0 else False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f857825a9e86686fd720c226f85e0674\",\n \"score\": \"0.6482086\",\n \"text\": \"def train():\\n # Build the data generators\\n train_generator, val_generator, eval_generator = build_data_pipelines()\\n # Get the classes mapping dictionnairy from the data generators\\n classes_dict = train_generator.class_indices\\n # Print the classes mapping\\n print(\\\"classes_dict : \\\\n \\\", classes_dict)\\n # Build the model to train\\n model = build_model(output_layer_dim=len(classes_dict))\\n # Prepare the callbacks\\n early_stopping, ckpt_saver = build_callbacks()\\n # Train the mdoel on the training data generator indicating the validation \\n # data generator to use at the end on each epochs.\\n model.fit(\\n train_generator,\\n steps_per_epoch=get_nb_step(DATA_DIR, 'training'),\\n validation_data=val_generator,\\n validation_steps=get_nb_step(DATA_DIR, 'validation'),\\n epochs=cfg.NB_EPOCHS,\\n callbacks=[early_stopping, ckpt_saver]\\n )\\n # Once the training is finished:\\n # Load best model\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"ignore\\\")\\n model.load_weights(os.path.join(OUTPUT_DIR, 'checkpoints'))\\n # Evaluate the model\\n print(\\\"[INFO] Evaluation phase...\\\")\\n # Make the predictions on the test images from the evaluation dataset\\n predictions = model.predict_generator(eval_generator)\\n predictions_idxs = np.argmax(predictions, axis=1)\\n # Build a classification report\\n my_classification_report = classification_report(eval_generator.classes,\\n predictions_idxs, target_names=eval_generator.class_indices.keys())\\n # Build a confusion matrix\\n my_confusion_matrix = confusion_matrix(eval_generator.classes,\\n predictions_idxs)\\n # Print the classifiyer performances\\n print(\\\"[INFO] Classification report : \\\")\\n print(my_classification_report)\\n\\n print(\\\"[INFO] Confusion matrix : \\\")\\n print(my_confusion_matrix)\\n\\n # Save the best model and it's results and config\\n model.save(os.path.join(OUTPUT_DIR, \\\"model.h5\\\"))\\n pickle.dump(my_classification_report, open(os.path.join(OUTPUT_DIR,\\n \\\"classification_report.pkl\\\"), \\\"wb\\\" ))\\n pickle.dump(my_confusion_matrix, open(os.path.join(OUTPUT_DIR,\\n \\\"confusion_matrix.pkl\\\"), \\\"wb\\\" ))\\n pickle.dump(classes_dict, open(os.path.join(OUTPUT_DIR,\\n \\\"classes_dict.pkl\\\"), \\\"wb\\\" ))\\n config_dict = cfg.get_config_dict() \\n pickle.dump(config_dict, open(os.path.join(OUTPUT_DIR,\\n \\\"config_dict.pkl\\\"), \\\"wb\\\" ))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17da67f37e8ffd1adb60d71627c0ce49\",\n \"score\": \"0.6469292\",\n \"text\": \"def test_kaggle_classification_data():\\n torch.manual_seed(100)\\n n_episodes = 20\\n a_space = _algorithm_space()\\n for dataset_name in [\\n \\\"kaggle.homesite_quote_conversion\\\",\\n \\\"kaggle.santander_customer_satisfaction\\\",\\n \\\"kaggle.bnp_paribas_cardif_claims_management\\\",\\n \\\"kaggle.poker_rule_induction\\\",\\n \\\"kaggle.costa_rican_household_poverty_prediction\\\",\\n ]:\\n t_env = _task_environment(\\n env_sources=[\\\"KAGGLE\\\"],\\n target_types=[\\\"BINARY\\\", \\\"MULTICLASS\\\"],\\n dataset_names=[dataset_name],\\n n_samples=500)\\n controller = _metalearn_controller(a_space, t_env)\\n reinforce = _metalearn_reinforce(controller, t_env)\\n reinforce.fit(\\n n_episodes=n_episodes,\\n **_fit_kwargs())\\n history = pd.DataFrame(reinforce.history)\\n assert history.shape[0] == n_episodes\\n assert history[\\\"n_successful_mlfs\\\"].sum() > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d86f3ba0c1c9b84474430f78e7c1af7\",\n \"score\": \"0.64522827\",\n \"text\": \"def get_train_test_valid_dataloaders(data_path, test_data_path, seed, image_size, batch_size):\\n def build_data(data_path):\\n content_list = []\\n labels_list = []\\n\\n for image in tqdm(os.listdir(data_path)):\\n if \\\".jpg\\\" in image:\\n content = cv2.imread(data_path + image)\\n content_list.append(content)\\n elif \\\".txt\\\" in image:\\n with open(data_path + image, \\\"r\\\") as f:\\n labels = f.read()\\n labels = np.array(labels.split(\\\" \\\"), dtype=int)\\n labels[0] = 0 if labels[0] == 1 else 1\\n labels = np.roll(labels, -1)\\n labels_list.append(labels)\\n data = np.array([list(a) for a in zip(content_list, labels_list)])\\n\\n return data\\n\\n train_data = build_data(data_path=data_path)\\n test_data = build_data(data_path=test_data_path)\\n\\n train_data, valid_data = train_test_split(train_data, shuffle=True, test_size=0.1, random_state=seed)\\n\\n train_clf_labels = [a[-1] for a in train_data[:, 1]]\\n\\n transform = Compose(\\n [\\n Resize(width=image_size, height=image_size),\\n HorizontalFlip(p=0.4),\\n # ShiftScaleRotate(p=0.3),\\n MedianBlur(blur_limit=7, always_apply=False, p=0.3),\\n IAAAdditiveGaussianNoise(scale=(0, 0.15 * 255), p=0.5),\\n HueSaturationValue(hue_shift_limit=0.2, sat_shift_limit=0.2, val_shift_limit=0.2, p=0.4),\\n RandomBrightnessContrast(brightness_limit=(-0.1, 0.1), contrast_limit=(-0.1, 0.1), p=0.5),\\n # in this implementation imagenet normalization is used\\n Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], max_pixel_value=255.0, p=1.0),\\n Cutout(p=0.4),\\n ToTensorV2(p=1.0),\\n ],\\n p=1.0,\\n bbox_params=A.BboxParams(format=\\\"pascal_voc\\\"),\\n )\\n\\n test_transform = Compose(\\n [\\n # only resize and normalization is used for testing\\n # no TTA is implemented in this solution\\n Resize(width=image_size, height=image_size),\\n Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], max_pixel_value=255.0, p=1.0),\\n ToTensorV2(p=1.0),\\n ],\\n p=1.0,\\n bbox_params=A.BboxParams(format=\\\"pascal_voc\\\"),\\n )\\n\\n train_dataset = Dataset(train_data, transforms=transform)\\n valid_dataset = Dataset(valid_data, transforms=transform)\\n test_dataset = Dataset(test_data, transforms=test_transform)\\n\\n train_dataloader = DataLoader(\\n train_dataset,\\n # balanced sampler is used to minimize harmful effects of dataset not being fully balanced\\n sampler=BalanceClassSampler(labels=train_clf_labels, mode=\\\"upsampling\\\"),\\n batch_size=batch_size,\\n )\\n test_dataloader = DataLoader(test_dataset, sampler=SequentialSampler(test_dataset), batch_size=1)\\n valid_dataloader = DataLoader(valid_dataset, sampler=SequentialSampler(valid_dataset), batch_size=batch_size)\\n\\n return train_dataloader, test_dataloader, valid_dataloader\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c39bc838fe64492c50e98365a188c9f1\",\n \"score\": \"0.643714\",\n \"text\": \"def train_model(model):\\n # Add your code here\\n\\n #Preprocessing \\n # Data augmentation - creation of more images to train on\\n train_datagen = ImageDataGenerator(\\n rescale = 1./255,\\n shear_range=0.2,\\n zoom_range=0.2,\\n width_shift_range=0.2,\\n\\t\\theight_shift_range=0.2,\\n rotation_range=20,\\n horizontal_flip=True)\\n\\n test_datagen = ImageDataGenerator(rescale = 1./255)\\n\\n training_set = train_datagen.flow_from_directory( #how can I check if I am actually making many more instances of pictures.\\n '/Users/harryrodger/Desktop/data',\\n target_size=(64,64),\\n batch_size=32,\\n class_mode='categorical'\\n )\\n\\n test_set = test_datagen.flow_from_directory( #how can I check if I am actually making many more instances of pictures.\\n '/Users/harryrodger/Desktop/ProjectCOMP309/ProjectCode/data/test',\\n target_size=(64,64),\\n batch_size=32,\\n class_mode='categorical'\\n )\\n\\n print('Data augmentation complete')\\n\\n model.fit_generator( \\n training_set,\\n steps_per_epoch=8000, \\n epochs = 50,\\n validation_data=test_set,\\n validation_steps = 15) \\n\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddc1d590f7f0d9c927c217dc46001088\",\n \"score\": \"0.6421587\",\n \"text\": \"def test_nn_models(num_models, raw_data, preprocess_tech, save_path, eval_tech = 'k-fold', list_of_models= None):\\n model_hist = [] ; model_results = pd.DataFrame(columns=['Hidden', 'Activations', 'Learning rate', 'Train Accuracy', 'Test Accuracy', 'Train AUC', 'Test AUC']) # TODO: Add tensorboard and model saving capabilities to this function\\n result_index = 0\\n if eval_tech not in ['k-fold', 'boot']: # must be a valid evaluation technique\\n raise NameError\\n\\n for i in range(num_models):\\n if not list_of_models: # if a list of models to test isn't provided\\n model_info = generate_random_model() # randomly generate models\\n model_info[\\\"Preprocessing\\\"] = preprocess_tech\\n model_info[\\\"Input size\\\"] = raw_data.shape[1] - 1 # set input data size\\n else:\\n assert num_models == len(list_of_models) # if we provide models, ensure the num_models param lines up\\n model_info = list_of_models[i] # get the ith model's info\\n\\n model_info[\\\"Model type\\\"] = 'NN' # this is for k-fold cross validation, since k-fold is able to evaluate both neural nets and logistic regression models\\n model = build_nn(model_info) # build & compile NN\\n\\n if eval_tech == 'k-fold':\\n results = k_fold_cross_validation(model_info, raw_data, preprocess_tech, model=model) # do k-fold cross validation on model\\n else:\\n print(\\\"currently not supporting bootstrap evaluation due to computational constraints\\\")\\n\\n train_acc, test_acc, train_auc, test_auc = zip(*results) # separate results into lists\\n\\n model_info[\\\"Avg Test AUC\\\"] = round(np.mean(test_auc), 3) # compute average metrics for each fold of the evaluation\\n model_info[\\\"Avg Train AUC\\\"] = round(np.mean(train_auc), 3)\\n model_info[\\\"Avg Train Accuracy\\\"] = round(np.mean(train_acc), 3) # these are averages from k-fold cross validation\\n model_info[\\\"Avg Test Accuracy\\\"] = round(np.mean(test_acc), 3)\\n model_hist.append(model_info) # add this model's info to the list\\n\\n model_results.loc[result_index] = (model_info['Hidden layers'], model_info['Activations'],\\\\\\n model_info['Learning rate'], model_info['Avg Train Accuracy'],\\\\\\n model_info[\\\"Avg Test Accuracy\\\"], \\\\\\n model_info[\\\"Avg Train AUC\\\"], model_info[\\\"Avg Test AUC\\\"]) # write model results to df\\n result_index += 1\\n\\n model_results.to_csv(save_path + \\\"K-fold NN Results.csv\\\") # save results to disk\\n display_k_fold_results(model_hist, top_models=1) # display the top models' performance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"665d6aca3d23932c887e6fb47be6e04d\",\n \"score\": \"0.6421472\",\n \"text\": \"def k_fold_cv(dataset, nbr_folds=5, m=10, alpha=0.0, feature_extraction=\\\"k-means distances\\\"):\\n # copy the data to avoid affecting reference\\n data = dataset.copy()\\n\\n # divide into k-folds\\n folds = assign_randomly(data, nbr_folds)\\n\\n accuracy_train, accuracy_test = [], []\\n MSE_train, MSE_test = [], []\\n\\n # train models for the folds\\n for i in range(nbr_folds):\\n\\n # distribute into train and test data\\n test_data, train_data = folds[i], []\\n for j in range(nbr_folds):\\n if i != j:\\n train_data.extend(folds[j])\\n\\n train_data = np.array(train_data)\\n test_data = np.array(test_data)\\n\\n # separate X (inputs) and Y (labels) for the training and testing data\\n x_train, y_train = train_data[:, :-TOTAL_DIGIT_CLASSES], train_data[:, -TOTAL_DIGIT_CLASSES:]\\n x_test, y_test = test_data[:, :-TOTAL_DIGIT_CLASSES], test_data[:, -TOTAL_DIGIT_CLASSES:]\\n\\n # Dimensionality reduction\\n if feature_extraction == \\\"k-means distances\\\":\\n clusters = k_means(x_train, m)\\n codebooks = get_codebooks(clusters)\\n x_train = select_features(x_train, codebooks)\\n x_test = select_features(x_test, codebooks)\\n elif feature_extraction == \\\"pca\\\":\\n components, x_bar = get_pca_components(x_train, m)\\n x_train = project_to_pca_comps(x_train, components)\\n x_test = project_to_pca_comps(x_test, components)\\n\\n # compute models for training data\\n model = compute_linreg(x_train, y_train, alpha)\\n\\n # prediction on training and testing data\\n pred_train = predict_hypothesis_matrix(model, x_train)\\n pred_test = predict_hypothesis_matrix(model, x_test)\\n\\n # assess accuracy\\n _acc_train = check_accuracy(pred_train, y_train)\\n _acc_test = check_accuracy(pred_test, y_test)\\n\\n accuracy_train.append(_acc_train)\\n accuracy_test.append(_acc_test)\\n\\n # assesss MSE\\n _MSE_train = check_MSE(pred_train, y_train)\\n _MSE_test = check_MSE(pred_test, y_test)\\n\\n MSE_train.append(_MSE_train)\\n MSE_test.append(_MSE_test)\\n\\n cv_accuracy_train = np.mean(accuracy_train)\\n cv_accuracy_test = np.mean(accuracy_test)\\n cv_MSE_train = np.mean(MSE_train)\\n cv_MSE_test = np.mean(MSE_test)\\n\\n return cv_accuracy_train, cv_accuracy_test, cv_MSE_train, cv_MSE_test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abac45c5186b9632817485064a2cda0e\",\n \"score\": \"0.6410815\",\n \"text\": \"def random_labeled_data(data_size,randomness):\\n\\n with open('config.yml') as ymlfile:\\n cgf = yaml.load(ymlfile, Loader=yaml.SafeLoader);\\n n = cgf['DATASET_TRAIN']['arguments']['grid_size']\\n input_shape = (n,n,1)\\n output_shape = (1)\\t\\n\\n weights_path = \\\"model/7/keras_model_files.h5\\\"\\t\\n model = mb.build_model_fc2(input_shape, output_shape,cgf['MODEL']['arguments'])\\n model.load_weights(weights_path)\\n\\n optimizer = cgf['TRAIN']['optim']['type']\\n loss_type= cgf['TRAIN']['loss']['type']\\n metric_list = list(cgf['TRAIN']['metrics'].values())\\n\\n model.compile(optimizer=optimizer,\\n loss=loss_type,\\n metrics= metric_list)\\n \\n if randomness == \\\"gaussian\\\":\\n #Gaussian with total mean 96 \\n data = np.random.normal(0.09375, size=(data_size, n, n, 1))\\n elif randomness == \\\"uniform\\\":\\t\\t\\t\\t\\n #data = np.random.uniform(low=0, high=0.1875, size=(data_size, n, n, 1)) \\n data = np.random.uniform(low=-0.5, high=2, size=(data_size, n, n, 1)) \\n\\n elif randomness == \\\"stripes\\\":\\n data_loader_test = Data_loader_stripe_test(cgf['DATASET_TEST']['arguments'])\\n data, _ = data_loader_test.load_data() \\n\\n sum_pixels = [i.sum() for i in data[:]]\\n\\n labels = model.predict(data)\\n return data, labels, sum_pixels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5770646ac9e05493f3a0a76c930a5fd\",\n \"score\": \"0.6401645\",\n \"text\": \"def cross_validate(model, cv_set, cv_target, n=..., shuffle=..., n_jobs=...):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a36652f9b73c547a618ff155e8f9161f\",\n \"score\": \"0.6391487\",\n \"text\": \"def main():\\n\\n configure_model_dir()\\n base = os.environ['DATA_ROOT']\\n\\n dp = dataset.DatasetProvider(\\n os.path.join(base, cfg.get('data', 'train')),\\n cfg.get('args', 'max_files'),\\n cfg.getint('args', 'max_cuis'),\\n cfg.getint('args', 'samples_per_doc'),\\n cfg.getint('bow', 'batch'),\\n cfg.getboolean('args', 'make_alphabet'),\\n cfg.getboolean('args', 'verbose'))\\n\\n max_cuis = int(cfg.get('args', 'max_cuis'))\\n model = get_model(max_cuis, max_cuis - 1)\\n optim = getattr(optimizers, cfg.get('bow', 'optimizer'))\\n\\n model.compile(\\n loss='binary_crossentropy',\\n optimizer=optim(lr=10**cfg.getint('bow', 'log10lr')),\\n metrics=['accuracy'])\\n\\n callback = ModelCheckpoint(\\n 'Model/model.h5',\\n verbose=1,\\n save_best_only=True)\\n\\n # load validation data\\n val_x, val_y = dp.load(os.path.join(base, cfg.get('data', 'dev')))\\n print('dev x, y shapes:', val_x.shape, val_y.shape)\\n\\n steps = math.ceil(dp.train_size / cfg.getint('bow', 'batch'))\\n print('steps per epoch:', steps)\\n\\n model.fit_generator(\\n dp.stream(),\\n\\t validation_data=(val_x, val_y),\\n epochs=cfg.getint('bow', 'epochs'),\\n steps_per_epoch=steps,\\n verbose=0,\\n callbacks=[callback])\\n\\n # save final model\\n model.save('Model/final.h5')\\n\\n # probability for each class; (test size, num of classes)\\n distribution = model.predict(val_x)\\n\\n # turn into an indicator matrix\\n distribution[distribution < 0.5] = 0\\n distribution[distribution >= 0.5] = 1\\n\\n f1 = f1_score(val_y, distribution, average='macro')\\n p = precision_score(val_y, distribution, average='macro')\\n r = recall_score(val_y, distribution, average='macro')\\n print(\\\"\\\\nmacro: p: %.3f - r: %.3f - f1: %.3f\\\" % (p, r, f1))\\n\\n f1 = f1_score(val_y, distribution, average='micro')\\n p = precision_score(val_y, distribution, average='micro')\\n r = recall_score(val_y, distribution, average='micro')\\n print(\\\"micro: p: %.3f - r: %.3f - f1: %.3f\\\" % (p, r, f1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4b5a2e8ccc39473c9e6240035c8ee52\",\n \"score\": \"0.63597065\",\n \"text\": \"def test_train_models_with_best_params_CD5(self, real_genomic_data, real_labels_cat, real_idx):\\n\\t\\tdisease_id = disease_IDs[int(1-1)]\\n\\t\\tchrom = 5\\n\\n\\n\\n\\t\\t# Load data, hp & labels\\n\\t\\tdata = real_genomic_data(disease_id, chrom)\\n\\t\\tfm = char_matrix_to_featmat(data, '3d', real_pnorm_feature_scaling)\\n\\n\\t\\tlabels_cat = real_labels_cat(disease_id)\\n\\n\\t\\thp = pickle.load(open(os.path.join(FINAL_RESULTS_DIR, 'hyperparams', disease_id, 'chrom{}.p'.format(chrom)), 'rb'))\\n\\t\\tprint(hp)\\n\\n\\t\\thp['epochs'] = int(hp['epochs'])\\n\\t\\thp['n_snps'] = int(fm.shape[1])\\n\\t\\t#hp['epochs'] = 250\\n\\t\\t#hp['hidden_neurons'] = 6\\n\\t\\t#hp['lr'] = 1e-4\\n\\t\\t#hp['l1_reg'] = 1e-5 # TODO change me back\\n\\t\\t# Train\\n\\t\\tos.makedirs(os.path.join(FINAL_RESULTS_DIR, 'csv_logs', disease_id), exist_ok=True)\\n\\n\\t\\tmodel = create_montaez_dense_model(hp)\\n\\t\\tmodel.fit(x=fm,\\n\\t\\t\\t\\t\\ty=labels_cat,\\n\\t\\t\\t\\t\\tepochs=hp['epochs'],\\n\\t\\t\\t\\t\\tcallbacks=[\\n\\t\\t\\t\\t\\t\\tCSVLogger(os.path.join(FINAL_RESULTS_DIR, 'csv_logs', disease_id, '{}'.format(chrom)))\\n\\t\\t\\t\\t\\t],\\n\\t\\t\\t\\t\\tverbose=0)\\n\\t\\t\\t\\t\\t\\n\\t\\t# Calculate AUC from the best model\\n\\t\\ty_pred = model.predict(x=fm)\\n\\n\\t\\t#print(classification_report(np.argmax(Y, axis=-1), np.argmax(y_pred, axis=-1), output_dict=False))\\n\\n\\t\\tauc = roc_auc_score(labels_cat, y_pred, average='weighted')\\n\\t\\tprec = average_precision_score(labels_cat, y_pred, average='weighted')\\n\\t\\tprint('ROC AUC score for best model: %f' % auc)\\t\\n\\t\\tprint('Prec-Rec AUC score for best model: %f' % prec)\\t\\n\\t\\t\\n\\t\\tfilename = os.path.join(FINAL_RESULTS_DIR, 'trained_models', disease_id, 'model{}.h5'.format(chrom))\\n\\t\\tos.makedirs(os.path.dirname(filename), exist_ok=True)\\n\\t\\tmodel.save(filename)\\n\\t\\tK.clear_session()\\n\\t\\tdel data, fm, model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4f5f13f17dc5afe9e9378e83539e768\",\n \"score\": \"0.63550985\",\n \"text\": \"def cvrand(model,\\n data,\\n traindatagen,\\n testdatagen,\\n itercol='subject',\\n n_iterations=3,\\n val_subjects=3,\\n epochs=1,\\n batch_size=32,\\n steps_per_epoch=None,\\n validation_steps=None,\\n target_size=(227, 227),\\n random_state=None,\\n min_delta=0,\\n patience=3):\\n\\n # Raise error if selected columns are numeric\\n if pd.api.types.is_numeric_dtype(data[itercol]):\\n raise TypeError('Columns must not be numeric')\\n\\n # set random seed\\n np.random.seed(random_state)\\n\\n # Create empty lists\\n sampledvalues = []\\n validation_accuracies = []\\n train_accuracies = []\\n\\n # Save initial default model weights\\n wsave = model.get_weights()\\n\\n # Designate model checkpoint and callbacks_list\\n checkpoint = ModelCheckpoint('weights.hdf5',\\n mode='max',\\n monitor='val_accuracy',\\n save_best_only=True)\\n\\n earlystop = EarlyStopping(monitor='val_accuracy', min_delta=min_delta, patience=patience)\\n\\n callbacks_list = [checkpoint, earlystop]\\n\\n # Initialize iteration counter\\n counter = 0\\n\\n # Pull unique values from itercol\\n valuelist = data[itercol].unique()\\n\\n for i in range(n_iterations):\\n sampledvalues.append(np.random.choice(valuelist, size=val_subjects, replace=False))\\n\\n # n_iteration for loop\\n for i in range(n_iterations):\\n # Substep counter\\n counter += 1\\n\\n # Print iteration and substep progress\\n print('CV iteration ' + str(counter) + ' of ' + str(n_iterations))\\n\\n # reset model states for fresh training\\n model.set_weights(wsave)\\n\\n # Sample the validation values\\n print('Validation subjects are ' + str(sampledvalues[i]))\\n\\n # Split train and test sets, iterating through each subject to be excluded from training\\n cvtest = data[data[itercol].isin(sampledvalues[i])]\\n cvtrain = data[~data[itercol].isin(sampledvalues[i])]\\n\\n # Split training data\\n train = traindatagen.flow_from_dataframe(cvtrain,\\n x_col='imgpath',\\n y_col='classname',\\n batch_size=batch_size,\\n target_size=target_size,\\n seed=random_state)\\n # Split validation data\\n val = testdatagen.flow_from_dataframe(cvtest,\\n x_col='imgpath',\\n y_col='classname',\\n target_size=target_size,\\n seed=random_state)\\n # Fit model\\n model.fit(train,\\n epochs=epochs,\\n steps_per_epoch=steps_per_epoch,\\n validation_data=val,\\n validation_steps=validation_steps,\\n callbacks=callbacks_list)\\n\\n # Append lists\\n valmax = max(model.history.history['val_accuracy'])\\n valmaxindex = model.history.history['val_accuracy'].index(valmax)\\n validation_accuracies.append(round(valmax, 3))\\n train_accuracies.append(round(model.history.history['accuracy'][valmaxindex], 3))\\n\\n # Fill dataframe with stats\\n dftemp = pd.DataFrame({'validation_subjects': sampledvalues,\\n 'train_accuracies': train_accuracies,\\n 'validation_accuracy': validation_accuracies})\\n\\n return dftemp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f029f3238380f5112a162693418c945\",\n \"score\": \"0.634898\",\n \"text\": \"def main():\\n ###!! Generate dataset at first.\\n generate_train_test_files()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93341f85a925dac3cb2a7d86eebd4847\",\n \"score\": \"0.6332978\",\n \"text\": \"def Classification_test(config):\\n\\n # 1. Retrieve information from config dict\\n device = config['device']\\n device_name = torch.cuda.get_device_name(device)\\n print('Device name: {}'.format(device_name))\\n input_shape = config['input_shape']\\n batch_size = config['batch_size']\\n number_of_tools = config['number_of_tools']\\n output_features = number_of_tools\\n random_frames = config['random_frames']\\n nr_videos = config['nr_videos']\\n nr_frames = config['nr_frames']\\n weight_decay = config['weight_decay']\\n msg_bot = config['msg_bot']\\n dataset_name = config['dataset']\\n model_name = config['model']\\n agent_name = 'TransNetAgent'\\n\\n\\n # 2. Define data\\n data = Data()\\n data.add_dataset(Cholec80(random_frames, nr_videos, nr_frames))\\n test_ds = (dataset_name, 'test')\\n\\n\\n # 3. Split data (0% train, 100% test) and define path\\n splits = dict()\\n for ds_name, ds in data.datasets.items():\\n splits[ds_name] = split_dataset(ds, test_ratio=1.0, \\n val_ratio=0, nr_repetitions=config['nr_runs'], \\n cross_validation=config['cross_validation'])\\n pathr = os.path.join(model_result_path, 'models', dataset_name+'_'+model, 'test_results')\\n if not os.path.exists(pathr):\\n os.makedirs(pathr)\\n else:\\n # Empty directory\\n shutil.rmtree(pathr)\\n os.makedirs(pathr)\\n\\n\\n # 4. Bring data to Pytorch format\\n print('Bring data to PyTorch format..')\\n # Repeat for each repition\\n for run_ix in range(config['nr_runs']):\\n # 5. Bring data to Pytorch format\\n datasets = dict()\\n for ds_name, ds in data.datasets.items():\\n for split, data_ixs in splits[ds_name][run_ix].items():\\n if len(data_ixs) > 0: # Sometimes val indexes may be an empty list\\n aug = config['augmentation'] if not('test' in split) else 'none'\\n datasets[(ds_name, split)] = PytorchClassification2DDataset(ds, \\n ix_lst=data_ixs, size=input_shape, aug_key=aug, \\n resize=config['resize'])\\n \\n # 6. Build test dataloader, and visualize\\n dl = DataLoader(datasets[(test_ds)], \\n batch_size=batch_size, shuffle=True,\\n num_workers=1)\\n\\n # 7. Load pretrained model\\n model = torch.load(os.path.join(model_result_path, 'models_', model_name, 'model.zip'))\\n model.eval()\\n model.to(device)\\n\\n # 8. Define loss and optimizer\\n loss_f = LossBCE(device=device)\\n \\n # 9. Test model\\n agent = getattr(agents, agent_name)(model=model, device=device)\\n print('Testing model in batches of {}..'.format(batch_size))\\n losses_test, _, accuracy_test, accuracy_det_test = agent.test(loss_f, dl, msg_bot=msg_bot)\\n\\n # 10. Save results\\n save_only_test_results(pathr, losses_test, accuracy_test, accuracy_det_test)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19b4953eda7a0d519efa29d1a869444b\",\n \"score\": \"0.63083833\",\n \"text\": \"def train_validation_split(dataset_path, output_path, split_ratio, seed=120):\\n img_categories = os.listdir(dataset_path) # all the image categories\\n if os.path.exists(output_path):\\n print('Dataset already exists at the given path')\\n else:\\n os.makedirs(output_path)\\n os.mkdir(output_path + '/train')\\n os.mkdir(output_path + '/validation')\\n\\n # for every image category in the dataset build train and val folders with images in them a/c to split_ratio\\n print('Splitting dataset into train and validation sets: ')\\n for img_category in img_categories:\\n print('.', end='')\\n # list all the images for this category\\n imgs = os.listdir(dataset_path + '/' + img_category)\\n # sort and shuffle images randomly\\n imgs.sort()\\n random.seed(seed)\\n random.shuffle(imgs)\\n # split the imgs into two halves train and test\\n train_split = imgs[:int(split_ratio * len(imgs))]\\n test_split = imgs[int(split_ratio * len(imgs)):]\\n\\n # built the train set and copy images\\n if not os.path.exists(os.path.join(output_path, 'train', img_category)):\\n os.mkdir(os.path.join(output_path, 'train', img_category))\\n for img in train_split:\\n source = os.path.join(dataset_path, img_category, img)\\n dest = os.path.join(output_path, 'train', img_category, img)\\n shutil.copy(source, dest)\\n\\n # built the test set and copy images\\n if not os.path.exists(os.path.join(output_path, 'validation', img_category)):\\n os.mkdir(os.path.join(output_path, 'validation', img_category))\\n for img in test_split:\\n source = os.path.join(dataset_path, img_category, img)\\n dest = os.path.join(output_path, 'validation', img_category, img)\\n shutil.copy(source, dest)\\n print('\\\\nSuccess!!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf6efe5146cb309dfdc58ab445a2fca0\",\n \"score\": \"0.6281842\",\n \"text\": \"def create_sets():\\n\\n X_training_1, Y_training_1, y_training_1 = LoadBatch('../../cifar-10-batches-py/data_batch_1')\\n X_training_2, Y_training_2, y_training_2 = LoadBatch('../../cifar-10-batches-py/data_batch_2')\\n X_training_3, Y_training_3, y_training_3 = LoadBatch('../../cifar-10-batches-py/data_batch_3')\\n X_training_4, Y_training_4, y_training_4 = LoadBatch('../../cifar-10-batches-py/data_batch_4')\\n X_training_5, Y_training_5, y_training_5 = LoadBatch('../../cifar-10-batches-py/data_batch_5')\\n\\n X_training = np.concatenate((X_training_1, X_training_3), axis=1)\\n X_training = np.copy(np.concatenate((X_training, X_training_4), axis=1))\\n X_training = np.copy(np.concatenate((X_training, X_training_5), axis=1))\\n\\n X_training = np.concatenate((X_training, X_training_2[:, :9000]), axis=1)\\n\\n Y_training = np.concatenate((Y_training_1, Y_training_3), axis=1)\\n Y_training = np.copy(np.concatenate((Y_training, Y_training_4), axis=1))\\n Y_training = np.copy(np.concatenate((Y_training, Y_training_5), axis=1))\\n\\n Y_training = np.concatenate((Y_training, Y_training_2[:, :9000]), axis=1)\\n\\n y_training = y_training_1 + y_training_3 + y_training_4 + y_training_5 + y_training_2[:9000]\\n\\n X_validation = np.copy(X_training_2[:, 9000:])\\n Y_validation = np.copy(Y_training_2[:, 9000:])\\n y_validation = y_training_2[9000:]\\n\\n X_test, _, y_test = LoadBatch('../../cifar-10-batches-py/test_batch')\\n\\n mean = np.mean(X_training)\\n X_training -= mean\\n X_validation -= mean\\n X_test -= mean\\n\\n return [X_training, Y_training, y_training], [X_validation, Y_validation, y_validation], [X_test, y_test]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d56f2c2b2a3f8242aba1a9f7544a3eb7\",\n \"score\": \"0.62642205\",\n \"text\": \"def train_test_original_dataset(norm, target_cat):\\n x_train, x_test, y_train, y_test = train_test_split(norm, target_cat, test_size=0.15)\\n\\n model = keras.Sequential()\\n model.add(keras.layers.Dense(10, input_dim=13, activation='relu'))\\n model.add(keras.layers.Dense(8, activation='relu'))\\n model.add(keras.layers.Dense(6, activation='relu'))\\n model.add(keras.layers.Dense(3, activation='softmax'))\\n\\n\\n model.compile(loss=\\\"categorical_crossentropy\\\", optimizer=\\\"adam\\\", metrics=['accuracy'])\\n\\n model.fit(x_train, y_train, batch_size=15, epochs=2000, verbose=0)\\n\\n test_loss, test_acc = model.evaluate(x_test, y_test)\\n\\n print('Original dataset - test accuracy:', test_acc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"236f3049bbff14ac691a0f3dff2befdb\",\n \"score\": \"0.62390447\",\n \"text\": \"def model_cv(make_model, data, featurizer, n_folds = 10, random_state = 1, getX = Features.getX, getY = Features.getY, stratified = False, **kwargs):\\n nobs = len(data)\\n cv_accuracies = []\\n if stratified:\\n folds = StratifiedKFold(getY(data), n_folds = n_folds, random_state = random_state, **kwargs)\\n else:\\n folds = KFold(n = nobs, n_folds= n_folds, random_state = random_state, **kwargs)\\n get_elems_at = lambda vals, indices: [vals[i] for i in indices]\\n for fold_id, (train_indices, test_indices) in enumerate(folds):\\n print \\\"Running fold %d\\\" % fold_id\\n train_data = get_elems_at(data, train_indices)\\n test_data = get_elems_at(data, test_indices)\\n # Featurize each time since our features can depend on training data\\n matrices = Features.make_experiment_matrices(train_data, test_data, featurizer, getX, getY)\\n # always make a new version of model...safer, not sure if model.fit would overwrite if re-trained\\n # as we want\\n model = make_model()\\n model.fit(matrices['train_X'], matrices['train_Y'])\\n accuracy = model.score(matrices['test_X'], matrices['test_Y'])\\n cv_accuracies.append(accuracy)\\n mean_accuracy = numpy.mean(cv_accuracies)\\n std_accuracy = numpy.std(cv_accuracies)\\n return (mean_accuracy, std_accuracy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad94c4de6b9d3187bd9e40b7ca5000ae\",\n \"score\": \"0.62292963\",\n \"text\": \"def compare_model_algorithms(data, Nrep=2, Nfolds=5):\\n\\t\\n\\t# set RNG\\n\\tnp.random.seed(0)\\n\\trandom.seed(0)\\n\\t\\n\\t# set KNN algorithm options\\n\\tuser_opt_cos = {\\\"name\\\":\\\"cosine\\\", \\\"user_based\\\":True}\\n\\titem_opt_cos = {\\\"name\\\":\\\"cosine\\\", \\\"user_based\\\":False}\\n\\t\\n\\t# The algorithms to cross-validate\\t\\n\\ts_SVD = SVD()\\n\\ts_SVDpp = SVDpp()\\n\\ts_NMF = NMF()\\n\\ts_SlopeOne = SlopeOne()\\n\\tu_KNNBasic = KNNBasic(sim_options=user_opt_cos)\\n\\tu_KNNWithMeans = KNNWithMeans(sim_options=user_opt_cos)\\n\\tu_KNNBaseline = KNNBaseline(sim_options=user_opt_cos)\\n\\ti_KNNBasic = KNNBasic(sim_options=item_opt_cos)\\n\\ti_KNNWithMeans = KNNWithMeans(sim_options=item_opt_cos)\\n\\ti_KNNBaseline = KNNBaseline(sim_options=item_opt_cos)\\n\\ts_CoClustering = CoClustering()\\n\\ts_BaselineOnly = BaselineOnly()\\n\\ts_NormalPredictor = NormalPredictor()\\n\\t\\n\\tclasses = [s_SVD, s_SVDpp, s_NMF, s_SlopeOne, u_KNNBasic, u_KNNWithMeans, \\n\\t\\t\\tu_KNNBaseline, i_KNNBasic, i_KNNWithMeans, i_KNNBaseline,\\n\\t\\t\\ts_CoClustering, s_BaselineOnly, s_NormalPredictor]\\n\\t\\n\\tclass_names = [\\\"SVD\\\", \\\"SVDpp\\\", \\\"NMF\\\", \\\"SlopeOne\\\", \\\"user-KNNBasic\\\", \\\"user-KNNWithMeans\\\", \\n\\t\\t\\t\\t \\\"user-KNNBaseline\\\", \\\"item-KNNBasic\\\", \\\"item-KNNWithMeans\\\", \\\"item-KNNBaseline\\\",\\n\\t\\t\\t\\t \\\"CoClustering\\\", \\\"BaselineOnly\\\", \\\"NormalPredictor\\\"]\\n\\t\\n\\t# repeat cross validation for different kfold splits for higher reliability\\n\\tperformance_list = []\\n\\theaders = ['RMSE', 'MAE', 'Time (min)']\\n\\tfor irep in range(0,Nrep):\\n\\t\\t\\n\\t\\t# cross validation folds will be the same for all algorithms. \\n\\t\\tkf = KFold(n_splits=Nfolds,random_state=0) \\n\\n\\t\\t# cross validate for each algorithm\\n\\t\\ttable = np.zeros((len(classes),len(headers)))\\n\\t\\tfor ik, klass in enumerate(classes):\\n\\t\\t start = time.time()\\n\\t\\t out = cross_validate(klass, data, ['rmse', 'mae'], kf)\\n\\t\\t cv_time = (time.time() - start) / 60\\n\\t\\t mean_rmse = np.mean(out['test_rmse'])\\n\\t\\t mean_mae = np.mean(out['test_mae'])\\n\\t\\t table[ik,:] = np.array([mean_rmse, mean_mae, cv_time])\\n\\t\\t\\t\\n\\t\\t# Accumulate results for each cross-validation\\t\\n\\t\\tperformance_list.append(table)\\n\\t\\n\\t# Show averaged results over cross validation iterations\\n\\tperformance = sum(performance_list)/len(performance_list)\\n\\tprint(tabulate(performance.tolist(), headers=headers, showindex=class_names))\\n\\n\\treturn performance_list, performance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93ec9a46bb7b1e1cef72b0996fd24145\",\n \"score\": \"0.62239105\",\n \"text\": \"def split_data(n_folds,output, path_train=False, path_test=False):\\n \\n #define stratified K-fold\\n kf = StratifiedKFold(n_splits=n_folds,shuffle=True,random_state=57)\\n \\n #loads files\\n if path_train:\\n X_train,Y_train = read_corpus(path_train)\\n if path_test:\\n X_test,Y_test = read_corpus(path_test)\\n\\n #cross-validation process with train and test in different input files\\n if path_train and path_test:\\n i=0\\n for train_index, test_index in kf.split(X_train,Y_train):\\n X_train = X_train[test_index]\\n y_train = Y_train[test_index]\\n\\n print(i)\\n print(X_train.shape, y_train.shape)\\n print(\\\" \\\")\\n\\n save_corpus(output+\\\"train\\\"+str(i), y_train, X_train)\\n \\n i=i+1\\n\\n i=0\\n for train_index, test_index in kf.split(X_test,Y_test):\\n\\n x_test = X_test[test_index]\\n y_test = Y_test[test_index]\\n\\n print(i)\\n print(x_test.shape, y_test.shape)\\n print(\\\" \\\")\\n\\n save_corpus(output+\\\"test\\\"+str(i), y_test,x_test)\\n i=i+1\\n \\n #cross-validation process with train and test in the same file \\n if path_train and not path_test:\\n i=0\\n for train_index, test_index in kf.split(X_train,Y_train):\\n X_train_fold, X_test_fold = X_train[train_index], X_train[test_index]\\n y_train_fold, y_test_fold = Y_train[train_index], Y_train[test_index]\\n \\n print(i)\\n print(X_train_fold.shape, y_train_fold.shape)\\n print(X_test_fold.shape, y_test_fold.shape)\\n print(\\\" \\\")\\n \\n save_corpus(output+\\\"train\\\"+str(i), train_index, y_train_fold,X_train_fold)\\n save_corpus(output+\\\"test\\\"+str(i), test_index, y_test_fold, X_test_fold)\\n save_corpus1(output+\\\"train\\\"+str(i)+'.csv', train_index, y_train_fold,X_train_fold)\\n save_corpus1(output+\\\"test\\\"+str(i)+'.csv', test_index, y_test_fold, X_test_fold)\\n i=i+1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aafd6739f852cd594573eafc608366\",\n \"score\": \"0.6206666\",\n \"text\": \"def trainModel(self):\\n\\n # load data throught the provided dataset object\\n X, y = self.dataset.load()\\n \\n # count number of classes\\n unique, _ = np.unique(y, return_counts=True)\\n \\n # call buildDNN method\\n model = self.buildDNN(n_classes = len(unique))\\n \\n # preprocess data thorugh the dataset class\\n X_train = self.dataset.preProcessData(X)\\n y_train = self.dataset.labalEncoding(y, n_classes = len(unique))\\n \\n # define optimizer\\n optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)\\n\\n # compile the model\\n model.compile(optimizer = optimizer , loss = \\\"categorical_crossentropy\\\", metrics= [\\\"accuracy\\\"])\\n\\n # set a learning rate annealer\\n learning_rate_reduction = ReduceLROnPlateau(monitor='acc', \\n patience=3, \\n verbose=1, \\n factor=0.5, \\n min_lr=0.00001)\\n \\n # data augmentation in order to improve model performance\\n datagen = ImageDataGenerator(\\n featurewise_center=False, # set input mean to 0 over the dataset\\n samplewise_center=False, # set each sample mean to 0\\n featurewise_std_normalization=False, # divide inputs by std of the dataset\\n samplewise_std_normalization=False, # divide each input by its std\\n zca_whitening=False, # apply ZCA whitening\\n rotation_range=10, # randomly rotate images\\n zoom_range = 0.1, # randomly zoom image \\n width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)\\n height_shift_range=0.1, # randomly shift images vertically (fraction of total height)\\n horizontal_flip=False, # randomly flip images\\n vertical_flip=False) # randomly flip images\\n\\n datagen.fit(X_train)\\n\\n # fit the model\\n history = model.fit_generator(datagen.flow(X_train,y_train, batch_size=self.batch_size),\\n epochs = self.epochs, steps_per_epoch=X_train.shape[0] // self.batch_size\\n ,callbacks=[learning_rate_reduction])\\n\\n # save the model\\n if self.char:\\n model.save(os.path.join(self.dirBin, 'model_char.h5'))\\n model.save_weights(os.path.join(self.dirBin, 'model_char_weights.h5'))\\n else:\\n model.save(os.path.join(self.dirBin, 'model_digit.h5'))\\n model.save_weights(os.path.join(self.dirBin, 'model_digit_weights.h5'))\\n logging.info('Model saved in {}'.format(self.dirBin))\\n \\n if self.verbose:\\n self.plotHistory(history)\\n \\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07fc7c74abfd563d4f5047f0410fa1b2\",\n \"score\": \"0.62013394\",\n \"text\": \"def cross_validation():\\n iris = datasets.load_iris()\\n X, y = iris.data, iris.target\\n\\n kf = KFold(n_splits=5, shuffle=True)\\n models = [NeuralNetwork([8, 2], alpha=1e-5), LogisticRegression(), GradientBoostingClassifier()]\\n predictions = [np.zeros(y.shape) for i in models]\\n for i, m in enumerate(models):\\n for train_index, test_index in kf.split(X):\\n m.fit(X[train_index], y[train_index])\\n predictions[i][test_index] = m.predict(X[test_index])\\n\\n return [f1_score(y, predict, average='micro') for predict in predictions]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afb5b0d1989fd237fa72cd69d2ae8cda\",\n \"score\": \"0.62008345\",\n \"text\": \"def test_model(config, test_dataloader, device, model):\\n model = model.to(device)\\n model.eval()\\n config[\\\"logger\\\"].info(\\\"Testing Model...\\\")\\n correct_top1 = 0\\n correct_top5 = 0\\n total = len(test_dataloader.dataset)\\n for (x, y, extra) in test_dataloader:\\n if config[\\\"test\\\"][\\\"task\\\"] == \\\"CNN\\\":\\n x = x[\\\"image\\\"].to(device)\\n else:\\n x = x.to(device)\\n y = y.to(device)\\n if config[\\\"train\\\"][\\\"task\\\"] == \\\"BDNN\\\":\\n out = model(x)[0]\\n else:\\n out = model(x)\\n y_pred = F.softmax(out, dim=1)\\n y_pred = y_pred.cpu()\\n y = y.cpu()\\n # Calc Top-1\\n pred = y_pred.argmax(dim=1)\\n correct_top1 += torch.eq(pred, y).sum().float().item()\\n # Calc Top-5\\n maxk = max((1, 5))\\n y_resize = y.view(-1, 1)\\n _, pred = y_pred.topk(maxk, 1, True, True)\\n correct_top5 += torch.eq(pred, y_resize).sum().float().item()\\n correct_top1 /= total\\n correct_top5 /= total\\n config[\\\"logger\\\"].info(\\\"============Testing Results============\\\")\\n config[\\\"logger\\\"].info(\\\"Top-1 Acc. {} Top-5 Acc. {}\\\".format(correct_top1, correct_top5))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a71666f94037f615546d1a382c18a5b1\",\n \"score\": \"0.6186508\",\n \"text\": \"def ex_2_1(X_train, y_train, X_test, y_test):\\n\\n randomSeed = np.random.randint(1, 100, 1)\\n\\n n_hidd = [100]\\n\\n score_train, score_test = [], []\\n\\n best_score = 0\\n\\n bestNetwork = MLPClassifier()\\n\\n classes = [\\\"T-shirt/top\\\", \\\"trousers/pants\\\", \\\"pullover shirt\\\", \\\"dress\\\", \\\"coat\\\", \\\"sandal\\\", \\\"shirt\\\", \\\"sneaker\\\", \\\"bag\\\", \\\"ankle boot\\\"]\\n\\n for n, n_h in enumerate(n_hidd):\\n for s, seed in enumerate(randomSeed):\\n nn = MLPClassifier(hidden_layer_sizes=(n_h,), activation='tanh', max_iter=50, random_state=seed)\\n\\n nn.fit(X_train, y_train)\\n\\n scoretrain = nn.score(X_train, y_train)\\n scoretest = nn.score(X_test, y_test)\\n\\n score_train.append(scoretrain)\\n score_test.append(scoretest)\\n\\n if scoretest > best_score:\\n bestNetwork = nn\\n best_score = scoretest\\n\\n print(100 / (len(n_hidd) * len(randomSeed)) * ((n*len(randomSeed)) + (s+1)), \\\"%\\\")\\n\\n \\n plot_boxplot(score_train, score_test)\\n\\n\\n prediction = bestNetwork.predict(X_test)\\n confusionMatrix = confusion_matrix(y_test, prediction)\\n\\n #confusion matrix\\n print(\\\"Confusion matrix:\\\")\\n print(classes)\\n\\n print(confusionMatrix)\\n\\n #Weight\\n print(len(bestNetwork.coefs_))\\n\\n plot_hidden_layer_weights(bestNetwork.coefs_[0])\\n\\n print(\\\"Misclassified Pictures\\\")\\n\\n falseList = prediction == y_test\\n\\n indexPosList = []\\n\\n for i, index in enumerate(falseList):\\n if index == False:\\n indexPosList.append(i)\\n\\n print(indexPosList)\\n\\n for i in range(5):\\n print(\\\"MLPClassifer think it is\\\", prediction[indexPosList[i]] + 1, \\\"but it is\\\", y_test[indexPosList[i]]+1)\\n plot_image(X_test[indexPosList[i]])\\n ## TODO\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49fbd35b39d01fd5d6ab4711badb3868\",\n \"score\": \"0.6181956\",\n \"text\": \"def make_problem(graphs, labels, n_validation=50, seed=0):\\n if seed is not None:\\n np.random.seed(seed)\\n \\n # convert label to 1 hot encoding\\n y = convertToOneHot(labels)\\n\\n # Shuffle\\n indexes = np.random.permutation(len(graphs))\\n graphs_shuffle = np.array(graphs)[indexes]\\n y_shuffle = np.array(y)[indexes]\\n\\n dataset_train = Dataset(graphs_shuffle[:-n_validation], y_shuffle[:-n_validation])\\n dataset_validation = Dataset(graphs_shuffle[-n_validation:], y_shuffle[-n_validation:])\\n return dataset_train, dataset_validation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47cf27be7827aae960dbc445f6eccda9\",\n \"score\": \"0.61763227\",\n \"text\": \"def test():\\n test_dataset, test_dataset_length = dataloader(args_opt.data_url, epoch=1, batch_size=1,\\n mode=args_opt.mode, shuffle=False)\\n predict = []\\n test_dataset_iter = test_dataset.create_dict_iterator()\\n print('Valid dataset length:', test_dataset_length)\\n deepid = get_network(args_opt, args_opt.num_class, True)\\n param_network = load_checkpoint(args_opt.ckpt_url)\\n load_param_into_net(deepid, param_network)\\n print('Start Testing!')\\n for data in test_dataset_iter:\\n img1 = \\\"img1\\\"\\n img2 = \\\"img2\\\"\\n img1_test = data['image1']\\n img2_test = data['image2']\\n label = data['label']\\n output1 = deepid(img1_test)\\n output2 = deepid(img2_test)\\n cosdistance = mindspore.ops.tensor_dot(output1, output2, (1, 1))\\n norm1 = P.norm(output1, dim=1)\\n norm2 = P.norm(output2, dim=1)\\n cosdistance = cosdistance / (norm1 * norm2 + 1e-5)\\n cosdistance = float(cosdistance.asnumpy())\\n predict.append('{}\\\\t{}\\\\t{}\\\\t{}\\\\n'.format(img1, img2, cosdistance, int(label.asnumpy())))\\n accuracy = []\\n thd = []\\n folds = KFold(n=3120, n_folds=10, shuffle=False)\\n thresholds = np.arange(-1.0, 1.0, 0.005)\\n ans = lambda line: line.strip('\\\\n').split()\\n predicts = np.array([ans(x) for x in predict])\\n for idx, (train, ktest) in enumerate(folds):\\n print(\\\"now the idx is %d\\\", idx)\\n best_thresh = find_best_threshold(thresholds, predicts[train])\\n accuracy.append(eval_acc(best_thresh, predicts[ktest]))\\n thd.append(best_thresh)\\n print('ACC={:.4f} std={:.4f} thd={:.4f}'.format(np.mean(accuracy), np.std(accuracy), np.mean(thd)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b286e936ab6cb1f5e29a430cdc5b803\",\n \"score\": \"0.6162955\",\n \"text\": \"def train_model(model):\\n\\n # Pre-processing\\n print('preprocessing')\\n # Scale the image down to 90x90 & Apply random transform,\\n datagenerator = ImageDataGenerator(\\n # featurewise_center=True, featurewise_std_normalization=True,\\n rotation_range=360, shear_range=0.6,\\n horizontal_flip=True, vertical_flip=True,\\n zoom_range=0.6, rescale=1./255,\\n # preprocessing_function=preprocess_data\\n )\\n \\n train_path = '/content/drive/My Drive/Colab Notebooks/Data/Train_data'\\n test_path = '/content/drive/My Drive/Colab Notebooks/Data/Test_data'\\n \\n train_batches = datagenerator.flow_from_directory(train_path, target_size=scaled_img_dimensions, classes=['cherry', 'strawberry', 'tomato'], batch_size=32, class_mode='categorical')\\n \\n # Training\\n # model.fit(generated_imgs, train_labels[0], epochs=20, batch_size=2000)\\n model.fit_generator(train_batches, shuffle=True, epochs=2, steps_per_epoch=4500) \\n\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76296cbc051c6cd750cbcb7ec6e89219\",\n \"score\": \"0.6159247\",\n \"text\": \"def Classification_initialize_and_train(config):\\n\\n # 1. Retrieve information from config dict\\n device = config['device']\\n device_name = torch.cuda.get_device_name(device)\\n print('Device name: {}'.format(device_name))\\n input_shape = config['input_shape']\\n batch_size = config['batch_size'] \\n number_of_tools = config['number_of_tools']\\n output_features = number_of_tools\\n random_frames = config['random_frames']\\n nr_videos = config['nr_videos']\\n nr_frames = config['nr_frames']\\n weight_decay = config['weight_decay']\\n save_interval = config['save_interval']\\n msg_bot = config['msg_bot']\\n bot_msg_interval = config['bot_msg_interval']\\n dataset_name = config['dataset']\\n model_name = config['model']\\n agent_name = 'TransNetAgent'\\n\\n\\n # 2. Define data\\n data = Data()\\n data.add_dataset(Cholec80(random_frames, nr_videos, nr_frames))\\n train_ds = (dataset_name, 'train')\\n val_ds = (dataset_name, 'val')\\n test_ds = (dataset_name, 'test')\\n\\n\\n # 3. Split data and define path\\n splits = dict()\\n for ds_name, ds in data.datasets.items():\\n splits[ds_name] = split_dataset(ds, test_ratio=config['test_ratio'], \\n val_ratio=config['val_ratio'], nr_repetitions=config['nr_runs'], \\n cross_validation=config['cross_validation'])\\n\\n # Include the model name, Alexnet, CNN, Resnet etc. what has been used\\n paths = os.path.join(storage_data_path, 'models', dataset_name+'_'+model_name, 'states')\\n pathr = os.path.join(model_result_path, 'models', dataset_name+'_'+model_name, 'results')\\n if not os.path.exists(paths):\\n os.makedirs(paths)\\n else:\\n # Empty directory\\n shutil.rmtree(paths)\\n os.makedirs(paths)\\n if not os.path.exists(pathr):\\n os.makedirs(pathr)\\n else:\\n # Empty directory\\n shutil.rmtree(pathr)\\n os.makedirs(pathr)\\n\\n # Save split\\n if splits is not None:\\n lr.save_json(splits, path=paths, name='data_splits')\\n\\n\\n # 4. Create data splits for each repetition\\n print('Bring data to PyTorch format..')\\n # Repeat for each repition\\n for run_ix in range(config['nr_runs']):\\n # 5. Bring data to Pytorch format\\n datasets = dict()\\n for ds_name, ds in data.datasets.items():\\n for split, data_ixs in splits[ds_name][run_ix].items():\\n if len(data_ixs) > 0: # Sometimes val indexes may be an empty list\\n aug = config['augmentation'] if not('test' in split) else 'none'\\n datasets[(ds_name, split)] = PytorchClassification2DDataset(ds, \\n ix_lst=data_ixs, size=input_shape, aug_key=aug, \\n resize=config['resize']) #TODO: Test with resize=True and without approach from dataset_classification.py\\n\\n # 6. Build train dataloader, and visualize\\n dl = DataLoader(datasets[(train_ds)], \\n batch_size=batch_size, shuffle=True,\\n num_workers=1)\\n dl_val = DataLoader(datasets[(val_ds)], \\n batch_size=batch_size, shuffle=True,\\n num_workers=1)\\n\\n # 7. Initialize model\\n model = getattr(models, model_name)(output_features)\\n model.to(device)\\n\\n # 8. Define loss and optimizer\\n loss_f = LossBCE(device=device)\\n optimizer = optim.Adam(model.parameters(), lr=config['lr'],\\n weight_decay=weight_decay)\\n\\n # 9. Train model\\n print('Training model in batches of {}..'.format(batch_size))\\n\\n agent = getattr(agents, agent_name)(model=model, device=device)\\n losses_train, losses_cum_train, losses_val, losses_cum_val,\\\\\\n accuracy_train, accuracy_det_train, accuracy_val,\\\\\\n accuracy_det_val = agent.train(optimizer, loss_f, dl,\\n dl_val, nr_epochs=config['nr_epochs'],\\n save_path=paths,\\n save_interval=save_interval, msg_bot=msg_bot,\\n bot_msg_interval=bot_msg_interval)\\n \\n # 10. Build test dataloader, and visualize\\n dl = DataLoader(datasets[(test_ds)], \\n batch_size=batch_size, shuffle=True)\\n \\n # 11. Test model\\n print('Testing model in batches of {}..'.format(batch_size))\\n losses_test, losses_cum_test, accuracy_test, accuracy_det_test = agent.test(loss_f, dl, msg_bot=msg_bot)\\n\\n # 12. Save results\\n save_results(model, model_name, dataset_name, paths, pathr, losses_train, losses_val, accuracy_train,\\n accuracy_det_train, accuracy_val, accuracy_det_val, losses_test, accuracy_test,\\n accuracy_det_test, losses_cum_train, losses_cum_val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e57cf40d2e063373cdc94bc0fc4667a8\",\n \"score\": \"0.6158664\",\n \"text\": \"def auth_train(client,random_class,train_samples):\\n T = 16\\n model = get_model()\\n model.load_weights('./weights/new_14.129-0.9992.h5')\\n print (len(model.layers))\\n model.pop()\\n frozen_layers = len(model.layers)-2\\n model.add(Dense(2, activation='sigmoid'))\\n for layer in model.layers[:frozen_layers]:\\n layer.trainable = False\\n model.summary()\\n\\n random_class[random_class.index(client)], random_class[0] = random_class[0], client\\n\\n rand_train_class = random_class[:50]\\n SGD = sgd(lr=0.01, momentum=0.9, decay=1e-3)\\n model.compile(loss='binary_crossentropy', optimizer=SGD, metrics=['accuracy'])\\n early_stop = EarlyStopping(patience=7, verbose=1, monitor='val_loss')\\n save_best = ModelCheckpoint('./weights/'+ str(client) + '.h5', monitor='val_loss', verbose=1,save_best_only=True)\\n train_gen = get_imagedata.ImageDataGenerator(preprocessing_function=preprocess, client=client,\\n height_shift_range=0.1,width_shift_range=0.1,\\n rotation_range=10,zoom_range=(0.9,1))\\n val_gen = get_imagedata.ImageDataGenerator(preprocessing_function=preprocess, client=client)\\n\\n model.fit_generator(\\n train_gen.flow_from_directory('../data/5sec10',sub_dir=train_samples[:3], sequence=T,\\n auth_rand_train=rand_train_class,\\n target_size=(100, 100),batch_size=32,authentication=True)\\n , steps_per_epoch=200, epochs=15, callbacks=[early_stop, save_best],\\n validation_data=val_gen.flow_from_directory('../data/5sec10', sub_dir=train_samples[3:],\\n sequence=T,auth_rand_train=rand_train_class,\\n target_size=(100, 100), batch_size=32,authentication=True)\\n , validation_steps=200, workers=4,use_multiprocessing=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddc5e9ee025b64e904f4501a4eb8bb03\",\n \"score\": \"0.61550874\",\n \"text\": \"def train_on_test():\\n create_cbk()\\n train()\\n test(\\\"res186/\\\", \\\"models_file186.txt\\\", \\\"train186.txt\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"397e5482e4215a42a3729ca79e7a3d4e\",\n \"score\": \"0.61392045\",\n \"text\": \"def cross_validate_all(model, data, labels):\\n\\n print(\\\"Running Cross Validation\\\")\\n model = model\\n dum_unifrom = DummyClassifier(strategy='uniform') #uniform most_frequent stratified\\n dum_stratified = DummyClassifier(strategy='stratified')\\n dum_freq = DummyClassifier(strategy='most_frequent')\\n\\n scoring = ['accuracy', 'f1_micro', 'f1_macro']\\n\\n cv = KFold(n_splits=5, shuffle=True, random_state=42)\\n scores = cross_validate(model, data, labels, scoring=scoring, cv=cv, return_train_score=False, n_jobs=-1)\\n dum_uniform_scores = cross_validate(dum_unifrom, data, labels, scoring=scoring, cv=cv, return_train_score=False, n_jobs=-1)\\n dum_stratified_scores = cross_validate(dum_stratified, data, labels, scoring=scoring, cv=cv, return_train_score=False, n_jobs=-1)\\n dum_freq_scores = cross_validate(dum_freq, data, labels, scoring=scoring, cv=cv, return_train_score=False, n_jobs=-1)\\n\\n acc_mean = np.around(scores['test_accuracy'].mean(), decimals=3)\\n acc_std = np.around(scores['test_accuracy'].std(), decimals=3)\\n\\n micro_mean = np.around(scores['test_f1_micro'].mean(), decimals=3)\\n micro_std = np.around(scores['test_f1_micro'].std(), decimals=3)\\n\\n macro_mean = np.around(scores['test_f1_macro'].mean(), decimals=3)\\n macro_std = np.around(scores['test_f1_macro'].std(), decimals=3)\\n\\n # calculate the lift over random score\\n dum_mean = dum_uniform_scores['test_f1_micro'].mean()\\n strat_mean = dum_stratified_scores['test_f1_micro'].mean()\\n freq_mean = dum_freq_scores['test_f1_micro'].mean()\\n\\n dum_lift_mean = np.around(((acc_mean-dum_mean)/dum_mean)*100, decimals=2)\\n strat_lift_mean = np.around(((acc_mean-strat_mean)/strat_mean)*100, decimals=2)\\n freq_lift_mean = np.around(((acc_mean-freq_mean)/freq_mean)*100, decimals=2)\\n\\n return acc_mean, acc_std, micro_mean, micro_std, macro_mean, macro_std, dum_lift_mean, strat_lift_mean, freq_lift_mean\\n\\n #acc = sk_ms.cross_val_score(model, data, labels, cv=10, scoring='accuracy', n_jobs=-1)\\n #f1_macro = sk_ms.cross_val_score(model, data, labels, cv=10, scoring='f1_macro', n_jobs=-1)\\n #f1_micro = sk_ms.cross_val_score(model, data, labels, cv=10, scoring='f1_micro', n_jobs=-1)\\n\\n #return acc, f1_macro, f1_micro\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bce4e31380d3fe6a6391c7c869f47861\",\n \"score\": \"0.61378807\",\n \"text\": \"def train():\\n # Set the random seeds for reproducibility\\n # np.random.seed(42)\\n\\n onehot_input, y, _ = cross_entropy_input_to_onehot()\\n\\n LEARNING_RATE_DEFAULT = 3e-3\\n MAX_STEPS_DEFAULT = 4000000\\n\\n cnet_a = CrossNet2(onehot_input.shape[1])\\n script_directory = os.path.split(os.path.abspath(__file__))[0]\\n filepath = 'grubbyStarCE2.model'\\n model_to_train = os.path.join(script_directory, filepath)\\n\\n cnet_b = SimpleMLP(onehot_input.shape[1])\\n script_directory_b = os.path.split(os.path.abspath(__file__))[0]\\n filepath_b = 'grubbyStarCrossEntropy.model'\\n model_b = os.path.join(script_directory_b, filepath_b)\\n\\n print(cnet_a)\\n print(onehot_input.shape)\\n print(onehot_input.shape[1])\\n\\n optimizer = torch.optim.SGD(cnet_a.parameters(), lr=1e-3, momentum=0.9, weight_decay=1e-5)\\n optimizer_b = torch.optim.SGD(cnet_b.parameters(), lr=1e-3, momentum=0.9, weight_decay=1e-5)\\n\\n accuracies = []\\n losses = []\\n vag_losses = []\\n min_loss = 100\\n min_loss_b = 100\\n\\n vag_games = get_validation_ids()\\n vag_games = np.array(vag_games)\\n vag_ids = vag_games[-200:]\\n validation_games = 100\\n vag_input = onehot_input[vag_ids, :]\\n vag_targets = y[vag_ids]\\n\\n for epoch in range(1):\\n val_ids = [i for i in range(onehot_input.shape[0]-validation_games, onehot_input.shape[0])]\\n val_ids = np.append(val_ids, vag_ids)\\n val_ids = np.unique(val_ids)\\n val_ids = np.array(val_ids)\\n print(len(val_ids), \\\"val ids\\\")\\n print(val_ids)\\n\\n train_ids = [i for i in range(onehot_input.shape[0]) if i not in val_ids]\\n\\n X_train = onehot_input[train_ids, :]\\n print(X_train.shape)\\n y_train = y[train_ids]\\n\\n X_test = onehot_input[val_ids, :]\\n y_test = y[val_ids]\\n\\n print(\\\"epoch \\\" + str(epoch))\\n saturation = 1\\n p = 1\\n bce = True\\n ace = True\\n\\n for iteration in range(MAX_STEPS_DEFAULT):\\n BATCH_SIZE_DEFAULT = 8\\n cnet_a.train()\\n cnet_b.train()\\n if iteration % 20000 == 0:\\n # saturation *= 0.5\\n # saturation = max(0.5, saturation)\\n print(iteration)\\n print(saturation)\\n\\n ids = np.random.choice(X_train.shape[0], size=BATCH_SIZE_DEFAULT, replace=False)\\n\\n X_train_batch = X_train[ids, :]\\n y_train_batch = y_train[ids]\\n\\n X_train_batch = np.reshape(X_train_batch, (BATCH_SIZE_DEFAULT, -1))\\n X_train_batch = Variable(torch.FloatTensor(X_train_batch))\\n\\n output = cnet_a.forward(X_train_batch)\\n output_b = cnet_b.forward(X_train_batch)\\n\\n y_train_batch = np.reshape(y_train_batch, (BATCH_SIZE_DEFAULT, -1))\\n y_train_batch = Variable(torch.FloatTensor(y_train_batch))\\n\\n if iteration % 1 == 0:\\n loss = center_my_loss(output, y_train_batch, output_b, saturation)\\n else:\\n loss = torch.nn.functional.binary_cross_entropy(output, y_train_batch)\\n\\n if True:\\n loss_b = center_my_loss(output_b, y_train_batch, output, saturation)\\n else:\\n loss_b = torch.nn.functional.binary_cross_entropy(output_b, y_train_batch)\\n\\n ce_loss = torch.nn.functional.binary_cross_entropy(output, y_train_batch)\\n ce_loss_b = torch.nn.functional.binary_cross_entropy(output_b, y_train_batch)\\n\\n if iteration % EVAL_FREQ_DEFAULT == 0:\\n cnet_a.eval()\\n cnet_b.eval()\\n\\n ids = np.array(range(len(X_test)))\\n\\n x = X_test[ids, :]\\n targets = y_test[ids]\\n\\n x = np.reshape(x, (len(X_test), -1))\\n\\n x = Variable(torch.FloatTensor(x))\\n\\n pred = cnet_a.forward(x)\\n pred_b = cnet_b.forward(x)\\n acc = accuracy(pred, targets)\\n\\n targets = np.reshape(targets, (len(X_test), -1))\\n targets = Variable(torch.FloatTensor(targets))\\n\\n calc_loss = torch.nn.functional.binary_cross_entropy(pred, targets)\\n calc_loss_b = torch.nn.functional.binary_cross_entropy(pred_b, targets)\\n\\n accuracies.append(acc)\\n\\n ###################\\n\\n if p*calc_loss.item()+(1-p)*ce_loss.item() < p*calc_loss_b.item()+(1-p)*ce_loss_b.item():\\n cnet_b.train()\\n cnet_b.zero_grad()\\n loss_b.backward(retain_graph=True)\\n optimizer_b.step()\\n cnet_b.eval()\\n\\n if min_loss_b > calc_loss_b.item():\\n min_loss_b = calc_loss_b.item()\\n torch.save(cnet_b, model_b)\\n\\n ids = np.array(range(len(X_train)))\\n x = X_train[ids, :]\\n targets = y_train[ids]\\n\\n x = np.reshape(x, (len(X_train), -1))\\n\\n x = Variable(torch.FloatTensor(x))\\n\\n pred_b = cnet_b.forward(x)\\n\\n train_acc = accuracy(pred_b, targets)\\n\\n targets = np.reshape(targets, (len(X_train), -1))\\n targets = Variable(torch.FloatTensor(targets))\\n\\n train_loss = torch.nn.functional.binary_cross_entropy(pred_b, targets)\\n losses.append(train_loss.item())\\n\\n print(\\\"iteration: \\\" + str(iteration) + \\\" train acc \\\" + str(train_acc) + \\\" val acc \\\" + str(\\n acc) + \\\" a \\\" + str(round(calc_loss.item()*1000)/1000) + \\\" b \\\" + str(\\n round(calc_loss_b.item() * 1000)/1000))\\n\\n if p*calc_loss.item()+(1-p)*ce_loss.item() > p*calc_loss_b.item()+(1-p)*ce_loss_b.item():\\n cnet_a.train()\\n cnet_a.zero_grad()\\n loss.backward(retain_graph=True)\\n optimizer.step()\\n cnet_a.eval()\\n\\n if min_loss > calc_loss.item():\\n min_loss = calc_loss.item()\\n torch.save(cnet_a, model_to_train)\\n\\n ids = np.array(range(len(X_train)))\\n x = X_train[ids, :]\\n targets = y_train[ids]\\n\\n x = np.reshape(x, (len(X_train), -1))\\n\\n x = Variable(torch.FloatTensor(x))\\n\\n pred = cnet_a.forward(x)\\n\\n train_acc = accuracy(pred, targets)\\n\\n targets = np.reshape(targets, (len(X_train), -1))\\n targets = Variable(torch.FloatTensor(targets))\\n\\n print(\\\"iteration: \\\" + str(iteration) + \\\" train acc \\\" + str(train_acc) + \\\" val acc \\\" + str(\\n acc) + \\\" a \\\" + str(round(calc_loss.item()*1000)/1000) + \\\" b \\\" + str(\\n round(calc_loss_b.item() * 1000)/1000))\\n\\n test_nn.test_all(model_to_train)\\n print(model_to_train)\\n\\n plt.plot(accuracies)\\n plt.ylabel('accuracies')\\n plt.show()\\n\\n plt.plot(vag_losses, 'r')\\n plt.plot(losses, 'b')\\n plt.ylabel('losses')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ce0d687e29d6e3788e4836fe28ef733\",\n \"score\": \"0.6130701\",\n \"text\": \"def train():\\n net = model(load=False, shape=(100, 100, 3))\\n X, y = get_X_y('./data/t1_1/driving_log.csv')\\n net.fit_generator(_generator(256, X, y), samples_per_epoch=20224, nb_epoch=2)\\n net.save('checkpoints/short.h5')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b79916dd2fd528c87eaac3cf7c4d4f1\",\n \"score\": \"0.61234725\",\n \"text\": \"def test_performance_cross_validation(dataset, classifier, label_col, n_folds, seed='46'):\\n rand_col = \\\"uid_rand\\\"\\n h = 1.0 / n_folds\\n df = dataset.select(\\\"*\\\", f.rand(seed).alias(rand_col))\\n\\n metrics_dict = {\\\"roc_auc\\\": [], # roc: y=tpr x=fpr\\n \\\"true_pos_rate\\\": [], # recall = true pos rate \\n \\\"false_pos_rate\\\": [],\\n \\\"precision\\\": [],\\n \\\"n_true_neg\\\": [],\\n \\\"n_false_neg\\\": [],\\n \\\"n_false_pos\\\": [],\\n \\\"n_true_pos\\\": [], }\\n\\n model = None\\n for i in range(n_folds):\\n\\n validate_lb = i * h # lower bound\\n validate_ub = (i + 1) * h # upper bound\\n condition = (df[rand_col] >= validate_lb) & (df[rand_col] < validate_ub)\\n validation = df.filter(condition)\\n train = df.filter(~condition)\\n \\n # train\\n model = classifier.fit(train)\\n \\n # predict\\n prediction = model.transform(validation)\\n \\n # assess performance metrics\\n prediction_and_labels = prediction.rdd.map(lambda x: (x['prediction'], x[label_col]))\\n print(prediction_and_labels)\\n metrics = MulticlassMetrics(prediction_and_labels)\\n metrics_areas = BinaryClassificationMetrics(prediction_and_labels) # gets roc and precRecall curves\\n metrics_dict['roc_auc'].append(metrics_areas.areaUnderROC)\\n # a bit slow, have to calc outside loop\\n cm = metrics.confusionMatrix().toArray()\\n n_true_neg = cm[0, 0]\\n n_false_neg = cm[1, 0]\\n n_true_pos = cm[1, 1]\\n n_false_pos = cm[0, 1]\\n #\\n metrics_dict['n_true_neg'].append(n_true_neg) \\n metrics_dict['n_false_neg'].append(n_false_neg)\\n metrics_dict['n_true_pos'].append(n_true_pos)\\n metrics_dict['n_false_pos'].append(n_false_pos) \\n metrics_dict['true_pos_rate'].append(n_true_pos / (n_true_pos+n_false_neg))\\n metrics_dict['false_pos_rate'].append(n_false_pos / (n_false_pos+n_true_neg))\\n metrics_dict['precision'].append(n_true_pos / (n_true_pos+n_false_pos))\\n\\n return model, metrics_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b6f4a8324ee07037fa8904b56f6e4dc\",\n \"score\": \"0.610934\",\n \"text\": \"def prep_datasets(test_size, validation_size):\\n X, y = load_data(DATA_PATH)\\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size)\\n X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=validation_size)\\n\\n return X_train, X_test, X_validation, y_train, y_test, y_validation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8be6173f0a97bc23d9661ba1438c9277\",\n \"score\": \"0.6109022\",\n \"text\": \"def few_label_classification(self, list_num_shots=[1, 10, 100, -1], num_runs=10, batch_size=1000, classifier='logistic'):\\n if not os.path.exists('results/few_label/'):\\n os.makedirs('results/few_label/')\\n results = {'Num_Shots': [], 'Mean': [], 'Std': []}\\n train_loader, im_h, im_w, im_channels = setup_data_loader(data=self.data, \\n data_dir=self.data_dir, \\n batch_size=batch_size, \\n train=True, \\n normalize=False if self.model_name in ['VAE', 'VAE_GMM'] else True, \\n shuffle=False, \\n shot_random_seed=None)\\n zs_train, ys_train = self.encode_dataset(train_loader)\\n \\n test_loader, im_h, im_w, im_channels = setup_data_loader(data=self.data, \\n data_dir=self.data_dir, \\n batch_size=batch_size, \\n train=False, \\n normalize=False if self.model_name in ['VAE', 'VAE_GMM'] else True, \\n shuffle=False, \\n shot_random_seed=None)\\n zs_test, ys_test = self.encode_dataset(test_loader)\\n \\n num_classes = len(np.unique(ys_train))\\n \\n for num_shots in tqdm(list_num_shots):\\n Accuracy = []\\n if num_shots == -1:\\n clf = LogisticRegression(random_state=0, \\n multi_class='auto', \\n solver='liblinear', \\n max_iter=10000).fit(zs_train, ys_train)\\n Accuracy.append(np.array([clf.score(zs_test, ys_test)])) \\n else:\\n for i in tqdm(range(num_runs)):\\n # torch.cuda.empty_cache()\\n zs_train_selected = []\\n ys_train_selected = []\\n set_seed(i)\\n for k in range(num_classes):\\n indk = np.argwhere(ys_train == k)[:,0]\\n ind_of_indk = np.random.permutation(len(indk))[:num_shots]\\n indk_selected = indk[ind_of_indk]\\n zs_train_selected.append(zs_train[indk_selected])\\n ys_train_selected.append(ys_train[indk_selected])\\n zs_train_selected = np.concatenate(zs_train_selected, 0)\\n ys_train_selected = np.concatenate(ys_train_selected, 0)\\n\\n# print(indk_selected)\\n \\n clf = LogisticRegression(random_state=0, \\n multi_class='auto', \\n solver='liblinear', \\n max_iter=10000).fit(zs_train_selected, ys_train_selected)\\n Accuracy.append(np.array([clf.score(zs_test, ys_test)])) \\n# return 0\\n Accuracy = np.concatenate(Accuracy)\\n results['Num_Shots'].append(num_shots)\\n results['Mean'].append(Accuracy.mean())\\n results['Std'].append(Accuracy.std())\\n pd.DataFrame.from_dict(results).to_csv('results/few_label/{}-{}-runs={}.csv'.format(self.model_name, self.data, num_runs), index=False)\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4dbe68065116d05964931885b20db3b\",\n \"score\": \"0.61042625\",\n \"text\": \"def main():\\r\\n print \\\"running now\\\"\\r\\n print \\\"reading in data\\\"\\r\\n inputs_test = pandas.read_csv(\\\"random_baseline_test_inputs.csv\\\").values\\r\\n labels_test = pandas.read_csv(\\\"random_baseline_test_labels.csv\\\").values\\r\\n inputs_train = pandas.read_csv(\\\"two_layer_generated2_train_inputs.csv\\\").values\\r\\n labels_train = pandas.read_csv(\\\"two_layer_generated2_train_labels.csv\\\").values\\r\\n\\r\\n #Keeps track of the number of samples in each dataset.\\r\\n train_size = len(labels_train)\\r\\n test_size = len(labels_test)\\r\\n\\r\\n \\\"\\\"\\\"Batch size is how many samples to train/evaluate the model on at once.\\r\\n In my experience, this doesn't effect performance of the model but if it is\\r\\n too high, your machine can run out of memory and crash and if it is too low\\r\\n your training can take too long. 100 has worked well so far.\\\"\\\"\\\"\\r\\n batch_size=100\\r\\n\\r\\n \\\"\\\"\\\"The model will train for a number of epochs equal to num_epochs. One epoch\\r\\n passes when the model has looked at every point in the dataset for training.\\r\\n The more epochs, the better trained the model will be and the better performance\\r\\n it will have. More complex models tend to take more epochs to train properly. \\r\\n There will be a point of diminishing returns with the epochs. For this model\\r\\n improvements start to flatten out around 10 epochs\\\"\\\"\\\"\\r\\n num_epochs = 10 \\r\\n\\r\\n \\\"\\\"\\\"This is a small constant used in batch regularization calculations to prevent\\r\\n divide by zero errors. At present batch regularization code is not implemented\\r\\n but is commented out (more on that further down)\\\"\\\"\\\"\\r\\n delta = 10^-8 \\r\\n\\r\\n \\\"\\\"\\\"These are paramaters for dropout. Dropout is a way of preventing overfitting by\\r\\n temporarily removing neurons (by setting them to 0) during training. The idea is \\r\\n that you are forcing the rest of the neurons to compensate for the lost neuron and\\r\\n you are preventing neurons from \\\"conspiring\\\" to overfit. drop_input and drop_hidden\\r\\n are the proportions (out of 1) of the dropout inputs that are kept when dropout is \\r\\n called with those variables. Standard practice is to keep .8 of the input units and \\r\\n .5 of the units in each hidden layer. Here both keep probabilities are set to 1 meaning\\r\\n no dropout occurs. This is because overfitting has not yet become an issue. If it does\\r\\n (signified by a large difference between train and test errors), dropout can be \\r\\n implemented as a potential fix by changing these variables.\\\"\\\"\\\"\\r\\n drop_input = 1\\r\\n drop_hidden = 1\\r\\n\\r\\n\\r\\n print \\\"creating model\\\"\\r\\n \\\"\\\"\\\" x is a placeholder for the input data that is going to be passed in to the model.\\r\\n Later on when we tell the model to start training, we will tell it to look at the \\r\\n data we took from the CSVs to use as values for x. A value of [None, 5] in the shape \\r\\n argument means that there can be an arbitrary amount of length 5 vectors passed in\\\"\\\"\\\"\\r\\n x = tf.placeholder(tf.float64, [None, 5], name = \\\"input_placeholder\\\")\\r\\n\\r\\n\\r\\n \\\"\\\"\\\"These are placeholders for the dropout keep probabilities. We want dropout to occur only during \\r\\n training and not testing so we make keep probabilities placeholders and pass in the value we want\\r\\n later when we know which (training/testing) we are doing.\\\"\\\"\\\"\\r\\n drop_input_placeholder = tf.placeholder(tf.float64, shape = (), name = \\\"drop_input_placeholder\\\")\\r\\n drop_hidden_placeholder = tf.placeholder(tf.float64, shape = (), name = \\\"drop_hidden_placeholder\\\")\\r\\n\\r\\n #Applies dropout to inputs\\r\\n x_drop = tf.nn.dropout(x, drop_input_placeholder)\\r\\n\\r\\n \\\"\\\"\\\"If reuse is set to false, weights and biases are initialized according to defaults. If true,\\r\\n it reads in the initial values of weights and biases from saved CSVs\\\"\\\"\\\"\\r\\n reuse = False\\r\\n\\r\\n \\\"\\\"\\\"This is the first layer of the neural network. This layer accepts [batch_size x 5] input vectors \\r\\n and multiplies them be a bias and adds a weight to create a [batch_size x 10] hidden vector. It then\\r\\n applies a ReLU activation function to force all elements of h_1 to be non-negative. This effectively\\r\\n either turns off each neuron or turns them on with an intensity linerly proportional to the value of\\r\\n the hidden unit. Subsequent layers are shaped similarly.\\\"\\\"\\\"\\r\\n\\r\\n if (reuse):\\r\\n W_1 = tf.Variable(pandas.read_csv(\\\"two_layer_weights_1.csv\\\", header = None).values)\\r\\n b_1 = tf.Variable(pandas.read_csv(\\\"two_layer_biases_1.csv\\\", header = None).values)\\r\\n else:\\r\\n W_1 = weight_variable([5,10])\\r\\n b_1 = bias_variable([10])\\r\\n h_1 = tf.nn.relu(tf.matmul(x_drop, W_1) + b_1)\\r\\n\\r\\n \\\"\\\"\\\"Batch normalization is a technique that has been shown in other cases to dramatically help convergence.\\r\\n The commented out lines here show the form that it would take in this implementation. If one wanted to \\r\\n apply batch normalization to this entire program, they would just have to uncomment the lines below and\\r\\n copy them in every step of the neural network. (e.g. change sig_1 to sig_2, mu_1 to mu_2 etc. and put the \\r\\n steps between the calculations of h_2 and h_2_drop.) Imperically this does not help the current model as it\\r\\n stands but it should be retried every once in a while if hte model is changing becuase it has been so \\r\\n effective in practice.\\r\\n mu_1 = tf.reduce_mean(h_1)\\r\\n sig_1 = tf.sqrt(tf.reduce_mean(tf.squared_difference(h_1, mu_1))+delta)\\r\\n h_1 = tf.divide((h_1-mu_1),sig_1)\\\"\\\"\\\"\\r\\n\\r\\n #Applies dropout to the first hidden layer.\\r\\n h_1_drop = tf.nn.dropout(h_1, drop_hidden_placeholder)\\r\\n\\r\\n #Second hidden layer, takes [batch_size x 10] input and gives [batch_size x 5] output\\r\\n if (reuse):\\r\\n W_2 = tf.Variable(pandas.read_csv(\\\"two_layer_weights_2.csv\\\", header = None).values)\\r\\n b_2 = tf.Variable(pandas.read_csv(\\\"two_layer_biases_2.csv\\\", header = None).values)\\r\\n else: \\r\\n W_2 = weight_variable([10,5])\\r\\n b_2 = bias_variable([5])\\r\\n h_2 = tf.nn.relu(tf.matmul(h_1_drop, W_2) + b_2)\\r\\n h_2_drop = tf.nn.dropout(h_2, drop_hidden_placeholder)\\r\\n\\r\\n\\r\\n #[batch_size x 5] -> [batch_size x 1]\\r\\n if (reuse):\\r\\n W_3 = tf.Variable(pandas.read_csv(\\\"two_layer_weights_3.csv\\\", header = None).values)\\r\\n b_3 = tf.Variable(pandas.read_csv(\\\"two_layer_biases_3.csv\\\", header = None).values)\\r\\n else: \\r\\n W_3 = weight_variable([5,1])\\r\\n b_3 = bias_variable([1])\\r\\n y = tf.matmul(h_2_drop, W_3) + b_3\\r\\n\\r\\n\\r\\n print \\\"making definitions\\\"\\r\\n\\r\\n\\r\\n # y_ is a placeholder for the actual costs. It will later be filled in with label data.\\r\\n y_ = tf.placeholder(tf.float64, [None,], name=\\\"label_placeholder\\\")\\r\\n\\r\\n\\r\\n \\\"\\\"\\\"This is the cost for the neural network (not the pedestrian). Higher cost reflects\\r\\n predictions being further from reality.\\\"\\\"\\\"\\r\\n cost = tf.squared_difference(y, y_)\\r\\n\\r\\n #Accuracy is the average cost for the batch\\r\\n accuracy = tf.reduce_mean(cost) \\r\\n\\r\\n \\\"\\\"\\\"This is where the magic happens. Your optimizer will modify the weight and bias variables\\r\\n to minimize this cost as best it can\\\"\\\"\\\"\\r\\n train_step = tf.train.AdamOptimizer().minimize(cost)\\r\\n\\r\\n #Creates the session in which your model will run\\r\\n sess = tf.InteractiveSession()\\r\\n\\r\\n #In different versions of tensorflow, initialize_all_variables is used instead of global_variables_initializer\\r\\n tf.global_variables_initializer().run() \\r\\n \\r\\n print \\\"training\\\"\\r\\n\\r\\n #Calculating the number of batches you need based on number of samples and batch size\\r\\n num_batches = int(np.floor(train_size/batch_size))\\r\\n\\r\\n\\r\\n \\\"\\\"\\\"The way I've set this up, the entire dataset doesn't get looked at, it can only look at up to a \\r\\n multiple of the batch size. There are ways to account for everything but it isn't super critical to\\r\\n the effectiveness of the model so I've let it slide for now. These printouts give you a sense of how \\r\\n much data you are ignoring\\\"\\\"\\\"\\r\\n print \\\"train size\\\"\\r\\n print train_size\\r\\n print \\\"actual size I'm sampling from\\\"\\r\\n print num_batches*batch_size\\r\\n\\r\\n for j in range (num_epochs): #This for loop iterated over epochs to train the model\\r\\n print \\\"epoch %d of %d\\\" % (j+1, num_epochs)\\r\\n\\r\\n \\\"\\\"\\\"In order to keep consecutive samples independent of each other and allow training to converge better,\\r\\n the dataset must be shuffled before being trained. The following lines shuffle the data once per epoch. \\r\\n It is important that inputs and labels are shuffled in the same order so that their rows still correspond\\r\\n to each other.\\\"\\\"\\\"\\r\\n permutation = np.random.permutation(len(labels_train))\\r\\n\\r\\n print \\\"shuffling\\\"\\r\\n shuffled_inputs_train = np.copy(inputs_train)\\r\\n shuffled_labels_train = np.copy(labels_train)\\r\\n\\r\\n for old_index, new_index in enumerate(permutation):\\r\\n shuffled_labels_train[new_index, 0] = np.copy(labels_train[old_index,0])\\r\\n shuffled_inputs_train[new_index, 0:5] = np.copy(inputs_train[old_index,0:5])\\r\\n \\r\\n print \\\"training\\\"\\r\\n\\r\\n #Iterates training over every batch \\r\\n batch_error_total = 0 #resets the error counter\\r\\n for i in range(num_batches): \\r\\n batch_xs = shuffled_inputs_train[i*batch_size:(i+1)*batch_size,0:5] #takes states and actions for the current batch\\r\\n batch_ys = shuffled_labels_train[i*batch_size:(i+1)*batch_size,0] #takes labels (costs) for the current batch\\r\\n #the line below initiates the training and keeps track of accuracy. The feed_dict shows where to look for placeholder values\\r\\n _, batch_error = sess.run([train_step, accuracy], feed_dict={x: batch_xs, y_: batch_ys, drop_hidden_placeholder: drop_hidden, drop_input_placeholder: drop_input})\\r\\n batch_error_total += float(batch_error)/num_batches #keeps track of error\\r\\n avg_train_error = batch_error_total\\r\\n print \\\"epoch %d has average training error of %d\\\" % (j+1, avg_train_error)\\r\\n \\r\\n\\r\\n print \\\"testing\\\"\\r\\n \\\"\\\"\\\"keeps track of the maximum and minimum predictions. This is useful to see how \\\"adventurous\\\" the model gets in\\r\\n its predictions. A small range means it isn't really making much different precitions every time while a larger range\\r\\n means it is picking up on patterns and is really able to discriminate between high likelihood and low likelihood crash\\r\\n scenarios\\\"\\\"\\\"\\r\\n y_max = tf.reduce_max(y)\\r\\n y_min = tf.reduce_min(y)\\r\\n\\r\\n\\r\\n #Similar to during training, our testing is batched\\r\\n num_batches = int(np.floor(test_size/batch_size))\\r\\n print \\\"test size\\\"\\r\\n print test_size\\r\\n print \\\"actual size I'm sampling from\\\"\\r\\n print num_batches*batch_size\\r\\n\\r\\n batch_error_total = 0\\r\\n ymax = 0\\r\\n ymin = 9999999999999999999999999999\\r\\n for i in range(num_batches): \\r\\n batch_xs = inputs_test[i*batch_size:(i+1)*batch_size,0:5] \\r\\n batch_ys = labels_test[i*batch_size:(i+1)*batch_size,0]\\r\\n batch_error, y_max_, y_min_ = sess.run([accuracy, y_max, y_min], feed_dict={x: batch_xs, y_: batch_ys, drop_input_placeholder: 1, drop_hidden_placeholder: 1})\\r\\n batch_error_total += float(batch_error)/num_batches\\r\\n #updates ymax and ymin from batch\\r\\n if (y_max_ > ymax):\\r\\n ymax = y_max_\\r\\n if (y_min_ < ymin):\\r\\n ymin = y_min_\\r\\n avg_test_error = batch_error_total\\r\\n\\r\\n \\r\\n print(\\\"Test Error: \\\", avg_test_error)\\r\\n print(\\\"Range of Predicted Outputs: \\\", ymin,\\\" - \\\", ymax)\\r\\n\\r\\n w1_ = W_1.eval()\\r\\n b1_ = b_1.eval()\\r\\n w2_ = W_2.eval()\\r\\n b2_ = b_2.eval()\\r\\n w3_ = W_3.eval()\\r\\n b3_ = b_3.eval()\\r\\n\\r\\n \\r\\n #save numpy arrays to CSV\\r\\n np.savetxt(\\\"two_layer_weights_1.csv\\\", w1_, delimiter=\\\",\\\")\\r\\n np.savetxt(\\\"two_layer_biases_1.csv\\\", b1_, delimiter=\\\",\\\")\\r\\n np.savetxt(\\\"two_layer_weights_2.csv\\\", w2_, delimiter=\\\",\\\")\\r\\n np.savetxt(\\\"two_layer_biases_2.csv\\\", b2_, delimiter=\\\",\\\")\\r\\n np.savetxt(\\\"two_layer_weights_3.csv\\\", w3_, delimiter=\\\",\\\")\\r\\n np.savetxt(\\\"two_layer_biases_3.csv\\\", b3_, delimiter=\\\",\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd96f3ccb40454f64b27151c187fc1df\",\n \"score\": \"0.609836\",\n \"text\": \"def gen_imgs_1_to_5(samples, batch_size, crop_size,\\n folder_to_save=False, shuffle=True):\\n\\n num_samples = len(samples)\\n while 1:\\n if shuffle:\\n # if frac = 1 will reorganized list randomly\\n samples = samples.sample(frac=1)\\n\\n for offset in range(0, num_samples, batch_size):\\n batch_samples = samples.iloc[offset:offset+batch_size]\\n images = []\\n labels = []\\n for _, batch_sample in batch_samples.iterrows():\\n img = io.imread(batch_sample.patch_path)\\n img = img[:, :, :3]\\n image_name = osp.splitext(\\n osp.basename(batch_sample.patch_path))[0]\\n\\n img = patch_aug_flip_rotate_crop_1_to_5(\\n img, crop_size, image_name, folder_to_save)\\n # img = patch_aug_flip_rotate_crop(\\n # img, crop_size, image_name, folder_to_save)\\n label = batch_sample['is_tumor']\\n\\n # images.append(img)\\n images.extend(img)\\n labels.append(label)\\n labels.append(label)\\n labels.append(label)\\n labels.append(label)\\n labels.append(label)\\n\\n X_train = np.array(images)\\n y_train = np.array(labels)\\n y_train = to_categorical(y_train, num_classes=2)\\n\\n yield X_train, y_train\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97bd1848fb338eac7a886eda8b7ed8e8\",\n \"score\": \"0.60963017\",\n \"text\": \"def train_CNNs(datasets, input_size, num_of_classes, num_of_epochs, batch_size):\\n i = 0\\n for X_train, y_train, X_test, y_test in datasets:\\n X_train, y_train, X_test, y_test = prepare_data(X_train, y_train, X_test, y_test, input_size)\\n model = simple_cnn(input_size, num_of_classes)\\n model.fit(X_train, y_train, epochs=num_of_epochs, batch_size=batch_size, validation_split=0.1, verbose=2)\\n # model.save('cifar-100_disjoint_dataset_models/cnn_model_%d.h5' % i)\\n i += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7ca3b812c59bde2937561c954ce682a\",\n \"score\": \"0.6095634\",\n \"text\": \"def build_basic_model():\\n model = Sequential(name='Age_CNN')\\n\\n model.add(Conv2D(filters=64,kernel_size=(5,5),input_shape=(48, 48, 1),activation='elu',padding='same',kernel_initializer='he_normal',name='conv2d_1'))\\n model.add(BatchNormalization(name='batchnorm_1'))\\n model.add(Conv2D(filters=64,kernel_size=(5,5),activation='elu',padding='same',kernel_initializer='he_normal',name='conv2d_2'))\\n model.add(BatchNormalization(name='batchnorm_2'))\\n \\n model.add(MaxPooling2D(pool_size=(2,2), name='maxpool2d_1'))\\n model.add(Dropout(0.4, name='dropout_1'))\\n\\n model.add(Conv2D(filters=128,kernel_size=(3,3),activation='elu',padding='same',kernel_initializer='he_normal',name='conv2d_3'))\\n model.add(BatchNormalization(name='batchnorm_3'))\\n model.add(Conv2D(filters=128,kernel_size=(3,3),activation='elu',padding='same',kernel_initializer='he_normal',name='conv2d_4'))\\n model.add(BatchNormalization(name='batchnorm_4'))\\n \\n model.add(MaxPooling2D(pool_size=(2,2), name='maxpool2d_2'))\\n model.add(Dropout(0.4, name='dropout_2'))\\n\\n model.add(Conv2D(filters=256,kernel_size=(3,3),activation='elu',padding='same',kernel_initializer='he_normal',name='conv2d_5'))\\n model.add(BatchNormalization(name='batchnorm_5'))\\n \\n model.add(Conv2D(filters=256,kernel_size=(3,3),activation='elu',padding='same',kernel_initializer='he_normal',name='conv2d_6'))\\n model.add(BatchNormalization(name='batchnorm_6'))\\n \\n model.add(MaxPooling2D(pool_size=(2,2), name='maxpool2d_3'))\\n model.add(Dropout(0.5, name='dropout_3'))\\n\\n model.add(Flatten(name='flatten')) \\n model.add(Dense(128,activation='elu',kernel_initializer='he_normal',name='dense_1'))\\n model.add(BatchNormalization(name='batchnorm_7'))\\n model.add(Dropout(0.6, name='dropout_4'))\\n \\n \\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"244d2d4b98e214ff4bfdb3fd37ebc29f\",\n \"score\": \"0.608587\",\n \"text\": \"def cross_validate(self, folds):\\n\\t\\tdata_size = len(self.MH.all_dat)\\n\\n\\t\\tif folds == -1:\\n\\t\\t\\tfolds = data_size # Leave one out cross validation\\n\\n\\t\\tgroup_size = int(data_size/folds)\\n\\n\\t\\trand_indices = random.sample([x for x in range(data_size)], data_size)\\n\\n\\t\\tvalidations = {}\\n\\t\\tfor header in self.MH.output_headers:\\n\\t\\t\\tvalidations[header] = []\\n\\n\\t\\t# Go through each fold, train on everything else, and test it\\n\\t\\tfor i in range(folds):\\n\\t\\t\\ttrain_indices = [x for x in (rand_indices[:i*group_size] + rand_indices[(i+1)*group_size:])]\\n\\t\\t\\tself.MH.make_train_data(train_indices)\\n\\t\\t\\tself.forward_model = ForwardModel()\\n\\t\\t\\ttest_indices = rand_indices[i*group_size:(i+1)*group_size]\\n\\t\\t\\ttest_dat = [self.MH.all_dat[x] for x in test_indices]\\n\\t\\t\\tfor i,dat_point in enumerate(test_dat):\\n\\t\\t\\t\\tret_vals = self.validate_model(dat_point)\\n\\t\\t\\t\\tfor header in ret_vals:\\n\\t\\t\\t\\t\\tvalidations[header].append(ret_vals[header]) # Validations dict says how well we did for the point\\n\\n\\t\\t# Data for the cross validation is written out at all_preds_droplet_size.csv and all_preds_generation_rate.csv\\n\\t\\t# This data only has the shown file headers. For bulk statistics (like coefficient of determination), you will\\n\\t\\t# need to run the data through DAFD/model_data/disp_graphs.py. See that file for more information.\\n\\t\\tfor header in validations:\\n\\t\\t\\tfile_headers = [\\\"actual_val\\\" ,\\\"pred_val\\\" ,\\\"deviation\\\", \\\"deviation_percent\\\",\\\"actual_regime\\\" ,\\\"pred_regime\\\" ,\\\"chip_number\\\"]\\n\\t\\t\\twith open(\\\"all_preds_\\\" + header + \\\".csv\\\" ,\\\"w\\\") as f:\\n\\t\\t\\t\\tf.write(\\\",\\\".join(file_headers) + \\\"\\\\n\\\")\\n\\t\\t\\t\\tfor x in validations[header]:\\n\\t\\t\\t\\t\\tf.write(\\\",\\\".join([str(xi) for xi in x]) + \\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1047f260623681f410855f4afdae04d8\",\n \"score\": \"0.6082897\",\n \"text\": \"def cross_validation_split(df, folds=10, is_shuffle=True):\\n dataset_split = []\\n df_copy = shuffle(df) if is_shuffle else df\\n fold_size = int(df_copy.shape[0] / folds)\\n training_dataset = []\\n testing_dataset = []\\n for i in range(folds):\\n fold = []\\n while len(fold) < fold_size:\\n r = randrange(df_copy.shape[0])\\n index = df_copy.index[r]\\n fold.append(df_copy.loc[[index]])\\n df_copy = df_copy.drop(index)\\n dataset_split.append(pd.concat(fold))\\n for i in range(folds):\\n r = list(range(folds))\\n r.pop(i)\\n for j in r :\\n if j == r[0]:\\n cv = dataset_split[j]\\n else: \\n cv=pd.concat([cv,dataset_split[j]])\\n training_data = cv.reset_index(drop=True)\\n testing_data = dataset_split[i].reset_index(drop=True)\\n training_dataset.append(training_data)\\n testing_dataset.append(testing_data)\\n return training_dataset, testing_dataset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93e1fba646891df795a989eba6ff3c8f\",\n \"score\": \"0.60722953\",\n \"text\": \"def main():\\n size = 200 #size of the images\\n imdim = size - 20 #strip 10 pixels buffer from each size\\n direct = \\\"../data/images\\\"+str(size)+\\\"/\\\" #directory containing the images\\n ld = listdir(direct) #contents of that directory\\n numEx = len(ld)\\n \\n \\n DUMP_WEIGHTS = True # will we dump the weights of conv layers for visualization\\n \\n shuffle(ld)\\n \\n trainTestSplit = 0.80\\n \\n with open(sys.argv[1]+\\\"testdata.csv\\\",'rb') as f:\\n testFs = [pn for pn in f.read().split(\\\"\\\\n\\\") if pn != '']\\n testL = len(testFs)\\n \\n print \\\"number of examples: \\\", numEx\\n print \\\"test examples : \\\", testL\\n \\n batch_size = 32\\n chunkSize = 2048\\n testChunkSize = testL\\n numTestEx = min(testL,testChunkSize)\\n \\n \\n ecfps = getECFPvecs()\\n atomlist = getAtomList()\\n \\n outsize = len(ecfps[ecfps.keys()[0]])\\n \\n testImages = np.zeros((testChunkSize,1,imdim,imdim),dtype=np.float)\\n testTargets = np.zeros((testChunkSize,outsize),dtype=np.float)\\n \\n \\n with open(sys.argv[1]+\\\"wholeModel.pickle\\\",'rb') as f:\\n model = cPickle.load(f)\\n \\n \\n numIterations = 1\\n for i in range(0,numIterations):\\n shuffle(testFs)\\n count = 0\\n cids = []\\n while count < testChunkSize: \\n for x in testFs: \\n if x.find(\\\".png\\\") > -1:\\n CID = x[:x.find(\\\".png\\\")]\\n cids.append(CID)\\n image = io.imread(direct+x,as_grey=True)[10:-10,10:-10] \\n #image = np.where(image > 0.1,1.0,0.0)\\n testImages[count,0,:,:] = image\\n testTargets[count] = ecfps[CID]\\n count +=1\\n \\n preds = model.predict(testImages)\\n RMSE = np.sqrt(mean_squared_error(testTargets, preds)) \\n ranks = [] \\n print RMSE\\n if RMSE < 300:\\n for ind1 in range(0,len(preds)):\\n p = [preds[ind1][ind2] for ind2 in range(0,len(preds[0]))]\\n t = [int(testTargets[ind1][ind2]) for ind2 in range(0,len(testTargets[0]))]\\n #printFormula(p,t,atomlist,cids[ind1])\\n ranks.append(getRank(cids[ind1],p,ecfps))\\n print ranks[ind1]\\n print np.mean(ranks)\\n with open(\\\"~/CHECKME.txt\\\",'wb') as f:\\n f.write('\\\\n'.join(ranks))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"281c22a40a9ccb649189afa665cbfa75\",\n \"score\": \"0.60718745\",\n \"text\": \"def preprocessing_test():\\n # create folders for test data\\n data_base_path = '../../../datasets/tiny-imagenet-200/'\\n val_path = data_base_path + 'val/'\\n test_path = data_base_path + 'test/'\\n print(val_path)\\n print(test_path)\\n with open(data_base_path + 'wnids.txt', 'r') as wnids:\\n i = 0\\n for line in wnids:\\n i += 1\\n label = line.split('\\\\n')[0]\\n print('%d. label: ' % i + label)\\n val_label_path = val_path + label + '/'\\n test_label_path = test_path + label + '/'\\n if os.path.exists(val_label_path):\\n if not os.path.exists(test_label_path):\\n os.mkdir(test_label_path)\\n # random choose images\\n path = os.listdir(val_label_path)\\n sample = random.sample(path, 25)\\n print(len(sample))\\n print(sample)\\n for name in sample:\\n shutil.move(val_label_path + name, test_label_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56056e5202e7ca56d503117d09dfcc0d\",\n \"score\": \"0.6071002\",\n \"text\": \"def train(batch, epochs, num_classes, size, weights, tclasses):\\n\\n train_generator, validation_generator, count1, count2 = generate(batch, size)\\n\\n train_generator = ImageDataGenerator(\\n rescale=1. / 255,\\n shear_range=0.2,\\n zoom_range=0.2,\\n rotation_range=90,\\n width_shift_range=0.2,\\n height_shift_range=0.2,\\n brightness_range=(1, 1.3),\\n horizontal_flip=True)\\n \\n directory=\\\"/home/gnss/Desktop/garbage_train\\\"\\n train_generator = MixupImageDataGenerator(generator=train_generator,\\n directory=directory,\\n batch_size=32,\\n img_height=224,\\n img_width=224,\\n subset='training')\\n\\n if weights:\\n model = MobileNetv2((size, size, 3), tclasses)\\n model = fine_tune(num_classes, weights, model)\\n print(num_classes)\\n else:\\n model = MobileNetv2((size, size, 3), num_classes)\\n print(num_classes)\\n\\n opt = Adam(1e-2)\\n # earlystop = EarlyStopping(monitor='val_acc', patience=30, verbose=0, mode='auto')\\n tensorboard = TensorBoard('/home/gnss/Desktop/MobileNetV2/logs',write_images=True)\\n # reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5,patience=3, verbose=0, mode='auto', epsilon=0.0001, cooldown=0, min_lr=0)\\n\\n warmup_epoch = 5\\n \\n warm_up_lr = WarmUpCosineDecayScheduler(learning_rate_base=1e-2,\\n total_steps=count1 // batch)\\n\\n checkpointer = ModelCheckpoint(filepath='/home/gnss/Desktop/MobileNetV2/mobilenet.h5',verbose=1,save_best_only=True)\\n\\n model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])\\n\\n \\n\\n # lr=XTensorBoard('/home/gnss/Desktop/MobileNetV2/logslr')\\n\\n hist = model.fit_generator(\\n train_generator,\\n validation_data=validation_generator,\\n steps_per_epoch=count1 // batch,\\n validation_steps=count2 // batch,\\n epochs=epochs,\\n callbacks=[warm_up_lr,tensorboard,checkpointer])\\n '''\\n learning_rate_base=1e-2\\n total_steps=count1 // batch\\n plt.plot(warm_up_lr.learning_rates)\\n plt.xlabel('Step', fontsize=20)\\n plt.ylabel('lr', fontsize=20)\\n plt.axis([0, total_steps, 0, learning_rate_base*1.1])\\n plt.xticks(np.arange(0, total_steps, 50))\\n plt.grid()\\n plt.title('Cosine decay with warmup', fontsize=20)\\n plt.show()\\n '''\\n if not os.path.exists('model'):\\n os.makedirs('model')\\n\\n df = pd.DataFrame.from_dict(hist.history)\\n df.to_csv('model/hist.csv', encoding='utf-8', index=False)\\n model.save_weights('model/weights.h5')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cabcd943cbdaf1e3270a11e84444f668\",\n \"score\": \"0.6066594\",\n \"text\": \"def _train_and_test_classifier(train_data, train_labels, test_data, test_labels, classifier_model):\\n if classifier_model == 'random_forest':\\n model = RandomForestClassifier()\\n model.fit(train_data, train_labels)\\n # out = codecs.open('/Users/mayankkejriwal/git-projects/dig-random-indexing-extractor/model', 'wb', 'utf-8')\\n # joblib.dump(model, '/Users/mayankkejriwal/git-projects/dig-random-indexing-extractor/test/model')\\n # out.close()\\n predicted_labels = model.predict(test_data)\\n print predicted_labels\\n predicted_probabilities = model.predict_proba(test_data)\\n # print predicted_labels[0:10]\\n # print predicted_probabilities[0:10]\\n elif classifier_model == 'knn':\\n k = 1\\n model = neighbors.KNeighborsClassifier(n_neighbors=k, weights='uniform')\\n model.fit(train_data, train_labels)\\n predicted_labels = model.predict(test_data)\\n predicted_probabilities = model.predict_proba(test_data)\\n elif classifier_model == 'manual_knn':\\n # this is not an scikit-learn model; does not support predicted_probabilities\\n k = 5\\n predicted_labels = list()\\n # print len(test_data)\\n for t in test_data:\\n scores_dict = dict()\\n for i in range(0, len(train_data)):\\n score = SimFunctions.SimFunctions.abs_dot_product_sim(train_data[i], t)\\n label = train_labels[i]\\n if score not in scores_dict:\\n scores_dict[score] = list()\\n scores_dict[score].append(label)\\n results = kNearestNeighbors._extract_top_k(scores_dict, k=k)\\n predicted_labels.append(TokenSupervised._compute_majority_label_in_vector(results))\\n predicted_labels = np.array(predicted_labels)\\n elif classifier_model == 'logistic_regression':\\n model = LogisticRegression()\\n model.fit(train_data, train_labels)\\n predicted_labels = model.predict(test_data)\\n predicted_probabilities = model.predict_proba(test_data)\\n elif classifier_model == 'linear_regression': # this is a regressor; be careful.\\n model = LinearRegression()\\n model.fit(train_data, train_labels)\\n predicted_labels = model.predict(test_data)\\n print 'AUC (Area Under Curve): ',\\n print roc_auc_score(test_labels, predicted_labels)\\n # precision, recall, thresholds = precision_recall_curve(test_labels, predicted_labels)\\n # plt.clf()\\n # plt.plot(recall, precision, label='precision-recall-curve')\\n # plt.xlabel('Recall')\\n # plt.ylabel('Precision')\\n # plt.ylim([0.0, 1.05])\\n # plt.xlim([0.0, 1.0])\\n # plt.title('Precision-Recall curve')\\n # plt.savefig('/home/mayankkejriwal/Downloads/memex-cp4-october/tmp/fig.png')\\n if classifier_model not in ['linear_regression']:\\n print 'Accuracy: ',\\n print accuracy_score(test_labels, predicted_labels)\\n # print precision_score(test_labels, predicted_labels)\\n prf = ['Precision: ', 'Recall: ', 'F-score: ', 'Support: ']\\n print 'Class 0\\\\tClass 1'\\n k = precision_recall_fscore_support(test_labels, predicted_labels)\\n for i in range(0, len(k)):\\n print prf[i],\\n print k[i]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ac78e1d8cb9eb12f562d133ce490d5c\",\n \"score\": \"0.6062178\",\n \"text\": \"def main():\\n\\n #I/O files\\n masterTrainingData = \\\"train_sample.csv\\\"\\n masterTestData = \\\"test.csv\\\"\\n sampleTrainingData = \\\"train_sample_10000.csv\\\"\\n submissionTemplate = \\\"sample_submission.csv\\\"\\n submissionOutput = \\\"mySubmission.csv\\\"\\n \\n # fix random seed for reproducibility\\n seed = 69\\n np.random.seed(seed)\\n\\n #load training data from csv\\n dataframe = pd.read_csv(masterTrainingData, header=0)\\n\\n # split into input (X) and output (Y) variables\\n x_train_master, y_train_master = preprocessTraining(dataframe);\\n\\n #print(x_train)\\n #print(len(x_train))\\n #print(y_train)\\n \\n #downsample to avoid unbalanced data\\n dataframe_train_neg = dataframe[(dataframe['is_attributed'] == 0)]\\n dataframe_train_pos = dataframe[(dataframe['is_attributed'] == 1)]\\n print(len(dataframe_train_neg))\\n print(len(dataframe_train_pos))\\n\\n dataframe_train_neg_sample = dataframe_train_neg.sample(n=4000)\\n dataframe_train_pos_sample = dataframe_train_pos.sample(n=227)\\n \\n print(len(dataframe_train_neg_sample))\\n print(len(dataframe_train_pos_sample))\\n\\n dataframe_train_comb = pd.concat([dataframe_train_neg_sample,dataframe_train_pos_sample])\\n x_train, y_train = preprocessTraining(dataframe_train_comb)\\n \\n #submission = pd.read_csv(submissionTemplate)\\n #submission['is_attributed'] = y_predss\\n #submission.to_csv(submissionOutput, index=False)\\n #print(submission.head())\\n\\n # create model\\n #model = Sequential()\\n #model.add(Dense(6, input_dim=9, kernel_initializer='normal', activation='relu'))\\n #model.add(Dense(3, kernel_initializer='normal', activation='relu'))\\n #model.add(Dense(1, kernel_initializer='normal', activation='tanh'))\\n # Compile model\\n #model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy', 'sparse_categorical_accuracy'])\\n #model.fit(x_train, y_train, epochs=8, batch_size=64)\\n \\n model = KerasRegressor(build_fn = create_model)\\n layers = [[5], [6,3], [6,5,4,3]]\\n activations = [relu, sigmoid]\\n param_grid = dict(layers = layers, activation = activations, batch_size = [32, 64], epochs = [4])\\n grid = GridSearchCV(estimator = model, param_grid = param_grid, scoring = 'neg_mean_squared_error')\\n\\n grid_result = grid.fit(x_train, y_train)\\n print(grid_result.best_score_)\\n print(grid_result.best_params_)\\n #[grid_result.best_score_, grid_result.best_params_]\\n\\n model.fit(x_train, y_train)\\n results = model.predict(x_train)\\n results = np.where(results > 0.5, 1, 0)\\n\\n negCount = 0\\n posCount = 0\\n for i in range(0, len(results)):\\n if results[i][0] == 1:\\n posCount += 1\\n else:\\n negCount += 1\\n print(negCount)\\n print(posCount)\\n \\n score = model.evaluate(x_train_master, y_train_master, batch_size=128)\\n print(score)\\n \\n #test on full training data\\n results = model.predict(x_train_master)\\n results = np.where(results > 0.5, 1, 0)\\n \\n negCount = 0\\n posCount = 0\\n for i in range(0, len(results)):\\n if results[i][0] == 1:\\n posCount += 1\\n else:\\n negCount += 1\\n print(negCount)\\n print(posCount)\\n false_positive_rate, recall, thresholds = roc_curve(y_train_master, results)\\n roc_auc = auc(false_positive_rate, recall)\\n print(roc_auc)\\n \\n #dataframe = pd.read_csv(masterTestData, header=0)\\n #x_test = preprocessTest(dataframe)\\n\\n #myPredictions = model.predict(x_test)\\n #myPredictions = np.where(myPredictions > 0.5, 1, 0)\\n\\n #output to a submission file\\n #mySubmission = pd.read_csv(submissionTemplate)\\n #mySubmission['is_attributed'] = myPredictions\\n #mySubmission.to_csv('mySubmission.csv', index=False)\\n #print(mySubmission.head())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fcacfc998e9f4ca1dfd924c0507673c\",\n \"score\": \"0.60598314\",\n \"text\": \"def FIDbenchmarking(models, df_ori, dropdummies = None, num_vars = None):\\n \\n # Set X and y for original data\\n X_ori = df_ori.iloc[:,:-1]\\n y_ori = df_ori.iloc[:,-1]\\n\\n # Split data up into 5 folds to execute gridsearch on 5 different folds to check stability of classifiers\\n\\n # Split into 5 stratified folds, so 5 \\\"different\\\" original sets with corresponding holdout validation sets are created\\n skf = StratifiedKFold(n_splits=5, random_state=8, shuffle=True)\\n\\n # Save indices per split so separate dataframes can be saved later\\n val_idx = []\\n for train_index, test_index in skf.split(X_ori, y_ori):\\n val_idx.append(test_index)\\n\\n # Create the 5 folds by saving the 5 validation sets\\n fold_1 = df_ori.loc[val_idx[0]]\\n fold_2 = df_ori.loc[val_idx[1]]\\n fold_3 = df_ori.loc[val_idx[2]]\\n fold_4 = df_ori.loc[val_idx[3]]\\n fold_5 = df_ori.loc[val_idx[4]]\\n\\n folds = [fold_1, fold_2, fold_3, fold_4, fold_5]\\n\\n i=1\\n DTscores = []\\n LRscores = []\\n for fold in folds:\\n print(\\\"fold\\\", i)\\n DTscores.extend((\\\"fold\\\", i))\\n LRscores.extend(('fold', i))\\n\\n for model in models:\\n pipe = Pipeline([('classifier' , model)])\\n\\n # Drop dummy variables in case of regression\\n fold_copy = fold.copy()\\n if model.__class__.__name__ == 'LogisticRegression':\\n if dropdummies == None:\\n print(\\\"Warning: no dummy columns specified to drop while regression is applied!\\\")\\n else:\\n df_ori_copy = fold_copy.drop(columns=dropdummies);\\n print(\\\"Dummy columns dropped:\\\", dropdummies)\\n\\n # Scale numerical variables in case of regression\\n if model.__class__.__name__ == 'LogisticRegression':\\n if num_vars == None:\\n print(\\\"Warning: no numerical variables specified to scale while regression is applied!\\\")\\n else:\\n fold_copy[num_vars] = preprocessing.minmax_scale(fold_copy[num_vars].astype(np.float64))\\n print(\\\"Numerical variables scaled:\\\", num_vars)\\n\\n # Set X and y for the fold for this model\\n X = fold_copy.iloc[:,:-1]\\n y = fold_copy.iloc[:,-1]\\n\\n # Create param grid\\n if model.__class__.__name__ == 'LogisticRegression':\\n param_grid = [\\n {'classifier' : [model],\\n 'classifier__penalty' : ['l2'],\\n 'classifier__C' : [0.0001, .001, .01, .1, 1, 10, 100],\\n 'classifier__solver' : ['lbfgs'],\\n 'classifier__max_iter' : [4000]}\\n ]\\n elif model.__class__.__name__ == 'DecisionTreeClassifier':\\n param_grid = [\\n {'classifier' : [model],\\n 'classifier__criterion' : ['entropy'], # both (also gini) are considered, but after experimentation to speed up the process only entropy is kept\\n 'classifier__min_samples_leaf' : [0.005], # Avoid overfitting by minimizing the number of samples in leaf node\\n 'classifier__max_depth' : list(range(1,6))} # Avoid overfitting by limiting the depth\\n ]\\n else:\\n print(\\\"param grid not available for this model\\\")\\n\\n # Create grid search object with random state for cv folds so DT and LR are tested on same folds\\n clf = GridSearchCV(pipe, param_grid = param_grid, cv = StratifiedKFold(5, shuffle=True, random_state=2))\\n\\n print(\\\"model\\\", model)\\n\\n # Fit on data\\n best_clf_ori = clf.fit(X, y)\\n\\n best_mean = best_clf_ori.cv_results_['mean_test_score'][best_clf_ori.best_index_] \\n best_std = best_clf_ori.cv_results_['std_test_score'][best_clf_ori.best_index_] \\n\\n print(\\\"best parameters:\\\", best_clf_ori.best_estimator_.get_params()['classifier'])\\n print(\\\"mean\\\", best_mean)\\n print(\\\"std\\\", best_std)\\n \\n if model.__class__.__name__ == 'LogisticRegression':\\n LRscores.extend((\\\"mean\\\", best_mean, \\\"std\\\", best_std))\\n elif model.__class__.__name__ == 'DecisionTreeClassifier': \\n DTscores.extend((\\\"mean\\\", best_mean, \\\"std\\\", best_std))\\n i+=1\\n\\n return LRscores, DTscores\",\n \"title\": \"\"\n },\n {\n \"docid\": \"294bbf1213338bdd9c9735f086c160b5\",\n \"score\": \"0.60585856\",\n \"text\": \"def crossvalidation(input_data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e551fa8e33e7f8992c3da7730c1ec9d\",\n \"score\": \"0.6049004\",\n \"text\": \"def training():\\n # red color training images\\n for f in os.listdir('../data/training_dataset/red'):\\n color_histogram_of_training_image('../data/training_dataset/red/' + f)\\n\\n # yellow color training images\\n for f in os.listdir('../data/training_dataset/yellow'):\\n color_histogram_of_training_image('../data/training_dataset/yellow/' + f)\\n\\n # green color training images\\n for f in os.listdir('../data/training_dataset/green'):\\n color_histogram_of_training_image('../data/training_dataset/green/' + f)\\n\\n # orange color training images\\n for f in os.listdir('../data/training_dataset/orange'):\\n color_histogram_of_training_image('../data/training_dataset/orange/' + f)\\n\\n # white color training images\\n for f in os.listdir('../data/training_dataset/white'):\\n color_histogram_of_training_image('../data/training_dataset/white/' + f)\\n\\n # black color training images\\n for f in os.listdir('../data/training_dataset/black'):\\n color_histogram_of_training_image('../data/training_dataset/black/' + f)\\n\\n # blue color training images\\n for f in os.listdir('../data/training_dataset/blue'):\\n color_histogram_of_training_image('../data/training_dataset/blue/' + f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8306dae9d8431df121243b23398e11f9\",\n \"score\": \"0.60411876\",\n \"text\": \"def training(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30405d30ee3c4c458cb2f520effae2aa\",\n \"score\": \"0.6039188\",\n \"text\": \"def random_search(self, model, fold_n, _iter, scaled_X_train, fold_y_train, scaled_X_test, fold_y_test, params):\\n if model == \\\"NN\\\":\\n self.clfs[model] = keras.Sequential(\\n [\\n keras.layers.Dense(\\n units=params[\\\"NN\\\"][_iter][\\\"neurons1\\\"],\\n input_shape=(scaled_X_train.shape[1],),\\n activation=params[\\\"NN\\\"][_iter][\\\"activation\\\"],\\n kernel_initializer=keras.initializers.RandomNormal(seed=random_state),\\n bias_initializer=keras.initializers.Constant(value=0.1),\\n )\\n ]\\n )\\n self.clfs[model].add(BatchNormalization())\\n self.clfs[model].add(Dropout(params[\\\"NN\\\"][_iter][\\\"dropout_rate\\\"]))\\n if params[\\\"NN\\\"][_iter][\\\"n_layers\\\"] == 2:\\n self.clfs[model].add(\\n layers.Dense(\\n units=params[\\\"NN\\\"][_iter][\\\"neurons2\\\"],\\n activation=params[\\\"NN\\\"][_iter][\\\"activation\\\"],\\n kernel_initializer=keras.initializers.RandomNormal(seed=random_state),\\n bias_initializer=keras.initializers.Constant(value=0.1),\\n )\\n )\\n self.clfs[model].add(Dropout(params[\\\"NN\\\"][_iter][\\\"dropout_rate\\\"]))\\n self.clfs[model].add(\\n layers.Dense(\\n 1,\\n activation=\\\"sigmoid\\\",\\n kernel_initializer=keras.initializers.RandomNormal(seed=random_state),\\n bias_initializer=keras.initializers.Constant(value=0.1),\\n )\\n )\\n\\n opt = adam_v2.Adam(\\n learning_rate=params[\\\"NN\\\"][_iter][\\\"learn_rate\\\"],\\n beta_1=params[\\\"NN\\\"][_iter][\\\"beta_1\\\"],\\n beta_2=params[\\\"NN\\\"][_iter][\\\"beta_1\\\"],\\n )\\n self.clfs[model].compile(loss=\\\"binary_crossentropy\\\", optimizer=opt, metrics=[keras.metrics.AUC()])\\n\\n # fit model\\n self.clfs[model].fit(\\n scaled_X_train,\\n fold_y_train,\\n batch_size=params[\\\"NN\\\"][_iter][\\\"batch_size\\\"],\\n epochs=params[\\\"NN\\\"][_iter][\\\"epochs\\\"],\\n shuffle=False,\\n verbose=False,\\n )\\n # predict\\n fold_pred = self.clfs[model].predict(scaled_X_test)\\n fold_pred = np.concatenate(fold_pred).ravel().tolist()\\n # calculate AUC\\n auc_score = roc_auc_score(fold_y_test, fold_pred)\\n # save results on next line\\n self.cv_randomsearch_results_df.loc[len(self.cv_randomsearch_results_df) + 1] = [\\n model,\\n fold_n,\\n _iter,\\n params[\\\"NN\\\"][_iter],\\n auc_score,\\n ]\\n elif model == \\\"XGBoost\\\":\\n self.clfs[model].set_params(**params[model][_iter])\\n # fit model\\n self.clfs[model].fit(scaled_X_train, fold_y_train, eval_metric=\\\"auc\\\")\\n # predict\\n fold_pred = self.clfs[model].predict_proba(scaled_X_test)[::, 1]\\n # calculate AUC\\n auc_score = roc_auc_score(fold_y_test, fold_pred)\\n # save results on next line\\n self.cv_randomsearch_results_df.loc[len(self.cv_randomsearch_results_df) + 1] = [\\n model,\\n fold_n,\\n _iter,\\n self.clfs[model].get_params(),\\n auc_score,\\n ]\\n else:\\n self.clfs[model].set_params(**params[model][_iter])\\n # fit model\\n self.clfs[model].fit(scaled_X_train, fold_y_train)\\n # predict\\n fold_pred = self.clfs[model].predict_proba(scaled_X_test)[:, 1]\\n # calculate AUC\\n auc_score = roc_auc_score(fold_y_test, fold_pred)\\n # save results on next line\\n self.cv_randomsearch_results_df.loc[len(self.cv_randomsearch_results_df) + 1] = [\\n model,\\n fold_n,\\n _iter,\\n self.clfs[model].get_params(),\\n auc_score,\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15825037f710339d5328e69419d7995b\",\n \"score\": \"0.60389423\",\n \"text\": \"def train(train_data, test_data, foldid: int = 0):\\n # Setup invariant repositories\\n # we'll take I many examples for each task with different answers for each fold\\n invariants = [select_invariants(train_data, i) for i in range(1, 5)] # T x (I, 1+L+1)\\n invariants = np.stack(invariants) # (T, I, 1+L+1)\\n # ---------------------------\\n # Setup model\\n model = UMLP(invariants)\\n cmodel = Classifier(model)\\n optimiser = C.optimizers.Adam(alpha=ARGS.learning_rate).setup(cmodel)\\n train_iter = C.iterators.SerialIterator(train_data, ARGS.batch_size)\\n updater = T.StandardUpdater(train_iter, optimiser, device=-1)\\n trainer = T.Trainer(updater, (2000, 'iteration'), out='results/umlp_result')\\n # ---------------------------\\n fname = (ARGS.name.format(foldid=foldid) if ARGS.name else '') or ('debug' if ARGS.debug else '') or str(uuid.uuid4())\\n # Setup trainer extensions\\n if ARGS.debug:\\n trainer.extend(print_vmap, trigger=(200, 'iteration'))\\n test_iter = C.iterators.SerialIterator(test_data, 128, repeat=False, shuffle=False)\\n trainer.extend(T.extensions.Evaluator(test_iter, cmodel, device=-1), name='test', trigger=(10, 'iteration'))\\n # trainer.extend(T.extensions.snapshot(filename=fname+'_latest.npz'), trigger=(100, 'iteration'))\\n trainer.extend(T.extensions.LogReport(log_name=fname+'_log.json', trigger=(10, 'iteration')))\\n trainer.extend(T.extensions.FailOnNonNumber())\\n train_keys = ['uloss', 'igloss', 'oloss', 'uacc', 'igacc', 'oacc', 'vloss']\\n test_keys = ['uloss', 'oloss', 'uacc', 'oacc']\\n trainer.extend(T.extensions.PrintReport(['iteration'] + ['main/'+k for k in train_keys] + ['test/main/'+k for k in test_keys] + ['elapsed_time']))\\n # ---------------------------\\n print(f\\\"---- FOLD {foldid} ----\\\")\\n try:\\n trainer.run()\\n except KeyboardInterrupt:\\n if not ARGS.debug:\\n return\\n # Save run parameters\\n params = ['length', 'symbols', 'invariants', 'embed', 'train_size', 'learning_rate', 'nouni', 'batch_size']\\n params = {k: vars(ARGS)[k] for k in params}\\n params['name'] = fname\\n params['foldid'] = foldid\\n with open(trainer.out + '/' + fname + '_params.json', 'w') as f:\\n json.dump(params, f)\\n # Save learned invariants\\n with open(trainer.out + '/' + fname + '.out', 'w') as f:\\n f.write(\\\"---- META ----\\\\n\\\")\\n train_data = np.stack(train_data)\\n test_data = np.stack(test_data)\\n meta = {'train': train_data.shape, 'train_tasks': np.unique(train_data[:,0], return_counts=True),\\n 'test': test_data.shape, 'test_tasks': np.unique(test_data[:,0], return_counts=True),\\n 'foldid': foldid}\\n f.write(str(meta))\\n f.write(\\\"\\\\n---- INVS ----\\\\n\\\")\\n f.write(str(model.inv_examples))\\n f.write(\\\"\\\\n--------\\\\n\\\")\\n f.write(str(model.log['vmap'][0].array))\\n for t in range(1, TASKS+1):\\n f.write(\\\"\\\\n---- SAMPLE ----\\\\n\\\")\\n test_data = np.stack(test_data) # (S, 1+L+1)\\n np.random.shuffle(test_data)\\n batch = test_data[test_data[:, 0] == t][:4] # (B, 1+L+1)\\n f.write(\\\"Input:\\\\n\\\")\\n f.write(str(batch))\\n out = model(batch) # (B, V)\\n f.write(\\\"\\\\nOutput:\\\\n\\\")\\n f.write(str(out.array))\\n f.write(\\\"\\\\nAtt:\\\\n\\\")\\n f.write(str(model.log['uniatt'][0].array))\\n f.write(\\\"\\\\n---- END ----\\\\n\\\")\\n if ARGS.debug:\\n print(batch)\\n import ipdb; ipdb.set_trace()\\n out = model(batch)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b66734712b88ba72ea46c914b473c20b\",\n \"score\": \"0.6036707\",\n \"text\": \"def load_6_class_classifier():\\n\\n required_input_shape = (*IMAGE_DIMS, IMAGE_NUM_CHANNELS)\\n\\n model = Sequential()\\n model.add(Conv2D(32, (3, 3), padding='valid', input_shape=required_input_shape))\\n model.add(BatchNormalization())\\n model.add(Activation('relu'))\\n model.add(MaxPooling2D(pool_size=(2, 2)))\\n \\n model.add(Conv2D(64, (3, 3)))\\n model.add(BatchNormalization())\\n model.add(Activation('relu'))\\n model.add(MaxPooling2D(pool_size=(2, 2)))\\n \\n model.add(Conv2D(128, (3, 3)))\\n model.add(BatchNormalization())\\n model.add(Activation('relu'))\\n model.add(MaxPooling2D(pool_size=(2, 2)))\\n \\n model.add(Dropout(0.5))\\n\\n model.add(Flatten())\\n \\n model.add(Dense(2048))\\n model.add(BatchNormalization())\\n model.add(Activation('relu'))\\n model.add(Dropout(0.5))\\n\\n \\n model.add(Dense(256))\\n model.add(BatchNormalization())\\n model.add(Activation('relu'))\\n model.add(Dropout(0.6))\\n \\n model.add(Dense(6))\\n model.add(Activation('softmax'))\\n model.summary()\\n\\n # load the model weights\\n model.load_weights(configurations.six_class_color_cnn_model_location)\\n \\n adam = optimizers.Adam(lr=0.00001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)\\n model.compile(loss='sparse_categorical_crossentropy', optimizer=adam, metrics=['accuracy'])\\n \\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e133f36ecf91955fb752183002c422c1\",\n \"score\": \"0.6036274\",\n \"text\": \"def setup_dataset(self):\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n train_path = \\\"small_dataset/images/nir/\\\"\\r\\n val_path = \\\"small_dataset/images/nir/\\\"\\r\\n test_path = \\\"small_dataset/images/nir/\\\"\\r\\n\\r\\n train_labels_path = \\\"small_dataset/labels/\\\"\\r\\n val_labels_path = \\\"small_dataset/labels/\\\"\\r\\n test_labels_path = \\\"small_dataset/labels/\\\"\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n train_path = f\\\"{self.cfg.data_dir}/images\\\"\\r\\n val_path = f\\\"{self.cfg.data_dir}/images\\\"\\r\\n test_path = f\\\"{self.cfg.data_dir}/images\\\"\\r\\n\\r\\n train_labels_path = f\\\"{self.cfg.data_dir}/labels/\\\"\\r\\n val_labels_path = f\\\"{self.cfg.data_dir}/labels/\\\"\\r\\n test_labels_path = f\\\"{self.cfg.data_dir}/labels/\\\"\\r\\n\\r\\n train = os.listdir(train_path)\\r\\n val = os.listdir(val_path)\\r\\n test = os.listdir(test_path)\\r\\n\\r\\n random.shuffle(train)\\r\\n random.shuffle(val)\\r\\n random.shuffle(test)\\r\\n\\r\\n train_img_names_index = train[:10000]\\r\\n val_img_names_index = val[:2000]\\r\\n test_img_names_index = test[:2000]\\r\\n\\r\\n labels_one_hot = {}\\r\\n k = self.cfg.classes # 8\\r\\n i=0\\r\\n for label in listdir_nohidden(train_labels_path):\\r\\n if label!=\\\"storm_damage\\\":\\r\\n labels_one_hot[label] = np.zeros((k,))\\r\\n labels_one_hot[label][i] = 1\\r\\n i+=1\\r\\n\\r\\n train_dataset = SegmentationDataset(\\\"train\\\", train_img_names_index, labels_one_hot, train_path, train_labels_path, use_cache=True)\\r\\n val_dataset = SegmentationDataset(\\\"validation\\\", val_img_names_index, labels_one_hot, val_path, val_labels_path, use_cache=True)\\r\\n # test_dataset = SegmentationDataset(\\\"test\\\", test_img_names_index, labels_one_hot, test_path, test_labels_path, use_cache=True)\\r\\n\\r\\n self.train_dataloader = DataLoader(train_dataset, batch_size=self.batch_size, shuffle=self.shuffle)\\r\\n self.val_dataloader = DataLoader(val_dataset, batch_size=self.batch_size, shuffle=self.shuffle)\\r\\n # self.test_dataloader = DataLoader(test_dataset, batch_size=self.batch_size)\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f5702df2c52b31ca43f0ceeda852595\",\n \"score\": \"0.6029016\",\n \"text\": \"def testUnseenDES2017(model, neural_network, n_splits): # from last time everything worked\\n\\n # def testUnseenDES2017(model, neural_network, n_splits, input_shape):\\n\\n known_des2017_array = getUnseenDES2017()\\n\\n num = 47\\n unknown_array = getUnseenNegative(num)\\n\\n image_test, labels_test = loadImage(known_des2017_array, unknown_array)\\n print(\\\"Image test shape: \\\", str(image_test.shape))\\n image_test = image_test.reshape(image_test.shape[0], input_shape[0], input_shape[1], input_shape[2])\\n\\n encoder = LabelEncoder()\\n y_image_labels = encoder.fit_transform(labels_test)\\n\\n y_pred = model.predict(image_test)\\n\\n y_test_index = np.round(y_pred)\\n ones = np.count_nonzero(y_test_index == 1)\\n zeroes = np.count_nonzero(y_test_index == 0)\\n\\n print(\\\"Ones: %s / 47\\\" % ones)\\n print(\\\"Zeroes: %s / 47\\\" % zeroes)\\n\\n # Get Accuracy Score tests DES2017 on the mlpclassifier:\\n _, acc = model.evaluate(image_test, y_image_labels, verbose=0)\\n accuracy_score_47 = acc * 100\\n print(\\\"Accuracy Score_47: \\\" + str(accuracy_score_47))\\n\\n # get the k fold accuracy after k fold cross validation\\n scores = cross_val_score(neural_network, image_test, y_image_labels, scoring='accuracy', cv=n_splits)\\n scores_mean = scores.mean() * 100\\n print(\\\"kFold47 Scores Mean: \\\" + str(scores_mean))\\n k_fold_std_47 = scores.std()\\n print(\\\"kFold47 Scores Std: \\\" + str(k_fold_std_47))\\n k_fold_accuracy_47 = scores_mean\\n\\n return known_des2017_array, accuracy_score_47, k_fold_accuracy_47, k_fold_std_47\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cdaa765ddb0e0fc7523a54313636171\",\n \"score\": \"0.6024136\",\n \"text\": \"def test_train_model(self):\\n opt = {\\n 'model': 'projects.self_feeding.self_feeding_agent:SelfFeedingAgent',\\n 'task': 'self_feeding:all',\\n 'max_train_time': 120,\\n 'dia_train': 'train_hh131k_hb60k.txt',\\n 'n_layers': 2,\\n 'n_heads': 2,\\n 'candidates': 'batch',\\n 'validation_metric': 'dia_acc',\\n 'optimizer': 'adamax',\\n 'learning_rate': 0.0025,\\n 'ffn_size': 32,\\n 'batchsize': 32,\\n 'embeddings_scale': False,\\n }\\n _, _, _ = testing_utils.train_model(opt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5de5956f3a979ffe84b3f90233b11164\",\n \"score\": \"0.60239935\",\n \"text\": \"def fit(data, n1, lr, bs, bayes_scope, model_num, args):\\n tf.set_random_seed(1234) # set tf seed to keep same weight initialization for each model\\n n_epochs = args.n_epochs # number of epochs\\n\\n # track losses and accuracies of each fold\\n loss = []\\n accuracy = []\\n test_list = []\\n # perform cross validation\\n with temp_seed(0):\\n for counter in range(len(data.y_train_list)):\\n bayes_scope += 1\\n with tf.Session() as sess:\\n x_train, y_train, x_test, y_test = data.return_cross_val(counter)\\n if args.noise:\\n x_train, y_train = data.training_augment(y_train, args.n_aug)\\n low_loss, low_accuracy = run(sess, x_train, y_train, x_test, y_test, n1, lr, bs, n_epochs, str(bayes_scope), counter, model_num)\\n loss.append(low_loss)\\n accuracy.append(low_accuracy)\\n sess.close()\\n tf.reset_default_graph()\\n test_list.append(y_test)\\n\\n # calculate mean and std of losses and accuracies across the folds\\n loss = np.array(loss)\\n accuracy = np.array(accuracy)\\n loss_mean = np.mean(loss)\\n loss_std = np.std(loss)\\n accuracy_mean = np.mean(accuracy)\\n accuracy_std = np.std(accuracy)\\n\\n return loss, accuracy, loss_mean, loss_std, accuracy_mean, accuracy_std, test_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aea65cd17178461f0d9488bb540e9918\",\n \"score\": \"0.60236037\",\n \"text\": \"def prepare_train_test_split(self, test_pct, val_pct, seed):\\n\\n\\n if self.data is None:\\n raise Exception('Error: Must preporcess data first')\\n\\n features = list(self.data.columns)\\n features.remove(\\\"genre\\\")\\n\\n #split to features and labels\\n X = self.data[features].values\\n y = self.data[\\\"genre\\\"].values\\n\\n #split the dataset to train, validation and test datasets\\n X_train_val, self.X_test, y_train_val, self.y_test = train_test_split(X, y, test_size=test_pct, shuffle=True,\\n stratify=y, random_state=seed)\\n self.X_train, self.X_val, self.y_train, self.y_val = train_test_split(X_train_val, y_train_val,\\n test_size=val_pct, shuffle=True,\\n stratify=y_train_val,random_state=seed)\\n\\n print(f\\\"Loaded train dataset containing: {len(self.X_train)} samples\\\")\\n print(f\\\"Loaded validation dataset containing: {len(self.X_val)} samples\\\")\\n print(f\\\"Loaded test dataset containing: {len(self.X_test)} samples\\\")\\n\\n images_files_df = pd.read_csv(os.path.join(self.image_source_path, self.source_images_list))\\n\\n\\n self.prepare_images_genres('images-train', self.X_train, self.y_train,\\n self.preprocessed_config[\\\"preprocess_col_order\\\"].index(\\\"book_id\\\"))\\n self.prepare_images_genres('images-val', self.X_val, self.y_val,\\n self.preprocessed_config[\\\"preprocess_col_order\\\"].index(\\\"book_id\\\"))\\n self.prepare_images_genres('images-test', self.X_test, self.y_test,\\n self.preprocessed_config[\\\"preprocess_col_order\\\"].index(\\\"book_id\\\"))\\n\\n print(f\\\"All images prepared in folders\\\")\\n\\n #increase the dimention of labels to be 2 dim array\\n self.y_train = self.y_train[:, np.newaxis]\\n self.y_val = self.y_val[:, np.newaxis]\\n self.y_test = self.y_test[:, np.newaxis]\\n\\n from . import dump_datasets_to_pickle, load_datasets_from_pickle\\n dump_datasets_to_pickle(self.data_path, np.hstack((self.X_train, self.y_train)), \\\"train.pkl\\\")\\n dump_datasets_to_pickle(self.data_path, np.hstack((self.X_val, self.y_val)), \\\"val.pkl\\\")\\n dump_datasets_to_pickle(self.data_path, np.hstack((self.X_test, self.y_test)), \\\"test.pkl\\\")\\n print(f\\\"Dataset splits saved as pickle files\\\")\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"244f93f0465b452b71453a0937425bb8\",\n \"score\": \"0.6017135\",\n \"text\": \"def train_cnn_old(self, img_folder,check_folder=params.directories['cnn_checkpoint_weights'],pre_trained_weight_path=''):\\n if os.path.exists(check_folder)==0:\\n os.mkdir(check_folder) \\n img_file_t, lable_t,visit_file_t, img_file_v, label_v, visit_file_v=load_class_balanced_files(img_folder,max_samples=-100)\\n \\n nb_epoch=500\\n batch_size=round(128)\\n n_batch_per_epoch=len(img_file_t)//batch_size\\n\\n num_val_sample=len(img_file_v)\\n n_batch_per_epoch_val=num_val_sample//batch_size\\n # data_train, label_train=input_load_train_data(img_file_t,visit_file_t, lable_t)\\n # data_val, label_val=input_load_train_data(img_file_v,visit_file_v, label_v)\\n train_generator=input_generator(img_file_t, lable_t,visit_file_t, batch_size)\\n val_generator = input_generator(img_file_v,label_v,visit_file_v,batch_size)\\n\\n model = get_cnn_model(self.params)\\n #model = make_parallel(model, 4)\\n if len(pre_trained_weight_path)>1:\\n model.load_weights(pre_trained_weight_path,True)\\n print('use pre-trained weights: ',pre_trained_weight_path) \\n model.compile(optimizer=Adam(lr=self.params.cnn_adam_learning_rate), loss='categorical_crossentropy', metrics=['accuracy'])\\n\\n print(\\\"training\\\")\\n filePath = os.path.join(check_folder, 'weights.{epoch:02d}.hdf5')\\n checkpoint = ModelCheckpoint(filepath=filePath, monitor='loss', verbose=0, save_best_only=False,\\n save_weights_only=False, mode='auto', period=5)\\n csv_logger = CSVLogger(os.path.join(check_folder,'train.csv'))\\n callbacks_list = [csv_logger,checkpoint]\\n\\n model.fit_generator(train_generator, steps_per_epoch=n_batch_per_epoch,\\n validation_data=val_generator, validation_steps=n_batch_per_epoch_val,\\n epochs=nb_epoch, callbacks=callbacks_list)\\n\\n #model.fit(data_train,label_train, batch_size=batch_size,validation_data=(data_val, label_val),\\n # epochs=nb_epoch, callbacks=callbacks_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25604d96783bc22ea17ba269d69f351c\",\n \"score\": \"0.601373\",\n \"text\": \"def do_kfold_validation(x, y, x_test, y_test):\\n # initializes kfold with 5 folds, including shuffling,\\n # using 9 as seed for the shuffling\\n kfold = KFold(n_splits=5, random_state=9, shuffle=True)\\n\\n # uses a Gaussian Naive Bayes classifier\\n model = GaussianNB()\\n\\n # splits datasets into folds and trains the model\\n for train_index, val_index in kfold.split(x):\\n x_train, x_val = x.loc[train_index], x.loc[val_index]\\n y_train, y_val = y.loc[train_index], y.loc[val_index]\\n # training of the model\\n model.fit(x_train, y_train)\\n\\n evaluate_accuracy(model, x_train, x_val, x_test, y_train, y_val, y_test)\\n # plot_correlation(x)\\n # plot_frequency(x, 'hesitations')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4a45402288d6f35db88557c8e6796d2\",\n \"score\": \"0.60123587\",\n \"text\": \"def valid_model(model,device,cls_cri):\\n # test datasets\\n test_set = cubdataset.CubDataset(config.DATA_ROOT,training = False,resize = config.RESIZE,crop = config.CROP)\\n test_loader = DataLoader(test_set,1,shuffle = False,num_workers = 1)\\n\\n # valid run.\\n low_cls_loss = 0\\n mid_cls_loss = 0\\n hig_cls_loss = 0\\n coa_cls_loss = 0\\n com_cls_loss = 0\\n\\n low_acc = 0\\n mid_acc = 0\\n hig_acc = 0\\n coa_acc = 0\\n com_acc = 0\\n fine_acc = 0\\n ens_acc = 0\\n total_num = len(test_loader)\\n\\n # change to eval mode.\\n model.eval()\\n with torch.no_grad():\\n for data in tqdm.tqdm(test_loader,desc = \\\"valid\\\"):\\n # data\\n imgs,targets = data\\n imgs = imgs.to(device)\\n targets = targets.to(device)\\n\\n # forward\\n low_logits,mid_logits,hig_logits,coa_logits,com_logits = model(imgs)\\n low_loss,mid_loss,hig_loss,coa_loss,com_loss = \\\\\\n cls_cri(low_logits,targets),cls_cri(mid_logits,targets),\\\\\\n cls_cri(hig_logits,targets),cls_cri(coa_logits,targets),cls_cri(com_logits,targets)\\n\\n low_cls_loss += low_loss.item()\\n mid_cls_loss += mid_loss.item()\\n hig_cls_loss += hig_loss.item()\\n coa_cls_loss += coa_loss.item()\\n com_cls_loss += com_loss.item()\\n\\n # evaluation.\\n b = targets.size()[0]\\n fine_logits = coa_logits + com_logits\\n ens_logits = low_logits + mid_logits + hig_logits + coa_logits + com_logits\\n total_logits = torch.cat((low_logits,mid_logits,hig_logits,coa_logits,com_logits,fine_logits,ens_logits),dim = 0)\\n total_probs = torch.softmax(total_logits,dim = -1)\\n total_preds = torch.argmax(total_probs,dim = -1)\\n\\n low_preds,mid_preds,hig_preds,coa_preds,com_preds,fine_preds,ens_preds = torch.split(total_preds,[b,b,b,b,b,b,b],dim = 0)\\n low_acc += torch.sum(low_preds == targets).item()\\n mid_acc += torch.sum(mid_preds == targets).item()\\n hig_acc += torch.sum(hig_preds == targets).item()\\n coa_acc += torch.sum(coa_preds == targets).item()\\n com_acc += torch.sum(com_preds == targets).item()\\n fine_acc += torch.sum(fine_preds == targets).item()\\n ens_acc += torch.sum(ens_preds == targets).item()\\n\\n return low_cls_loss/total_num,mid_cls_loss/total_num,hig_cls_loss/total_num,\\\\\\n coa_cls_loss/total_num,com_cls_loss/total_num,\\\\\\n low_acc/total_num * 100,mid_acc/total_num * 100,hig_acc/total_num * 100,\\\\\\n coa_acc/total_num * 100,com_acc/total_num * 100,fine_acc/total_num * 100,ens_acc/total_num * 100\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8ab4fdaf53fbfcf52709f5a3362d73b\",\n \"score\": \"0.6010357\",\n \"text\": \"def main():\\n augment_train_ds = transforms.Compose([\\n transforms.RandomCrop(64, padding=2),\\n transforms.RandomHorizontalFlip(),\\n transforms.ToTensor(),\\n transforms.Normalize((0.5, 0.5, 0.5), (0.2, 0.2, 0.2)),\\n ])\\n \\\"\\\"\\\"Normalizing Test Dataset\\\"\\\"\\\"\\n augment_test_ds = transforms.Compose([\\n transforms.ToTensor(),\\n transforms.Normalize((0.5, 0.5, 0.5), (0.2, 0.2, 0.2)),\\n ])\\n\\n \\\"\\\"\\\"Set seed.\\\"\\\"\\\"\\n np.random.seed(0)\\n random.seed(0)\\n torch.manual_seed(0)\\n torch.cuda.manual_seed(0)\\n\\n train_dir = '/u/training/tra287/scratch/tiny-imagenet-200/train'\\n train_ds = datasets.ImageFolder(train_dir, transform=augment_train_ds)\\n # print(train_ds.class_to_idx)\\n train_ds_loader = data.DataLoader(train_ds, batch_size=batch_size_train, shuffle=True, num_workers=8)\\n\\n val_dir = '/u/training/tra287/scratch/tiny-imagenet-200/val/'\\n\\n # print(\\\"Now working on Val Dir\\\")\\n if 'val_' in os.listdir(val_dir+'images/')[0]:\\n # print(\\\"Calling create_val_dir() with val_dir: \\\", val_dir)\\n create_val_folder(val_dir)\\n val_dir = val_dir + 'images/'\\n # print(\\\"changed val_dir to : \\\", val_dir)\\n else:\\n # print(\\\"Didnt call create_val_dir\\\")\\n val_dir = val_dir + 'images/'\\n #train_ds = torchvision.datasets.ImageNet(train_dir, split='train', download=False, transform=augment_train_ds)\\n # train_ds_loader = data.DataLoader(train_ds, batch_size=batch_size_train, shuffle=True, num_workers=8)\\n # test_ds = torchvision.datasets.ImageNet(val_dir, split='val', download=False, transform=augment_test_ds)\\n # test_ds_loader = data.DataLoader(test_ds, batch_size=batch_size_test, shuffle=False, num_workers=8)\\n\\n test_ds = datasets.ImageFolder(val_dir, transform=augment_test_ds)\\n #print(test_ds.class_to_idx)\\n test_ds_loader = torch.utils.data.DataLoader(test_ds, batch_size=batch_size_test, shuffle=False, num_workers=8)\\n\\n device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\\n\\n print(\\\"Initializing Model\\\")\\n basic_block = BasicBlock\\n res_net = ResNet(basic_block=basic_block, num_basic_blocks_list=[2, 4, 4, 2], num_classes=200,\\n linear_layer_num_input=2304, max_pool_stride=2)\\n res_net = res_net.to(device)\\n start_epoch = 0\\n\\n if load_chkpt:\\n print(\\\"Saved Model is being loaded\\\")\\n chkpt = torch.load('./Checkpoint/model_state.pt')\\n res_net.load_state_dict(chkpt['res_net_model'])\\n start_epoch = chkpt['epoch']\\n\\n \\\"\\\"\\\"If multiple GPUs are available then use asynchronous training \\\"\\\"\\\"\\n if device == 'cuda':\\n res_net = torch.nn.DataParallel(res_net)\\n cudnn.benchmark = True\\n\\n \\\"\\\"\\\"___________ Training ___________\\\"\\\"\\\"\\n\\n print(\\\"Starting Training\\\")\\n\\n \\\"\\\"\\\"Criterion Function: Softmax + Log-Likelihood\\\"\\\"\\\"\\n loss_fn = nn.CrossEntropyLoss()\\n \\\"\\\"\\\"Adam Optimizer (as it takes advantage of both RMSDrop and Momentum\\\"\\\"\\\"\\n optimizer = optim.Adam(res_net.parameters(), lr=learning_rate)\\n\\n test_acc_list = []\\n epochs_list = [x for x in range(epochs)]\\n\\n for epoch in range(start_epoch, epochs):\\n\\n cur_loss = 0.0\\n total_correct = 0\\n total_samples = 0\\n\\n \\\"\\\"\\\" Overflow error in the optimizer if the step size is not reset.\\\"\\\"\\\"\\n if epoch > 8:\\n for group in optimizer.param_groups:\\n for p in group['params']:\\n state = optimizer.state[p]\\n if state['step'] >= 1024:\\n state['step'] = 1000\\n\\n for i, (inputs, labels) in enumerate(train_ds_loader):\\n\\n \\\"\\\"\\\"Transfer inputs and labels to CUDA if available\\\"\\\"\\\"\\n inputs = Variable(inputs).to(device)\\n labels = Variable(labels).to(device)\\n\\n \\\"\\\"\\\"Loss function requires the inputs to be wrapped in variables\\\"\\\"\\\"\\n # inputs = Variable(inputs)\\n\\n \\\"\\\"\\\"Torch tends to take cumulative gradients which is not required so setting it to zero after each batch\\\"\\\"\\\"\\n optimizer.zero_grad()\\n\\n outputs = res_net(inputs)\\n loss = loss_fn(outputs, labels)\\n loss.backward()\\n optimizer.step()\\n\\n cur_loss += loss.item()\\n avg_loss = cur_loss / (i + 1)\\n\\n _, predicted_label = torch.max(outputs, 1)\\n # print(predicted_label.shape, labels.shape)\\n total_samples += labels.shape[0]\\n # arr = (predicted_label == labels).numpy()\\n # print(np.sum(arr))\\n \\\"\\\"\\\"can not use numpy as the tensors are in CUDA\\\"\\\"\\\"\\n total_correct += predicted_label.eq(labels.long()).float().sum().item()\\n accuracy = total_correct / total_samples\\n\\n if i % 100 == 0:\\n print('Training [epoch: %d, batch: %d] loss: %.3f, accuracy: %.5f' %\\n (epoch + 1, i + 1, avg_loss, accuracy))\\n\\n test_acc_list.append(test(device, loss_fn, res_net, test_ds_loader))\\n\\n \\\"\\\"\\\"Saving model after every 5 epochs\\\"\\\"\\\"\\n if (epoch + 1) % 5 == 0:\\n print('==> Saving model ...')\\n state = {\\n 'res_net_model': res_net.state_dict(),\\n 'epoch': epoch,\\n }\\n if not os.path.isdir('./Checkpoint'):\\n os.mkdir('Checkpoint')\\n torch.save(state, './Checkpoint/model_state.pt')\\n\\n print(\\\"Training Completed!\\\")\\n\\n \\\"\\\"\\\"___________ Testing ____________\\\"\\\"\\\"\\n print(\\\"Testing Started\\\")\\n \\\"\\\"\\\"Puts model in testing state\\\"\\\"\\\"\\n res_net.eval()\\n\\n accuracy = test(device, loss_fn, res_net, test_ds_loader)\\n\\n print(\\\"Testing Completed with accuracy:\\\" + str(accuracy))\\n\\n with open('graph_resnet_tinyimagenet.csv', 'w') as result_file:\\n wr = csv.writer(result_file, dialect='excel')\\n wr.writerow(test_acc_list)\\n\\n print(\\\"Saved Test Accuracy list for graph\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"232173d16e2d32eed36666657126e8f9\",\n \"score\": \"0.6008869\",\n \"text\": \"def test_DCAKNN(noise_dataset):\\n X = noise_dataset\\n model = DCAKNN(d=1, T=10)\\n model.fit(X)\\n model.transform(X)\\n model.fit_transform(X)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5d1416b2cbec5bfd4f15e5b20dd166b\",\n \"score\": \"0.6007508\",\n \"text\": \"def toy5():\\r\\n\\tX_train = torch.stack((torch.arange(2.0).repeat_interleave(10), torch.arange(0.1, 1.1, 0.1).repeat(2))).t()\\r\\n\\tY_train = torch.tensor([0] * 2 + [1] * 8 + [0] * 8 + [1] * 2)\\r\\n\\r\\n\\tX_train_min = [0.0, 0.0]\\r\\n\\tX_train_max = [1.0, 1.0]\\r\\n\\treturn {\\\"dataset_name\\\": \\\"toy5\\\", \\\"X_train\\\": X_train, \\\"Y_train\\\": Y_train, \\\"X_train_min\\\": X_train_min, \\\"X_train_max\\\": X_train_max}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b8787c50ad17cac715273ab2ce03320\",\n \"score\": \"0.60015285\",\n \"text\": \"def _run_training(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f3dba3af1538f8dde86449fc01e48cf\",\n \"score\": \"0.5996881\",\n \"text\": \"def initialisedataset():\\r\\n import initialdefs\\r\\n import math\\r\\n\\r\\n task_file, alltasks = initialdefs.starup()\\r\\n\\r\\n X = np.array([[np.zeros([32, 32]), np.zeros([32, 32])]])\\r\\n Y = [0]\\r\\n\\r\\n # make prelim Y's - labels for which problems are patterns. Prelim because we'll make more samples from each problem\\r\\n # so we'll only use these to inform us what label we should use\\r\\n Yprelim = [0] * 400\\r\\n\\r\\n # from manually going through and seeing what tasks were filling in repeating patterns / mosaics\\r\\n for i in [16, 60, 73, 109, 241, 286, 304, 312, 350, 399]:\\r\\n Yprelim[i] = 1\\r\\n\\r\\n for taskno in range(len(alltasks)):\\r\\n print(taskno)\\r\\n task = alltasks[taskno]\\r\\n train = task['train']\\r\\n\\r\\n # check the input & output are the same size: if not, don't use (too different, would cause too many problems)\\r\\n check = train[0]\\r\\n checkinput = np.array(check['input'])\\r\\n checkoutput = np.array(check['output'])\\r\\n\\r\\n # if they are the same, we can use as sample for the model.\\r\\n if checkoutput.shape == checkinput.shape:\\r\\n for trainno in range(len(train)):\\r\\n # dim0: samples dim1: channels (2: input, out), dim3: x dim4: y\\r\\n imagepair = train[trainno]\\r\\n imageinput = imagepair['input']\\r\\n imageoutput = imagepair['output']\\r\\n sz0l = math.floor((32 - np.size(imageinput, 0))/2) # padding for the left of dimension 0\\r\\n sz0r = math.ceil((32 - np.size(imageinput, 0))/2) # padding for the right of dimension 0\\r\\n sz1l = math.floor((32 - np.size(imageinput, 1))/2) # padding for the left of dimension 1\\r\\n sz1r = math.ceil((32 - np.size(imageinput, 1))/2) # padding for the right of dimension 1\\r\\n ippad = np.pad(imageinput, ((sz0l, sz0r), (sz1l, sz1r)), constant_values=(0, 0))\\r\\n oppad = np.pad(imageoutput, ((sz0l, sz0r), (sz1l, sz1r)), constant_values=(0, 0))\\r\\n\\r\\n newsample = np.array([[ippad, oppad]])\\r\\n\\r\\n X = np.concatenate((X, newsample), axis=0)\\r\\n Y.append(Yprelim[taskno])\\r\\n\\r\\n # create more images from the rotated versions\\r\\n for i in range(3):\\r\\n ippad = np.rot90(ippad)\\r\\n oppad = np.rot90(oppad)\\r\\n\\r\\n newsample = np.array([[ippad, oppad]])\\r\\n\\r\\n X = np.concatenate((X, newsample), axis=0)\\r\\n Y.append(Yprelim[taskno])\\r\\n\\r\\n # create more images from the transposed & rotated versions\\r\\n ippad = ippad.T\\r\\n oppad = oppad.T\\r\\n\\r\\n newsample = np.array([[ippad, oppad]])\\r\\n\\r\\n X = np.concatenate((X, newsample), axis=0)\\r\\n Y.append(Yprelim[taskno])\\r\\n\\r\\n for i in range(3):\\r\\n ippad = np.rot90(ippad)\\r\\n oppad = np.rot90(oppad)\\r\\n\\r\\n newsample = np.array([[ippad, oppad]])\\r\\n\\r\\n X = np.concatenate((X, newsample), axis=0)\\r\\n Y.append(Yprelim[taskno])\\r\\n\\r\\n X = np.delete(X, 0, axis=0)\\r\\n Y.__delitem__(0)\\r\\n\\r\\n # make channel the last dim\\r\\n X = np.moveaxis(X, 1, -1)\\r\\n\\r\\n return X, Y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"595a5ffcf5e2ea58fea3e0e6cce83d4c\",\n \"score\": \"0.59949315\",\n \"text\": \"def test_cv_shuffled(self):\\n _, y = data_iris()\\n X = np.array([[i] for i in range(100)])\\n pred = solution.test_cv(DummyShuffleLearner(), X, y, k=4)\\n self.assertIsNotNone(pred)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5eb25dde381ce47ffc7f7e85fbed0bc4\",\n \"score\": \"0.5986747\",\n \"text\": \"def train(\\n model, img_width, img_height, train_data_path,\\n validation_data_path, no_of_epochs):\\n nb_train_samples = sum(len(files)\\n for _, _, files in os.walk(train_data_path))\\n nb_validation_samples = sum(len(files)\\n for _, _, files in os.walk(\\n validation_data_path))\\n\\n epochs = no_of_epochs\\n batch_size = 3\\n checkpoint = ModelCheckpoint(\\n filepath=\\\"checkpoint_srkw-{epoch:02d}-{val_accuracy:.2f}.h5\\\",\\n monitor=\\\"val_accuracy\\\", verbose=0, save_best_only=True)\\n\\n reduce_lr = ReduceLROnPlateau(monitor=\\\"val_loss\\\", factor=0.1,\\n patience=100, min_lr=1e-8)\\n\\n train_datagen = ImageDataGenerator(rescale=1. / 255,\\n shear_range=0.2,\\n zoom_range=0.2)\\n\\n # only rescaling\\n test_datagen = ImageDataGenerator(rescale=1. / 255)\\n\\n train_batchsize = 1\\n val_batchsize = 1\\n\\n train_generator = train_datagen.flow_from_directory(\\n train_data_path,\\n target_size=(img_width, img_height),\\n batch_size=train_batchsize,\\n class_mode=\\\"binary\\\",\\n shuffle=True)\\n\\n validation_generator = test_datagen.flow_from_directory(\\n validation_data_path,\\n target_size=(img_width, img_height),\\n batch_size=val_batchsize,\\n class_mode=\\\"binary\\\",\\n shuffle=False)\\n\\n model.fit_generator(\\n train_generator,\\n steps_per_epoch=nb_train_samples // batch_size,\\n epochs=epochs,\\n validation_data=validation_generator,\\n validation_steps=nb_validation_samples // batch_size,\\n callbacks=[checkpoint, reduce_lr])\\n\\n model.save(\\\"srkw_cnn.h5\\\")\\n\\n logger.info(\\\"Detection Model saved\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35f184aa90c03c358209230f835e0f86\",\n \"score\": \"0.5976398\",\n \"text\": \"def train_model(train: Train, ds:Dataset(\\\"catsdogs\\\"), pf: Featureset(\\\"cat_and_dog_features\\\")) -> Any:\\n # train: Train, df:Dataset(\\\"cats-and-dogs-classification\\\"), pf: Featureset(\\\"animal_features\\\")\\n df = ds.to_pandas().merge(pf.to_pandas(), on='id')\\n training_set = df[(df['path'] == 'training_set/dogs') | (df['path'] == 'training_set/cats')]\\n testing_set = df[(df['path'] == 'test_set/dogs') | (df['path'] == 'test_set/cats')]\\n X_train = np.stack(training_set['content'].map(load_process_images))\\n X_test = np.stack(testing_set['content'].map(load_process_images))\\n train.register_input(X_train)\\n train.register_output(df['category'])\\n train_datagen = ImageDataGenerator(rescale=1. / 255,\\n shear_range=0.2,\\n zoom_range=0.2,\\n horizontal_flip=True,\\n width_shift_range=0.1,\\n height_shift_range=0.1\\n )\\n train_datagen.fit(X_train)\\n training_data = train_datagen.flow(X_train, training_set['category'], batch_size=32)\\n validation_gen = ImageDataGenerator(rescale=1. / 255)\\n testing_data = validation_gen.flow(X_test, testing_set['category'], batch_size=32)\\n\\n model = Sequential([\\n Conv2D(filters=32, kernel_size=(3, 3), input_shape=(224, 224, 3), activation='relu'),\\n MaxPooling2D(pool_size=(2, 2)),\\n\\n Conv2D(filters=32, kernel_size=(3, 3), activation='relu'),\\n MaxPooling2D(pool_size=(2, 2)),\\n Dropout(0.25),\\n\\n Conv2D(filters=64, kernel_size=(3, 3), activation='relu'),\\n MaxPooling2D(pool_size=(2, 2)),\\n Dropout(0.25),\\n\\n Flatten(),\\n Dense(128, activation='relu'),\\n Dropout(0.25),\\n Dense(1, activation='sigmoid')\\n ])\\n model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\\n callbacks = [\\n EarlyStopping(patience=2),\\n\\n ]\\n epochs = 10\\n model.fit(\\n training_data,\\n epochs=epochs,\\n validation_data=testing_data,\\n callbacks=callbacks\\n )\\n test_loss, test_accuracy = model.evaluate(testing_data)\\n train_loss, train_accuracy = model.evaluate(training_data)\\n\\n train.log_metric(\\\"Testing Accuracy\\\", test_accuracy)\\n train.log_metric(\\\"Testing Loss\\\", test_loss)\\n\\n train.log_metric(\\\"Training Accuracy\\\", train_accuracy)\\n train.log_metric(\\\"Training Loss\\\", train_loss)\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ef953872049cc8735a5f3564ce27a78\",\n \"score\": \"0.5974803\",\n \"text\": \"def classify(DD, CD):\\n\\n # do K-fold cross validation\\n all_data = np.concatenate((DD,CD))\\n all_class = np.vstack((np.ones((DD.shape[0], 1)), np.zeros((CD.shape[0], 1))))\\n N = all_data.shape[0]\\n K = 5\\n accuracies = np.zeros((K, 1))\\n sensitivities = np.zeros((K, 1))\\n specificities = np.zeros((K, 1))\\n randIndices = np.random.permutation(range(0, N))\\n\\n for fold in range(0, K) :\\n i_test = randIndices[fold * (N // K) : (fold + 1) * (N // K)]\\n i_train = [val for val in randIndices if val not in i_test]\\n c_train = all_class[i_train]\\n c_test = all_class[i_test] \\n\\n # train the model using features all_data[i_train,:] and classes c_train \\n # TODO\\n \\n\\n # compute the % errors using all_data[i_test,:], c_test, and the model you trained\\n # TODO\\n \\n\\n # report result mean and variance, over all folds, of each of accuracy, specificity, and sensitivity\\n # TODO\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4f9cdb59bf6e995c9d2b296fac83e37\",\n \"score\": \"0.59640414\",\n \"text\": \"def train_models(models=model_list, file_name=pr.np_save, in_dims=input_dims, lrs=learning_rates, epochs=epoch_counts,\\r\\n n_splits=splits, saveFile=save_file):\\r\\n X, Y = shape_data(file_name)\\r\\n X = X.reshape((8732, in_dims[0], in_dims[1], in_dims[2]))\\r\\n kfold = KFold(n_splits=n_splits, shuffle=True)\\r\\n with open(saveFile, 'w') as file:\\r\\n writer = csv.writer(file)\\r\\n writer.writerow(['Model Number', 'Learning Rate', 'Number of epochs', 'Accuracy', 'STD', 'Loss'])\\r\\n for lr in lrs:\\r\\n opt = opts.Adam(learning_rate=lr)\\r\\n for batch_size in batch_sizes:\\r\\n for epoch_count in epochs:\\r\\n for model_num in range(len(models)):\\r\\n fold_no = 0\\r\\n model = models[model_num]\\r\\n acc_per_fold = []\\r\\n loss_per_fold = []\\r\\n for train, test in kfold.split(X):\\r\\n fold_no += 1\\r\\n model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\\r\\n model.fit(X[train], Y[train], epochs=epoch_count, batch_size=batch_size,\\r\\n validation_data=(X[test], Y[test]))\\r\\n model.summary()\\r\\n scores = model.evaluate(X[test], Y[test])\\r\\n print(\\r\\n f'Score for fold {fold_no}: {model.metrics_names[0]} of {scores[0]}; '\\r\\n f'{model.metrics_names[1]} of {scores[1] * 100}%')\\r\\n acc_per_fold.append(scores[1] * 100)\\r\\n loss_per_fold.append(scores[0])\\r\\n if fold_no == 1:\\r\\n predictions = model.predict(X[test])\\r\\n preds = np.argmax(predictions, axis=1)\\r\\n conf_matrix = confusion_matrix(np.argmax(Y[test], axis=1), preds)\\r\\n print('Confusion Matrix for fold 1')\\r\\n print(conf_matrix)\\r\\n print('------------------------------------------------------------------------')\\r\\n print('Score per fold')\\r\\n for i in range(0, len(acc_per_fold)):\\r\\n print('------------------------------------------------------------------------')\\r\\n print(f'> Fold {i + 1} - Loss: {loss_per_fold[i]} - Accuracy: {acc_per_fold[i]}%')\\r\\n print('------------------------------------------------------------------------')\\r\\n print('Average scores for all folds:')\\r\\n print(f'> Accuracy: {np.mean(acc_per_fold)} (+- {np.std(acc_per_fold)})')\\r\\n print(f'> Loss: {np.mean(loss_per_fold)}')\\r\\n print('------------------------------------------------------------------------')\\r\\n with open(saveFile, 'a') as file:\\r\\n writer = csv.writer(file)\\r\\n writer.writerow(\\r\\n [model_num + 1, lr, epoch_count, np.mean(acc_per_fold), np.std(acc_per_fold),\\r\\n np.mean(loss_per_fold)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91769a32f38873175c4167a220486de4\",\n \"score\": \"0.59445524\",\n \"text\": \"def cross_validate(model, X, Y, k_fold=10, verbose=False, plot=False):\\r\\n global rand_gen, np_rand_gen\\r\\n\\r\\n n_samples = X.shape[1]\\r\\n n_samples_validation = n_samples / k_fold\\r\\n n_samples_train = n_samples - n_samples_validation\\r\\n fold_stats = []\\r\\n X, Y = model.shuffle(X, Y)\\r\\n\\r\\n for fold in range(k_fold):\\r\\n rand_gen = lib.random.RandomState(seed=random_generator_seed)\\r\\n np_rand_gen = numpy.random.RandomState(seed=random_generator_seed)\\r\\n\\r\\n _model = MultiClassNeuralNetwork(\\r\\n data_dim=model._data_dim,\\r\\n activation_func=model._activation_func,\\r\\n l1_hidden_size=model._l1_hidden_size,\\r\\n l2_hidden_size=model._l2_hidden_size,\\r\\n num_of_classes=model._num_of_classes,\\r\\n lr=model._lr,\\r\\n initial_lr=model._initial_lr,\\r\\n epochs=model._epochs,\\r\\n batch_size=model._batch_size,\\r\\n reg=model._reg,\\r\\n input_noise_p=model._input_noise_p,\\r\\n dropout_p=model._dropout_p,\\r\\n early_stop_max_epochs=model._early_stop_max_epochs,\\r\\n input_z_score_normalization=model._input_z_score_normalization,\\r\\n init_weights_mu=model._init_weights_mu,\\r\\n init_weights_sigma=model._init_weights_sigma\\r\\n )\\r\\n\\r\\n sliding_window_left = fold * n_samples_validation\\r\\n sliding_window_right = (fold + 1) * n_samples_validation\\r\\n\\r\\n X_validation = X[:, sliding_window_left:sliding_window_right]\\r\\n Y_validation = Y[:, sliding_window_left:sliding_window_right]\\r\\n\\r\\n X_train_left = X[:, 0:sliding_window_left]\\r\\n X_train_right = X[:, sliding_window_right:n_samples]\\r\\n Y_train_left = Y[:, 0:sliding_window_left]\\r\\n Y_train_right = Y[:, sliding_window_right:n_samples]\\r\\n\\r\\n X_train = lib.concatenate((X_train_left, X_train_right), axis=1)\\r\\n Y_train = lib.concatenate((Y_train_left, Y_train_right), axis=1)\\r\\n\\r\\n performance = _model.fit(\\r\\n X_train, Y_train, X_validation, Y_validation, verbose)\\r\\n predictions = _model.predict(X_validation)\\r\\n accuracy = _model.evaluate(predictions, Y_validation)\\r\\n fold_stats.append(accuracy)\\r\\n\\r\\n if plot is True:\\r\\n print(\\\"\\\")\\r\\n _model.plot(performance)\\r\\n print(\\\"\\\")\\r\\n\\r\\n print(\\\"\\\\nFold ({fold}) test accuracy: {accuracy} %\\\\n\\\".format(\\r\\n fold=fold + 1, accuracy=accuracy))\\r\\n\\r\\n total_avg_accuracy = round(float(lib.average(fold_stats)), 2)\\r\\n\\r\\n return total_avg_accuracy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8363ed89ffcb5af9c876b7a99e9e4a7\",\n \"score\": \"0.5938936\",\n \"text\": \"def main():\\r\\n \\r\\n cudnn.enabled = True\\r\\n cudnn.benchmark = True \\r\\n os.environ['CUDA_VISIBLE_DEVICES'] = args.gpus\\r\\n \\r\\n ############# Create mask-guided classification network.\\r\\n model = DSI_Net(config, K = args.K)\\r\\n optimizer = torch.optim.Adam(model.parameters(), lr=config.LEARNING_RATE, weight_decay =config.WEIGHT_DECAY)\\r\\n model.cuda()\\r\\n if config.FP16 is True:\\r\\n model, optimizer = amp.initialize(model, optimizer, opt_level=\\\"O1\\\")\\r\\n model = torch.nn.DataParallel(model)\\r\\n \\r\\n ############# Load pretrained weights\\r\\n pretrained_dict = torch.load(model_urls['deeplabv3plus_xception'])\\r\\n net_dict = model.state_dict()\\r\\n pretrained_dict = {k: v for k, v in pretrained_dict.items() if (k in net_dict) and (v.shape == net_dict[k].shape)}\\r\\n net_dict.update(pretrained_dict)\\r\\n model.load_state_dict(net_dict)\\r\\n print(len(net_dict))\\r\\n print(len(pretrained_dict))\\r\\n model.train()\\r\\n model.float()\\r\\n ce_loss = nn.CrossEntropyLoss()\\r\\n dice_loss = Dice_Loss()\\r\\n task_interaction_loss = Task_Interaction_Loss()\\r\\n \\r\\n ############# Load training and validation data\\r\\n trainloader = data.DataLoader(CADCAPDataset(config.DATA_ROOT, args.image_list, config.SIZE, data_type='train', mode = 'train'), batch_size=config.BATCH_SIZE, shuffle=True, \\r\\n num_workers=config.NUM_WORKERS, pin_memory=True, drop_last = config.DROP_LAST)\\r\\n testloader = data.DataLoader(CADCAPDataset(config.DATA_ROOT, args.image_list,config.SIZE, data_type = 'test', mode='test'), \\r\\n batch_size=1, shuffle=False, num_workers=config.NUM_WORKERS, pin_memory=True)\\r\\n train_testloader = data.DataLoader(CADCAPDataset(config.DATA_ROOT, args.image_list,config.SIZE, data_type = 'train', mode = 'test'), \\r\\n batch_size=1, shuffle=False, num_workers=config.NUM_WORKERS, pin_memory=True) \\r\\n \\r\\n \\r\\n if not os.path.isdir(config.SAVE_PATH):\\r\\n os.mkdir(config.SAVE_PATH)\\r\\n if not os.path.isdir(config.SAVE_PATH+'Seg_results/'):\\r\\n os.mkdir(config.SAVE_PATH+'Seg_results/')\\r\\n if not os.path.isdir(config.LOG_PATH):\\r\\n os.mkdir(config.LOG_PATH) \\r\\n \\r\\n f_path = config.LOG_PATH + 'training_output.log'\\r\\n logfile = open(f_path, 'a')\\r\\n \\r\\n print_f(os.getcwd(), f=logfile)\\r\\n print_f('Device: {}'.format(args.gpus), f=logfile)\\r\\n print_f('==={}==='.format(time.strftime(\\\"%Y-%m-%d %H:%M:%S\\\", time.localtime())), f=logfile)\\r\\n print_f('===Setting===', f=logfile)\\r\\n print_f(' Data_list: {}'.format(args.image_list), f=logfile)\\r\\n print_f(' K: {}'.format(args.K), f=logfile)\\r\\n print_f(' Lost_weight: {}'.format(args.alpha), f=logfile)\\r\\n print_f(' LR: {}'.format(config.LEARNING_RATE), f=logfile) \\r\\n\\r\\n OA_bulk_train = []\\r\\n CK_bulk_train = []\\r\\n DI_bulk_train = []\\r\\n JA_bulk_train = []\\r\\n SE_bulk_train = []\\r\\n\\r\\n OA_bulk_test = []\\r\\n CK_bulk_test = []\\r\\n DI_bulk_test = []\\r\\n JA_bulk_test = []\\r\\n SE_bulk_test = [] \\r\\n \\r\\n \\r\\n for epoch in range(config.EPOCH): \\r\\n #cls\\r\\n cls_train_loss = []\\r\\n seg_train_loss = []\\r\\n train_inter_loss = [] \\r\\n ############# Start the training\\r\\n for i_iter, batch in enumerate(trainloader):\\r\\n step = (config.TRAIN_NUM/config.BATCH_SIZE)*epoch+i_iter \\r\\n images, masks, labels, name = batch\\r\\n images = images.cuda()\\r\\n labels = labels.cuda().long()\\r\\n masks = masks.cuda().squeeze(1)\\r\\n optimizer.zero_grad()\\r\\n lr = adjust_learning_rate(optimizer, step)\\r\\n model.train()\\r\\n preds_seg_coarse, preds_seg_fine, preds_cls = model(images)\\r\\n cls_loss = ce_loss(preds_cls, labels) \\r\\n seg_loss_fine = dice_loss(preds_seg_fine, masks)\\r\\n seg_loss_coarse = dice_loss(preds_seg_coarse, masks) \\r\\n inter_loss = task_interaction_loss(preds_cls, preds_seg_fine, labels)\\r\\n loss = cls_loss + seg_loss_fine + seg_loss_coarse + args.alpha * inter_loss\\r\\n \\r\\n if config.FP16 is True:\\r\\n with amp.scale_loss(loss, optimizer) as scaled_loss:\\r\\n scaled_loss.backward()\\r\\n else:\\r\\n loss.backward()\\r\\n optimizer.step()\\r\\n #cls\\r\\n cls_train_loss.append(cls_loss.cpu().data.numpy()) \\r\\n seg_train_loss.append(seg_loss_fine.cpu().data.numpy())\\r\\n train_inter_loss.append(inter_loss.cpu().data.numpy()) \\r\\n \\r\\n ############ train log\\r\\n line = \\\"Train-Epoch [%d/%d] [All]: Seg_loss = %.6f, Class_loss = %.6f, Inter_loss = %.6f, LR = %0.9f\\\\n\\\" % (epoch, config.EPOCH, np.nanmean(seg_train_loss), np.nanmean(cls_train_loss), np.nanmean(train_inter_loss), lr)\\r\\n print_f(line, f=logfile) \\r\\n \\r\\n result = test(train_testloader, model, epoch, verbose=False)\\r\\n #cls\\r\\n [CK, OA, EREC] = result['cls']\\r\\n OA_bulk_train.append(OA)\\r\\n CK_bulk_train.append(CK)\\r\\n \\r\\n # seg \\r\\n [AC, DI, SE, SP, JA] = result['seg'] \\r\\n JA_bulk_train.append(np.nanmean(JA)) \\r\\n DI_bulk_train.append(np.nanmean(DI))\\r\\n SE_bulk_train.append(np.nanmean(SE)) \\r\\n \\r\\n ############# Start the test\\r\\n result = test(testloader, model, epoch, config.SAVE_PATH+'Seg_results/' , verbose = config.VERBOSE)\\r\\n #cls\\r\\n [CK, OA, EREC] = result['cls']\\r\\n line = \\\"Test -Epoch [%d/%d] [Cls]: CK-Score = %f, OA = %f, Rec-N = %f, Rec-V = %f, Rec-I=%f \\\\n\\\" % (epoch, config.EPOCH, CK, OA, EREC[0],EREC[1],EREC[2] )\\r\\n print_f(line, f=logfile)\\r\\n OA_bulk_test.append(OA)\\r\\n CK_bulk_test.append(CK)\\r\\n \\r\\n # seg \\r\\n [AC, DI, SE, SP, JA] = result['seg'] \\r\\n line = \\\"Test -Epoch [%d/%d] [Seg]: AC = %f, DI = %f, SE = %f, SP = %f, JA = %f \\\\n\\\" % (epoch, config.EPOCH, np.nanmean(AC), np.nanmean(DI), np.nanmean(SE), np.nanmean(SP), np.nanmean(JA))\\r\\n print_f(line, f=logfile)\\r\\n \\r\\n JA_bulk_test.append(np.nanmean(JA)) \\r\\n DI_bulk_test.append(np.nanmean(DI))\\r\\n SE_bulk_test.append(np.nanmean(SE))\\r\\n \\r\\n ############# Plot val curve\\r\\n filename = os.path.join(config.LOG_PATH, 'cls_curves.png')\\r\\n data_list = [OA_bulk_train, OA_bulk_test, CK_bulk_train, CK_bulk_test]\\r\\n label_list = ['OA_train','OA_test','CK_train','CK_test']\\r\\n draw_curves(data_list = data_list, label_list = label_list, color_list = config.COLOR[0:4], filename = filename)\\r\\n filename = os.path.join(config.LOG_PATH, 'seg_curves.png')\\r\\n data_list = [JA_bulk_train, JA_bulk_test, DI_bulk_train, DI_bulk_test, SE_bulk_train, SE_bulk_test]\\r\\n label_list = ['JA_train','JA_test','DI_train','DI_test', 'SE_train','SE_test'] \\r\\n draw_curves(data_list = data_list, label_list = label_list, color_list = config.COLOR[0:6], filename = filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0796727cc4b78bb5a1a5ead43d7e85aa\",\n \"score\": \"0.59347063\",\n \"text\": \"def ms_classification(\\n self,\\n data_dir = \\\"PATDATA\\\",\\n patient_file_path = \\\"PATSTAT\\\",\\n model_path = \\\"MODELPATH\\\",\\n model_name = \\\"model_test\\\",\\n num_layers = 8,\\n lr = 0.0005,\\n drop_rate = 0.2,\\n num_folds = 1,\\n num_splits = 1,\\n flip = False,\\n random_seed = None,\\n test_fraction = 0.1,\\n filter_age = None,\\n batch_size = 32,\\n early_stopping = True,\\n epochs = 100,\\n log_dir = \\\"./logs/fit/\\\",\\n use_spectrogram = False,\\n ms_only = False,\\n use_lstm = False,\\n verbose = True\\n ):\\n job_id = str(uuid.uuid4())\\n logger.info(f\\\"Job ID for this run: {job_id}\\\")\\n \\n # Create the paths to the directories \\n data_dir = os.environ[data_dir]\\n patient_file_path = os.environ[patient_file_path]\\n model_path = os.environ[model_path]\\n traces = data_manipulation.readin_traces(\\n data_dir, patient_file_path, start_index=100\\n )\\n\\n if filter_age is not None:\\n traces = [t for t in traces if int(t.sub_age.item()) < filter_age]\\n \\n if verbose:\\n print(f\\\"Number of patient traces: {len(traces)}\\\")\\n\\n # Convert the images to Eye trace features \\n if ms_only:\\n X, y, subject_ids = pre_processing.build_using_edss(traces)\\n\\n # Augment Severe EDSS indices to get rid of data imbalance\\n severe_indices = np.where(y == 1)\\n X_aug, y_aug, sub_id_aug = pre_processing.flip_augment(X, y, \\n subject_ids,\\n severe_indices)\\n # Append these Augmented samples \\n print(subject_ids[severe_indices].shape)\\n X = np.concatenate((X, X_aug))\\n y = np.concatenate((y, y_aug))\\n subject_ids = np.concatenate((subject_ids, sub_id_aug))\\n \\n else:\\n X, y, subject_ids = pre_processing.traces_to_feature(traces, \\n mean_center=False, scale_std=False)\\n\\n # Use only distance and test it out \\n # X = pre_processing.use_dist(X)\\n\\n # Prepare the Data. Augment the data. \\n X = pre_processing.downsample_traces(X, 128)\\n \\n # Use spectrograms if use_spectrogram is set to True\\n if use_spectrogram:\\n X = pre_processing.spec_build(X)\\n freq_bins = X.shape[2]\\n time_sets = X.shape[3]\\n X = np.reshape(X, newshape=(-1, 2, freq_bins * time_sets))\\n\\n if verbose:\\n print(f\\\"Number of samples with Label 0:{y[y == False].shape[0]}\\\")\\n print(f\\\"Number of samples with Label 1:{y[y == True].shape[0]}\\\")\\n print(f\\\"Number of features used from Eye trace: {X.shape[1]}\\\")\\n \\n # Create the test train split here \\n (\\n X_train,\\n X_test,\\n y_train,\\n y_test,\\n subject_ids_train,\\n subject_ids_test,\\n ) = grouped_train_test_split(\\n X, y, subject_ids, test_fraction, random_seed=random_seed\\n )\\n\\n if verbose:\\n print(f\\\"Shape of the Input is : {X_train.shape}\\\")\\n \\n # Double the dataset by including the flipped version of the inputs\\n if flip:\\n X_train, y_train, subject_ids_train = data_manipulation.flip_y_traces(\\n X_train, y_train, subject_ids_train\\n )\\n\\n X_train = data_manipulation.channel_last(X_train)\\n X_test = data_manipulation.channel_last(X_test)\\n y_train, y_test = (\\n y_train.astype(np.int32).squeeze(),\\n y_test.astype(np.int32).squeeze(),\\n )\\n \\n if verbose:\\n print(f\\\"Shape of the Input after setting channel last is {X_train.shape}\\\")\\n \\n input_shape = tuple(list(X_train.shape)[1:])\\n print(f\\\"Shape of the input is: {input_shape}\\\") \\n \\n if use_lstm:\\n model = LSTM_Model(input_shape) \\n \\n if verbose:\\n model.model.summary()\\n \\n model.run_model(X_train, y_train, \\n lr = lr, epochs = epochs,\\n batch_size = batch_size)\\n # Save the model.\\n model.save_model(model_path, model_name)\\n\\n if verbose:\\n print(f\\\"Model has been saved to {os.path.join(model_path, model_name) + '.h5'}\\\")\\n else:\\n # Build the Model \\n # Any new model should have this Keras Classifier wrapper.\\n log_dir = log_dir + datetime.datetime.now().strftime(\\\"%Y%m%d-%H%M%S\\\")\\n model = KerasClassifier(\\n resnet_build_fn_closure(input_shape, log_dir=log_dir,\\n drop_rate=drop_rate), \\n batch_size=batch_size, \\n epochs=epochs)\\n \\n # How many input channels \\n in_channel = X_train.shape[2]\\n if verbose:\\n print(f\\\"Number of Input Channels: {in_channel}\\\")\\n print(f\\\"Shape of X : {X.shape}\\\")\\n print(f\\\"Shape of Y: {y.shape}\\\")\\n # Parameters that are used for the model \\n param_grid = {\\n \\\"num_layers\\\": [num_layers],\\n \\\"n_feature_maps\\\": [in_channel],\\n \\\"lr\\\": [lr],\\n \\\"early_stopping\\\": [early_stopping],\\n \\\"kernel_multiple\\\": [1],\\n }\\n\\n cv_results = manual_grid_search_with_validation(\\n X_train, y_train, subject_ids_train, \\n num_folds, model, param_grid,\\n model_path, model_name)\\n \\n save_path = Path(\\\"~\\\").expanduser() / \\\"cv_runs\\\"\\n # Create the directory if it doesn't exist\\n save_path.mkdir(parents=True, exist_ok=True)\\n save_file = save_path / f\\\"cv_run_{job_id}.csv\\\"\\n cv_results.to_csv(save_file)\\n \\n # Use the X_test set to calculate some metrics \\n y_pred = model.predict_proba(X_test)\\n test_accuracy = accuracy_score(y_test, np.argmax(y_pred, axis=1))\\n\\n # Test Accuracy Calculation\\n print(f\\\"Test Accuracy of the model is: {test_accuracy:.2f} when tested on {X_test.shape[0]} samples.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27c36c1cd92a24e9acb50b85e9ed76c5\",\n \"score\": \"0.5934338\",\n \"text\": \"def train_model(model_params):\\n cars = glob.glob('util_images/vehicles/**/*.png')\\n not_cars = glob.glob('util_images/non-vehicles/**/*.png')\\n sample_size = model_params[\\\"sample_size\\\"]\\n cars = cars[0:sample_size]\\n not_cars = not_cars[0:sample_size]\\n\\n color_item = model_params[\\\"color_item\\\"]\\n orient = model_params[\\\"orient\\\"]\\n pix_per_cell = model_params[\\\"pix_per_cell\\\"]\\n cells_per_block = 2\\n hog_channels = model_params[\\\"hog_channel\\\"]\\n\\n\\n print_parameters(color_item, orient, pix_per_cell, cells_per_block, hog_channels)\\n\\n car_features = object_detection_utils.extract_features(cars, color_space=color_item, orient=orient,\\n pix_per_cell=pix_per_cell, cell_per_block=cells_per_block,\\n hog_channel=hog_channels)\\n notcar_features = object_detection_utils.extract_features(not_cars, color_space=color_item, orient=orient,\\n pix_per_cell=pix_per_cell, cell_per_block=cells_per_block,\\n hog_channel=hog_channels)\\n # Create an array stack of feature vectors\\n X = np.vstack((car_features, notcar_features)).astype(np.float64)\\n # Define the labels vector\\n y = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features))))\\n # Split up data into randomized training and test sets\\n rand_state = np.random.randint(0, 100)\\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=rand_state)\\n # Fit a per-column scaler\\n X_scaler = StandardScaler().fit(X_train)\\n # Apply the scaler to X\\n X_train = X_scaler.transform(X_train)\\n X_test = X_scaler.transform(X_test)\\n # Use a linear SVC\\n svc = LinearSVC()\\n # Check the training time for the SVC\\n svc.fit(X_train, y_train)\\n # Check the score of the SVC\\n accuracy = round(svc.score(X_test, y_test), 4)\\n model = dict()\\n model[\\\"linearSVC\\\"] = svc\\n model[\\\"X_scaler\\\"] = X_scaler\\n model[\\\"color_item\\\"] = model_params[\\\"color_item\\\"]\\n model[\\\"orient\\\"] = model_params[\\\"orient\\\"]\\n model[\\\"pix_per_cell\\\"] = model_params[\\\"pix_per_cell\\\"]\\n model[\\\"cells_per_block\\\"] = 2\\n model[\\\"hog_channel\\\"] = model_params[\\\"hog_channel\\\"]\\n model[\\\"sample_size\\\"] = model_params[\\\"sample_size\\\"]\\n model[\\\"spatial_feat\\\"] = True\\n model[\\\"hist_feat\\\"] = True\\n model[\\\"hog_feat\\\"] = True\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d33ed3dd79e174c6177e1851c8ce0b24\",\n \"score\": \"0.5933601\",\n \"text\": \"def generate_attack_inputs(sess,model,x_test,y_test,class_num,nb_imgs, load_imgs = False,load_robust = True,file_path = 'local_info/'):\\n if not load_imgs:\\n # if load_robust:\\n # original_predict = sess.run(model.pre_softmax,feed_dict = {model.x_input:x_test.reshape(-1,784)})\\n # else:\\n original_predict = model.predict_prob(x_test)\\n original_class = np.argmax(original_predict, axis = 1)\\n true_class = np.argmax(y_test, axis = 1)\\n mask = true_class == original_class\\n corr_idx = np.where(true_class == original_class)[0]\\n print(np.sum(mask), \\\"out of\\\", mask.size, \\\"are correctly labeled,\\\", len(x_test[mask])) \\n\\n # generate targeted labels, choose least likely class, untargeted attack does not require\\n orig_class_vec = range(class_num)\\n target_ys_one_hot = []\\n target_ys = []\\n orig_images = []\\n orig_labels = []\\n all_true_ids = []\\n trans_test_images = []\\n for orig_class in orig_class_vec:\\n # guarantees that same sets of images are selected...\\n np.random.seed(1234)\\n cls_idx = np.where(true_class == orig_class)[0]\\n corr_cls_idx = np.intersect1d(cls_idx,corr_idx)\\n #random sample, this is doubled because I want to set aside a test set which is to measure transferability\\n corr_cls_idx = np.random.choice(corr_cls_idx, size=nb_imgs*2, replace=False)\\n # print(\\\"selected img ids:\\\",corr_cls_idx)\\n x_sel = x_test[corr_cls_idx]\\n orig_labels_tmp = y_test[corr_cls_idx]\\n y_sel = model.predict_prob(x_sel)\\n cls_count = np.bincount(np.argmin(y_sel,axis=1))# count number of occurence\\n tar_class = np.argmax(cls_count)\\n print(\\\"Orig: {}, Tar: {}\\\".format(orig_class,tar_class))\\n # store the adversarial samples related files\\n target_ys.extend([tar_class]*int(len(corr_cls_idx)/2))\\n target_ys_one_hot.extend([np.eye(class_num)[tar_class]]*int(len(corr_cls_idx)/2))\\n orig_images.extend(instance for instance in x_sel[:int(len(x_sel)/2)])\\n orig_labels.extend(lab for lab in orig_labels_tmp[:int(len(orig_labels_tmp)/2)])\\n all_true_ids.extend(idx for idx in corr_cls_idx[:int(len(corr_cls_idx)/2)])\\n # store the transferability test related files\\n trans_test_images.extend(instance for instance in x_sel[int(len(x_sel)/2):])\\n\\n target_ys_one_hot = np.array(target_ys_one_hot)\\n orig_images = np.array(orig_images)\\n target_ys = np.array(target_ys)\\n orig_labels = np.array(orig_labels)\\n all_true_ids = np.array(all_true_ids)\\n trans_test_images = np.array(trans_test_images)\\n\\n # can uncomment below if not needed\\n fname = '/target_ys_one_hot_{}.npy'.format(nb_imgs)\\n np.save(file_path+fname,target_ys_one_hot)\\n fname = '/orig_images_{}.npy'.format(nb_imgs)\\n np.save(file_path+fname,orig_images)\\n fname = '/orig_labels_{}.npy'.format(nb_imgs)\\n np.save(file_path+fname,orig_labels)\\n fname = '/all_true_ids_{}.npy'.format(nb_imgs)\\n np.save(file_path+fname,all_true_ids)\\n fname = '/target_ys_{}.npy'.format(nb_imgs)\\n np.save(file_path+fname,target_ys)\\n fname = '/trans_test_images_{}.npy'.format(nb_imgs)\\n np.save(file_path+fname,trans_test_images)\\n else:\\n fname = '/target_ys_one_hot_{}.npy'.format(nb_imgs)\\n target_ys_one_hot = np.load(file_path+fname)\\n fname = '/orig_images_{}.npy'.format(nb_imgs)\\n orig_images = np.load(file_path+fname)\\n fname = '/orig_labels_{}.npy'.format(nb_imgs)\\n orig_labels = np.load(file_path+fname)\\n fname = '/all_true_ids_{}.npy'.format(nb_imgs)\\n all_true_ids = np.load(file_path+fname)\\n fname = '/target_ys_{}.npy'.format(nb_imgs)\\n target_ys = np.load(file_path+fname)\\n fname = '/trans_test_images_{}.npy'.format(nb_imgs)\\n trans_test_images = np.load(file_path+fname)\\n return target_ys_one_hot,orig_images,target_ys,orig_labels,all_true_ids, trans_test_images\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e73201314edf20d5183214b6e3104c5\",\n \"score\": \"0.5933446\",\n \"text\": \"def learn_test(self):\\r\\n self.net.train_step()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32d8d6f2faf543f1badf64caac4a023c\",\n \"score\": \"0.59314454\",\n \"text\": \"def train(self):\\n steps_for_validation = self.data.validation_data.n//self.data.validation_data.batch_size\\n steps_for_train = self.data.training_data.n//self.data.training_data.batch_size\\n\\n # Compilation process\\n self.model.compile(\\n optimizer=\\\"rmsprop\\\", loss=\\\"categorical_crossentropy\\\", metrics=[\\\"accuracy\\\"])\\n\\n # Training process\\n self.model.fit_generator(self.data.training_data, epochs=5, steps_per_epoch=steps_for_train,\\n validation_data=self.data.validation_data, validation_steps=steps_for_validation)\\n\\n # Evaluation process\\n evaluation = self.model.evaluate_generator(\\n generator=self.data.validation_data, steps=steps_for_validation)\\n print(\\n f\\\"EVALUATION:\\\\n Loss {evaluation[0]}\\\\nAccuracy: {evaluation[1]}\\\\n\\\")\\n\\n self.model.save(\\\"magichands_model.h5\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13ac53ff48da1bb7ec2ed1361735ecbc\",\n \"score\": \"0.59222907\",\n \"text\": \"def _generate_train_batch(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cad73916bbe322fa35bd7d7f8483a5cb\",\n \"score\": \"0.59176\",\n \"text\": \"def create_validation(args):\\n \\n dataset_dir = args.dataset_dir\\n workspace = args.workspace\\n folds_num = 3\\n \\n rs = np.random.RandomState(0)\\n \\n filenames = ['BirdVoxDCASE20k_csvpublic.csv', \\n 'ff1010bird_metadata_2018.csv', \\n 'warblrb10k_public_metadata_2018.csv']\\n \\n # Read dataframe\\n dataframes = []\\n \\n for (n, filename) in enumerate(filenames):\\n \\n filepath = os.path.join(dataset_dir, filename)\\n \\n df = pd.read_csv(filepath)\\n df = pd.DataFrame(df)\\n df['fold'] = n + 1\\n \\n dataframes.append(df)\\n \\n dataframes = pd.concat(dataframes)\\n \\n # Write out to csv\\n out_path = os.path.join(workspace, 'validation.csv')\\n dataframes.to_csv(out_path)\\n print(\\\"Write out to {}\\\".format(out_path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16e58975c6c39ba9ad4c420830817f5c\",\n \"score\": \"0.5915359\",\n \"text\": \"def __init__(self, Examples, Labels, n_folds=10, epochs=50):\\r\\n \\r\\n # Set up an n-fold test and call train_and_evaluate_model\\r\\n # for each fold. Keep statistics and report summary\\r\\n # information of mean and variance.\\r\\n \\r\\n # Randmize examples within each fold\\r\\n kfold = StratifiedKFold(n_folds, shuffle=True)\\r\\n one_hot_labels = np_utils.to_categorical(Labels)\\r\\n # Generate indices that can be used to split into training\\r\\n # and test data, e.g. examples[train_idx]\\r\\n accuracy = []\\r\\n for (train_idx, test_idx) in kfold.split(Examples, Labels):\\r\\n # normally, we would gather results about each fold\\r\\n accuracy.append(self.train_and_evaluate__model(Examples, one_hot_labels, train_idx, test_idx, 100, epochs))\\r\\n accuracy = np.array(accuracy)\\r\\n mu, sd = np.mean(accuracy), np.std(accuracy)\\r\\n print()\\r\\n print('mean accuracy {} and standard deviation {}'.format(mu,sd))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8704d8db46bfdc6fb2723045f09b85dd\",\n \"score\": \"0.5912254\",\n \"text\": \"def main():\\n logger = logging.getLogger(__name__)\\n logger.info('making final data set from raw data')\\n\\n split_train_test()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d15329cda45e293524f75eab42e1b7b0\",\n \"score\": \"0.591102\",\n \"text\": \"def train_model(self,valid_dicom_npy_lst, boolean_mask_lst):\\n train_data = []\\n train_target = []\\n\\n # Using the saved numpy information files from the DICOM images and contour files,\\n for dicom_npy_file in valid_dicom_npy_lst:\\n train_data.append(np.load(dicom_npy_file))\\n\\n for mask_npy_file in boolean_mask_lst:\\n train_target.append(np.load(mask_npy_file))\\n\\n # Forming the single numpy array for the entire data set\\n train_data = np.array(train_data, dtype=np.float32)\\n train_target = np.array(train_target, dtype=np.float32)\\n\\n for i in range(self.nb_epoch) :\\n print('Epoch {} of {}'.format(i, self.nb_epoch))\\n # get the single batch of train data consists of one numpy array for images & one numpy array for targets.\\n mini_random_batches = self.get_random_mini_batches(train_data, train_target)\\n print 'Random Mini Batch Length : {} out of {} entire samples'.format(mini_random_batches.__len__(),train_data.__len__())\\n for random_batch in mini_random_batches:\\n train_data_batch = random_batch[0]\\n train_target_batch = random_batch[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9d7e22c90369d59220d5e7ea08937f5\",\n \"score\": \"0.59054047\",\n \"text\": \"def __init_dataset(self):\\r\\n\\r\\n if self.__mode == 'test':\\r\\n self.__test_data = pd.read_csv(self.__label_root)\\r\\n self.__get_column_name(self.__test_data)\\r\\n self.__test_img_list = [os.path.join(self.__data_root, img_name) for img_name in\\r\\n self.__test_data[self.__img_column_name].values]\\r\\n else:\\r\\n self.__train_csv_data = pd.read_csv(self.__label_root)\\r\\n self.__get_column_name(self.__train_csv_data)\\r\\n # 全数据训练\\r\\n if config.train_proportion == 1.0:\\r\\n\\r\\n if self.__mode == 'train':\\r\\n self.__train_img_list = [os.path.join(self.__data_root, img_name) for img_name in\\r\\n self.__train_csv_data[self.__img_column_name].values]\\r\\n self.__train_label_list = self.__train_csv_data[self.__label_column_name].values\\r\\n if self.__mode == 'val':\\r\\n val_data = self.__train_csv_data[round(0.8*len(self.__train_csv_data)):]\\r\\n self.__val_img_list = [os.path.join(self.__data_root, img_name) for img_name in\\r\\n val_data[self.__img_column_name].values]\\r\\n self.__val_label_list = val_data[self.__label_column_name].values\\r\\n\\r\\n\\r\\n else:\\r\\n # random split\\r\\n train_img, train_label, val_img, val_label = train_test_split(\\r\\n self.__train_csv_data[self.__img_column_name].values,\\r\\n self.__train_csv_data[self.__label_column_name].values,\\r\\n train_size=config.train_proportion,\\r\\n test_size=1-config.train_proportion\\r\\n )\\r\\n # data for training\\r\\n if self.__mode == 'train':\\r\\n self.__train_img_list = train_img\\r\\n self.__train_label_list = train_label\\r\\n # data for validating\\r\\n if self.__mode == 'val':\\r\\n self.__val_img_list = val_img\\r\\n self.__val_label_list = val_label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f12b9848d19a13693b83ac4081c97df\",\n \"score\": \"0.5903171\",\n \"text\": \"def prepare_data_cross_validation(pos_data, neg_data, ADD_FEATURE=False):\\n\\n _pos_cross_valid_all = get_valid_data(pos_data)\\n _neg_cross_valid_all = get_valid_data(neg_data)\\n\\n for i in range(5):\\n [_pos_4_train, _pos_4_test] = _pos_cross_valid_all[i]\\n [_neg_4_train, _neg_4_test] = _neg_cross_valid_all[i]\\n _neg_4_train = random.sample(_neg_4_train, len(_pos_4_train))\\n test_data = _pos_4_test + _neg_4_test[:len(_pos_4_test)]\\n train_data = _pos_4_train + _neg_4_train\\n print '---------------------------------'\\n print 'cross_validation id:' + str(i)\\n print 'pos for training:' + len(_pos_4_train).__str__()\\n print 'neg for training:' + len(_neg_4_train).__str__()\\n print 'pos for testing:' + len(_pos_4_test).__str__()\\n print 'neg for testing:' + len(_neg_4_test[:len(_pos_4_test)]).__str__()\\n print '---------------------------------'\\n vec_train, label_train, trigger_train, aux_train = get_data_and_lable_trigger(train_data)\\n vec_test, label_test, trigger_test, aux_test = get_data_and_lable_trigger(test_data)\\n datas = [(np.array(vec_train), np.array(label_train),\\n trigger_train, aux_train, np.array(vec_test),\\n np.array(label_test), trigger_test, aux_test)]\\n _path_to_save_datas = '5-cross_data/prepared_datas_with_feature_{}'.format(i) if ADD_FEATURE else '5-cross_data/prepared_datas_{}'.format(i)\\n with open(_path_to_save_datas, 'w') as f:\\n pickle.dump(datas, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce5c3099dfe5c5a1aaf838214a3540e8\",\n \"score\": \"0.5902428\",\n \"text\": \"def cv_split_classification(self):\\n # Define base data size and size of validation\\n data_size = len(self.transformed_data)\\n validation_size = int(data_size / 10)\\n\\n # Check and set the random seed\\n if self.random_state:\\n np.random.seed(self.random_state)\\n\\n # Sample for validation\\n validation_splitter = []\\n\\n # Randomize a number between 0 and 10 and multiply by the index to randomly pick observations over data set\\n for index in range(validation_size):\\n validation_splitter.append(np.random.choice(a=10) + (10 * index))\\n self.tune_data = self.transformed_data.iloc[validation_splitter]\\n\\n # Determine the remaining index that weren't picked for validation\\n remainder = list(set(self.transformed_data.index) - set(validation_splitter))\\n remainder_df = pd.DataFrame(self.transformed_data.iloc[remainder]['Class'])\\n\\n # Assign a random number\\n remainder_df['Random_Number'] = np.random.randint(0, len(remainder), remainder_df.shape[0])\\n\\n # Sort over class and the random number\\n remainder_df.sort_values(by=['Class', 'Random_Number'], inplace=True)\\n remainder_df.reset_index(inplace=True)\\n\\n # Sample for CV\\n for index in range(5):\\n # Mod the index by 5 and there will be 5 remainder groups for the CV split\\n splitter = remainder_df.loc[remainder_df.index % 5 == index]['index']\\n\\n # Update our attribute with the dictionary for this index\\n self.test_split.update({\\n index: self.transformed_data.iloc[splitter]\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23ef1af4360bbaa010b3774d4a46929d\",\n \"score\": \"0.58991945\",\n \"text\": \"def test_if_the_name_is_correct(model_name):\\n \\n possible_datasets = ['IconArt_v1','RMN','RASTA','Paintings']\\n possible_lr = ['_big0001_modif','_small001_modif','_big001_modif','_small01_modif','_big01_modif']\\n possible_llt = ['_GAP','_GMP']\\n # For the last layer transformation !!! GlobalAveragePooling2D\\n possible_opt = ['_adam','_Adadelta','_RMSprop']\\n possible_freeze= ['_unfreeze50','_unfreeze84','_unfreeze44','_unfreeze20','_unfreeze8']\\n # _unfreeze50 for InceptionV1 to train starting at mixed4a_3x3_bottleneck_pre_relu\\n # but _unfreeze84 for InceptionV1_slim to train at \\n # Mixed_4b_Branch_1_a_1x1_conv : because the name of the layer are not the same !\\n possible_loss= ['_cosineloss']\\n possibleInit = ['_RandInit','_RandForUnfreezed']\\n possible_crop = ['_randomCrop']\\n possible_Sup = ['_deepSupervision']\\n possible_Aug = ['_dataAug','_SmallDataAug','_MediumDataAug']\\n possible_epochs = ['_ep120','_ep200','_ep1']\\n possible_clipnorm = ['_cn1','_cn10']\\n possible_LRSched = ['_LRschedG','_RedLROnPlat'] # For LR scheduler\\n possible_dropout = ['_dropout04','_dropout070704'] # For LR scheduler\\n # For the parameters based on : https://www.analyticsvidhya.com/blog/2018/10/understanding-inception-network-from-scratch/\\n # Use the learning rate at 0.01 and the list dropout\\n possible_lastEpochs = ['_LastEpoch']\\n correct_name = False\\n for dataset in possible_datasets:\\n #print(dataset,model_name)\\n if dataset in model_name:\\n correct_name = True\\n model_name_new = model_name.replace(dataset,'')\\n break\\n if not(correct_name):\\n if not(model_name in [None,'RandForUnfreezed','imagenet']):\\n print('Dataset is missing in',model_name)\\n return(False)\\n #raise(ValueError('Dataset is missing'))\\n correct_name = False\\n for lr in possible_lr:\\n if lr in model_name:\\n correct_name = True\\n model_name_new = model_name_new.replace(lr,'')\\n break\\n if not(correct_name):\\n print('lr is missing in ',model_name)\\n return(False)\\n #raise(ValueError('lr is missing'))\\n\\n for llt in possible_llt:\\n model_name_new = model_name_new.replace(llt,'')\\n \\n for opt in possible_opt:\\n model_name_new = model_name_new.replace(opt,'')\\n for f in possible_freeze:\\n model_name_new = model_name_new.replace(f,'')\\n for loss in possible_loss:\\n model_name_new = model_name_new.replace(loss,'')\\n for init in possibleInit:\\n model_name_new = model_name_new.replace(init,'')\\n for crop in possible_crop:\\n model_name_new = model_name_new.replace(crop,'')\\n for sup in possible_Sup:\\n model_name_new = model_name_new.replace(sup,'')\\n for aug in possible_Aug:\\n model_name_new = model_name_new.replace(aug,'')\\n for ep in possible_epochs:\\n model_name_new = model_name_new.replace(ep,'')\\n for le in possible_lastEpochs:\\n model_name_new = model_name_new.replace(le,'')\\n for c in possible_clipnorm:\\n model_name_new = model_name_new.replace(c,'')\\n for ls in possible_LRSched:\\n model_name_new = model_name_new.replace(ls,'')\\n for dp in possible_dropout:\\n model_name_new = model_name_new.replace(dp,'')\\n \\n if not(model_name_new==''):\\n print(model_name_new + ' parameter is unknonw sorry.')\\n return(False)\\n #raise(ValueError(model_name_new + 'is unknonw sorry.'))\\n \\n return(True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85239ab57ee0ec63f4f87cf0827597a0\",\n \"score\": \"0.5899114\",\n \"text\": \"def test(self, sample_every=30, verbose=True):\\n print('start testing the cnn')\\n start = time.time()\\n\\n # visualize the test model\\n merged = tf.summary.merge_all()\\n writter = tf.summary.FileWriter('graphs/{0}/test'.format(self.name), self.sess.graph) \\n\\n # restore saved model\\n saver = tf.train.Saver()\\n save_path = \\\"model/{0}.ckpt\\\".format(self.name)\\n saver.restore(self.sess, save_path)\\n\\n # total prediction in each class {Normal, AF, Other, Noise}\\n total = {0: 0, 1: 0, 2: 0, 3: 0}\\n # total correct prediction in each class {Normal, AF, Other, Noise}\\n corrects = {0: 0, 1: 0, 2: 0, 3: 0}\\n\\n # run through every single data in test set\\n for i in range(self.ecg.ntests):\\n # run single forward pass\\n X_test, Y_test = self.ecg.get_test(i)\\n\\n # no drop out in testing\\n loss, logit, summary = self.sess.run([self.loss, self.logits, merged], feed_dict={self.X: X_test, self.Y: Y_test, self.keep_prob: 1})\\n\\n # get the prediction\\n writter.add_summary(summary, i)\\n probs = self.sess.run(tf.nn.softmax(logit))\\n pred = self.sess.run(tf.argmax(probs, 1))[0]\\n\\n correct = np.argmax(Y_test)\\n\\n total[pred] += 1\\n if pred == correct:\\n corrects[pred] += 1\\n\\n if verbose:\\n # plot(X_test)\\n print('True label is {0}'.format(self.id_to_class_name[correct]), '- The model predicts', self.id_to_class_name[pred])\\n\\n # calculate the accuracy, base Scoring part at https://physionet.org/challenge/2017/#preparing\\n fn = 2.0 * corrects[0] / (total[0] + self.ecg.N)\\n fa = 2.0 * corrects[1] / (total[1] + self.ecg.A)\\n fo = 2.0 * corrects[2] / (total[2] + self.ecg.O)\\n fp = 2.0 * corrects[3] / (total[3] + self.ecg.P)\\n f = (fn + fa + fo + fp) / 4.0\\n print('Accuracy in the validation set is {0}'.format(f))\\n print('Testing time {0}'.format(time.time() - start))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70077f7954fa6195bfbf2ddacb50c2d8\",\n \"score\": \"0.58966506\",\n \"text\": \"def KNN_test_objectnet_shuffle(val_loader, model, args, train_list, path_visualization_results=None):\\n # crucial for use when loading a model with bn, if we don't run eval, its very bad\\n model.eval()\\n\\n if len(train_list)==2:\\n emb_list, label_list = train_list\\n else:\\n emb_list, label_list, output_list = train_list\\n\\n try:\\n emb_list = torch.from_numpy(emb_list)\\n output_list = torch.from_numpy(output_list)\\n except:\\n pass\\n\\n cnt=0\\n top1_cnt = 0\\n top1_cnt_old = 0\\n top5_cnt = 0\\n\\n test_hidden=True\\n \\n if args.visualize:\\n labels_path = os.getenv(\\\"HOME\\\") + '/.torch/models/imagenet_class_index.json'\\n with open(labels_path) as json_data:\\n idx_to_labels = json.load(json_data)\\n\\n\\n with torch.no_grad():\\n for i, example in enumerate(val_loader):\\n images = example['images']\\n target = example['labels']\\n path = example['path']\\n if args.gpu is not None:\\n images = images.cuda(args.gpu, non_blocking=True)\\n # target = target.cuda(args.gpu, non_blocking=True)\\n\\n\\n # print(\\\"target\\\", target)\\n # exit(0)\\n\\n # print('img', images.size())\\n output, fea, norm = model(images)\\n # fea, _ = normalize_l2(fea)\\n # fea = MLP(fea)\\n\\n if test_hidden:\\n score_mat = torch.mm(fea.cpu(), emb_list.t()) # test_num * train_num\\n else:\\n score_mat = torch.mm(output.cpu(), output_list.t()) # test_num * train_num\\n # print('topk', TOPK_NN)\\n score, pred = torch.topk(score_mat, k=TOPK_NN, dim=1, largest=True, sorted=False)\\n # print(\\\"done\\\")\\n pred_np = pred.data.cpu().numpy()\\n # print(\\\"sim\\\", score_mat)\\n # print(\\\"pred\\\", pred)\\n\\n # print(\\\"Getting scores: \\\", score, pred)\\n\\n # print('target b4', target)\\n # The way to organize is that, i-th sublist in the target, contains the i-th label for all test examples\\n target_num = len(target)\\n target = [each.numpy() for each in target]\\n # print('target after', target)\\n\\n pred_label = []\\n\\n for each in range(pred_np.shape[0]): # for each test images\\n # label_gt_all = target[each]\\n label_gt_list = []\\n # for mogu in label_gt_all:\\n # label_gt_list.append(mogu.numpy()[0])\\n # print('label gt', label_gt)\\n for kk in range(target_num):\\n label_gt_list.append(target[kk][each]) # Since target size is: \\\"the multilabel dim, number of test examples\\\"\\n\\n cnt += 1\\n\\n index = pred[each]\\n retrieved_labels = label_list[index.cpu().numpy()]\\n\\n retrieved_labels = np.sort(retrieved_labels)\\n # print(\\\"retrieved labels\\\", len(retrieved_labels))\\n\\n label_num_dict = {}\\n last = None\\n num = None\\n\\n for kk in range(retrieved_labels.shape[0]):\\n if kk==0:\\n last = retrieved_labels[0]\\n num=1\\n\\n else:\\n if retrieved_labels[kk] == last:\\n num += 1\\n else:\\n label_num_dict[retrieved_labels[kk-1]] = num\\n num = 1\\n last = retrieved_labels[kk]\\n\\n if kk == retrieved_labels.shape[0] - 1:\\n label_num_dict[retrieved_labels[kk]] = num\\n # print(label_num_dict)\\n\\n # print(\\\"label dict num\\\", label_num_dict)\\n # Sort dict from most frequent to least frequent\\n sort_topk_neighbor = sorted(label_num_dict.items(), key=lambda kv: (-kv[1], kv[0]))\\n\\n # print(\\\"sort topk neighbor\\\", sort_topk_neighbor, len(label_gt_list), label_gt_list,)\\n\\n\\n # Most frequent label for this test image is in sort_topk_neighbour[0][0], and gt is label_gt_list, so check if our top prediction is among\\n if args.visualize:\\n #path_viz = os.path.join(path_visualization_results,'knn' )\\n #path_viz = os.path.join(path_visualization_results,
/knn_prediction/ )\\n \\n\\n path_image = path[each]\\n list_path_split = path_image.split('/')\\n objnet_category = list_path_split[-2]\\n image_basename = list_path_split[-1]\\n path_to_append = objnet_category + '/' + 'knn_output' + '/' + image_basename\\n path_destination_image = os.path.join(path_visualization_results, path_to_append)\\n # print(\\\"__path__\\\",path[each])\\n # path_dest_visualization_img = os.path.join(path_viz, 'dfd')\\n \\n true_label = objnet_category\\n predicted_labels_id = [kv[0] for kv in sort_topk_neighbor[:5]]\\n predicted_labels = [idx_to_labels[str(id)][1] for id in predicted_labels_id]\\n predicted_label = objnet_category \\n \\n temp = images[each].detach().numpy()\\n temp = np.moveaxis(temp, 0, 2)\\n mean = np.array([0.485, 0.456, 0.406])\\n std = np.array([0.229, 0.224, 0.225])\\n temp = np.clip((temp*std)+mean,0,1)\\n f,ax = plt.subplots()\\n ax.imshow(temp)\\n str_title = 'Truth Objnet category: {} \\\\n Predicted labels: {} '.format(true_label,predicted_labels)\\n f.suptitle(str_title, fontsize=12)\\n print(\\\"savign at : \\\", path_destination_image)\\n if i%500 ==0:\\n print(i, 'done') \\n dirname = os.path.dirname(path_destination_image)\\n if not os.path.exists(dirname):\\n os.makedirs(dirname)\\n f.savefig(path_destination_image,bbox_inches='tight')\\n plt.close()\\n\\n top1_cnt_old=top1_cnt\\n try:\\n for label_gt in label_gt_list:\\n if sort_topk_neighbor[0][0] == label_gt:\\n top1_cnt += 1\\n break\\n except:\\n print(sort_topk_neighbor, label_gt_list, top1_cnt)\\n\\n if top1_cnt-top1_cnt_old > 1:\\n print(top1_cnt-top1_cnt_old, label_gt_list)\\n\\n # Make sure if see one category correct, then stop loop\\n flag = True\\n for nnn in range(5):\\n try:\\n if flag == False:\\n break\\n for label_gt in label_gt_list:\\n if sort_topk_neighbor[nnn][0] == label_gt:\\n top5_cnt += 1\\n flag = False\\n break\\n except:\\n # print(\\\"top 5 but number less than 5\\\")\\n break\\n\\n print('{} Overlapping ImgNet'.format(args.dataset), i, \\\"/%d top 1: %.5f, top 5: %.5f\\\" %\\n (len(val_loader), top1_cnt*100.0/cnt, top5_cnt*100.0/cnt))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80c777305f27dcd901af5ad15a5837cf\",\n \"score\": \"0.58955616\",\n \"text\": \"def main():\\n # Data loaders\\n if check_for_augmented_data(\\\"./data\\\"):\\n tr_loader, va_loader, te_loader, _ = get_train_val_test_loaders(\\n task=\\\"target\\\", batch_size=config(\\\"cnn.batch_size\\\"), augment=True\\n )\\n else:\\n tr_loader, va_loader, te_loader, _ = get_train_val_test_loaders(\\n task=\\\"target\\\",\\n batch_size=config(\\\"cnn.batch_size\\\"),\\n )\\n # Model\\n model = Target()\\n\\n # TODO: define loss function, and optimizer\\n criterion = torch.nn.CrossEntropyLoss()\\n optimizer = torch.optim.Adam(model.parameters(), lr = 1e-3)\\n #\\n\\n print(\\\"Number of float-valued parameters:\\\", count_parameters(model))\\n\\n # Attempts to restore the latest checkpoint if exists\\n print(\\\"Loading cnn...\\\")\\n model, start_epoch, stats = restore_checkpoint(model, config(\\\"cnn.checkpoint\\\"))\\n\\n axes = utils.make_training_plot()\\n\\n # Evaluate the randomly initialized model\\n evaluate_epoch(\\n axes, tr_loader, va_loader, te_loader, model, criterion, start_epoch, stats\\n )\\n\\n # initial val loss for early stopping\\n prev_val_loss = stats[0][1]\\n\\n # TODO: define patience for early stopping\\n patience = 5\\n curr_patience = 0\\n #\\n\\n # Loop over the entire dataset multiple times\\n # for epoch in range(start_epoch, config('cnn.num_epochs')):\\n epoch = start_epoch\\n while curr_patience < patience:\\n # Train model\\n train_epoch(tr_loader, model, criterion, optimizer)\\n\\n # Evaluate model\\n evaluate_epoch(\\n axes, tr_loader, va_loader, te_loader, model, criterion, epoch + 1, stats\\n )\\n\\n # Save model parameters\\n save_checkpoint(model, epoch + 1, config(\\\"cnn.checkpoint\\\"), stats)\\n\\n # update early stopping parameters\\n curr_patience, prev_val_loss = early_stopping(\\n stats, curr_patience, prev_val_loss\\n )\\n\\n epoch += 1\\n print(\\\"Finished Training\\\")\\n # Save figure and keep plot open\\n utils.save_cnn_training_plot()\\n utils.hold_training_plot()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fbe1449e57be9c34d14dfcbde67c564\",\n \"score\": \"0.5892119\",\n \"text\": \"def Classification_restore_and_train(config):\\n\\n # 1. Retrieve information from config dict\\n device = config['device']\\n device_name = torch.cuda.get_device_name(device)\\n print('Device name: {}'.format(device_name))\\n input_shape = config['input_shape']\\n batch_size = config['batch_size'] \\n number_of_tools = config['number_of_tools']\\n output_features = number_of_tools\\n random_frames = config['random_frames']\\n nr_videos = config['nr_videos']\\n nr_frames = config['nr_frames']\\n weight_decay = config['weight_decay']\\n save_interval = config['save_interval']\\n msg_bot = config['msg_bot']\\n bot_msg_interval = config['bot_msg_interval']\\n dataset_name = config['dataset']\\n model_name = config['model']\\n agent_name = 'TransNetAgent'\\n\\n\\n # 2. Define data to restore dataset\\n data = Data()\\n data.add_dataset(Cholec80Restored())\\n train_ds = (dataset_name, 'train')\\n val_ds = (dataset_name, 'val')\\n test_ds = (dataset_name, 'test')\\n\\n\\n # 3. Restore and define path\\n paths = os.path.join(storage_data_path, 'models', dataset_name+'_'+model_name, 'states')\\n pathr = os.path.join(model_result_path, 'models', dataset_name+'_'+model_name, 'results')\\n splits = lr.load_json(path=paths, name='data_splits')\\n print('Restored existing splits')\\n\\n # 4. Create data splits for each repetition\\n print('Bring data to PyTorch format..')\\n # Repeat for each repition\\n for run_ix in range(config['nr_runs']):\\n # 5. Bring data to Pytorch format\\n datasets = dict()\\n for ds_name, ds in data.datasets.items():\\n for split, data_ixs in splits[ds_name][run_ix].items():\\n if len(data_ixs) > 0: # Sometimes val indexes may be an empty list\\n aug = config['augmentation'] if not('test' in split) else 'none'\\n datasets[(ds_name, split)] = PytorchClassification2DDataset(ds, \\n ix_lst=data_ixs, size=input_shape, aug_key=aug, \\n resize=config['resize'])\\n\\n # 6. Build train dataloader, and visualize\\n dl = DataLoader(datasets[(train_ds)], \\n batch_size=batch_size, shuffle=True,\\n num_workers=0)\\n dl_val = DataLoader(datasets[(val_ds)], \\n batch_size=batch_size, shuffle=True,\\n num_workers=1)\\n\\n # 7. Initialize model\\n model = getattr(models, model_name)(output_features)\\n model.to(device)\\n\\n # 8. Define loss and optimizer\\n loss_f = LossBCE(device=device)\\n optimizer = optim.Adam(model.parameters(), lr=config['lr'],\\n weight_decay=weight_decay)\\n\\n # 9. Train model\\n state_names = [name for name in os.listdir(paths) if '.' not in name]\\n state_name = state_names[0].split('_')[0]\\n for idx, state in enumerate(state_names):\\n state_names[idx] = int(state.split('_')[-1])\\n state_names.sort()\\n state_name += '_' + str(state_names[-1])\\n\\n print('Restore last saved model from epoch {}..'.format(state_name.split('_')[-1]))\\n agent = getattr(agents, agent_name)(model=model, device=device)\\n restored, restored_results = agent.restore_state(paths, state_name, optimizer=optimizer)\\n if not restored:\\n print(\\\"Desired state could not be recovered. --> Error!\\\")\\n raise FileNotFoundError\\n\\n losses_train_r, losses_cum_train_r, losses_val_r, losses_cum_val_r, accuracy_train_r,\\\\\\n accuracy_det_train_r, accuracy_val_r, accuracy_det_val_r = restored_results\\n\\n print('Training model in batches of {}..'.format(batch_size))\\n losses_train, losses_cum_train, losses_val, losses_cum_val,\\\\\\n accuracy_train, accuracy_det_train, accuracy_val,\\\\\\n accuracy_det_val = agent.train(optimizer, loss_f, dl,\\n dl_val, nr_epochs=config['nr_epochs'],\\n start_epoch=int(state_name.split('_')[-1]),\\n save_path=paths, losses=losses_train_r.tolist(),\\n losses_cum=losses_cum_train_r.tolist(),\\n losses_val=losses_val_r.tolist(),\\n losses_cum_val=losses_cum_val_r.tolist(),\\n accuracy=accuracy_train_r.tolist(),\\n accuracy_detailed=accuracy_det_train_r.tolist(),\\n accuracy_val=accuracy_val_r.tolist(),\\n accuracy_val_detailed=accuracy_det_val_r.tolist(),\\n save_interval=save_interval, msg_bot=msg_bot,\\n bot_msg_interval=bot_msg_interval)\\n\\n # 10. Build test dataloader, and visualize\\n dl = DataLoader(datasets[(test_ds)], \\n batch_size=batch_size, shuffle=True)\\n \\n # 11. Test model\\n print('Testing model in batches of {}..'.format(batch_size))\\n losses_test, losses_cum_test, accuracy_test, accuracy_det_test = agent.test(loss_f, dl, msg_bot=msg_bot)\\n\\n # 12. Save results\\n save_results(model, model_name, dataset_name, paths, pathr, losses_train, losses_val, accuracy_train,\\n accuracy_det_train, accuracy_val, accuracy_det_val, losses_test, accuracy_test,\\n accuracy_det_test, losses_cum_train, losses_cum_val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5a2ed71f70a2f7851513e0fa81e36e1\",\n \"score\": \"0.58881956\",\n \"text\": \"def main(feature_pkl='C:\\\\\\\\Users\\\\Cory\\\\\\\\Documents\\\\\\\\DataScienceWorkshop\\\\\\\\avito_kaggle\\\\\\\\new-feat-full\\\\\\\\train_data.pkl', model=SGDClassifier(loss='log',penalty='l2',alpha=1e-4,class_weight='auto'), KFOLD=10):\\n # DEFAULT MODEL:\\n # Stochastic Gradient Descent (online learning)\\n # loss (cost) = log ~ Logistic Regression\\n # L2 norm used for cost, alpha ~ Regularization\\n # class_weight = auto\\n\\n # Wrapper function may pre-load these large variables and pass as tuple instead of doing this step iteratively.\\n if type(feature_pkl) is tuple: \\n featureIndex, trainFeatures, trainTargets, trainItemIds, testFeatures, testItemIds = feature_pkl\\n else:\\n print 'Loading .pkl data for fitting/cross-validation...'\\n if feature_pkl.find('new-feat')>-1:\\n # Benchmark code did not save column names (featureIndex)\\n trainFeatures, trainTargets, trainItemIds, testFeatures, testItemIds = joblib.load(feature_pkl)\\n else:\\n featureIndex, trainFeatures, trainTargets, trainItemIds, testFeatures, testItemIds = joblib.load(feature_pkl)\\n if type(model) is str:\\n model = eval(model)\\n KFOLD = int(KFOLD)\\n \\n font = {'family' : 'normal',\\n 'weight' : 'bold',\\n 'size' : 22}\\n matplotlib.rc('font', **font)\\n \\n # convert features to CSR for row-slicing\\n trainFeatures = trainFeatures.tocsr()\\n\\n #Cross validation split into 10 folds for cross-validation\\n kf_total = cross_validation.KFold(len(trainItemIds),n_folds=KFOLD,shuffle=True,indices=True)\\n \\n #conversion of targets to numpy \\n trainTargets = np.asarray(trainTargets)\\n count = 0\\n total_conf=np.zeros(shape=(2,2))\\n mean_tpr = 0\\n mean_fpr = np.linspace(0, 1, 100)\\n \\n #Iterate through the folds of the dataset\\n for train_indices, test_indices in kf_total:\\n #Calculation of the confusion matrix values for each fold \\n model.fit(trainFeatures[train_indices], trainTargets[train_indices])\\n predicted = model.predict(trainFeatures[test_indices])\\n conf_arr = metrics.confusion_matrix(trainTargets[test_indices],predicted)\\n norm_conf = [] \\n for i in conf_arr:\\n a = 0\\n tmp_arr = []\\n a = sum(i, 0)\\n for j in i:\\n tmp_arr.append(float(j)/float(a))\\n norm_conf.append(tmp_arr)\\n total_conf += norm_conf\\n #Calculation of the ROC/AUC for each fold\\n prob = model_predicted_prob(model,trainFeatures[test_indices])\\n fpr, tpr, thresholds = metrics.roc_curve(trainTargets[test_indices],prob)\\n mean_tpr += interp(mean_fpr, fpr, tpr)\\n mean_tpr[0] = 0.0\\n print \\\"Finished with fold number \\\" + str(count+1)\\n count += 1\\n \\n #Calculate mean values and plot the results\\n mean_tpr /= KFOLD\\n mean_tpr[-1] = 1.0\\n total_conf /= KFOLD\\n \\n #Plot the confusion matrix\\n labels = ['not blocked','blocked']\\n fig = plt.figure(figsize=(10,8))\\n plt.clf()\\n ax = fig.add_subplot(111)\\n cax = ax.matshow(np.array(norm_conf), cmap=plt.cm.jet, \\n interpolation='nearest')\\n plt.title('Confusion matrix')\\n fig.colorbar(cax)\\n ax.set_xticklabels([''] + labels)\\n ax.set_yticklabels([''] + labels)\\n plt.xlabel('Predicted')\\n plt.ylabel('True')\\n \\n #Add confusion matrix values to the graph\\n width = len(norm_conf)\\n height = len(norm_conf[0])\\n for x in xrange(width):\\n for y in xrange(height):\\n ax.annotate('%.4f' % norm_conf[x][y], xy=(y, x), \\n horizontalalignment='center',\\n verticalalignment='center')\\n print \\\"Confusion Matrix \\\\n\\\" + str(total_conf)\\n for ext in ['.png','.pdf','.jpg']:\\n fig.savefig('confusion'+ext)\\n \\n #Plot the ROC\\n fig = plt.figure(figsize=(10,8))\\n plt.plot(mean_fpr,mean_tpr)\\n plt.xlim([-0.05, 1.05])\\n plt.ylim([-0.05, 1.05])\\n plt.xlabel('False Positive Rate')\\n plt.ylabel('True Positive Rate')\\n plt.title('Receiver operating characteristic')\\n for ext in ['.png','.pdf','.jpg']:\\n fig.savefig('roc'+ext)\\n \\n auc_score = metrics.auc(mean_fpr,mean_tpr)\\n print \\\"AUC score\\\\n\\\" + str(auc_score)\\n \\n logging.info(\\\"Done with cross-validation\\\")\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4903e00d13c6d1a5d4967e817001609\",\n \"score\": \"0.58879054\",\n \"text\": \"def dataset_setup(self):\\n settings = self.settings\\n if settings.crowd_dataset == 'World Expo':\\n train_transform = torchvision.transforms.Compose([data.RandomlySelectPathWithNoPerspectiveRescale(),\\n data.RandomHorizontalFlip(),\\n data.NegativeOneToOneNormalizeImage(),\\n data.NumpyArraysToTorchTensors()])\\n validation_transform = torchvision.transforms.Compose([data.RandomlySelectPathWithNoPerspectiveRescale(),\\n data.NegativeOneToOneNormalizeImage(),\\n data.NumpyArraysToTorchTensors()])\\n dataset_path = '../World Expo/'\\n with open(os.path.join(dataset_path, 'viable_with_validation_and_random_test.json')) as json_file:\\n cameras_dict = json.load(json_file)\\n self.train_dataset = CrowdDataset(dataset_path, camera_names=cameras_dict['train'],\\n number_of_cameras=settings.number_of_cameras,\\n number_of_images_per_camera=settings.number_of_images_per_camera,\\n transform=train_transform, seed=settings.labeled_dataset_seed)\\n self.train_dataset_loader = DataLoader(self.train_dataset, batch_size=settings.batch_size, shuffle=True,\\n pin_memory=True, num_workers=settings.number_of_data_workers)\\n # self.unlabeled_dataset = CrowdDataset(dataset_path, camera_names=cameras_dict['validation'],\\n # transform=train_transform, unlabeled=True,\\n # seed=100)\\n self.unlabeled_dataset = CrowdDataset(dataset_path, camera_names=cameras_dict['train'],\\n number_of_cameras=settings.number_of_cameras,\\n transform=train_transform, unlabeled=True,\\n seed=settings.labeled_dataset_seed)\\n self.unlabeled_dataset_loader = DataLoader(self.unlabeled_dataset, batch_size=settings.batch_size, shuffle=True,\\n pin_memory=True, num_workers=settings.number_of_data_workers)\\n self.validation_dataset = CrowdDataset(dataset_path, camera_names=cameras_dict['validation'],\\n transform=validation_transform, seed=101)\\n elif settings.crowd_dataset == 'ShanghaiTech':\\n train_transform = torchvision.transforms.Compose([data.ExtractPatchForRandomPosition(),\\n data.RandomHorizontalFlip(),\\n data.NegativeOneToOneNormalizeImage(),\\n data.NumpyArraysToTorchTensors()])\\n validation_transform = torchvision.transforms.Compose([data.ExtractPatchForRandomPosition(),\\n data.NegativeOneToOneNormalizeImage(),\\n data.NumpyArraysToTorchTensors()])\\n self.train_dataset = ShanghaiTechDataset(transform=train_transform, seed=settings.labeled_dataset_seed)\\n self.train_dataset_loader = DataLoader(self.train_dataset, batch_size=settings.batch_size, shuffle=True,\\n pin_memory=True, num_workers=settings.number_of_data_workers)\\n self.unlabeled_dataset = ShanghaiTechDataset(transform=train_transform, seed=settings.labeled_dataset_seed,\\n unlabeled=True)\\n self.unlabeled_dataset_loader = DataLoader(self.unlabeled_dataset, batch_size=settings.batch_size, shuffle=True,\\n pin_memory=True, num_workers=settings.number_of_data_workers)\\n self.validation_dataset = ShanghaiTechDataset(dataset='test', transform=validation_transform, seed=101)\\n else:\\n raise ValueError('{} is not an understood crowd dataset.'.format(settings.crowd_dataset))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"914e7b701c70b5ff67c1331a07c46010\",\n \"score\": \"0.588063\",\n \"text\": \"def initiate_training(augmentation, backbone, batch_size, dataset_path, epochs, model, random_occlusions,\\n snapshot_base_name, snapshot_path, steps_per_epoch, tensorboard_dir, validation_set,\\n validation_split, counting_model, balance_datasets=False):\\n if augmentation:\\n transform_generator = random_transform_generator(\\n min_rotation=-0.1,\\n max_rotation=0.1,\\n min_translation=(-0.1, -0.1),\\n max_translation=(0.1, 0.1),\\n min_shear=-0.1,\\n max_shear=0.1,\\n min_scaling=(0.9, 0.9),\\n max_scaling=(1.1, 1.1),\\n flip_x_chance=0.5,\\n flip_y_chance=0.5)\\n else:\\n transform_generator = random_transform_generator(flip_x_chance=0.5)\\n\\n dataset = CarsDataset(dataset_path, validation_split=validation_split, validation_set=validation_set,\\n balance_datasets=balance_datasets)\\n\\n val_generator = None\\n validation_steps = None\\n train_generator = CarsGenerator(dataset.train,\\n preprocess_image=backbone.get_preprocess_image(),\\n counting_model=counting_model,\\n transform_generator=transform_generator,\\n batch_size=batch_size,\\n image_min_side=720,\\n image_max_side=1280,\\n perform_random_occlusions=random_occlusions)\\n\\n if dataset.validation:\\n val_generator = CarsGenerator(dataset.validation,\\n preprocess_image=backbone.get_preprocess_image(),\\n counting_model=counting_model,\\n batch_size=batch_size,\\n image_min_side=720,\\n image_max_side=1280,\\n perform_random_occlusions=False)\\n validation_steps = len(val_generator)\\n os.makedirs(snapshot_path, exist_ok=True)\\n with open('{}/validation.txt'.format(snapshot_path), \\\"wt\\\") as f:\\n for img_path in dataset.validation.keys():\\n print(img_path, file=f)\\n\\n callbacks = create_callbacks(model,\\n batch_size=batch_size,\\n tensorboard_dir=tensorboard_dir,\\n snapshot_path=snapshot_path,\\n snapshot_name_base=snapshot_base_name)\\n if steps_per_epoch is None:\\n steps_per_epoch = len(train_generator)\\n\\n model.fit_generator(\\n generator=train_generator,\\n steps_per_epoch=steps_per_epoch,\\n callbacks=callbacks,\\n epochs=epochs,\\n validation_data=val_generator,\\n validation_steps=validation_steps,\\n verbose=1,\\n workers=1,\\n use_multiprocessing=True,\\n max_queue_size=10\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"703a4c5a748da4a3e27519a47d5fd13e\",\n \"score\": \"0.587915\",\n \"text\": \"def evaluate_lenet5(learning_rate=0.1, n_epochs=200,\\n dataset='mnist.pkl.gz',\\n nkerns=[20,50],batch_size=500):\\n \\n rng = numpy.random.RandomState(1234)\\n print('Loading Data'+'.'*20)\\n datasets = load_data(dataset)\\n \\n trainSetX, trainSetY = datasets[0]\\n validSetX, validSetY = datasets[1]\\n testSetX, testSetY = datasets[2]\\n \\n n_train_batches = trainSetX.get_value(borrow=True).shape[0] // batch_size\\n n_valid_batches = validSetX.get_value(borrow=True).shape[0] // batch_size\\n n_test_batches = testSetX.get_value(borrow=True).shape[0] // batch_size\\n \\n print('Building Data'+'.'*20)\\n \\n index = T.lscalar('index')\\n x = T.matrix('x')\\n y = T.ivector('y')\\n \\n # Reshape matrix of rasterized images of shape (batch_size, 28*28)\\n # to a 4D tensor, compatible with our LeNetConvPoolLayer\\n layer0_input = x.reshape((batch_size,1,28,28))\\n \\n # construct the first convolutional pooling layer\\n # filtering reduces the image size to (28-5+1,28-5+1) = (24, 24)\\n # maxpooling reduces this further to (24/2, 24/2) = (12, 12)\\n # 4D output tensor is thus of shape (batch_size,nkerns[0],12,12)\\n layer0 = LeNetConvPoolLayer(\\n rng=rng,\\n input=layer0_input,\\n image_shape=(batch_size,1,28,28),\\n filter_shape=(nkerns[0],1,5,5),\\n poolsize=(2,2)\\n )\\n \\n # construct the second convolutional pooling layer\\n # filtering reduces the image size to (12-5+1,12-5+1) = (8, 8)\\n # maxpooling reduces this further to (8/2, 8/2) = (4, 4)\\n # 4D output tensor is thus of shape (batch_size,nkerns[1],4,4)\\n layer1 = LeNetConvPoolLayer(\\n rng=rng,\\n input=layer0.output,\\n image_shape=(batch_size,nkerns[0],12,12),\\n filter_shape=(nkerns[1],nkerns[0],5,5),\\n poolsize=(2,2)\\n )\\n \\n layer2_input = layer1.output.flatten(2)\\n layer2 = HiddenLayer(\\n rng=rng,\\n input=layer2_input,\\n n_in=nkerns[1]*4*4,\\n n_out=500,\\n activation=T.tanh\\n )\\n \\n layer3 = LogisticRegression(input=layer2.output,n_in=500,n_out=10)\\n \\n testModel = theano.function(\\n inputs=[index],\\n outputs=layer3.errors(y),\\n givens={\\n x:testSetX[index*batch_size:(index+1)*batch_size], \\n y:testSetY[index*batch_size:(index+1)*batch_size] \\n } \\n )\\n validModel = theano.function(\\n inputs=[index],\\n outputs=layer3.errors(y),\\n givens={\\n x:validSetX[index*batch_size:(index+1)*batch_size], \\n y:validSetY[index*batch_size:(index+1)*batch_size] \\n }\\n )\\n \\n params = layer3.params+layer2.params+layer1.params+layer0.params\\n cost = layer3.negative_log_likelihood(y)\\n grads = T.grad(cost,params)\\n \\n updates= [(param, param - learning_rate*grad)\\n for param,grad in zip(params,grads)\\n ]\\n \\n trainModel = theano.function(\\n inputs=[index],\\n outputs=cost,\\n updates=updates,\\n givens={\\n x:trainSetX[index*batch_size:(index+1)*batch_size], \\n y:trainSetY[index*batch_size:(index+1)*batch_size] \\n } \\n )\\n \\n print('Training'+'.'*20)\\n \\n patience = 10000\\n patience_increase = 2\\n improvement_threshold = 2\\n validation_frequence = min(n_train_batches, patience/2)\\n \\n best_validation_loss = numpy.inf\\n best_iter = 0\\n test_score =0.\\n start_time = timeit.default_timer()\\n \\n epoch = 0\\n done_looping = False\\n \\n while (epoch < n_epochs) and (not done_looping):\\n epoch += 1\\n \\n for mini_batch_index in range(n_train_batches):\\n iter = (epoch - 1) * n_train_batches + mini_batch_index\\n if iter % 100 == 0:\\n print('training @ iter = ' , iter)\\n \\n cost_ij = trainModel(mini_batch_index)\\n if (iter + 1) % validation_frequence ==0:\\n validation_losses = [validModel(i) for i \\n in range(n_valid_batches)]\\n this_validation_losses = numpy.mean(validation_losses)\\n print('epoch %i, minibatch %i/%i, validation error %f %%' %\\n (epoch, mini_batch_index+1, n_train_batches,\\n this_validation_losses*100)\\n )\\n if this_validation_losses < best_validation_loss:\\n best_validation_loss = this_validation_losses\\n best_iter = iter\\n if this_validation_losses < best_validation_loss * \\\\\\n improvement_threshold:\\n patience = max(patience, patience*patience_increase)\\n \\n test_losses = [testModel(i) for i in range(n_test_batches)]\\n test_score = numpy.mean(test_losses)\\n \\n print(' epoch %i, minibatch %i/%i, test error of'\\n 'best model %f %%'%\\n (epoch, mini_batch_index+1, n_train_batches,\\n this_validation_losses*100)\\n )\\n if patience <= iter:\\n done_looping = True\\n break\\n endtime = timeit.default_timer()\\n print('Optimization complete.')\\n print('Best validation score of %f %% obtained at iteration %i, '\\n ' with test performance %f %%' %\\n (best_validation_loss*100, best_iter+1, test_score*100.)\\n )\\n \\n print('The code for file ' + os.path.split(__file__)[1]+\\n ' ran for %.2fm' % (endtime - start_time)/60. \\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf981ff48b6e7565b993d0c026bd7c2e\",\n \"score\": \"0.58766633\",\n \"text\": \"def ModelTraindata():\\n data = request.get_json()\\n with open(app.config[\\\"DATA_PATH\\\"] + \\\"/config/params.json\\\") as json_file:\\n default_configs = json.load(json_file)\\n default_configs[\\\"experiment\\\"][\\\"model\\\"] = data[\\\"model\\\"]\\n default_configs[\\\"experiment\\\"][\\\"batch_size\\\"] = int(data[\\\"batch_size\\\"])\\n default_configs[\\\"experiment\\\"][\\\"epochs\\\"] = int(data[\\\"epochs\\\"])\\n default_configs[\\\"experiment\\\"][\\\"learning_rate\\\"] = float(\\n data[\\\"learning_rate\\\"])\\n default_configs[\\\"experiment\\\"][\\\"class_ids\\\"] = list(\\n range(0, 43)) + (np.array(data[\\\"class_ids\\\"]) + 43).tolist()\\n default_configs[\\\"experiment\\\"][\\\"num_classes\\\"] = int(\\n data[\\\"num_classes\\\"]) + 43\\n\\n max_train = -1\\n for file in os.listdir(app.config[\\\"DATA_PATH\\\"] + \\\"checkpoints/logs/\\\"):\\n if file.startswith(\\\"temp\\\"):\\n number = int(file.split(\\\"_\\\")[-1])\\n max_train = max(max_train, number)\\n\\n max_train+=1\\n next_config = \\\"temp_config_\\\"+ str(max_train)\\n with open(app.config[\\\"DATA_PATH\\\"] + \\\"config/\\\" + next_config + \\\".json\\\", \\\"w\\\") as outfile:\\n json.dump(default_configs, outfile, indent=4)\\n\\n try:\\n shutil.move(os.path.join(app.config[\\\"DATA_PATH\\\"], \\\"dataset/EXTRA\\\"),\\n os.path.join(app.config[\\\"DATA_PATH\\\"], \\\"dataset/EXTRA_copy\\\"))\\n remake_EXTRA_folder(\\n 0, 0, new_classes=default_configs[\\\"experiment\\\"][\\\"class_ids\\\"], next_config = max_train)\\n remake_EXTRA_folder(\\n 0, 1, new_classes=default_configs[\\\"experiment\\\"][\\\"class_ids\\\"], next_config = max_train)\\n remake_EXTRA_folder(\\n 1, 0, new_classes=default_configs[\\\"experiment\\\"][\\\"class_ids\\\"], next_config = max_train)\\n \\n \\n t1 = threading.Thread(target=train.train, args=[next_config])\\n t1.start()\\n while t1.is_alive():\\n continue\\n\\n except KeyboardInterrupt:\\n \\\"Stopped Abruptly\\\"\\n finally:\\n print(\\\"finally\\\")\\n f = open(os.path.join(\\\"data/traffic_sign_interiit/dataset/\\\", \\\"TrainInfo.txt\\\"), \\\"w+\\\")\\n f.close()\\n shutil.rmtree(app.config[\\\"DATA_PATH\\\"] + \\\"dataset/EXTRA\\\")\\n shutil.move(os.path.join(app.config[\\\"DATA_PATH\\\"], \\\"dataset/EXTRA_copy\\\"),\\n os.path.join(app.config[\\\"DATA_PATH\\\"], \\\"dataset/EXTRA\\\"))\\n\\n check = True\\n if check:\\n return make_response(jsonify({\\\"message\\\": \\\"Model Trained Successfuly! Saved as: \\\"+ next_config +\\\".pt. Reload to see it in test\\\"}), 200)\\n else:\\n return make_response(jsonify({\\\"error\\\": \\\"Something is Wrong. Try Again!\\\"}), 400)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e135a1d790910c67e738f3c6dfc769e\",\n \"score\": \"0.5874946\",\n \"text\": \"def train_coxnet_model_for_baseline(cancer_data_path, folds_data_path,\\r\\n test_fold_index, max_iter, top_k_variance_limit,\\r\\n selected_features_path, selected_features_count, present_output=True,\\r\\n model_output_path=\\\"\\\"):\\r\\n\\r\\n # create train and test sets\\r\\n X_train, y_train = create_X_and_y(cancer_data_path, top_k_variance_limit, folds_data_path,\\r\\n [i for i in range(5) if i != test_fold_index], selected_features_path,\\r\\n selected_features_count)\\r\\n X_test, y_test = create_X_and_y(cancer_data_path, top_k_variance_limit, folds_data_path, [test_fold_index],\\r\\n selected_features_path, selected_features_count)\\r\\n\\r\\n # train a simple coxnet model to get a range of possible alphas\\r\\n cox_net = CoxnetSurvivalAnalysis(max_iter=max_iter)\\r\\n cox_net.fit(X_train, y_train)\\r\\n initial_alphas = cox_net.alphas_\\r\\n\\r\\n params = {\\\"alphas\\\": [[v] for i, v in enumerate(initial_alphas) if i % 5 == 0] + [[v * 10] for i, v in\\r\\n enumerate(initial_alphas) if\\r\\n i % 5 == 0]\\r\\n , \\\"l1_ratio\\\": [0.00101, 0.1, 0.5, 0.7, 1]}\\r\\n\\r\\n gcv_results = get_grid_search_results(params, X_train, y_train, max_iter)\\r\\n gcv_results_table = pd.DataFrame(gcv_results.cv_results_)\\r\\n best_model = gcv_results.best_estimator_\\r\\n alphas = gcv_results_table.param_alphas.map(lambda x: x[0])\\r\\n\\r\\n best_l1_ratio = best_model.l1_ratio\\r\\n best_coefs = pd.DataFrame(\\r\\n best_model.coef_,\\r\\n index=X_train.columns,\\r\\n columns=[\\\"coefficient\\\"]\\r\\n )\\r\\n\\r\\n best_model_cv = get_ci_score(best_model, X_test, y_test)\\r\\n\\r\\n if present_output:\\r\\n # present results\\r\\n print(\\\"Best model C.I for test set on fold #{} is: {}\\\".format(test_fold_index, best_model_cv))\\r\\n print(\\\"Best alpha: {}\\\".format(best_model.alphas_))\\r\\n print(\\\"Best l1 ratio: {}\\\".format(best_l1_ratio))\\r\\n visualize_alpha_values(best_model, best_coefs, alphas)\\r\\n visualize_ci_for_alpha_values(gcv_results_table, gcv_results, alphas)\\r\\n print(\\\"Internal CV scores while training: \\\")\\r\\n print(cross_val_score(best_model, X_train, y_train,\\r\\n cv=5)) # check what to actually cross validate on - holdout or all?\\r\\n\\r\\n # save model\\r\\n if model_output_path:\\r\\n pickle.dump(best_model, open(model_output_path, \\\"wb\\\"))\\r\\n return best_model, best_model_cv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8f59f72f592409cc0bd15f81fb5b465\",\n \"score\": \"0.5869422\",\n \"text\": \"def setup(data, \\n target, \\n train_size = 0.7,\\n sampling = True,\\n sample_estimator = None,\\n categorical_features = None,\\n categorical_imputation = 'constant',\\n ordinal_features = None,\\n high_cardinality_features = None, \\n high_cardinality_method = 'frequency', \\n numeric_features = None,\\n numeric_imputation = 'mean',\\n date_features = None,\\n ignore_features = None,\\n normalize = False,\\n normalize_method = 'zscore',\\n transformation = False,\\n transformation_method = 'yeo-johnson',\\n handle_unknown_categorical = True, \\n unknown_categorical_method = 'least_frequent',\\n pca = False,\\n pca_method = 'linear',\\n pca_components = None, \\n ignore_low_variance = False, \\n combine_rare_levels = False,\\n rare_level_threshold = 0.10,\\n bin_numeric_features = None, \\n remove_outliers = False,\\n outliers_threshold = 0.05,\\n remove_multicollinearity = False,\\n multicollinearity_threshold = 0.9,\\n remove_perfect_collinearity = False, #added in pycaret==2.0.0\\n create_clusters = False,\\n cluster_iter = 20,\\n polynomial_features = False, \\n polynomial_degree = 2,\\n trigonometry_features = False,\\n polynomial_threshold = 0.1,\\n group_features = None,\\n group_names = None,\\n feature_selection = False,\\n feature_selection_threshold = 0.8,\\n feature_interaction = False,\\n feature_ratio = False,\\n interaction_threshold = 0.01, \\n transform_target = False,\\n transform_target_method = 'box-cox',\\n data_split_shuffle = True, #added in pycaret==2.0.0\\n folds_shuffle = False, #added in pycaret==2.0.0\\n n_jobs = -1, #added in pycaret==2.0.0\\n use_gpu = False, #added in pycaret==2.1\\n html = True, #added in pycaret==2.0.0\\n session_id = None,\\n log_experiment = False, #added in pycaret==2.0.0\\n experiment_name = None, #added in pycaret==2.0.0\\n log_plots = False, #added in pycaret==2.0.0\\n log_profile = False, #added in pycaret==2.0.0\\n log_data = False, #added in pycaret==2.0.0\\n silent = False,\\n verbose = True, #added in pycaret==2.0.0\\n profile = False):\\n \\n #exception checking \\n import sys\\n \\n from pycaret.utils import __version__\\n ver = __version__()\\n\\n import logging\\n\\n # create logger\\n global logger\\n\\n logger = logging.getLogger('logs')\\n logger.setLevel(logging.DEBUG)\\n \\n # create console handler and set level to debug\\n\\n if logger.hasHandlers():\\n logger.handlers.clear()\\n \\n ch = logging.FileHandler('logs.log')\\n ch.setLevel(logging.DEBUG)\\n\\n # create formatter\\n formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(message)s')\\n\\n # add formatter to ch\\n ch.setFormatter(formatter)\\n\\n # add ch to logger\\n logger.addHandler(ch)\\n\\n logger.info(\\\"PyCaret Regression Module\\\")\\n logger.info('version ' + str(ver))\\n logger.info(\\\"Initializing setup()\\\")\\n\\n #generate USI for mlflow tracking\\n import secrets\\n global USI\\n USI = secrets.token_hex(nbytes=2)\\n logger.info('USI: ' + str(USI))\\n\\n logger.info(\\\"\\\"\\\"setup(data={}, target={}, train_size={}, sampling={}, sample_estimator={}, categorical_features={}, categorical_imputation={}, ordinal_features={},\\n high_cardinality_features={}, high_cardinality_method={}, numeric_features={}, numeric_imputation={}, date_features={}, ignore_features={}, normalize={},\\n normalize_method={}, transformation={}, transformation_method={}, handle_unknown_categorical={}, unknown_categorical_method={}, pca={}, pca_method={},\\n pca_components={}, ignore_low_variance={}, combine_rare_levels={}, rare_level_threshold={}, bin_numeric_features={}, remove_outliers={}, outliers_threshold={},\\n remove_multicollinearity={}, multicollinearity_threshold={}, remove_perfect_collinearity={}, create_clusters={}, cluster_iter={},\\n polynomial_features={}, polynomial_degree={}, trigonometry_features={}, polynomial_threshold={}, group_features={},\\n group_names={}, feature_selection={}, feature_selection_threshold={}, feature_interaction={}, feature_ratio={}, interaction_threshold={}, transform_target={},\\n transform_target_method={}, data_split_shuffle={}, folds_shuffle={}, n_jobs={}, html={}, session_id={}, log_experiment={},\\n experiment_name={}, log_plots={}, log_profile={}, log_data={}, silent={}, verbose={}, profile={})\\\"\\\"\\\".format(\\\\\\n str(data.shape), str(target), str(train_size), str(sampling), str(sample_estimator), str(categorical_features), str(categorical_imputation), str(ordinal_features),\\\\\\n str(high_cardinality_features), str(high_cardinality_method), str(numeric_features), str(numeric_imputation), str(date_features), str(ignore_features),\\\\\\n str(normalize), str(normalize_method), str(transformation), str(transformation_method), str(handle_unknown_categorical), str(unknown_categorical_method), str(pca),\\\\\\n str(pca_method), str(pca_components), str(ignore_low_variance), str(combine_rare_levels), str(rare_level_threshold), str(bin_numeric_features), str(remove_outliers),\\\\\\n str(outliers_threshold), str(remove_multicollinearity), str(multicollinearity_threshold), str(remove_perfect_collinearity), str(create_clusters), str(cluster_iter),\\\\\\n str(polynomial_features), str(polynomial_degree), str(trigonometry_features), str(polynomial_threshold), str(group_features), str(group_names),\\\\\\n str(feature_selection), str(feature_selection_threshold), str(feature_interaction), str(feature_ratio), str(interaction_threshold), str(transform_target),\\\\\\n str(transform_target_method), str(data_split_shuffle), str(folds_shuffle), str(n_jobs), str(html), str(session_id),\\\\\\n str(log_experiment), str(experiment_name), str(log_plots), str(log_profile), str(log_data), str(silent), str(verbose), str(profile)))\\n\\n #logging environment and libraries\\n logger.info(\\\"Checking environment\\\")\\n \\n from platform import python_version, platform, python_build, machine\\n\\n try:\\n logger.info(\\\"python_version: \\\" + str(python_version()))\\n except:\\n logger.warning(\\\"cannot find platform.python_version\\\")\\n\\n try:\\n logger.info(\\\"python_build: \\\" + str(python_build()))\\n except:\\n logger.warning(\\\"cannot find platform.python_build\\\")\\n\\n try:\\n logger.info(\\\"machine: \\\" + str(machine()))\\n except:\\n logger.warning(\\\"cannot find platform.machine\\\")\\n\\n try:\\n logger.info(\\\"platform: \\\" + str(platform()))\\n except:\\n logger.warning(\\\"cannot find platform.platform\\\")\\n\\n try:\\n import psutil\\n logger.info(\\\"Memory: \\\" + str(psutil.virtual_memory()))\\n logger.info(\\\"Physical Core: \\\" + str(psutil.cpu_count(logical=False)))\\n logger.info(\\\"Logical Core: \\\" + str(psutil.cpu_count(logical=True)))\\n except:\\n logger.warning(\\\"cannot find psutil installation. memory not traceable. Install psutil using pip to enable memory logging. \\\")\\n \\n logger.info(\\\"Checking libraries\\\")\\n\\n try:\\n from pandas import __version__\\n logger.info(\\\"pd==\\\" + str(__version__))\\n except:\\n logger.warning(\\\"pandas not found\\\")\\n\\n try:\\n from numpy import __version__\\n logger.info(\\\"numpy==\\\" + str(__version__))\\n except:\\n logger.warning(\\\"numpy not found\\\")\\n\\n try:\\n from sklearn import __version__\\n logger.info(\\\"sklearn==\\\" + str(__version__))\\n except:\\n logger.warning(\\\"sklearn not found\\\")\\n\\n try:\\n from xgboost import __version__\\n logger.info(\\\"xgboost==\\\" + str(__version__))\\n except:\\n logger.warning(\\\"xgboost not found\\\")\\n\\n try:\\n from lightgbm import __version__\\n logger.info(\\\"lightgbm==\\\" + str(__version__))\\n except:\\n logger.warning(\\\"lightgbm not found\\\")\\n\\n try:\\n from catboost import __version__\\n logger.info(\\\"catboost==\\\" + str(__version__))\\n except:\\n logger.warning(\\\"catboost not found\\\")\\n\\n try:\\n from mlflow.version import VERSION\\n import warnings\\n warnings.filterwarnings('ignore') \\n logger.info(\\\"mlflow==\\\" + str(VERSION))\\n except:\\n logger.warning(\\\"mlflow not found\\\")\\n\\n #run_time\\n import datetime, time\\n runtime_start = time.time()\\n\\n logger.info(\\\"Checking Exceptions\\\")\\n\\n #checking data type\\n if hasattr(data,'shape') is False:\\n sys.exit('(Type Error): data passed must be of type pandas.DataFrame')\\n\\n #checking train size parameter\\n if type(train_size) is not float:\\n sys.exit('(Type Error): train_size parameter only accepts float value.')\\n \\n #checking sampling parameter\\n if type(sampling) is not bool:\\n sys.exit('(Type Error): sampling parameter only accepts True or False.')\\n \\n #checking sampling parameter\\n if target not in data.columns:\\n sys.exit('(Value Error): Target parameter doesnt exist in the data provided.') \\n\\n #checking session_id\\n if session_id is not None:\\n if type(session_id) is not int:\\n sys.exit('(Type Error): session_id parameter must be an integer.') \\n \\n #checking sampling parameter\\n if type(profile) is not bool:\\n sys.exit('(Type Error): profile parameter only accepts True or False.')\\n \\n #checking normalize parameter\\n if type(normalize) is not bool:\\n sys.exit('(Type Error): normalize parameter only accepts True or False.')\\n \\n #checking transformation parameter\\n if type(transformation) is not bool:\\n sys.exit('(Type Error): transformation parameter only accepts True or False.')\\n \\n #checking categorical imputation\\n allowed_categorical_imputation = ['constant', 'mode']\\n if categorical_imputation not in allowed_categorical_imputation:\\n sys.exit(\\\"(Value Error): categorical_imputation param only accepts 'constant' or 'mode' \\\")\\n \\n #ordinal_features\\n if ordinal_features is not None:\\n if type(ordinal_features) is not dict:\\n sys.exit(\\\"(Type Error): ordinal_features must be of type dictionary with column name as key and ordered values as list. \\\")\\n \\n #ordinal features check\\n if ordinal_features is not None:\\n data_cols = data.columns\\n data_cols = data_cols.drop(target)\\n ord_keys = ordinal_features.keys()\\n \\n for i in ord_keys:\\n if i not in data_cols:\\n sys.exit(\\\"(Value Error) Column name passed as a key in ordinal_features param doesnt exist. \\\")\\n \\n for k in ord_keys:\\n if data[k].nunique() != len(ordinal_features.get(k)):\\n sys.exit(\\\"(Value Error) Levels passed in ordinal_features param doesnt match with levels in data. \\\")\\n\\n for i in ord_keys:\\n value_in_keys = ordinal_features.get(i)\\n value_in_data = list(data[i].unique().astype(str))\\n for j in value_in_keys:\\n if j not in value_in_data:\\n text = \\\"Column name '\\\" + str(i) + \\\"' doesnt contain any level named '\\\" + str(j) + \\\"'.\\\"\\n sys.exit(text)\\n \\n #high_cardinality_features\\n if high_cardinality_features is not None:\\n if type(high_cardinality_features) is not list:\\n sys.exit(\\\"(Type Error): high_cardinality_features param only accepts name of columns as a list. \\\")\\n \\n if high_cardinality_features is not None:\\n data_cols = data.columns\\n data_cols = data_cols.drop(target)\\n for i in high_cardinality_features:\\n if i not in data_cols:\\n sys.exit(\\\"(Value Error): Column type forced is either target column or doesn't exist in the dataset.\\\")\\n \\n #checking numeric imputation\\n allowed_numeric_imputation = ['mean', 'median']\\n if numeric_imputation not in allowed_numeric_imputation:\\n sys.exit(\\\"(Value Error): numeric_imputation param only accepts 'mean' or 'median' \\\")\\n \\n #checking normalize method\\n allowed_normalize_method = ['zscore', 'minmax', 'maxabs', 'robust']\\n if normalize_method not in allowed_normalize_method:\\n sys.exit(\\\"(Value Error): normalize_method param only accepts 'zscore', 'minxmax', 'maxabs' or 'robust'. \\\") \\n \\n #checking transformation method\\n allowed_transformation_method = ['yeo-johnson', 'quantile']\\n if transformation_method not in allowed_transformation_method:\\n sys.exit(\\\"(Value Error): transformation_method param only accepts 'yeo-johnson' or 'quantile' \\\") \\n \\n #handle unknown categorical\\n if type(handle_unknown_categorical) is not bool:\\n sys.exit('(Type Error): handle_unknown_categorical parameter only accepts True or False.')\\n \\n #unknown categorical method\\n unknown_categorical_method_available = ['least_frequent', 'most_frequent']\\n \\n if unknown_categorical_method not in unknown_categorical_method_available:\\n sys.exit(\\\"(Type Error): unknown_categorical_method only accepts 'least_frequent' or 'most_frequent'.\\\")\\n \\n #check pca\\n if type(pca) is not bool:\\n sys.exit('(Type Error): PCA parameter only accepts True or False.')\\n \\n #pca method check\\n allowed_pca_methods = ['linear', 'kernel', 'incremental',]\\n if pca_method not in allowed_pca_methods:\\n sys.exit(\\\"(Value Error): pca method param only accepts 'linear', 'kernel', or 'incremental'. \\\") \\n \\n #pca components check\\n if pca is True:\\n if pca_method != 'linear':\\n if pca_components is not None:\\n if(type(pca_components)) is not int:\\n sys.exit(\\\"(Type Error): pca_components parameter must be integer when pca_method is not 'linear'. \\\")\\n\\n #pca components check 2\\n if pca is True:\\n if pca_method != 'linear':\\n if pca_components is not None:\\n if pca_components > len(data.columns)-1:\\n sys.exit(\\\"(Type Error): pca_components parameter cannot be greater than original features space.\\\") \\n \\n #pca components check 3\\n if pca is True:\\n if pca_method == 'linear':\\n if pca_components is not None:\\n if type(pca_components) is not float:\\n if pca_components > len(data.columns)-1: \\n sys.exit(\\\"(Type Error): pca_components parameter cannot be greater than original features space or float between 0 - 1.\\\") \\n \\n #check ignore_low_variance\\n if type(ignore_low_variance) is not bool:\\n sys.exit('(Type Error): ignore_low_variance parameter only accepts True or False.')\\n \\n #check ignore_low_variance\\n if type(combine_rare_levels) is not bool:\\n sys.exit('(Type Error): combine_rare_levels parameter only accepts True or False.')\\n \\n #check rare_level_threshold\\n if type(rare_level_threshold) is not float:\\n sys.exit('(Type Error): rare_level_threshold must be a float between 0 and 1. ')\\n \\n #bin numeric features\\n if bin_numeric_features is not None:\\n all_cols = list(data.columns)\\n all_cols.remove(target)\\n \\n for i in bin_numeric_features:\\n if i not in all_cols:\\n sys.exit(\\\"(Value Error): Column type forced is either target column or doesn't exist in the dataset.\\\")\\n \\n #check transform_target\\n if type(transform_target) is not bool:\\n sys.exit('(Type Error): transform_target parameter only accepts True or False.')\\n \\n #transform_target_method\\n allowed_transform_target_method = ['box-cox', 'yeo-johnson']\\n if transform_target_method not in allowed_transform_target_method:\\n sys.exit(\\\"(Value Error): transform_target_method param only accepts 'box-cox' or 'yeo-johnson'. \\\") \\n \\n #remove_outliers\\n if type(remove_outliers) is not bool:\\n sys.exit('(Type Error): remove_outliers parameter only accepts True or False.') \\n \\n #outliers_threshold\\n if type(outliers_threshold) is not float:\\n sys.exit('(Type Error): outliers_threshold must be a float between 0 and 1. ') \\n \\n #remove_multicollinearity\\n if type(remove_multicollinearity) is not bool:\\n sys.exit('(Type Error): remove_multicollinearity parameter only accepts True or False.')\\n \\n #multicollinearity_threshold\\n if type(multicollinearity_threshold) is not float:\\n sys.exit('(Type Error): multicollinearity_threshold must be a float between 0 and 1. ') \\n \\n #create_clusters\\n if type(create_clusters) is not bool:\\n sys.exit('(Type Error): create_clusters parameter only accepts True or False.')\\n \\n #cluster_iter\\n if type(cluster_iter) is not int:\\n sys.exit('(Type Error): cluster_iter must be a integer greater than 1. ') \\n \\n #polynomial_features\\n if type(polynomial_features) is not bool:\\n sys.exit('(Type Error): polynomial_features only accepts True or False. ') \\n \\n #polynomial_degree\\n if type(polynomial_degree) is not int:\\n sys.exit('(Type Error): polynomial_degree must be an integer. ')\\n \\n #polynomial_features\\n if type(trigonometry_features) is not bool:\\n sys.exit('(Type Error): trigonometry_features only accepts True or False. ') \\n \\n #polynomial threshold\\n if type(polynomial_threshold) is not float:\\n sys.exit('(Type Error): polynomial_threshold must be a float between 0 and 1. ') \\n \\n #group features\\n if group_features is not None:\\n if type(group_features) is not list:\\n sys.exit('(Type Error): group_features must be of type list. ') \\n \\n if group_names is not None:\\n if type(group_names) is not list:\\n sys.exit('(Type Error): group_names must be of type list. ') \\n \\n #cannot drop target\\n if ignore_features is not None:\\n if target in ignore_features:\\n sys.exit(\\\"(Value Error): cannot drop target column. \\\") \\n \\n #feature_selection\\n if type(feature_selection) is not bool:\\n sys.exit('(Type Error): feature_selection only accepts True or False. ') \\n \\n #feature_selection_threshold\\n if type(feature_selection_threshold) is not float:\\n sys.exit('(Type Error): feature_selection_threshold must be a float between 0 and 1. ') \\n \\n #feature_interaction\\n if type(feature_interaction) is not bool:\\n sys.exit('(Type Error): feature_interaction only accepts True or False. ') \\n \\n #feature_ratio\\n if type(feature_ratio) is not bool:\\n sys.exit('(Type Error): feature_ratio only accepts True or False. ') \\n \\n #interaction_threshold\\n if type(interaction_threshold) is not float:\\n sys.exit('(Type Error): interaction_threshold must be a float between 0 and 1. ') \\n\\n #cannot drop target\\n if ignore_features is not None:\\n if target in ignore_features:\\n sys.exit(\\\"(Value Error): cannot drop target column. \\\") \\n \\n #forced type check\\n all_cols = list(data.columns)\\n all_cols.remove(target)\\n \\n #categorical\\n if categorical_features is not None:\\n for i in categorical_features:\\n if i not in all_cols:\\n sys.exit(\\\"(Value Error): Column type forced is either target column or doesn't exist in the dataset.\\\")\\n \\n #numeric\\n if numeric_features is not None:\\n for i in numeric_features:\\n if i not in all_cols:\\n sys.exit(\\\"(Value Error): Column type forced is either target column or doesn't exist in the dataset.\\\") \\n \\n #date features\\n if date_features is not None:\\n for i in date_features:\\n if i not in all_cols:\\n sys.exit(\\\"(Value Error): Column type forced is either target column or doesn't exist in the dataset.\\\") \\n \\n #drop features\\n if ignore_features is not None:\\n for i in ignore_features:\\n if i not in all_cols:\\n sys.exit(\\\"(Value Error): Feature ignored is either target column or doesn't exist in the dataset.\\\") \\n \\n #silent\\n if type(silent) is not bool:\\n sys.exit(\\\"(Type Error): silent parameter only accepts True or False. \\\")\\n\\n #remove_perfect_collinearity\\n if type(remove_perfect_collinearity) is not bool:\\n sys.exit('(Type Error): remove_perfect_collinearity parameter only accepts True or False.')\\n \\n #html\\n if type(html) is not bool:\\n sys.exit('(Type Error): html parameter only accepts True or False.')\\n\\n #folds_shuffle\\n if type(folds_shuffle) is not bool:\\n sys.exit('(Type Error): folds_shuffle parameter only accepts True or False.')\\n\\n #data_split_shuffle\\n if type(data_split_shuffle) is not bool:\\n sys.exit('(Type Error): data_split_shuffle parameter only accepts True or False.')\\n\\n #log_experiment\\n if type(log_experiment) is not bool:\\n sys.exit('(Type Error): log_experiment parameter only accepts True or False.')\\n\\n #log_plots\\n if type(log_plots) is not bool:\\n sys.exit('(Type Error): log_plots parameter only accepts True or False.')\\n\\n #log_data\\n if type(log_data) is not bool:\\n sys.exit('(Type Error): log_data parameter only accepts True or False.')\\n\\n #log_profile\\n if type(log_profile) is not bool:\\n sys.exit('(Type Error): log_profile parameter only accepts True or False.')\\n\\n logger.info(\\\"Preloading libraries\\\")\\n\\n #pre-load libraries\\n import pandas as pd\\n import ipywidgets as ipw\\n from IPython.display import display, HTML, clear_output, update_display\\n import datetime, time\\n import os\\n\\n #pandas option\\n pd.set_option('display.max_columns', 500)\\n pd.set_option('display.max_rows', 500)\\n \\n #global html_param\\n global html_param\\n \\n #create html_param\\n html_param = html\\n\\n #silent parameter to also set sampling to False\\n if silent:\\n sampling = False\\n\\n logger.info(\\\"Preparing display monitor\\\")\\n\\n #progress bar\\n if sampling:\\n max = 10 + 3\\n else:\\n max = 3\\n \\n progress = ipw.IntProgress(value=0, min=0, max=max, step=1 , description='Processing: ')\\n if verbose:\\n if html_param:\\n display(progress)\\n \\n timestampStr = datetime.datetime.now().strftime(\\\"%H:%M:%S\\\")\\n monitor = pd.DataFrame( [ ['Initiated' , '. . . . . . . . . . . . . . . . . .', timestampStr ], \\n ['Status' , '. . . . . . . . . . . . . . . . . .' , 'Loading Dependencies' ],\\n ['ETC' , '. . . . . . . . . . . . . . . . . .', 'Calculating ETC'] ],\\n columns=['', ' ', ' ']).set_index('')\\n \\n if verbose:\\n if html_param:\\n display(monitor, display_id = 'monitor')\\n \\n logger.info(\\\"Importing libraries\\\")\\n\\n #general dependencies\\n import numpy as np\\n from sklearn.linear_model import LinearRegression\\n from sklearn.model_selection import train_test_split\\n from sklearn import metrics\\n import random\\n import seaborn as sns\\n import matplotlib.pyplot as plt\\n import plotly.express as px\\n \\n #setting sklearn config to print all parameters including default\\n import sklearn\\n sklearn.set_config(print_changed_only=False)\\n \\n #define highlight function for function grid to display\\n def highlight_max(s):\\n is_max = s == True\\n return ['background-color: yellow' if v else '' for v in is_max]\\n \\n #cufflinks\\n import cufflinks as cf\\n cf.go_offline()\\n cf.set_config_file(offline=False, world_readable=True)\\n \\n #ignore warnings\\n import warnings\\n warnings.filterwarnings('ignore') \\n \\n\\n logger.info(\\\"Declaring global variables\\\")\\n\\n #declaring global variables to be accessed by other functions\\n global X, y, X_train, X_test, y_train, y_test, seed, prep_pipe, target_inverse_transformer, experiment__,\\\\\\n preprocess, folds_shuffle_param, n_jobs_param, create_model_container, master_model_container,\\\\\\n display_container, exp_name_log, logging_param, log_plots_param, data_before_preprocess, target_param,\\\\\\n gpu_param\\n\\n logger.info(\\\"Copying data for preprocessing\\\")\\n #copy original data for pandas profiler\\n data_before_preprocess = data.copy()\\n \\n #generate seed to be used globally\\n if session_id is None:\\n seed = random.randint(150,9000)\\n else:\\n seed = session_id\\n \\n\\n \\\"\\\"\\\"\\n preprocessing starts here\\n \\\"\\\"\\\"\\n \\n monitor.iloc[1,1:] = 'Preparing Data for Modeling'\\n if verbose:\\n if html_param:\\n update_display(monitor, display_id = 'monitor')\\n \\n #define parameters for preprocessor\\n \\n logger.info(\\\"Declaring preprocessing parameters\\\")\\n\\n #categorical features\\n if categorical_features is None:\\n cat_features_pass = []\\n else:\\n cat_features_pass = categorical_features\\n \\n #numeric features\\n if numeric_features is None:\\n numeric_features_pass = []\\n else:\\n numeric_features_pass = numeric_features\\n \\n #drop features\\n if ignore_features is None:\\n ignore_features_pass = []\\n else:\\n ignore_features_pass = ignore_features\\n \\n #date features\\n if date_features is None:\\n date_features_pass = []\\n else:\\n date_features_pass = date_features\\n \\n #categorical imputation strategy\\n if categorical_imputation == 'constant':\\n categorical_imputation_pass = 'not_available'\\n elif categorical_imputation == 'mode':\\n categorical_imputation_pass = 'most frequent'\\n \\n #transformation method strategy\\n if transformation_method == 'yeo-johnson':\\n trans_method_pass = 'yj'\\n elif transformation_method == 'quantile':\\n trans_method_pass = 'quantile'\\n \\n #pass method\\n if pca_method == 'linear':\\n pca_method_pass = 'pca_liner'\\n \\n elif pca_method == 'kernel':\\n pca_method_pass = 'pca_kernal'\\n \\n elif pca_method == 'incremental':\\n pca_method_pass = 'incremental'\\n \\n elif pca_method == 'pls':\\n pca_method_pass = 'pls'\\n \\n #pca components\\n if pca is True:\\n if pca_components is None:\\n if pca_method == 'linear':\\n pca_components_pass = 0.99\\n else:\\n pca_components_pass = int((len(data.columns)-1)*0.5)\\n \\n else:\\n pca_components_pass = pca_components\\n \\n else:\\n pca_components_pass = 0.99\\n \\n if bin_numeric_features is None:\\n apply_binning_pass = False\\n features_to_bin_pass = []\\n \\n else:\\n apply_binning_pass = True\\n features_to_bin_pass = bin_numeric_features\\n \\n #trignometry\\n if trigonometry_features is False:\\n trigonometry_features_pass = []\\n else:\\n trigonometry_features_pass = ['sin', 'cos', 'tan']\\n \\n #group features\\n #=============#\\n \\n #apply grouping\\n if group_features is not None:\\n apply_grouping_pass = True\\n else:\\n apply_grouping_pass = False\\n \\n #group features listing\\n if apply_grouping_pass is True:\\n \\n if type(group_features[0]) is str:\\n group_features_pass = []\\n group_features_pass.append(group_features)\\n else:\\n group_features_pass = group_features\\n \\n else:\\n \\n group_features_pass = [[]]\\n \\n #group names\\n if apply_grouping_pass is True:\\n\\n if (group_names is None) or (len(group_names) != len(group_features_pass)):\\n group_names_pass = list(np.arange(len(group_features_pass)))\\n group_names_pass = ['group_' + str(i) for i in group_names_pass]\\n\\n else:\\n group_names_pass = group_names\\n \\n else:\\n group_names_pass = []\\n \\n #feature interactions\\n \\n if feature_interaction or feature_ratio:\\n apply_feature_interactions_pass = True\\n else:\\n apply_feature_interactions_pass = False\\n \\n interactions_to_apply_pass = []\\n \\n if feature_interaction:\\n interactions_to_apply_pass.append('multiply')\\n \\n if feature_ratio:\\n interactions_to_apply_pass.append('divide')\\n \\n #unknown categorical\\n if unknown_categorical_method == 'least_frequent':\\n unknown_categorical_method_pass = 'least frequent'\\n elif unknown_categorical_method == 'most_frequent':\\n unknown_categorical_method_pass = 'most frequent'\\n\\n #ordinal_features\\n if ordinal_features is not None:\\n apply_ordinal_encoding_pass = True\\n else:\\n apply_ordinal_encoding_pass = False\\n \\n if apply_ordinal_encoding_pass is True:\\n ordinal_columns_and_categories_pass = ordinal_features\\n else:\\n ordinal_columns_and_categories_pass = {}\\n \\n if high_cardinality_features is not None:\\n apply_cardinality_reduction_pass = True\\n else:\\n apply_cardinality_reduction_pass = False\\n \\n if high_cardinality_method == 'frequency':\\n cardinal_method_pass = 'count'\\n elif high_cardinality_method == 'clustering':\\n cardinal_method_pass = 'cluster'\\n \\n if apply_cardinality_reduction_pass:\\n cardinal_features_pass = high_cardinality_features\\n else:\\n cardinal_features_pass = []\\n \\n if silent:\\n display_dtypes_pass = False\\n else:\\n display_dtypes_pass = True\\n \\n #transform target method\\n if transform_target_method == 'box-cox':\\n transform_target_method_pass = 'bc'\\n elif transform_target_method == 'yeo-johnson':\\n transform_target_method_pass = 'yj'\\n\\n logger.info(\\\"Importing preprocessing module\\\")\\n \\n #import library\\n import pycaret.preprocess as preprocess\\n \\n logger.info(\\\"Creating preprocessing pipeline\\\")\\n\\n data = preprocess.Preprocess_Path_One(train_data = data, \\n target_variable = target,\\n categorical_features = cat_features_pass,\\n apply_ordinal_encoding = apply_ordinal_encoding_pass, \\n ordinal_columns_and_categories = ordinal_columns_and_categories_pass, \\n apply_cardinality_reduction = apply_cardinality_reduction_pass,\\n cardinal_method = cardinal_method_pass, \\n cardinal_features = cardinal_features_pass,\\n numerical_features = numeric_features_pass,\\n time_features = date_features_pass,\\n features_todrop = ignore_features_pass,\\n numeric_imputation_strategy = numeric_imputation,\\n categorical_imputation_strategy = categorical_imputation_pass,\\n scale_data = normalize,\\n scaling_method = normalize_method,\\n Power_transform_data = transformation,\\n Power_transform_method = trans_method_pass,\\n apply_untrained_levels_treatment= handle_unknown_categorical,\\n untrained_levels_treatment_method = unknown_categorical_method_pass, \\n apply_pca = pca, \\n pca_method = pca_method_pass, \\n pca_variance_retained_or_number_of_components = pca_components_pass, \\n apply_zero_nearZero_variance = ignore_low_variance,\\n club_rare_levels = combine_rare_levels,\\n rara_level_threshold_percentage = rare_level_threshold,\\n apply_binning = apply_binning_pass,\\n features_to_binn = features_to_bin_pass,\\n remove_outliers = remove_outliers,\\n outlier_contamination_percentage = outliers_threshold,\\n outlier_methods = ['pca'], #pca hardcoded\\n remove_multicollinearity = remove_multicollinearity,\\n maximum_correlation_between_features = multicollinearity_threshold,\\n remove_perfect_collinearity = remove_perfect_collinearity, \\n cluster_entire_data = create_clusters, \\n range_of_clusters_to_try = cluster_iter, \\n apply_polynomial_trigonometry_features = polynomial_features, \\n max_polynomial = polynomial_degree, \\n trigonometry_calculations = trigonometry_features_pass, \\n top_poly_trig_features_to_select_percentage = polynomial_threshold, \\n apply_grouping = apply_grouping_pass, \\n features_to_group_ListofList = group_features_pass, \\n group_name = group_names_pass, \\n apply_feature_selection = feature_selection, \\n feature_selection_top_features_percentage = feature_selection_threshold, \\n apply_feature_interactions = apply_feature_interactions_pass, \\n feature_interactions_to_apply = interactions_to_apply_pass, \\n feature_interactions_top_features_to_select_percentage=interaction_threshold, \\n display_types = display_dtypes_pass, \\n target_transformation = transform_target, \\n target_transformation_method = transform_target_method_pass, \\n random_state = seed)\\n\\n progress.value += 1\\n logger.info(\\\"Preprocessing pipeline created successfully\\\")\\n \\n if hasattr(preprocess.dtypes, 'replacement'):\\n label_encoded = preprocess.dtypes.replacement\\n label_encoded = str(label_encoded).replace(\\\"'\\\", '')\\n label_encoded = str(label_encoded).replace(\\\"{\\\", '')\\n label_encoded = str(label_encoded).replace(\\\"}\\\", '')\\n\\n else:\\n label_encoded = 'None'\\n\\n try:\\n res_type = ['quit','Quit','exit','EXIT','q','Q','e','E','QUIT','Exit']\\n res = preprocess.dtypes.response\\n if res in res_type:\\n sys.exit(\\\"(Process Exit): setup has been interupted with user command 'quit'. setup must rerun.\\\" )\\n except:\\n pass\\n \\n #save prep pipe\\n prep_pipe = preprocess.pipe\\n \\n\\n #save target inverse transformer\\n try:\\n target_inverse_transformer = preprocess.pt_target.p_transform_target\\n except:\\n target_inverse_transformer = None\\n logger.info(\\\"No inverse transformer found\\\")\\n\\n \\n logger.info(\\\"Creating grid variables\\\")\\n\\n #generate values for grid show\\n missing_values = data_before_preprocess.isna().sum().sum()\\n if missing_values > 0:\\n missing_flag = True\\n else:\\n missing_flag = False\\n \\n if normalize is True:\\n normalize_grid = normalize_method\\n else:\\n normalize_grid = 'None'\\n \\n if transformation is True:\\n transformation_grid = transformation_method\\n else:\\n transformation_grid = 'None'\\n \\n if pca is True:\\n pca_method_grid = pca_method\\n else:\\n pca_method_grid = 'None'\\n \\n if pca is True:\\n pca_components_grid = pca_components_pass\\n else:\\n pca_components_grid = 'None'\\n \\n if combine_rare_levels:\\n rare_level_threshold_grid = rare_level_threshold\\n else:\\n rare_level_threshold_grid = 'None'\\n \\n if bin_numeric_features is None:\\n numeric_bin_grid = False\\n else:\\n numeric_bin_grid = True\\n \\n if remove_outliers is False:\\n outliers_threshold_grid = None\\n else:\\n outliers_threshold_grid = outliers_threshold\\n \\n if remove_multicollinearity is False:\\n multicollinearity_threshold_grid = None\\n else:\\n multicollinearity_threshold_grid = multicollinearity_threshold\\n \\n if create_clusters is False:\\n cluster_iter_grid = None\\n else:\\n cluster_iter_grid = cluster_iter\\n \\n if polynomial_features:\\n polynomial_degree_grid = polynomial_degree\\n else:\\n polynomial_degree_grid = None\\n \\n if polynomial_features or trigonometry_features:\\n polynomial_threshold_grid = polynomial_threshold\\n else:\\n polynomial_threshold_grid = None\\n \\n if feature_selection:\\n feature_selection_threshold_grid = feature_selection_threshold\\n else:\\n feature_selection_threshold_grid = None\\n \\n if feature_interaction or feature_ratio:\\n interaction_threshold_grid = interaction_threshold\\n else:\\n interaction_threshold_grid = None\\n \\n if ordinal_features is not None:\\n ordinal_features_grid = True\\n else:\\n ordinal_features_grid = False\\n \\n if handle_unknown_categorical:\\n unknown_categorical_method_grid = unknown_categorical_method\\n else:\\n unknown_categorical_method_grid = None\\n \\n if group_features is not None:\\n group_features_grid = True\\n else:\\n group_features_grid = False\\n \\n if high_cardinality_features is not None:\\n high_cardinality_features_grid = True\\n else:\\n high_cardinality_features_grid = False\\n \\n if high_cardinality_features_grid:\\n high_cardinality_method_grid = high_cardinality_method\\n else:\\n high_cardinality_method_grid = None\\n \\n learned_types = preprocess.dtypes.learent_dtypes\\n learned_types.drop(target, inplace=True)\\n\\n float_type = 0 \\n cat_type = 0\\n\\n for i in preprocess.dtypes.learent_dtypes:\\n if 'float' in str(i):\\n float_type += 1\\n elif 'object' in str(i):\\n cat_type += 1\\n elif 'int' in str(i):\\n float_type += 1\\n \\n #target transformation method\\n if transform_target is False:\\n transform_target_method_grid = None\\n else:\\n transform_target_method_grid = preprocess.pt_target.function_to_apply\\n \\n \\\"\\\"\\\"\\n preprocessing ends here\\n \\\"\\\"\\\"\\n \\n #reset pandas option\\n pd.reset_option(\\\"display.max_rows\\\")\\n pd.reset_option(\\\"display.max_columns\\\")\\n \\n logger.info(\\\"Creating global containers\\\")\\n\\n #create an empty list for pickling later.\\n experiment__ = []\\n \\n #create folds_shuffle_param\\n folds_shuffle_param = folds_shuffle\\n\\n #create n_jobs_param\\n n_jobs_param = n_jobs\\n\\n #create create_model_container\\n create_model_container = []\\n\\n #create master_model_container\\n master_model_container = []\\n\\n #create display container\\n display_container = []\\n\\n #create logging parameter\\n logging_param = log_experiment\\n\\n #create exp_name_log param incase logging is False\\n exp_name_log = 'no_logging'\\n\\n #create an empty log_plots_param\\n if log_plots:\\n log_plots_param = True\\n else:\\n log_plots_param = False\\n\\n # create target param\\n target_param = target\\n\\n # create gpu param\\n gpu_param = use_gpu\\n\\n #sample estimator\\n if sample_estimator is None:\\n model = LinearRegression(n_jobs=n_jobs_param)\\n else:\\n model = sample_estimator\\n \\n model_name = str(model).split(\\\"(\\\")[0]\\n \\n if 'CatBoostRegressor' in model_name:\\n model_name = 'CatBoostRegressor'\\n \\n #creating variables to be used later in the function\\n X = data.drop(target,axis=1)\\n y = data[target]\\n \\n progress.value += 1\\n \\n if sampling is True and data.shape[0] > 25000: #change back to 25000\\n \\n split_perc = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.99]\\n split_perc_text = ['10%','20%','30%','40%','50%','60%', '70%', '80%', '90%', '100%']\\n split_perc_tt = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.99]\\n split_perc_tt_total = []\\n split_percent = []\\n\\n metric_results = []\\n metric_name = []\\n \\n counter = 0\\n \\n for i in split_perc:\\n \\n progress.value += 1\\n \\n t0 = time.time()\\n \\n '''\\n MONITOR UPDATE STARTS\\n '''\\n \\n perc_text = split_perc_text[counter]\\n monitor.iloc[1,1:] = 'Fitting Model on ' + perc_text + ' sample'\\n if verbose:\\n if html_param:\\n update_display(monitor, display_id = 'monitor')\\n\\n '''\\n MONITOR UPDATE ENDS\\n '''\\n \\n X_, X__, y_, y__ = train_test_split(X, y, test_size=1-i, random_state=seed, shuffle=data_split_shuffle)\\n X_train, X_test, y_train, y_test = train_test_split(X_, y_, test_size=1-train_size, random_state=seed, shuffle=data_split_shuffle)\\n model.fit(X_train,y_train)\\n pred_ = model.predict(X_test)\\n \\n r2 = metrics.r2_score(y_test,pred_)\\n metric_results.append(r2)\\n metric_name.append('R2')\\n split_percent.append(i)\\n \\n t1 = time.time()\\n \\n '''\\n Time calculation begins\\n '''\\n \\n tt = t1 - t0\\n total_tt = tt / i\\n split_perc_tt.pop(0)\\n \\n for remain in split_perc_tt:\\n ss = total_tt * remain\\n split_perc_tt_total.append(ss)\\n \\n ttt = sum(split_perc_tt_total) / 60\\n ttt = np.around(ttt, 2)\\n \\n if ttt < 1:\\n ttt = str(np.around((ttt * 60), 2))\\n ETC = ttt + ' Seconds Remaining'\\n\\n else:\\n ttt = str (ttt)\\n ETC = ttt + ' Minutes Remaining'\\n \\n monitor.iloc[2,1:] = ETC\\n if verbose:\\n if html_param:\\n update_display(monitor, display_id = 'monitor')\\n \\n \\n '''\\n Time calculation Ends\\n '''\\n \\n split_perc_tt_total = []\\n counter += 1\\n\\n model_results = pd.DataFrame({'Sample' : split_percent, 'Metric' : metric_results, 'Metric Name': metric_name})\\n fig = px.line(model_results, x='Sample', y='Metric', color='Metric Name', line_shape='linear', range_y = [0,1])\\n fig.update_layout(plot_bgcolor='rgb(245,245,245)')\\n title= str(model_name) + ' Metric and Sample %'\\n fig.update_layout(title={'text': title, 'y':0.95,'x':0.45,'xanchor': 'center','yanchor': 'top'})\\n fig.show()\\n \\n monitor.iloc[1,1:] = 'Waiting for input'\\n if verbose:\\n if html_param:\\n update_display(monitor, display_id = 'monitor')\\n \\n \\n print('Please Enter the sample % of data you would like to use for modeling. Example: Enter 0.3 for 30%.')\\n print('Press Enter if you would like to use 100% of the data.')\\n \\n print(' ')\\n \\n sample_size = input(\\\"Sample Size: \\\")\\n \\n if sample_size == '' or sample_size == '1':\\n \\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1-train_size, random_state=seed, shuffle=data_split_shuffle)\\n\\n else:\\n \\n sample_n = float(sample_size)\\n X_selected, X_discard, y_selected, y_discard = train_test_split(X, y, test_size=1-sample_n, \\n random_state=seed, shuffle=data_split_shuffle)\\n \\n X_train, X_test, y_train, y_test = train_test_split(X_selected, y_selected, test_size=1-train_size, \\n random_state=seed, shuffle=data_split_shuffle)\\n\\n else:\\n \\n monitor.iloc[1,1:] = 'Splitting Data'\\n if verbose:\\n if html_param:\\n update_display(monitor, display_id = 'monitor')\\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1-train_size, random_state=seed, shuffle=data_split_shuffle)\\n progress.value += 1\\n\\n '''\\n Final display Starts\\n '''\\n clear_output()\\n if verbose:\\n print(' ')\\n if profile:\\n print('Setup Succesfully Completed. Loading Profile Now... Please Wait!')\\n else:\\n if verbose:\\n print('Setup Succesfully Completed.')\\n functions = pd.DataFrame ( [ ['session_id', seed ],\\n ['Transform Target ', transform_target],\\n ['Transform Target Method', transform_target_method_grid],\\n ['Original Data', data_before_preprocess.shape ],\\n ['Missing Values ', missing_flag],\\n ['Numeric Features ', str(float_type) ],\\n ['Categorical Features ', str(cat_type) ],\\n ['Ordinal Features ', ordinal_features_grid],\\n ['High Cardinality Features ', high_cardinality_features_grid],\\n ['High Cardinality Method ', high_cardinality_method_grid],\\n ['Sampled Data', '(' + str(X_train.shape[0] + X_test.shape[0]) + ', ' + str(data_before_preprocess.shape[1]) + ')' ], \\n ['Transformed Train Set', X_train.shape ], \\n ['Transformed Test Set',X_test.shape ],\\n ['Numeric Imputer ', numeric_imputation],\\n ['Categorical Imputer ', categorical_imputation],\\n ['Normalize ', normalize ],\\n ['Normalize Method ', normalize_grid ],\\n ['Transformation ', transformation ],\\n ['Transformation Method ', transformation_grid ],\\n ['PCA ', pca],\\n ['PCA Method ', pca_method_grid],\\n ['PCA Components ', pca_components_grid],\\n ['Ignore Low Variance ', ignore_low_variance],\\n ['Combine Rare Levels ', combine_rare_levels],\\n ['Rare Level Threshold ', rare_level_threshold_grid],\\n ['Numeric Binning ', numeric_bin_grid],\\n ['Remove Outliers ', remove_outliers],\\n ['Outliers Threshold ', outliers_threshold_grid],\\n ['Remove Multicollinearity ', remove_multicollinearity],\\n ['Multicollinearity Threshold ', multicollinearity_threshold_grid],\\n ['Clustering ', create_clusters],\\n ['Clustering Iteration ', cluster_iter_grid],\\n ['Polynomial Features ', polynomial_features],\\n ['Polynomial Degree ', polynomial_degree_grid],\\n ['Trignometry Features ', trigonometry_features],\\n ['Polynomial Threshold ', polynomial_threshold_grid], \\n ['Group Features ', group_features_grid],\\n ['Feature Selection ', feature_selection],\\n ['Features Selection Threshold ', feature_selection_threshold_grid],\\n ['Feature Interaction ', feature_interaction],\\n ['Feature Ratio ', feature_ratio],\\n ['Interaction Threshold ', interaction_threshold_grid],\\n ], columns = ['Description', 'Value'] )\\n \\n functions_ = functions.style.apply(highlight_max)\\n if verbose:\\n if html_param:\\n display(functions_)\\n else:\\n print(functions_.data)\\n \\n if profile:\\n try:\\n import pandas_profiling\\n pf = pandas_profiling.ProfileReport(data_before_preprocess)\\n clear_output()\\n display(pf)\\n except:\\n print('Data Profiler Failed. No output to show, please continue with Modeling.')\\n \\n '''\\n Final display Ends\\n ''' \\n \\n #log into experiment\\n experiment__.append(('Regression Setup Config', functions))\\n experiment__.append(('X_training Set', X_train))\\n experiment__.append(('y_training Set', y_train))\\n experiment__.append(('X_test Set', X_test))\\n experiment__.append(('y_test Set', y_test))\\n experiment__.append(('Transformation Pipeline', prep_pipe))\\n try:\\n experiment__.append(('Target Inverse Transformer', target_inverse_transformer))\\n except:\\n pass\\n \\n #end runtime\\n runtime_end = time.time()\\n runtime = np.array(runtime_end - runtime_start).round(2)\\n\\n if logging_param:\\n \\n logger.info(\\\"Logging experiment in MLFlow\\\")\\n \\n import mlflow\\n from pathlib import Path\\n\\n if experiment_name is None:\\n exp_name_ = 'reg-default-name'\\n else:\\n exp_name_ = experiment_name\\n\\n URI = secrets.token_hex(nbytes=4) \\n exp_name_log = exp_name_\\n \\n try:\\n mlflow.create_experiment(exp_name_log)\\n except:\\n pass\\n\\n #mlflow logging\\n mlflow.set_experiment(exp_name_log)\\n\\n run_name_ = 'Session Initialized ' + str(USI)\\n with mlflow.start_run(run_name=run_name_) as run:\\n\\n # Get active run to log as tag\\n RunID = mlflow.active_run().info.run_id\\n \\n k = functions.copy()\\n k.set_index('Description',drop=True,inplace=True)\\n kdict = k.to_dict()\\n params = kdict.get('Value')\\n mlflow.log_params(params)\\n\\n #set tag of compare_models\\n mlflow.set_tag(\\\"Source\\\", \\\"setup\\\")\\n \\n import secrets\\n URI = secrets.token_hex(nbytes=4)\\n mlflow.set_tag(\\\"URI\\\", URI)\\n mlflow.set_tag(\\\"USI\\\", USI) \\n mlflow.set_tag(\\\"Run Time\\\", runtime)\\n mlflow.set_tag(\\\"Run ID\\\", RunID)\\n\\n # Log the transformation pipeline\\n logger.info(\\\"SubProcess save_model() called ==================================\\\")\\n save_model(prep_pipe, 'Transformation Pipeline', verbose=False)\\n logger.info(\\\"SubProcess save_model() end ==================================\\\")\\n mlflow.log_artifact('Transformation Pipeline' + '.pkl')\\n os.remove('Transformation Pipeline.pkl')\\n\\n # Log pandas profile\\n if log_profile:\\n import pandas_profiling\\n pf = pandas_profiling.ProfileReport(data_before_preprocess)\\n pf.to_file(\\\"Data Profile.html\\\")\\n mlflow.log_artifact(\\\"Data Profile.html\\\")\\n os.remove(\\\"Data Profile.html\\\")\\n clear_output()\\n display(functions_)\\n\\n # Log training and testing set\\n if log_data:\\n X_train.join(y_train).to_csv('Train.csv')\\n X_test.join(y_test).to_csv('Test.csv')\\n mlflow.log_artifact(\\\"Train.csv\\\")\\n mlflow.log_artifact(\\\"Test.csv\\\")\\n os.remove('Train.csv')\\n os.remove('Test.csv')\\n\\n logger.info(\\\"create_model_container: \\\" + str(len(create_model_container)))\\n logger.info(\\\"master_model_container: \\\" + str(len(master_model_container)))\\n logger.info(\\\"display_container: \\\" + str(len(display_container)))\\n\\n logger.info(\\\"setup() succesfully completed......................................\\\")\\n\\n return X, y, X_train, X_test, y_train, y_test, seed, prep_pipe, target_inverse_transformer,\\\\\\n experiment__, folds_shuffle_param, n_jobs_param, html_param, create_model_container,\\\\\\n master_model_container, display_container, exp_name_log, logging_param, log_plots_param, USI,\\\\\\n data_before_preprocess, target_param\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":787,"cells":{"query_id":{"kind":"string","value":"012e9d167c4fb65431c816f0d2bcbe37"},"query":{"kind":"string","value":"The name of the category."},"positive_passages":{"kind":"list like","value":[{"docid":"e9c3dee8d88435fba7afa382d2833fa5","score":"0.0","text":"def name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"name\")","title":""}],"string":"[\n {\n \"docid\": \"e9c3dee8d88435fba7afa382d2833fa5\",\n \"score\": \"0.0\",\n \"text\": \"def name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"name\\\")\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"89d5292e894ed33a2743b5ac6e40c96c","score":"0.9356435","text":"def category_name(self) -> str:\n return pulumi.get(self, \"category_name\")","title":""},{"docid":"0708909944d1765b981bfd898b8dc4cb","score":"0.93421","text":"def category_name(self):\n return self.category.name","title":""},{"docid":"837b2e5849f05c1d3a5c0c8c54b14d6d","score":"0.8533845","text":"def display_category(self):\n return self.category.name","title":""},{"docid":"847ddfc9482fb33002fac12dfe2a54ac","score":"0.8499749","text":"def category_name(self):\r\n for short, name in Log.CATEGORIES:\r\n if short == self.category:\r\n return name","title":""},{"docid":"d64baca15ee050ea116217e1713805d9","score":"0.8397046","text":"def category_string(self):\n return self.category.category","title":""},{"docid":"6fbfa1cd29b03bac59ef2d15304c2d49","score":"0.8353404","text":"def category(self) -> str:\n return pulumi.get(self, \"category\")","title":""},{"docid":"6fbfa1cd29b03bac59ef2d15304c2d49","score":"0.8353404","text":"def category(self) -> str:\n return pulumi.get(self, \"category\")","title":""},{"docid":"cc084d9b86ab30a8c39027b34a50ae84","score":"0.82416683","text":"def category(self) -> str:\n return self.__category","title":""},{"docid":"784e11c23c379d212eda73f408b01c39","score":"0.8241514","text":"def category(self) -> str:\n return self._category","title":""},{"docid":"784e11c23c379d212eda73f408b01c39","score":"0.8241514","text":"def category(self) -> str:\n return self._category","title":""},{"docid":"bb9fa73c2381cb09ce34ed6335171b31","score":"0.8165807","text":"def get_category(self) -> str:\n return self.category","title":""},{"docid":"bb9fa73c2381cb09ce34ed6335171b31","score":"0.8165807","text":"def get_category(self) -> str:\n return self.category","title":""},{"docid":"1dd37b8c9e970c52b2838189a550ddd5","score":"0.8058707","text":"def __str__(self):\n return self.category_name","title":""},{"docid":"0b193e584f63e1639bfb75e19c6f946c","score":"0.79800016","text":"def __str__(self):\n return f'{self.category_name}'","title":""},{"docid":"0dc710ad2f02611012cdcbc487823c8b","score":"0.79722446","text":"def category(self):\n return self.name.rsplit('/', 1)[0] + '/'","title":""},{"docid":"06374a70d4477972bd07ac41a314264f","score":"0.7918446","text":"def category() -> str:","title":""},{"docid":"3c3f8f7b4b813ea0bddad61c6bf90c67","score":"0.7842589","text":"def category(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"category\")","title":""},{"docid":"3c3f8f7b4b813ea0bddad61c6bf90c67","score":"0.7842589","text":"def category(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"category\")","title":""},{"docid":"77ff1a6143a22e370cd3a6a6f7e521fd","score":"0.77653986","text":"def name(self):\r\n return self.cat_constructor.Name","title":""},{"docid":"95a247d99194548d29a1d23c029c3dcd","score":"0.7652714","text":"def __str__(self):\r\n return 'Category %s - %s' % (self.uuid, self.name)","title":""},{"docid":"78645ae986863b172ce083ee07a5f12a","score":"0.7547565","text":"def category(self) -> Optional[str]:\n return pulumi.get(self, \"category\")","title":""},{"docid":"78645ae986863b172ce083ee07a5f12a","score":"0.7547565","text":"def category(self) -> Optional[str]:\n return pulumi.get(self, \"category\")","title":""},{"docid":"78645ae986863b172ce083ee07a5f12a","score":"0.7547565","text":"def category(self) -> Optional[str]:\n return pulumi.get(self, \"category\")","title":""},{"docid":"78645ae986863b172ce083ee07a5f12a","score":"0.7547565","text":"def category(self) -> Optional[str]:\n return pulumi.get(self, \"category\")","title":""},{"docid":"78645ae986863b172ce083ee07a5f12a","score":"0.7547565","text":"def category(self) -> Optional[str]:\n return pulumi.get(self, \"category\")","title":""},{"docid":"a57544f9261000503d64e99de99db7e4","score":"0.7472379","text":"def category(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"category\")","title":""},{"docid":"4a855dd9f24799bbcfbed783efb17b92","score":"0.7465466","text":"def display_category(self):\n return ', '.join(category.name for category in self.category.all()[:3])","title":""},{"docid":"4a855dd9f24799bbcfbed783efb17b92","score":"0.7465466","text":"def display_category(self):\n return ', '.join(category.name for category in self.category.all()[:3])","title":""},{"docid":"7a1ae1c7692d99ade070e5b08bd50d0b","score":"0.74586797","text":"def category(self):\n return self._spec.get(\"category\", \"cat1\")","title":""},{"docid":"72ac3527e07c63658a0e557a89bec324","score":"0.74584407","text":"def display_category_names(self):\n print(\"Categories in '{}':\".format(self.name))\n for x in self.categories:\n print(\" {}\".format(x.get_category()))","title":""},{"docid":"1657d74940af32240709239acb26e1d6","score":"0.7458107","text":"def atomCatName(self):\n return \"/\".join([self.category, self.package])","title":""},{"docid":"21af89cdf7e410fa22fad75d418ec611","score":"0.74382997","text":"def category(self):\r\n\t\treturn self._category","title":""},{"docid":"f32c49533b9a54ab9eb1fbcf5a54abc8","score":"0.7434136","text":"def category(self):\n return self._category","title":""},{"docid":"f32c49533b9a54ab9eb1fbcf5a54abc8","score":"0.7434136","text":"def category(self):\n return self._category","title":""},{"docid":"f32c49533b9a54ab9eb1fbcf5a54abc8","score":"0.7434136","text":"def category(self):\n return self._category","title":""},{"docid":"f32c49533b9a54ab9eb1fbcf5a54abc8","score":"0.7434136","text":"def category(self):\n return self._category","title":""},{"docid":"1a0d0a787f626a0f90d29e2250f74fd8","score":"0.7415323","text":"def __unicode__(self):\n return '%s' % (self.category_name)","title":""},{"docid":"d2660afd93fca59c30089d0744754271","score":"0.7400903","text":"def display_category(self):\n return \", \".join(category.name for category in self.category.all()[:3])","title":""},{"docid":"dd5bee67836f1b8eca6d998ee562dee9","score":"0.7382513","text":"def category(self):\n return self.data.get('category', None)","title":""},{"docid":"d74a87d0efd15837adfe8fbcbeef56fa","score":"0.73162085","text":"def getcategory(self):\n return self._category","title":""},{"docid":"d906b9603a9910e2aa620469645f0078","score":"0.7305942","text":"def __str__(self):\n return \"{}_{}\".format(self.naics_code, self.category)","title":""},{"docid":"abb0f496a90b8fa3b807169ea66e4710","score":"0.73001766","text":"def __repr__(self):\n return \"\".format(self.name)","title":""},{"docid":"dc3ea140787dcb7fa956825f78c1cdcf","score":"0.7292876","text":"def get_category(self):\n return self.category","title":""},{"docid":"8a469419a3fb137705f86403cfe1dec9","score":"0.7285824","text":"def category_control_name(v):\n return \"%s_category\" % (str(v.key()))","title":""},{"docid":"d60f3e48b3561d736891b1aa8476c110","score":"0.72473085","text":"def name_subcategory(self):\n name_cat = sc.select()\n return name_cat","title":""},{"docid":"307206fa4cddf4e8180af82b538bca12","score":"0.7234101","text":"def full_name(self):\n #Todo category.name to str\n names = [unicode(category.name) for category in self.get_ancestors_and_self()]\n return self._full_name_separator.join(names)","title":""},{"docid":"6a0dca4c22be27656fb9052f77fd4393","score":"0.72217125","text":"def getCategory(self):\n\t\treturn self.category","title":""},{"docid":"70ec38ab3039627e38d4f3bc3dd6b110","score":"0.7205014","text":"def getCategory(self):\n return self.category","title":""},{"docid":"00ad380e04978509eb45ba207d3b8189","score":"0.7178186","text":"def get_formatted_category(self):\n if (self.category == \"dinner\"):\n return \"Dinner\"\n elif (self.category == \"pregame\"):\n return \"Pregame\"\n elif (self.category == \"mainEvent\"):\n return \"Main Event\"","title":""},{"docid":"deb598c830491d4dd73290aa76ee3871","score":"0.71753734","text":"def getCategory(self):\r\n return self._category","title":""},{"docid":"fc37e7c612504a7bc7a075be43fcc727","score":"0.71447045","text":"def __repr__(self):\n return f\"Category('{self.name}', '{self.link}')\"","title":""},{"docid":"cabedc836d9d0b6efe6a18c61f0c911e","score":"0.7112214","text":"def category(self):\n first_slash = self._category.find('/')\n return self._category[first_slash+1:]","title":""},{"docid":"980cc4ca2d2bfed62c4c539e2f29acac","score":"0.7083927","text":"def __str__(self):\n return \"{}_{}\".format(self.o_net_soc_code, self.category_name)","title":""},{"docid":"d0077bcc85160eb5ee87403a2706536c","score":"0.7082638","text":"def category(self) -> int:\n return pulumi.get(self, \"category\")","title":""},{"docid":"b36ea215313f96dddf7b2ae0a7bf4281","score":"0.7078961","text":"def get_cat_name(self, catIds):\n coco_cat = self.coco.loadCats(self.coco.getCatIds())\n cat_id_list = [cat['id'] for cat in coco_cat]\n if catIds in cat_id_list:\n idx = cat_id_list.index(catIds)\n return coco_cat[idx]['name']\n else:\n print('Category ID: {} is not found'.format(catIds))","title":""},{"docid":"e61c3f38f95d7548bb62d8f86bbc1c5b","score":"0.7058682","text":"def get_category_name(self, val):\n for x,y in self.categories.iteritems():\n if val == y:\n return x","title":""},{"docid":"db58e08706bfa0189036feac713d888b","score":"0.70486426","text":"def category(self):\r\n return self.family.category","title":""},{"docid":"8559ebcd0f10e79f10a33f692a55a3c6","score":"0.70230514","text":"def category(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"category\")","title":""},{"docid":"8559ebcd0f10e79f10a33f692a55a3c6","score":"0.70230514","text":"def category(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"category\")","title":""},{"docid":"8559ebcd0f10e79f10a33f692a55a3c6","score":"0.70230514","text":"def category(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"category\")","title":""},{"docid":"e81567a46738762e670c313b26e83e92","score":"0.7003943","text":"def __unicode__(self):\n return '%s' % (self.category)","title":""},{"docid":"04e61fdcad64605a82bffa55e70e636d","score":"0.68851626","text":"def __repr__(self):\n return (\n f\"Category=(id={self.id},category_name={self.category_name}\"\n f\",category_slug={self.category_slug})\"\n )","title":""},{"docid":"132385416c41c8a88f2d74b077a54a78","score":"0.68466485","text":"def category(self, session) -> str:\n return None","title":""},{"docid":"132385416c41c8a88f2d74b077a54a78","score":"0.68466485","text":"def category(self, session) -> str:\n return None","title":""},{"docid":"09f79861b611cd9716d7a0dd4e6ff761","score":"0.6819605","text":"def get_category_name_from_file(self, file_path):\n\t\treturn os.path.basename(file_path).split(\"_\")[0].lower()","title":""},{"docid":"78426cf0ec4c27103dc9c5c81f641b79","score":"0.67999417","text":"def getCat(self):\n return self.__cat","title":""},{"docid":"0f4baac6e9e2ee62778ab614918eac22","score":"0.67872024","text":"def name_get(self):\n if self._context.get('base_category_display') == 'short':\n return super(BaseCategory, self).name_get()\n\n res = []\n for cat in self:\n names = []\n current = cat\n while current:\n names.append(current.name)\n current = current.parent_id\n res.append((cat.id, ' / '.join(reversed(names))))\n return res","title":""},{"docid":"ec4fa44ae9c32427f82d1f86209f8cae","score":"0.67868376","text":"def _getCategory(self):\n return self._category","title":""},{"docid":"58c2687b3486d37c06537a719680373a","score":"0.6763114","text":"def category_for_sidebar(self):\n if self.category == '/':\n return self.name + '/'\n else:\n return self.category","title":""},{"docid":"5481b7a5b087a23d40cf673ab900756a","score":"0.6749293","text":"def get_category(self, item_name):\n\t\treturn self._get_inner(item_name, \"category\")","title":""},{"docid":"131ebf67d44098cb9345aa71c2fa0484","score":"0.6714853","text":"def category(self):\r\n if len(self.name) > 0:\r\n return \"{0} Commands\".format(self.name)\r\n else:\r\n return \"Commands\"","title":""},{"docid":"3e9e273c04faa5f31a9d8ecc2837dc05","score":"0.6709203","text":"def __repr__(self):\r\n\r\n return \"\" % (self.category_id,\r\n self.name)","title":""},{"docid":"d3c8382079a50d9066a6b6b7eb36702f","score":"0.6706454","text":"def category_names(self):\n return self._category_names","title":""},{"docid":"f3abc7d54f964dd2a02568ca92aa6288","score":"0.6704538","text":"def category_name(value):\n return {\n 0: \"idle\",\n 1: \"unassigned\",\n 2: \"work\",\n 3: \"private\",\n 4: \"break\",\n }.get(value, \"?\")","title":""},{"docid":"7a7b61604dda3071777b0ac773df792d","score":"0.6700998","text":"def name(self) -> str:\n return self.mcls_data[\"name\"]","title":""},{"docid":"72131489f884be4ee198b3f775d36bc6","score":"0.66930056","text":"def add_category(self, name):\n pass","title":""},{"docid":"d4ab0a3b54bb0838df83bb4f7a2e44cf","score":"0.6680661","text":"def category(self) -> str:\n return self._match_json[\"rule\"][\"category\"][\"id\"]","title":""},{"docid":"e0845856b3b4613543378128ee2696b5","score":"0.66691476","text":"def description(self):\r\n return \"List of {0}\".format(self.category)","title":""},{"docid":"1ce8cbd5fcad52b41ad30ed17c7d600a","score":"0.66662973","text":"def category(self) -> str:\n return self._match_json[\"sentence\"]","title":""},{"docid":"859cedc6a3acc81eee9bfe33949e594c","score":"0.66522664","text":"def _classifier_name(self):\n pass","title":""},{"docid":"45b3e084e12e07d059d13bc9c39c85ae","score":"0.66405225","text":"def category(self):\r\n return None","title":""},{"docid":"0f98736dac789271f2092ab254ca1f78","score":"0.6631525","text":"def __unicode__(self):\n return '{cat} ({name})'.format(cat=self.cat, name=self.name)","title":""},{"docid":"1256cc671eed3e9c05ac7098aa629bc3","score":"0.6622872","text":"def ccle_name(self) -> str:\n return self._ccle_name","title":""},{"docid":"a7c7b7feeeb4368fd49ec71bd21a782c","score":"0.6616305","text":"def get_name(self):\n return self.nacp.title[0].get_name()","title":""},{"docid":"4e0676a63812c08b59a27a79805cd8d3","score":"0.6608744","text":"def name(self):\n return self._channel.display_name","title":""},{"docid":"2942a49de69b38fe0787f4947804c86b","score":"0.65840465","text":"def name(self):\n return self.get_name()","title":""},{"docid":"2942a49de69b38fe0787f4947804c86b","score":"0.65840465","text":"def name(self):\n return self.get_name()","title":""},{"docid":"2d6fffbfd28ae4bbf8b3adfe2759111f","score":"0.6575162","text":"def name(self):\n return self.title","title":""},{"docid":"8662746c1da3997d5994d94cb6a21e60","score":"0.6558912","text":"def name(self):\n\n return self.get_name()","title":""},{"docid":"55d93fe321b4c0aae22e789ba10cb083","score":"0.65544754","text":"def name(self, value):\r\n self.cat_constructor.Name = value","title":""},{"docid":"30537caf198aed0fefa485c28010e25c","score":"0.65399724","text":"def get_category_by_name(self, category_name):\n category = self.st.category.find(filters=\"name=%s\" % category_name)\n return category","title":""},{"docid":"2e5a1670aa466fd329bb723522948fea","score":"0.6515061","text":"def primary_category(self) -> str:\n assert self.primary_classification is not None\n return str(self.primary_classification.category)","title":""},{"docid":"cfedbc43ed83e949155c875195d82c3f","score":"0.649954","text":"def display_categories(self):\n return \", \".join([category.title for category in self.categories.all()])","title":""},{"docid":"18f03ba6408b62ee86f7f67aa07c8d55","score":"0.6494066","text":"def label(self) -> str:\n return self._str_representation_for(\"name\")","title":""},{"docid":"961ff47bd328452e856e4e249ea6ff51","score":"0.6491235","text":"def get_name(self):\n return","title":""},{"docid":"bf02a874f1168c6a3c8ab1b8ea4513e5","score":"0.64903516","text":"def name(self):\r\n return self.name_","title":""},{"docid":"3c617e4b1cdbe7f4d6973fcc7c3a40bc","score":"0.6483566","text":"def __repr__(self):\n\n return f\"\"\"< Resource Category: code = {self.code}, \n name = {self.name} >\"\"\"","title":""},{"docid":"d09b4e6cb66b2ab70cf087407fcbb8e5","score":"0.64835054","text":"def get_name(self):\r\n\r\n return self.get_title()","title":""},{"docid":"d09b4e6cb66b2ab70cf087407fcbb8e5","score":"0.64835054","text":"def get_name(self):\r\n\r\n return self.get_title()","title":""},{"docid":"db0061ef3cf4c041256576c24e2ad0ed","score":"0.6476098","text":"def get_name(self) -> str:\r\n\r\n return self.name","title":""},{"docid":"c99c902a9a1c269a9dea7384547d60da","score":"0.6466613","text":"def GetName(self):\n return _lldb.SBTypeCategory_GetName(self)","title":""}],"string":"[\n {\n \"docid\": \"89d5292e894ed33a2743b5ac6e40c96c\",\n \"score\": \"0.9356435\",\n \"text\": \"def category_name(self) -> str:\\n return pulumi.get(self, \\\"category_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0708909944d1765b981bfd898b8dc4cb\",\n \"score\": \"0.93421\",\n \"text\": \"def category_name(self):\\n return self.category.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"837b2e5849f05c1d3a5c0c8c54b14d6d\",\n \"score\": \"0.8533845\",\n \"text\": \"def display_category(self):\\n return self.category.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"847ddfc9482fb33002fac12dfe2a54ac\",\n \"score\": \"0.8499749\",\n \"text\": \"def category_name(self):\\r\\n for short, name in Log.CATEGORIES:\\r\\n if short == self.category:\\r\\n return name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d64baca15ee050ea116217e1713805d9\",\n \"score\": \"0.8397046\",\n \"text\": \"def category_string(self):\\n return self.category.category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fbfa1cd29b03bac59ef2d15304c2d49\",\n \"score\": \"0.8353404\",\n \"text\": \"def category(self) -> str:\\n return pulumi.get(self, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fbfa1cd29b03bac59ef2d15304c2d49\",\n \"score\": \"0.8353404\",\n \"text\": \"def category(self) -> str:\\n return pulumi.get(self, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc084d9b86ab30a8c39027b34a50ae84\",\n \"score\": \"0.82416683\",\n \"text\": \"def category(self) -> str:\\n return self.__category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"784e11c23c379d212eda73f408b01c39\",\n \"score\": \"0.8241514\",\n \"text\": \"def category(self) -> str:\\n return self._category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"784e11c23c379d212eda73f408b01c39\",\n \"score\": \"0.8241514\",\n \"text\": \"def category(self) -> str:\\n return self._category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb9fa73c2381cb09ce34ed6335171b31\",\n \"score\": \"0.8165807\",\n \"text\": \"def get_category(self) -> str:\\n return self.category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb9fa73c2381cb09ce34ed6335171b31\",\n \"score\": \"0.8165807\",\n \"text\": \"def get_category(self) -> str:\\n return self.category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dd37b8c9e970c52b2838189a550ddd5\",\n \"score\": \"0.8058707\",\n \"text\": \"def __str__(self):\\n return self.category_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b193e584f63e1639bfb75e19c6f946c\",\n \"score\": \"0.79800016\",\n \"text\": \"def __str__(self):\\n return f'{self.category_name}'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dc710ad2f02611012cdcbc487823c8b\",\n \"score\": \"0.79722446\",\n \"text\": \"def category(self):\\n return self.name.rsplit('/', 1)[0] + '/'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06374a70d4477972bd07ac41a314264f\",\n \"score\": \"0.7918446\",\n \"text\": \"def category() -> str:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c3f8f7b4b813ea0bddad61c6bf90c67\",\n \"score\": \"0.7842589\",\n \"text\": \"def category(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c3f8f7b4b813ea0bddad61c6bf90c67\",\n \"score\": \"0.7842589\",\n \"text\": \"def category(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77ff1a6143a22e370cd3a6a6f7e521fd\",\n \"score\": \"0.77653986\",\n \"text\": \"def name(self):\\r\\n return self.cat_constructor.Name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95a247d99194548d29a1d23c029c3dcd\",\n \"score\": \"0.7652714\",\n \"text\": \"def __str__(self):\\r\\n return 'Category %s - %s' % (self.uuid, self.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78645ae986863b172ce083ee07a5f12a\",\n \"score\": \"0.7547565\",\n \"text\": \"def category(self) -> Optional[str]:\\n return pulumi.get(self, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78645ae986863b172ce083ee07a5f12a\",\n \"score\": \"0.7547565\",\n \"text\": \"def category(self) -> Optional[str]:\\n return pulumi.get(self, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78645ae986863b172ce083ee07a5f12a\",\n \"score\": \"0.7547565\",\n \"text\": \"def category(self) -> Optional[str]:\\n return pulumi.get(self, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78645ae986863b172ce083ee07a5f12a\",\n \"score\": \"0.7547565\",\n \"text\": \"def category(self) -> Optional[str]:\\n return pulumi.get(self, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78645ae986863b172ce083ee07a5f12a\",\n \"score\": \"0.7547565\",\n \"text\": \"def category(self) -> Optional[str]:\\n return pulumi.get(self, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a57544f9261000503d64e99de99db7e4\",\n \"score\": \"0.7472379\",\n \"text\": \"def category(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a855dd9f24799bbcfbed783efb17b92\",\n \"score\": \"0.7465466\",\n \"text\": \"def display_category(self):\\n return ', '.join(category.name for category in self.category.all()[:3])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a855dd9f24799bbcfbed783efb17b92\",\n \"score\": \"0.7465466\",\n \"text\": \"def display_category(self):\\n return ', '.join(category.name for category in self.category.all()[:3])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a1ae1c7692d99ade070e5b08bd50d0b\",\n \"score\": \"0.74586797\",\n \"text\": \"def category(self):\\n return self._spec.get(\\\"category\\\", \\\"cat1\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72ac3527e07c63658a0e557a89bec324\",\n \"score\": \"0.74584407\",\n \"text\": \"def display_category_names(self):\\n print(\\\"Categories in '{}':\\\".format(self.name))\\n for x in self.categories:\\n print(\\\" {}\\\".format(x.get_category()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1657d74940af32240709239acb26e1d6\",\n \"score\": \"0.7458107\",\n \"text\": \"def atomCatName(self):\\n return \\\"/\\\".join([self.category, self.package])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21af89cdf7e410fa22fad75d418ec611\",\n \"score\": \"0.74382997\",\n \"text\": \"def category(self):\\r\\n\\t\\treturn self._category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f32c49533b9a54ab9eb1fbcf5a54abc8\",\n \"score\": \"0.7434136\",\n \"text\": \"def category(self):\\n return self._category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f32c49533b9a54ab9eb1fbcf5a54abc8\",\n \"score\": \"0.7434136\",\n \"text\": \"def category(self):\\n return self._category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f32c49533b9a54ab9eb1fbcf5a54abc8\",\n \"score\": \"0.7434136\",\n \"text\": \"def category(self):\\n return self._category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f32c49533b9a54ab9eb1fbcf5a54abc8\",\n \"score\": \"0.7434136\",\n \"text\": \"def category(self):\\n return self._category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a0d0a787f626a0f90d29e2250f74fd8\",\n \"score\": \"0.7415323\",\n \"text\": \"def __unicode__(self):\\n return '%s' % (self.category_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2660afd93fca59c30089d0744754271\",\n \"score\": \"0.7400903\",\n \"text\": \"def display_category(self):\\n return \\\", \\\".join(category.name for category in self.category.all()[:3])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd5bee67836f1b8eca6d998ee562dee9\",\n \"score\": \"0.7382513\",\n \"text\": \"def category(self):\\n return self.data.get('category', None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d74a87d0efd15837adfe8fbcbeef56fa\",\n \"score\": \"0.73162085\",\n \"text\": \"def getcategory(self):\\n return self._category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d906b9603a9910e2aa620469645f0078\",\n \"score\": \"0.7305942\",\n \"text\": \"def __str__(self):\\n return \\\"{}_{}\\\".format(self.naics_code, self.category)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abb0f496a90b8fa3b807169ea66e4710\",\n \"score\": \"0.73001766\",\n \"text\": \"def __repr__(self):\\n return \\\"\\\".format(self.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc3ea140787dcb7fa956825f78c1cdcf\",\n \"score\": \"0.7292876\",\n \"text\": \"def get_category(self):\\n return self.category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a469419a3fb137705f86403cfe1dec9\",\n \"score\": \"0.7285824\",\n \"text\": \"def category_control_name(v):\\n return \\\"%s_category\\\" % (str(v.key()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d60f3e48b3561d736891b1aa8476c110\",\n \"score\": \"0.72473085\",\n \"text\": \"def name_subcategory(self):\\n name_cat = sc.select()\\n return name_cat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"307206fa4cddf4e8180af82b538bca12\",\n \"score\": \"0.7234101\",\n \"text\": \"def full_name(self):\\n #Todo category.name to str\\n names = [unicode(category.name) for category in self.get_ancestors_and_self()]\\n return self._full_name_separator.join(names)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a0dca4c22be27656fb9052f77fd4393\",\n \"score\": \"0.72217125\",\n \"text\": \"def getCategory(self):\\n\\t\\treturn self.category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70ec38ab3039627e38d4f3bc3dd6b110\",\n \"score\": \"0.7205014\",\n \"text\": \"def getCategory(self):\\n return self.category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00ad380e04978509eb45ba207d3b8189\",\n \"score\": \"0.7178186\",\n \"text\": \"def get_formatted_category(self):\\n if (self.category == \\\"dinner\\\"):\\n return \\\"Dinner\\\"\\n elif (self.category == \\\"pregame\\\"):\\n return \\\"Pregame\\\"\\n elif (self.category == \\\"mainEvent\\\"):\\n return \\\"Main Event\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"deb598c830491d4dd73290aa76ee3871\",\n \"score\": \"0.71753734\",\n \"text\": \"def getCategory(self):\\r\\n return self._category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc37e7c612504a7bc7a075be43fcc727\",\n \"score\": \"0.71447045\",\n \"text\": \"def __repr__(self):\\n return f\\\"Category('{self.name}', '{self.link}')\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cabedc836d9d0b6efe6a18c61f0c911e\",\n \"score\": \"0.7112214\",\n \"text\": \"def category(self):\\n first_slash = self._category.find('/')\\n return self._category[first_slash+1:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"980cc4ca2d2bfed62c4c539e2f29acac\",\n \"score\": \"0.7083927\",\n \"text\": \"def __str__(self):\\n return \\\"{}_{}\\\".format(self.o_net_soc_code, self.category_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0077bcc85160eb5ee87403a2706536c\",\n \"score\": \"0.7082638\",\n \"text\": \"def category(self) -> int:\\n return pulumi.get(self, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b36ea215313f96dddf7b2ae0a7bf4281\",\n \"score\": \"0.7078961\",\n \"text\": \"def get_cat_name(self, catIds):\\n coco_cat = self.coco.loadCats(self.coco.getCatIds())\\n cat_id_list = [cat['id'] for cat in coco_cat]\\n if catIds in cat_id_list:\\n idx = cat_id_list.index(catIds)\\n return coco_cat[idx]['name']\\n else:\\n print('Category ID: {} is not found'.format(catIds))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e61c3f38f95d7548bb62d8f86bbc1c5b\",\n \"score\": \"0.7058682\",\n \"text\": \"def get_category_name(self, val):\\n for x,y in self.categories.iteritems():\\n if val == y:\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db58e08706bfa0189036feac713d888b\",\n \"score\": \"0.70486426\",\n \"text\": \"def category(self):\\r\\n return self.family.category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8559ebcd0f10e79f10a33f692a55a3c6\",\n \"score\": \"0.70230514\",\n \"text\": \"def category(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8559ebcd0f10e79f10a33f692a55a3c6\",\n \"score\": \"0.70230514\",\n \"text\": \"def category(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8559ebcd0f10e79f10a33f692a55a3c6\",\n \"score\": \"0.70230514\",\n \"text\": \"def category(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e81567a46738762e670c313b26e83e92\",\n \"score\": \"0.7003943\",\n \"text\": \"def __unicode__(self):\\n return '%s' % (self.category)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04e61fdcad64605a82bffa55e70e636d\",\n \"score\": \"0.68851626\",\n \"text\": \"def __repr__(self):\\n return (\\n f\\\"Category=(id={self.id},category_name={self.category_name}\\\"\\n f\\\",category_slug={self.category_slug})\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"132385416c41c8a88f2d74b077a54a78\",\n \"score\": \"0.68466485\",\n \"text\": \"def category(self, session) -> str:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"132385416c41c8a88f2d74b077a54a78\",\n \"score\": \"0.68466485\",\n \"text\": \"def category(self, session) -> str:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09f79861b611cd9716d7a0dd4e6ff761\",\n \"score\": \"0.6819605\",\n \"text\": \"def get_category_name_from_file(self, file_path):\\n\\t\\treturn os.path.basename(file_path).split(\\\"_\\\")[0].lower()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78426cf0ec4c27103dc9c5c81f641b79\",\n \"score\": \"0.67999417\",\n \"text\": \"def getCat(self):\\n return self.__cat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f4baac6e9e2ee62778ab614918eac22\",\n \"score\": \"0.67872024\",\n \"text\": \"def name_get(self):\\n if self._context.get('base_category_display') == 'short':\\n return super(BaseCategory, self).name_get()\\n\\n res = []\\n for cat in self:\\n names = []\\n current = cat\\n while current:\\n names.append(current.name)\\n current = current.parent_id\\n res.append((cat.id, ' / '.join(reversed(names))))\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec4fa44ae9c32427f82d1f86209f8cae\",\n \"score\": \"0.67868376\",\n \"text\": \"def _getCategory(self):\\n return self._category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58c2687b3486d37c06537a719680373a\",\n \"score\": \"0.6763114\",\n \"text\": \"def category_for_sidebar(self):\\n if self.category == '/':\\n return self.name + '/'\\n else:\\n return self.category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5481b7a5b087a23d40cf673ab900756a\",\n \"score\": \"0.6749293\",\n \"text\": \"def get_category(self, item_name):\\n\\t\\treturn self._get_inner(item_name, \\\"category\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"131ebf67d44098cb9345aa71c2fa0484\",\n \"score\": \"0.6714853\",\n \"text\": \"def category(self):\\r\\n if len(self.name) > 0:\\r\\n return \\\"{0} Commands\\\".format(self.name)\\r\\n else:\\r\\n return \\\"Commands\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e9e273c04faa5f31a9d8ecc2837dc05\",\n \"score\": \"0.6709203\",\n \"text\": \"def __repr__(self):\\r\\n\\r\\n return \\\"\\\" % (self.category_id,\\r\\n self.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3c8382079a50d9066a6b6b7eb36702f\",\n \"score\": \"0.6706454\",\n \"text\": \"def category_names(self):\\n return self._category_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3abc7d54f964dd2a02568ca92aa6288\",\n \"score\": \"0.6704538\",\n \"text\": \"def category_name(value):\\n return {\\n 0: \\\"idle\\\",\\n 1: \\\"unassigned\\\",\\n 2: \\\"work\\\",\\n 3: \\\"private\\\",\\n 4: \\\"break\\\",\\n }.get(value, \\\"?\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a7b61604dda3071777b0ac773df792d\",\n \"score\": \"0.6700998\",\n \"text\": \"def name(self) -> str:\\n return self.mcls_data[\\\"name\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72131489f884be4ee198b3f775d36bc6\",\n \"score\": \"0.66930056\",\n \"text\": \"def add_category(self, name):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4ab0a3b54bb0838df83bb4f7a2e44cf\",\n \"score\": \"0.6680661\",\n \"text\": \"def category(self) -> str:\\n return self._match_json[\\\"rule\\\"][\\\"category\\\"][\\\"id\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0845856b3b4613543378128ee2696b5\",\n \"score\": \"0.66691476\",\n \"text\": \"def description(self):\\r\\n return \\\"List of {0}\\\".format(self.category)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ce8cbd5fcad52b41ad30ed17c7d600a\",\n \"score\": \"0.66662973\",\n \"text\": \"def category(self) -> str:\\n return self._match_json[\\\"sentence\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"859cedc6a3acc81eee9bfe33949e594c\",\n \"score\": \"0.66522664\",\n \"text\": \"def _classifier_name(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45b3e084e12e07d059d13bc9c39c85ae\",\n \"score\": \"0.66405225\",\n \"text\": \"def category(self):\\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f98736dac789271f2092ab254ca1f78\",\n \"score\": \"0.6631525\",\n \"text\": \"def __unicode__(self):\\n return '{cat} ({name})'.format(cat=self.cat, name=self.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1256cc671eed3e9c05ac7098aa629bc3\",\n \"score\": \"0.6622872\",\n \"text\": \"def ccle_name(self) -> str:\\n return self._ccle_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7c7b7feeeb4368fd49ec71bd21a782c\",\n \"score\": \"0.6616305\",\n \"text\": \"def get_name(self):\\n return self.nacp.title[0].get_name()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e0676a63812c08b59a27a79805cd8d3\",\n \"score\": \"0.6608744\",\n \"text\": \"def name(self):\\n return self._channel.display_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2942a49de69b38fe0787f4947804c86b\",\n \"score\": \"0.65840465\",\n \"text\": \"def name(self):\\n return self.get_name()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2942a49de69b38fe0787f4947804c86b\",\n \"score\": \"0.65840465\",\n \"text\": \"def name(self):\\n return self.get_name()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d6fffbfd28ae4bbf8b3adfe2759111f\",\n \"score\": \"0.6575162\",\n \"text\": \"def name(self):\\n return self.title\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8662746c1da3997d5994d94cb6a21e60\",\n \"score\": \"0.6558912\",\n \"text\": \"def name(self):\\n\\n return self.get_name()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55d93fe321b4c0aae22e789ba10cb083\",\n \"score\": \"0.65544754\",\n \"text\": \"def name(self, value):\\r\\n self.cat_constructor.Name = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30537caf198aed0fefa485c28010e25c\",\n \"score\": \"0.65399724\",\n \"text\": \"def get_category_by_name(self, category_name):\\n category = self.st.category.find(filters=\\\"name=%s\\\" % category_name)\\n return category\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e5a1670aa466fd329bb723522948fea\",\n \"score\": \"0.6515061\",\n \"text\": \"def primary_category(self) -> str:\\n assert self.primary_classification is not None\\n return str(self.primary_classification.category)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfedbc43ed83e949155c875195d82c3f\",\n \"score\": \"0.649954\",\n \"text\": \"def display_categories(self):\\n return \\\", \\\".join([category.title for category in self.categories.all()])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18f03ba6408b62ee86f7f67aa07c8d55\",\n \"score\": \"0.6494066\",\n \"text\": \"def label(self) -> str:\\n return self._str_representation_for(\\\"name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"961ff47bd328452e856e4e249ea6ff51\",\n \"score\": \"0.6491235\",\n \"text\": \"def get_name(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf02a874f1168c6a3c8ab1b8ea4513e5\",\n \"score\": \"0.64903516\",\n \"text\": \"def name(self):\\r\\n return self.name_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c617e4b1cdbe7f4d6973fcc7c3a40bc\",\n \"score\": \"0.6483566\",\n \"text\": \"def __repr__(self):\\n\\n return f\\\"\\\"\\\"< Resource Category: code = {self.code}, \\n name = {self.name} >\\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d09b4e6cb66b2ab70cf087407fcbb8e5\",\n \"score\": \"0.64835054\",\n \"text\": \"def get_name(self):\\r\\n\\r\\n return self.get_title()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d09b4e6cb66b2ab70cf087407fcbb8e5\",\n \"score\": \"0.64835054\",\n \"text\": \"def get_name(self):\\r\\n\\r\\n return self.get_title()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db0061ef3cf4c041256576c24e2ad0ed\",\n \"score\": \"0.6476098\",\n \"text\": \"def get_name(self) -> str:\\r\\n\\r\\n return self.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c99c902a9a1c269a9dea7384547d60da\",\n \"score\": \"0.6466613\",\n \"text\": \"def GetName(self):\\n return _lldb.SBTypeCategory_GetName(self)\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":788,"cells":{"query_id":{"kind":"string","value":"25b75ea7bf5cb76a12789e7989129fb6"},"query":{"kind":"string","value":"Returns a ApplicationCache Object to interact with the browser app cache."},"positive_passages":{"kind":"list like","value":[{"docid":"30b841e126a587fbf9af547ebf5dc15d","score":"0.83621204","text":"def get_application_cache(self) -> ApplicationCache:\n return self._selenium_web_driver().application_cache","title":""}],"string":"[\n {\n \"docid\": \"30b841e126a587fbf9af547ebf5dc15d\",\n \"score\": \"0.83621204\",\n \"text\": \"def get_application_cache(self) -> ApplicationCache:\\n return self._selenium_web_driver().application_cache\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"ee705e4bfe2f7971b08deb8b63d3f503","score":"0.6836842","text":"def create_cache():\n class _App(object):\n config = {}\n\n cache_config = {\n 'CACHE_TYPE': 'filesystem',\n 'CACHE_DEFAULT_TIMEOUT': 60 * 60 * 24 * 7,\n 'CACHE_THRESHOLD': 100,\n 'CACHE_DIR': CACHE_DIR\n }\n return Cache(app=_App(), with_jinja2_ext=False, config=cache_config)","title":""},{"docid":"334a688276c7862ca0bb5905d0a49fd0","score":"0.6681711","text":"def register_cache(app):\n cache.init_app(app, config=app.config['FLASK_CACHING'])\n return cache","title":""},{"docid":"7642dc511277a83d32862396659c17f1","score":"0.6571262","text":"def get_app(self, app_name: str) -> 'Application':\n return self._cache.get(Application, app_name)","title":""},{"docid":"869dfebcc5223afdd7f3a0efe886514c","score":"0.6241348","text":"def request_bingo_cache(self):\n if self.is_requesting_archives():\n return BingoCache.load_from_datastore(archives=True)\n else:\n return BingoCache.get()","title":""},{"docid":"67664eccbc30c0e7d178394b6d8b3ad6","score":"0.6231686","text":"def cache(self):\n\t\tif self.is_cache_outdated():\n\t\t\tself.build_cache()\n\t\treturn self._cache","title":""},{"docid":"153b33b20666b9c4cedce670d4310f24","score":"0.61012405","text":"def GetCache(self, *args, **kwargs):\n pass","title":""},{"docid":"289d4072202e1d90878dcb9292403b8d","score":"0.60995215","text":"def get_cache() -> dict:\n return cache_data","title":""},{"docid":"de836b5da6ef2795c54a70be15ae3ff8","score":"0.59789747","text":"def __init__(self, app, config=None, environ_key='beaker.cache', **kwargs):\r\n self.app = app\r\n config = config or {}\r\n \r\n self.options = {}\r\n \r\n # Update the options with the parsed config\r\n self.options.update(parse_cache_config_options(config))\r\n \r\n # Add any options from kwargs, but leave out the defaults this\r\n # time\r\n self.options.update(\r\n parse_cache_config_options(kwargs, include_defaults=False))\r\n \r\n # Assume all keys are intended for cache if none are prefixed with\r\n # 'cache.'\r\n if not self.options and config:\r\n self.options = config\r\n \r\n self.options.update(kwargs)\r\n self.cache_manager = CacheManager(**self.options)\r\n self.environ_key = environ_key","title":""},{"docid":"c476ea367ecbb6929bd81bc289a10d6d","score":"0.5942076","text":"def _cache(self):\r\n if getattr(self, '_client', None) is None:\r\n self._client = self._lib.Client(self._servers)\r\n\r\n return self._client","title":""},{"docid":"bca59bca7ecf59bdae92c0021209b061","score":"0.5933238","text":"def cache(self):\n return self.__cache","title":""},{"docid":"3d301b63ac638716d047a343058c1758","score":"0.5932765","text":"def getApplication(self, request):\n return self.app","title":""},{"docid":"a5b049f6b924c0d60adde586af89db49","score":"0.59322166","text":"def _cache(self) -> Any:\n return caches[self.cache_alias]","title":""},{"docid":"28af4f8e54bd26c7cd73a2cf5ea2fdf1","score":"0.5918129","text":"def get_cache_item(cls, app_name, name, project=None):\n cls._check_app_name(app_name)\n query_string = {'app_name': app_name, 'name': name}\n if project:\n query_string['project'] = project\n return JSONCacheItem.objects.filter(**query_string).first()","title":""},{"docid":"05dd9a0157a7106df30e4a315653f530","score":"0.5873467","text":"def cache(self):\n return self._cache","title":""},{"docid":"19c0bbeba4aae70fe2b037660d1f5e44","score":"0.58454627","text":"def __get_application(self):\n if self.request.method in ['GET', 'DELETE']:\n access_token = self.request.get('accessToken')\n else:\n try:\n access_token = loads(self.request.body).get('accessToken')\n except ValueError:\n access_token = None\n if access_token is None:\n return None\n application_key = get_application_key(access_token)\n if not application_key:\n return None\n return Application.get_by_id(application_key)","title":""},{"docid":"baf7ac30be61fcda29e2c1467fe74286","score":"0.57707256","text":"def _cache(self):\n if hasattr(self, '_c_cache'):\n return self._c_cache","title":""},{"docid":"8871d716d8b9fc95d49422015115f9d1","score":"0.5753878","text":"def __init__(self):\n self.cache = Cache.Cache()","title":""},{"docid":"f0dba87b29c1fb9cdab9b96416208122","score":"0.5749011","text":"def get_system_cache_configuration(self):\n return SystemCacheConfiguration.from_json(self.service_client.GET('configurations/cache'))","title":""},{"docid":"5e06a73f248ad6fa75d9c193c09452ec","score":"0.5745871","text":"async def get_application(self):\n self.api_server = RestApi()\n return self.api_server.app","title":""},{"docid":"02b22474b0390b3b51772482ab1e97ae","score":"0.57296216","text":"async def get_application(self):\n app = web.Application()\n setup_routes(app)\n await read_json(app, NEWS_FILENAME, COMMENTS_FILENAME)\n return app","title":""},{"docid":"cf9facd2e37038a2167d32b13ef1af14","score":"0.57231057","text":"def get_cache_backend():\n cache_alias = getattr(settings, 'METATAGS_CACHE_ALIAS', DEFAULT_CACHE_ALIAS)\n return caches[cache_alias]","title":""},{"docid":"9fe2fbaf73204d994e5343659fa9a5f4","score":"0.5628217","text":"def load(self, app: OrmCacheApp) -> _T:\n\n session = app.session_manager.session()\n\n # before accessing any cached values we need to make sure that all\n # pending changes are properly flushed -> this leads to some extra cpu\n # cycles spent but eliminates the chance of accessing a stale entry\n # after a change\n if session.dirty:\n session.flush()\n\n # we use a secondary request cache for even more lookup speed and to\n # make sure that inside a request we always get the exact same instance\n # (otherwise we don't see changes reflected)\n if self.cache_key in app.request_cache:\n\n # it is possible for objects in the request cache to become\n # detached - in this case we need to merge them again\n # (the merge function only does this if necessary)\n return self.merge(session, app.request_cache[self.cache_key])\n\n else:\n obj = app.cache.get_or_create(\n key=self.cache_key,\n creator=lambda: self.create(app)\n )\n\n # named tuples\n if isinstance(obj, tuple) and hasattr(obj.__class__, '_make'):\n obj = obj._make(self.merge(session, o) for o in obj) # type:ignore\n\n # lists (we can save some memory here)\n elif isinstance(obj, list):\n for ix, o in enumerate(obj):\n obj[ix] = self.merge(session, o)\n\n # generic iterables\n elif isinstance(obj, (tuple, set)):\n obj = obj.__class__(self.merge(session, o) for o in obj)\n\n # generic objects\n else:\n obj = self.merge(session, obj)\n\n app.request_cache[self.cache_key] = obj\n\n return obj","title":""},{"docid":"db4887c5a66ab5ae0f5986923dfe4261","score":"0.5611781","text":"def GetApplication():\r\n global applicationinstance\r\n if applicationinstance is None:\r\n applicationinstance = Application()\r\n return applicationinstance","title":""},{"docid":"5a17577055d30528e507006564db0a89","score":"0.5601422","text":"def get_backend(self):\n try:\n cache = caches[machina_settings.ATTACHMENT_CACHE_NAME]\n except InvalidCacheBackendError:\n raise ImproperlyConfigured(\n 'The attachment cache backend ({}) is not configured'.format(\n machina_settings.ATTACHMENT_CACHE_NAME,\n ),\n )\n return cache","title":""},{"docid":"3cfb9d05acb64afffe315c5561f7c5ed","score":"0.5581193","text":"async def get_application(self):\n\n return self.api_server.app","title":""},{"docid":"b70c4becdaada755059aa07437d4c524","score":"0.5558913","text":"def cache_controller_in_ram():\r\n\r\n t = time.ctime()\r\n return dict(time=t, link=A('click to reload', _href=URL(r=request)))","title":""},{"docid":"66f96f0b609062c43880b76822d679dd","score":"0.5556888","text":"def db_cache() -> DbCache:\n return DbCache()","title":""},{"docid":"ace87eb211e4148baa97d2cf1c61bab1","score":"0.55439925","text":"def repopulate_app_cache(self):\r\n cache._populate()","title":""},{"docid":"3832c8bc07725352eed7d22b1008df38","score":"0.54964185","text":"def getApplication(self):\n return FacebookApplication(self.base.get(\"application\", []))","title":""},{"docid":"13cb7837c3f4c82e42eb920acc825d86","score":"0.5493108","text":"def setup_cache(self):\n if config.get('redis.host'):\n if 'redis.timeout' in config:\n default_timeout = asint(config['redis.timeout'])\n else:\n default_timeout = None\n cache = RedisCache(\n host=config['redis.host'],\n port=asint(config.get('redis.port', 6379)),\n db=asint(config.get('redis.db', 0)),\n prefix=config.get('redis.prefix', ''),\n default_timeout=default_timeout\n )\n else:\n cache = None\n\n config['pylons.app_globals'].cache = cache","title":""},{"docid":"ca1e444b3ac520643889a7a48a17951c","score":"0.5489794","text":"def get_cache(cache_class, namespace, max_entries=1000,\n redis_host='localhost', redis_port=6379, redis_protocol=None):\n\n def got_connection(client):\n return cache_class(namespace, max_entries=max_entries,\n client=client)\n\n d = get_redis_client(redis_host, redis_port, redis_protocol)\n d.addCallback(got_connection)\n return d","title":""},{"docid":"d04ba07ed214d1d987240b5b6a464de4","score":"0.54842114","text":"def get_object_cache(self, key, **kwargs):\n if key not in self._object_caches:\n return self.create_object_cache(key, **kwargs)\n return self._object_caches[key]['cache']","title":""},{"docid":"ee9dce7b8e65156d08727068dcb8d6b4","score":"0.5458705","text":"def registry(self):\n if webapp2.get_app().registry:\n return webapp2.get_app().registry\n else:\n return AppCachedProperty.GLOBAL_CACHE","title":""},{"docid":"42af6e00cbfc49d384648dc212af1b3c","score":"0.5456047","text":"def init_cache(app):\r\n if not hasattr(app.env, 'programoutput_cache'):\r\n app.env.programoutput_cache = ProgramOutputCache()","title":""},{"docid":"3a4b88fb5fb29ec511ce0e004d4edcca","score":"0.5446816","text":"def _get_form_cache(self):\n\t\tcache = FormCache.get_form_cache(cache_name=self.cache_name)\n\t\tif cache is None:\n\t\t\tcache = self.default_cache\n\t\t\tFormCache.set_form_cache(\n\t\t\t\tcache_name=self.cache_name,\n\t\t\t\tvalues=cache,\n\t\t\t)\n\t\treturn cache","title":""},{"docid":"e73b8c3756d0a2bb5d74d0a08ddd2e9a","score":"0.5431631","text":"def application(self):\n return self.__instance__.application","title":""},{"docid":"91441c5ce0580d43fe81d264c119a49d","score":"0.5430361","text":"def memcached(self):\n if self._memcached is None:\n self._memcached = MemcachedConnect()\n return self._memcached","title":""},{"docid":"91441c5ce0580d43fe81d264c119a49d","score":"0.5430361","text":"def memcached(self):\n if self._memcached is None:\n self._memcached = MemcachedConnect()\n return self._memcached","title":""},{"docid":"b1517e2db769d7f9eb9d90956f0bb1fd","score":"0.54203856","text":"def cache(self) -> typing.Optional[hikari.api.Cache]:","title":""},{"docid":"b1517e2db769d7f9eb9d90956f0bb1fd","score":"0.54203856","text":"def cache(self) -> typing.Optional[hikari.api.Cache]:","title":""},{"docid":"0796dc1f4c91c06c9a14153e5aaa4e93","score":"0.5419042","text":"async def get_application(self):\n parser, args = httpproxy.parse_args(self.get_args())\n return httpproxy.get_app(args)","title":""},{"docid":"6696ca05a9b63e58dcf94a1e9366ece7","score":"0.5381189","text":"def application(self):\r\n return Live.Application.get_application()","title":""},{"docid":"1f4e7f91068174b1f596a5964a4c1ea0","score":"0.5379811","text":"def cache(self):\n self.is_cached = True\n self.persist(StorageLevel.MEMORY_ONLY_SER)\n return self","title":""},{"docid":"e0c06854fdff64f5801b9ceb5e7f3389","score":"0.5374801","text":"def app_cache_restorer():\n state = _app_cache_deepcopy(apps.__dict__)\n try:\n yield state\n finally:\n with apps_lock():\n apps.__dict__ = state\n # Rebind the app registry models cache to\n # individual app config ones.\n for app_conf in apps.get_app_configs():\n app_conf.models = apps.all_models[app_conf.label]\n apps.clear_cache()","title":""},{"docid":"6010386b54023d11ab2b9e1343cbeb01","score":"0.53698903","text":"def get_application(routes):\n cookie_secret = None\n if not on_dev_server():\n cookie_secret = get_project_metadata('cookie_secret')\n else:\n cookie_secret = get_dev_secrets()\n\n config = {}\n config['webapp2_extras.sessions'] = {\n 'secret_key': str(cookie_secret),\n }\n\n if cookie_secret:\n return webapp2.WSGIApplication(routes=routes, config=config)\n else:\n return error_app(get_cookie_secret_error())","title":""},{"docid":"8c455cac6227c2052174516c27f9a823","score":"0.53665507","text":"def __init__(self, simplecache=None):\n if not simplecache:\n from simplecache import SimpleCache\n self.cache = SimpleCache()\n else:\n self.cache = simplecache","title":""},{"docid":"59c4f863d36bcaa265d22d8af732f9ba","score":"0.5362422","text":"def cache(self, *args, **kwargs):\n return self.get_query_set().cache(*args, **kwargs)","title":""},{"docid":"c0c2da69bbe01bfedc16759813f47b0c","score":"0.5360249","text":"def __call__(self, *args, **kwargs):\n return self._cache_wrapper(None, *args, **kwargs)","title":""},{"docid":"152df095861d66b1e00b698bae74a35d","score":"0.5358146","text":"def add_get_application_json_to_weak_caching(context):\n registry = getUtility(IRegistry)\n try:\n from plone.app.caching.interfaces import IPloneCacheSettings\n except ImportError:\n # plone.app.caching is optional.\n return\n\n try:\n settings = registry.forInterface(IPloneCacheSettings)\n except KeyError:\n # It is available, but not activated. Nothing to do.\n return\n mapping = settings.templateRulesetMapping\n key = \"GET_application_json_\"\n if key in mapping:\n # already set, do not change\n return\n mapping[key] = \"plone.content.folderView\"\n # Note: if we edit templateRulesetMapping, our change will not be persisted,\n # because it is a simple dict. We have to set the entire mapping.\n settings.templateRulesetMapping = mapping","title":""},{"docid":"9d71866de901d0a4f6fe621ae055dfa8","score":"0.53514236","text":"def cache():\n pass","title":""},{"docid":"19bdd5a6c663d5975005537d1ddad3cb","score":"0.5350763","text":"def cache(self, cache = None):\n try:\n currentCache = self.__request[\"cache\"]\n except Exception:\n currentCache = None\n if type(cache) is bool:\n self.__request[\"cache\"] = cache\n elif not (cache is None):\n raise Exception(\"cache must be of type bool\") \n return(currentCache)","title":""},{"docid":"09cc63cffc95a64464f289ff60a972a0","score":"0.5331978","text":"def get(self):\n return pickle.loads(self.db.get(self.cache_key))","title":""},{"docid":"143320186be55d43f35a0cbec3bb1a6c","score":"0.5324998","text":"def get_project_cache(cls, project, data_type='json'):\n cls._check_data_type(data_type)\n return JSONCacheItem.objects.filter(project=project)","title":""},{"docid":"4e63ad3426854737590231ef1f5a14a2","score":"0.5324482","text":"def cache(self):\n self.rdd.cache()\n return self","title":""},{"docid":"b9cb2ff8bf3587177d0a21c392d0a3aa","score":"0.5307121","text":"def __init__(self, cache, cache_key=None):\n self.cache = cache\n self.cache_key = cache_key","title":""},{"docid":"7cda2a535c305eb27ff3312122683ee2","score":"0.5298131","text":"async def get_application(self):\n return await servinit()","title":""},{"docid":"223ac8ecb438e90a6a126a73aa19c711","score":"0.52853745","text":"def _get_activity(_cache=[]):\n if _cache:\n return _cache[0]\n # See MainActivity.onCreate() for initialization of .singletonThis:\n # https://github.com/beeware/briefcase-android-gradle-template/blob/3.7/%7B%7B%20cookiecutter.formal_name%20%7D%7D/app/src/main/java/org/beeware/android/MainActivity.java\n # This can't be tested because if it isn't set, nothing else will work.\n if not MainActivity.singletonThis: # pragma: no cover\n raise ValueError(\n \"Unable to find MainActivity.singletonThis from Python. This is typically set by \"\n \"org.beeware.android.MainActivity.onCreate().\"\n )\n _cache.append(MainActivity.singletonThis.__global__())\n return _cache[0]","title":""},{"docid":"117a4865f41357039d3e9afaa14aadbf","score":"0.52616775","text":"def create_app(config_name):\n\n # instantiate an app where config will be in an instance\n app = Flask(__name__, instance_relative_config=True)\n api = Api(app)\n cor_app = CORS(app)\n\n # Set the config parameters\n app.config.from_object(config[config_name])\n app.config.from_pyfile('config.py')\n \n # Initialize the cache using the config params\n cache = Cache(\n app.config['NUMBER_OF_SLOTS'], \n app.config['TIME_TO_LIVE'], \n EvictionStrategies(app.config['EVICTION_POLICY'])\n )\n CacheApi.initialize_cache(cache)\n\n # Set the routes\n \n api.add_resource(CacheApi, '/object/')\n\n return app","title":""},{"docid":"2150e2dc9809741e315158ab94b40be0","score":"0.524874","text":"def _init_cache(self):\n self.cache = Cache()","title":""},{"docid":"1d53deeaee79dd16889659342055f392","score":"0.52353615","text":"def cache_in_ram():\r\n\r\n t = cache.ram('time', lambda: time.ctime(), time_expire=5)\r\n return dict(time=t, link=A('click to reload', _href=URL(r=request)))","title":""},{"docid":"4cce37b55384c1355090c31bcabae321","score":"0.5233077","text":"def cache_controller_on_disk():\r\n\r\n t = time.ctime()\r\n return dict(time=t, link=A('click to reload', _href=URL(r=request)))","title":""},{"docid":"1d9e34cb1536560b88113c751ed5d066","score":"0.5232964","text":"def cache(context):\n pass","title":""},{"docid":"21781c1219feb51c4c32df6a3bdfc805","score":"0.5228117","text":"def copy(self) -> \"LRUCache\":\n rv = self.__class__(self.capacity)\n rv._mapping.update(self._mapping)\n rv._queue.extend(self._queue)\n return rv","title":""},{"docid":"25bbc181be207acf18ae5ad7ca2ac443","score":"0.52091885","text":"def application(self):\n return self._application","title":""},{"docid":"25bbc181be207acf18ae5ad7ca2ac443","score":"0.52091885","text":"def application(self):\n return self._application","title":""},{"docid":"25bbc181be207acf18ae5ad7ca2ac443","score":"0.52091885","text":"def application(self):\n return self._application","title":""},{"docid":"25bbc181be207acf18ae5ad7ca2ac443","score":"0.52091885","text":"def application(self):\n return self._application","title":""},{"docid":"c2637b40860ccde8ff311f2db227d1a9","score":"0.51805556","text":"def synchronize_apps_cache():\n sync_id = start_synchronization()\n try:\n cached_requests = get_cached_session()\n synced_apps = _sync_golab_translations(cached_requests, force_reload = False)\n _sync_regular_apps(cached_requests, synced_apps, force_reload = False)\n finally:\n end_synchronization(sync_id)","title":""},{"docid":"e881a491de4108eb2309b0abc0489a0e","score":"0.51803803","text":"def appinit(self):\n return Appinit(self)","title":""},{"docid":"0955005ce38d9fb33f9c61ea62849cf5","score":"0.5178926","text":"def load(self):\n return app","title":""},{"docid":"aa5be44577fef68e21bd261cbb8eb343","score":"0.5176882","text":"def cache_object(*args, **kwargs):\n global __loop__\n if __loop__ is None:\n __loop__ = DefaultEventLoop(GLOBAL_NAME)\n\n __loop__.cache_object(*args, **kwargs)","title":""},{"docid":"826ff4c5dfa46ce200d716f8c979c71f","score":"0.51712286","text":"def cache(self) -> Path:\n return self._impl.get_cache_path()","title":""},{"docid":"ba34225b33d8ee6d77a88f36f6da0eb1","score":"0.5167296","text":"def create_app(self):\n # Create APP and set configuration\n app = APP\n config = Config()\n\n app.config['TESTING'] = True\n app.config['LIVESERVER_PORT'] = config.agent_api_ip_bind_port()\n os.environ['FLASK_ENV'] = 'development'\n\n # Clear the flask cache\n cache = Cache(config={'CACHE_TYPE': 'null'})\n cache.init_app(app)\n\n # Return\n return app","title":""},{"docid":"ea095adf9d10118bfac112c4d08bab27","score":"0.5156244","text":"def __get__(self, instance: RedisCache, owner: Any) -> RedisCache:\n if self.bot:\n return self\n\n if self._namespace is None:\n error_message = \"RedisCache must be a class attribute.\"\n log.error(error_message)\n raise NoNamespaceError(error_message)\n\n if instance is None:\n error_message = (\n \"You must access the RedisCache instance through the cog instance \"\n \"before accessing it using the cog's class object.\"\n )\n log.error(error_message)\n raise NoParentInstanceError(error_message)\n\n for attribute in vars(instance).values():\n if isinstance(attribute, Bot):\n self.bot = attribute\n self._redis = self.bot.redis_session\n return self\n else:\n error_message = (\n \"Critical error: RedisCache has no `Bot` instance. \"\n \"This happens when the class RedisCache was created in doesn't \"\n \"have a Bot instance. Please make sure that you're instantiating \"\n \"the RedisCache inside a class that has a Bot instance attribute.\"\n )\n log.error(error_message)\n raise NoBotInstanceError(error_message)","title":""},{"docid":"ecdcf580e6dadac9b79940b89047d257","score":"0.5144671","text":"def get_app(self):\n return app","title":""},{"docid":"ecdcf580e6dadac9b79940b89047d257","score":"0.5144671","text":"def get_app(self):\n return app","title":""},{"docid":"6376a6e74ef6bcf425e908c4432620fd","score":"0.5133707","text":"def __install_cache(self) -> None:\n if USE_CACHE:\n CACHE_PATH.mkdir(exist_ok=True)\n requests_cache.install_cache(\n str(CACHE_PATH.joinpath(self.get_class_name()))\n )","title":""},{"docid":"5da1adaa6297fe43915b98a452f62088","score":"0.51205796","text":"def static_config(self):\n\n return CacheConfig(1024, None)","title":""},{"docid":"b16be2098fbca620e32869fc48ffd6ac","score":"0.51037395","text":"def __call__(self, environ, start_response):\n ctx = ndb.get_context()\n ctx.set_cache_policy(False)\n\n response = self.application(environ, start_response)\n\n ctx.clear_cache()\n return response","title":""},{"docid":"b5977509f45af093701976e2ade8bac2","score":"0.5083971","text":"def get_metadata_cache_manager():\n global _METADATA_CACHE_MANAGER\n return _METADATA_CACHE_MANAGER","title":""},{"docid":"7fc4e69a79bbf77e3395e0aaa6245980","score":"0.5079018","text":"def get_full_cache(self):\n self.update_time()\n return self._cache","title":""},{"docid":"25af86466512750d1c59fda9de5954c4","score":"0.50715613","text":"def _get_cache_obj(self, type, id):\n cache = self._resolve_cache(type)\n return cache.get(id, None)","title":""},{"docid":"773e0199280cb8d13f98978e26d54757","score":"0.5059492","text":"def get(cls) -> MemCacheHandlerBase:\n if not hasattr(cls, \"instance\"):\n cls_name = cls.__class__.__name__\n raise MemCacheHandlerError(f\"Before calling {cls_name}.get(), you should call {cls_name}.create() first.\")\n\n return cls.instance","title":""},{"docid":"03c3efd180677017fc9a9577570c04f2","score":"0.5054254","text":"def get_app(self):\n if self.app is None:\n self._register_app()\n return self.app","title":""},{"docid":"153fed25f31f98f5702716cb7a2be054","score":"0.5037384","text":"def get_app(self):\n return self.app","title":""},{"docid":"e44ac8b2c1de1c94cd56bd19fe5aa44a","score":"0.50314593","text":"def __init__(self, app):\n self._initialize_app(app)\n self._initialize_cache(SortedDict(key=lambda p: p.date, reverse=True))","title":""},{"docid":"54cc4030140cae06ac04f930aa3ab44a","score":"0.5023157","text":"def app(self):\n return self._app","title":""},{"docid":"38c7a5050d751a7deed2a178db436be1","score":"0.5017547","text":"def load_cache(cache_file):\n fd = open(cache_file)\n cache_data = load(fd)\n fd.close()\n return cache_data","title":""},{"docid":"d1e74660c616410ec8202c90cda4c051","score":"0.50134623","text":"def get(self, url):\n session = self.sessionmaker()\n try:\n cache_result = session.query(HTTPCacheContent) \\\n .filter(HTTPCacheContent.url == url) \\\n .one_or_none()\n\n # if expiration is enabled then don't return anything that is expired\n if cache_result is not None and \\\n not self.dont_expire and \\\n cache_result.expire_on_dt is not None and \\\n cache_result.expire_on_dt < datetime.utcnow():\n print(\n f\"URL '{url}' found in cache, but set for expiration in the past at \"\n f\"{cache_result.expire_on_dt}, so not returned.\"\n )\n cache_result = None\n finally:\n session.close()\n\n if cache_result is None:\n return None\n elif cache_result.content is not None:\n return cache_result.content\n else:\n assert cache_result.content_bzip2 is not None\n return bz2.decompress(cache_result.content_bzip2)","title":""},{"docid":"abb9359623ada198ed4ffdf06225b3fc","score":"0.500541","text":"def api_with_cache(epirr_id):\n if epirr_id not in cache:\n cache[epirr_id] = api_call_epirr(epirr_id)\n return cache[epirr_id]","title":""},{"docid":"5cd670ae42f6218ae3950ce5d1b15fac","score":"0.50006336","text":"def _get_cache(self, param, namespace = None):\n\n return self._get_cache_map([param], namespace).get(param, None)","title":""},{"docid":"6db3e2c53e4f23305597543bddd7e1f0","score":"0.49994013","text":"def get_route_cache(cls):\n if hasattr(cls.thread_locals, 'request'):\n request = cls.thread_locals.request\n if not hasattr(request, 'route_cache'):\n request.route_cache = {}\n return request.route_cache\n return {}","title":""},{"docid":"5d085c0843cd6a2fcaecbd641b4c7cb7","score":"0.4996278","text":"def get_session(cls, app: object = None) -> 'SessionStore':\n config = get_application_config(app)\n host = config.get('REDIS_HOST', 'localhost')\n port = int(config.get('REDIS_PORT', '7000'))\n db = int(config.get('REDIS_DATABASE', '0'))\n token = config.get('REDIS_TOKEN', None)\n cluster = config.get('REDIS_CLUSTER', '1') == '1'\n secret = config['JWT_SECRET']\n duration = int(config.get('SESSION_DURATION', '7200'))\n fake = config.get('REDIS_FAKE', False)\n return cls(host, port, db, secret, duration, token=token,\n cluster=cluster, fake=fake)","title":""},{"docid":"92662aad18d3d08d9394e5a41bd4354b","score":"0.4986594","text":"def cache_data(self):\n # Initialize key variables\n result = self.data['cache_data']\n return result","title":""},{"docid":"0ba0b7031e90c85452d1a720f6a03a9e","score":"0.49527672","text":"def get_app_singleton():\n return _singleton","title":""},{"docid":"1d671bb769197d57a80411b7f2fe1661","score":"0.4948296","text":"def get_cache(self, queue_name: str) -> dict:\n\n # Check queue name. Raise exception if not exists\n self.check_queue_name(queue_name)\n\n # Get cache from queue\n cache = self.queues[queue_name].get_cache()\n return cache","title":""},{"docid":"b35e91efbb19a6fb8e708214fffa593b","score":"0.49419135","text":"def get_default_cache_ctrl():\n\n if settings.get(\"DEFAULT_CACHE\") is None: # missing or None\n return CacheCtrl([])\n\n return make_cache_ctrl(settings[\"DEFAULT_CACHE\"])","title":""},{"docid":"679b837e1b0576c33d4c25c0bed3086d","score":"0.4934507","text":"def _create_session(self):\n kwargs = {\"connection\": self.memcached, \"ip_address\": self.request.remote_ip}\n new_session = MemcachedSession(**kwargs)\n new_session.save()\n return new_session","title":""},{"docid":"d7e28d69713a7047707a1f602dceba8d","score":"0.49208707","text":"def save_in_cache(self):\n memcache.set(self.code(), self)","title":""},{"docid":"ede37ea1f20c4e14732e6445d4722ac0","score":"0.49168393","text":"def get_application():\n description = '''Turbinia API server'''\n _app = FastAPI(\n title='Turbinia API Server', description=description, version='1.0.0',\n license_info={\n 'name': 'Apache License 2.0',\n 'url': 'https://www.apache.org/licenses/LICENSE-2.0.html'\n }, routes=router.routes)\n return _app","title":""}],"string":"[\n {\n \"docid\": \"ee705e4bfe2f7971b08deb8b63d3f503\",\n \"score\": \"0.6836842\",\n \"text\": \"def create_cache():\\n class _App(object):\\n config = {}\\n\\n cache_config = {\\n 'CACHE_TYPE': 'filesystem',\\n 'CACHE_DEFAULT_TIMEOUT': 60 * 60 * 24 * 7,\\n 'CACHE_THRESHOLD': 100,\\n 'CACHE_DIR': CACHE_DIR\\n }\\n return Cache(app=_App(), with_jinja2_ext=False, config=cache_config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"334a688276c7862ca0bb5905d0a49fd0\",\n \"score\": \"0.6681711\",\n \"text\": \"def register_cache(app):\\n cache.init_app(app, config=app.config['FLASK_CACHING'])\\n return cache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7642dc511277a83d32862396659c17f1\",\n \"score\": \"0.6571262\",\n \"text\": \"def get_app(self, app_name: str) -> 'Application':\\n return self._cache.get(Application, app_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"869dfebcc5223afdd7f3a0efe886514c\",\n \"score\": \"0.6241348\",\n \"text\": \"def request_bingo_cache(self):\\n if self.is_requesting_archives():\\n return BingoCache.load_from_datastore(archives=True)\\n else:\\n return BingoCache.get()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67664eccbc30c0e7d178394b6d8b3ad6\",\n \"score\": \"0.6231686\",\n \"text\": \"def cache(self):\\n\\t\\tif self.is_cache_outdated():\\n\\t\\t\\tself.build_cache()\\n\\t\\treturn self._cache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"153b33b20666b9c4cedce670d4310f24\",\n \"score\": \"0.61012405\",\n \"text\": \"def GetCache(self, *args, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"289d4072202e1d90878dcb9292403b8d\",\n \"score\": \"0.60995215\",\n \"text\": \"def get_cache() -> dict:\\n return cache_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de836b5da6ef2795c54a70be15ae3ff8\",\n \"score\": \"0.59789747\",\n \"text\": \"def __init__(self, app, config=None, environ_key='beaker.cache', **kwargs):\\r\\n self.app = app\\r\\n config = config or {}\\r\\n \\r\\n self.options = {}\\r\\n \\r\\n # Update the options with the parsed config\\r\\n self.options.update(parse_cache_config_options(config))\\r\\n \\r\\n # Add any options from kwargs, but leave out the defaults this\\r\\n # time\\r\\n self.options.update(\\r\\n parse_cache_config_options(kwargs, include_defaults=False))\\r\\n \\r\\n # Assume all keys are intended for cache if none are prefixed with\\r\\n # 'cache.'\\r\\n if not self.options and config:\\r\\n self.options = config\\r\\n \\r\\n self.options.update(kwargs)\\r\\n self.cache_manager = CacheManager(**self.options)\\r\\n self.environ_key = environ_key\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c476ea367ecbb6929bd81bc289a10d6d\",\n \"score\": \"0.5942076\",\n \"text\": \"def _cache(self):\\r\\n if getattr(self, '_client', None) is None:\\r\\n self._client = self._lib.Client(self._servers)\\r\\n\\r\\n return self._client\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bca59bca7ecf59bdae92c0021209b061\",\n \"score\": \"0.5933238\",\n \"text\": \"def cache(self):\\n return self.__cache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d301b63ac638716d047a343058c1758\",\n \"score\": \"0.5932765\",\n \"text\": \"def getApplication(self, request):\\n return self.app\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5b049f6b924c0d60adde586af89db49\",\n \"score\": \"0.59322166\",\n \"text\": \"def _cache(self) -> Any:\\n return caches[self.cache_alias]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28af4f8e54bd26c7cd73a2cf5ea2fdf1\",\n \"score\": \"0.5918129\",\n \"text\": \"def get_cache_item(cls, app_name, name, project=None):\\n cls._check_app_name(app_name)\\n query_string = {'app_name': app_name, 'name': name}\\n if project:\\n query_string['project'] = project\\n return JSONCacheItem.objects.filter(**query_string).first()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05dd9a0157a7106df30e4a315653f530\",\n \"score\": \"0.5873467\",\n \"text\": \"def cache(self):\\n return self._cache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19c0bbeba4aae70fe2b037660d1f5e44\",\n \"score\": \"0.58454627\",\n \"text\": \"def __get_application(self):\\n if self.request.method in ['GET', 'DELETE']:\\n access_token = self.request.get('accessToken')\\n else:\\n try:\\n access_token = loads(self.request.body).get('accessToken')\\n except ValueError:\\n access_token = None\\n if access_token is None:\\n return None\\n application_key = get_application_key(access_token)\\n if not application_key:\\n return None\\n return Application.get_by_id(application_key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"baf7ac30be61fcda29e2c1467fe74286\",\n \"score\": \"0.57707256\",\n \"text\": \"def _cache(self):\\n if hasattr(self, '_c_cache'):\\n return self._c_cache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8871d716d8b9fc95d49422015115f9d1\",\n \"score\": \"0.5753878\",\n \"text\": \"def __init__(self):\\n self.cache = Cache.Cache()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0dba87b29c1fb9cdab9b96416208122\",\n \"score\": \"0.5749011\",\n \"text\": \"def get_system_cache_configuration(self):\\n return SystemCacheConfiguration.from_json(self.service_client.GET('configurations/cache'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e06a73f248ad6fa75d9c193c09452ec\",\n \"score\": \"0.5745871\",\n \"text\": \"async def get_application(self):\\n self.api_server = RestApi()\\n return self.api_server.app\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02b22474b0390b3b51772482ab1e97ae\",\n \"score\": \"0.57296216\",\n \"text\": \"async def get_application(self):\\n app = web.Application()\\n setup_routes(app)\\n await read_json(app, NEWS_FILENAME, COMMENTS_FILENAME)\\n return app\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf9facd2e37038a2167d32b13ef1af14\",\n \"score\": \"0.57231057\",\n \"text\": \"def get_cache_backend():\\n cache_alias = getattr(settings, 'METATAGS_CACHE_ALIAS', DEFAULT_CACHE_ALIAS)\\n return caches[cache_alias]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fe2fbaf73204d994e5343659fa9a5f4\",\n \"score\": \"0.5628217\",\n \"text\": \"def load(self, app: OrmCacheApp) -> _T:\\n\\n session = app.session_manager.session()\\n\\n # before accessing any cached values we need to make sure that all\\n # pending changes are properly flushed -> this leads to some extra cpu\\n # cycles spent but eliminates the chance of accessing a stale entry\\n # after a change\\n if session.dirty:\\n session.flush()\\n\\n # we use a secondary request cache for even more lookup speed and to\\n # make sure that inside a request we always get the exact same instance\\n # (otherwise we don't see changes reflected)\\n if self.cache_key in app.request_cache:\\n\\n # it is possible for objects in the request cache to become\\n # detached - in this case we need to merge them again\\n # (the merge function only does this if necessary)\\n return self.merge(session, app.request_cache[self.cache_key])\\n\\n else:\\n obj = app.cache.get_or_create(\\n key=self.cache_key,\\n creator=lambda: self.create(app)\\n )\\n\\n # named tuples\\n if isinstance(obj, tuple) and hasattr(obj.__class__, '_make'):\\n obj = obj._make(self.merge(session, o) for o in obj) # type:ignore\\n\\n # lists (we can save some memory here)\\n elif isinstance(obj, list):\\n for ix, o in enumerate(obj):\\n obj[ix] = self.merge(session, o)\\n\\n # generic iterables\\n elif isinstance(obj, (tuple, set)):\\n obj = obj.__class__(self.merge(session, o) for o in obj)\\n\\n # generic objects\\n else:\\n obj = self.merge(session, obj)\\n\\n app.request_cache[self.cache_key] = obj\\n\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db4887c5a66ab5ae0f5986923dfe4261\",\n \"score\": \"0.5611781\",\n \"text\": \"def GetApplication():\\r\\n global applicationinstance\\r\\n if applicationinstance is None:\\r\\n applicationinstance = Application()\\r\\n return applicationinstance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a17577055d30528e507006564db0a89\",\n \"score\": \"0.5601422\",\n \"text\": \"def get_backend(self):\\n try:\\n cache = caches[machina_settings.ATTACHMENT_CACHE_NAME]\\n except InvalidCacheBackendError:\\n raise ImproperlyConfigured(\\n 'The attachment cache backend ({}) is not configured'.format(\\n machina_settings.ATTACHMENT_CACHE_NAME,\\n ),\\n )\\n return cache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cfb9d05acb64afffe315c5561f7c5ed\",\n \"score\": \"0.5581193\",\n \"text\": \"async def get_application(self):\\n\\n return self.api_server.app\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b70c4becdaada755059aa07437d4c524\",\n \"score\": \"0.5558913\",\n \"text\": \"def cache_controller_in_ram():\\r\\n\\r\\n t = time.ctime()\\r\\n return dict(time=t, link=A('click to reload', _href=URL(r=request)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66f96f0b609062c43880b76822d679dd\",\n \"score\": \"0.5556888\",\n \"text\": \"def db_cache() -> DbCache:\\n return DbCache()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ace87eb211e4148baa97d2cf1c61bab1\",\n \"score\": \"0.55439925\",\n \"text\": \"def repopulate_app_cache(self):\\r\\n cache._populate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3832c8bc07725352eed7d22b1008df38\",\n \"score\": \"0.54964185\",\n \"text\": \"def getApplication(self):\\n return FacebookApplication(self.base.get(\\\"application\\\", []))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13cb7837c3f4c82e42eb920acc825d86\",\n \"score\": \"0.5493108\",\n \"text\": \"def setup_cache(self):\\n if config.get('redis.host'):\\n if 'redis.timeout' in config:\\n default_timeout = asint(config['redis.timeout'])\\n else:\\n default_timeout = None\\n cache = RedisCache(\\n host=config['redis.host'],\\n port=asint(config.get('redis.port', 6379)),\\n db=asint(config.get('redis.db', 0)),\\n prefix=config.get('redis.prefix', ''),\\n default_timeout=default_timeout\\n )\\n else:\\n cache = None\\n\\n config['pylons.app_globals'].cache = cache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca1e444b3ac520643889a7a48a17951c\",\n \"score\": \"0.5489794\",\n \"text\": \"def get_cache(cache_class, namespace, max_entries=1000,\\n redis_host='localhost', redis_port=6379, redis_protocol=None):\\n\\n def got_connection(client):\\n return cache_class(namespace, max_entries=max_entries,\\n client=client)\\n\\n d = get_redis_client(redis_host, redis_port, redis_protocol)\\n d.addCallback(got_connection)\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d04ba07ed214d1d987240b5b6a464de4\",\n \"score\": \"0.54842114\",\n \"text\": \"def get_object_cache(self, key, **kwargs):\\n if key not in self._object_caches:\\n return self.create_object_cache(key, **kwargs)\\n return self._object_caches[key]['cache']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee9dce7b8e65156d08727068dcb8d6b4\",\n \"score\": \"0.5458705\",\n \"text\": \"def registry(self):\\n if webapp2.get_app().registry:\\n return webapp2.get_app().registry\\n else:\\n return AppCachedProperty.GLOBAL_CACHE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42af6e00cbfc49d384648dc212af1b3c\",\n \"score\": \"0.5456047\",\n \"text\": \"def init_cache(app):\\r\\n if not hasattr(app.env, 'programoutput_cache'):\\r\\n app.env.programoutput_cache = ProgramOutputCache()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a4b88fb5fb29ec511ce0e004d4edcca\",\n \"score\": \"0.5446816\",\n \"text\": \"def _get_form_cache(self):\\n\\t\\tcache = FormCache.get_form_cache(cache_name=self.cache_name)\\n\\t\\tif cache is None:\\n\\t\\t\\tcache = self.default_cache\\n\\t\\t\\tFormCache.set_form_cache(\\n\\t\\t\\t\\tcache_name=self.cache_name,\\n\\t\\t\\t\\tvalues=cache,\\n\\t\\t\\t)\\n\\t\\treturn cache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e73b8c3756d0a2bb5d74d0a08ddd2e9a\",\n \"score\": \"0.5431631\",\n \"text\": \"def application(self):\\n return self.__instance__.application\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91441c5ce0580d43fe81d264c119a49d\",\n \"score\": \"0.5430361\",\n \"text\": \"def memcached(self):\\n if self._memcached is None:\\n self._memcached = MemcachedConnect()\\n return self._memcached\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91441c5ce0580d43fe81d264c119a49d\",\n \"score\": \"0.5430361\",\n \"text\": \"def memcached(self):\\n if self._memcached is None:\\n self._memcached = MemcachedConnect()\\n return self._memcached\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1517e2db769d7f9eb9d90956f0bb1fd\",\n \"score\": \"0.54203856\",\n \"text\": \"def cache(self) -> typing.Optional[hikari.api.Cache]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1517e2db769d7f9eb9d90956f0bb1fd\",\n \"score\": \"0.54203856\",\n \"text\": \"def cache(self) -> typing.Optional[hikari.api.Cache]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0796dc1f4c91c06c9a14153e5aaa4e93\",\n \"score\": \"0.5419042\",\n \"text\": \"async def get_application(self):\\n parser, args = httpproxy.parse_args(self.get_args())\\n return httpproxy.get_app(args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6696ca05a9b63e58dcf94a1e9366ece7\",\n \"score\": \"0.5381189\",\n \"text\": \"def application(self):\\r\\n return Live.Application.get_application()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f4e7f91068174b1f596a5964a4c1ea0\",\n \"score\": \"0.5379811\",\n \"text\": \"def cache(self):\\n self.is_cached = True\\n self.persist(StorageLevel.MEMORY_ONLY_SER)\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0c06854fdff64f5801b9ceb5e7f3389\",\n \"score\": \"0.5374801\",\n \"text\": \"def app_cache_restorer():\\n state = _app_cache_deepcopy(apps.__dict__)\\n try:\\n yield state\\n finally:\\n with apps_lock():\\n apps.__dict__ = state\\n # Rebind the app registry models cache to\\n # individual app config ones.\\n for app_conf in apps.get_app_configs():\\n app_conf.models = apps.all_models[app_conf.label]\\n apps.clear_cache()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6010386b54023d11ab2b9e1343cbeb01\",\n \"score\": \"0.53698903\",\n \"text\": \"def get_application(routes):\\n cookie_secret = None\\n if not on_dev_server():\\n cookie_secret = get_project_metadata('cookie_secret')\\n else:\\n cookie_secret = get_dev_secrets()\\n\\n config = {}\\n config['webapp2_extras.sessions'] = {\\n 'secret_key': str(cookie_secret),\\n }\\n\\n if cookie_secret:\\n return webapp2.WSGIApplication(routes=routes, config=config)\\n else:\\n return error_app(get_cookie_secret_error())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c455cac6227c2052174516c27f9a823\",\n \"score\": \"0.53665507\",\n \"text\": \"def __init__(self, simplecache=None):\\n if not simplecache:\\n from simplecache import SimpleCache\\n self.cache = SimpleCache()\\n else:\\n self.cache = simplecache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59c4f863d36bcaa265d22d8af732f9ba\",\n \"score\": \"0.5362422\",\n \"text\": \"def cache(self, *args, **kwargs):\\n return self.get_query_set().cache(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0c2da69bbe01bfedc16759813f47b0c\",\n \"score\": \"0.5360249\",\n \"text\": \"def __call__(self, *args, **kwargs):\\n return self._cache_wrapper(None, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"152df095861d66b1e00b698bae74a35d\",\n \"score\": \"0.5358146\",\n \"text\": \"def add_get_application_json_to_weak_caching(context):\\n registry = getUtility(IRegistry)\\n try:\\n from plone.app.caching.interfaces import IPloneCacheSettings\\n except ImportError:\\n # plone.app.caching is optional.\\n return\\n\\n try:\\n settings = registry.forInterface(IPloneCacheSettings)\\n except KeyError:\\n # It is available, but not activated. Nothing to do.\\n return\\n mapping = settings.templateRulesetMapping\\n key = \\\"GET_application_json_\\\"\\n if key in mapping:\\n # already set, do not change\\n return\\n mapping[key] = \\\"plone.content.folderView\\\"\\n # Note: if we edit templateRulesetMapping, our change will not be persisted,\\n # because it is a simple dict. We have to set the entire mapping.\\n settings.templateRulesetMapping = mapping\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d71866de901d0a4f6fe621ae055dfa8\",\n \"score\": \"0.53514236\",\n \"text\": \"def cache():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19bdd5a6c663d5975005537d1ddad3cb\",\n \"score\": \"0.5350763\",\n \"text\": \"def cache(self, cache = None):\\n try:\\n currentCache = self.__request[\\\"cache\\\"]\\n except Exception:\\n currentCache = None\\n if type(cache) is bool:\\n self.__request[\\\"cache\\\"] = cache\\n elif not (cache is None):\\n raise Exception(\\\"cache must be of type bool\\\") \\n return(currentCache)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09cc63cffc95a64464f289ff60a972a0\",\n \"score\": \"0.5331978\",\n \"text\": \"def get(self):\\n return pickle.loads(self.db.get(self.cache_key))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"143320186be55d43f35a0cbec3bb1a6c\",\n \"score\": \"0.5324998\",\n \"text\": \"def get_project_cache(cls, project, data_type='json'):\\n cls._check_data_type(data_type)\\n return JSONCacheItem.objects.filter(project=project)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e63ad3426854737590231ef1f5a14a2\",\n \"score\": \"0.5324482\",\n \"text\": \"def cache(self):\\n self.rdd.cache()\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9cb2ff8bf3587177d0a21c392d0a3aa\",\n \"score\": \"0.5307121\",\n \"text\": \"def __init__(self, cache, cache_key=None):\\n self.cache = cache\\n self.cache_key = cache_key\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cda2a535c305eb27ff3312122683ee2\",\n \"score\": \"0.5298131\",\n \"text\": \"async def get_application(self):\\n return await servinit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"223ac8ecb438e90a6a126a73aa19c711\",\n \"score\": \"0.52853745\",\n \"text\": \"def _get_activity(_cache=[]):\\n if _cache:\\n return _cache[0]\\n # See MainActivity.onCreate() for initialization of .singletonThis:\\n # https://github.com/beeware/briefcase-android-gradle-template/blob/3.7/%7B%7B%20cookiecutter.formal_name%20%7D%7D/app/src/main/java/org/beeware/android/MainActivity.java\\n # This can't be tested because if it isn't set, nothing else will work.\\n if not MainActivity.singletonThis: # pragma: no cover\\n raise ValueError(\\n \\\"Unable to find MainActivity.singletonThis from Python. This is typically set by \\\"\\n \\\"org.beeware.android.MainActivity.onCreate().\\\"\\n )\\n _cache.append(MainActivity.singletonThis.__global__())\\n return _cache[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"117a4865f41357039d3e9afaa14aadbf\",\n \"score\": \"0.52616775\",\n \"text\": \"def create_app(config_name):\\n\\n # instantiate an app where config will be in an instance\\n app = Flask(__name__, instance_relative_config=True)\\n api = Api(app)\\n cor_app = CORS(app)\\n\\n # Set the config parameters\\n app.config.from_object(config[config_name])\\n app.config.from_pyfile('config.py')\\n \\n # Initialize the cache using the config params\\n cache = Cache(\\n app.config['NUMBER_OF_SLOTS'], \\n app.config['TIME_TO_LIVE'], \\n EvictionStrategies(app.config['EVICTION_POLICY'])\\n )\\n CacheApi.initialize_cache(cache)\\n\\n # Set the routes\\n \\n api.add_resource(CacheApi, '/object/')\\n\\n return app\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2150e2dc9809741e315158ab94b40be0\",\n \"score\": \"0.524874\",\n \"text\": \"def _init_cache(self):\\n self.cache = Cache()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d53deeaee79dd16889659342055f392\",\n \"score\": \"0.52353615\",\n \"text\": \"def cache_in_ram():\\r\\n\\r\\n t = cache.ram('time', lambda: time.ctime(), time_expire=5)\\r\\n return dict(time=t, link=A('click to reload', _href=URL(r=request)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cce37b55384c1355090c31bcabae321\",\n \"score\": \"0.5233077\",\n \"text\": \"def cache_controller_on_disk():\\r\\n\\r\\n t = time.ctime()\\r\\n return dict(time=t, link=A('click to reload', _href=URL(r=request)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d9e34cb1536560b88113c751ed5d066\",\n \"score\": \"0.5232964\",\n \"text\": \"def cache(context):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21781c1219feb51c4c32df6a3bdfc805\",\n \"score\": \"0.5228117\",\n \"text\": \"def copy(self) -> \\\"LRUCache\\\":\\n rv = self.__class__(self.capacity)\\n rv._mapping.update(self._mapping)\\n rv._queue.extend(self._queue)\\n return rv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25bbc181be207acf18ae5ad7ca2ac443\",\n \"score\": \"0.52091885\",\n \"text\": \"def application(self):\\n return self._application\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25bbc181be207acf18ae5ad7ca2ac443\",\n \"score\": \"0.52091885\",\n \"text\": \"def application(self):\\n return self._application\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25bbc181be207acf18ae5ad7ca2ac443\",\n \"score\": \"0.52091885\",\n \"text\": \"def application(self):\\n return self._application\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25bbc181be207acf18ae5ad7ca2ac443\",\n \"score\": \"0.52091885\",\n \"text\": \"def application(self):\\n return self._application\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2637b40860ccde8ff311f2db227d1a9\",\n \"score\": \"0.51805556\",\n \"text\": \"def synchronize_apps_cache():\\n sync_id = start_synchronization()\\n try:\\n cached_requests = get_cached_session()\\n synced_apps = _sync_golab_translations(cached_requests, force_reload = False)\\n _sync_regular_apps(cached_requests, synced_apps, force_reload = False)\\n finally:\\n end_synchronization(sync_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e881a491de4108eb2309b0abc0489a0e\",\n \"score\": \"0.51803803\",\n \"text\": \"def appinit(self):\\n return Appinit(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0955005ce38d9fb33f9c61ea62849cf5\",\n \"score\": \"0.5178926\",\n \"text\": \"def load(self):\\n return app\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa5be44577fef68e21bd261cbb8eb343\",\n \"score\": \"0.5176882\",\n \"text\": \"def cache_object(*args, **kwargs):\\n global __loop__\\n if __loop__ is None:\\n __loop__ = DefaultEventLoop(GLOBAL_NAME)\\n\\n __loop__.cache_object(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"826ff4c5dfa46ce200d716f8c979c71f\",\n \"score\": \"0.51712286\",\n \"text\": \"def cache(self) -> Path:\\n return self._impl.get_cache_path()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba34225b33d8ee6d77a88f36f6da0eb1\",\n \"score\": \"0.5167296\",\n \"text\": \"def create_app(self):\\n # Create APP and set configuration\\n app = APP\\n config = Config()\\n\\n app.config['TESTING'] = True\\n app.config['LIVESERVER_PORT'] = config.agent_api_ip_bind_port()\\n os.environ['FLASK_ENV'] = 'development'\\n\\n # Clear the flask cache\\n cache = Cache(config={'CACHE_TYPE': 'null'})\\n cache.init_app(app)\\n\\n # Return\\n return app\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea095adf9d10118bfac112c4d08bab27\",\n \"score\": \"0.5156244\",\n \"text\": \"def __get__(self, instance: RedisCache, owner: Any) -> RedisCache:\\n if self.bot:\\n return self\\n\\n if self._namespace is None:\\n error_message = \\\"RedisCache must be a class attribute.\\\"\\n log.error(error_message)\\n raise NoNamespaceError(error_message)\\n\\n if instance is None:\\n error_message = (\\n \\\"You must access the RedisCache instance through the cog instance \\\"\\n \\\"before accessing it using the cog's class object.\\\"\\n )\\n log.error(error_message)\\n raise NoParentInstanceError(error_message)\\n\\n for attribute in vars(instance).values():\\n if isinstance(attribute, Bot):\\n self.bot = attribute\\n self._redis = self.bot.redis_session\\n return self\\n else:\\n error_message = (\\n \\\"Critical error: RedisCache has no `Bot` instance. \\\"\\n \\\"This happens when the class RedisCache was created in doesn't \\\"\\n \\\"have a Bot instance. Please make sure that you're instantiating \\\"\\n \\\"the RedisCache inside a class that has a Bot instance attribute.\\\"\\n )\\n log.error(error_message)\\n raise NoBotInstanceError(error_message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecdcf580e6dadac9b79940b89047d257\",\n \"score\": \"0.5144671\",\n \"text\": \"def get_app(self):\\n return app\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecdcf580e6dadac9b79940b89047d257\",\n \"score\": \"0.5144671\",\n \"text\": \"def get_app(self):\\n return app\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6376a6e74ef6bcf425e908c4432620fd\",\n \"score\": \"0.5133707\",\n \"text\": \"def __install_cache(self) -> None:\\n if USE_CACHE:\\n CACHE_PATH.mkdir(exist_ok=True)\\n requests_cache.install_cache(\\n str(CACHE_PATH.joinpath(self.get_class_name()))\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5da1adaa6297fe43915b98a452f62088\",\n \"score\": \"0.51205796\",\n \"text\": \"def static_config(self):\\n\\n return CacheConfig(1024, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b16be2098fbca620e32869fc48ffd6ac\",\n \"score\": \"0.51037395\",\n \"text\": \"def __call__(self, environ, start_response):\\n ctx = ndb.get_context()\\n ctx.set_cache_policy(False)\\n\\n response = self.application(environ, start_response)\\n\\n ctx.clear_cache()\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5977509f45af093701976e2ade8bac2\",\n \"score\": \"0.5083971\",\n \"text\": \"def get_metadata_cache_manager():\\n global _METADATA_CACHE_MANAGER\\n return _METADATA_CACHE_MANAGER\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fc4e69a79bbf77e3395e0aaa6245980\",\n \"score\": \"0.5079018\",\n \"text\": \"def get_full_cache(self):\\n self.update_time()\\n return self._cache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25af86466512750d1c59fda9de5954c4\",\n \"score\": \"0.50715613\",\n \"text\": \"def _get_cache_obj(self, type, id):\\n cache = self._resolve_cache(type)\\n return cache.get(id, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"773e0199280cb8d13f98978e26d54757\",\n \"score\": \"0.5059492\",\n \"text\": \"def get(cls) -> MemCacheHandlerBase:\\n if not hasattr(cls, \\\"instance\\\"):\\n cls_name = cls.__class__.__name__\\n raise MemCacheHandlerError(f\\\"Before calling {cls_name}.get(), you should call {cls_name}.create() first.\\\")\\n\\n return cls.instance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03c3efd180677017fc9a9577570c04f2\",\n \"score\": \"0.5054254\",\n \"text\": \"def get_app(self):\\n if self.app is None:\\n self._register_app()\\n return self.app\",\n \"title\": \"\"\n },\n {\n \"docid\": \"153fed25f31f98f5702716cb7a2be054\",\n \"score\": \"0.5037384\",\n \"text\": \"def get_app(self):\\n return self.app\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e44ac8b2c1de1c94cd56bd19fe5aa44a\",\n \"score\": \"0.50314593\",\n \"text\": \"def __init__(self, app):\\n self._initialize_app(app)\\n self._initialize_cache(SortedDict(key=lambda p: p.date, reverse=True))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54cc4030140cae06ac04f930aa3ab44a\",\n \"score\": \"0.5023157\",\n \"text\": \"def app(self):\\n return self._app\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38c7a5050d751a7deed2a178db436be1\",\n \"score\": \"0.5017547\",\n \"text\": \"def load_cache(cache_file):\\n fd = open(cache_file)\\n cache_data = load(fd)\\n fd.close()\\n return cache_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1e74660c616410ec8202c90cda4c051\",\n \"score\": \"0.50134623\",\n \"text\": \"def get(self, url):\\n session = self.sessionmaker()\\n try:\\n cache_result = session.query(HTTPCacheContent) \\\\\\n .filter(HTTPCacheContent.url == url) \\\\\\n .one_or_none()\\n\\n # if expiration is enabled then don't return anything that is expired\\n if cache_result is not None and \\\\\\n not self.dont_expire and \\\\\\n cache_result.expire_on_dt is not None and \\\\\\n cache_result.expire_on_dt < datetime.utcnow():\\n print(\\n f\\\"URL '{url}' found in cache, but set for expiration in the past at \\\"\\n f\\\"{cache_result.expire_on_dt}, so not returned.\\\"\\n )\\n cache_result = None\\n finally:\\n session.close()\\n\\n if cache_result is None:\\n return None\\n elif cache_result.content is not None:\\n return cache_result.content\\n else:\\n assert cache_result.content_bzip2 is not None\\n return bz2.decompress(cache_result.content_bzip2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abb9359623ada198ed4ffdf06225b3fc\",\n \"score\": \"0.500541\",\n \"text\": \"def api_with_cache(epirr_id):\\n if epirr_id not in cache:\\n cache[epirr_id] = api_call_epirr(epirr_id)\\n return cache[epirr_id]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cd670ae42f6218ae3950ce5d1b15fac\",\n \"score\": \"0.50006336\",\n \"text\": \"def _get_cache(self, param, namespace = None):\\n\\n return self._get_cache_map([param], namespace).get(param, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6db3e2c53e4f23305597543bddd7e1f0\",\n \"score\": \"0.49994013\",\n \"text\": \"def get_route_cache(cls):\\n if hasattr(cls.thread_locals, 'request'):\\n request = cls.thread_locals.request\\n if not hasattr(request, 'route_cache'):\\n request.route_cache = {}\\n return request.route_cache\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d085c0843cd6a2fcaecbd641b4c7cb7\",\n \"score\": \"0.4996278\",\n \"text\": \"def get_session(cls, app: object = None) -> 'SessionStore':\\n config = get_application_config(app)\\n host = config.get('REDIS_HOST', 'localhost')\\n port = int(config.get('REDIS_PORT', '7000'))\\n db = int(config.get('REDIS_DATABASE', '0'))\\n token = config.get('REDIS_TOKEN', None)\\n cluster = config.get('REDIS_CLUSTER', '1') == '1'\\n secret = config['JWT_SECRET']\\n duration = int(config.get('SESSION_DURATION', '7200'))\\n fake = config.get('REDIS_FAKE', False)\\n return cls(host, port, db, secret, duration, token=token,\\n cluster=cluster, fake=fake)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92662aad18d3d08d9394e5a41bd4354b\",\n \"score\": \"0.4986594\",\n \"text\": \"def cache_data(self):\\n # Initialize key variables\\n result = self.data['cache_data']\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ba0b7031e90c85452d1a720f6a03a9e\",\n \"score\": \"0.49527672\",\n \"text\": \"def get_app_singleton():\\n return _singleton\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d671bb769197d57a80411b7f2fe1661\",\n \"score\": \"0.4948296\",\n \"text\": \"def get_cache(self, queue_name: str) -> dict:\\n\\n # Check queue name. Raise exception if not exists\\n self.check_queue_name(queue_name)\\n\\n # Get cache from queue\\n cache = self.queues[queue_name].get_cache()\\n return cache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b35e91efbb19a6fb8e708214fffa593b\",\n \"score\": \"0.49419135\",\n \"text\": \"def get_default_cache_ctrl():\\n\\n if settings.get(\\\"DEFAULT_CACHE\\\") is None: # missing or None\\n return CacheCtrl([])\\n\\n return make_cache_ctrl(settings[\\\"DEFAULT_CACHE\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"679b837e1b0576c33d4c25c0bed3086d\",\n \"score\": \"0.4934507\",\n \"text\": \"def _create_session(self):\\n kwargs = {\\\"connection\\\": self.memcached, \\\"ip_address\\\": self.request.remote_ip}\\n new_session = MemcachedSession(**kwargs)\\n new_session.save()\\n return new_session\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7e28d69713a7047707a1f602dceba8d\",\n \"score\": \"0.49208707\",\n \"text\": \"def save_in_cache(self):\\n memcache.set(self.code(), self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ede37ea1f20c4e14732e6445d4722ac0\",\n \"score\": \"0.49168393\",\n \"text\": \"def get_application():\\n description = '''Turbinia API server'''\\n _app = FastAPI(\\n title='Turbinia API Server', description=description, version='1.0.0',\\n license_info={\\n 'name': 'Apache License 2.0',\\n 'url': 'https://www.apache.org/licenses/LICENSE-2.0.html'\\n }, routes=router.routes)\\n return _app\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":789,"cells":{"query_id":{"kind":"string","value":"e6be7d743ec3c3a768eef75172fd2f8f"},"query":{"kind":"string","value":"find users with their username that start with letter that user has entered"},"positive_passages":{"kind":"list like","value":[{"docid":"2c1b08376ea3c1ba98ed3949f10267b2","score":"0.83855855","text":"def users(letter):\n return User.objects.filter(username__istartswith=letter)","title":""}],"string":"[\n {\n \"docid\": \"2c1b08376ea3c1ba98ed3949f10267b2\",\n \"score\": \"0.83855855\",\n \"text\": \"def users(letter):\\n return User.objects.filter(username__istartswith=letter)\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"d8e566029b9c771acb958a39552c0017","score":"0.85593385","text":"def getUsersStartingWith(letters):","title":""},{"docid":"8664fa1d80739b81e26240163caa7420","score":"0.6641168","text":"def check_username(self, username):\n match = re.findall(r\"^[a-zA-Z]+\\w+$\", username)\n if match and username == match[0] and len(username) >= 4:\n if not settings.first_run:\n query = f\"SELECT user FROM {settings.users_table}\"\n users = database.get_record(query)\n if username in users[0]:\n return None, \"Your username is taken\\n\"\n return username, \"\"\n else:\n return None, \"Username is incorrect, check the tip in the input field\\n\"","title":""},{"docid":"b0d07a24adf7d116bc2430e9a8b3be28","score":"0.6440513","text":"def search_users_byusername(self, uid):\n raise RBFatalError(\"NOT IMLEMENTED YET\")\n self.cur.execute(\n ('SELECT username, usertype, id, name, course, year, email '\n 'FROM users WHERE username LIKE %s'), ('%%%s%%' % uid, ))\n return self.cur.fetchall()","title":""},{"docid":"53d6cdc39eb7021e73e9ba4c4996a088","score":"0.64390105","text":"def searchname(partialname, maxresults=32):\n people = Person.select().where( \\\n Person.name.contains(partialname) | \\\n Person.username.contains(partialname)) \\\n .order_by(Person.username).limit(maxresults)\n return [p.username for p in people]","title":""},{"docid":"afc18538883cccf5725b2dce2f8c60a0","score":"0.6423979","text":"def usernames_in(message):\n # Don't parse usernames in the commands\n if _command_re.match(message.split(\" \", 1)[0]):\n message = message.split(\" \", 1)[1]\n\n # Strip email addresses from the message, in order to avoid matching the\n # user's domain. Also strip URLs, in order to avoid usernames in them.\n message = strip_urls(message)\n\n results = []\n for result in _username_re.finditer(message):\n if result.group(1):\n results.append(result.group(1))\n\n return results","title":""},{"docid":"fc4f8574b1f5698bd6006dda181c5d0b","score":"0.6417107","text":"def get_username(self):\n is_valid_username = False\n while not is_valid_username:\n print(\"Username: \", end=\"\")\n username = input().strip()\n user_regex = re.compile(r\"^[A-z0-9]+$\")\n is_valid_username = user_regex.match(username) is not None\n if not is_valid_username:\n print(\"{} is not a valid username\".format(username))\n # check if username already exists in the system\n if self.db.is_username_exists(username):\n is_valid_username = False\n print(\"The username {} already exists\".format(username))\n return username","title":""},{"docid":"e43cb84cbc70ae2360760c291046ee1c","score":"0.63649565","text":"def usernames(request):\n term = request.GET.get('term', '')\n query = request.GET.get('query', '')\n pre = term or query\n\n if not pre:\n return []\n if not request.user.is_authenticated():\n return []\n with statsd.timer('users.api.usernames.search'):\n profiles = (\n Profile.objects.filter(Q(name__istartswith=pre))\n .values_list('user_id', flat=True))\n users = (\n User.objects.filter(\n Q(username__istartswith=pre) | Q(id__in=profiles))\n .extra(select={'length': 'Length(username)'})\n .order_by('length').select_related('profile'))\n\n if not waffle.switch_is_active('users-dont-limit-by-login'):\n last_login = datetime.now() - timedelta(weeks=12)\n users = users.filter(last_login__gte=last_login)\n\n return [{'username': u.username,\n 'display_name': display_name_or_none(u)}\n for u in users[:10]]","title":""},{"docid":"08a2c22b67b0f2a978e7780ae7c0336d","score":"0.6349212","text":"def username_condition(environ, match):\n map_user = match.get('map_username')\n if not map_user:\n return False \n if len(map_user) < 4:\n return False\n if map_user in CONFIG.BANNED_USERNAMES:\n return False\n\n return True","title":""},{"docid":"2a13f74a9806a22a6449e0636d9524fe","score":"0.6297197","text":"def findUsers(conn, username):\n curs = dbi.dict_cursor(conn)\n curs.execute('''select * from user_accounts where username like %s ''', \n ['%' + username + '%'])\n return curs.fetchall()","title":""},{"docid":"594beefe0a7b7fdf252067fc0a2c3576","score":"0.6248869","text":"def get_usernames(keyword):\n con = get_binded_connection()\n usernames = [result[1][\"uid\"][0] for result in\\\n con.search_s(\"%s,%s\" % (settings.USERS_OU, settings.DC),\n ldap.SCOPE_SUBTREE, \"(uid=*)\", ['uid'])]\n con.unbind_s()\n return filter(lambda username: re.search(keyword, username, re.IGNORECASE), usernames)","title":""},{"docid":"2a8f38999dd1d2fd8616deec79bde372","score":"0.6243985","text":"def valid_name(username):\n return username and re.match(r'[a-zA-Z][a-zA-Z0-9_]{2,}$', username)","title":""},{"docid":"edbdd034511c2b18b1c0215116f2b0f0","score":"0.62313473","text":"def matching_user(self, name):","title":""},{"docid":"dccf8958baf7b0b08b5e164ed7102675","score":"0.6210032","text":"def check_username(cls, username):\n return cls.db.keys('user:' + username)","title":""},{"docid":"bb3730a4ec076f9fc8d1347cddd7de83","score":"0.6201446","text":"def check_username(username):\n\n usernames = db.execute(\"SELECT * FROM users WHERE username = :username\", username=username)\n\n return usernames","title":""},{"docid":"c5acc83ec17386263af6a5d412e0fd45","score":"0.616268","text":"def search_users_byname(self, name):\n raise RBFatalError(\"NOT IMLEMENTED YET\")\n return self.search_users('name ILIKE', name)","title":""},{"docid":"cc1ef7ea82d61048aa09dd3ed3e44c15","score":"0.6128579","text":"def search_usernames(self, query):\n\t\tresults = self.t.users.search(q=query)\n\t\tusernames = [(user['name'], user['screen_name']) for user in results]\n\t\treturn usernames","title":""},{"docid":"df9359dba65ecb00cd096116cbc81acd","score":"0.60037506","text":"def test_user_is_a_johnny(self):\n one_user = User.objects.first()\n self.assertTrue(one_user.username.startswith('Johnny'))","title":""},{"docid":"c06d376bfd8ed8b7d44e8d44ecd462c9","score":"0.5999096","text":"def validate_username(self, username_field):\n if re.match(r'[A-Z]', username_field.data):\n raise ValidationError(\"you only can using lowercase for username\")\n \n if User.query.filter_by(username=username_field.data).first():\n raise ValidationError(\"username already registered, try another one\")","title":""},{"docid":"0d3ad88fbc1aaab9ad1becb48ae653c5","score":"0.5974542","text":"def search_users(self, where, var):\n raise RBFatalError(\"NOT IMLEMENTED YET\")\n self.cur.execute(\n ('SELECT username, usertype, id, name, course, year, email '\n 'FROM users WHERE ') + where + ' %s', ('%%%s%%' % var, ))\n return self.cur.fetchall()","title":""},{"docid":"4b384e75efcc1d67949f0ef8c80505b6","score":"0.5971448","text":"def search_for_users(self, query: str) -> list:","title":""},{"docid":"a02b78a0e0147084bb2f8d657650deb0","score":"0.59693974","text":"def search(request):\n global unknow_user_list\n unknow_user_list = []\n user_name = request.POST['res_user']\n first = user_name.split(' ')[0]\n second = user_name.split(' ')[1]\n for i in SCUser.objects.filter(first_name=first,last_name=second)[:]:\n unknow_user_list.append(i)","title":""},{"docid":"ffd486a999e8dc1924a47854a645327c","score":"0.59549856","text":"def valid_username(username):\n return username and USER_RE.match(username)","title":""},{"docid":"00be54a5087127aa2f0dc169f7128251","score":"0.5947252","text":"def userPrefix(self):\n return self.userName[0].lower()","title":""},{"docid":"368ecd47b076d6bda76aab3a613cba06","score":"0.5938804","text":"def username(Username):\n\treturn Username.strip().lower().replace(\"_\", \"\").replace(\" \", \"-\")","title":""},{"docid":"717f9b0b7330473e2adc7cf854da0e6e","score":"0.59299797","text":"def user_searcher(self, bot, name: str, max_users=5) -> List[any]:\n username, discriminator = self.username_parser(name)\n if discriminator:\n users = [\n x\n for x in bot.get_all_members()\n if x.name.lower() == username.lower()\n and x.discriminator == discriminator\n ]\n else:\n users = [\n x for x in bot.get_all_members() if x.name.lower() == username.lower()\n ]\n if not users:\n name = re.escape(name)\n users = [\n x\n for x in bot.get_all_members()\n if re.search(name.lower(), x.display_name.lower())\n ]\n if len(users) > max_users:\n raise ValueError(\"Too many users returned.\")\n return users","title":""},{"docid":"49e9d832ff89507a1c9d784f031875b5","score":"0.5920381","text":"def find_account(cls,username):\n for account in cls.users:\n if account.firstName == username:\n return account","title":""},{"docid":"cf9af93bf0b1284fc4bd295ec2b5eef8","score":"0.5917308","text":"def _findUserNameCLEARCHAT(self):\n\t\ttry:\n\t\t\tuserName = re.sub('[^a-zA-Z0-9_]','', self.data.split(':')[2])\n\t\t\treturn userName\n\t\texcept:\n\t\t\treturn \"error.finding.userName\"","title":""},{"docid":"cb6ee67c968d2081cd7cd8f23dba9b5f","score":"0.59135354","text":"def clean_username(self):\n if not ALNUM_REGEXP.search(self.cleaned_data['username']):\n raise forms.ValidationError(_(u'Uživatelské jméno může obsahovat pouze písmena, číslice a podtržítko'))\n try:\n user = User.objects.get(username__iexact=self.cleaned_data['username'])\n except User.DoesNotExist:\n return self.cleaned_data['username']\n raise forms.ValidationError(_(u'Toto uživatelské jméno už je zabráno'))","title":""},{"docid":"45977a361e20948d23517a0937d6ba6b","score":"0.5911364","text":"def test_contains_lowercase(self):\n assert re.search(\"[a-z]\", PasswordGenerator().create())","title":""},{"docid":"1747b598c0bd18602f0a68ac2c4399ca","score":"0.58801764","text":"def userfilter(text):\n return stringutil.shortuser(text)","title":""},{"docid":"df0d9d1c946f81589b520c6d0eb77721","score":"0.5878371","text":"def get_user_input():\r\n valid_inputs = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't',\r\n 'u', 'v', 'w', 'x', 'y', 'z', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N',\r\n 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', ' ']\r\n username = input()\r\n if len(username) < 1:\r\n print(\"Name must be at least one letter. Please enter another name:\")\r\n username = get_user_input()\r\n for letter in username:\r\n if letter not in valid_inputs:\r\n print(\"Username can only contain letters. Please enter another name:\")\r\n username = get_user_input()\r\n return username","title":""},{"docid":"009a78a17b2eecd6200119ecaf2d1859","score":"0.58749855","text":"def validate_username(self, username):\n regex = re.compile(r'[@_!#$%^&*()<>?/\\|}{~:]')\n check_username = User.objects.filter(username=username)\n if check_username.exists():\n raise serializers.ValidationError(\"Provided username already exist, please provide a different one\")\n elif regex.search(username):\n raise serializers.ValidationError(\"should not contain special characters @_!#$%^&*()<>?/\\|}{~:\")\n elif len(username.split()) > 1:\n raise serializers.ValidationError(\n \"Username should not contain spaces\"\n )","title":""},{"docid":"dfebcbddfded50c6d9ec60be675c99bd","score":"0.587404","text":"def clean_username(self):\n username = self.cleaned_data[\"username\"]\n\n if Albergatore.objects.filter(username=username).exists():\n raise forms.ValidationError('Username non disponibile!')\n else:\n return username","title":""},{"docid":"31b35357c4c9938c12a2a94fd81842d0","score":"0.5864838","text":"def find_user(self):\n users = []\n place = -1\n not_found = True\n # until a user is chosen\n while not_found:\n name = raw_input(\"Name: \")\n users = self.searchForUsers(name)\n # Printout 4 best matches\n for i, user in enumerate(users[:4], 1):\n print(f'#{i} {user.name}')\n place = int(input(\"Enter number, in case the user is not listed, press enter and try their name again:\"))\n if 0 <= place <= 3:\n not_found = False\n return users[place - 1]","title":""},{"docid":"3d070a3ee437c821903d36715620acb2","score":"0.58620286","text":"def username_validator(form, field):\n username = field.data\n if len(username) < 3:\n raise ValidationError(_('Username must be at least 3 characters long'))\n valid_chars = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789-._'\n chars = list(username)\n for char in chars:\n if char not in valid_chars:\n raise ValidationError(_(\"Username may only contain letters, numbers, '-', '.' and '_'.\"))","title":""},{"docid":"6480d21680c8dd6952e8aea6c8929864","score":"0.5858849","text":"def clean_username(self):\r\n existing = User.objects.filter(username__iexact=self.cleaned_data['username'])\r\n if existing.exists():\r\n raise forms.ValidationError(_(\"O email já se encontra registado.\"))\r\n else:\r\n return self.cleaned_data['username']","title":""},{"docid":"bb509753ebe1a402b4655be04aca08b9","score":"0.585632","text":"def check():\n userNames = db.execute(\"SELECT username FROM users\")\n userName = request.args.get(\"username\")\n search = {'username' : userName}\n if search in userNames:\n print(\"user name found\")\n return jsonify(\"false\")\n return jsonify(\"TODO\")","title":""},{"docid":"c50cb820f73822e2b0f5cc90572b7b68","score":"0.5852539","text":"def valid_username(self, username):\n return self.USER_RE.match(username)","title":""},{"docid":"4695547fd1518cdc497f17e06805a09c","score":"0.58498293","text":"def valid_username(self, username):\n return username and USER_RE.match(username)","title":""},{"docid":"97828fd3b7f52be6efdb1d914cf15d1b","score":"0.584555","text":"def get_username(infile):\n username = 0\n myfile = open(infile, mode=\"r\", encoding=\"latin_1\")\n what_to_look = 'Uploaded by '\n for line_username in myfile:\n if what_to_look in line_username:\n username_length = line_username.find('')\n username = line_username[58:username_length]\n print(\"Username: \" + username.strip())\n return username.strip()\n myfile.close()","title":""},{"docid":"8b086f5cca7f18c70d9deeb692a32faf","score":"0.58317703","text":"def process_username(self, value) -> str:","title":""},{"docid":"0cde03d4228e8685827660495aa1a6b1","score":"0.58283216","text":"def clean_username(self):\n\t\tusername = self.cleaned_data['username']\n\t\tif not re.search(r'^\\w+$', username):\n\t\t\traise forms.ValidationError ('Username can only contain '\n\t\t\t'alphanumeric characters and the underscore.')\n\t\ttry:\n\t\t\tUser.objects.get(username=username)\n\t\texcept User.DoesNotExist:\n\t\t\treturn username\n\t\traise forms.ValidationError('Username already taken.')","title":""},{"docid":"886622e20a72c5ffc26f7711c383fbfc","score":"0.5823204","text":"def clean_username(self):\n existing = Users.objects.filter(username=self.cleaned_data['username'])\n if existing.exists():\n raise forms.ValidationError(_('A user with that username already exists.'))\n else:\n return self.cleaned_data['username']","title":""},{"docid":"e27cb3f8ef14d1b9c8665d9cd4f0f134","score":"0.5819259","text":"def handle_search(request):\n\n query = request.GET.get('query')\n if query:\n users = User.objects.filter(username__icontains=query\n ).values_list('username', flat=True\n ).order_by('profile__score')\n else:\n users = User.objects.order_by('profile__score').values_list('username', flat=True)\n\n users = list(users[:20])\n # Return list of users matching username\n return ajax_success(users=users, msg=\"Username searched\")","title":""},{"docid":"402f6542db3a31a58cd1343b3fc307c8","score":"0.5805711","text":"def validate_username(cls, username, skip_uniqueness_check=False):\n un = username.lower()\n\n if (un in Config['blocked_usernames']\n or any(fragment in un for fragment in Config['blocked_username_fragments'])\n or any(fragment in un for fragment in Config['autoflag_words'])):\n return \"Sorry, this username is not allowed.\"\n\n if not un:\n return \"Please enter a username.\"\n elif len(un) < 3:\n return \"Username must be 3 or more characters.\"\n elif len(un) > 16:\n return \"Username must be 16 characters or less.\"\n\n alphabet = string.lowercase + string.uppercase + string.digits + '_'\n if not all(char in alphabet for char in username):\n return \"Usernames can only contain letters, digits and underscores.\"\n\n if not skip_uniqueness_check:\n if cls.objects.filter(username__iexact=username):\n return \"Sorry! This username is taken. Please pick a different username.\"","title":""},{"docid":"ccf82e5c207b54640641b69971396aec","score":"0.5800986","text":"def clean_username(self):\n username = self.cleaned_data['username'].lower()\n if (User.objects.filter(username__iexact=username).exists() or\n username in settings.ACCOUNT_USERNAME_BLACKLIST):\n raise ValidationError(_('A user with that username already exists.'))\n return username","title":""},{"docid":"8b2719c2a52bb1843d98111dca318d8e","score":"0.5790296","text":"def get_users_like(self, name):\n name_length = len(name)\n if name_length > USER_ALL_NAME_LENGTH_MAX_WITH_DISCRIMINATOR:\n return []\n \n users = self.users\n \n # name with discriminator\n \n name_with_discriminator = _parse_name_with_discriminator(name)\n if (name_with_discriminator is not None):\n for user in users.values():\n if _is_user_matching_name_with_discriminator(user, name_with_discriminator):\n return [user]\n \n if name_length > USER_ALL_NAME_LENGTH_MAX:\n return []\n \n user_name_pattern = re_compile('.*?'.join(re_escape(char) for char in name), re_ignore_case)\n matches = []\n guild_id = self.id\n \n for user in users.values():\n # name\n \n parsed = user_name_pattern.search(user.name)\n if (parsed is not None):\n match_start = parsed.start()\n match_length = parsed.end() - match_start\n \n match_rate = (USER_MATCH_WEIGHT_NAME, match_length, match_start)\n \n matches.append((user, match_rate))\n continue\n \n # display_name\n \n user_display_name = user.display_name\n if (user_display_name is not None):\n parsed = user_name_pattern.search(user_display_name)\n if (parsed is not None):\n match_start = parsed.start()\n match_length = parsed.end() - match_start\n \n match_rate = (USER_MATCH_WEIGHT_DISPLAY_NAME, match_length, match_start)\n \n matches.append((user, match_rate))\n continue\n \n # nick\n \n try:\n guild_profile = user.guild_profiles[guild_id]\n except KeyError:\n pass\n else:\n user_nick = guild_profile.nick\n if (user_nick is not None):\n parsed = user_name_pattern.search(user_nick)\n if (parsed is not None):\n match_start = parsed.start()\n match_length = parsed.end() - match_start\n \n match_rate = (USER_MATCH_WEIGHT_NICK, match_length, match_start)\n \n matches.append((user, match_rate))\n continue\n \n \n return [item[0] for item in sorted(matches, key = _user_match_sort_key)]","title":""},{"docid":"b9555fdcc9e54fd5f5f4380bc80d68d4","score":"0.57825446","text":"def validate_username_field(value):\n msg = []\n if value:\n if not value.isalnum() and \"_\" not in value:\n msg.append(\n \"field mustn't contain any special character other than _\")\n if not ((re.search(\"[a-z]\", value))or(re.search(\"[A-Z]\", value))):\n msg.append(\"field must contain atleast one character\")\n if len(msg) > 0:\n raise serializers.ValidationError(msg)","title":""},{"docid":"a35b0a5cfdaf998533757ab81ef80b7a","score":"0.5759538","text":"def validate_username(username: str) -> bool:\n return True if re.match(\"^([a-zA-Z0-9]|\\.){6,32}$\", username) else False","title":""},{"docid":"bccfcb1a4faa69fafb35985b1726fcc8","score":"0.5750044","text":"def sanity_check_username(name):\n VALID_CHARACTERS = string.ascii_letters+string.digits+\"_-.\"\n rules = [ \n len(name) > 3, # User name is longer than 3 characters\n all([k in VALID_CHARACTERS for k in list(name)]) # Username is made of valid characters\n ]\n return all(rules)","title":""},{"docid":"73419325271abbbdc5c616ca0cd524bf","score":"0.574605","text":"def usersname_length(username):\n try:\n username = str(username)\n # if username.length() > 3 and < 21:\n if (len(username) >= 3) and (len(username) < 21):\n return True\n except ValueError:\n return False","title":""},{"docid":"901627988b4967b6959f5399749d0ce6","score":"0.57419056","text":"def is_valid_username(u_name):\n if not re.match(r\"^[A-Za-z\\.\\+_-]*$\", u_name) != None:\n return False\n return True","title":""},{"docid":"921e0723b5599cc772717ee38636de85","score":"0.57408136","text":"def findUsersByName(conn, name):\n query = \"SELECT * FROM users WHERE name LIKE %s\"\n params = [\"%\" + name + \"%\"]\n return getSqlQuery(conn, query, params)","title":""},{"docid":"11dd260e48c8b537315631ad539f4f7a","score":"0.5739077","text":"def clean_username(self):\n username = self.cleaned_data[\"username\"]\n if not Albergatore.objects.filter(username=username).exists():\n raise forms.ValidationError(\"Username non registrato\")\n else:\n return username","title":""},{"docid":"10457fa52cefb6a629aa87718148e306","score":"0.5726845","text":"def check_user_name(self, username):\n query = \"SELECT username FROM users WHERE username = '%s'\" % (username)\n result = self.fetch_single_data_row(query)\n if result is None:\n return False\n return True","title":""},{"docid":"e0f0e453b2520b68c9cb1c756d008987","score":"0.57148904","text":"def check_valid_username(self, username : str):\n\t\treturn True","title":""},{"docid":"c9c54c91fd6a80bbb5edc552561ef5b2","score":"0.57078695","text":"def security_check_username(self, bot, update):\n\n full_name = (update.message.from_user.first_name + \" \"\n + update.message.from_user.last_name)\n if self.name_ban_re and self.name_ban_re.search(full_name):\n # Logging\n log_message = \"Ban match full name: {}\".format(full_name.encode('utf-8'))\n logger.info(log_message)\n for notify_user_id in self.notify_user_ids:\n logger.info(notify_user_id,\"gets notified\")\n bot.send_message(\n chat_id=notify_user_id,\n text=log_message)\n # Ban the user\n self.ban_user(update)\n # Log in database\n\n if self.name_ban_re and self.name_ban_re.search(update.message.from_user.username or ''):\n # Logging\n log_message = \"Ban match username: {}\".format(update.message.from_user.username.encode('utf-8'))\n logger.info(log_message)\n for notify_user_id in self.notify_user_ids:\n bot.send_message(\n chat_id=notify_user_id,\n text=log_message)\n # Ban the user\n self.ban_user(update)","title":""},{"docid":"60497a226d11be3cafe143f5f4baf5a3","score":"0.56966424","text":"def clean_username(self):\r\n username = self.cleaned_data[\"username\"]\r\n if '@' in username:\r\n # The user entered an email, so try to log them in by e-mail\r\n emails = ContactValue.objects.filter(value=username,\r\n field__field_type='email',\r\n contact__trash=False,\r\n contact__related_user__isnull=False)\r\n users = [email.contact.related_user.user for email in emails]\r\n else:\r\n users = User.objects.filter(user__username=username)\r\n if len(users) == 0:\r\n raise forms.ValidationError(\r\n _(\"Sorry, we don't know that user or e-mail.\"))\r\n else:\r\n username = users[0]\r\n return username","title":""},{"docid":"c1353c764d75f75d8f8558f24aaa1ab2","score":"0.5692759","text":"def clean_username(self):\n try:\n user = User.objects.get(username__iexact=self.cleaned_data['username'])\n except User.DoesNotExist:\n return self.cleaned_data['username']\n raise forms.ValidationError(_(\"A user with that username already exists.\"))","title":""},{"docid":"c3de59944fb73806d9a0d76fdef129df","score":"0.5677581","text":"def auto_complete_model(request):\n if request.is_ajax():\n searchString=request.POST[\"search\"]\n search_qs = User.objects.filter(username__startswith=searchString)\n print search_qs\n results = []\n for r in search_qs:\n results.append(r.username)\n print results\n return HttpResponse(json.dumps(results), content_type=\"application/json\", status=200)","title":""},{"docid":"a5e0005f4d22a3f09122843f9e940b11","score":"0.5675287","text":"def is_letter_in_pattern(user_input, word):\r\n for letter in list(word):\r\n if user_input == letter:\r\n return True\r\n return False","title":""},{"docid":"7de1e6e4e7af2ec958fd08cc5adad7c4","score":"0.56643456","text":"def get_userName():\n userName = input(\"Tapez Votre nom d'utilisateur: \")\n\n userName = userName.capitalize() # 1st letter in cap\n\n if not userName.isalnum() or len(userName)<4:\n\n print(\"Nom invalide\")\n return get_userName()\n\n else:\n return userName","title":""},{"docid":"11bb76360b33e6a116242866fab5edd2","score":"0.5660173","text":"def _check_username(self, username):\n # Username cannot contain spaces\n if ' ' in username:\n return False\n else:\n return True","title":""},{"docid":"dc451def748fc8dda4ebad1e3f2dd02c","score":"0.5657971","text":"def clean_username(self):\n username = self.cleaned_data.get('username')\n if len(username) < 3:\n raise forms.ValidationError(\"001,Your username must be longer than 3 words.\")\n elif len(username) > 20:\n raise forms.ValidationError(\"002,Your username must be shorter than 20 words.\")\n else:\n\n #To check whether the username already exists\n \n checkresult = User_Info.objects.filter(username=username)\n if len(checkresult) > 0:\n raise forms.ValidationError(\"003,Your username already exists.\")\n return username","title":""},{"docid":"0411422ddb7c66213d92669fbfea4606","score":"0.5654673","text":"def find(word, letter, start):\n\n index = start\n hits = []\n\n while index < len(word):\n if word[index].lower() == letter.lower():\n hits.append(index)\n index += 1\n\n return(hits)","title":""},{"docid":"fe06db84edc8d4419919a5d393e0d0da","score":"0.5648181","text":"def user_name():\n while 1:\n username = input(\"enter username, please: \")\n if not len(username) == 0:\n return username","title":""},{"docid":"9c82479f2897275c5c78a4cb5bfe4b88","score":"0.56426215","text":"def clean_username(self, username):\n return username","title":""},{"docid":"58c8af63dd87814589e29192c85d4b4c","score":"0.5637166","text":"def clean_username(self):\n username = self.cleaned_data['username']\n if User.objects.filter(username=username):\n raise forms.ValidationError('Este correo electronico ya existe')\n return username","title":""},{"docid":"1e7833e36e5978bb94e34dd72ed08206","score":"0.5629253","text":"def _contains_lowercase_name(self, name):\n return name in (x.lower() for x in self._distinct())","title":""},{"docid":"4746a4438fcd4d437bbb5a83cd66e336","score":"0.56258893","text":"def userSearch(self, username, pwd=\"abc\", usneed=False):\n try:\n self.cur.execute('''\n SELECT * FROM \"Student\"\n WHERE \"UserName\" = ?''', (username,))\n\n rows = self.cur.fetchall()\n # print(rows)\n if usneed:\n return rows[0][1]\n elif len(rows) == 0:\n return \"Empty\" # if no account yet created or wrong username\n elif len(rows) == 1 and rows[0][4] == pwd:\n return True\n return False # if wrong password inserted\n except Exception as e:\n print(\"userSearch(), error -\", e)\n self.con.close()\n return -1","title":""},{"docid":"cc295b480fcf5c6b853d0df7ea8f1d4f","score":"0.561518","text":"def _is_first_name(self, word):\n names = self._first_name_list\n word = word.lower()\n ind = bisect.bisect_left(names, word)\n if ind < len(names) and names[ind] == word:\n return True\n return False","title":""},{"docid":"0d71da97eb837b9720e897203c7687db","score":"0.56023335","text":"def test_get_user_by_username_r(self):\n pass","title":""},{"docid":"280fe52791e9ae728929c968bfb34157","score":"0.5601374","text":"def filter_user(tweets):\n \n return tweets.str.replace('', '', case=False)","title":""},{"docid":"801fc57aa073f025190e7da688415a5e","score":"0.559805","text":"def _findUserNamePRIVMSG(self):\n\t\ttry:\n\t\t\tuserName = re.sub('[^a-zA-Z0-9_]','', self.data.split('!')[0])\n\t\t\treturn userName\n\t\texcept:\n\t\t\treturn \"error.finding.userName\"","title":""},{"docid":"f6d7f959bb24b39ec9fb5bb435b9539a","score":"0.55943894","text":"def search_for_letters(phrase:str,letters:str='aeiou') ->set:\r\n return set(letters).intersection(set(phrase))","title":""},{"docid":"38c88ffee191a5c1841c81b8482b5ccf","score":"0.558165","text":"def get_service_usernames():\n usernames = [k[:-len('_passwd')] for k in leader_get()\n if k.endswith('_passwd') and k != 'admin_passwd']\n # now match against the service list.\n valid_service_names = valid_services.keys()\n known_service_usernames = []\n for username in usernames:\n # if a username has '_' in it, then it is a compound name.\n parts = username.split('_')\n if 'keystone' in parts:\n continue\n if not all(p in valid_service_names for p in parts):\n continue\n known_service_usernames.append(username)\n return known_service_usernames","title":""},{"docid":"39cb53ddf55ec2a3546f1b818d47090e","score":"0.5574706","text":"def clean_username(self):\n username = self.cleaned_data['username']\n if User.objects.filter(username=username):\n raise forms.ValidationError('Nombre de usuario ya registrado.')\n return username","title":""},{"docid":"1d4427c955f075306234e38ed62c4af2","score":"0.5553871","text":"def generate_username(name):\n username = ''.join(name.split(' ')).lower()\n if not User.objects.filter(username=username).exists():\n return username\n else:\n random_username = username + str(randint(0, 1000))\n return generate_username(random_username)","title":""},{"docid":"aa644612e55a8ebbd61835fc8da001c0","score":"0.5548313","text":"def parse_name(guild, username):\r\n if '@' in username:\r\n try:\r\n return guild.get_member(int(username[3:-1]))\r\n except:\r\n raise NameError(username)\r\n else:\r\n return get_member_from_guild(guild.members, username)","title":""},{"docid":"8970f68548e77c567d874853c18c5454","score":"0.55467904","text":"def check():\r\n username = request.args.get(\"username\")\r\n if len(username) >= 1:\r\n usernames = [dct[\"username\"] for dct in db.execute(\"SELECT username FROM users\")]\r\n if username not in usernames:\r\n return jsonify(True)\r\n else:\r\n return jsonify(False)","title":""},{"docid":"d7f90c6d8429bb1d64141f7955edbd74","score":"0.55457246","text":"def username_exists_validator(value):\n try:\n User.objects.get(username=value)\n\n except User.DoesNotExist:\n raise ValidationError(_('Requested username does not exist.'))","title":""},{"docid":"e76690e229888ca6a3b5515f7b6a088f","score":"0.55418396","text":"def validate_username(cls, nick):\n if nick and nick != \"\":\n nick = nick.strip()\n if len(nick) > 0:\n if re.match(\"^[A-z0-9_\\-]+$\", nick):\n return nick, \"\"\n else:\n return \"\", \"Username can only contain letters, numbers, underscores, and dashes.\"\n return \"\", \"Username must not be empty.\"","title":""},{"docid":"566aa32677a8d6f412d11e734703c54a","score":"0.55270416","text":"def check_username_lower_limit(username):\n if len(username) >= 4:\n return True","title":""},{"docid":"8286201ec8571426d3ce568b4ff7d700","score":"0.55268127","text":"def get_by_natural_key(self, username):\r\n anycase_username_field = f'{self.model.USERNAME_FIELD}__iexact'\r\n return self.get(**{anycase_username_field: username})","title":""},{"docid":"40e99474b264ed23dbb9d1046298888f","score":"0.552202","text":"def check_username(cls, uid):\n if not uid:\n raise RBFatalError('Username must be given')\n if re.search(r'[^a-z0-9_.-]', uid):\n raise RBFatalError(\"Invalid characters in username\")\n if len(uid) > rbconfig.maxlen_uname:\n raise RBFatalError(\"Username can not be longer than %d characters\"\n % rbconfig.maxlen_uname)\n if re.search(r'^[^a-z0-9]', uid):\n raise RBFatalError(\"Username must begin with letter or number\")","title":""},{"docid":"bf3b9bd84177442eda8c6433534e636c","score":"0.5512801","text":"def validate_username(self, username):\n # onyl allow alphanumeric character, . _ -\n if username is not None:\n pattern = r\"^[a-zA-Z0-9_.-]+$\"\n if len(username) < 1:\n raise ValidationError('Invalid username, minimum is 1 character')\n if len(username) > 32:\n raise ValidationError('Invalid username, max is 32 character')\n if re.match(pattern, username) is None:\n raise ValidationError('Invalid username, only alphanumeric, . _ - allowed')","title":""},{"docid":"28fd7d5e2650faf027dced619ce0ed7e","score":"0.5505782","text":"def get_first_name(self):\n is_valid_first_name = False\n while not is_valid_first_name:\n print(\"First Name: \", end=\"\")\n fname = input().strip()\n fn_regex = re.compile(r\"^[A-z]+$\")\n is_valid_first_name = fn_regex.match(fname) is not None\n if not is_valid_first_name:\n print(\"{} is not a valid first name\".format(fname))\n return fname","title":""},{"docid":"d677b66b8bcbcc5abf29fbca1af46b39","score":"0.54982305","text":"def lookup_by_letter(letter):\n letterlist = []\n if len(letter) == 1 and letter.isalpha(): #tests if input is a letter and only one letter\n letter = letter.capitalize() #allows for case insensitivity, makes uppercase to match dictionary keys\n for i in name_dict.keys():\n if i[0] == letter:\n letterlist.append(i)\n letterlist.sort()\n return (letterlist)\n else:\n return (\"Invalid input\")","title":""},{"docid":"451862806c0c5103f25ac4f009c789e5","score":"0.54910004","text":"def checkUsername(self, username, cursor, UserAccount):\n \n query = \"SELECT username FROM accounts WHERE username = %s\"\n cursor.execute(query, (username,))\n result = cursor.fetchall()\n\n while result:\n print(\"\\n\\n\\n\\n\\nSorry that username is taken\\n\")\n username = input(\"Please select another username: \")\n query = \"SELECT username FROM accounts WHERE username = %s\"\n cursor.execute(query, (username,))\n result = cursor.fetchall()\n UserAccount.username = username","title":""},{"docid":"bcaf8476203abba535b097330b03e65a","score":"0.54815954","text":"def UIDStartsWith(uid: Text, prefix: Text) -> bool:\n return uid.find(prefix + '.') == 0","title":""},{"docid":"e9c7c4389e4800fba66a37280252c525","score":"0.5481196","text":"def get_user_by_username_or_user_id(cls, string):\n username = str(string).replace(\"@\", \"\").strip().lower()\n if username.isdigit(): # user_id\n return cls.objects.filter(user_id=int(username)).first()\n return cls.objects.filter(username__iexact=username).first()","title":""},{"docid":"ec61766f47b6fa904df432ea8eee3942","score":"0.54762703","text":"def find_person(self, name=None):\n try:\n if name != '' or name != ' ':\n fname = lname = ''\n if ' ' in name:\n fname = name.split(' ')[0]\n lname = name.split(' ')[1]\n else:\n fname = lname = name\n return (x.first_name for x in\n self.user.select().where(Person.last_name.contains(lname) & Person.first_name.contains(fname)))\n except Exception as ex:\n logging.error(\"Error in searching Person\" + str(ex))","title":""},{"docid":"880446522c0701f47fb1caa6d64f0ad7","score":"0.5466264","text":"def searchUser(self, nick=None):\n with open('/usr/local/apache2/cookies.csv', 'r') as csvFile:\n reader = csv.reader(csvFile)\n for row in reader:\n if nick == row[0]:\n return True\n return False","title":""},{"docid":"6183092b076a2e135b74342a8d89997e","score":"0.54581034","text":"def clean_username(self):\n\t\tusername = self.cleaned_data['username']\n\t\tusername = username.strip()\n\t\tif not username:\n\t\t\traise ValidationError('Nickname mein harf likhna zaruri hain')\n\t\tvalidate_nickname_chars(username)\n\t\tif username[:1] == '.':\n\t\t\traise ValidationError('Nickname ke shuru mein . nah dalein')\n\t\tif username[-1:] == '.':\n\t\t\traise ValidationError('Nickname ke akhir mein . nah dalein')\n\t\texists = nick_already_exists(nickname=username)\n\t\taltered = {}\n\t\tif exists is None:\n\t\t\t#the redis DB is compromised, use PSQL DB. Check nick against DB, that's it\n\t\t\tif ' ' in username:\n\t\t\t\tusername_original = username\n\t\t\t\tusername = ''.join(username.split())\n\t\t\t\taltered = {'status':'joined'}\n\t\t\t\tif User.objects.filter(username__iexact=username).exists():\n\t\t\t\t\traise ValidationError('\"%s\" kisi aur ka nickname hai. Kuch aur likhein' % username_original)\n\t\t\telse:\n\t\t\t\tif User.objects.filter(username__iexact=username).exists():\n\t\t\t\t\traise ValidationError('\"%s\" kisi aur ka nickname hai. Kuch aur likhein' % username)\n\t\t\treturn [username], altered, username\n\t\t############################################\n\t\telse:\n\t\t\t# form variants and suggestions\n\t\t\t# check al against redis DB\n\t\t\tif ' ' in username:\n\t\t\t\talternatives = form_variants(username) #returns list of tuples containing variants and their statuses\n\t\t\t\talt_choices = process_choices(alternatives)\n\t\t\t\tif not alt_choices:\n\t\t\t\t\t# no suggestions could be unearthed\n\t\t\t\t\traise ValidationError('\"%s\" kisi aur ka nickname hai. Kuch aur likhein' % username)\n\t\t\t\telse:\n\t\t\t\t\t# some suggestions unearthed\n\t\t\t\t\taltered = {'status':'joined'}\n\t\t\t\t\treturn alt_choices, altered, username\n\t\t\telse:\n\t\t\t\tif exists:\n\t\t\t\t\t# nick is not available\n\t\t\t\t\talternatives = form_suggestions(username) #returns list of tuples containing suggestions and their statuses\n\t\t\t\t\talt_choices = process_choices(alternatives)\n\t\t\t\t\tif not alt_choices:\n\t\t\t\t\t\traise ValidationError('\"%s\" kisi aur ka nickname hai. Kuch aur likhein' % username)\n\t\t\t\t\telse:\n\t\t\t\t\t\taltered = {'status':'replaced'}\n\t\t\t\t\t\treturn alt_choices, altered, username\n\t\t\t\telse:\n\t\t\t\t\t#nick is available\n\t\t\t\t\t# altered = False\n\t\t\t\t\treturn [username], altered, username","title":""},{"docid":"00e4c3f38dce907d0d53bcbc58efa430","score":"0.54509556","text":"def _validate_username(self):\n username = self.cleaned_data.get(\"username\")\n\n if not username:\n return\n\n if settings.ST_CASE_INSENSITIVE_USERNAMES:\n username = username.lower()\n\n is_found = (\n User.objects\n .filter(username=username)\n .exists())\n if is_found:\n return\n\n if settings.ST_CASE_INSENSITIVE_EMAILS:\n username = username.lower()\n\n is_found_email = (\n User.objects\n .filter(email=username)\n .exists())\n if is_found_email:\n return\n\n raise forms.ValidationError(\n _(\"No account matches %(username)s.\") % {\n 'username': username})","title":""},{"docid":"9db4028ad184739fd82612b64ce1df4d","score":"0.5444739","text":"def fuzzy_nickname_search(request):\n logger('views', 'main: {}'.format(request.json_body))\n return fuzzy_string_matcher.get_nicknames(request.validated['user'], request.validated['value'])","title":""},{"docid":"ddc4a07d8aba14ec188a596bc8b9f270","score":"0.5443438","text":"def searchLetters(phrase: str, letters: str = 'aeiou') -> set:\n return set(letters).intersection(set(phrase))","title":""},{"docid":"373f0e4b464d81fd513c3c7bf09ae95f","score":"0.5439793","text":"def security_check_username(self, bot, update):\n\n full_name = ((update.message.from_user.first_name or \"\") + \" \"\n + (update.message.from_user.last_name or \"\"))\n if self.name_ban_re and self.name_ban_re.search(full_name):\n # Logging\n log_message = \"Ban match full name: {}\".format(\n full_name.encode('utf-8'))\n if self.debug:\n update.message.reply_text(log_message)\n print(log_message)\n for notify_user_id in self.notify_user_ids:\n print(notify_user_id, \"gets notified\")\n bot.send_message(\n chat_id=notify_user_id,\n text=log_message)\n # Ban the user\n self.ban_user(update)\n # Log in database\n s = session()\n userBan = UserBan(\n user_id=update.message.from_user.id,\n reason=log_message)\n s.add(userBan)\n s.commit()\n s.close()\n\n if self.name_ban_re and self.name_ban_re.search(update.message.from_user.username or ''):\n # Logging\n log_message = \"Ban match username: {}\".format(\n update.message.from_user.username.encode('utf-8'))\n if self.debug:\n update.message.reply_text(log_message)\n print(log_message)\n for notify_user_id in self.notify_user_ids:\n bot.send_message(\n chat_id=notify_user_id,\n text=log_message)\n # Ban the user\n self.ban_user(update)\n # Log in database\n s = session()\n userBan = UserBan(\n user_id=update.message.from_user.id,\n reason=log_message)\n s.add(userBan)\n s.commit()\n s.close()","title":""},{"docid":"b46757792a081b3c67557f1b7341f309","score":"0.5436486","text":"def justice_league_by_real_name(real_name):\n print(\"i'm here though\")\n canonicalized = real_name.replace(\" \", \"\").lower()\n for character in justice_league_members:\n search_term = character[\"real_name\"].replace(\" \", \"\").lower()\n\n if search_term == canonicalized:\n return jsonify(character)\n\n return jsonify({\"error\": f\"Character with real_name {real_name} not found.\"}), 404","title":""},{"docid":"caded9656e227eede695bae6071645e2","score":"0.54354316","text":"def validate_first_name(self, value):\n if not all(c in string.ascii_letters for c in value.lower().replace(\" \", \"\")):\n raise serializers.ValidationError(\"First name is not valid, please enter again\")\n return value","title":""}],"string":"[\n {\n \"docid\": \"d8e566029b9c771acb958a39552c0017\",\n \"score\": \"0.85593385\",\n \"text\": \"def getUsersStartingWith(letters):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8664fa1d80739b81e26240163caa7420\",\n \"score\": \"0.6641168\",\n \"text\": \"def check_username(self, username):\\n match = re.findall(r\\\"^[a-zA-Z]+\\\\w+$\\\", username)\\n if match and username == match[0] and len(username) >= 4:\\n if not settings.first_run:\\n query = f\\\"SELECT user FROM {settings.users_table}\\\"\\n users = database.get_record(query)\\n if username in users[0]:\\n return None, \\\"Your username is taken\\\\n\\\"\\n return username, \\\"\\\"\\n else:\\n return None, \\\"Username is incorrect, check the tip in the input field\\\\n\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0d07a24adf7d116bc2430e9a8b3be28\",\n \"score\": \"0.6440513\",\n \"text\": \"def search_users_byusername(self, uid):\\n raise RBFatalError(\\\"NOT IMLEMENTED YET\\\")\\n self.cur.execute(\\n ('SELECT username, usertype, id, name, course, year, email '\\n 'FROM users WHERE username LIKE %s'), ('%%%s%%' % uid, ))\\n return self.cur.fetchall()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53d6cdc39eb7021e73e9ba4c4996a088\",\n \"score\": \"0.64390105\",\n \"text\": \"def searchname(partialname, maxresults=32):\\n people = Person.select().where( \\\\\\n Person.name.contains(partialname) | \\\\\\n Person.username.contains(partialname)) \\\\\\n .order_by(Person.username).limit(maxresults)\\n return [p.username for p in people]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afc18538883cccf5725b2dce2f8c60a0\",\n \"score\": \"0.6423979\",\n \"text\": \"def usernames_in(message):\\n # Don't parse usernames in the commands\\n if _command_re.match(message.split(\\\" \\\", 1)[0]):\\n message = message.split(\\\" \\\", 1)[1]\\n\\n # Strip email addresses from the message, in order to avoid matching the\\n # user's domain. Also strip URLs, in order to avoid usernames in them.\\n message = strip_urls(message)\\n\\n results = []\\n for result in _username_re.finditer(message):\\n if result.group(1):\\n results.append(result.group(1))\\n\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc4f8574b1f5698bd6006dda181c5d0b\",\n \"score\": \"0.6417107\",\n \"text\": \"def get_username(self):\\n is_valid_username = False\\n while not is_valid_username:\\n print(\\\"Username: \\\", end=\\\"\\\")\\n username = input().strip()\\n user_regex = re.compile(r\\\"^[A-z0-9]+$\\\")\\n is_valid_username = user_regex.match(username) is not None\\n if not is_valid_username:\\n print(\\\"{} is not a valid username\\\".format(username))\\n # check if username already exists in the system\\n if self.db.is_username_exists(username):\\n is_valid_username = False\\n print(\\\"The username {} already exists\\\".format(username))\\n return username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e43cb84cbc70ae2360760c291046ee1c\",\n \"score\": \"0.63649565\",\n \"text\": \"def usernames(request):\\n term = request.GET.get('term', '')\\n query = request.GET.get('query', '')\\n pre = term or query\\n\\n if not pre:\\n return []\\n if not request.user.is_authenticated():\\n return []\\n with statsd.timer('users.api.usernames.search'):\\n profiles = (\\n Profile.objects.filter(Q(name__istartswith=pre))\\n .values_list('user_id', flat=True))\\n users = (\\n User.objects.filter(\\n Q(username__istartswith=pre) | Q(id__in=profiles))\\n .extra(select={'length': 'Length(username)'})\\n .order_by('length').select_related('profile'))\\n\\n if not waffle.switch_is_active('users-dont-limit-by-login'):\\n last_login = datetime.now() - timedelta(weeks=12)\\n users = users.filter(last_login__gte=last_login)\\n\\n return [{'username': u.username,\\n 'display_name': display_name_or_none(u)}\\n for u in users[:10]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08a2c22b67b0f2a978e7780ae7c0336d\",\n \"score\": \"0.6349212\",\n \"text\": \"def username_condition(environ, match):\\n map_user = match.get('map_username')\\n if not map_user:\\n return False \\n if len(map_user) < 4:\\n return False\\n if map_user in CONFIG.BANNED_USERNAMES:\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a13f74a9806a22a6449e0636d9524fe\",\n \"score\": \"0.6297197\",\n \"text\": \"def findUsers(conn, username):\\n curs = dbi.dict_cursor(conn)\\n curs.execute('''select * from user_accounts where username like %s ''', \\n ['%' + username + '%'])\\n return curs.fetchall()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"594beefe0a7b7fdf252067fc0a2c3576\",\n \"score\": \"0.6248869\",\n \"text\": \"def get_usernames(keyword):\\n con = get_binded_connection()\\n usernames = [result[1][\\\"uid\\\"][0] for result in\\\\\\n con.search_s(\\\"%s,%s\\\" % (settings.USERS_OU, settings.DC),\\n ldap.SCOPE_SUBTREE, \\\"(uid=*)\\\", ['uid'])]\\n con.unbind_s()\\n return filter(lambda username: re.search(keyword, username, re.IGNORECASE), usernames)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a8f38999dd1d2fd8616deec79bde372\",\n \"score\": \"0.6243985\",\n \"text\": \"def valid_name(username):\\n return username and re.match(r'[a-zA-Z][a-zA-Z0-9_]{2,}$', username)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edbdd034511c2b18b1c0215116f2b0f0\",\n \"score\": \"0.62313473\",\n \"text\": \"def matching_user(self, name):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dccf8958baf7b0b08b5e164ed7102675\",\n \"score\": \"0.6210032\",\n \"text\": \"def check_username(cls, username):\\n return cls.db.keys('user:' + username)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb3730a4ec076f9fc8d1347cddd7de83\",\n \"score\": \"0.6201446\",\n \"text\": \"def check_username(username):\\n\\n usernames = db.execute(\\\"SELECT * FROM users WHERE username = :username\\\", username=username)\\n\\n return usernames\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5acc83ec17386263af6a5d412e0fd45\",\n \"score\": \"0.616268\",\n \"text\": \"def search_users_byname(self, name):\\n raise RBFatalError(\\\"NOT IMLEMENTED YET\\\")\\n return self.search_users('name ILIKE', name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc1ef7ea82d61048aa09dd3ed3e44c15\",\n \"score\": \"0.6128579\",\n \"text\": \"def search_usernames(self, query):\\n\\t\\tresults = self.t.users.search(q=query)\\n\\t\\tusernames = [(user['name'], user['screen_name']) for user in results]\\n\\t\\treturn usernames\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df9359dba65ecb00cd096116cbc81acd\",\n \"score\": \"0.60037506\",\n \"text\": \"def test_user_is_a_johnny(self):\\n one_user = User.objects.first()\\n self.assertTrue(one_user.username.startswith('Johnny'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c06d376bfd8ed8b7d44e8d44ecd462c9\",\n \"score\": \"0.5999096\",\n \"text\": \"def validate_username(self, username_field):\\n if re.match(r'[A-Z]', username_field.data):\\n raise ValidationError(\\\"you only can using lowercase for username\\\")\\n \\n if User.query.filter_by(username=username_field.data).first():\\n raise ValidationError(\\\"username already registered, try another one\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d3ad88fbc1aaab9ad1becb48ae653c5\",\n \"score\": \"0.5974542\",\n \"text\": \"def search_users(self, where, var):\\n raise RBFatalError(\\\"NOT IMLEMENTED YET\\\")\\n self.cur.execute(\\n ('SELECT username, usertype, id, name, course, year, email '\\n 'FROM users WHERE ') + where + ' %s', ('%%%s%%' % var, ))\\n return self.cur.fetchall()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b384e75efcc1d67949f0ef8c80505b6\",\n \"score\": \"0.5971448\",\n \"text\": \"def search_for_users(self, query: str) -> list:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a02b78a0e0147084bb2f8d657650deb0\",\n \"score\": \"0.59693974\",\n \"text\": \"def search(request):\\n global unknow_user_list\\n unknow_user_list = []\\n user_name = request.POST['res_user']\\n first = user_name.split(' ')[0]\\n second = user_name.split(' ')[1]\\n for i in SCUser.objects.filter(first_name=first,last_name=second)[:]:\\n unknow_user_list.append(i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffd486a999e8dc1924a47854a645327c\",\n \"score\": \"0.59549856\",\n \"text\": \"def valid_username(username):\\n return username and USER_RE.match(username)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00be54a5087127aa2f0dc169f7128251\",\n \"score\": \"0.5947252\",\n \"text\": \"def userPrefix(self):\\n return self.userName[0].lower()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"368ecd47b076d6bda76aab3a613cba06\",\n \"score\": \"0.5938804\",\n \"text\": \"def username(Username):\\n\\treturn Username.strip().lower().replace(\\\"_\\\", \\\"\\\").replace(\\\" \\\", \\\"-\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"717f9b0b7330473e2adc7cf854da0e6e\",\n \"score\": \"0.59299797\",\n \"text\": \"def user_searcher(self, bot, name: str, max_users=5) -> List[any]:\\n username, discriminator = self.username_parser(name)\\n if discriminator:\\n users = [\\n x\\n for x in bot.get_all_members()\\n if x.name.lower() == username.lower()\\n and x.discriminator == discriminator\\n ]\\n else:\\n users = [\\n x for x in bot.get_all_members() if x.name.lower() == username.lower()\\n ]\\n if not users:\\n name = re.escape(name)\\n users = [\\n x\\n for x in bot.get_all_members()\\n if re.search(name.lower(), x.display_name.lower())\\n ]\\n if len(users) > max_users:\\n raise ValueError(\\\"Too many users returned.\\\")\\n return users\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49e9d832ff89507a1c9d784f031875b5\",\n \"score\": \"0.5920381\",\n \"text\": \"def find_account(cls,username):\\n for account in cls.users:\\n if account.firstName == username:\\n return account\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf9af93bf0b1284fc4bd295ec2b5eef8\",\n \"score\": \"0.5917308\",\n \"text\": \"def _findUserNameCLEARCHAT(self):\\n\\t\\ttry:\\n\\t\\t\\tuserName = re.sub('[^a-zA-Z0-9_]','', self.data.split(':')[2])\\n\\t\\t\\treturn userName\\n\\t\\texcept:\\n\\t\\t\\treturn \\\"error.finding.userName\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb6ee67c968d2081cd7cd8f23dba9b5f\",\n \"score\": \"0.59135354\",\n \"text\": \"def clean_username(self):\\n if not ALNUM_REGEXP.search(self.cleaned_data['username']):\\n raise forms.ValidationError(_(u'Uživatelské jméno může obsahovat pouze písmena, číslice a podtržítko'))\\n try:\\n user = User.objects.get(username__iexact=self.cleaned_data['username'])\\n except User.DoesNotExist:\\n return self.cleaned_data['username']\\n raise forms.ValidationError(_(u'Toto uživatelské jméno už je zabráno'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45977a361e20948d23517a0937d6ba6b\",\n \"score\": \"0.5911364\",\n \"text\": \"def test_contains_lowercase(self):\\n assert re.search(\\\"[a-z]\\\", PasswordGenerator().create())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1747b598c0bd18602f0a68ac2c4399ca\",\n \"score\": \"0.58801764\",\n \"text\": \"def userfilter(text):\\n return stringutil.shortuser(text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df0d9d1c946f81589b520c6d0eb77721\",\n \"score\": \"0.5878371\",\n \"text\": \"def get_user_input():\\r\\n valid_inputs = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't',\\r\\n 'u', 'v', 'w', 'x', 'y', 'z', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N',\\r\\n 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', ' ']\\r\\n username = input()\\r\\n if len(username) < 1:\\r\\n print(\\\"Name must be at least one letter. Please enter another name:\\\")\\r\\n username = get_user_input()\\r\\n for letter in username:\\r\\n if letter not in valid_inputs:\\r\\n print(\\\"Username can only contain letters. Please enter another name:\\\")\\r\\n username = get_user_input()\\r\\n return username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"009a78a17b2eecd6200119ecaf2d1859\",\n \"score\": \"0.58749855\",\n \"text\": \"def validate_username(self, username):\\n regex = re.compile(r'[@_!#$%^&*()<>?/\\\\|}{~:]')\\n check_username = User.objects.filter(username=username)\\n if check_username.exists():\\n raise serializers.ValidationError(\\\"Provided username already exist, please provide a different one\\\")\\n elif regex.search(username):\\n raise serializers.ValidationError(\\\"should not contain special characters @_!#$%^&*()<>?/\\\\|}{~:\\\")\\n elif len(username.split()) > 1:\\n raise serializers.ValidationError(\\n \\\"Username should not contain spaces\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfebcbddfded50c6d9ec60be675c99bd\",\n \"score\": \"0.587404\",\n \"text\": \"def clean_username(self):\\n username = self.cleaned_data[\\\"username\\\"]\\n\\n if Albergatore.objects.filter(username=username).exists():\\n raise forms.ValidationError('Username non disponibile!')\\n else:\\n return username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b35357c4c9938c12a2a94fd81842d0\",\n \"score\": \"0.5864838\",\n \"text\": \"def find_user(self):\\n users = []\\n place = -1\\n not_found = True\\n # until a user is chosen\\n while not_found:\\n name = raw_input(\\\"Name: \\\")\\n users = self.searchForUsers(name)\\n # Printout 4 best matches\\n for i, user in enumerate(users[:4], 1):\\n print(f'#{i} {user.name}')\\n place = int(input(\\\"Enter number, in case the user is not listed, press enter and try their name again:\\\"))\\n if 0 <= place <= 3:\\n not_found = False\\n return users[place - 1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d070a3ee437c821903d36715620acb2\",\n \"score\": \"0.58620286\",\n \"text\": \"def username_validator(form, field):\\n username = field.data\\n if len(username) < 3:\\n raise ValidationError(_('Username must be at least 3 characters long'))\\n valid_chars = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789-._'\\n chars = list(username)\\n for char in chars:\\n if char not in valid_chars:\\n raise ValidationError(_(\\\"Username may only contain letters, numbers, '-', '.' and '_'.\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6480d21680c8dd6952e8aea6c8929864\",\n \"score\": \"0.5858849\",\n \"text\": \"def clean_username(self):\\r\\n existing = User.objects.filter(username__iexact=self.cleaned_data['username'])\\r\\n if existing.exists():\\r\\n raise forms.ValidationError(_(\\\"O email já se encontra registado.\\\"))\\r\\n else:\\r\\n return self.cleaned_data['username']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb509753ebe1a402b4655be04aca08b9\",\n \"score\": \"0.585632\",\n \"text\": \"def check():\\n userNames = db.execute(\\\"SELECT username FROM users\\\")\\n userName = request.args.get(\\\"username\\\")\\n search = {'username' : userName}\\n if search in userNames:\\n print(\\\"user name found\\\")\\n return jsonify(\\\"false\\\")\\n return jsonify(\\\"TODO\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c50cb820f73822e2b0f5cc90572b7b68\",\n \"score\": \"0.5852539\",\n \"text\": \"def valid_username(self, username):\\n return self.USER_RE.match(username)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4695547fd1518cdc497f17e06805a09c\",\n \"score\": \"0.58498293\",\n \"text\": \"def valid_username(self, username):\\n return username and USER_RE.match(username)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97828fd3b7f52be6efdb1d914cf15d1b\",\n \"score\": \"0.584555\",\n \"text\": \"def get_username(infile):\\n username = 0\\n myfile = open(infile, mode=\\\"r\\\", encoding=\\\"latin_1\\\")\\n what_to_look = 'Uploaded by '\\n for line_username in myfile:\\n if what_to_look in line_username:\\n username_length = line_username.find('')\\n username = line_username[58:username_length]\\n print(\\\"Username: \\\" + username.strip())\\n return username.strip()\\n myfile.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b086f5cca7f18c70d9deeb692a32faf\",\n \"score\": \"0.58317703\",\n \"text\": \"def process_username(self, value) -> str:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cde03d4228e8685827660495aa1a6b1\",\n \"score\": \"0.58283216\",\n \"text\": \"def clean_username(self):\\n\\t\\tusername = self.cleaned_data['username']\\n\\t\\tif not re.search(r'^\\\\w+$', username):\\n\\t\\t\\traise forms.ValidationError ('Username can only contain '\\n\\t\\t\\t'alphanumeric characters and the underscore.')\\n\\t\\ttry:\\n\\t\\t\\tUser.objects.get(username=username)\\n\\t\\texcept User.DoesNotExist:\\n\\t\\t\\treturn username\\n\\t\\traise forms.ValidationError('Username already taken.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"886622e20a72c5ffc26f7711c383fbfc\",\n \"score\": \"0.5823204\",\n \"text\": \"def clean_username(self):\\n existing = Users.objects.filter(username=self.cleaned_data['username'])\\n if existing.exists():\\n raise forms.ValidationError(_('A user with that username already exists.'))\\n else:\\n return self.cleaned_data['username']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e27cb3f8ef14d1b9c8665d9cd4f0f134\",\n \"score\": \"0.5819259\",\n \"text\": \"def handle_search(request):\\n\\n query = request.GET.get('query')\\n if query:\\n users = User.objects.filter(username__icontains=query\\n ).values_list('username', flat=True\\n ).order_by('profile__score')\\n else:\\n users = User.objects.order_by('profile__score').values_list('username', flat=True)\\n\\n users = list(users[:20])\\n # Return list of users matching username\\n return ajax_success(users=users, msg=\\\"Username searched\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"402f6542db3a31a58cd1343b3fc307c8\",\n \"score\": \"0.5805711\",\n \"text\": \"def validate_username(cls, username, skip_uniqueness_check=False):\\n un = username.lower()\\n\\n if (un in Config['blocked_usernames']\\n or any(fragment in un for fragment in Config['blocked_username_fragments'])\\n or any(fragment in un for fragment in Config['autoflag_words'])):\\n return \\\"Sorry, this username is not allowed.\\\"\\n\\n if not un:\\n return \\\"Please enter a username.\\\"\\n elif len(un) < 3:\\n return \\\"Username must be 3 or more characters.\\\"\\n elif len(un) > 16:\\n return \\\"Username must be 16 characters or less.\\\"\\n\\n alphabet = string.lowercase + string.uppercase + string.digits + '_'\\n if not all(char in alphabet for char in username):\\n return \\\"Usernames can only contain letters, digits and underscores.\\\"\\n\\n if not skip_uniqueness_check:\\n if cls.objects.filter(username__iexact=username):\\n return \\\"Sorry! This username is taken. Please pick a different username.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccf82e5c207b54640641b69971396aec\",\n \"score\": \"0.5800986\",\n \"text\": \"def clean_username(self):\\n username = self.cleaned_data['username'].lower()\\n if (User.objects.filter(username__iexact=username).exists() or\\n username in settings.ACCOUNT_USERNAME_BLACKLIST):\\n raise ValidationError(_('A user with that username already exists.'))\\n return username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b2719c2a52bb1843d98111dca318d8e\",\n \"score\": \"0.5790296\",\n \"text\": \"def get_users_like(self, name):\\n name_length = len(name)\\n if name_length > USER_ALL_NAME_LENGTH_MAX_WITH_DISCRIMINATOR:\\n return []\\n \\n users = self.users\\n \\n # name with discriminator\\n \\n name_with_discriminator = _parse_name_with_discriminator(name)\\n if (name_with_discriminator is not None):\\n for user in users.values():\\n if _is_user_matching_name_with_discriminator(user, name_with_discriminator):\\n return [user]\\n \\n if name_length > USER_ALL_NAME_LENGTH_MAX:\\n return []\\n \\n user_name_pattern = re_compile('.*?'.join(re_escape(char) for char in name), re_ignore_case)\\n matches = []\\n guild_id = self.id\\n \\n for user in users.values():\\n # name\\n \\n parsed = user_name_pattern.search(user.name)\\n if (parsed is not None):\\n match_start = parsed.start()\\n match_length = parsed.end() - match_start\\n \\n match_rate = (USER_MATCH_WEIGHT_NAME, match_length, match_start)\\n \\n matches.append((user, match_rate))\\n continue\\n \\n # display_name\\n \\n user_display_name = user.display_name\\n if (user_display_name is not None):\\n parsed = user_name_pattern.search(user_display_name)\\n if (parsed is not None):\\n match_start = parsed.start()\\n match_length = parsed.end() - match_start\\n \\n match_rate = (USER_MATCH_WEIGHT_DISPLAY_NAME, match_length, match_start)\\n \\n matches.append((user, match_rate))\\n continue\\n \\n # nick\\n \\n try:\\n guild_profile = user.guild_profiles[guild_id]\\n except KeyError:\\n pass\\n else:\\n user_nick = guild_profile.nick\\n if (user_nick is not None):\\n parsed = user_name_pattern.search(user_nick)\\n if (parsed is not None):\\n match_start = parsed.start()\\n match_length = parsed.end() - match_start\\n \\n match_rate = (USER_MATCH_WEIGHT_NICK, match_length, match_start)\\n \\n matches.append((user, match_rate))\\n continue\\n \\n \\n return [item[0] for item in sorted(matches, key = _user_match_sort_key)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9555fdcc9e54fd5f5f4380bc80d68d4\",\n \"score\": \"0.57825446\",\n \"text\": \"def validate_username_field(value):\\n msg = []\\n if value:\\n if not value.isalnum() and \\\"_\\\" not in value:\\n msg.append(\\n \\\"field mustn't contain any special character other than _\\\")\\n if not ((re.search(\\\"[a-z]\\\", value))or(re.search(\\\"[A-Z]\\\", value))):\\n msg.append(\\\"field must contain atleast one character\\\")\\n if len(msg) > 0:\\n raise serializers.ValidationError(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a35b0a5cfdaf998533757ab81ef80b7a\",\n \"score\": \"0.5759538\",\n \"text\": \"def validate_username(username: str) -> bool:\\n return True if re.match(\\\"^([a-zA-Z0-9]|\\\\.){6,32}$\\\", username) else False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bccfcb1a4faa69fafb35985b1726fcc8\",\n \"score\": \"0.5750044\",\n \"text\": \"def sanity_check_username(name):\\n VALID_CHARACTERS = string.ascii_letters+string.digits+\\\"_-.\\\"\\n rules = [ \\n len(name) > 3, # User name is longer than 3 characters\\n all([k in VALID_CHARACTERS for k in list(name)]) # Username is made of valid characters\\n ]\\n return all(rules)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73419325271abbbdc5c616ca0cd524bf\",\n \"score\": \"0.574605\",\n \"text\": \"def usersname_length(username):\\n try:\\n username = str(username)\\n # if username.length() > 3 and < 21:\\n if (len(username) >= 3) and (len(username) < 21):\\n return True\\n except ValueError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"901627988b4967b6959f5399749d0ce6\",\n \"score\": \"0.57419056\",\n \"text\": \"def is_valid_username(u_name):\\n if not re.match(r\\\"^[A-Za-z\\\\.\\\\+_-]*$\\\", u_name) != None:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"921e0723b5599cc772717ee38636de85\",\n \"score\": \"0.57408136\",\n \"text\": \"def findUsersByName(conn, name):\\n query = \\\"SELECT * FROM users WHERE name LIKE %s\\\"\\n params = [\\\"%\\\" + name + \\\"%\\\"]\\n return getSqlQuery(conn, query, params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11dd260e48c8b537315631ad539f4f7a\",\n \"score\": \"0.5739077\",\n \"text\": \"def clean_username(self):\\n username = self.cleaned_data[\\\"username\\\"]\\n if not Albergatore.objects.filter(username=username).exists():\\n raise forms.ValidationError(\\\"Username non registrato\\\")\\n else:\\n return username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10457fa52cefb6a629aa87718148e306\",\n \"score\": \"0.5726845\",\n \"text\": \"def check_user_name(self, username):\\n query = \\\"SELECT username FROM users WHERE username = '%s'\\\" % (username)\\n result = self.fetch_single_data_row(query)\\n if result is None:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0f0e453b2520b68c9cb1c756d008987\",\n \"score\": \"0.57148904\",\n \"text\": \"def check_valid_username(self, username : str):\\n\\t\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9c54c91fd6a80bbb5edc552561ef5b2\",\n \"score\": \"0.57078695\",\n \"text\": \"def security_check_username(self, bot, update):\\n\\n full_name = (update.message.from_user.first_name + \\\" \\\"\\n + update.message.from_user.last_name)\\n if self.name_ban_re and self.name_ban_re.search(full_name):\\n # Logging\\n log_message = \\\"Ban match full name: {}\\\".format(full_name.encode('utf-8'))\\n logger.info(log_message)\\n for notify_user_id in self.notify_user_ids:\\n logger.info(notify_user_id,\\\"gets notified\\\")\\n bot.send_message(\\n chat_id=notify_user_id,\\n text=log_message)\\n # Ban the user\\n self.ban_user(update)\\n # Log in database\\n\\n if self.name_ban_re and self.name_ban_re.search(update.message.from_user.username or ''):\\n # Logging\\n log_message = \\\"Ban match username: {}\\\".format(update.message.from_user.username.encode('utf-8'))\\n logger.info(log_message)\\n for notify_user_id in self.notify_user_ids:\\n bot.send_message(\\n chat_id=notify_user_id,\\n text=log_message)\\n # Ban the user\\n self.ban_user(update)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60497a226d11be3cafe143f5f4baf5a3\",\n \"score\": \"0.56966424\",\n \"text\": \"def clean_username(self):\\r\\n username = self.cleaned_data[\\\"username\\\"]\\r\\n if '@' in username:\\r\\n # The user entered an email, so try to log them in by e-mail\\r\\n emails = ContactValue.objects.filter(value=username,\\r\\n field__field_type='email',\\r\\n contact__trash=False,\\r\\n contact__related_user__isnull=False)\\r\\n users = [email.contact.related_user.user for email in emails]\\r\\n else:\\r\\n users = User.objects.filter(user__username=username)\\r\\n if len(users) == 0:\\r\\n raise forms.ValidationError(\\r\\n _(\\\"Sorry, we don't know that user or e-mail.\\\"))\\r\\n else:\\r\\n username = users[0]\\r\\n return username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1353c764d75f75d8f8558f24aaa1ab2\",\n \"score\": \"0.5692759\",\n \"text\": \"def clean_username(self):\\n try:\\n user = User.objects.get(username__iexact=self.cleaned_data['username'])\\n except User.DoesNotExist:\\n return self.cleaned_data['username']\\n raise forms.ValidationError(_(\\\"A user with that username already exists.\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3de59944fb73806d9a0d76fdef129df\",\n \"score\": \"0.5677581\",\n \"text\": \"def auto_complete_model(request):\\n if request.is_ajax():\\n searchString=request.POST[\\\"search\\\"]\\n search_qs = User.objects.filter(username__startswith=searchString)\\n print search_qs\\n results = []\\n for r in search_qs:\\n results.append(r.username)\\n print results\\n return HttpResponse(json.dumps(results), content_type=\\\"application/json\\\", status=200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5e0005f4d22a3f09122843f9e940b11\",\n \"score\": \"0.5675287\",\n \"text\": \"def is_letter_in_pattern(user_input, word):\\r\\n for letter in list(word):\\r\\n if user_input == letter:\\r\\n return True\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7de1e6e4e7af2ec958fd08cc5adad7c4\",\n \"score\": \"0.56643456\",\n \"text\": \"def get_userName():\\n userName = input(\\\"Tapez Votre nom d'utilisateur: \\\")\\n\\n userName = userName.capitalize() # 1st letter in cap\\n\\n if not userName.isalnum() or len(userName)<4:\\n\\n print(\\\"Nom invalide\\\")\\n return get_userName()\\n\\n else:\\n return userName\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11bb76360b33e6a116242866fab5edd2\",\n \"score\": \"0.5660173\",\n \"text\": \"def _check_username(self, username):\\n # Username cannot contain spaces\\n if ' ' in username:\\n return False\\n else:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc451def748fc8dda4ebad1e3f2dd02c\",\n \"score\": \"0.5657971\",\n \"text\": \"def clean_username(self):\\n username = self.cleaned_data.get('username')\\n if len(username) < 3:\\n raise forms.ValidationError(\\\"001,Your username must be longer than 3 words.\\\")\\n elif len(username) > 20:\\n raise forms.ValidationError(\\\"002,Your username must be shorter than 20 words.\\\")\\n else:\\n\\n #To check whether the username already exists\\n \\n checkresult = User_Info.objects.filter(username=username)\\n if len(checkresult) > 0:\\n raise forms.ValidationError(\\\"003,Your username already exists.\\\")\\n return username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0411422ddb7c66213d92669fbfea4606\",\n \"score\": \"0.5654673\",\n \"text\": \"def find(word, letter, start):\\n\\n index = start\\n hits = []\\n\\n while index < len(word):\\n if word[index].lower() == letter.lower():\\n hits.append(index)\\n index += 1\\n\\n return(hits)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe06db84edc8d4419919a5d393e0d0da\",\n \"score\": \"0.5648181\",\n \"text\": \"def user_name():\\n while 1:\\n username = input(\\\"enter username, please: \\\")\\n if not len(username) == 0:\\n return username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c82479f2897275c5c78a4cb5bfe4b88\",\n \"score\": \"0.56426215\",\n \"text\": \"def clean_username(self, username):\\n return username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58c8af63dd87814589e29192c85d4b4c\",\n \"score\": \"0.5637166\",\n \"text\": \"def clean_username(self):\\n username = self.cleaned_data['username']\\n if User.objects.filter(username=username):\\n raise forms.ValidationError('Este correo electronico ya existe')\\n return username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e7833e36e5978bb94e34dd72ed08206\",\n \"score\": \"0.5629253\",\n \"text\": \"def _contains_lowercase_name(self, name):\\n return name in (x.lower() for x in self._distinct())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4746a4438fcd4d437bbb5a83cd66e336\",\n \"score\": \"0.56258893\",\n \"text\": \"def userSearch(self, username, pwd=\\\"abc\\\", usneed=False):\\n try:\\n self.cur.execute('''\\n SELECT * FROM \\\"Student\\\"\\n WHERE \\\"UserName\\\" = ?''', (username,))\\n\\n rows = self.cur.fetchall()\\n # print(rows)\\n if usneed:\\n return rows[0][1]\\n elif len(rows) == 0:\\n return \\\"Empty\\\" # if no account yet created or wrong username\\n elif len(rows) == 1 and rows[0][4] == pwd:\\n return True\\n return False # if wrong password inserted\\n except Exception as e:\\n print(\\\"userSearch(), error -\\\", e)\\n self.con.close()\\n return -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc295b480fcf5c6b853d0df7ea8f1d4f\",\n \"score\": \"0.561518\",\n \"text\": \"def _is_first_name(self, word):\\n names = self._first_name_list\\n word = word.lower()\\n ind = bisect.bisect_left(names, word)\\n if ind < len(names) and names[ind] == word:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d71da97eb837b9720e897203c7687db\",\n \"score\": \"0.56023335\",\n \"text\": \"def test_get_user_by_username_r(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"280fe52791e9ae728929c968bfb34157\",\n \"score\": \"0.5601374\",\n \"text\": \"def filter_user(tweets):\\n \\n return tweets.str.replace('', '', case=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"801fc57aa073f025190e7da688415a5e\",\n \"score\": \"0.559805\",\n \"text\": \"def _findUserNamePRIVMSG(self):\\n\\t\\ttry:\\n\\t\\t\\tuserName = re.sub('[^a-zA-Z0-9_]','', self.data.split('!')[0])\\n\\t\\t\\treturn userName\\n\\t\\texcept:\\n\\t\\t\\treturn \\\"error.finding.userName\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6d7f959bb24b39ec9fb5bb435b9539a\",\n \"score\": \"0.55943894\",\n \"text\": \"def search_for_letters(phrase:str,letters:str='aeiou') ->set:\\r\\n return set(letters).intersection(set(phrase))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38c88ffee191a5c1841c81b8482b5ccf\",\n \"score\": \"0.558165\",\n \"text\": \"def get_service_usernames():\\n usernames = [k[:-len('_passwd')] for k in leader_get()\\n if k.endswith('_passwd') and k != 'admin_passwd']\\n # now match against the service list.\\n valid_service_names = valid_services.keys()\\n known_service_usernames = []\\n for username in usernames:\\n # if a username has '_' in it, then it is a compound name.\\n parts = username.split('_')\\n if 'keystone' in parts:\\n continue\\n if not all(p in valid_service_names for p in parts):\\n continue\\n known_service_usernames.append(username)\\n return known_service_usernames\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39cb53ddf55ec2a3546f1b818d47090e\",\n \"score\": \"0.5574706\",\n \"text\": \"def clean_username(self):\\n username = self.cleaned_data['username']\\n if User.objects.filter(username=username):\\n raise forms.ValidationError('Nombre de usuario ya registrado.')\\n return username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d4427c955f075306234e38ed62c4af2\",\n \"score\": \"0.5553871\",\n \"text\": \"def generate_username(name):\\n username = ''.join(name.split(' ')).lower()\\n if not User.objects.filter(username=username).exists():\\n return username\\n else:\\n random_username = username + str(randint(0, 1000))\\n return generate_username(random_username)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa644612e55a8ebbd61835fc8da001c0\",\n \"score\": \"0.5548313\",\n \"text\": \"def parse_name(guild, username):\\r\\n if '@' in username:\\r\\n try:\\r\\n return guild.get_member(int(username[3:-1]))\\r\\n except:\\r\\n raise NameError(username)\\r\\n else:\\r\\n return get_member_from_guild(guild.members, username)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8970f68548e77c567d874853c18c5454\",\n \"score\": \"0.55467904\",\n \"text\": \"def check():\\r\\n username = request.args.get(\\\"username\\\")\\r\\n if len(username) >= 1:\\r\\n usernames = [dct[\\\"username\\\"] for dct in db.execute(\\\"SELECT username FROM users\\\")]\\r\\n if username not in usernames:\\r\\n return jsonify(True)\\r\\n else:\\r\\n return jsonify(False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7f90c6d8429bb1d64141f7955edbd74\",\n \"score\": \"0.55457246\",\n \"text\": \"def username_exists_validator(value):\\n try:\\n User.objects.get(username=value)\\n\\n except User.DoesNotExist:\\n raise ValidationError(_('Requested username does not exist.'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e76690e229888ca6a3b5515f7b6a088f\",\n \"score\": \"0.55418396\",\n \"text\": \"def validate_username(cls, nick):\\n if nick and nick != \\\"\\\":\\n nick = nick.strip()\\n if len(nick) > 0:\\n if re.match(\\\"^[A-z0-9_\\\\-]+$\\\", nick):\\n return nick, \\\"\\\"\\n else:\\n return \\\"\\\", \\\"Username can only contain letters, numbers, underscores, and dashes.\\\"\\n return \\\"\\\", \\\"Username must not be empty.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"566aa32677a8d6f412d11e734703c54a\",\n \"score\": \"0.55270416\",\n \"text\": \"def check_username_lower_limit(username):\\n if len(username) >= 4:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8286201ec8571426d3ce568b4ff7d700\",\n \"score\": \"0.55268127\",\n \"text\": \"def get_by_natural_key(self, username):\\r\\n anycase_username_field = f'{self.model.USERNAME_FIELD}__iexact'\\r\\n return self.get(**{anycase_username_field: username})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40e99474b264ed23dbb9d1046298888f\",\n \"score\": \"0.552202\",\n \"text\": \"def check_username(cls, uid):\\n if not uid:\\n raise RBFatalError('Username must be given')\\n if re.search(r'[^a-z0-9_.-]', uid):\\n raise RBFatalError(\\\"Invalid characters in username\\\")\\n if len(uid) > rbconfig.maxlen_uname:\\n raise RBFatalError(\\\"Username can not be longer than %d characters\\\"\\n % rbconfig.maxlen_uname)\\n if re.search(r'^[^a-z0-9]', uid):\\n raise RBFatalError(\\\"Username must begin with letter or number\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf3b9bd84177442eda8c6433534e636c\",\n \"score\": \"0.5512801\",\n \"text\": \"def validate_username(self, username):\\n # onyl allow alphanumeric character, . _ -\\n if username is not None:\\n pattern = r\\\"^[a-zA-Z0-9_.-]+$\\\"\\n if len(username) < 1:\\n raise ValidationError('Invalid username, minimum is 1 character')\\n if len(username) > 32:\\n raise ValidationError('Invalid username, max is 32 character')\\n if re.match(pattern, username) is None:\\n raise ValidationError('Invalid username, only alphanumeric, . _ - allowed')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28fd7d5e2650faf027dced619ce0ed7e\",\n \"score\": \"0.5505782\",\n \"text\": \"def get_first_name(self):\\n is_valid_first_name = False\\n while not is_valid_first_name:\\n print(\\\"First Name: \\\", end=\\\"\\\")\\n fname = input().strip()\\n fn_regex = re.compile(r\\\"^[A-z]+$\\\")\\n is_valid_first_name = fn_regex.match(fname) is not None\\n if not is_valid_first_name:\\n print(\\\"{} is not a valid first name\\\".format(fname))\\n return fname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d677b66b8bcbcc5abf29fbca1af46b39\",\n \"score\": \"0.54982305\",\n \"text\": \"def lookup_by_letter(letter):\\n letterlist = []\\n if len(letter) == 1 and letter.isalpha(): #tests if input is a letter and only one letter\\n letter = letter.capitalize() #allows for case insensitivity, makes uppercase to match dictionary keys\\n for i in name_dict.keys():\\n if i[0] == letter:\\n letterlist.append(i)\\n letterlist.sort()\\n return (letterlist)\\n else:\\n return (\\\"Invalid input\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"451862806c0c5103f25ac4f009c789e5\",\n \"score\": \"0.54910004\",\n \"text\": \"def checkUsername(self, username, cursor, UserAccount):\\n \\n query = \\\"SELECT username FROM accounts WHERE username = %s\\\"\\n cursor.execute(query, (username,))\\n result = cursor.fetchall()\\n\\n while result:\\n print(\\\"\\\\n\\\\n\\\\n\\\\n\\\\nSorry that username is taken\\\\n\\\")\\n username = input(\\\"Please select another username: \\\")\\n query = \\\"SELECT username FROM accounts WHERE username = %s\\\"\\n cursor.execute(query, (username,))\\n result = cursor.fetchall()\\n UserAccount.username = username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcaf8476203abba535b097330b03e65a\",\n \"score\": \"0.54815954\",\n \"text\": \"def UIDStartsWith(uid: Text, prefix: Text) -> bool:\\n return uid.find(prefix + '.') == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9c7c4389e4800fba66a37280252c525\",\n \"score\": \"0.5481196\",\n \"text\": \"def get_user_by_username_or_user_id(cls, string):\\n username = str(string).replace(\\\"@\\\", \\\"\\\").strip().lower()\\n if username.isdigit(): # user_id\\n return cls.objects.filter(user_id=int(username)).first()\\n return cls.objects.filter(username__iexact=username).first()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec61766f47b6fa904df432ea8eee3942\",\n \"score\": \"0.54762703\",\n \"text\": \"def find_person(self, name=None):\\n try:\\n if name != '' or name != ' ':\\n fname = lname = ''\\n if ' ' in name:\\n fname = name.split(' ')[0]\\n lname = name.split(' ')[1]\\n else:\\n fname = lname = name\\n return (x.first_name for x in\\n self.user.select().where(Person.last_name.contains(lname) & Person.first_name.contains(fname)))\\n except Exception as ex:\\n logging.error(\\\"Error in searching Person\\\" + str(ex))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"880446522c0701f47fb1caa6d64f0ad7\",\n \"score\": \"0.5466264\",\n \"text\": \"def searchUser(self, nick=None):\\n with open('/usr/local/apache2/cookies.csv', 'r') as csvFile:\\n reader = csv.reader(csvFile)\\n for row in reader:\\n if nick == row[0]:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6183092b076a2e135b74342a8d89997e\",\n \"score\": \"0.54581034\",\n \"text\": \"def clean_username(self):\\n\\t\\tusername = self.cleaned_data['username']\\n\\t\\tusername = username.strip()\\n\\t\\tif not username:\\n\\t\\t\\traise ValidationError('Nickname mein harf likhna zaruri hain')\\n\\t\\tvalidate_nickname_chars(username)\\n\\t\\tif username[:1] == '.':\\n\\t\\t\\traise ValidationError('Nickname ke shuru mein . nah dalein')\\n\\t\\tif username[-1:] == '.':\\n\\t\\t\\traise ValidationError('Nickname ke akhir mein . nah dalein')\\n\\t\\texists = nick_already_exists(nickname=username)\\n\\t\\taltered = {}\\n\\t\\tif exists is None:\\n\\t\\t\\t#the redis DB is compromised, use PSQL DB. Check nick against DB, that's it\\n\\t\\t\\tif ' ' in username:\\n\\t\\t\\t\\tusername_original = username\\n\\t\\t\\t\\tusername = ''.join(username.split())\\n\\t\\t\\t\\taltered = {'status':'joined'}\\n\\t\\t\\t\\tif User.objects.filter(username__iexact=username).exists():\\n\\t\\t\\t\\t\\traise ValidationError('\\\"%s\\\" kisi aur ka nickname hai. Kuch aur likhein' % username_original)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tif User.objects.filter(username__iexact=username).exists():\\n\\t\\t\\t\\t\\traise ValidationError('\\\"%s\\\" kisi aur ka nickname hai. Kuch aur likhein' % username)\\n\\t\\t\\treturn [username], altered, username\\n\\t\\t############################################\\n\\t\\telse:\\n\\t\\t\\t# form variants and suggestions\\n\\t\\t\\t# check al against redis DB\\n\\t\\t\\tif ' ' in username:\\n\\t\\t\\t\\talternatives = form_variants(username) #returns list of tuples containing variants and their statuses\\n\\t\\t\\t\\talt_choices = process_choices(alternatives)\\n\\t\\t\\t\\tif not alt_choices:\\n\\t\\t\\t\\t\\t# no suggestions could be unearthed\\n\\t\\t\\t\\t\\traise ValidationError('\\\"%s\\\" kisi aur ka nickname hai. Kuch aur likhein' % username)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t# some suggestions unearthed\\n\\t\\t\\t\\t\\taltered = {'status':'joined'}\\n\\t\\t\\t\\t\\treturn alt_choices, altered, username\\n\\t\\t\\telse:\\n\\t\\t\\t\\tif exists:\\n\\t\\t\\t\\t\\t# nick is not available\\n\\t\\t\\t\\t\\talternatives = form_suggestions(username) #returns list of tuples containing suggestions and their statuses\\n\\t\\t\\t\\t\\talt_choices = process_choices(alternatives)\\n\\t\\t\\t\\t\\tif not alt_choices:\\n\\t\\t\\t\\t\\t\\traise ValidationError('\\\"%s\\\" kisi aur ka nickname hai. Kuch aur likhein' % username)\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\taltered = {'status':'replaced'}\\n\\t\\t\\t\\t\\t\\treturn alt_choices, altered, username\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t#nick is available\\n\\t\\t\\t\\t\\t# altered = False\\n\\t\\t\\t\\t\\treturn [username], altered, username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00e4c3f38dce907d0d53bcbc58efa430\",\n \"score\": \"0.54509556\",\n \"text\": \"def _validate_username(self):\\n username = self.cleaned_data.get(\\\"username\\\")\\n\\n if not username:\\n return\\n\\n if settings.ST_CASE_INSENSITIVE_USERNAMES:\\n username = username.lower()\\n\\n is_found = (\\n User.objects\\n .filter(username=username)\\n .exists())\\n if is_found:\\n return\\n\\n if settings.ST_CASE_INSENSITIVE_EMAILS:\\n username = username.lower()\\n\\n is_found_email = (\\n User.objects\\n .filter(email=username)\\n .exists())\\n if is_found_email:\\n return\\n\\n raise forms.ValidationError(\\n _(\\\"No account matches %(username)s.\\\") % {\\n 'username': username})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9db4028ad184739fd82612b64ce1df4d\",\n \"score\": \"0.5444739\",\n \"text\": \"def fuzzy_nickname_search(request):\\n logger('views', 'main: {}'.format(request.json_body))\\n return fuzzy_string_matcher.get_nicknames(request.validated['user'], request.validated['value'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddc4a07d8aba14ec188a596bc8b9f270\",\n \"score\": \"0.5443438\",\n \"text\": \"def searchLetters(phrase: str, letters: str = 'aeiou') -> set:\\n return set(letters).intersection(set(phrase))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"373f0e4b464d81fd513c3c7bf09ae95f\",\n \"score\": \"0.5439793\",\n \"text\": \"def security_check_username(self, bot, update):\\n\\n full_name = ((update.message.from_user.first_name or \\\"\\\") + \\\" \\\"\\n + (update.message.from_user.last_name or \\\"\\\"))\\n if self.name_ban_re and self.name_ban_re.search(full_name):\\n # Logging\\n log_message = \\\"Ban match full name: {}\\\".format(\\n full_name.encode('utf-8'))\\n if self.debug:\\n update.message.reply_text(log_message)\\n print(log_message)\\n for notify_user_id in self.notify_user_ids:\\n print(notify_user_id, \\\"gets notified\\\")\\n bot.send_message(\\n chat_id=notify_user_id,\\n text=log_message)\\n # Ban the user\\n self.ban_user(update)\\n # Log in database\\n s = session()\\n userBan = UserBan(\\n user_id=update.message.from_user.id,\\n reason=log_message)\\n s.add(userBan)\\n s.commit()\\n s.close()\\n\\n if self.name_ban_re and self.name_ban_re.search(update.message.from_user.username or ''):\\n # Logging\\n log_message = \\\"Ban match username: {}\\\".format(\\n update.message.from_user.username.encode('utf-8'))\\n if self.debug:\\n update.message.reply_text(log_message)\\n print(log_message)\\n for notify_user_id in self.notify_user_ids:\\n bot.send_message(\\n chat_id=notify_user_id,\\n text=log_message)\\n # Ban the user\\n self.ban_user(update)\\n # Log in database\\n s = session()\\n userBan = UserBan(\\n user_id=update.message.from_user.id,\\n reason=log_message)\\n s.add(userBan)\\n s.commit()\\n s.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b46757792a081b3c67557f1b7341f309\",\n \"score\": \"0.5436486\",\n \"text\": \"def justice_league_by_real_name(real_name):\\n print(\\\"i'm here though\\\")\\n canonicalized = real_name.replace(\\\" \\\", \\\"\\\").lower()\\n for character in justice_league_members:\\n search_term = character[\\\"real_name\\\"].replace(\\\" \\\", \\\"\\\").lower()\\n\\n if search_term == canonicalized:\\n return jsonify(character)\\n\\n return jsonify({\\\"error\\\": f\\\"Character with real_name {real_name} not found.\\\"}), 404\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caded9656e227eede695bae6071645e2\",\n \"score\": \"0.54354316\",\n \"text\": \"def validate_first_name(self, value):\\n if not all(c in string.ascii_letters for c in value.lower().replace(\\\" \\\", \\\"\\\")):\\n raise serializers.ValidationError(\\\"First name is not valid, please enter again\\\")\\n return value\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":790,"cells":{"query_id":{"kind":"string","value":"60a13f80bbbd52ab881d64ae67e7d878"},"query":{"kind":"string","value":"Test LimitTracker used in auvimport."},"positive_passages":{"kind":"list like","value":[{"docid":"376124658927b62692caa4f6e6bed547","score":"0.5882347","text":"def test_limit_tracker(self):\n\n # check direct values\n direct_track = LimitTracker()\n\n direct_track.check(1.0)\n\n self.assertEqual(direct_track.minimum, 1.0)\n self.assertEqual(direct_track.maximum, 1.0)\n\n direct_track.check(10.0)\n direct_track.check(-10.0)\n\n self.assertEqual(direct_track.minimum, -10.0)\n self.assertEqual(direct_track.maximum, 10.0)\n\n # check values in a dictionary\n dict_value_track = LimitTracker('val')\n\n dict_value_track.check({'val': 1.0})\n\n self.assertEqual(dict_value_track.minimum, 1.0)\n\n dict_value_track.check({'val': 10.0})\n dict_value_track.check({'val': -10.0})\n\n self.assertEqual(dict_value_track.maximum, 10.0)\n self.assertEqual(dict_value_track.minimum, -10.0)\n\n # the final (odd) cases\n # should throw error... or silently ignore?\n\n # using a dict, but give it a value\n self.assertRaises(TypeError, dict_value_track.check, (20.0))\n # or a dict with the wrong key\n dict_value_track.check({'other': -20.0})\n\n self.assertEqual(dict_value_track.maximum, 10.0)\n self.assertEqual(dict_value_track.minimum, -10.0)\n\n # using a value but give it a dict\n self.assertRaises(TypeError, direct_track.check, ({'val': 20.0}))\n\n self.assertEqual(direct_track.minimum, -10.0)\n self.assertEqual(direct_track.maximum, 10.0)","title":""}],"string":"[\n {\n \"docid\": \"376124658927b62692caa4f6e6bed547\",\n \"score\": \"0.5882347\",\n \"text\": \"def test_limit_tracker(self):\\n\\n # check direct values\\n direct_track = LimitTracker()\\n\\n direct_track.check(1.0)\\n\\n self.assertEqual(direct_track.minimum, 1.0)\\n self.assertEqual(direct_track.maximum, 1.0)\\n\\n direct_track.check(10.0)\\n direct_track.check(-10.0)\\n\\n self.assertEqual(direct_track.minimum, -10.0)\\n self.assertEqual(direct_track.maximum, 10.0)\\n\\n # check values in a dictionary\\n dict_value_track = LimitTracker('val')\\n\\n dict_value_track.check({'val': 1.0})\\n\\n self.assertEqual(dict_value_track.minimum, 1.0)\\n\\n dict_value_track.check({'val': 10.0})\\n dict_value_track.check({'val': -10.0})\\n\\n self.assertEqual(dict_value_track.maximum, 10.0)\\n self.assertEqual(dict_value_track.minimum, -10.0)\\n\\n # the final (odd) cases\\n # should throw error... or silently ignore?\\n\\n # using a dict, but give it a value\\n self.assertRaises(TypeError, dict_value_track.check, (20.0))\\n # or a dict with the wrong key\\n dict_value_track.check({'other': -20.0})\\n\\n self.assertEqual(dict_value_track.maximum, 10.0)\\n self.assertEqual(dict_value_track.minimum, -10.0)\\n\\n # using a value but give it a dict\\n self.assertRaises(TypeError, direct_track.check, ({'val': 20.0}))\\n\\n self.assertEqual(direct_track.minimum, -10.0)\\n self.assertEqual(direct_track.maximum, 10.0)\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"5e84c8ee36f03c4d423562b65adc6087","score":"0.6538664","text":"def test_rate_limit_interface():\n per_user_rate_def = RateDefinition(per_week=50000, per_day=13000, per_second=.001)\n min_rate_def = RateDefinition(per_second=10)\n per_user_rate_def = PerUserRateDefinition(per_user_rate_def, min_rate_def)\n my_feature_rate_limiter = RateLimiter('my_feature', per_user_rate_def.get_rate_limits)\n if my_feature_rate_limiter.allow_usage('my_domain'):\n # ...do stuff...\n my_feature_rate_limiter.report_usage('my_domain')","title":""},{"docid":"3d93729a630c34495d64c4b65d915e78","score":"0.6360713","text":"def test_activity_api_limits(self):\n\n async def run_test():\n with self.assertRaises(ValueError):\n await self.test_camera.query_activity_history(limit=ACTIVITY_API_LIMIT + 1)\n\n self.loop.run_until_complete(run_test())","title":""},{"docid":"577aaff1b88b187d68a0fc9eb0d0a150","score":"0.60561013","text":"def on_limit(self, track):\n print 'Limitation notice has arrived...'\n return","title":""},{"docid":"f731d809af601dfc71952e039aedadcb","score":"0.5922246","text":"def test_no_participations(self):\n user = users.models.User.objects.create_user('test-user')\n limit = self.limiter.get_limit(user)\n self.assertEqual(limit.min_level, self.c_prime)","title":""},{"docid":"cc47a7b5642e0be6e1843560d450fa4d","score":"0.58270365","text":"def test_limit_reached(self):\r\n chip_counter = Chips()\r\n limit_reached(chip_counter)\r\n self.assertEqual(chip_counter.total, 2000)","title":""},{"docid":"7f073275b6c9022ea447378594acfed2","score":"0.57859415","text":"def setUp(self):\n super(LimitsControllerTestV21, self).setUp()\n self.controller = wsgi.Resource(self.limits_controller())\n self.ctrler = self.limits_controller()","title":""},{"docid":"3e651565f2fa68e798f85ce96e80e1e5","score":"0.5757954","text":"def testCPULimit(self):\n result = self.RunFlow(\"CPULimitFlow\", cpu_limit=300)\n self.assertEqual(result[\"cpulimit\"], [300, 295, 255])","title":""},{"docid":"028e2c972cedb228cc34afa6e47ecdd1","score":"0.5748576","text":"def test_lagLimitExceeded(self):\n logger = OperationLogger(outfile=StringIO())\n for lag in [100.0, 1100.0, 1200.0]:\n logger.observe(dict(\n type='operation', phase='start', user='user01',\n label='testing', lag=lag)\n )\n self.assertEqual(\n [\"Median TESTING scheduling lag greater than 1000.0ms\"],\n logger.failures())","title":""},{"docid":"f0ddee65968dc0db1bba54298341b73b","score":"0.5728882","text":"def test_rate_limiting_error(self):\n self.sources[0].RATE_LIMIT_HTTP_CODES = ('429', '456')\n\n error_body = json_dumps({\"meta\": {\n \"code\": 429, \"error_message\": \"The maximum number of requests...\",\n \"error_type\": \"OAuthRateLimitException\"}})\n self.expect_get_activities().AndRaise(\n urllib.error.HTTPError('url', 429, 'Rate limited', {},\n io.StringIO(error_body)))\n\n self.post_task(expect_poll=FakeSource.RATE_LIMITED_POLL,\n expect_last_polled=util.EPOCH)\n source = self.sources[0].key.get()\n self.assertEqual('error', source.poll_status)\n self.assertTrue(source.rate_limited)","title":""},{"docid":"429a1b753c13a4d10d6c92ab2a74cd5b","score":"0.57227284","text":"def l_rate_limit(self):","title":""},{"docid":"ce44371742d077c6779a50b7ed42e1ea","score":"0.5719269","text":"def test_failureLimitExceeded(self):\n logger = OperationLogger(outfile=StringIO())\n for _ignore in range(98):\n logger.observe(dict(\n type='operation', phase='end', user='user01',\n duration=0.25, label='testing', success=True)\n )\n logger.observe(dict(\n type='operation', phase='end', user='user01',\n duration=0.25, label='testing', success=False)\n )\n self.assertEqual(\n [\"Greater than 1% TESTING failed\"],\n logger.failures())","title":""},{"docid":"e227dcc7289ee272f60fa0da1a07c9ce","score":"0.5711501","text":"def test_limit_complete(self):\n limit = Limit(item_limit=0)\n self.assertTrue(limit.complete)","title":""},{"docid":"0feeedd5f4c50591d83f4d5fcc7f880b","score":"0.57075286","text":"def patch_max_test_time(limit):\n from corehq.tests.noseplugins.timing import patch_max_test_time\n return patch_max_test_time(limit)","title":""},{"docid":"3cf5b7cc716afd8c4bbca17ff7c3acd0","score":"0.57040715","text":"def testNetworkLimit(self):\n result = self.RunFlow(\"NetworkLimitFlow\", network_bytes_limit=10000)\n self.assertEqual(result[\"networklimit\"], [10000, 9820, 8820, 8240])","title":""},{"docid":"ae6c67987520abd015cd9de754883cf2","score":"0.5701207","text":"def read_limits(self, for_who):","title":""},{"docid":"ff22e7cb9c2a3e700ca9155736fb84ab","score":"0.5698678","text":"def test_throttle(throttle_app: App, test_request):\n\n app = throttle_app\n\n # clear counter from previous test run\n clear_throttle(test_request, \"throttle_sample\")\n\n app.get(\"/\", status=200)\n\n # We exceeded the limit of 1 request per hour\n app.get(\"/\", status=429)\n\n # Let's clear the counter\n clear_throttle(test_request, \"throttle_sample\")\n\n app.get(\"/\", status=200)","title":""},{"docid":"5aaa63580662051245a41e49f13c4230","score":"0.56840473","text":"def setUp(self):\n self.max_integer = __import__('6-max_integer').max_integer","title":""},{"docid":"2543c7d0beb0ba1c91f9362f2f265831","score":"0.56803393","text":"def test_p_rate_limit_switch(self):\n return self._rate_limit_switch(apk_type='P')","title":""},{"docid":"e9ed7d357f3e9e2d9f4dc79158c4f26f","score":"0.5655471","text":"def test_custom_threshold_works(self, mock_req):\n uri = re.compile(\n r\"http://services\\.swpc\\.noaa\\.gov/text/aurora-nowcast-map\\.txt\"\n )\n mock_req.get(uri, text=load_fixture(\"aurora.txt\"))\n\n entities = []\n\n def mock_add_entities(new_entities, update_before_add=False):\n \"\"\"Mock add entities.\"\"\"\n if update_before_add:\n for entity in new_entities:\n entity.update()\n\n for entity in new_entities:\n entities.append(entity)\n\n config = {\"name\": \"Test\", \"forecast_threshold\": 1}\n self.hass.config.longitude = 18.987\n self.hass.config.latitude = 69.648\n\n aurora.setup_platform(self.hass, config, mock_add_entities)\n\n aurora_component = entities[0]\n assert aurora_component.aurora_data.visibility_level == \"16\"\n assert aurora_component.is_on","title":""},{"docid":"85bf876290c6c3c5d0355470cbad4ed4","score":"0.5648686","text":"def test_below_threshold(self):\n throughput_difference = self.throughput_difference\n\n self.set_limits_for_all_users(self.resource_name, self.resource_limit)\n\n # Check that a throughput below the limit succeeds\n for task in self.tasks:\n task.set_throughput(self.resource_limit * self.units - throughput_difference)\n\n for task in self.tasks:\n self.get_throughput = task.get_throughput() + self.error\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(),\n task.get_throughput(), self.error)))\n\n self.get_stats(self.get_throughput, False)\n\n self.set_throughput_to_zero()","title":""},{"docid":"141469ad46b520837dd553482f37f6fe","score":"0.5639672","text":"def test_change_default_throttling_settings_ws_with_overwrite_throttled_rate_above_50():","title":""},{"docid":"0469a79b4e91fdf1eb75b14522575b4f","score":"0.5631249","text":"def test_invocation_unchanged_limits(self):\n self.test_response[\"X-RateLimit-Usage\"] = \"0, 0\"\n self.test_response[\"X-RateLimit-Limit\"] = \"10000, 1000000\"\n rule = SleepingRateLimitRule()\n self.assertEqual(10000, rule.short_limit)\n self.assertEqual(1000000, rule.long_limit)\n rule(self.test_response)\n self.assertEqual(10000, rule.short_limit)\n self.assertEqual(1000000, rule.long_limit)","title":""},{"docid":"2599d758adb11097d473b0bfb0780c11","score":"0.56051433","text":"def _set_limits(self):\n raise NotImplementedError","title":""},{"docid":"a2319fb6aad76ef1c6ddf1cae92cd6c5","score":"0.5590528","text":"def __init__(self, reason, lim=0):\n self.successes = 0\n self.tests = 0\n self.reason = reason\n self.limit = lim","title":""},{"docid":"c4e9f03b8b2adbadb4b28bae3405579f","score":"0.5579144","text":"def test_invocation_changed_limits(self):\n self.test_response[\"X-RateLimit-Usage\"] = \"0, 0\"\n self.test_response[\"X-RateLimit-Limit\"] = \"600, 30000\"\n\n # without limit enforcement (default)\n rule = SleepingRateLimitRule()\n rule(self.test_response)\n self.assertEqual(600, rule.short_limit)\n self.assertEqual(30000, rule.long_limit)\n\n # with limit enforcement\n rule = SleepingRateLimitRule(force_limits=True)\n rule(self.test_response)\n self.assertEqual(10000, rule.short_limit)\n self.assertEqual(1000000, rule.long_limit)","title":""},{"docid":"a03eb84647800d9093f40be90d5ba017","score":"0.5568088","text":"def test_validate_number_limits(self):\n # Reset limits first.\n self.reset_limits()\n\n # Request as often as allowed.\n for i in range(0, self._app.limit_amount):\n self.http_client.fetch(self.get_url('/validate_number/?number=%2B49176123456'), self.stop)\n response = self.wait()\n self.assert_json_response(response, {'status': 'ok'})\n\n # Request again and expect error.\n self.http_client.fetch(self.get_url('/validate_number/?number=%2B49176123456'), self.stop)\n response = self.wait()\n self.assert_json_response(response, {'status': 'error', \"error\": \"limit_acceded\"})","title":""},{"docid":"eca65a20a04453254c85360acb20ea5e","score":"0.55434823","text":"def test_statistics_challenge(self):\n pass","title":""},{"docid":"df7e313bfb7a80e371ff9385d8417c86","score":"0.5538704","text":"def test_change_default_throttling_settings_ws_with_overwrite_throttled_rate_above_account_quota():","title":""},{"docid":"d018af22930d29f4c353b8dbfa60cafe","score":"0.5533047","text":"def test_no_quota(self, mock_google_credential):\n ae_api_client = ae.AppEngineClient(global_configs={})\n self.assertEqual(None, ae_api_client.repository._rate_limiter)","title":""},{"docid":"c788ea84b5ffad0c5ab893d8e475ac34","score":"0.5523266","text":"def test_below_to_above_threshold(self):\n throughput_difference = self.throughput_difference\n\n self.set_limits_for_all_users(self.resource_name, self.resource_limit)\n\n # Check that a throughput below the limit succeeds\n for task in self.tasks:\n task.set_throughput(self.resource_limit * self.units - throughput_difference)\n\n for task in self.tasks:\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(), task.get_throughput(), self.error)))\n\n self.sleep(5)\n\n # Check that a throughput above the threshold is constrained by the\n # resource limit\n for task in self.tasks:\n task.set_throughput(self.resource_limit * self.units + throughput_difference)\n\n for task in self.tasks:\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(), self.resource_limit * self.units, self.error)))\n\n self.set_throughput_to_zero()","title":""},{"docid":"21efddcaf5513005cc4e51e4780125a8","score":"0.552136","text":"def test_too_high_limit_value(self):\n self.mockRequest.args['limit'] = ['100']\n d = self.app.paginate_me(self.mockRequest)\n self.assertEqual(self.successResultOf(d), {'limit': 10})","title":""},{"docid":"fb84850547b9daa3559033fa86466ce2","score":"0.55199075","text":"def test_vehicle_error_quota_limit() -> None:\n response: models.KamereonVehicleDataResponse = fixtures.get_file_content_as_schema(\n f\"{fixtures.KAMEREON_FIXTURE_PATH}/error/quota_limit.json\",\n schemas.KamereonVehicleDataResponseSchema,\n )\n with pytest.raises(exceptions.QuotaLimitException) as excinfo:\n response.raise_for_error_code()\n assert excinfo.value.error_code == \"err.func.wired.overloaded\"\n assert excinfo.value.error_details == \"You have reached your quota limit\"","title":""},{"docid":"2db0e6d6b9538d21be903b3567d6778a","score":"0.551946","text":"def test_limits_boundary_values(self):\n def check_error_msg(status, output, storagelimit=False):\n import json\n if status == False:\n content = json.loads(output)[\"errors\"]\n if storagelimit:\n actual_error = content[\"dataStorageLimit\"]\n expected_error = '\"dataStorageLimit\" must be an integer between -1 and 100000'\n else:\n actual_error = content[\"dataThrottleLimit\"]\n expected_error = '\"dataThrottleLimit\" must be an integer between -1 and 2147483647'\n self.assertEqual(actual_error, expected_error)\n else:\n self.fail(\"expected to fail but passsed\")\n\n bucket = self.bucket_util.get_all_buckets(self.cluster)[0]\n server = random.choice(bucket.servers)\n bucket_helper = BucketHelper(server)\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\n throttle_limit=-2)\n check_error_msg(status, content)\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\n throttle_limit=2147483648)\n check_error_msg(status, content)\n\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\n storage_limit=-2)\n check_error_msg(status, content, True)\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\n storage_limit=2147483648)\n check_error_msg(status, content, True)\n\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\n throttle_limit=-2,\n storage_limit=-2)\n check_error_msg(status, content)\n check_error_msg(status, content, True)\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\n throttle_limit=2147483648,\n storage_limit=2147483648)\n check_error_msg(status, content)\n check_error_msg(status, content, True)","title":""},{"docid":"2c8cfd4e6547c5da4018fce338552fc1","score":"0.55178887","text":"def test_absLimits_get(self):\n # To check if all limits are present in the response (will be checked\n # by schema)\n self.client.show_limits()","title":""},{"docid":"f4559bad2a7fa45c28b461e0872b9faa","score":"0.55167836","text":"def testModel10(self):\n m = self.createModel ( 10 )\n ulComp = UpperLimitComputer( cl=.95 )\n ulProf = ulComp.getUpperLimitOnMu( m )\n self.assertAlmostEqual( ulProf / 365.6091713369213, 1.0, 2 )\n ## Nick's profiling code gets for n=10 ul=357.568","title":""},{"docid":"605f7410262f6675d7c4426a9b4c6acf","score":"0.5482882","text":"def test_above_to_below_threshold(self):\n throughput_difference = self.throughput_difference\n\n self.set_limits_for_all_users(self.resource_name, self.resource_limit)\n\n # Check that a throughput above the threshold is constrained by the\n # resource limit\n for task in self.tasks:\n task.set_throughput(self.resource_limit * self.units + throughput_difference)\n\n for task in self.tasks:\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(), self.resource_limit * self.units, self.error)))\n\n self.sleep(5)\n\n # Check that a throughput below the limit succeeds\n for task in self.tasks:\n task.set_throughput(self.resource_limit * self.units - throughput_difference)\n\n for task in self.tasks:\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(), task.get_throughput(), self.error)))\n\n self.set_throughput_to_zero()","title":""},{"docid":"d52e7cbd76427c029fb67e8abebba80c","score":"0.5475557","text":"def test_investment_limit(self):\n bus1 = solph.Bus(label='Bus1')\n solph.components.GenericStorage(\n label='storage_invest_limit',\n invest_relation_input_capacity=0.2,\n invest_relation_output_capacity=0.2,\n inputs={bus1: solph.Flow()},\n outputs={bus1: solph.Flow()},\n investment=solph.Investment(ep_costs=145))\n solph.Source(label='Source', outputs={bus1: solph.Flow(\n investment=solph.Investment(ep_costs=123))})\n om = self.get_om()\n solph.constraints.investment_limit(om, limit=900)\n\n self.compare_lp_files('investment_limit.lp', my_om=om)","title":""},{"docid":"b7463779b823b34dc5aa1bf07a24eca5","score":"0.5472236","text":"def __init__(self, \n loading_limit: float = 1.0,\n voltage_limit: dict = {\n 'overvoltage_threshold': 1.05,\n 'undervoltage_threshold': 0.95\n }\n ):\n\n self.loading_limit = data_model.ThermalLoadingLimit(threshold=loading_limit)\n self.voltage_limit = data_model.VoltageViolationLimit(**voltage_limit)\n self.sardi_aggregated = 0\n self.counter = 0","title":""},{"docid":"c4e2b672d4dc7204199b80307baf975e","score":"0.5468406","text":"def setUp(self):\n self.maxDiff = None\n pass","title":""},{"docid":"7bc25d8909e6aa209e9f2e2d0be7b68e","score":"0.54441905","text":"def test_warn_count_all_unnecessary(marker_trackerstore: TrackerStore):\n with pytest.warns(UserWarning):\n MarkerTrackerLoader(marker_trackerstore, STRATEGY_ALL, 3)","title":""},{"docid":"14d2bfa5e14c5ed85e25032ec06138e7","score":"0.54241455","text":"def _monkey_update_limits(self):\n self.monkey_update_limits_counter += 1","title":""},{"docid":"147689e8ba9730d21bd90b07a88450ca","score":"0.5423483","text":"async def test_warn_count_exceeds_store(marker_trackerstore: TrackerStore):\n loader = MarkerTrackerLoader(marker_trackerstore, STRATEGY_SAMPLE_N, 6)\n with pytest.warns(UserWarning):\n # Need to force the generator to evaluate to produce the warning\n [tracker async for tracker in loader.load()]","title":""},{"docid":"f563c2e499abfb074d540962c3d9af1f","score":"0.54190344","text":"def test_change_default_throttling_settings_ws_with_overwrite_throttled_burst_above_50():","title":""},{"docid":"861ab1172fa87007229aece7506960ca","score":"0.5406477","text":"def setUp(self):\n self.maxDiff = None","title":""},{"docid":"861ab1172fa87007229aece7506960ca","score":"0.5406477","text":"def setUp(self):\n self.maxDiff = None","title":""},{"docid":"effe8908484025bd651d30f0924e1980","score":"0.5385588","text":"def test_backend_job_limit(self):\n job_limit = self.backend.job_limit()\n self.assertIsNotNone(job_limit)\n self.assertIsNotNone(job_limit.active_jobs)\n if job_limit.maximum_jobs:\n self.assertGreater(job_limit.maximum_jobs, 0)","title":""},{"docid":"e6a5ca508718aa8278ec74bef4ab52dd","score":"0.5382612","text":"def test_api_loss_livestock_read(self):\n pass","title":""},{"docid":"aedb392579d434e71a5f9e1599a33c1a","score":"0.53748673","text":"def test_above_threshold(self):\n throughput_difference = self.throughput_difference\n\n self.set_limits_for_all_users(self.resource_name, self.resource_limit)\n\n # Check that a throughput above the threshold is constrained by the\n # resource limit\n for task in self.tasks:\n task.set_throughput(self.resource_limit * self.units + throughput_difference)\n\n for task in self.tasks:\n self.get_throughput = self.resource_limit * self.units\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(),\n self.get_throughput, self.error)))\n\n # Once above threshold, ensure the expected error message is thrown\n if self.tasks and self.tasks[0].expected_error():\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check_error(self.tasks[0].error(), self.tasks[0].expected_error())))\n\n self.run_compaction()\n\n # set a higher limit to validate stats\n self.set_limits_for_all_users(self.resource_name, self.resource_limit*2)\n\n self.get_stats(self.get_throughput)\n self.set_throughput_to_zero()","title":""},{"docid":"6e9d17c01cda4ebe3809accad1368a5f","score":"0.5370684","text":"def test_change_default_throttling_settings_ws_with_overwrite_throttled_burst_above_account_quota():","title":""},{"docid":"22680aa0d2c40892f6f36114bc2bf5e0","score":"0.53467035","text":"def test_change_default_throttling_settings_ws_with_overwrite_throttled():","title":""},{"docid":"1483b065c225ad7c2faed1cc34cd61e5","score":"0.5340418","text":"def test_api_pings():","title":""},{"docid":"01008ac4145ca5c9d4ec1f51c92bfdf4","score":"0.5332597","text":"def test_datapoints_agent(self):\n pass","title":""},{"docid":"8e5e5b0d87f58a1a1bfbdbbd9d919cd0","score":"0.53280985","text":"def test_api_vulnerability_read(self):\n pass","title":""},{"docid":"8f91b4ce1ad365a009b2d61390489c8c","score":"0.53207844","text":"def test_get_stats_works(fixture):\n\tpass","title":""},{"docid":"8e9dc9174213a944552c00c74e42c711","score":"0.53145677","text":"def test_post_view_hits_rate_limit_will_return_429(self, throttled_function):\n url = reverse(self.VIEW_NAME)\n\n text = \"I am eating a hamburger\"\n data = {\"text\": text, \"email\": text, \"title\": text}\n\n client = self.registered_user_client\n\n for _ in range(3):\n response = client.post(url, data=data)\n\n # by the 3rd request, it should have hit the rate limit\n self.assertEqual(response.status_code, 429)","title":""},{"docid":"f7ce635d7ca31075dfd703bbb75fad7e","score":"0.5311275","text":"def test_stats_get(self):\n pass","title":""},{"docid":"9613fa7ebf25c4158b0f0913a2a1fffe","score":"0.53091764","text":"def test_api_rejects_invalid_limit_parameter(self):\n\n res = self.client().get(\"/bucketlists/v1.0/?limit=dkj\",\n headers={\"Authorization\": self.token}\n )\n self.assertEqual(res.status_code, 400)\n self.assertIn('\"limit\": \"Limit has to be a number between 1 and 100\"', str(res.data))","title":""},{"docid":"c38455204f979970e200d0fbc8af07fa","score":"0.5301058","text":"def test_setup(self):\n self.assertEqual(8, len(self.raw_metrics_list), \"incorrect number json files read\")","title":""},{"docid":"35f8ec51e964a32376250ad4abfdc991","score":"0.53001773","text":"def test_api_pollution_read(self):\n pass","title":""},{"docid":"1c00c0289ce5b30ecc63191737bf7a91","score":"0.5295825","text":"def test_no_quota(self, mock_google_credential):\n iam_api_client = iam.IAMClient(global_configs={})\n self.assertEqual(None, iam_api_client.repository._rate_limiter)","title":""},{"docid":"140403ea97ead15f21e0234c6e3bb831","score":"0.5290854","text":"def monkey_update_limits(self):\n self._monkey_update_limits_counter += 1","title":""},{"docid":"5210bac5e5864645dce2f6149ab7bd66","score":"0.52772236","text":"def throttle_failure(self):\n ...","title":""},{"docid":"cc2cb6f3650dbdde8c333a597208dd49","score":"0.5274103","text":"def test_4(self):\n pass","title":""},{"docid":"311741480132ebdb87164609f0028ed4","score":"0.526988","text":"def test_get_task_counts_for_project(self):\n pass","title":""},{"docid":"4bfcc5fa516f4fe2340f52fde18e6100","score":"0.5268455","text":"def test_limit_batches():\n command = [\n \"run.py\",\n \"trainer=default\",\n \"trainer.max_epochs=1\",\n \"trainer.limit_train_batches=0.25\",\n \"trainer.limit_val_batches=0.25\",\n \"trainer.limit_test_batches=0.25\",\n ]\n run_command(command)","title":""},{"docid":"791f0692a94751a7eb9dc52bcefd5e5c","score":"0.5268286","text":"def limit(self, count):\n ...","title":""},{"docid":"f988e2113eb41b9ef4a78efee021d3e0","score":"0.5256502","text":"def test_calc_requests_used_most_recent_quarterly_finished_mdm_import_dataset(self):\n pass","title":""},{"docid":"5d7cacf4f06d0c7199faf623f92e22eb","score":"0.52531564","text":"def test_get_loyalty_statistics(self):\n pass","title":""},{"docid":"90b4c969440d7d9ad5e7d45c2049f85c","score":"0.5247421","text":"def test_api_vulnerability_list(self):\n pass","title":""},{"docid":"eeadd5017709442f6f24c683f78a6ce2","score":"0.5246757","text":"def limits(self):\n pass","title":""},{"docid":"c8b4b1cdf058f1a8088794045a3af17d","score":"0.5246362","text":"def test_defaultmessage_limits(self):\n # Reset limits first.\n self.reset_limits()\n\n # Request as often as allowed.\n for i in range(0, self._app.limit_amount):\n self.http_client.fetch(self.get_url('/message/?receiver=%2B49176123456'), self.stop)\n response = self.wait()\n self.assert_json_response(response, {'status': 'ok'})\n\n # Request again and expect error.\n self.http_client.fetch(self.get_url('/message/?receiver=%2B49176123456'), self.stop)\n response = self.wait()\n self.assert_json_response(response, {'status': 'error', \"error\": \"limit_acceded\"})","title":""},{"docid":"ba552db0941824167440874d2c526ad2","score":"0.5243879","text":"def test_api_loss_livestock_list(self):\n pass","title":""},{"docid":"ca5463fd9a59ec9898f825406dd45249","score":"0.52336663","text":"def test_assign_resources_on_sdp_in_low():","title":""},{"docid":"64401e16acebcdf491cc6144a1a120cc","score":"0.5232181","text":"def test_q_rate_limit_switch(self):\n return self._rate_limit_switch(apk_type='Q')","title":""},{"docid":"5648c8fcec5cc214d4d05a429bde0046","score":"0.5230283","text":"def test_mock_handle_rate_limiting_429(self, app, worker):\n mock_exception = SpotifyException(http_status=429, code=-1, msg=\"Mock exception\", headers={'Retry-After': 1})\n job = findartist.spotify.handle_rate_limiting(mock_exception)\n\n assert job in app.task_queue.jobs\n worker.work(burst=True)\n assert job not in app.task_queue.jobs\n assert job.is_finished","title":""},{"docid":"af1ca4a11a40dbf7e53ee6a5ca9c4704","score":"0.52235043","text":"def monkey_update_limits(self, *args):\n self._monkey_update_limits_counter += 1","title":""},{"docid":"65a1187552ae4ec4473c938eb901fbb1","score":"0.52184767","text":"def test_update_limits(self, monkeypatch):\n # call function to test\n monkeypatch.setattr(DoublyFedInductionMotor, '__init__', self.monkey_init)\n\n test_object = DoublyFedInductionMotor()\n test_object._motor_parameter = self._motor_parameter\n test_object._limits = self._limit_values\n test_object._nominal_values = self._nominal_values\n # verify the expected results\n test_object._update_limits()\n\n assert test_object._limits['u_rbeta'] == 0.5 * self._limit_values['u']\n assert test_object._nominal_values['i_ralpha'] == (test_object._nominal_values.get('i', None) or test_object._nominal_values['u_ralpha'] / test_object._motor_parameter['r_r'])\n\n monkeypatch.setattr(InductionMotor, '_update_limits', self.monkey_super_update_limits)\n test_object._update_limits()\n assert self._monkey_super_update_limits_counter == 1, 'super().update_limits() is not called once'","title":""},{"docid":"6cfebc8db03c686495c135c813f57f06","score":"0.52165115","text":"def test_set_real_limit(self):\n ## Create a card\n self.app.post('/wallet/cards',\n data={**self.card_arguments,\n 'due_day': 27,\n 'max_limit': 1000.0},\n headers=dict(token=self.token), follow_redirects=True)\n\n # set real limit\n result = self.app.put('/wallet/real_limit/{}'.format(500.0), headers=dict(token=self.token),\n follow_redirects=True)\n self.assertEqual(result.status_code, 200)\n\n # Get wallet information and test free limit\n result = self.app.get('/wallet', headers=dict(token=self.token), follow_redirects=True)\n wallet = json.loads(result.get_data(as_text=True))\n\n self.assertEqual(wallet['real_limit'], 500.0)","title":""},{"docid":"5c001b9151f78ba6fa1c78d66717bde8","score":"0.52106315","text":"def test_1_0(self):\n pass","title":""},{"docid":"4eca299efee3d1176cf39ea28978c28a","score":"0.5209944","text":"def test_add_vote(self):\n pass","title":""},{"docid":"fc0a30a1ff8e7d599561ab6219a85a5a","score":"0.5197893","text":"def metrics_to_use(self):","title":""},{"docid":"e584f5b1e1944ae9128eb9a748fbacfe","score":"0.51940686","text":"def limit( bw=10, cpu=.1 ):\n intf = custom( TCIntf, bw=bw )\n myTopo = TreeTopo( depth=1, fanout=2 )\n for sched in 'rt', 'cfs':\n info( '*** Testing with', sched, 'bandwidth limiting\\n' )\n if sched == 'rt':\n release = quietRun( 'uname -r' ).strip('\\r\\n')\n output = quietRun( 'grep CONFIG_RT_GROUP_SCHED /boot/config-%s'\n % release )\n if output == '# CONFIG_RT_GROUP_SCHED is not set\\n':\n info( '*** RT Scheduler is not enabled in your kernel. '\n 'Skipping this test\\n' )\n continue\n host = custom( CPULimitedHost, sched=sched, cpu=cpu )\n net = Mininet( topo=myTopo, intf=intf, host=host )\n net.start()\n testLinkLimit( net, bw=bw )\n net.runCpuLimitTest( cpu=cpu )\n net.stop()","title":""},{"docid":"2dc1e789b8060a80568434a0a92ef969","score":"0.51926","text":"def test_get_target_low(self):\n self.assertEqual(90, int(self.decider.get_target_low()))","title":""},{"docid":"689d8b3e3e9cdd38fcfe0e2e1c61ad6f","score":"0.5189289","text":"def test_mock_handle_rate_limiting_other(self, app, worker):\n mock_exception = SpotifyException(http_status=500, code=-1, msg=\"Mock exception\")\n job = findartist.spotify.handle_rate_limiting(mock_exception)\n assert job is None","title":""},{"docid":"46fcb98abd2ca1365a843b1d2872353e","score":"0.51855177","text":"def test_invalid_usage_limits_count(self):\n kwargs = {'usage_limits_count': 'invalid'}\n self.assertRaisesRegex(\n TypeError,\n \"Usage limits count must be an integer.\",\n payloads.CheckRequestPayload,\n **kwargs\n )\n\n payload = payloads.CheckRequestPayload()\n args = (payload, 'usage_limits_count', 'invalid')\n self.assertRaisesRegex(\n TypeError,\n \"Usage limits count must be an integer.\",\n setattr,\n *args\n )","title":""},{"docid":"552576c0a98079e2d0328df7aa439700","score":"0.51854384","text":"def test_add_loyalty_points(self):\n pass","title":""},{"docid":"90285dc105febdd849e5bb9c230d1dc5","score":"0.5181207","text":"def test_4_0(self):\n pass","title":""},{"docid":"99da783c6eb090328bfabeb8870bf314","score":"0.51730084","text":"def setUp(self):\n\n Test.maxDiff = None","title":""},{"docid":"5a92616e769a5d1cb3217b13b20a9983","score":"0.5170803","text":"def test_non_algorithmic(self):\n user = users.models.User.objects.create_user('test-user')\n user.school_participations.create(\n school=self.prev_school_1, parallel=self.s1_c_prime)\n user.school_participations.create(\n school=self.prev_school_2, parallel=self.s2_p)\n limit = self.limiter.get_limit(user)\n self.assertEqual(limit.min_level, self.c)","title":""},{"docid":"e2de9a73ba436aa8b5014b39fd686afd","score":"0.5166248","text":"def test_change_default_throttling_settings_ws_with_overwrite_not_throttled():","title":""},{"docid":"25f824ca954e055d2f435e9035469b80","score":"0.516279","text":"def test_replica_rebuild_per_volume_limit():\n pass","title":""},{"docid":"ed82c281f0922ac9a7ecd142073448e4","score":"0.51620966","text":"def test_get_account_analytics(self):\n pass","title":""},{"docid":"9c2bcb75ddb3b932aa7bab229253b583","score":"0.51556665","text":"def test_case_4(self):","title":""},{"docid":"f464cac8f81208d141ebf17c1a20cacd","score":"0.51537263","text":"def test_functionality(self):\n pass","title":""},{"docid":"46fd953d06a0aa2f1c6ab23ae66d95a8","score":"0.515275","text":"def test_redundancy_increased_when_not_max(self, mock_client):\n n_answers = 3\n target = 'example.com'\n task = self.ctx.create_task(n_answers, target, max_answers=4)\n for i in range(n_answers):\n TaskRunFactory.create(task=task, info={\n 'reference': i,\n 'control_number': i,\n 'comments': ''\n })\n result = self.result_repo.filter_by(project_id=task.project_id)[0]\n fake_search = MagicMock()\n fake_search.return_value = []\n mock_client.search_annotations = fake_search\n self.z3950_analyst.analyse(result.id)\n assert_equal(mock_client.create_annotation.called, False)\n\n updated_task = self.task_repo.get_task(task.id)\n assert_equal(updated_task.n_answers, n_answers + 1)","title":""},{"docid":"927d00ec51cb64265439588d482b3f5f","score":"0.5147975","text":"def testCaseTBA(self):","title":""},{"docid":"96093b7512519cf8aa209c17e16fbbd9","score":"0.51442605","text":"def test_invalid_usage_limits_count(self):\n kwargs = {'usage_limits_count': 'invalid'}\n self.assertRaisesRegex(\n TypeError,\n \"Usage limits count must be an integer.\",\n payloads.CheckResponsePayload,\n **kwargs\n )\n\n payload = payloads.CheckResponsePayload()\n args = (payload, 'usage_limits_count', 'invalid')\n self.assertRaisesRegex(\n TypeError,\n \"Usage limits count must be an integer.\",\n setattr,\n *args\n )","title":""},{"docid":"0605695f28a7671760cd03cd4dd896e0","score":"0.5142084","text":"def test_redundancy_not_increased_when_max(self, mock_client):\n n_answers = 3\n target = 'example.com'\n task = self.ctx.create_task(n_answers, target, max_answers=3)\n for i in range(n_answers):\n TaskRunFactory.create(task=task, info={\n 'reference': i,\n 'control_number': i,\n 'comments': ''\n })\n result = self.result_repo.filter_by(project_id=task.project_id)[0]\n fake_search = MagicMock()\n fake_search.return_value = []\n mock_client.search_annotations = fake_search\n self.z3950_analyst.analyse(result.id)\n assert_equal(mock_client.create_annotation.called, False)\n\n updated_task = self.task_repo.get_task(task.id)\n assert_equal(updated_task.n_answers, n_answers)","title":""},{"docid":"c61b8cf2b199172f15801994e9c81fea","score":"0.513838","text":"def test_api_loss_list(self):\n pass","title":""},{"docid":"001da76050180758b4e0921641d6f7fe","score":"0.51352376","text":"def test_max_local_block_devices_0_force_bfv(self):\n self.flags(max_local_block_devices=0)\n server = self._build_server()\n ex = self.assertRaises(api_client.OpenStackApiException,\n self.admin_api.post_server,\n {'server': server})\n self.assertEqual(400, ex.response.status_code)\n self.assertIn('You specified more local devices than the limit allows',\n str(ex))","title":""}],"string":"[\n {\n \"docid\": \"5e84c8ee36f03c4d423562b65adc6087\",\n \"score\": \"0.6538664\",\n \"text\": \"def test_rate_limit_interface():\\n per_user_rate_def = RateDefinition(per_week=50000, per_day=13000, per_second=.001)\\n min_rate_def = RateDefinition(per_second=10)\\n per_user_rate_def = PerUserRateDefinition(per_user_rate_def, min_rate_def)\\n my_feature_rate_limiter = RateLimiter('my_feature', per_user_rate_def.get_rate_limits)\\n if my_feature_rate_limiter.allow_usage('my_domain'):\\n # ...do stuff...\\n my_feature_rate_limiter.report_usage('my_domain')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d93729a630c34495d64c4b65d915e78\",\n \"score\": \"0.6360713\",\n \"text\": \"def test_activity_api_limits(self):\\n\\n async def run_test():\\n with self.assertRaises(ValueError):\\n await self.test_camera.query_activity_history(limit=ACTIVITY_API_LIMIT + 1)\\n\\n self.loop.run_until_complete(run_test())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"577aaff1b88b187d68a0fc9eb0d0a150\",\n \"score\": \"0.60561013\",\n \"text\": \"def on_limit(self, track):\\n print 'Limitation notice has arrived...'\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f731d809af601dfc71952e039aedadcb\",\n \"score\": \"0.5922246\",\n \"text\": \"def test_no_participations(self):\\n user = users.models.User.objects.create_user('test-user')\\n limit = self.limiter.get_limit(user)\\n self.assertEqual(limit.min_level, self.c_prime)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc47a7b5642e0be6e1843560d450fa4d\",\n \"score\": \"0.58270365\",\n \"text\": \"def test_limit_reached(self):\\r\\n chip_counter = Chips()\\r\\n limit_reached(chip_counter)\\r\\n self.assertEqual(chip_counter.total, 2000)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f073275b6c9022ea447378594acfed2\",\n \"score\": \"0.57859415\",\n \"text\": \"def setUp(self):\\n super(LimitsControllerTestV21, self).setUp()\\n self.controller = wsgi.Resource(self.limits_controller())\\n self.ctrler = self.limits_controller()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e651565f2fa68e798f85ce96e80e1e5\",\n \"score\": \"0.5757954\",\n \"text\": \"def testCPULimit(self):\\n result = self.RunFlow(\\\"CPULimitFlow\\\", cpu_limit=300)\\n self.assertEqual(result[\\\"cpulimit\\\"], [300, 295, 255])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"028e2c972cedb228cc34afa6e47ecdd1\",\n \"score\": \"0.5748576\",\n \"text\": \"def test_lagLimitExceeded(self):\\n logger = OperationLogger(outfile=StringIO())\\n for lag in [100.0, 1100.0, 1200.0]:\\n logger.observe(dict(\\n type='operation', phase='start', user='user01',\\n label='testing', lag=lag)\\n )\\n self.assertEqual(\\n [\\\"Median TESTING scheduling lag greater than 1000.0ms\\\"],\\n logger.failures())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0ddee65968dc0db1bba54298341b73b\",\n \"score\": \"0.5728882\",\n \"text\": \"def test_rate_limiting_error(self):\\n self.sources[0].RATE_LIMIT_HTTP_CODES = ('429', '456')\\n\\n error_body = json_dumps({\\\"meta\\\": {\\n \\\"code\\\": 429, \\\"error_message\\\": \\\"The maximum number of requests...\\\",\\n \\\"error_type\\\": \\\"OAuthRateLimitException\\\"}})\\n self.expect_get_activities().AndRaise(\\n urllib.error.HTTPError('url', 429, 'Rate limited', {},\\n io.StringIO(error_body)))\\n\\n self.post_task(expect_poll=FakeSource.RATE_LIMITED_POLL,\\n expect_last_polled=util.EPOCH)\\n source = self.sources[0].key.get()\\n self.assertEqual('error', source.poll_status)\\n self.assertTrue(source.rate_limited)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"429a1b753c13a4d10d6c92ab2a74cd5b\",\n \"score\": \"0.57227284\",\n \"text\": \"def l_rate_limit(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce44371742d077c6779a50b7ed42e1ea\",\n \"score\": \"0.5719269\",\n \"text\": \"def test_failureLimitExceeded(self):\\n logger = OperationLogger(outfile=StringIO())\\n for _ignore in range(98):\\n logger.observe(dict(\\n type='operation', phase='end', user='user01',\\n duration=0.25, label='testing', success=True)\\n )\\n logger.observe(dict(\\n type='operation', phase='end', user='user01',\\n duration=0.25, label='testing', success=False)\\n )\\n self.assertEqual(\\n [\\\"Greater than 1% TESTING failed\\\"],\\n logger.failures())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e227dcc7289ee272f60fa0da1a07c9ce\",\n \"score\": \"0.5711501\",\n \"text\": \"def test_limit_complete(self):\\n limit = Limit(item_limit=0)\\n self.assertTrue(limit.complete)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0feeedd5f4c50591d83f4d5fcc7f880b\",\n \"score\": \"0.57075286\",\n \"text\": \"def patch_max_test_time(limit):\\n from corehq.tests.noseplugins.timing import patch_max_test_time\\n return patch_max_test_time(limit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cf5b7cc716afd8c4bbca17ff7c3acd0\",\n \"score\": \"0.57040715\",\n \"text\": \"def testNetworkLimit(self):\\n result = self.RunFlow(\\\"NetworkLimitFlow\\\", network_bytes_limit=10000)\\n self.assertEqual(result[\\\"networklimit\\\"], [10000, 9820, 8820, 8240])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae6c67987520abd015cd9de754883cf2\",\n \"score\": \"0.5701207\",\n \"text\": \"def read_limits(self, for_who):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff22e7cb9c2a3e700ca9155736fb84ab\",\n \"score\": \"0.5698678\",\n \"text\": \"def test_throttle(throttle_app: App, test_request):\\n\\n app = throttle_app\\n\\n # clear counter from previous test run\\n clear_throttle(test_request, \\\"throttle_sample\\\")\\n\\n app.get(\\\"/\\\", status=200)\\n\\n # We exceeded the limit of 1 request per hour\\n app.get(\\\"/\\\", status=429)\\n\\n # Let's clear the counter\\n clear_throttle(test_request, \\\"throttle_sample\\\")\\n\\n app.get(\\\"/\\\", status=200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5aaa63580662051245a41e49f13c4230\",\n \"score\": \"0.56840473\",\n \"text\": \"def setUp(self):\\n self.max_integer = __import__('6-max_integer').max_integer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2543c7d0beb0ba1c91f9362f2f265831\",\n \"score\": \"0.56803393\",\n \"text\": \"def test_p_rate_limit_switch(self):\\n return self._rate_limit_switch(apk_type='P')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9ed7d357f3e9e2d9f4dc79158c4f26f\",\n \"score\": \"0.5655471\",\n \"text\": \"def test_custom_threshold_works(self, mock_req):\\n uri = re.compile(\\n r\\\"http://services\\\\.swpc\\\\.noaa\\\\.gov/text/aurora-nowcast-map\\\\.txt\\\"\\n )\\n mock_req.get(uri, text=load_fixture(\\\"aurora.txt\\\"))\\n\\n entities = []\\n\\n def mock_add_entities(new_entities, update_before_add=False):\\n \\\"\\\"\\\"Mock add entities.\\\"\\\"\\\"\\n if update_before_add:\\n for entity in new_entities:\\n entity.update()\\n\\n for entity in new_entities:\\n entities.append(entity)\\n\\n config = {\\\"name\\\": \\\"Test\\\", \\\"forecast_threshold\\\": 1}\\n self.hass.config.longitude = 18.987\\n self.hass.config.latitude = 69.648\\n\\n aurora.setup_platform(self.hass, config, mock_add_entities)\\n\\n aurora_component = entities[0]\\n assert aurora_component.aurora_data.visibility_level == \\\"16\\\"\\n assert aurora_component.is_on\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85bf876290c6c3c5d0355470cbad4ed4\",\n \"score\": \"0.5648686\",\n \"text\": \"def test_below_threshold(self):\\n throughput_difference = self.throughput_difference\\n\\n self.set_limits_for_all_users(self.resource_name, self.resource_limit)\\n\\n # Check that a throughput below the limit succeeds\\n for task in self.tasks:\\n task.set_throughput(self.resource_limit * self.units - throughput_difference)\\n\\n for task in self.tasks:\\n self.get_throughput = task.get_throughput() + self.error\\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(),\\n task.get_throughput(), self.error)))\\n\\n self.get_stats(self.get_throughput, False)\\n\\n self.set_throughput_to_zero()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"141469ad46b520837dd553482f37f6fe\",\n \"score\": \"0.5639672\",\n \"text\": \"def test_change_default_throttling_settings_ws_with_overwrite_throttled_rate_above_50():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0469a79b4e91fdf1eb75b14522575b4f\",\n \"score\": \"0.5631249\",\n \"text\": \"def test_invocation_unchanged_limits(self):\\n self.test_response[\\\"X-RateLimit-Usage\\\"] = \\\"0, 0\\\"\\n self.test_response[\\\"X-RateLimit-Limit\\\"] = \\\"10000, 1000000\\\"\\n rule = SleepingRateLimitRule()\\n self.assertEqual(10000, rule.short_limit)\\n self.assertEqual(1000000, rule.long_limit)\\n rule(self.test_response)\\n self.assertEqual(10000, rule.short_limit)\\n self.assertEqual(1000000, rule.long_limit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2599d758adb11097d473b0bfb0780c11\",\n \"score\": \"0.56051433\",\n \"text\": \"def _set_limits(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2319fb6aad76ef1c6ddf1cae92cd6c5\",\n \"score\": \"0.5590528\",\n \"text\": \"def __init__(self, reason, lim=0):\\n self.successes = 0\\n self.tests = 0\\n self.reason = reason\\n self.limit = lim\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4e9f03b8b2adbadb4b28bae3405579f\",\n \"score\": \"0.5579144\",\n \"text\": \"def test_invocation_changed_limits(self):\\n self.test_response[\\\"X-RateLimit-Usage\\\"] = \\\"0, 0\\\"\\n self.test_response[\\\"X-RateLimit-Limit\\\"] = \\\"600, 30000\\\"\\n\\n # without limit enforcement (default)\\n rule = SleepingRateLimitRule()\\n rule(self.test_response)\\n self.assertEqual(600, rule.short_limit)\\n self.assertEqual(30000, rule.long_limit)\\n\\n # with limit enforcement\\n rule = SleepingRateLimitRule(force_limits=True)\\n rule(self.test_response)\\n self.assertEqual(10000, rule.short_limit)\\n self.assertEqual(1000000, rule.long_limit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a03eb84647800d9093f40be90d5ba017\",\n \"score\": \"0.5568088\",\n \"text\": \"def test_validate_number_limits(self):\\n # Reset limits first.\\n self.reset_limits()\\n\\n # Request as often as allowed.\\n for i in range(0, self._app.limit_amount):\\n self.http_client.fetch(self.get_url('/validate_number/?number=%2B49176123456'), self.stop)\\n response = self.wait()\\n self.assert_json_response(response, {'status': 'ok'})\\n\\n # Request again and expect error.\\n self.http_client.fetch(self.get_url('/validate_number/?number=%2B49176123456'), self.stop)\\n response = self.wait()\\n self.assert_json_response(response, {'status': 'error', \\\"error\\\": \\\"limit_acceded\\\"})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eca65a20a04453254c85360acb20ea5e\",\n \"score\": \"0.55434823\",\n \"text\": \"def test_statistics_challenge(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df7e313bfb7a80e371ff9385d8417c86\",\n \"score\": \"0.5538704\",\n \"text\": \"def test_change_default_throttling_settings_ws_with_overwrite_throttled_rate_above_account_quota():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d018af22930d29f4c353b8dbfa60cafe\",\n \"score\": \"0.5533047\",\n \"text\": \"def test_no_quota(self, mock_google_credential):\\n ae_api_client = ae.AppEngineClient(global_configs={})\\n self.assertEqual(None, ae_api_client.repository._rate_limiter)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c788ea84b5ffad0c5ab893d8e475ac34\",\n \"score\": \"0.5523266\",\n \"text\": \"def test_below_to_above_threshold(self):\\n throughput_difference = self.throughput_difference\\n\\n self.set_limits_for_all_users(self.resource_name, self.resource_limit)\\n\\n # Check that a throughput below the limit succeeds\\n for task in self.tasks:\\n task.set_throughput(self.resource_limit * self.units - throughput_difference)\\n\\n for task in self.tasks:\\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(), task.get_throughput(), self.error)))\\n\\n self.sleep(5)\\n\\n # Check that a throughput above the threshold is constrained by the\\n # resource limit\\n for task in self.tasks:\\n task.set_throughput(self.resource_limit * self.units + throughput_difference)\\n\\n for task in self.tasks:\\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(), self.resource_limit * self.units, self.error)))\\n\\n self.set_throughput_to_zero()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21efddcaf5513005cc4e51e4780125a8\",\n \"score\": \"0.552136\",\n \"text\": \"def test_too_high_limit_value(self):\\n self.mockRequest.args['limit'] = ['100']\\n d = self.app.paginate_me(self.mockRequest)\\n self.assertEqual(self.successResultOf(d), {'limit': 10})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb84850547b9daa3559033fa86466ce2\",\n \"score\": \"0.55199075\",\n \"text\": \"def test_vehicle_error_quota_limit() -> None:\\n response: models.KamereonVehicleDataResponse = fixtures.get_file_content_as_schema(\\n f\\\"{fixtures.KAMEREON_FIXTURE_PATH}/error/quota_limit.json\\\",\\n schemas.KamereonVehicleDataResponseSchema,\\n )\\n with pytest.raises(exceptions.QuotaLimitException) as excinfo:\\n response.raise_for_error_code()\\n assert excinfo.value.error_code == \\\"err.func.wired.overloaded\\\"\\n assert excinfo.value.error_details == \\\"You have reached your quota limit\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2db0e6d6b9538d21be903b3567d6778a\",\n \"score\": \"0.551946\",\n \"text\": \"def test_limits_boundary_values(self):\\n def check_error_msg(status, output, storagelimit=False):\\n import json\\n if status == False:\\n content = json.loads(output)[\\\"errors\\\"]\\n if storagelimit:\\n actual_error = content[\\\"dataStorageLimit\\\"]\\n expected_error = '\\\"dataStorageLimit\\\" must be an integer between -1 and 100000'\\n else:\\n actual_error = content[\\\"dataThrottleLimit\\\"]\\n expected_error = '\\\"dataThrottleLimit\\\" must be an integer between -1 and 2147483647'\\n self.assertEqual(actual_error, expected_error)\\n else:\\n self.fail(\\\"expected to fail but passsed\\\")\\n\\n bucket = self.bucket_util.get_all_buckets(self.cluster)[0]\\n server = random.choice(bucket.servers)\\n bucket_helper = BucketHelper(server)\\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\\n throttle_limit=-2)\\n check_error_msg(status, content)\\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\\n throttle_limit=2147483648)\\n check_error_msg(status, content)\\n\\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\\n storage_limit=-2)\\n check_error_msg(status, content, True)\\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\\n storage_limit=2147483648)\\n check_error_msg(status, content, True)\\n\\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\\n throttle_limit=-2,\\n storage_limit=-2)\\n check_error_msg(status, content)\\n check_error_msg(status, content, True)\\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\\n throttle_limit=2147483648,\\n storage_limit=2147483648)\\n check_error_msg(status, content)\\n check_error_msg(status, content, True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c8cfd4e6547c5da4018fce338552fc1\",\n \"score\": \"0.55178887\",\n \"text\": \"def test_absLimits_get(self):\\n # To check if all limits are present in the response (will be checked\\n # by schema)\\n self.client.show_limits()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4559bad2a7fa45c28b461e0872b9faa\",\n \"score\": \"0.55167836\",\n \"text\": \"def testModel10(self):\\n m = self.createModel ( 10 )\\n ulComp = UpperLimitComputer( cl=.95 )\\n ulProf = ulComp.getUpperLimitOnMu( m )\\n self.assertAlmostEqual( ulProf / 365.6091713369213, 1.0, 2 )\\n ## Nick's profiling code gets for n=10 ul=357.568\",\n \"title\": \"\"\n },\n {\n \"docid\": \"605f7410262f6675d7c4426a9b4c6acf\",\n \"score\": \"0.5482882\",\n \"text\": \"def test_above_to_below_threshold(self):\\n throughput_difference = self.throughput_difference\\n\\n self.set_limits_for_all_users(self.resource_name, self.resource_limit)\\n\\n # Check that a throughput above the threshold is constrained by the\\n # resource limit\\n for task in self.tasks:\\n task.set_throughput(self.resource_limit * self.units + throughput_difference)\\n\\n for task in self.tasks:\\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(), self.resource_limit * self.units, self.error)))\\n\\n self.sleep(5)\\n\\n # Check that a throughput below the limit succeeds\\n for task in self.tasks:\\n task.set_throughput(self.resource_limit * self.units - throughput_difference)\\n\\n for task in self.tasks:\\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(), task.get_throughput(), self.error)))\\n\\n self.set_throughput_to_zero()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d52e7cbd76427c029fb67e8abebba80c\",\n \"score\": \"0.5475557\",\n \"text\": \"def test_investment_limit(self):\\n bus1 = solph.Bus(label='Bus1')\\n solph.components.GenericStorage(\\n label='storage_invest_limit',\\n invest_relation_input_capacity=0.2,\\n invest_relation_output_capacity=0.2,\\n inputs={bus1: solph.Flow()},\\n outputs={bus1: solph.Flow()},\\n investment=solph.Investment(ep_costs=145))\\n solph.Source(label='Source', outputs={bus1: solph.Flow(\\n investment=solph.Investment(ep_costs=123))})\\n om = self.get_om()\\n solph.constraints.investment_limit(om, limit=900)\\n\\n self.compare_lp_files('investment_limit.lp', my_om=om)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7463779b823b34dc5aa1bf07a24eca5\",\n \"score\": \"0.5472236\",\n \"text\": \"def __init__(self, \\n loading_limit: float = 1.0,\\n voltage_limit: dict = {\\n 'overvoltage_threshold': 1.05,\\n 'undervoltage_threshold': 0.95\\n }\\n ):\\n\\n self.loading_limit = data_model.ThermalLoadingLimit(threshold=loading_limit)\\n self.voltage_limit = data_model.VoltageViolationLimit(**voltage_limit)\\n self.sardi_aggregated = 0\\n self.counter = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4e2b672d4dc7204199b80307baf975e\",\n \"score\": \"0.5468406\",\n \"text\": \"def setUp(self):\\n self.maxDiff = None\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bc25d8909e6aa209e9f2e2d0be7b68e\",\n \"score\": \"0.54441905\",\n \"text\": \"def test_warn_count_all_unnecessary(marker_trackerstore: TrackerStore):\\n with pytest.warns(UserWarning):\\n MarkerTrackerLoader(marker_trackerstore, STRATEGY_ALL, 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14d2bfa5e14c5ed85e25032ec06138e7\",\n \"score\": \"0.54241455\",\n \"text\": \"def _monkey_update_limits(self):\\n self.monkey_update_limits_counter += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"147689e8ba9730d21bd90b07a88450ca\",\n \"score\": \"0.5423483\",\n \"text\": \"async def test_warn_count_exceeds_store(marker_trackerstore: TrackerStore):\\n loader = MarkerTrackerLoader(marker_trackerstore, STRATEGY_SAMPLE_N, 6)\\n with pytest.warns(UserWarning):\\n # Need to force the generator to evaluate to produce the warning\\n [tracker async for tracker in loader.load()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f563c2e499abfb074d540962c3d9af1f\",\n \"score\": \"0.54190344\",\n \"text\": \"def test_change_default_throttling_settings_ws_with_overwrite_throttled_burst_above_50():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"861ab1172fa87007229aece7506960ca\",\n \"score\": \"0.5406477\",\n \"text\": \"def setUp(self):\\n self.maxDiff = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"861ab1172fa87007229aece7506960ca\",\n \"score\": \"0.5406477\",\n \"text\": \"def setUp(self):\\n self.maxDiff = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"effe8908484025bd651d30f0924e1980\",\n \"score\": \"0.5385588\",\n \"text\": \"def test_backend_job_limit(self):\\n job_limit = self.backend.job_limit()\\n self.assertIsNotNone(job_limit)\\n self.assertIsNotNone(job_limit.active_jobs)\\n if job_limit.maximum_jobs:\\n self.assertGreater(job_limit.maximum_jobs, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6a5ca508718aa8278ec74bef4ab52dd\",\n \"score\": \"0.5382612\",\n \"text\": \"def test_api_loss_livestock_read(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aedb392579d434e71a5f9e1599a33c1a\",\n \"score\": \"0.53748673\",\n \"text\": \"def test_above_threshold(self):\\n throughput_difference = self.throughput_difference\\n\\n self.set_limits_for_all_users(self.resource_name, self.resource_limit)\\n\\n # Check that a throughput above the threshold is constrained by the\\n # resource limit\\n for task in self.tasks:\\n task.set_throughput(self.resource_limit * self.units + throughput_difference)\\n\\n for task in self.tasks:\\n self.get_throughput = self.resource_limit * self.units\\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(),\\n self.get_throughput, self.error)))\\n\\n # Once above threshold, ensure the expected error message is thrown\\n if self.tasks and self.tasks[0].expected_error():\\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check_error(self.tasks[0].error(), self.tasks[0].expected_error())))\\n\\n self.run_compaction()\\n\\n # set a higher limit to validate stats\\n self.set_limits_for_all_users(self.resource_name, self.resource_limit*2)\\n\\n self.get_stats(self.get_throughput)\\n self.set_throughput_to_zero()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e9d17c01cda4ebe3809accad1368a5f\",\n \"score\": \"0.5370684\",\n \"text\": \"def test_change_default_throttling_settings_ws_with_overwrite_throttled_burst_above_account_quota():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22680aa0d2c40892f6f36114bc2bf5e0\",\n \"score\": \"0.53467035\",\n \"text\": \"def test_change_default_throttling_settings_ws_with_overwrite_throttled():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1483b065c225ad7c2faed1cc34cd61e5\",\n \"score\": \"0.5340418\",\n \"text\": \"def test_api_pings():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01008ac4145ca5c9d4ec1f51c92bfdf4\",\n \"score\": \"0.5332597\",\n \"text\": \"def test_datapoints_agent(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e5e5b0d87f58a1a1bfbdbbd9d919cd0\",\n \"score\": \"0.53280985\",\n \"text\": \"def test_api_vulnerability_read(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f91b4ce1ad365a009b2d61390489c8c\",\n \"score\": \"0.53207844\",\n \"text\": \"def test_get_stats_works(fixture):\\n\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e9dc9174213a944552c00c74e42c711\",\n \"score\": \"0.53145677\",\n \"text\": \"def test_post_view_hits_rate_limit_will_return_429(self, throttled_function):\\n url = reverse(self.VIEW_NAME)\\n\\n text = \\\"I am eating a hamburger\\\"\\n data = {\\\"text\\\": text, \\\"email\\\": text, \\\"title\\\": text}\\n\\n client = self.registered_user_client\\n\\n for _ in range(3):\\n response = client.post(url, data=data)\\n\\n # by the 3rd request, it should have hit the rate limit\\n self.assertEqual(response.status_code, 429)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7ce635d7ca31075dfd703bbb75fad7e\",\n \"score\": \"0.5311275\",\n \"text\": \"def test_stats_get(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9613fa7ebf25c4158b0f0913a2a1fffe\",\n \"score\": \"0.53091764\",\n \"text\": \"def test_api_rejects_invalid_limit_parameter(self):\\n\\n res = self.client().get(\\\"/bucketlists/v1.0/?limit=dkj\\\",\\n headers={\\\"Authorization\\\": self.token}\\n )\\n self.assertEqual(res.status_code, 400)\\n self.assertIn('\\\"limit\\\": \\\"Limit has to be a number between 1 and 100\\\"', str(res.data))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c38455204f979970e200d0fbc8af07fa\",\n \"score\": \"0.5301058\",\n \"text\": \"def test_setup(self):\\n self.assertEqual(8, len(self.raw_metrics_list), \\\"incorrect number json files read\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35f8ec51e964a32376250ad4abfdc991\",\n \"score\": \"0.53001773\",\n \"text\": \"def test_api_pollution_read(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c00c0289ce5b30ecc63191737bf7a91\",\n \"score\": \"0.5295825\",\n \"text\": \"def test_no_quota(self, mock_google_credential):\\n iam_api_client = iam.IAMClient(global_configs={})\\n self.assertEqual(None, iam_api_client.repository._rate_limiter)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"140403ea97ead15f21e0234c6e3bb831\",\n \"score\": \"0.5290854\",\n \"text\": \"def monkey_update_limits(self):\\n self._monkey_update_limits_counter += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5210bac5e5864645dce2f6149ab7bd66\",\n \"score\": \"0.52772236\",\n \"text\": \"def throttle_failure(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc2cb6f3650dbdde8c333a597208dd49\",\n \"score\": \"0.5274103\",\n \"text\": \"def test_4(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"311741480132ebdb87164609f0028ed4\",\n \"score\": \"0.526988\",\n \"text\": \"def test_get_task_counts_for_project(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bfcc5fa516f4fe2340f52fde18e6100\",\n \"score\": \"0.5268455\",\n \"text\": \"def test_limit_batches():\\n command = [\\n \\\"run.py\\\",\\n \\\"trainer=default\\\",\\n \\\"trainer.max_epochs=1\\\",\\n \\\"trainer.limit_train_batches=0.25\\\",\\n \\\"trainer.limit_val_batches=0.25\\\",\\n \\\"trainer.limit_test_batches=0.25\\\",\\n ]\\n run_command(command)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"791f0692a94751a7eb9dc52bcefd5e5c\",\n \"score\": \"0.5268286\",\n \"text\": \"def limit(self, count):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f988e2113eb41b9ef4a78efee021d3e0\",\n \"score\": \"0.5256502\",\n \"text\": \"def test_calc_requests_used_most_recent_quarterly_finished_mdm_import_dataset(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d7cacf4f06d0c7199faf623f92e22eb\",\n \"score\": \"0.52531564\",\n \"text\": \"def test_get_loyalty_statistics(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90b4c969440d7d9ad5e7d45c2049f85c\",\n \"score\": \"0.5247421\",\n \"text\": \"def test_api_vulnerability_list(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eeadd5017709442f6f24c683f78a6ce2\",\n \"score\": \"0.5246757\",\n \"text\": \"def limits(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8b4b1cdf058f1a8088794045a3af17d\",\n \"score\": \"0.5246362\",\n \"text\": \"def test_defaultmessage_limits(self):\\n # Reset limits first.\\n self.reset_limits()\\n\\n # Request as often as allowed.\\n for i in range(0, self._app.limit_amount):\\n self.http_client.fetch(self.get_url('/message/?receiver=%2B49176123456'), self.stop)\\n response = self.wait()\\n self.assert_json_response(response, {'status': 'ok'})\\n\\n # Request again and expect error.\\n self.http_client.fetch(self.get_url('/message/?receiver=%2B49176123456'), self.stop)\\n response = self.wait()\\n self.assert_json_response(response, {'status': 'error', \\\"error\\\": \\\"limit_acceded\\\"})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba552db0941824167440874d2c526ad2\",\n \"score\": \"0.5243879\",\n \"text\": \"def test_api_loss_livestock_list(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca5463fd9a59ec9898f825406dd45249\",\n \"score\": \"0.52336663\",\n \"text\": \"def test_assign_resources_on_sdp_in_low():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64401e16acebcdf491cc6144a1a120cc\",\n \"score\": \"0.5232181\",\n \"text\": \"def test_q_rate_limit_switch(self):\\n return self._rate_limit_switch(apk_type='Q')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5648c8fcec5cc214d4d05a429bde0046\",\n \"score\": \"0.5230283\",\n \"text\": \"def test_mock_handle_rate_limiting_429(self, app, worker):\\n mock_exception = SpotifyException(http_status=429, code=-1, msg=\\\"Mock exception\\\", headers={'Retry-After': 1})\\n job = findartist.spotify.handle_rate_limiting(mock_exception)\\n\\n assert job in app.task_queue.jobs\\n worker.work(burst=True)\\n assert job not in app.task_queue.jobs\\n assert job.is_finished\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af1ca4a11a40dbf7e53ee6a5ca9c4704\",\n \"score\": \"0.52235043\",\n \"text\": \"def monkey_update_limits(self, *args):\\n self._monkey_update_limits_counter += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65a1187552ae4ec4473c938eb901fbb1\",\n \"score\": \"0.52184767\",\n \"text\": \"def test_update_limits(self, monkeypatch):\\n # call function to test\\n monkeypatch.setattr(DoublyFedInductionMotor, '__init__', self.monkey_init)\\n\\n test_object = DoublyFedInductionMotor()\\n test_object._motor_parameter = self._motor_parameter\\n test_object._limits = self._limit_values\\n test_object._nominal_values = self._nominal_values\\n # verify the expected results\\n test_object._update_limits()\\n\\n assert test_object._limits['u_rbeta'] == 0.5 * self._limit_values['u']\\n assert test_object._nominal_values['i_ralpha'] == (test_object._nominal_values.get('i', None) or test_object._nominal_values['u_ralpha'] / test_object._motor_parameter['r_r'])\\n\\n monkeypatch.setattr(InductionMotor, '_update_limits', self.monkey_super_update_limits)\\n test_object._update_limits()\\n assert self._monkey_super_update_limits_counter == 1, 'super().update_limits() is not called once'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cfebc8db03c686495c135c813f57f06\",\n \"score\": \"0.52165115\",\n \"text\": \"def test_set_real_limit(self):\\n ## Create a card\\n self.app.post('/wallet/cards',\\n data={**self.card_arguments,\\n 'due_day': 27,\\n 'max_limit': 1000.0},\\n headers=dict(token=self.token), follow_redirects=True)\\n\\n # set real limit\\n result = self.app.put('/wallet/real_limit/{}'.format(500.0), headers=dict(token=self.token),\\n follow_redirects=True)\\n self.assertEqual(result.status_code, 200)\\n\\n # Get wallet information and test free limit\\n result = self.app.get('/wallet', headers=dict(token=self.token), follow_redirects=True)\\n wallet = json.loads(result.get_data(as_text=True))\\n\\n self.assertEqual(wallet['real_limit'], 500.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c001b9151f78ba6fa1c78d66717bde8\",\n \"score\": \"0.52106315\",\n \"text\": \"def test_1_0(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4eca299efee3d1176cf39ea28978c28a\",\n \"score\": \"0.5209944\",\n \"text\": \"def test_add_vote(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc0a30a1ff8e7d599561ab6219a85a5a\",\n \"score\": \"0.5197893\",\n \"text\": \"def metrics_to_use(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e584f5b1e1944ae9128eb9a748fbacfe\",\n \"score\": \"0.51940686\",\n \"text\": \"def limit( bw=10, cpu=.1 ):\\n intf = custom( TCIntf, bw=bw )\\n myTopo = TreeTopo( depth=1, fanout=2 )\\n for sched in 'rt', 'cfs':\\n info( '*** Testing with', sched, 'bandwidth limiting\\\\n' )\\n if sched == 'rt':\\n release = quietRun( 'uname -r' ).strip('\\\\r\\\\n')\\n output = quietRun( 'grep CONFIG_RT_GROUP_SCHED /boot/config-%s'\\n % release )\\n if output == '# CONFIG_RT_GROUP_SCHED is not set\\\\n':\\n info( '*** RT Scheduler is not enabled in your kernel. '\\n 'Skipping this test\\\\n' )\\n continue\\n host = custom( CPULimitedHost, sched=sched, cpu=cpu )\\n net = Mininet( topo=myTopo, intf=intf, host=host )\\n net.start()\\n testLinkLimit( net, bw=bw )\\n net.runCpuLimitTest( cpu=cpu )\\n net.stop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dc1e789b8060a80568434a0a92ef969\",\n \"score\": \"0.51926\",\n \"text\": \"def test_get_target_low(self):\\n self.assertEqual(90, int(self.decider.get_target_low()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"689d8b3e3e9cdd38fcfe0e2e1c61ad6f\",\n \"score\": \"0.5189289\",\n \"text\": \"def test_mock_handle_rate_limiting_other(self, app, worker):\\n mock_exception = SpotifyException(http_status=500, code=-1, msg=\\\"Mock exception\\\")\\n job = findartist.spotify.handle_rate_limiting(mock_exception)\\n assert job is None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46fcb98abd2ca1365a843b1d2872353e\",\n \"score\": \"0.51855177\",\n \"text\": \"def test_invalid_usage_limits_count(self):\\n kwargs = {'usage_limits_count': 'invalid'}\\n self.assertRaisesRegex(\\n TypeError,\\n \\\"Usage limits count must be an integer.\\\",\\n payloads.CheckRequestPayload,\\n **kwargs\\n )\\n\\n payload = payloads.CheckRequestPayload()\\n args = (payload, 'usage_limits_count', 'invalid')\\n self.assertRaisesRegex(\\n TypeError,\\n \\\"Usage limits count must be an integer.\\\",\\n setattr,\\n *args\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"552576c0a98079e2d0328df7aa439700\",\n \"score\": \"0.51854384\",\n \"text\": \"def test_add_loyalty_points(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90285dc105febdd849e5bb9c230d1dc5\",\n \"score\": \"0.5181207\",\n \"text\": \"def test_4_0(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99da783c6eb090328bfabeb8870bf314\",\n \"score\": \"0.51730084\",\n \"text\": \"def setUp(self):\\n\\n Test.maxDiff = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a92616e769a5d1cb3217b13b20a9983\",\n \"score\": \"0.5170803\",\n \"text\": \"def test_non_algorithmic(self):\\n user = users.models.User.objects.create_user('test-user')\\n user.school_participations.create(\\n school=self.prev_school_1, parallel=self.s1_c_prime)\\n user.school_participations.create(\\n school=self.prev_school_2, parallel=self.s2_p)\\n limit = self.limiter.get_limit(user)\\n self.assertEqual(limit.min_level, self.c)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2de9a73ba436aa8b5014b39fd686afd\",\n \"score\": \"0.5166248\",\n \"text\": \"def test_change_default_throttling_settings_ws_with_overwrite_not_throttled():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25f824ca954e055d2f435e9035469b80\",\n \"score\": \"0.516279\",\n \"text\": \"def test_replica_rebuild_per_volume_limit():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed82c281f0922ac9a7ecd142073448e4\",\n \"score\": \"0.51620966\",\n \"text\": \"def test_get_account_analytics(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c2bcb75ddb3b932aa7bab229253b583\",\n \"score\": \"0.51556665\",\n \"text\": \"def test_case_4(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f464cac8f81208d141ebf17c1a20cacd\",\n \"score\": \"0.51537263\",\n \"text\": \"def test_functionality(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46fd953d06a0aa2f1c6ab23ae66d95a8\",\n \"score\": \"0.515275\",\n \"text\": \"def test_redundancy_increased_when_not_max(self, mock_client):\\n n_answers = 3\\n target = 'example.com'\\n task = self.ctx.create_task(n_answers, target, max_answers=4)\\n for i in range(n_answers):\\n TaskRunFactory.create(task=task, info={\\n 'reference': i,\\n 'control_number': i,\\n 'comments': ''\\n })\\n result = self.result_repo.filter_by(project_id=task.project_id)[0]\\n fake_search = MagicMock()\\n fake_search.return_value = []\\n mock_client.search_annotations = fake_search\\n self.z3950_analyst.analyse(result.id)\\n assert_equal(mock_client.create_annotation.called, False)\\n\\n updated_task = self.task_repo.get_task(task.id)\\n assert_equal(updated_task.n_answers, n_answers + 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"927d00ec51cb64265439588d482b3f5f\",\n \"score\": \"0.5147975\",\n \"text\": \"def testCaseTBA(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96093b7512519cf8aa209c17e16fbbd9\",\n \"score\": \"0.51442605\",\n \"text\": \"def test_invalid_usage_limits_count(self):\\n kwargs = {'usage_limits_count': 'invalid'}\\n self.assertRaisesRegex(\\n TypeError,\\n \\\"Usage limits count must be an integer.\\\",\\n payloads.CheckResponsePayload,\\n **kwargs\\n )\\n\\n payload = payloads.CheckResponsePayload()\\n args = (payload, 'usage_limits_count', 'invalid')\\n self.assertRaisesRegex(\\n TypeError,\\n \\\"Usage limits count must be an integer.\\\",\\n setattr,\\n *args\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0605695f28a7671760cd03cd4dd896e0\",\n \"score\": \"0.5142084\",\n \"text\": \"def test_redundancy_not_increased_when_max(self, mock_client):\\n n_answers = 3\\n target = 'example.com'\\n task = self.ctx.create_task(n_answers, target, max_answers=3)\\n for i in range(n_answers):\\n TaskRunFactory.create(task=task, info={\\n 'reference': i,\\n 'control_number': i,\\n 'comments': ''\\n })\\n result = self.result_repo.filter_by(project_id=task.project_id)[0]\\n fake_search = MagicMock()\\n fake_search.return_value = []\\n mock_client.search_annotations = fake_search\\n self.z3950_analyst.analyse(result.id)\\n assert_equal(mock_client.create_annotation.called, False)\\n\\n updated_task = self.task_repo.get_task(task.id)\\n assert_equal(updated_task.n_answers, n_answers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c61b8cf2b199172f15801994e9c81fea\",\n \"score\": \"0.513838\",\n \"text\": \"def test_api_loss_list(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"001da76050180758b4e0921641d6f7fe\",\n \"score\": \"0.51352376\",\n \"text\": \"def test_max_local_block_devices_0_force_bfv(self):\\n self.flags(max_local_block_devices=0)\\n server = self._build_server()\\n ex = self.assertRaises(api_client.OpenStackApiException,\\n self.admin_api.post_server,\\n {'server': server})\\n self.assertEqual(400, ex.response.status_code)\\n self.assertIn('You specified more local devices than the limit allows',\\n str(ex))\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":791,"cells":{"query_id":{"kind":"string","value":"ec66effa18f2468179bea5514cc33f27"},"query":{"kind":"string","value":"Changes each face when a turn is made"},"positive_passages":{"kind":"list like","value":[{"docid":"fbd8f96f5e48ea6fb17378d7c6c7e8a1","score":"0.0","text":"def turnSide(side, copy1, copy2, copy3, numMoves, isPrime,\n falseSide1, falseSide2,\n falseCopy1, falseCopy2,\n trueSide1, trueSide2, \n trueCopy1, trueCopy2,\n twoSide1, twoSide2, \n twoCopy1, twoCopy2,\n ):\n if numMoves == 1:\n if not isPrime:\n side[falseSide1][falseSide2] = copy1[falseCopy1][falseCopy2]\n elif isPrime:\n side[trueSide1][trueSide2] = copy2[trueCopy1][trueCopy2]\n elif numMoves == 2:\n side[twoSide1][twoSide2] = copy3[twoCopy1][twoCopy2]","title":""}],"string":"[\n {\n \"docid\": \"fbd8f96f5e48ea6fb17378d7c6c7e8a1\",\n \"score\": \"0.0\",\n \"text\": \"def turnSide(side, copy1, copy2, copy3, numMoves, isPrime,\\n falseSide1, falseSide2,\\n falseCopy1, falseCopy2,\\n trueSide1, trueSide2, \\n trueCopy1, trueCopy2,\\n twoSide1, twoSide2, \\n twoCopy1, twoCopy2,\\n ):\\n if numMoves == 1:\\n if not isPrime:\\n side[falseSide1][falseSide2] = copy1[falseCopy1][falseCopy2]\\n elif isPrime:\\n side[trueSide1][trueSide2] = copy2[trueCopy1][trueCopy2]\\n elif numMoves == 2:\\n side[twoSide1][twoSide2] = copy3[twoCopy1][twoCopy2]\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"ffc8669634c0df6b38b564f4c3edc344","score":"0.6458799","text":"def __init__(self):\n self.face = 0","title":""},{"docid":"0695201c5269bde707b3ec1907317f36","score":"0.6264643","text":"def changeTurn(self):\n self._players[self._current].deactivateAll()\n self._current += 1\n self._current %= 2\n self._players[self._current].activateAll()","title":""},{"docid":"5ca7ba0f0334c899a03277ab756a5f20","score":"0.6256967","text":"def faces(self, faces_list):\n self._faces = faces_list.copy()","title":""},{"docid":"d429617ce921bc8119fb2e901c727028","score":"0.62325376","text":"def faces(self) -> int:\n ...","title":""},{"docid":"1c1c70ce28713225d7cbc55049a7fbc4","score":"0.6092525","text":"def turn(self):\n pass","title":""},{"docid":"e08a8bc7b41d149c96a19da9d6335b8c","score":"0.60666806","text":"def face_north():\n while not_facing_north():\n turn_left()","title":""},{"docid":"e08a8bc7b41d149c96a19da9d6335b8c","score":"0.60666806","text":"def face_north():\n while not_facing_north():\n turn_left()","title":""},{"docid":"f0baaefbddfd7d10faccd30853b301b8","score":"0.60647076","text":"def setFaceValue(self, faceValue): # Private Method Please\n\t\tself.faceValue = faceValue","title":""},{"docid":"a177cbec4a85d538f345a12d654ba86f","score":"0.59847814","text":"def cube_perform_action(self, action):\n\n if action == 'left':\n self.turn_left()\n elif action == 'right':\n self.turn_right()\n elif action == 'front':\n self.turn_front()\n elif action == 'back':\n self.turn_back()\n elif action == 'up':\n self.turn_up()\n elif action == 'down':\n self.turn_down()\n self.__faces__ = [self.front(), self.back(), self.left(), self.right(), self.up(), self.down()]","title":""},{"docid":"a061d87f98199f1f9668b8d1a691a827","score":"0.5982775","text":"def __init__(self, model, size, faces_list):\n self.model = model\n self.screen = pygame.display.set_mode(size)\n self.faceImages = []\n for face in faces_list: #puts images of all available faces in a list\n self.faceImages.append(pygame.image.load('faces/'+face))\n for i in range(len(self.faceImages)): #scales the images to 80 x 80\n self.faceImages[i] = pygame.transform.scale(self.faceImages[i],(80,80))\n self.my_font = pygame.font.SysFont('Comic Sans MS', 32)\n self.text_playing = self.my_font.render('press spacebar to stack heads',\n False, (200,200,200))\n self.text_won = self.my_font.render('You Win! Press Enter to play again'\n , False, (127,255,63))","title":""},{"docid":"bff17381e1e3637051f3ef1d1ebe8330","score":"0.5980718","text":"def flip_card(self):\n if self.facedown:\n self.image = self.face\n self.facedown = False\n else:\n self.image = self.back\n self.facedown = True","title":""},{"docid":"700dc088225caa6f7c58cafc3c90f8cd","score":"0.5977117","text":"def _flip_all_card_face_up(self):\n card_update_dict = {}\n for card in self:\n card_update_dict[card.value]='U'\n card.face = 'U'\n new_event = gamestate.Event(\n type='Flip',\n cards_status=card_update_dict,\n )\n self._update_event_handle(new_event)","title":""},{"docid":"24eaf5602f6a342992aedd3c61992df8","score":"0.5971587","text":"def rotate_face(self, face):\n new_face = [[], [], []]\n for i in reversed(range(self.size)):\n for y in range(self.size):\n new_face[self.size - 1 - i].append(face[i][self.size - 1 - y])\n return new_face","title":""},{"docid":"f51b0f91c874812753159befb0d33d42","score":"0.5963223","text":"def update_ai_turn(self):\n self.is_ai_turn ^= True","title":""},{"docid":"e8aa1309b0c0dfe363ec073df8c793fa","score":"0.59623414","text":"def next(self):\n\n for i in range(len(self.face_names)):\n\n if self.face_names[i][0] == \"\":\n\n display_size_image = 600\n display_scale_image = display_size_image / 1000\n\n img_full = cv2.imread(self.face_rcg.get_known_face_files()[self.face_rcg.get_known_face_ids().index(self.face_ids[i][0])]) #, cv2.IMREAD_UNCHANGED)\n scale_percent = display_scale_image*1000/img_full.shape[1] # percent of original size\n width = int(img_full.shape[1] * scale_percent )\n height = int(img_full.shape[0] * scale_percent )\n dim = (width, height)\n # resize image\n img_bgr = cv2.resize(img_full, dim)\n\n (top, right, bottom, left) = self.face_rcg.get_known_face_locations()[self.face_rcg.get_known_face_ids().index(self.face_ids[i][0])]\n\n top = int(display_scale_image*top)\n right = int(display_scale_image*right)\n bottom = int(display_scale_image*bottom)\n left = int(display_scale_image*left)\n\n # Draw a box around the face\n cv2.rectangle(img_bgr, (left, top), (right, bottom), (255, 255, 255), 2)\n\n # Draw a label with a name below the face\n cv2.rectangle(img_bgr, (left, bottom), (right+120, bottom+30), (255, 255, 255), cv2.FILLED)\n font = cv2.FONT_HERSHEY_DUPLEX\n cv2.putText(img_bgr, self.face_ids[i][0], (left + 6, bottom + 25), font, 0.6, (0, 0, 0), 2)\n\n # change color channel\n b,g,r = cv2.split(img_bgr)\n img = cv2.merge((r,g,b))\n\n # Convert the Image object into a TkPhoto object\n im = Image.fromarray(img)\n imgtk = ImageTk.PhotoImage(image=im) \n self.canvas.configure(image=imgtk)\n self.canvas.image = imgtk\n\n\n self.status.set(\"Please type in the name of \" + self.face_ids[i][0] + \" and click 'Save' and 'Next'\")\n break\n\n else:\n\n if self.new_file_list:\n\n self.new_file_list = False\n\n self.face_rcg.encode([file[1] for file in self.file_list])\n\n self.face_clusters = self.face_rcg.cluster_faces_dbscan()\n #self.face_clusters = self.face_rcg.cluster_faces()\n self.face_ids,self.face_names,self.face_cluster_id = self.face_rcg.get_processed_clusters(self.face_clusters)\n\n self.status.set(\"Faces recognized. Click 'Next'\")\n\n \"\"\"else:\n\n self.face_rcg.write_images_with_names(\"/home/manuel/Pictures/test_face_recognition/known/\")\n self.status.set(\"Finished. Type path and click 'Get file list' to start new.\")\"\"\"","title":""},{"docid":"128d7614f3f4e709ea448f91f67b08b3","score":"0.59122604","text":"def alternateFace(self, alternateFace):\n pass","title":""},{"docid":"128d7614f3f4e709ea448f91f67b08b3","score":"0.591115","text":"def alternateFace(self, alternateFace):\n pass","title":""},{"docid":"2591ed688bb5b393fd66d89278a57ae7","score":"0.5884116","text":"def cycle_state_variable(self, step):\n if self.detection_state is DetectionState.FACELETS:\n self.curr_facelet_index = (self.curr_facelet_index + step) % len(self.facelets)\n if self.detection_state is DetectionState.COLORS:\n self.write_data()\n self.curr_face_index = (self.curr_face_index + step) % len(self.faces)","title":""},{"docid":"da933096f83382cb6f8e3a32d9ac169c","score":"0.58704185","text":"def before_turn(self):","title":""},{"docid":"86474847e2f3e487405a26113e2d7bb8","score":"0.58687705","text":"def make_turn(self, current_set):\n pass","title":""},{"docid":"603495cb7320afd8f075a41dd5a741b2","score":"0.57598776","text":"def flip_turn(self):\n self.current_player = self.other_player","title":""},{"docid":"1bba631564d46848b6f1088d1c18c4f5","score":"0.57119805","text":"def _turn(self):\n\n if self.dyingCounter:\n return\n for i in range(0, len(self.idle_anim)):\n self.idle_anim[i] = pygame.transform.flip(self.idle_anim[i], True, False)\n for i in range(0, len(self.sprint_anim)):\n self.sprint_anim[i] = pygame.transform.flip(\n self.sprint_anim[i], True, False\n )\n for i in range(0, len(self.death_anim)):\n self.death_anim[i] = pygame.transform.flip(self.death_anim[i], True, False)","title":""},{"docid":"6784a6fa82f3e8bc5c6a69e64aaa58aa","score":"0.5680338","text":"def face_west():\n while not_facing_west():\n turn_left()","title":""},{"docid":"6784a6fa82f3e8bc5c6a69e64aaa58aa","score":"0.5680338","text":"def face_west():\n while not_facing_west():\n turn_left()","title":""},{"docid":"549706dee04edf2bd2a164a4b62a7607","score":"0.5671197","text":"def change_turn(self):\n player = self.get_player_turn()\n if player == 1:\n self.set_player_turn(2) # change to player 2's turn\n elif player == 2:\n self.set_player_turn(1) # change to player 1's turn","title":""},{"docid":"aac0ed475a41f2c2daddd82cfcd1d9fe","score":"0.5666877","text":"def turn_to_face_v(self, direction=Vector3()):\n if direction.is_zero_vector():\n return\n direction = normalize(direction)\n forward = self.get_forward()\n axis = cross(forward, direction)\n angle = math.degrees(math.acos(dot(forward, direction)))\n self.rotate(angle=angle, axis=axis)","title":""},{"docid":"5ea4cf811fc403de72b690b5f67d56ef","score":"0.56437415","text":"def face_south():\n while not_facing_south():\n turn_left()","title":""},{"docid":"5ea4cf811fc403de72b690b5f67d56ef","score":"0.56437415","text":"def face_south():\n while not_facing_south():\n turn_left()","title":""},{"docid":"b5e928f657c1906b34842d1250d8fff6","score":"0.56321895","text":"def circularFace(self, circularFace):\n pass","title":""},{"docid":"189760f95d5ce7e234edde57548b548b","score":"0.5585415","text":"def face_east():\n while not_facing_east():\n turn_left()","title":""},{"docid":"189760f95d5ce7e234edde57548b548b","score":"0.5585415","text":"def face_east():\n while not_facing_east():\n turn_left()","title":""},{"docid":"ed4b503b10258e58a5c6803affaa2ed9","score":"0.55851406","text":"def update_turn(self):\n if self.get_turn() is True:\n self._turn = False\n elif self.get_turn() is False:\n self._turn = True","title":""},{"docid":"bb44be567a173befa4572a4772b065a6","score":"0.55826795","text":"def changePerspective(self):\n\n # We only need to loop through the 'top' of the board\n for index in range(32):\n # Set our current row and column with respect to index\n row = index//8\n column = index % 8\n # If we are currently on one of our peices\n if self.boardArray[row][column].isupper():\n flipPeice = self.boardArray[row][column].lower() # Set it as an enemy peice\n else:\n flipPeice = self.boardArray[row][column].upper() # Set enemy peice as friendly otherwise\n\n # We compute the same evaluations as above but for the peice at the opposite corner\n # If currently one of our peices\n if self.boardArray[7-row][7-column].isupper():\n self.boardArray[row][column] = self.boardArray[7-row][7-column].lower() # Set current peice to enemy peice\n else:\n self.boardArray[row][column] = self.boardArray[7-row][7-column].upper() # Set enemy peice as friendly otherwise\n\n self.boardArray[7-row][7-column] = flipPeice # Set peice to flipped peice\n\n kingFlipped = self.kingPosition_White # Set temporary kingFlipped variable for white position\n # Set new white and black king positions\n self.kingPosition_White = 63 - self.kingPosition_Black\n self.kingPosition_Black = 63 - kingFlipped","title":""},{"docid":"488f70727fcc85e584cfc0d19d827c55","score":"0.5572259","text":"def change_game(self,game):\r\n for i in range(0,len(game)):\r\n if(game[i] == UNEXPOSED):\r\n self.replace_character_at_index(i,UNEXPOSED)\r\n elif(game[i] == POKEMON):\r\n self.replace_character_at_index(i,POKEMON)\r\n elif(game[i] == FLAG):\r\n self.replace_character_at_index(i,FLAG)\r\n else:\r\n self.replace_character_at_index(i,game[i])","title":""},{"docid":"b8370e789cd50eff989a7a93fb7d4fe2","score":"0.5558109","text":"def SetFaceAttribute(*args):\r\n return _pynewton.TreeCollision_SetFaceAttribute(*args)","title":""},{"docid":"b69495d8208a4864bde80fc6e5d84f93","score":"0.5540498","text":"def on_face_known(self, faces):\n for person in faces:\n if self.is_greeting_appropriate(person.name):\n self.say(\"Hello, {}!\".format(person.name))","title":""},{"docid":"47e9fdbf3407bf11b9cddd6bc48483fa","score":"0.5509818","text":"def flip_faceverts(faces):\n flipped_faces = []\n for face in faces:\n flipped_face = []\n for vert in reversed(face):\n flipped_face.append(vert)\n flipped_faces.append(tuple(flipped_face))\n return flipped_faces","title":""},{"docid":"7863737e7a3789fdc123f7429e7e3abc","score":"0.5505532","text":"def turn(self, angle):\r\n self.angle += math.radians(angle)","title":""},{"docid":"170d87b5663fb2ec759bdc462737210a","score":"0.55026007","text":"def update(self):\n self.game_manager.play_turn()","title":""},{"docid":"6d48510aa9b5615f55195f6c7d5543b6","score":"0.54960114","text":"def GetFaces(self):\n ...","title":""},{"docid":"7cd6d059d0b5ecc8a587f69906f0edd0","score":"0.54950124","text":"def do_turn(self, player_index, game_info):","title":""},{"docid":"ff37c865ae0d503c86e439146c2d490a","score":"0.549461","text":"def poppy_turn(poppy, facee, frame):\r\n # Format of facee: [x, y, w, h]\r\n # print(facee)\r\n w = facee[2]\r\n x = facee[0] + 0.5 * w\r\n\r\n f_dist = find_distance(w)\r\n theta = find_angle(x - x_centre, f_dist)\r\n\r\n # print(\"angle: {0}\".format(theta)) \r\n # print(\"distance: {0}\".format(f_dist))\r\n # print()\r\n cv2.putText(frame, \"angle: \" + str(theta), \r\n (10, 100),\r\n cv2.FONT_HERSHEY_SIMPLEX,\r\n 1, (0, 255, 0), 2)\r\n\r\n if -TOLERANCE < theta < TOLERANCE:\r\n wave()\r\n else:\r\n poppy.head_z.goto_position(theta, wait=True)","title":""},{"docid":"ee602941d266468d60ff79ca0ad7f44d","score":"0.5484665","text":"def switch_turn(self):\n if self.whose_turn == self.player1:\n self.whose_turn = self.player2\n else:\n self.whose_turn = self.player1","title":""},{"docid":"9fc217b92dd7f13d55b1d8fe06e5bf9f","score":"0.54783547","text":"def switch_turn(self):\n self.black_turn = not self.black_turn","title":""},{"docid":"6c5dd10bcddb3d490fb9bd7562ec5609","score":"0.5469843","text":"def find_faces(self):\n if self.is_face:\n self.image = self.get_image()\n self.is_face = False\n self.rotate_right()\n else:\n if self.is_gray:\n self.set_image_colorful()\n self.is_gray = True\n gray = cv2.cvtColor(self.image, cv2.COLOR_BGR2GRAY)\n faces = FACE_CASCADE.detectMultiScale(gray, 1.3, 5)\n for (x, y, w, h) in faces:\n cv2.rectangle(self.image, (x, y), (x+w, y+h), (255, 0, 0), 2)\n self.is_face = True","title":""},{"docid":"10358d736ae3f6c576c6bbcb9c6a8731","score":"0.5469517","text":"def rot(cube, face, direction):\n\n cube = np.copy(cube)\n if face == Man.U.value:\n cube[0:4] = np.roll(cube[0:4], -direction, axis=0)\n cube[4] = np.rot90(cube[4], k=-direction)\n cube[5] = np.rot90(cube[5], k=direction)\n elif face == Man.L.value:\n sl = [cube[Man.F.value], cube[Man.D.value], cube[Man.B.value, ::-1], cube[Man.U.value]]\n sl = np.roll(sl, direction, axis=0)\n cube[Man.F.value] = sl[0]\n cube[Man.D.value] = sl[1]\n cube[Man.B.value, ::-1] = sl[2]\n cube[Man.U.value] = sl[3]\n cube[Man.L.value] = np.rot90(cube[Man.L.value], k=-direction)\n cube[Man.R.value] = np.rot90(cube[Man.R.value], k=direction)\n elif face == Man.F.value:\n sl = np.array([cube[Man.U.value], cube[Man.R.value], cube[Man.D.value], cube[Man.L.value]])\n sl = np.rot90(sl, k = -direction, axes=(1,2))\n sl = np.roll(sl, direction, axis=0)\n cube[Man.U.value] = sl[0]\n cube[Man.R.value] = sl[1]\n cube[Man.D.value] = sl[2]\n cube[Man.L.value] = sl[3]\n cube[Man.F.value] = np.rot90(cube[Man.F.value], k=-direction)\n cube[Man.B.value] = np.rot90(cube[Man.B.value], k=direction)\n return cube","title":""},{"docid":"7300ea72bb79fa732ac3314284a70b79","score":"0.54683673","text":"def _update_turn(self) -> None:\n self._player = self._next_player(self._player)\n self._turns += 1","title":""},{"docid":"d90968dcc01c6270f40ffe213d050cb8","score":"0.54581696","text":"def do_viewfaces(self, arg):\n if len(self.inner) == 0:\n print(\"There are no faces to view. 1. loadchoices default 2. alterproperty 3. loadfaces personal 4. savechoices\")\n if len(self.inner) > con.MAX_PORTRAITS:\n print(\"Over 50 images have been selected. If you would like to view all fifty then adjust def do_viewfaces(self, arg)\")\n print(\"Otherwise, narrow your selection.\")\n return False\n filenames = []\n for face in self.inner:\n filename = face[0][0:-3]\n filenames.append(\"{}png\\n\".format(filename))\n with open(con.SAVE_IMAGE_NAMES, 'w') as f:\n for filename in filenames:\n # print(filename)\n f.write(filename)\n # for face in self.inner:\n # print(\"elem[0]: \", face[0])\n # # ----\n # filename = face[0][0:-3]\n # filename = \"{}png\".format(filename)\n # filepath = os.path.join(IMAGES_DIRECTORY, filename)\n # # print(filepath)\n # print(filepath)\n # img = mpimg.imread(filepath)\n # imgplot = plt.imshow(img)\n # plt.show()","title":""},{"docid":"81af7233c98af91768a4284a206a2ea5","score":"0.54511875","text":"def turn(self) -> None:\n #current position reset\n self.current = 0\n #increase sequence and play it\n self.addseq()\n self.playseq()","title":""},{"docid":"cdb50a9792a666e9020ee95cb88b7d81","score":"0.544412","text":"def turn_to_face_p(self, target=Point3()):\n my_pos = self.position()\n if my_pos == target:\n return\n direction = normalize(target - my_pos)\n self.turn_to_face_v(direction)","title":""},{"docid":"7cc4f5944b094acbfaeefcca5129c1f7","score":"0.5429818","text":"def draw(self):\n # rect_head = (self.head[0] * UNIT_SIZE, self.head[1] * UNIT_SIZE, UNIT_SIZE - 0.5, UNIT_SIZE - 0.5)\n # pygame.draw.rect(screen, GREY + ALPHA, rect_head, 0)\n ''' I want to add two eyes! OR, use FACE and NECK! '''\n ball_face = (int((self.head[0] + 0.5) * UNIT_SIZE), int((self.head[1] + 0.5) * UNIT_SIZE))\n pygame.draw.circle(screen, GREY + ALPHA, ball_face, int(UNIT_SIZE / 2) - 1)\n if self.__direction == 'U':\n rect_neck = (self.head[0] * UNIT_SIZE, int((self.head[1] + 0.5) * UNIT_SIZE),\n UNIT_SIZE - 0.5, UNIT_SIZE / 2 - 0.5)\n # ball_eye1 = (self.head[0] * UNIT_SIZE, self.head[1] * UNIT_SIZE)\n # ball_eye2 = ((self.head[0] + 1) * UNIT_SIZE, self.head[1] * UNIT_SIZE)\n elif self.__direction == 'D':\n rect_neck = (self.head[0] * UNIT_SIZE, self.head[1] * UNIT_SIZE,\n UNIT_SIZE - 0.5, UNIT_SIZE / 2 - 0.5)\n elif self.__direction == 'L':\n rect_neck = (int((self.head[0] + 0.5) * UNIT_SIZE), self.head[1] * UNIT_SIZE,\n UNIT_SIZE / 2 - 0.5, UNIT_SIZE - 0.5)\n else:\n rect_neck = (self.head[0] * UNIT_SIZE, self.head[1] * UNIT_SIZE,\n UNIT_SIZE / 2 - 0.5, UNIT_SIZE - 0.5)\n\n pygame.draw.rect(screen, GREY + ALPHA, rect_neck, 0)\n # pygame.draw.circle(screen, WHITE + ALPHA, ball_eye1, 5)\n # pygame.draw.circle(screen, WHITE + ALPHA, ball_eye2, 5)\n for body_node in self.body:\n rect_node = (body_node[0] * UNIT_SIZE, body_node[1] * UNIT_SIZE, UNIT_SIZE - 0.5, UNIT_SIZE - 0.5)\n pygame.draw.rect(screen, GREY + ALPHA, rect_node, 0)","title":""},{"docid":"e005cb9924b5e7e58763ca9d7dd07829","score":"0.54284596","text":"def find_faces():\n\n labels = {}\n with open(\"./labels.pickle\", 'rb') as f:\n org_labels = pickle.load(f)\n labels = {v: k for k, v in org_labels.items()}\n\n face_cascade = CascadeClassifier(\n './haarcascade/haarcascade_frontalface_default.xml')\n recognizer = cv2.face.LBPHFaceRecognizer_create()\n recognizer.read(\"./trainer.yml\")\n\n lefteye_cascade = CascadeClassifier('./haarcascade/haarcascade_lefteye.xml')\n video_capture = cv2.VideoCapture(0)\n picture_counter = 0\n\n picture_flag = False\n while True:\n\n # Capture Frame by Frame\n ret, frame = video_capture.read()\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n faces = face_cascade.detectMultiScale(\n gray, scaleFactor=1.05, minNeighbors=8)\n face_dic = {\n \"known\": 0,\n \"total\": 0}\n \n # Draws a rectagle around the face\n for(x, y, w, h) in faces:\n face_dic[\"total\"] += 1\n roi_gray = gray[y:y+h, x:x+w]\n roi_color = frame[y:y+h, x:x+w]\n id_, conf = recognizer.predict(roi_gray)\n eyes = lefteye_cascade.detectMultiScale(roi_gray)\n test = [i for i in eyes]\n if test:\n frame = cv2.rectangle(\n frame, (x, y,), (x+w, y+h), (0, 255, 0), 3)\n\n # conf is the confidence level that detected face has been trained\n if conf >= 85:\n print(id_)\n face_dic[\"known\"] += 1\n name = labels[id_]\n font = cv2. FONT_HERSHEY_SIMPLEX\n color = (255, 255, 255)\n stroke = 2\n cv2.putText(frame, name, (x, y), font, 1,\n color, stroke, cv2.LINE_AA)\n\n for (ex, ey, ew, eh) in eyes:\n cv2.rectangle(roi_color, (ex, ey),\n (ex+ew, ey+eh), (255, 0, 0), 3)\n else:\n for (ex, ey, ew, eh) in eyes:\n cv2.rectangle(roi_color, (ex, ey),\n (ex+ew, ey+eh), (255, 0, 255), 3)\n \n \n # Shows Number faces dectected/known in frame\n cv2.putText(frame, \"Number of faces detected: \" + str(\n face_dic[\"total\"]), (0, 100), cv2.FONT_HERSHEY_TRIPLEX, 0.5, (255, 0, 0), 1)\n cv2.putText(frame, \"Number of faces known: \" + str(\n face_dic[\"known\"]), (0, 120), cv2.FONT_HERSHEY_TRIPLEX, 0.5, (255, 0, 0), 1)\n\n # Dipsplay the resulting frame\n cv2.imshow('Video', frame)\n \n \n #Saves Picture \n if face_dic[\"known\"] == 0 and face_dic[\"total\"] >= 1:\n if not picture_flag:\n picture_counter += 1\n cv2.imwrite(\"./test_subjects/\" +\n str(picture_counter)+\".png\", frame)\n picture_flag = True\n picture_counter += 1\n if picture_counter == 120:\n picture_flag = False\n picture_counter = 0\n\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\n video_capture.release()\n cv2.destroyAllWindows()","title":""},{"docid":"aca6c77e4382d682458f04ff0ba933f6","score":"0.5426279","text":"def roll(cube, face, direction):\n #print([\"L\", \"F\", \"R\", \"B\", \"U\", \"D\"][face] + str(direction))\n cube = np.copy(cube)\n if face == Man.U.value:\n cube[0:4, 0] = np.roll(cube[0:4, 0], -direction, axis=0)\n cube[Man.U.value] = np.rot90(cube[Man.U.value], k=-direction)\n elif face == Man.D.value:\n cube[0:4, -1] = np.roll(cube[0:4, -1], direction, axis=0)\n cube[Man.D.value] = np.rot90(cube[Man.D.value], k=-direction)\n elif face == Man.F.value:\n sl = np.array([cube[Man.U.value, -1, :], cube[Man.R.value, :, 0],\n cube[Man.D.value, 0, ::-1], cube[Man.L.value, ::-1, -1]])\n sl = np.roll(sl, direction, axis=0)\n cube[Man.U.value, -1, :] = sl[0]\n cube[Man.R.value, :, 0] = sl[1]\n cube[Man.D.value, 0, ::-1] = sl[2]\n cube[Man.L.value, ::-1, -1] = sl[3]\n cube[Man.F.value] = np.rot90(cube[Man.F.value], k=-direction)#TODO: Use map function\n elif face == Man.B.value:\n sl = np.array([cube[Man.U.value, 0, :], cube[Man.R.value, :, -1], cube[Man.D.value, -1, ::-1], cube[Man.L.value, ::-1, 0]])\n sl = np.roll(sl, -direction, axis=0)\n cube[Man.U.value, 0, :] = sl[0]\n cube[Man.R.value, :, -1] = sl[1]\n cube[Man.D.value, -1, ::-1] = sl[2]\n cube[Man.L.value, ::-1, 0] = sl[3]\n cube[Man.B.value] = np.rot90(cube[Man.B.value], k=-direction)#TODO: Use map function\n elif face == Man.L.value:\n sl = np.array([cube[Man.F.value, :, 0], cube[Man.D.value, :, 0], cube[Man.B.value, ::-1, -1], cube[Man.U.value, :, 0]])\n sl = np.roll(sl, direction, axis=0)\n cube[Man.F.value, :, 0] = sl[0]\n cube[Man.D.value, :, 0] = sl[1]\n cube[Man.B.value, ::-1, -1] = sl[2]\n cube[Man.U.value, :, 0] = sl[3]\n cube[Man.L.value] = np.rot90(cube[Man.L.value], k=-direction)\n elif face == Man.R.value:\n sl = np.array([cube[Man.F.value, :, -1], cube[Man.D.value, :, -1], cube[Man.B.value, ::-1, 0], cube[Man.U.value, :, -1]])\n sl = np.roll(sl, -direction, axis=0)\n cube[Man.F.value, :, -1] = sl[0]\n cube[Man.D.value, :, -1] = sl[1]\n cube[Man.B.value, ::-1, 0] = sl[2]\n cube[Man.U.value, :, -1] = sl[3]\n cube[Man.R.value] = np.rot90(cube[Man.R.value], k=-direction)\n return cube\n #print(Man.__str__())","title":""},{"docid":"004a42186846ed8f1490595eec53dab7","score":"0.54207855","text":"def _run_initial_turn(self) -> None:","title":""},{"docid":"ba9e97f7f2908033657be66d1f35b5b9","score":"0.54142267","text":"def ChangeTurn(self):\r\n self.lsPlayer[self.turn] = self.ActivePlayer.copy()\r\n self.turn = (self.turn + 1) % self.numPlayers\r\n self.ActivePlayer = self.lsPlayer[self.turn].copy()\r\n if not self.CheckForValidMoves(self.ActivePlayer):\r\n self.ChangeTurn()","title":""},{"docid":"e3e708f7fd07391ba176ad2189cd3335","score":"0.5413524","text":"def faces(self):\n raise NotImplementedError(f\"faces not implemented for {type(self)}\")","title":""},{"docid":"36d5ea98cfd7541db5061101436cfeb6","score":"0.5406411","text":"def advance(self):\n self.x_shape_rot += self.x_rot_speed\n self.x_shape_rot %= 360\n self.y_shape_rot += self.y_rot_speed\n self.y_shape_rot %= 360\n self.z_shape_rot += self.z_rot_speed\n self.z_shape_rot %= 360\n self.updateGL()","title":""},{"docid":"fcb9346c79e67186b2b99f01f562d783","score":"0.53899795","text":"def turnLeft(self):","title":""},{"docid":"ce23e46ad1a8999548893082b51db47f","score":"0.5377675","text":"def setNext(self,game):\r\n self.nextFixture = game","title":""},{"docid":"85bf3aa342f06d6abe03dfabe04ff9e8","score":"0.53709286","text":"def faces(self):\r\n\t\tif self._faces is None:\r\n\t\t\tself._faces = [Face(self, i) for i in xrange(len(self.faceVertArray))]\r\n\t\treturn self._faces","title":""},{"docid":"4666aaedfe037607c73a5c0fa530b353","score":"0.53502315","text":"def targetFaces(self, targetFaces):\n pass","title":""},{"docid":"4666aaedfe037607c73a5c0fa530b353","score":"0.5349725","text":"def targetFaces(self, targetFaces):\n pass","title":""},{"docid":"9e7321b77232091343736848210ba289","score":"0.5335583","text":"def save_loop(camera, name: str) -> None:\n faces = []\n process_this = True\n face_to_be_saved = None\n while True:\n ret, frame = camera.read()\n if process_this:\n faces = prc.process_faces(frame)\n process_this = not process_this\n # changing name of each face to empty string\n message = ''\n for face in faces:\n face.assign_name('')\n if len(faces) == 1:\n face_to_be_saved = faces[0]\n elif len(faces) > 1:\n message = 'More than 1 face detected'\n else:\n message = 'No face detected'\n prc.mark_faces(frame, faces)\n cv2.putText(frame, message, (20, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 255), 2)\n cv2.imshow('Video', frame)\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n if cv2.getWindowProperty('Video',cv2.WND_PROP_VISIBLE) < 1:\n break;\n\n if face_to_be_saved is not None:\n save(face_to_be_saved, name)\n else:\n print(\"Couldn't save the face\")","title":""},{"docid":"f83a0ae88bdaf135a50b3fd54acc6388","score":"0.53346217","text":"def on_face_new(self, faces):\n\n if self.is_greeting_appropriate(\"new\"):\n self.say(\"I see a new person!, Hello stranger!\")","title":""},{"docid":"0a042e9a13a6051ebb7a09d57af4c404","score":"0.53295404","text":"def face_detect(self):\n self.face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')\n \n self.gray = cv2.cvtColor(self.cv2img, cv2.COLOR_BGR2GRAY)\n self.cv2img = cv2.cvtColor(self.cv2img, cv2.COLOR_BGR2RGB)\n \n # create an array of the positions of each face \n # and then iterates through it to draw a rectangle around all faces\n faces = self.face_cascade.detectMultiScale(self.gray, 1.3, 5)\n for (x, y, w, h) in faces:\n self.cv2img = cv2.rectangle(self.cv2img, (x, y), (x+w, y+h), (255, 0, 0), 2)\n \n self.cv2img = cv2.cvtColor(self.cv2img, cv2.COLOR_BGR2RGB)\n faceimg = Image.fromarray(self.cv2img)\n faceimg = ImageTk.PhotoImage(faceimg)\n self.panel.configure(image = faceimg)\n self.panel.image = faceimg\n \n #makes the button for crop faces visible\n self.btn.pack(side = 'right')\n\n self.states.append(self.cv2img)","title":""},{"docid":"05dddf2a206011046beb22ae40ed5ec9","score":"0.5327832","text":"def switch_turns(self):\n # TODO: This can be a one-liner, too lazy to do it now. Somehow it's\n # easier to write a comment about something that would take 10 seconds\n # to do than actually doing it\n if self.turn == self.tictactoe.player1:\n self.turn = self.tictactoe.player2\n else:\n self.turn = self.tictactoe.player1","title":""},{"docid":"8388d88bb7a30ab65434d4545028ee86","score":"0.53252715","text":"def flip(self):\r\n self.buffer,self.backbuffer = self.backbuffer,self.buffer\r\n self.Refresh()","title":""},{"docid":"264d4cbd2ab4610bba0e52cfc6791f00","score":"0.53248394","text":"def move_forward_or_back(self, face):\n\n while not ((self.get_face_distance(face) < 1.1) or (self.get_face_distance(face) > .9)):\n # checks if face is too small, then moves back\n if self.get_face_distance(face) < .9:\n self.move_bot(300)\n # checks if face is too big, then moves forward\n elif self.get_face_distance(face) > 1.1:\n self.move_bot(-300)\n self.motors = 6000\n self.tango.setTarget(self.MOTORS, self.motors)","title":""},{"docid":"8f1a405824a24625be6ae6172b8f61fd","score":"0.5323996","text":"def assBody(self):\n \n \n self.surfaceBody = self.spriteList[self.spriteIndex]\n self.surfaceBody.set_colorkey(constants.TRANS)","title":""},{"docid":"4717439d1124655b3c1bcf0d0ccb4854","score":"0.532059","text":"def update(self):\n self.surface.fill((0, 0, 0))\n # head\n self.surface.blit(pygame.transform.rotate(self.images[\"head\"],\n -90 * self.directions[0]),\n (CASE_SIZE[0] * self.coords[0][0],\n CASE_SIZE[1] * self.coords[0][1]))\n # body\n size_x, size_y = CASE_SIZE\n\n for i in range(len(self.coords[1:-1])):\n x, y = self.coords[i + 1]\n direction = self.directions[i + 1]\n direction_prec = self.directions[i]\n if direction == direction_prec: # straight\n self.surface.blit(self.rotate(\"body_straight\", -90*direction),\n (size_x * x, size_y * y))\n # turn right>top or botom>left\n if (direction == 0 and direction_prec == 1) or \\\n (direction == 3 and direction_prec == 2):\n self.surface.blit(self.rotate(\"body_turn\",0),\n (size_x * x, size_y * y))\n # turn left>top or botom>right\n if (direction == 0 and direction_prec == 3) or \\\n (direction == 1 and direction_prec == 2):\n self.surface.blit(self.rotate(\"body_turn\",-90),\n (size_x * x, size_y * y))\n # turn left>botom or top>right\n if (direction == 2 and direction_prec == 3) or \\\n (direction == 1 and direction_prec == 0):\n self.surface.blit(self.rotate(\"body_turn\",180),\n (size_x * x, size_y * y))\n # turn top>left or right>botom\n if (direction == 3 and direction_prec == 0) or \\\n (direction == 2 and direction_prec == 1):\n self.surface.blit(self.rotate(\"body_turn\",90),\n (size_x * x, size_y * y))\n # Tail\n self.surface.blit(pygame.transform.rotate(self.images[\"tail\"],\n -90 * self.directions[-2]),\n (size_x * self.coords[-1][0],\n size_y * self.coords[-1][1]))","title":""},{"docid":"9e9993859b70cb4a82352b878a93e3a4","score":"0.53183514","text":"def EditSkin(\n self,\n name: str = \"\",\n faces: tuple[Face] = (),\n edges: tuple[Edge] = (),\n elementFaces: tuple[MeshFace] = (),\n elementEdges: tuple[MeshEdge] = (),\n ):\n pass","title":""},{"docid":"4697beee66114fbfa28fcab3d916a7b4","score":"0.5313693","text":"def update(self, image):\n\n self._faces = []\n\n if (utils.is_gray(image)):\n image = cv2.equalizeHist(image)\n else:\n image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n image = cv2.equalizeHist(image)\n\n min_size = utils.widthHeightDividedBy(image, 8)\n\n face_rects = self._face_classifier.detectMultiScale(\n image, self.scale_factor, self.min_neighbors, self.flags, min_size)\n\n if (face_rects is not None):\n for face_rect in face_rects:\n face = Face()\n face.face_rect = face_rect\n x, y, w, h = face_rect\n\n # look for an eye in the upper-left part of the face\n search_rect = (x+w//7, y, w*2//7, h//2)\n face.left_eye_rect = self._detect_one_object(\n self._eye_classifier, image, search_rect, 64\n )\n\n # look for an eye in the upper-right part of the face\n search_rect = (x + (w*4)//7, y, w*2//7, h//2)\n face.right_eye_rect = self._detect_one_object(\n self._eye_classifier, image, search_rect, 64\n )\n\n # look for an node in the middle part of the face\n search_rect = (x+w//4, y+h//4, w*2, h//2)\n face.nose_rect = self._detect_one_object(\n self._nose_classifier, image, search_rect, 32\n )\n\n # look for an mouth in the lower-middle part of the face\n search_rect = (x+w//6, y + (h*2)//3, w*2//3, h//3)\n face.mouth_rect = self._detect_one_object(\n self._mouth_classifier, image, search_rect, 16\n )\n\n self._faces.append(face)","title":""},{"docid":"2f9a35c4e36f54cadb68c18c698cde3f","score":"0.5311607","text":"def faceSet(self):\r\n\t\tret = FaceSet(self, [])\r\n\t\tret.update(range(len(self.faceVertArray)))\r\n\t\treturn ret","title":""},{"docid":"7ee3af718f04065414eca6b36fe668b4","score":"0.53067994","text":"def face_amount(self, face_amount):\n\n self._face_amount = face_amount","title":""},{"docid":"b21fe36df6c0fae07d86a2b23bd7f9bb","score":"0.5304956","text":"def add_face_to_list(self, points):\n ids = [point.id for point in points]\n self.faces.append(ids)","title":""},{"docid":"a4bb2a8e7b4881fca6ca45efc0183e63","score":"0.53038234","text":"def online_face_recognition(profile_names, n_pictures=15):\n images = []\n labels = []\n label_names = []\n for i, name in enumerate(profile_names):\n p = load_profile(name)\n p = p[0:n_pictures, ]\n images += [p]\n labels += [np.ones(p.shape[0]) * (i + 1)]\n label_names += [name]\n faces = np.vstack(images)\n labels = np.hstack(labels).astype(np.int)\n # Generate model\n model = IncrementalKCenters(faces, labels)\n # Start camera\n cam = cv.VideoCapture(0)\n while True:\n ret_val, img = cam.read()\n grey_image = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\n working_image = cv.bilateralFilter(grey_image, 9, 75, 75)\n working_image = cv.equalizeHist(working_image)\n working_image = cv.GaussianBlur(working_image, (5, 5), 0)\n box = face_haar_cascade.detectMultiScale(working_image)\n for b0 in box:\n x, y = b0[0], b0[1]\n x_range, y_range = b0[2], b0[3]\n # look for eye classifier\n local_image = img[y:(y + y_range), x:(x + x_range)]\n eye_box = eye_haar_cascade.detectMultiScale(local_image)\n if len(eye_box) == 0:\n cv.rectangle(img, tuple([b0[0] - 4, b0[1] - 4]), tuple([b0[0] + b0[2] + 4, b0[1] + b0[3] + 4]),\n (0, 0, 255), 2)\n continue\n # select face\n local_image = grey_image[y:(y + y_range), x:(x + x_range)]\n x_t = preprocess_face(local_image)\n\n \"\"\"\n Centroids are updated here\n \"\"\"\n model.online_ssl_update_centroids(x_t)\n p1, p2 = tuple([b0[0] - 4, b0[1] - 4]), tuple([b0[0] + b0[2] + 4, b0[1] + b0[3] + 4])\n\n \n \"\"\"\n HardHFS solution is computed here\n \"\"\"\n f = model.online_ssl_compute_solution()\n lab = np.argsort(f)\n \n \n \"\"\"\n Change False by something else to be able to disregard faces it cannot recognize (question 3.4)\n \"\"\" \n \n mean_dist_new_to_labeled = np.mean(distance.cdist(np.array([model.centroids[model.last_face]]), faces)[0])\n \n mean_dist_existing=0\n for person in set(labels):\n #Average distance between the faces of a same person\n mean_dist_existing += np.mean(distance.cdist(faces[labels==person], faces[labels==person])[0])\n mean_dist_existing=mean_dist_existing/len(list(set(labels))) \n \n if any(f==0.) and mean_dist_new_to_labeled>mean_dist_existing:\n color = (100, 100, 100)\n txt = \"unknown\"\n cv.putText(img, txt, (p1[0], p1[1] - 5), cv.FONT_HERSHEY_COMPLEX_SMALL, 1, color)\n else:\n for i, l in enumerate(lab):\n color = [(0, 255, 0), (255, 0, 0), (0, 0, 255)][l]\n txt = label_names[l] + \" \" + ('%.4f' % np.abs(f[l]))\n cv.putText(img, txt, (p1[0], p1[1] - 5 - 10 * i), cv.FONT_HERSHEY_COMPLEX_SMALL,\n 0.5 + 0.5 * (i == f.shape[0] - 1), color)\n cv.rectangle(img, p1, p2, color, 2)\n cv.imshow(\"cam\", img)\n key = cv.waitKey(1)\n if key in [27, 101]:\n break\n if key == ord('s'):\n # Save face\n print('saved')\n cv.imwrite(\"frame.png\", img)\n ## cv.waitKey(1)\n cv.destroyAllWindows()","title":""},{"docid":"5a8f35e3b27d9bfb457cba006ae270ae","score":"0.5301556","text":"def turn():\n global o_both_steering\n\n o_both_steering.on_for_rotations(100, SpeedPercent(40), 1) # OPTIMAL VALUE 1","title":""},{"docid":"391dc969f17266ea0bca1dc38375864f","score":"0.5300656","text":"def generator(surface, angles):\n original_surf = surface.copy()\n for angle in angles:\n x, y = self.rect.center\n self.surface = pygame.transform.rotate(original_surf, angle)\n self.instant_move(x, y)\n yield True","title":""},{"docid":"f6eceb20ea200d1fa8569da8eae1e7bc","score":"0.5300277","text":"def updateCameraVectors(self):\n # Calculate the new Front vector\n x = np.cos(np.radians(self.Yaw)) * np.cos(np.radians(self.Pitch))\n y = np.sin(np.radians(self.Pitch))\n z = np.sin(np.radians(self.Yaw)) * np.cos(np.radians(self.Pitch))\n \n front = np.array([x, y, z], dtype=GLfloat)\n print(front)\n self.Front = front / np.sqrt(x**2 + y**2 + z**2)\n print(self.Front)\n \n temp = np.cross(self.Front, self.WorldUp)\n self.Right = temp / np.sqrt(temp[0]**2 + temp[1]**2 + temp[2]**2)\n print(self.Right)\n \n temp = np.cross(self.Right, self.Front)\n self.Up = temp / np.sqrt(temp[0]**2 + temp[1]**2 + temp[2]**2)\n print(self.Up)\n \n \n \n \n # front = glm.vec3(x, y, z)\n # print('*=========')\n # self.Front = np.array(glm.normalize(front), dtype=GLfloat)\n # print(self.Front)\n # print('#=========')\n #\n # # Also re-calculate the Right and Up vector\n # # Normalize the vectors, because their length gets closer to 0 the more you\n # # look up or down which results in slower movement.\n # print('**=========')\n # temp = np.cross(self.Front, self.WorldUp)\n # print(temp)\n # print('##=========')\n # self.Right = np.array(glm.normalize(glm.vec3(temp)), dtype=GLfloat)\n #\n # print('***=========')\n # temp = np.cross(self.Right, self.Front)\n # print(temp)\n # self.Up = np.array(glm.normalize(glm.vec3(temp)), dtype=GLfloat)\n # print('###=========')","title":""},{"docid":"8904960cd5a8b6234de2d8d57a573dcd","score":"0.5300243","text":"def inputFaces(self, inputFaces):\n pass","title":""},{"docid":"8904960cd5a8b6234de2d8d57a573dcd","score":"0.5300041","text":"def inputFaces(self, inputFaces):\n pass","title":""},{"docid":"8904960cd5a8b6234de2d8d57a573dcd","score":"0.5300041","text":"def inputFaces(self, inputFaces):\n pass","title":""},{"docid":"8904960cd5a8b6234de2d8d57a573dcd","score":"0.5300041","text":"def inputFaces(self, inputFaces):\n pass","title":""},{"docid":"ad3aa8cbac3e97b9e7256bd79a6f9a6c","score":"0.5299885","text":"def next_turn(self):\n temp = self.current_player\n self.current_player = self.opponent\n self.opponent = temp","title":""},{"docid":"57f4d58e0f16c22078ce9dd38381e616","score":"0.52914584","text":"def test_batch_redetect_with_one_face(self):\n for detector in self.detectors:\n with self.subTest(detectorType=detector.detectorType):\n detection = detector.detectOne(image=VLIMAGE_ONE_FACE)\n redetect = detector.redetect(\n images=[ImageForRedetection(image=VLIMAGE_ONE_FACE, bBoxes=[detection.boundingBox.rect])]\n )[0]\n self.assertFaceDetection(redetect, VLIMAGE_ONE_FACE)","title":""},{"docid":"37798494830af70cc4a3e549576796e7","score":"0.52862424","text":"def build_face():\n masterScale = 0.2\n create_hooks(masterScale)\n inputHooks = create_joint_drivers(masterScale)\n\n for poseInfo in faceposes:\n zone = poseInfo['zone']\n pose = poseInfo['pose']\n poseDriver = poseInfo['driver']\n poseRange = poseInfo['mapping']\n poseOverrides = poseInfo['overrides']\n poseOverrideMaps = poseInfo['overridemaps']\n\n create_pose_node(pose, zone, inputHooks, masterScale, poseDriver, poseRange, poseOverrides, poseOverrideMaps)\n print 'done'","title":""},{"docid":"7596177b3c91fc479cf4def251516cac","score":"0.5286083","text":"def UpdateRectColors(self, selectedRectId):\n\n for (rectId, rect) in enumerate(self.rects):\n\n (x, y, w, h) = rect\n color = (0,255,0) if rectId == selectedRectId-1 else (0,0,255) #selection is 1 based\n\n cv2.rectangle(self.img500, (x, y), ((x + w), (y + h)), color, 2)\n\n # show the face number\n cv2.putText(self.img500,\n \"Face #{}\".format(rectId + 1),\n (x - 10, y - 10),\n cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)\n\n cv2.imshow(self.faceSelectionWin, self.img500)","title":""},{"docid":"07ac0645a023800fc4dcf11143ec82b7","score":"0.5282528","text":"def setup_face_indices(self, reset=True):\n if reset or not self.fis:\n fa = self.domain.get_facets(force_faces=True)[1]\n\n if self.faces:\n faces = self.faces\n else:\n faces = self.edges\n\n self.fis = {}\n for ig in self.igs:\n rfaces = faces[ig]\n fi = fa.indices[rfaces]\n assert_(nm.all(fi[:,0] == ig))\n self.fis[ig] = fi[:,1:].copy()","title":""},{"docid":"8e58cb333ee43454817f059a30915849","score":"0.5273543","text":"def another_turn(self, pit: PitData) -> None:\n self.update_color(pit, 0)","title":""},{"docid":"4fc7f93fc6c3dd1ed8fcd5dd822f522a","score":"0.52639437","text":"def on_draw(self):\n arcade.start_render()","title":""},{"docid":"4b0b0f3d2a4cf4ba6735ee43f6bad2a4","score":"0.5263633","text":"def update_turn(self):\n self.p1.turn = not self.p1.turn\n self.p2.turn = not self.p2.turn\n self.current_player = self.p1 or self.p2","title":""},{"docid":"65698da0371ffe24258d721bc412f014","score":"0.52610844","text":"def updateCams(self, interface=\"match\"):\n activecams = getActiveCams(len(self.cameras[interface]))\n for activeind in activecams:\n camera = self.cameras[interface][activeind]\n if camera in self.gridded:\n camera.updateImgOnLabel()","title":""},{"docid":"09cf5d165983357f2dcc6a505dae41e3","score":"0.5259303","text":"def RubiksCube():\n solved = False\n while (input(\"Start game?: \") == \"yes\") and (not solved):\n c = Cube()\n c.shuffle()\n plotCube(c)\n plt.ion()\n plt.show()\n plt.pause(1)\n # pause needed to update matplotlib plots with keyboard inputs\n print(\"The possible faces to rotate are: U,D,L,R,F,B.\\n The possible directions to rotate are: clockwise, counterclockwise.\\n The possible numbers of rotations are: 1,2,3.\\n\")\n while not solved:\n interact(c)\n plt.close()\n plotCube(c)\n plt.show()\n plt.pause(1)\n if c._p == Cube()._p:\n solved = True\n if solved:\n print(\"You win!\")","title":""},{"docid":"0a4f80d41e6e547f5490b5de12845018","score":"0.52588135","text":"def get_any_face(self):\n return choice(list(self.faces()))","title":""},{"docid":"f2d84dce15832a9e4b877937b180ce8e","score":"0.5258472","text":"def Turn(self, amount):\n \n self.Angle -= amount\n self.Angle %= 360\n\n self.Image = pygame.transform.rotate(self.Orig, self.Angle)\n self.UpdateMask()\n \n newSize = self.Image.get_size()\n self.ModX = -(newSize[0]-self.Size[0])/2\n self.ModY = -(newSize[1]-self.Size[1])/2","title":""},{"docid":"ee707779d583c98c6499bd7652922ed3","score":"0.524997","text":"def toggle_turn(self):\n if self._turn == \"player1\":\n self._turn = \"player2\"\n else:\n self._turn = \"player1\"","title":""},{"docid":"c18c82af0b0743c846de867ee1593851","score":"0.52392316","text":"def update_surface(self):\n self.surface = pygame.surfarray.make_surface(self.arr)","title":""},{"docid":"2a23ba794220991e6386c2a7006d82da","score":"0.5238691","text":"def getSurface(self):\n self.frame += 1\n if self.frame == 3:\n self.isFirstPic = not self.isFirstPic\n self.frame = 0\n\n if self.direction == 0:\n self.surface = Pacman.images[self.isFirstPic]\n elif self.direction == 1:\n self.surface = pygame.transform.rotate(Pacman.images[self.isFirstPic], 90)\n elif self.direction == 2:\n self.surface = pygame.transform.rotate(Pacman.images[self.isFirstPic], 180)\n elif self.direction == 3:\n self.surface = pygame.transform.rotate(Pacman.images[self.isFirstPic], 270)","title":""},{"docid":"9a00db76457ab7532cc3af1fdac12f64","score":"0.5236641","text":"def turn_inc(self) -> None:\n self.__current_turn += 1","title":""},{"docid":"b7e1b59538be1a46c3c180e932f6b9ca","score":"0.52359444","text":"def draw_changing(self):","title":""},{"docid":"20f025928f844ba5664d57864501e178","score":"0.5231487","text":"def turn(self):\n\n #print(self.coords[0] ,self.dx,self.dy, self.field.width,self.field.height)\n mis=list(self.field.mice.keys())\n if len(mis)>0:\n x=self.coords[0][0]\n y=self.coords[0][1]\n mx=1000\n my=1000\n for (tx,ty) in mis:\n if abs(x-tx)+abs(y-ty) mx:\n if self.dx!=-1:\n if random.randint(0,10) < 5:\n if random.randint(0,1) == 1:\n self.turn_left()\n else:\n self.turn_right()\n else:\n if self.coords[0][1] < my:\n if self.dy!=1:\n if random.randint(0,10) < 5:\n if random.randint(0,1) == 1:\n self.turn_left()\n else:\n self.turn_right()\n elif self.coords[0][1] > my:\n if self.dy!=-1:\n if random.randint(0,10) < 5:\n if random.randint(0,1) == 1:\n self.turn_left()\n else:\n self.turn_right()","title":""}],"string":"[\n {\n \"docid\": \"ffc8669634c0df6b38b564f4c3edc344\",\n \"score\": \"0.6458799\",\n \"text\": \"def __init__(self):\\n self.face = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0695201c5269bde707b3ec1907317f36\",\n \"score\": \"0.6264643\",\n \"text\": \"def changeTurn(self):\\n self._players[self._current].deactivateAll()\\n self._current += 1\\n self._current %= 2\\n self._players[self._current].activateAll()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ca7ba0f0334c899a03277ab756a5f20\",\n \"score\": \"0.6256967\",\n \"text\": \"def faces(self, faces_list):\\n self._faces = faces_list.copy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d429617ce921bc8119fb2e901c727028\",\n \"score\": \"0.62325376\",\n \"text\": \"def faces(self) -> int:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c1c70ce28713225d7cbc55049a7fbc4\",\n \"score\": \"0.6092525\",\n \"text\": \"def turn(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e08a8bc7b41d149c96a19da9d6335b8c\",\n \"score\": \"0.60666806\",\n \"text\": \"def face_north():\\n while not_facing_north():\\n turn_left()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e08a8bc7b41d149c96a19da9d6335b8c\",\n \"score\": \"0.60666806\",\n \"text\": \"def face_north():\\n while not_facing_north():\\n turn_left()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0baaefbddfd7d10faccd30853b301b8\",\n \"score\": \"0.60647076\",\n \"text\": \"def setFaceValue(self, faceValue): # Private Method Please\\n\\t\\tself.faceValue = faceValue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a177cbec4a85d538f345a12d654ba86f\",\n \"score\": \"0.59847814\",\n \"text\": \"def cube_perform_action(self, action):\\n\\n if action == 'left':\\n self.turn_left()\\n elif action == 'right':\\n self.turn_right()\\n elif action == 'front':\\n self.turn_front()\\n elif action == 'back':\\n self.turn_back()\\n elif action == 'up':\\n self.turn_up()\\n elif action == 'down':\\n self.turn_down()\\n self.__faces__ = [self.front(), self.back(), self.left(), self.right(), self.up(), self.down()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a061d87f98199f1f9668b8d1a691a827\",\n \"score\": \"0.5982775\",\n \"text\": \"def __init__(self, model, size, faces_list):\\n self.model = model\\n self.screen = pygame.display.set_mode(size)\\n self.faceImages = []\\n for face in faces_list: #puts images of all available faces in a list\\n self.faceImages.append(pygame.image.load('faces/'+face))\\n for i in range(len(self.faceImages)): #scales the images to 80 x 80\\n self.faceImages[i] = pygame.transform.scale(self.faceImages[i],(80,80))\\n self.my_font = pygame.font.SysFont('Comic Sans MS', 32)\\n self.text_playing = self.my_font.render('press spacebar to stack heads',\\n False, (200,200,200))\\n self.text_won = self.my_font.render('You Win! Press Enter to play again'\\n , False, (127,255,63))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff17381e1e3637051f3ef1d1ebe8330\",\n \"score\": \"0.5980718\",\n \"text\": \"def flip_card(self):\\n if self.facedown:\\n self.image = self.face\\n self.facedown = False\\n else:\\n self.image = self.back\\n self.facedown = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"700dc088225caa6f7c58cafc3c90f8cd\",\n \"score\": \"0.5977117\",\n \"text\": \"def _flip_all_card_face_up(self):\\n card_update_dict = {}\\n for card in self:\\n card_update_dict[card.value]='U'\\n card.face = 'U'\\n new_event = gamestate.Event(\\n type='Flip',\\n cards_status=card_update_dict,\\n )\\n self._update_event_handle(new_event)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24eaf5602f6a342992aedd3c61992df8\",\n \"score\": \"0.5971587\",\n \"text\": \"def rotate_face(self, face):\\n new_face = [[], [], []]\\n for i in reversed(range(self.size)):\\n for y in range(self.size):\\n new_face[self.size - 1 - i].append(face[i][self.size - 1 - y])\\n return new_face\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f51b0f91c874812753159befb0d33d42\",\n \"score\": \"0.5963223\",\n \"text\": \"def update_ai_turn(self):\\n self.is_ai_turn ^= True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8aa1309b0c0dfe363ec073df8c793fa\",\n \"score\": \"0.59623414\",\n \"text\": \"def next(self):\\n\\n for i in range(len(self.face_names)):\\n\\n if self.face_names[i][0] == \\\"\\\":\\n\\n display_size_image = 600\\n display_scale_image = display_size_image / 1000\\n\\n img_full = cv2.imread(self.face_rcg.get_known_face_files()[self.face_rcg.get_known_face_ids().index(self.face_ids[i][0])]) #, cv2.IMREAD_UNCHANGED)\\n scale_percent = display_scale_image*1000/img_full.shape[1] # percent of original size\\n width = int(img_full.shape[1] * scale_percent )\\n height = int(img_full.shape[0] * scale_percent )\\n dim = (width, height)\\n # resize image\\n img_bgr = cv2.resize(img_full, dim)\\n\\n (top, right, bottom, left) = self.face_rcg.get_known_face_locations()[self.face_rcg.get_known_face_ids().index(self.face_ids[i][0])]\\n\\n top = int(display_scale_image*top)\\n right = int(display_scale_image*right)\\n bottom = int(display_scale_image*bottom)\\n left = int(display_scale_image*left)\\n\\n # Draw a box around the face\\n cv2.rectangle(img_bgr, (left, top), (right, bottom), (255, 255, 255), 2)\\n\\n # Draw a label with a name below the face\\n cv2.rectangle(img_bgr, (left, bottom), (right+120, bottom+30), (255, 255, 255), cv2.FILLED)\\n font = cv2.FONT_HERSHEY_DUPLEX\\n cv2.putText(img_bgr, self.face_ids[i][0], (left + 6, bottom + 25), font, 0.6, (0, 0, 0), 2)\\n\\n # change color channel\\n b,g,r = cv2.split(img_bgr)\\n img = cv2.merge((r,g,b))\\n\\n # Convert the Image object into a TkPhoto object\\n im = Image.fromarray(img)\\n imgtk = ImageTk.PhotoImage(image=im) \\n self.canvas.configure(image=imgtk)\\n self.canvas.image = imgtk\\n\\n\\n self.status.set(\\\"Please type in the name of \\\" + self.face_ids[i][0] + \\\" and click 'Save' and 'Next'\\\")\\n break\\n\\n else:\\n\\n if self.new_file_list:\\n\\n self.new_file_list = False\\n\\n self.face_rcg.encode([file[1] for file in self.file_list])\\n\\n self.face_clusters = self.face_rcg.cluster_faces_dbscan()\\n #self.face_clusters = self.face_rcg.cluster_faces()\\n self.face_ids,self.face_names,self.face_cluster_id = self.face_rcg.get_processed_clusters(self.face_clusters)\\n\\n self.status.set(\\\"Faces recognized. Click 'Next'\\\")\\n\\n \\\"\\\"\\\"else:\\n\\n self.face_rcg.write_images_with_names(\\\"/home/manuel/Pictures/test_face_recognition/known/\\\")\\n self.status.set(\\\"Finished. Type path and click 'Get file list' to start new.\\\")\\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"128d7614f3f4e709ea448f91f67b08b3\",\n \"score\": \"0.59122604\",\n \"text\": \"def alternateFace(self, alternateFace):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"128d7614f3f4e709ea448f91f67b08b3\",\n \"score\": \"0.591115\",\n \"text\": \"def alternateFace(self, alternateFace):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2591ed688bb5b393fd66d89278a57ae7\",\n \"score\": \"0.5884116\",\n \"text\": \"def cycle_state_variable(self, step):\\n if self.detection_state is DetectionState.FACELETS:\\n self.curr_facelet_index = (self.curr_facelet_index + step) % len(self.facelets)\\n if self.detection_state is DetectionState.COLORS:\\n self.write_data()\\n self.curr_face_index = (self.curr_face_index + step) % len(self.faces)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da933096f83382cb6f8e3a32d9ac169c\",\n \"score\": \"0.58704185\",\n \"text\": \"def before_turn(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86474847e2f3e487405a26113e2d7bb8\",\n \"score\": \"0.58687705\",\n \"text\": \"def make_turn(self, current_set):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"603495cb7320afd8f075a41dd5a741b2\",\n \"score\": \"0.57598776\",\n \"text\": \"def flip_turn(self):\\n self.current_player = self.other_player\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bba631564d46848b6f1088d1c18c4f5\",\n \"score\": \"0.57119805\",\n \"text\": \"def _turn(self):\\n\\n if self.dyingCounter:\\n return\\n for i in range(0, len(self.idle_anim)):\\n self.idle_anim[i] = pygame.transform.flip(self.idle_anim[i], True, False)\\n for i in range(0, len(self.sprint_anim)):\\n self.sprint_anim[i] = pygame.transform.flip(\\n self.sprint_anim[i], True, False\\n )\\n for i in range(0, len(self.death_anim)):\\n self.death_anim[i] = pygame.transform.flip(self.death_anim[i], True, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6784a6fa82f3e8bc5c6a69e64aaa58aa\",\n \"score\": \"0.5680338\",\n \"text\": \"def face_west():\\n while not_facing_west():\\n turn_left()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6784a6fa82f3e8bc5c6a69e64aaa58aa\",\n \"score\": \"0.5680338\",\n \"text\": \"def face_west():\\n while not_facing_west():\\n turn_left()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"549706dee04edf2bd2a164a4b62a7607\",\n \"score\": \"0.5671197\",\n \"text\": \"def change_turn(self):\\n player = self.get_player_turn()\\n if player == 1:\\n self.set_player_turn(2) # change to player 2's turn\\n elif player == 2:\\n self.set_player_turn(1) # change to player 1's turn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aac0ed475a41f2c2daddd82cfcd1d9fe\",\n \"score\": \"0.5666877\",\n \"text\": \"def turn_to_face_v(self, direction=Vector3()):\\n if direction.is_zero_vector():\\n return\\n direction = normalize(direction)\\n forward = self.get_forward()\\n axis = cross(forward, direction)\\n angle = math.degrees(math.acos(dot(forward, direction)))\\n self.rotate(angle=angle, axis=axis)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ea4cf811fc403de72b690b5f67d56ef\",\n \"score\": \"0.56437415\",\n \"text\": \"def face_south():\\n while not_facing_south():\\n turn_left()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ea4cf811fc403de72b690b5f67d56ef\",\n \"score\": \"0.56437415\",\n \"text\": \"def face_south():\\n while not_facing_south():\\n turn_left()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5e928f657c1906b34842d1250d8fff6\",\n \"score\": \"0.56321895\",\n \"text\": \"def circularFace(self, circularFace):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"189760f95d5ce7e234edde57548b548b\",\n \"score\": \"0.5585415\",\n \"text\": \"def face_east():\\n while not_facing_east():\\n turn_left()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"189760f95d5ce7e234edde57548b548b\",\n \"score\": \"0.5585415\",\n \"text\": \"def face_east():\\n while not_facing_east():\\n turn_left()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed4b503b10258e58a5c6803affaa2ed9\",\n \"score\": \"0.55851406\",\n \"text\": \"def update_turn(self):\\n if self.get_turn() is True:\\n self._turn = False\\n elif self.get_turn() is False:\\n self._turn = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb44be567a173befa4572a4772b065a6\",\n \"score\": \"0.55826795\",\n \"text\": \"def changePerspective(self):\\n\\n # We only need to loop through the 'top' of the board\\n for index in range(32):\\n # Set our current row and column with respect to index\\n row = index//8\\n column = index % 8\\n # If we are currently on one of our peices\\n if self.boardArray[row][column].isupper():\\n flipPeice = self.boardArray[row][column].lower() # Set it as an enemy peice\\n else:\\n flipPeice = self.boardArray[row][column].upper() # Set enemy peice as friendly otherwise\\n\\n # We compute the same evaluations as above but for the peice at the opposite corner\\n # If currently one of our peices\\n if self.boardArray[7-row][7-column].isupper():\\n self.boardArray[row][column] = self.boardArray[7-row][7-column].lower() # Set current peice to enemy peice\\n else:\\n self.boardArray[row][column] = self.boardArray[7-row][7-column].upper() # Set enemy peice as friendly otherwise\\n\\n self.boardArray[7-row][7-column] = flipPeice # Set peice to flipped peice\\n\\n kingFlipped = self.kingPosition_White # Set temporary kingFlipped variable for white position\\n # Set new white and black king positions\\n self.kingPosition_White = 63 - self.kingPosition_Black\\n self.kingPosition_Black = 63 - kingFlipped\",\n \"title\": \"\"\n },\n {\n \"docid\": \"488f70727fcc85e584cfc0d19d827c55\",\n \"score\": \"0.5572259\",\n \"text\": \"def change_game(self,game):\\r\\n for i in range(0,len(game)):\\r\\n if(game[i] == UNEXPOSED):\\r\\n self.replace_character_at_index(i,UNEXPOSED)\\r\\n elif(game[i] == POKEMON):\\r\\n self.replace_character_at_index(i,POKEMON)\\r\\n elif(game[i] == FLAG):\\r\\n self.replace_character_at_index(i,FLAG)\\r\\n else:\\r\\n self.replace_character_at_index(i,game[i])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8370e789cd50eff989a7a93fb7d4fe2\",\n \"score\": \"0.5558109\",\n \"text\": \"def SetFaceAttribute(*args):\\r\\n return _pynewton.TreeCollision_SetFaceAttribute(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b69495d8208a4864bde80fc6e5d84f93\",\n \"score\": \"0.5540498\",\n \"text\": \"def on_face_known(self, faces):\\n for person in faces:\\n if self.is_greeting_appropriate(person.name):\\n self.say(\\\"Hello, {}!\\\".format(person.name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47e9fdbf3407bf11b9cddd6bc48483fa\",\n \"score\": \"0.5509818\",\n \"text\": \"def flip_faceverts(faces):\\n flipped_faces = []\\n for face in faces:\\n flipped_face = []\\n for vert in reversed(face):\\n flipped_face.append(vert)\\n flipped_faces.append(tuple(flipped_face))\\n return flipped_faces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7863737e7a3789fdc123f7429e7e3abc\",\n \"score\": \"0.5505532\",\n \"text\": \"def turn(self, angle):\\r\\n self.angle += math.radians(angle)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"170d87b5663fb2ec759bdc462737210a\",\n \"score\": \"0.55026007\",\n \"text\": \"def update(self):\\n self.game_manager.play_turn()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d48510aa9b5615f55195f6c7d5543b6\",\n \"score\": \"0.54960114\",\n \"text\": \"def GetFaces(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cd6d059d0b5ecc8a587f69906f0edd0\",\n \"score\": \"0.54950124\",\n \"text\": \"def do_turn(self, player_index, game_info):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff37c865ae0d503c86e439146c2d490a\",\n \"score\": \"0.549461\",\n \"text\": \"def poppy_turn(poppy, facee, frame):\\r\\n # Format of facee: [x, y, w, h]\\r\\n # print(facee)\\r\\n w = facee[2]\\r\\n x = facee[0] + 0.5 * w\\r\\n\\r\\n f_dist = find_distance(w)\\r\\n theta = find_angle(x - x_centre, f_dist)\\r\\n\\r\\n # print(\\\"angle: {0}\\\".format(theta)) \\r\\n # print(\\\"distance: {0}\\\".format(f_dist))\\r\\n # print()\\r\\n cv2.putText(frame, \\\"angle: \\\" + str(theta), \\r\\n (10, 100),\\r\\n cv2.FONT_HERSHEY_SIMPLEX,\\r\\n 1, (0, 255, 0), 2)\\r\\n\\r\\n if -TOLERANCE < theta < TOLERANCE:\\r\\n wave()\\r\\n else:\\r\\n poppy.head_z.goto_position(theta, wait=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee602941d266468d60ff79ca0ad7f44d\",\n \"score\": \"0.5484665\",\n \"text\": \"def switch_turn(self):\\n if self.whose_turn == self.player1:\\n self.whose_turn = self.player2\\n else:\\n self.whose_turn = self.player1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fc217b92dd7f13d55b1d8fe06e5bf9f\",\n \"score\": \"0.54783547\",\n \"text\": \"def switch_turn(self):\\n self.black_turn = not self.black_turn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c5dd10bcddb3d490fb9bd7562ec5609\",\n \"score\": \"0.5469843\",\n \"text\": \"def find_faces(self):\\n if self.is_face:\\n self.image = self.get_image()\\n self.is_face = False\\n self.rotate_right()\\n else:\\n if self.is_gray:\\n self.set_image_colorful()\\n self.is_gray = True\\n gray = cv2.cvtColor(self.image, cv2.COLOR_BGR2GRAY)\\n faces = FACE_CASCADE.detectMultiScale(gray, 1.3, 5)\\n for (x, y, w, h) in faces:\\n cv2.rectangle(self.image, (x, y), (x+w, y+h), (255, 0, 0), 2)\\n self.is_face = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10358d736ae3f6c576c6bbcb9c6a8731\",\n \"score\": \"0.5469517\",\n \"text\": \"def rot(cube, face, direction):\\n\\n cube = np.copy(cube)\\n if face == Man.U.value:\\n cube[0:4] = np.roll(cube[0:4], -direction, axis=0)\\n cube[4] = np.rot90(cube[4], k=-direction)\\n cube[5] = np.rot90(cube[5], k=direction)\\n elif face == Man.L.value:\\n sl = [cube[Man.F.value], cube[Man.D.value], cube[Man.B.value, ::-1], cube[Man.U.value]]\\n sl = np.roll(sl, direction, axis=0)\\n cube[Man.F.value] = sl[0]\\n cube[Man.D.value] = sl[1]\\n cube[Man.B.value, ::-1] = sl[2]\\n cube[Man.U.value] = sl[3]\\n cube[Man.L.value] = np.rot90(cube[Man.L.value], k=-direction)\\n cube[Man.R.value] = np.rot90(cube[Man.R.value], k=direction)\\n elif face == Man.F.value:\\n sl = np.array([cube[Man.U.value], cube[Man.R.value], cube[Man.D.value], cube[Man.L.value]])\\n sl = np.rot90(sl, k = -direction, axes=(1,2))\\n sl = np.roll(sl, direction, axis=0)\\n cube[Man.U.value] = sl[0]\\n cube[Man.R.value] = sl[1]\\n cube[Man.D.value] = sl[2]\\n cube[Man.L.value] = sl[3]\\n cube[Man.F.value] = np.rot90(cube[Man.F.value], k=-direction)\\n cube[Man.B.value] = np.rot90(cube[Man.B.value], k=direction)\\n return cube\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7300ea72bb79fa732ac3314284a70b79\",\n \"score\": \"0.54683673\",\n \"text\": \"def _update_turn(self) -> None:\\n self._player = self._next_player(self._player)\\n self._turns += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d90968dcc01c6270f40ffe213d050cb8\",\n \"score\": \"0.54581696\",\n \"text\": \"def do_viewfaces(self, arg):\\n if len(self.inner) == 0:\\n print(\\\"There are no faces to view. 1. loadchoices default 2. alterproperty 3. loadfaces personal 4. savechoices\\\")\\n if len(self.inner) > con.MAX_PORTRAITS:\\n print(\\\"Over 50 images have been selected. If you would like to view all fifty then adjust def do_viewfaces(self, arg)\\\")\\n print(\\\"Otherwise, narrow your selection.\\\")\\n return False\\n filenames = []\\n for face in self.inner:\\n filename = face[0][0:-3]\\n filenames.append(\\\"{}png\\\\n\\\".format(filename))\\n with open(con.SAVE_IMAGE_NAMES, 'w') as f:\\n for filename in filenames:\\n # print(filename)\\n f.write(filename)\\n # for face in self.inner:\\n # print(\\\"elem[0]: \\\", face[0])\\n # # ----\\n # filename = face[0][0:-3]\\n # filename = \\\"{}png\\\".format(filename)\\n # filepath = os.path.join(IMAGES_DIRECTORY, filename)\\n # # print(filepath)\\n # print(filepath)\\n # img = mpimg.imread(filepath)\\n # imgplot = plt.imshow(img)\\n # plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81af7233c98af91768a4284a206a2ea5\",\n \"score\": \"0.54511875\",\n \"text\": \"def turn(self) -> None:\\n #current position reset\\n self.current = 0\\n #increase sequence and play it\\n self.addseq()\\n self.playseq()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdb50a9792a666e9020ee95cb88b7d81\",\n \"score\": \"0.544412\",\n \"text\": \"def turn_to_face_p(self, target=Point3()):\\n my_pos = self.position()\\n if my_pos == target:\\n return\\n direction = normalize(target - my_pos)\\n self.turn_to_face_v(direction)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cc4f5944b094acbfaeefcca5129c1f7\",\n \"score\": \"0.5429818\",\n \"text\": \"def draw(self):\\n # rect_head = (self.head[0] * UNIT_SIZE, self.head[1] * UNIT_SIZE, UNIT_SIZE - 0.5, UNIT_SIZE - 0.5)\\n # pygame.draw.rect(screen, GREY + ALPHA, rect_head, 0)\\n ''' I want to add two eyes! OR, use FACE and NECK! '''\\n ball_face = (int((self.head[0] + 0.5) * UNIT_SIZE), int((self.head[1] + 0.5) * UNIT_SIZE))\\n pygame.draw.circle(screen, GREY + ALPHA, ball_face, int(UNIT_SIZE / 2) - 1)\\n if self.__direction == 'U':\\n rect_neck = (self.head[0] * UNIT_SIZE, int((self.head[1] + 0.5) * UNIT_SIZE),\\n UNIT_SIZE - 0.5, UNIT_SIZE / 2 - 0.5)\\n # ball_eye1 = (self.head[0] * UNIT_SIZE, self.head[1] * UNIT_SIZE)\\n # ball_eye2 = ((self.head[0] + 1) * UNIT_SIZE, self.head[1] * UNIT_SIZE)\\n elif self.__direction == 'D':\\n rect_neck = (self.head[0] * UNIT_SIZE, self.head[1] * UNIT_SIZE,\\n UNIT_SIZE - 0.5, UNIT_SIZE / 2 - 0.5)\\n elif self.__direction == 'L':\\n rect_neck = (int((self.head[0] + 0.5) * UNIT_SIZE), self.head[1] * UNIT_SIZE,\\n UNIT_SIZE / 2 - 0.5, UNIT_SIZE - 0.5)\\n else:\\n rect_neck = (self.head[0] * UNIT_SIZE, self.head[1] * UNIT_SIZE,\\n UNIT_SIZE / 2 - 0.5, UNIT_SIZE - 0.5)\\n\\n pygame.draw.rect(screen, GREY + ALPHA, rect_neck, 0)\\n # pygame.draw.circle(screen, WHITE + ALPHA, ball_eye1, 5)\\n # pygame.draw.circle(screen, WHITE + ALPHA, ball_eye2, 5)\\n for body_node in self.body:\\n rect_node = (body_node[0] * UNIT_SIZE, body_node[1] * UNIT_SIZE, UNIT_SIZE - 0.5, UNIT_SIZE - 0.5)\\n pygame.draw.rect(screen, GREY + ALPHA, rect_node, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e005cb9924b5e7e58763ca9d7dd07829\",\n \"score\": \"0.54284596\",\n \"text\": \"def find_faces():\\n\\n labels = {}\\n with open(\\\"./labels.pickle\\\", 'rb') as f:\\n org_labels = pickle.load(f)\\n labels = {v: k for k, v in org_labels.items()}\\n\\n face_cascade = CascadeClassifier(\\n './haarcascade/haarcascade_frontalface_default.xml')\\n recognizer = cv2.face.LBPHFaceRecognizer_create()\\n recognizer.read(\\\"./trainer.yml\\\")\\n\\n lefteye_cascade = CascadeClassifier('./haarcascade/haarcascade_lefteye.xml')\\n video_capture = cv2.VideoCapture(0)\\n picture_counter = 0\\n\\n picture_flag = False\\n while True:\\n\\n # Capture Frame by Frame\\n ret, frame = video_capture.read()\\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\\n faces = face_cascade.detectMultiScale(\\n gray, scaleFactor=1.05, minNeighbors=8)\\n face_dic = {\\n \\\"known\\\": 0,\\n \\\"total\\\": 0}\\n \\n # Draws a rectagle around the face\\n for(x, y, w, h) in faces:\\n face_dic[\\\"total\\\"] += 1\\n roi_gray = gray[y:y+h, x:x+w]\\n roi_color = frame[y:y+h, x:x+w]\\n id_, conf = recognizer.predict(roi_gray)\\n eyes = lefteye_cascade.detectMultiScale(roi_gray)\\n test = [i for i in eyes]\\n if test:\\n frame = cv2.rectangle(\\n frame, (x, y,), (x+w, y+h), (0, 255, 0), 3)\\n\\n # conf is the confidence level that detected face has been trained\\n if conf >= 85:\\n print(id_)\\n face_dic[\\\"known\\\"] += 1\\n name = labels[id_]\\n font = cv2. FONT_HERSHEY_SIMPLEX\\n color = (255, 255, 255)\\n stroke = 2\\n cv2.putText(frame, name, (x, y), font, 1,\\n color, stroke, cv2.LINE_AA)\\n\\n for (ex, ey, ew, eh) in eyes:\\n cv2.rectangle(roi_color, (ex, ey),\\n (ex+ew, ey+eh), (255, 0, 0), 3)\\n else:\\n for (ex, ey, ew, eh) in eyes:\\n cv2.rectangle(roi_color, (ex, ey),\\n (ex+ew, ey+eh), (255, 0, 255), 3)\\n \\n \\n # Shows Number faces dectected/known in frame\\n cv2.putText(frame, \\\"Number of faces detected: \\\" + str(\\n face_dic[\\\"total\\\"]), (0, 100), cv2.FONT_HERSHEY_TRIPLEX, 0.5, (255, 0, 0), 1)\\n cv2.putText(frame, \\\"Number of faces known: \\\" + str(\\n face_dic[\\\"known\\\"]), (0, 120), cv2.FONT_HERSHEY_TRIPLEX, 0.5, (255, 0, 0), 1)\\n\\n # Dipsplay the resulting frame\\n cv2.imshow('Video', frame)\\n \\n \\n #Saves Picture \\n if face_dic[\\\"known\\\"] == 0 and face_dic[\\\"total\\\"] >= 1:\\n if not picture_flag:\\n picture_counter += 1\\n cv2.imwrite(\\\"./test_subjects/\\\" +\\n str(picture_counter)+\\\".png\\\", frame)\\n picture_flag = True\\n picture_counter += 1\\n if picture_counter == 120:\\n picture_flag = False\\n picture_counter = 0\\n\\n if cv2.waitKey(1) & 0xFF == ord('q'):\\n break\\n\\n video_capture.release()\\n cv2.destroyAllWindows()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aca6c77e4382d682458f04ff0ba933f6\",\n \"score\": \"0.5426279\",\n \"text\": \"def roll(cube, face, direction):\\n #print([\\\"L\\\", \\\"F\\\", \\\"R\\\", \\\"B\\\", \\\"U\\\", \\\"D\\\"][face] + str(direction))\\n cube = np.copy(cube)\\n if face == Man.U.value:\\n cube[0:4, 0] = np.roll(cube[0:4, 0], -direction, axis=0)\\n cube[Man.U.value] = np.rot90(cube[Man.U.value], k=-direction)\\n elif face == Man.D.value:\\n cube[0:4, -1] = np.roll(cube[0:4, -1], direction, axis=0)\\n cube[Man.D.value] = np.rot90(cube[Man.D.value], k=-direction)\\n elif face == Man.F.value:\\n sl = np.array([cube[Man.U.value, -1, :], cube[Man.R.value, :, 0],\\n cube[Man.D.value, 0, ::-1], cube[Man.L.value, ::-1, -1]])\\n sl = np.roll(sl, direction, axis=0)\\n cube[Man.U.value, -1, :] = sl[0]\\n cube[Man.R.value, :, 0] = sl[1]\\n cube[Man.D.value, 0, ::-1] = sl[2]\\n cube[Man.L.value, ::-1, -1] = sl[3]\\n cube[Man.F.value] = np.rot90(cube[Man.F.value], k=-direction)#TODO: Use map function\\n elif face == Man.B.value:\\n sl = np.array([cube[Man.U.value, 0, :], cube[Man.R.value, :, -1], cube[Man.D.value, -1, ::-1], cube[Man.L.value, ::-1, 0]])\\n sl = np.roll(sl, -direction, axis=0)\\n cube[Man.U.value, 0, :] = sl[0]\\n cube[Man.R.value, :, -1] = sl[1]\\n cube[Man.D.value, -1, ::-1] = sl[2]\\n cube[Man.L.value, ::-1, 0] = sl[3]\\n cube[Man.B.value] = np.rot90(cube[Man.B.value], k=-direction)#TODO: Use map function\\n elif face == Man.L.value:\\n sl = np.array([cube[Man.F.value, :, 0], cube[Man.D.value, :, 0], cube[Man.B.value, ::-1, -1], cube[Man.U.value, :, 0]])\\n sl = np.roll(sl, direction, axis=0)\\n cube[Man.F.value, :, 0] = sl[0]\\n cube[Man.D.value, :, 0] = sl[1]\\n cube[Man.B.value, ::-1, -1] = sl[2]\\n cube[Man.U.value, :, 0] = sl[3]\\n cube[Man.L.value] = np.rot90(cube[Man.L.value], k=-direction)\\n elif face == Man.R.value:\\n sl = np.array([cube[Man.F.value, :, -1], cube[Man.D.value, :, -1], cube[Man.B.value, ::-1, 0], cube[Man.U.value, :, -1]])\\n sl = np.roll(sl, -direction, axis=0)\\n cube[Man.F.value, :, -1] = sl[0]\\n cube[Man.D.value, :, -1] = sl[1]\\n cube[Man.B.value, ::-1, 0] = sl[2]\\n cube[Man.U.value, :, -1] = sl[3]\\n cube[Man.R.value] = np.rot90(cube[Man.R.value], k=-direction)\\n return cube\\n #print(Man.__str__())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"004a42186846ed8f1490595eec53dab7\",\n \"score\": \"0.54207855\",\n \"text\": \"def _run_initial_turn(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba9e97f7f2908033657be66d1f35b5b9\",\n \"score\": \"0.54142267\",\n \"text\": \"def ChangeTurn(self):\\r\\n self.lsPlayer[self.turn] = self.ActivePlayer.copy()\\r\\n self.turn = (self.turn + 1) % self.numPlayers\\r\\n self.ActivePlayer = self.lsPlayer[self.turn].copy()\\r\\n if not self.CheckForValidMoves(self.ActivePlayer):\\r\\n self.ChangeTurn()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3e708f7fd07391ba176ad2189cd3335\",\n \"score\": \"0.5413524\",\n \"text\": \"def faces(self):\\n raise NotImplementedError(f\\\"faces not implemented for {type(self)}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36d5ea98cfd7541db5061101436cfeb6\",\n \"score\": \"0.5406411\",\n \"text\": \"def advance(self):\\n self.x_shape_rot += self.x_rot_speed\\n self.x_shape_rot %= 360\\n self.y_shape_rot += self.y_rot_speed\\n self.y_shape_rot %= 360\\n self.z_shape_rot += self.z_rot_speed\\n self.z_shape_rot %= 360\\n self.updateGL()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcb9346c79e67186b2b99f01f562d783\",\n \"score\": \"0.53899795\",\n \"text\": \"def turnLeft(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce23e46ad1a8999548893082b51db47f\",\n \"score\": \"0.5377675\",\n \"text\": \"def setNext(self,game):\\r\\n self.nextFixture = game\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85bf3aa342f06d6abe03dfabe04ff9e8\",\n \"score\": \"0.53709286\",\n \"text\": \"def faces(self):\\r\\n\\t\\tif self._faces is None:\\r\\n\\t\\t\\tself._faces = [Face(self, i) for i in xrange(len(self.faceVertArray))]\\r\\n\\t\\treturn self._faces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4666aaedfe037607c73a5c0fa530b353\",\n \"score\": \"0.53502315\",\n \"text\": \"def targetFaces(self, targetFaces):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4666aaedfe037607c73a5c0fa530b353\",\n \"score\": \"0.5349725\",\n \"text\": \"def targetFaces(self, targetFaces):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e7321b77232091343736848210ba289\",\n \"score\": \"0.5335583\",\n \"text\": \"def save_loop(camera, name: str) -> None:\\n faces = []\\n process_this = True\\n face_to_be_saved = None\\n while True:\\n ret, frame = camera.read()\\n if process_this:\\n faces = prc.process_faces(frame)\\n process_this = not process_this\\n # changing name of each face to empty string\\n message = ''\\n for face in faces:\\n face.assign_name('')\\n if len(faces) == 1:\\n face_to_be_saved = faces[0]\\n elif len(faces) > 1:\\n message = 'More than 1 face detected'\\n else:\\n message = 'No face detected'\\n prc.mark_faces(frame, faces)\\n cv2.putText(frame, message, (20, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 255), 2)\\n cv2.imshow('Video', frame)\\n if cv2.waitKey(1) & 0xFF == ord('q'):\\n break\\n if cv2.getWindowProperty('Video',cv2.WND_PROP_VISIBLE) < 1:\\n break;\\n\\n if face_to_be_saved is not None:\\n save(face_to_be_saved, name)\\n else:\\n print(\\\"Couldn't save the face\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f83a0ae88bdaf135a50b3fd54acc6388\",\n \"score\": \"0.53346217\",\n \"text\": \"def on_face_new(self, faces):\\n\\n if self.is_greeting_appropriate(\\\"new\\\"):\\n self.say(\\\"I see a new person!, Hello stranger!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a042e9a13a6051ebb7a09d57af4c404\",\n \"score\": \"0.53295404\",\n \"text\": \"def face_detect(self):\\n self.face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')\\n \\n self.gray = cv2.cvtColor(self.cv2img, cv2.COLOR_BGR2GRAY)\\n self.cv2img = cv2.cvtColor(self.cv2img, cv2.COLOR_BGR2RGB)\\n \\n # create an array of the positions of each face \\n # and then iterates through it to draw a rectangle around all faces\\n faces = self.face_cascade.detectMultiScale(self.gray, 1.3, 5)\\n for (x, y, w, h) in faces:\\n self.cv2img = cv2.rectangle(self.cv2img, (x, y), (x+w, y+h), (255, 0, 0), 2)\\n \\n self.cv2img = cv2.cvtColor(self.cv2img, cv2.COLOR_BGR2RGB)\\n faceimg = Image.fromarray(self.cv2img)\\n faceimg = ImageTk.PhotoImage(faceimg)\\n self.panel.configure(image = faceimg)\\n self.panel.image = faceimg\\n \\n #makes the button for crop faces visible\\n self.btn.pack(side = 'right')\\n\\n self.states.append(self.cv2img)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05dddf2a206011046beb22ae40ed5ec9\",\n \"score\": \"0.5327832\",\n \"text\": \"def switch_turns(self):\\n # TODO: This can be a one-liner, too lazy to do it now. Somehow it's\\n # easier to write a comment about something that would take 10 seconds\\n # to do than actually doing it\\n if self.turn == self.tictactoe.player1:\\n self.turn = self.tictactoe.player2\\n else:\\n self.turn = self.tictactoe.player1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8388d88bb7a30ab65434d4545028ee86\",\n \"score\": \"0.53252715\",\n \"text\": \"def flip(self):\\r\\n self.buffer,self.backbuffer = self.backbuffer,self.buffer\\r\\n self.Refresh()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"264d4cbd2ab4610bba0e52cfc6791f00\",\n \"score\": \"0.53248394\",\n \"text\": \"def move_forward_or_back(self, face):\\n\\n while not ((self.get_face_distance(face) < 1.1) or (self.get_face_distance(face) > .9)):\\n # checks if face is too small, then moves back\\n if self.get_face_distance(face) < .9:\\n self.move_bot(300)\\n # checks if face is too big, then moves forward\\n elif self.get_face_distance(face) > 1.1:\\n self.move_bot(-300)\\n self.motors = 6000\\n self.tango.setTarget(self.MOTORS, self.motors)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f1a405824a24625be6ae6172b8f61fd\",\n \"score\": \"0.5323996\",\n \"text\": \"def assBody(self):\\n \\n \\n self.surfaceBody = self.spriteList[self.spriteIndex]\\n self.surfaceBody.set_colorkey(constants.TRANS)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4717439d1124655b3c1bcf0d0ccb4854\",\n \"score\": \"0.532059\",\n \"text\": \"def update(self):\\n self.surface.fill((0, 0, 0))\\n # head\\n self.surface.blit(pygame.transform.rotate(self.images[\\\"head\\\"],\\n -90 * self.directions[0]),\\n (CASE_SIZE[0] * self.coords[0][0],\\n CASE_SIZE[1] * self.coords[0][1]))\\n # body\\n size_x, size_y = CASE_SIZE\\n\\n for i in range(len(self.coords[1:-1])):\\n x, y = self.coords[i + 1]\\n direction = self.directions[i + 1]\\n direction_prec = self.directions[i]\\n if direction == direction_prec: # straight\\n self.surface.blit(self.rotate(\\\"body_straight\\\", -90*direction),\\n (size_x * x, size_y * y))\\n # turn right>top or botom>left\\n if (direction == 0 and direction_prec == 1) or \\\\\\n (direction == 3 and direction_prec == 2):\\n self.surface.blit(self.rotate(\\\"body_turn\\\",0),\\n (size_x * x, size_y * y))\\n # turn left>top or botom>right\\n if (direction == 0 and direction_prec == 3) or \\\\\\n (direction == 1 and direction_prec == 2):\\n self.surface.blit(self.rotate(\\\"body_turn\\\",-90),\\n (size_x * x, size_y * y))\\n # turn left>botom or top>right\\n if (direction == 2 and direction_prec == 3) or \\\\\\n (direction == 1 and direction_prec == 0):\\n self.surface.blit(self.rotate(\\\"body_turn\\\",180),\\n (size_x * x, size_y * y))\\n # turn top>left or right>botom\\n if (direction == 3 and direction_prec == 0) or \\\\\\n (direction == 2 and direction_prec == 1):\\n self.surface.blit(self.rotate(\\\"body_turn\\\",90),\\n (size_x * x, size_y * y))\\n # Tail\\n self.surface.blit(pygame.transform.rotate(self.images[\\\"tail\\\"],\\n -90 * self.directions[-2]),\\n (size_x * self.coords[-1][0],\\n size_y * self.coords[-1][1]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e9993859b70cb4a82352b878a93e3a4\",\n \"score\": \"0.53183514\",\n \"text\": \"def EditSkin(\\n self,\\n name: str = \\\"\\\",\\n faces: tuple[Face] = (),\\n edges: tuple[Edge] = (),\\n elementFaces: tuple[MeshFace] = (),\\n elementEdges: tuple[MeshEdge] = (),\\n ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4697beee66114fbfa28fcab3d916a7b4\",\n \"score\": \"0.5313693\",\n \"text\": \"def update(self, image):\\n\\n self._faces = []\\n\\n if (utils.is_gray(image)):\\n image = cv2.equalizeHist(image)\\n else:\\n image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\\n image = cv2.equalizeHist(image)\\n\\n min_size = utils.widthHeightDividedBy(image, 8)\\n\\n face_rects = self._face_classifier.detectMultiScale(\\n image, self.scale_factor, self.min_neighbors, self.flags, min_size)\\n\\n if (face_rects is not None):\\n for face_rect in face_rects:\\n face = Face()\\n face.face_rect = face_rect\\n x, y, w, h = face_rect\\n\\n # look for an eye in the upper-left part of the face\\n search_rect = (x+w//7, y, w*2//7, h//2)\\n face.left_eye_rect = self._detect_one_object(\\n self._eye_classifier, image, search_rect, 64\\n )\\n\\n # look for an eye in the upper-right part of the face\\n search_rect = (x + (w*4)//7, y, w*2//7, h//2)\\n face.right_eye_rect = self._detect_one_object(\\n self._eye_classifier, image, search_rect, 64\\n )\\n\\n # look for an node in the middle part of the face\\n search_rect = (x+w//4, y+h//4, w*2, h//2)\\n face.nose_rect = self._detect_one_object(\\n self._nose_classifier, image, search_rect, 32\\n )\\n\\n # look for an mouth in the lower-middle part of the face\\n search_rect = (x+w//6, y + (h*2)//3, w*2//3, h//3)\\n face.mouth_rect = self._detect_one_object(\\n self._mouth_classifier, image, search_rect, 16\\n )\\n\\n self._faces.append(face)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f9a35c4e36f54cadb68c18c698cde3f\",\n \"score\": \"0.5311607\",\n \"text\": \"def faceSet(self):\\r\\n\\t\\tret = FaceSet(self, [])\\r\\n\\t\\tret.update(range(len(self.faceVertArray)))\\r\\n\\t\\treturn ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ee3af718f04065414eca6b36fe668b4\",\n \"score\": \"0.53067994\",\n \"text\": \"def face_amount(self, face_amount):\\n\\n self._face_amount = face_amount\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b21fe36df6c0fae07d86a2b23bd7f9bb\",\n \"score\": \"0.5304956\",\n \"text\": \"def add_face_to_list(self, points):\\n ids = [point.id for point in points]\\n self.faces.append(ids)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4bb2a8e7b4881fca6ca45efc0183e63\",\n \"score\": \"0.53038234\",\n \"text\": \"def online_face_recognition(profile_names, n_pictures=15):\\n images = []\\n labels = []\\n label_names = []\\n for i, name in enumerate(profile_names):\\n p = load_profile(name)\\n p = p[0:n_pictures, ]\\n images += [p]\\n labels += [np.ones(p.shape[0]) * (i + 1)]\\n label_names += [name]\\n faces = np.vstack(images)\\n labels = np.hstack(labels).astype(np.int)\\n # Generate model\\n model = IncrementalKCenters(faces, labels)\\n # Start camera\\n cam = cv.VideoCapture(0)\\n while True:\\n ret_val, img = cam.read()\\n grey_image = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\\n working_image = cv.bilateralFilter(grey_image, 9, 75, 75)\\n working_image = cv.equalizeHist(working_image)\\n working_image = cv.GaussianBlur(working_image, (5, 5), 0)\\n box = face_haar_cascade.detectMultiScale(working_image)\\n for b0 in box:\\n x, y = b0[0], b0[1]\\n x_range, y_range = b0[2], b0[3]\\n # look for eye classifier\\n local_image = img[y:(y + y_range), x:(x + x_range)]\\n eye_box = eye_haar_cascade.detectMultiScale(local_image)\\n if len(eye_box) == 0:\\n cv.rectangle(img, tuple([b0[0] - 4, b0[1] - 4]), tuple([b0[0] + b0[2] + 4, b0[1] + b0[3] + 4]),\\n (0, 0, 255), 2)\\n continue\\n # select face\\n local_image = grey_image[y:(y + y_range), x:(x + x_range)]\\n x_t = preprocess_face(local_image)\\n\\n \\\"\\\"\\\"\\n Centroids are updated here\\n \\\"\\\"\\\"\\n model.online_ssl_update_centroids(x_t)\\n p1, p2 = tuple([b0[0] - 4, b0[1] - 4]), tuple([b0[0] + b0[2] + 4, b0[1] + b0[3] + 4])\\n\\n \\n \\\"\\\"\\\"\\n HardHFS solution is computed here\\n \\\"\\\"\\\"\\n f = model.online_ssl_compute_solution()\\n lab = np.argsort(f)\\n \\n \\n \\\"\\\"\\\"\\n Change False by something else to be able to disregard faces it cannot recognize (question 3.4)\\n \\\"\\\"\\\" \\n \\n mean_dist_new_to_labeled = np.mean(distance.cdist(np.array([model.centroids[model.last_face]]), faces)[0])\\n \\n mean_dist_existing=0\\n for person in set(labels):\\n #Average distance between the faces of a same person\\n mean_dist_existing += np.mean(distance.cdist(faces[labels==person], faces[labels==person])[0])\\n mean_dist_existing=mean_dist_existing/len(list(set(labels))) \\n \\n if any(f==0.) and mean_dist_new_to_labeled>mean_dist_existing:\\n color = (100, 100, 100)\\n txt = \\\"unknown\\\"\\n cv.putText(img, txt, (p1[0], p1[1] - 5), cv.FONT_HERSHEY_COMPLEX_SMALL, 1, color)\\n else:\\n for i, l in enumerate(lab):\\n color = [(0, 255, 0), (255, 0, 0), (0, 0, 255)][l]\\n txt = label_names[l] + \\\" \\\" + ('%.4f' % np.abs(f[l]))\\n cv.putText(img, txt, (p1[0], p1[1] - 5 - 10 * i), cv.FONT_HERSHEY_COMPLEX_SMALL,\\n 0.5 + 0.5 * (i == f.shape[0] - 1), color)\\n cv.rectangle(img, p1, p2, color, 2)\\n cv.imshow(\\\"cam\\\", img)\\n key = cv.waitKey(1)\\n if key in [27, 101]:\\n break\\n if key == ord('s'):\\n # Save face\\n print('saved')\\n cv.imwrite(\\\"frame.png\\\", img)\\n ## cv.waitKey(1)\\n cv.destroyAllWindows()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a8f35e3b27d9bfb457cba006ae270ae\",\n \"score\": \"0.5301556\",\n \"text\": \"def turn():\\n global o_both_steering\\n\\n o_both_steering.on_for_rotations(100, SpeedPercent(40), 1) # OPTIMAL VALUE 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"391dc969f17266ea0bca1dc38375864f\",\n \"score\": \"0.5300656\",\n \"text\": \"def generator(surface, angles):\\n original_surf = surface.copy()\\n for angle in angles:\\n x, y = self.rect.center\\n self.surface = pygame.transform.rotate(original_surf, angle)\\n self.instant_move(x, y)\\n yield True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6eceb20ea200d1fa8569da8eae1e7bc\",\n \"score\": \"0.5300277\",\n \"text\": \"def updateCameraVectors(self):\\n # Calculate the new Front vector\\n x = np.cos(np.radians(self.Yaw)) * np.cos(np.radians(self.Pitch))\\n y = np.sin(np.radians(self.Pitch))\\n z = np.sin(np.radians(self.Yaw)) * np.cos(np.radians(self.Pitch))\\n \\n front = np.array([x, y, z], dtype=GLfloat)\\n print(front)\\n self.Front = front / np.sqrt(x**2 + y**2 + z**2)\\n print(self.Front)\\n \\n temp = np.cross(self.Front, self.WorldUp)\\n self.Right = temp / np.sqrt(temp[0]**2 + temp[1]**2 + temp[2]**2)\\n print(self.Right)\\n \\n temp = np.cross(self.Right, self.Front)\\n self.Up = temp / np.sqrt(temp[0]**2 + temp[1]**2 + temp[2]**2)\\n print(self.Up)\\n \\n \\n \\n \\n # front = glm.vec3(x, y, z)\\n # print('*=========')\\n # self.Front = np.array(glm.normalize(front), dtype=GLfloat)\\n # print(self.Front)\\n # print('#=========')\\n #\\n # # Also re-calculate the Right and Up vector\\n # # Normalize the vectors, because their length gets closer to 0 the more you\\n # # look up or down which results in slower movement.\\n # print('**=========')\\n # temp = np.cross(self.Front, self.WorldUp)\\n # print(temp)\\n # print('##=========')\\n # self.Right = np.array(glm.normalize(glm.vec3(temp)), dtype=GLfloat)\\n #\\n # print('***=========')\\n # temp = np.cross(self.Right, self.Front)\\n # print(temp)\\n # self.Up = np.array(glm.normalize(glm.vec3(temp)), dtype=GLfloat)\\n # print('###=========')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8904960cd5a8b6234de2d8d57a573dcd\",\n \"score\": \"0.5300243\",\n \"text\": \"def inputFaces(self, inputFaces):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8904960cd5a8b6234de2d8d57a573dcd\",\n \"score\": \"0.5300041\",\n \"text\": \"def inputFaces(self, inputFaces):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8904960cd5a8b6234de2d8d57a573dcd\",\n \"score\": \"0.5300041\",\n \"text\": \"def inputFaces(self, inputFaces):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8904960cd5a8b6234de2d8d57a573dcd\",\n \"score\": \"0.5300041\",\n \"text\": \"def inputFaces(self, inputFaces):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad3aa8cbac3e97b9e7256bd79a6f9a6c\",\n \"score\": \"0.5299885\",\n \"text\": \"def next_turn(self):\\n temp = self.current_player\\n self.current_player = self.opponent\\n self.opponent = temp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57f4d58e0f16c22078ce9dd38381e616\",\n \"score\": \"0.52914584\",\n \"text\": \"def test_batch_redetect_with_one_face(self):\\n for detector in self.detectors:\\n with self.subTest(detectorType=detector.detectorType):\\n detection = detector.detectOne(image=VLIMAGE_ONE_FACE)\\n redetect = detector.redetect(\\n images=[ImageForRedetection(image=VLIMAGE_ONE_FACE, bBoxes=[detection.boundingBox.rect])]\\n )[0]\\n self.assertFaceDetection(redetect, VLIMAGE_ONE_FACE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37798494830af70cc4a3e549576796e7\",\n \"score\": \"0.52862424\",\n \"text\": \"def build_face():\\n masterScale = 0.2\\n create_hooks(masterScale)\\n inputHooks = create_joint_drivers(masterScale)\\n\\n for poseInfo in faceposes:\\n zone = poseInfo['zone']\\n pose = poseInfo['pose']\\n poseDriver = poseInfo['driver']\\n poseRange = poseInfo['mapping']\\n poseOverrides = poseInfo['overrides']\\n poseOverrideMaps = poseInfo['overridemaps']\\n\\n create_pose_node(pose, zone, inputHooks, masterScale, poseDriver, poseRange, poseOverrides, poseOverrideMaps)\\n print 'done'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7596177b3c91fc479cf4def251516cac\",\n \"score\": \"0.5286083\",\n \"text\": \"def UpdateRectColors(self, selectedRectId):\\n\\n for (rectId, rect) in enumerate(self.rects):\\n\\n (x, y, w, h) = rect\\n color = (0,255,0) if rectId == selectedRectId-1 else (0,0,255) #selection is 1 based\\n\\n cv2.rectangle(self.img500, (x, y), ((x + w), (y + h)), color, 2)\\n\\n # show the face number\\n cv2.putText(self.img500,\\n \\\"Face #{}\\\".format(rectId + 1),\\n (x - 10, y - 10),\\n cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)\\n\\n cv2.imshow(self.faceSelectionWin, self.img500)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07ac0645a023800fc4dcf11143ec82b7\",\n \"score\": \"0.5282528\",\n \"text\": \"def setup_face_indices(self, reset=True):\\n if reset or not self.fis:\\n fa = self.domain.get_facets(force_faces=True)[1]\\n\\n if self.faces:\\n faces = self.faces\\n else:\\n faces = self.edges\\n\\n self.fis = {}\\n for ig in self.igs:\\n rfaces = faces[ig]\\n fi = fa.indices[rfaces]\\n assert_(nm.all(fi[:,0] == ig))\\n self.fis[ig] = fi[:,1:].copy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e58cb333ee43454817f059a30915849\",\n \"score\": \"0.5273543\",\n \"text\": \"def another_turn(self, pit: PitData) -> None:\\n self.update_color(pit, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fc7f93fc6c3dd1ed8fcd5dd822f522a\",\n \"score\": \"0.52639437\",\n \"text\": \"def on_draw(self):\\n arcade.start_render()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b0b0f3d2a4cf4ba6735ee43f6bad2a4\",\n \"score\": \"0.5263633\",\n \"text\": \"def update_turn(self):\\n self.p1.turn = not self.p1.turn\\n self.p2.turn = not self.p2.turn\\n self.current_player = self.p1 or self.p2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65698da0371ffe24258d721bc412f014\",\n \"score\": \"0.52610844\",\n \"text\": \"def updateCams(self, interface=\\\"match\\\"):\\n activecams = getActiveCams(len(self.cameras[interface]))\\n for activeind in activecams:\\n camera = self.cameras[interface][activeind]\\n if camera in self.gridded:\\n camera.updateImgOnLabel()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09cf5d165983357f2dcc6a505dae41e3\",\n \"score\": \"0.5259303\",\n \"text\": \"def RubiksCube():\\n solved = False\\n while (input(\\\"Start game?: \\\") == \\\"yes\\\") and (not solved):\\n c = Cube()\\n c.shuffle()\\n plotCube(c)\\n plt.ion()\\n plt.show()\\n plt.pause(1)\\n # pause needed to update matplotlib plots with keyboard inputs\\n print(\\\"The possible faces to rotate are: U,D,L,R,F,B.\\\\n The possible directions to rotate are: clockwise, counterclockwise.\\\\n The possible numbers of rotations are: 1,2,3.\\\\n\\\")\\n while not solved:\\n interact(c)\\n plt.close()\\n plotCube(c)\\n plt.show()\\n plt.pause(1)\\n if c._p == Cube()._p:\\n solved = True\\n if solved:\\n print(\\\"You win!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a4f80d41e6e547f5490b5de12845018\",\n \"score\": \"0.52588135\",\n \"text\": \"def get_any_face(self):\\n return choice(list(self.faces()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2d84dce15832a9e4b877937b180ce8e\",\n \"score\": \"0.5258472\",\n \"text\": \"def Turn(self, amount):\\n \\n self.Angle -= amount\\n self.Angle %= 360\\n\\n self.Image = pygame.transform.rotate(self.Orig, self.Angle)\\n self.UpdateMask()\\n \\n newSize = self.Image.get_size()\\n self.ModX = -(newSize[0]-self.Size[0])/2\\n self.ModY = -(newSize[1]-self.Size[1])/2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee707779d583c98c6499bd7652922ed3\",\n \"score\": \"0.524997\",\n \"text\": \"def toggle_turn(self):\\n if self._turn == \\\"player1\\\":\\n self._turn = \\\"player2\\\"\\n else:\\n self._turn = \\\"player1\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c18c82af0b0743c846de867ee1593851\",\n \"score\": \"0.52392316\",\n \"text\": \"def update_surface(self):\\n self.surface = pygame.surfarray.make_surface(self.arr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a23ba794220991e6386c2a7006d82da\",\n \"score\": \"0.5238691\",\n \"text\": \"def getSurface(self):\\n self.frame += 1\\n if self.frame == 3:\\n self.isFirstPic = not self.isFirstPic\\n self.frame = 0\\n\\n if self.direction == 0:\\n self.surface = Pacman.images[self.isFirstPic]\\n elif self.direction == 1:\\n self.surface = pygame.transform.rotate(Pacman.images[self.isFirstPic], 90)\\n elif self.direction == 2:\\n self.surface = pygame.transform.rotate(Pacman.images[self.isFirstPic], 180)\\n elif self.direction == 3:\\n self.surface = pygame.transform.rotate(Pacman.images[self.isFirstPic], 270)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a00db76457ab7532cc3af1fdac12f64\",\n \"score\": \"0.5236641\",\n \"text\": \"def turn_inc(self) -> None:\\n self.__current_turn += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7e1b59538be1a46c3c180e932f6b9ca\",\n \"score\": \"0.52359444\",\n \"text\": \"def draw_changing(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20f025928f844ba5664d57864501e178\",\n \"score\": \"0.5231487\",\n \"text\": \"def turn(self):\\n\\n #print(self.coords[0] ,self.dx,self.dy, self.field.width,self.field.height)\\n mis=list(self.field.mice.keys())\\n if len(mis)>0:\\n x=self.coords[0][0]\\n y=self.coords[0][1]\\n mx=1000\\n my=1000\\n for (tx,ty) in mis:\\n if abs(x-tx)+abs(y-ty) mx:\\n if self.dx!=-1:\\n if random.randint(0,10) < 5:\\n if random.randint(0,1) == 1:\\n self.turn_left()\\n else:\\n self.turn_right()\\n else:\\n if self.coords[0][1] < my:\\n if self.dy!=1:\\n if random.randint(0,10) < 5:\\n if random.randint(0,1) == 1:\\n self.turn_left()\\n else:\\n self.turn_right()\\n elif self.coords[0][1] > my:\\n if self.dy!=-1:\\n if random.randint(0,10) < 5:\\n if random.randint(0,1) == 1:\\n self.turn_left()\\n else:\\n self.turn_right()\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":792,"cells":{"query_id":{"kind":"string","value":"9003693612e2496d1b8ec20c13e967fc"},"query":{"kind":"string","value":"Takes a project UUID and either creates a new document in the desprojects index or updates the document if one already exists for that project."},"positive_passages":{"kind":"list like","value":[{"docid":"809657dc207ab465236a7ea3711a9617","score":"0.74024814","text":"def index_or_update_project(self, uuid):\n from designsafe.apps.api.projects.models import Project\n\n client = get_service_account_client()\n project_model = Project(client)\n project = project_model.search({'uuid': uuid}, client)[0]\n project_meta = project.to_dict()\n\n to_index = {key: value for key, value in project_meta.iteritems() if key != '_links'}\n to_index['value'] = {key: value for key, value in project_meta['value'].iteritems() if key != 'teamMember'}\n\n project_search = IndexedProject.search().filter(\n Q({'term': \n {'uuid._exact': uuid}\n })\n )\n res = project_search.execute()\n\n if res.hits.total == 0:\n # Create an ES record for the new metadata.\n # project_info_args = {key:value for key,value in project_info.iteritems() if key != '_links'}\n project_ES = IndexedProject(**to_index)\n project_ES.save()\n elif res.hits.total == 1:\n # Update the record.\n doc = res[0]\n doc.update(**to_index)\n else:\n # If we're here we've somehow indexed the same project multiple times. \n # Delete all records and replace with the metadata passed to the task.\n for doc in res:\n doc.delete()\n project_ES = IndexedProject(**to_index) \n project_ES.save()","title":""}],"string":"[\n {\n \"docid\": \"809657dc207ab465236a7ea3711a9617\",\n \"score\": \"0.74024814\",\n \"text\": \"def index_or_update_project(self, uuid):\\n from designsafe.apps.api.projects.models import Project\\n\\n client = get_service_account_client()\\n project_model = Project(client)\\n project = project_model.search({'uuid': uuid}, client)[0]\\n project_meta = project.to_dict()\\n\\n to_index = {key: value for key, value in project_meta.iteritems() if key != '_links'}\\n to_index['value'] = {key: value for key, value in project_meta['value'].iteritems() if key != 'teamMember'}\\n\\n project_search = IndexedProject.search().filter(\\n Q({'term': \\n {'uuid._exact': uuid}\\n })\\n )\\n res = project_search.execute()\\n\\n if res.hits.total == 0:\\n # Create an ES record for the new metadata.\\n # project_info_args = {key:value for key,value in project_info.iteritems() if key != '_links'}\\n project_ES = IndexedProject(**to_index)\\n project_ES.save()\\n elif res.hits.total == 1:\\n # Update the record.\\n doc = res[0]\\n doc.update(**to_index)\\n else:\\n # If we're here we've somehow indexed the same project multiple times. \\n # Delete all records and replace with the metadata passed to the task.\\n for doc in res:\\n doc.delete()\\n project_ES = IndexedProject(**to_index) \\n project_ES.save()\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"3eb70541f86495a1a812503637a60949","score":"0.58575755","text":"def reindex_projects(self):\n client = get_service_account_client()\n query = {'name': 'designsafe.project'}\n\n in_loop = True\n offset = 0\n while in_loop:\n listing = client.meta.listMetadata(q=json.dumps(query), offset=offset, limit=100)\n offset += 100\n\n if len(listing) == 0:\n in_loop = False\n else:\n for project in listing:\n index_or_update_project.apply_async(args=[project.uuid], queue='api')","title":""},{"docid":"25f60d64bc8a92d3f3948d857b5134ec","score":"0.57789236","text":"def create(self, document):\n self.index(document)","title":""},{"docid":"1e467fdbb386338ac8ce8d4cd52f433a","score":"0.57180494","text":"def create_or_update_doc(self, index, doc):\n body = self.bulk_body[index]\n body.write(json.dumps({'index': {'_id': doc['id']}}))\n body.write('\\n')\n body.write(json.dumps(doc))\n body.write('\\n')","title":""},{"docid":"56f028452184cb6eea1ae6844841d69e","score":"0.5655644","text":"def update_document(self, **fields):\r\n \r\n # Check which of the supplied fields are unique\r\n unique_fields = [name for name, field\r\n in self.index.schema.fields()\r\n if name in fields and field.unique]\r\n if not unique_fields:\r\n raise IndexingError(\"None of the fields in %r are unique\" % fields.keys())\r\n \r\n # Delete documents in which the supplied unique fields match\r\n s = self.searcher()\r\n for name in unique_fields:\r\n self.delete_by_term(name, fields[name])\r\n \r\n # Add the given fields\r\n self.add_document(**fields)","title":""},{"docid":"8b403dda7150c949fc6ce049474e9990","score":"0.56132174","text":"def create_document(doc_id, data):\n conn = get_conn()\n for alias in get_active_aliases(conn, object_types=[data[\"object_type\"]]):\n conn.create(index=alias, doc_type=GLOBAL_DOC_TYPE, body=data, id=doc_id)","title":""},{"docid":"a5241a27b6867e7f2755bbd925f12a74","score":"0.5546349","text":"def update_doc(c, i, d, u=False):\n try:\n c.update_one({'_id': i}, {'$set': d}, upsert = u)\n return True\n except:\n return False","title":""},{"docid":"4b4b83ea1b879b88eda2c72b53e3c52c","score":"0.5532153","text":"def post_project_document(project_id,\n document_type,\n document_name,\n document_content_type,\n document_content,\n document_preamble,\n document_legal_entity_name,\n new_major_version=None,\n username=None):\n project = get_project_instance()\n try:\n project.load(str(project_id))\n except DoesNotExist as err:\n return {'errors': {'project_id': str(err)}}\n project_acl_verify(username, project)\n document = get_document_instance()\n document.set_document_name(document_name)\n document.set_document_content_type(document_content_type)\n document.set_document_content(document_content)\n document.set_document_preamble(document_preamble)\n document.set_document_legal_entity_name(document_legal_entity_name)\n if document_type == 'individual':\n major, minor = cla.utils.get_last_version(project.get_project_individual_documents())\n if new_major_version:\n document.set_document_major_version(major + 1)\n document.set_document_minor_version(0)\n else:\n if major == 0:\n major = 1\n document.set_document_major_version(major)\n document.set_document_minor_version(minor + 1)\n project.add_project_individual_document(document)\n else:\n major, minor = cla.utils.get_last_version(project.get_project_corporate_documents())\n if new_major_version:\n document.set_document_major_version(major + 1)\n document.set_document_minor_version(0)\n else:\n if major == 0:\n major = 1\n document.set_document_major_version(major)\n document.set_document_minor_version(minor + 1)\n project.add_project_corporate_document(document)\n project.save()\n return project.to_dict()","title":""},{"docid":"f019a8aa87ac1017e067247c87599fa5","score":"0.5523262","text":"def process_project(project_data: dict):\n project_id = project_data.get('projectid')\n\n if project_id is not None:\n Project.objects.update_or_create(\n ona_pk=project_id,\n defaults={\n 'name': project_data.get('name'),\n 'deleted_at': project_data.get('deleted_at'),\n 'last_updated': project_data.get('date_modified'),\n 'json': project_data,\n })","title":""},{"docid":"a74cc25a8d7944eb373349e99b2e6179","score":"0.5489646","text":"def update_project(uuid, **kwargs):\n project = Project.query.get(uuid)\n\n if project is None:\n raise NotFound(\"The specified project does not exist\")\n\n if \"name\" in kwargs:\n name = kwargs[\"name\"]\n if name != project.name:\n check_project_name = db_session.query(Project).filter_by(name=name).first()\n if check_project_name:\n raise BadRequest(\"a project with that name already exists\")\n\n data = {\"updated_at\": datetime.utcnow()}\n data.update(kwargs)\n\n try:\n db_session.query(Project).filter_by(uuid=uuid).update(data)\n db_session.commit()\n except (InvalidRequestError, ProgrammingError) as e:\n raise BadRequest(str(e))\n\n return project.as_dict()","title":""},{"docid":"968af9fd450edbe0146f7b5d25cc0cd7","score":"0.5369272","text":"def post_project_document_template(project_id,\n document_type,\n document_name,\n document_preamble,\n document_legal_entity_name,\n template_name,\n new_major_version=None,\n username=None):\n project = get_project_instance()\n try:\n project.load(str(project_id))\n except DoesNotExist as err:\n return {'errors': {'project_id': str(err)}}\n project_acl_verify(username, project)\n document = get_document_instance()\n document.set_document_name(document_name)\n document.set_document_preamble(document_preamble)\n document.set_document_legal_entity_name(document_legal_entity_name)\n if document_type == 'individual':\n major, minor = cla.utils.get_last_version(project.get_project_individual_documents())\n if new_major_version:\n document.set_document_major_version(major + 1)\n document.set_document_minor_version(0)\n else:\n document.set_document_minor_version(minor + 1)\n project.add_project_individual_document(document)\n else:\n major, minor = cla.utils.get_last_version(project.get_project_corporate_documents())\n if new_major_version:\n document.set_document_major_version(major + 1)\n document.set_document_minor_version(0)\n else:\n document.set_document_minor_version(minor + 1)\n project.add_project_corporate_document(document)\n # Need to take the template, inject the preamble and legal entity name, and add the tabs.\n tmplt = getattr(cla.resources.contract_templates, template_name)\n template = tmplt(document_type=document_type.capitalize(),\n major_version=document.get_document_major_version(),\n minor_version=document.get_document_minor_version())\n content = template.get_html_contract(document_legal_entity_name, document_preamble)\n pdf_generator = get_pdf_service()\n pdf_content = pdf_generator.generate(content)\n document.set_document_content_type('storage+pdf')\n document.set_document_content(pdf_content, b64_encoded=False)\n document.set_raw_document_tabs(template.get_tabs())\n project.save()\n return project.to_dict()","title":""},{"docid":"18db39f126f585a872029737f5e29364","score":"0.52629536","text":"def create_document(self, entity, wait=False):\n docs = self.build_document(entity, 'single')\n for doc in docs:\n logger.info(f\"Making Doc={doc}\")\n res = self.es.index(index=self.index_name(entity.project.pk),\n id=doc['_id'],\n refresh=wait,\n routing=1,\n body={**doc['_source']})","title":""},{"docid":"397a433b2bb4643fa322625c06217544","score":"0.5236747","text":"def setup():\n index_template = Doc._index.as_template(\"base\")\n index_template.save()","title":""},{"docid":"cd1f125fa2856f065487eb0b6eafe3e6","score":"0.5214013","text":"def update_project(self, project_name, from_path):\n session = self.session\n\n # check if project exists.\n query_result = session.query(Project).filter_by(\n name=project_name).first()\n if not query_result:\n logging.info(f\"{project_name} not found, skipping.\")\n return\n\n if query_result.fully_indexed and query_result.external:\n logging.debug(\n f\"{project_name} already fully indexed, skipping.\")\n return\n\n if query_result.external and from_path:\n module_name = Path(from_path).name\n if query_result.is_module_in_project(module_name):\n logging.info(f\"{project_name} is already enough indexed, skipping.\")\n return\n\n logging.info(f\"Indexing {project_name} from {from_path}.\")\n query_result.index(from_path)\n logging.info(f\"Building {project_name}.\")\n query_result.build()\n\n session.commit()\n # session.close()","title":""},{"docid":"f69d0545c80cf079a73906a79de1b5e3","score":"0.52051336","text":"def create_index(index=None):\n index = index or INDEX\n document_types = ['project', 'component', 'registration', 'user', 'file', 'institution', 'preprint', 'collectionSubmission']\n guid_metadata_types = ['project', 'component', 'registration', 'preprint', 'file']\n\n client().indices.create(index, ignore=[400]) # HTTP 400 if index already exists\n for type_ in document_types:\n if type_ == 'collectionSubmission':\n mapping = {\n 'properties': {\n 'collectedType': NOT_ANALYZED_PROPERTY,\n 'subjects': NOT_ANALYZED_PROPERTY,\n 'status': NOT_ANALYZED_PROPERTY,\n 'issue': NOT_ANALYZED_PROPERTY,\n 'volume': NOT_ANALYZED_PROPERTY,\n 'programArea': NOT_ANALYZED_PROPERTY,\n 'provider': NOT_ANALYZED_PROPERTY,\n 'title': ENGLISH_ANALYZER_PROPERTY,\n 'abstract': ENGLISH_ANALYZER_PROPERTY,\n 'schoolType': NOT_ANALYZED_PROPERTY,\n 'studyDesign': NOT_ANALYZED_PROPERTY,\n }\n }\n else:\n mapping = {\n 'properties': {\n 'tags': NOT_ANALYZED_PROPERTY,\n 'license': {\n 'properties': {\n 'id': NOT_ANALYZED_PROPERTY,\n 'name': NOT_ANALYZED_PROPERTY,\n # Elasticsearch automatically infers mappings from content-type. `year` needs to\n # be explicitly mapped as a string to allow date ranges, which break on the inferred type\n 'year': {'type': 'string'},\n }\n },\n\n }\n }\n if type_ in guid_metadata_types:\n mapping['properties'].update({\n 'title': ENGLISH_ANALYZER_PROPERTY,\n 'description': ENGLISH_ANALYZER_PROPERTY,\n 'language': NOT_ANALYZED_PROPERTY,\n 'resource_type_general': NOT_ANALYZED_PROPERTY,\n 'funder_name': {'type': 'string'},\n 'funder_identifier': NOT_ANALYZED_PROPERTY,\n 'award_number': NOT_ANALYZED_PROPERTY,\n 'award_uri': NOT_ANALYZED_PROPERTY,\n 'award_title': {'type': 'string'},\n })\n\n if type_ == 'user':\n fields = {\n 'job': {\n 'type': 'string',\n 'boost': '1',\n },\n 'all_jobs': {\n 'type': 'string',\n 'boost': '0.01',\n },\n 'school': {\n 'type': 'string',\n 'boost': '1',\n },\n 'all_schools': {\n 'type': 'string',\n 'boost': '0.01'\n },\n }\n mapping['properties'].update(fields)\n client().indices.put_mapping(index=index, doc_type=type_, body=mapping, ignore=[400, 404])","title":""},{"docid":"8f0e31de144dba037550aa04b481e8d6","score":"0.5165092","text":"def update_index():\n # TODO: check that it's been more than a day\n deb.update_index()","title":""},{"docid":"105792b5a3b9e9dbd05f13689425dbf9","score":"0.5146721","text":"def update_projects():","title":""},{"docid":"105792b5a3b9e9dbd05f13689425dbf9","score":"0.5146721","text":"def update_projects():","title":""},{"docid":"68dbdd7332da5f3c6e2202cf083bbe01","score":"0.51065445","text":"def update(self, document):\n pass","title":""},{"docid":"dfb05f5d631251af0f718137c6fa6bec","score":"0.5101003","text":"async def replace_project(project_id: ProjectID, _replace: ProjectReplace):","title":""},{"docid":"05a62e64c02e498d0d8bdea8b6527342","score":"0.5084844","text":"def reindex_document(document):\n\n try:\n record = document._record or DocumentRecord.objects.get(pk=document.id)\n except DocumentRecord.DoesNotExist:\n return\n\n qs = TokenFieldIndex.objects.filter(\n record_id=document.id,\n revision=document.revision\n )\n defer_iteration_with_finalize(\n qs, _destroy_record, _finalize, _shards=1, _queue=_SEARCH_QUEUE\n )\n\n # Generate a brand new revision ID for this document\n record.revision = uuid.uuid4()\n record.save()\n\n index_document(document.index_name, document)","title":""},{"docid":"2bf09988a232c0f4a78f1201328c3b11","score":"0.50620395","text":"def test_get_project_by_id(app):\n\n # insert a projects into the database","title":""},{"docid":"33d3852abae5ff5e392637b5bcc96cc4","score":"0.5043024","text":"def addDocumentToIndex(self, name, vrr, nodeIdNum, title, content, tags):\n raise NotImplementedError","title":""},{"docid":"e705ba684fa2a02064f83326f33c8ec5","score":"0.5042402","text":"def projects_delete(self, perun_id):\n self.projects_update(perun_id, scratched=True)","title":""},{"docid":"3ba7858e4f524c1eb763fba5c6cd64be","score":"0.50327057","text":"def add(self, document):\n return self.db.update({document['id']: document})","title":""},{"docid":"4bcd77babd05f969641766c089631885","score":"0.5025321","text":"def delete_project_document(project_id, document_type, major_version, minor_version, username=None):\n project = get_project_instance()\n try:\n project.load(str(project_id))\n except DoesNotExist as err:\n return {'errors': {'project_id': str(err)}}\n project_acl_verify(username, project)\n document = cla.utils.get_project_document(project, document_type, major_version, minor_version)\n if document is None:\n return {'errors': {'document': 'Document version not found'}}\n if document_type == 'individual':\n project.remove_project_individual_document(document)\n else:\n project.remove_project_corporate_document(document)\n project.save()\n return {'success': True}","title":""},{"docid":"437d1058dc5f0f770d721c6f2b83c40c","score":"0.50245684","text":"def update_project(self, database):\n opended_after_140630 = comp_dates('2014-06-30', self.ordered_opened)\n try:\n LOG.info('Handeling {proj}'.format(proj = self.name))\n project = database.ProjectDB(lims, self.id, self.samp_db)\n key = find_proj_from_view(self.proj_db, self.name)\n project.obj['_id'] = find_or_make_key(key)\n if not opended_after_140630:\n project.obj = load_status_from_google_docs.get(self.name, project.obj)\n if self.upload_data:\n info = save_couchdb_obj(self.proj_db, project.obj)\n else:\n info = self.print_couchdb_obj_to_file(project.obj)\n return \"project {name} is handled and {info}: _id = {id}\".format(\n name=self.name, info=info, id=project.obj['_id'])\n except:\n return ('Issues geting info for {name}. The \"Application\" udf might'\n ' be missing'.format(name = self.name))","title":""},{"docid":"311ac2a59dda5e4adc79b73d50426ccc","score":"0.5006261","text":"def create_day_doc(doc_datetime):\n timestampStr = doc_datetime.strftime(\"%Y-%m-%d\")\n\n if not COL_TELEMETRY.document(timestampStr).get().exists:\n try:\n COL_TELEMETRY.document(timestampStr).set({\"Date\": timestampStr})\n return \"Documents Created\", 201\n except Exception as e:\n reporting_client.report_exception()\n return f\"An Error Occured: {e}\", 400\n return \"Document already exists\", 200","title":""},{"docid":"5b85dff9afb87d73bc6a1318ae161f8e","score":"0.5004508","text":"def create_legacy_projects():\n for slug, title, pubdate in dbs:\n r = requests.get('https://physionet.org/physiobank/database/{}/HEADER.shtml'.format(slug))\n if r.status_code != 200:\n r = requests.get('https://physionet.org/physiobank/database/{}/index.shtml'.format(slug))\n if r.status_code != 200:\n print('{} does not exist'.format(slug))\n continue\n content = load_legacy_html(slug=slug)\n p = LegacyProject.objects.create(title=title, slug=slug,\n full_description=content, publish_date=datetime.datetime.strptime(pubdate.strip(), '%d %B %Y'))","title":""},{"docid":"f2401688b53bc9cf60745ef665dd2c14","score":"0.5003635","text":"def update_project(self):\n pass","title":""},{"docid":"192c820a883dfe716f2d4126f0080317","score":"0.49894994","text":"def test_create_project(self):\n payload = {\n \"name\": {\"en\": \"Test project\", \"fr\": \"Projet test\"},\n \"description\": {\"en\": [\"Test desc\"], \"fr\": [\"Test desc\"]}\n }\n res = self.admin_client.post(\n PROJECT_URL, payload, format=\"json\")\n self.assertEqual(res.status_code, status.HTTP_201_CREATED)\n exists = Project.objects.all().filter(\n name=payload['name']).exists()\n self.assertTrue(exists)\n Project.objects.all().filter(name=payload['name']).delete()","title":""},{"docid":"98e81e7036e3f2d0f04ad8b471ec4984","score":"0.49818078","text":"def update_document(id, body):\n\n es.update(\n index=INDEX_NAME,\n doc_type=DOC_TYPE,\n id=id,\n body=body\n )","title":""},{"docid":"0130808ec40d1e1a19bb584b322e719c","score":"0.4969886","text":"def test_update(dvc):\n index = Index.from_repo(dvc)\n new_stage = Stage(dvc, path=\"path1\")\n new_index = index.update({new_stage})\n\n assert not index.stages\n assert new_index.stages == [new_stage]\n\n dup_stage1 = Stage(dvc, path=\"path1\")\n dup_stage2 = Stage(dvc, path=\"path2\")\n dup_index = index.update([dup_stage1, dup_stage2])\n assert not index.stages\n assert len(new_index.stages) == 1\n assert new_index.stages[0] is new_stage\n assert set(map(id, dup_index.stages)) == {id(dup_stage1), id(dup_stage2)}","title":""},{"docid":"61353c81ed9deebb3ce5644b5255533a","score":"0.4959378","text":"def projects_create(self, perun_id, name=None, description=None, members=None, enabled=True):\n\n perun_id = str(perun_id)\n if name is None:\n name = perun_id\n\n if not self.ro:\n os_project = self.keystone.projects.create(name=str(name),\n perun_id=perun_id,\n domain=self.target_domain_id,\n description=description,\n enabled=bool(enabled),\n scratched=False,\n flag=self.flag,\n parent=self.parent_project_id if self.nested else None)\n denbi_project = {'id': str(os_project.id),\n 'name': str(os_project.name),\n 'perun_id': str(os_project.perun_id),\n 'description': os_project.description,\n 'enabled': bool(os_project.enabled),\n 'scratched': bool(os_project.scratched),\n 'members': []}\n else:\n denbi_project = {'id': 'read-only-fake',\n 'name': name,\n 'perun_id': perun_id,\n 'description': description,\n 'enabled': enabled,\n 'scratched': False,\n 'members': []}\n # Log keystone update\n self.log2.debug(f\"project [{denbi_project['perun_id']},{denbi_project['id']}]: created.\")\n\n self.denbi_project_map[denbi_project['perun_id']] = denbi_project\n self.__project_id2perun_id__[denbi_project['id']] = denbi_project['perun_id']\n\n # if a list of members is given append them to current project\n if members:\n for member in members:\n self.projects_append_user(perun_id, member)\n\n return denbi_project","title":""},{"docid":"f37cbd0c4d6a7707fd3977bd2ed8fbb1","score":"0.49592552","text":"def projects_create(context, values):\n session = get_session()\n project = models.Project()\n if not values.get('id'):\n values['id'] = uuid.uuid4()\n with session.begin():\n project.update(values)\n project.save(session)\n return project","title":""},{"docid":"a68db4d9ea28e8d68eef7684f1c15da2","score":"0.4956459","text":"def put_design(self, designname, doc, rebuild=True):\n response = self.server._GET(self.name, \"_design\", designname,\n errors={404: None})\n if response.status_code == 200:\n current_doc = response.json(object_pairs_hook=odict)\n doc[\"_id\"] = current_doc[\"_id\"]\n doc[\"_rev\"] = current_doc[\"_rev\"]\n if doc == current_doc:\n return False\n response = self.server._PUT(self.name, \"_design\", designname, json=doc)\n if rebuild:\n for view in doc.get(\"views\", {}):\n self.view(designname, view, limit=1)\n return True","title":""},{"docid":"8b15b5501a521c21a539203e0e8fe5b9","score":"0.49440858","text":"def check_and_update_run(self, run_number):\n run_doc = self.get_run(run_number)\n if not run_doc:\n run_doc = self.run_doc_class.build_document(run_number)\n if run_doc:\n print \"Run %i is not in database, inserting...\" % int(run_number)\n self.insert_rundoc(run_doc)","title":""},{"docid":"1e868dbc23aaa9f84999b81f877cb4a9","score":"0.49372107","text":"def _add_document(doc_id, data, database=MAIN_DB):\n return requests.put(\n os.path.join(COUCH_URL, database, doc_id),\n json=data,\n headers={\"Content-Type\": \"application/json\"}\n )","title":""},{"docid":"4667675eb9f0fee441a3449ca58fd4e8","score":"0.4923755","text":"def test_project_index(project_client, session, project, project2):\n assert project.releases\n assert project.tags\n assert project.comments\n assert project.user\n assert project.user.projects\n assert project.user.projectcomments\n assert project.tag_counts == [('arcade', 2, 16), ('game', 1, 14)]\n\n resp = project_client.get('/project/1/')\n assert resp.status_code == 200\n assert b'Some project title 1' in resp.data\n assert b'Some project title 2' not in resp.data\n assert b'game' in resp.data\n assert b'arcade' in resp.data\n\n resp = project_client.get('/project-blabla+bla-1-.html')\n assert resp.status_code == 200, 'because this url works too.'\n assert b'Some project title 1' in resp.data\n\n resp = project_client.get('/project/1/1')\n assert resp.status_code == 200\n assert b'A release title.' in resp.data\n\n resp = project_client.get('/project-blabla+blasbla+-1-1.html')\n assert resp.status_code == 200, 'because this url works too.'\n assert b'A release title.' in resp.data\n\n resp = project_client.get('/project/66/')\n assert resp.status_code == 404, 'when the project is not there'\n resp = project_client.get('/project/1/66')\n assert resp.status_code == 404, 'when the release is not there either'","title":""},{"docid":"3899bcc0fbdc4a74c83b775d8510dce1","score":"0.49015632","text":"def set_document(request, doc_id, body, errors):\n if doc_id in DB:\n errors['doc_id'].add('Document already exists')\n\n if errors:\n raise errors\n\n body['doc_id'] = doc_id\n DB[doc_id] = body\n\n # dict response for jsonify middleware\n return body","title":""},{"docid":"68855beaa907077bdd1096e7fe3a3d2d","score":"0.48790357","text":"def get_or_create(self, index_name): \r\n raise NotImplementedError","title":""},{"docid":"68855beaa907077bdd1096e7fe3a3d2d","score":"0.48790357","text":"def get_or_create(self, index_name): \r\n raise NotImplementedError","title":""},{"docid":"6423f40815445f520211d18edecdff79","score":"0.487891","text":"def create_doc_instances(self, **kwargs):\n kwargs[u\"cdb_project_id\"] = self.cdb_project_id\n return self.Super(ProjectTemplateDocRef).create_doc_instances(**kwargs)","title":""},{"docid":"8ef3b2bce4ef4353b9113c49562ff49c","score":"0.4876358","text":"def add_project():\n\n # Get an access class\n da=DomainAccess()\n ea=EmployeeAccess()\n pa=ProjectAccess()\n\n # Acquire form data\n title = request.form[\"Title\"]\n maxStudents = request.form[\"Maxstudents\"]\n descriptionTextNl = request.form[\"nlDescription\"]\n descriptionTextEng = request.form[\"engDescription\"]\n\n researchGroupNrs = request.form.getlist(\"Researchgroup\")\n typeNrs = request.form.getlist(\"Type\")\n disciplineNrs = request.form.getlist(\"Discipline\")\n tags = request.form.getlist(\"Tags\")\n related = request.form.getlist(\"Related\")\n promotorNames = request.form.getlist(\"Promotors\")\n supervisorNames = request.form.getlist(\"Supervisors\")\n externNames = request.form.getlist(\"Extern\")\n\n # Acquire request files\n files_nl = request.files.getlist(\"nlUploads\")\n files_en = request.files.getlist(\"engUploads\")\n\n # Create basic project\n project = Project(None, title, maxStudents, True)\n\n # Assign research group ID's\n for researchGroupNr in researchGroupNrs:\n if int(researchGroupNr) == 0:\n continue\n project.researchGroup.append(int(researchGroupNr))\n\n # Create dutch document\n docNL = Document(None, \"dutch\", descriptionTextNl)\n # Link attachments to document\n this_dir = os.path.dirname(__file__)\n upload_folder = os.path.join(this_dir, app.config['UPLOAD_FOLDER'])\n for file in files_nl:\n nameFile = secure_filename(title + '_' + file.filename)\n file.save(os.path.join(upload_folder, nameFile))\n docNL.attachment.append(nameFile)\n # Assign document as description\n project.desc.append(docNL)\n\n # Create english document\n docEn = Document(None, \"english\", descriptionTextEng)\n # Link attachments to document\n for file in files_en:\n nameFile = secure_filename(title + '_' + file.filename)\n file.save(os.path.join(os.path.join(upload_folder, nameFile)))\n docEn.attachment.append(nameFile)\n # Assign document as description\n project.desc.append(docEn)\n\n # Append types to the project\n typeOptions = da.get_projectType()\n for typeNr in typeNrs:\n if int(typeNr) == 0:\n continue\n project.type.append(typeOptions[int(typeNr) - 1])\n\n # Append disciplines to the project\n disciplineOptions = da.get_disciplines()\n for disciplineNr in disciplineNrs:\n if int(disciplineNr) == 0:\n continue\n project.discipline.append(disciplineOptions[int(disciplineNr) - 1])\n\n # Fetch all employees from the database\n employeeOptions = ea.get_employees()\n\n # Add promotors to the project\n # Create dictionary of promotor name and his id, used to easily append ID to the project\n promotor_id_dict = {promotorOption.name: promotorOption.id for promotorOption in employeeOptions}\n # Loop over names and connect the associated ID's to the project\n for promotorName in promotorNames:\n if promotorName in promotor_id_dict:\n project.promotors.append(promotor_id_dict[promotorName])\n\n # Add supervisors to the project\n # Create dictionary of supervisor name and his id, used to easily append ID to the project\n supervisor_id_dict = {staffOption.name: staffOption.id for staffOption in\n employeeOptions}\n # Loop over names and connect the associated ID's to the project\n for staffName in supervisorNames:\n if staffName in supervisor_id_dict:\n project.supervisors.append(supervisor_id_dict[staffName])\n\n # Add extern employees to the project\n for name in externNames:\n project.extern_employees.append(name)\n\n # Add tags to the project\n project.tag = list(tags)\n\n # Add related projects to the project\n # Fetch all projects from the database\n relatedProjectOptions = pa.get_projects()\n # Create dictionary of project title and its id, used to easily find the required ID\n related_project_id_dict = {relatedProjectOption.title: relatedProjectOption.ID for relatedProjectOption in\n relatedProjectOptions}\n # Loop over related projects and append the associated ID to the project relations\n for relatedProjectTitle in related:\n if relatedProjectTitle in related_project_id_dict:\n project.relatedProject.append(related_project_id_dict[relatedProjectTitle])\n\n # Assign active year\n now = datetime.now()\n project.activeYear.append(now.year)\n\n # Finalize project and add it to the database\n pa.add_project(project)\n findTag(project)\n\n # Return result to javascript\n return jsonify(result=True)","title":""},{"docid":"dcbe91f25d0a54837a18c3ca816e5125","score":"0.4869392","text":"def add_doc():\n c_id = request.args[\"c_id\"]\n doc_number = request.args[\"doc_number\"]\n\n if s.query(Documents). \\\n filter(and_(Documents.c_id == c_id,\n Documents.qpulse_no == doc_number)). \\\n count() == 0:\n doc = Documents(c_id=c_id, qpulse_no=doc_number)\n s.add(doc)\n s.commit()\n return jsonify({\"success\": True})","title":""},{"docid":"198535543a3034d8ac97e5d93610f18c","score":"0.48673","text":"def test_existing_index(self, orion_db):\n assert (\n \"new_field_1\" not in get_db(orion_db)[\"new_collection\"].index_information()\n )\n\n orion_db.ensure_index(\"new_collection\", \"new_field\", unique=True)\n assert \"new_field_1\" in get_db(orion_db)[\"new_collection\"].index_information()\n\n # reattempt\n orion_db.ensure_index(\"new_collection\", \"new_field\", unique=True)\n assert \"new_field_1\" in get_db(orion_db)[\"new_collection\"].index_information()","title":""},{"docid":"c466a7bd7ef34e6dfc1db2eba47deba4","score":"0.4845863","text":"def update_inv_index(self, processed_tokens, document_id):\n for token_index in range(0, len(processed_tokens)):\n if processed_tokens[token_index] not in self.terms:\n self.terms.append(processed_tokens[token_index])\n new_postings_list = list()\n if self.purpose == \"vsm\":\n new_doc = Document(document_id, store_term_weights=True)\n else:\n new_doc = Document(document_id, token_index + 1)\n new_postings_list.append(new_doc)\n self.posting_lists.append(new_postings_list)\n else:\n existing_posting_list = self.get_postings_list(\n processed_tokens[token_index])\n doc_exists = False\n for i in range(len(existing_posting_list)):\n if existing_posting_list[i].id == document_id:\n if self.purpose == \"vsm\":\n existing_posting_list[i].increment_frequency()\n else:\n existing_posting_list[i].add_position(\n token_index + 1)\n doc_exists = True\n if doc_exists is False:\n if self.purpose == \"vsm\":\n new_doc = Document(\n document_id, store_term_weights=True)\n else:\n new_doc = Document(document_id, token_index + 1)\n existing_posting_list.append(new_doc)","title":""},{"docid":"c0486cd11ffc6867d49d30635859999a","score":"0.48408973","text":"def create_index():\n try:\n print(\"[*] creating an index in elasticsearch\")\n r = requests.put(INDEX_URL)\n if \"index_already_exists_exception\" in r.text:\n print(\"[*] index already exists\")\n return\n mapping = {\n \"capture\": {\n \"properties\": { \n \"timestamp\": {\n \"type\": \"date\",\n },\n \"capture\": {\n \"type\": \"string\",\n },\n }\n }\n }\n r = requests.put(TYPE_URL + \"/_mapping\", data=json.dumps(mapping))\n print(r.text)\n except:\n raise Exception(\"Elasticsearch is not running\")","title":""},{"docid":"79550ceba64c00d7cd1ed2e8ab6d2e3b","score":"0.48389933","text":"def insert(self, doc):\n params = self._params_for_insert\n params.update({'body': doc, 'refresh': True})\n if not self._index_exists():\n self._create_index()\n doc = ELASTICSEARCH.index(**params)\n return doc['_id']","title":""},{"docid":"91c9aafa4e4dd3f4de10a590538ed54f","score":"0.48380858","text":"def create(self, id: str, data: Optional[JsonDict] = None) -> Document:\n if any(id == d.id for d in self._docs):\n raise ValueError(\n f\"There is already another Document instance for {id} part of the BulkOperation\"\n )\n\n doc = Document(self._database, id, data=data)\n self._docs.append(doc)\n\n return doc","title":""},{"docid":"c338b5a256e496f6123de3c7a5c4fc5b","score":"0.48324662","text":"def _import_project_data(self):\n for data in self._get_fyle_data(\n self.fyle_api.Projects, {'active_only': False}):\n defaults = {\n 'name': data['name'],\n 'description': data['description'],\n 'active': data['active'],\n }\n Project.objects.update_or_create(\n id=data['id'], user=self.batch.user, defaults=defaults)","title":""},{"docid":"8266958ea8686836d449b0440c676007","score":"0.48165312","text":"def index(self, doctype, docid, document):\n dbname = self.dbname\n self.broker.DB.index(index=dbname, doc_type=doctype, id=docid, body=document)","title":""},{"docid":"b95af5d41d231f0ee4aff7660a7f0d47","score":"0.48141915","text":"def add_new_doc(self, document):\n document_dictionary = document.term_doc_dictionary\n document_capitals = document.capital_letter_indexer\n document_date = document.tweet_date\n for key_term in document_capitals:\n if key_term not in self.global_capitals:\n self.global_capitals[key_term] = document_capitals[key_term]\n else:\n if not document_capitals[key_term]:\n self.global_capitals[key_term] = False\n\n document_entities = document.named_entities\n\n for entity in document_entities:\n self.entities_dict[entity] += 1\n document_vec = np.zeros(shape=25)\n is_covid = False\n for term in document_dictionary:\n if term == 'covid':\n is_covid = True\n if term in self.glove_dict:\n document_vec += self.glove_dict[term]\n document_vec /= len(document_dictionary)\n # document_vec, # numpy array of size 25 which\n # represents the document in 25 dimensional space(GloVe)\n if is_covid:\n self.document_posting_covid[document.tweet_id] = (document_vec, document_date)\n self.document_dict[document.tweet_id] = \"doc_posting_covid\" + str(self.doc_posting_covid_counter)\n else:\n self.doc_posting_dict[document.tweet_id] = (document_vec, document_date)\n self.document_dict[document.tweet_id] = \"doc_posting\"+str(self.doc_posting_counter)\n\n if len(self.doc_posting_dict) == 100000:\n self.save_doc_posting()\n if len(self.document_posting_covid) == 100000:\n self.save_doc_covid()\n\n\n # Go over each term in the doc\n for term in document_dictionary.keys():\n try:\n if term in self.inverted_idx:\n if term not in self.posting_dict:\n self.posting_dict[term] = None\n self.inverted_idx[term][0] += 1\n\n else:\n self.inverted_idx[term] = [1, str(self.counter_of_postings)]\n self.posting_dict[term] = None\n\n insert_tuple = (document.tweet_id, # tweet id\n document.doc_length, # total number of words in tweet\n document.max_tf, # number of occurrences of most common term in tweet\n document.unique_terms, # number of unique words in tweet\n document_dictionary[term], # number of times term is in tweet - tf!\n )\n # if there're no documents for the current term, insert the first document\n if self.posting_dict[term] is None:\n self.posting_list.append((term, [insert_tuple]))\n self.posting_dict[term] = len(self.posting_list) - 1\n else:\n tuple_idx = self.posting_dict[term]\n self.posting_list[tuple_idx][1].append(insert_tuple)\n\n # check if posting_dict is full\n if len(self.posting_list) == self.postingDict_size:\n self.save_postings()\n\n except:\n print('problem with the following key {}'.format(term))","title":""},{"docid":"3ad5d272bb1d56172468a84a457c5e10","score":"0.4809561","text":"def test_indexed_doc(es):\n investor_company = CompanyFactory()\n\n large_investor_profile = LargeCapitalInvestorProfileFactory(\n investor_company=investor_company,\n )\n\n doc = ESLargeInvestorProfile.es_document(large_investor_profile)\n elasticsearch.bulk(actions=(doc, ), chunk_size=1)\n\n es.indices.refresh()\n\n indexed_large_investor_profile = es.get(\n index=ESLargeInvestorProfile.get_write_index(),\n id=large_investor_profile.pk,\n )\n\n assert indexed_large_investor_profile['_id'] == str(large_investor_profile.pk)\n assert indexed_large_investor_profile['_source'] == {\n '_document_type': LargeInvestorProfileSearchApp.name,\n 'id': str(large_investor_profile.pk),\n 'asset_classes_of_interest': [],\n 'country_of_origin': {\n 'id': str(large_investor_profile.country_of_origin.pk),\n 'name': str(large_investor_profile.country_of_origin.name),\n },\n 'investor_company': {\n 'id': str(investor_company.pk),\n 'name': str(investor_company.name),\n 'trading_names': investor_company.trading_names,\n },\n 'created_by': None,\n 'investor_type': None,\n 'required_checks_conducted': None,\n 'deal_ticket_sizes': [],\n 'investment_types': [],\n 'minimum_return_rate': None,\n 'time_horizons': [],\n 'restrictions': [],\n 'construction_risks': [],\n 'minimum_equity_percentage': None,\n 'desired_deal_roles': [],\n 'uk_region_locations': [],\n 'other_countries_being_considered': [],\n 'investable_capital': None,\n 'investor_description': '',\n 'created_on': '2019-01-01T00:00:00+00:00',\n 'notes_on_locations': '',\n 'global_assets_under_management': None,\n 'modified_on': '2019-01-01T00:00:00+00:00',\n }","title":""},{"docid":"51f2637b70c16067a94a6bd4e0258048","score":"0.48014212","text":"def read_document(self, d):\n if not self.in_collection(d):\n self.documents[d] = self.index.add_document(d)\n\n if len(self.index) >= READ_LIMIT_TO_WRITE_TO_MONGO:\n self.flush_to_mongo()","title":""},{"docid":"ce5a9df77a8dea388f8abce2e0a3ba20","score":"0.47926098","text":"def project(name, description, skip_if_exists):\n if skip_if_exists:\n result = Client().graphql(\n query={\n \"query\": {\n with_args(\"project\", {\"where\": {\"name\": {\"_eq\": name}}}): {\n \"id\": True\n }\n }\n }\n )\n if result.data.project:\n click.secho(\"{} already exists\".format(name), fg=\"green\")\n return\n\n try:\n Client().create_project(project_name=name, project_description=description)\n except ClientError as exc:\n click.echo(f\"{type(exc).__name__}: {exc}\")\n click.secho(\"Error creating project\", fg=\"red\")\n return\n\n click.secho(\"{} created\".format(name), fg=\"green\")","title":""},{"docid":"d308c3fb5e0f7925f8a90beabd7769b1","score":"0.47813076","text":"def test_create(self):\n doc = Document(DocumentID=\"123\")","title":""},{"docid":"3653cfc90294d606c691f6002160e051","score":"0.47683114","text":"async def update_project(project_id: ProjectID, update: ProjectUpdate):","title":""},{"docid":"3cb2ea4fbdea7b30cfb8fc035ec33b63","score":"0.4765857","text":"def delete(project, version=None):","title":""},{"docid":"304ed14f24d509d80cc30d9e20a000bb","score":"0.47605696","text":"def update_document(self, document_id, mappings):\n\n if len(mappings) == 0:\n return None\n\n requests = []\n for (src, dest) in mappings.items():\n requests.append(\n {\n \"replaceAllText\": {\n \"containsText\": {\n \"text\": src,\n \"matchCase\": \"true\",\n },\n \"replaceText\": dest,\n }\n }\n )\n\n body = {\"requests\": requests}\n results = self.docs.batchUpdate(documentId=document_id, body=body).execute()\n\n return results","title":""},{"docid":"0b0ff57f645529136b9fca1f29ca8a3d","score":"0.47586724","text":"def assign_weather_to_project(delphin_id: str, weather_ids: list) -> str:\n\n # Save climate class to delphin document\n delphin_document = delphin_db.Delphin.objects(id=delphin_id).first()\n\n for id_ in weather_ids:\n weather_document = weather_db.Weather.objects(id=id_).first()\n delphin_document.update(push__weather=weather_document)\n\n return delphin_document.id","title":""},{"docid":"da1b60c6b85b7a75cb23675cb817f78d","score":"0.4754898","text":"def insert_document(self, index_name, type, document_id, document):\n\n try:\n if self.es_client is None or not self.es_client.ping():\n self.connect()\n\n if document_id is not None and document_id != \"\":\n return self.es_client.index(index=index_name, doc_type=type, id=document_id, body=document, refresh='wait_for', request_timeout=30)\n else:\n return self.es_client.index(index=index_name, doc_type=type, body=document, refresh='wait_for', request_timeout=30)\n except:\n logger.exception(\"could not insert document is %s in index %s \"%(document_id, index_name))","title":""},{"docid":"1f767eaef4e45cf976afc1f230f7464f","score":"0.47510174","text":"def upsert(self):\n\n # Raise error if index is not writable\n if not self.config.get(\"writable\"):\n raise ReadOnlyError(\"Attempting to upsert a read-only index (writable != True)\")\n\n if self.embeddings and self.documents:\n with self.lock:\n # Run upsert\n self.embeddings.upsert(self.documents)\n\n # Save index if path available, otherwise this is an memory-only index\n if self.config.get(\"path\"):\n self.embeddings.save(self.config[\"path\"], self.config.get(\"cloud\"))\n\n # Reset document stream\n self.documents.close()\n self.documents = None","title":""},{"docid":"da970d3a07959bb70343da54b9e0f1dc","score":"0.4724849","text":"def updateDocs():\n docs = Dataset.search()\n for doc in docs.scan():\n created = getattr(getattr(doc, \"_ts\", None), \"date_created\", None)\n updated = getattr(getattr(doc, \"_ts\", None), \"last_updated\", None)\n if not created and not updated:\n existing_dc = getattr(getattr(doc, \"_meta\", None), \"date_created\", None)\n existing_lu = getattr(getattr(doc, \"_meta\", None), \"last_updated\", None)\n\n if existing_dc and existing_lu:\n doc.update(**{\"_ts\": {\"date_created\": existing_dc, \"last_updated\": existing_lu}})\n logging.info(\"Updating fields for doc with existing\")\n else:\n doc.update(\n **{\n \"_ts\": {\n \"date_created\": default_create,\n \"last_updated\": default_last_updated,\n }\n }\n )\n logging.info(\"Adding fields for doc with defaults\")\n else:\n created_bug = getattr(getattr(doc, \"_ts\", None), \"guide\", None)\n if created_bug:\n doc.update(\n **{\n \"_ts\": {\n \"date_created\": default_create,\n \"last_updated\": default_last_updated,\n }\n }\n )\n else:\n logging.info(\"Doc already has TS last updated field\")","title":""},{"docid":"4c0614bb9a344bbcb7774f40d315a663","score":"0.4718103","text":"def _write_projects(projects):\n path = _get_projects_index_file()\n contents = util.read_yaml(path)\n contents.update({'projects': projects})\n util.write_yaml(path, contents)","title":""},{"docid":"f3b73dd77df050a21496f43cdef09149","score":"0.47118863","text":"def delete(self, project, query):\n self.es.delete_by_query(\n index=self.index_name(project),\n body=query,\n conflicts='proceed',\n )","title":""},{"docid":"3024c1ab542c8ad983221027239d933a","score":"0.47102568","text":"def add_doc(self, doc_id, contents):\n self.__es.index(index=self.__index_name, doc_type=self.DOC_TYPE, id=doc_id, body=contents)","title":""},{"docid":"af9147664a4952bc73e1c324e25ce1b2","score":"0.47069666","text":"def test_designs_id_put(self):\n pass","title":""},{"docid":"6d6dd2c023bd9b98b8654db1f500b71a","score":"0.4705125","text":"def insert(dictObject, db):\n\n \"\"\" res = db[\"menus\"].update_one(\n {\"id\": dictObject[\"id\"],\n \"date\": dictObject[\"date\"],\n \"mensaName\": dictObject[\"mensaName\"],\n \"lang\": dictObject[\"lang\"]\n }, {\"$set\": dictObject}, upsert=True)\n\n print(\"modifed: id: \" + str(dictObject[\"id\"].encode('utf-8')) + \" Date: \" + dictObject[\"date\"] + \" lang: \" +\n dictObject[\"lang\"])\n if res.upserted_id is None:\n print(\"res: modified: \" + str(res.modified_count) + \" matched: \" + str(res.matched_count))\n else:\n print(\"res: inserted\") \"\"\"","title":""},{"docid":"876fbf36475b5fdab80415b990d77feb","score":"0.46986815","text":"def scrape_new_data(index=\"verdicts\"):\n if es.indices.exists(index=index):\n tic = time.time()\n scraper.update_database()\n toc = time.time()\n print(\"Scraping done! Time needed: {}\".format(str(toc - tic)))\n else:\n print(\"Index to be updated does not exists!\")","title":""},{"docid":"7842c0d6747dbdef941acf436cb28de4","score":"0.4698259","text":"def create(self, index_name):\r\n raise NotImplementedError","title":""},{"docid":"7842c0d6747dbdef941acf436cb28de4","score":"0.4698259","text":"def create(self, index_name):\r\n raise NotImplementedError","title":""},{"docid":"7842c0d6747dbdef941acf436cb28de4","score":"0.4698259","text":"def create(self, index_name):\r\n raise NotImplementedError","title":""},{"docid":"620ced752c4b779874c9474695caa26d","score":"0.4695401","text":"def create_doc_instances(self, **kwargs):\n kwargs[u\"cdb_project_id\"] = self.cdb_project_id\n return self.Super(TaskTemplateDocRef).create_doc_instances(**kwargs)","title":""},{"docid":"de775a146f47d74e09cd6d9324f8dbab","score":"0.4694352","text":"def create_update_project(request, slug=None):\n request_user = request.user\n if slug is not None:\n project = Project.objects.get(slug=slug)\n if project.owner != request.user:\n raise PermissionDenied\n else:\n project_form = CreateProjectForm(instance=project)\n position_formset = PositionFormset(\n queryset=Position.objects.filter(\n project=project\n ))\n else:\n project = None\n project_form = CreateProjectForm()\n position_formset = PositionFormset(\n queryset=Position.objects.none()\n )\n\n if request.method == 'POST':\n project_form = CreateProjectForm(\n request.POST, \n instance=project\n )\n position_formset = PositionFormset(\n request.POST,\n queryset=Position.objects.filter(project=project)\n )\n if project_form.is_valid() and position_formset.is_valid():\n project = project_form.save(commit=False)\n project.owner = request_user\n new_project = False\n if project.status is None:\n new_project = True\n project.status = 'A'\n project.save()\n positions = position_formset.save(commit=False)\n for position in positions:\n position.project = project\n if position.status not in ('E', 'F'):\n position.status = 'E'\n position.save()\n position_formset.save_m2m()\n position_formset.save()\n if new_project:\n messages.success(\n request, 'Project {} created!'.format(project.title))\n else:\n messages.success(\n request, 'Project {} updated!'.format(project.title))\n return HttpResponseRedirect(reverse('projects:project', kwargs={'slug': project.slug}))\n\n context = {\n 'project_form': project_form,\n 'position_formset': position_formset,\n }\n return render(request, 'projects/create_update_project.html', context)","title":""},{"docid":"1df57eb7f6c6f2712debcc484e3347d8","score":"0.46942607","text":"def test_update_project(self):\n pass","title":""},{"docid":"c17dc383ee2e366b1a5fc4e1604f9b99","score":"0.4692427","text":"def refresh(self, project):\n self.es.indices.refresh(index=self.index_name(project))","title":""},{"docid":"6d957caf110468efdc93c17755a223f5","score":"0.46918374","text":"def test_rud_document(self):\n\n # upload a documnet\n self.upload_document()\n\n # retrieve document PK to test\n test_document = Document.objects.all().first()\n\n # test retrive document with invvalid pk\n rud_document_url = reverse(\n 'documents:document',\n kwargs={'pk': 99}\n )\n response = self.client.get(\n rud_document_url,\n )\n self.assertEqual(response.status_code, 404)\n\n # test update document with invvalid pk\n response = self.client.patch(\n rud_document_url,\n {\n 'language': 'af',\n 'document_type': 'pdf'\n }\n )\n self.assertEqual(response.status_code, 404)\n\n # test delete document with invvalid pk\n response = self.client.delete(\n rud_document_url\n )\n self.assertEqual(response.status_code, 404)\n\n # test retrieve document with pk\n rud_document_url = reverse(\n 'documents:document',\n kwargs={'pk': test_document.pk}\n )\n response = self.client.get(\n rud_document_url,\n )\n self.assertEqual(response.status_code, 200)\n\n # test update document with pk\n rud_document_url = reverse(\n 'documents:document',\n kwargs={'pk': test_document.pk}\n )\n response = self.client.patch(\n rud_document_url,\n {\n 'language': 'af',\n 'document_type': 'pdf'\n }\n )\n test_document.refresh_from_db()\n self.assertEqual(response.status_code, 200)\n self.assertEqual(test_document.language.short_name, 'af')\n self.assertEqual(test_document.document_type.short_name, 'pdf')\n self.assertEqual(Document.objects.all().count(), 1)\n\n # test delete document with pk\n rud_document_url = reverse(\n 'documents:document',\n kwargs={'pk': test_document.pk}\n )\n response = self.client.delete(\n rud_document_url\n )\n self.assertEqual(response.status_code, 204)\n self.assertEqual(Document.objects.all().count(), 0)","title":""},{"docid":"a42d660681fa0dea1386fc66c5c6f7ae","score":"0.46850002","text":"def indexDocElement(es_Url, awsauth, docData):\n try:\n headers = {\"Content-Type\": \"application/json\"}\n resp = requests.put(es_Url, auth=awsauth,\n headers=headers, json=docData)\n print(resp.content)\n if resp.status_code == 201:\n logger.info('INFO: Successfully created element into ES')\n elif resp.status_code == 200:\n logger.info('INFO: Successfully updated element into ES')\n else:\n logger.error(f'FAILURE: Unable to index element {resp.content}')\n raise\n except Exception as e:\n logger.error(f'ERROR: {str(e)}')\n logger.error(f\"ERROR: Unable to index line:{docData['content']}\")\n raise","title":""},{"docid":"11e4d54117e769ca06d0c90afeab7608","score":"0.46833104","text":"def unindex_object(documentId):","title":""},{"docid":"7a6374eccd3ecd3ac8f9c0c4077d249a","score":"0.46808988","text":"async def update_data(doc_id: int, response: IntakeData,\n db_file: str = tinydb_file):\n db0 = await get_db(db_file)\n db0.update(response.dict(), doc_ids=[doc_id])\n return db0","title":""},{"docid":"f6ca95cb848f9130b79bfd3e7d693228","score":"0.4669554","text":"def uploadDesignDocuments(self,directory):\n\n import glob\n pattern = os.path.join(directory,'*.py')\n viewFiles = glob.glob(pattern)\n for viewFile in viewFiles:\n execfile(viewFile)\n for view in views:\n viewID = os.path.join('_design',view)\n print (\"uploading \", view, \" of \", viewFile, \" to \", self.databaseName)\n document = self.db.get(viewID)\n if document:\n self.db.delete(document)\n self.db[viewID] = views[view]","title":""},{"docid":"4c6b0bfb6c8ba313b8b13141fe7abd40","score":"0.46691608","text":"def update(self, index, document, **options):\n\n index = base._resolve_index(index)\n\n index_name = index.get_name()\n index_doc_type = options.pop('doc_type', index.get_doc_type())\n\n id, adapted_document = index.adapt_document(document)\n\n if id:\n options.setdefault(\"id\", id)\n\n self._es.index(index_name, index_doc_type, adapted_document, **options)","title":""},{"docid":"e43a3a310e72a471695196fe25c4e8b9","score":"0.46609166","text":"def test_delete_project(self):\n name = {\"en\": \"Test project\", \"fr\": \"Projet test\"}\n project = Project.objects.create(\n name=name,\n description={\"en\": [\"Test desc\"], \"fr\": [\"Test desc\"]}\n )\n detail_url = reverse('rest:project-detail', kwargs={'pk': project.id})\n res = self.admin_client.delete(detail_url)\n self.assertEqual(res.status_code, status.HTTP_204_NO_CONTENT)\n exists = Project.objects.all().filter(name=name).exists()\n self.assertFalse(exists)","title":""},{"docid":"0e611165d35dc8ad0d765ab9c84890b1","score":"0.4655304","text":"def update_project(project_id, update_dict):\n # update_dict = convert_project_object_to_dict(update_dict)\n # try:\n Project.objects.get_or_404(id=project_id).update(**update_dict)\n return (True, \"success\")\n # except Exception, e:\n # return (False, repr(e))","title":""},{"docid":"4ec582f4f431c4caf8adbe8f976d8607","score":"0.46348995","text":"def project(id):\n\t#Call load() in the data layer to load the database and store it's contents in the variable db\n\tdb = data.load('data.json')\n\t#Call get_project() in the data layer and store the project in the variable project\n\tproject = data.get_project(db, id)\n\t#If None was returned by get_project():\n\t\t#No project with the specified ID exists, ergo a nonexistent page has been visited, ergo abort with a 404\n\tif project is None:\n\t\tabort(404)\n\telse:\n\t\t#adding a counter to the project-page.\n\t\tcounter = open(\"doc/counter.log\", \"r+\")\t\t\t\t\t#opens counter.log\n\t\tcount = 1\n\t\tpid = str(\"project \")+str(project['project_id'])+\"\\n\"\n\t\tfor line in counter:\n\t\t\tif pid in line:\n\t\t\t\tcount += 1\n\t\tproject['counter'] = count\n\t\tcounter.write(pid)\n\t\tcounter.close()\n\t\t#Render project.html with:\n\t\t\t#project --> used to display information about the project\n\t\treturn render_template('project.html', project=project)","title":""},{"docid":"cb39d6df0b7b3498a570ea1bba71feb3","score":"0.46262908","text":"def _update_document_by_id(doc_id, body, object_type, *, retry_on_conflict=0, **kwargs):\n conn = get_conn()\n for alias in get_active_aliases(conn, object_types=[object_type]):\n try:\n conn.update(\n index=alias,\n doc_type=GLOBAL_DOC_TYPE,\n body=body,\n id=doc_id,\n params={\"retry_on_conflict\": retry_on_conflict, **kwargs},\n )\n # Our policy for document update-related version conflicts right now is to log them\n # and allow the app to continue as normal.\n except ConflictError:\n log.error(\n \"Update API request resulted in a version conflict (alias: %s, doc id: %s)\",\n alias,\n doc_id,\n )","title":""},{"docid":"331d110c41a2530b147f583ddccbeb0a","score":"0.46188155","text":"def create_opensearch_index(index_name: str):\n with open(Path(__file__).parent / \"pipeline_logs_mapping.json5\") as fd:\n index_mappings = pyjson5.load(fd)\n res = requests.put(\n f\"{OPENSEARCH_ENDPOINT}/{index_name}\",\n data=json.dumps(index_mappings),\n headers={\"Content-Type\": \"application/json\"},\n auth=(OPENSEARCH_USERNAME, OPENSEARCH_PASSWORD),\n )\n if res.status_code >= 400:\n logging.error(\n f'Failed to create opensearch index \"{index_name}\", '\n f\"server responded with status {res.status_code}\"\n )\n try:\n logging.error(res.json())\n except json.JSONDecodeError:\n logging.error(res.text)","title":""},{"docid":"74d8d50b59c45411d8f14763398d3d38","score":"0.46130043","text":"def createDoc(self, docInfos):\n if docInfos['id'] != '':\n newDocId = self.plominoDatabase.invokeFactory(docInfos['type'], \n id=docInfos['id'])\n newDoc = self.plominoDatabase.getDocument(docInfos['id'])\n else:\n newDoc = self.plominoDatabase.createDocument()\n \n if newDoc is not None:\n if self.plominoDatabase.getForm(docInfos['form']) is not None:\n newDoc.setItem('Form', docInfos['form'])\n\n # TODO: create a default form if form does not exist ? in a later version\n # REM: doc is not saved if not associated with a existing form\n \n # Set the items of this document\n for itemInfos in docInfos['items']:\n newDoc.setItem(itemInfos['name'], itemInfos['value'])\n # TODO: check the value before set ?\n #self.createItem(itemInfos, newDoc)\n\n # Add the files inserted in the document\n if docInfos['files'] != []:\n if not hasattr(newDoc.getForm(), 'imported_files'):\n fieldId = newDoc.getForm().invokeFactory('PlominoField', \n id='imported_files',\n title='Imported Files',\n FieldType='ATTACHMENT')\n newDoc.getForm().setFormLayout(newDoc.getForm().getFormLayout() + \\\n '
Imported files: imported_files
')\n for fileInfos in docInfos['files']:\n newDoc.setfile(a2b_base64(str(fileInfos['content'])), fileInfos['name'])\n\n newDoc.save()\n\n else:\n raise Exception\n\n self.plominoDatabase.getIndex().refresh()","title":""},{"docid":"a5eea1dedb85eb1c7282df2ad603ce7f","score":"0.46129036","text":"def upsert_document(doc_id, doc, object_type, *, retry_on_conflict=0, **kwargs):\n _update_document_by_id(\n doc_id,\n {\"doc\": doc, \"doc_as_upsert\": True},\n object_type,\n retry_on_conflict=retry_on_conflict,\n **kwargs,\n )","title":""},{"docid":"10ab54ba9546b01682521f015050756d","score":"0.46108896","text":"def delete_project(uuid):\n project = Project.query.get(uuid)\n\n if project is None:\n raise NotFound(\"The specified project does not exist\")\n\n experiments = Experiment.query.filter(Experiment.project_id == uuid).all()\n for experiment in experiments:\n # remove dependencies\n operators = db_session.query(Operator).filter(Operator.experiment_id == experiment.uuid).all()\n for operator in operators:\n Dependency.query.filter(Dependency.operator_id == operator.uuid).delete()\n\n # remove operators\n Operator.query.filter(Operator.experiment_id == experiment.uuid).delete()\n\n Experiment.query.filter(Experiment.project_id == uuid).delete()\n\n db_session.delete(project)\n db_session.commit()\n\n prefix = join(\"experiments\", uuid)\n remove_objects(prefix=prefix)\n\n return {\"message\": \"Project deleted\"}","title":""},{"docid":"1acfc33f6ce86672083340d2f05e83f7","score":"0.46102795","text":"def index_document(body):\n es.index(\n index='documents',\n doc_type='document',\n body=body\n )","title":""},{"docid":"ce2ba6ec3062bc8b00d66042e7c58f95","score":"0.46075854","text":"def post(self, request, *args, **kwargs):\n self.project_slug = self.kwargs.get('project_slug', None)\n self.project = Project.objects.get(slug=self.project_slug)\n return super(VersionDeleteView, self).post(request, *args, **kwargs)","title":""},{"docid":"7cca9d7d23d55aea633a1d309353db55","score":"0.46073022","text":"def add(self, document_or_documents):\n\n added_document_ids = []\n\n if isinstance(document_or_documents, Document):\n was_list = False\n documents = [document_or_documents]\n else:\n was_list = True\n documents = document_or_documents[:]\n\n # First-pass validation\n self._validate_documents(documents)\n\n with transaction.atomic(independent=True):\n for document in documents:\n record = document._record\n\n # We go through the document fields, pull out the values that have been set\n # then we index them.\n field_data = {\n f: getattr(document, document.get_field(f).attname)\n for f in document.get_fields() if f != \"id\"\n }\n\n # Generate a database representation of this Document use\n # the passed ID if there is one\n record, created = DocumentRecord.objects.update_or_create(\n pk=document.id,\n defaults={\n \"index_stats\": self.index,\n \"data\": field_data\n }\n )\n document.id = record.id\n document._record = record\n\n if created:\n index_document(self.name, document)\n added_document_ids.append(record.id)\n else:\n # This wipes out any existing document, bumps the revision\n # and then indexes this one\n reindex_document(document)\n\n return added_document_ids if was_list else (added_document_ids[0] if added_document_ids else 0)","title":""},{"docid":"7df4233a622d8556ec5399e88e8dcaf1","score":"0.4595747","text":"def create_document(record):\n # TODO: Write an iterator over the INDEX_MAP to fetch fields from the record and\n # return a dictionary representing the document.\n return None","title":""},{"docid":"b994723d25f0a197f745292df18fa72c","score":"0.45953628","text":"def test_update_project(self):\n project = Project.objects.create(\n name={\"en\": \"Test project\", \"fr\": \"Projet test\"},\n description={\"en\": [\"Test desc\"], \"fr\": [\"Test desc\"]}\n )\n payload = {\"name\": {\"en\": \"New name\", \"fr\": \"Nouveau nom\"}}\n detail_url = reverse('rest:project-detail', kwargs={'pk': project.id})\n res = self.admin_client.patch(detail_url, payload, format=\"json\")\n self.assertEqual(res.status_code, status.HTTP_200_OK)\n project.refresh_from_db()\n self.assertEqual(project.name, payload['name'])","title":""},{"docid":"4b3a5c81140b78c9a4413be72272e6f4","score":"0.45898458","text":"def CreateProjects(projRef):\r\n\r\n print(\"Creating projects\")\r\n # make root directory with permit date\r\n if (len(projRef) == 0):\r\n print(\"No project available\")\r\n return\r\n\r\n dt = str(projRef.values()[0].PERMIT_DATE).split(\" \")[0]\r\n project_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), os.path.join(NFOutputPath.OUTPUT, dt))\r\n if not os.path.exists(project_root):\r\n os.makedirs(project_root)\r\n\r\n summary = []\r\n\r\n try:\r\n edit = arcpy.da.Editor(NFDataSource.COMMONGIS_CONN)\r\n edit.startEditing(False, False)\r\n edit.startOperation()\r\n editCounter = 0\r\n\r\n # create one folder for each project ref\r\n for key in projRef:\r\n arcpy.AddMessage(key)\r\n\r\n project_folder = os.path.join(project_root, key)\r\n\r\n # if the project folder already contains plans - skip; this project has been imported before; will not be included in new MaxDia report too\r\n if not os.path.exists(project_folder):\r\n os.makedirs(project_folder)\r\n file_list = []\r\n else:\r\n file_list = os.listdir(project_folder)\r\n\r\n pdf_exists = False\r\n\r\n for f in file_list:\r\n\r\n if \".pdf\" in f:\r\n arcpy.AddMessage(\"PDF in project folder: \" + project_folder)\r\n pdf_exists = True\r\n break\r\n if pdf_exists:\r\n continue\r\n\r\n cwdPdf = os.path.join(os.path.dirname(os.path.abspath(__file__)),\r\n os.path.join(project_folder, \"{0}-{1}.pdf\".format(key, NFAnnex.CWD)))\r\n\r\n UpdateCWD(projRef[key])\r\n GenerateCWD(arcpy.mapping.MapDocument(NFTemplate.CWDTEMPLATE), projRef[key], cwdPdf, [], \"\")\r\n\r\n wsnPdf = os.path.join(os.path.dirname(os.path.abspath(__file__)),\r\n os.path.join(project_folder, \"{0}-{1}.pdf\".format(key, NFAnnex.WSN)))\r\n wsnasset = UpdateWSN(projRef[key])\r\n\r\n if (len(wsnasset) > 0):\r\n # GenerateCWD(arcpy.mapping.MapDocument(NFTemplate.WSNTEMPLATE), projRef[key], wsnPdf, wsnasset, \"WSN\")\r\n print \"wsnasset\"\r\n\r\n wrnPdf = os.path.join(os.path.dirname(os.path.abspath(__file__)),\r\n os.path.join(project_folder, \"{0}-{1}.pdf\".format(key, NFAnnex.WRN)))\r\n wrnasset = UpdateWRN(projRef[key])\r\n\r\n if (len(wrnasset) > 0):\r\n GenerateCWD(arcpy.mapping.MapDocument(NFTemplate.WRNTEMPLATE), projRef[key], wrnPdf, wrnasset, \"WRN\")\r\n summary.extend(GenerateReport(projRef[key], wrnasset))\r\n\r\n editCounter = editCounter + 1\r\n\r\n if (editCounter == 10):\r\n print (\"intermediate save @ 10\")\r\n edit.stopOperation()\r\n edit.stopEditing(True)\r\n\r\n edit.startEditing(False, False)\r\n edit.startOperation()\r\n editCounter = 0\r\n\r\n edit.stopOperation()\r\n edit.stopEditing(True)\r\n del edit\r\n\r\n max_dia_report_name = GenerateMaxDiaReport(summary, dt, projRef)\r\n arcpy.AddMessage(\"END Creating projects\")\r\n arcpy.AddMessage(max_dia_report_name)\r\n print(\"Generated maxdia report\")\r\n return max_dia_report_name\r\n\r\n except Exception as err:\r\n print err\r\n Error_Handler(\"Create Projects Failed\")\r\n Error_Handler(repr(traceback.format_exc()))","title":""},{"docid":"86d93df5e9bcd0bbd1dbc95aa839899d","score":"0.45813572","text":"def insert_into_index(es_object, index_name, record): \n try:\n outcome = es_object.index(index=index_name, body=record)\n print(f\"Inserted object into Index '{index_name}'.\")\n print(str(record))\n except Exception as ex:\n print('Error in indexing data')\n print(str(ex))","title":""},{"docid":"f4a20f89cdf668abaec4a099362659d0","score":"0.4577445","text":"def test_get_or_create_project__call():\n project_name = str(uuid.uuid1())\n project = synapseclient.Project(name=project_name)\n returned = synapseclient.Project(name=project_name,\n id=str(uuid.uuid1()))\n with patch.object(CREATE_CLS,\n \"_find_by_obj_or_create\",\n return_value=returned) as patch_find_or_create:\n new_project = CREATE_CLS.get_or_create_project(name=project_name)\n assert new_project == returned\n patch_find_or_create.assert_called_once_with(project)","title":""},{"docid":"01878f3733f7e6fa22c36b45e3f46ae8","score":"0.45740703","text":"def test_create_project_adds_a_project(db: Session) -> None:\n\n # there is no project to start with\n assert db.query(models.Project).count() == 0\n\n # create a project\n project = schemas.ProjectCreate(\n name=\"My Project\", directory=\"/wherever\", command=\"whatever\"\n )\n create_project(db, project)\n\n # there is a project now...\n assert db.query(models.Project).count() == 1\n\n # ... and it has the correct content\n db_project = db.query(models.Project).first()\n assert db_project.id is not None\n assert db_project.command == project.command\n assert db_project.directory == project.directory\n assert db_project.name == project.name","title":""},{"docid":"8dd10092dad3a1e670dc70a758f1a80d","score":"0.45728135","text":"def add_guid_index():\n catalog = api.portal.get_tool('portal_catalog')\n if GUID_INDEX_NAME not in catalog.indexes():\n log.info(\"Adding GUID index %r\" % GUID_INDEX_NAME)\n catalog.addIndex(GUID_INDEX_NAME, 'FieldIndex')\n else:\n log.info(\"GUID index %r already exists\" % GUID_INDEX_NAME)","title":""}],"string":"[\n {\n \"docid\": \"3eb70541f86495a1a812503637a60949\",\n \"score\": \"0.58575755\",\n \"text\": \"def reindex_projects(self):\\n client = get_service_account_client()\\n query = {'name': 'designsafe.project'}\\n\\n in_loop = True\\n offset = 0\\n while in_loop:\\n listing = client.meta.listMetadata(q=json.dumps(query), offset=offset, limit=100)\\n offset += 100\\n\\n if len(listing) == 0:\\n in_loop = False\\n else:\\n for project in listing:\\n index_or_update_project.apply_async(args=[project.uuid], queue='api')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25f60d64bc8a92d3f3948d857b5134ec\",\n \"score\": \"0.57789236\",\n \"text\": \"def create(self, document):\\n self.index(document)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e467fdbb386338ac8ce8d4cd52f433a\",\n \"score\": \"0.57180494\",\n \"text\": \"def create_or_update_doc(self, index, doc):\\n body = self.bulk_body[index]\\n body.write(json.dumps({'index': {'_id': doc['id']}}))\\n body.write('\\\\n')\\n body.write(json.dumps(doc))\\n body.write('\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56f028452184cb6eea1ae6844841d69e\",\n \"score\": \"0.5655644\",\n \"text\": \"def update_document(self, **fields):\\r\\n \\r\\n # Check which of the supplied fields are unique\\r\\n unique_fields = [name for name, field\\r\\n in self.index.schema.fields()\\r\\n if name in fields and field.unique]\\r\\n if not unique_fields:\\r\\n raise IndexingError(\\\"None of the fields in %r are unique\\\" % fields.keys())\\r\\n \\r\\n # Delete documents in which the supplied unique fields match\\r\\n s = self.searcher()\\r\\n for name in unique_fields:\\r\\n self.delete_by_term(name, fields[name])\\r\\n \\r\\n # Add the given fields\\r\\n self.add_document(**fields)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b403dda7150c949fc6ce049474e9990\",\n \"score\": \"0.56132174\",\n \"text\": \"def create_document(doc_id, data):\\n conn = get_conn()\\n for alias in get_active_aliases(conn, object_types=[data[\\\"object_type\\\"]]):\\n conn.create(index=alias, doc_type=GLOBAL_DOC_TYPE, body=data, id=doc_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5241a27b6867e7f2755bbd925f12a74\",\n \"score\": \"0.5546349\",\n \"text\": \"def update_doc(c, i, d, u=False):\\n try:\\n c.update_one({'_id': i}, {'$set': d}, upsert = u)\\n return True\\n except:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b4b83ea1b879b88eda2c72b53e3c52c\",\n \"score\": \"0.5532153\",\n \"text\": \"def post_project_document(project_id,\\n document_type,\\n document_name,\\n document_content_type,\\n document_content,\\n document_preamble,\\n document_legal_entity_name,\\n new_major_version=None,\\n username=None):\\n project = get_project_instance()\\n try:\\n project.load(str(project_id))\\n except DoesNotExist as err:\\n return {'errors': {'project_id': str(err)}}\\n project_acl_verify(username, project)\\n document = get_document_instance()\\n document.set_document_name(document_name)\\n document.set_document_content_type(document_content_type)\\n document.set_document_content(document_content)\\n document.set_document_preamble(document_preamble)\\n document.set_document_legal_entity_name(document_legal_entity_name)\\n if document_type == 'individual':\\n major, minor = cla.utils.get_last_version(project.get_project_individual_documents())\\n if new_major_version:\\n document.set_document_major_version(major + 1)\\n document.set_document_minor_version(0)\\n else:\\n if major == 0:\\n major = 1\\n document.set_document_major_version(major)\\n document.set_document_minor_version(minor + 1)\\n project.add_project_individual_document(document)\\n else:\\n major, minor = cla.utils.get_last_version(project.get_project_corporate_documents())\\n if new_major_version:\\n document.set_document_major_version(major + 1)\\n document.set_document_minor_version(0)\\n else:\\n if major == 0:\\n major = 1\\n document.set_document_major_version(major)\\n document.set_document_minor_version(minor + 1)\\n project.add_project_corporate_document(document)\\n project.save()\\n return project.to_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f019a8aa87ac1017e067247c87599fa5\",\n \"score\": \"0.5523262\",\n \"text\": \"def process_project(project_data: dict):\\n project_id = project_data.get('projectid')\\n\\n if project_id is not None:\\n Project.objects.update_or_create(\\n ona_pk=project_id,\\n defaults={\\n 'name': project_data.get('name'),\\n 'deleted_at': project_data.get('deleted_at'),\\n 'last_updated': project_data.get('date_modified'),\\n 'json': project_data,\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a74cc25a8d7944eb373349e99b2e6179\",\n \"score\": \"0.5489646\",\n \"text\": \"def update_project(uuid, **kwargs):\\n project = Project.query.get(uuid)\\n\\n if project is None:\\n raise NotFound(\\\"The specified project does not exist\\\")\\n\\n if \\\"name\\\" in kwargs:\\n name = kwargs[\\\"name\\\"]\\n if name != project.name:\\n check_project_name = db_session.query(Project).filter_by(name=name).first()\\n if check_project_name:\\n raise BadRequest(\\\"a project with that name already exists\\\")\\n\\n data = {\\\"updated_at\\\": datetime.utcnow()}\\n data.update(kwargs)\\n\\n try:\\n db_session.query(Project).filter_by(uuid=uuid).update(data)\\n db_session.commit()\\n except (InvalidRequestError, ProgrammingError) as e:\\n raise BadRequest(str(e))\\n\\n return project.as_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"968af9fd450edbe0146f7b5d25cc0cd7\",\n \"score\": \"0.5369272\",\n \"text\": \"def post_project_document_template(project_id,\\n document_type,\\n document_name,\\n document_preamble,\\n document_legal_entity_name,\\n template_name,\\n new_major_version=None,\\n username=None):\\n project = get_project_instance()\\n try:\\n project.load(str(project_id))\\n except DoesNotExist as err:\\n return {'errors': {'project_id': str(err)}}\\n project_acl_verify(username, project)\\n document = get_document_instance()\\n document.set_document_name(document_name)\\n document.set_document_preamble(document_preamble)\\n document.set_document_legal_entity_name(document_legal_entity_name)\\n if document_type == 'individual':\\n major, minor = cla.utils.get_last_version(project.get_project_individual_documents())\\n if new_major_version:\\n document.set_document_major_version(major + 1)\\n document.set_document_minor_version(0)\\n else:\\n document.set_document_minor_version(minor + 1)\\n project.add_project_individual_document(document)\\n else:\\n major, minor = cla.utils.get_last_version(project.get_project_corporate_documents())\\n if new_major_version:\\n document.set_document_major_version(major + 1)\\n document.set_document_minor_version(0)\\n else:\\n document.set_document_minor_version(minor + 1)\\n project.add_project_corporate_document(document)\\n # Need to take the template, inject the preamble and legal entity name, and add the tabs.\\n tmplt = getattr(cla.resources.contract_templates, template_name)\\n template = tmplt(document_type=document_type.capitalize(),\\n major_version=document.get_document_major_version(),\\n minor_version=document.get_document_minor_version())\\n content = template.get_html_contract(document_legal_entity_name, document_preamble)\\n pdf_generator = get_pdf_service()\\n pdf_content = pdf_generator.generate(content)\\n document.set_document_content_type('storage+pdf')\\n document.set_document_content(pdf_content, b64_encoded=False)\\n document.set_raw_document_tabs(template.get_tabs())\\n project.save()\\n return project.to_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18db39f126f585a872029737f5e29364\",\n \"score\": \"0.52629536\",\n \"text\": \"def create_document(self, entity, wait=False):\\n docs = self.build_document(entity, 'single')\\n for doc in docs:\\n logger.info(f\\\"Making Doc={doc}\\\")\\n res = self.es.index(index=self.index_name(entity.project.pk),\\n id=doc['_id'],\\n refresh=wait,\\n routing=1,\\n body={**doc['_source']})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"397a433b2bb4643fa322625c06217544\",\n \"score\": \"0.5236747\",\n \"text\": \"def setup():\\n index_template = Doc._index.as_template(\\\"base\\\")\\n index_template.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd1f125fa2856f065487eb0b6eafe3e6\",\n \"score\": \"0.5214013\",\n \"text\": \"def update_project(self, project_name, from_path):\\n session = self.session\\n\\n # check if project exists.\\n query_result = session.query(Project).filter_by(\\n name=project_name).first()\\n if not query_result:\\n logging.info(f\\\"{project_name} not found, skipping.\\\")\\n return\\n\\n if query_result.fully_indexed and query_result.external:\\n logging.debug(\\n f\\\"{project_name} already fully indexed, skipping.\\\")\\n return\\n\\n if query_result.external and from_path:\\n module_name = Path(from_path).name\\n if query_result.is_module_in_project(module_name):\\n logging.info(f\\\"{project_name} is already enough indexed, skipping.\\\")\\n return\\n\\n logging.info(f\\\"Indexing {project_name} from {from_path}.\\\")\\n query_result.index(from_path)\\n logging.info(f\\\"Building {project_name}.\\\")\\n query_result.build()\\n\\n session.commit()\\n # session.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f69d0545c80cf079a73906a79de1b5e3\",\n \"score\": \"0.52051336\",\n \"text\": \"def create_index(index=None):\\n index = index or INDEX\\n document_types = ['project', 'component', 'registration', 'user', 'file', 'institution', 'preprint', 'collectionSubmission']\\n guid_metadata_types = ['project', 'component', 'registration', 'preprint', 'file']\\n\\n client().indices.create(index, ignore=[400]) # HTTP 400 if index already exists\\n for type_ in document_types:\\n if type_ == 'collectionSubmission':\\n mapping = {\\n 'properties': {\\n 'collectedType': NOT_ANALYZED_PROPERTY,\\n 'subjects': NOT_ANALYZED_PROPERTY,\\n 'status': NOT_ANALYZED_PROPERTY,\\n 'issue': NOT_ANALYZED_PROPERTY,\\n 'volume': NOT_ANALYZED_PROPERTY,\\n 'programArea': NOT_ANALYZED_PROPERTY,\\n 'provider': NOT_ANALYZED_PROPERTY,\\n 'title': ENGLISH_ANALYZER_PROPERTY,\\n 'abstract': ENGLISH_ANALYZER_PROPERTY,\\n 'schoolType': NOT_ANALYZED_PROPERTY,\\n 'studyDesign': NOT_ANALYZED_PROPERTY,\\n }\\n }\\n else:\\n mapping = {\\n 'properties': {\\n 'tags': NOT_ANALYZED_PROPERTY,\\n 'license': {\\n 'properties': {\\n 'id': NOT_ANALYZED_PROPERTY,\\n 'name': NOT_ANALYZED_PROPERTY,\\n # Elasticsearch automatically infers mappings from content-type. `year` needs to\\n # be explicitly mapped as a string to allow date ranges, which break on the inferred type\\n 'year': {'type': 'string'},\\n }\\n },\\n\\n }\\n }\\n if type_ in guid_metadata_types:\\n mapping['properties'].update({\\n 'title': ENGLISH_ANALYZER_PROPERTY,\\n 'description': ENGLISH_ANALYZER_PROPERTY,\\n 'language': NOT_ANALYZED_PROPERTY,\\n 'resource_type_general': NOT_ANALYZED_PROPERTY,\\n 'funder_name': {'type': 'string'},\\n 'funder_identifier': NOT_ANALYZED_PROPERTY,\\n 'award_number': NOT_ANALYZED_PROPERTY,\\n 'award_uri': NOT_ANALYZED_PROPERTY,\\n 'award_title': {'type': 'string'},\\n })\\n\\n if type_ == 'user':\\n fields = {\\n 'job': {\\n 'type': 'string',\\n 'boost': '1',\\n },\\n 'all_jobs': {\\n 'type': 'string',\\n 'boost': '0.01',\\n },\\n 'school': {\\n 'type': 'string',\\n 'boost': '1',\\n },\\n 'all_schools': {\\n 'type': 'string',\\n 'boost': '0.01'\\n },\\n }\\n mapping['properties'].update(fields)\\n client().indices.put_mapping(index=index, doc_type=type_, body=mapping, ignore=[400, 404])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f0e31de144dba037550aa04b481e8d6\",\n \"score\": \"0.5165092\",\n \"text\": \"def update_index():\\n # TODO: check that it's been more than a day\\n deb.update_index()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"105792b5a3b9e9dbd05f13689425dbf9\",\n \"score\": \"0.5146721\",\n \"text\": \"def update_projects():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"105792b5a3b9e9dbd05f13689425dbf9\",\n \"score\": \"0.5146721\",\n \"text\": \"def update_projects():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68dbdd7332da5f3c6e2202cf083bbe01\",\n \"score\": \"0.51065445\",\n \"text\": \"def update(self, document):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfb05f5d631251af0f718137c6fa6bec\",\n \"score\": \"0.5101003\",\n \"text\": \"async def replace_project(project_id: ProjectID, _replace: ProjectReplace):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05a62e64c02e498d0d8bdea8b6527342\",\n \"score\": \"0.5084844\",\n \"text\": \"def reindex_document(document):\\n\\n try:\\n record = document._record or DocumentRecord.objects.get(pk=document.id)\\n except DocumentRecord.DoesNotExist:\\n return\\n\\n qs = TokenFieldIndex.objects.filter(\\n record_id=document.id,\\n revision=document.revision\\n )\\n defer_iteration_with_finalize(\\n qs, _destroy_record, _finalize, _shards=1, _queue=_SEARCH_QUEUE\\n )\\n\\n # Generate a brand new revision ID for this document\\n record.revision = uuid.uuid4()\\n record.save()\\n\\n index_document(document.index_name, document)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bf09988a232c0f4a78f1201328c3b11\",\n \"score\": \"0.50620395\",\n \"text\": \"def test_get_project_by_id(app):\\n\\n # insert a projects into the database\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33d3852abae5ff5e392637b5bcc96cc4\",\n \"score\": \"0.5043024\",\n \"text\": \"def addDocumentToIndex(self, name, vrr, nodeIdNum, title, content, tags):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e705ba684fa2a02064f83326f33c8ec5\",\n \"score\": \"0.5042402\",\n \"text\": \"def projects_delete(self, perun_id):\\n self.projects_update(perun_id, scratched=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ba7858e4f524c1eb763fba5c6cd64be\",\n \"score\": \"0.50327057\",\n \"text\": \"def add(self, document):\\n return self.db.update({document['id']: document})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bcd77babd05f969641766c089631885\",\n \"score\": \"0.5025321\",\n \"text\": \"def delete_project_document(project_id, document_type, major_version, minor_version, username=None):\\n project = get_project_instance()\\n try:\\n project.load(str(project_id))\\n except DoesNotExist as err:\\n return {'errors': {'project_id': str(err)}}\\n project_acl_verify(username, project)\\n document = cla.utils.get_project_document(project, document_type, major_version, minor_version)\\n if document is None:\\n return {'errors': {'document': 'Document version not found'}}\\n if document_type == 'individual':\\n project.remove_project_individual_document(document)\\n else:\\n project.remove_project_corporate_document(document)\\n project.save()\\n return {'success': True}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"437d1058dc5f0f770d721c6f2b83c40c\",\n \"score\": \"0.50245684\",\n \"text\": \"def update_project(self, database):\\n opended_after_140630 = comp_dates('2014-06-30', self.ordered_opened)\\n try:\\n LOG.info('Handeling {proj}'.format(proj = self.name))\\n project = database.ProjectDB(lims, self.id, self.samp_db)\\n key = find_proj_from_view(self.proj_db, self.name)\\n project.obj['_id'] = find_or_make_key(key)\\n if not opended_after_140630:\\n project.obj = load_status_from_google_docs.get(self.name, project.obj)\\n if self.upload_data:\\n info = save_couchdb_obj(self.proj_db, project.obj)\\n else:\\n info = self.print_couchdb_obj_to_file(project.obj)\\n return \\\"project {name} is handled and {info}: _id = {id}\\\".format(\\n name=self.name, info=info, id=project.obj['_id'])\\n except:\\n return ('Issues geting info for {name}. The \\\"Application\\\" udf might'\\n ' be missing'.format(name = self.name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"311ac2a59dda5e4adc79b73d50426ccc\",\n \"score\": \"0.5006261\",\n \"text\": \"def create_day_doc(doc_datetime):\\n timestampStr = doc_datetime.strftime(\\\"%Y-%m-%d\\\")\\n\\n if not COL_TELEMETRY.document(timestampStr).get().exists:\\n try:\\n COL_TELEMETRY.document(timestampStr).set({\\\"Date\\\": timestampStr})\\n return \\\"Documents Created\\\", 201\\n except Exception as e:\\n reporting_client.report_exception()\\n return f\\\"An Error Occured: {e}\\\", 400\\n return \\\"Document already exists\\\", 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b85dff9afb87d73bc6a1318ae161f8e\",\n \"score\": \"0.5004508\",\n \"text\": \"def create_legacy_projects():\\n for slug, title, pubdate in dbs:\\n r = requests.get('https://physionet.org/physiobank/database/{}/HEADER.shtml'.format(slug))\\n if r.status_code != 200:\\n r = requests.get('https://physionet.org/physiobank/database/{}/index.shtml'.format(slug))\\n if r.status_code != 200:\\n print('{} does not exist'.format(slug))\\n continue\\n content = load_legacy_html(slug=slug)\\n p = LegacyProject.objects.create(title=title, slug=slug,\\n full_description=content, publish_date=datetime.datetime.strptime(pubdate.strip(), '%d %B %Y'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2401688b53bc9cf60745ef665dd2c14\",\n \"score\": \"0.5003635\",\n \"text\": \"def update_project(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"192c820a883dfe716f2d4126f0080317\",\n \"score\": \"0.49894994\",\n \"text\": \"def test_create_project(self):\\n payload = {\\n \\\"name\\\": {\\\"en\\\": \\\"Test project\\\", \\\"fr\\\": \\\"Projet test\\\"},\\n \\\"description\\\": {\\\"en\\\": [\\\"Test desc\\\"], \\\"fr\\\": [\\\"Test desc\\\"]}\\n }\\n res = self.admin_client.post(\\n PROJECT_URL, payload, format=\\\"json\\\")\\n self.assertEqual(res.status_code, status.HTTP_201_CREATED)\\n exists = Project.objects.all().filter(\\n name=payload['name']).exists()\\n self.assertTrue(exists)\\n Project.objects.all().filter(name=payload['name']).delete()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98e81e7036e3f2d0f04ad8b471ec4984\",\n \"score\": \"0.49818078\",\n \"text\": \"def update_document(id, body):\\n\\n es.update(\\n index=INDEX_NAME,\\n doc_type=DOC_TYPE,\\n id=id,\\n body=body\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0130808ec40d1e1a19bb584b322e719c\",\n \"score\": \"0.4969886\",\n \"text\": \"def test_update(dvc):\\n index = Index.from_repo(dvc)\\n new_stage = Stage(dvc, path=\\\"path1\\\")\\n new_index = index.update({new_stage})\\n\\n assert not index.stages\\n assert new_index.stages == [new_stage]\\n\\n dup_stage1 = Stage(dvc, path=\\\"path1\\\")\\n dup_stage2 = Stage(dvc, path=\\\"path2\\\")\\n dup_index = index.update([dup_stage1, dup_stage2])\\n assert not index.stages\\n assert len(new_index.stages) == 1\\n assert new_index.stages[0] is new_stage\\n assert set(map(id, dup_index.stages)) == {id(dup_stage1), id(dup_stage2)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61353c81ed9deebb3ce5644b5255533a\",\n \"score\": \"0.4959378\",\n \"text\": \"def projects_create(self, perun_id, name=None, description=None, members=None, enabled=True):\\n\\n perun_id = str(perun_id)\\n if name is None:\\n name = perun_id\\n\\n if not self.ro:\\n os_project = self.keystone.projects.create(name=str(name),\\n perun_id=perun_id,\\n domain=self.target_domain_id,\\n description=description,\\n enabled=bool(enabled),\\n scratched=False,\\n flag=self.flag,\\n parent=self.parent_project_id if self.nested else None)\\n denbi_project = {'id': str(os_project.id),\\n 'name': str(os_project.name),\\n 'perun_id': str(os_project.perun_id),\\n 'description': os_project.description,\\n 'enabled': bool(os_project.enabled),\\n 'scratched': bool(os_project.scratched),\\n 'members': []}\\n else:\\n denbi_project = {'id': 'read-only-fake',\\n 'name': name,\\n 'perun_id': perun_id,\\n 'description': description,\\n 'enabled': enabled,\\n 'scratched': False,\\n 'members': []}\\n # Log keystone update\\n self.log2.debug(f\\\"project [{denbi_project['perun_id']},{denbi_project['id']}]: created.\\\")\\n\\n self.denbi_project_map[denbi_project['perun_id']] = denbi_project\\n self.__project_id2perun_id__[denbi_project['id']] = denbi_project['perun_id']\\n\\n # if a list of members is given append them to current project\\n if members:\\n for member in members:\\n self.projects_append_user(perun_id, member)\\n\\n return denbi_project\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f37cbd0c4d6a7707fd3977bd2ed8fbb1\",\n \"score\": \"0.49592552\",\n \"text\": \"def projects_create(context, values):\\n session = get_session()\\n project = models.Project()\\n if not values.get('id'):\\n values['id'] = uuid.uuid4()\\n with session.begin():\\n project.update(values)\\n project.save(session)\\n return project\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a68db4d9ea28e8d68eef7684f1c15da2\",\n \"score\": \"0.4956459\",\n \"text\": \"def put_design(self, designname, doc, rebuild=True):\\n response = self.server._GET(self.name, \\\"_design\\\", designname,\\n errors={404: None})\\n if response.status_code == 200:\\n current_doc = response.json(object_pairs_hook=odict)\\n doc[\\\"_id\\\"] = current_doc[\\\"_id\\\"]\\n doc[\\\"_rev\\\"] = current_doc[\\\"_rev\\\"]\\n if doc == current_doc:\\n return False\\n response = self.server._PUT(self.name, \\\"_design\\\", designname, json=doc)\\n if rebuild:\\n for view in doc.get(\\\"views\\\", {}):\\n self.view(designname, view, limit=1)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b15b5501a521c21a539203e0e8fe5b9\",\n \"score\": \"0.49440858\",\n \"text\": \"def check_and_update_run(self, run_number):\\n run_doc = self.get_run(run_number)\\n if not run_doc:\\n run_doc = self.run_doc_class.build_document(run_number)\\n if run_doc:\\n print \\\"Run %i is not in database, inserting...\\\" % int(run_number)\\n self.insert_rundoc(run_doc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e868dbc23aaa9f84999b81f877cb4a9\",\n \"score\": \"0.49372107\",\n \"text\": \"def _add_document(doc_id, data, database=MAIN_DB):\\n return requests.put(\\n os.path.join(COUCH_URL, database, doc_id),\\n json=data,\\n headers={\\\"Content-Type\\\": \\\"application/json\\\"}\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4667675eb9f0fee441a3449ca58fd4e8\",\n \"score\": \"0.4923755\",\n \"text\": \"def test_project_index(project_client, session, project, project2):\\n assert project.releases\\n assert project.tags\\n assert project.comments\\n assert project.user\\n assert project.user.projects\\n assert project.user.projectcomments\\n assert project.tag_counts == [('arcade', 2, 16), ('game', 1, 14)]\\n\\n resp = project_client.get('/project/1/')\\n assert resp.status_code == 200\\n assert b'Some project title 1' in resp.data\\n assert b'Some project title 2' not in resp.data\\n assert b'game' in resp.data\\n assert b'arcade' in resp.data\\n\\n resp = project_client.get('/project-blabla+bla-1-.html')\\n assert resp.status_code == 200, 'because this url works too.'\\n assert b'Some project title 1' in resp.data\\n\\n resp = project_client.get('/project/1/1')\\n assert resp.status_code == 200\\n assert b'A release title.' in resp.data\\n\\n resp = project_client.get('/project-blabla+blasbla+-1-1.html')\\n assert resp.status_code == 200, 'because this url works too.'\\n assert b'A release title.' in resp.data\\n\\n resp = project_client.get('/project/66/')\\n assert resp.status_code == 404, 'when the project is not there'\\n resp = project_client.get('/project/1/66')\\n assert resp.status_code == 404, 'when the release is not there either'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3899bcc0fbdc4a74c83b775d8510dce1\",\n \"score\": \"0.49015632\",\n \"text\": \"def set_document(request, doc_id, body, errors):\\n if doc_id in DB:\\n errors['doc_id'].add('Document already exists')\\n\\n if errors:\\n raise errors\\n\\n body['doc_id'] = doc_id\\n DB[doc_id] = body\\n\\n # dict response for jsonify middleware\\n return body\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68855beaa907077bdd1096e7fe3a3d2d\",\n \"score\": \"0.48790357\",\n \"text\": \"def get_or_create(self, index_name): \\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68855beaa907077bdd1096e7fe3a3d2d\",\n \"score\": \"0.48790357\",\n \"text\": \"def get_or_create(self, index_name): \\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6423f40815445f520211d18edecdff79\",\n \"score\": \"0.487891\",\n \"text\": \"def create_doc_instances(self, **kwargs):\\n kwargs[u\\\"cdb_project_id\\\"] = self.cdb_project_id\\n return self.Super(ProjectTemplateDocRef).create_doc_instances(**kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ef3b2bce4ef4353b9113c49562ff49c\",\n \"score\": \"0.4876358\",\n \"text\": \"def add_project():\\n\\n # Get an access class\\n da=DomainAccess()\\n ea=EmployeeAccess()\\n pa=ProjectAccess()\\n\\n # Acquire form data\\n title = request.form[\\\"Title\\\"]\\n maxStudents = request.form[\\\"Maxstudents\\\"]\\n descriptionTextNl = request.form[\\\"nlDescription\\\"]\\n descriptionTextEng = request.form[\\\"engDescription\\\"]\\n\\n researchGroupNrs = request.form.getlist(\\\"Researchgroup\\\")\\n typeNrs = request.form.getlist(\\\"Type\\\")\\n disciplineNrs = request.form.getlist(\\\"Discipline\\\")\\n tags = request.form.getlist(\\\"Tags\\\")\\n related = request.form.getlist(\\\"Related\\\")\\n promotorNames = request.form.getlist(\\\"Promotors\\\")\\n supervisorNames = request.form.getlist(\\\"Supervisors\\\")\\n externNames = request.form.getlist(\\\"Extern\\\")\\n\\n # Acquire request files\\n files_nl = request.files.getlist(\\\"nlUploads\\\")\\n files_en = request.files.getlist(\\\"engUploads\\\")\\n\\n # Create basic project\\n project = Project(None, title, maxStudents, True)\\n\\n # Assign research group ID's\\n for researchGroupNr in researchGroupNrs:\\n if int(researchGroupNr) == 0:\\n continue\\n project.researchGroup.append(int(researchGroupNr))\\n\\n # Create dutch document\\n docNL = Document(None, \\\"dutch\\\", descriptionTextNl)\\n # Link attachments to document\\n this_dir = os.path.dirname(__file__)\\n upload_folder = os.path.join(this_dir, app.config['UPLOAD_FOLDER'])\\n for file in files_nl:\\n nameFile = secure_filename(title + '_' + file.filename)\\n file.save(os.path.join(upload_folder, nameFile))\\n docNL.attachment.append(nameFile)\\n # Assign document as description\\n project.desc.append(docNL)\\n\\n # Create english document\\n docEn = Document(None, \\\"english\\\", descriptionTextEng)\\n # Link attachments to document\\n for file in files_en:\\n nameFile = secure_filename(title + '_' + file.filename)\\n file.save(os.path.join(os.path.join(upload_folder, nameFile)))\\n docEn.attachment.append(nameFile)\\n # Assign document as description\\n project.desc.append(docEn)\\n\\n # Append types to the project\\n typeOptions = da.get_projectType()\\n for typeNr in typeNrs:\\n if int(typeNr) == 0:\\n continue\\n project.type.append(typeOptions[int(typeNr) - 1])\\n\\n # Append disciplines to the project\\n disciplineOptions = da.get_disciplines()\\n for disciplineNr in disciplineNrs:\\n if int(disciplineNr) == 0:\\n continue\\n project.discipline.append(disciplineOptions[int(disciplineNr) - 1])\\n\\n # Fetch all employees from the database\\n employeeOptions = ea.get_employees()\\n\\n # Add promotors to the project\\n # Create dictionary of promotor name and his id, used to easily append ID to the project\\n promotor_id_dict = {promotorOption.name: promotorOption.id for promotorOption in employeeOptions}\\n # Loop over names and connect the associated ID's to the project\\n for promotorName in promotorNames:\\n if promotorName in promotor_id_dict:\\n project.promotors.append(promotor_id_dict[promotorName])\\n\\n # Add supervisors to the project\\n # Create dictionary of supervisor name and his id, used to easily append ID to the project\\n supervisor_id_dict = {staffOption.name: staffOption.id for staffOption in\\n employeeOptions}\\n # Loop over names and connect the associated ID's to the project\\n for staffName in supervisorNames:\\n if staffName in supervisor_id_dict:\\n project.supervisors.append(supervisor_id_dict[staffName])\\n\\n # Add extern employees to the project\\n for name in externNames:\\n project.extern_employees.append(name)\\n\\n # Add tags to the project\\n project.tag = list(tags)\\n\\n # Add related projects to the project\\n # Fetch all projects from the database\\n relatedProjectOptions = pa.get_projects()\\n # Create dictionary of project title and its id, used to easily find the required ID\\n related_project_id_dict = {relatedProjectOption.title: relatedProjectOption.ID for relatedProjectOption in\\n relatedProjectOptions}\\n # Loop over related projects and append the associated ID to the project relations\\n for relatedProjectTitle in related:\\n if relatedProjectTitle in related_project_id_dict:\\n project.relatedProject.append(related_project_id_dict[relatedProjectTitle])\\n\\n # Assign active year\\n now = datetime.now()\\n project.activeYear.append(now.year)\\n\\n # Finalize project and add it to the database\\n pa.add_project(project)\\n findTag(project)\\n\\n # Return result to javascript\\n return jsonify(result=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcbe91f25d0a54837a18c3ca816e5125\",\n \"score\": \"0.4869392\",\n \"text\": \"def add_doc():\\n c_id = request.args[\\\"c_id\\\"]\\n doc_number = request.args[\\\"doc_number\\\"]\\n\\n if s.query(Documents). \\\\\\n filter(and_(Documents.c_id == c_id,\\n Documents.qpulse_no == doc_number)). \\\\\\n count() == 0:\\n doc = Documents(c_id=c_id, qpulse_no=doc_number)\\n s.add(doc)\\n s.commit()\\n return jsonify({\\\"success\\\": True})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"198535543a3034d8ac97e5d93610f18c\",\n \"score\": \"0.48673\",\n \"text\": \"def test_existing_index(self, orion_db):\\n assert (\\n \\\"new_field_1\\\" not in get_db(orion_db)[\\\"new_collection\\\"].index_information()\\n )\\n\\n orion_db.ensure_index(\\\"new_collection\\\", \\\"new_field\\\", unique=True)\\n assert \\\"new_field_1\\\" in get_db(orion_db)[\\\"new_collection\\\"].index_information()\\n\\n # reattempt\\n orion_db.ensure_index(\\\"new_collection\\\", \\\"new_field\\\", unique=True)\\n assert \\\"new_field_1\\\" in get_db(orion_db)[\\\"new_collection\\\"].index_information()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c466a7bd7ef34e6dfc1db2eba47deba4\",\n \"score\": \"0.4845863\",\n \"text\": \"def update_inv_index(self, processed_tokens, document_id):\\n for token_index in range(0, len(processed_tokens)):\\n if processed_tokens[token_index] not in self.terms:\\n self.terms.append(processed_tokens[token_index])\\n new_postings_list = list()\\n if self.purpose == \\\"vsm\\\":\\n new_doc = Document(document_id, store_term_weights=True)\\n else:\\n new_doc = Document(document_id, token_index + 1)\\n new_postings_list.append(new_doc)\\n self.posting_lists.append(new_postings_list)\\n else:\\n existing_posting_list = self.get_postings_list(\\n processed_tokens[token_index])\\n doc_exists = False\\n for i in range(len(existing_posting_list)):\\n if existing_posting_list[i].id == document_id:\\n if self.purpose == \\\"vsm\\\":\\n existing_posting_list[i].increment_frequency()\\n else:\\n existing_posting_list[i].add_position(\\n token_index + 1)\\n doc_exists = True\\n if doc_exists is False:\\n if self.purpose == \\\"vsm\\\":\\n new_doc = Document(\\n document_id, store_term_weights=True)\\n else:\\n new_doc = Document(document_id, token_index + 1)\\n existing_posting_list.append(new_doc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0486cd11ffc6867d49d30635859999a\",\n \"score\": \"0.48408973\",\n \"text\": \"def create_index():\\n try:\\n print(\\\"[*] creating an index in elasticsearch\\\")\\n r = requests.put(INDEX_URL)\\n if \\\"index_already_exists_exception\\\" in r.text:\\n print(\\\"[*] index already exists\\\")\\n return\\n mapping = {\\n \\\"capture\\\": {\\n \\\"properties\\\": { \\n \\\"timestamp\\\": {\\n \\\"type\\\": \\\"date\\\",\\n },\\n \\\"capture\\\": {\\n \\\"type\\\": \\\"string\\\",\\n },\\n }\\n }\\n }\\n r = requests.put(TYPE_URL + \\\"/_mapping\\\", data=json.dumps(mapping))\\n print(r.text)\\n except:\\n raise Exception(\\\"Elasticsearch is not running\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79550ceba64c00d7cd1ed2e8ab6d2e3b\",\n \"score\": \"0.48389933\",\n \"text\": \"def insert(self, doc):\\n params = self._params_for_insert\\n params.update({'body': doc, 'refresh': True})\\n if not self._index_exists():\\n self._create_index()\\n doc = ELASTICSEARCH.index(**params)\\n return doc['_id']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91c9aafa4e4dd3f4de10a590538ed54f\",\n \"score\": \"0.48380858\",\n \"text\": \"def create(self, id: str, data: Optional[JsonDict] = None) -> Document:\\n if any(id == d.id for d in self._docs):\\n raise ValueError(\\n f\\\"There is already another Document instance for {id} part of the BulkOperation\\\"\\n )\\n\\n doc = Document(self._database, id, data=data)\\n self._docs.append(doc)\\n\\n return doc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c338b5a256e496f6123de3c7a5c4fc5b\",\n \"score\": \"0.48324662\",\n \"text\": \"def _import_project_data(self):\\n for data in self._get_fyle_data(\\n self.fyle_api.Projects, {'active_only': False}):\\n defaults = {\\n 'name': data['name'],\\n 'description': data['description'],\\n 'active': data['active'],\\n }\\n Project.objects.update_or_create(\\n id=data['id'], user=self.batch.user, defaults=defaults)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8266958ea8686836d449b0440c676007\",\n \"score\": \"0.48165312\",\n \"text\": \"def index(self, doctype, docid, document):\\n dbname = self.dbname\\n self.broker.DB.index(index=dbname, doc_type=doctype, id=docid, body=document)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b95af5d41d231f0ee4aff7660a7f0d47\",\n \"score\": \"0.48141915\",\n \"text\": \"def add_new_doc(self, document):\\n document_dictionary = document.term_doc_dictionary\\n document_capitals = document.capital_letter_indexer\\n document_date = document.tweet_date\\n for key_term in document_capitals:\\n if key_term not in self.global_capitals:\\n self.global_capitals[key_term] = document_capitals[key_term]\\n else:\\n if not document_capitals[key_term]:\\n self.global_capitals[key_term] = False\\n\\n document_entities = document.named_entities\\n\\n for entity in document_entities:\\n self.entities_dict[entity] += 1\\n document_vec = np.zeros(shape=25)\\n is_covid = False\\n for term in document_dictionary:\\n if term == 'covid':\\n is_covid = True\\n if term in self.glove_dict:\\n document_vec += self.glove_dict[term]\\n document_vec /= len(document_dictionary)\\n # document_vec, # numpy array of size 25 which\\n # represents the document in 25 dimensional space(GloVe)\\n if is_covid:\\n self.document_posting_covid[document.tweet_id] = (document_vec, document_date)\\n self.document_dict[document.tweet_id] = \\\"doc_posting_covid\\\" + str(self.doc_posting_covid_counter)\\n else:\\n self.doc_posting_dict[document.tweet_id] = (document_vec, document_date)\\n self.document_dict[document.tweet_id] = \\\"doc_posting\\\"+str(self.doc_posting_counter)\\n\\n if len(self.doc_posting_dict) == 100000:\\n self.save_doc_posting()\\n if len(self.document_posting_covid) == 100000:\\n self.save_doc_covid()\\n\\n\\n # Go over each term in the doc\\n for term in document_dictionary.keys():\\n try:\\n if term in self.inverted_idx:\\n if term not in self.posting_dict:\\n self.posting_dict[term] = None\\n self.inverted_idx[term][0] += 1\\n\\n else:\\n self.inverted_idx[term] = [1, str(self.counter_of_postings)]\\n self.posting_dict[term] = None\\n\\n insert_tuple = (document.tweet_id, # tweet id\\n document.doc_length, # total number of words in tweet\\n document.max_tf, # number of occurrences of most common term in tweet\\n document.unique_terms, # number of unique words in tweet\\n document_dictionary[term], # number of times term is in tweet - tf!\\n )\\n # if there're no documents for the current term, insert the first document\\n if self.posting_dict[term] is None:\\n self.posting_list.append((term, [insert_tuple]))\\n self.posting_dict[term] = len(self.posting_list) - 1\\n else:\\n tuple_idx = self.posting_dict[term]\\n self.posting_list[tuple_idx][1].append(insert_tuple)\\n\\n # check if posting_dict is full\\n if len(self.posting_list) == self.postingDict_size:\\n self.save_postings()\\n\\n except:\\n print('problem with the following key {}'.format(term))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ad5d272bb1d56172468a84a457c5e10\",\n \"score\": \"0.4809561\",\n \"text\": \"def test_indexed_doc(es):\\n investor_company = CompanyFactory()\\n\\n large_investor_profile = LargeCapitalInvestorProfileFactory(\\n investor_company=investor_company,\\n )\\n\\n doc = ESLargeInvestorProfile.es_document(large_investor_profile)\\n elasticsearch.bulk(actions=(doc, ), chunk_size=1)\\n\\n es.indices.refresh()\\n\\n indexed_large_investor_profile = es.get(\\n index=ESLargeInvestorProfile.get_write_index(),\\n id=large_investor_profile.pk,\\n )\\n\\n assert indexed_large_investor_profile['_id'] == str(large_investor_profile.pk)\\n assert indexed_large_investor_profile['_source'] == {\\n '_document_type': LargeInvestorProfileSearchApp.name,\\n 'id': str(large_investor_profile.pk),\\n 'asset_classes_of_interest': [],\\n 'country_of_origin': {\\n 'id': str(large_investor_profile.country_of_origin.pk),\\n 'name': str(large_investor_profile.country_of_origin.name),\\n },\\n 'investor_company': {\\n 'id': str(investor_company.pk),\\n 'name': str(investor_company.name),\\n 'trading_names': investor_company.trading_names,\\n },\\n 'created_by': None,\\n 'investor_type': None,\\n 'required_checks_conducted': None,\\n 'deal_ticket_sizes': [],\\n 'investment_types': [],\\n 'minimum_return_rate': None,\\n 'time_horizons': [],\\n 'restrictions': [],\\n 'construction_risks': [],\\n 'minimum_equity_percentage': None,\\n 'desired_deal_roles': [],\\n 'uk_region_locations': [],\\n 'other_countries_being_considered': [],\\n 'investable_capital': None,\\n 'investor_description': '',\\n 'created_on': '2019-01-01T00:00:00+00:00',\\n 'notes_on_locations': '',\\n 'global_assets_under_management': None,\\n 'modified_on': '2019-01-01T00:00:00+00:00',\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51f2637b70c16067a94a6bd4e0258048\",\n \"score\": \"0.48014212\",\n \"text\": \"def read_document(self, d):\\n if not self.in_collection(d):\\n self.documents[d] = self.index.add_document(d)\\n\\n if len(self.index) >= READ_LIMIT_TO_WRITE_TO_MONGO:\\n self.flush_to_mongo()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce5a9df77a8dea388f8abce2e0a3ba20\",\n \"score\": \"0.47926098\",\n \"text\": \"def project(name, description, skip_if_exists):\\n if skip_if_exists:\\n result = Client().graphql(\\n query={\\n \\\"query\\\": {\\n with_args(\\\"project\\\", {\\\"where\\\": {\\\"name\\\": {\\\"_eq\\\": name}}}): {\\n \\\"id\\\": True\\n }\\n }\\n }\\n )\\n if result.data.project:\\n click.secho(\\\"{} already exists\\\".format(name), fg=\\\"green\\\")\\n return\\n\\n try:\\n Client().create_project(project_name=name, project_description=description)\\n except ClientError as exc:\\n click.echo(f\\\"{type(exc).__name__}: {exc}\\\")\\n click.secho(\\\"Error creating project\\\", fg=\\\"red\\\")\\n return\\n\\n click.secho(\\\"{} created\\\".format(name), fg=\\\"green\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d308c3fb5e0f7925f8a90beabd7769b1\",\n \"score\": \"0.47813076\",\n \"text\": \"def test_create(self):\\n doc = Document(DocumentID=\\\"123\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3653cfc90294d606c691f6002160e051\",\n \"score\": \"0.47683114\",\n \"text\": \"async def update_project(project_id: ProjectID, update: ProjectUpdate):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cb2ea4fbdea7b30cfb8fc035ec33b63\",\n \"score\": \"0.4765857\",\n \"text\": \"def delete(project, version=None):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"304ed14f24d509d80cc30d9e20a000bb\",\n \"score\": \"0.47605696\",\n \"text\": \"def update_document(self, document_id, mappings):\\n\\n if len(mappings) == 0:\\n return None\\n\\n requests = []\\n for (src, dest) in mappings.items():\\n requests.append(\\n {\\n \\\"replaceAllText\\\": {\\n \\\"containsText\\\": {\\n \\\"text\\\": src,\\n \\\"matchCase\\\": \\\"true\\\",\\n },\\n \\\"replaceText\\\": dest,\\n }\\n }\\n )\\n\\n body = {\\\"requests\\\": requests}\\n results = self.docs.batchUpdate(documentId=document_id, body=body).execute()\\n\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b0ff57f645529136b9fca1f29ca8a3d\",\n \"score\": \"0.47586724\",\n \"text\": \"def assign_weather_to_project(delphin_id: str, weather_ids: list) -> str:\\n\\n # Save climate class to delphin document\\n delphin_document = delphin_db.Delphin.objects(id=delphin_id).first()\\n\\n for id_ in weather_ids:\\n weather_document = weather_db.Weather.objects(id=id_).first()\\n delphin_document.update(push__weather=weather_document)\\n\\n return delphin_document.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da1b60c6b85b7a75cb23675cb817f78d\",\n \"score\": \"0.4754898\",\n \"text\": \"def insert_document(self, index_name, type, document_id, document):\\n\\n try:\\n if self.es_client is None or not self.es_client.ping():\\n self.connect()\\n\\n if document_id is not None and document_id != \\\"\\\":\\n return self.es_client.index(index=index_name, doc_type=type, id=document_id, body=document, refresh='wait_for', request_timeout=30)\\n else:\\n return self.es_client.index(index=index_name, doc_type=type, body=document, refresh='wait_for', request_timeout=30)\\n except:\\n logger.exception(\\\"could not insert document is %s in index %s \\\"%(document_id, index_name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f767eaef4e45cf976afc1f230f7464f\",\n \"score\": \"0.47510174\",\n \"text\": \"def upsert(self):\\n\\n # Raise error if index is not writable\\n if not self.config.get(\\\"writable\\\"):\\n raise ReadOnlyError(\\\"Attempting to upsert a read-only index (writable != True)\\\")\\n\\n if self.embeddings and self.documents:\\n with self.lock:\\n # Run upsert\\n self.embeddings.upsert(self.documents)\\n\\n # Save index if path available, otherwise this is an memory-only index\\n if self.config.get(\\\"path\\\"):\\n self.embeddings.save(self.config[\\\"path\\\"], self.config.get(\\\"cloud\\\"))\\n\\n # Reset document stream\\n self.documents.close()\\n self.documents = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da970d3a07959bb70343da54b9e0f1dc\",\n \"score\": \"0.4724849\",\n \"text\": \"def updateDocs():\\n docs = Dataset.search()\\n for doc in docs.scan():\\n created = getattr(getattr(doc, \\\"_ts\\\", None), \\\"date_created\\\", None)\\n updated = getattr(getattr(doc, \\\"_ts\\\", None), \\\"last_updated\\\", None)\\n if not created and not updated:\\n existing_dc = getattr(getattr(doc, \\\"_meta\\\", None), \\\"date_created\\\", None)\\n existing_lu = getattr(getattr(doc, \\\"_meta\\\", None), \\\"last_updated\\\", None)\\n\\n if existing_dc and existing_lu:\\n doc.update(**{\\\"_ts\\\": {\\\"date_created\\\": existing_dc, \\\"last_updated\\\": existing_lu}})\\n logging.info(\\\"Updating fields for doc with existing\\\")\\n else:\\n doc.update(\\n **{\\n \\\"_ts\\\": {\\n \\\"date_created\\\": default_create,\\n \\\"last_updated\\\": default_last_updated,\\n }\\n }\\n )\\n logging.info(\\\"Adding fields for doc with defaults\\\")\\n else:\\n created_bug = getattr(getattr(doc, \\\"_ts\\\", None), \\\"guide\\\", None)\\n if created_bug:\\n doc.update(\\n **{\\n \\\"_ts\\\": {\\n \\\"date_created\\\": default_create,\\n \\\"last_updated\\\": default_last_updated,\\n }\\n }\\n )\\n else:\\n logging.info(\\\"Doc already has TS last updated field\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c0614bb9a344bbcb7774f40d315a663\",\n \"score\": \"0.4718103\",\n \"text\": \"def _write_projects(projects):\\n path = _get_projects_index_file()\\n contents = util.read_yaml(path)\\n contents.update({'projects': projects})\\n util.write_yaml(path, contents)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3b73dd77df050a21496f43cdef09149\",\n \"score\": \"0.47118863\",\n \"text\": \"def delete(self, project, query):\\n self.es.delete_by_query(\\n index=self.index_name(project),\\n body=query,\\n conflicts='proceed',\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3024c1ab542c8ad983221027239d933a\",\n \"score\": \"0.47102568\",\n \"text\": \"def add_doc(self, doc_id, contents):\\n self.__es.index(index=self.__index_name, doc_type=self.DOC_TYPE, id=doc_id, body=contents)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af9147664a4952bc73e1c324e25ce1b2\",\n \"score\": \"0.47069666\",\n \"text\": \"def test_designs_id_put(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d6dd2c023bd9b98b8654db1f500b71a\",\n \"score\": \"0.4705125\",\n \"text\": \"def insert(dictObject, db):\\n\\n \\\"\\\"\\\" res = db[\\\"menus\\\"].update_one(\\n {\\\"id\\\": dictObject[\\\"id\\\"],\\n \\\"date\\\": dictObject[\\\"date\\\"],\\n \\\"mensaName\\\": dictObject[\\\"mensaName\\\"],\\n \\\"lang\\\": dictObject[\\\"lang\\\"]\\n }, {\\\"$set\\\": dictObject}, upsert=True)\\n\\n print(\\\"modifed: id: \\\" + str(dictObject[\\\"id\\\"].encode('utf-8')) + \\\" Date: \\\" + dictObject[\\\"date\\\"] + \\\" lang: \\\" +\\n dictObject[\\\"lang\\\"])\\n if res.upserted_id is None:\\n print(\\\"res: modified: \\\" + str(res.modified_count) + \\\" matched: \\\" + str(res.matched_count))\\n else:\\n print(\\\"res: inserted\\\") \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"876fbf36475b5fdab80415b990d77feb\",\n \"score\": \"0.46986815\",\n \"text\": \"def scrape_new_data(index=\\\"verdicts\\\"):\\n if es.indices.exists(index=index):\\n tic = time.time()\\n scraper.update_database()\\n toc = time.time()\\n print(\\\"Scraping done! Time needed: {}\\\".format(str(toc - tic)))\\n else:\\n print(\\\"Index to be updated does not exists!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7842c0d6747dbdef941acf436cb28de4\",\n \"score\": \"0.4698259\",\n \"text\": \"def create(self, index_name):\\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7842c0d6747dbdef941acf436cb28de4\",\n \"score\": \"0.4698259\",\n \"text\": \"def create(self, index_name):\\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7842c0d6747dbdef941acf436cb28de4\",\n \"score\": \"0.4698259\",\n \"text\": \"def create(self, index_name):\\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"620ced752c4b779874c9474695caa26d\",\n \"score\": \"0.4695401\",\n \"text\": \"def create_doc_instances(self, **kwargs):\\n kwargs[u\\\"cdb_project_id\\\"] = self.cdb_project_id\\n return self.Super(TaskTemplateDocRef).create_doc_instances(**kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de775a146f47d74e09cd6d9324f8dbab\",\n \"score\": \"0.4694352\",\n \"text\": \"def create_update_project(request, slug=None):\\n request_user = request.user\\n if slug is not None:\\n project = Project.objects.get(slug=slug)\\n if project.owner != request.user:\\n raise PermissionDenied\\n else:\\n project_form = CreateProjectForm(instance=project)\\n position_formset = PositionFormset(\\n queryset=Position.objects.filter(\\n project=project\\n ))\\n else:\\n project = None\\n project_form = CreateProjectForm()\\n position_formset = PositionFormset(\\n queryset=Position.objects.none()\\n )\\n\\n if request.method == 'POST':\\n project_form = CreateProjectForm(\\n request.POST, \\n instance=project\\n )\\n position_formset = PositionFormset(\\n request.POST,\\n queryset=Position.objects.filter(project=project)\\n )\\n if project_form.is_valid() and position_formset.is_valid():\\n project = project_form.save(commit=False)\\n project.owner = request_user\\n new_project = False\\n if project.status is None:\\n new_project = True\\n project.status = 'A'\\n project.save()\\n positions = position_formset.save(commit=False)\\n for position in positions:\\n position.project = project\\n if position.status not in ('E', 'F'):\\n position.status = 'E'\\n position.save()\\n position_formset.save_m2m()\\n position_formset.save()\\n if new_project:\\n messages.success(\\n request, 'Project {} created!'.format(project.title))\\n else:\\n messages.success(\\n request, 'Project {} updated!'.format(project.title))\\n return HttpResponseRedirect(reverse('projects:project', kwargs={'slug': project.slug}))\\n\\n context = {\\n 'project_form': project_form,\\n 'position_formset': position_formset,\\n }\\n return render(request, 'projects/create_update_project.html', context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1df57eb7f6c6f2712debcc484e3347d8\",\n \"score\": \"0.46942607\",\n \"text\": \"def test_update_project(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c17dc383ee2e366b1a5fc4e1604f9b99\",\n \"score\": \"0.4692427\",\n \"text\": \"def refresh(self, project):\\n self.es.indices.refresh(index=self.index_name(project))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d957caf110468efdc93c17755a223f5\",\n \"score\": \"0.46918374\",\n \"text\": \"def test_rud_document(self):\\n\\n # upload a documnet\\n self.upload_document()\\n\\n # retrieve document PK to test\\n test_document = Document.objects.all().first()\\n\\n # test retrive document with invvalid pk\\n rud_document_url = reverse(\\n 'documents:document',\\n kwargs={'pk': 99}\\n )\\n response = self.client.get(\\n rud_document_url,\\n )\\n self.assertEqual(response.status_code, 404)\\n\\n # test update document with invvalid pk\\n response = self.client.patch(\\n rud_document_url,\\n {\\n 'language': 'af',\\n 'document_type': 'pdf'\\n }\\n )\\n self.assertEqual(response.status_code, 404)\\n\\n # test delete document with invvalid pk\\n response = self.client.delete(\\n rud_document_url\\n )\\n self.assertEqual(response.status_code, 404)\\n\\n # test retrieve document with pk\\n rud_document_url = reverse(\\n 'documents:document',\\n kwargs={'pk': test_document.pk}\\n )\\n response = self.client.get(\\n rud_document_url,\\n )\\n self.assertEqual(response.status_code, 200)\\n\\n # test update document with pk\\n rud_document_url = reverse(\\n 'documents:document',\\n kwargs={'pk': test_document.pk}\\n )\\n response = self.client.patch(\\n rud_document_url,\\n {\\n 'language': 'af',\\n 'document_type': 'pdf'\\n }\\n )\\n test_document.refresh_from_db()\\n self.assertEqual(response.status_code, 200)\\n self.assertEqual(test_document.language.short_name, 'af')\\n self.assertEqual(test_document.document_type.short_name, 'pdf')\\n self.assertEqual(Document.objects.all().count(), 1)\\n\\n # test delete document with pk\\n rud_document_url = reverse(\\n 'documents:document',\\n kwargs={'pk': test_document.pk}\\n )\\n response = self.client.delete(\\n rud_document_url\\n )\\n self.assertEqual(response.status_code, 204)\\n self.assertEqual(Document.objects.all().count(), 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a42d660681fa0dea1386fc66c5c6f7ae\",\n \"score\": \"0.46850002\",\n \"text\": \"def indexDocElement(es_Url, awsauth, docData):\\n try:\\n headers = {\\\"Content-Type\\\": \\\"application/json\\\"}\\n resp = requests.put(es_Url, auth=awsauth,\\n headers=headers, json=docData)\\n print(resp.content)\\n if resp.status_code == 201:\\n logger.info('INFO: Successfully created element into ES')\\n elif resp.status_code == 200:\\n logger.info('INFO: Successfully updated element into ES')\\n else:\\n logger.error(f'FAILURE: Unable to index element {resp.content}')\\n raise\\n except Exception as e:\\n logger.error(f'ERROR: {str(e)}')\\n logger.error(f\\\"ERROR: Unable to index line:{docData['content']}\\\")\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11e4d54117e769ca06d0c90afeab7608\",\n \"score\": \"0.46833104\",\n \"text\": \"def unindex_object(documentId):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a6374eccd3ecd3ac8f9c0c4077d249a\",\n \"score\": \"0.46808988\",\n \"text\": \"async def update_data(doc_id: int, response: IntakeData,\\n db_file: str = tinydb_file):\\n db0 = await get_db(db_file)\\n db0.update(response.dict(), doc_ids=[doc_id])\\n return db0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6ca95cb848f9130b79bfd3e7d693228\",\n \"score\": \"0.4669554\",\n \"text\": \"def uploadDesignDocuments(self,directory):\\n\\n import glob\\n pattern = os.path.join(directory,'*.py')\\n viewFiles = glob.glob(pattern)\\n for viewFile in viewFiles:\\n execfile(viewFile)\\n for view in views:\\n viewID = os.path.join('_design',view)\\n print (\\\"uploading \\\", view, \\\" of \\\", viewFile, \\\" to \\\", self.databaseName)\\n document = self.db.get(viewID)\\n if document:\\n self.db.delete(document)\\n self.db[viewID] = views[view]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c6b0bfb6c8ba313b8b13141fe7abd40\",\n \"score\": \"0.46691608\",\n \"text\": \"def update(self, index, document, **options):\\n\\n index = base._resolve_index(index)\\n\\n index_name = index.get_name()\\n index_doc_type = options.pop('doc_type', index.get_doc_type())\\n\\n id, adapted_document = index.adapt_document(document)\\n\\n if id:\\n options.setdefault(\\\"id\\\", id)\\n\\n self._es.index(index_name, index_doc_type, adapted_document, **options)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e43a3a310e72a471695196fe25c4e8b9\",\n \"score\": \"0.46609166\",\n \"text\": \"def test_delete_project(self):\\n name = {\\\"en\\\": \\\"Test project\\\", \\\"fr\\\": \\\"Projet test\\\"}\\n project = Project.objects.create(\\n name=name,\\n description={\\\"en\\\": [\\\"Test desc\\\"], \\\"fr\\\": [\\\"Test desc\\\"]}\\n )\\n detail_url = reverse('rest:project-detail', kwargs={'pk': project.id})\\n res = self.admin_client.delete(detail_url)\\n self.assertEqual(res.status_code, status.HTTP_204_NO_CONTENT)\\n exists = Project.objects.all().filter(name=name).exists()\\n self.assertFalse(exists)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e611165d35dc8ad0d765ab9c84890b1\",\n \"score\": \"0.4655304\",\n \"text\": \"def update_project(project_id, update_dict):\\n # update_dict = convert_project_object_to_dict(update_dict)\\n # try:\\n Project.objects.get_or_404(id=project_id).update(**update_dict)\\n return (True, \\\"success\\\")\\n # except Exception, e:\\n # return (False, repr(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ec582f4f431c4caf8adbe8f976d8607\",\n \"score\": \"0.46348995\",\n \"text\": \"def project(id):\\n\\t#Call load() in the data layer to load the database and store it's contents in the variable db\\n\\tdb = data.load('data.json')\\n\\t#Call get_project() in the data layer and store the project in the variable project\\n\\tproject = data.get_project(db, id)\\n\\t#If None was returned by get_project():\\n\\t\\t#No project with the specified ID exists, ergo a nonexistent page has been visited, ergo abort with a 404\\n\\tif project is None:\\n\\t\\tabort(404)\\n\\telse:\\n\\t\\t#adding a counter to the project-page.\\n\\t\\tcounter = open(\\\"doc/counter.log\\\", \\\"r+\\\")\\t\\t\\t\\t\\t#opens counter.log\\n\\t\\tcount = 1\\n\\t\\tpid = str(\\\"project \\\")+str(project['project_id'])+\\\"\\\\n\\\"\\n\\t\\tfor line in counter:\\n\\t\\t\\tif pid in line:\\n\\t\\t\\t\\tcount += 1\\n\\t\\tproject['counter'] = count\\n\\t\\tcounter.write(pid)\\n\\t\\tcounter.close()\\n\\t\\t#Render project.html with:\\n\\t\\t\\t#project --> used to display information about the project\\n\\t\\treturn render_template('project.html', project=project)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb39d6df0b7b3498a570ea1bba71feb3\",\n \"score\": \"0.46262908\",\n \"text\": \"def _update_document_by_id(doc_id, body, object_type, *, retry_on_conflict=0, **kwargs):\\n conn = get_conn()\\n for alias in get_active_aliases(conn, object_types=[object_type]):\\n try:\\n conn.update(\\n index=alias,\\n doc_type=GLOBAL_DOC_TYPE,\\n body=body,\\n id=doc_id,\\n params={\\\"retry_on_conflict\\\": retry_on_conflict, **kwargs},\\n )\\n # Our policy for document update-related version conflicts right now is to log them\\n # and allow the app to continue as normal.\\n except ConflictError:\\n log.error(\\n \\\"Update API request resulted in a version conflict (alias: %s, doc id: %s)\\\",\\n alias,\\n doc_id,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"331d110c41a2530b147f583ddccbeb0a\",\n \"score\": \"0.46188155\",\n \"text\": \"def create_opensearch_index(index_name: str):\\n with open(Path(__file__).parent / \\\"pipeline_logs_mapping.json5\\\") as fd:\\n index_mappings = pyjson5.load(fd)\\n res = requests.put(\\n f\\\"{OPENSEARCH_ENDPOINT}/{index_name}\\\",\\n data=json.dumps(index_mappings),\\n headers={\\\"Content-Type\\\": \\\"application/json\\\"},\\n auth=(OPENSEARCH_USERNAME, OPENSEARCH_PASSWORD),\\n )\\n if res.status_code >= 400:\\n logging.error(\\n f'Failed to create opensearch index \\\"{index_name}\\\", '\\n f\\\"server responded with status {res.status_code}\\\"\\n )\\n try:\\n logging.error(res.json())\\n except json.JSONDecodeError:\\n logging.error(res.text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74d8d50b59c45411d8f14763398d3d38\",\n \"score\": \"0.46130043\",\n \"text\": \"def createDoc(self, docInfos):\\n if docInfos['id'] != '':\\n newDocId = self.plominoDatabase.invokeFactory(docInfos['type'], \\n id=docInfos['id'])\\n newDoc = self.plominoDatabase.getDocument(docInfos['id'])\\n else:\\n newDoc = self.plominoDatabase.createDocument()\\n \\n if newDoc is not None:\\n if self.plominoDatabase.getForm(docInfos['form']) is not None:\\n newDoc.setItem('Form', docInfos['form'])\\n\\n # TODO: create a default form if form does not exist ? in a later version\\n # REM: doc is not saved if not associated with a existing form\\n \\n # Set the items of this document\\n for itemInfos in docInfos['items']:\\n newDoc.setItem(itemInfos['name'], itemInfos['value'])\\n # TODO: check the value before set ?\\n #self.createItem(itemInfos, newDoc)\\n\\n # Add the files inserted in the document\\n if docInfos['files'] != []:\\n if not hasattr(newDoc.getForm(), 'imported_files'):\\n fieldId = newDoc.getForm().invokeFactory('PlominoField', \\n id='imported_files',\\n title='Imported Files',\\n FieldType='ATTACHMENT')\\n newDoc.getForm().setFormLayout(newDoc.getForm().getFormLayout() + \\\\\\n '
Imported files: imported_files
')\\n for fileInfos in docInfos['files']:\\n newDoc.setfile(a2b_base64(str(fileInfos['content'])), fileInfos['name'])\\n\\n newDoc.save()\\n\\n else:\\n raise Exception\\n\\n self.plominoDatabase.getIndex().refresh()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5eea1dedb85eb1c7282df2ad603ce7f\",\n \"score\": \"0.46129036\",\n \"text\": \"def upsert_document(doc_id, doc, object_type, *, retry_on_conflict=0, **kwargs):\\n _update_document_by_id(\\n doc_id,\\n {\\\"doc\\\": doc, \\\"doc_as_upsert\\\": True},\\n object_type,\\n retry_on_conflict=retry_on_conflict,\\n **kwargs,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10ab54ba9546b01682521f015050756d\",\n \"score\": \"0.46108896\",\n \"text\": \"def delete_project(uuid):\\n project = Project.query.get(uuid)\\n\\n if project is None:\\n raise NotFound(\\\"The specified project does not exist\\\")\\n\\n experiments = Experiment.query.filter(Experiment.project_id == uuid).all()\\n for experiment in experiments:\\n # remove dependencies\\n operators = db_session.query(Operator).filter(Operator.experiment_id == experiment.uuid).all()\\n for operator in operators:\\n Dependency.query.filter(Dependency.operator_id == operator.uuid).delete()\\n\\n # remove operators\\n Operator.query.filter(Operator.experiment_id == experiment.uuid).delete()\\n\\n Experiment.query.filter(Experiment.project_id == uuid).delete()\\n\\n db_session.delete(project)\\n db_session.commit()\\n\\n prefix = join(\\\"experiments\\\", uuid)\\n remove_objects(prefix=prefix)\\n\\n return {\\\"message\\\": \\\"Project deleted\\\"}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1acfc33f6ce86672083340d2f05e83f7\",\n \"score\": \"0.46102795\",\n \"text\": \"def index_document(body):\\n es.index(\\n index='documents',\\n doc_type='document',\\n body=body\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce2ba6ec3062bc8b00d66042e7c58f95\",\n \"score\": \"0.46075854\",\n \"text\": \"def post(self, request, *args, **kwargs):\\n self.project_slug = self.kwargs.get('project_slug', None)\\n self.project = Project.objects.get(slug=self.project_slug)\\n return super(VersionDeleteView, self).post(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cca9d7d23d55aea633a1d309353db55\",\n \"score\": \"0.46073022\",\n \"text\": \"def add(self, document_or_documents):\\n\\n added_document_ids = []\\n\\n if isinstance(document_or_documents, Document):\\n was_list = False\\n documents = [document_or_documents]\\n else:\\n was_list = True\\n documents = document_or_documents[:]\\n\\n # First-pass validation\\n self._validate_documents(documents)\\n\\n with transaction.atomic(independent=True):\\n for document in documents:\\n record = document._record\\n\\n # We go through the document fields, pull out the values that have been set\\n # then we index them.\\n field_data = {\\n f: getattr(document, document.get_field(f).attname)\\n for f in document.get_fields() if f != \\\"id\\\"\\n }\\n\\n # Generate a database representation of this Document use\\n # the passed ID if there is one\\n record, created = DocumentRecord.objects.update_or_create(\\n pk=document.id,\\n defaults={\\n \\\"index_stats\\\": self.index,\\n \\\"data\\\": field_data\\n }\\n )\\n document.id = record.id\\n document._record = record\\n\\n if created:\\n index_document(self.name, document)\\n added_document_ids.append(record.id)\\n else:\\n # This wipes out any existing document, bumps the revision\\n # and then indexes this one\\n reindex_document(document)\\n\\n return added_document_ids if was_list else (added_document_ids[0] if added_document_ids else 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7df4233a622d8556ec5399e88e8dcaf1\",\n \"score\": \"0.4595747\",\n \"text\": \"def create_document(record):\\n # TODO: Write an iterator over the INDEX_MAP to fetch fields from the record and\\n # return a dictionary representing the document.\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b994723d25f0a197f745292df18fa72c\",\n \"score\": \"0.45953628\",\n \"text\": \"def test_update_project(self):\\n project = Project.objects.create(\\n name={\\\"en\\\": \\\"Test project\\\", \\\"fr\\\": \\\"Projet test\\\"},\\n description={\\\"en\\\": [\\\"Test desc\\\"], \\\"fr\\\": [\\\"Test desc\\\"]}\\n )\\n payload = {\\\"name\\\": {\\\"en\\\": \\\"New name\\\", \\\"fr\\\": \\\"Nouveau nom\\\"}}\\n detail_url = reverse('rest:project-detail', kwargs={'pk': project.id})\\n res = self.admin_client.patch(detail_url, payload, format=\\\"json\\\")\\n self.assertEqual(res.status_code, status.HTTP_200_OK)\\n project.refresh_from_db()\\n self.assertEqual(project.name, payload['name'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b3a5c81140b78c9a4413be72272e6f4\",\n \"score\": \"0.45898458\",\n \"text\": \"def CreateProjects(projRef):\\r\\n\\r\\n print(\\\"Creating projects\\\")\\r\\n # make root directory with permit date\\r\\n if (len(projRef) == 0):\\r\\n print(\\\"No project available\\\")\\r\\n return\\r\\n\\r\\n dt = str(projRef.values()[0].PERMIT_DATE).split(\\\" \\\")[0]\\r\\n project_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), os.path.join(NFOutputPath.OUTPUT, dt))\\r\\n if not os.path.exists(project_root):\\r\\n os.makedirs(project_root)\\r\\n\\r\\n summary = []\\r\\n\\r\\n try:\\r\\n edit = arcpy.da.Editor(NFDataSource.COMMONGIS_CONN)\\r\\n edit.startEditing(False, False)\\r\\n edit.startOperation()\\r\\n editCounter = 0\\r\\n\\r\\n # create one folder for each project ref\\r\\n for key in projRef:\\r\\n arcpy.AddMessage(key)\\r\\n\\r\\n project_folder = os.path.join(project_root, key)\\r\\n\\r\\n # if the project folder already contains plans - skip; this project has been imported before; will not be included in new MaxDia report too\\r\\n if not os.path.exists(project_folder):\\r\\n os.makedirs(project_folder)\\r\\n file_list = []\\r\\n else:\\r\\n file_list = os.listdir(project_folder)\\r\\n\\r\\n pdf_exists = False\\r\\n\\r\\n for f in file_list:\\r\\n\\r\\n if \\\".pdf\\\" in f:\\r\\n arcpy.AddMessage(\\\"PDF in project folder: \\\" + project_folder)\\r\\n pdf_exists = True\\r\\n break\\r\\n if pdf_exists:\\r\\n continue\\r\\n\\r\\n cwdPdf = os.path.join(os.path.dirname(os.path.abspath(__file__)),\\r\\n os.path.join(project_folder, \\\"{0}-{1}.pdf\\\".format(key, NFAnnex.CWD)))\\r\\n\\r\\n UpdateCWD(projRef[key])\\r\\n GenerateCWD(arcpy.mapping.MapDocument(NFTemplate.CWDTEMPLATE), projRef[key], cwdPdf, [], \\\"\\\")\\r\\n\\r\\n wsnPdf = os.path.join(os.path.dirname(os.path.abspath(__file__)),\\r\\n os.path.join(project_folder, \\\"{0}-{1}.pdf\\\".format(key, NFAnnex.WSN)))\\r\\n wsnasset = UpdateWSN(projRef[key])\\r\\n\\r\\n if (len(wsnasset) > 0):\\r\\n # GenerateCWD(arcpy.mapping.MapDocument(NFTemplate.WSNTEMPLATE), projRef[key], wsnPdf, wsnasset, \\\"WSN\\\")\\r\\n print \\\"wsnasset\\\"\\r\\n\\r\\n wrnPdf = os.path.join(os.path.dirname(os.path.abspath(__file__)),\\r\\n os.path.join(project_folder, \\\"{0}-{1}.pdf\\\".format(key, NFAnnex.WRN)))\\r\\n wrnasset = UpdateWRN(projRef[key])\\r\\n\\r\\n if (len(wrnasset) > 0):\\r\\n GenerateCWD(arcpy.mapping.MapDocument(NFTemplate.WRNTEMPLATE), projRef[key], wrnPdf, wrnasset, \\\"WRN\\\")\\r\\n summary.extend(GenerateReport(projRef[key], wrnasset))\\r\\n\\r\\n editCounter = editCounter + 1\\r\\n\\r\\n if (editCounter == 10):\\r\\n print (\\\"intermediate save @ 10\\\")\\r\\n edit.stopOperation()\\r\\n edit.stopEditing(True)\\r\\n\\r\\n edit.startEditing(False, False)\\r\\n edit.startOperation()\\r\\n editCounter = 0\\r\\n\\r\\n edit.stopOperation()\\r\\n edit.stopEditing(True)\\r\\n del edit\\r\\n\\r\\n max_dia_report_name = GenerateMaxDiaReport(summary, dt, projRef)\\r\\n arcpy.AddMessage(\\\"END Creating projects\\\")\\r\\n arcpy.AddMessage(max_dia_report_name)\\r\\n print(\\\"Generated maxdia report\\\")\\r\\n return max_dia_report_name\\r\\n\\r\\n except Exception as err:\\r\\n print err\\r\\n Error_Handler(\\\"Create Projects Failed\\\")\\r\\n Error_Handler(repr(traceback.format_exc()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86d93df5e9bcd0bbd1dbc95aa839899d\",\n \"score\": \"0.45813572\",\n \"text\": \"def insert_into_index(es_object, index_name, record): \\n try:\\n outcome = es_object.index(index=index_name, body=record)\\n print(f\\\"Inserted object into Index '{index_name}'.\\\")\\n print(str(record))\\n except Exception as ex:\\n print('Error in indexing data')\\n print(str(ex))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4a20f89cdf668abaec4a099362659d0\",\n \"score\": \"0.4577445\",\n \"text\": \"def test_get_or_create_project__call():\\n project_name = str(uuid.uuid1())\\n project = synapseclient.Project(name=project_name)\\n returned = synapseclient.Project(name=project_name,\\n id=str(uuid.uuid1()))\\n with patch.object(CREATE_CLS,\\n \\\"_find_by_obj_or_create\\\",\\n return_value=returned) as patch_find_or_create:\\n new_project = CREATE_CLS.get_or_create_project(name=project_name)\\n assert new_project == returned\\n patch_find_or_create.assert_called_once_with(project)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01878f3733f7e6fa22c36b45e3f46ae8\",\n \"score\": \"0.45740703\",\n \"text\": \"def test_create_project_adds_a_project(db: Session) -> None:\\n\\n # there is no project to start with\\n assert db.query(models.Project).count() == 0\\n\\n # create a project\\n project = schemas.ProjectCreate(\\n name=\\\"My Project\\\", directory=\\\"/wherever\\\", command=\\\"whatever\\\"\\n )\\n create_project(db, project)\\n\\n # there is a project now...\\n assert db.query(models.Project).count() == 1\\n\\n # ... and it has the correct content\\n db_project = db.query(models.Project).first()\\n assert db_project.id is not None\\n assert db_project.command == project.command\\n assert db_project.directory == project.directory\\n assert db_project.name == project.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dd10092dad3a1e670dc70a758f1a80d\",\n \"score\": \"0.45728135\",\n \"text\": \"def add_guid_index():\\n catalog = api.portal.get_tool('portal_catalog')\\n if GUID_INDEX_NAME not in catalog.indexes():\\n log.info(\\\"Adding GUID index %r\\\" % GUID_INDEX_NAME)\\n catalog.addIndex(GUID_INDEX_NAME, 'FieldIndex')\\n else:\\n log.info(\\\"GUID index %r already exists\\\" % GUID_INDEX_NAME)\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":793,"cells":{"query_id":{"kind":"string","value":"68e37ec088ae4267c593779e2ace4a03"},"query":{"kind":"string","value":"r\"\"\"The number of graphs in the dataset."},"positive_passages":{"kind":"list like","value":[{"docid":"179a0ee94a6a7e7d1a87da777afe7be5","score":"0.0","text":"def __len__(self): # -> Literal[1]:\n ...","title":""}],"string":"[\n {\n \"docid\": \"179a0ee94a6a7e7d1a87da777afe7be5\",\n \"score\": \"0.0\",\n \"text\": \"def __len__(self): # -> Literal[1]:\\n ...\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"0355f1833468a8bd7a3a10e653f39a43","score":"0.8215049","text":"def number_of_graphs(self):\n return self.get_table_row_count(self.GRAPH_TABLE._name_)","title":""},{"docid":"d5cf06f4008d0a5795c841c5b8657580","score":"0.75362337","text":"def n_datasets(self) -> int:\n n_others = len(self._dcs().keys())\n\n return n_others + 1","title":""},{"docid":"bfee6f97e0581fbcdbcde40548c486ef","score":"0.73872626","text":"def number_of_edges(self):\n ...","title":""},{"docid":"fb0738fdb10f050aab0ef67e1a4adca5","score":"0.73707473","text":"def n_plots(self):\n return 0","title":""},{"docid":"08bad50437dea7ff65c93298e103c51c","score":"0.7311434","text":"def number_of_nodes(self):\n ...","title":""},{"docid":"6d31cacdd1126a7080e038de5ebd6812","score":"0.71918505","text":"def size(self):\n\t\treturn len(self._datasets)","title":""},{"docid":"1d4a94f52e9f13a3c54d0cf45633160c","score":"0.71886915","text":"def number_of_nodes(self):\r\n return len(self.nodes)","title":""},{"docid":"d2591fef6647a6fc549f24fb36a1d180","score":"0.71552324","text":"def number_of_nodes(self):\n return len(self.nodes)","title":""},{"docid":"3558f2eb5a7ab0338cede409267fcf02","score":"0.71441734","text":"def number_of_edges(self):\r\n return len(self.edges)","title":""},{"docid":"0d8b2036bbf102da85429eefe047c8bb","score":"0.7133862","text":"def number_of_nodes(self):\n return self.number_of_vertices()","title":""},{"docid":"b1aa83c24a0ee2ef2a53638baa576fba","score":"0.70328444","text":"def num_of_node(graph):\r\n return len(list(graph.nodes()))","title":""},{"docid":"5dfad0d59845d099bb0f3b0393f7ef0a","score":"0.7019014","text":"def get_number_of_nodes(self):\r\n return len(self.node_Ids)","title":""},{"docid":"76154412818646208517a4ae81add28b","score":"0.6967071","text":"def n_nodes(self):\n return self._G.number_of_nodes()","title":""},{"docid":"df2614d63ee491efd9a8e9666e3a4e4b","score":"0.6942869","text":"def get_number_of_nodes(self):\n return len(self.__adjacency_list)","title":""},{"docid":"6afa6f8cf49e64e4b7e5c061e51ef1ae","score":"0.6938715","text":"def count(self) -> int:\n return self.vis[\"count\"]","title":""},{"docid":"e93592d0020887f7c40a21c6d1cb6144","score":"0.6929193","text":"def getGraphSize(self):\n return self.graphSize","title":""},{"docid":"2edb31256fae3bd25a640fed242c1827","score":"0.692044","text":"def number_of_nodes(self):\r\n return len(self.node)","title":""},{"docid":"04898e55628d19987e50d0a139129b10","score":"0.68733644","text":"def n_data_points(self)->int:\n return self._n_data_points","title":""},{"docid":"04898e55628d19987e50d0a139129b10","score":"0.68733644","text":"def n_data_points(self)->int:\n return self._n_data_points","title":""},{"docid":"4a5be17166b29fc298d961638225e1c0","score":"0.68576306","text":"def number_of_edges(self):\n return sum(1 for _ in self.edges())","title":""},{"docid":"1f728335c8c6b9ff1422511a87bbe86c","score":"0.6832707","text":"def num_edges(self):\n return","title":""},{"docid":"bcc6f50b575f6617d5901ee7b86f07fe","score":"0.6823159","text":"def number_of_nodes(self):\n return sum(1 for _ in self.nodes_iter())","title":""},{"docid":"4e249348a7eb7edd50e8b905bb41dfed","score":"0.68057483","text":"def dataset_image_count(self):\n return self._dataset_image_count","title":""},{"docid":"474979992ba794a05d9cb3e18ca10e96","score":"0.6793165","text":"def n_edges(self) -> int:\n return len(self.edges())","title":""},{"docid":"62ae97345eb9260c4e91c922f20583df","score":"0.67880934","text":"def num_nodes(self):\n return len(self.nodes())","title":""},{"docid":"89ac151061059e2521eaf9256758b63b","score":"0.67822707","text":"def num_nodes(self):\n return len(self.adj_list)","title":""},{"docid":"5c116b37f3519214798b58931e4d1faa","score":"0.6774768","text":"def node_count(self):\n return len(self.nodes)","title":""},{"docid":"08727f4e597c0ab82643a0042d218190","score":"0.6736805","text":"def numNodes(self):\n return len(self.dicN)","title":""},{"docid":"ab03c6856efb9d62a0fac78658e743a5","score":"0.67211145","text":"def number_of_hidden_nodes(self):\r\n return len(self.hidden_nodes)","title":""},{"docid":"c920b53677faaa969bbfeff023116cf0","score":"0.6689106","text":"def number_of_hidden_edges(self):\r\n return len(self.hidden_edges)","title":""},{"docid":"87c4b1315c12da4ea656b9cbb315a03c","score":"0.6685002","text":"def connection_count(self):\n\n connections = 0\n if \"num-connections\" in self.profile:\n connections = self.profile[\"num-connections\"]\n\n return connections","title":""},{"docid":"690e135c7c131dc14231b535172a0282","score":"0.66796535","text":"def nconsiderednodes(self):\n assert self._node_count is not None\n return self._node_count","title":""},{"docid":"3e1aa29ec90d6b77fd0e7c4c31b8563f","score":"0.66766924","text":"def size(self):\n\n return self.n_edges","title":""},{"docid":"e6faad0a0d83c063cfbc933606dce2f7","score":"0.6675746","text":"def number_of_edges(self):\n num_edges = graph_wrapper.number_of_edges(self.graph_ptr)\n\n return num_edges","title":""},{"docid":"cf57ddcd35b62ee350b622ab89f97184","score":"0.66686505","text":"def n_nodes(self) -> int:\n raise NotImplementedError","title":""},{"docid":"cf57ddcd35b62ee350b622ab89f97184","score":"0.66686505","text":"def n_nodes(self) -> int:\n raise NotImplementedError","title":""},{"docid":"936e2734149056a985cf042958e7a1e8","score":"0.66666746","text":"def numNodes(self) -> int:\n return self._call_java(\"numNodes\")","title":""},{"docid":"a4f7605f501cc4a7659341266aecc6d0","score":"0.6652915","text":"def number_of_docs(self) -> int:\n return self._G.number_of_nodes()","title":""},{"docid":"d1efe6a5c532bd8a32dcb9d7931a89fe","score":"0.66516787","text":"def num_edges(self):\n return lib.graph_num_edges(self._obj)","title":""},{"docid":"540eef509583d426ab07af850429427c","score":"0.6622153","text":"def num_nodes(self) -> int:\n return self._c_csc_graph.num_nodes()","title":""},{"docid":"8f56c5b5b9c9b332a52dd9b25e86da94","score":"0.6622133","text":"def _get_number_of_instances(self):\n return self.__number_of_instances","title":""},{"docid":"8f56c5b5b9c9b332a52dd9b25e86da94","score":"0.6622133","text":"def _get_number_of_instances(self):\n return self.__number_of_instances","title":""},{"docid":"d3bbbace7d5e517b7c1f0c9cd2c64b76","score":"0.66186965","text":"def example_count(self):\n return len(self.torch_loader.dataset)","title":""},{"docid":"78f630810ff63c673af6a4e80f02b9fd","score":"0.6618202","text":"def number_of_vertices(self):\n num_vertices = graph_wrapper.number_of_vertices(self.graph_ptr)\n\n return num_vertices","title":""},{"docid":"a3a4abff35b148272092124f43be0f8b","score":"0.6610146","text":"def dataset_count():\n count = 0\n result = toolkit.get_action('package_search')({}, {'rows': 1})\n if result.get('count'):\n count = result.get('count')\n return count","title":""},{"docid":"6574458beda02c6c4c70bbfeed0306cd","score":"0.66042715","text":"def get_point_count(self):\n return len(self.edges)","title":""},{"docid":"49918347258b797a343003e19f1f7cfb","score":"0.6601892","text":"def GetNodeCount(self):\r\n pass","title":""},{"docid":"ed8ffb037c28d99430f82940f7ff1ebe","score":"0.65973693","text":"def num_instances(self):\n return _NvRules.IMetric_num_instances(self)","title":""},{"docid":"d071495a425e30a7937c01a0e1cd00d3","score":"0.6597358","text":"def nnodes(self):\n return len(self.matrix.keys())","title":""},{"docid":"5bb32be2f53661002c27c87273f37b8d","score":"0.65965056","text":"def num_datapoints(self):\n\t\treturn self.predictions.shape[0]","title":""},{"docid":"3464e5965295ae9a8d80778d86c6411c","score":"0.6596318","text":"def count(self):\n return self._label_fdist.N()","title":""},{"docid":"d0b4f79eedd7716a980fec7fd1fcc86c","score":"0.65911674","text":"def size(self):\n return self.nodes.size()","title":""},{"docid":"a0788dee9a35d6dfc6c7c25db7fe1e68","score":"0.65862036","text":"def size(self):\n return self.digr.num_vertices()","title":""},{"docid":"1e40b28ac771634a9df3b7c41ccd5c0c","score":"0.658214","text":"def number_of_nodes(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"number_of_nodes\")","title":""},{"docid":"6b5270f9cba24d292600124909b27116","score":"0.6581056","text":"def plot_rank(self):\n return len(self.plot_shape)","title":""},{"docid":"4748f20ccc2d3768007027e69e5cabb6","score":"0.65771747","text":"def node_count(graph):\n return len(graph.keys())","title":""},{"docid":"958ed92b89a2de823e72cf2d18df1875","score":"0.6570017","text":"def num_nodes(self):\n return len(self._nodes)","title":""},{"docid":"460f42002a43c9de8ba845e635ed3be5","score":"0.6568672","text":"def dataset_size(self):\n return self.dataset.size","title":""},{"docid":"a5956dcf005f5a551ed08f05bb482489","score":"0.6568589","text":"def number_of_paths(self):\n return self.num_paths","title":""},{"docid":"562189beb582fc5db5475ef106ed37f2","score":"0.65611106","text":"def __len__(self) -> int:\n return len(list(self.graph.items(self.uri)))","title":""},{"docid":"d90f41a954dbd4af2a4f891ba47a0b2f","score":"0.65585494","text":"def totalConnections(analyzer):\n return gr.numEdges(analyzer['connections'])","title":""},{"docid":"5b99a28bdd0c7483f89649d3657a11fa","score":"0.6558188","text":"def num_edges(self) -> int:\n return self._c_csc_graph.num_edges()","title":""},{"docid":"0af950419a6e9ba755be627bf4a55852","score":"0.6527257","text":"def __len__(self):\n return self.num_edges","title":""},{"docid":"9edc1dad30b39aefcfd0285b42b8a328","score":"0.65193063","text":"def number_of_monitors(): ###\n return len(monitors())","title":""},{"docid":"d15ec172779f9d6b32207a8a088d435d","score":"0.65185875","text":"def numNodes(self):\n return len(self.G)","title":""},{"docid":"2fc7ddd99e64240ecf316bf6d1594e93","score":"0.65136","text":"def get_frame_count(self):\n\n return len(self.plot_list)","title":""},{"docid":"34cb4e9110db43ea35180523b3ca3d70","score":"0.6512665","text":"def dimensions(self):\n return self._N","title":""},{"docid":"4aee5267e5f73468783933e887a0efdd","score":"0.65110767","text":"def n_records(self) -> int:\n\n n_records = sum([len(data.initial_molecules) for data in self.dataset.values()])\n return n_records","title":""},{"docid":"719d02468234d1aa3c2dec1d8689c58b","score":"0.65079","text":"def getNdim(self):\n try:\n ndim = self.segments.ndim\n except AttributeError:\n ndim = self._ndim\n return ndim","title":""},{"docid":"dddcc96fb145ad82d5cb2cacd1bd7931","score":"0.6505912","text":"def get_num_nodes(self):\n return len([sec for sec in self.config.sections if self.is_node(sec)])","title":""},{"docid":"a6b1e31b262dd6bc1276e39245392cb2","score":"0.6504585","text":"def num_datapoints(self):\n\t\treturn self.pred_probs.shape[0]","title":""},{"docid":"9cd1df339740f4fb4043640e049d656c","score":"0.6499093","text":"def n_scales(self):\n return len(self.scales)","title":""},{"docid":"9cd1df339740f4fb4043640e049d656c","score":"0.6499093","text":"def n_scales(self):\n return len(self.scales)","title":""},{"docid":"df460ebad4eecf19324ad19e3c7f59de","score":"0.64894354","text":"def num_subgraphs(self, subgraph):\n return self.num_subgraphs_by_parameters(\n subgraph.num_verts(),\n subgraph.num_edges(),\n subgraph.automorphism_group().order()\n )","title":""},{"docid":"df52ea3bf5d0498742b099a84939ac22","score":"0.64827776","text":"def __len__(self):\n\t\treturn self._nnodes","title":""},{"docid":"94ce00d1dbe4096809876f0f4d3ae624","score":"0.6477803","text":"def n_nodes(self):\n return len(self.xs)","title":""},{"docid":"df97dcfd539cfe6c227b63c8b4f920c5","score":"0.64721024","text":"def num_per_epoch(self):\n\n return len(self.images)","title":""},{"docid":"52821838253f58673b5a1a49ed8e44ff","score":"0.64649993","text":"def getDimension(self):\n return len(self.components)","title":""},{"docid":"b4a960f137d0cfa646f638b56d43f468","score":"0.64609027","text":"def __len__(self):\n return self._n_nodes","title":""},{"docid":"4a26f0ff4891642c6b0af7c69c3bfb35","score":"0.6458422","text":"def _get_dataset_size(self):","title":""},{"docid":"717dba2db4cbce87a82ddae8a8cd9c7f","score":"0.6457629","text":"def dimension(self):\r\n return self.n","title":""},{"docid":"98d322b0f1269415c560ad757aef43fe","score":"0.64518964","text":"def get_count(self):\n return len(self._bars)","title":""},{"docid":"d413aebbb9a88ec559169b4fa899caac","score":"0.6450254","text":"def dimension(self):\n return self.n+1","title":""},{"docid":"c49bcb084caca4964216fc2fefd6e5af","score":"0.6448196","text":"def nums(self):\n return len(self.data)","title":""},{"docid":"0f345f1295ad50bfc21811d69dea3f0d","score":"0.6447515","text":"def get_count(self) -> int:\n return len(self._bars)","title":""},{"docid":"8bc33751eeb84b817b313f95c0b2585c","score":"0.64436454","text":"def number_of_layers(self):\n return self.len","title":""},{"docid":"4ec5df087a97bd61e2a2f090a708bec3","score":"0.6435416","text":"def get_dataset_size(self, dataset):\n \n i = 0\n for batch, (image, audio, label) in enumerate(dataset):\n assert tf.shape(image)[0] == tf.shape(audio)[0] == tf.shape(label)[0]\n i += tf.shape(image)[0]\n \n return i","title":""},{"docid":"6e5b492619ae4b1681fc7d6804c9e76e","score":"0.642131","text":"def __len__(self):\r\n return len(Dataset(self._id).shape)","title":""},{"docid":"383ab9ad77c69e54df5b8d330e1e757c","score":"0.6417246","text":"def qg8_graph_get_number_chunks(graph: qg8_graph):\n if not isinstance(graph, qg8_graph):\n raise TypeError(\"Argument is not a qg8_graph\")\n return len(graph.chunks)","title":""},{"docid":"640890e72855245f1a2545c881ce98fb","score":"0.640243","text":"def num_node_groups(self) -> int:\n return pulumi.get(self, \"num_node_groups\")","title":""},{"docid":"725a9f9c5e60e3ed272d60d7960d7fb6","score":"0.6393146","text":"def dim(self):\n return len(self)","title":""},{"docid":"c5fbe652b1d9985ecc168146e0cfc634","score":"0.63930035","text":"def num_nodes(self):\n return len(self.myDict.keys())","title":""},{"docid":"4a45541a366dd2442b53dbbeb223cb07","score":"0.6390137","text":"def getNodes(self):\n return self.getParameter(\"NODE_COUNT\")","title":""},{"docid":"ba0f614b73a3c53ccb3754672c4c9142","score":"0.63867104","text":"def N(self):\n return self.inputdata().GetNumberOfPoints()","title":""},{"docid":"54c448bb78dddb0d6d8e65382178e863","score":"0.6385197","text":"def number_of_selfloops(self):\r\n return len(self.selfloop_edges())","title":""},{"docid":"ba62341117af99dc48f054cda06be3a1","score":"0.6381643","text":"def totalNumNodes(self) -> int:\n return self._call_java(\"totalNumNodes\")","title":""},{"docid":"79be64a597a7eff7ef9ee35a4799f694","score":"0.63810533","text":"def num_of_edge(graph):\r\n return len(list(graph.edges()))","title":""},{"docid":"9b618bf04b85a724d0516cc64b89edc5","score":"0.6379052","text":"def dim(self) -> int:\n return self._dim","title":""},{"docid":"9b618bf04b85a724d0516cc64b89edc5","score":"0.6379052","text":"def dim(self) -> int:\n return self._dim","title":""},{"docid":"e1c525285756cd9712e8ee8baecfed8d","score":"0.63684386","text":"def number_of_values(self):","title":""},{"docid":"d31cf30662de8229efe8544a66397df2","score":"0.63648313","text":"def num_frames( self ):\r\n return self.data.num_frames","title":""}],"string":"[\n {\n \"docid\": \"0355f1833468a8bd7a3a10e653f39a43\",\n \"score\": \"0.8215049\",\n \"text\": \"def number_of_graphs(self):\\n return self.get_table_row_count(self.GRAPH_TABLE._name_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5cf06f4008d0a5795c841c5b8657580\",\n \"score\": \"0.75362337\",\n \"text\": \"def n_datasets(self) -> int:\\n n_others = len(self._dcs().keys())\\n\\n return n_others + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfee6f97e0581fbcdbcde40548c486ef\",\n \"score\": \"0.73872626\",\n \"text\": \"def number_of_edges(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb0738fdb10f050aab0ef67e1a4adca5\",\n \"score\": \"0.73707473\",\n \"text\": \"def n_plots(self):\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08bad50437dea7ff65c93298e103c51c\",\n \"score\": \"0.7311434\",\n \"text\": \"def number_of_nodes(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d31cacdd1126a7080e038de5ebd6812\",\n \"score\": \"0.71918505\",\n \"text\": \"def size(self):\\n\\t\\treturn len(self._datasets)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d4a94f52e9f13a3c54d0cf45633160c\",\n \"score\": \"0.71886915\",\n \"text\": \"def number_of_nodes(self):\\r\\n return len(self.nodes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2591fef6647a6fc549f24fb36a1d180\",\n \"score\": \"0.71552324\",\n \"text\": \"def number_of_nodes(self):\\n return len(self.nodes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3558f2eb5a7ab0338cede409267fcf02\",\n \"score\": \"0.71441734\",\n \"text\": \"def number_of_edges(self):\\r\\n return len(self.edges)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d8b2036bbf102da85429eefe047c8bb\",\n \"score\": \"0.7133862\",\n \"text\": \"def number_of_nodes(self):\\n return self.number_of_vertices()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1aa83c24a0ee2ef2a53638baa576fba\",\n \"score\": \"0.70328444\",\n \"text\": \"def num_of_node(graph):\\r\\n return len(list(graph.nodes()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dfad0d59845d099bb0f3b0393f7ef0a\",\n \"score\": \"0.7019014\",\n \"text\": \"def get_number_of_nodes(self):\\r\\n return len(self.node_Ids)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76154412818646208517a4ae81add28b\",\n \"score\": \"0.6967071\",\n \"text\": \"def n_nodes(self):\\n return self._G.number_of_nodes()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df2614d63ee491efd9a8e9666e3a4e4b\",\n \"score\": \"0.6942869\",\n \"text\": \"def get_number_of_nodes(self):\\n return len(self.__adjacency_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6afa6f8cf49e64e4b7e5c061e51ef1ae\",\n \"score\": \"0.6938715\",\n \"text\": \"def count(self) -> int:\\n return self.vis[\\\"count\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e93592d0020887f7c40a21c6d1cb6144\",\n \"score\": \"0.6929193\",\n \"text\": \"def getGraphSize(self):\\n return self.graphSize\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2edb31256fae3bd25a640fed242c1827\",\n \"score\": \"0.692044\",\n \"text\": \"def number_of_nodes(self):\\r\\n return len(self.node)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04898e55628d19987e50d0a139129b10\",\n \"score\": \"0.68733644\",\n \"text\": \"def n_data_points(self)->int:\\n return self._n_data_points\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04898e55628d19987e50d0a139129b10\",\n \"score\": \"0.68733644\",\n \"text\": \"def n_data_points(self)->int:\\n return self._n_data_points\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a5be17166b29fc298d961638225e1c0\",\n \"score\": \"0.68576306\",\n \"text\": \"def number_of_edges(self):\\n return sum(1 for _ in self.edges())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f728335c8c6b9ff1422511a87bbe86c\",\n \"score\": \"0.6832707\",\n \"text\": \"def num_edges(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcc6f50b575f6617d5901ee7b86f07fe\",\n \"score\": \"0.6823159\",\n \"text\": \"def number_of_nodes(self):\\n return sum(1 for _ in self.nodes_iter())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e249348a7eb7edd50e8b905bb41dfed\",\n \"score\": \"0.68057483\",\n \"text\": \"def dataset_image_count(self):\\n return self._dataset_image_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"474979992ba794a05d9cb3e18ca10e96\",\n \"score\": \"0.6793165\",\n \"text\": \"def n_edges(self) -> int:\\n return len(self.edges())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62ae97345eb9260c4e91c922f20583df\",\n \"score\": \"0.67880934\",\n \"text\": \"def num_nodes(self):\\n return len(self.nodes())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89ac151061059e2521eaf9256758b63b\",\n \"score\": \"0.67822707\",\n \"text\": \"def num_nodes(self):\\n return len(self.adj_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c116b37f3519214798b58931e4d1faa\",\n \"score\": \"0.6774768\",\n \"text\": \"def node_count(self):\\n return len(self.nodes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08727f4e597c0ab82643a0042d218190\",\n \"score\": \"0.6736805\",\n \"text\": \"def numNodes(self):\\n return len(self.dicN)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab03c6856efb9d62a0fac78658e743a5\",\n \"score\": \"0.67211145\",\n \"text\": \"def number_of_hidden_nodes(self):\\r\\n return len(self.hidden_nodes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c920b53677faaa969bbfeff023116cf0\",\n \"score\": \"0.6689106\",\n \"text\": \"def number_of_hidden_edges(self):\\r\\n return len(self.hidden_edges)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87c4b1315c12da4ea656b9cbb315a03c\",\n \"score\": \"0.6685002\",\n \"text\": \"def connection_count(self):\\n\\n connections = 0\\n if \\\"num-connections\\\" in self.profile:\\n connections = self.profile[\\\"num-connections\\\"]\\n\\n return connections\",\n \"title\": \"\"\n },\n {\n \"docid\": \"690e135c7c131dc14231b535172a0282\",\n \"score\": \"0.66796535\",\n \"text\": \"def nconsiderednodes(self):\\n assert self._node_count is not None\\n return self._node_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e1aa29ec90d6b77fd0e7c4c31b8563f\",\n \"score\": \"0.66766924\",\n \"text\": \"def size(self):\\n\\n return self.n_edges\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6faad0a0d83c063cfbc933606dce2f7\",\n \"score\": \"0.6675746\",\n \"text\": \"def number_of_edges(self):\\n num_edges = graph_wrapper.number_of_edges(self.graph_ptr)\\n\\n return num_edges\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf57ddcd35b62ee350b622ab89f97184\",\n \"score\": \"0.66686505\",\n \"text\": \"def n_nodes(self) -> int:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf57ddcd35b62ee350b622ab89f97184\",\n \"score\": \"0.66686505\",\n \"text\": \"def n_nodes(self) -> int:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"936e2734149056a985cf042958e7a1e8\",\n \"score\": \"0.66666746\",\n \"text\": \"def numNodes(self) -> int:\\n return self._call_java(\\\"numNodes\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4f7605f501cc4a7659341266aecc6d0\",\n \"score\": \"0.6652915\",\n \"text\": \"def number_of_docs(self) -> int:\\n return self._G.number_of_nodes()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1efe6a5c532bd8a32dcb9d7931a89fe\",\n \"score\": \"0.66516787\",\n \"text\": \"def num_edges(self):\\n return lib.graph_num_edges(self._obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"540eef509583d426ab07af850429427c\",\n \"score\": \"0.6622153\",\n \"text\": \"def num_nodes(self) -> int:\\n return self._c_csc_graph.num_nodes()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f56c5b5b9c9b332a52dd9b25e86da94\",\n \"score\": \"0.6622133\",\n \"text\": \"def _get_number_of_instances(self):\\n return self.__number_of_instances\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f56c5b5b9c9b332a52dd9b25e86da94\",\n \"score\": \"0.6622133\",\n \"text\": \"def _get_number_of_instances(self):\\n return self.__number_of_instances\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3bbbace7d5e517b7c1f0c9cd2c64b76\",\n \"score\": \"0.66186965\",\n \"text\": \"def example_count(self):\\n return len(self.torch_loader.dataset)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78f630810ff63c673af6a4e80f02b9fd\",\n \"score\": \"0.6618202\",\n \"text\": \"def number_of_vertices(self):\\n num_vertices = graph_wrapper.number_of_vertices(self.graph_ptr)\\n\\n return num_vertices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3a4abff35b148272092124f43be0f8b\",\n \"score\": \"0.6610146\",\n \"text\": \"def dataset_count():\\n count = 0\\n result = toolkit.get_action('package_search')({}, {'rows': 1})\\n if result.get('count'):\\n count = result.get('count')\\n return count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6574458beda02c6c4c70bbfeed0306cd\",\n \"score\": \"0.66042715\",\n \"text\": \"def get_point_count(self):\\n return len(self.edges)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49918347258b797a343003e19f1f7cfb\",\n \"score\": \"0.6601892\",\n \"text\": \"def GetNodeCount(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed8ffb037c28d99430f82940f7ff1ebe\",\n \"score\": \"0.65973693\",\n \"text\": \"def num_instances(self):\\n return _NvRules.IMetric_num_instances(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d071495a425e30a7937c01a0e1cd00d3\",\n \"score\": \"0.6597358\",\n \"text\": \"def nnodes(self):\\n return len(self.matrix.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bb32be2f53661002c27c87273f37b8d\",\n \"score\": \"0.65965056\",\n \"text\": \"def num_datapoints(self):\\n\\t\\treturn self.predictions.shape[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3464e5965295ae9a8d80778d86c6411c\",\n \"score\": \"0.6596318\",\n \"text\": \"def count(self):\\n return self._label_fdist.N()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0b4f79eedd7716a980fec7fd1fcc86c\",\n \"score\": \"0.65911674\",\n \"text\": \"def size(self):\\n return self.nodes.size()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0788dee9a35d6dfc6c7c25db7fe1e68\",\n \"score\": \"0.65862036\",\n \"text\": \"def size(self):\\n return self.digr.num_vertices()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e40b28ac771634a9df3b7c41ccd5c0c\",\n \"score\": \"0.658214\",\n \"text\": \"def number_of_nodes(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"number_of_nodes\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b5270f9cba24d292600124909b27116\",\n \"score\": \"0.6581056\",\n \"text\": \"def plot_rank(self):\\n return len(self.plot_shape)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4748f20ccc2d3768007027e69e5cabb6\",\n \"score\": \"0.65771747\",\n \"text\": \"def node_count(graph):\\n return len(graph.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"958ed92b89a2de823e72cf2d18df1875\",\n \"score\": \"0.6570017\",\n \"text\": \"def num_nodes(self):\\n return len(self._nodes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"460f42002a43c9de8ba845e635ed3be5\",\n \"score\": \"0.6568672\",\n \"text\": \"def dataset_size(self):\\n return self.dataset.size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5956dcf005f5a551ed08f05bb482489\",\n \"score\": \"0.6568589\",\n \"text\": \"def number_of_paths(self):\\n return self.num_paths\",\n \"title\": \"\"\n },\n {\n \"docid\": \"562189beb582fc5db5475ef106ed37f2\",\n \"score\": \"0.65611106\",\n \"text\": \"def __len__(self) -> int:\\n return len(list(self.graph.items(self.uri)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d90f41a954dbd4af2a4f891ba47a0b2f\",\n \"score\": \"0.65585494\",\n \"text\": \"def totalConnections(analyzer):\\n return gr.numEdges(analyzer['connections'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b99a28bdd0c7483f89649d3657a11fa\",\n \"score\": \"0.6558188\",\n \"text\": \"def num_edges(self) -> int:\\n return self._c_csc_graph.num_edges()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0af950419a6e9ba755be627bf4a55852\",\n \"score\": \"0.6527257\",\n \"text\": \"def __len__(self):\\n return self.num_edges\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9edc1dad30b39aefcfd0285b42b8a328\",\n \"score\": \"0.65193063\",\n \"text\": \"def number_of_monitors(): ###\\n return len(monitors())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d15ec172779f9d6b32207a8a088d435d\",\n \"score\": \"0.65185875\",\n \"text\": \"def numNodes(self):\\n return len(self.G)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc7ddd99e64240ecf316bf6d1594e93\",\n \"score\": \"0.65136\",\n \"text\": \"def get_frame_count(self):\\n\\n return len(self.plot_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34cb4e9110db43ea35180523b3ca3d70\",\n \"score\": \"0.6512665\",\n \"text\": \"def dimensions(self):\\n return self._N\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4aee5267e5f73468783933e887a0efdd\",\n \"score\": \"0.65110767\",\n \"text\": \"def n_records(self) -> int:\\n\\n n_records = sum([len(data.initial_molecules) for data in self.dataset.values()])\\n return n_records\",\n \"title\": \"\"\n },\n {\n \"docid\": \"719d02468234d1aa3c2dec1d8689c58b\",\n \"score\": \"0.65079\",\n \"text\": \"def getNdim(self):\\n try:\\n ndim = self.segments.ndim\\n except AttributeError:\\n ndim = self._ndim\\n return ndim\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dddcc96fb145ad82d5cb2cacd1bd7931\",\n \"score\": \"0.6505912\",\n \"text\": \"def get_num_nodes(self):\\n return len([sec for sec in self.config.sections if self.is_node(sec)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6b1e31b262dd6bc1276e39245392cb2\",\n \"score\": \"0.6504585\",\n \"text\": \"def num_datapoints(self):\\n\\t\\treturn self.pred_probs.shape[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cd1df339740f4fb4043640e049d656c\",\n \"score\": \"0.6499093\",\n \"text\": \"def n_scales(self):\\n return len(self.scales)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cd1df339740f4fb4043640e049d656c\",\n \"score\": \"0.6499093\",\n \"text\": \"def n_scales(self):\\n return len(self.scales)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df460ebad4eecf19324ad19e3c7f59de\",\n \"score\": \"0.64894354\",\n \"text\": \"def num_subgraphs(self, subgraph):\\n return self.num_subgraphs_by_parameters(\\n subgraph.num_verts(),\\n subgraph.num_edges(),\\n subgraph.automorphism_group().order()\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df52ea3bf5d0498742b099a84939ac22\",\n \"score\": \"0.64827776\",\n \"text\": \"def __len__(self):\\n\\t\\treturn self._nnodes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94ce00d1dbe4096809876f0f4d3ae624\",\n \"score\": \"0.6477803\",\n \"text\": \"def n_nodes(self):\\n return len(self.xs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df97dcfd539cfe6c227b63c8b4f920c5\",\n \"score\": \"0.64721024\",\n \"text\": \"def num_per_epoch(self):\\n\\n return len(self.images)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52821838253f58673b5a1a49ed8e44ff\",\n \"score\": \"0.64649993\",\n \"text\": \"def getDimension(self):\\n return len(self.components)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4a960f137d0cfa646f638b56d43f468\",\n \"score\": \"0.64609027\",\n \"text\": \"def __len__(self):\\n return self._n_nodes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a26f0ff4891642c6b0af7c69c3bfb35\",\n \"score\": \"0.6458422\",\n \"text\": \"def _get_dataset_size(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"717dba2db4cbce87a82ddae8a8cd9c7f\",\n \"score\": \"0.6457629\",\n \"text\": \"def dimension(self):\\r\\n return self.n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98d322b0f1269415c560ad757aef43fe\",\n \"score\": \"0.64518964\",\n \"text\": \"def get_count(self):\\n return len(self._bars)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d413aebbb9a88ec559169b4fa899caac\",\n \"score\": \"0.6450254\",\n \"text\": \"def dimension(self):\\n return self.n+1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c49bcb084caca4964216fc2fefd6e5af\",\n \"score\": \"0.6448196\",\n \"text\": \"def nums(self):\\n return len(self.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f345f1295ad50bfc21811d69dea3f0d\",\n \"score\": \"0.6447515\",\n \"text\": \"def get_count(self) -> int:\\n return len(self._bars)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bc33751eeb84b817b313f95c0b2585c\",\n \"score\": \"0.64436454\",\n \"text\": \"def number_of_layers(self):\\n return self.len\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ec5df087a97bd61e2a2f090a708bec3\",\n \"score\": \"0.6435416\",\n \"text\": \"def get_dataset_size(self, dataset):\\n \\n i = 0\\n for batch, (image, audio, label) in enumerate(dataset):\\n assert tf.shape(image)[0] == tf.shape(audio)[0] == tf.shape(label)[0]\\n i += tf.shape(image)[0]\\n \\n return i\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e5b492619ae4b1681fc7d6804c9e76e\",\n \"score\": \"0.642131\",\n \"text\": \"def __len__(self):\\r\\n return len(Dataset(self._id).shape)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"383ab9ad77c69e54df5b8d330e1e757c\",\n \"score\": \"0.6417246\",\n \"text\": \"def qg8_graph_get_number_chunks(graph: qg8_graph):\\n if not isinstance(graph, qg8_graph):\\n raise TypeError(\\\"Argument is not a qg8_graph\\\")\\n return len(graph.chunks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"640890e72855245f1a2545c881ce98fb\",\n \"score\": \"0.640243\",\n \"text\": \"def num_node_groups(self) -> int:\\n return pulumi.get(self, \\\"num_node_groups\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"725a9f9c5e60e3ed272d60d7960d7fb6\",\n \"score\": \"0.6393146\",\n \"text\": \"def dim(self):\\n return len(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5fbe652b1d9985ecc168146e0cfc634\",\n \"score\": \"0.63930035\",\n \"text\": \"def num_nodes(self):\\n return len(self.myDict.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a45541a366dd2442b53dbbeb223cb07\",\n \"score\": \"0.6390137\",\n \"text\": \"def getNodes(self):\\n return self.getParameter(\\\"NODE_COUNT\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba0f614b73a3c53ccb3754672c4c9142\",\n \"score\": \"0.63867104\",\n \"text\": \"def N(self):\\n return self.inputdata().GetNumberOfPoints()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54c448bb78dddb0d6d8e65382178e863\",\n \"score\": \"0.6385197\",\n \"text\": \"def number_of_selfloops(self):\\r\\n return len(self.selfloop_edges())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba62341117af99dc48f054cda06be3a1\",\n \"score\": \"0.6381643\",\n \"text\": \"def totalNumNodes(self) -> int:\\n return self._call_java(\\\"totalNumNodes\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79be64a597a7eff7ef9ee35a4799f694\",\n \"score\": \"0.63810533\",\n \"text\": \"def num_of_edge(graph):\\r\\n return len(list(graph.edges()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b618bf04b85a724d0516cc64b89edc5\",\n \"score\": \"0.6379052\",\n \"text\": \"def dim(self) -> int:\\n return self._dim\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b618bf04b85a724d0516cc64b89edc5\",\n \"score\": \"0.6379052\",\n \"text\": \"def dim(self) -> int:\\n return self._dim\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1c525285756cd9712e8ee8baecfed8d\",\n \"score\": \"0.63684386\",\n \"text\": \"def number_of_values(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d31cf30662de8229efe8544a66397df2\",\n \"score\": \"0.63648313\",\n \"text\": \"def num_frames( self ):\\r\\n return self.data.num_frames\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":794,"cells":{"query_id":{"kind":"string","value":"e436a651273cc71240d278e3bae026c5"},"query":{"kind":"string","value":"Show the given variable on the plot. Undoes `ignore_variable`. If a dataframe name is specified, all of its columns will be shown."},"positive_passages":{"kind":"list like","value":[{"docid":"13c4e6f3f61e09eaa750fd207f32f767","score":"0.4953621","text":"def show_variable(self, toast: Toast, var_name: str) -> None:\n try:\n return self.active_view.show_variable(toast, var_name)\n except NotImplementedError:\n toast.show(f\"{self._active} does not implement showing variables\", ToastType.warning)","title":""}],"string":"[\n {\n \"docid\": \"13c4e6f3f61e09eaa750fd207f32f767\",\n \"score\": \"0.4953621\",\n \"text\": \"def show_variable(self, toast: Toast, var_name: str) -> None:\\n try:\\n return self.active_view.show_variable(toast, var_name)\\n except NotImplementedError:\\n toast.show(f\\\"{self._active} does not implement showing variables\\\", ToastType.warning)\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"f3416a3c42880a57556602bd26db1797","score":"0.57033026","text":"def ignore_variable(self, toast: Toast, var_name: str) -> None:\n try:\n return self.active_view.ignore_variable(toast, var_name)\n except NotImplementedError:\n toast.show(f\"{self._active} does not implement ignoring variables\", ToastType.warning)","title":""},{"docid":"a30be2073bb4ee8b98885fcb1f16c1f5","score":"0.5620072","text":"def plot_proper(df: pd.DataFrame, xax: str, yax: str, var: str, title: str) -> None:\n pd.options.plotting.backend = \"plotly\"\n fig = df.plot(title=title, template=\"simple_white\",\n labels=dict(index=xax, value=yax, variable=var))\n fig.update_layout(\n xaxis_title=xax,\n yaxis_title=yax,\n )\n fig.show()","title":""},{"docid":"0372b371887346e02e57dac3f55b655a","score":"0.545876","text":"def ignore_variable(self, toast: Toast, var_name: str) -> None:\n raise NotImplementedError","title":""},{"docid":"b21493415e595924fcbbdc5510e9902e","score":"0.51881444","text":"def show(*args, block=True, **kwargs):\n # Remove reserved rewords\n try:\n kwargs.pop('block')\n except:\n pass\n # Get the variable names in the scope show() was called from\n callers_local_vars = inspect.currentframe().f_back.f_locals.items()\n\n # Make a dictionary of the DataFrames from the position args and get their variable names using inspect\n dataframes = {}\n for i, df_object in enumerate(args):\n df_name = 'untitled' + str(i + 1)\n\n for var_name, var_val in callers_local_vars:\n if var_val is df_object:\n df_name = var_name\n\n dataframes[df_name] = df_object\n\n # Add the dictionary of positional args to the kwargs\n if (any([key in kwargs.keys() for key in dataframes.keys()])):\n print(\"Warning! Duplicate DataFrame names were given, duplicates were ignored.\")\n kwargs = {**kwargs, **dataframes}\n\n pandas_gui = PandasGUI(**kwargs)\n\n if block:\n pandas_gui.app.exec_()","title":""},{"docid":"a51ef5db5e8ccb230c8b182b1588e161","score":"0.51808095","text":"def no_detour_boxplot(df,country,save=False,fig=None, ax=None,**kwargs):\n df = df.loc[df['country'] == country]\n\n if (fig == None and ax == None): # if No axes and no figure is provided\n fig, ax = plt.subplots(figsize=(12, 6))\n\n df.boxplot(by='AoI combinations', column='no detour', grid=False, ax=ax,**kwargs)\n ax.set_xlabel(\"Number of combinations of flood events (AoI)\")\n ax.set_ylabel(\"% No detour\")\n ax.set_title(\"% routes between NUTS-3 regions in {} without detour\".format(country))\n\n #Todo (possible): give fig ax as args; enable saving possiblity\n\n if save: # TODO REPLACE ALL INSTANCES OF THIS PART OF CODE WITH A SPECIAL FUNCTION\n save_figs = load_config(config_file)['paths']['output_images'] / 'no_detour_boxplot'\n if not save_figs.exists(): save_figs.mkdir()\n filename = \"noDT_boxplot_{}.png\".format(country)\n fig.savefig(save_figs / filename)\n\n return fig,ax","title":""},{"docid":"53a5704bd479ea2ddbc6c7a3c6679479","score":"0.51140994","text":"def display_data_frame(data_frame):\n\n with pd.option_context('display.max_rows', None, 'display.max_columns', None): # more options can be specified also\n print(data_frame)","title":""},{"docid":"cf1e92a529a23ea32701ac6ec9e46e36","score":"0.5068718","text":"def plot_log(df, var) -> None:\n\n df = df.copy()\n df[var] = np.log(df[var] + 1)\n sns.displot(df, x=var)","title":""},{"docid":"0af0d61f97e9c1df8737454662caad4a","score":"0.5064501","text":"def plot_continuous(df, var) -> None:\n\n df = df.copy()\n df['Income'] = df['Income'].astype('category')\n var_name = var\n if var in config.SKEWED_NUMERIC_VARS:\n df[var] = df[var]\n var_name = 'Log of ' + var_name\n ax = sns.boxplot(x='Income', y=var, data=df)\n ax.set(ylabel=var_name, title=f\"Effect of {var} on Income\")\n plt.show()","title":""},{"docid":"005efd6f5fe0c2f341addce0816a3c8e","score":"0.50505435","text":"def visualize_continuous_variables(df, label, method={'type': 'histogram', 'bins': 20}, outlier='on'):\n # create vector of the variable of interest\n v = df[label]\n # define mean and standard deviation\n m = v.mean()\n s = v.std()\n # prep the figure\n fig, ax = plt.subplots(1, 2, figsize=(14, 4))\n ax[0].set_title('Distribution of ' + label)\n ax[1].set_title('Tip % by ' + label)\n if outlier == 'off': # remove outliers accordingly and update titles\n v = v[(v - m) <= 3 * s]\n ax[0].set_title('Distribution of ' + label + '(no outliers)')\n ax[1].set_title('Tip % by ' + label + '(no outliers)')\n if method['type'] == 'histogram': # plot the histogram\n v.hist(bins=method['bins'], ax=ax[0])\n if method['type'] == 'boxplot': # plot the box plot\n df.loc[v.index].boxplot(label, ax=ax[0])\n ax[1].plot(v, df.loc[v.index].tip_percentage, '.', alpha=0.4)\n ax[0].set_xlabel(label)\n ax[1].set_xlabel(label)\n ax[0].set_ylabel('Count')\n ax[1].set_ylabel('Tip (%)')","title":""},{"docid":"a3c79f8c6106edcd31610020abb8ff8b","score":"0.5001463","text":"def plot_continuous_column(df, target_variable, column_name, fig_size=(9, 12)):\n f, (ax1, ax2) = plt.subplots(2, figsize=fig_size)\n tmp_df = df.dropna()\n sns.distplot(tmp_df[column_name], ax=ax1)\n sns.boxplot(x=target_variable, y=column_name, data=tmp_df, ax=ax2)","title":""},{"docid":"db0fbaeaed5291893aca4beb006c0d75","score":"0.49557826","text":"def set_df_violin_plot(data, header):\n df = records_to_df(data)\n try:\n df[['user', 'condition', 'block', 'task']] = df[['user', 'condition', 'block', 'task']].astype('category')\n except KeyError:\n col_names = [c['id'] for c in header]\n df = pd.DataFrame(columns=col_names)\n df.rename(columns={'df1 mean': 'df1', 'df2 mean': 'df2'}, inplace=True)\n fig = plotting.generate_violin_figure(df, columns=['df1', 'df2'], ytitle='Mean', legend_title=\"DOF\")\n return fig","title":""},{"docid":"e2c885c87d0ff416a36c8e524bf40d4a","score":"0.49128348","text":"def plot_var(var='positiveIncrease',\n state='NY'):\n assert type(var) == str, \"Expected string as the variable name\"\n assert type(state) == str, \"Expected string as the state name\"\n\n y = df[df['state'] == state][var]\n x = df[df['state'] == state]['date']\n plt.figure(figsize=(12, 4))\n plt.title(\"Plot of \\\"{}\\\" for {}\".format(var, state), fontsize=18)\n plt.bar(x=x, height=y, edgecolor='k', color='orange')\n plt.grid(True)\n plt.xticks(fontsize=14, rotation=45)\n plt.yticks(fontsize=14)\n plt.show()","title":""},{"docid":"5c80fdeda37d80a6aae7f35d9019dc81","score":"0.48900345","text":"def show_labels(self, var, datasets, text_key):\n\t\tif datasets == ['all']: datasets = self.ds_names\n\t\tds = [self[datasets]] if len(datasets)==1 else self[datasets]\n\t\talias = self._get_alias(datasets)\n\t\ttk = text_key.split('~')\n\t\tetk = tk[1].split()[0] if len(tk) > 1 else None\n\t\ttk = tk[0]\n\t\tall_df = []\n\t\tfor v in var:\n\t\t\tif not all(v in d for d in ds): continue\n\t\t\ttexts = [d.text(v, False, tk, etk) if v in d else None\n\t\t\t\t\t for d in ds]\n\t\t\tindex = pd.MultiIndex.from_tuples([(v, a) for a in alias])\n\t\t\tdf = pd.DataFrame({text_key: texts}, index=index)\n\t\t\tall_df.append(df)\n\t\tif not all_df:\n\t\t\tprint('No variables to show.')\n\t\telse:\n\t\t\treturn pd.concat(all_df, axis=0)","title":""},{"docid":"7f303d01580491b054b8ee02bf003a5d","score":"0.48365968","text":"def plot(ds: DatasetLike.TYPE,\n var: VarName.TYPE,\n indexers: DictLike.TYPE = None,\n title: str = None,\n properties: DictLike.TYPE = None,\n file: str = None) -> Figure:\n ds = DatasetLike.convert(ds)\n\n var_name = VarName.convert(var)\n if not var_name:\n raise ValidationError(\"Missing name for 'var'\")\n var = ds[var_name]\n\n indexers = DictLike.convert(indexers)\n properties = DictLike.convert(properties) or {}\n\n figure = plt.figure()\n ax = figure.add_subplot(111)\n\n var_data = get_var_data(var, indexers)\n var_data.plot(ax=ax, **properties)\n\n if title:\n ax.set_title(title)\n\n figure.tight_layout()\n\n if file:\n figure.savefig(file)\n\n return figure if not in_notebook() else None","title":""},{"docid":"147a7cca10cae4658a76af694603d9ef","score":"0.47928968","text":"def removeVariable(self, *args):\n return _libsedml.SedDataGenerator_removeVariable(self, *args)","title":""},{"docid":"739695fe3ea743b4e0a8fb833a68fa2c","score":"0.4776736","text":"def plot_discrete(df, var) -> None:\n\n temp = df[['Income', var]].groupby(var).mean().reset_index()\n axis = sns.barplot(x=var, y='Income', data=temp)\n axis.set(ylabel=\"Probability of earning over 50K\")\n plt.title(f\"Effect of {var} on Income\")\n if var == 'weeks worked in year':\n plt.title(f\"Effect of Months worked in year on Income\")\n axis.set(xlabel=\"Months worked in year\")\n plt.show()","title":""},{"docid":"4ce3d4781b9c8774a39083c0bd03c66a","score":"0.4768098","text":"def plot_missing(self, axis = 1, show=True, **figure_kwargs):\n if axis == 1:\n if self.record_missing_cols is None:\n raise NotImplementedError(\"Columns missing values have not been calculated. Run `find_missing with axis = 1`\")\n elif axis == 0:\n if self.record_missing_rows is None:\n raise NotImplementedError(\"Rows missing values have not been calculated. Run `find_missing with axis = 0`\")\n self.reset_plot()\n # Histogram of missing values\n plt.style.use('seaborn-white')\n plt.figure(figsize = (7, 5))\n if axis == 1:\n plt.hist(self.missing_stats_cols['missing_fraction'], bins = np.linspace(0, 1, 11), edgecolor = 'k', linewidth = 0.5)\n elif axis == 0:\n plt.hist(self.missing_stats_rows['missing_fraction'], bins = np.linspace(0, 1, 11), edgecolor = 'k', linewidth = 0.5)\n plt.xticks(np.linspace(0, 1, 11))\n plt.xlabel('Missing Fraction', size = 14)\n plt.ylabel('Count of Features', size = 14)\n plt.title(\"Fraction of Missing Values Histogram\", size = 16)\n missing_plot = self.prepro_dir + '/missing.png'\n plt.savefig(missing_plot, dpi=300)\n if self.isDisplay == False:\n plt.close()","title":""},{"docid":"f81b4edbff3dfdaaacd61d1269c7a70d","score":"0.47633633","text":"def displayDataFrame(dataFrame):\n pd.set_option('display.max_rows', dataFrame.shape[0] + 1)\n pd.set_option('display.max_columns', dataFrame.shape[1] + 1)\n print(dataFrame)\n pd.reset_option('display.max_rows')\n pd.reset_option('display.max_columns')","title":""},{"docid":"d6b28d65ea32c34efacc762f1e62c65c","score":"0.4750205","text":"def removeVariable(self, *args):\n return _libsedml.SedComputeChange_removeVariable(self, *args)","title":""},{"docid":"c57f8fcc11be295aa3a4e5d2127121d3","score":"0.47035733","text":"def plot_graph(dfs,\n variables_to_plot,\n titles,\n ylim=None,\n xlim=None,\n save=None,\n eventname=None,\n show=True):\n answer = {} \n custom_lim = np.ndarray((2,1))\n def printspanselector(xmin, xmax):\n if isinstance(xmin, np.datetime64):\n xmin = date2num(xmin)\n xmax = date2num(xmax)\n\n ind = np.logical_and(date2num(df.index) >= xmin,\n date2num(df.index) <= xmax)\n axs[-1].plot(df.index[ind],\n df[v][ind],\n 'r+',\n gid='selected')\n if 'delete' not in answer:\n answer['delete'] = []\n answer['delete'].append([xmin, xmax])\n\n def onclick(event, ax):\n # Only clicks inside this axis are valid.\n try: # use try/except in case we are not using Qt backend\n zooming_panning = ( fig.canvas.cursor().shape() != 0 ) # 0 is the arrow, which means we are not zooming or panning.\n except:\n zooming_panning = False\n if zooming_panning: \n return\n\n if eventname == 'choose':\n plt.close('all')\n answer['axes'] = event.inaxes.get_title()\n \n \n plt.close('all')\n fig = plt.figure(figsize = (11.69*(2/3), 8.27/2))\n gs1 = gridspec.GridSpec(len(dfs), 1)\n axs=[]\n for n, df in enumerate(dfs):\n if n == 0:\n axs.append(fig.add_subplot(gs1[n],) )\n else:\n axs.append(fig.add_subplot(gs1[n], sharex=axs[0]) )\n\n plt.title(titles[n])\n\n for i,v in enumerate(variables_to_plot):\n if v in df:\n if not check_timeseries(df[v].values):\n typ = '-'\n else:\n typ = '+'\n\n axs[-1].plot(df.index,\n df[v],\n typ,\n label=v)\n\n if xlim:\n plt.xlim(xlim[0], xlim[1])\n\n if ylim:\n plt.ylim(ylim[0], ylim[1])\n\n plt.legend(bbox_to_anchor=(0.0, -.5, 1, 0),\n loc=\"lower left\",\n mode=\"expand\", ncol=2)\n \n fig.autofmt_xdate()\n\n if save:\n plt.savefig(save,\n format='png',\n bbox_inches=\"tight\")\n\n if eventname=='choose':\n cid = fig.canvas.mpl_connect('button_press_event', lambda event: onclick(event, axs[-1]))\n plt.show()\n elif eventname=='clean':\n selector = SpanSelector(axs[-1],\n onselect=printspanselector,\n direction='horizontal',\n useblit=True,\n span_stays=False,\n button=1,\n rectprops={'facecolor': 'grey', 'alpha': 0.3})\n fig.canvas.mpl_connect('key_press_event',\n selector)\n plt.show()\n else:\n if show:\n plt.show()\n\n return answer","title":""},{"docid":"84b3cc6dad55e823688a60a9dd9f1d6a","score":"0.47027907","text":"def drop_superfluous_trace_vars(data):\n return data.drop([\"P0\", \"co2vmr\", \"gw\", \"hyai\", \"date\", \"date_written\",\n \"datesec\", \"hyai\", \"hyam\", \"hybi\", \"hybm\", \"mdt\",\n \"nbdate\", \"nbsec\", \"ndbase\", \"ndcur\", \"nlon\", \"nsbase\",\n \"nscur\", \"nsteph\", \"ntrk\", \"ntrm\", \"ntrn\",\n \"time_written\", \"wnummax\"])","title":""},{"docid":"62e424f4c821b66a9f0683dc1362dd3c","score":"0.468913","text":"def plot_variable(lon, lat, variable, varname):\n\n # Create a figure\n plt.figure(figsize=(12,12))\n ax = plt.axes(projection=ccrs.PlateCarree())\n ax.coastlines()\n \n # Plot data\n plt.pcolor(lon, lat, variable, transform=ccrs.PlateCarree(), cmap='RdYlBu_r')\n plt.title(varname)\n plt.colorbar(orientation=\"horizontal\", pad=0.06)#fraction=0.05)\n \n # Extent from metadata: ULC: 060.00 -120.00 LRC: 000.00 -030.00 RES:1.00 1.0\n ax.set_xticks([-120,-60,-30])\n ax.set_yticks([60,30,0])\n \n plt.savefig('snapshot_' + varname + '.png', dpi=300, bbox_inches='tight')\n plt.show()\n plt.close()","title":""},{"docid":"7e890b9f93a94f0a34c0d76af1446424","score":"0.4683768","text":"def show_variable(self, toast: Toast, var_name: str) -> None:\n raise NotImplementedError","title":""},{"docid":"b971ee4f2989e077c9d161fa3847216b","score":"0.46831873","text":"def plot_3d(df, target_variable):\n unique_labels = target_variable.unique()\n ordinal_encoding = [np.where(unique_labels == label)[0][0]\n for label in target_variable]\n color_dict = {0: 'red', 1: 'green', 2: 'blue'}\n colors = [color_dict[each] for each in ordinal_encoding]\n threedee = plt.figure().gca(projection='3d')\n threedee.scatter(df[[0]], df[[1]],\n df[[2]], color=colors)\n threedee.set_xlabel(df.columns.values[0])\n threedee.set_ylabel(df.columns.values[1])\n threedee.set_zlabel(df.columns.values[2])\n plt.show()\n return None","title":""},{"docid":"fbd0f849e14a511d0e2aa2a2fe32b21e","score":"0.46819878","text":"def visualize(self, df:DataFrame):# -> dict:\n pass","title":""},{"docid":"bc766e7a0cd6d7c24b37fe6c46efb114","score":"0.46767002","text":"def get_var(data, variable):\n\n var = data.variables[variable]\n var_plot = ma.masked_equal(var, var.missing_value)\n\n lons = data.variables['lon'][...]\n lats = data.variables['lat'][...]\n\n return var_plot, lons, lats","title":""},{"docid":"0ad4c798ac7dc8256ba71957ce08c2f2","score":"0.46681195","text":"def plot_solution(self, variable):\n all_points = [*(self.nodes), *self.internal_points]\n x = sorted(set([point.coords[0] for point in all_points]))\n y = sorted(set([point.coords[1] for point in all_points]))\n z = [[None] * len(x)] * len(y)\n variable = variable.lower()\n for point in all_points:\n point_x = x.index(point.coords[0])\n point_y = y.index(point.coords[1])\n if variable == \"ux\":\n z[point_y][point_x] = point.u[0]\n elif variable == \"uy\":\n z[point_y][point_x] = point.u[1]\n elif variable == \"sx\":\n z[point_y][point_x] = point.p[0]\n elif variable == \"sy\":\n z[point_y][point_x] = point.p[1]\n elif variable == \"sxy\":\n z[point_y][point_x] = point.p[2]\n else:\n print(f\"Invalid option: {variable}\")\n self.plot_model(auto_open=False)\n self.fig.add_contour(\n name=variable,\n z=z,\n x=x,\n y=y,\n connectgaps=True,\n contours=dict(coloring=\"heatmap\", showlabels=True),\n colorbar=dict(title=variable, titleside=\"right\", x=1.2),\n )\n self.fig[\"layout\"].update(title=f\"{self.name} ({variable})\")\n return plotly.offline.plot(self.fig, filename=f\"{self.name}-{variable}.html\")","title":""},{"docid":"aa5d60f3a2f6d30cef38d85daa28dec1","score":"0.46618274","text":"def set_proj_var_plot(data):\n df = records_to_df(data)\n long_df = analysis.wide_to_long(df, ['parallel', 'orthogonal'], suffixes='variance', j='projection')\n fig = plotting.generate_lines_plot(long_df, \"variance\", by='user', color_col='projection')\n return fig","title":""},{"docid":"ee269e097d579cb2a24ab667fa58037b","score":"0.46423653","text":"def plot_against_target(self, feature):\n assert feature != self._target_variable\n\n if feature in self.numeric_features:\n self._dataset[[feature, self._target_variable]].plot.scatter(x=feature,\n y=self._target_variable,\n alpha=0.1,\n title='{0} vs. target (`{1}`)'.\n format(feature,\n self._target_variable))\n else:\n title = '{0} vs. target (`{1}`)'.format(feature, self.target_variable)\n self._dataset[[feature, self._target_variable]].boxplot(by=feature)\n plt.ylabel(self.target_variable)\n plt.title(title)\n plt.suptitle(\"\")\n plt.tight_layout()","title":""},{"docid":"9e9637d4e9abc6369930a68c1502b95e","score":"0.46198583","text":"def set_variance_graph(table_data):\n df = records_to_df(table_data)\n df.dropna(inplace=True)\n return plotting.generate_means_figure(df)","title":""},{"docid":"82efd8c8b683ed6091a8576b3df1086d","score":"0.46060088","text":"def plotData(dataFrame, titleString, saveToFile=False, nsamples = 100, vars=None):\n\n\t#plot nsamples random elements from the sample\n\trandom_elements = random.sample(range(dataFrame.shape[0]), nsamples)\n\tdataFrame_plot = dataFrame.iloc[random_elements]\n\n\tif vars != None:\n\t\tp = sns.pairplot(dataFrame_plot, hue='AHUNumber', diag_kind='kde', vars=vars, dropna=True)\n\telse:\n\t\tp = sns.pairplot(dataFrame_plot, hue='AHUNumber', diag_kind='kde', dropna=True)\n\n\tplt.suptitle(titleString, y = 1.025)\n\n\tif saveToFile == True:\n\t\tplt.savefig('testfig.png', format='png', pad_inches=0.5, bbox_inches='tight')\n\t\tplt.close()\n\telse:\n\t\tpass\n\t\t#plt.show()","title":""},{"docid":"174b0d1fc32bac49ceac43e70e10a012","score":"0.46051374","text":"def plot(self, **kwargs):\n plotdata = self.plottable_data\n if plotdata:\n plotdata.plot(**kwargs)\n else:\n raise NeXusError(\"There is no plottable data\")","title":""},{"docid":"d347fcfa2ffeceefe3ec788c6b9ec835","score":"0.45513475","text":"def removeVariable(self, *args):\n return _libsedml.SedSetValue_removeVariable(self, *args)","title":""},{"docid":"52d761854d1c29b36faeda1e5af7e583","score":"0.4534381","text":"def show_cats(self, var, text_key):\n\t\ttext_key = text_key.split('~')\n\t\tetk = text_key[1].split()[0] if len(text_key) > 1 else None\n\t\ttext_key = text_key[0]\n\t\tdf_all_v = []\n\t\tfor v in var:\n\t\t\tall_df = []\n\t\t\tfor name in list(self.ds_alias.values()):\n\t\t\t\tif v in self[name]:\n\t\t\t\t\tval = self[name].value_texts(v, text_key, etk)\n\t\t\t\t\tcodes = self[name].codes(v)\n\t\t\t\t\tindex = pd.MultiIndex.from_tuples([(v, c) for c in codes])\n\t\t\t\t\tdf = pd.DataFrame(val, index=index, columns=[name])\n\t\t\t\t\tall_df.append(df)\n\t\t\tall_df = pd.concat(all_df, axis=1)\n\t\t\tdf_all_v.append(all_df)\n\t\tif not df_all_v:\n\t\t\tprint('No variables to show.')\n\t\telse:\n\t\t\treturn pd.concat(df_all_v, axis=0)","title":""},{"docid":"c460d6f20fc4d69bed840f326ee71a3a","score":"0.44996905","text":"def plot_categorical_column(df, target_variable, column_name, fig_size=(9, 12), y_lim=None):\n f, (ax1, ax2) = plt.subplots(2, figsize=fig_size)\n tmp_df = df.dropna()\n sns.countplot(x=column_name, data=tmp_df, ax=ax1)\n sns.pointplot(x=column_name, y=target_variable, data=tmp_df, ax=ax2)\n if y_lim is not None:\n ax2.set_ylim(0, y_lim)","title":""},{"docid":"31dcef4c8847e4789643aaf5cb79239e","score":"0.44970563","text":"def require_variable(df, variable, unit=None, year=None, exclude_on_fail=False,\n **kwargs):\n fdf = df.filter(**kwargs)\n if len(fdf.data) > 0:\n vdf = fdf.require_variable(variable=variable, unit=unit, year=year,\n exclude_on_fail=exclude_on_fail)\n df.meta['exclude'] |= fdf.meta['exclude'] # update if any excluded\n return vdf","title":""},{"docid":"5a9b710f33eaca9bece16fa9e4362da0","score":"0.44876218","text":"def unsuppress(self):\n self._pipe.send((None, 'show', None))","title":""},{"docid":"9a073a4e1e8b5823d0c2be47feae53f7","score":"0.4475288","text":"def plot(self, df, plotdir):\n df.plot()","title":""},{"docid":"4aed811f4a0431f31970dec2f4ca576a","score":"0.44718388","text":"def plot_single_variable(self, results, xlab=None, ylab=None, save=None):\n\n n, m = self.x.shape\n if m != 2:\n raise ValueError('Error: can only plot single explanatory variable. Current X shape: ({},{})'.format(n, m))\n X = self.x.drop('const', axis=1)\n X = np.squeeze(X.values)\n Y = np.squeeze(self.y.values)\n plt.plot(X, Y, 'o', c='steelblue')\n Xmin = np.argmin(X)\n Xmax = np.argmax(X)\n plt.plot([X[Xmin], X[Xmax]], [self.preds[Xmin], self.preds[Xmax]], c='darkorange')\n if xlab:\n plt.xlabel(xlab)\n if ylab:\n plt.ylabel(ylab)\n if save:\n plt.savefig('{}.png'.format(save), dpi=200)\n plt.show()","title":""},{"docid":"acb0f674d7c2303a1019fee9596426f2","score":"0.44626507","text":"def display(df):","title":""},{"docid":"7f92ec7c3a117c72a81c791bdbfc54a3","score":"0.4445461","text":"def print_dataframe(data):\n with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n print(data)","title":""},{"docid":"215c3df97025653743c1890ed0d2c221","score":"0.44444445","text":"def remove_variable(self, varname):\n self.graph.remove_node(self.variables[varname])\n \n #removes the class of varname (should there be no other variables that share that class) \n #from classes_imported set \n for key,value in self.classes_available.items():\n if isinstance(self.variables[varname], value):\n self.classes_imported.remove(key) \n\n #remove the varname from the variables dictionary \n del self.variables[varname]\n self.convert_to_script()\n self.graph_changed_event = True","title":""},{"docid":"edc07226f64f3c33738730d8089e6428","score":"0.44427666","text":"def _plot_var(self, ax, **kwargs):\n self.__xmin, self.__xmax = ax.get_xlim()\n self.__ymin, self.__ymax = ax.get_ylim()\n plot_digit = kwargs.get('plot_digit', None)\n if plot_digit is not None:\n self.__prec = '%.' + '%d' % plot_digit + 'f'","title":""},{"docid":"05543b631cc28eab1499d4a0303f5622","score":"0.44299516","text":"def set_dropextradata(self, dropextradata):\n self.options['dropextradata'] = dropextradata","title":""},{"docid":"498c7472384572bf9b63e89977b3b9a2","score":"0.4422164","text":"def box_plot() -> str:\n\n session_manager.cache_analysis_option()\n return StatsModel().get_box_plot()","title":""},{"docid":"2b2024b879b7e0c085a117ee9ab652b2","score":"0.4414972","text":"def logplot(self, **kwargs):\n plotdata = self.plottable_data\n if plotdata:\n plotdata.logplot(**kwargs)\n else:\n raise NeXusError(\"There is no plottable data\")","title":""},{"docid":"bc7637e0f986c8d599c82f0c7bfa9bbd","score":"0.44135877","text":"def plot(filename, column):\n df=pd.read_csv(filename)\n if column is None:\n df.hist();\n else:\n df[column].hist()\n plt.show() # if slow, this blocks all subsequent code from running # it waits for the window to close before continuing","title":""},{"docid":"c17307ab1248182037bdaadea9cc6336","score":"0.44075608","text":"def x_vs_y_plots(X,y,save_toggle=False,file_prefix=''):\n if len(file_prefix)>0:\n file_prefix += '_'\n \n df = pd.concat([X, pd.DataFrame(y, index=X.index)], axis=1)\n for x in X.columns:\n df[[x,y.name]].groupby(x).mean().plot()\n if save_toggle:\n plt.savefig(file_prefix+x+'_vs_'+y.name+'.png')\n plt.show()\n return","title":""},{"docid":"0889a47d34fccef58e97603b04c39123","score":"0.43964615","text":"def show_figure(self, *args, **kwargs):\n if 'divider' in dir(self):\n del self.divider\n plt.show(*args, **kwargs)","title":""},{"docid":"e42c056ce7bb2a7c997fe17f18e2da84","score":"0.43961328","text":"def implot(self, **kwargs):\n plotdata = self.plottable_data\n if plotdata:\n plotdata.implot(**kwargs)\n else:\n raise NeXusError(\"There is no plottable data\")","title":""},{"docid":"94c1e8bd58d88d907afa606c72266977","score":"0.4391739","text":"def plot_xy(varx='totalTestResultsIncrease',\n vary='positiveIncrease',\n state='NY'):\n assert type(varx) == str, \"Expected string as the variable x name\"\n assert type(vary) == str, \"Expected string as the variable y name\"\n\n y = df[df['state'] == state][vary]\n x = df[df['state'] == state][varx]\n if (x.nunique() != 1) and (y.nunique() != 1):\n plt.figure(figsize=(12, 4))\n plt.title(\"Plot of \\\"{}\\\" vs. \\\"{}\\\" for {}\".format(varx, vary, state), fontsize=18)\n plt.scatter(x=x, y=y, edgecolor='k', color='lightgreen', s=100)\n plt.grid(True)\n plt.xticks(fontsize=14, rotation=45)\n plt.yticks(fontsize=14)\n plt.show()\n else:\n print(\"Some of the data unavailable for a scatter plot. Sorry!\")","title":""},{"docid":"bb8afd28e29829a317cacd43faa15d70","score":"0.4388029","text":"def plot(\n self,\n components=None,\n *,\n ax=None,\n logy=False,\n show=False,\n dates=False,\n legend=True,\n grid=True,\n ):\n ax = ax or plt.gca()\n kwargs = {\"logy\": logy, \"ax\": ax, \"grid\": grid, \"legend\": legend}\n\n def get_column(col):\n if dates:\n col += \":dates\"\n data = self[col]\n return data\n\n components = self.meta.variables if components is None else components\n for col in components:\n data = get_column(col)\n data.plot(**kwargs)\n if show:\n plt.show()","title":""},{"docid":"3cf0aa71c26b31f4c997c8c2afaf8893","score":"0.4385104","text":"def print_df(df: pd.DataFrame) -> None:\n\twith pd.option_context('display.max_rows', None, 'display.max_columns', None):\n\t\tprint(df)","title":""},{"docid":"72e57320b475c20dc2f96fd48da6fca7","score":"0.43605706","text":"def display_na(df):\r\n \r\n display(HTML(\"Percentage of missing variables for each feature:\"))\r\n print(df.isnull().sum(axis=0) * 100 / len(df))","title":""},{"docid":"7d004de844130a45592187867f7c68fd","score":"0.4355372","text":"def show(self, *args, **kwargs):\n self.get_plot(*args, **kwargs).show()","title":""},{"docid":"c08b4f4854337048144d53f798e073eb","score":"0.43472216","text":"def plot_data(df, title=\"Stock prices\", xlabel=\"Date\", ylabel=\"Price\"):\n ax = df.plot(title=title, fontsize=12)\n ax.set_xlabel(xlabel)\n ax.set_ylabel(ylabel)\n plt.show() # must be called to show plots in some environments","title":""},{"docid":"2e2c6fd0d9dca232109a4340a5c5a1a9","score":"0.43427277","text":"def extra_plot_variance_explained(var_explained, component,var_explained_2, component_2, *args):\n opt_n_comp = np.argmin(var_explained) - 1\n stdout.write(\"\\n\")\n # plot var_explained for each component\n with plt.style.context((\"ggplot\")):\n ax = plt.subplot(111)\n plt.plot(component, np.array(var_explained), \"-v\", color=\"red\", mfc=\"red\", label=\"train\")\n plt.plot(\n component[opt_n_comp], np.array(var_explained)[opt_n_comp], \"P\", ms=10, mfc=\"red\"\n )\n plt.plot(component_2, np.array(var_explained_2), \"-v\", color=\"blue\", mfc=\"blue\", label=\"test\")\n plt.plot(\n component_2[opt_n_comp], np.array(var_explained_2)[opt_n_comp], \"P\", ms=10, mfc=\"red\"\n )\n plt.xlabel(\"Number of PLS components\")\n plt.ylabel(\"Variance explained %\")\n plt.title(\"PLS\")\n plt.xlim(0-1)\n ax.legend()\n plt.show()","title":""},{"docid":"0a4f7b6cbfef0a5defdf6be992e65669","score":"0.4340944","text":"def plot_runner(ds, variable, s3upload=False) -> str:\n\n model_type = get_model_type(ds)\n if model_type == 'roms':\n imagename = plot_roms(ds_ofs, var, s3upload=True)\n elif model_type == 'fvcom':\n imagename = plot_fvcom(ds_ofs, var, s3upload=True)\n else:\n print(f\"ERROR: Unsupported model type - {modeltype}\")","title":""},{"docid":"473bf765662306bc925b6e3848dabee6","score":"0.43398735","text":"def hide_column(self, column_name):\n if not isinstance(column_name, str):\n raise TypeError(\"column_name must be of type str\")\n\n column_names = self.get_column_names()\n if column_name not in column_names:\n raise ValueError(f\"{column_name} not in available columns ({column_names})\")\n new_column_names = [cn for cn in self.get_column_names(True)\n if cn not in column_name]\n return self.set_visible_columns(new_column_names)","title":""},{"docid":"715c6a713767779ca425cadeff548c21","score":"0.43344194","text":"def display(self, *fields):\n try:\n if self.df is None:\n self.warning(\"Dataframe is empty: nothing to display\")\n return\n df2 = self.df[list(fields)]\n return df2.head()\n except Exception as e:\n self.err(e, self.display, \"Can not display dataframe\")","title":""},{"docid":"ab189fb4de3b5e34800eba35dea524ba","score":"0.43340218","text":"def plot_hovmoeller(ds: xr.Dataset,\n var: VarName.TYPE = None,\n x_axis: DimName.TYPE = None,\n y_axis: DimName.TYPE = None,\n method: str = 'mean',\n contour: bool = True,\n title: str = None,\n file: str = None,\n monitor: Monitor = Monitor.NONE,\n **kwargs) -> Figure:\n var_name = None\n if not var:\n for key in ds.data_vars.keys():\n var_name = key\n break\n else:\n var_name = VarName.convert(var)\n var = ds[var_name]\n\n if not x_axis:\n x_axis = var.dims[0]\n else:\n x_axis = DimName.convert(x_axis)\n\n if not y_axis:\n try:\n y_axis = var.dims[1]\n except IndexError:\n raise ValidationError('Given dataset variable should have at least two dimensions.')\n else:\n y_axis = DimName.convert(y_axis)\n\n if x_axis == y_axis:\n raise ValidationError('Dimensions should differ between plot axis.')\n\n dims = list(var.dims)\n try:\n dims.remove(x_axis)\n dims.remove(y_axis)\n except ValueError:\n raise ValidationError('Given dataset variable: {} does not feature requested dimensions:\\\n {}, {}.'.format(var_name, x_axis, y_axis))\n\n ufuncs = {'min': np.nanmin, 'max': np.nanmax, 'mean': np.nanmean,\n 'median': np.nanmedian, 'sum': np.nansum}\n\n with monitor.starting(\"Plot Hovmoeller\", total_work=100):\n monitor.progress(5)\n with monitor.child(90).observing(\"Aggregate\"):\n var = var.reduce(ufuncs[method], dim=dims)\n monitor.progress(5)\n\n figure = plt.figure()\n ax = figure.add_subplot(111)\n if x_axis == 'time':\n figure.autofmt_xdate()\n\n if contour:\n var.plot.contourf(ax=ax, x=x_axis, y=y_axis, **kwargs)\n else:\n var.plot.pcolormesh(ax=ax, x=x_axis, y=y_axis, **kwargs)\n\n if title:\n ax.set_title(title)\n\n figure.tight_layout()\n\n if file:\n figure.savefig(file)\n\n return figure if not in_notebook() else None","title":""},{"docid":"e45e26120e690f3dad49ac9e218a7480","score":"0.43270665","text":"def remove_variable(self, var):\n self.vars.remove(var)","title":""},{"docid":"6f45be729aa0e30132fc58c88ddb34ca","score":"0.4326928","text":"def subset_variables(ds, variableList): # {{{\n\n allvars = ds.data_vars.keys()\n\n # get set of variables to drop (all ds variables not in vlist)\n dropvars = set(allvars) - set(variableList)\n\n # drop spurious variables\n ds = ds.drop(dropvars)\n\n # must also drop all coordinates that are not associated with the variables\n coords = set()\n for avar in ds.data_vars.keys():\n coords |= set(ds[avar].coords.keys())\n dropcoords = set(ds.coords.keys()) - coords\n\n # drop spurious coordinates\n ds = ds.drop(dropcoords)\n\n if len(ds.data_vars.keys()) == 0:\n raise ValueError(\n 'Empty dataset is returned.\\n'\n 'Variables {}\\n'\n 'are not found within the dataset '\n 'variables: {}.'.format(variableList, allvars))\n\n return ds # }}}","title":""},{"docid":"64cfecc9efd06e5098ce91f2f6a118a5","score":"0.43260407","text":"def distribution_plot(dataframe: pd.DataFrame, var: str, target: str = None, **kwargs):\n\n row = kwargs.get('row', None)\n col = kwargs.get('col', None)\n\n size = kwargs.get('size', None)\n aspect = kwargs.get('aspect', None)\n\n facet = sns.FacetGrid(\n dataframe,\n hue=target,\n size=size if size else 3,\n aspect=aspect if aspect else 4,\n row=row,\n col=col\n )\n\n facet.map(sns.kdeplot, var, shade=True)\n facet.set(title=\"Distribution of '{}' over '{}'\".format(var, target))\n\n # Defines limits\n if kwargs.get('clean', False):\n xmin = dataframe[var].quantile(0.02)\n xmax = dataframe[var].quantile(0.98)\n facet.set(xlim=(xmin, xmax))\n\n facet.add_legend()\n plt.show()","title":""},{"docid":"54230d6985c38be8af9232a0bf01ee18","score":"0.43237677","text":"def rename_variable(self, toast: Toast, var_name: str, display_name: str) -> None:\n try:\n return self.active_view.rename_variable(toast, var_name, display_name)\n except NotImplementedError:\n toast.show(f\"{self._active} does not implement variable renaming\", ToastType.warning)","title":""},{"docid":"5290b0dbee071b9ff806e7ed58e3ceb1","score":"0.43195853","text":"def setShowInX(self, x):\r\n\t\tself.__showInX=x","title":""},{"docid":"1ca95d946d23156598f83a8c528ad2ac","score":"0.43146688","text":"def plot_dis(df_master, df_dis, units):\n modul = df_master.modul\n stor = '{}_stor'.format(modul)\n loss = '{}_loss'.format(modul)\n relax = '{}_relax'.format(modul)\n\n if df_master.domain == 'freq':\n fig, (ax1, ax2) = plt.subplots(1,2, figsize=(8,0.75*4))\n df_master.plot(x='f', y=[stor], label=[\"{}'(filter)\".format(modul)],\n ax=ax1, logx=True, logy=True, color=['C0'], alpha=0.5)\n df_master.plot(x='f', y=[loss], label=[\"{}''(filter)\".format(modul)],\n ax=ax2, logx=True, logy=True, color=['C1'], alpha=0.5)\n df_dis.plot(x='f', y=[stor], label=['tau_i'], ax=ax1, \n logx=True, logy=True, ls='', marker='o', color=['C0'])\n df_dis.plot(x='f', y=[loss], label=['tau_i'], ax=ax2, \n logx=True, logy=True, ls='', marker='o', color=['C1'])\n ax1.set_xlabel('Frequency (Hz)')\n ax1.set_ylabel('Storage modulus ({})'.format(units[stor]))\n ax2.set_xlabel('Frequency (Hz)')\n ax2.set_ylabel('Loss modulus ({})'.format(units[stor])) \n ax1.legend()\n ax2.legend()\n fig.show()\n return fig\n elif df_master.domain == 'time':\n fig, ax1 = plt.subplots(figsize=(4,0.75*4))\n df_master.plot(x='t', y=[relax], \n ax=ax1, logx=True, logy=True, color=['k'])\n df_dis.plot(x='t', y=[relax], label = ['tau_i'], \n ax=ax1, logx=True, logy=True, ls='', marker='o', color=['red'])\n ax1.set_xlabel('Time ({})'.format(units['t']))\n ax1.set_ylabel('Relaxation modulus ({})'.format(units[relax]))\n ax1.legend()\n fig.show()\n return fig","title":""},{"docid":"35832c1e396d3f8c41dcf63d2a46273b","score":"0.43139654","text":"def hide_xticks(ax):\n for tick in ax.axes.get_xticklabels():\n tick.set_visible(False)","title":""},{"docid":"5e63b785758284832390cce3c328b022","score":"0.43101996","text":"def plot_data(df, title=\"Stock prices\", xlabel=\"Date\", ylabel=\"Price\"):\n ax = df.plot(title=title, fontsize=12)\n ax.set_xlabel(xlabel)\n ax.set_ylabel(ylabel)\n plt.show()","title":""},{"docid":"5e63b785758284832390cce3c328b022","score":"0.43101996","text":"def plot_data(df, title=\"Stock prices\", xlabel=\"Date\", ylabel=\"Price\"):\n ax = df.plot(title=title, fontsize=12)\n ax.set_xlabel(xlabel)\n ax.set_ylabel(ylabel)\n plt.show()","title":""},{"docid":"eb45a6f55ba0db29f2a75fa25b824f96","score":"0.43083325","text":"def show(self, *args, **kwds):\n self.plot(*args, **kwds).show()\n return","title":""},{"docid":"0b798880aba7d93b422ba79a8dafd758","score":"0.43063605","text":"def plot_discharge(self, data, **kw):\n x = 0\n print 'data', data\n for key in data:\n print 'key', key\n if key == '11462125 peak':\n print 'skipping peak discharge df'\n elif key == 'CLV - Cloverdale hourly':\n for col in self._col_list[:2]:\n ser = data[key]['Dataframe'][col]\n if not ser.empty:\n self._plot(col, key, ser, **kw)\n\n elif key == 'COY - Coyote hourly':\n for col in self._col_list:\n ser = data[key]['Dataframe'][col]\n if not ser.empty:\n self._plot(col, key, ser, **kw)\n else:\n for col in self._col_list:\n ser = data[key]['Dataframe'][col]\n print 'nan values: {}'.format(count_nonzero(isnan(ser)))\n print 'len ser: {}'.format(len(ser))\n if len(ser) != count_nonzero(isnan(ser)):\n self._plot(col, key, ser, **kw)","title":""},{"docid":"8ae8dc9da6845384c103464f8a1f1451","score":"0.43056202","text":"def set_display():\r\n # Plots display settings\r\n plt.style.use('fivethirtyeight')\r\n plt.rcParams['figure.figsize'] = 12, 8\r\n plt.rcParams.update({'font.size': 14})\r\n # DataFrame display settings\r\n pd.set_option('display.max_columns', None)\r\n pd.set_option('display.max_rows', None)\r\n pd.options.display.float_format = '{:.4f}'.format","title":""},{"docid":"30b66c30a50d93c31d6e8fbe9796c40a","score":"0.42991424","text":"def boxplot_one_country(df,country,AoIs= 'All',save=False,fig=None,ax=None,**kwargs):\n df = df.loc[df['country'] == country]\n #if AoIs != 'All': df2 = df2.loc[df2['AoI combinations'].isin(AoIs)]\n\n if (fig == None and ax == None): # if No axes and no figure is provided\n fig, ax = plt.subplots(figsize=(12, 6))\n\n df.boxplot(by='AoI combinations', column='disrupted', ax=ax,grid=False,**kwargs)\n ax.set_xlabel(\"Number of combinations of flood events (AoI)\")\n ax.set_ylabel(\"% preferred routes disrupted\")\n ax.set_title(\"% routes between NUTS-3 regions in {} disrupted\".format(country))\n\n if save:\n save_figs = load_config(config_file)['paths']['output_images'] / 'disrupted'\n if not save_figs.exists(): save_figs.mkdir()\n filename = \"disrupted_boxplot_{}.png\".format(country)\n fig.savefig(save_figs / filename)\n\n return fig,ax","title":""},{"docid":"7cf843c3246768fbe1cd025b86929885","score":"0.42943284","text":"def plot_categoricial(df, var) -> None:\n temp = df[['Income', var]].groupby(var).mean().reset_index()\n axis = sns.barplot(x=var, y='Income', data=temp)\n axis.set(ylabel=\"Probability of earning over 50K\")\n axis.set_xticklabels(config.vis_dict[var], rotation=75)\n plt.title(f\"Effect of {var} on Income\")\n plt.show()","title":""},{"docid":"e6b30d96205aae6597f9e8a95779b74e","score":"0.42883715","text":"def plot_line_chart(df: pd.DataFrame, title: str, y: str = 'y', x: str = 'x',):\n px.line(df, x=x, y=y, title=title).show()","title":""},{"docid":"99f63f08277116886413b5dfbbc45efa","score":"0.42838115","text":"def datatoplot(self, track_choice, track_no=None, track_index=None):\r\n if track_choice == 'evo':\r\n tracks = self.data\r\n elif track_choice == 'ranged':\r\n if self.ranged_tracks is None:\r\n raise NameError('No ranged tracks!')\r\n else: tracks = self.ranged_tracks\r\n else: raise NameError('Wrong track name!')\r\n \r\n max_tracks = tracks['track_no'].nunique()\r\n uniques = tracks['track_no'].unique()\r\n if track_no != None:\r\n if max_tracks > track_no:\r\n if type(track_index)==type(None):\r\n track_index=np.random.choice(uniques, track_no)\r\n else: track_index = uniques\r\n else: track_index = uniques\r\n if len(track_index)>200:\r\n raise ValueError('Too many tracks, are you sure you want to plot '+str(len(track_index))+' tracks??')\r\n \r\n selected = tracks.loc[tracks['track_no'].isin(track_index)]\r\n plot_tracks = [selected['Teff'], selected['L']]\r\n plot_m = selected['mass']\r\n return plot_tracks, plot_m","title":""},{"docid":"5513626174155422f31e167cfb56c003","score":"0.42826742","text":"def set_df_mean_plot(data):\n df = records_to_df(data)\n try:\n # For error bars we need standard deviation.\n df[['df1 std', 'df2 std']] = df[['df1 variance', 'df2 variance']].transform(np.sqrt)\n except KeyError:\n pass\n # Convert to long format for easier plotting.\n long_df = analysis.wide_to_long(df, stubs=['df1', 'df2'], suffixes=['mean', 'std'], j='dof')\n fig = plotting.generate_lines_plot(long_df, \"mean\", by='user', color_col='dof', errors='std', jitter=True)\n return fig","title":""},{"docid":"a2d4e630a8059c1b12652584c43c00fd","score":"0.4279645","text":"def easy_print_data_frame(data_frame):\n\n print(data_frame.to_string())","title":""},{"docid":"eef7324d0a5850a8c8ac3a856b4b9dfb","score":"0.42760804","text":"def _plot_variance_explained(var_explained, component, *args):\n opt_n_comp = np.argmin(var_explained) - 1\n stdout.write(\"\\n\")\n # plot var_explained for each component\n with plt.style.context((\"ggplot\")):\n plt.plot(component, np.array(var_explained), \"-v\", color=\"blue\", mfc=\"blue\")\n plt.plot(\n component[opt_n_comp], np.array(var_explained)[opt_n_comp], \"P\", ms=10, mfc=\"red\"\n )\n plt.xlabel(\"Number of PLS components\")\n plt.ylabel(\"Variance explained %\")\n plt.title(\"PLS\")\n plt.xlim(0-1)\n\n plt.show()","title":""},{"docid":"d83e8777568cac18f63c1b5039fa7e43","score":"0.4275368","text":"def show():\n plt.show(block=False)","title":""},{"docid":"4441f564cdf782290c8b56b76d3b9153","score":"0.4270931","text":"def show_plot(self, table):\n assert (self._current_panel is not None)\n graph = wxtbx.plots.iotbx_data_plot_base(\n parent=self._current_panel,\n tables=[table],\n size=(TEXT_WIDTH - 40, min(500, TEXT_WIDTH * 0.75)))\n graph.set_plot(table.only_plot())\n self._graphs.append((graph, table.title))\n self._current_sizer.Add(graph, 0, wx.ALL|wx.EXPAND, 5)","title":""},{"docid":"a774101cb8988ac9fa189c9a6f651b21","score":"0.42703143","text":"def plot_two_vars(self,\n xy_data, # ['my_datadict_out', e.g., 'merge_summstat_out']\n x_name, # [name of x variable to use for x-axis]\n y_name, # [name of y variable to use for y-axis]\n ent_axis = 'col', # [<'row', 'col'>] => how variables are situated]\n err_data = None, # [ => e.g., 'merge_summstat_out']\n x_err = None, # [ => create bubbles]\n y_err = None, # [ => create bubbles]\n color_by = None, # [ => color-code bubbles]\n cosine_correct = 'coord|corr', # [ => correct for cosine between variables]\n max_cos = 0.95, # => trigger exception when cos(x, y) > max_cos]\n plot = None, # [ => see docs to customize]\n savefig = 'xy_plot.png' # [ => how to output chart]\n ):\n\n if self.verbose is True:\n print 'plot_two_vars() is working...\\n'\n\n # Run the damon utility\n plot_two_vars_out = dmn.utils._plot_two_vars(locals())\n self.plot_two_vars_out = plot_two_vars_out\n\n if self.verbose is True:\n print 'plot_two_vars() is done -- see my_obj.plot_two_vars_out'\n\n return None","title":""},{"docid":"0d4531cd130c5272a5d9ffa4d886b3ee","score":"0.42631972","text":"def _plot_skipped_well_file(config, raw_data, well_name):\n if config.plot_skipped:\n plot_params = dict()\n plot_params['savepath'] = config.work_dir\n plot_params['well_name'] = well_name\n plot_params['fg_conc_edges'] = config.fg_conc_edges\n plot_params['fg_fret_edges'] = config.fg_fret_edges\n plot_params['data_df'] = raw_data\n plot_params['fine_grid_xlim'] = None\n plot_params['fine_grid_ylim'] = None\n plot_params['plot_type'] = config.plot_type\n plot_fine_grid_profiles(plot_params)","title":""},{"docid":"11adb8baf5e82cdd7884c2a1f79cd1e1","score":"0.42607546","text":"def do_variable(self, arg):\n self.display_cmake_help('variable', arg)","title":""},{"docid":"1862e5d6a2dd0e4bdab216750261911f","score":"0.42592025","text":"def strip_data(df: pd.DataFrame, data_to_keep: dict):\n gene_name = 'symbol'\n if df.index.name == data_to_keep.get(gene_name):\n data_to_keep.pop(gene_name)\n new_df = df[data_to_keep.values()]\n return new_df","title":""},{"docid":"a745c78232fa241c5ee33a4d7e4af63e","score":"0.4254119","text":"def bokeh_show(bokeh_object, filename=None, **save_kwargs):\n if filename is None:\n filename = \"bokehplot_00000000.html\"\n i = 1\n while i < 99999999 and os.path.isfile(filename):\n filename = \"bokehplot_{0:08d}.html\".format(i)\n i += 1\n\n if i == 99999999:\n raise RuntimeError(\n \"Cannot generate generic file name, as all filenames are used.\"\n )\n\n if \"resources\" in save_kwargs:\n resources = save_kwargs.pop(\"resources\")\n else:\n resources = bokeh.resources.CDN\n\n if \"title\" in save_kwargs:\n title = save_kwargs.pop(\"title\")\n else:\n title = \"Bokeh Plot\"\n\n bokeh.io.save(\n bokeh_object, filename=filename, resources=resources, title=title, **save_kwargs\n )\n\n return HTML(filename)","title":""},{"docid":"338a9905885d36026a7596b50df165de","score":"0.42535764","text":"def oplot(self, **kwargs):\n plotdata = self.plottable_data\n if plotdata:\n plotdata.oplot(**kwargs)\n else:\n raise NeXusError(\"There is no plottable data\")","title":""},{"docid":"8d7c065190303a0310e9f02a8760beaa","score":"0.4251952","text":"def print_1d_visual(self, df, tuning_param):\n axis = {\n \"accuracy\": \"Accuracy (%)\",\n \"false_positive\": \"False Positive Rate (%)\",\n \"false_negative\": \"False Negative Rate (%)\"\n }\n plt.clf()\n sns.set(style='darkgrid')\n sns.lineplot(x=tuning_param, y=axis[self.metric], data=df)\n fig = plt.gcf()\n fig.savefig('%s/OptimizationLineGraph.png' % self.path, bbox_inches='tight')\n plt.close()\n df.to_csv(\"%s/optimize_results.csv\" % self.path)","title":""},{"docid":"11a762413a0129120eb397564ca2dc97","score":"0.42496905","text":"def plot(self, variables_values, axes=None):\n assert len(self.variables) == len(variables_values), \\\n f\"You need to pass as many variable values as this visualiser has variables. Required:\" \\\n f\"{len(self.variables)}, Given: {len(variables_values)}\"\n\n fig = None\n if axes is None:\n fig, axes = self.create_default_axes()\n # TODO: Probably should not do that, but it prevents an empty plot from popping up in IPython\n plt.close(fig)\n\n self.plot_init(variables_values, axes)\n self.plot_update(variables_values, axes)\n if fig is not None and in_ipython_context:\n ip_display(fig)","title":""},{"docid":"81280fdc16829c5aafa79a33822634eb","score":"0.4248225","text":"def plot_my_data(x,y,verbose=True):\n\n # Note the indentation for the if block\n if (verbose):\n print \"\"\n print \"Now plotting the data.\"\n\n # In the plot command below, the 'bo' tells the program to plot blue ('b')\n # circles ('o')\n print \"\"\n #print \"Close the plot window to continue...\"\n plt.plot(x,y,'bo')","title":""},{"docid":"45ad991ab71a43a553c913e63ea1f2af","score":"0.42471483","text":"def variable_importance_plot(model, log, result_path=\"result\"):\n algo = model.params['model_id']['actual']['name']\n\n if model.varimp() is not None:\n plt.tight_layout()\n\n plt.figure(figsize=(12, 8))\n var_imp = model.varimp_plot()\n plt.title(\"Variable Importance : \" + algo)\n plt.xlabel(\"Relative Importance\")\n\n plt.savefig(result_path + \"/\" + \"variable_importance.png\", dpi=600)\n plt.close()\n else:\n log.warning(\"[\" + algo + \"]\" + \"This model doesn't have variable importances\")","title":""},{"docid":"2e224ac6f166a66c30181dc30cb08c5b","score":"0.42448515","text":"def turn_visualization_off(self):\n self.visualize = False","title":""},{"docid":"aaa4f7f0ce31f350d847feb2209d7261","score":"0.4238392","text":"def drop(self, var_index):\n return self.masked([i != var_index for i in range(self.num_vars)])","title":""},{"docid":"da9570ff3d6ed2dd5db6f03efbc01781","score":"0.4237229","text":"def display_only(self, frame):\n cv2.imshow(self.display_name, frame)","title":""},{"docid":"f997c2c75ad0ef1126c60105cf176510","score":"0.42370698","text":"def remove_NoVal_col(df: pd.core.frame.DataFrame, label: str) -> pd.core.series.Series:\n mask = df[label] != 99.0\n col = df[label][mask]\n return col","title":""},{"docid":"fa0ae72e1dcc3d68e8cfcd5580d6d362","score":"0.42370147","text":"def display_data(df):\n response= ['yes', 'no']\n rawdata = ''\n #counter variable is initialized as a tag to ensure only details from a particular point is displayed\n counter = 0\n while rawdata not in response:\n print(\"\\nDo you wish to view the raw data?\")\n print(\"\\nAccepted responses:\\nYes or no\")\n rawdata = input().lower()\n #the raw data from the df is displayed if user opts for it\n if rawdata == \"yes\":\n print(df.head())\n elif rawdata not in response:\n print(\"\\nPlease check your input.\")\n print(\"Input does not seem to match any of the accepted responses.\")\n print(\"\\nRestarting...\\n\")\n\n #Extra while loop here to ask user if they want to continue viewing data\n while rawdata == 'yes':\n print(\"Do you wish to view raw data?\")\n counter += 5\n rawdata = input().lower()\n #If user opts for it, this displays next 5 rows of data\n if rawdata == \"yes\":\n print(df[counter:counter+5])\n elif rawdata != \"yes\":\n break\n\n print('-'*80)","title":""},{"docid":"d09bbfaaacfe251e374b812438d2d174","score":"0.42354107","text":"def viewVariables(dataset):\r\n for entry in dataset['DATA'][0]:\r\n pprint.pprint(str(entry) + \" : \" + str(dataset['VARIABLES'][str(entry)]))","title":""},{"docid":"227b29295221cfa88a8937ea82c250ad","score":"0.42240646","text":"def remove_plot(self):\n \n if self.has_plant: \n #Updates the variables to show that the plot is empty\n self.plant_type = None\n self.has_plant = False\n self.age = 0\n self.time_planted = 0\n self.can_harvest = False\n \n else:\n #Once the plot itself is removed, the switch turns off and the plot is disabled\n self.plotted = False","title":""},{"docid":"68478c1344086aa8df9dc257fd0cc6cd","score":"0.42235586","text":"def suppress(self):\n self._pipe.send((None, 'hide', None))","title":""}],"string":"[\n {\n \"docid\": \"f3416a3c42880a57556602bd26db1797\",\n \"score\": \"0.57033026\",\n \"text\": \"def ignore_variable(self, toast: Toast, var_name: str) -> None:\\n try:\\n return self.active_view.ignore_variable(toast, var_name)\\n except NotImplementedError:\\n toast.show(f\\\"{self._active} does not implement ignoring variables\\\", ToastType.warning)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a30be2073bb4ee8b98885fcb1f16c1f5\",\n \"score\": \"0.5620072\",\n \"text\": \"def plot_proper(df: pd.DataFrame, xax: str, yax: str, var: str, title: str) -> None:\\n pd.options.plotting.backend = \\\"plotly\\\"\\n fig = df.plot(title=title, template=\\\"simple_white\\\",\\n labels=dict(index=xax, value=yax, variable=var))\\n fig.update_layout(\\n xaxis_title=xax,\\n yaxis_title=yax,\\n )\\n fig.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0372b371887346e02e57dac3f55b655a\",\n \"score\": \"0.545876\",\n \"text\": \"def ignore_variable(self, toast: Toast, var_name: str) -> None:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b21493415e595924fcbbdc5510e9902e\",\n \"score\": \"0.51881444\",\n \"text\": \"def show(*args, block=True, **kwargs):\\n # Remove reserved rewords\\n try:\\n kwargs.pop('block')\\n except:\\n pass\\n # Get the variable names in the scope show() was called from\\n callers_local_vars = inspect.currentframe().f_back.f_locals.items()\\n\\n # Make a dictionary of the DataFrames from the position args and get their variable names using inspect\\n dataframes = {}\\n for i, df_object in enumerate(args):\\n df_name = 'untitled' + str(i + 1)\\n\\n for var_name, var_val in callers_local_vars:\\n if var_val is df_object:\\n df_name = var_name\\n\\n dataframes[df_name] = df_object\\n\\n # Add the dictionary of positional args to the kwargs\\n if (any([key in kwargs.keys() for key in dataframes.keys()])):\\n print(\\\"Warning! Duplicate DataFrame names were given, duplicates were ignored.\\\")\\n kwargs = {**kwargs, **dataframes}\\n\\n pandas_gui = PandasGUI(**kwargs)\\n\\n if block:\\n pandas_gui.app.exec_()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a51ef5db5e8ccb230c8b182b1588e161\",\n \"score\": \"0.51808095\",\n \"text\": \"def no_detour_boxplot(df,country,save=False,fig=None, ax=None,**kwargs):\\n df = df.loc[df['country'] == country]\\n\\n if (fig == None and ax == None): # if No axes and no figure is provided\\n fig, ax = plt.subplots(figsize=(12, 6))\\n\\n df.boxplot(by='AoI combinations', column='no detour', grid=False, ax=ax,**kwargs)\\n ax.set_xlabel(\\\"Number of combinations of flood events (AoI)\\\")\\n ax.set_ylabel(\\\"% No detour\\\")\\n ax.set_title(\\\"% routes between NUTS-3 regions in {} without detour\\\".format(country))\\n\\n #Todo (possible): give fig ax as args; enable saving possiblity\\n\\n if save: # TODO REPLACE ALL INSTANCES OF THIS PART OF CODE WITH A SPECIAL FUNCTION\\n save_figs = load_config(config_file)['paths']['output_images'] / 'no_detour_boxplot'\\n if not save_figs.exists(): save_figs.mkdir()\\n filename = \\\"noDT_boxplot_{}.png\\\".format(country)\\n fig.savefig(save_figs / filename)\\n\\n return fig,ax\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53a5704bd479ea2ddbc6c7a3c6679479\",\n \"score\": \"0.51140994\",\n \"text\": \"def display_data_frame(data_frame):\\n\\n with pd.option_context('display.max_rows', None, 'display.max_columns', None): # more options can be specified also\\n print(data_frame)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf1e92a529a23ea32701ac6ec9e46e36\",\n \"score\": \"0.5068718\",\n \"text\": \"def plot_log(df, var) -> None:\\n\\n df = df.copy()\\n df[var] = np.log(df[var] + 1)\\n sns.displot(df, x=var)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0af0d61f97e9c1df8737454662caad4a\",\n \"score\": \"0.5064501\",\n \"text\": \"def plot_continuous(df, var) -> None:\\n\\n df = df.copy()\\n df['Income'] = df['Income'].astype('category')\\n var_name = var\\n if var in config.SKEWED_NUMERIC_VARS:\\n df[var] = df[var]\\n var_name = 'Log of ' + var_name\\n ax = sns.boxplot(x='Income', y=var, data=df)\\n ax.set(ylabel=var_name, title=f\\\"Effect of {var} on Income\\\")\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"005efd6f5fe0c2f341addce0816a3c8e\",\n \"score\": \"0.50505435\",\n \"text\": \"def visualize_continuous_variables(df, label, method={'type': 'histogram', 'bins': 20}, outlier='on'):\\n # create vector of the variable of interest\\n v = df[label]\\n # define mean and standard deviation\\n m = v.mean()\\n s = v.std()\\n # prep the figure\\n fig, ax = plt.subplots(1, 2, figsize=(14, 4))\\n ax[0].set_title('Distribution of ' + label)\\n ax[1].set_title('Tip % by ' + label)\\n if outlier == 'off': # remove outliers accordingly and update titles\\n v = v[(v - m) <= 3 * s]\\n ax[0].set_title('Distribution of ' + label + '(no outliers)')\\n ax[1].set_title('Tip % by ' + label + '(no outliers)')\\n if method['type'] == 'histogram': # plot the histogram\\n v.hist(bins=method['bins'], ax=ax[0])\\n if method['type'] == 'boxplot': # plot the box plot\\n df.loc[v.index].boxplot(label, ax=ax[0])\\n ax[1].plot(v, df.loc[v.index].tip_percentage, '.', alpha=0.4)\\n ax[0].set_xlabel(label)\\n ax[1].set_xlabel(label)\\n ax[0].set_ylabel('Count')\\n ax[1].set_ylabel('Tip (%)')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3c79f8c6106edcd31610020abb8ff8b\",\n \"score\": \"0.5001463\",\n \"text\": \"def plot_continuous_column(df, target_variable, column_name, fig_size=(9, 12)):\\n f, (ax1, ax2) = plt.subplots(2, figsize=fig_size)\\n tmp_df = df.dropna()\\n sns.distplot(tmp_df[column_name], ax=ax1)\\n sns.boxplot(x=target_variable, y=column_name, data=tmp_df, ax=ax2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db0fbaeaed5291893aca4beb006c0d75\",\n \"score\": \"0.49557826\",\n \"text\": \"def set_df_violin_plot(data, header):\\n df = records_to_df(data)\\n try:\\n df[['user', 'condition', 'block', 'task']] = df[['user', 'condition', 'block', 'task']].astype('category')\\n except KeyError:\\n col_names = [c['id'] for c in header]\\n df = pd.DataFrame(columns=col_names)\\n df.rename(columns={'df1 mean': 'df1', 'df2 mean': 'df2'}, inplace=True)\\n fig = plotting.generate_violin_figure(df, columns=['df1', 'df2'], ytitle='Mean', legend_title=\\\"DOF\\\")\\n return fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2c885c87d0ff416a36c8e524bf40d4a\",\n \"score\": \"0.49128348\",\n \"text\": \"def plot_var(var='positiveIncrease',\\n state='NY'):\\n assert type(var) == str, \\\"Expected string as the variable name\\\"\\n assert type(state) == str, \\\"Expected string as the state name\\\"\\n\\n y = df[df['state'] == state][var]\\n x = df[df['state'] == state]['date']\\n plt.figure(figsize=(12, 4))\\n plt.title(\\\"Plot of \\\\\\\"{}\\\\\\\" for {}\\\".format(var, state), fontsize=18)\\n plt.bar(x=x, height=y, edgecolor='k', color='orange')\\n plt.grid(True)\\n plt.xticks(fontsize=14, rotation=45)\\n plt.yticks(fontsize=14)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c80fdeda37d80a6aae7f35d9019dc81\",\n \"score\": \"0.48900345\",\n \"text\": \"def show_labels(self, var, datasets, text_key):\\n\\t\\tif datasets == ['all']: datasets = self.ds_names\\n\\t\\tds = [self[datasets]] if len(datasets)==1 else self[datasets]\\n\\t\\talias = self._get_alias(datasets)\\n\\t\\ttk = text_key.split('~')\\n\\t\\tetk = tk[1].split()[0] if len(tk) > 1 else None\\n\\t\\ttk = tk[0]\\n\\t\\tall_df = []\\n\\t\\tfor v in var:\\n\\t\\t\\tif not all(v in d for d in ds): continue\\n\\t\\t\\ttexts = [d.text(v, False, tk, etk) if v in d else None\\n\\t\\t\\t\\t\\t for d in ds]\\n\\t\\t\\tindex = pd.MultiIndex.from_tuples([(v, a) for a in alias])\\n\\t\\t\\tdf = pd.DataFrame({text_key: texts}, index=index)\\n\\t\\t\\tall_df.append(df)\\n\\t\\tif not all_df:\\n\\t\\t\\tprint('No variables to show.')\\n\\t\\telse:\\n\\t\\t\\treturn pd.concat(all_df, axis=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f303d01580491b054b8ee02bf003a5d\",\n \"score\": \"0.48365968\",\n \"text\": \"def plot(ds: DatasetLike.TYPE,\\n var: VarName.TYPE,\\n indexers: DictLike.TYPE = None,\\n title: str = None,\\n properties: DictLike.TYPE = None,\\n file: str = None) -> Figure:\\n ds = DatasetLike.convert(ds)\\n\\n var_name = VarName.convert(var)\\n if not var_name:\\n raise ValidationError(\\\"Missing name for 'var'\\\")\\n var = ds[var_name]\\n\\n indexers = DictLike.convert(indexers)\\n properties = DictLike.convert(properties) or {}\\n\\n figure = plt.figure()\\n ax = figure.add_subplot(111)\\n\\n var_data = get_var_data(var, indexers)\\n var_data.plot(ax=ax, **properties)\\n\\n if title:\\n ax.set_title(title)\\n\\n figure.tight_layout()\\n\\n if file:\\n figure.savefig(file)\\n\\n return figure if not in_notebook() else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"147a7cca10cae4658a76af694603d9ef\",\n \"score\": \"0.47928968\",\n \"text\": \"def removeVariable(self, *args):\\n return _libsedml.SedDataGenerator_removeVariable(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"739695fe3ea743b4e0a8fb833a68fa2c\",\n \"score\": \"0.4776736\",\n \"text\": \"def plot_discrete(df, var) -> None:\\n\\n temp = df[['Income', var]].groupby(var).mean().reset_index()\\n axis = sns.barplot(x=var, y='Income', data=temp)\\n axis.set(ylabel=\\\"Probability of earning over 50K\\\")\\n plt.title(f\\\"Effect of {var} on Income\\\")\\n if var == 'weeks worked in year':\\n plt.title(f\\\"Effect of Months worked in year on Income\\\")\\n axis.set(xlabel=\\\"Months worked in year\\\")\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ce3d4781b9c8774a39083c0bd03c66a\",\n \"score\": \"0.4768098\",\n \"text\": \"def plot_missing(self, axis = 1, show=True, **figure_kwargs):\\n if axis == 1:\\n if self.record_missing_cols is None:\\n raise NotImplementedError(\\\"Columns missing values have not been calculated. Run `find_missing with axis = 1`\\\")\\n elif axis == 0:\\n if self.record_missing_rows is None:\\n raise NotImplementedError(\\\"Rows missing values have not been calculated. Run `find_missing with axis = 0`\\\")\\n self.reset_plot()\\n # Histogram of missing values\\n plt.style.use('seaborn-white')\\n plt.figure(figsize = (7, 5))\\n if axis == 1:\\n plt.hist(self.missing_stats_cols['missing_fraction'], bins = np.linspace(0, 1, 11), edgecolor = 'k', linewidth = 0.5)\\n elif axis == 0:\\n plt.hist(self.missing_stats_rows['missing_fraction'], bins = np.linspace(0, 1, 11), edgecolor = 'k', linewidth = 0.5)\\n plt.xticks(np.linspace(0, 1, 11))\\n plt.xlabel('Missing Fraction', size = 14)\\n plt.ylabel('Count of Features', size = 14)\\n plt.title(\\\"Fraction of Missing Values Histogram\\\", size = 16)\\n missing_plot = self.prepro_dir + '/missing.png'\\n plt.savefig(missing_plot, dpi=300)\\n if self.isDisplay == False:\\n plt.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f81b4edbff3dfdaaacd61d1269c7a70d\",\n \"score\": \"0.47633633\",\n \"text\": \"def displayDataFrame(dataFrame):\\n pd.set_option('display.max_rows', dataFrame.shape[0] + 1)\\n pd.set_option('display.max_columns', dataFrame.shape[1] + 1)\\n print(dataFrame)\\n pd.reset_option('display.max_rows')\\n pd.reset_option('display.max_columns')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6b28d65ea32c34efacc762f1e62c65c\",\n \"score\": \"0.4750205\",\n \"text\": \"def removeVariable(self, *args):\\n return _libsedml.SedComputeChange_removeVariable(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c57f8fcc11be295aa3a4e5d2127121d3\",\n \"score\": \"0.47035733\",\n \"text\": \"def plot_graph(dfs,\\n variables_to_plot,\\n titles,\\n ylim=None,\\n xlim=None,\\n save=None,\\n eventname=None,\\n show=True):\\n answer = {} \\n custom_lim = np.ndarray((2,1))\\n def printspanselector(xmin, xmax):\\n if isinstance(xmin, np.datetime64):\\n xmin = date2num(xmin)\\n xmax = date2num(xmax)\\n\\n ind = np.logical_and(date2num(df.index) >= xmin,\\n date2num(df.index) <= xmax)\\n axs[-1].plot(df.index[ind],\\n df[v][ind],\\n 'r+',\\n gid='selected')\\n if 'delete' not in answer:\\n answer['delete'] = []\\n answer['delete'].append([xmin, xmax])\\n\\n def onclick(event, ax):\\n # Only clicks inside this axis are valid.\\n try: # use try/except in case we are not using Qt backend\\n zooming_panning = ( fig.canvas.cursor().shape() != 0 ) # 0 is the arrow, which means we are not zooming or panning.\\n except:\\n zooming_panning = False\\n if zooming_panning: \\n return\\n\\n if eventname == 'choose':\\n plt.close('all')\\n answer['axes'] = event.inaxes.get_title()\\n \\n \\n plt.close('all')\\n fig = plt.figure(figsize = (11.69*(2/3), 8.27/2))\\n gs1 = gridspec.GridSpec(len(dfs), 1)\\n axs=[]\\n for n, df in enumerate(dfs):\\n if n == 0:\\n axs.append(fig.add_subplot(gs1[n],) )\\n else:\\n axs.append(fig.add_subplot(gs1[n], sharex=axs[0]) )\\n\\n plt.title(titles[n])\\n\\n for i,v in enumerate(variables_to_plot):\\n if v in df:\\n if not check_timeseries(df[v].values):\\n typ = '-'\\n else:\\n typ = '+'\\n\\n axs[-1].plot(df.index,\\n df[v],\\n typ,\\n label=v)\\n\\n if xlim:\\n plt.xlim(xlim[0], xlim[1])\\n\\n if ylim:\\n plt.ylim(ylim[0], ylim[1])\\n\\n plt.legend(bbox_to_anchor=(0.0, -.5, 1, 0),\\n loc=\\\"lower left\\\",\\n mode=\\\"expand\\\", ncol=2)\\n \\n fig.autofmt_xdate()\\n\\n if save:\\n plt.savefig(save,\\n format='png',\\n bbox_inches=\\\"tight\\\")\\n\\n if eventname=='choose':\\n cid = fig.canvas.mpl_connect('button_press_event', lambda event: onclick(event, axs[-1]))\\n plt.show()\\n elif eventname=='clean':\\n selector = SpanSelector(axs[-1],\\n onselect=printspanselector,\\n direction='horizontal',\\n useblit=True,\\n span_stays=False,\\n button=1,\\n rectprops={'facecolor': 'grey', 'alpha': 0.3})\\n fig.canvas.mpl_connect('key_press_event',\\n selector)\\n plt.show()\\n else:\\n if show:\\n plt.show()\\n\\n return answer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84b3cc6dad55e823688a60a9dd9f1d6a\",\n \"score\": \"0.47027907\",\n \"text\": \"def drop_superfluous_trace_vars(data):\\n return data.drop([\\\"P0\\\", \\\"co2vmr\\\", \\\"gw\\\", \\\"hyai\\\", \\\"date\\\", \\\"date_written\\\",\\n \\\"datesec\\\", \\\"hyai\\\", \\\"hyam\\\", \\\"hybi\\\", \\\"hybm\\\", \\\"mdt\\\",\\n \\\"nbdate\\\", \\\"nbsec\\\", \\\"ndbase\\\", \\\"ndcur\\\", \\\"nlon\\\", \\\"nsbase\\\",\\n \\\"nscur\\\", \\\"nsteph\\\", \\\"ntrk\\\", \\\"ntrm\\\", \\\"ntrn\\\",\\n \\\"time_written\\\", \\\"wnummax\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62e424f4c821b66a9f0683dc1362dd3c\",\n \"score\": \"0.468913\",\n \"text\": \"def plot_variable(lon, lat, variable, varname):\\n\\n # Create a figure\\n plt.figure(figsize=(12,12))\\n ax = plt.axes(projection=ccrs.PlateCarree())\\n ax.coastlines()\\n \\n # Plot data\\n plt.pcolor(lon, lat, variable, transform=ccrs.PlateCarree(), cmap='RdYlBu_r')\\n plt.title(varname)\\n plt.colorbar(orientation=\\\"horizontal\\\", pad=0.06)#fraction=0.05)\\n \\n # Extent from metadata: ULC: 060.00 -120.00 LRC: 000.00 -030.00 RES:1.00 1.0\\n ax.set_xticks([-120,-60,-30])\\n ax.set_yticks([60,30,0])\\n \\n plt.savefig('snapshot_' + varname + '.png', dpi=300, bbox_inches='tight')\\n plt.show()\\n plt.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e890b9f93a94f0a34c0d76af1446424\",\n \"score\": \"0.4683768\",\n \"text\": \"def show_variable(self, toast: Toast, var_name: str) -> None:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b971ee4f2989e077c9d161fa3847216b\",\n \"score\": \"0.46831873\",\n \"text\": \"def plot_3d(df, target_variable):\\n unique_labels = target_variable.unique()\\n ordinal_encoding = [np.where(unique_labels == label)[0][0]\\n for label in target_variable]\\n color_dict = {0: 'red', 1: 'green', 2: 'blue'}\\n colors = [color_dict[each] for each in ordinal_encoding]\\n threedee = plt.figure().gca(projection='3d')\\n threedee.scatter(df[[0]], df[[1]],\\n df[[2]], color=colors)\\n threedee.set_xlabel(df.columns.values[0])\\n threedee.set_ylabel(df.columns.values[1])\\n threedee.set_zlabel(df.columns.values[2])\\n plt.show()\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbd0f849e14a511d0e2aa2a2fe32b21e\",\n \"score\": \"0.46819878\",\n \"text\": \"def visualize(self, df:DataFrame):# -> dict:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc766e7a0cd6d7c24b37fe6c46efb114\",\n \"score\": \"0.46767002\",\n \"text\": \"def get_var(data, variable):\\n\\n var = data.variables[variable]\\n var_plot = ma.masked_equal(var, var.missing_value)\\n\\n lons = data.variables['lon'][...]\\n lats = data.variables['lat'][...]\\n\\n return var_plot, lons, lats\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ad4c798ac7dc8256ba71957ce08c2f2\",\n \"score\": \"0.46681195\",\n \"text\": \"def plot_solution(self, variable):\\n all_points = [*(self.nodes), *self.internal_points]\\n x = sorted(set([point.coords[0] for point in all_points]))\\n y = sorted(set([point.coords[1] for point in all_points]))\\n z = [[None] * len(x)] * len(y)\\n variable = variable.lower()\\n for point in all_points:\\n point_x = x.index(point.coords[0])\\n point_y = y.index(point.coords[1])\\n if variable == \\\"ux\\\":\\n z[point_y][point_x] = point.u[0]\\n elif variable == \\\"uy\\\":\\n z[point_y][point_x] = point.u[1]\\n elif variable == \\\"sx\\\":\\n z[point_y][point_x] = point.p[0]\\n elif variable == \\\"sy\\\":\\n z[point_y][point_x] = point.p[1]\\n elif variable == \\\"sxy\\\":\\n z[point_y][point_x] = point.p[2]\\n else:\\n print(f\\\"Invalid option: {variable}\\\")\\n self.plot_model(auto_open=False)\\n self.fig.add_contour(\\n name=variable,\\n z=z,\\n x=x,\\n y=y,\\n connectgaps=True,\\n contours=dict(coloring=\\\"heatmap\\\", showlabels=True),\\n colorbar=dict(title=variable, titleside=\\\"right\\\", x=1.2),\\n )\\n self.fig[\\\"layout\\\"].update(title=f\\\"{self.name} ({variable})\\\")\\n return plotly.offline.plot(self.fig, filename=f\\\"{self.name}-{variable}.html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa5d60f3a2f6d30cef38d85daa28dec1\",\n \"score\": \"0.46618274\",\n \"text\": \"def set_proj_var_plot(data):\\n df = records_to_df(data)\\n long_df = analysis.wide_to_long(df, ['parallel', 'orthogonal'], suffixes='variance', j='projection')\\n fig = plotting.generate_lines_plot(long_df, \\\"variance\\\", by='user', color_col='projection')\\n return fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee269e097d579cb2a24ab667fa58037b\",\n \"score\": \"0.46423653\",\n \"text\": \"def plot_against_target(self, feature):\\n assert feature != self._target_variable\\n\\n if feature in self.numeric_features:\\n self._dataset[[feature, self._target_variable]].plot.scatter(x=feature,\\n y=self._target_variable,\\n alpha=0.1,\\n title='{0} vs. target (`{1}`)'.\\n format(feature,\\n self._target_variable))\\n else:\\n title = '{0} vs. target (`{1}`)'.format(feature, self.target_variable)\\n self._dataset[[feature, self._target_variable]].boxplot(by=feature)\\n plt.ylabel(self.target_variable)\\n plt.title(title)\\n plt.suptitle(\\\"\\\")\\n plt.tight_layout()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e9637d4e9abc6369930a68c1502b95e\",\n \"score\": \"0.46198583\",\n \"text\": \"def set_variance_graph(table_data):\\n df = records_to_df(table_data)\\n df.dropna(inplace=True)\\n return plotting.generate_means_figure(df)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82efd8c8b683ed6091a8576b3df1086d\",\n \"score\": \"0.46060088\",\n \"text\": \"def plotData(dataFrame, titleString, saveToFile=False, nsamples = 100, vars=None):\\n\\n\\t#plot nsamples random elements from the sample\\n\\trandom_elements = random.sample(range(dataFrame.shape[0]), nsamples)\\n\\tdataFrame_plot = dataFrame.iloc[random_elements]\\n\\n\\tif vars != None:\\n\\t\\tp = sns.pairplot(dataFrame_plot, hue='AHUNumber', diag_kind='kde', vars=vars, dropna=True)\\n\\telse:\\n\\t\\tp = sns.pairplot(dataFrame_plot, hue='AHUNumber', diag_kind='kde', dropna=True)\\n\\n\\tplt.suptitle(titleString, y = 1.025)\\n\\n\\tif saveToFile == True:\\n\\t\\tplt.savefig('testfig.png', format='png', pad_inches=0.5, bbox_inches='tight')\\n\\t\\tplt.close()\\n\\telse:\\n\\t\\tpass\\n\\t\\t#plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"174b0d1fc32bac49ceac43e70e10a012\",\n \"score\": \"0.46051374\",\n \"text\": \"def plot(self, **kwargs):\\n plotdata = self.plottable_data\\n if plotdata:\\n plotdata.plot(**kwargs)\\n else:\\n raise NeXusError(\\\"There is no plottable data\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d347fcfa2ffeceefe3ec788c6b9ec835\",\n \"score\": \"0.45513475\",\n \"text\": \"def removeVariable(self, *args):\\n return _libsedml.SedSetValue_removeVariable(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52d761854d1c29b36faeda1e5af7e583\",\n \"score\": \"0.4534381\",\n \"text\": \"def show_cats(self, var, text_key):\\n\\t\\ttext_key = text_key.split('~')\\n\\t\\tetk = text_key[1].split()[0] if len(text_key) > 1 else None\\n\\t\\ttext_key = text_key[0]\\n\\t\\tdf_all_v = []\\n\\t\\tfor v in var:\\n\\t\\t\\tall_df = []\\n\\t\\t\\tfor name in list(self.ds_alias.values()):\\n\\t\\t\\t\\tif v in self[name]:\\n\\t\\t\\t\\t\\tval = self[name].value_texts(v, text_key, etk)\\n\\t\\t\\t\\t\\tcodes = self[name].codes(v)\\n\\t\\t\\t\\t\\tindex = pd.MultiIndex.from_tuples([(v, c) for c in codes])\\n\\t\\t\\t\\t\\tdf = pd.DataFrame(val, index=index, columns=[name])\\n\\t\\t\\t\\t\\tall_df.append(df)\\n\\t\\t\\tall_df = pd.concat(all_df, axis=1)\\n\\t\\t\\tdf_all_v.append(all_df)\\n\\t\\tif not df_all_v:\\n\\t\\t\\tprint('No variables to show.')\\n\\t\\telse:\\n\\t\\t\\treturn pd.concat(df_all_v, axis=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c460d6f20fc4d69bed840f326ee71a3a\",\n \"score\": \"0.44996905\",\n \"text\": \"def plot_categorical_column(df, target_variable, column_name, fig_size=(9, 12), y_lim=None):\\n f, (ax1, ax2) = plt.subplots(2, figsize=fig_size)\\n tmp_df = df.dropna()\\n sns.countplot(x=column_name, data=tmp_df, ax=ax1)\\n sns.pointplot(x=column_name, y=target_variable, data=tmp_df, ax=ax2)\\n if y_lim is not None:\\n ax2.set_ylim(0, y_lim)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31dcef4c8847e4789643aaf5cb79239e\",\n \"score\": \"0.44970563\",\n \"text\": \"def require_variable(df, variable, unit=None, year=None, exclude_on_fail=False,\\n **kwargs):\\n fdf = df.filter(**kwargs)\\n if len(fdf.data) > 0:\\n vdf = fdf.require_variable(variable=variable, unit=unit, year=year,\\n exclude_on_fail=exclude_on_fail)\\n df.meta['exclude'] |= fdf.meta['exclude'] # update if any excluded\\n return vdf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a9b710f33eaca9bece16fa9e4362da0\",\n \"score\": \"0.44876218\",\n \"text\": \"def unsuppress(self):\\n self._pipe.send((None, 'show', None))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a073a4e1e8b5823d0c2be47feae53f7\",\n \"score\": \"0.4475288\",\n \"text\": \"def plot(self, df, plotdir):\\n df.plot()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4aed811f4a0431f31970dec2f4ca576a\",\n \"score\": \"0.44718388\",\n \"text\": \"def plot_single_variable(self, results, xlab=None, ylab=None, save=None):\\n\\n n, m = self.x.shape\\n if m != 2:\\n raise ValueError('Error: can only plot single explanatory variable. Current X shape: ({},{})'.format(n, m))\\n X = self.x.drop('const', axis=1)\\n X = np.squeeze(X.values)\\n Y = np.squeeze(self.y.values)\\n plt.plot(X, Y, 'o', c='steelblue')\\n Xmin = np.argmin(X)\\n Xmax = np.argmax(X)\\n plt.plot([X[Xmin], X[Xmax]], [self.preds[Xmin], self.preds[Xmax]], c='darkorange')\\n if xlab:\\n plt.xlabel(xlab)\\n if ylab:\\n plt.ylabel(ylab)\\n if save:\\n plt.savefig('{}.png'.format(save), dpi=200)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acb0f674d7c2303a1019fee9596426f2\",\n \"score\": \"0.44626507\",\n \"text\": \"def display(df):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f92ec7c3a117c72a81c791bdbfc54a3\",\n \"score\": \"0.4445461\",\n \"text\": \"def print_dataframe(data):\\n with pd.option_context('display.max_rows', None, 'display.max_columns', None):\\n print(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"215c3df97025653743c1890ed0d2c221\",\n \"score\": \"0.44444445\",\n \"text\": \"def remove_variable(self, varname):\\n self.graph.remove_node(self.variables[varname])\\n \\n #removes the class of varname (should there be no other variables that share that class) \\n #from classes_imported set \\n for key,value in self.classes_available.items():\\n if isinstance(self.variables[varname], value):\\n self.classes_imported.remove(key) \\n\\n #remove the varname from the variables dictionary \\n del self.variables[varname]\\n self.convert_to_script()\\n self.graph_changed_event = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edc07226f64f3c33738730d8089e6428\",\n \"score\": \"0.44427666\",\n \"text\": \"def _plot_var(self, ax, **kwargs):\\n self.__xmin, self.__xmax = ax.get_xlim()\\n self.__ymin, self.__ymax = ax.get_ylim()\\n plot_digit = kwargs.get('plot_digit', None)\\n if plot_digit is not None:\\n self.__prec = '%.' + '%d' % plot_digit + 'f'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05543b631cc28eab1499d4a0303f5622\",\n \"score\": \"0.44299516\",\n \"text\": \"def set_dropextradata(self, dropextradata):\\n self.options['dropextradata'] = dropextradata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"498c7472384572bf9b63e89977b3b9a2\",\n \"score\": \"0.4422164\",\n \"text\": \"def box_plot() -> str:\\n\\n session_manager.cache_analysis_option()\\n return StatsModel().get_box_plot()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b2024b879b7e0c085a117ee9ab652b2\",\n \"score\": \"0.4414972\",\n \"text\": \"def logplot(self, **kwargs):\\n plotdata = self.plottable_data\\n if plotdata:\\n plotdata.logplot(**kwargs)\\n else:\\n raise NeXusError(\\\"There is no plottable data\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc7637e0f986c8d599c82f0c7bfa9bbd\",\n \"score\": \"0.44135877\",\n \"text\": \"def plot(filename, column):\\n df=pd.read_csv(filename)\\n if column is None:\\n df.hist();\\n else:\\n df[column].hist()\\n plt.show() # if slow, this blocks all subsequent code from running # it waits for the window to close before continuing\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c17307ab1248182037bdaadea9cc6336\",\n \"score\": \"0.44075608\",\n \"text\": \"def x_vs_y_plots(X,y,save_toggle=False,file_prefix=''):\\n if len(file_prefix)>0:\\n file_prefix += '_'\\n \\n df = pd.concat([X, pd.DataFrame(y, index=X.index)], axis=1)\\n for x in X.columns:\\n df[[x,y.name]].groupby(x).mean().plot()\\n if save_toggle:\\n plt.savefig(file_prefix+x+'_vs_'+y.name+'.png')\\n plt.show()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0889a47d34fccef58e97603b04c39123\",\n \"score\": \"0.43964615\",\n \"text\": \"def show_figure(self, *args, **kwargs):\\n if 'divider' in dir(self):\\n del self.divider\\n plt.show(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e42c056ce7bb2a7c997fe17f18e2da84\",\n \"score\": \"0.43961328\",\n \"text\": \"def implot(self, **kwargs):\\n plotdata = self.plottable_data\\n if plotdata:\\n plotdata.implot(**kwargs)\\n else:\\n raise NeXusError(\\\"There is no plottable data\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94c1e8bd58d88d907afa606c72266977\",\n \"score\": \"0.4391739\",\n \"text\": \"def plot_xy(varx='totalTestResultsIncrease',\\n vary='positiveIncrease',\\n state='NY'):\\n assert type(varx) == str, \\\"Expected string as the variable x name\\\"\\n assert type(vary) == str, \\\"Expected string as the variable y name\\\"\\n\\n y = df[df['state'] == state][vary]\\n x = df[df['state'] == state][varx]\\n if (x.nunique() != 1) and (y.nunique() != 1):\\n plt.figure(figsize=(12, 4))\\n plt.title(\\\"Plot of \\\\\\\"{}\\\\\\\" vs. \\\\\\\"{}\\\\\\\" for {}\\\".format(varx, vary, state), fontsize=18)\\n plt.scatter(x=x, y=y, edgecolor='k', color='lightgreen', s=100)\\n plt.grid(True)\\n plt.xticks(fontsize=14, rotation=45)\\n plt.yticks(fontsize=14)\\n plt.show()\\n else:\\n print(\\\"Some of the data unavailable for a scatter plot. Sorry!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb8afd28e29829a317cacd43faa15d70\",\n \"score\": \"0.4388029\",\n \"text\": \"def plot(\\n self,\\n components=None,\\n *,\\n ax=None,\\n logy=False,\\n show=False,\\n dates=False,\\n legend=True,\\n grid=True,\\n ):\\n ax = ax or plt.gca()\\n kwargs = {\\\"logy\\\": logy, \\\"ax\\\": ax, \\\"grid\\\": grid, \\\"legend\\\": legend}\\n\\n def get_column(col):\\n if dates:\\n col += \\\":dates\\\"\\n data = self[col]\\n return data\\n\\n components = self.meta.variables if components is None else components\\n for col in components:\\n data = get_column(col)\\n data.plot(**kwargs)\\n if show:\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cf0aa71c26b31f4c997c8c2afaf8893\",\n \"score\": \"0.4385104\",\n \"text\": \"def print_df(df: pd.DataFrame) -> None:\\n\\twith pd.option_context('display.max_rows', None, 'display.max_columns', None):\\n\\t\\tprint(df)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72e57320b475c20dc2f96fd48da6fca7\",\n \"score\": \"0.43605706\",\n \"text\": \"def display_na(df):\\r\\n \\r\\n display(HTML(\\\"Percentage of missing variables for each feature:\\\"))\\r\\n print(df.isnull().sum(axis=0) * 100 / len(df))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d004de844130a45592187867f7c68fd\",\n \"score\": \"0.4355372\",\n \"text\": \"def show(self, *args, **kwargs):\\n self.get_plot(*args, **kwargs).show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c08b4f4854337048144d53f798e073eb\",\n \"score\": \"0.43472216\",\n \"text\": \"def plot_data(df, title=\\\"Stock prices\\\", xlabel=\\\"Date\\\", ylabel=\\\"Price\\\"):\\n ax = df.plot(title=title, fontsize=12)\\n ax.set_xlabel(xlabel)\\n ax.set_ylabel(ylabel)\\n plt.show() # must be called to show plots in some environments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e2c6fd0d9dca232109a4340a5c5a1a9\",\n \"score\": \"0.43427277\",\n \"text\": \"def extra_plot_variance_explained(var_explained, component,var_explained_2, component_2, *args):\\n opt_n_comp = np.argmin(var_explained) - 1\\n stdout.write(\\\"\\\\n\\\")\\n # plot var_explained for each component\\n with plt.style.context((\\\"ggplot\\\")):\\n ax = plt.subplot(111)\\n plt.plot(component, np.array(var_explained), \\\"-v\\\", color=\\\"red\\\", mfc=\\\"red\\\", label=\\\"train\\\")\\n plt.plot(\\n component[opt_n_comp], np.array(var_explained)[opt_n_comp], \\\"P\\\", ms=10, mfc=\\\"red\\\"\\n )\\n plt.plot(component_2, np.array(var_explained_2), \\\"-v\\\", color=\\\"blue\\\", mfc=\\\"blue\\\", label=\\\"test\\\")\\n plt.plot(\\n component_2[opt_n_comp], np.array(var_explained_2)[opt_n_comp], \\\"P\\\", ms=10, mfc=\\\"red\\\"\\n )\\n plt.xlabel(\\\"Number of PLS components\\\")\\n plt.ylabel(\\\"Variance explained %\\\")\\n plt.title(\\\"PLS\\\")\\n plt.xlim(0-1)\\n ax.legend()\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a4f7b6cbfef0a5defdf6be992e65669\",\n \"score\": \"0.4340944\",\n \"text\": \"def plot_runner(ds, variable, s3upload=False) -> str:\\n\\n model_type = get_model_type(ds)\\n if model_type == 'roms':\\n imagename = plot_roms(ds_ofs, var, s3upload=True)\\n elif model_type == 'fvcom':\\n imagename = plot_fvcom(ds_ofs, var, s3upload=True)\\n else:\\n print(f\\\"ERROR: Unsupported model type - {modeltype}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"473bf765662306bc925b6e3848dabee6\",\n \"score\": \"0.43398735\",\n \"text\": \"def hide_column(self, column_name):\\n if not isinstance(column_name, str):\\n raise TypeError(\\\"column_name must be of type str\\\")\\n\\n column_names = self.get_column_names()\\n if column_name not in column_names:\\n raise ValueError(f\\\"{column_name} not in available columns ({column_names})\\\")\\n new_column_names = [cn for cn in self.get_column_names(True)\\n if cn not in column_name]\\n return self.set_visible_columns(new_column_names)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"715c6a713767779ca425cadeff548c21\",\n \"score\": \"0.43344194\",\n \"text\": \"def display(self, *fields):\\n try:\\n if self.df is None:\\n self.warning(\\\"Dataframe is empty: nothing to display\\\")\\n return\\n df2 = self.df[list(fields)]\\n return df2.head()\\n except Exception as e:\\n self.err(e, self.display, \\\"Can not display dataframe\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab189fb4de3b5e34800eba35dea524ba\",\n \"score\": \"0.43340218\",\n \"text\": \"def plot_hovmoeller(ds: xr.Dataset,\\n var: VarName.TYPE = None,\\n x_axis: DimName.TYPE = None,\\n y_axis: DimName.TYPE = None,\\n method: str = 'mean',\\n contour: bool = True,\\n title: str = None,\\n file: str = None,\\n monitor: Monitor = Monitor.NONE,\\n **kwargs) -> Figure:\\n var_name = None\\n if not var:\\n for key in ds.data_vars.keys():\\n var_name = key\\n break\\n else:\\n var_name = VarName.convert(var)\\n var = ds[var_name]\\n\\n if not x_axis:\\n x_axis = var.dims[0]\\n else:\\n x_axis = DimName.convert(x_axis)\\n\\n if not y_axis:\\n try:\\n y_axis = var.dims[1]\\n except IndexError:\\n raise ValidationError('Given dataset variable should have at least two dimensions.')\\n else:\\n y_axis = DimName.convert(y_axis)\\n\\n if x_axis == y_axis:\\n raise ValidationError('Dimensions should differ between plot axis.')\\n\\n dims = list(var.dims)\\n try:\\n dims.remove(x_axis)\\n dims.remove(y_axis)\\n except ValueError:\\n raise ValidationError('Given dataset variable: {} does not feature requested dimensions:\\\\\\n {}, {}.'.format(var_name, x_axis, y_axis))\\n\\n ufuncs = {'min': np.nanmin, 'max': np.nanmax, 'mean': np.nanmean,\\n 'median': np.nanmedian, 'sum': np.nansum}\\n\\n with monitor.starting(\\\"Plot Hovmoeller\\\", total_work=100):\\n monitor.progress(5)\\n with monitor.child(90).observing(\\\"Aggregate\\\"):\\n var = var.reduce(ufuncs[method], dim=dims)\\n monitor.progress(5)\\n\\n figure = plt.figure()\\n ax = figure.add_subplot(111)\\n if x_axis == 'time':\\n figure.autofmt_xdate()\\n\\n if contour:\\n var.plot.contourf(ax=ax, x=x_axis, y=y_axis, **kwargs)\\n else:\\n var.plot.pcolormesh(ax=ax, x=x_axis, y=y_axis, **kwargs)\\n\\n if title:\\n ax.set_title(title)\\n\\n figure.tight_layout()\\n\\n if file:\\n figure.savefig(file)\\n\\n return figure if not in_notebook() else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e45e26120e690f3dad49ac9e218a7480\",\n \"score\": \"0.43270665\",\n \"text\": \"def remove_variable(self, var):\\n self.vars.remove(var)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f45be729aa0e30132fc58c88ddb34ca\",\n \"score\": \"0.4326928\",\n \"text\": \"def subset_variables(ds, variableList): # {{{\\n\\n allvars = ds.data_vars.keys()\\n\\n # get set of variables to drop (all ds variables not in vlist)\\n dropvars = set(allvars) - set(variableList)\\n\\n # drop spurious variables\\n ds = ds.drop(dropvars)\\n\\n # must also drop all coordinates that are not associated with the variables\\n coords = set()\\n for avar in ds.data_vars.keys():\\n coords |= set(ds[avar].coords.keys())\\n dropcoords = set(ds.coords.keys()) - coords\\n\\n # drop spurious coordinates\\n ds = ds.drop(dropcoords)\\n\\n if len(ds.data_vars.keys()) == 0:\\n raise ValueError(\\n 'Empty dataset is returned.\\\\n'\\n 'Variables {}\\\\n'\\n 'are not found within the dataset '\\n 'variables: {}.'.format(variableList, allvars))\\n\\n return ds # }}}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64cfecc9efd06e5098ce91f2f6a118a5\",\n \"score\": \"0.43260407\",\n \"text\": \"def distribution_plot(dataframe: pd.DataFrame, var: str, target: str = None, **kwargs):\\n\\n row = kwargs.get('row', None)\\n col = kwargs.get('col', None)\\n\\n size = kwargs.get('size', None)\\n aspect = kwargs.get('aspect', None)\\n\\n facet = sns.FacetGrid(\\n dataframe,\\n hue=target,\\n size=size if size else 3,\\n aspect=aspect if aspect else 4,\\n row=row,\\n col=col\\n )\\n\\n facet.map(sns.kdeplot, var, shade=True)\\n facet.set(title=\\\"Distribution of '{}' over '{}'\\\".format(var, target))\\n\\n # Defines limits\\n if kwargs.get('clean', False):\\n xmin = dataframe[var].quantile(0.02)\\n xmax = dataframe[var].quantile(0.98)\\n facet.set(xlim=(xmin, xmax))\\n\\n facet.add_legend()\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54230d6985c38be8af9232a0bf01ee18\",\n \"score\": \"0.43237677\",\n \"text\": \"def rename_variable(self, toast: Toast, var_name: str, display_name: str) -> None:\\n try:\\n return self.active_view.rename_variable(toast, var_name, display_name)\\n except NotImplementedError:\\n toast.show(f\\\"{self._active} does not implement variable renaming\\\", ToastType.warning)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5290b0dbee071b9ff806e7ed58e3ceb1\",\n \"score\": \"0.43195853\",\n \"text\": \"def setShowInX(self, x):\\r\\n\\t\\tself.__showInX=x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ca95d946d23156598f83a8c528ad2ac\",\n \"score\": \"0.43146688\",\n \"text\": \"def plot_dis(df_master, df_dis, units):\\n modul = df_master.modul\\n stor = '{}_stor'.format(modul)\\n loss = '{}_loss'.format(modul)\\n relax = '{}_relax'.format(modul)\\n\\n if df_master.domain == 'freq':\\n fig, (ax1, ax2) = plt.subplots(1,2, figsize=(8,0.75*4))\\n df_master.plot(x='f', y=[stor], label=[\\\"{}'(filter)\\\".format(modul)],\\n ax=ax1, logx=True, logy=True, color=['C0'], alpha=0.5)\\n df_master.plot(x='f', y=[loss], label=[\\\"{}''(filter)\\\".format(modul)],\\n ax=ax2, logx=True, logy=True, color=['C1'], alpha=0.5)\\n df_dis.plot(x='f', y=[stor], label=['tau_i'], ax=ax1, \\n logx=True, logy=True, ls='', marker='o', color=['C0'])\\n df_dis.plot(x='f', y=[loss], label=['tau_i'], ax=ax2, \\n logx=True, logy=True, ls='', marker='o', color=['C1'])\\n ax1.set_xlabel('Frequency (Hz)')\\n ax1.set_ylabel('Storage modulus ({})'.format(units[stor]))\\n ax2.set_xlabel('Frequency (Hz)')\\n ax2.set_ylabel('Loss modulus ({})'.format(units[stor])) \\n ax1.legend()\\n ax2.legend()\\n fig.show()\\n return fig\\n elif df_master.domain == 'time':\\n fig, ax1 = plt.subplots(figsize=(4,0.75*4))\\n df_master.plot(x='t', y=[relax], \\n ax=ax1, logx=True, logy=True, color=['k'])\\n df_dis.plot(x='t', y=[relax], label = ['tau_i'], \\n ax=ax1, logx=True, logy=True, ls='', marker='o', color=['red'])\\n ax1.set_xlabel('Time ({})'.format(units['t']))\\n ax1.set_ylabel('Relaxation modulus ({})'.format(units[relax]))\\n ax1.legend()\\n fig.show()\\n return fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35832c1e396d3f8c41dcf63d2a46273b\",\n \"score\": \"0.43139654\",\n \"text\": \"def hide_xticks(ax):\\n for tick in ax.axes.get_xticklabels():\\n tick.set_visible(False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e63b785758284832390cce3c328b022\",\n \"score\": \"0.43101996\",\n \"text\": \"def plot_data(df, title=\\\"Stock prices\\\", xlabel=\\\"Date\\\", ylabel=\\\"Price\\\"):\\n ax = df.plot(title=title, fontsize=12)\\n ax.set_xlabel(xlabel)\\n ax.set_ylabel(ylabel)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e63b785758284832390cce3c328b022\",\n \"score\": \"0.43101996\",\n \"text\": \"def plot_data(df, title=\\\"Stock prices\\\", xlabel=\\\"Date\\\", ylabel=\\\"Price\\\"):\\n ax = df.plot(title=title, fontsize=12)\\n ax.set_xlabel(xlabel)\\n ax.set_ylabel(ylabel)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb45a6f55ba0db29f2a75fa25b824f96\",\n \"score\": \"0.43083325\",\n \"text\": \"def show(self, *args, **kwds):\\n self.plot(*args, **kwds).show()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b798880aba7d93b422ba79a8dafd758\",\n \"score\": \"0.43063605\",\n \"text\": \"def plot_discharge(self, data, **kw):\\n x = 0\\n print 'data', data\\n for key in data:\\n print 'key', key\\n if key == '11462125 peak':\\n print 'skipping peak discharge df'\\n elif key == 'CLV - Cloverdale hourly':\\n for col in self._col_list[:2]:\\n ser = data[key]['Dataframe'][col]\\n if not ser.empty:\\n self._plot(col, key, ser, **kw)\\n\\n elif key == 'COY - Coyote hourly':\\n for col in self._col_list:\\n ser = data[key]['Dataframe'][col]\\n if not ser.empty:\\n self._plot(col, key, ser, **kw)\\n else:\\n for col in self._col_list:\\n ser = data[key]['Dataframe'][col]\\n print 'nan values: {}'.format(count_nonzero(isnan(ser)))\\n print 'len ser: {}'.format(len(ser))\\n if len(ser) != count_nonzero(isnan(ser)):\\n self._plot(col, key, ser, **kw)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ae8dc9da6845384c103464f8a1f1451\",\n \"score\": \"0.43056202\",\n \"text\": \"def set_display():\\r\\n # Plots display settings\\r\\n plt.style.use('fivethirtyeight')\\r\\n plt.rcParams['figure.figsize'] = 12, 8\\r\\n plt.rcParams.update({'font.size': 14})\\r\\n # DataFrame display settings\\r\\n pd.set_option('display.max_columns', None)\\r\\n pd.set_option('display.max_rows', None)\\r\\n pd.options.display.float_format = '{:.4f}'.format\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30b66c30a50d93c31d6e8fbe9796c40a\",\n \"score\": \"0.42991424\",\n \"text\": \"def boxplot_one_country(df,country,AoIs= 'All',save=False,fig=None,ax=None,**kwargs):\\n df = df.loc[df['country'] == country]\\n #if AoIs != 'All': df2 = df2.loc[df2['AoI combinations'].isin(AoIs)]\\n\\n if (fig == None and ax == None): # if No axes and no figure is provided\\n fig, ax = plt.subplots(figsize=(12, 6))\\n\\n df.boxplot(by='AoI combinations', column='disrupted', ax=ax,grid=False,**kwargs)\\n ax.set_xlabel(\\\"Number of combinations of flood events (AoI)\\\")\\n ax.set_ylabel(\\\"% preferred routes disrupted\\\")\\n ax.set_title(\\\"% routes between NUTS-3 regions in {} disrupted\\\".format(country))\\n\\n if save:\\n save_figs = load_config(config_file)['paths']['output_images'] / 'disrupted'\\n if not save_figs.exists(): save_figs.mkdir()\\n filename = \\\"disrupted_boxplot_{}.png\\\".format(country)\\n fig.savefig(save_figs / filename)\\n\\n return fig,ax\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cf843c3246768fbe1cd025b86929885\",\n \"score\": \"0.42943284\",\n \"text\": \"def plot_categoricial(df, var) -> None:\\n temp = df[['Income', var]].groupby(var).mean().reset_index()\\n axis = sns.barplot(x=var, y='Income', data=temp)\\n axis.set(ylabel=\\\"Probability of earning over 50K\\\")\\n axis.set_xticklabels(config.vis_dict[var], rotation=75)\\n plt.title(f\\\"Effect of {var} on Income\\\")\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6b30d96205aae6597f9e8a95779b74e\",\n \"score\": \"0.42883715\",\n \"text\": \"def plot_line_chart(df: pd.DataFrame, title: str, y: str = 'y', x: str = 'x',):\\n px.line(df, x=x, y=y, title=title).show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99f63f08277116886413b5dfbbc45efa\",\n \"score\": \"0.42838115\",\n \"text\": \"def datatoplot(self, track_choice, track_no=None, track_index=None):\\r\\n if track_choice == 'evo':\\r\\n tracks = self.data\\r\\n elif track_choice == 'ranged':\\r\\n if self.ranged_tracks is None:\\r\\n raise NameError('No ranged tracks!')\\r\\n else: tracks = self.ranged_tracks\\r\\n else: raise NameError('Wrong track name!')\\r\\n \\r\\n max_tracks = tracks['track_no'].nunique()\\r\\n uniques = tracks['track_no'].unique()\\r\\n if track_no != None:\\r\\n if max_tracks > track_no:\\r\\n if type(track_index)==type(None):\\r\\n track_index=np.random.choice(uniques, track_no)\\r\\n else: track_index = uniques\\r\\n else: track_index = uniques\\r\\n if len(track_index)>200:\\r\\n raise ValueError('Too many tracks, are you sure you want to plot '+str(len(track_index))+' tracks??')\\r\\n \\r\\n selected = tracks.loc[tracks['track_no'].isin(track_index)]\\r\\n plot_tracks = [selected['Teff'], selected['L']]\\r\\n plot_m = selected['mass']\\r\\n return plot_tracks, plot_m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5513626174155422f31e167cfb56c003\",\n \"score\": \"0.42826742\",\n \"text\": \"def set_df_mean_plot(data):\\n df = records_to_df(data)\\n try:\\n # For error bars we need standard deviation.\\n df[['df1 std', 'df2 std']] = df[['df1 variance', 'df2 variance']].transform(np.sqrt)\\n except KeyError:\\n pass\\n # Convert to long format for easier plotting.\\n long_df = analysis.wide_to_long(df, stubs=['df1', 'df2'], suffixes=['mean', 'std'], j='dof')\\n fig = plotting.generate_lines_plot(long_df, \\\"mean\\\", by='user', color_col='dof', errors='std', jitter=True)\\n return fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2d4e630a8059c1b12652584c43c00fd\",\n \"score\": \"0.4279645\",\n \"text\": \"def easy_print_data_frame(data_frame):\\n\\n print(data_frame.to_string())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eef7324d0a5850a8c8ac3a856b4b9dfb\",\n \"score\": \"0.42760804\",\n \"text\": \"def _plot_variance_explained(var_explained, component, *args):\\n opt_n_comp = np.argmin(var_explained) - 1\\n stdout.write(\\\"\\\\n\\\")\\n # plot var_explained for each component\\n with plt.style.context((\\\"ggplot\\\")):\\n plt.plot(component, np.array(var_explained), \\\"-v\\\", color=\\\"blue\\\", mfc=\\\"blue\\\")\\n plt.plot(\\n component[opt_n_comp], np.array(var_explained)[opt_n_comp], \\\"P\\\", ms=10, mfc=\\\"red\\\"\\n )\\n plt.xlabel(\\\"Number of PLS components\\\")\\n plt.ylabel(\\\"Variance explained %\\\")\\n plt.title(\\\"PLS\\\")\\n plt.xlim(0-1)\\n\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d83e8777568cac18f63c1b5039fa7e43\",\n \"score\": \"0.4275368\",\n \"text\": \"def show():\\n plt.show(block=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4441f564cdf782290c8b56b76d3b9153\",\n \"score\": \"0.4270931\",\n \"text\": \"def show_plot(self, table):\\n assert (self._current_panel is not None)\\n graph = wxtbx.plots.iotbx_data_plot_base(\\n parent=self._current_panel,\\n tables=[table],\\n size=(TEXT_WIDTH - 40, min(500, TEXT_WIDTH * 0.75)))\\n graph.set_plot(table.only_plot())\\n self._graphs.append((graph, table.title))\\n self._current_sizer.Add(graph, 0, wx.ALL|wx.EXPAND, 5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a774101cb8988ac9fa189c9a6f651b21\",\n \"score\": \"0.42703143\",\n \"text\": \"def plot_two_vars(self,\\n xy_data, # ['my_datadict_out', e.g., 'merge_summstat_out']\\n x_name, # [name of x variable to use for x-axis]\\n y_name, # [name of y variable to use for y-axis]\\n ent_axis = 'col', # [<'row', 'col'>] => how variables are situated]\\n err_data = None, # [ => e.g., 'merge_summstat_out']\\n x_err = None, # [ => create bubbles]\\n y_err = None, # [ => create bubbles]\\n color_by = None, # [ => color-code bubbles]\\n cosine_correct = 'coord|corr', # [ => correct for cosine between variables]\\n max_cos = 0.95, # => trigger exception when cos(x, y) > max_cos]\\n plot = None, # [ => see docs to customize]\\n savefig = 'xy_plot.png' # [ => how to output chart]\\n ):\\n\\n if self.verbose is True:\\n print 'plot_two_vars() is working...\\\\n'\\n\\n # Run the damon utility\\n plot_two_vars_out = dmn.utils._plot_two_vars(locals())\\n self.plot_two_vars_out = plot_two_vars_out\\n\\n if self.verbose is True:\\n print 'plot_two_vars() is done -- see my_obj.plot_two_vars_out'\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d4531cd130c5272a5d9ffa4d886b3ee\",\n \"score\": \"0.42631972\",\n \"text\": \"def _plot_skipped_well_file(config, raw_data, well_name):\\n if config.plot_skipped:\\n plot_params = dict()\\n plot_params['savepath'] = config.work_dir\\n plot_params['well_name'] = well_name\\n plot_params['fg_conc_edges'] = config.fg_conc_edges\\n plot_params['fg_fret_edges'] = config.fg_fret_edges\\n plot_params['data_df'] = raw_data\\n plot_params['fine_grid_xlim'] = None\\n plot_params['fine_grid_ylim'] = None\\n plot_params['plot_type'] = config.plot_type\\n plot_fine_grid_profiles(plot_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11adb8baf5e82cdd7884c2a1f79cd1e1\",\n \"score\": \"0.42607546\",\n \"text\": \"def do_variable(self, arg):\\n self.display_cmake_help('variable', arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1862e5d6a2dd0e4bdab216750261911f\",\n \"score\": \"0.42592025\",\n \"text\": \"def strip_data(df: pd.DataFrame, data_to_keep: dict):\\n gene_name = 'symbol'\\n if df.index.name == data_to_keep.get(gene_name):\\n data_to_keep.pop(gene_name)\\n new_df = df[data_to_keep.values()]\\n return new_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a745c78232fa241c5ee33a4d7e4af63e\",\n \"score\": \"0.4254119\",\n \"text\": \"def bokeh_show(bokeh_object, filename=None, **save_kwargs):\\n if filename is None:\\n filename = \\\"bokehplot_00000000.html\\\"\\n i = 1\\n while i < 99999999 and os.path.isfile(filename):\\n filename = \\\"bokehplot_{0:08d}.html\\\".format(i)\\n i += 1\\n\\n if i == 99999999:\\n raise RuntimeError(\\n \\\"Cannot generate generic file name, as all filenames are used.\\\"\\n )\\n\\n if \\\"resources\\\" in save_kwargs:\\n resources = save_kwargs.pop(\\\"resources\\\")\\n else:\\n resources = bokeh.resources.CDN\\n\\n if \\\"title\\\" in save_kwargs:\\n title = save_kwargs.pop(\\\"title\\\")\\n else:\\n title = \\\"Bokeh Plot\\\"\\n\\n bokeh.io.save(\\n bokeh_object, filename=filename, resources=resources, title=title, **save_kwargs\\n )\\n\\n return HTML(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"338a9905885d36026a7596b50df165de\",\n \"score\": \"0.42535764\",\n \"text\": \"def oplot(self, **kwargs):\\n plotdata = self.plottable_data\\n if plotdata:\\n plotdata.oplot(**kwargs)\\n else:\\n raise NeXusError(\\\"There is no plottable data\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d7c065190303a0310e9f02a8760beaa\",\n \"score\": \"0.4251952\",\n \"text\": \"def print_1d_visual(self, df, tuning_param):\\n axis = {\\n \\\"accuracy\\\": \\\"Accuracy (%)\\\",\\n \\\"false_positive\\\": \\\"False Positive Rate (%)\\\",\\n \\\"false_negative\\\": \\\"False Negative Rate (%)\\\"\\n }\\n plt.clf()\\n sns.set(style='darkgrid')\\n sns.lineplot(x=tuning_param, y=axis[self.metric], data=df)\\n fig = plt.gcf()\\n fig.savefig('%s/OptimizationLineGraph.png' % self.path, bbox_inches='tight')\\n plt.close()\\n df.to_csv(\\\"%s/optimize_results.csv\\\" % self.path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11a762413a0129120eb397564ca2dc97\",\n \"score\": \"0.42496905\",\n \"text\": \"def plot(self, variables_values, axes=None):\\n assert len(self.variables) == len(variables_values), \\\\\\n f\\\"You need to pass as many variable values as this visualiser has variables. Required:\\\" \\\\\\n f\\\"{len(self.variables)}, Given: {len(variables_values)}\\\"\\n\\n fig = None\\n if axes is None:\\n fig, axes = self.create_default_axes()\\n # TODO: Probably should not do that, but it prevents an empty plot from popping up in IPython\\n plt.close(fig)\\n\\n self.plot_init(variables_values, axes)\\n self.plot_update(variables_values, axes)\\n if fig is not None and in_ipython_context:\\n ip_display(fig)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81280fdc16829c5aafa79a33822634eb\",\n \"score\": \"0.4248225\",\n \"text\": \"def plot_my_data(x,y,verbose=True):\\n\\n # Note the indentation for the if block\\n if (verbose):\\n print \\\"\\\"\\n print \\\"Now plotting the data.\\\"\\n\\n # In the plot command below, the 'bo' tells the program to plot blue ('b')\\n # circles ('o')\\n print \\\"\\\"\\n #print \\\"Close the plot window to continue...\\\"\\n plt.plot(x,y,'bo')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45ad991ab71a43a553c913e63ea1f2af\",\n \"score\": \"0.42471483\",\n \"text\": \"def variable_importance_plot(model, log, result_path=\\\"result\\\"):\\n algo = model.params['model_id']['actual']['name']\\n\\n if model.varimp() is not None:\\n plt.tight_layout()\\n\\n plt.figure(figsize=(12, 8))\\n var_imp = model.varimp_plot()\\n plt.title(\\\"Variable Importance : \\\" + algo)\\n plt.xlabel(\\\"Relative Importance\\\")\\n\\n plt.savefig(result_path + \\\"/\\\" + \\\"variable_importance.png\\\", dpi=600)\\n plt.close()\\n else:\\n log.warning(\\\"[\\\" + algo + \\\"]\\\" + \\\"This model doesn't have variable importances\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e224ac6f166a66c30181dc30cb08c5b\",\n \"score\": \"0.42448515\",\n \"text\": \"def turn_visualization_off(self):\\n self.visualize = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaa4f7f0ce31f350d847feb2209d7261\",\n \"score\": \"0.4238392\",\n \"text\": \"def drop(self, var_index):\\n return self.masked([i != var_index for i in range(self.num_vars)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da9570ff3d6ed2dd5db6f03efbc01781\",\n \"score\": \"0.4237229\",\n \"text\": \"def display_only(self, frame):\\n cv2.imshow(self.display_name, frame)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f997c2c75ad0ef1126c60105cf176510\",\n \"score\": \"0.42370698\",\n \"text\": \"def remove_NoVal_col(df: pd.core.frame.DataFrame, label: str) -> pd.core.series.Series:\\n mask = df[label] != 99.0\\n col = df[label][mask]\\n return col\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa0ae72e1dcc3d68e8cfcd5580d6d362\",\n \"score\": \"0.42370147\",\n \"text\": \"def display_data(df):\\n response= ['yes', 'no']\\n rawdata = ''\\n #counter variable is initialized as a tag to ensure only details from a particular point is displayed\\n counter = 0\\n while rawdata not in response:\\n print(\\\"\\\\nDo you wish to view the raw data?\\\")\\n print(\\\"\\\\nAccepted responses:\\\\nYes or no\\\")\\n rawdata = input().lower()\\n #the raw data from the df is displayed if user opts for it\\n if rawdata == \\\"yes\\\":\\n print(df.head())\\n elif rawdata not in response:\\n print(\\\"\\\\nPlease check your input.\\\")\\n print(\\\"Input does not seem to match any of the accepted responses.\\\")\\n print(\\\"\\\\nRestarting...\\\\n\\\")\\n\\n #Extra while loop here to ask user if they want to continue viewing data\\n while rawdata == 'yes':\\n print(\\\"Do you wish to view raw data?\\\")\\n counter += 5\\n rawdata = input().lower()\\n #If user opts for it, this displays next 5 rows of data\\n if rawdata == \\\"yes\\\":\\n print(df[counter:counter+5])\\n elif rawdata != \\\"yes\\\":\\n break\\n\\n print('-'*80)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d09bbfaaacfe251e374b812438d2d174\",\n \"score\": \"0.42354107\",\n \"text\": \"def viewVariables(dataset):\\r\\n for entry in dataset['DATA'][0]:\\r\\n pprint.pprint(str(entry) + \\\" : \\\" + str(dataset['VARIABLES'][str(entry)]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"227b29295221cfa88a8937ea82c250ad\",\n \"score\": \"0.42240646\",\n \"text\": \"def remove_plot(self):\\n \\n if self.has_plant: \\n #Updates the variables to show that the plot is empty\\n self.plant_type = None\\n self.has_plant = False\\n self.age = 0\\n self.time_planted = 0\\n self.can_harvest = False\\n \\n else:\\n #Once the plot itself is removed, the switch turns off and the plot is disabled\\n self.plotted = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68478c1344086aa8df9dc257fd0cc6cd\",\n \"score\": \"0.42235586\",\n \"text\": \"def suppress(self):\\n self._pipe.send((None, 'hide', None))\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":795,"cells":{"query_id":{"kind":"string","value":"2be6d369e19ba2cf155c71d55a982262"},"query":{"kind":"string","value":"Start a player off with the default starting deck, an empty hand, and an empty discard pile. Set the game stage to 'early_game'. Initialize a turn counter. Draw a random starting hand from the deck."},"positive_passages":{"kind":"list like","value":[{"docid":"9ab052ff693422df14ac76b389410470","score":"0.7242505","text":"def __init__(self):\n\n self.hand = [] # empty hand\n self.discard = [] # empty discard pile\n self.deck = 3 * [estate] + 7 * [copper] # starting deck\n self.game_stage = GameStage.early_game # start the game in early stage\n self.turn_count = 0\n\n random.shuffle(\n self.deck\n ) # shuffle the deck before drawing (for the first time)\n self.draw_hand() # start by drawing a hand","title":""}],"string":"[\n {\n \"docid\": \"9ab052ff693422df14ac76b389410470\",\n \"score\": \"0.7242505\",\n \"text\": \"def __init__(self):\\n\\n self.hand = [] # empty hand\\n self.discard = [] # empty discard pile\\n self.deck = 3 * [estate] + 7 * [copper] # starting deck\\n self.game_stage = GameStage.early_game # start the game in early stage\\n self.turn_count = 0\\n\\n random.shuffle(\\n self.deck\\n ) # shuffle the deck before drawing (for the first time)\\n self.draw_hand() # start by drawing a hand\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"5100836fd6ace5c376bcbe31fb141463","score":"0.7105495","text":"def start_new_game(self, player_hand):\n self.deck_of_cards += self.hand\n self.deck_of_cards += player_hand\n self.deck_of_cards.shuffle()","title":""},{"docid":"73b8d8128052bfd7bd5a587d480672ce","score":"0.6954077","text":"def start_game(self):\n initial_draw = int((len(self.stock.cards) * 0.5) / len(self.participants))\n\n if initial_draw == 0:\n initial_draw = 1\n\n for player in self.participants:\n cards = [self.stock.deal() for x in range(initial_draw)]\n player.hand = cards\n\n self.act_player = self.participants[0]\n self.turn = self.next_turn()","title":""},{"docid":"6662d1a0a763307463d759a7f7232e3a","score":"0.66947067","text":"def init_game(self):\n if self.is_basic: #create quick simple game\n p1 = 6 #priest\n p2 = 7 #rogue\n deck1 = random_draft(CardClass(p1))\n deck2 = random_draft(CardClass(p2))\n self.players[0] = Player(\"Player1\", deck1, CardClass(p1).default_hero)\n self.players[1] = Player(\"Player2\", deck2, CardClass(p2).default_hero)\n self.game = Game(players=self.players)\n self.game.start()\n\n #Skip mulligan\n for player in self.game.players:\n cards_to_mulligan = random.sample(player.choice.cards, 0)\n player.choice.choose(*cards_to_mulligan)\n\n return self.game\n\n else:\n p1 = random.randint(1, 9)\n p2 = random.randint(1, 9)\n #initialize players and randomly draft decks\n #pdb.set_trace()\n deck1 = random_draft(CardClass(p1))\n deck2 = random_draft(CardClass(p2))\n self.players[0] = Player(\"Player1\", deck1, CardClass(p1).default_hero)\n self.players[1] = Player(\"Player2\", deck2, CardClass(p2).default_hero)\n #begin the game\n self.game = Game(players=self.players)\n self.game.start()\n\n #Skip mulligan for now\n for player in self.game.players:\n cards_to_mulligan = random.sample(player.choice.cards, 0)\n player.choice.choose(*cards_to_mulligan)\n\n return self.game","title":""},{"docid":"db336ace779d9c035eacad3d1e43a2c3","score":"0.6674635","text":"def play_hand(self, player):\n if not player.has_blackjack():\n while player.get_hand_value() <= 21:\n self.print_current_state(player, sd=False)\n action = player.get_action()\n if action == 'Hit':\n self.deal_top(player)\n if action == 'Stand':\n break\n if action == 'Split':\n pass\n if action == 'Double':\n player.stack -= player.amount_bet\n player.amount_bet *= 2\n self.deal_top(player)\n break\n # If player did not bust dealer plays\n if (player.get_hand_value() <= 21):\n self.dealer_plays()\n self.print_current_state(player, sd=True)\n self.declare_winner(player)\n self.reset_table()\n print(self.players[0])","title":""},{"docid":"cefe9cdd368f1bc5c556b592bd28b0ba","score":"0.6657983","text":"def computer_turn():\n global winner, winning_hand, time\n computer.draw_card(deck.pile[0])\n winning_hand = computer.declare_game()\n if winning_hand[0]:\n #winning condition\n winner = computer\n round_over()\n stage[0] += 1\n else:\n score = [0] * 14\n min_pile = (0, 100)\n for i in range(len(computer.hand)):\n score[i] = calculate_score(computer.hand[i], computer.hand)\n for i in range(len(score)):\n if score[i] < min_pile[1]:\n min_pile = (i,score[i])\n elif score[i] == min_pile[1]:\n if computer.hand[i].rank >= computer.hand[min_pile[0]].rank:\n min_pile = (i, score[i])\n\n computer.discard_card(deck.pile[0])\n\n joker_card = Card('3','spades',True)\n computer.draw_card(joker_card)\n score = [0] * 14\n min_deck = (0, 100)\n for i in range(len(computer.hand)):\n score[i] = calculate_score(computer.hand[i], computer.hand)\n for i in range(len(score)):\n if score[i] < min_deck[1]:\n min_deck = (i,score[i])\n elif score[i] == min_deck[1]:\n if computer.hand[i].rank >= computer.hand[min_deck[0]].rank:\n min_deck = (i, score[i])\n computer.discard_card(joker_card)\n\n\n if min_pile[1] <= min_deck[1]:\n #draw from pile\n if time == 0 :\n computer.draw_card(deck.pile.pop(0))\n deal_card_sound.play()\n time += 1\n # print(\"pile initialisation\")\n elif time >= computer_delay:\n #DELAY\n deck.update_pile(computer.hand[min_pile[0]])\n computer.discard_card(computer.hand[min_pile[0]])\n deal_card_sound.play()\n # print(\"pile time condition\")\n computer.hand = sort_hand(computer.hand)\n computer.turn = False\n user.turn = True\n time = 0\n else:\n # print(\"pile increment\")\n time += 1\n else:\n #draw from deck\n #DELAY\n if time == 0:\n computer.draw_card(deck.draw_card())\n deal_card_sound.play()\n # print(\"deck initialisation\")\n time += 1\n elif time >= computer_delay:\n # print(\"Deck time condition\")\n winning_hand = computer.declare_hand()\n if winning_hand[0] :\n #winning condition\n winner = computer\n round_over()\n stage[0] += 1\n time = 0\n else:\n score = [0] * 14\n min_deck = (0, 100)\n for i in range(len(computer.hand)):\n score[i] = calculate_score(computer.hand[i], computer.hand)\n for i in range(len(score)):\n if score[i] < min_deck[1]:\n min_deck = (i,score[i])\n elif score[i] == min_deck[1]:\n if computer.hand[i].rank >= computer.hand[min_deck[0]].rank:\n min_deck = (i, score[i])\n deck.update_pile(computer.hand[min_deck[0]])\n computer.discard_card(computer.hand[min_deck[0]])\n time = 0\n deal_card_sound.play()\n computer.hand = sort_hand(computer.hand)\n computer.turn = False\n user.turn = True\n else:\n # print(\"deck increment condition\")\n time += 1","title":""},{"docid":"ae5ce86f91fa809eba12df1bb5fd7837","score":"0.66456765","text":"def start_hand(player, dealer, bet_amount):\n\n player.place_bet(bet_amount)\n player.get_hand(dealer.give_card())\n player.get_hand(dealer.give_card())\n\n print(player.show_hand())\n\n dealer.get_hand(dealer.give_card())\n dealer.get_hand(dealer.give_card())\n\n print(dealer.show_first_hand())\n\n if player.hand_total_value() == 21:\n player.has_blackjack = True\n\n if dealer.hand_total_value() == 21:\n dealer.has_blackjack = True\n\n if player.has_blackjack:\n print(\"Blackjack!!!\")","title":""},{"docid":"f23e94ed0fa96fae14e8750676a9722f","score":"0.65526325","text":"def play(self):\n\n #############\n # Game loop #\n #############\n number_bets = 0\n print \"###############\"\n print \"# Game starts #\"\n print \"###############\"\n while self.player.chips > 0:\n self.play_hands()\n number_bets += 1\n #wants_exit = raw_input(\"> Press e for exit \").lower()\n #if wants_exit == 'e':\n # break\n print \"###############\"\n print \"#########################\"\n print \"# Game end with %s bets #\" % (number_bets)\n print \"#########################\"","title":""},{"docid":"cb175cb8d195e3b844ef7dd03c55afb8","score":"0.6526096","text":"def initialSetUp():\n deck = cardGame.getDeck()\n cardGame.shuffleDeck(deck)\n numberOfPlayers = getNumberOfPlayers()\n hands = cardGame.dealCards(deck, 0, numberOfPlayers)\n sortHands(hands)\n startingPlayer = getStartingPlayer(hands)\n return deck, hands, numberOfPlayers, startingPlayer","title":""},{"docid":"b1ec6eee21dc39ea3e1cc703759281ee","score":"0.6525598","text":"def start(self):\n\n self.view.display_game_start()\n self.cardgame.add_players()\n\n game_over = False\n\n while not game_over:\n for player in self.cardgame.get_players():\n move = self.parse_move(player.name)\n self.cardgame.step(player, move)\n self.view.display_hand(player.name, player.get_hand())\n game_over = self.cardgame.is_game_over()\n\n winner, score = self.cardgame.pick_winner()\n\n if not winner:\n self.view.display_draw_game()\n else:\n self.view.display_winner(winner.name, score, winner.get_hand())","title":""},{"docid":"a3e313dae13a0e559e08ea35e111a9f2","score":"0.65084654","text":"def new_game():\n global cards, exposed, hand, state\n global card_1, card_2, turns\n\n hand = cards[:]\n random.shuffle(hand) # hand for the game\n\n exposed = 16 * [False] # initialize uncovered cards\n card_1 = None\n card_2 = None\n turns = 0\n\n state = 0 # number of cards initially uncovered","title":""},{"docid":"fee53164e852b2bd36967c77375c4a68","score":"0.64621794","text":"def setup_game(self):\n if self.is_ready():\n for player in self.players:\n player.draw(5)\n self.wild.draw(5)\n self.starting_positions = [p.player_id for p in self.players]\n self.current_player = self.next_player()\n else:\n print 'Not enough players to setup game'","title":""},{"docid":"caf9e7f2d74b953dbf52004e56ba4100","score":"0.6429044","text":"def start_hand(self):\n [self.draw() for _ in range(5)] #initially draw 5","title":""},{"docid":"d389423337a21da474a9da118a77413d","score":"0.64132434","text":"def start_game(self):\n self.display_info()\n temporary_board = Board_game(self.board_size, self.number_of_ships,\n \"Computer\", player=False)\n self.computer_board = temporary_board\n player_name = input(\"Please enter your name: \\n\")\n print(\"-\" * 60)\n temporary_board = Board_game(self.board_size, self.number_of_ships,\n player_name, player=True)\n self.player_board = temporary_board\n\n self.play_game()","title":""},{"docid":"eb5a90f61dcbcc1ddbbe1831103505af","score":"0.6401434","text":"def reset_new_game(self):\n\t\tself._piles['deck'] = Pile('copper',7)\n\t\tself._piles['deck'].add_new_card_to_pile('estate',3)\n\t\tself._piles['deck'].shuffle()\n\t\tself._piles['hand'] = Pile()\n\t\tself._piles['discard'] = Pile()\n\t\tself._piles['in_play'] = Pile()\n\t\tself.draw_n(5)","title":""},{"docid":"6d74b8502b3dc12a43ce7f6e15d3c209","score":"0.6401383","text":"def play_hands(self):\n\n #############\n # First hand\n #############\n # Deal two cards to each opponent\n self.player.cards = self.dealer.deal(2)\n self.dealer.cards = self.dealer.deal(2)\n\n # Show hand\n self.player.show_hand()\n self.dealer.show_hand(1) # hide second card\n\n # End if player has blackjack\n if self.player.has_blackjack():\n self.end_bet()\n return\n\n #################################\n # Hand loop for player and dealer\n #################################\n opponents = [self.player, self.dealer]\n dealer_turn = 0 # 0: is player turn, 1: is dealer turn\n continue_bet = True\n round = 1\n # Bet\n self.player.chips -= self.player.chips_per_bet\n # Repeat until bet end condition\n while continue_bet:\n # Set who plays: player or dealer\n opponent = opponents[dealer_turn]\n continue_hand = True\n while (continue_hand):\n # Ask for action: hit or stand?\n action = opponent.choose_action()\n # If hit, deal cards and show hand\n if action == settings.ACTION_HIT:\n opponent.cards.extend(self.dealer.deal())\n opponent.show_hand()\n # check end conditions\n if opponent.has_blackjack() or opponent.has_busted():\n continue_hand = False\n continue_bet = False\n # If stand, it's dealer's turn\n elif action == settings.ACTION_STAND:\n dealer_turn = 1 - dealer_turn # pass turn to other opponent\n round+=1\n continue_hand = False\n # Player stands and dealer stands so end bet\n if round == 4:\n continue_bet = False\n else:\n raise Exception(\"Unknown action: '%s'\" % action)\n self.end_bet()","title":""},{"docid":"bd044cf068a3a9a276486c7f13ec9422","score":"0.6370955","text":"def start_game(self):\n print(\"\"\"\n Welcome to the Hi/Lo game!\n \n Guess if the next card will be higher\n or lower than the last drawn card.\n If correct, you get 100pts! Guess wrong \n and you lose 75pts!\n \n You lose if you hit, or go below, 0pts.\n You win if you score 1,000 or more pts.\n \n Good luck!\n \"\"\")\n while self.keep_playing == True:\n print(f\"The card is : {self.old_card}\")\n self.player.guess()\n self.draw_card()\n self.display_card()\n self.get_points()\n print(f\"Score: {self.points}\")\n self.keep_playing = self.to_play()\n self.card_update()","title":""},{"docid":"a74aa24f1c77ebf72c7629e86f7d25ca","score":"0.6350413","text":"def main():\n newdeck = deck.Deck()\n newdeck.shuffle()\n firstplayer_cards = []\n secondplayer_cards = []\n for x in range(5):\n firstplayer_cards.append(newdeck.dealacard())\n secondplayer_cards.append(newdeck.dealacard())\n playing_game(firstplayer_cards, secondplayer_cards)","title":""},{"docid":"c6a583f2c39d810c3d4c6a06f580b22f","score":"0.6337075","text":"def start(self):\n while self._pyramid[0][0].rank is not None: # if top card is not None\n self._menu()\n command = input('\\nCommand: ').strip()\n if self._enter_command(command) is None:\n system('cls')\n continue\n else:\n system('cls')\n input('\\nGame Over')\n print(game.instruction())\n input('Press any key to continue...') # wait user\n system('cls')\n self.start()","title":""},{"docid":"293c5613cb568b29524346857329a8d3","score":"0.6327848","text":"def reset(self) -> None:\n\n self.deck = make_cards()\n self.player = Player([self.deck.pop(), self.deck.pop()], 0, self.player_name)\n self.dealer = Dealer([self.deck.pop(), self.deck.pop()])\n self.player_split = Player([], 0, self.player_name + \" hand_two\")\n self.split = False","title":""},{"docid":"b7122669f6b8f76c9b6655a98923e290","score":"0.63025635","text":"def start_round(self):\r\n self.round_number += 1\r\n self.dealer = (self.dealer + 1) % 4\r\n self.round = Round(self.round_number, self.dealer)\r\n trump_suit = None\r\n self.deal()\r\n self.round.starting_hands = [player.cards[:] for player in self.players] + [\r\n self.kitty.cards[:]\r\n ]\r\n\r\n print(\"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\")\r\n print(\"Round %s\" % (self.round_number))\r\n print(\"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\")","title":""},{"docid":"e90754c4fcc810d1e8439fabac48bf0d","score":"0.6301797","text":"def start_game(self):\n\t\tpass\n\t\t# Welcome the player\n\t\t# Get the player name\n\t\t# Tell the player how to navigate the game\n\t\t\t# How to access the rules\n\t\t\t# How to quit at any input \n\t\t# Reveal the blank game board \n\t\t# \"Ready to start? (Y)es or (N)o\"","title":""},{"docid":"5f40568fed3de59176404cedb08d0e3a","score":"0.62817025","text":"def user_start_play(tower, discard):\n\n print('\\nNOW IT\\'S YOUR TURN!')\n print('Your Tower', end = ': ')\n print(tower)\n\n top_discard_brick = see_top_brick_discard(discard)\n print('The top brick on the discard pile is', top_discard_brick)","title":""},{"docid":"713dc46b4c49cbc94a8c0af15906bb56","score":"0.6278261","text":"def game_reset():\n global deck, user, computer, draw, swap, insert, sort, declare, discard, showCP, user_name, user_name_rect, computer_name, computer_name_rect\n global name_text, name_rect, score_text, score_rect, discard_mode, swap_mode, insert_mode, index\n deck = Deck(2)\n deck.shuffle_cards()\n deck.set_joker()\n user.deal_cards(deck)\n computer.deal_cards(deck)\n cardss = deck.draw_card()\n deck.update_pile(cardss)\n user.turn = True\n discard_mode = False\n swap_mode = False\n insert_mode = False\n index = []","title":""},{"docid":"4235cb633939d598736e432d17bdfc7f","score":"0.62765485","text":"def play_turn(game):\n print('=================================')\n if game.is_current_player_a_computer():\n print(game.get_current_player_name() + \"'s turn to play\")\n time.sleep(0.5)\n cards_drawn = game.play_computer_turn()\n for card in cards_drawn:\n if card is not None:\n print(\"Hit! Drew card %s\" % card)\n time.sleep(0.5)\n else:\n print(\"Deck is empty! Finished turn.\")\n if (game.current_player.is_over_21()):\n print(game.get_current_player_name() + \" lost! \" + game.get_current_player_name() + \" is out.\")\n time.sleep(0.5)\n else:\n if game.get_current_player_name != 'Dealer':\n print(game.get_current_player_name() + \" finished with count of \" + str(game.get_current_player_hand_count()))\n time.sleep(0.5)\n else:\n print(\"Your turn to play! Your count is: %s\" % game.get_current_player_hand_count())\n print(\"Your hand is: %s\" % game.get_current_player_hand())\n while game.human_can_draw() and input(\"Do you want to hit? [y/N] \") is 'y':\n card = game.human_player_draw()\n if card is not None:\n print(\"Hit! Delt card %s. Your count is now: %s\" % (card, game.get_current_player_hand_count()))\n print(\"Your hand is: %s\" % game.get_current_player_hand())\n else:\n print(\"Deck is empty! Finished turn.\")\n break\n if game.is_current_player_over_21():\n print(\"You lost! You score is over %s.\" % game.max_count())","title":""},{"docid":"2222c78926dcf1f22dafb55d4be49df5","score":"0.626647","text":"def computer_turn(self):\n print(\"You have \" + str(len(self.player1.deck)) + \" cards\")\n print(self.player2.name + \" has \" + str(len(self.player2.deck)) + \" cards\")\n print(\"Here is your top card\\n\")\n print_card(self.player1.deck[0])\n print(\"\\nYour opponent is choosing a trump stat\")\n rand_num = randint(1, 6)\n chosen_stat = None\n\n if rand_num == 1:\n chosen_stat = \"hp\"\n elif rand_num == 2:\n chosen_stat = \"attack\"\n elif rand_num == 3:\n chosen_stat = \"defense\"\n elif rand_num == 4:\n chosen_stat = \"speed\"\n elif rand_num == 5:\n chosen_stat = \"special attack\"\n else:\n chosen_stat = \"special defense\"\n time.sleep(2)\n print(\"\\n Your opponent picked \" + chosen_stat + \"!\\n\")\n\n self.check_stats(chosen_stat)","title":""},{"docid":"334b7600b2adc90874380ae9d38902c7","score":"0.625842","text":"def new_game():\n global exposed, deck_cards, state, turns\n turns = 0\n # Game state\n state = 0\n exposed = [False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False] \n # STEP 1: Model the deck of cards \n deck_cards = [1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8]\n correct_cards = [False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False] \n # STEP 3: Shuffle the deck using random.shuffle\n random.shuffle(deck_cards)","title":""},{"docid":"98b10c5fbb5553c5b37d0874b9667ae6","score":"0.624704","text":"def __init__(self):\n self.keep_playing = True\n self.old_card = r.randint(1,13)\n self.new_card = 0\n self.player = Player()\n self.guess = \"\"\n self.points = 300","title":""},{"docid":"2efe4a4c4937f3e8e134bfdda416f06d","score":"0.62439346","text":"def reset_game(self):\n # The player always goes first.\n self.current_turn = 'player'\n\n # Set the current cards to an empty array.\n self.cards = {\n 'dealer': [],\n 'player': []\n }\n\n # Set the totals for each player.\n self.totals = {\n 'dealer': 0,\n 'player': 0\n }\n\n # Create a new new deck of cards.\n self.deck = self.deck_generator.get_new_deck()","title":""},{"docid":"4315b4a7c52331d868ebcd95500313ae","score":"0.6240726","text":"def take_initial_turn(self, player, dealer, deck_list):\n\n dist_counter = 0\n\n # Distribute first 2 cards to player and dealer\n while dist_counter < 2:\n \n # Take 1 card for player\n player, deck_list = self.take_1_card(player, deck_list)\n print(f'{player.name} hand: {[pair[0] for pair in player.hand]}\\nCurrent deck len: {len(deck_list)}')\n \n # Take 1 card for dealer, showing only the first drawn card\n dealer, deck_list = self.take_1_card(dealer, deck_list)\n if dist_counter == 0:\n print(f'{dealer.name} hand: {[pair[0] for pair in dealer.hand]}\\nCurrent deck len: {len(deck_list)}')\n else:\n print(f'Current deck len: {len(deck_list)}')\n\n dist_counter += 1\n \n print('\\nEnd of initial distribution.')\n\n return player, dealer, deck_list","title":""},{"docid":"aeccbe58a2576153b7edf0796e66999c","score":"0.62265307","text":"def black_jack_hand(player1, dealer): \n #this should be the black_jack hand, while the begging should be setting up a new game\n dealer.get_all_bets(player1)\n ##deal two cards each, one to the dealer should be set to face down\n for x in xrange(1,3):\n dealer.give_card_to_player(player1)\n dealer.give_card_to_player(dealer)\n\n ###Now the game is ready to start showing the hands of the dealer and the player should be done\n print \"Dealer's Hand\"\n dealer.get_hand().showHand()\n print \"Player's Hand\"\n player1.get_hand().showHand()\n while(player1.want_card()):\n dealer.give_card_to_player(player1)\n print \"Players Hand\"\n player1.get_hand().showHand()\n\n \n dealer.flip_card_over()\n while(dealer.want_card()):\n dealer.give_card_to_player(dealer)\n \n print \"Dealers Hand\"\n dealer.get_hand().showHand()\n\n dealer.set_winner(player1)","title":""},{"docid":"06a062fdec911de20686003a63b70bb4","score":"0.6199962","text":"def play_game(self):\n\n # Print the beginning game message.\n print(\"\\nBeginning \" + str(self))\n\n time.sleep(1.5)\n\n os.system('cls' if os.name == 'nt' else 'clear')\n\n # It is the initial turn.\n turn = 0\n\n # This is the game loop. When the game ends we will exit this loop.\n has_ended = False\n while not has_ended:\n\n # Increment the turn number.\n turn += 1\n\n # Iterate through each player that is playing.\n for player_num, player in enumerate(self.config.players):\n \n # Set the current player.\n self.config.set_current_player(player_num)\n \n # Before each player's turn, check to see if the game ended.\n if self.config.supply.check_end_game():\n has_ended = True\n break\n \n # The player's turn starts here.\n try:\n print(f\"\\n********** Turn #{turn} **********\")\n player.turn(self.config)\n time.sleep(1.5)\n os.system('cls' if os.name == 'nt' else 'clear')\n \n # Here we catch unexpected errors while playing the game.\n except DominionError as e:\n print(\"Encountered an error while playing. \" + e)\n return\n\n # If anyone quits, exit the game here.\n except DominionQuitError:\n print(f\"Game quit by {player.name}\")\n return\n\n # Once the game has ended, run the end_game method.\n self.end_game(turn)","title":""},{"docid":"e85dcd6a656f2d43ae2291615c21760e","score":"0.6190354","text":"def start_game(self):\n\n # Change starting screen to Chess board\n self.board = Board(square_width=self.view.square_width, \n square_height=self.view.square_height,\n is_flipped=self.is_flipped)\n\n # Create players\n self.board.player_list.append('Human')\n \n if self.settings['vs_computer']: self.board.player_list.append('AI')\n else: self.board.player_list.append('Human')","title":""},{"docid":"f4717ddff3d01a3cc83e1621cdcdf452","score":"0.6188858","text":"def playAgain(self):\n self.game_setup()","title":""},{"docid":"08f12d4c17062b04529efa4006ecc7bb","score":"0.6175251","text":"def _start_new_game(self):\n D = DeckGenerator()\n D.shuffle()\n T = TableCard(D)\n T.generate_pyramid()\n gL = GameLogic(T)\n gL.level = self._level\n for item in T.pyramid_deck[-1]:\n item.status = True\n return Game(D, T, gL)","title":""},{"docid":"6dcbee6750907da85989d529de59453d","score":"0.6170448","text":"def initTurn(self,players,current_player,draw):\n if self.__oneSide:\n for player in players:\n i=0\n for card in player.getHand():\n if callable(getattr(player, \"getSpecial\", None)):\n self.__canvas.itemconfigure(self.__canvas_items[\"player{}-card-{}\".format(player.getId(),i)],image=self.__imgs[\"back\"])\n else:\n self.__canvas.itemconfigure(self.__canvas_items[\"player{}-card-{}\".format(player.getId(),i)],image=self.__imgs[card.getName()])\n i+=1\n i=0\n for card in player.getDiscard():\n self.__canvas.itemconfigure(self.__canvas_items[\"player{}-discard-{}\".format(player.getId(),i)],image=self.__imgs[card.getName()])\n i+=1\n else:\n for player in players:\n i=0\n for card in player.getHand():\n if current_player.getId() == player.getId():\n self.__canvas.itemconfigure(self.__canvas_items[\"player{}-card-{}\".format(player.getId(),i)],image=self.__imgs[\"back\"])\n self.__canvas.move(self.__canvas_items[\"player{}-card-{}\".format(player.getId(),i)],0,-716)\n else:\n self.__canvas.itemconfigure(self.__canvas_items[\"player{}-card-{}\".format(player.getId(),i)],image=self.__imgs[card.getName()])\n self.__canvas.move(self.__canvas_items[\"player{}-card-{}\".format(player.getId(),i)],0,716)\n i+=1\n i=0\n for card in player.getDiscard():\n if current_player.getId() == player.getId():\n self.__canvas.itemconfigure(self.__canvas_items[\"player{}-discard-{}\".format(player.getId(),i)],image=self.__imgs[card.getName()])\n self.__canvas.move(self.__canvas_items[\"player{}-discard-{}\".format(player.getId(),i)],0,-358)\n else :\n self.__canvas.itemconfigure(self.__canvas_items[\"player{}-discard-{}\".format(player.getId(),i)],image=self.__imgs[card.getName()])\n self.__canvas.move(self.__canvas_items[\"player{}-discard-{}\".format(player.getId(),i)],0,358)\n i+=1\n if len(draw) == 0:\n self.__canvas.itemconfigure(self.__canvas_items[\"draw\"],image=\"\")\n self.__updateDisplay()","title":""},{"docid":"020fa0581c40ec60e6532db6e2adb650","score":"0.6161986","text":"def main():\n game = poker_game()\n game.add_player(player_1) #Temp solution to add players to game\n game.add_player(player_2)\n game.form_player_hands() #Deal phase one, creating card objects and card_pack \n game.update_all_hands() #Used after each round, to sort hand + calculate each persons strongest hand\n game.deal_flop()\n game.update_all_hands()\n game.deal_turn()\n game.update_all_hands()\n game.deal_river()\n game.update_all_hands()\n print(game.player_list[1].hand_value) #Prints hand_value of player at index 1\n #hand_compare.hand_compare(player_hands, board) --Incomplete\n return","title":""},{"docid":"8e986e6106c6e9afad81891509fe2a59","score":"0.6153859","text":"def start_game(self):\n if self.gameover:\n self.init_game()\n self.gameover = False","title":""},{"docid":"ff428e6a7a8d16851ada513c3fdd2f4b","score":"0.61526495","text":"def run_play_loop(self): \n player = Player('Player', STARTING_CHIP_NUMBER)\n dealer = Player('Dealer', STARTING_CHIP_NUMBER)\n self.gui.create_play_screen()\n self.pause(500)\n \n while True:\n deck = Deck()\n\n # Prompt the user to place a wager\n wager = self.get_wager(player, dealer)\n\n # Handle the card selection process in triple pocket holdem\n # Present the user with his/her options for cards and have him/her select the cards\n self.select_cards(deck, player, dealer)\n community_cards = deck.draw_five_community_cards()\n \n # Now, reveal the cards held by the user and the dealer, as well as the \n # five community cards\n self.gui.explain_card_reveal()\n self.pause(2000)\n self.gui.reveal_player_cards(player, dealer)\n self.pause(2000)\n self.gui.reveal_common_cards(community_cards)\n self.pause(5000)\n\n # Determine the best hand held by each player and who won the round\n # Add or subtract chips from the player and dealer's totals depending on the outcome\n # Output to the user which poker hand the dealer had and what the consequent result was\n (player_hand, dealer_hand, player_wager_multiple, dealer_wager_multiple) = determine_outcome(\n player.hands[0], \n dealer.hands[0], \n dealer.hands[1], \n community_cards\n )\n player.alter_chip_balance(player_wager_multiple * wager)\n dealer.alter_chip_balance(dealer_wager_multiple * wager)\n self.gui.explain_outcome(\n player_hand, \n dealer_hand, \n player_wager_multiple, \n player.num_chips, \n dealer.num_chips\n )\n self.pause(4000)\n \n # Get the next action from the user\n # If the game has ended (i.e. the user or dealer has run out of chips),\n # the user can elect to play again. Otherwise, the user can elect to continue with\n # another round in the current game or to exit\n next_action = self.get_action_at_round_end(player, dealer)\n if next_action == MENU_NOW:\n return next_action\n elif next_action == PLAY_AGAIN:\n player = Player('Player', STARTING_CHIP_NUMBER)\n dealer = Player('Dealer', STARTING_CHIP_NUMBER)\n else:\n player.clear_hands()\n dealer.clear_hands()","title":""},{"docid":"94b5e7b4a39d3ad3dd48208c7ef542b8","score":"0.614256","text":"def play(self):\n # Clear/Reset attributes/values\n self.winners = []\n self.count = 0\n for player in self.players:\n # Reset hands\n del(player.cards[:])\n # Increment number of unique games played\n player.gamesPlayed += 1.0\n # Increment total number of games played\n self.totalGamesPlayed += 1.0\n # Prepare the deck, deal the cards\n self.deck = Deck()\n self.deck.shuffle()\n self.deck.deal(3, self)\n # Top card is placed onto discard pile\n self.discard.add(self.deck.pop())\n # While no one has satisfied winning conditions, take turns\n while not self.check_won():\n self.turn_seq()","title":""},{"docid":"ad995044d769ec0161ae9924becdf18b","score":"0.61161804","text":"def initiate_deck(self):\n\n deck = Deck()\n deck.build_deck()\n deck.shuffle()\n self.deck1, self.deck2 = deck.split_deck()","title":""},{"docid":"0706d71c979b7b26af67ecd63b9ff690","score":"0.61084616","text":"def reset_hands(self):\n self.my_player.reset_hand()\n self.my_computer.reset_hand()","title":""},{"docid":"b7dc1ffc7670027b24ea1494978535af","score":"0.61073416","text":"def reset(self, test=None):\n self.health = self.start_health\n self.armor = 0\n self.this_turn_mana = 0\n self._init_heropower()\n self._init_deck(self.fix_deck)\n self.inhands = [] # cards in self hands\n self.intable = [] # cards on self table\n self.used_cards = defaultdict(int) # used card counts. key: cidx, value: used counts\n if self.first_player:\n self.draw_as_first_player()\n else:\n self.draw_as_second_player()\n if test is not None:\n self.test = test","title":""},{"docid":"f1a212e0c2daeb3463048aa45a876849","score":"0.60954237","text":"def setup_game():\n\t\n\tglobal bullet_chamber\n\tglobal current_chamber\n\tglobal player_turn\n\t\n\tbullet_chamber = math.ceil(random.random()*CHAMBER_COUNT)\n\tcurrent_chamber = 1\n\t\n\tprint \"Gun prepared...\"\n\t# determine who's first\n\tif random.random() >= 0.5:\n\t\tplayer_turn = True\n\t\tprint \"Player goes first\"\n\telse:\n\t\tplayer_turn = False\n\t\tprint \"Computer goes first\"","title":""},{"docid":"dd4b311a54ba395986edf37e3444a0a1","score":"0.60823447","text":"def start_new_game(players):\n game = SchnapsenGame()\n game.create_cards()\n game.shuffle_cards()\n for player in players:\n for dummy in range(5):\n card = game.draw_from_stock()\n player.add_card(card)\n return game","title":""},{"docid":"b8c0d6666ca0db5b99f471105ab90a1f","score":"0.6074004","text":"def show_next_hand(*args):\n global twentyfive_cards\n # disable show_next and enable show_best\n # show_best25_hand_button[\"state\"] = \"normal\"\n # show_next_hand_button[\"state\"] = \"disabled\"\n\n window.title(\"Play Pyramid Poker\")\n w.delete(\"all\") # clear out last hand\n show_twentyfive_cards()","title":""},{"docid":"0d3d9925f75608d29e6f3f04e3b60fe8","score":"0.60738045","text":"def start_hand(deck, onCoin):\n size = 4 if onCoin else 3\n return [deck.pop() for i in range(size)]","title":""},{"docid":"abc03548cf453e649529ab559af2bfe6","score":"0.607288","text":"def initialise_deck(self):\n deck = Deck()\n shuffle(deck.card)\n self.deck = deck\n return","title":""},{"docid":"40e5d29991114394fd48b8ffdeb3fb70","score":"0.60725707","text":"def main():\n numHands = eval(input('Enter number of player: '))\n while (numHands < 2 or numHands > 10):\n numHands = eval(input('Enter number of hands to play[2~6]: '))\n game = Poker(numHands)\n print(\"## Game start\")\n game.play()\n print(\"## Caculating hands\")\n print(game.evaluate(game.hands))","title":""},{"docid":"d4317741c257ecaff2039978f0c82dd3","score":"0.60713416","text":"def choose_hand(self):\n\n w = random.randint(0, 4)\n if len(Player.history) > 0:\n if Player.history[len(Player.history)-1] == \"rock\":\n w = 4\n self.weapon = Weapons.weapon[w]\n self.turtle.shape(Weapons.computer_images[w])","title":""},{"docid":"de83074b480ba3c367ce9ddc04565f9a","score":"0.60338426","text":"def start(self):\n self.start_game()\n self.init_playing_order()\n while self.winning_camel is None:\n self.run_leg()\n self.report()\n print(\"------------------- End leg ---------------------\")\n self.game_stage = \"result\"\n self.losing_camel = self.orders[-1]\n self.game_scoring_round()\n self.determine_game_result()","title":""},{"docid":"5f2270c443ff6f39200566891a90e09b","score":"0.60226154","text":"def playUpgrade(self, card, target = None):\n logging.info(card)\n active = self.activePlayer.board[\"Creature\"]\n inactive = self.inactivePlayer.board[\"Creature\"]\n hand = self.activePlayer.hand\n broken = False\n \n if target:\n self.activePlayer.board[\"Upgrade\"].append(card)\n if card in hand:\n hand.remove(card)\n side, choice = target\n if side == \"fr\":\n active[choice].upgrade.append(card)\n else:\n inactive[choice].upgrade.append(card)\n eval(f\"upgrade.{card.title}(self, card, side, choice)\")\n self.cardChanged(True) # this would actually make the play abilities I put on blood of titans, a bit redundant, but redundancy is ok\n return\n \n drawMe = []\n # surfs\n selectedSurf = Surface((self.target_cardw, self.target_cardh))\n selectedSurf.convert_alpha()\n selectedSurf.set_alpha(80)\n selectedSurf.fill(COLORS[\"LIGHT_GREEN\"])\n\n selectedSurfTapped = Surface((self.target_cardh, self.target_cardw))\n selectedSurfTapped.convert_alpha()\n selectedSurfTapped.set_alpha(80)\n selectedSurfTapped.fill(COLORS[\"LIGHT_GREEN\"])\n # draw all targets\n for temp_card in active + inactive:\n if temp_card.ready:\n drawMe.append((selectedSurf, temp_card.rect))\n else:\n drawMe.append((selectedSurfTapped, temp_card.tapped_rect))\n \n if self.canDiscard(card, reset = False):\n ## discard stuff here - if you can play it, you can discard it\n discSurf = Surface((self.target_cardw, self.target_cardh))\n discSurf.convert_alpha()\n discSurf.set_alpha(80)\n discSurf.fill(COLORS[\"LIGHT_GREEN\"])\n discRect = discSurf.get_rect()\n discRect.topleft = self.discard1_rect.topleft\n drawMe.append((discSurf, discRect))\n \n while True:\n self.extraDraws = drawMe.copy()\n \n for e in pygame.event.get():\n if e.type == MOUSEMOTION:\n #update mouse position\n self.mousex, self.mousey = e.pos\n \n if e.type == QUIT:\n pygame.quit()\n\n if e.type == MOUSEBUTTONUP and e.button == 1:\n activeHit = [(Rect.collidepoint(x.rect, (self.mousex, self.mousey)) or Rect.collidepoint(x.tapped_rect, (self.mousex, self.mousey))) for x in active]\n inactiveHit = [(Rect.collidepoint(x.rect, (self.mousex, self.mousey)) or Rect.collidepoint(x.tapped_rect, (self.mousex, self.mousey))) for x in inactive]\n if Rect.collidepoint(self.hand1_rect, (self.mousex, self.mousey)):\n l = len(hand)\n for x in range(len(hand)):\n temp_card = hand[x]\n if x == 0 and self.mousex < temp_card.rect.centerx:\n hand.insert(0, self.dragging.pop())\n self.extraDraws = []\n self.cardChanged()\n return\n elif temp_card.rect.centerx < self.mousex and x < l-1 and self.mousex < hand[x+1].rect.centerx:\n hand.insert(x + 1, self.dragging.pop())\n self.extraDraws = []\n self.cardChanged()\n return\n hand.append(self.dragging.pop())\n self.extraDraws = []\n return\n elif True in activeHit:\n self.activePlayer.board[\"Upgrade\"].append(self.dragging.pop())\n choice = activeHit.index(True)\n target = active[choice]\n target.upgrade.append(card)\n eval(f\"upgrade.{card.title}(self, card, target)\")\n broken = True\n break\n elif True in inactiveHit:\n self.activePlayer.board[\"Upgrade\"].append(self.dragging.pop())\n choice = inactiveHit.index(True)\n target = active[choice]\n target.upgrade.append(card)\n eval(f\"upgrade.{card.title}(self, card, target)\")\n broken = True\n break\n elif self.canDiscard(card, reset=False) and Rect.collidepoint(discRect, (self.mousex, self.mousey)):\n hand.append(self.dragging.pop())\n self.discardCard(-1)\n self.extraDraws = []\n self.cardChanged()\n return\n else:\n hand.append(self.dragging.pop())\n self.extraDraws = []\n self.cardChanged()\n return\n\n if e.type == MOUSEBUTTONDOWN and e.button == 1:\n logging.error(\"You shouldn't be able to trigger MOUSEBUTTONDOWN in dragCard.\")\n\n if Rect.collidepoint(self.hand1_rect, (self.mousex, self.mousey)):\n l = len(hand)\n for x in range(len(hand)):\n temp_card = hand[x]\n if x == 0 and self.mousex < temp_card.rect.centerx:\n hand.insert(0, self.invisicard)\n self.cardChanged()\n break\n elif temp_card.rect.centerx < self.mousex and x < l-1 and self.mousex < hand[x+1].rect.centerx:\n hand.insert(x + 1, self.invisicard)\n self.cardChanged()\n break\n\n self.CLOCK.tick(self.FPS)\n self.hovercard = []\n self.check_hover()\n self.draw(False)\n try:\n hand.remove(self.invisicard)\n except:\n pass\n pygame.display.flip()\n self.extraDraws = []\n if broken:\n self.cardChanged()\n break\n \n if not target and card.amber > 0:\n self.activePlayer.gainAmber(card.amber, self)\n logging.info(f\"{card.title} gave you {str(card.amber)} amber. You now have {str(self.activePlayer.amber)} amber.\\n\\nChange to a log when you fix the amber display issue.\"\"\")\n if card not in self.playedThisTurn:\n self.playedThisTurn[card] = 1\n else:\n self.playedThisTurn[card] += 1\n self.cardChanged(True)\n return","title":""},{"docid":"c09bb9eea918d6798de1ca216dd3a63a","score":"0.60097355","text":"def player_start(self):\n for card in self._target_hand:\n if card.pip is False:\n self._dealer.flip_deal(card)\n return self._target_hand_stats","title":""},{"docid":"fe63242c7be7c36cf950c836bb9a76e8","score":"0.60043347","text":"def initialize_game():\n\n MIN_SCORE = 0\n\n p1_card = create_score_card()\n p2_card = create_score_card()\n p1_bonus = MIN_SCORE\n p2_bonus = MIN_SCORE\n\n round_ongoing = True\n\n while round_ongoing:\n if -1 in p1_card.values():\n print(\"-- IT IS PLAYER ONE'S TURN --\")\n take_turn(p1_card, p1_bonus)\n\n print(\"\\n\")\n\n if -1 in p2_card.values():\n print(\"-- IT IS PLAYER TWO'S TURN --\")\n take_turn(p2_card, p2_bonus)\n\n if -1 not in p1_card.values() and -1 not in p2_card.values():\n round_ongoing = False\n\n game_verdict(calculate_score(p1_card), p1_bonus, calculate_score(p2_card), p2_bonus)","title":""},{"docid":"45c738d403cfef32cc315c30b913debe","score":"0.6004059","text":"def __init__(self):\n self.shoe = deck.Deck(False)\n self.shuffle_shoe()\n self.hand = []","title":""},{"docid":"d0f4de22f0f5ef385e52aa0594aba3bc","score":"0.6000726","text":"def begin_game(self):\n pass","title":""},{"docid":"307a4bb97dc9ff3d6387bcf1682a04b0","score":"0.59878075","text":"def play(self):\n # randomly remove a Cards in cards\n card_picked = choice(self.cards)\n self.cards.remove(card_picked)\n\n # add the Card to the Player's history\n self.history.append(card_picked)\n\n # increment the Player's turn count by 1\n self.turn_count += 1\n\n # decrement the Player's number of cards by 1\n self.number_of_cards -= 1\n \n # if self.number_of_cards == 0:\n # print(f\"{self.name} {self.turn_count} has no card\")\n\n # display the play\n print(f\"{self.name} {self.turn_count} played: {card_picked}\")\n\n return card_picked","title":""},{"docid":"06c3a907ddc1c52dcecd2b011db5a5c4","score":"0.5981489","text":"def black_jack():\n dealer = Dealer()\n player1 = Player(input(\"How much money does the player have?\"))\n want_to_play = 1\n while(want_to_play == 1):\n dealer.shuffle_deck()\n dealer.ready_for_next_hand()\n player1.ready_for_next_hand()\n\n black_jack_hand(player1, dealer)\n want_to_play = input(\"Would you like to play another hand? Select 1 for Yes and 0 for no\")","title":""},{"docid":"3cdc313404535e52d43ab7e2d590aff0","score":"0.59767616","text":"def startTurn():\n\tglobal current_piece\n\tcurrent_piece = 0\n\tlimbL.move_to_joint_positions(coords.ResetPos)\n\tlimbL.move_to_joint_positions(coords.PickU)\n\tlimbL.move_to_joint_positions(coords.PickD)\n\ti = 0\n\twhile current_piece == 0:\n\t\tcurrent_piece = IDBlock(left_image)\n\t\trospy.sleep(5)\n\t\ti+=1\t\t\n\t\tif i >= 6:#After 30 seconds of inaction by the human player, Lucas assumes victory\n\t\t\tprint \"Huzzah! Another glorious victory for your robotic overlord!\"\n\t\t\tsys.exit()\n\tlimbL.move_to_joint_positions(coords.PickU)\n\tlimbL.move_to_joint_positions(coords.ResetPos)\n\tscanBoard()\n\tlimbL.move_to_joint_positions(coords.ResetPos)","title":""},{"docid":"9c181062fcadfcfb7154f204b991d32b","score":"0.5975167","text":"def play_one_turn(self):\n #XXX: My laziness knows no bounds. Only allows two players.\n #XXX: I pass the whole game object through to players. Ugh.\n \n if self.current_player == 1:\n self.p1.new_hand()\n self.p1.play_one_turn(self)\n self.current_player = 2\n else:\n self.p2.new_hand()\n self.p2.play_one_turn(self)\n self.current_player = 1","title":""},{"docid":"cd5f840201383788c385fab45b96b7da","score":"0.59595335","text":"def reset(self, bank=50):\n self.gm = Game(\"P1\", \"Qtable\", \"P2\", \"Qtable\", bank)\n\n # Random starting money distribution p=0.4\n if self.rc is not None:\n if random() < self.rc:\n left = round(random() * ((bank * 2) - 4))\n self.gm.sb_player().bank = left + 2\n self.gm.bb_player().bank = ((bank * 2) - 4) - left + 2\n\n self.gm.place_blinds()\n if self.dc:\n self.gm.players_draw_cards()\n # Return observation\n # [ Small Blind Player, Big Blind Player ]\n self.ob = [self.gm.sb_player(), self.gm.bb_player()]\n return self.ob","title":""},{"docid":"21166b5cdbe4125442fd133b0444ff07","score":"0.5957577","text":"def test_playcard_cost0(self):\n self.plr.play_card(self.card)\n self.plr.gain_card(\"Copper\")\n self.plr.test_input = [\"end phase\", \"end phase\"]\n self.plr.turn()\n self.assertNotIn(\"Horse\", self.plr.piles[Piles.DISCARD])","title":""},{"docid":"6436110836fe4d65b16594b43045efd8","score":"0.59550923","text":"def play_round(self):\n playing_players = self.enter_bets()\n self.give_cards()\n for player in self.players:\n if player.name in playing_players:\n self.play_hand(player)","title":""},{"docid":"60e9ce3200b27a029258f903fb33497d","score":"0.5942199","text":"def play_1(player):\r\n playing = True\r\n deck = Deck()\r\n deck.shuffle()\r\n score = 0\r\n print(input(\"Welcome to the casino, \" + player + \". Press Enter to play the game.\"))\r\n while playing:\r\n if deck.out():\r\n print(\"Deck is out of cards. Game over. Your score was \", str(score))\r\n playing = False\r\n else:\r\n hand_a = PokerHand()\r\n hand_b = PokerHand()\r\n for i in range(0, MAX_HAND_SIZE):\r\n card = deck.deal()\r\n hand_a.add_card(card)\r\n for i in range(0, MAX_HAND_SIZE):\r\n card = deck.deal()\r\n hand_b.add_card(card)\r\n print(\"Hand #1: \", hand_a)\r\n print(\"Hand #2: \", hand_b, \"\\n\")\r\n user_answer = int(input(\"Which hand is worth more? \"\r\n \"Type 1 if Hand #1, -1 if Hand #2, and 0 if they are worth the same\\n \"))\r\n correct_answer = hand_a.compare_to(hand_b)\r\n if user_answer == correct_answer:\r\n print(\"Correct! Let's try again\\n\")\r\n score += 1\r\n else:\r\n print(\"Game over. Your score was \", str(score))\r\n playing = False","title":""},{"docid":"e2e8b14207df9b875bc1f700b9f25012","score":"0.5939224","text":"def start_self_play(self, player, display=False):\n player.reset_player()\n self.init_game_state()\n\n states, mcts_probs, current_players = [], [], []\n while True:\n move, move_probs = player.get_action(self.game_state)\n\n if move == PASS_MOVE:\n move = PASS_MOVE\n else:\n move = (move // self.game_state.size, move % self.game_state.size)\n\n # store the data\n states.append(self.game_state.copy())\n mcts_probs.append(move_probs)\n current_players.append(self.game_state.current_player)\n\n # perform a move\n is_game_end = self.game_state.do_move(move)\n if display:\n self.print_board(self.game_state, str(player), str(player), move_probs)\n if is_game_end:\n winner_id = self.game_state.get_winner()\n # winner from the perspective of the current player of each state\n winners_z = np.zeros(len(current_players))\n if winner_id is not None:\n winners_z[np.array(current_players) == winner_id] = 1.0\n winners_z[np.array(current_players) != winner_id] = -1.0\n if display:\n if winner_id is not None:\n print(\n \"Game end. Winner is player:\",\n \"X\" if winner_id == go.BLACK else \"O\",\n )\n else:\n print(\"Game end. Tie\")\n return winner_id, zip(states, mcts_probs, winners_z)","title":""},{"docid":"a2d452612ccda9a3382eabefd3e5e2c2","score":"0.5927983","text":"def UCTPlaySampleGame():\n # state = OthelloState(4) # uncomment to play Othello on a square board of the given size\n # state = OXOState() # uncomment to play OXO\n # state = NimState(15) # uncomment to play Nim with the given number of starting chips\n # state = SimpState((5,5)) # uncomment to play Simp with the given number of starting chips\n state = CapGoState(sz) # uncomment to play Capture Go with the given board size\n UCTPlayGame(state)","title":""},{"docid":"3c125026b6cbd995192ce2d8c656d968","score":"0.59240186","text":"def resetGame():\n del CompHand[:] \n del UserHand[:]\n del CompWinningList[:]\n del UserWinningList[:]\n del deck[:]\n deck.append(\"Ah\")\n deck.append(\"Ad\")\n deck.append(\"Ac\")\n deck.append(\"As\")\n deck.append(\"2h\")\n deck.append(\"2d\")\n deck.append(\"2c\")\n deck.append(\"2s\")\n deck.append(\"3h\")\n deck.append(\"3d\")\n deck.append(\"3k\")\n deck.append(\"3s\")\n deck.append(\"4h\")\n deck.append(\"4d\")\n deck.append(\"4c\")\n deck.append(\"4s\")\n deck.append(\"5h\")\n deck.append(\"5d\")\n deck.append(\"5c\")\n deck.append(\"5s\")\n deck.append(\"6h\")\n deck.append(\"6d\")\n deck.append(\"6c\")\n deck.append(\"6s\")\n deck.append(\"7h\")\n deck.append(\"7d\")\n deck.append(\"7c\")\n deck.append(\"7s\")\n deck.append(\"8h\")\n deck.append(\"8d\")\n deck.append(\"8c\")\n deck.append(\"8s\")\n deck.append(\"9h\")\n deck.append(\"9c\")\n deck.append(\"9d\")\n deck.append(\"9s\")\n deck.append(\"10h\")\n deck.append(\"10d\")\n deck.append(\"10c\")\n deck.append(\"10s\")\n deck.append(\"Jh\")\n deck.append(\"Jd\")\n deck.append(\"Jc\")\n deck.append(\"Js\")\n deck.append(\"Qh\")\n deck.append(\"Qd\")\n deck.append(\"Qc\")\n deck.append(\"Qs\")\n deck.append(\"Kh\")\n deck.append(\"Kd\")\n deck.append(\"Kc\")\n deck.append(\"Ks\") \n return deck","title":""},{"docid":"55054711f5324eaa84db3999ab4d245c","score":"0.59172213","text":"def test_play_no_pickup(self):\n self.plr.piles[Piles.HAND].set(\"Copper\", \"Copper\", \"Estate\", \"Duchy\")\n self.plr.piles[Piles.DECK].set(\"Gold\", \"Silver\", \"Copper\", \"Copper\")\n self.plr.add_card(self.card, Piles.HAND)\n self.plr.test_input = [\n \"Discard\",\n \"Discard Estate\",\n \"Discard Duchy\",\n \"Finish\",\n \"nothing\",\n ]\n self.plr.play_card(self.card)\n self.assertEqual(self.plr.coins.get(), 2)\n self.assertEqual(self.plr.piles[Piles.HAND].size(), 4 + 1 - 2)\n self.assertNotIn(\"Duchy\", self.plr.piles[Piles.HAND])\n self.assertNotIn(\"Silver\", self.plr.piles[Piles.HAND])","title":""},{"docid":"f18317543fe0e81636342952f64a9f20","score":"0.5912951","text":"def start_game():\n\tprint intro_msg\n\tplayer_name = raw_input(\"What' do they call you son ? \")\n\tend = False\t\t\n\twhile not end :\n\t\tchoice,door = show_options()\n\t\tif choice == 'yes':\n\t\t\t#Enter the door chosen\n\t\t\tproceed = proceed_in_door(door)\n\t\t\tif proceed:\n\t\t\t\tprint \"Proceeding....\"\n\t\t\t\ttime.sleep(3)\n\t\t\t\tos.system('clear')\n\t\t\t\tcontinue\t\n\t\t\telse :\n\t\t\t\tend = True\n\n\t\telif choice == 'no' :\n\t\t\t#Show the random waiting message\n\t\t\tprint random.randint(0,len(waiting_messages))\n\t\t\ttime.sleep(5) #wait\n\n\t\telif choice == 'info':\n\t\t\t#shows the player info\n\t\t\tshow_player_info()\n\t\telse :\n\t\t\tprint \"Whaaaaaaaat the heck is that ?, Just YES or no\"","title":""},{"docid":"754749c503bb5ff4f5d8ff50d7501951","score":"0.5910271","text":"def start_game(self, name):\n\t\tself.guesses = ''\n\t\tself.incorrect = 0\n\t\tself.correct = 0\n\t\tself.gameover = False\n\t\tself.won = False\n\t\tself.username = name\n\n\t\tword_list = ['3dhubs', 'marvin', 'print', 'filament', 'order', 'layer']\n\t\tself.target = word_list[random.randint(0, 5)]","title":""},{"docid":"f5c861602d872a650894343f6703dbbb","score":"0.5908629","text":"def start(self):\n # <<-- Creer-Merge: start -->> - Code you add between this comment and the end comment will be preserved between Creer re-runs.\n # replace with your start logic\n self.game_data = GameData()\n\n self.random = random.random()\n # <<-- /Creer-Merge: start -->>\n\n self.game_data.humans = [human for human in self.player.units if human.job.title == 'fresh human']\n\n self.first_run = True\n self.no_income = True\n self.harvester_returning = False","title":""},{"docid":"c9805fd2876acedb227ef8bbf31c53e4","score":"0.59040797","text":"def reset_game():\n global tmp_score, current_player\n for data in players:\n data[1] = 0\n tmp_score = 0\n current_player = 0 # 'the user always starts'\n cls()\n start_game()","title":""},{"docid":"2a582deca9da8cb98d30740d848ec22b","score":"0.5902361","text":"def startGame(self):\n while True:\n whiteType = input('Welcome to King-Queen Endgame Simulatior \\nWould you like a (1) Computer or (2) Human to play as White (the attacker with a queen and a king)?\\n')\n if whiteType == (1 or 'computer' or 'Computer'):\n whiteType = 'comp'\n break\n elif whiteType == (2 or 'human' or 'Human'):\n whiteType = 'human'\n break\n else:\n print 'Please input 1 or 2.'\n while True:\n blackType = input('Would you like a (1) Computer or (2) Human to play as Black (the defender with a king)?\\n')\n if blackType == 1 or blackType=='computer' or blackType=='Computer':\n blackType = 'comp'\n break\n elif blackType == 2 or blackType=='human' or blackType=='Human':\n blackType = 'human'\n break\n else:\n print 'Please input 1 or 2.'\n self.setBoard('Q')\n print b\n self.playGame(whiteType, blackType)","title":""},{"docid":"c413ae6f91a0d0648f1e7f342387a287","score":"0.58975714","text":"def _start_game(self):\n\t\tself.settings.initialize_dynamic_settings()\n\n\t\t# Reset the game statistics\n\t\tself.stats.reset_stats()\n\t\tself.stats.game_active = True\n\t\tself.sb.prep_score()\n\t\tself.sb.prep_level()\n\t\tself.sb.prep_strikers()\n\n\t\t# Get rid of any remaining kids and candy.\n# \t\tself.kids.empty()\n# \t\tself.candies.empty()\n\n\t\t# Hide the mouse cursor.\n\t\tpygame.mouse.set_visible(False)\n\n\t\tpygame.mixer.music.set_endevent(self.SONG_END)\n\t\tpygame.mixer.music.load(self.song_fn)\n\t\tpygame.mixer.music.play()","title":""},{"docid":"26f3a2bfdeb59a4fb079e12aaccf6ffa","score":"0.5896248","text":"def create_random(cls, players, deck='52-card'):\n\n current_deck = Card.get_shuffled_deck(size=int(deck[:2]))\n\n bottom_card = current_deck[0]\n\n player_hands = {}\n\n for uid, name in players:\n player_hands[uid] = set(current_deck.pop() for _ in xrange(6))\n\n if deck[-4:] == 'fast':\n current_deck = current_deck[:len(players)]\n\n ordered_players = list(players)\n urandom_shuffle_inplace(ordered_players)\n\n cls._choose_first_player(bottom_card.suit, ordered_players, player_hands)\n\n return GameState(ordered_players, player_hands, [], current_deck, [], bottom_card)","title":""},{"docid":"ca6db0fb483345ac87b4cf31144f780c","score":"0.58839583","text":"def initialize_trump(self) -> None:\r\n input(\"Time to decide trump. Pass the computer to \" + self.first_player.name)\r\n euchre_utils.clear_screen()\r\n starting = self.players.index(self.first_player)\r\n i=starting\r\n\r\n self.add_card(self.deck.get_card())\r\n \r\n while i <= starting+7:\r\n player = self.players[i%4]\r\n self.print_status_update(player)\r\n face_up_card: Card = self.cards_on_table[0]\r\n\r\n if i <= starting+3: #first time around\r\n answer = euchre_utils.get_yes_no(\"Do you want the dealer\"+\r\n \" to pick up the card on the table and that card to\" +\r\n \" become trump?\".format(player.name))\r\n if answer:\r\n #alone_answer = euchre_utils.get_yes_no(\"Do you want to go alone?\")\r\n euchre_utils.clear_screen()\r\n input(\"\\nOK, Announce that {} is trump. Pass the computer to the dealer({})\".format(\r\n face_up_card.suit, self.dealer.name))\r\n euchre_utils.clear_screen()\r\n self.dealer.new_card(face_up_card)\r\n self.clear_cards_on_table()\r\n self.trump = face_up_card.suit\r\n self.player_that_called_suit = player\r\n \r\n #dealer chooses which cards to remove\r\n self.dealer.sort_hand(self.trump)\r\n self.print_status_update(self.dealer)\r\n print(\"Dealer, {} told you to pick it up. \".format(player.name)+\r\n \"Which card in your hand do you want to remove?\")\r\n self.dealer.remove_card(self.dealer.select_card(self.dealer.hand))\r\n euchre_utils.clear_screen()\r\n input(\"Pass the computer to {} to start the round\".format(self.first_player.name))\r\n euchre_utils.clear_screen()\r\n break\r\n else: \r\n euchre_utils.clear_screen()\r\n input(\"\\nPass the computer to {}\".format(self.players[(i+1)%4].name))\r\n euchre_utils.clear_screen()\r\n \r\n else: #second time around, choose which suit to be trump\r\n response = euchre_utils.get_answer_no(\"Do you want to select the trump suit?\"+\r\n \" If so, say which. You're playing 'stick the dealer'\\n\", \"diamonds|hearts|spades|clubs\")\r\n if response and response != face_up_card.suit: #if they say an availible suit\r\n print(\"OK, {} is Trump. Pass the computer to {} to start the round\".format(\r\n response, self.first_player.name))\r\n self.trump = response\r\n self.player_that_called_suit = player\r\n self.clear_cards_on_table()\r\n break\r\n elif response == face_up_card.suit:\r\n input(\"Sorry, you can't choose the suit that was initially face up. Try again\")\r\n i-=1 # loop around to the same player again to give another chance\r\n elif i == starting+7: #STICK THE DEALER\r\n #this if clause needs to come after the first two to take lower precedence\r\n input(\"You're playing 'stick the dealer', so the dealer has to choose a suit.\")\r\n i-=1\r\n else: #empty string, they said no\r\n euchre_utils.clear_screen()\r\n input(\"\\nOK, pass the computer to {}\".format(self.players[(i+1)%4].name))\r\n euchre_utils.clear_screen()\r\n \r\n euchre_utils.clear_screen()\r\n i+=1\r\n euchre_utils.clear_screen()\r\n #sort everybodys hands according to trump\r\n [player.sort_hand(self.trump) for player in self.players]","title":""},{"docid":"25edc669fdded7b1f82cd0c502985d64","score":"0.5880057","text":"def make_step(self, action=\"reset\"):\n reward = 0\n if not self.active and action != \"reset\":\n print(\"You specified an action, but no active game is being played. Please reset the game using action='reset'.\")\n elif self.active and action == \"reset\":\n print(\"WARNING! You specified action='reset' although the last game has not ended, yet.\")\n elif action == \"reset\":\n self.reset_game()\n if self.player_sum == 21:\n self.active = False\n if self.verbose:\n print(\"Player has Blackjack!\")\n print(\"The dealer's cards are:\", [int(x) for x in self.dealer_cards])\n if np.sum(self.dealer_cards == 1) == 1 and np.sum(self.dealer_cards == 10) == 1:\n if self.verbose:\n print(\"Dealer has Blackjack, too!\")\n print(\"DRAW!\")\n else:\n if self.verbose:\n print(\"PLAYER WINS!\")\n reward = 1\n elif action == \"hit\":\n new_card = self.draw_cards(size=1)\n if self.verbose:\n print(\"Player draws card:\", new_card)\n self.player_sum += new_card\n if self.player_sum > 21:\n if self.player_has_usable_ace:\n if self.verbose:\n print(\"Player converts a usable ace (11) into 1.\")\n self.player_cards[self.player_cards == 11] = 1\n self.player_has_usable_ace = False\n self.player_sum -= 10\n if self.player_sum > 21:\n # Player goes bust\n if self.verbose:\n print(\"Player goes BUST!\")\n self.active = False\n reward = -1\n else:\n self.state = np.array([int(self.player_sum), self.dealers_showing_card, self.player_has_usable_ace])\n if self.verbose:\n print(\"New sum of player's cards:\", self.player_sum)\n reward = 0\n elif action == \"stick\":\n self.active = False\n if self.verbose:\n print(\"The dealer's cards are:\", [int(x) for x in self.dealer_cards])\n print(\"The dealer has\", np.sum(self.dealer_cards), \"points.\")\n # Let dealer play\n if np.any(self.dealer_cards == 1) and 17 <= self.dealer_sum + 10 <= 21:\n if self.verbose:\n print(\"Dealer converts 1 into 11\")\n self.dealer_sum += 10\n self.dealer_cards[self.dealer_cards == 1] = 11\n while self.dealer_sum < 17:\n new_card = self.draw_cards(size=1)\n if self.verbose:\n print(\"Dealer draws card:\", new_card)\n self.dealer_cards = np.append(self.dealer_cards, new_card)\n self.dealer_sum += new_card\n if np.any(self.dealer_cards == 1) and 17 <= self.dealer_sum + 10 <= 21:\n if self.verbose:\n print(\"Dealer converts 1 into 11\")\n self.dealer_sum += 10\n self.dealer_cards[np.where(self.dealer_cards == 1)[0]] = 11\n if self.verbose:\n print(\"New dealer sum\", self.dealer_sum)\n\n # Check whether dealer goes bust.\n dealer_difference = 21 - self.dealer_sum\n if dealer_difference < 0:\n if self.verbose:\n print(\"Dealer goes BUST!\")\n reward = 1\n else:\n # Compare player to dealer\n player_difference = 21 - self.player_sum\n if player_difference == dealer_difference:\n if self.verbose:\n print(\"DRAW!\")\n reward = 0\n elif player_difference < dealer_difference:\n if self.verbose:\n print(\"PLAYER WINS!\")\n reward = 1\n else:\n if self.verbose:\n print(\"DEALER WINS!\")\n reward = -1\n else:\n raise ValueError(\"'action' has to be either 'stick' or 'hit' when gameover is False.\")\n\n if not self.active:\n self.state = np.array([-1, -1, -1])\n\n return copy.deepcopy(self.state), reward","title":""},{"docid":"2b5ed06f45caf7e937b1b9ad3a3a65f4","score":"0.5879681","text":"def test_others_playing(self):\n while True:\n card = self.g[\"Clashes\"].remove()\n if card.name == \"Warlord\":\n break\n self.plr.add_card(card, Piles.HAND)\n self.plr.play_card(card)\n mil = self.g[\"Militia\"].remove()\n self.oth.add_card(mil, Piles.HAND)\n self.oth.piles[Piles.PLAYED].set(\"Militia\", \"Militia\", \"Copper\")\n self.oth.play_card(mil)\n self.g.print_state()","title":""},{"docid":"2063217bde657141e0f5d1728c6fe9fb","score":"0.5867167","text":"def playCard(self, chosen: int, played_from: str = \"Hand\", cheat: bool = False, flank = \"Right\", ask = True):\n logging.info(f\"numPlays: {sum(v for k,v in self.playedThisTurn.items())}\")\n if played_from == \"Deck\":\n source = self.activePlayer.deck\n elif played_from == \"Discard\":\n source = self.activePlayer.discard\n elif played_from == \"Mimicry\":\n source = self.inactivePlayer.discard\n else:\n source = self.activePlayer.hand\n card = source[chosen]\n if not self.canPlay(card, cheat = cheat):\n return\n # canPlay() makes sure that you can't try to play a card you're not allowed to play\n # Increases amber, adds the card to the action section of the board, then calls the card's play function\n if card.amber > 0:\n self.activePlayer.gainAmber(card.amber, self)\n logging.info(f\"{source[chosen].title} gave {self.activePlayer.name} {card.amber} amber. {self.activePlayer.name} now has {self.activePlayer.amber} amber.\")\n if ask:\n if card.type == \"Creature\" and len(self.activePlayer.board[\"Creature\"]) > 0:\n flank = self.chooseFlank(card)\n # left flank\n if card.type != \"Upgrade\" and flank == \"Left\":\n self.activePlayer.board[card.type].insert(0, source.pop(chosen))\n logging.info(f\"numPlays: {sum(v for k,v in self.playedThisTurn.items())}\")\n # default case: right flank\n elif card.type != \"Upgrade\" and played_from != \"Mimicry\":\n self.activePlayer.board[card.type].append(source.pop(chosen))\n logging.info(f\"numPlays: {sum(v for k,v in self.playedThisTurn.items())}\")\n elif played_from == \"Mimicry\":\n self.activePlayer.board[card.type].append(source[chosen])\n logging.info(f\"Mimicry branch.\")\n else:\n targeted = self.chooseCards(\"Creature\", \"Choose a creature to attach the upgrade to:\")\n if targeted:\n targeted = targeted[0]\n else:\n return\n self.playUpgrade(card, targeted)\n self.cardChanged()\n return\n #once the card has been added, then we trigger any play effects (eg smaaash will target himself if played on an empty board), use stored new position\n if card not in self.playedThisTurn:\n self.playedThisTurn[card] = 1\n else:\n self.playedThisTurn[card] += 1\n self.cardChanged(True) # definitely need to recalc power here, in case we play something next to a staunch knight so now it is dead\n self.draw()\n pygame.display.update()\n card.play(self, card)\n logging.info(f\"{card.title} play ability resolved.\")\n logging.info(f\"numPlays: {sum(v for k,v in self.playedThisTurn.items())}\")\n # if the card is an action, now add it to the discard pile - remote access or poltergeist or nexus on masterplan can potentially play cards that belong to the other player\n if card.type == \"Action\" and played_from != \"Mimicry\":\n if card.title == \"library_access\":\n if card not in self.activePlayer.board[\"Action\"]:\n return\n if card.deck == self.activePlayer.name:\n self.activePlayer.purged.append(self.activePlayer.board[\"Action\"].pop())\n else:\n self.inactivePlayer.purged.append(self.activePlayer.board[\"Action\"].pop())\n else:\n if card not in self.activePlayer.board[\"Action\"]:\n return\n if card.deck == self.activePlayer.name:\n self.activePlayer.discard.append(self.activePlayer.board[\"Action\"].pop())\n else:\n self.inactivePlayer.discard.append(self.activePlayer.board[\"Action\"].pop())\n elif played_from == \"Mimicry\":\n if card.title == \"library_access\":\n purge_mimic = True\n self.activePlayer.board[\"Action\"].pop()\n logging.info(f\"Removing Mimicried card.\")\n if purge_mimic:\n if card.deck == self.activePlayer.name:\n self.activePlayer.purged.append(self.activePlayer.board[\"Action\"].pop())\n else:\n self.inactivePlayer.purged.append(self.activePlayer.board[\"Action\"].pop())\n self.cardChanged(True)","title":""},{"docid":"430302c7890fa57ebc3b3eed949f5b6c","score":"0.5861373","text":"def new_game():\n \n # Initialize global variable that will hold the \"deck\n # of cards\"; we model the \"deck of cards\" as a list \n # consisting of 16 numbers with each number lying in \n # the range [0,8) and appearing twice. The list is\n # created by concatenating two lists with range [0,8) \n # together. Although Player can play the game with\n # \"textual numbers\" or \"images\", the above mentioned \n # technique is being used modeling the game in both\n # game \"modes\". \n global deck_of_cards\n deck_of_cards = range(CARDS_NUMBER // 2) + range(CARDS_NUMBER // 2)\n # Shuffle the \"deck\".\n random.shuffle(deck_of_cards)\n # Remove comment if in DEBUG mode.\n #print deck_of_cards\n \n # Initialize global variable that will hold the a list,\n # with size equal to the size of the \"deck of cards\"\n # consisting of boolean values. The boolean value\n # at a certain list index indicates whether the \"card\"\n # is \"exposed\" (True) or not (False). Particularly,\n # the ith entry should be \"True\" if the ith card is \n # face up and its value is visible or \"False\" if the \n # ith card is face down and it's value is hidden. \n global deck_of_cards_exposed\n deck_of_cards_exposed = [False] * CARDS_NUMBER\n \n # Initialize global variable that will hold the game\n # state (0,1 and 2), i.e. beginning of the game, single \n # \"exposed\" unpaired \"card\" and end of a \"turn\" \n # respectively (have a look at the comments of \n # \"mouseclick()\" for a detailed description \n # concerning this variable).\n global state\n state = 0 \n \n # Initialize global variable that will hold the number\n # of \"turns\" playing the game.\n global turn \n turn = 0\n label.set_text(\"Turns = \" + str(turn))\n \n # Initialize global variable that will hold a \"helper\"\n # list, keeping the index of the cards \"exposed\" in \n # a single \"turn\". \n global index_of_cards_exposed_in_a_turn \n index_of_cards_exposed_in_a_turn = [-1, -1]\n \n return None","title":""},{"docid":"92bac313e3373abca83de4f803c9c470","score":"0.5860306","text":"def play(deck, verbose):\r\n\r\n\t#generate first 2 'visible' cards from deck\r\n\tvisible = [deck.pop(0), deck.pop(0)]\r\n\t\r\n\twhile len(visible) <= 9: #game over if there are more than 9 'piles'\r\n\t\t\t\r\n\t\twhile add_to_11(visible) or jqk(visible):\t\t\r\n\t\t\r\n\t\t\tif add_to_11(visible):\r\n\t\t\t\r\n\t\t\t\t#avoid popping from empty list:\r\n\t\t\t\tif len(deck) >= 2:\r\n\t\t\t\t\t\r\n\t\t\t\t\tfor a in add_to_11(visible):\r\n\t\t\t\t\t\tvisible[a] = deck.pop(0) #'cover' cards\r\n\t\t\t\t\t\t\r\n\t\t\t\t\tif verbose:\r\n\t\t\t\t\t\tprint(visible)\r\n\t\t\t\r\n\t\t\t\telse: \r\n\t\t\t\t\treturn 0 \r\n\t\t\t\t\t#if len(deck) < 2 and 2 cards need to be covered then player wins\r\n\t\r\n\t\t\tif jqk(visible):\r\n\t\r\n\t\t\t\t#avoid popping from empty list:\r\n\t\t\t\tif len(deck) >= 3 : \r\n\t\t\t\t\t\r\n\t\t\t\t\tfor j in jqk(visible):\r\n\t\t\t\t\t\tvisible[j] = deck.pop(0)\r\n\t\t\t\t\t\t\r\n\t\t\t\t\tif verbose:\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t\tprint(visible)\r\n\t\t\r\n\t\t\t\telse:\r\n\t\t\t\t\treturn 0\r\n\t\r\n\t\tif len(visible) <= 9 and len(deck) > 0: \r\n\t\t# no cards can be covered but game not over:\r\n\t\r\n\t\t\tvisible.append(deck.pop(0)) #add new pile\r\n\t\t\t\r\n\t\t\tif verbose:\r\n\t\t\t\tprint(visible)\r\n\t\t\t\r\n\t\tif len(visible) > 9 or len(deck) == 0: \r\n\t\t\tbreak #once there are 10 piles/deck empty game is over\r\n\t\r\n\treturn(len(deck))","title":""},{"docid":"d95982ee89a1979bb0988195bad9f0f4","score":"0.58343667","text":"def reset_game(self):\n if self.verbose:\n print(\"The game is reset.\")\n self.active = True\n self.player_cards = []\n self.player_sum = 0\n self.player_has_usable_ace = False\n while self.player_sum < 12:\n new_card = self.draw_cards(size=1)\n if new_card == 1 and self.player_sum < 11:\n new_card = 11\n self.player_has_usable_ace = True\n self.player_cards = np.append(self.player_cards, new_card)\n self.player_sum += new_card\n\n self.dealer_cards = self.draw_cards(size=2)\n self.dealer_sum = np.sum(self.dealer_cards)\n self.dealers_showing_card = self.dealer_cards[1]\n\n self.state = np.array([int(self.player_sum), self.dealers_showing_card, self.player_has_usable_ace])\n if self.verbose:\n print(\"Player's cards:\", [int(x) for x in self.player_cards]) # , \"; player sum:\", self.player_sum[0]\n print(\"Dealer's showing card:\", [self.dealer_cards[1]])","title":""},{"docid":"6159fbc53dc2432a972ab889872f9fc7","score":"0.5832458","text":"def start_turn(self, character):\n character.db.combat_actionsleft = ACTIONS_PER_TURN # Replenish actions\n # Prompt the character for their turn and give some information.\n character.msg(\"|wIt's your turn! You have %i HP remaining.|n\" % character.db.hp)","title":""},{"docid":"53efab2740a1c1c957c7cc649f014514","score":"0.5830657","text":"def initial(self, state):\n\n self.player_number = min(state.turn_order, 0) % self.size\n self.current_player = self.players[self.player_number]","title":""},{"docid":"d948450c72319e3b6261eb24a0525aa5","score":"0.581744","text":"def play(self):\n print('Welcome to Blackjack v1.0')\n # give dealer 2 cards\n # give player 2 cards\n # player goes first in the gameplay, dealer hits until the player stays\n # dealer then goes\n # if player wins, they double their bet\n\n # if both the player and dealer are the same value, it's called a PUSH and\n # no money changes hands.\n\n # it's a 1:1 payout in blackjack, if you put down 10, you win another 10.\n\n # if you hit BLACKJACK! - 21, then you will get a 3:1 payout, so for putting down\n # 10, you get 15.\n\n while True:\n\n break","title":""},{"docid":"f9ef2eb3926070baf2d966906da516c8","score":"0.5817214","text":"def shuffle_deck(self):\n self.__deck = Deck()","title":""},{"docid":"dece00dfe6866bb147e30f9a31ea4d1c","score":"0.58131605","text":"def select_cards(self, deck, player, dealer):\n self.gui.show_hand_select_instructions()\n self.pause(2000)\n \n first_hand = deck.draw_two_card_hand()\n second_hand = deck.draw_two_card_hand()\n third_hand = deck.draw_two_card_hand()\n \n self.gui.show_first_hand(first_hand[0].img_path, first_hand[1].img_path)\n if self.picked_up_cards():\n player.add_hand(first_hand)\n dealer.add_hand(second_hand)\n dealer.add_hand(third_hand)\n else:\n dealer.add_hand(first_hand)\n self.gui.show_second_hand(second_hand[0].img_path, second_hand[1].img_path)\n\n if self.picked_up_cards():\n player.add_hand(second_hand)\n dealer.add_hand(third_hand)\n else:\n dealer.add_hand(second_hand)\n player.add_hand(third_hand)\n self.gui.alert_to_third_hand()\n self.pause(1500)","title":""},{"docid":"2589a760be7715f758b3933ef4b784ee","score":"0.58054376","text":"def game(self) -> None:\n print(RULES) \n print(f\"You have been credited with ${self.start_amount}\")\n\n # main game loop, loop infinitely until\n # player decides to quit or player looses\n # all his/her money\n while True: \n if self.start_amount < 1:\n print(\"You've run out of money\")\n print(\"Good thing you weren't using real money.\")\n print(\"Try to seek professional help for your gambling problems.\")\n sys.exit() #player is broke, exit game \n\n print(f\"Money available: ${self.start_amount}\") \n\n # Get the amount player wants to stake.\n amt_to_bet = self.entity_bet(self.player, self.start_amount)\n print(f\"Player bet ${amt_to_bet}\")\n\n # Display player scores\n self.display_scores()\n\n # play for both player and player's second\n # hand if he does split\n self.entity_play(self.player)\n self.entity_play(self.player_split)\n\n # after player busts or (St)ands\n # check if dealer needs to play\n self.check_if_dealer_plays()\n\n # check which hand - player main or second hand\n # if existent- won.\n self.check_who_wins(self.player)\n self.check_who_wins(self.player_split)\n\n # start all over again\n input(\"Press Enter to continue...\")\n print('\\n')\n\n # reset stats.\n self.reset()\n # clear console\n os.system('cls') if sys.platform == \"win32\" else os.system('clear')","title":""},{"docid":"35cf530c7c6f7b8e383a4fe612d5570d","score":"0.58013165","text":"def __init__(self, deck=None, p1_class=Player, p2_class=Player):\n #XXX: Currently defaults to playing with VP and Money only.\n self.card_set = 'trivial'\n self.current_player = 1\n self.banked_cards = copy.deepcopy(cards.CARD_SETS[self.card_set])\n\n # Variables for game stats\n self.turn_num = 1\n self.winning_player = []\n self.player_vp = []\n\n initial_deck = deck or cards.STARTING_DECK\n self.p1 = p1_class(initial_deck)\n self.p2 = p2_class(initial_deck)","title":""},{"docid":"fa0e423f621be5257e0f20a164254566","score":"0.5795721","text":"def main():\n\n print(\"Welcome to BlackJack!!!\")\n\n # Ask for number of players\n n_players = get_num_players()\n\n # Initialize the game\n human_name = 'You'\n game = Game(n_players, human_name=human_name)\n\n while (True):\n # Deal\n print_deal(game.deal())\n\n # Play one turn per player\n while game.can_play():\n play_turn(game)\n game.next_player()\n\n # Output results\n print_score(game, *game.get_winners())\n if (input(\"Play again? [Y/n] \") is 'n'):\n break\n\n # End the game\n print(\"Thanks for playing! Have a great day!\")","title":""},{"docid":"74b353cb03f1abea549580a71817cbcb","score":"0.57908016","text":"def play(self):\n self._shuffle()\n self._deal()\n self._display_all()\n results = {}\n for player in self.player_list:\n score = player.play_hand()\n results[f'{player.type} {player.name}'] = score\n print('Dealer flips over second card...')\n print(self._dealer.hand)\n dealer_score = self._dealer.play_hand()\n print('\\n---------------------------------\\n')\n for name, score in results.items():\n if score == 0:\n print(f'{name} lost')\n elif score > dealer_score:\n print(f'{name} wins')\n elif score == dealer_score:\n print(f'{name} pushes')\n else:\n print(f'{name} lost')","title":""},{"docid":"64492e3d35024b21d7f7db18a48eadff","score":"0.57886285","text":"def draw_initial_cards(self):\n\n # Draw cards for player and dealer.\n for current in ('dealer', 'player'):\n for i in range(0, 2):\n card = self.draw_card()\n self.cards[current].append(card)","title":""},{"docid":"a3cba110962d7ea7079e68e96fc17b49","score":"0.5776046","text":"def main():\n game = WarGame()\n game.deal()\n while not game.winner():\n game.step()\n print(game)\n print(game.winner())","title":""},{"docid":"2ad22698074c27bd70352a6b42bde555","score":"0.57631284","text":"def test_Engine__insure_zero(engine, hand):\n player = players.BetterPlayer(name='Eric', chips=100)\n player.bet = 20\n player.hands = (cards.Hand(),)\n engine.dealer.hands = (hand,)\n engine._insure(player)\n assert player.insured == 0\n assert player.chips == 100\n assert engine.ui.mock_calls == []","title":""},{"docid":"b156f8f72b972997f3e5d63cee14036e","score":"0.5762807","text":"def pollPlay(self, state):\n hand = self.player.getHand()\n if not len(hand) == 0:\n cardOfChoice = random.choice(hand)\n # returns the choosen card here\n return cardOfChoice\n print(\"Oops, you are trying to play a card from an empty hand\")\n return None","title":""},{"docid":"7976e1729bf6638650ad8b2cd77acfb2","score":"0.5760314","text":"def start(self):\n Screen.clear()\n print('STARTING NEW GAME')\n print('ENTERING MAIN GAME STATE IN A MOMENT...')\n self.pause(pause_time=1)","title":""},{"docid":"39032af7fa0996e1f761e776ab5a59b6","score":"0.5759432","text":"def drawPlayerCard(self):\n player = self.level.players[0]\n card = self.level.playerDeck.draw()\n if card is not None:\n controller = PlayerDrawController(player, card)\n controller.run()","title":""},{"docid":"c4bb3a70ea09d37ce93a834cc0900ec8","score":"0.57525396","text":"def resetHand(self):\n self.hand = Hand()","title":""},{"docid":"b29b99ebb0ebc7579986cd9ef088010b","score":"0.5751541","text":"def prep(self, hand):\r\n self.hand = hand\r\n active_count = 0\r\n\r\n for player in self.players:\r\n if player.stack >= self.BLINDS[1]:\r\n player.activate()\r\n active_count += 1\r\n else:\r\n player.deactivate()\r\n \r\n if active_count < 2:\r\n c = \"Table needs at least 2 active players for a game.\"\r\n raise StructureError(c)\r\n\r\n if self.button == None:\r\n self.button = 0\r\n else:\r\n self.button += 1","title":""},{"docid":"2114aa08ab856de7ecb735cbc2255963","score":"0.574097","text":"def start_game(self):\n self.round = 0\n self.drawing_player = None\n self.score = {}\n\n for client_id in self.clients:\n self.score[client_id] = 0\n\n self.notify_all('game_starting')\n self.next_turn()","title":""},{"docid":"1fdb99a447976b8d96b16eef1d320019","score":"0.5738883","text":"def PlayRound(self):\n with self._lock:\n if self._active_round:\n logging.error('HypeJack game already active.')\n return\n bets = self._core.bets.LookupBets(\n self.name, resolver=self._core.name.lower())\n if not bets:\n logging.error('Attempted to start HypeJack with no players.')\n return\n\n self._pending_start = False\n\n # Shuffle the deck when it gets low. We assume a reasonable number of\n # cards needed per player, but with lots of splits / low cards we may\n # still run out of cards to play the hand.\n if len(self._shoe) < (len(self._peeps) + 1) * 7:\n self._ShuffleCards()\n\n # Deal cards to plebs.\n for user_id, user_bets in bets.items():\n hand = Hand(user_bets[0], self._shoe.pop(), self._shoe.pop())\n self._peeps[user_id] = [hand]\n self._DisplayUser(user_bets[0].user)\n\n # Deal cards to hypebot.\n self._dealer_hand = Hand(None, self._shoe.pop(), self._shoe.pop())\n # self._dealer_hand = Hand(playing_cards_lib.Card('Hearts', 8),\n # playing_cards_lib.Card('Spades', 8))\n self._msg_fn(None, 'Dealer: [%s, %s]' % (self._dealer_hand.cards[0], '🂠'))\n\n self._active_round = True\n\n # Short-circuit game play if the dealer has a hypejack or if all peeps\n # have hypejacks.\n if not self._dealer_hand.IsHypeJack() and any(\n [self._IsActive(user_id) for user_id in self._peeps.keys()]):\n # Force the round to end after some time if some peep ran away. Waiting\n # on a condition releases the lock while waiting, then reacquires it\n # automatically. Will shortcircuit if notified when all peeps have\n # finished their hands.\n self._game_ender.wait(timeout=self.MAX_ROUND_LENGTH)\n\n # Complete dealer hand.\n self._msg_fn(None, 'Dealer: %s' % self._dealer_hand)\n while self._dealer_hand.Score() < 17:\n self._dealer_hand.cards.append(self._shoe.pop())\n self._msg_fn(None, 'Dealer: %s' % self._dealer_hand)\n\n self._core.bets.SettleBets(self, self._core.name.lower(), self._msg_fn)\n\n # Reset game state.\n self._peeps = {}\n self._active_round = False","title":""}],"string":"[\n {\n \"docid\": \"5100836fd6ace5c376bcbe31fb141463\",\n \"score\": \"0.7105495\",\n \"text\": \"def start_new_game(self, player_hand):\\n self.deck_of_cards += self.hand\\n self.deck_of_cards += player_hand\\n self.deck_of_cards.shuffle()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73b8d8128052bfd7bd5a587d480672ce\",\n \"score\": \"0.6954077\",\n \"text\": \"def start_game(self):\\n initial_draw = int((len(self.stock.cards) * 0.5) / len(self.participants))\\n\\n if initial_draw == 0:\\n initial_draw = 1\\n\\n for player in self.participants:\\n cards = [self.stock.deal() for x in range(initial_draw)]\\n player.hand = cards\\n\\n self.act_player = self.participants[0]\\n self.turn = self.next_turn()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6662d1a0a763307463d759a7f7232e3a\",\n \"score\": \"0.66947067\",\n \"text\": \"def init_game(self):\\n if self.is_basic: #create quick simple game\\n p1 = 6 #priest\\n p2 = 7 #rogue\\n deck1 = random_draft(CardClass(p1))\\n deck2 = random_draft(CardClass(p2))\\n self.players[0] = Player(\\\"Player1\\\", deck1, CardClass(p1).default_hero)\\n self.players[1] = Player(\\\"Player2\\\", deck2, CardClass(p2).default_hero)\\n self.game = Game(players=self.players)\\n self.game.start()\\n\\n #Skip mulligan\\n for player in self.game.players:\\n cards_to_mulligan = random.sample(player.choice.cards, 0)\\n player.choice.choose(*cards_to_mulligan)\\n\\n return self.game\\n\\n else:\\n p1 = random.randint(1, 9)\\n p2 = random.randint(1, 9)\\n #initialize players and randomly draft decks\\n #pdb.set_trace()\\n deck1 = random_draft(CardClass(p1))\\n deck2 = random_draft(CardClass(p2))\\n self.players[0] = Player(\\\"Player1\\\", deck1, CardClass(p1).default_hero)\\n self.players[1] = Player(\\\"Player2\\\", deck2, CardClass(p2).default_hero)\\n #begin the game\\n self.game = Game(players=self.players)\\n self.game.start()\\n\\n #Skip mulligan for now\\n for player in self.game.players:\\n cards_to_mulligan = random.sample(player.choice.cards, 0)\\n player.choice.choose(*cards_to_mulligan)\\n\\n return self.game\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db336ace779d9c035eacad3d1e43a2c3\",\n \"score\": \"0.6674635\",\n \"text\": \"def play_hand(self, player):\\n if not player.has_blackjack():\\n while player.get_hand_value() <= 21:\\n self.print_current_state(player, sd=False)\\n action = player.get_action()\\n if action == 'Hit':\\n self.deal_top(player)\\n if action == 'Stand':\\n break\\n if action == 'Split':\\n pass\\n if action == 'Double':\\n player.stack -= player.amount_bet\\n player.amount_bet *= 2\\n self.deal_top(player)\\n break\\n # If player did not bust dealer plays\\n if (player.get_hand_value() <= 21):\\n self.dealer_plays()\\n self.print_current_state(player, sd=True)\\n self.declare_winner(player)\\n self.reset_table()\\n print(self.players[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cefe9cdd368f1bc5c556b592bd28b0ba\",\n \"score\": \"0.6657983\",\n \"text\": \"def computer_turn():\\n global winner, winning_hand, time\\n computer.draw_card(deck.pile[0])\\n winning_hand = computer.declare_game()\\n if winning_hand[0]:\\n #winning condition\\n winner = computer\\n round_over()\\n stage[0] += 1\\n else:\\n score = [0] * 14\\n min_pile = (0, 100)\\n for i in range(len(computer.hand)):\\n score[i] = calculate_score(computer.hand[i], computer.hand)\\n for i in range(len(score)):\\n if score[i] < min_pile[1]:\\n min_pile = (i,score[i])\\n elif score[i] == min_pile[1]:\\n if computer.hand[i].rank >= computer.hand[min_pile[0]].rank:\\n min_pile = (i, score[i])\\n\\n computer.discard_card(deck.pile[0])\\n\\n joker_card = Card('3','spades',True)\\n computer.draw_card(joker_card)\\n score = [0] * 14\\n min_deck = (0, 100)\\n for i in range(len(computer.hand)):\\n score[i] = calculate_score(computer.hand[i], computer.hand)\\n for i in range(len(score)):\\n if score[i] < min_deck[1]:\\n min_deck = (i,score[i])\\n elif score[i] == min_deck[1]:\\n if computer.hand[i].rank >= computer.hand[min_deck[0]].rank:\\n min_deck = (i, score[i])\\n computer.discard_card(joker_card)\\n\\n\\n if min_pile[1] <= min_deck[1]:\\n #draw from pile\\n if time == 0 :\\n computer.draw_card(deck.pile.pop(0))\\n deal_card_sound.play()\\n time += 1\\n # print(\\\"pile initialisation\\\")\\n elif time >= computer_delay:\\n #DELAY\\n deck.update_pile(computer.hand[min_pile[0]])\\n computer.discard_card(computer.hand[min_pile[0]])\\n deal_card_sound.play()\\n # print(\\\"pile time condition\\\")\\n computer.hand = sort_hand(computer.hand)\\n computer.turn = False\\n user.turn = True\\n time = 0\\n else:\\n # print(\\\"pile increment\\\")\\n time += 1\\n else:\\n #draw from deck\\n #DELAY\\n if time == 0:\\n computer.draw_card(deck.draw_card())\\n deal_card_sound.play()\\n # print(\\\"deck initialisation\\\")\\n time += 1\\n elif time >= computer_delay:\\n # print(\\\"Deck time condition\\\")\\n winning_hand = computer.declare_hand()\\n if winning_hand[0] :\\n #winning condition\\n winner = computer\\n round_over()\\n stage[0] += 1\\n time = 0\\n else:\\n score = [0] * 14\\n min_deck = (0, 100)\\n for i in range(len(computer.hand)):\\n score[i] = calculate_score(computer.hand[i], computer.hand)\\n for i in range(len(score)):\\n if score[i] < min_deck[1]:\\n min_deck = (i,score[i])\\n elif score[i] == min_deck[1]:\\n if computer.hand[i].rank >= computer.hand[min_deck[0]].rank:\\n min_deck = (i, score[i])\\n deck.update_pile(computer.hand[min_deck[0]])\\n computer.discard_card(computer.hand[min_deck[0]])\\n time = 0\\n deal_card_sound.play()\\n computer.hand = sort_hand(computer.hand)\\n computer.turn = False\\n user.turn = True\\n else:\\n # print(\\\"deck increment condition\\\")\\n time += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae5ce86f91fa809eba12df1bb5fd7837\",\n \"score\": \"0.66456765\",\n \"text\": \"def start_hand(player, dealer, bet_amount):\\n\\n player.place_bet(bet_amount)\\n player.get_hand(dealer.give_card())\\n player.get_hand(dealer.give_card())\\n\\n print(player.show_hand())\\n\\n dealer.get_hand(dealer.give_card())\\n dealer.get_hand(dealer.give_card())\\n\\n print(dealer.show_first_hand())\\n\\n if player.hand_total_value() == 21:\\n player.has_blackjack = True\\n\\n if dealer.hand_total_value() == 21:\\n dealer.has_blackjack = True\\n\\n if player.has_blackjack:\\n print(\\\"Blackjack!!!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f23e94ed0fa96fae14e8750676a9722f\",\n \"score\": \"0.65526325\",\n \"text\": \"def play(self):\\n\\n #############\\n # Game loop #\\n #############\\n number_bets = 0\\n print \\\"###############\\\"\\n print \\\"# Game starts #\\\"\\n print \\\"###############\\\"\\n while self.player.chips > 0:\\n self.play_hands()\\n number_bets += 1\\n #wants_exit = raw_input(\\\"> Press e for exit \\\").lower()\\n #if wants_exit == 'e':\\n # break\\n print \\\"###############\\\"\\n print \\\"#########################\\\"\\n print \\\"# Game end with %s bets #\\\" % (number_bets)\\n print \\\"#########################\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb175cb8d195e3b844ef7dd03c55afb8\",\n \"score\": \"0.6526096\",\n \"text\": \"def initialSetUp():\\n deck = cardGame.getDeck()\\n cardGame.shuffleDeck(deck)\\n numberOfPlayers = getNumberOfPlayers()\\n hands = cardGame.dealCards(deck, 0, numberOfPlayers)\\n sortHands(hands)\\n startingPlayer = getStartingPlayer(hands)\\n return deck, hands, numberOfPlayers, startingPlayer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1ec6eee21dc39ea3e1cc703759281ee\",\n \"score\": \"0.6525598\",\n \"text\": \"def start(self):\\n\\n self.view.display_game_start()\\n self.cardgame.add_players()\\n\\n game_over = False\\n\\n while not game_over:\\n for player in self.cardgame.get_players():\\n move = self.parse_move(player.name)\\n self.cardgame.step(player, move)\\n self.view.display_hand(player.name, player.get_hand())\\n game_over = self.cardgame.is_game_over()\\n\\n winner, score = self.cardgame.pick_winner()\\n\\n if not winner:\\n self.view.display_draw_game()\\n else:\\n self.view.display_winner(winner.name, score, winner.get_hand())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3e313dae13a0e559e08ea35e111a9f2\",\n \"score\": \"0.65084654\",\n \"text\": \"def new_game():\\n global cards, exposed, hand, state\\n global card_1, card_2, turns\\n\\n hand = cards[:]\\n random.shuffle(hand) # hand for the game\\n\\n exposed = 16 * [False] # initialize uncovered cards\\n card_1 = None\\n card_2 = None\\n turns = 0\\n\\n state = 0 # number of cards initially uncovered\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fee53164e852b2bd36967c77375c4a68\",\n \"score\": \"0.64621794\",\n \"text\": \"def setup_game(self):\\n if self.is_ready():\\n for player in self.players:\\n player.draw(5)\\n self.wild.draw(5)\\n self.starting_positions = [p.player_id for p in self.players]\\n self.current_player = self.next_player()\\n else:\\n print 'Not enough players to setup game'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caf9e7f2d74b953dbf52004e56ba4100\",\n \"score\": \"0.6429044\",\n \"text\": \"def start_hand(self):\\n [self.draw() for _ in range(5)] #initially draw 5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d389423337a21da474a9da118a77413d\",\n \"score\": \"0.64132434\",\n \"text\": \"def start_game(self):\\n self.display_info()\\n temporary_board = Board_game(self.board_size, self.number_of_ships,\\n \\\"Computer\\\", player=False)\\n self.computer_board = temporary_board\\n player_name = input(\\\"Please enter your name: \\\\n\\\")\\n print(\\\"-\\\" * 60)\\n temporary_board = Board_game(self.board_size, self.number_of_ships,\\n player_name, player=True)\\n self.player_board = temporary_board\\n\\n self.play_game()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb5a90f61dcbcc1ddbbe1831103505af\",\n \"score\": \"0.6401434\",\n \"text\": \"def reset_new_game(self):\\n\\t\\tself._piles['deck'] = Pile('copper',7)\\n\\t\\tself._piles['deck'].add_new_card_to_pile('estate',3)\\n\\t\\tself._piles['deck'].shuffle()\\n\\t\\tself._piles['hand'] = Pile()\\n\\t\\tself._piles['discard'] = Pile()\\n\\t\\tself._piles['in_play'] = Pile()\\n\\t\\tself.draw_n(5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d74b8502b3dc12a43ce7f6e15d3c209\",\n \"score\": \"0.6401383\",\n \"text\": \"def play_hands(self):\\n\\n #############\\n # First hand\\n #############\\n # Deal two cards to each opponent\\n self.player.cards = self.dealer.deal(2)\\n self.dealer.cards = self.dealer.deal(2)\\n\\n # Show hand\\n self.player.show_hand()\\n self.dealer.show_hand(1) # hide second card\\n\\n # End if player has blackjack\\n if self.player.has_blackjack():\\n self.end_bet()\\n return\\n\\n #################################\\n # Hand loop for player and dealer\\n #################################\\n opponents = [self.player, self.dealer]\\n dealer_turn = 0 # 0: is player turn, 1: is dealer turn\\n continue_bet = True\\n round = 1\\n # Bet\\n self.player.chips -= self.player.chips_per_bet\\n # Repeat until bet end condition\\n while continue_bet:\\n # Set who plays: player or dealer\\n opponent = opponents[dealer_turn]\\n continue_hand = True\\n while (continue_hand):\\n # Ask for action: hit or stand?\\n action = opponent.choose_action()\\n # If hit, deal cards and show hand\\n if action == settings.ACTION_HIT:\\n opponent.cards.extend(self.dealer.deal())\\n opponent.show_hand()\\n # check end conditions\\n if opponent.has_blackjack() or opponent.has_busted():\\n continue_hand = False\\n continue_bet = False\\n # If stand, it's dealer's turn\\n elif action == settings.ACTION_STAND:\\n dealer_turn = 1 - dealer_turn # pass turn to other opponent\\n round+=1\\n continue_hand = False\\n # Player stands and dealer stands so end bet\\n if round == 4:\\n continue_bet = False\\n else:\\n raise Exception(\\\"Unknown action: '%s'\\\" % action)\\n self.end_bet()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd044cf068a3a9a276486c7f13ec9422\",\n \"score\": \"0.6370955\",\n \"text\": \"def start_game(self):\\n print(\\\"\\\"\\\"\\n Welcome to the Hi/Lo game!\\n \\n Guess if the next card will be higher\\n or lower than the last drawn card.\\n If correct, you get 100pts! Guess wrong \\n and you lose 75pts!\\n \\n You lose if you hit, or go below, 0pts.\\n You win if you score 1,000 or more pts.\\n \\n Good luck!\\n \\\"\\\"\\\")\\n while self.keep_playing == True:\\n print(f\\\"The card is : {self.old_card}\\\")\\n self.player.guess()\\n self.draw_card()\\n self.display_card()\\n self.get_points()\\n print(f\\\"Score: {self.points}\\\")\\n self.keep_playing = self.to_play()\\n self.card_update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a74aa24f1c77ebf72c7629e86f7d25ca\",\n \"score\": \"0.6350413\",\n \"text\": \"def main():\\n newdeck = deck.Deck()\\n newdeck.shuffle()\\n firstplayer_cards = []\\n secondplayer_cards = []\\n for x in range(5):\\n firstplayer_cards.append(newdeck.dealacard())\\n secondplayer_cards.append(newdeck.dealacard())\\n playing_game(firstplayer_cards, secondplayer_cards)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6a583f2c39d810c3d4c6a06f580b22f\",\n \"score\": \"0.6337075\",\n \"text\": \"def start(self):\\n while self._pyramid[0][0].rank is not None: # if top card is not None\\n self._menu()\\n command = input('\\\\nCommand: ').strip()\\n if self._enter_command(command) is None:\\n system('cls')\\n continue\\n else:\\n system('cls')\\n input('\\\\nGame Over')\\n print(game.instruction())\\n input('Press any key to continue...') # wait user\\n system('cls')\\n self.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"293c5613cb568b29524346857329a8d3\",\n \"score\": \"0.6327848\",\n \"text\": \"def reset(self) -> None:\\n\\n self.deck = make_cards()\\n self.player = Player([self.deck.pop(), self.deck.pop()], 0, self.player_name)\\n self.dealer = Dealer([self.deck.pop(), self.deck.pop()])\\n self.player_split = Player([], 0, self.player_name + \\\" hand_two\\\")\\n self.split = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7122669f6b8f76c9b6655a98923e290\",\n \"score\": \"0.63025635\",\n \"text\": \"def start_round(self):\\r\\n self.round_number += 1\\r\\n self.dealer = (self.dealer + 1) % 4\\r\\n self.round = Round(self.round_number, self.dealer)\\r\\n trump_suit = None\\r\\n self.deal()\\r\\n self.round.starting_hands = [player.cards[:] for player in self.players] + [\\r\\n self.kitty.cards[:]\\r\\n ]\\r\\n\\r\\n print(\\\"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\\")\\r\\n print(\\\"Round %s\\\" % (self.round_number))\\r\\n print(\\\"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e90754c4fcc810d1e8439fabac48bf0d\",\n \"score\": \"0.6301797\",\n \"text\": \"def start_game(self):\\n\\t\\tpass\\n\\t\\t# Welcome the player\\n\\t\\t# Get the player name\\n\\t\\t# Tell the player how to navigate the game\\n\\t\\t\\t# How to access the rules\\n\\t\\t\\t# How to quit at any input \\n\\t\\t# Reveal the blank game board \\n\\t\\t# \\\"Ready to start? (Y)es or (N)o\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f40568fed3de59176404cedb08d0e3a\",\n \"score\": \"0.62817025\",\n \"text\": \"def user_start_play(tower, discard):\\n\\n print('\\\\nNOW IT\\\\'S YOUR TURN!')\\n print('Your Tower', end = ': ')\\n print(tower)\\n\\n top_discard_brick = see_top_brick_discard(discard)\\n print('The top brick on the discard pile is', top_discard_brick)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"713dc46b4c49cbc94a8c0af15906bb56\",\n \"score\": \"0.6278261\",\n \"text\": \"def game_reset():\\n global deck, user, computer, draw, swap, insert, sort, declare, discard, showCP, user_name, user_name_rect, computer_name, computer_name_rect\\n global name_text, name_rect, score_text, score_rect, discard_mode, swap_mode, insert_mode, index\\n deck = Deck(2)\\n deck.shuffle_cards()\\n deck.set_joker()\\n user.deal_cards(deck)\\n computer.deal_cards(deck)\\n cardss = deck.draw_card()\\n deck.update_pile(cardss)\\n user.turn = True\\n discard_mode = False\\n swap_mode = False\\n insert_mode = False\\n index = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4235cb633939d598736e432d17bdfc7f\",\n \"score\": \"0.62765485\",\n \"text\": \"def play_turn(game):\\n print('=================================')\\n if game.is_current_player_a_computer():\\n print(game.get_current_player_name() + \\\"'s turn to play\\\")\\n time.sleep(0.5)\\n cards_drawn = game.play_computer_turn()\\n for card in cards_drawn:\\n if card is not None:\\n print(\\\"Hit! Drew card %s\\\" % card)\\n time.sleep(0.5)\\n else:\\n print(\\\"Deck is empty! Finished turn.\\\")\\n if (game.current_player.is_over_21()):\\n print(game.get_current_player_name() + \\\" lost! \\\" + game.get_current_player_name() + \\\" is out.\\\")\\n time.sleep(0.5)\\n else:\\n if game.get_current_player_name != 'Dealer':\\n print(game.get_current_player_name() + \\\" finished with count of \\\" + str(game.get_current_player_hand_count()))\\n time.sleep(0.5)\\n else:\\n print(\\\"Your turn to play! Your count is: %s\\\" % game.get_current_player_hand_count())\\n print(\\\"Your hand is: %s\\\" % game.get_current_player_hand())\\n while game.human_can_draw() and input(\\\"Do you want to hit? [y/N] \\\") is 'y':\\n card = game.human_player_draw()\\n if card is not None:\\n print(\\\"Hit! Delt card %s. Your count is now: %s\\\" % (card, game.get_current_player_hand_count()))\\n print(\\\"Your hand is: %s\\\" % game.get_current_player_hand())\\n else:\\n print(\\\"Deck is empty! Finished turn.\\\")\\n break\\n if game.is_current_player_over_21():\\n print(\\\"You lost! You score is over %s.\\\" % game.max_count())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2222c78926dcf1f22dafb55d4be49df5\",\n \"score\": \"0.626647\",\n \"text\": \"def computer_turn(self):\\n print(\\\"You have \\\" + str(len(self.player1.deck)) + \\\" cards\\\")\\n print(self.player2.name + \\\" has \\\" + str(len(self.player2.deck)) + \\\" cards\\\")\\n print(\\\"Here is your top card\\\\n\\\")\\n print_card(self.player1.deck[0])\\n print(\\\"\\\\nYour opponent is choosing a trump stat\\\")\\n rand_num = randint(1, 6)\\n chosen_stat = None\\n\\n if rand_num == 1:\\n chosen_stat = \\\"hp\\\"\\n elif rand_num == 2:\\n chosen_stat = \\\"attack\\\"\\n elif rand_num == 3:\\n chosen_stat = \\\"defense\\\"\\n elif rand_num == 4:\\n chosen_stat = \\\"speed\\\"\\n elif rand_num == 5:\\n chosen_stat = \\\"special attack\\\"\\n else:\\n chosen_stat = \\\"special defense\\\"\\n time.sleep(2)\\n print(\\\"\\\\n Your opponent picked \\\" + chosen_stat + \\\"!\\\\n\\\")\\n\\n self.check_stats(chosen_stat)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"334b7600b2adc90874380ae9d38902c7\",\n \"score\": \"0.625842\",\n \"text\": \"def new_game():\\n global exposed, deck_cards, state, turns\\n turns = 0\\n # Game state\\n state = 0\\n exposed = [False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False] \\n # STEP 1: Model the deck of cards \\n deck_cards = [1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8]\\n correct_cards = [False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False] \\n # STEP 3: Shuffle the deck using random.shuffle\\n random.shuffle(deck_cards)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98b10c5fbb5553c5b37d0874b9667ae6\",\n \"score\": \"0.624704\",\n \"text\": \"def __init__(self):\\n self.keep_playing = True\\n self.old_card = r.randint(1,13)\\n self.new_card = 0\\n self.player = Player()\\n self.guess = \\\"\\\"\\n self.points = 300\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2efe4a4c4937f3e8e134bfdda416f06d\",\n \"score\": \"0.62439346\",\n \"text\": \"def reset_game(self):\\n # The player always goes first.\\n self.current_turn = 'player'\\n\\n # Set the current cards to an empty array.\\n self.cards = {\\n 'dealer': [],\\n 'player': []\\n }\\n\\n # Set the totals for each player.\\n self.totals = {\\n 'dealer': 0,\\n 'player': 0\\n }\\n\\n # Create a new new deck of cards.\\n self.deck = self.deck_generator.get_new_deck()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4315b4a7c52331d868ebcd95500313ae\",\n \"score\": \"0.6240726\",\n \"text\": \"def take_initial_turn(self, player, dealer, deck_list):\\n\\n dist_counter = 0\\n\\n # Distribute first 2 cards to player and dealer\\n while dist_counter < 2:\\n \\n # Take 1 card for player\\n player, deck_list = self.take_1_card(player, deck_list)\\n print(f'{player.name} hand: {[pair[0] for pair in player.hand]}\\\\nCurrent deck len: {len(deck_list)}')\\n \\n # Take 1 card for dealer, showing only the first drawn card\\n dealer, deck_list = self.take_1_card(dealer, deck_list)\\n if dist_counter == 0:\\n print(f'{dealer.name} hand: {[pair[0] for pair in dealer.hand]}\\\\nCurrent deck len: {len(deck_list)}')\\n else:\\n print(f'Current deck len: {len(deck_list)}')\\n\\n dist_counter += 1\\n \\n print('\\\\nEnd of initial distribution.')\\n\\n return player, dealer, deck_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aeccbe58a2576153b7edf0796e66999c\",\n \"score\": \"0.62265307\",\n \"text\": \"def black_jack_hand(player1, dealer): \\n #this should be the black_jack hand, while the begging should be setting up a new game\\n dealer.get_all_bets(player1)\\n ##deal two cards each, one to the dealer should be set to face down\\n for x in xrange(1,3):\\n dealer.give_card_to_player(player1)\\n dealer.give_card_to_player(dealer)\\n\\n ###Now the game is ready to start showing the hands of the dealer and the player should be done\\n print \\\"Dealer's Hand\\\"\\n dealer.get_hand().showHand()\\n print \\\"Player's Hand\\\"\\n player1.get_hand().showHand()\\n while(player1.want_card()):\\n dealer.give_card_to_player(player1)\\n print \\\"Players Hand\\\"\\n player1.get_hand().showHand()\\n\\n \\n dealer.flip_card_over()\\n while(dealer.want_card()):\\n dealer.give_card_to_player(dealer)\\n \\n print \\\"Dealers Hand\\\"\\n dealer.get_hand().showHand()\\n\\n dealer.set_winner(player1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06a062fdec911de20686003a63b70bb4\",\n \"score\": \"0.6199962\",\n \"text\": \"def play_game(self):\\n\\n # Print the beginning game message.\\n print(\\\"\\\\nBeginning \\\" + str(self))\\n\\n time.sleep(1.5)\\n\\n os.system('cls' if os.name == 'nt' else 'clear')\\n\\n # It is the initial turn.\\n turn = 0\\n\\n # This is the game loop. When the game ends we will exit this loop.\\n has_ended = False\\n while not has_ended:\\n\\n # Increment the turn number.\\n turn += 1\\n\\n # Iterate through each player that is playing.\\n for player_num, player in enumerate(self.config.players):\\n \\n # Set the current player.\\n self.config.set_current_player(player_num)\\n \\n # Before each player's turn, check to see if the game ended.\\n if self.config.supply.check_end_game():\\n has_ended = True\\n break\\n \\n # The player's turn starts here.\\n try:\\n print(f\\\"\\\\n********** Turn #{turn} **********\\\")\\n player.turn(self.config)\\n time.sleep(1.5)\\n os.system('cls' if os.name == 'nt' else 'clear')\\n \\n # Here we catch unexpected errors while playing the game.\\n except DominionError as e:\\n print(\\\"Encountered an error while playing. \\\" + e)\\n return\\n\\n # If anyone quits, exit the game here.\\n except DominionQuitError:\\n print(f\\\"Game quit by {player.name}\\\")\\n return\\n\\n # Once the game has ended, run the end_game method.\\n self.end_game(turn)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e85dcd6a656f2d43ae2291615c21760e\",\n \"score\": \"0.6190354\",\n \"text\": \"def start_game(self):\\n\\n # Change starting screen to Chess board\\n self.board = Board(square_width=self.view.square_width, \\n square_height=self.view.square_height,\\n is_flipped=self.is_flipped)\\n\\n # Create players\\n self.board.player_list.append('Human')\\n \\n if self.settings['vs_computer']: self.board.player_list.append('AI')\\n else: self.board.player_list.append('Human')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4717ddff3d01a3cc83e1621cdcdf452\",\n \"score\": \"0.6188858\",\n \"text\": \"def playAgain(self):\\n self.game_setup()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08f12d4c17062b04529efa4006ecc7bb\",\n \"score\": \"0.6175251\",\n \"text\": \"def _start_new_game(self):\\n D = DeckGenerator()\\n D.shuffle()\\n T = TableCard(D)\\n T.generate_pyramid()\\n gL = GameLogic(T)\\n gL.level = self._level\\n for item in T.pyramid_deck[-1]:\\n item.status = True\\n return Game(D, T, gL)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6dcbee6750907da85989d529de59453d\",\n \"score\": \"0.6170448\",\n \"text\": \"def initTurn(self,players,current_player,draw):\\n if self.__oneSide:\\n for player in players:\\n i=0\\n for card in player.getHand():\\n if callable(getattr(player, \\\"getSpecial\\\", None)):\\n self.__canvas.itemconfigure(self.__canvas_items[\\\"player{}-card-{}\\\".format(player.getId(),i)],image=self.__imgs[\\\"back\\\"])\\n else:\\n self.__canvas.itemconfigure(self.__canvas_items[\\\"player{}-card-{}\\\".format(player.getId(),i)],image=self.__imgs[card.getName()])\\n i+=1\\n i=0\\n for card in player.getDiscard():\\n self.__canvas.itemconfigure(self.__canvas_items[\\\"player{}-discard-{}\\\".format(player.getId(),i)],image=self.__imgs[card.getName()])\\n i+=1\\n else:\\n for player in players:\\n i=0\\n for card in player.getHand():\\n if current_player.getId() == player.getId():\\n self.__canvas.itemconfigure(self.__canvas_items[\\\"player{}-card-{}\\\".format(player.getId(),i)],image=self.__imgs[\\\"back\\\"])\\n self.__canvas.move(self.__canvas_items[\\\"player{}-card-{}\\\".format(player.getId(),i)],0,-716)\\n else:\\n self.__canvas.itemconfigure(self.__canvas_items[\\\"player{}-card-{}\\\".format(player.getId(),i)],image=self.__imgs[card.getName()])\\n self.__canvas.move(self.__canvas_items[\\\"player{}-card-{}\\\".format(player.getId(),i)],0,716)\\n i+=1\\n i=0\\n for card in player.getDiscard():\\n if current_player.getId() == player.getId():\\n self.__canvas.itemconfigure(self.__canvas_items[\\\"player{}-discard-{}\\\".format(player.getId(),i)],image=self.__imgs[card.getName()])\\n self.__canvas.move(self.__canvas_items[\\\"player{}-discard-{}\\\".format(player.getId(),i)],0,-358)\\n else :\\n self.__canvas.itemconfigure(self.__canvas_items[\\\"player{}-discard-{}\\\".format(player.getId(),i)],image=self.__imgs[card.getName()])\\n self.__canvas.move(self.__canvas_items[\\\"player{}-discard-{}\\\".format(player.getId(),i)],0,358)\\n i+=1\\n if len(draw) == 0:\\n self.__canvas.itemconfigure(self.__canvas_items[\\\"draw\\\"],image=\\\"\\\")\\n self.__updateDisplay()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"020fa0581c40ec60e6532db6e2adb650\",\n \"score\": \"0.6161986\",\n \"text\": \"def main():\\n game = poker_game()\\n game.add_player(player_1) #Temp solution to add players to game\\n game.add_player(player_2)\\n game.form_player_hands() #Deal phase one, creating card objects and card_pack \\n game.update_all_hands() #Used after each round, to sort hand + calculate each persons strongest hand\\n game.deal_flop()\\n game.update_all_hands()\\n game.deal_turn()\\n game.update_all_hands()\\n game.deal_river()\\n game.update_all_hands()\\n print(game.player_list[1].hand_value) #Prints hand_value of player at index 1\\n #hand_compare.hand_compare(player_hands, board) --Incomplete\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e986e6106c6e9afad81891509fe2a59\",\n \"score\": \"0.6153859\",\n \"text\": \"def start_game(self):\\n if self.gameover:\\n self.init_game()\\n self.gameover = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff428e6a7a8d16851ada513c3fdd2f4b\",\n \"score\": \"0.61526495\",\n \"text\": \"def run_play_loop(self): \\n player = Player('Player', STARTING_CHIP_NUMBER)\\n dealer = Player('Dealer', STARTING_CHIP_NUMBER)\\n self.gui.create_play_screen()\\n self.pause(500)\\n \\n while True:\\n deck = Deck()\\n\\n # Prompt the user to place a wager\\n wager = self.get_wager(player, dealer)\\n\\n # Handle the card selection process in triple pocket holdem\\n # Present the user with his/her options for cards and have him/her select the cards\\n self.select_cards(deck, player, dealer)\\n community_cards = deck.draw_five_community_cards()\\n \\n # Now, reveal the cards held by the user and the dealer, as well as the \\n # five community cards\\n self.gui.explain_card_reveal()\\n self.pause(2000)\\n self.gui.reveal_player_cards(player, dealer)\\n self.pause(2000)\\n self.gui.reveal_common_cards(community_cards)\\n self.pause(5000)\\n\\n # Determine the best hand held by each player and who won the round\\n # Add or subtract chips from the player and dealer's totals depending on the outcome\\n # Output to the user which poker hand the dealer had and what the consequent result was\\n (player_hand, dealer_hand, player_wager_multiple, dealer_wager_multiple) = determine_outcome(\\n player.hands[0], \\n dealer.hands[0], \\n dealer.hands[1], \\n community_cards\\n )\\n player.alter_chip_balance(player_wager_multiple * wager)\\n dealer.alter_chip_balance(dealer_wager_multiple * wager)\\n self.gui.explain_outcome(\\n player_hand, \\n dealer_hand, \\n player_wager_multiple, \\n player.num_chips, \\n dealer.num_chips\\n )\\n self.pause(4000)\\n \\n # Get the next action from the user\\n # If the game has ended (i.e. the user or dealer has run out of chips),\\n # the user can elect to play again. Otherwise, the user can elect to continue with\\n # another round in the current game or to exit\\n next_action = self.get_action_at_round_end(player, dealer)\\n if next_action == MENU_NOW:\\n return next_action\\n elif next_action == PLAY_AGAIN:\\n player = Player('Player', STARTING_CHIP_NUMBER)\\n dealer = Player('Dealer', STARTING_CHIP_NUMBER)\\n else:\\n player.clear_hands()\\n dealer.clear_hands()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94b5e7b4a39d3ad3dd48208c7ef542b8\",\n \"score\": \"0.614256\",\n \"text\": \"def play(self):\\n # Clear/Reset attributes/values\\n self.winners = []\\n self.count = 0\\n for player in self.players:\\n # Reset hands\\n del(player.cards[:])\\n # Increment number of unique games played\\n player.gamesPlayed += 1.0\\n # Increment total number of games played\\n self.totalGamesPlayed += 1.0\\n # Prepare the deck, deal the cards\\n self.deck = Deck()\\n self.deck.shuffle()\\n self.deck.deal(3, self)\\n # Top card is placed onto discard pile\\n self.discard.add(self.deck.pop())\\n # While no one has satisfied winning conditions, take turns\\n while not self.check_won():\\n self.turn_seq()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad995044d769ec0161ae9924becdf18b\",\n \"score\": \"0.61161804\",\n \"text\": \"def initiate_deck(self):\\n\\n deck = Deck()\\n deck.build_deck()\\n deck.shuffle()\\n self.deck1, self.deck2 = deck.split_deck()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0706d71c979b7b26af67ecd63b9ff690\",\n \"score\": \"0.61084616\",\n \"text\": \"def reset_hands(self):\\n self.my_player.reset_hand()\\n self.my_computer.reset_hand()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7dc1ffc7670027b24ea1494978535af\",\n \"score\": \"0.61073416\",\n \"text\": \"def reset(self, test=None):\\n self.health = self.start_health\\n self.armor = 0\\n self.this_turn_mana = 0\\n self._init_heropower()\\n self._init_deck(self.fix_deck)\\n self.inhands = [] # cards in self hands\\n self.intable = [] # cards on self table\\n self.used_cards = defaultdict(int) # used card counts. key: cidx, value: used counts\\n if self.first_player:\\n self.draw_as_first_player()\\n else:\\n self.draw_as_second_player()\\n if test is not None:\\n self.test = test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1a212e0c2daeb3463048aa45a876849\",\n \"score\": \"0.60954237\",\n \"text\": \"def setup_game():\\n\\t\\n\\tglobal bullet_chamber\\n\\tglobal current_chamber\\n\\tglobal player_turn\\n\\t\\n\\tbullet_chamber = math.ceil(random.random()*CHAMBER_COUNT)\\n\\tcurrent_chamber = 1\\n\\t\\n\\tprint \\\"Gun prepared...\\\"\\n\\t# determine who's first\\n\\tif random.random() >= 0.5:\\n\\t\\tplayer_turn = True\\n\\t\\tprint \\\"Player goes first\\\"\\n\\telse:\\n\\t\\tplayer_turn = False\\n\\t\\tprint \\\"Computer goes first\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd4b311a54ba395986edf37e3444a0a1\",\n \"score\": \"0.60823447\",\n \"text\": \"def start_new_game(players):\\n game = SchnapsenGame()\\n game.create_cards()\\n game.shuffle_cards()\\n for player in players:\\n for dummy in range(5):\\n card = game.draw_from_stock()\\n player.add_card(card)\\n return game\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8c0d6666ca0db5b99f471105ab90a1f\",\n \"score\": \"0.6074004\",\n \"text\": \"def show_next_hand(*args):\\n global twentyfive_cards\\n # disable show_next and enable show_best\\n # show_best25_hand_button[\\\"state\\\"] = \\\"normal\\\"\\n # show_next_hand_button[\\\"state\\\"] = \\\"disabled\\\"\\n\\n window.title(\\\"Play Pyramid Poker\\\")\\n w.delete(\\\"all\\\") # clear out last hand\\n show_twentyfive_cards()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d3d9925f75608d29e6f3f04e3b60fe8\",\n \"score\": \"0.60738045\",\n \"text\": \"def start_hand(deck, onCoin):\\n size = 4 if onCoin else 3\\n return [deck.pop() for i in range(size)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abc03548cf453e649529ab559af2bfe6\",\n \"score\": \"0.607288\",\n \"text\": \"def initialise_deck(self):\\n deck = Deck()\\n shuffle(deck.card)\\n self.deck = deck\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40e5d29991114394fd48b8ffdeb3fb70\",\n \"score\": \"0.60725707\",\n \"text\": \"def main():\\n numHands = eval(input('Enter number of player: '))\\n while (numHands < 2 or numHands > 10):\\n numHands = eval(input('Enter number of hands to play[2~6]: '))\\n game = Poker(numHands)\\n print(\\\"## Game start\\\")\\n game.play()\\n print(\\\"## Caculating hands\\\")\\n print(game.evaluate(game.hands))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4317741c257ecaff2039978f0c82dd3\",\n \"score\": \"0.60713416\",\n \"text\": \"def choose_hand(self):\\n\\n w = random.randint(0, 4)\\n if len(Player.history) > 0:\\n if Player.history[len(Player.history)-1] == \\\"rock\\\":\\n w = 4\\n self.weapon = Weapons.weapon[w]\\n self.turtle.shape(Weapons.computer_images[w])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de83074b480ba3c367ce9ddc04565f9a\",\n \"score\": \"0.60338426\",\n \"text\": \"def start(self):\\n self.start_game()\\n self.init_playing_order()\\n while self.winning_camel is None:\\n self.run_leg()\\n self.report()\\n print(\\\"------------------- End leg ---------------------\\\")\\n self.game_stage = \\\"result\\\"\\n self.losing_camel = self.orders[-1]\\n self.game_scoring_round()\\n self.determine_game_result()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f2270c443ff6f39200566891a90e09b\",\n \"score\": \"0.60226154\",\n \"text\": \"def playUpgrade(self, card, target = None):\\n logging.info(card)\\n active = self.activePlayer.board[\\\"Creature\\\"]\\n inactive = self.inactivePlayer.board[\\\"Creature\\\"]\\n hand = self.activePlayer.hand\\n broken = False\\n \\n if target:\\n self.activePlayer.board[\\\"Upgrade\\\"].append(card)\\n if card in hand:\\n hand.remove(card)\\n side, choice = target\\n if side == \\\"fr\\\":\\n active[choice].upgrade.append(card)\\n else:\\n inactive[choice].upgrade.append(card)\\n eval(f\\\"upgrade.{card.title}(self, card, side, choice)\\\")\\n self.cardChanged(True) # this would actually make the play abilities I put on blood of titans, a bit redundant, but redundancy is ok\\n return\\n \\n drawMe = []\\n # surfs\\n selectedSurf = Surface((self.target_cardw, self.target_cardh))\\n selectedSurf.convert_alpha()\\n selectedSurf.set_alpha(80)\\n selectedSurf.fill(COLORS[\\\"LIGHT_GREEN\\\"])\\n\\n selectedSurfTapped = Surface((self.target_cardh, self.target_cardw))\\n selectedSurfTapped.convert_alpha()\\n selectedSurfTapped.set_alpha(80)\\n selectedSurfTapped.fill(COLORS[\\\"LIGHT_GREEN\\\"])\\n # draw all targets\\n for temp_card in active + inactive:\\n if temp_card.ready:\\n drawMe.append((selectedSurf, temp_card.rect))\\n else:\\n drawMe.append((selectedSurfTapped, temp_card.tapped_rect))\\n \\n if self.canDiscard(card, reset = False):\\n ## discard stuff here - if you can play it, you can discard it\\n discSurf = Surface((self.target_cardw, self.target_cardh))\\n discSurf.convert_alpha()\\n discSurf.set_alpha(80)\\n discSurf.fill(COLORS[\\\"LIGHT_GREEN\\\"])\\n discRect = discSurf.get_rect()\\n discRect.topleft = self.discard1_rect.topleft\\n drawMe.append((discSurf, discRect))\\n \\n while True:\\n self.extraDraws = drawMe.copy()\\n \\n for e in pygame.event.get():\\n if e.type == MOUSEMOTION:\\n #update mouse position\\n self.mousex, self.mousey = e.pos\\n \\n if e.type == QUIT:\\n pygame.quit()\\n\\n if e.type == MOUSEBUTTONUP and e.button == 1:\\n activeHit = [(Rect.collidepoint(x.rect, (self.mousex, self.mousey)) or Rect.collidepoint(x.tapped_rect, (self.mousex, self.mousey))) for x in active]\\n inactiveHit = [(Rect.collidepoint(x.rect, (self.mousex, self.mousey)) or Rect.collidepoint(x.tapped_rect, (self.mousex, self.mousey))) for x in inactive]\\n if Rect.collidepoint(self.hand1_rect, (self.mousex, self.mousey)):\\n l = len(hand)\\n for x in range(len(hand)):\\n temp_card = hand[x]\\n if x == 0 and self.mousex < temp_card.rect.centerx:\\n hand.insert(0, self.dragging.pop())\\n self.extraDraws = []\\n self.cardChanged()\\n return\\n elif temp_card.rect.centerx < self.mousex and x < l-1 and self.mousex < hand[x+1].rect.centerx:\\n hand.insert(x + 1, self.dragging.pop())\\n self.extraDraws = []\\n self.cardChanged()\\n return\\n hand.append(self.dragging.pop())\\n self.extraDraws = []\\n return\\n elif True in activeHit:\\n self.activePlayer.board[\\\"Upgrade\\\"].append(self.dragging.pop())\\n choice = activeHit.index(True)\\n target = active[choice]\\n target.upgrade.append(card)\\n eval(f\\\"upgrade.{card.title}(self, card, target)\\\")\\n broken = True\\n break\\n elif True in inactiveHit:\\n self.activePlayer.board[\\\"Upgrade\\\"].append(self.dragging.pop())\\n choice = inactiveHit.index(True)\\n target = active[choice]\\n target.upgrade.append(card)\\n eval(f\\\"upgrade.{card.title}(self, card, target)\\\")\\n broken = True\\n break\\n elif self.canDiscard(card, reset=False) and Rect.collidepoint(discRect, (self.mousex, self.mousey)):\\n hand.append(self.dragging.pop())\\n self.discardCard(-1)\\n self.extraDraws = []\\n self.cardChanged()\\n return\\n else:\\n hand.append(self.dragging.pop())\\n self.extraDraws = []\\n self.cardChanged()\\n return\\n\\n if e.type == MOUSEBUTTONDOWN and e.button == 1:\\n logging.error(\\\"You shouldn't be able to trigger MOUSEBUTTONDOWN in dragCard.\\\")\\n\\n if Rect.collidepoint(self.hand1_rect, (self.mousex, self.mousey)):\\n l = len(hand)\\n for x in range(len(hand)):\\n temp_card = hand[x]\\n if x == 0 and self.mousex < temp_card.rect.centerx:\\n hand.insert(0, self.invisicard)\\n self.cardChanged()\\n break\\n elif temp_card.rect.centerx < self.mousex and x < l-1 and self.mousex < hand[x+1].rect.centerx:\\n hand.insert(x + 1, self.invisicard)\\n self.cardChanged()\\n break\\n\\n self.CLOCK.tick(self.FPS)\\n self.hovercard = []\\n self.check_hover()\\n self.draw(False)\\n try:\\n hand.remove(self.invisicard)\\n except:\\n pass\\n pygame.display.flip()\\n self.extraDraws = []\\n if broken:\\n self.cardChanged()\\n break\\n \\n if not target and card.amber > 0:\\n self.activePlayer.gainAmber(card.amber, self)\\n logging.info(f\\\"{card.title} gave you {str(card.amber)} amber. You now have {str(self.activePlayer.amber)} amber.\\\\n\\\\nChange to a log when you fix the amber display issue.\\\"\\\"\\\")\\n if card not in self.playedThisTurn:\\n self.playedThisTurn[card] = 1\\n else:\\n self.playedThisTurn[card] += 1\\n self.cardChanged(True)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c09bb9eea918d6798de1ca216dd3a63a\",\n \"score\": \"0.60097355\",\n \"text\": \"def player_start(self):\\n for card in self._target_hand:\\n if card.pip is False:\\n self._dealer.flip_deal(card)\\n return self._target_hand_stats\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe63242c7be7c36cf950c836bb9a76e8\",\n \"score\": \"0.60043347\",\n \"text\": \"def initialize_game():\\n\\n MIN_SCORE = 0\\n\\n p1_card = create_score_card()\\n p2_card = create_score_card()\\n p1_bonus = MIN_SCORE\\n p2_bonus = MIN_SCORE\\n\\n round_ongoing = True\\n\\n while round_ongoing:\\n if -1 in p1_card.values():\\n print(\\\"-- IT IS PLAYER ONE'S TURN --\\\")\\n take_turn(p1_card, p1_bonus)\\n\\n print(\\\"\\\\n\\\")\\n\\n if -1 in p2_card.values():\\n print(\\\"-- IT IS PLAYER TWO'S TURN --\\\")\\n take_turn(p2_card, p2_bonus)\\n\\n if -1 not in p1_card.values() and -1 not in p2_card.values():\\n round_ongoing = False\\n\\n game_verdict(calculate_score(p1_card), p1_bonus, calculate_score(p2_card), p2_bonus)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45c738d403cfef32cc315c30b913debe\",\n \"score\": \"0.6004059\",\n \"text\": \"def __init__(self):\\n self.shoe = deck.Deck(False)\\n self.shuffle_shoe()\\n self.hand = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0f4de22f0f5ef385e52aa0594aba3bc\",\n \"score\": \"0.6000726\",\n \"text\": \"def begin_game(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"307a4bb97dc9ff3d6387bcf1682a04b0\",\n \"score\": \"0.59878075\",\n \"text\": \"def play(self):\\n # randomly remove a Cards in cards\\n card_picked = choice(self.cards)\\n self.cards.remove(card_picked)\\n\\n # add the Card to the Player's history\\n self.history.append(card_picked)\\n\\n # increment the Player's turn count by 1\\n self.turn_count += 1\\n\\n # decrement the Player's number of cards by 1\\n self.number_of_cards -= 1\\n \\n # if self.number_of_cards == 0:\\n # print(f\\\"{self.name} {self.turn_count} has no card\\\")\\n\\n # display the play\\n print(f\\\"{self.name} {self.turn_count} played: {card_picked}\\\")\\n\\n return card_picked\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06c3a907ddc1c52dcecd2b011db5a5c4\",\n \"score\": \"0.5981489\",\n \"text\": \"def black_jack():\\n dealer = Dealer()\\n player1 = Player(input(\\\"How much money does the player have?\\\"))\\n want_to_play = 1\\n while(want_to_play == 1):\\n dealer.shuffle_deck()\\n dealer.ready_for_next_hand()\\n player1.ready_for_next_hand()\\n\\n black_jack_hand(player1, dealer)\\n want_to_play = input(\\\"Would you like to play another hand? Select 1 for Yes and 0 for no\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cdc313404535e52d43ab7e2d590aff0\",\n \"score\": \"0.59767616\",\n \"text\": \"def startTurn():\\n\\tglobal current_piece\\n\\tcurrent_piece = 0\\n\\tlimbL.move_to_joint_positions(coords.ResetPos)\\n\\tlimbL.move_to_joint_positions(coords.PickU)\\n\\tlimbL.move_to_joint_positions(coords.PickD)\\n\\ti = 0\\n\\twhile current_piece == 0:\\n\\t\\tcurrent_piece = IDBlock(left_image)\\n\\t\\trospy.sleep(5)\\n\\t\\ti+=1\\t\\t\\n\\t\\tif i >= 6:#After 30 seconds of inaction by the human player, Lucas assumes victory\\n\\t\\t\\tprint \\\"Huzzah! Another glorious victory for your robotic overlord!\\\"\\n\\t\\t\\tsys.exit()\\n\\tlimbL.move_to_joint_positions(coords.PickU)\\n\\tlimbL.move_to_joint_positions(coords.ResetPos)\\n\\tscanBoard()\\n\\tlimbL.move_to_joint_positions(coords.ResetPos)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c181062fcadfcfb7154f204b991d32b\",\n \"score\": \"0.5975167\",\n \"text\": \"def play_one_turn(self):\\n #XXX: My laziness knows no bounds. Only allows two players.\\n #XXX: I pass the whole game object through to players. Ugh.\\n \\n if self.current_player == 1:\\n self.p1.new_hand()\\n self.p1.play_one_turn(self)\\n self.current_player = 2\\n else:\\n self.p2.new_hand()\\n self.p2.play_one_turn(self)\\n self.current_player = 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd5f840201383788c385fab45b96b7da\",\n \"score\": \"0.59595335\",\n \"text\": \"def reset(self, bank=50):\\n self.gm = Game(\\\"P1\\\", \\\"Qtable\\\", \\\"P2\\\", \\\"Qtable\\\", bank)\\n\\n # Random starting money distribution p=0.4\\n if self.rc is not None:\\n if random() < self.rc:\\n left = round(random() * ((bank * 2) - 4))\\n self.gm.sb_player().bank = left + 2\\n self.gm.bb_player().bank = ((bank * 2) - 4) - left + 2\\n\\n self.gm.place_blinds()\\n if self.dc:\\n self.gm.players_draw_cards()\\n # Return observation\\n # [ Small Blind Player, Big Blind Player ]\\n self.ob = [self.gm.sb_player(), self.gm.bb_player()]\\n return self.ob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21166b5cdbe4125442fd133b0444ff07\",\n \"score\": \"0.5957577\",\n \"text\": \"def test_playcard_cost0(self):\\n self.plr.play_card(self.card)\\n self.plr.gain_card(\\\"Copper\\\")\\n self.plr.test_input = [\\\"end phase\\\", \\\"end phase\\\"]\\n self.plr.turn()\\n self.assertNotIn(\\\"Horse\\\", self.plr.piles[Piles.DISCARD])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6436110836fe4d65b16594b43045efd8\",\n \"score\": \"0.59550923\",\n \"text\": \"def play_round(self):\\n playing_players = self.enter_bets()\\n self.give_cards()\\n for player in self.players:\\n if player.name in playing_players:\\n self.play_hand(player)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60e9ce3200b27a029258f903fb33497d\",\n \"score\": \"0.5942199\",\n \"text\": \"def play_1(player):\\r\\n playing = True\\r\\n deck = Deck()\\r\\n deck.shuffle()\\r\\n score = 0\\r\\n print(input(\\\"Welcome to the casino, \\\" + player + \\\". Press Enter to play the game.\\\"))\\r\\n while playing:\\r\\n if deck.out():\\r\\n print(\\\"Deck is out of cards. Game over. Your score was \\\", str(score))\\r\\n playing = False\\r\\n else:\\r\\n hand_a = PokerHand()\\r\\n hand_b = PokerHand()\\r\\n for i in range(0, MAX_HAND_SIZE):\\r\\n card = deck.deal()\\r\\n hand_a.add_card(card)\\r\\n for i in range(0, MAX_HAND_SIZE):\\r\\n card = deck.deal()\\r\\n hand_b.add_card(card)\\r\\n print(\\\"Hand #1: \\\", hand_a)\\r\\n print(\\\"Hand #2: \\\", hand_b, \\\"\\\\n\\\")\\r\\n user_answer = int(input(\\\"Which hand is worth more? \\\"\\r\\n \\\"Type 1 if Hand #1, -1 if Hand #2, and 0 if they are worth the same\\\\n \\\"))\\r\\n correct_answer = hand_a.compare_to(hand_b)\\r\\n if user_answer == correct_answer:\\r\\n print(\\\"Correct! Let's try again\\\\n\\\")\\r\\n score += 1\\r\\n else:\\r\\n print(\\\"Game over. Your score was \\\", str(score))\\r\\n playing = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2e8b14207df9b875bc1f700b9f25012\",\n \"score\": \"0.5939224\",\n \"text\": \"def start_self_play(self, player, display=False):\\n player.reset_player()\\n self.init_game_state()\\n\\n states, mcts_probs, current_players = [], [], []\\n while True:\\n move, move_probs = player.get_action(self.game_state)\\n\\n if move == PASS_MOVE:\\n move = PASS_MOVE\\n else:\\n move = (move // self.game_state.size, move % self.game_state.size)\\n\\n # store the data\\n states.append(self.game_state.copy())\\n mcts_probs.append(move_probs)\\n current_players.append(self.game_state.current_player)\\n\\n # perform a move\\n is_game_end = self.game_state.do_move(move)\\n if display:\\n self.print_board(self.game_state, str(player), str(player), move_probs)\\n if is_game_end:\\n winner_id = self.game_state.get_winner()\\n # winner from the perspective of the current player of each state\\n winners_z = np.zeros(len(current_players))\\n if winner_id is not None:\\n winners_z[np.array(current_players) == winner_id] = 1.0\\n winners_z[np.array(current_players) != winner_id] = -1.0\\n if display:\\n if winner_id is not None:\\n print(\\n \\\"Game end. Winner is player:\\\",\\n \\\"X\\\" if winner_id == go.BLACK else \\\"O\\\",\\n )\\n else:\\n print(\\\"Game end. Tie\\\")\\n return winner_id, zip(states, mcts_probs, winners_z)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2d452612ccda9a3382eabefd3e5e2c2\",\n \"score\": \"0.5927983\",\n \"text\": \"def UCTPlaySampleGame():\\n # state = OthelloState(4) # uncomment to play Othello on a square board of the given size\\n # state = OXOState() # uncomment to play OXO\\n # state = NimState(15) # uncomment to play Nim with the given number of starting chips\\n # state = SimpState((5,5)) # uncomment to play Simp with the given number of starting chips\\n state = CapGoState(sz) # uncomment to play Capture Go with the given board size\\n UCTPlayGame(state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c125026b6cbd995192ce2d8c656d968\",\n \"score\": \"0.59240186\",\n \"text\": \"def resetGame():\\n del CompHand[:] \\n del UserHand[:]\\n del CompWinningList[:]\\n del UserWinningList[:]\\n del deck[:]\\n deck.append(\\\"Ah\\\")\\n deck.append(\\\"Ad\\\")\\n deck.append(\\\"Ac\\\")\\n deck.append(\\\"As\\\")\\n deck.append(\\\"2h\\\")\\n deck.append(\\\"2d\\\")\\n deck.append(\\\"2c\\\")\\n deck.append(\\\"2s\\\")\\n deck.append(\\\"3h\\\")\\n deck.append(\\\"3d\\\")\\n deck.append(\\\"3k\\\")\\n deck.append(\\\"3s\\\")\\n deck.append(\\\"4h\\\")\\n deck.append(\\\"4d\\\")\\n deck.append(\\\"4c\\\")\\n deck.append(\\\"4s\\\")\\n deck.append(\\\"5h\\\")\\n deck.append(\\\"5d\\\")\\n deck.append(\\\"5c\\\")\\n deck.append(\\\"5s\\\")\\n deck.append(\\\"6h\\\")\\n deck.append(\\\"6d\\\")\\n deck.append(\\\"6c\\\")\\n deck.append(\\\"6s\\\")\\n deck.append(\\\"7h\\\")\\n deck.append(\\\"7d\\\")\\n deck.append(\\\"7c\\\")\\n deck.append(\\\"7s\\\")\\n deck.append(\\\"8h\\\")\\n deck.append(\\\"8d\\\")\\n deck.append(\\\"8c\\\")\\n deck.append(\\\"8s\\\")\\n deck.append(\\\"9h\\\")\\n deck.append(\\\"9c\\\")\\n deck.append(\\\"9d\\\")\\n deck.append(\\\"9s\\\")\\n deck.append(\\\"10h\\\")\\n deck.append(\\\"10d\\\")\\n deck.append(\\\"10c\\\")\\n deck.append(\\\"10s\\\")\\n deck.append(\\\"Jh\\\")\\n deck.append(\\\"Jd\\\")\\n deck.append(\\\"Jc\\\")\\n deck.append(\\\"Js\\\")\\n deck.append(\\\"Qh\\\")\\n deck.append(\\\"Qd\\\")\\n deck.append(\\\"Qc\\\")\\n deck.append(\\\"Qs\\\")\\n deck.append(\\\"Kh\\\")\\n deck.append(\\\"Kd\\\")\\n deck.append(\\\"Kc\\\")\\n deck.append(\\\"Ks\\\") \\n return deck\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55054711f5324eaa84db3999ab4d245c\",\n \"score\": \"0.59172213\",\n \"text\": \"def test_play_no_pickup(self):\\n self.plr.piles[Piles.HAND].set(\\\"Copper\\\", \\\"Copper\\\", \\\"Estate\\\", \\\"Duchy\\\")\\n self.plr.piles[Piles.DECK].set(\\\"Gold\\\", \\\"Silver\\\", \\\"Copper\\\", \\\"Copper\\\")\\n self.plr.add_card(self.card, Piles.HAND)\\n self.plr.test_input = [\\n \\\"Discard\\\",\\n \\\"Discard Estate\\\",\\n \\\"Discard Duchy\\\",\\n \\\"Finish\\\",\\n \\\"nothing\\\",\\n ]\\n self.plr.play_card(self.card)\\n self.assertEqual(self.plr.coins.get(), 2)\\n self.assertEqual(self.plr.piles[Piles.HAND].size(), 4 + 1 - 2)\\n self.assertNotIn(\\\"Duchy\\\", self.plr.piles[Piles.HAND])\\n self.assertNotIn(\\\"Silver\\\", self.plr.piles[Piles.HAND])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f18317543fe0e81636342952f64a9f20\",\n \"score\": \"0.5912951\",\n \"text\": \"def start_game():\\n\\tprint intro_msg\\n\\tplayer_name = raw_input(\\\"What' do they call you son ? \\\")\\n\\tend = False\\t\\t\\n\\twhile not end :\\n\\t\\tchoice,door = show_options()\\n\\t\\tif choice == 'yes':\\n\\t\\t\\t#Enter the door chosen\\n\\t\\t\\tproceed = proceed_in_door(door)\\n\\t\\t\\tif proceed:\\n\\t\\t\\t\\tprint \\\"Proceeding....\\\"\\n\\t\\t\\t\\ttime.sleep(3)\\n\\t\\t\\t\\tos.system('clear')\\n\\t\\t\\t\\tcontinue\\t\\n\\t\\t\\telse :\\n\\t\\t\\t\\tend = True\\n\\n\\t\\telif choice == 'no' :\\n\\t\\t\\t#Show the random waiting message\\n\\t\\t\\tprint random.randint(0,len(waiting_messages))\\n\\t\\t\\ttime.sleep(5) #wait\\n\\n\\t\\telif choice == 'info':\\n\\t\\t\\t#shows the player info\\n\\t\\t\\tshow_player_info()\\n\\t\\telse :\\n\\t\\t\\tprint \\\"Whaaaaaaaat the heck is that ?, Just YES or no\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"754749c503bb5ff4f5d8ff50d7501951\",\n \"score\": \"0.5910271\",\n \"text\": \"def start_game(self, name):\\n\\t\\tself.guesses = ''\\n\\t\\tself.incorrect = 0\\n\\t\\tself.correct = 0\\n\\t\\tself.gameover = False\\n\\t\\tself.won = False\\n\\t\\tself.username = name\\n\\n\\t\\tword_list = ['3dhubs', 'marvin', 'print', 'filament', 'order', 'layer']\\n\\t\\tself.target = word_list[random.randint(0, 5)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5c861602d872a650894343f6703dbbb\",\n \"score\": \"0.5908629\",\n \"text\": \"def start(self):\\n # <<-- Creer-Merge: start -->> - Code you add between this comment and the end comment will be preserved between Creer re-runs.\\n # replace with your start logic\\n self.game_data = GameData()\\n\\n self.random = random.random()\\n # <<-- /Creer-Merge: start -->>\\n\\n self.game_data.humans = [human for human in self.player.units if human.job.title == 'fresh human']\\n\\n self.first_run = True\\n self.no_income = True\\n self.harvester_returning = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9805fd2876acedb227ef8bbf31c53e4\",\n \"score\": \"0.59040797\",\n \"text\": \"def reset_game():\\n global tmp_score, current_player\\n for data in players:\\n data[1] = 0\\n tmp_score = 0\\n current_player = 0 # 'the user always starts'\\n cls()\\n start_game()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a582deca9da8cb98d30740d848ec22b\",\n \"score\": \"0.5902361\",\n \"text\": \"def startGame(self):\\n while True:\\n whiteType = input('Welcome to King-Queen Endgame Simulatior \\\\nWould you like a (1) Computer or (2) Human to play as White (the attacker with a queen and a king)?\\\\n')\\n if whiteType == (1 or 'computer' or 'Computer'):\\n whiteType = 'comp'\\n break\\n elif whiteType == (2 or 'human' or 'Human'):\\n whiteType = 'human'\\n break\\n else:\\n print 'Please input 1 or 2.'\\n while True:\\n blackType = input('Would you like a (1) Computer or (2) Human to play as Black (the defender with a king)?\\\\n')\\n if blackType == 1 or blackType=='computer' or blackType=='Computer':\\n blackType = 'comp'\\n break\\n elif blackType == 2 or blackType=='human' or blackType=='Human':\\n blackType = 'human'\\n break\\n else:\\n print 'Please input 1 or 2.'\\n self.setBoard('Q')\\n print b\\n self.playGame(whiteType, blackType)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c413ae6f91a0d0648f1e7f342387a287\",\n \"score\": \"0.58975714\",\n \"text\": \"def _start_game(self):\\n\\t\\tself.settings.initialize_dynamic_settings()\\n\\n\\t\\t# Reset the game statistics\\n\\t\\tself.stats.reset_stats()\\n\\t\\tself.stats.game_active = True\\n\\t\\tself.sb.prep_score()\\n\\t\\tself.sb.prep_level()\\n\\t\\tself.sb.prep_strikers()\\n\\n\\t\\t# Get rid of any remaining kids and candy.\\n# \\t\\tself.kids.empty()\\n# \\t\\tself.candies.empty()\\n\\n\\t\\t# Hide the mouse cursor.\\n\\t\\tpygame.mouse.set_visible(False)\\n\\n\\t\\tpygame.mixer.music.set_endevent(self.SONG_END)\\n\\t\\tpygame.mixer.music.load(self.song_fn)\\n\\t\\tpygame.mixer.music.play()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26f3a2bfdeb59a4fb079e12aaccf6ffa\",\n \"score\": \"0.5896248\",\n \"text\": \"def create_random(cls, players, deck='52-card'):\\n\\n current_deck = Card.get_shuffled_deck(size=int(deck[:2]))\\n\\n bottom_card = current_deck[0]\\n\\n player_hands = {}\\n\\n for uid, name in players:\\n player_hands[uid] = set(current_deck.pop() for _ in xrange(6))\\n\\n if deck[-4:] == 'fast':\\n current_deck = current_deck[:len(players)]\\n\\n ordered_players = list(players)\\n urandom_shuffle_inplace(ordered_players)\\n\\n cls._choose_first_player(bottom_card.suit, ordered_players, player_hands)\\n\\n return GameState(ordered_players, player_hands, [], current_deck, [], bottom_card)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca6db0fb483345ac87b4cf31144f780c\",\n \"score\": \"0.58839583\",\n \"text\": \"def initialize_trump(self) -> None:\\r\\n input(\\\"Time to decide trump. Pass the computer to \\\" + self.first_player.name)\\r\\n euchre_utils.clear_screen()\\r\\n starting = self.players.index(self.first_player)\\r\\n i=starting\\r\\n\\r\\n self.add_card(self.deck.get_card())\\r\\n \\r\\n while i <= starting+7:\\r\\n player = self.players[i%4]\\r\\n self.print_status_update(player)\\r\\n face_up_card: Card = self.cards_on_table[0]\\r\\n\\r\\n if i <= starting+3: #first time around\\r\\n answer = euchre_utils.get_yes_no(\\\"Do you want the dealer\\\"+\\r\\n \\\" to pick up the card on the table and that card to\\\" +\\r\\n \\\" become trump?\\\".format(player.name))\\r\\n if answer:\\r\\n #alone_answer = euchre_utils.get_yes_no(\\\"Do you want to go alone?\\\")\\r\\n euchre_utils.clear_screen()\\r\\n input(\\\"\\\\nOK, Announce that {} is trump. Pass the computer to the dealer({})\\\".format(\\r\\n face_up_card.suit, self.dealer.name))\\r\\n euchre_utils.clear_screen()\\r\\n self.dealer.new_card(face_up_card)\\r\\n self.clear_cards_on_table()\\r\\n self.trump = face_up_card.suit\\r\\n self.player_that_called_suit = player\\r\\n \\r\\n #dealer chooses which cards to remove\\r\\n self.dealer.sort_hand(self.trump)\\r\\n self.print_status_update(self.dealer)\\r\\n print(\\\"Dealer, {} told you to pick it up. \\\".format(player.name)+\\r\\n \\\"Which card in your hand do you want to remove?\\\")\\r\\n self.dealer.remove_card(self.dealer.select_card(self.dealer.hand))\\r\\n euchre_utils.clear_screen()\\r\\n input(\\\"Pass the computer to {} to start the round\\\".format(self.first_player.name))\\r\\n euchre_utils.clear_screen()\\r\\n break\\r\\n else: \\r\\n euchre_utils.clear_screen()\\r\\n input(\\\"\\\\nPass the computer to {}\\\".format(self.players[(i+1)%4].name))\\r\\n euchre_utils.clear_screen()\\r\\n \\r\\n else: #second time around, choose which suit to be trump\\r\\n response = euchre_utils.get_answer_no(\\\"Do you want to select the trump suit?\\\"+\\r\\n \\\" If so, say which. You're playing 'stick the dealer'\\\\n\\\", \\\"diamonds|hearts|spades|clubs\\\")\\r\\n if response and response != face_up_card.suit: #if they say an availible suit\\r\\n print(\\\"OK, {} is Trump. Pass the computer to {} to start the round\\\".format(\\r\\n response, self.first_player.name))\\r\\n self.trump = response\\r\\n self.player_that_called_suit = player\\r\\n self.clear_cards_on_table()\\r\\n break\\r\\n elif response == face_up_card.suit:\\r\\n input(\\\"Sorry, you can't choose the suit that was initially face up. Try again\\\")\\r\\n i-=1 # loop around to the same player again to give another chance\\r\\n elif i == starting+7: #STICK THE DEALER\\r\\n #this if clause needs to come after the first two to take lower precedence\\r\\n input(\\\"You're playing 'stick the dealer', so the dealer has to choose a suit.\\\")\\r\\n i-=1\\r\\n else: #empty string, they said no\\r\\n euchre_utils.clear_screen()\\r\\n input(\\\"\\\\nOK, pass the computer to {}\\\".format(self.players[(i+1)%4].name))\\r\\n euchre_utils.clear_screen()\\r\\n \\r\\n euchre_utils.clear_screen()\\r\\n i+=1\\r\\n euchre_utils.clear_screen()\\r\\n #sort everybodys hands according to trump\\r\\n [player.sort_hand(self.trump) for player in self.players]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25edc669fdded7b1f82cd0c502985d64\",\n \"score\": \"0.5880057\",\n \"text\": \"def make_step(self, action=\\\"reset\\\"):\\n reward = 0\\n if not self.active and action != \\\"reset\\\":\\n print(\\\"You specified an action, but no active game is being played. Please reset the game using action='reset'.\\\")\\n elif self.active and action == \\\"reset\\\":\\n print(\\\"WARNING! You specified action='reset' although the last game has not ended, yet.\\\")\\n elif action == \\\"reset\\\":\\n self.reset_game()\\n if self.player_sum == 21:\\n self.active = False\\n if self.verbose:\\n print(\\\"Player has Blackjack!\\\")\\n print(\\\"The dealer's cards are:\\\", [int(x) for x in self.dealer_cards])\\n if np.sum(self.dealer_cards == 1) == 1 and np.sum(self.dealer_cards == 10) == 1:\\n if self.verbose:\\n print(\\\"Dealer has Blackjack, too!\\\")\\n print(\\\"DRAW!\\\")\\n else:\\n if self.verbose:\\n print(\\\"PLAYER WINS!\\\")\\n reward = 1\\n elif action == \\\"hit\\\":\\n new_card = self.draw_cards(size=1)\\n if self.verbose:\\n print(\\\"Player draws card:\\\", new_card)\\n self.player_sum += new_card\\n if self.player_sum > 21:\\n if self.player_has_usable_ace:\\n if self.verbose:\\n print(\\\"Player converts a usable ace (11) into 1.\\\")\\n self.player_cards[self.player_cards == 11] = 1\\n self.player_has_usable_ace = False\\n self.player_sum -= 10\\n if self.player_sum > 21:\\n # Player goes bust\\n if self.verbose:\\n print(\\\"Player goes BUST!\\\")\\n self.active = False\\n reward = -1\\n else:\\n self.state = np.array([int(self.player_sum), self.dealers_showing_card, self.player_has_usable_ace])\\n if self.verbose:\\n print(\\\"New sum of player's cards:\\\", self.player_sum)\\n reward = 0\\n elif action == \\\"stick\\\":\\n self.active = False\\n if self.verbose:\\n print(\\\"The dealer's cards are:\\\", [int(x) for x in self.dealer_cards])\\n print(\\\"The dealer has\\\", np.sum(self.dealer_cards), \\\"points.\\\")\\n # Let dealer play\\n if np.any(self.dealer_cards == 1) and 17 <= self.dealer_sum + 10 <= 21:\\n if self.verbose:\\n print(\\\"Dealer converts 1 into 11\\\")\\n self.dealer_sum += 10\\n self.dealer_cards[self.dealer_cards == 1] = 11\\n while self.dealer_sum < 17:\\n new_card = self.draw_cards(size=1)\\n if self.verbose:\\n print(\\\"Dealer draws card:\\\", new_card)\\n self.dealer_cards = np.append(self.dealer_cards, new_card)\\n self.dealer_sum += new_card\\n if np.any(self.dealer_cards == 1) and 17 <= self.dealer_sum + 10 <= 21:\\n if self.verbose:\\n print(\\\"Dealer converts 1 into 11\\\")\\n self.dealer_sum += 10\\n self.dealer_cards[np.where(self.dealer_cards == 1)[0]] = 11\\n if self.verbose:\\n print(\\\"New dealer sum\\\", self.dealer_sum)\\n\\n # Check whether dealer goes bust.\\n dealer_difference = 21 - self.dealer_sum\\n if dealer_difference < 0:\\n if self.verbose:\\n print(\\\"Dealer goes BUST!\\\")\\n reward = 1\\n else:\\n # Compare player to dealer\\n player_difference = 21 - self.player_sum\\n if player_difference == dealer_difference:\\n if self.verbose:\\n print(\\\"DRAW!\\\")\\n reward = 0\\n elif player_difference < dealer_difference:\\n if self.verbose:\\n print(\\\"PLAYER WINS!\\\")\\n reward = 1\\n else:\\n if self.verbose:\\n print(\\\"DEALER WINS!\\\")\\n reward = -1\\n else:\\n raise ValueError(\\\"'action' has to be either 'stick' or 'hit' when gameover is False.\\\")\\n\\n if not self.active:\\n self.state = np.array([-1, -1, -1])\\n\\n return copy.deepcopy(self.state), reward\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b5ed06f45caf7e937b1b9ad3a3a65f4\",\n \"score\": \"0.5879681\",\n \"text\": \"def test_others_playing(self):\\n while True:\\n card = self.g[\\\"Clashes\\\"].remove()\\n if card.name == \\\"Warlord\\\":\\n break\\n self.plr.add_card(card, Piles.HAND)\\n self.plr.play_card(card)\\n mil = self.g[\\\"Militia\\\"].remove()\\n self.oth.add_card(mil, Piles.HAND)\\n self.oth.piles[Piles.PLAYED].set(\\\"Militia\\\", \\\"Militia\\\", \\\"Copper\\\")\\n self.oth.play_card(mil)\\n self.g.print_state()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2063217bde657141e0f5d1728c6fe9fb\",\n \"score\": \"0.5867167\",\n \"text\": \"def playCard(self, chosen: int, played_from: str = \\\"Hand\\\", cheat: bool = False, flank = \\\"Right\\\", ask = True):\\n logging.info(f\\\"numPlays: {sum(v for k,v in self.playedThisTurn.items())}\\\")\\n if played_from == \\\"Deck\\\":\\n source = self.activePlayer.deck\\n elif played_from == \\\"Discard\\\":\\n source = self.activePlayer.discard\\n elif played_from == \\\"Mimicry\\\":\\n source = self.inactivePlayer.discard\\n else:\\n source = self.activePlayer.hand\\n card = source[chosen]\\n if not self.canPlay(card, cheat = cheat):\\n return\\n # canPlay() makes sure that you can't try to play a card you're not allowed to play\\n # Increases amber, adds the card to the action section of the board, then calls the card's play function\\n if card.amber > 0:\\n self.activePlayer.gainAmber(card.amber, self)\\n logging.info(f\\\"{source[chosen].title} gave {self.activePlayer.name} {card.amber} amber. {self.activePlayer.name} now has {self.activePlayer.amber} amber.\\\")\\n if ask:\\n if card.type == \\\"Creature\\\" and len(self.activePlayer.board[\\\"Creature\\\"]) > 0:\\n flank = self.chooseFlank(card)\\n # left flank\\n if card.type != \\\"Upgrade\\\" and flank == \\\"Left\\\":\\n self.activePlayer.board[card.type].insert(0, source.pop(chosen))\\n logging.info(f\\\"numPlays: {sum(v for k,v in self.playedThisTurn.items())}\\\")\\n # default case: right flank\\n elif card.type != \\\"Upgrade\\\" and played_from != \\\"Mimicry\\\":\\n self.activePlayer.board[card.type].append(source.pop(chosen))\\n logging.info(f\\\"numPlays: {sum(v for k,v in self.playedThisTurn.items())}\\\")\\n elif played_from == \\\"Mimicry\\\":\\n self.activePlayer.board[card.type].append(source[chosen])\\n logging.info(f\\\"Mimicry branch.\\\")\\n else:\\n targeted = self.chooseCards(\\\"Creature\\\", \\\"Choose a creature to attach the upgrade to:\\\")\\n if targeted:\\n targeted = targeted[0]\\n else:\\n return\\n self.playUpgrade(card, targeted)\\n self.cardChanged()\\n return\\n #once the card has been added, then we trigger any play effects (eg smaaash will target himself if played on an empty board), use stored new position\\n if card not in self.playedThisTurn:\\n self.playedThisTurn[card] = 1\\n else:\\n self.playedThisTurn[card] += 1\\n self.cardChanged(True) # definitely need to recalc power here, in case we play something next to a staunch knight so now it is dead\\n self.draw()\\n pygame.display.update()\\n card.play(self, card)\\n logging.info(f\\\"{card.title} play ability resolved.\\\")\\n logging.info(f\\\"numPlays: {sum(v for k,v in self.playedThisTurn.items())}\\\")\\n # if the card is an action, now add it to the discard pile - remote access or poltergeist or nexus on masterplan can potentially play cards that belong to the other player\\n if card.type == \\\"Action\\\" and played_from != \\\"Mimicry\\\":\\n if card.title == \\\"library_access\\\":\\n if card not in self.activePlayer.board[\\\"Action\\\"]:\\n return\\n if card.deck == self.activePlayer.name:\\n self.activePlayer.purged.append(self.activePlayer.board[\\\"Action\\\"].pop())\\n else:\\n self.inactivePlayer.purged.append(self.activePlayer.board[\\\"Action\\\"].pop())\\n else:\\n if card not in self.activePlayer.board[\\\"Action\\\"]:\\n return\\n if card.deck == self.activePlayer.name:\\n self.activePlayer.discard.append(self.activePlayer.board[\\\"Action\\\"].pop())\\n else:\\n self.inactivePlayer.discard.append(self.activePlayer.board[\\\"Action\\\"].pop())\\n elif played_from == \\\"Mimicry\\\":\\n if card.title == \\\"library_access\\\":\\n purge_mimic = True\\n self.activePlayer.board[\\\"Action\\\"].pop()\\n logging.info(f\\\"Removing Mimicried card.\\\")\\n if purge_mimic:\\n if card.deck == self.activePlayer.name:\\n self.activePlayer.purged.append(self.activePlayer.board[\\\"Action\\\"].pop())\\n else:\\n self.inactivePlayer.purged.append(self.activePlayer.board[\\\"Action\\\"].pop())\\n self.cardChanged(True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"430302c7890fa57ebc3b3eed949f5b6c\",\n \"score\": \"0.5861373\",\n \"text\": \"def new_game():\\n \\n # Initialize global variable that will hold the \\\"deck\\n # of cards\\\"; we model the \\\"deck of cards\\\" as a list \\n # consisting of 16 numbers with each number lying in \\n # the range [0,8) and appearing twice. The list is\\n # created by concatenating two lists with range [0,8) \\n # together. Although Player can play the game with\\n # \\\"textual numbers\\\" or \\\"images\\\", the above mentioned \\n # technique is being used modeling the game in both\\n # game \\\"modes\\\". \\n global deck_of_cards\\n deck_of_cards = range(CARDS_NUMBER // 2) + range(CARDS_NUMBER // 2)\\n # Shuffle the \\\"deck\\\".\\n random.shuffle(deck_of_cards)\\n # Remove comment if in DEBUG mode.\\n #print deck_of_cards\\n \\n # Initialize global variable that will hold the a list,\\n # with size equal to the size of the \\\"deck of cards\\\"\\n # consisting of boolean values. The boolean value\\n # at a certain list index indicates whether the \\\"card\\\"\\n # is \\\"exposed\\\" (True) or not (False). Particularly,\\n # the ith entry should be \\\"True\\\" if the ith card is \\n # face up and its value is visible or \\\"False\\\" if the \\n # ith card is face down and it's value is hidden. \\n global deck_of_cards_exposed\\n deck_of_cards_exposed = [False] * CARDS_NUMBER\\n \\n # Initialize global variable that will hold the game\\n # state (0,1 and 2), i.e. beginning of the game, single \\n # \\\"exposed\\\" unpaired \\\"card\\\" and end of a \\\"turn\\\" \\n # respectively (have a look at the comments of \\n # \\\"mouseclick()\\\" for a detailed description \\n # concerning this variable).\\n global state\\n state = 0 \\n \\n # Initialize global variable that will hold the number\\n # of \\\"turns\\\" playing the game.\\n global turn \\n turn = 0\\n label.set_text(\\\"Turns = \\\" + str(turn))\\n \\n # Initialize global variable that will hold a \\\"helper\\\"\\n # list, keeping the index of the cards \\\"exposed\\\" in \\n # a single \\\"turn\\\". \\n global index_of_cards_exposed_in_a_turn \\n index_of_cards_exposed_in_a_turn = [-1, -1]\\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92bac313e3373abca83de4f803c9c470\",\n \"score\": \"0.5860306\",\n \"text\": \"def play(deck, verbose):\\r\\n\\r\\n\\t#generate first 2 'visible' cards from deck\\r\\n\\tvisible = [deck.pop(0), deck.pop(0)]\\r\\n\\t\\r\\n\\twhile len(visible) <= 9: #game over if there are more than 9 'piles'\\r\\n\\t\\t\\t\\r\\n\\t\\twhile add_to_11(visible) or jqk(visible):\\t\\t\\r\\n\\t\\t\\r\\n\\t\\t\\tif add_to_11(visible):\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t\\t#avoid popping from empty list:\\r\\n\\t\\t\\t\\tif len(deck) >= 2:\\r\\n\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t\\t\\tfor a in add_to_11(visible):\\r\\n\\t\\t\\t\\t\\t\\tvisible[a] = deck.pop(0) #'cover' cards\\r\\n\\t\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t\\t\\tif verbose:\\r\\n\\t\\t\\t\\t\\t\\tprint(visible)\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t\\telse: \\r\\n\\t\\t\\t\\t\\treturn 0 \\r\\n\\t\\t\\t\\t\\t#if len(deck) < 2 and 2 cards need to be covered then player wins\\r\\n\\t\\r\\n\\t\\t\\tif jqk(visible):\\r\\n\\t\\r\\n\\t\\t\\t\\t#avoid popping from empty list:\\r\\n\\t\\t\\t\\tif len(deck) >= 3 : \\r\\n\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t\\t\\tfor j in jqk(visible):\\r\\n\\t\\t\\t\\t\\t\\tvisible[j] = deck.pop(0)\\r\\n\\t\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t\\t\\tif verbose:\\r\\n\\t\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t\\t\\t\\tprint(visible)\\r\\n\\t\\t\\r\\n\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\treturn 0\\r\\n\\t\\r\\n\\t\\tif len(visible) <= 9 and len(deck) > 0: \\r\\n\\t\\t# no cards can be covered but game not over:\\r\\n\\t\\r\\n\\t\\t\\tvisible.append(deck.pop(0)) #add new pile\\r\\n\\t\\t\\t\\r\\n\\t\\t\\tif verbose:\\r\\n\\t\\t\\t\\tprint(visible)\\r\\n\\t\\t\\t\\r\\n\\t\\tif len(visible) > 9 or len(deck) == 0: \\r\\n\\t\\t\\tbreak #once there are 10 piles/deck empty game is over\\r\\n\\t\\r\\n\\treturn(len(deck))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d95982ee89a1979bb0988195bad9f0f4\",\n \"score\": \"0.58343667\",\n \"text\": \"def reset_game(self):\\n if self.verbose:\\n print(\\\"The game is reset.\\\")\\n self.active = True\\n self.player_cards = []\\n self.player_sum = 0\\n self.player_has_usable_ace = False\\n while self.player_sum < 12:\\n new_card = self.draw_cards(size=1)\\n if new_card == 1 and self.player_sum < 11:\\n new_card = 11\\n self.player_has_usable_ace = True\\n self.player_cards = np.append(self.player_cards, new_card)\\n self.player_sum += new_card\\n\\n self.dealer_cards = self.draw_cards(size=2)\\n self.dealer_sum = np.sum(self.dealer_cards)\\n self.dealers_showing_card = self.dealer_cards[1]\\n\\n self.state = np.array([int(self.player_sum), self.dealers_showing_card, self.player_has_usable_ace])\\n if self.verbose:\\n print(\\\"Player's cards:\\\", [int(x) for x in self.player_cards]) # , \\\"; player sum:\\\", self.player_sum[0]\\n print(\\\"Dealer's showing card:\\\", [self.dealer_cards[1]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6159fbc53dc2432a972ab889872f9fc7\",\n \"score\": \"0.5832458\",\n \"text\": \"def start_turn(self, character):\\n character.db.combat_actionsleft = ACTIONS_PER_TURN # Replenish actions\\n # Prompt the character for their turn and give some information.\\n character.msg(\\\"|wIt's your turn! You have %i HP remaining.|n\\\" % character.db.hp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53efab2740a1c1c957c7cc649f014514\",\n \"score\": \"0.5830657\",\n \"text\": \"def initial(self, state):\\n\\n self.player_number = min(state.turn_order, 0) % self.size\\n self.current_player = self.players[self.player_number]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d948450c72319e3b6261eb24a0525aa5\",\n \"score\": \"0.581744\",\n \"text\": \"def play(self):\\n print('Welcome to Blackjack v1.0')\\n # give dealer 2 cards\\n # give player 2 cards\\n # player goes first in the gameplay, dealer hits until the player stays\\n # dealer then goes\\n # if player wins, they double their bet\\n\\n # if both the player and dealer are the same value, it's called a PUSH and\\n # no money changes hands.\\n\\n # it's a 1:1 payout in blackjack, if you put down 10, you win another 10.\\n\\n # if you hit BLACKJACK! - 21, then you will get a 3:1 payout, so for putting down\\n # 10, you get 15.\\n\\n while True:\\n\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9ef2eb3926070baf2d966906da516c8\",\n \"score\": \"0.5817214\",\n \"text\": \"def shuffle_deck(self):\\n self.__deck = Deck()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dece00dfe6866bb147e30f9a31ea4d1c\",\n \"score\": \"0.58131605\",\n \"text\": \"def select_cards(self, deck, player, dealer):\\n self.gui.show_hand_select_instructions()\\n self.pause(2000)\\n \\n first_hand = deck.draw_two_card_hand()\\n second_hand = deck.draw_two_card_hand()\\n third_hand = deck.draw_two_card_hand()\\n \\n self.gui.show_first_hand(first_hand[0].img_path, first_hand[1].img_path)\\n if self.picked_up_cards():\\n player.add_hand(first_hand)\\n dealer.add_hand(second_hand)\\n dealer.add_hand(third_hand)\\n else:\\n dealer.add_hand(first_hand)\\n self.gui.show_second_hand(second_hand[0].img_path, second_hand[1].img_path)\\n\\n if self.picked_up_cards():\\n player.add_hand(second_hand)\\n dealer.add_hand(third_hand)\\n else:\\n dealer.add_hand(second_hand)\\n player.add_hand(third_hand)\\n self.gui.alert_to_third_hand()\\n self.pause(1500)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2589a760be7715f758b3933ef4b784ee\",\n \"score\": \"0.58054376\",\n \"text\": \"def game(self) -> None:\\n print(RULES) \\n print(f\\\"You have been credited with ${self.start_amount}\\\")\\n\\n # main game loop, loop infinitely until\\n # player decides to quit or player looses\\n # all his/her money\\n while True: \\n if self.start_amount < 1:\\n print(\\\"You've run out of money\\\")\\n print(\\\"Good thing you weren't using real money.\\\")\\n print(\\\"Try to seek professional help for your gambling problems.\\\")\\n sys.exit() #player is broke, exit game \\n\\n print(f\\\"Money available: ${self.start_amount}\\\") \\n\\n # Get the amount player wants to stake.\\n amt_to_bet = self.entity_bet(self.player, self.start_amount)\\n print(f\\\"Player bet ${amt_to_bet}\\\")\\n\\n # Display player scores\\n self.display_scores()\\n\\n # play for both player and player's second\\n # hand if he does split\\n self.entity_play(self.player)\\n self.entity_play(self.player_split)\\n\\n # after player busts or (St)ands\\n # check if dealer needs to play\\n self.check_if_dealer_plays()\\n\\n # check which hand - player main or second hand\\n # if existent- won.\\n self.check_who_wins(self.player)\\n self.check_who_wins(self.player_split)\\n\\n # start all over again\\n input(\\\"Press Enter to continue...\\\")\\n print('\\\\n')\\n\\n # reset stats.\\n self.reset()\\n # clear console\\n os.system('cls') if sys.platform == \\\"win32\\\" else os.system('clear')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35cf530c7c6f7b8e383a4fe612d5570d\",\n \"score\": \"0.58013165\",\n \"text\": \"def __init__(self, deck=None, p1_class=Player, p2_class=Player):\\n #XXX: Currently defaults to playing with VP and Money only.\\n self.card_set = 'trivial'\\n self.current_player = 1\\n self.banked_cards = copy.deepcopy(cards.CARD_SETS[self.card_set])\\n\\n # Variables for game stats\\n self.turn_num = 1\\n self.winning_player = []\\n self.player_vp = []\\n\\n initial_deck = deck or cards.STARTING_DECK\\n self.p1 = p1_class(initial_deck)\\n self.p2 = p2_class(initial_deck)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa0e423f621be5257e0f20a164254566\",\n \"score\": \"0.5795721\",\n \"text\": \"def main():\\n\\n print(\\\"Welcome to BlackJack!!!\\\")\\n\\n # Ask for number of players\\n n_players = get_num_players()\\n\\n # Initialize the game\\n human_name = 'You'\\n game = Game(n_players, human_name=human_name)\\n\\n while (True):\\n # Deal\\n print_deal(game.deal())\\n\\n # Play one turn per player\\n while game.can_play():\\n play_turn(game)\\n game.next_player()\\n\\n # Output results\\n print_score(game, *game.get_winners())\\n if (input(\\\"Play again? [Y/n] \\\") is 'n'):\\n break\\n\\n # End the game\\n print(\\\"Thanks for playing! Have a great day!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74b353cb03f1abea549580a71817cbcb\",\n \"score\": \"0.57908016\",\n \"text\": \"def play(self):\\n self._shuffle()\\n self._deal()\\n self._display_all()\\n results = {}\\n for player in self.player_list:\\n score = player.play_hand()\\n results[f'{player.type} {player.name}'] = score\\n print('Dealer flips over second card...')\\n print(self._dealer.hand)\\n dealer_score = self._dealer.play_hand()\\n print('\\\\n---------------------------------\\\\n')\\n for name, score in results.items():\\n if score == 0:\\n print(f'{name} lost')\\n elif score > dealer_score:\\n print(f'{name} wins')\\n elif score == dealer_score:\\n print(f'{name} pushes')\\n else:\\n print(f'{name} lost')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64492e3d35024b21d7f7db18a48eadff\",\n \"score\": \"0.57886285\",\n \"text\": \"def draw_initial_cards(self):\\n\\n # Draw cards for player and dealer.\\n for current in ('dealer', 'player'):\\n for i in range(0, 2):\\n card = self.draw_card()\\n self.cards[current].append(card)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3cba110962d7ea7079e68e96fc17b49\",\n \"score\": \"0.5776046\",\n \"text\": \"def main():\\n game = WarGame()\\n game.deal()\\n while not game.winner():\\n game.step()\\n print(game)\\n print(game.winner())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ad22698074c27bd70352a6b42bde555\",\n \"score\": \"0.57631284\",\n \"text\": \"def test_Engine__insure_zero(engine, hand):\\n player = players.BetterPlayer(name='Eric', chips=100)\\n player.bet = 20\\n player.hands = (cards.Hand(),)\\n engine.dealer.hands = (hand,)\\n engine._insure(player)\\n assert player.insured == 0\\n assert player.chips == 100\\n assert engine.ui.mock_calls == []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b156f8f72b972997f3e5d63cee14036e\",\n \"score\": \"0.5762807\",\n \"text\": \"def pollPlay(self, state):\\n hand = self.player.getHand()\\n if not len(hand) == 0:\\n cardOfChoice = random.choice(hand)\\n # returns the choosen card here\\n return cardOfChoice\\n print(\\\"Oops, you are trying to play a card from an empty hand\\\")\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7976e1729bf6638650ad8b2cd77acfb2\",\n \"score\": \"0.5760314\",\n \"text\": \"def start(self):\\n Screen.clear()\\n print('STARTING NEW GAME')\\n print('ENTERING MAIN GAME STATE IN A MOMENT...')\\n self.pause(pause_time=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39032af7fa0996e1f761e776ab5a59b6\",\n \"score\": \"0.5759432\",\n \"text\": \"def drawPlayerCard(self):\\n player = self.level.players[0]\\n card = self.level.playerDeck.draw()\\n if card is not None:\\n controller = PlayerDrawController(player, card)\\n controller.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4bb3a70ea09d37ce93a834cc0900ec8\",\n \"score\": \"0.57525396\",\n \"text\": \"def resetHand(self):\\n self.hand = Hand()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b29b99ebb0ebc7579986cd9ef088010b\",\n \"score\": \"0.5751541\",\n \"text\": \"def prep(self, hand):\\r\\n self.hand = hand\\r\\n active_count = 0\\r\\n\\r\\n for player in self.players:\\r\\n if player.stack >= self.BLINDS[1]:\\r\\n player.activate()\\r\\n active_count += 1\\r\\n else:\\r\\n player.deactivate()\\r\\n \\r\\n if active_count < 2:\\r\\n c = \\\"Table needs at least 2 active players for a game.\\\"\\r\\n raise StructureError(c)\\r\\n\\r\\n if self.button == None:\\r\\n self.button = 0\\r\\n else:\\r\\n self.button += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2114aa08ab856de7ecb735cbc2255963\",\n \"score\": \"0.574097\",\n \"text\": \"def start_game(self):\\n self.round = 0\\n self.drawing_player = None\\n self.score = {}\\n\\n for client_id in self.clients:\\n self.score[client_id] = 0\\n\\n self.notify_all('game_starting')\\n self.next_turn()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fdb99a447976b8d96b16eef1d320019\",\n \"score\": \"0.5738883\",\n \"text\": \"def PlayRound(self):\\n with self._lock:\\n if self._active_round:\\n logging.error('HypeJack game already active.')\\n return\\n bets = self._core.bets.LookupBets(\\n self.name, resolver=self._core.name.lower())\\n if not bets:\\n logging.error('Attempted to start HypeJack with no players.')\\n return\\n\\n self._pending_start = False\\n\\n # Shuffle the deck when it gets low. We assume a reasonable number of\\n # cards needed per player, but with lots of splits / low cards we may\\n # still run out of cards to play the hand.\\n if len(self._shoe) < (len(self._peeps) + 1) * 7:\\n self._ShuffleCards()\\n\\n # Deal cards to plebs.\\n for user_id, user_bets in bets.items():\\n hand = Hand(user_bets[0], self._shoe.pop(), self._shoe.pop())\\n self._peeps[user_id] = [hand]\\n self._DisplayUser(user_bets[0].user)\\n\\n # Deal cards to hypebot.\\n self._dealer_hand = Hand(None, self._shoe.pop(), self._shoe.pop())\\n # self._dealer_hand = Hand(playing_cards_lib.Card('Hearts', 8),\\n # playing_cards_lib.Card('Spades', 8))\\n self._msg_fn(None, 'Dealer: [%s, %s]' % (self._dealer_hand.cards[0], '🂠'))\\n\\n self._active_round = True\\n\\n # Short-circuit game play if the dealer has a hypejack or if all peeps\\n # have hypejacks.\\n if not self._dealer_hand.IsHypeJack() and any(\\n [self._IsActive(user_id) for user_id in self._peeps.keys()]):\\n # Force the round to end after some time if some peep ran away. Waiting\\n # on a condition releases the lock while waiting, then reacquires it\\n # automatically. Will shortcircuit if notified when all peeps have\\n # finished their hands.\\n self._game_ender.wait(timeout=self.MAX_ROUND_LENGTH)\\n\\n # Complete dealer hand.\\n self._msg_fn(None, 'Dealer: %s' % self._dealer_hand)\\n while self._dealer_hand.Score() < 17:\\n self._dealer_hand.cards.append(self._shoe.pop())\\n self._msg_fn(None, 'Dealer: %s' % self._dealer_hand)\\n\\n self._core.bets.SettleBets(self, self._core.name.lower(), self._msg_fn)\\n\\n # Reset game state.\\n self._peeps = {}\\n self._active_round = False\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":796,"cells":{"query_id":{"kind":"string","value":"878430aedb1cf2faa0d7a646f80b93dd"},"query":{"kind":"string","value":"Returns True if `n` is a prime number. Uses a sieve based approach."},"positive_passages":{"kind":"list like","value":[{"docid":"f6562e93978739d735e33a2f03e547ef","score":"0.82913023","text":"def is_prime(n):\n if n < 2:\n return False\n ps = primes()\n p = next(ps)\n while p * p <= n:\n if n % p == 0:\n return False\n p = next(ps)\n return True","title":""}],"string":"[\n {\n \"docid\": \"f6562e93978739d735e33a2f03e547ef\",\n \"score\": \"0.82913023\",\n \"text\": \"def is_prime(n):\\n if n < 2:\\n return False\\n ps = primes()\\n p = next(ps)\\n while p * p <= n:\\n if n % p == 0:\\n return False\\n p = next(ps)\\n return True\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"78a25bc4f1590805de217f19646d793b","score":"0.8519759","text":"def isPrime(n):\n\tif n > 1:\n\t\tsqrtN = int(math.sqrt(n))\n\t\tfor i in xrange(2, sqrtN + 1):\n\t\t\tif n % i == 0:\n\t\t\t\treturn False\n\t\treturn True\n\telse:\n\t\treturn False","title":""},{"docid":"dc7a4b3b514e80626aae9ddf8b01d248","score":"0.85149896","text":"def is_prime(n):\n return divisors_count(n) == 2","title":""},{"docid":"8552f164b83d92b64ee1a20078ac78f5","score":"0.85020345","text":"def is_prime(n):\n n=int(n)\n if n <= 1:\n return False\n else:\n val = round(m.sqrt(n))\n for i in range(2, val + 1):\n if n % i == 0:\n return False\n return True","title":""},{"docid":"270a8e44a4956d1698003b4d5c40e13c","score":"0.8487371","text":"def isPrime(n):\n primes=getPrimesLT(n)\n return n in primes","title":""},{"docid":"ba5d05a02c5d60a51d738a646aff60ba","score":"0.8479271","text":"def is_prime(self, n: int) -> bool:\n assert isinstance(n, int) and n >= 2, \"n is an integer greater than 1\"\n\n s = math.sqrt(n)\n for p in self.first_1e6_prime:\n if p <= s and n % p == 0:\n return False\n\n while p <= s:\n p += 2\n if n % p == 0:\n return False\n\n return True","title":""},{"docid":"b19d63e9d42c2b218f79ec6c0d98fd6f","score":"0.84770465","text":"def isprime(n):\n # range starts with 2 and only needs to go up the squareroot of n\n if n < 2:\n return False\n for x in xrange(2, int(n ** 0.5) + 1):\n if n % x == 0:\n return False\n return True","title":""},{"docid":"477eda70153ff8b8c7ccaf2fc536d8b1","score":"0.8416152","text":"def IsPrime(n):\n if n%2 == 0:\n return False\n else:\n prime = True\n for j in range(2,int(np.sqrt(n))+1):\n if n%j == 0:\n prime = False\n break\n return prime","title":""},{"docid":"901a0f3a40b40d64ffc53c9ea7bf1c30","score":"0.84116584","text":"def isprime(n):\n if n < 2:\n return False\n if n == 2:\n return True\n for i in range(2, int(math.ceil(math.sqrt(n)))+1):\n if n % i == 0:\n return False\n return True","title":""},{"docid":"dfeec13bc07ab476d32355abbc4c85d4","score":"0.8396946","text":"def prime(n):\n\tif n <= 1:\n\t\treturn False\n\tfor i in range(2, int(sqrt(n) + 1)):\n\t\tif n % i == 0:\n\t\t\treturn False\n\treturn True","title":""},{"docid":"36a9b3e3ec56f716139c2690f52c09f0","score":"0.8389916","text":"def isPrime(n):\n result = False;\n if isinstance(n, int) == False:\n raise TypeError(\"Input is not an Integer.\");\n elif n <= 0:\n raise ValueError(\"Input is zero or less.\");\n elif n < 2:\n result = False;\n elif n == 2:\n result = True;\n else:\n for i in range(2, n):\n if i == n:\n return;\n elif n % i == 0:\n result = False;\n break;\n else:\n result = True;\n\n return result;","title":""},{"docid":"50906bb1d2d6ef7da5820bf7438979a9","score":"0.8389429","text":"def isprime(n):\r\n prime_ind = False\r\n init_primes = [\r\n 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61,\r\n 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137,\r\n 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, 193, 197, 199\r\n ]\r\n\r\n if n in init_primes:\r\n prime_ind = True\r\n\r\n elif (n<=1 or n%2==0 or n%3==0 or n%5==0):\r\n prime_ind = False\r\n\r\n else:\r\n # Determine upper limit of test range.\r\n ulimit = (int(math.ceil(math.sqrt(n)))+1)\r\n prime_ind = not any(n%k==0 for k in range(3, ulimit, 2))\r\n return(prime_ind)","title":""},{"docid":"60a114091880ba14ed9eca16e709d998","score":"0.83860743","text":"def is_prime(n):\n\tfor num in range(2, floor(sqrt(n)) + 1):\n\t\tif n % num == 0:\n\t\t\treturn False\n\treturn True","title":""},{"docid":"8d62ce4c046658b6480d22f00130fb9f","score":"0.8377889","text":"def is_prime(n):\n\n\t# Find the square root of n.\n\n\tp = int(math.sqrt(n))\n\n\twhile p > 1:\n\t\tif n % p == 0:\n\t\t\treturn 0\n\n\t\tp = p - 1\n\n\treturn 1","title":""},{"docid":"56dadae9af134db9fa497ca8a6ff4e8b","score":"0.8371619","text":"def is_prime(n):\n if n in (0, 1):\n return False\n for i in range(2, n):\n\tif n % i == 0:\n\t return False\n return True","title":""},{"docid":"7635e87c98a226c663071d70fe5328cb","score":"0.83457136","text":"def is_prime(n):\n for i in range(2,n):\n if n % i == 0:\n return False\n return True","title":""},{"docid":"3c2d0c0ed09c36e073b03e7a086dc47b","score":"0.83377486","text":"def is_prime(n):\n\tfor i in range(2,int(abs(n)**0.5)+1):\n\t\tif n%i == 0:\n\t\t\treturn False\n\treturn True","title":""},{"docid":"856b5803e82eb6c5d40330c721906474","score":"0.8323594","text":"def prime_test(n):\n if n < 2:\n return False\n for number in islice(count(2), int(sqrt(n)-1)):\n if not n % number:\n return False\n return True","title":""},{"docid":"589b2f8b21d0d8b67d7c6e53a4057c7e","score":"0.83179694","text":"def is_prime(n):\n \n if n == 2: return True\n if n % 2 == 0: \n return False\n root_n = int(math.sqrt(n))+1\n up_to_root_n = root_n\n quotients = map(lambda divisor: n % divisor, \n range(3, up_to_root_n))\n n_is_evenly_divisible = 0 in quotients\n return False if n_is_evenly_divisible else True","title":""},{"docid":"272d0471d12b21e6b69f97f48baac25a","score":"0.83177346","text":"def is_prime(n: int) -> bool:\n result: bool = True\n\n if n <= 1 or (n % 2 == 0 and n > 2) or (n % 3 == 0 and n > 3):\n result = False\n\n if n == 2:\n result = True\n\n for x in range(3, int(math.sqrt(n)) + 1, 2):\n if n % x == 0:\n result = False\n\n return result","title":""},{"docid":"dd2265a5869ef4466755a84a4f2bdbe7","score":"0.8312044","text":"def is_prime(n):\n if n == 1:\n return False\n\n if n == 2:\n return True\n\n if n % 2 == 0:\n return False\n\n for i in range(3, math.ceil(math.sqrt(n)) + 1, 2):\n if n % i == 0:\n return False\n\n return True","title":""},{"docid":"74aa430d379576b0fc6af3d55d5f6748","score":"0.8284265","text":"def is_prime(n):","title":""},{"docid":"0fed4b5a70448034b4da8242726ede89","score":"0.82800364","text":"def is_prime(n):\n from math import sqrt\n\n # Primes starts on 2\n if n <= 1: return False\n # Caso especial da porra\n if n == 2: return True\n # Numeros pares > 2 nao sao primos\n if n % 2 == 0: return False\n\n square_root = int(sqrt(n))\n for m in xrange(3, square_root + 1, 2): # lista de nums impares ate sqrt\n if n % m == 0:\n return False\n return True","title":""},{"docid":"5f6716ef495b945079b2a2119c7894cf","score":"0.82623875","text":"def is_prime(n):\n if n < 2:\n return False\n else:\n for x in range(2,n):\n if(n % x == 0):\n return False\n return True","title":""},{"docid":"511006c76add766235762394700bec07","score":"0.8257107","text":"def is_prime(n):\n if n == 2:\n return True\n if n == 0 or n == 1 or n % 2 == 0:\n return False\n for i in range(3, int(math.sqrt(n))+1, 2):\n if n % i == 0:\n return False\n return True","title":""},{"docid":"79fb7fa454db5d1e238880467ff4ddee","score":"0.82552886","text":"def is_prime(n):\n\n if n <= 1:\n return False # 0 and 1 are prime numbers\n if n == 2:\n return True # 2 is a prime number\n if n % 2 == 0:\n return False # even numbers greater than 2 are not prime\n # For every integer 1 < i < n, if n is divisible by i,\n # then n is not prime. We only need to check from 3 to\n # sqrt(n), because sqrt(n) is the second largest factor of n,\n # and we can increase i by 2 at every iteration, since we know\n # that n is not even\n limit = math.floor( math.sqrt(n) ) + 1\n for i in range(3, limit, 2):\n if n % i == 0:\n return False\n return True","title":""},{"docid":"2afa11d6ef7e50b2a833a1cf51f971e6","score":"0.82412285","text":"def is_prime(n):\n if n <= 0:\n return False\n if n == 1:\n return False\n if n == 2:\n return True\n for m in range (2, n): # We only have to check until n/2 \n if n % m == 0:\n return False\n return True","title":""},{"docid":"422b20b7c441b1037d7b6a7cc0e0ae1b","score":"0.8240911","text":"def is_prime(n):\n if n<2:\n return False\n\n i=int(n**0.5)\n\n if has_divisor_smaller_than(n, i) == False:\n return True\n return False","title":""},{"docid":"f6e0478c7ea72acda213a781f826bddd","score":"0.82399696","text":"def is_prime(n: int) -> bool: # not solved\n if n == 2 or n == 3:\n return True\n if n % 2 == 0 or n % 3 == 0 or n == 1:\n return False\n else:\n for i in range(2, int(n**.5) + 1):\n if n % i == 0:\n return False\n return True","title":""},{"docid":"725ba95a76ce6d82f495fe8d1132f873","score":"0.82367325","text":"def isprime(n):\n # make sure n is a positive integer\n n = abs(int(n))\n # 0 and 1 are not primes\n if n < 2:\n return False\n # 2 is the only even prime number\n if n == 2:\n return True\n # all other even numbers are not primes\n if not n & 1:\n return False\n # range starts with 3 and only needs to go up the square root of n\n # for all odd numbers\n for x in range(3, int(n**0.5)+1, 2):\n if n % x == 0:\n return False\n return True","title":""},{"docid":"fc31081f55ec0723a789771bb6d30774","score":"0.82352555","text":"def is_prime(n):\n if n==1:\n return False\n elif n==2:\n return True\n else:\n for i in range(2,n):\n if(n % i==0):\n return False\n return True","title":""},{"docid":"07348f41c7be602f12b436214ff72d1a","score":"0.82299316","text":"def isprime(n):\n n = abs(n)\n if n < 2:\n return False\n if n == 2:\n return True\n if not n % 1 == 0:\n return False\n if n % 2 == 0:\n return False\n for x in range(3, round(math.sqrt(n)) + 1, 2):\n if n % x == 0:\n return False\n return True","title":""},{"docid":"08cee8ee7adddb537a8b7d371a94a6ce","score":"0.8216398","text":"def prime(n):\n if n == 2:\n return True\n elif n < 2 or n % 2 == 0:\n return False\n else:\n return not any(n % x == 0\n for x in range(3, math.ceil(math.sqrt(n)) + 1, 2))","title":""},{"docid":"a2ce1f52c9105aa1973c3712ae2bb925","score":"0.82160985","text":"def is_prime(n: int) -> bool:\n # simple test for small n: 2 and 3 are prime, but 1 is not\n if n <= 3:\n return n > 1\n\n # check if multiple of 2 or 3\n if n % 2 == 0 or n % 3 == 0:\n return False\n\n # search for subsequent prime factors around multiples of 6\n max_factor = int(math.sqrt(n))\n for i in range(5, max_factor + 1, 6):\n if n % i == 0 or n % (i + 2) == 0:\n return False\n return True","title":""},{"docid":"a2ce1f52c9105aa1973c3712ae2bb925","score":"0.82160985","text":"def is_prime(n: int) -> bool:\n # simple test for small n: 2 and 3 are prime, but 1 is not\n if n <= 3:\n return n > 1\n\n # check if multiple of 2 or 3\n if n % 2 == 0 or n % 3 == 0:\n return False\n\n # search for subsequent prime factors around multiples of 6\n max_factor = int(math.sqrt(n))\n for i in range(5, max_factor + 1, 6):\n if n % i == 0 or n % (i + 2) == 0:\n return False\n return True","title":""},{"docid":"f0820534545c1052093724d0be6a8cb0","score":"0.82139057","text":"def isprime(n):\n if n == 2: return True\n if n == 3: return True\n if n % 2 == 0: return False\n if n % 3 == 0: return False\n\n i = 5\n w = 2\n while i * i <= n:\n if n % i == 0:\n return False\n\n i += w\n w = 6 - w\n\n return True","title":""},{"docid":"09f7f61a6383ab12105f728487bdd534","score":"0.82113105","text":"def isprime(n):\n if n % 2 == 0 and n > 2:\n return False\n for i in range(3, n):\n if n % i == 0:\n return False\n return True","title":""},{"docid":"d0cccf3620b1352e5c60bf0346de7b01","score":"0.82056844","text":"def is_prime(n):\n if n <= 1:\n return False\n m = 2\n while m <= sqrt(n):\n if n % m == 0:\n return False\n m += 1\n return True","title":""},{"docid":"635525dbae85557bdc340c8b9b10edc4","score":"0.8201573","text":"def is_prime(n):\n\n for i in range(2, int(n**(.5))+1):\n if n % i == 0:\n return False\n return True","title":""},{"docid":"41ac96a2995b84c5582172f7c60743c7","score":"0.81952757","text":"def is_prime(n):\n if n < 2: return False\n if n % 2 == 0: return n == 2\n d = int(math.sqrt(n))\n for k in range(3, d+1, 2):\n if n % k == 0: return False\n return True","title":""},{"docid":"d7bafae931b71a704e193fea70413010","score":"0.8193707","text":"def is_prime(n):\n if n < 2:\n return False\n for i in range(2, int(n**0.5) + 1):\n if n % i == 0:\n return False\n return True","title":""},{"docid":"af516e3c8b4214cefaaeb5e242c9e5de","score":"0.8186336","text":"def is_prime(n):\r\n end = int(sqrt(n))\r\n for i in range(2, end + 1):\r\n if n % i == 0:\r\n return False\r\n return True","title":""},{"docid":"c278d5413913dd21598d0be7b8b5beeb","score":"0.81617874","text":"def is_prime(n):\n if n < 2:\n return False\n\n if n == 2:\n return True\n \n if n % 2 == 0:\n return False\n \n for i in range(3, n):\n if n % i == 0:\n return False\n \n return True","title":""},{"docid":"bc24c2eda47040b95d6de2a18c0f451a","score":"0.81556326","text":"def is_prime(n):\n for i in range(2,int(abs(n)**0.5)+1):\n if(n%i == 0):\n return False\n return True","title":""},{"docid":"1b4342cdcfc8e1b7e350391ff6fdd457","score":"0.8154981","text":"def is_prime(n):\n if n < 2:\n return False\n i = 2\n while i*i <= n:\n if n % i == 0:\n return False\n i = i + 1\n return True","title":""},{"docid":"0e4c66c8caa96007d8a7ed30223725ce","score":"0.81473047","text":"def isPrime(n):\n n = abs(n)\n if n < 2:\n return False\n if n == 2:\n return True\n if not n % 1 == 0:\n return False\n if n % 2 == 0:\n return False\n for x in range(3, round(math.sqrt(n)) + 1, 2):\n if n % x == 0:\n return False\n return True","title":""},{"docid":"0e4c66c8caa96007d8a7ed30223725ce","score":"0.81473047","text":"def isPrime(n):\n n = abs(n)\n if n < 2:\n return False\n if n == 2:\n return True\n if not n % 1 == 0:\n return False\n if n % 2 == 0:\n return False\n for x in range(3, round(math.sqrt(n)) + 1, 2):\n if n % x == 0:\n return False\n return True","title":""},{"docid":"78c57cc85d45fb203d479ef719ae28e9","score":"0.81402576","text":"def isprime(n):\n # range starts with 2 and only needs to go up the squareroot of n\n if n % 2 == 0:\n return False\n for x in range(3, int(n**0.5)+1,2):\n if n % x == 0:\n return False\n return True","title":""},{"docid":"0718b32ecb5751a279a2c58ec2ec1cf7","score":"0.8138055","text":"def is_prime(n):\n if n == 2:\n return True\n if n == 3:\n return True\n if n % 2 == 0:\n return False\n if n % 3 == 0:\n return False\n\n i = 5\n w = 2\n while i*i <= n:\n if n % i == 0:\n return False\n i += w\n w = 6 - w\n\n return True","title":""},{"docid":"54ae235b788cc942b94ba04ca81c3c9d","score":"0.8134483","text":"def is_prime(n):\n for i in range(2, int(n ** 0.5) + 1):\n if not n % i:\n return False\n return True","title":""},{"docid":"6809b1cd33dd1d0ebe49e0e30145952f","score":"0.8124355","text":"def is_prime(n):\n prime = True # assume prime until proven otherwise\n\n for i in range(2, int(sqrt(n) + 1)):\n if n % i == 0: # factor found -> not prime\n prime = False\n break\n\n return prime","title":""},{"docid":"a1e711a66dbcaeb4c4f5cd3fef7cac5c","score":"0.81242764","text":"def is_prime_v2(n):\n if n == 1:\n return False\n\n max_divisor = math.floor(math.sqrt(n))\n for d in range(2, 1 + max_divisor):\n if n % d == 0:\n return False\n return True","title":""},{"docid":"5d81ef705192336ab08758c7cb15039d","score":"0.81232667","text":"def isPrime(n):\n\n if n == 0 or n == 1:\n return False\n\n for div in range(2, int(n**0.5)+1):\n if not n % div:\n return False\n return True","title":""},{"docid":"148222020cd78bf2d22c5e4326000805","score":"0.81167865","text":"def is_prime(n):\n if n == 2:\n return True\n if n == 3:\n return True\n if n % 2 == 0:\n return False\n if n % 3 == 0:\n return False\n\n i = 5\n w = 2\n\n while i * i <= n:\n if n % i == 0:\n return False\n\n i += w\n w = 6 - w\n\n return True","title":""},{"docid":"4c5a33abd4d265953025486942673076","score":"0.81161934","text":"def isPrime(n):\n\tif n <= 1: return False\n\telif n < 4: return True #2, 3\n\telif n % 2 == 0: return False\n\telif n < 9: return True #5, 7\n\telif n % 3 == 0: return False\n\telse:\n\t\t#make use of the fact that all primes > 5 have have the form 6k +- 1\n\t\tr = math.floor(math.sqrt(n))\n\t\tf = 5\n\t\twhile f <= r:\n\t\t\tif n % f == 0: return False\n\t\t\tif n % (f + 2) == 0: return False\n\t\t\tf += 6\n\t\treturn True","title":""},{"docid":"18968f383dac5e1e0b0a60d0bb0988ba","score":"0.80963194","text":"def is_prime(n):\n if n < 2:\n return False\n if n == 2:\n return True\n d = 2\n while d <= int(math.sqrt(n)) + 1:\n if n % d == 0:\n return False\n d += 1\n return True","title":""},{"docid":"40d0e26c0162295a4d5cec18cca267db","score":"0.8092706","text":"def isprime(n):\n return len(factors(n)) == 2","title":""},{"docid":"bda83ee0a8ba13b9b662c98c048c8736","score":"0.80838317","text":"def isprime(n):\n if n == 2:\n return True\n if n == 3:\n return True\n if n % 2 == 0:\n return False\n if n % 3 == 0:\n return False\n\n i = 5\n w = 2\n\n while i * i <= n:\n if n % i == 0:\n return False\n\n i += w\n w = 6 - w\n\n return True","title":""},{"docid":"6f088fa3e414e6a8725d83eb028394b2","score":"0.8081018","text":"def is_prime_v1(n):\n if n == 1:\n return False\n\n for d in range(2, n):\n if n % d == 0:\n return False\n return True","title":""},{"docid":"ae0faea9835cf4d2c6ce4970efa17b8f","score":"0.8067031","text":"def is_prime(n):\n def divisible_by_known_prime(n):\n for x in known_primes:\n if divides(x, n):\n return x\n return False\n\n # Quick filters\n if n < 2:\n return False\n elif n in known_primes:\n return True\n elif divisible_by_known_prime(n):\n return False\n else:\n # Start searching again at the max known prime\n start = known_primes[-1] + 2\n for y in xrange(start, int(sqrt(n)), 2):\n if divisible_by_known_prime(y):\n continue\n else:\n if n % y == 0:\n return False\n known_primes.append(y)\n return True","title":""},{"docid":"464021a33de3106c49185f714bf98982","score":"0.8051832","text":"def test_prime(n):\n if n==2:\n return True\n #we only need to check divisors up to square root of a number\n for i in range(2,int(n**(.5))+1):\n if n % i ==0:\n return False\n else:\n return True","title":""},{"docid":"4c6829a46657473e9890c8e4a7c737a3","score":"0.8046635","text":"def isprime(n):\n\n if n == 2:\n return True\n if n == 3:\n return True\n if n % 2 == 0:\n return False\n if n % 3 == 0:\n return False\n i = 5\n w = 2\n while i * i <= n:\n if n % i == 0:\n return False\n i += w\n w = 6 - w\n return True","title":""},{"docid":"4c6829a46657473e9890c8e4a7c737a3","score":"0.8046635","text":"def isprime(n):\n\n if n == 2:\n return True\n if n == 3:\n return True\n if n % 2 == 0:\n return False\n if n % 3 == 0:\n return False\n i = 5\n w = 2\n while i * i <= n:\n if n % i == 0:\n return False\n i += w\n w = 6 - w\n return True","title":""},{"docid":"e993486b8ff4626cd7a77a6f4541959e","score":"0.80411047","text":"def is_prime_number(n:int) -> bool:\r\n\r\n i = 0\r\n is_prime = True\r\n\r\n if (n == 2 or n == 3):\r\n return True\r\n while(i < len(_prime_list)):\r\n if (not n % _prime_list[i] and is_prime):\r\n is_prime = False\r\n return is_prime\r\n i += 1\r\n if (is_prime):\r\n _prime_list.append(n)\r\n return is_prime","title":""},{"docid":"bc04899d0ad229677ff3b7c4f040ac5d","score":"0.8024999","text":"def isPrime(n):\n Dmax = scipy.sqrt(n)\n if n == 2:\n return True\n if isEven(n):\n return False\n d = 3\n while n%d != 0 and d <= Dmax:\n d += 2\n return d > Dmax","title":""},{"docid":"b5a485022c94922bb7f2ae2c0922016c","score":"0.7937437","text":"def is_prime(n):\n test_vals = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37]\n if n in test_vals:\n return True\n d = n - 1\n s = 0\n while not d & 1:\n d = d >> 1\n s += 1\n for a in test_vals:\n for r in range(0, s):\n if (a ** (d * (1 << r))) % n != (n - 1) \\\n and (a ** d) % n != 1:\n return False\n return True","title":""},{"docid":"7a6588f6a60f59af54b5b30212aef90f","score":"0.78840125","text":"def is_prime(n):\n if n < 2 :\n return False\n\n # OFF BY ONE Error, solution is to add +1 \n #for i in range (2, int(math.sqrt(n)) + 1 ):\n for i in range (2, int(math.sqrt(n)) ):\n if n % i == 0 :\n return False\n return True","title":""},{"docid":"8c240bfae6683bf9bd96ef3272a3f9b7","score":"0.78501946","text":"def prime_number(n):\n\n if n > 1:\n\n for divisor in range(2,n):\n\n if n%divisor == 0:\n #if n and divisor cannot complete full division, n is not a \n #prime number\n\n return False\n\n return True\n\n else:\n #negative number is not prime number\n\n return False","title":""},{"docid":"9168442a99017fb7004b5deea6142afb","score":"0.7763757","text":"def is_prime(n):\n \n if n == 2:\n return True\n if n < 2 or n % 2 == 0:\n return False\n \n trail_factor = 3\n \n root = sqrt(n)\n \n while trail_factor <= root:\n if n % trail_factor == 0:\n return False\n trail_factor += 2 \n \n return True","title":""},{"docid":"e931ff2a59cbd8806ee6f819d4efc108","score":"0.77405703","text":"def is_prime(n):\n def prime_helper(index):\n if index == n:\n return True\n elif n % index == 0 or n == 1:\n return False\n else:\n return prime_helper(index + 1)\n return prime_helper(2)","title":""},{"docid":"1ad782a50c1962258b23b6d5a2dfd97c","score":"0.7739098","text":"def primality(n):\n if n == 1:\n return NOT_PRIME\n return PRIME if all(n % i for i in range(2, int(sqrt(n)) + 1)) else NOT_PRIME","title":""},{"docid":"2440dc2a1445e687d40f229c33daa7cd","score":"0.77390164","text":"def isprime(n):\r\n if n % 3 == 0:\r\n return (False, 3)\r\n\r\n i = 5\r\n w = 2\r\n\r\n while i * i <= n:\r\n if n % i == 0:\r\n return (False, i)\r\n\r\n i += w\r\n w = 6 - w\r\n\r\n return (True, None)","title":""},{"docid":"1373ba4fdce84a62e9becedccf9c9fad","score":"0.7735379","text":"def is_prime(n):\n prime = True\n d = 2\n while prime and d <= round(n**(1/2)):\n if n%d != 0:\n d += 1\n else:\n prime = False\n return prime","title":""},{"docid":"5fb53f51042964c6e86ff53aeb84cd0f","score":"0.76981086","text":"def is_prime_sieve(x):\n\n if (x <= 1):\n return False\n elif (x <= 3):\n return True\n x_mod_6 = x % 6\n if ((x_mod_6 != 1) and (x_mod_6 != 5)):\n return False\n\n sqrt_x = int_sqrt(x)\n for i in generate_primes(sqrt_x):\n if ((x % i) == 0):\n return False\n return True","title":""},{"docid":"ebc0590b9895849fc3e3f49891941a7f","score":"0.7671711","text":"def is_prime(n):\n\n # (This is used to study the risk of false positives:)\n global miller_rabin_test_count\n\n miller_rabin_test_count = 0\n\n if n <= smallprimes[-1]:\n if n in smallprimes:\n return True\n else:\n return False\n\n if gcd(n, 2 * 3 * 5 * 7 * 11) != 1:\n return False\n\n # Choose a number of iterations sufficient to reduce the\n # probability of accepting a composite below 2**-80\n # (from Menezes et al. Table 4.4):\n\n t = 40\n n_bits = 1 + int(math.log(n, 2))\n for k, tt in (\n (100, 27),\n (150, 18),\n (200, 15),\n (250, 12),\n (300, 9),\n (350, 8),\n (400, 7),\n (450, 6),\n (550, 5),\n (650, 4),\n (850, 3),\n (1300, 2),\n ):\n if n_bits < k:\n break\n t = tt\n\n # Run the test t times:\n\n s = 0\n r = n - 1\n while (r % 2) == 0:\n s = s + 1\n r = r // 2\n for i in xrange(t):\n a = smallprimes[i]\n y = pow(a, r, n)\n if y != 1 and y != n - 1:\n j = 1\n while j <= s - 1 and y != n - 1:\n y = pow(y, 2, n)\n if y == 1:\n miller_rabin_test_count = i + 1\n return False\n j = j + 1\n if y != n - 1:\n miller_rabin_test_count = i + 1\n return False\n return True","title":""},{"docid":"07e51bd8d669673e6777f4dbb96d36b8","score":"0.76641953","text":"def is_prime(n):\n\n # Checks if given a list...\n if type(n) == list:\n if_prime_list = [False, ] * len(n)\n c = 0\n for i in n:\n if is_prime(i):\n if_prime_list[c] = True\n c += 1\n # and returns one with weather or not it's prime in place\n return if_prime_list\n\n # When a single number ..\n\n if n < 2:\n return False\n if n == 2 or n == 3:\n return True\n\n for i in range(2, int(sqrt(n))+1):\n if n % i == 0:\n return False\n return True","title":""},{"docid":"b167f7b47ee5341083441f6a31a32272","score":"0.7655478","text":"def isPrime(n):\n\n if n % 2 == 0:\n return n == 2\n d = 3\n while d * d <= n and n % d != 0:\n d += 2\n return d * d > n","title":""},{"docid":"0be316dbe44f91fe2b2e34bac2ceba31","score":"0.7647462","text":"def is_prime_cacheless(n):\n if n < 2:\n return False\n for i in xrange(2, int(math.sqrt(n)) + 1):\n if n % i == 0:\n return False\n return True","title":""},{"docid":"5809882f1b0d4c24bf26aadb26e9c4cb","score":"0.7642497","text":"def is_superprime(n):\n while n > 0:\n if not is_prime(n):\n return False\n n = n // 10\n return True","title":""},{"docid":"6f9804bea1d4da046f3722898d4b15ae","score":"0.7615486","text":"def is_prime_exact(n):\n if n <= 3:\n return n >= 2\n if n % 2 == 0 or n % 3 == 0:\n return False\n for i in range(5, int(n ** 0.5) + 1, 6):\n if n % i == 0 or n % (i + 2) == 0:\n return False\n return True","title":""},{"docid":"5259c60cb0e66026bd62a7cf1ba6f28e","score":"0.76105756","text":"def is_prime(number: int) -> bool:\n prop_div = proper_divisors(number)\n return len(prop_div) == 1","title":""},{"docid":"2e333a6a531610d72ac921f35578efc7","score":"0.76068914","text":"def is_prime_v3(n):\n if n == 1:\n return False\n\n # If it's even and not 2, then it's not prime\n if n == 2:\n return True\n if n > 2 and n % 2 == 0:\n return False\n\n max_divisor = math.floor(math.sqrt(n))\n for d in range(3, 1 + max_divisor, 2):\n if n % d == 0:\n return False\n return True","title":""},{"docid":"2e333a6a531610d72ac921f35578efc7","score":"0.76068914","text":"def is_prime_v3(n):\n if n == 1:\n return False\n\n # If it's even and not 2, then it's not prime\n if n == 2:\n return True\n if n > 2 and n % 2 == 0:\n return False\n\n max_divisor = math.floor(math.sqrt(n))\n for d in range(3, 1 + max_divisor, 2):\n if n % d == 0:\n return False\n return True","title":""},{"docid":"bd441534aae1f3e8f4a3213bf868fdb0","score":"0.75916487","text":"def is_prime(n):\n\n # (This is used to study the risk of false positives:)\n global miller_rabin_test_count\n\n miller_rabin_test_count = 0\n\n if n <= smallprimes[-1]:\n if n in smallprimes:\n return True\n else:\n return False\n\n if gcd(n, 2 * 3 * 5 * 7 * 11) != 1:\n return False\n\n # Choose a number of iterations sufficient to reduce the\n # probability of accepting a composite below 2**-80\n # (from Menezes et al. Table 4.4):\n\n t = 40\n n_bits = 1 + int(math.log(n, 2))\n for k, tt in ((100, 27),\n (150, 18),\n (200, 15),\n (250, 12),\n (300, 9),\n (350, 8),\n (400, 7),\n (450, 6),\n (550, 5),\n (650, 4),\n (850, 3),\n (1300, 2),\n ):\n if n_bits < k:\n break\n t = tt\n\n # Run the test t times:\n\n s = 0\n r = n - 1\n while (r % 2) == 0:\n s = s + 1\n r = r // 2\n for i in range(t):\n a = smallprimes[i]\n y = modular_exp(a, r, n)\n if y != 1 and y != n - 1:\n j = 1\n while j <= s - 1 and y != n - 1:\n y = modular_exp(y, 2, n)\n if y == 1:\n miller_rabin_test_count = i + 1\n return False\n j = j + 1\n if y != n - 1:\n miller_rabin_test_count = i + 1\n return False\n return True","title":""},{"docid":"31c7eb72160691d2a58b025131a986ac","score":"0.758809","text":"def is_prime(n):\n r = 0\n d = n - 1\n while d % 2 == 0:\n r += 1\n d //= 2\n\n for _ in range(40):\n a = random.randint(2, n - 2)\n x = pow(a, d, n)\n if x == 1 or x == n - 1:\n continue\n \n for _ in range(r - 1):\n x = pow(x, 2, n)\n if x == n - 1:\n break\n\n else:\n return False\n\n return True","title":""},{"docid":"0b537c6a55e7ef96df0d686053d637c4","score":"0.75501156","text":"def is_prime(i, primes):\n pass","title":""},{"docid":"1fbbb15cd7dd7e9b4d3490d51da7e317","score":"0.74986815","text":"def is_prime(number):\n for i in range(2, number):\n if number % i == 0:\n return False\n\n return True","title":""},{"docid":"d3e14a0482f5920322f17e25db1e4d4f","score":"0.7495828","text":"def is_prime(n, prime_numbers = []):\n if n < 2:\n return False\n\n if len(prime_numbers) == 0 or prime_numbers[-1] < n:\n prime_numbers = _generate_primes(n + 1, prime_numbers)\n\n for p in prime_numbers:\n if n < p:\n return False\n if n == p:\n return True\n\n return False","title":""},{"docid":"83d11c11d34ac275d0984b8b705ea571","score":"0.7481891","text":"def is_prime(number):\r\n if number <= 0:\r\n return False\r\n elif number > 2:\r\n for i in range(2, number):\r\n if number % i == 0:\r\n return False\r\n return True","title":""},{"docid":"2bbe1ee06cee20708acd22de4008554f","score":"0.7473216","text":"def prime(n):\n # https://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n-in-python/3035188#3035188\n sieve = np.ones(int(n/3 + (n%6==2)), dtype=np.bool)\n sieve[0] = False\n for i in range(int(n**0.5/3+1)):\n if sieve[i]:\n k=3*i+1|1\n sieve[ int((k*k)/3) ::2*k] = False\n sieve[int((k*k+4*k-2*k*(i&1))/3)::2*k] = False\n return np.r_[2,3,((3*np.nonzero(sieve)[0]+1)|1)]","title":""},{"docid":"589dda1ed65ef55d34a2407a75760f86","score":"0.74541706","text":"def is_prime(num):\r\n for n in range(2, num):\r\n if num % n == 0:\r\n return False\r\n return True","title":""},{"docid":"557caac191fadc8d79ddadae1a0b768c","score":"0.74398","text":"def _is_prime(number):\n if number <= 1:\n return False\n\n divisor = 2\n while divisor < number:\n if ((number % divisor) == 0) or (divisor > (number / 2)):\n return False\n divisor += 1\n return True","title":""},{"docid":"07f0a11ff3c7fc1e8870986532afedf0","score":"0.7438905","text":"def is_prime(number: int) -> bool:\n min_divisor = 2\n max_divisor = math.ceil(math.sqrt(number))\n for divisor in range(min_divisor, max_divisor + 1):\n if number % divisor == 0:\n return False\n return True","title":""},{"docid":"0b7752fe5a1cf97814e82aa201face01","score":"0.7425838","text":"def probable_prime(n):\n return pow(2, n-1, n) == 1","title":""},{"docid":"89e87a69f7b9cd088605a16cdbb01c27","score":"0.74213374","text":"def is_prime_v1(n):\n if n == 1:\n return False # 1 is not prime (1 is not composite, either)\n # d stands for divisor. Since any number greater than 1 (prime or composite) can be exactly divided by 1 and itself,\n # we skip 1 and n and only iterate over the items in the sequence from 2 through n - 1 (Remember in the range()\n # function the stop value is not included.)\n for d in range(2, n):\n # % is the modulus operator. The right-hand side is the remainder of the operation of division of n by d.\n # If n is divisible by d, the remainder is equal to 0; if not, the remainder is not equal to 0.\n # So the test_expression of n % d == 0 means n is divisible by d. If the test_expression is True, control of the\n # program flows to the body of if and execute the return statement. The function exits and meanwhile the boolean\n # value of False is returned.\n if n % d == 0:\n return False\n # If each items in the sequence generated from the range doesn't make the test_expression True, meaning n is not\n # divisible by any smaller number, then we can consider n is prime and return True after the items in the sequence\n # exhaust without the for loop being stopped halfway.\n return True","title":""},{"docid":"460b8ab60da7496aa79fef6e6731ca9f","score":"0.74172294","text":"def isPrime(p):\n\n return True","title":""},{"docid":"6248ff0357b4b512961650451529bc9c","score":"0.7411656","text":"def is_prime(number: int) -> bool:\n\n if 1 < number < 4:\n # 2 and 3 are primes\n return True\n elif number < 2 or number % 2 == 0 or number % 3 == 0:\n # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes\n return False\n\n # All primes number are in format of 6k +/- 1\n for i in range(5, int(math.sqrt(number) + 1), 6):\n if number % i == 0 or number % (i + 2) == 0:\n return False\n return True","title":""},{"docid":"6d50325cb8c5ccefa49472a84ce23913","score":"0.7410716","text":"def is_prime(number):\n if number <= 1:\n return False\n if number == 2:\n return True\n else:\n maximum = int(math.sqrt(number)) + 1\n for i in range(2, maximum):\n if number % i == 0:\n return False\n return True","title":""},{"docid":"b2b3903b7ed71035196f37c9778c5e81","score":"0.74050426","text":"def check_prime(number):\n if number in primes:\n return True\n return False","title":""},{"docid":"1a3617d3ebdd114f5eeefb2a8c559fad","score":"0.73954546","text":"def primes(n):\n sieve = [True] * n\n for i in range(3,int(n**0.5)+1,2): #in py3, range and xrange are same\n if sieve[i]:\n sieve[i*i::2*i]=[False]*((n-i*i-1)//(2*i)+1)\n return [2] + [i for i in range(3,n,2) if sieve[i]]","title":""},{"docid":"9d7593a29f9a46b123950c8a508ec279","score":"0.7380684","text":"def is_prime(number: int) -> bool:\r\n\r\n if 1 < number < 4:\r\n # 2 and 3 are primes\r\n return True\r\n elif number < 2 or number % 2 == 0 or number % 3 == 0:\r\n # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes\r\n return False\r\n\r\n # All primes number are in format of 6k +/- 1\r\n for i in range(5, int(math.sqrt(number) + 1), 6):\r\n if number % i == 0 or number % (i + 2) == 0:\r\n return False\r\n return True","title":""}],"string":"[\n {\n \"docid\": \"78a25bc4f1590805de217f19646d793b\",\n \"score\": \"0.8519759\",\n \"text\": \"def isPrime(n):\\n\\tif n > 1:\\n\\t\\tsqrtN = int(math.sqrt(n))\\n\\t\\tfor i in xrange(2, sqrtN + 1):\\n\\t\\t\\tif n % i == 0:\\n\\t\\t\\t\\treturn False\\n\\t\\treturn True\\n\\telse:\\n\\t\\treturn False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc7a4b3b514e80626aae9ddf8b01d248\",\n \"score\": \"0.85149896\",\n \"text\": \"def is_prime(n):\\n return divisors_count(n) == 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8552f164b83d92b64ee1a20078ac78f5\",\n \"score\": \"0.85020345\",\n \"text\": \"def is_prime(n):\\n n=int(n)\\n if n <= 1:\\n return False\\n else:\\n val = round(m.sqrt(n))\\n for i in range(2, val + 1):\\n if n % i == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"270a8e44a4956d1698003b4d5c40e13c\",\n \"score\": \"0.8487371\",\n \"text\": \"def isPrime(n):\\n primes=getPrimesLT(n)\\n return n in primes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba5d05a02c5d60a51d738a646aff60ba\",\n \"score\": \"0.8479271\",\n \"text\": \"def is_prime(self, n: int) -> bool:\\n assert isinstance(n, int) and n >= 2, \\\"n is an integer greater than 1\\\"\\n\\n s = math.sqrt(n)\\n for p in self.first_1e6_prime:\\n if p <= s and n % p == 0:\\n return False\\n\\n while p <= s:\\n p += 2\\n if n % p == 0:\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b19d63e9d42c2b218f79ec6c0d98fd6f\",\n \"score\": \"0.84770465\",\n \"text\": \"def isprime(n):\\n # range starts with 2 and only needs to go up the squareroot of n\\n if n < 2:\\n return False\\n for x in xrange(2, int(n ** 0.5) + 1):\\n if n % x == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"477eda70153ff8b8c7ccaf2fc536d8b1\",\n \"score\": \"0.8416152\",\n \"text\": \"def IsPrime(n):\\n if n%2 == 0:\\n return False\\n else:\\n prime = True\\n for j in range(2,int(np.sqrt(n))+1):\\n if n%j == 0:\\n prime = False\\n break\\n return prime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"901a0f3a40b40d64ffc53c9ea7bf1c30\",\n \"score\": \"0.84116584\",\n \"text\": \"def isprime(n):\\n if n < 2:\\n return False\\n if n == 2:\\n return True\\n for i in range(2, int(math.ceil(math.sqrt(n)))+1):\\n if n % i == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfeec13bc07ab476d32355abbc4c85d4\",\n \"score\": \"0.8396946\",\n \"text\": \"def prime(n):\\n\\tif n <= 1:\\n\\t\\treturn False\\n\\tfor i in range(2, int(sqrt(n) + 1)):\\n\\t\\tif n % i == 0:\\n\\t\\t\\treturn False\\n\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36a9b3e3ec56f716139c2690f52c09f0\",\n \"score\": \"0.8389916\",\n \"text\": \"def isPrime(n):\\n result = False;\\n if isinstance(n, int) == False:\\n raise TypeError(\\\"Input is not an Integer.\\\");\\n elif n <= 0:\\n raise ValueError(\\\"Input is zero or less.\\\");\\n elif n < 2:\\n result = False;\\n elif n == 2:\\n result = True;\\n else:\\n for i in range(2, n):\\n if i == n:\\n return;\\n elif n % i == 0:\\n result = False;\\n break;\\n else:\\n result = True;\\n\\n return result;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50906bb1d2d6ef7da5820bf7438979a9\",\n \"score\": \"0.8389429\",\n \"text\": \"def isprime(n):\\r\\n prime_ind = False\\r\\n init_primes = [\\r\\n 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61,\\r\\n 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137,\\r\\n 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, 193, 197, 199\\r\\n ]\\r\\n\\r\\n if n in init_primes:\\r\\n prime_ind = True\\r\\n\\r\\n elif (n<=1 or n%2==0 or n%3==0 or n%5==0):\\r\\n prime_ind = False\\r\\n\\r\\n else:\\r\\n # Determine upper limit of test range.\\r\\n ulimit = (int(math.ceil(math.sqrt(n)))+1)\\r\\n prime_ind = not any(n%k==0 for k in range(3, ulimit, 2))\\r\\n return(prime_ind)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60a114091880ba14ed9eca16e709d998\",\n \"score\": \"0.83860743\",\n \"text\": \"def is_prime(n):\\n\\tfor num in range(2, floor(sqrt(n)) + 1):\\n\\t\\tif n % num == 0:\\n\\t\\t\\treturn False\\n\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d62ce4c046658b6480d22f00130fb9f\",\n \"score\": \"0.8377889\",\n \"text\": \"def is_prime(n):\\n\\n\\t# Find the square root of n.\\n\\n\\tp = int(math.sqrt(n))\\n\\n\\twhile p > 1:\\n\\t\\tif n % p == 0:\\n\\t\\t\\treturn 0\\n\\n\\t\\tp = p - 1\\n\\n\\treturn 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56dadae9af134db9fa497ca8a6ff4e8b\",\n \"score\": \"0.8371619\",\n \"text\": \"def is_prime(n):\\n if n in (0, 1):\\n return False\\n for i in range(2, n):\\n\\tif n % i == 0:\\n\\t return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7635e87c98a226c663071d70fe5328cb\",\n \"score\": \"0.83457136\",\n \"text\": \"def is_prime(n):\\n for i in range(2,n):\\n if n % i == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c2d0c0ed09c36e073b03e7a086dc47b\",\n \"score\": \"0.83377486\",\n \"text\": \"def is_prime(n):\\n\\tfor i in range(2,int(abs(n)**0.5)+1):\\n\\t\\tif n%i == 0:\\n\\t\\t\\treturn False\\n\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"856b5803e82eb6c5d40330c721906474\",\n \"score\": \"0.8323594\",\n \"text\": \"def prime_test(n):\\n if n < 2:\\n return False\\n for number in islice(count(2), int(sqrt(n)-1)):\\n if not n % number:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"589b2f8b21d0d8b67d7c6e53a4057c7e\",\n \"score\": \"0.83179694\",\n \"text\": \"def is_prime(n):\\n \\n if n == 2: return True\\n if n % 2 == 0: \\n return False\\n root_n = int(math.sqrt(n))+1\\n up_to_root_n = root_n\\n quotients = map(lambda divisor: n % divisor, \\n range(3, up_to_root_n))\\n n_is_evenly_divisible = 0 in quotients\\n return False if n_is_evenly_divisible else True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"272d0471d12b21e6b69f97f48baac25a\",\n \"score\": \"0.83177346\",\n \"text\": \"def is_prime(n: int) -> bool:\\n result: bool = True\\n\\n if n <= 1 or (n % 2 == 0 and n > 2) or (n % 3 == 0 and n > 3):\\n result = False\\n\\n if n == 2:\\n result = True\\n\\n for x in range(3, int(math.sqrt(n)) + 1, 2):\\n if n % x == 0:\\n result = False\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd2265a5869ef4466755a84a4f2bdbe7\",\n \"score\": \"0.8312044\",\n \"text\": \"def is_prime(n):\\n if n == 1:\\n return False\\n\\n if n == 2:\\n return True\\n\\n if n % 2 == 0:\\n return False\\n\\n for i in range(3, math.ceil(math.sqrt(n)) + 1, 2):\\n if n % i == 0:\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74aa430d379576b0fc6af3d55d5f6748\",\n \"score\": \"0.8284265\",\n \"text\": \"def is_prime(n):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fed4b5a70448034b4da8242726ede89\",\n \"score\": \"0.82800364\",\n \"text\": \"def is_prime(n):\\n from math import sqrt\\n\\n # Primes starts on 2\\n if n <= 1: return False\\n # Caso especial da porra\\n if n == 2: return True\\n # Numeros pares > 2 nao sao primos\\n if n % 2 == 0: return False\\n\\n square_root = int(sqrt(n))\\n for m in xrange(3, square_root + 1, 2): # lista de nums impares ate sqrt\\n if n % m == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f6716ef495b945079b2a2119c7894cf\",\n \"score\": \"0.82623875\",\n \"text\": \"def is_prime(n):\\n if n < 2:\\n return False\\n else:\\n for x in range(2,n):\\n if(n % x == 0):\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"511006c76add766235762394700bec07\",\n \"score\": \"0.8257107\",\n \"text\": \"def is_prime(n):\\n if n == 2:\\n return True\\n if n == 0 or n == 1 or n % 2 == 0:\\n return False\\n for i in range(3, int(math.sqrt(n))+1, 2):\\n if n % i == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79fb7fa454db5d1e238880467ff4ddee\",\n \"score\": \"0.82552886\",\n \"text\": \"def is_prime(n):\\n\\n if n <= 1:\\n return False # 0 and 1 are prime numbers\\n if n == 2:\\n return True # 2 is a prime number\\n if n % 2 == 0:\\n return False # even numbers greater than 2 are not prime\\n # For every integer 1 < i < n, if n is divisible by i,\\n # then n is not prime. We only need to check from 3 to\\n # sqrt(n), because sqrt(n) is the second largest factor of n,\\n # and we can increase i by 2 at every iteration, since we know\\n # that n is not even\\n limit = math.floor( math.sqrt(n) ) + 1\\n for i in range(3, limit, 2):\\n if n % i == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2afa11d6ef7e50b2a833a1cf51f971e6\",\n \"score\": \"0.82412285\",\n \"text\": \"def is_prime(n):\\n if n <= 0:\\n return False\\n if n == 1:\\n return False\\n if n == 2:\\n return True\\n for m in range (2, n): # We only have to check until n/2 \\n if n % m == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"422b20b7c441b1037d7b6a7cc0e0ae1b\",\n \"score\": \"0.8240911\",\n \"text\": \"def is_prime(n):\\n if n<2:\\n return False\\n\\n i=int(n**0.5)\\n\\n if has_divisor_smaller_than(n, i) == False:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6e0478c7ea72acda213a781f826bddd\",\n \"score\": \"0.82399696\",\n \"text\": \"def is_prime(n: int) -> bool: # not solved\\n if n == 2 or n == 3:\\n return True\\n if n % 2 == 0 or n % 3 == 0 or n == 1:\\n return False\\n else:\\n for i in range(2, int(n**.5) + 1):\\n if n % i == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"725ba95a76ce6d82f495fe8d1132f873\",\n \"score\": \"0.82367325\",\n \"text\": \"def isprime(n):\\n # make sure n is a positive integer\\n n = abs(int(n))\\n # 0 and 1 are not primes\\n if n < 2:\\n return False\\n # 2 is the only even prime number\\n if n == 2:\\n return True\\n # all other even numbers are not primes\\n if not n & 1:\\n return False\\n # range starts with 3 and only needs to go up the square root of n\\n # for all odd numbers\\n for x in range(3, int(n**0.5)+1, 2):\\n if n % x == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc31081f55ec0723a789771bb6d30774\",\n \"score\": \"0.82352555\",\n \"text\": \"def is_prime(n):\\n if n==1:\\n return False\\n elif n==2:\\n return True\\n else:\\n for i in range(2,n):\\n if(n % i==0):\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07348f41c7be602f12b436214ff72d1a\",\n \"score\": \"0.82299316\",\n \"text\": \"def isprime(n):\\n n = abs(n)\\n if n < 2:\\n return False\\n if n == 2:\\n return True\\n if not n % 1 == 0:\\n return False\\n if n % 2 == 0:\\n return False\\n for x in range(3, round(math.sqrt(n)) + 1, 2):\\n if n % x == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08cee8ee7adddb537a8b7d371a94a6ce\",\n \"score\": \"0.8216398\",\n \"text\": \"def prime(n):\\n if n == 2:\\n return True\\n elif n < 2 or n % 2 == 0:\\n return False\\n else:\\n return not any(n % x == 0\\n for x in range(3, math.ceil(math.sqrt(n)) + 1, 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2ce1f52c9105aa1973c3712ae2bb925\",\n \"score\": \"0.82160985\",\n \"text\": \"def is_prime(n: int) -> bool:\\n # simple test for small n: 2 and 3 are prime, but 1 is not\\n if n <= 3:\\n return n > 1\\n\\n # check if multiple of 2 or 3\\n if n % 2 == 0 or n % 3 == 0:\\n return False\\n\\n # search for subsequent prime factors around multiples of 6\\n max_factor = int(math.sqrt(n))\\n for i in range(5, max_factor + 1, 6):\\n if n % i == 0 or n % (i + 2) == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2ce1f52c9105aa1973c3712ae2bb925\",\n \"score\": \"0.82160985\",\n \"text\": \"def is_prime(n: int) -> bool:\\n # simple test for small n: 2 and 3 are prime, but 1 is not\\n if n <= 3:\\n return n > 1\\n\\n # check if multiple of 2 or 3\\n if n % 2 == 0 or n % 3 == 0:\\n return False\\n\\n # search for subsequent prime factors around multiples of 6\\n max_factor = int(math.sqrt(n))\\n for i in range(5, max_factor + 1, 6):\\n if n % i == 0 or n % (i + 2) == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0820534545c1052093724d0be6a8cb0\",\n \"score\": \"0.82139057\",\n \"text\": \"def isprime(n):\\n if n == 2: return True\\n if n == 3: return True\\n if n % 2 == 0: return False\\n if n % 3 == 0: return False\\n\\n i = 5\\n w = 2\\n while i * i <= n:\\n if n % i == 0:\\n return False\\n\\n i += w\\n w = 6 - w\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09f7f61a6383ab12105f728487bdd534\",\n \"score\": \"0.82113105\",\n \"text\": \"def isprime(n):\\n if n % 2 == 0 and n > 2:\\n return False\\n for i in range(3, n):\\n if n % i == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0cccf3620b1352e5c60bf0346de7b01\",\n \"score\": \"0.82056844\",\n \"text\": \"def is_prime(n):\\n if n <= 1:\\n return False\\n m = 2\\n while m <= sqrt(n):\\n if n % m == 0:\\n return False\\n m += 1\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"635525dbae85557bdc340c8b9b10edc4\",\n \"score\": \"0.8201573\",\n \"text\": \"def is_prime(n):\\n\\n for i in range(2, int(n**(.5))+1):\\n if n % i == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41ac96a2995b84c5582172f7c60743c7\",\n \"score\": \"0.81952757\",\n \"text\": \"def is_prime(n):\\n if n < 2: return False\\n if n % 2 == 0: return n == 2\\n d = int(math.sqrt(n))\\n for k in range(3, d+1, 2):\\n if n % k == 0: return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7bafae931b71a704e193fea70413010\",\n \"score\": \"0.8193707\",\n \"text\": \"def is_prime(n):\\n if n < 2:\\n return False\\n for i in range(2, int(n**0.5) + 1):\\n if n % i == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af516e3c8b4214cefaaeb5e242c9e5de\",\n \"score\": \"0.8186336\",\n \"text\": \"def is_prime(n):\\r\\n end = int(sqrt(n))\\r\\n for i in range(2, end + 1):\\r\\n if n % i == 0:\\r\\n return False\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c278d5413913dd21598d0be7b8b5beeb\",\n \"score\": \"0.81617874\",\n \"text\": \"def is_prime(n):\\n if n < 2:\\n return False\\n\\n if n == 2:\\n return True\\n \\n if n % 2 == 0:\\n return False\\n \\n for i in range(3, n):\\n if n % i == 0:\\n return False\\n \\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc24c2eda47040b95d6de2a18c0f451a\",\n \"score\": \"0.81556326\",\n \"text\": \"def is_prime(n):\\n for i in range(2,int(abs(n)**0.5)+1):\\n if(n%i == 0):\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b4342cdcfc8e1b7e350391ff6fdd457\",\n \"score\": \"0.8154981\",\n \"text\": \"def is_prime(n):\\n if n < 2:\\n return False\\n i = 2\\n while i*i <= n:\\n if n % i == 0:\\n return False\\n i = i + 1\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e4c66c8caa96007d8a7ed30223725ce\",\n \"score\": \"0.81473047\",\n \"text\": \"def isPrime(n):\\n n = abs(n)\\n if n < 2:\\n return False\\n if n == 2:\\n return True\\n if not n % 1 == 0:\\n return False\\n if n % 2 == 0:\\n return False\\n for x in range(3, round(math.sqrt(n)) + 1, 2):\\n if n % x == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e4c66c8caa96007d8a7ed30223725ce\",\n \"score\": \"0.81473047\",\n \"text\": \"def isPrime(n):\\n n = abs(n)\\n if n < 2:\\n return False\\n if n == 2:\\n return True\\n if not n % 1 == 0:\\n return False\\n if n % 2 == 0:\\n return False\\n for x in range(3, round(math.sqrt(n)) + 1, 2):\\n if n % x == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78c57cc85d45fb203d479ef719ae28e9\",\n \"score\": \"0.81402576\",\n \"text\": \"def isprime(n):\\n # range starts with 2 and only needs to go up the squareroot of n\\n if n % 2 == 0:\\n return False\\n for x in range(3, int(n**0.5)+1,2):\\n if n % x == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0718b32ecb5751a279a2c58ec2ec1cf7\",\n \"score\": \"0.8138055\",\n \"text\": \"def is_prime(n):\\n if n == 2:\\n return True\\n if n == 3:\\n return True\\n if n % 2 == 0:\\n return False\\n if n % 3 == 0:\\n return False\\n\\n i = 5\\n w = 2\\n while i*i <= n:\\n if n % i == 0:\\n return False\\n i += w\\n w = 6 - w\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54ae235b788cc942b94ba04ca81c3c9d\",\n \"score\": \"0.8134483\",\n \"text\": \"def is_prime(n):\\n for i in range(2, int(n ** 0.5) + 1):\\n if not n % i:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6809b1cd33dd1d0ebe49e0e30145952f\",\n \"score\": \"0.8124355\",\n \"text\": \"def is_prime(n):\\n prime = True # assume prime until proven otherwise\\n\\n for i in range(2, int(sqrt(n) + 1)):\\n if n % i == 0: # factor found -> not prime\\n prime = False\\n break\\n\\n return prime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1e711a66dbcaeb4c4f5cd3fef7cac5c\",\n \"score\": \"0.81242764\",\n \"text\": \"def is_prime_v2(n):\\n if n == 1:\\n return False\\n\\n max_divisor = math.floor(math.sqrt(n))\\n for d in range(2, 1 + max_divisor):\\n if n % d == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d81ef705192336ab08758c7cb15039d\",\n \"score\": \"0.81232667\",\n \"text\": \"def isPrime(n):\\n\\n if n == 0 or n == 1:\\n return False\\n\\n for div in range(2, int(n**0.5)+1):\\n if not n % div:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"148222020cd78bf2d22c5e4326000805\",\n \"score\": \"0.81167865\",\n \"text\": \"def is_prime(n):\\n if n == 2:\\n return True\\n if n == 3:\\n return True\\n if n % 2 == 0:\\n return False\\n if n % 3 == 0:\\n return False\\n\\n i = 5\\n w = 2\\n\\n while i * i <= n:\\n if n % i == 0:\\n return False\\n\\n i += w\\n w = 6 - w\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c5a33abd4d265953025486942673076\",\n \"score\": \"0.81161934\",\n \"text\": \"def isPrime(n):\\n\\tif n <= 1: return False\\n\\telif n < 4: return True #2, 3\\n\\telif n % 2 == 0: return False\\n\\telif n < 9: return True #5, 7\\n\\telif n % 3 == 0: return False\\n\\telse:\\n\\t\\t#make use of the fact that all primes > 5 have have the form 6k +- 1\\n\\t\\tr = math.floor(math.sqrt(n))\\n\\t\\tf = 5\\n\\t\\twhile f <= r:\\n\\t\\t\\tif n % f == 0: return False\\n\\t\\t\\tif n % (f + 2) == 0: return False\\n\\t\\t\\tf += 6\\n\\t\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18968f383dac5e1e0b0a60d0bb0988ba\",\n \"score\": \"0.80963194\",\n \"text\": \"def is_prime(n):\\n if n < 2:\\n return False\\n if n == 2:\\n return True\\n d = 2\\n while d <= int(math.sqrt(n)) + 1:\\n if n % d == 0:\\n return False\\n d += 1\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40d0e26c0162295a4d5cec18cca267db\",\n \"score\": \"0.8092706\",\n \"text\": \"def isprime(n):\\n return len(factors(n)) == 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bda83ee0a8ba13b9b662c98c048c8736\",\n \"score\": \"0.80838317\",\n \"text\": \"def isprime(n):\\n if n == 2:\\n return True\\n if n == 3:\\n return True\\n if n % 2 == 0:\\n return False\\n if n % 3 == 0:\\n return False\\n\\n i = 5\\n w = 2\\n\\n while i * i <= n:\\n if n % i == 0:\\n return False\\n\\n i += w\\n w = 6 - w\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f088fa3e414e6a8725d83eb028394b2\",\n \"score\": \"0.8081018\",\n \"text\": \"def is_prime_v1(n):\\n if n == 1:\\n return False\\n\\n for d in range(2, n):\\n if n % d == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae0faea9835cf4d2c6ce4970efa17b8f\",\n \"score\": \"0.8067031\",\n \"text\": \"def is_prime(n):\\n def divisible_by_known_prime(n):\\n for x in known_primes:\\n if divides(x, n):\\n return x\\n return False\\n\\n # Quick filters\\n if n < 2:\\n return False\\n elif n in known_primes:\\n return True\\n elif divisible_by_known_prime(n):\\n return False\\n else:\\n # Start searching again at the max known prime\\n start = known_primes[-1] + 2\\n for y in xrange(start, int(sqrt(n)), 2):\\n if divisible_by_known_prime(y):\\n continue\\n else:\\n if n % y == 0:\\n return False\\n known_primes.append(y)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"464021a33de3106c49185f714bf98982\",\n \"score\": \"0.8051832\",\n \"text\": \"def test_prime(n):\\n if n==2:\\n return True\\n #we only need to check divisors up to square root of a number\\n for i in range(2,int(n**(.5))+1):\\n if n % i ==0:\\n return False\\n else:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c6829a46657473e9890c8e4a7c737a3\",\n \"score\": \"0.8046635\",\n \"text\": \"def isprime(n):\\n\\n if n == 2:\\n return True\\n if n == 3:\\n return True\\n if n % 2 == 0:\\n return False\\n if n % 3 == 0:\\n return False\\n i = 5\\n w = 2\\n while i * i <= n:\\n if n % i == 0:\\n return False\\n i += w\\n w = 6 - w\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c6829a46657473e9890c8e4a7c737a3\",\n \"score\": \"0.8046635\",\n \"text\": \"def isprime(n):\\n\\n if n == 2:\\n return True\\n if n == 3:\\n return True\\n if n % 2 == 0:\\n return False\\n if n % 3 == 0:\\n return False\\n i = 5\\n w = 2\\n while i * i <= n:\\n if n % i == 0:\\n return False\\n i += w\\n w = 6 - w\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e993486b8ff4626cd7a77a6f4541959e\",\n \"score\": \"0.80411047\",\n \"text\": \"def is_prime_number(n:int) -> bool:\\r\\n\\r\\n i = 0\\r\\n is_prime = True\\r\\n\\r\\n if (n == 2 or n == 3):\\r\\n return True\\r\\n while(i < len(_prime_list)):\\r\\n if (not n % _prime_list[i] and is_prime):\\r\\n is_prime = False\\r\\n return is_prime\\r\\n i += 1\\r\\n if (is_prime):\\r\\n _prime_list.append(n)\\r\\n return is_prime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc04899d0ad229677ff3b7c4f040ac5d\",\n \"score\": \"0.8024999\",\n \"text\": \"def isPrime(n):\\n Dmax = scipy.sqrt(n)\\n if n == 2:\\n return True\\n if isEven(n):\\n return False\\n d = 3\\n while n%d != 0 and d <= Dmax:\\n d += 2\\n return d > Dmax\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5a485022c94922bb7f2ae2c0922016c\",\n \"score\": \"0.7937437\",\n \"text\": \"def is_prime(n):\\n test_vals = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37]\\n if n in test_vals:\\n return True\\n d = n - 1\\n s = 0\\n while not d & 1:\\n d = d >> 1\\n s += 1\\n for a in test_vals:\\n for r in range(0, s):\\n if (a ** (d * (1 << r))) % n != (n - 1) \\\\\\n and (a ** d) % n != 1:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a6588f6a60f59af54b5b30212aef90f\",\n \"score\": \"0.78840125\",\n \"text\": \"def is_prime(n):\\n if n < 2 :\\n return False\\n\\n # OFF BY ONE Error, solution is to add +1 \\n #for i in range (2, int(math.sqrt(n)) + 1 ):\\n for i in range (2, int(math.sqrt(n)) ):\\n if n % i == 0 :\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c240bfae6683bf9bd96ef3272a3f9b7\",\n \"score\": \"0.78501946\",\n \"text\": \"def prime_number(n):\\n\\n if n > 1:\\n\\n for divisor in range(2,n):\\n\\n if n%divisor == 0:\\n #if n and divisor cannot complete full division, n is not a \\n #prime number\\n\\n return False\\n\\n return True\\n\\n else:\\n #negative number is not prime number\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9168442a99017fb7004b5deea6142afb\",\n \"score\": \"0.7763757\",\n \"text\": \"def is_prime(n):\\n \\n if n == 2:\\n return True\\n if n < 2 or n % 2 == 0:\\n return False\\n \\n trail_factor = 3\\n \\n root = sqrt(n)\\n \\n while trail_factor <= root:\\n if n % trail_factor == 0:\\n return False\\n trail_factor += 2 \\n \\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e931ff2a59cbd8806ee6f819d4efc108\",\n \"score\": \"0.77405703\",\n \"text\": \"def is_prime(n):\\n def prime_helper(index):\\n if index == n:\\n return True\\n elif n % index == 0 or n == 1:\\n return False\\n else:\\n return prime_helper(index + 1)\\n return prime_helper(2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ad782a50c1962258b23b6d5a2dfd97c\",\n \"score\": \"0.7739098\",\n \"text\": \"def primality(n):\\n if n == 1:\\n return NOT_PRIME\\n return PRIME if all(n % i for i in range(2, int(sqrt(n)) + 1)) else NOT_PRIME\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2440dc2a1445e687d40f229c33daa7cd\",\n \"score\": \"0.77390164\",\n \"text\": \"def isprime(n):\\r\\n if n % 3 == 0:\\r\\n return (False, 3)\\r\\n\\r\\n i = 5\\r\\n w = 2\\r\\n\\r\\n while i * i <= n:\\r\\n if n % i == 0:\\r\\n return (False, i)\\r\\n\\r\\n i += w\\r\\n w = 6 - w\\r\\n\\r\\n return (True, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1373ba4fdce84a62e9becedccf9c9fad\",\n \"score\": \"0.7735379\",\n \"text\": \"def is_prime(n):\\n prime = True\\n d = 2\\n while prime and d <= round(n**(1/2)):\\n if n%d != 0:\\n d += 1\\n else:\\n prime = False\\n return prime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fb53f51042964c6e86ff53aeb84cd0f\",\n \"score\": \"0.76981086\",\n \"text\": \"def is_prime_sieve(x):\\n\\n if (x <= 1):\\n return False\\n elif (x <= 3):\\n return True\\n x_mod_6 = x % 6\\n if ((x_mod_6 != 1) and (x_mod_6 != 5)):\\n return False\\n\\n sqrt_x = int_sqrt(x)\\n for i in generate_primes(sqrt_x):\\n if ((x % i) == 0):\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebc0590b9895849fc3e3f49891941a7f\",\n \"score\": \"0.7671711\",\n \"text\": \"def is_prime(n):\\n\\n # (This is used to study the risk of false positives:)\\n global miller_rabin_test_count\\n\\n miller_rabin_test_count = 0\\n\\n if n <= smallprimes[-1]:\\n if n in smallprimes:\\n return True\\n else:\\n return False\\n\\n if gcd(n, 2 * 3 * 5 * 7 * 11) != 1:\\n return False\\n\\n # Choose a number of iterations sufficient to reduce the\\n # probability of accepting a composite below 2**-80\\n # (from Menezes et al. Table 4.4):\\n\\n t = 40\\n n_bits = 1 + int(math.log(n, 2))\\n for k, tt in (\\n (100, 27),\\n (150, 18),\\n (200, 15),\\n (250, 12),\\n (300, 9),\\n (350, 8),\\n (400, 7),\\n (450, 6),\\n (550, 5),\\n (650, 4),\\n (850, 3),\\n (1300, 2),\\n ):\\n if n_bits < k:\\n break\\n t = tt\\n\\n # Run the test t times:\\n\\n s = 0\\n r = n - 1\\n while (r % 2) == 0:\\n s = s + 1\\n r = r // 2\\n for i in xrange(t):\\n a = smallprimes[i]\\n y = pow(a, r, n)\\n if y != 1 and y != n - 1:\\n j = 1\\n while j <= s - 1 and y != n - 1:\\n y = pow(y, 2, n)\\n if y == 1:\\n miller_rabin_test_count = i + 1\\n return False\\n j = j + 1\\n if y != n - 1:\\n miller_rabin_test_count = i + 1\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07e51bd8d669673e6777f4dbb96d36b8\",\n \"score\": \"0.76641953\",\n \"text\": \"def is_prime(n):\\n\\n # Checks if given a list...\\n if type(n) == list:\\n if_prime_list = [False, ] * len(n)\\n c = 0\\n for i in n:\\n if is_prime(i):\\n if_prime_list[c] = True\\n c += 1\\n # and returns one with weather or not it's prime in place\\n return if_prime_list\\n\\n # When a single number ..\\n\\n if n < 2:\\n return False\\n if n == 2 or n == 3:\\n return True\\n\\n for i in range(2, int(sqrt(n))+1):\\n if n % i == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b167f7b47ee5341083441f6a31a32272\",\n \"score\": \"0.7655478\",\n \"text\": \"def isPrime(n):\\n\\n if n % 2 == 0:\\n return n == 2\\n d = 3\\n while d * d <= n and n % d != 0:\\n d += 2\\n return d * d > n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0be316dbe44f91fe2b2e34bac2ceba31\",\n \"score\": \"0.7647462\",\n \"text\": \"def is_prime_cacheless(n):\\n if n < 2:\\n return False\\n for i in xrange(2, int(math.sqrt(n)) + 1):\\n if n % i == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5809882f1b0d4c24bf26aadb26e9c4cb\",\n \"score\": \"0.7642497\",\n \"text\": \"def is_superprime(n):\\n while n > 0:\\n if not is_prime(n):\\n return False\\n n = n // 10\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f9804bea1d4da046f3722898d4b15ae\",\n \"score\": \"0.7615486\",\n \"text\": \"def is_prime_exact(n):\\n if n <= 3:\\n return n >= 2\\n if n % 2 == 0 or n % 3 == 0:\\n return False\\n for i in range(5, int(n ** 0.5) + 1, 6):\\n if n % i == 0 or n % (i + 2) == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5259c60cb0e66026bd62a7cf1ba6f28e\",\n \"score\": \"0.76105756\",\n \"text\": \"def is_prime(number: int) -> bool:\\n prop_div = proper_divisors(number)\\n return len(prop_div) == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e333a6a531610d72ac921f35578efc7\",\n \"score\": \"0.76068914\",\n \"text\": \"def is_prime_v3(n):\\n if n == 1:\\n return False\\n\\n # If it's even and not 2, then it's not prime\\n if n == 2:\\n return True\\n if n > 2 and n % 2 == 0:\\n return False\\n\\n max_divisor = math.floor(math.sqrt(n))\\n for d in range(3, 1 + max_divisor, 2):\\n if n % d == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e333a6a531610d72ac921f35578efc7\",\n \"score\": \"0.76068914\",\n \"text\": \"def is_prime_v3(n):\\n if n == 1:\\n return False\\n\\n # If it's even and not 2, then it's not prime\\n if n == 2:\\n return True\\n if n > 2 and n % 2 == 0:\\n return False\\n\\n max_divisor = math.floor(math.sqrt(n))\\n for d in range(3, 1 + max_divisor, 2):\\n if n % d == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd441534aae1f3e8f4a3213bf868fdb0\",\n \"score\": \"0.75916487\",\n \"text\": \"def is_prime(n):\\n\\n # (This is used to study the risk of false positives:)\\n global miller_rabin_test_count\\n\\n miller_rabin_test_count = 0\\n\\n if n <= smallprimes[-1]:\\n if n in smallprimes:\\n return True\\n else:\\n return False\\n\\n if gcd(n, 2 * 3 * 5 * 7 * 11) != 1:\\n return False\\n\\n # Choose a number of iterations sufficient to reduce the\\n # probability of accepting a composite below 2**-80\\n # (from Menezes et al. Table 4.4):\\n\\n t = 40\\n n_bits = 1 + int(math.log(n, 2))\\n for k, tt in ((100, 27),\\n (150, 18),\\n (200, 15),\\n (250, 12),\\n (300, 9),\\n (350, 8),\\n (400, 7),\\n (450, 6),\\n (550, 5),\\n (650, 4),\\n (850, 3),\\n (1300, 2),\\n ):\\n if n_bits < k:\\n break\\n t = tt\\n\\n # Run the test t times:\\n\\n s = 0\\n r = n - 1\\n while (r % 2) == 0:\\n s = s + 1\\n r = r // 2\\n for i in range(t):\\n a = smallprimes[i]\\n y = modular_exp(a, r, n)\\n if y != 1 and y != n - 1:\\n j = 1\\n while j <= s - 1 and y != n - 1:\\n y = modular_exp(y, 2, n)\\n if y == 1:\\n miller_rabin_test_count = i + 1\\n return False\\n j = j + 1\\n if y != n - 1:\\n miller_rabin_test_count = i + 1\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31c7eb72160691d2a58b025131a986ac\",\n \"score\": \"0.758809\",\n \"text\": \"def is_prime(n):\\n r = 0\\n d = n - 1\\n while d % 2 == 0:\\n r += 1\\n d //= 2\\n\\n for _ in range(40):\\n a = random.randint(2, n - 2)\\n x = pow(a, d, n)\\n if x == 1 or x == n - 1:\\n continue\\n \\n for _ in range(r - 1):\\n x = pow(x, 2, n)\\n if x == n - 1:\\n break\\n\\n else:\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b537c6a55e7ef96df0d686053d637c4\",\n \"score\": \"0.75501156\",\n \"text\": \"def is_prime(i, primes):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fbbb15cd7dd7e9b4d3490d51da7e317\",\n \"score\": \"0.74986815\",\n \"text\": \"def is_prime(number):\\n for i in range(2, number):\\n if number % i == 0:\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3e14a0482f5920322f17e25db1e4d4f\",\n \"score\": \"0.7495828\",\n \"text\": \"def is_prime(n, prime_numbers = []):\\n if n < 2:\\n return False\\n\\n if len(prime_numbers) == 0 or prime_numbers[-1] < n:\\n prime_numbers = _generate_primes(n + 1, prime_numbers)\\n\\n for p in prime_numbers:\\n if n < p:\\n return False\\n if n == p:\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83d11c11d34ac275d0984b8b705ea571\",\n \"score\": \"0.7481891\",\n \"text\": \"def is_prime(number):\\r\\n if number <= 0:\\r\\n return False\\r\\n elif number > 2:\\r\\n for i in range(2, number):\\r\\n if number % i == 0:\\r\\n return False\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bbe1ee06cee20708acd22de4008554f\",\n \"score\": \"0.7473216\",\n \"text\": \"def prime(n):\\n # https://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n-in-python/3035188#3035188\\n sieve = np.ones(int(n/3 + (n%6==2)), dtype=np.bool)\\n sieve[0] = False\\n for i in range(int(n**0.5/3+1)):\\n if sieve[i]:\\n k=3*i+1|1\\n sieve[ int((k*k)/3) ::2*k] = False\\n sieve[int((k*k+4*k-2*k*(i&1))/3)::2*k] = False\\n return np.r_[2,3,((3*np.nonzero(sieve)[0]+1)|1)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"589dda1ed65ef55d34a2407a75760f86\",\n \"score\": \"0.74541706\",\n \"text\": \"def is_prime(num):\\r\\n for n in range(2, num):\\r\\n if num % n == 0:\\r\\n return False\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"557caac191fadc8d79ddadae1a0b768c\",\n \"score\": \"0.74398\",\n \"text\": \"def _is_prime(number):\\n if number <= 1:\\n return False\\n\\n divisor = 2\\n while divisor < number:\\n if ((number % divisor) == 0) or (divisor > (number / 2)):\\n return False\\n divisor += 1\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07f0a11ff3c7fc1e8870986532afedf0\",\n \"score\": \"0.7438905\",\n \"text\": \"def is_prime(number: int) -> bool:\\n min_divisor = 2\\n max_divisor = math.ceil(math.sqrt(number))\\n for divisor in range(min_divisor, max_divisor + 1):\\n if number % divisor == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b7752fe5a1cf97814e82aa201face01\",\n \"score\": \"0.7425838\",\n \"text\": \"def probable_prime(n):\\n return pow(2, n-1, n) == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89e87a69f7b9cd088605a16cdbb01c27\",\n \"score\": \"0.74213374\",\n \"text\": \"def is_prime_v1(n):\\n if n == 1:\\n return False # 1 is not prime (1 is not composite, either)\\n # d stands for divisor. Since any number greater than 1 (prime or composite) can be exactly divided by 1 and itself,\\n # we skip 1 and n and only iterate over the items in the sequence from 2 through n - 1 (Remember in the range()\\n # function the stop value is not included.)\\n for d in range(2, n):\\n # % is the modulus operator. The right-hand side is the remainder of the operation of division of n by d.\\n # If n is divisible by d, the remainder is equal to 0; if not, the remainder is not equal to 0.\\n # So the test_expression of n % d == 0 means n is divisible by d. If the test_expression is True, control of the\\n # program flows to the body of if and execute the return statement. The function exits and meanwhile the boolean\\n # value of False is returned.\\n if n % d == 0:\\n return False\\n # If each items in the sequence generated from the range doesn't make the test_expression True, meaning n is not\\n # divisible by any smaller number, then we can consider n is prime and return True after the items in the sequence\\n # exhaust without the for loop being stopped halfway.\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"460b8ab60da7496aa79fef6e6731ca9f\",\n \"score\": \"0.74172294\",\n \"text\": \"def isPrime(p):\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6248ff0357b4b512961650451529bc9c\",\n \"score\": \"0.7411656\",\n \"text\": \"def is_prime(number: int) -> bool:\\n\\n if 1 < number < 4:\\n # 2 and 3 are primes\\n return True\\n elif number < 2 or number % 2 == 0 or number % 3 == 0:\\n # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes\\n return False\\n\\n # All primes number are in format of 6k +/- 1\\n for i in range(5, int(math.sqrt(number) + 1), 6):\\n if number % i == 0 or number % (i + 2) == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d50325cb8c5ccefa49472a84ce23913\",\n \"score\": \"0.7410716\",\n \"text\": \"def is_prime(number):\\n if number <= 1:\\n return False\\n if number == 2:\\n return True\\n else:\\n maximum = int(math.sqrt(number)) + 1\\n for i in range(2, maximum):\\n if number % i == 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2b3903b7ed71035196f37c9778c5e81\",\n \"score\": \"0.74050426\",\n \"text\": \"def check_prime(number):\\n if number in primes:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a3617d3ebdd114f5eeefb2a8c559fad\",\n \"score\": \"0.73954546\",\n \"text\": \"def primes(n):\\n sieve = [True] * n\\n for i in range(3,int(n**0.5)+1,2): #in py3, range and xrange are same\\n if sieve[i]:\\n sieve[i*i::2*i]=[False]*((n-i*i-1)//(2*i)+1)\\n return [2] + [i for i in range(3,n,2) if sieve[i]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d7593a29f9a46b123950c8a508ec279\",\n \"score\": \"0.7380684\",\n \"text\": \"def is_prime(number: int) -> bool:\\r\\n\\r\\n if 1 < number < 4:\\r\\n # 2 and 3 are primes\\r\\n return True\\r\\n elif number < 2 or number % 2 == 0 or number % 3 == 0:\\r\\n # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes\\r\\n return False\\r\\n\\r\\n # All primes number are in format of 6k +/- 1\\r\\n for i in range(5, int(math.sqrt(number) + 1), 6):\\r\\n if number % i == 0 or number % (i + 2) == 0:\\r\\n return False\\r\\n return True\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":797,"cells":{"query_id":{"kind":"string","value":"c4d430e66bb3d21f6d5a3c0b14362f75"},"query":{"kind":"string","value":"Gets the EMD language between the two languages"},"positive_passages":{"kind":"list like","value":[{"docid":"62965b2c20affbafeeb5c0cc7a3c475e","score":"0.0","text":"def apply(lang1, lang2, variant=Variants.PYEMD, parameters=None):\n return exec_utils.get_variant(variant).apply(lang1, lang2, parameters=parameters)","title":""}],"string":"[\n {\n \"docid\": \"62965b2c20affbafeeb5c0cc7a3c475e\",\n \"score\": \"0.0\",\n \"text\": \"def apply(lang1, lang2, variant=Variants.PYEMD, parameters=None):\\n return exec_utils.get_variant(variant).apply(lang1, lang2, parameters=parameters)\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"7d19c984c03b3e59c0e29927e52e446b","score":"0.6914142","text":"def get_language(self):","title":""},{"docid":"38cf8daef8d0e9165e88916da0f08868","score":"0.67045707","text":"def getLanguage(self) -> ghidra.program.model.lang.Language:\n ...","title":""},{"docid":"88efbc8344a0bc887062d526ad6868f2","score":"0.6524406","text":"def Language(self) -> int:","title":""},{"docid":"25f46e6f2f129aea0b62e208273c88ba","score":"0.64493454","text":"def get_translation(language):","title":""},{"docid":"92e349e11ef96d37ee720c9e44ff81c6","score":"0.6397181","text":"def language(self):\n return self._mapped_lang","title":""},{"docid":"83cfc614d26d41e5c5b4cd9dcfed6d54","score":"0.6361355","text":"def get_translated_languages():","title":""},{"docid":"72af2abe9ac887512114fc6b329f6a58","score":"0.6298945","text":"def language(self):\n return getattr(\n self,\n constants.CONST_LANGUAGE,\n constants.DEFAULT_LANGUAGE,\n )","title":""},{"docid":"7374b79e2ca26f3f6ef3c6f14ae42a97","score":"0.62752056","text":"def getLanguage (self,c,setting):\n \n language = self.getString(c,setting)\n # g.trace(setting,language)\n \n return language","title":""},{"docid":"d018b5936457ce548d59d069e02048a5","score":"0.626775","text":"def get_language(event, context):\n logger.info('Calling get_language with event')\n logger.info(event)\n\n # Use UTF-8 encoding for comprehend\n if 'Text' in event:\n text = str(event['Text'])\n else:\n logger.error(\"There is no Text!\")\n raise Exception('Please provide Text!')\n\n # comprehend accepts up to 5000 UTF-8 encoded characters\n if len(text) >= 4900:\n text = text[:4899]\n elif len(text) < 20:\n logger.warning(\"Text is shorter than 20 characters!\")\n raise Exception('Please provide a text of at least 20 characters!')\n # logger.info(text)\n\n try:\n response = comprehend.detect_dominant_language(Text=text)\n except ClientError as e:\n logger.error(\"Received error: %s\", e, exc_info=True)\n raise\n except ParamValidationError as e:\n logger.error(\"The provided parameters are incorrect: %s\", e, exc_info=True)\n raise\n\n # logger.info(response)\n return response['Languages'][0]['LanguageCode']","title":""},{"docid":"91f4df655801a0f81560021370fb0325","score":"0.6215751","text":"def language(self):\n try:\n return self._get_response(\n url=self.LANGID_URL, sentence=self.sentence)\n except:\n return None","title":""},{"docid":"a6852d214fd73ca3c8b3c76ed0880cbe","score":"0.62144786","text":"def lang(self):\n # pick a sample to find language (don't pick the truth file)\n sample = [choose for choose in os.listdir(self.folder)\n if 'truth' not in choose][0]\n with open(os.path.join(self.folder, sample), 'r') as sample_file:\n first_line = sample_file.readlines()[0]\n lang = [match.group('lang')\n for match in re.finditer(Pan.LANG_REGEX, first_line)][0]\n return lang.lower()","title":""},{"docid":"64b70a35a3529c04521ca03c1783af78","score":"0.62131405","text":"def language(self):\n return self.language_version()[0]","title":""},{"docid":"762ed9baf7428d3c2c27c92906c3f34d","score":"0.61848724","text":"def language(self):\n lang=None\n if self.__dict__['TAG:language']:\n lang=self.__dict__['TAG:language']\n return lang","title":""},{"docid":"d9442ebc089fe4982af945a7c4eee87f","score":"0.6183641","text":"def lang(self):\n return self.lex_meta.lang","title":""},{"docid":"ff6c3d8673b836167af732a1190347c6","score":"0.617456","text":"def test_lang() -> str:\n lang = locale.getdefaultlocale()[0][0:2]\n if lang == 'fr':\n return 'dtc'\n return 'qdb'","title":""},{"docid":"86b093671c104aa8770479841f816a10","score":"0.615984","text":"def gncEmployeeGetLanguage(*args):\n return _gnucash_core_c.gncEmployeeGetLanguage(*args)","title":""},{"docid":"036561cb4fe47e070a4c9ddc370ac2e2","score":"0.60845643","text":"def get_language(self):\n\n return self.__msg_lang","title":""},{"docid":"99a60ae13aaed279959529adedcdb43d","score":"0.6080517","text":"def Language (self):\n return (self.headers.get ('index_language', 'any'),\n self.headers.get ('contents_language', 'any'))","title":""},{"docid":"1790cd720d52235c595dab8ffb7999cd","score":"0.6073051","text":"def get_lang():\n if os() == 'Windows':\n # Windows\n lang = locale.windows_locale[ctypes.windll.kernel32.GetUserDefaultUILanguage()]\n else:\n # Linux || MacOS\n lang = locale.getlocale(locale.LC_MESSAGES)[0]\n return lang","title":""},{"docid":"8f04f28b59d21d0f9e23d1b811974cc3","score":"0.6020242","text":"def moment_lang(self):\n return load_moment_locales().get(self.lang, 'en')","title":""},{"docid":"e019d75c7257253db6436ddc0730b7da","score":"0.5989116","text":"def lang(self):\n return self._lang","title":""},{"docid":"3065207a9592be2eab56e31bb0e0f886","score":"0.59747165","text":"def get_language(self):\n locale = self.get_locale()\n language = locale\n if '_' in locale:\n language = locale[0:locale.index('_')]\n return language","title":""},{"docid":"5123c9ed66643d7c0daa4ede7f2b7ffc","score":"0.5971464","text":"def check_language(text):\n if detect(text) == 'de':\n language = 'de'\n elif detect(text) == 'en':\n language = 'en'\n else:\n language = None\n return language","title":""},{"docid":"65d1e9eae9ab232b88af6f359a4eb6c0","score":"0.596331","text":"def language_tag(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"language_tag\")","title":""},{"docid":"6c4045103df8f4bf07a4b594e60c7fc2","score":"0.5960778","text":"def get_locale():\r\n # this is a demo case, we use url to get locale\r\n code = request.args.get('lang', 'en')\r\n return code","title":""},{"docid":"3250b795723d483231050f75ffc0f981","score":"0.59486884","text":"def language(self):\n return self.__language","title":""},{"docid":"3250b795723d483231050f75ffc0f981","score":"0.59486884","text":"def language(self):\n return self.__language","title":""},{"docid":"3250b795723d483231050f75ffc0f981","score":"0.59486884","text":"def language(self):\n return self.__language","title":""},{"docid":"12f6c63ad26a4e47296bddee9b9653f2","score":"0.5934641","text":"def lang_(self):\n return self.doc.vocab.store[self.lex_meta.lang]","title":""},{"docid":"216919c5a4cf9279f362cc048048645b","score":"0.5924688","text":"def get_main_language(project_report):\n required1 = {\"MetaDataCollector\": {\"main_language\": \"\"}}\n required2 = {\"LanguageDetector\": {\"main_language\": \"\"}}\n\n if containedStructure(required1, project_report):\n lang = project_report[\"MetaDataCollector\"][\"main_language\"]\n return shorten_language(lang)\n elif containedStructure(required2, project_report):\n lang = project_report[\"LanguageDetector\"][\"main_language\"]\n return shorten_language(lang)\n return '-'","title":""},{"docid":"3125c2dce33044e62c80ca613fa15952","score":"0.59202147","text":"def convert_language(language):\n\n if language == 'StoreKing_NW':\n language = 'Kannada'\n if language not in LANGUAGES:\n language = 'Hindi'\n\n return language","title":""},{"docid":"8ce21ad58cb3eab50ede578a17775fc6","score":"0.59194607","text":"def get_locale():\n if \"lang\" in session:\n return session[\"lang\"]\n #Decides the most suitable language option\n #fi=Finnish has higher priority\n lang = request.accept_languages.best_match([\"fi\", \"en\"])\n return lang","title":""},{"docid":"fb14f5c2099e436683e91382ce62e904","score":"0.59086585","text":"def language_code(self):\n raise NotImplementedError()","title":""},{"docid":"71d7bb6cef50f1f302e8a531796e5ef0","score":"0.590221","text":"def __get_language__(self, lang_code: str) -> str:\n\n if lang_code not in self.ALLOWED_LANGUAGES.keys():\n raise ValueError('{} is not a supported language code'.format(lang_code))\n\n return self.ALLOWED_LANGUAGES[lang_code]","title":""},{"docid":"c660a06708cf14ec63c36cbcdc98b5f0","score":"0.5900211","text":"def get_lang(country_iso: str) -> str:\n res = country_lang.get(country_iso)\n if res:\n return res\n else:\n return 'en'","title":""},{"docid":"f71b058ed50499dc5964b614bdebe35c","score":"0.5899539","text":"def get_lang(self, language):\n if language not in self.compilers:\n self.console.print(\"Language is not supported!\", style=\"bold red\")\n Utils.close()\n return language","title":""},{"docid":"31f035ca7232dd83ae68d0fd8680c087","score":"0.5898755","text":"def getAvailableLanguages():\n\t#Make a list of all the locales found in NVDA's locale dir\n\tl=[x for x in os.listdir(\"locales\") if not x.startswith('.')]\n\tl=[x for x in l if os.path.isfile('locales/%s/LC_MESSAGES/twblue-documentation.mo' % x)]\n\t#Make sure that en (english) is in the list as it may not have any locale files, but is default\n\tif 'en' not in l:\n\t\tl.append('en')\n\t\tl.sort()\n\t#For each locale, ask Windows for its human readable display name\n\td=[]\n\tfor i in l:\n\t\tdesc=getLanguageDescription(i)\n\t\tlabel=\"%s, %s\"%(desc,i) if desc else i\n\t\td.append(label)\n\t#include a 'user default, windows' language, which just represents the default language for this user account\n\tl.append(\"system\")\n\t# Translators: the label for the Windows default NVDA interface language.\n\td.append(_(\"User default\"))\n\t#return a zipped up version of both the lists (a list with tuples of locale,label)\n\treturn zip(l,d)","title":""},{"docid":"f8a9d1153b9d1d09c331da8bf1f45873","score":"0.5883816","text":"def get_language(self):\n return self._language","title":""},{"docid":"29738bd87cc78dd4e2ff5aa73bc07f2f","score":"0.58760846","text":"def lang(self):\n return self.config_core.get('lang')","title":""},{"docid":"d7cf60544184706f9f81cdc9296ec99b","score":"0.5870653","text":"def language(self) -> Optional[Text]:\n\n return self.get(\"language\")","title":""},{"docid":"49d004470495efbf7e4526a851502aa6","score":"0.58666897","text":"def l_get(\n language_dict: Dict[str, Type[T_Lang]], update_msg_user_or_language_code: T_update_msg_user_or_language_code = None\n) -> Type[T_Lang]:\n lang = get_language_code(update_msg_user_or_language_code)\n\n # if it is None (default, or getting from message failed), use the default\n if lang is None:\n return language_dict['default']\n # end if\n\n if lang in language_dict:\n return language_dict[lang]\n # end if\n\n # try replacing \"de_DE\" => \"de-DE\"\n part = lang.replace(\"_\", \"-\")\n if part != lang and part in language_dict:\n return language_dict[part]\n # end of\n\n # try splitting it \"de-DE\" => \"de\"\n part = lang.split('-')[0]\n if part != lang and part in language_dict:\n return language_dict[part]\n # end of\n\n # try splitting it \"de_DE\" => \"de\"\n part = lang.split('_')[0]\n if part != lang and part in language_dict:\n return language_dict[part]\n # end if\n\n # nothing did match, use the default\n return language_dict['default']","title":""},{"docid":"8235dc2fa6170ed344df28e1700dbbc2","score":"0.5864678","text":"def getLanguage(self):\n spd = self.get(sessionPkgDataId, {})\n return spd.get('language', None)","title":""},{"docid":"16a9b23a1950dc54cc078c8a113ee638","score":"0.58440614","text":"def language(self) -> LangEnum:\n return self._lang_enum","title":""},{"docid":"e305eccb0d97870a645edc54619fa11d","score":"0.58301246","text":"def get_language(self):\n # Look through ancestors of this page for its language homepage\n # The language homepage is located at depth 3\n language_homepage = self.get_ancestors(inclusive=True).get(depth=3)\n\n # The slug of language homepages should always be set to the language code\n return language_homepage.slug","title":""},{"docid":"01a7c30c4fb5b54b7e1163687d05331e","score":"0.58280015","text":"def get_keyboard_language():\n # only language not variant layout\n user32 = ctypes.WinDLL('user32', use_last_error=True)\n curr_window = user32.GetForegroundWindow()\n thread_id = user32.GetWindowThreadProcessId(curr_window, 0)\n klid = user32.GetKeyboardLayout(thread_id)\n lid = klid & (2 ** 16 - 1)\n lid_hex = f\"{lid:#0{6}x}\"\n root = os.path.dirname(__file__)\n with open(root + '/misc/win-language-id.json', 'r') as f:\n win_layout = json.load(f)\n return win_layout[lid_hex]","title":""},{"docid":"35060b60835a1adab92ba7649db7b1db","score":"0.5827729","text":"def language_code(self):\n return self._language_code","title":""},{"docid":"c83856b033f38001233ddd1ccea8962e","score":"0.58120084","text":"def language(self):\n return self._language","title":""},{"docid":"c83856b033f38001233ddd1ccea8962e","score":"0.58120084","text":"def language(self):\n return self._language","title":""},{"docid":"c83856b033f38001233ddd1ccea8962e","score":"0.58120084","text":"def language(self):\n return self._language","title":""},{"docid":"60cd8d4c2b50df7167c55b050d8b4fed","score":"0.57999927","text":"def getLanguageDescription(language):\n\tdesc=None\n\tif platform.system() == \"Windows\":\n\t\tLCID=localeNameToWindowsLCID(language)\n\t\tif LCID!=0:\n\t\t\tbuf=ctypes.create_unicode_buffer(1024)\n\t\t\tif '_' not in language:\n\t\t\t\tres=ctypes.windll.kernel32.GetLocaleInfoW(LCID,LOCALE_SLANGDISPLAYNAME,buf,1024)\n\t\t\telse:\n\t\t\t\tres=0\n\t\t\tif res==0:\n\t\t\t\tres=ctypes.windll.kernel32.GetLocaleInfoW(LCID,LOCALE_SLANGUAGE,buf,1024)\n\t\t\tdesc=buf.value\n\telif platform.system() == \"Linux\" or not desc:\n\t\tdesc={\n\t\t\t\"am\":pgettext(\"languageName\",\"Amharic\"),\n\t\t\t\"an\":pgettext(\"languageName\",\"Aragonese\"),\n\t\t\t\"es\":pgettext(\"languageName\",\"Spanish\"),\n\t\t\t\"pt\":pgettext(\"languageName\",\"Portuguese\"),\n\t\t\t\"ru\":pgettext(\"languageName\",\"Russian\"),\n\t\t\t\"it\":pgettext(\"languageName\",\"italian\"),\n\t\t\t\"tr\":pgettext(\"languageName\",\"Turkey\"),\n\t\t\t\"gl\":pgettext(\"languageName\",\"Galician\"),\n\t\t\t\"ca\":pgettext(\"languageName\",\"Catala\"),\n\t\t\t\"eu\":pgettext(\"languageName\",\"Vasque\"),\n\t\t\t\"pl\":pgettext(\"languageName\",\"polish\"),\n\t\t\t\"ar\":pgettext(\"languageName\",\"Arabic\"),\n\t\t\t\"ne\":pgettext(\"languageName\",\"Nepali\"),\n\t\t\t\"sr\":pgettext(\"languageName\",\"Serbian (Latin)\"),\n\t\t}.get(language,None)\n\treturn desc","title":""},{"docid":"2e521127c2c1644e5b57354454f398c9","score":"0.5784645","text":"def SelectSupportedLanguage(language_codes):\n for lang in map(NormalizeLang, language_codes.split(',')):\n if lang in ALL_LANGUAGES:\n return lang\n first = lang.split('-')[0]\n if first in ALL_LANGUAGES:\n return first\n return None","title":""},{"docid":"45fcd201506a586053c88430aa858de5","score":"0.57824755","text":"def language(self):\n return self.json[\"response\"].get(\"language\", \"\")","title":""},{"docid":"8a89e360fd1b1356d970be4fb6b21d74","score":"0.57764006","text":"def test_23_language_detection_greek(self):\n res = detect_language(self.greek_text)\n self.assertEqual(res, 'el')\n return res","title":""},{"docid":"ef21f9e15c70ba159a29472690cf3950","score":"0.5774823","text":"def get_language(source, code, language=None):\n\n if language is not None:\n for l in extensions_mapping.values():\n if l.name == language:\n return l\n else:\n raise ValueError(\"Unknown forced language: \" + language)\n\n m = re.match(r'.*(\\..+)', os.path.basename(source))\n\n if m and m.group(1) in extensions_mapping:\n return extensions_mapping[m.group(1)]\n else:\n lang = lexers.guess_lexer(code).name.lower()\n for l in extensions_mapping.values():\n if l.name == lang:\n return l\n else:\n return None","title":""},{"docid":"8da823ae121d2b255c5a33172318a03e","score":"0.57725","text":"def Language(self):\n return \"\"","title":""},{"docid":"8bc68f83d53055dd7588e2b309bc48f3","score":"0.5762527","text":"def format_language(language):\n\n if language == ACCEPTED_LANGUAGES[0]:\n return 'eng'\n elif language == ACCEPTED_LANGUAGES[1]:\n return 'spa'\n elif language == ACCEPTED_LANGUAGES[2]:\n return 'fra'\n elif language == ACCEPTED_LANGUAGES[3]:\n return 'hin'","title":""},{"docid":"37f64e0382dccc912af07a8ecffe82e2","score":"0.5761884","text":"def get(ietf_tag: str) -> int:\n ietf_tag = ietf_tag.lower()\n languages = {\n \"en\": SupportedLanguage.EN,\n \"en-us\": SupportedLanguage.EN,\n \"en-gb\": SupportedLanguage.EN\n }\n\n return languages.get(ietf_tag, SupportedLanguage.EN)","title":""},{"docid":"49dcf7e294119248c293aa1a1ecfa77a","score":"0.5746692","text":"def get_language_from_message(message):\n if hasattr(message,'language'):\n return message.language\n return get_language_from_connection(message.persistant_connection)","title":""},{"docid":"fd487efa338808a4a785366461405f65","score":"0.5726152","text":"def language(self):\n\n return self._repo_data[\"language\"]","title":""},{"docid":"3dd19c7078cc54387873d7772600e4f7","score":"0.57207394","text":"def get_language(khoros_object, community_details=None):\n return get_community_field(khoros_object, 'language', community_details)","title":""},{"docid":"daadb6a97a97c036e36905d7fcc266de","score":"0.57153803","text":"def max_delang (self):\n return self._max_delang","title":""},{"docid":"a0c7697bba718c2fda31db17eabbad33","score":"0.5704921","text":"def get_default_language():\n return properties.LANGUAGE_CODE","title":""},{"docid":"4347a1ff6618c88e66f09364dd7fa2ae","score":"0.5693334","text":"def test_english(self):\n self.assertEqual(get_language('this is english'), 'en')","title":""},{"docid":"4459ad0d764cdc5ea1d93398d52beea2","score":"0.56914985","text":"def nb_language(self, engine_name, nb, language=None):\n return self.get_engine(engine_name).nb_language(nb, language)","title":""},{"docid":"8aa2db27f0e2c9c6eec736f9e249e08f","score":"0.5668158","text":"def get_language_code(self, handler):\n l = self.get_locale(handler)\n return str(l)","title":""},{"docid":"5ea780555d4bf7864d77a55a9e794d2e","score":"0.5646527","text":"def langsvlakheid(self):\n return self._langsvlakheid.get_waarde()","title":""},{"docid":"39076118f799675f003835a70c26b96e","score":"0.5592201","text":"def default_language(self):\n return self._lang","title":""},{"docid":"36cc39483704c665adf2d8a5f9ef33c2","score":"0.55814254","text":"def learning_language(self):\n if getattr(self, '_learning_language', None) is None:\n self._learning_language = self.app.get_setting(\n u'learning_language', u'en')\n return self._learning_language","title":""},{"docid":"597c596cd22ca4a0fb9414329da19b56","score":"0.5578478","text":"def get_language_choices():\n return properties.LANGUAGES","title":""},{"docid":"1314574931b7caa48350cdb3e69b9ef0","score":"0.5577607","text":"def language_GetCapLang(self):\n status = False\n ch_langs = ['de', 'deu', 'de-CH', 'ger', # Deutsch\n 'en', 'eng', 'en-US', 'en-GB', 'en-CA', # Englisch\n 'fr', 'fra', 'fr-CH', 'fre', # Französisch\n 'it', 'ita', 'it-CH', # Italienisch\n 'roh', 'rm'] # Romanisch\n \n url_status = False\n url_results = []\n langs_found = []\n results = []\n \n \"\"\"\n By GetCapabilities\n \"\"\"\n f = None\n if self.restful:\n url = urllib.basejoin(self.base_url, self.service_settings.REST.GetCapabilities)\n else:\n kvp = {\n 'request': 'GetCapabilities',\n 'service': self.service,\n 'version': self.version\n #'language':'de-CH'\n }\n url = self.build_kvp_url(self.base_url, kvp, swapcases=self.swapcases)\n try:\n f = URL2File(url, auth=self.auth)\n for line in f.readlines():\n # \n l = re.compile('xml:lang=\"(\\w+)\"')\n found = l.findall(line)\n if found:\n langs_found.append(found[0])\n except Exception, e:\n url_results.append(\"%s for URL %s\" %(e, url))\n\n finally:\n if f:\n f.close()\n \n # Check Capabilities.Languages\n try:\n for lang in dict2list(self.gc_xmlroot.Languages.Language): \n langs_found.append(lang.value)\n results.append('Found following languages: ' + ', '.join(langs_found))\n except KeyError:\n results.append('XML Element \"Languages\" not found.')\n \n if DEBUG: print langs_found\n \n \"\"\"\n LANG-02\n Suche in Operation.Parameter(name=Language).Value\n \"\"\"\n # Get the Service Operations (same as in meta_ServiceOperations)\n \"\"\"\n if self.ows_common:\n #OWS_Common Workaround \n service_ops = dict2list(self.gc_xmlroot.OperationsMetadata.Operation)\n else:\n service_ops = dict2list(self.gc_xmlroot.Capability.Request)\n \n #if DEBUG: print service_ops\n langs_in_param_found = []\n for operation in service_ops:\n if not operation.has_key('Parameter'):\n continue\n else:\n params = dict2list(operation.Parameter)\n for param in params:\n if \"language\" == param.name.lower():\n for lang in dict2list(param.Value):\n langs_in_param_found.append(lang.value)\n langs_found.append(lang.value)\n \n if len(langs_in_param_found):\n self.setResultsOverview(\"LANG-02\", True, \"Found following languages: \" + ', '.join(langs_in_param_found))\n else:\n self.setResultsOverview(\"LANG-02\", False, \"Found no languages in Parameters\")\n\n \"\"\"\n\n \"\"\"\n By URL-naming\n LANG-03\n LANG-04\n \"\"\"\n #else:\n urls = self.base_url.split(\"/\")\n url_results = \"Service does not support languages via URL-Path\"\n for url_lang in urls:\n if url_lang in ch_langs:\n url_status = True\n langs_found.append(url_lang)\n results.append(\"Supported language %s found\" %url_lang)\n url_results = \"Supports languages via URL-Path '%s'\"%url_lang\n break\n \n \"\"\"\n Checking for HTTP status 300\n \"\"\"\n \n if url_status:\n http_status_code_redirect = 300\n http_status_code = None\n http_300 = False \n http_300_results = 'Server responded with HTTP status code 300 (multiple choices)'\n \n base_url = self.base_url.replace(\"/%s/\" %(url_lang), \"/\")\n url = self.build_kvp_url(base_url, kvp, self.swapcases)\n results.append('Checking for URL %s' %(url))\n try: \n f = URL2File(url, auth=self.auth)\n http_status_code = f.code \n except Exception, e:\n http_status_code = e.code\n finally:\n f.close()\n \n http_300 = http_status_code_redirect == http_status_code\n if not http_300:\n http_300_results = 'Server responded with HTTP status code %i (should be 300)' %(http_status_code)\n \n else:\n http_300 = False\n http_300_results = 'Service does not support redirection (HTTP Status Code 300)'\n self.setResultsOverview(\"LANG-04\", http_300, http_300_results)\n \n \"\"\"\n Checking equalness\n \"\"\"\n equal_langs = []\n \n for l in ch_langs:\n if l in langs_found:\n equal_langs.append(l)\n else:\n pass\n results.append(\"Found %s eCH-languages (%s) in GetCapabilities.\" %(len(equal_langs), ', '.join(equal_langs)))\n status = bool(len(equal_langs))\n \n if not status:\n results.append(\"Service does not support languages (via GetCapabilities)\")\n if not url_status:\n url_results = \"Service does not support languages (via URL)\"\n \n hints = \"LANG-xx\"\n # Anzahl > 0 der gefundenen übereinstimmenden Sprachangaben erfüllt LANG-01\n equal_langs = [\"'%s'\" %l for l in equal_langs]\n self.setResultsOverview(\"LANG-01\", status, results, data={'code':', '.join(equal_langs)})\n self.setResultsOverview(\"LANG-03\", url_status, url_results, data={'code':url_lang})\n\n #status = any([status, url_status, http_300, langs_in_param_found])\n status = any([status, url_status, http_300])\n return ResponseDict('language_GetCapLang', results, status, hints)","title":""},{"docid":"70af76ab2e7ccc1894522298d7193001","score":"0.5564876","text":"def language_tag(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"language_tag\")","title":""},{"docid":"b825cbe6d7edd3f663e27a2df5e2a694","score":"0.55642813","text":"def get_language_code(language):\n\n # if SelfcheckLanguage[language] in SelfcheckLanguage:\n\n try:\n return SelfcheckLanguage[language].value\n except KeyError:\n return SelfcheckLanguage.UNKNOWN.value","title":""},{"docid":"b2b80e66f39eb3694cc93f0dfc6eb6c8","score":"0.55572534","text":"def _example_word(lang):\n return {\n \"de\": \"mann\",\n \"es\": \"hombre\"\n }.get(lang)","title":""},{"docid":"23e8ef306206adbf0712ef6783425a84","score":"0.5551857","text":"def test_22_language_detection_georgian(self):\n res = detect_language(self.georgian_text)\n self.assertEqual(res, 'ka')\n return res","title":""},{"docid":"bbe8eabf96228e3318a7dbef78bed21e","score":"0.55382514","text":"def get_restricted_translation(language):","title":""},{"docid":"6e1ae8d5c5b2b0da951bde342b697e7f","score":"0.55368966","text":"def detect_language(html) :\n h = html2text.HTML2Text()\n return langdetect.detect(h.handle(html))","title":""},{"docid":"f1b043acb4293797c4d7d8782085b0fb","score":"0.5533975","text":"def SelectLanguage(*langs):\n for lang in langs:\n if lang:\n supported_lang = SelectSupportedLanguage(lang)\n if supported_lang:\n return supported_lang\n # All arguments were None or invalid.\n return DEFAULT_LANGUAGE","title":""},{"docid":"b2519838fae7143708da9ef1a0071e33","score":"0.55263555","text":"def get_langinfo(isocode):\n page = \"http://www.ethnologue.com/language/\"+isocode","title":""},{"docid":"116ebdb2170d7d99284a0fc999bedc0c","score":"0.5515722","text":"def detect_language(text):\n\n languages = langdetect.detect_langs(text)\n # This is a list, I should return the topmost language 2 letters code in uppercase\n\n language = str(languages[0]).upper()\n language = language[0:2]\n return language","title":""},{"docid":"de840d1a4f23b9a8c04ad4ee0c78d02c","score":"0.5501173","text":"def get_language(self, request):\n if request.META.get('HTTP_ACCEPT_LANGUAGE'):\n best = self.get_best_language(\n request.META['HTTP_ACCEPT_LANGUAGE'])\n if best:\n return best\n return settings.LANGUAGE_CODE","title":""},{"docid":"812a0731fd886aff6bfc486fda7c291e","score":"0.5500768","text":"def detect_B(text):\n\n if text is None:\n return \"None\"\n try:\n lang = detect(text)\n\n if lang == 'es':\n return lang\n else:\n return 'en'\n except UnicodeDecodeError:\n return \"None\"","title":""},{"docid":"12550f06daa0c939cd1f11a17e4b4901","score":"0.54871047","text":"def find_language(regnskab_dict):\n for key, regnskab_tuples in regnskab_dict.items():\n for t in regnskab_tuples:\n if t.unitIdXbrl is not None and t.unitIdXbrl[0:5] == 'lang:':\n return t.unitIdXbrl[5:]\n return 'da'","title":""},{"docid":"c0c7a68ed0fd1e6022fd42027c80e24e","score":"0.54816854","text":"def nb_language(cls, nb, language=None):\n return nb_language(nb, language)","title":""},{"docid":"53339468ec2621b4d9b68bb8c646d467","score":"0.54690015","text":"def language_tag(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"language_tag\")","title":""},{"docid":"5aceab5793405918a4af8ac38437e72d","score":"0.5465471","text":"def gengo_language_to_locale(lang):\n lang = REVERSE_LANGMAP.get(lang, lang)\n parts = lang.partition('-')\n return parts[0] + parts[1].replace('-', '_') + parts[2].upper()","title":""},{"docid":"fc684fc552261aa1a0a111055fcf3db9","score":"0.5461347","text":"def test_get_default_language():\n assert messages.get('UNKNOWN', 'foo') == messages.get('UNKNOWN', 'en')","title":""},{"docid":"b1490a48a304c69ef904edc4f23508b1","score":"0.5448692","text":"def get_language(self):\n return translation.get_language() # Assumes that middleware has set this properly.","title":""},{"docid":"4ba269d2ce9b75575efa709c03ac1679","score":"0.54374075","text":"def getLocations(self, language: str):\n if language not in self.locations.keys():\n return self.locations['de']\n else:\n return self.locations[language]","title":""},{"docid":"17e2768c96b0c43adf784c2cc7a648b3","score":"0.54363334","text":"def _language_name(self):\n languages = dict(settings.ALL_LANGUAGES)\n try:\n return languages[self.course_team.language]\n except KeyError:\n return self.course_team.language","title":""},{"docid":"c050815f7d7c5a0e33dbd062754bf878","score":"0.5428089","text":"def babel_language_in_locale(locale_id = 'en'):\n locale_obj = Locale.parse(flask_babel.get_locale())\n return locale_obj.get_language_name(locale_id)","title":""},{"docid":"6c2feee86d66db6f05c9ff0d67fc3f37","score":"0.5426774","text":"def get_default_programming_language(\n language: str\n) -> str:\n language = language.lower()\n for lang in config.DEFAULT_PROGRAMMING_LANGUAGES:\n if lang.replace('\\\\', '').lower() == language:\n return lang\n return \"\"","title":""},{"docid":"025879eba30135849ee96cfb440d0bcc","score":"0.54237074","text":"def language_version(self):\n return (None, None)","title":""},{"docid":"2e76f4cb6729101e7eac8a3c2918919f","score":"0.5418951","text":"def target_language(self):\n return self.target.config.name","title":""},{"docid":"ada6b3bf7115085ac89d052826564c84","score":"0.54109055","text":"def english(self) -> str:\n return self._english","title":""},{"docid":"277d65c1ed69f90c859004a3071a4fc7","score":"0.53834516","text":"def ui_language(self):\n return self._ui_language","title":""},{"docid":"90711d26c4fbf62f59ed7f992d8a1552","score":"0.5372895","text":"def canonizeLanguage(language):\n for code, name in LANGUAGES:\n if language == code or language == name:\n return code\n return ''","title":""},{"docid":"00223d98f7771684b2488a26b6afcac4","score":"0.53686786","text":"def LanguageIndex(self) -> int:","title":""},{"docid":"d041a335c9018f52897117de0d6b43ec","score":"0.5344354","text":"def current_language(request):\n languages = [x.split(';', 1)[0].lower() for x in request.META.get('HTTP_ACCEPT_LANGUAGE', '').split(',')]\n for language in languages:\n if language.startswith('zh'):\n return 'zh-tw'\n return 'en'","title":""},{"docid":"31c0766968b51ecda9a7dd834d2a9841","score":"0.53424793","text":"def get_lang(uid):\n return webdb.select('USERS', where='uid = \"' + str(uid) + '\"', limit=1)[0]['lang']","title":""},{"docid":"87e595a25126aeef4b825d780e5a1ebf","score":"0.5316689","text":"def get_lang_ext(self, lang):\n if lang not in self.language_extensions:\n return None\n return self.language_extensions[lang]","title":""},{"docid":"dc3a93bf0886f81449c29a183972b5bc","score":"0.53152317","text":"def get_language_with_ip(request):\n country = get_country_with_request(request)\n if country:\n return country.language_code\n else: # defaults to English\n return 'en'","title":""}],"string":"[\n {\n \"docid\": \"7d19c984c03b3e59c0e29927e52e446b\",\n \"score\": \"0.6914142\",\n \"text\": \"def get_language(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38cf8daef8d0e9165e88916da0f08868\",\n \"score\": \"0.67045707\",\n \"text\": \"def getLanguage(self) -> ghidra.program.model.lang.Language:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88efbc8344a0bc887062d526ad6868f2\",\n \"score\": \"0.6524406\",\n \"text\": \"def Language(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25f46e6f2f129aea0b62e208273c88ba\",\n \"score\": \"0.64493454\",\n \"text\": \"def get_translation(language):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92e349e11ef96d37ee720c9e44ff81c6\",\n \"score\": \"0.6397181\",\n \"text\": \"def language(self):\\n return self._mapped_lang\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83cfc614d26d41e5c5b4cd9dcfed6d54\",\n \"score\": \"0.6361355\",\n \"text\": \"def get_translated_languages():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72af2abe9ac887512114fc6b329f6a58\",\n \"score\": \"0.6298945\",\n \"text\": \"def language(self):\\n return getattr(\\n self,\\n constants.CONST_LANGUAGE,\\n constants.DEFAULT_LANGUAGE,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7374b79e2ca26f3f6ef3c6f14ae42a97\",\n \"score\": \"0.62752056\",\n \"text\": \"def getLanguage (self,c,setting):\\n \\n language = self.getString(c,setting)\\n # g.trace(setting,language)\\n \\n return language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d018b5936457ce548d59d069e02048a5\",\n \"score\": \"0.626775\",\n \"text\": \"def get_language(event, context):\\n logger.info('Calling get_language with event')\\n logger.info(event)\\n\\n # Use UTF-8 encoding for comprehend\\n if 'Text' in event:\\n text = str(event['Text'])\\n else:\\n logger.error(\\\"There is no Text!\\\")\\n raise Exception('Please provide Text!')\\n\\n # comprehend accepts up to 5000 UTF-8 encoded characters\\n if len(text) >= 4900:\\n text = text[:4899]\\n elif len(text) < 20:\\n logger.warning(\\\"Text is shorter than 20 characters!\\\")\\n raise Exception('Please provide a text of at least 20 characters!')\\n # logger.info(text)\\n\\n try:\\n response = comprehend.detect_dominant_language(Text=text)\\n except ClientError as e:\\n logger.error(\\\"Received error: %s\\\", e, exc_info=True)\\n raise\\n except ParamValidationError as e:\\n logger.error(\\\"The provided parameters are incorrect: %s\\\", e, exc_info=True)\\n raise\\n\\n # logger.info(response)\\n return response['Languages'][0]['LanguageCode']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91f4df655801a0f81560021370fb0325\",\n \"score\": \"0.6215751\",\n \"text\": \"def language(self):\\n try:\\n return self._get_response(\\n url=self.LANGID_URL, sentence=self.sentence)\\n except:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6852d214fd73ca3c8b3c76ed0880cbe\",\n \"score\": \"0.62144786\",\n \"text\": \"def lang(self):\\n # pick a sample to find language (don't pick the truth file)\\n sample = [choose for choose in os.listdir(self.folder)\\n if 'truth' not in choose][0]\\n with open(os.path.join(self.folder, sample), 'r') as sample_file:\\n first_line = sample_file.readlines()[0]\\n lang = [match.group('lang')\\n for match in re.finditer(Pan.LANG_REGEX, first_line)][0]\\n return lang.lower()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64b70a35a3529c04521ca03c1783af78\",\n \"score\": \"0.62131405\",\n \"text\": \"def language(self):\\n return self.language_version()[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"762ed9baf7428d3c2c27c92906c3f34d\",\n \"score\": \"0.61848724\",\n \"text\": \"def language(self):\\n lang=None\\n if self.__dict__['TAG:language']:\\n lang=self.__dict__['TAG:language']\\n return lang\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9442ebc089fe4982af945a7c4eee87f\",\n \"score\": \"0.6183641\",\n \"text\": \"def lang(self):\\n return self.lex_meta.lang\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff6c3d8673b836167af732a1190347c6\",\n \"score\": \"0.617456\",\n \"text\": \"def test_lang() -> str:\\n lang = locale.getdefaultlocale()[0][0:2]\\n if lang == 'fr':\\n return 'dtc'\\n return 'qdb'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86b093671c104aa8770479841f816a10\",\n \"score\": \"0.615984\",\n \"text\": \"def gncEmployeeGetLanguage(*args):\\n return _gnucash_core_c.gncEmployeeGetLanguage(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"036561cb4fe47e070a4c9ddc370ac2e2\",\n \"score\": \"0.60845643\",\n \"text\": \"def get_language(self):\\n\\n return self.__msg_lang\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99a60ae13aaed279959529adedcdb43d\",\n \"score\": \"0.6080517\",\n \"text\": \"def Language (self):\\n return (self.headers.get ('index_language', 'any'),\\n self.headers.get ('contents_language', 'any'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1790cd720d52235c595dab8ffb7999cd\",\n \"score\": \"0.6073051\",\n \"text\": \"def get_lang():\\n if os() == 'Windows':\\n # Windows\\n lang = locale.windows_locale[ctypes.windll.kernel32.GetUserDefaultUILanguage()]\\n else:\\n # Linux || MacOS\\n lang = locale.getlocale(locale.LC_MESSAGES)[0]\\n return lang\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f04f28b59d21d0f9e23d1b811974cc3\",\n \"score\": \"0.6020242\",\n \"text\": \"def moment_lang(self):\\n return load_moment_locales().get(self.lang, 'en')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e019d75c7257253db6436ddc0730b7da\",\n \"score\": \"0.5989116\",\n \"text\": \"def lang(self):\\n return self._lang\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3065207a9592be2eab56e31bb0e0f886\",\n \"score\": \"0.59747165\",\n \"text\": \"def get_language(self):\\n locale = self.get_locale()\\n language = locale\\n if '_' in locale:\\n language = locale[0:locale.index('_')]\\n return language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5123c9ed66643d7c0daa4ede7f2b7ffc\",\n \"score\": \"0.5971464\",\n \"text\": \"def check_language(text):\\n if detect(text) == 'de':\\n language = 'de'\\n elif detect(text) == 'en':\\n language = 'en'\\n else:\\n language = None\\n return language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65d1e9eae9ab232b88af6f359a4eb6c0\",\n \"score\": \"0.596331\",\n \"text\": \"def language_tag(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"language_tag\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c4045103df8f4bf07a4b594e60c7fc2\",\n \"score\": \"0.5960778\",\n \"text\": \"def get_locale():\\r\\n # this is a demo case, we use url to get locale\\r\\n code = request.args.get('lang', 'en')\\r\\n return code\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3250b795723d483231050f75ffc0f981\",\n \"score\": \"0.59486884\",\n \"text\": \"def language(self):\\n return self.__language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3250b795723d483231050f75ffc0f981\",\n \"score\": \"0.59486884\",\n \"text\": \"def language(self):\\n return self.__language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3250b795723d483231050f75ffc0f981\",\n \"score\": \"0.59486884\",\n \"text\": \"def language(self):\\n return self.__language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12f6c63ad26a4e47296bddee9b9653f2\",\n \"score\": \"0.5934641\",\n \"text\": \"def lang_(self):\\n return self.doc.vocab.store[self.lex_meta.lang]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"216919c5a4cf9279f362cc048048645b\",\n \"score\": \"0.5924688\",\n \"text\": \"def get_main_language(project_report):\\n required1 = {\\\"MetaDataCollector\\\": {\\\"main_language\\\": \\\"\\\"}}\\n required2 = {\\\"LanguageDetector\\\": {\\\"main_language\\\": \\\"\\\"}}\\n\\n if containedStructure(required1, project_report):\\n lang = project_report[\\\"MetaDataCollector\\\"][\\\"main_language\\\"]\\n return shorten_language(lang)\\n elif containedStructure(required2, project_report):\\n lang = project_report[\\\"LanguageDetector\\\"][\\\"main_language\\\"]\\n return shorten_language(lang)\\n return '-'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3125c2dce33044e62c80ca613fa15952\",\n \"score\": \"0.59202147\",\n \"text\": \"def convert_language(language):\\n\\n if language == 'StoreKing_NW':\\n language = 'Kannada'\\n if language not in LANGUAGES:\\n language = 'Hindi'\\n\\n return language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ce21ad58cb3eab50ede578a17775fc6\",\n \"score\": \"0.59194607\",\n \"text\": \"def get_locale():\\n if \\\"lang\\\" in session:\\n return session[\\\"lang\\\"]\\n #Decides the most suitable language option\\n #fi=Finnish has higher priority\\n lang = request.accept_languages.best_match([\\\"fi\\\", \\\"en\\\"])\\n return lang\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb14f5c2099e436683e91382ce62e904\",\n \"score\": \"0.59086585\",\n \"text\": \"def language_code(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71d7bb6cef50f1f302e8a531796e5ef0\",\n \"score\": \"0.590221\",\n \"text\": \"def __get_language__(self, lang_code: str) -> str:\\n\\n if lang_code not in self.ALLOWED_LANGUAGES.keys():\\n raise ValueError('{} is not a supported language code'.format(lang_code))\\n\\n return self.ALLOWED_LANGUAGES[lang_code]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c660a06708cf14ec63c36cbcdc98b5f0\",\n \"score\": \"0.5900211\",\n \"text\": \"def get_lang(country_iso: str) -> str:\\n res = country_lang.get(country_iso)\\n if res:\\n return res\\n else:\\n return 'en'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f71b058ed50499dc5964b614bdebe35c\",\n \"score\": \"0.5899539\",\n \"text\": \"def get_lang(self, language):\\n if language not in self.compilers:\\n self.console.print(\\\"Language is not supported!\\\", style=\\\"bold red\\\")\\n Utils.close()\\n return language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31f035ca7232dd83ae68d0fd8680c087\",\n \"score\": \"0.5898755\",\n \"text\": \"def getAvailableLanguages():\\n\\t#Make a list of all the locales found in NVDA's locale dir\\n\\tl=[x for x in os.listdir(\\\"locales\\\") if not x.startswith('.')]\\n\\tl=[x for x in l if os.path.isfile('locales/%s/LC_MESSAGES/twblue-documentation.mo' % x)]\\n\\t#Make sure that en (english) is in the list as it may not have any locale files, but is default\\n\\tif 'en' not in l:\\n\\t\\tl.append('en')\\n\\t\\tl.sort()\\n\\t#For each locale, ask Windows for its human readable display name\\n\\td=[]\\n\\tfor i in l:\\n\\t\\tdesc=getLanguageDescription(i)\\n\\t\\tlabel=\\\"%s, %s\\\"%(desc,i) if desc else i\\n\\t\\td.append(label)\\n\\t#include a 'user default, windows' language, which just represents the default language for this user account\\n\\tl.append(\\\"system\\\")\\n\\t# Translators: the label for the Windows default NVDA interface language.\\n\\td.append(_(\\\"User default\\\"))\\n\\t#return a zipped up version of both the lists (a list with tuples of locale,label)\\n\\treturn zip(l,d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8a9d1153b9d1d09c331da8bf1f45873\",\n \"score\": \"0.5883816\",\n \"text\": \"def get_language(self):\\n return self._language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29738bd87cc78dd4e2ff5aa73bc07f2f\",\n \"score\": \"0.58760846\",\n \"text\": \"def lang(self):\\n return self.config_core.get('lang')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7cf60544184706f9f81cdc9296ec99b\",\n \"score\": \"0.5870653\",\n \"text\": \"def language(self) -> Optional[Text]:\\n\\n return self.get(\\\"language\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49d004470495efbf7e4526a851502aa6\",\n \"score\": \"0.58666897\",\n \"text\": \"def l_get(\\n language_dict: Dict[str, Type[T_Lang]], update_msg_user_or_language_code: T_update_msg_user_or_language_code = None\\n) -> Type[T_Lang]:\\n lang = get_language_code(update_msg_user_or_language_code)\\n\\n # if it is None (default, or getting from message failed), use the default\\n if lang is None:\\n return language_dict['default']\\n # end if\\n\\n if lang in language_dict:\\n return language_dict[lang]\\n # end if\\n\\n # try replacing \\\"de_DE\\\" => \\\"de-DE\\\"\\n part = lang.replace(\\\"_\\\", \\\"-\\\")\\n if part != lang and part in language_dict:\\n return language_dict[part]\\n # end of\\n\\n # try splitting it \\\"de-DE\\\" => \\\"de\\\"\\n part = lang.split('-')[0]\\n if part != lang and part in language_dict:\\n return language_dict[part]\\n # end of\\n\\n # try splitting it \\\"de_DE\\\" => \\\"de\\\"\\n part = lang.split('_')[0]\\n if part != lang and part in language_dict:\\n return language_dict[part]\\n # end if\\n\\n # nothing did match, use the default\\n return language_dict['default']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8235dc2fa6170ed344df28e1700dbbc2\",\n \"score\": \"0.5864678\",\n \"text\": \"def getLanguage(self):\\n spd = self.get(sessionPkgDataId, {})\\n return spd.get('language', None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16a9b23a1950dc54cc078c8a113ee638\",\n \"score\": \"0.58440614\",\n \"text\": \"def language(self) -> LangEnum:\\n return self._lang_enum\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e305eccb0d97870a645edc54619fa11d\",\n \"score\": \"0.58301246\",\n \"text\": \"def get_language(self):\\n # Look through ancestors of this page for its language homepage\\n # The language homepage is located at depth 3\\n language_homepage = self.get_ancestors(inclusive=True).get(depth=3)\\n\\n # The slug of language homepages should always be set to the language code\\n return language_homepage.slug\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01a7c30c4fb5b54b7e1163687d05331e\",\n \"score\": \"0.58280015\",\n \"text\": \"def get_keyboard_language():\\n # only language not variant layout\\n user32 = ctypes.WinDLL('user32', use_last_error=True)\\n curr_window = user32.GetForegroundWindow()\\n thread_id = user32.GetWindowThreadProcessId(curr_window, 0)\\n klid = user32.GetKeyboardLayout(thread_id)\\n lid = klid & (2 ** 16 - 1)\\n lid_hex = f\\\"{lid:#0{6}x}\\\"\\n root = os.path.dirname(__file__)\\n with open(root + '/misc/win-language-id.json', 'r') as f:\\n win_layout = json.load(f)\\n return win_layout[lid_hex]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35060b60835a1adab92ba7649db7b1db\",\n \"score\": \"0.5827729\",\n \"text\": \"def language_code(self):\\n return self._language_code\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c83856b033f38001233ddd1ccea8962e\",\n \"score\": \"0.58120084\",\n \"text\": \"def language(self):\\n return self._language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c83856b033f38001233ddd1ccea8962e\",\n \"score\": \"0.58120084\",\n \"text\": \"def language(self):\\n return self._language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c83856b033f38001233ddd1ccea8962e\",\n \"score\": \"0.58120084\",\n \"text\": \"def language(self):\\n return self._language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60cd8d4c2b50df7167c55b050d8b4fed\",\n \"score\": \"0.57999927\",\n \"text\": \"def getLanguageDescription(language):\\n\\tdesc=None\\n\\tif platform.system() == \\\"Windows\\\":\\n\\t\\tLCID=localeNameToWindowsLCID(language)\\n\\t\\tif LCID!=0:\\n\\t\\t\\tbuf=ctypes.create_unicode_buffer(1024)\\n\\t\\t\\tif '_' not in language:\\n\\t\\t\\t\\tres=ctypes.windll.kernel32.GetLocaleInfoW(LCID,LOCALE_SLANGDISPLAYNAME,buf,1024)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tres=0\\n\\t\\t\\tif res==0:\\n\\t\\t\\t\\tres=ctypes.windll.kernel32.GetLocaleInfoW(LCID,LOCALE_SLANGUAGE,buf,1024)\\n\\t\\t\\tdesc=buf.value\\n\\telif platform.system() == \\\"Linux\\\" or not desc:\\n\\t\\tdesc={\\n\\t\\t\\t\\\"am\\\":pgettext(\\\"languageName\\\",\\\"Amharic\\\"),\\n\\t\\t\\t\\\"an\\\":pgettext(\\\"languageName\\\",\\\"Aragonese\\\"),\\n\\t\\t\\t\\\"es\\\":pgettext(\\\"languageName\\\",\\\"Spanish\\\"),\\n\\t\\t\\t\\\"pt\\\":pgettext(\\\"languageName\\\",\\\"Portuguese\\\"),\\n\\t\\t\\t\\\"ru\\\":pgettext(\\\"languageName\\\",\\\"Russian\\\"),\\n\\t\\t\\t\\\"it\\\":pgettext(\\\"languageName\\\",\\\"italian\\\"),\\n\\t\\t\\t\\\"tr\\\":pgettext(\\\"languageName\\\",\\\"Turkey\\\"),\\n\\t\\t\\t\\\"gl\\\":pgettext(\\\"languageName\\\",\\\"Galician\\\"),\\n\\t\\t\\t\\\"ca\\\":pgettext(\\\"languageName\\\",\\\"Catala\\\"),\\n\\t\\t\\t\\\"eu\\\":pgettext(\\\"languageName\\\",\\\"Vasque\\\"),\\n\\t\\t\\t\\\"pl\\\":pgettext(\\\"languageName\\\",\\\"polish\\\"),\\n\\t\\t\\t\\\"ar\\\":pgettext(\\\"languageName\\\",\\\"Arabic\\\"),\\n\\t\\t\\t\\\"ne\\\":pgettext(\\\"languageName\\\",\\\"Nepali\\\"),\\n\\t\\t\\t\\\"sr\\\":pgettext(\\\"languageName\\\",\\\"Serbian (Latin)\\\"),\\n\\t\\t}.get(language,None)\\n\\treturn desc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e521127c2c1644e5b57354454f398c9\",\n \"score\": \"0.5784645\",\n \"text\": \"def SelectSupportedLanguage(language_codes):\\n for lang in map(NormalizeLang, language_codes.split(',')):\\n if lang in ALL_LANGUAGES:\\n return lang\\n first = lang.split('-')[0]\\n if first in ALL_LANGUAGES:\\n return first\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45fcd201506a586053c88430aa858de5\",\n \"score\": \"0.57824755\",\n \"text\": \"def language(self):\\n return self.json[\\\"response\\\"].get(\\\"language\\\", \\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a89e360fd1b1356d970be4fb6b21d74\",\n \"score\": \"0.57764006\",\n \"text\": \"def test_23_language_detection_greek(self):\\n res = detect_language(self.greek_text)\\n self.assertEqual(res, 'el')\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef21f9e15c70ba159a29472690cf3950\",\n \"score\": \"0.5774823\",\n \"text\": \"def get_language(source, code, language=None):\\n\\n if language is not None:\\n for l in extensions_mapping.values():\\n if l.name == language:\\n return l\\n else:\\n raise ValueError(\\\"Unknown forced language: \\\" + language)\\n\\n m = re.match(r'.*(\\\\..+)', os.path.basename(source))\\n\\n if m and m.group(1) in extensions_mapping:\\n return extensions_mapping[m.group(1)]\\n else:\\n lang = lexers.guess_lexer(code).name.lower()\\n for l in extensions_mapping.values():\\n if l.name == lang:\\n return l\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8da823ae121d2b255c5a33172318a03e\",\n \"score\": \"0.57725\",\n \"text\": \"def Language(self):\\n return \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bc68f83d53055dd7588e2b309bc48f3\",\n \"score\": \"0.5762527\",\n \"text\": \"def format_language(language):\\n\\n if language == ACCEPTED_LANGUAGES[0]:\\n return 'eng'\\n elif language == ACCEPTED_LANGUAGES[1]:\\n return 'spa'\\n elif language == ACCEPTED_LANGUAGES[2]:\\n return 'fra'\\n elif language == ACCEPTED_LANGUAGES[3]:\\n return 'hin'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37f64e0382dccc912af07a8ecffe82e2\",\n \"score\": \"0.5761884\",\n \"text\": \"def get(ietf_tag: str) -> int:\\n ietf_tag = ietf_tag.lower()\\n languages = {\\n \\\"en\\\": SupportedLanguage.EN,\\n \\\"en-us\\\": SupportedLanguage.EN,\\n \\\"en-gb\\\": SupportedLanguage.EN\\n }\\n\\n return languages.get(ietf_tag, SupportedLanguage.EN)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49dcf7e294119248c293aa1a1ecfa77a\",\n \"score\": \"0.5746692\",\n \"text\": \"def get_language_from_message(message):\\n if hasattr(message,'language'):\\n return message.language\\n return get_language_from_connection(message.persistant_connection)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd487efa338808a4a785366461405f65\",\n \"score\": \"0.5726152\",\n \"text\": \"def language(self):\\n\\n return self._repo_data[\\\"language\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dd19c7078cc54387873d7772600e4f7\",\n \"score\": \"0.57207394\",\n \"text\": \"def get_language(khoros_object, community_details=None):\\n return get_community_field(khoros_object, 'language', community_details)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daadb6a97a97c036e36905d7fcc266de\",\n \"score\": \"0.57153803\",\n \"text\": \"def max_delang (self):\\n return self._max_delang\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0c7697bba718c2fda31db17eabbad33\",\n \"score\": \"0.5704921\",\n \"text\": \"def get_default_language():\\n return properties.LANGUAGE_CODE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4347a1ff6618c88e66f09364dd7fa2ae\",\n \"score\": \"0.5693334\",\n \"text\": \"def test_english(self):\\n self.assertEqual(get_language('this is english'), 'en')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4459ad0d764cdc5ea1d93398d52beea2\",\n \"score\": \"0.56914985\",\n \"text\": \"def nb_language(self, engine_name, nb, language=None):\\n return self.get_engine(engine_name).nb_language(nb, language)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8aa2db27f0e2c9c6eec736f9e249e08f\",\n \"score\": \"0.5668158\",\n \"text\": \"def get_language_code(self, handler):\\n l = self.get_locale(handler)\\n return str(l)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ea780555d4bf7864d77a55a9e794d2e\",\n \"score\": \"0.5646527\",\n \"text\": \"def langsvlakheid(self):\\n return self._langsvlakheid.get_waarde()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39076118f799675f003835a70c26b96e\",\n \"score\": \"0.5592201\",\n \"text\": \"def default_language(self):\\n return self._lang\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36cc39483704c665adf2d8a5f9ef33c2\",\n \"score\": \"0.55814254\",\n \"text\": \"def learning_language(self):\\n if getattr(self, '_learning_language', None) is None:\\n self._learning_language = self.app.get_setting(\\n u'learning_language', u'en')\\n return self._learning_language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"597c596cd22ca4a0fb9414329da19b56\",\n \"score\": \"0.5578478\",\n \"text\": \"def get_language_choices():\\n return properties.LANGUAGES\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1314574931b7caa48350cdb3e69b9ef0\",\n \"score\": \"0.5577607\",\n \"text\": \"def language_GetCapLang(self):\\n status = False\\n ch_langs = ['de', 'deu', 'de-CH', 'ger', # Deutsch\\n 'en', 'eng', 'en-US', 'en-GB', 'en-CA', # Englisch\\n 'fr', 'fra', 'fr-CH', 'fre', # Französisch\\n 'it', 'ita', 'it-CH', # Italienisch\\n 'roh', 'rm'] # Romanisch\\n \\n url_status = False\\n url_results = []\\n langs_found = []\\n results = []\\n \\n \\\"\\\"\\\"\\n By GetCapabilities\\n \\\"\\\"\\\"\\n f = None\\n if self.restful:\\n url = urllib.basejoin(self.base_url, self.service_settings.REST.GetCapabilities)\\n else:\\n kvp = {\\n 'request': 'GetCapabilities',\\n 'service': self.service,\\n 'version': self.version\\n #'language':'de-CH'\\n }\\n url = self.build_kvp_url(self.base_url, kvp, swapcases=self.swapcases)\\n try:\\n f = URL2File(url, auth=self.auth)\\n for line in f.readlines():\\n # <Title xml:lang=\\\"fr\\\">\\n l = re.compile('xml:lang=\\\"(\\\\w+)\\\"')\\n found = l.findall(line)\\n if found:\\n langs_found.append(found[0])\\n except Exception, e:\\n url_results.append(\\\"%s for URL %s\\\" %(e, url))\\n\\n finally:\\n if f:\\n f.close()\\n \\n # Check Capabilities.Languages\\n try:\\n for lang in dict2list(self.gc_xmlroot.Languages.Language): \\n langs_found.append(lang.value)\\n results.append('Found following languages: ' + ', '.join(langs_found))\\n except KeyError:\\n results.append('XML Element \\\"Languages\\\" not found.')\\n \\n if DEBUG: print langs_found\\n \\n \\\"\\\"\\\"\\n LANG-02\\n Suche in Operation.Parameter(name=Language).Value\\n \\\"\\\"\\\"\\n # Get the Service Operations (same as in meta_ServiceOperations)\\n \\\"\\\"\\\"\\n if self.ows_common:\\n #OWS_Common Workaround \\n service_ops = dict2list(self.gc_xmlroot.OperationsMetadata.Operation)\\n else:\\n service_ops = dict2list(self.gc_xmlroot.Capability.Request)\\n \\n #if DEBUG: print service_ops\\n langs_in_param_found = []\\n for operation in service_ops:\\n if not operation.has_key('Parameter'):\\n continue\\n else:\\n params = dict2list(operation.Parameter)\\n for param in params:\\n if \\\"language\\\" == param.name.lower():\\n for lang in dict2list(param.Value):\\n langs_in_param_found.append(lang.value)\\n langs_found.append(lang.value)\\n \\n if len(langs_in_param_found):\\n self.setResultsOverview(\\\"LANG-02\\\", True, \\\"Found following languages: \\\" + ', '.join(langs_in_param_found))\\n else:\\n self.setResultsOverview(\\\"LANG-02\\\", False, \\\"Found no languages in Parameters\\\")\\n\\n \\\"\\\"\\\"\\n\\n \\\"\\\"\\\"\\n By URL-naming\\n LANG-03\\n LANG-04\\n \\\"\\\"\\\"\\n #else:\\n urls = self.base_url.split(\\\"/\\\")\\n url_results = \\\"Service does not support languages via URL-Path\\\"\\n for url_lang in urls:\\n if url_lang in ch_langs:\\n url_status = True\\n langs_found.append(url_lang)\\n results.append(\\\"Supported language %s found\\\" %url_lang)\\n url_results = \\\"Supports languages via URL-Path '%s'\\\"%url_lang\\n break\\n \\n \\\"\\\"\\\"\\n Checking for HTTP status 300\\n \\\"\\\"\\\"\\n \\n if url_status:\\n http_status_code_redirect = 300\\n http_status_code = None\\n http_300 = False \\n http_300_results = 'Server responded with HTTP status code 300 (multiple choices)'\\n \\n base_url = self.base_url.replace(\\\"/%s/\\\" %(url_lang), \\\"/\\\")\\n url = self.build_kvp_url(base_url, kvp, self.swapcases)\\n results.append('Checking for URL %s' %(url))\\n try: \\n f = URL2File(url, auth=self.auth)\\n http_status_code = f.code \\n except Exception, e:\\n http_status_code = e.code\\n finally:\\n f.close()\\n \\n http_300 = http_status_code_redirect == http_status_code\\n if not http_300:\\n http_300_results = 'Server responded with HTTP status code %i (should be 300)' %(http_status_code)\\n \\n else:\\n http_300 = False\\n http_300_results = 'Service does not support redirection (HTTP Status Code 300)'\\n self.setResultsOverview(\\\"LANG-04\\\", http_300, http_300_results)\\n \\n \\\"\\\"\\\"\\n Checking equalness\\n \\\"\\\"\\\"\\n equal_langs = []\\n \\n for l in ch_langs:\\n if l in langs_found:\\n equal_langs.append(l)\\n else:\\n pass\\n results.append(\\\"Found %s eCH-languages (%s) in GetCapabilities.\\\" %(len(equal_langs), ', '.join(equal_langs)))\\n status = bool(len(equal_langs))\\n \\n if not status:\\n results.append(\\\"Service does not support languages (via GetCapabilities)\\\")\\n if not url_status:\\n url_results = \\\"Service does not support languages (via URL)\\\"\\n \\n hints = \\\"LANG-xx\\\"\\n # Anzahl > 0 der gefundenen übereinstimmenden Sprachangaben erfüllt LANG-01\\n equal_langs = [\\\"'%s'\\\" %l for l in equal_langs]\\n self.setResultsOverview(\\\"LANG-01\\\", status, results, data={'code':', '.join(equal_langs)})\\n self.setResultsOverview(\\\"LANG-03\\\", url_status, url_results, data={'code':url_lang})\\n\\n #status = any([status, url_status, http_300, langs_in_param_found])\\n status = any([status, url_status, http_300])\\n return ResponseDict('language_GetCapLang', results, status, hints)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70af76ab2e7ccc1894522298d7193001\",\n \"score\": \"0.5564876\",\n \"text\": \"def language_tag(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"language_tag\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b825cbe6d7edd3f663e27a2df5e2a694\",\n \"score\": \"0.55642813\",\n \"text\": \"def get_language_code(language):\\n\\n # if SelfcheckLanguage[language] in SelfcheckLanguage:\\n\\n try:\\n return SelfcheckLanguage[language].value\\n except KeyError:\\n return SelfcheckLanguage.UNKNOWN.value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2b80e66f39eb3694cc93f0dfc6eb6c8\",\n \"score\": \"0.55572534\",\n \"text\": \"def _example_word(lang):\\n return {\\n \\\"de\\\": \\\"mann\\\",\\n \\\"es\\\": \\\"hombre\\\"\\n }.get(lang)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23e8ef306206adbf0712ef6783425a84\",\n \"score\": \"0.5551857\",\n \"text\": \"def test_22_language_detection_georgian(self):\\n res = detect_language(self.georgian_text)\\n self.assertEqual(res, 'ka')\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbe8eabf96228e3318a7dbef78bed21e\",\n \"score\": \"0.55382514\",\n \"text\": \"def get_restricted_translation(language):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e1ae8d5c5b2b0da951bde342b697e7f\",\n \"score\": \"0.55368966\",\n \"text\": \"def detect_language(html) :\\n h = html2text.HTML2Text()\\n return langdetect.detect(h.handle(html))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1b043acb4293797c4d7d8782085b0fb\",\n \"score\": \"0.5533975\",\n \"text\": \"def SelectLanguage(*langs):\\n for lang in langs:\\n if lang:\\n supported_lang = SelectSupportedLanguage(lang)\\n if supported_lang:\\n return supported_lang\\n # All arguments were None or invalid.\\n return DEFAULT_LANGUAGE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2519838fae7143708da9ef1a0071e33\",\n \"score\": \"0.55263555\",\n \"text\": \"def get_langinfo(isocode):\\n page = \\\"http://www.ethnologue.com/language/\\\"+isocode\",\n \"title\": \"\"\n },\n {\n \"docid\": \"116ebdb2170d7d99284a0fc999bedc0c\",\n \"score\": \"0.5515722\",\n \"text\": \"def detect_language(text):\\n\\n languages = langdetect.detect_langs(text)\\n # This is a list, I should return the topmost language 2 letters code in uppercase\\n\\n language = str(languages[0]).upper()\\n language = language[0:2]\\n return language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de840d1a4f23b9a8c04ad4ee0c78d02c\",\n \"score\": \"0.5501173\",\n \"text\": \"def get_language(self, request):\\n if request.META.get('HTTP_ACCEPT_LANGUAGE'):\\n best = self.get_best_language(\\n request.META['HTTP_ACCEPT_LANGUAGE'])\\n if best:\\n return best\\n return settings.LANGUAGE_CODE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"812a0731fd886aff6bfc486fda7c291e\",\n \"score\": \"0.5500768\",\n \"text\": \"def detect_B(text):\\n\\n if text is None:\\n return \\\"None\\\"\\n try:\\n lang = detect(text)\\n\\n if lang == 'es':\\n return lang\\n else:\\n return 'en'\\n except UnicodeDecodeError:\\n return \\\"None\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12550f06daa0c939cd1f11a17e4b4901\",\n \"score\": \"0.54871047\",\n \"text\": \"def find_language(regnskab_dict):\\n for key, regnskab_tuples in regnskab_dict.items():\\n for t in regnskab_tuples:\\n if t.unitIdXbrl is not None and t.unitIdXbrl[0:5] == 'lang:':\\n return t.unitIdXbrl[5:]\\n return 'da'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0c7a68ed0fd1e6022fd42027c80e24e\",\n \"score\": \"0.54816854\",\n \"text\": \"def nb_language(cls, nb, language=None):\\n return nb_language(nb, language)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53339468ec2621b4d9b68bb8c646d467\",\n \"score\": \"0.54690015\",\n \"text\": \"def language_tag(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"language_tag\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5aceab5793405918a4af8ac38437e72d\",\n \"score\": \"0.5465471\",\n \"text\": \"def gengo_language_to_locale(lang):\\n lang = REVERSE_LANGMAP.get(lang, lang)\\n parts = lang.partition('-')\\n return parts[0] + parts[1].replace('-', '_') + parts[2].upper()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc684fc552261aa1a0a111055fcf3db9\",\n \"score\": \"0.5461347\",\n \"text\": \"def test_get_default_language():\\n assert messages.get('UNKNOWN', 'foo') == messages.get('UNKNOWN', 'en')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1490a48a304c69ef904edc4f23508b1\",\n \"score\": \"0.5448692\",\n \"text\": \"def get_language(self):\\n return translation.get_language() # Assumes that middleware has set this properly.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ba269d2ce9b75575efa709c03ac1679\",\n \"score\": \"0.54374075\",\n \"text\": \"def getLocations(self, language: str):\\n if language not in self.locations.keys():\\n return self.locations['de']\\n else:\\n return self.locations[language]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17e2768c96b0c43adf784c2cc7a648b3\",\n \"score\": \"0.54363334\",\n \"text\": \"def _language_name(self):\\n languages = dict(settings.ALL_LANGUAGES)\\n try:\\n return languages[self.course_team.language]\\n except KeyError:\\n return self.course_team.language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c050815f7d7c5a0e33dbd062754bf878\",\n \"score\": \"0.5428089\",\n \"text\": \"def babel_language_in_locale(locale_id = 'en'):\\n locale_obj = Locale.parse(flask_babel.get_locale())\\n return locale_obj.get_language_name(locale_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c2feee86d66db6f05c9ff0d67fc3f37\",\n \"score\": \"0.5426774\",\n \"text\": \"def get_default_programming_language(\\n language: str\\n) -> str:\\n language = language.lower()\\n for lang in config.DEFAULT_PROGRAMMING_LANGUAGES:\\n if lang.replace('\\\\\\\\', '').lower() == language:\\n return lang\\n return \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"025879eba30135849ee96cfb440d0bcc\",\n \"score\": \"0.54237074\",\n \"text\": \"def language_version(self):\\n return (None, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e76f4cb6729101e7eac8a3c2918919f\",\n \"score\": \"0.5418951\",\n \"text\": \"def target_language(self):\\n return self.target.config.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ada6b3bf7115085ac89d052826564c84\",\n \"score\": \"0.54109055\",\n \"text\": \"def english(self) -> str:\\n return self._english\",\n \"title\": \"\"\n },\n {\n \"docid\": \"277d65c1ed69f90c859004a3071a4fc7\",\n \"score\": \"0.53834516\",\n \"text\": \"def ui_language(self):\\n return self._ui_language\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90711d26c4fbf62f59ed7f992d8a1552\",\n \"score\": \"0.5372895\",\n \"text\": \"def canonizeLanguage(language):\\n for code, name in LANGUAGES:\\n if language == code or language == name:\\n return code\\n return ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00223d98f7771684b2488a26b6afcac4\",\n \"score\": \"0.53686786\",\n \"text\": \"def LanguageIndex(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d041a335c9018f52897117de0d6b43ec\",\n \"score\": \"0.5344354\",\n \"text\": \"def current_language(request):\\n languages = [x.split(';', 1)[0].lower() for x in request.META.get('HTTP_ACCEPT_LANGUAGE', '').split(',')]\\n for language in languages:\\n if language.startswith('zh'):\\n return 'zh-tw'\\n return 'en'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31c0766968b51ecda9a7dd834d2a9841\",\n \"score\": \"0.53424793\",\n \"text\": \"def get_lang(uid):\\n return webdb.select('USERS', where='uid = \\\"' + str(uid) + '\\\"', limit=1)[0]['lang']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87e595a25126aeef4b825d780e5a1ebf\",\n \"score\": \"0.5316689\",\n \"text\": \"def get_lang_ext(self, lang):\\n if lang not in self.language_extensions:\\n return None\\n return self.language_extensions[lang]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc3a93bf0886f81449c29a183972b5bc\",\n \"score\": \"0.53152317\",\n \"text\": \"def get_language_with_ip(request):\\n country = get_country_with_request(request)\\n if country:\\n return country.language_code\\n else: # defaults to English\\n return 'en'\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":798,"cells":{"query_id":{"kind":"string","value":"dcb6c0a8a52a7bb0ac92e1bdf7df1718"},"query":{"kind":"string","value":"get all the bookmars that a certai project has"},"positive_passages":{"kind":"list like","value":[{"docid":"4705871157183a2403386664a34c0255","score":"0.0","text":"def get_projectBookmarks(self, projectId):\n from Bookmark import Bookmark\n cursor = self.dbconnect.get_cursor()\n cursor.execute('select * from bookmark where project=%s', (projectId,))\n bookmarks = list()\n for row in cursor:\n bookmark = Bookmark(row[0], row[1])\n bookmarks.append(bookmark)\n return bookmarks","title":""}],"string":"[\n {\n \"docid\": \"4705871157183a2403386664a34c0255\",\n \"score\": \"0.0\",\n \"text\": \"def get_projectBookmarks(self, projectId):\\n from Bookmark import Bookmark\\n cursor = self.dbconnect.get_cursor()\\n cursor.execute('select * from bookmark where project=%s', (projectId,))\\n bookmarks = list()\\n for row in cursor:\\n bookmark = Bookmark(row[0], row[1])\\n bookmarks.append(bookmark)\\n return bookmarks\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"edff7cd81254a3f14281af7177212839","score":"0.6072002","text":"def getBooks():\n ret = {}\n books = []\n iter = model.getBooks()\n while iter.next():\n books.append((iter.id, iter.name))\n\n ret['books'] = books\n return ret","title":""},{"docid":"c20ccd70c0b4dc0f21126c5b35a2add6","score":"0.60219944","text":"def get_all_books():\n\n # return Book.query.all()\n return Book.query.order_by(Book.title).all()","title":""},{"docid":"7bf9f7f1f1c9dad337225ac773f549ca","score":"0.5911421","text":"def books(self):\n return self._results_dict['books']","title":""},{"docid":"ffaac922289e589946ee40ee82d085cd","score":"0.59017754","text":"def makeBooksArray():\n books=[]\n metadata = metainfo.readmetadata()\n for key in metadata:\n if( (len(metadata[key]['subjects']) > 0) and( metadata[key]['subjects'] is not None) and (metadata[key]['downloads']>75) and ( metadata[key]['author'] is not None) and (\"en\" in metadata[key]['language']) ):\n #authorNames = extractNames(metadata[key]['author'])\n genreString = ', '.join(metadata[key]['subjects'])\n books.append(Book(key,metadata[key]['title'],metadata[key]['author'],genreString,0,0))\n return books","title":""},{"docid":"dd0291c5e68e86d1df97ac53b11c36a3","score":"0.5664254","text":"def bookkeeping_all():\n result = handlers.BookKeeping().retrieve_bookkeeping_all()\n return result","title":""},{"docid":"d1614d4de7151421f0c9791faa015e5c","score":"0.5599402","text":"def get_all_books() -> List[Dict]:\n with open(BOOKS, 'r') as in_file:\n return [_parse_from_line(line) for line in in_file]","title":""},{"docid":"237be531bc784d39ef4a4f3921d9d799","score":"0.5585883","text":"def get_books():\n books = Book.query.all()\n json_books = []\n for book in books:\n json_books.append(book.to_json())\n return jsonify(json_books), 200","title":""},{"docid":"ef68a6b4baab0918e4133a86782e5c38","score":"0.5565488","text":"def getClips(book_id):\n ret = {}\n clips = []\n it = model.getClipsByBookId(book_id)\n while it.next():\n clips.append((it.id, it.content))\n pass\n\n ret['clips'] = clips\n return ret","title":""},{"docid":"9822c9b8267f4bbe3be1a2a68d825825","score":"0.5564752","text":"def books():\n import raw\n return jsonify(raw.get_books())","title":""},{"docid":"dbc1d921dc9c507e31bd65a8ccb389bf","score":"0.55092233","text":"def get_book(self, top_n=None):\n return {\n 'asks': self.get_asks(top_n=top_n),\n 'bids': self.get_bids(top_n=top_n)\n }","title":""},{"docid":"97c3fb06b8fb3ae945e9ec38ceb52e37","score":"0.5496954","text":"def get_all_books_alphabetical(self):\n raise NotImplementedError","title":""},{"docid":"4e546d6dc1b8c1cf1b01fb12e3d27bfc","score":"0.54952437","text":"def get_all_books_release_year(self):\n raise NotImplementedError","title":""},{"docid":"3530625f47dfbaac23e3bb2548db518b","score":"0.5468389","text":"def getBorrowedBooks(self):\n return self.__borrowedBooks","title":""},{"docid":"9732865d0e328131cc0a5c7aed478c34","score":"0.546294","text":"def get_all_books(self, book_type):\n validate_type(book_type)\n return [book.serialize() for book in self.list_books if book.type == Type(book_type)]","title":""},{"docid":"1a73881df25c2175e71e8c0915b410a8","score":"0.5461085","text":"def get_bible(out):\n for book in BOOKS:\n book_chapters = IBook(book).chapter_to_text(version=\"basicenglish\")\n with open(Path(out) / f\"{book}.json\", \"w\") as out_json:\n json.dump(book_chapters, out_json)","title":""},{"docid":"df876f5293768f809cb674acdd1282ae","score":"0.5460983","text":"def get_books(self):\n files = [f for f in listdir(self.text_dir)]\n for author, title, period, url in self.json():\n filename = format_filename(author, title)\n if not filename in files:\n logger.debug(\"Getting %s\" % filename)\n book = self.extractor.download_book(url, False, author, title, period)\n else:\n logger.debug(\"%s already downloaded\" % filename)","title":""},{"docid":"1c25455989ae0ff94927d623ecc64382","score":"0.54381627","text":"def getNyTimesBooks():\n lists = [lst['list_name_encoded'] for lst in requests.get('https://api.nytimes.com/svc/books/v3/lists/names.json?api-key=1bfa24a95061415dbc8d4a4f136329a5').json()['results']]\n global printout\n for l in lists[:1]:\n results = requests.get('https://api.nytimes.com/svc/books/v3/lists.json?list='+l+'&api-key=1bfa24a95061415dbc8d4a4f136329a5').json()\n for r in results['results']:\n isbn = r['book_details'][0]['primary_isbn13']\n list = r['list_name']\n try:\n GRBook_req = requests.get('https://www.goodreads.com/book/isbn_to_id/'+isbn+'?key='+API_KEY['GOODREADS'])\n if(GRBook_req.status_code == 200):\n getGRBookByID(int(GRBook_req.text), list)\n except ExpatError as e:\n print(e)","title":""},{"docid":"48efe85434fc0b68dd59a1906bbfe648","score":"0.54263264","text":"def citations(self):\n return []","title":""},{"docid":"48ee710991bee60d2ba6ceafc328780f","score":"0.5416807","text":"def getLooks(self):\n pass","title":""},{"docid":"b3cb373aca17c84403726111cf0ef93f","score":"0.5414597","text":"def report_books(self):\n for book in self._books.itervalues():\n print(book.report())","title":""},{"docid":"6304b84bc10f74f192ff44ede1114e7a","score":"0.5382276","text":"def get_all_project_details() -> list:\n\n list_project = db_connector.collection(Collections.PROJECTS).find(\n {}, {\"_id\": 0}).sort(\"project_name\", pymongo.ASCENDING)\n list_projects_to_be_send = []\n for project in list_project:\n list_projects_to_be_send.append(project)\n return list_projects_to_be_send","title":""},{"docid":"672c738042ef32362a7eaa5ce36e731a","score":"0.5363474","text":"def get_chapters():\n q = 'project/'\n chapters = exec_jira_query(q)\n tcs = []\n for c in chapters:\n k = c['key']\n if k in relevant_chapter_keys:\n tcs.append(_build_chapter(c))\n return tcs","title":""},{"docid":"8ad5bfe68833e5763701cec6d8345a9c","score":"0.5345248","text":"def get_books(self, shelf):\n titles = self.parse_titles(shelf)\n isbns = self.parse_isbns(shelf)\n isbn13s = self.parse_isbn13s(shelf)\n authors = self.parse_author(shelf)\n\n books = []\n for i in range(len(titles)):\n b = Book(titles[i], isbns[i], isbn13s[i], authors[i])\n books.append(b)\n return books","title":""},{"docid":"74cf32b3febfbaa7e8c684f007192f04","score":"0.53447914","text":"def books(self):\n if not hasattr(self, '_books'):\n self._books = list(Bestseller.select().where(Bestseller.author == self))\n\n return self._books","title":""},{"docid":"e1a081d8885482958cb5ffbf91794e50","score":"0.53246725","text":"def bookshelves_subjects(self, book_id):\n select = \"SELECT DISTINCT ?subject ?bookshelf\"\n pattern = f\"\"\"\n WHERE {{\n <{book_id}> dcterms:subject [dcterms:title ?subject];\n pgterms:bookshelf [dcterms:title ?bookshelf] ;\n dcterms:type dcmitype:Text. \n }}\n ORDER BY ?subject\n \"\"\"\n query = self._namespace + select + pattern\n query = self._get_query_results(query)\n\n subjects = [result[\"subject\"] for result in query]\n bookshelves = [result[\"bookshelf\"] for result in query]\n\n return {\"bookshelves\": set(bookshelves), \"subjects\": set(subjects)}","title":""},{"docid":"035f5538e005f363b6d8bec20bd36a8e","score":"0.5317984","text":"def book_list(username):\n reviews=Review.objects.filter(creator__username__contains=username)\n return {'books_read':[r.book.title for r in reviews]}","title":""},{"docid":"bd584a6e594699b465eb8376480b73b8","score":"0.5293692","text":"def get_all_book_ids(self):\n raise NotImplementedError","title":""},{"docid":"be9122f82f2ad38b2c9797839d77dae1","score":"0.5280353","text":"def test_get_all_company_fundamentals(self):\n pass","title":""},{"docid":"a1fb51aa792d13daf074d9ca509842b5","score":"0.5274824","text":"def get_bibliography(self, id):\n select = \"SELECT DISTINCT ?title\"\n pattern = f\"\"\"\n WHERE {{\n ?book_id purl:creator <{id}> .\n {self._books}\n }}\n ORDER BY ?title\n \"\"\"\n\n query = self._namespace + select + pattern\n return [result[\"title\"] for result in self._get_query_results(query)]","title":""},{"docid":"35ed5a246c763eda36d66954f9ac9472","score":"0.5273621","text":"def getCaptials():\n query_strings = request.args.get('query')\n search_strings = request.args.get('search')\n book = capital.Capital()\n if not query_strings and not search_strings:\n return jsonify(book.fetch_20Capitals()), 200\n else:\n result = book.fetch_capitals(query_strings, search_strings)\n return jsonify(result), 200","title":""},{"docid":"f94f6c712c4fb595164c705a98aca96a","score":"0.5262072","text":"def report_all_books(self):\n print(\"= All Books at {library} =\".format(library=self))\n print(\"+ {:^30} | {:^30} | {:^10} +\"\n .format(\"Title\", \"Author\", \"Copies\"))\n for shelf in self._shelves.itervalues():\n print(\"Shelf - {shelf}\".format(shelf=shelf))\n shelf.report_books()","title":""},{"docid":"852fd0932e249ca1b9b0513846f2beb5","score":"0.52414143","text":"def get_all(self):\n\n self._init_dict_chal_keys()\n\n # REC FUTURE BIG : woups. Seems that underscore is a special char in the LIKE clauses. Will fix that later.\n # Gets award informations\n filter_award_name = 'plugin_intermflag_' + str(self.chal_id) + '_%'\n awards_query_result = db.session.query(\n Awards.id.label('award_id'), Awards.date, Awards.name.label('award_name'),\n Teams.id.label('team_id'), Teams.name.label('team_name')\n ).join(Teams).filter(Awards.name.like(filter_award_name)).order_by(Awards.date).all()\n\n # Joins award datas and key datas\n awards = [\n (award.award_id, award.date, award.team_id, award.team_name, self._award_infos(award.award_name))\n for award in awards_query_result\n ]\n awards = [\n award\n for award in awards\n if award[-1] is not None\n ]\n\n # Hides descrip, icon and scores that the team should not know.\n key_ids_of_team = [\n award_infos['key_id']\n for award_id, _, award_team_id, __, award_infos in awards\n if award_team_id == self.team_id ]\n for award in awards:\n award_id, _, award_team_id, __, award_infos = award\n if award_infos['key_id'] not in key_ids_of_team and not award_infos['public']:\n award_infos['congrat_msg'] = None\n award_infos['congrat_img_url'] = None\n award_infos['score'] = None\n\n # Removes unneeded fields\n awards = [\n (award_id, award_date.strftime('%Y-%m-%d %H:%M:%S'), award_team_name, award_infos['congrat_msg'], award_infos['congrat_img_url'], award_infos['score'])\n for award_id, award_date, award_team_id, award_team_name, award_infos in awards ]\n return awards","title":""},{"docid":"a10e9faa2f17a446fabe0eb42492f79a","score":"0.52363074","text":"def book(items):\n list1 = [ ]\n for i in items:\n if i[1] == 'book':\n list1.append(i)\n \n\n #print(list1)\n for i in list1:\n print(i[0],i[1],i[2],i[3])\n \n #print('\\n')\n #print(\"Book Category List: \")","title":""},{"docid":"9011e7ed69e422937eb5327e0fd5a057","score":"0.52341586","text":"def get_popularAuthors():","title":""},{"docid":"5bae812189214d0eca9a33a1b8283017","score":"0.5205308","text":"def all_books(self, date_format=DATE_FORMAT):\n return self.execute(queries.ALL_BOOKS, (date_format,)).fetchall()","title":""},{"docid":"8dab7fefb4df6626bf48f6bf501e8abe","score":"0.52029043","text":"def fetch_titles_from_page(page_number, get_books=True) -> ([Book], bool):\n assert type(page_number) is int\n assert type(get_books) is bool\n\n logger.debug(f\"Fetching catalog page #{page_number}... \")\n url = TITLES_URL + f\"&search_page={page_number}\"\n result = requests.get(url, headers=_get_scrape_headers())\n\n if result.status_code != 200:\n return [], False\n\n json_result = json.loads(result.text)\n if json_result[\"status\"] != \"SUCCESS\":\n return [], False\n\n if json_result[\"results\"] == \"No results\":\n return [], False\n\n books = []\n if get_books:\n html_results = json_result[\"results\"]\n scraper = BeautifulSoup(html_results, 'html.parser')\n catalog_results = scraper.find_all(\"li\", class_=\"catalog-result\")\n\n if catalog_results:\n for catalog_result in catalog_results:\n book = Book()\n result_data = catalog_result.find(\"div\", class_=\"result-data\")\n book.title = result_data.a.text.strip()\n # Remove enclosing \" or ' if any\n if book.title[0] is \"\\\"\" or book.title[0] is \"'\":\n book.title = book.title[1:]\n\n if book.title[-1] is \"\\\"\" or book.title[-1] is \"'\":\n book.title = book.title[:-1]\n\n book.url = result_data.a.attrs[\"href\"]\n book_author = result_data.find(class_=\"book-author\")\n if book_author and book_author.text:\n book.author = book_author.text.strip()\n else:\n logger.error(f\"Failed to scrape the author for book \\\"{book.url}\\\" \"\n f\"in catalog page #{page_number}\")\n if book_author and book_author.a and book_author.a.attrs[\"href\"]:\n book.author_url = book_author.a.attrs[\"href\"]\n else:\n # Books with various authors don't have an author url so this might be fine\n logger.warn(f\"Failed to scrape the author url for book \\\"{book.url}\\\" \"\n f\"in catalog page #{page_number}\")\n book.download_url = catalog_result.find(class_=\"download-btn\").a.attrs[\"href\"]\n book.size = catalog_result.find(class_=\"download-btn\").span.text.strip()\n books.append(book)\n logger.debug(f\"Fetched metadata for {len(books)} books from page #{page_number}\")\n\n else:\n logger.warn(f\"Failed to fetch books from the catalog page #{page_number}\")\n\n else:\n logger.debug(f\"Skipping scraping page #{page_number} for book metadata...\")\n\n return books, True","title":""},{"docid":"24177aea31d1974e83f92029e9fdd9e4","score":"0.51874995","text":"def get_project_cla_groups() -> List[dict]:\n return [project.to_dict() for project in get_project_cla_group_instance().all()]","title":""},{"docid":"20a060a2a5bce25663f6e65a1093082b","score":"0.51766706","text":"def get_book_all(self) -> (tuple, list):\n sql_expr = \\\n '''\n SELECT * FROM Books;\n '''\n sql_result = self._my_connect.execute_sql_read(sql_expr)\n return sql_result","title":""},{"docid":"4e5e47b31b5d480b438d8b922ac26c47","score":"0.5172181","text":"def allReviews(self,book):\n return self.query.filter_by(book_id=book.id).all()","title":""},{"docid":"c34a455b5b6e5dacc98dd48267a7c8db","score":"0.5162416","text":"def get_all_obgyn():\n \n ##first find where this condition is true for facility for obgyn\n obgyn_facility_list = Facility.query.filter_by(facility_category_name_english='Obstetrics and Gynecology').all()\n \n return obgyn_facility_list","title":""},{"docid":"36d5129ff7c35b11a63cd60cba04f239","score":"0.51620466","text":"def get_bible_books() -> {int: dict}:\n genres = get_bible_book_genres()\n\n with open(KEY_ENGLISH_PATH, 'r') as csvfile:\n reader = csv.reader(csvfile)\n headers = next(reader)\n\n book_index = headers.index(BOOK_KEY)\n name_index = headers.index(NAME_KEY)\n testament_index = headers.index(TESTAMENT_KEY)\n genre_index = headers.index(GENRE_KEY)\n dataset_index = headers.index(DATASET_KEY)\n\n return {\n int(book[book_index]): {\n 'name': book[name_index],\n 'testament': book[testament_index],\n 'genre_id': int(book[genre_index]),\n 'genre': genres[int(book[genre_index])],\n 'dataset': book[dataset_index]\n } for book in reader\n }","title":""},{"docid":"d1039cac6e05df4019fe627b9cd9b488","score":"0.5157313","text":"def harvest_all():\n db = db_connect()\n dois = None\n get_commits()\n get_citations(db, dois)\n get_mentions(since_version=None)\n list_records(dois)","title":""},{"docid":"7ea38c63d3d310e833fa6010f016c207","score":"0.5141538","text":"def cli(ctx, biomaterial_name=\"\", provider_id=\"\", biomaterial_id=\"\", organism_id=\"\", dbxref_id=\"\"):\n return ctx.gi.expression.get_biomaterials(biomaterial_name=biomaterial_name, provider_id=provider_id, biomaterial_id=biomaterial_id, organism_id=organism_id, dbxref_id=dbxref_id)","title":""},{"docid":"0552a8791165f00bd82c35a9076ab387","score":"0.5130115","text":"def get_test_bible_book_ids() -> {int}:\n return {book_id for (book_id, book) in get_bible_books().items() if book['dataset'] == 'test' }","title":""},{"docid":"b90c8edc53393d9501476ac214728a50","score":"0.5111732","text":"def get_all_books():\n\n book_info = db.books.find({}, {\"_id\": 0})\n book_info_cur = list(book_info)\n # print(book_info_cur[0][\"rating\"])\n sorted_cur = sorted(book_info_cur, key = lambda i: int(i.get('rating', 0)), reverse=True)\n if len(book_info_cur) != 0:\n return json.dumps(sorted_cur, indent=4), 200\n else:\n return \"Empty book collection in database\", 404","title":""},{"docid":"10b924ef88152a9fac406b6d482606bf","score":"0.51091206","text":"def get_all_cb_comps():\n u = urllib2.urlopen('http://api.crunchbase.com/v/1/companies.js')\n \n comps = u.read()\n\n return comps","title":""},{"docid":"37744c1787adacba8b8ce6ee9b38eb8a","score":"0.5083007","text":"def fetch_all_books(start_page=1, end_page=MAX_KNOWN_PAGE, need_update_page=False) -> [Book]:\n assert type(start_page) is int\n assert type(end_page) is int\n assert start_page < end_page\n assert type(need_update_page) is bool\n\n while need_update_page:\n logger.debug(f\"Checking if the # of LibriVox catalog pages is larger than {end_page}...\")\n _, need_update_page = fetch_titles_from_page(end_page, False)\n if need_update_page:\n end_page += 1\n logger.debug(f\"Found new catalog page #{end_page}\")\n\n logger.info(f\"Fetching LibriVox's book catalog from pages #{start_page} till #{end_page}...\")\n\n all_books = []\n \"\"\"\n # Commenting out multiprocess code for now as it crashes in Python 3.6.2 with the error:\n # \"__NSPlaceholderDate initialize] may have been in progress in another thread when fork() was called.\"\n fetched_titles = []\n with Pool(NUM_PROCESSES) as pool:\n fetched_titles = pool.map(fetch_titles_from_page, [n for n in range(start_page, end_page)])\n for result in fetched_titles:\n if result and result[1]:\n all_books.extend(result[0])\n \"\"\"\n for page_num in range(start_page, end_page):\n catalog_page, could_fetch = fetch_titles_from_page(page_num)\n if could_fetch and catalog_page:\n all_books.extend(catalog_page)\n\n return all_books","title":""},{"docid":"8f76e593d6b23f2a302b628d8dc58ba0","score":"0.5065584","text":"def listConsents(self):\n Response = self.make_request()\n return Response","title":""},{"docid":"8e6648cba3aa87f3160051c3d7766ec1","score":"0.5063357","text":"def get_all(self):\n return [DocumentCorpus(self.collection, document) for document in self.collection.find()]","title":""},{"docid":"df84247f23d395f335bc7958cdf6d32f","score":"0.5062308","text":"def fetchCompanies():\n return desk.companies.list_companies()","title":""},{"docid":"afb6780e1b14530393c586bdff951aa2","score":"0.50400996","text":"def get_citations_needed_report(URL):\n # print('top report')\n response = requests.get(URL)\n content = response.content\n soup = BeautifulSoup(content, 'html.parser')\n result = soup.find_all('a', href=\"/wiki/Wikipedia:Citation_needed\")\n\n dictionary = []\n\n for link in result:\n P = link.parent.parent.parent\n P = P.text.strip()\n dictionary.append(P)\n\n # print('above dictionary')\n for P in dictionary:\n print(P, sep='\\n')\n print('\\n')\n\n return dictionary","title":""},{"docid":"2c0aea7c5e824b0c9aecad3c418b9157","score":"0.5029921","text":"def books_title(self):\n return [i.title for i in shelf.books_list]","title":""},{"docid":"a1ad1e8add187059eb6f82e5671699c0","score":"0.5021048","text":"def getBucketlists():","title":""},{"docid":"900b206cfe54379257569656e674a907","score":"0.5011742","text":"def test_books_get(self):\n pass","title":""},{"docid":"c0920b7316709655aa523d2dd5b7398d","score":"0.49976185","text":"def fetch_all_chapters(book) -> [Chapter]:\n logger.debug(f\"Downloading info for chapters in book: \\\"{book.title[:70]}\\\"...\")\n session = download_session.make_session()\n try:\n book_page = session.get(book.url, headers=_get_scrape_headers(), timeout=70)\n except download_session.get_download_exceptions() as e:\n logger.error(f\"Scraping timed out to download chapters from book \\\"{book.url}\\\"\")\n logger.error(e)\n return []\n\n if book_page.status_code != 200:\n logger.warn(f\"Failed to download chapters information for book \\\"{book.url}\\\"\")\n return []\n\n scraper = BeautifulSoup(book_page.text, \"html.parser\")\n _fetch_missing_book_metadata(book, scraper)\n\n # Get the chapters information\n chapters = []\n chapters_table = scraper.find(\"table\", class_=\"chapter-download\")\n if not chapters_table:\n logger.error(f\"Scraping failed to find the chapters table for book \\\"{book.url}\\\"\")\n return chapters\n\n chapter_rows = chapters_table.find_all(\"tr\")[1:] # The first row is the table headers\n if not chapter_rows:\n logger.error(f\"Scraping failed to find the chapters rows in the chapters table for book \\\"{book.url}\\\"\")\n return chapters\n\n num_row_elements = len(chapter_rows[0].find_all(\"td\"))\n if num_row_elements == 7:\n for row in chapter_rows:\n chapter = Chapter(book)\n chapter.title = row.find(\"a\", class_=\"chapter-name\").text\n chapter.download_url = row.find(\"a\", class_=\"chapter-name\").attrs[\"href\"]\n row_elements = row.find_all(\"td\")\n if not row_elements or not row_elements[0] or not row_elements[0].a:\n logger.error(f\"Scraping failed for chapter metadata of book \\\"{book.url}\\\"\")\n break\n\n chapter.number = int(row_elements[0].text.replace(row_elements[0].a.text, \"\").strip())\n if len(row_elements) > 2:\n chapter.author = row_elements[2].text.strip()\n if row_elements[2].a:\n chapter.author_url = row_elements[2].a.attrs[\"href\"]\n\n if len(row_elements) > 3:\n chapter.source_text = row_elements[3].text.strip()\n if row_elements[3].a:\n chapter.source_text_url = row_elements[3].a.attrs[\"href\"]\n\n if len(row_elements) > 4:\n chapter.reader_name = row_elements[4].text.strip()\n if row_elements[4].a:\n chapter.reader_url = row_elements[4].a.attrs[\"href\"]\n\n if len(row_elements) > 5:\n chapter.duration = row_elements[5].text.strip()\n\n if len(row_elements) > 6:\n chapter.language_code = row_elements[6].text.strip()\n # If no language_code was found, we fallback to use the book's language\n # This is not perfect as the book's language can be \"multilingual\" and chapters\n # are supposed to be recorded in only one language\n if len(row_elements) < 6:\n logger.warn(f\"Found an unexpected number of columns in the chapter row \\\"{chapter.title}\\\"\")\n\n chapters.append(chapter)\n\n else:\n logger.error(f\"Scraping failed for chapter metadata of book \\\"{book.url}\\\"\")\n\n elif num_row_elements == 4:\n for row in chapter_rows:\n chapter = Chapter(book)\n chapter.title = row.find(\"a\", class_=\"chapter-name\").text\n chapter.download_url = row.find(\"a\", class_=\"chapter-name\").attrs[\"href\"]\n row_elements = row.find_all(\"td\")\n if not row_elements or not row_elements[0] or not row_elements[0].a:\n logger.error(f\"Scraping failed for chapter metadata of book \\\"{book.url}\\\"\")\n break\n\n chapter.number = int(row_elements[0].text.replace(row_elements[0].a.text, \"\").strip())\n if len(row_elements) > 2:\n chapter.reader_name = row_elements[2].text.strip()\n # Chapters read by a group of people don't have a link to the reader's profile\n if row_elements[2].a:\n chapter.reader_url = row_elements[2].a.attrs[\"href\"]\n\n if len(row_elements) > 3:\n chapter.duration = row_elements[3].text.strip()\n # The chapter's language will be set to the book's language via the chapter's init\n chapter.author = book.author\n chapters.append(chapter)\n\n else:\n logger.error(f\"Scraping failed for metadata of chapter \\\"{chapter.title}\\\"\")\n else:\n logger.error(f\"Found an unknown number ({num_row_elements}) of chapter_rows in \"\n f\"the book page of \\\"{book.url}\\\"\")\n\n logger.debug(f\"Finished downloading info for {len(chapters)} chapters in book \\\"{book.title[:50]}\\\"\")\n return chapters","title":""},{"docid":"82f50f6b850e4250535ba4a318356de3","score":"0.49940458","text":"def get_campos(self):\n campos = DBSession.query(Campos).all()\n return campos","title":""},{"docid":"04b04e5dc6586c5a26cd4999c0e5428a","score":"0.49917156","text":"def group_by_author(self, author: Author):\n return list(filter(lambda book: book.author == author, self.books))\n # return [book for book in self.books if book.author == author]","title":""},{"docid":"575ee14284891d05a646ecd5e8b93d04","score":"0.49862415","text":"def get_books(date, list_name):\n \n response = requests.get('http://api.nytimes.com/svc/books/v3/lists/%s/%s.json?api-key=%s'%(str(date), list_name, key))\n response = response.json()\n\n num_results = response['num_results']\n fetched = 20\n names = [book['title'] for book in response['results']['books']]\n\n while num_results > fetched:\n time.sleep(0.125)\n response = requests.get('http://api.nytimes.com/svc/books/v3/lists/%s/%s.json?offset=%d&api-key=%s'%(str(date), list_name, fetched, key))\n response = response.json()\n names += [book['title'] for book in response['results']['books']]\n fetched += 20\n\n return set(names), datetime.datetime.strptime(response['results']['published_date'], \"%Y-%m-%d\")\n\n import datetime","title":""},{"docid":"6754e5ddfdd274ebad8c7dc16e92ecf5","score":"0.49814323","text":"def get_books_by_author_by(author_id):\n\n return Book.query.filter(Book.author_id == author_id).all()","title":""},{"docid":"059da709ae06cf5dc3abb55bfd751068","score":"0.49789444","text":"def address_book():\n return [(beings[k], locales[v]) for k,v in residencies.items()]","title":""},{"docid":"13ed1135b97bc4d8ad273ec5643edb03","score":"0.49710968","text":"def get_books(self, top_k=None, cache=True):\n # Reset the books saved in the cache if its length is under the threshold\n if top_k and len(self._books) < top_k:\n self._books = []\n # Get the top-k books, ordered from the author's book page\n books = []\n for i, book in enumerate(self.books(cache=cache)):\n book.register_author(self)\n books.append(book)\n if top_k and i + 1 >= top_k:\n break\n return books","title":""},{"docid":"fa7897ea3b6bb81356248b187a8dd014","score":"0.4967275","text":"def get_books_contained_by_version(bible_version: dict) -> [int]:\n table_path = TABLE_DIRECTORY / TABLE_NAME_FORMAT.format(table = bible_version['table'])\n\n with open(table_path, 'r', encoding='utf-8') as csvfile:\n reader = csv.reader(csvfile)\n\n headers = next(reader)\n book_index = headers.index(BOOK_KEY)\n\n return sorted({int(verse[book_index]) for verse in reader})","title":""},{"docid":"ad55ea24f2e82e232ae5f2893b4d496c","score":"0.49647325","text":"def get_bills(self):\n return Bill.objects.filter(club=self)","title":""},{"docid":"75f7e52d5e64c5f32df2d433bc20aaa9","score":"0.4963884","text":"def get_finished_books_by_criteria(search_criteria, patron_id):\n criteria = '%' + search_criteria + '%'\n search_result = (db_session.query(Finished_Book,\n Book,\n Book_Author,\n Author)\n .filter(Finished_Book.book_id == Book.id,\n Finished_Book.patron_id == patron_id,\n Book.id == Book_Author.book_id,\n Book_Author.author_id == Author.id,\n Book.title.like(criteria) |\n Author.name.like(criteria))\n .order_by(Book.title).all()) \n\n books_grouped_by_id = itertools.groupby(search_result, lambda x: x[1].id)\n\n list_of_books = []\n for book_id, result_set in books_grouped_by_id:\n\n\n for d in result_set:\n list_of_books += [(d[1].title, d[1].cover_png, d[0].date, d[3].name,\n \"\".join(r[3].name for r in result_set))]\n #end for\n #end for\n\n return list_of_books","title":""},{"docid":"8eefcef1d4d158585d11392c8d9cf137","score":"0.49480593","text":"def html_sample(self,limit=20):\n books = self.query.order_by(self.isbn).all()\n books = books[:limit]\n data = []\n for book in books:\n isbn = book.isbn\n desc = GoodReads.getDescription(isbn)\n data.append(desc)\n return data","title":""},{"docid":"f9fb78dccddb588333440f822406b273","score":"0.49452752","text":"def testBook():\n\n # book build\n books = []\n books.append(Book(\"The Hobbit\", \"J.R.R. Tolkien\", \"Doug\", [\"Wendy\"]))\n books.append(Book(\"Greek Myths\", \"Troy Miller\", \"Bob\", []))\n books.append(Book(\"Python Programming\", \"Ken Lambert\", \"Wendy\", [\"Bob\", \"Doug\"]))\n books.append(Book(\"Introduction to Computers\", \"Ted Murphy\", \"Wendy\", []))\n books.append(Book(\"P.H.P. Basics\", \"Michael Smith\", \"Wendy\", [\"Bob\"]))\n\n print(\"Current Books:\")\n print()\n for i in range(len(books)):\n print(\"\\tTitle: \", books[i].getTitle())\n print(\"\\tAuthor: \", books[i].getAuthor())\n print(\"\\tChecked Out To: \", books[i].getCurrentPatron())\n print(\"\\tCurrent Waiting List: \", books[i].getWaitingList())\n print()","title":""},{"docid":"4f274280e00eaca4f1c5bad1369dd8c2","score":"0.49442163","text":"def books(self, cache=True):\n if len(self._books) > 0 and cache:\n yield from self._books\n else:\n yield from self._search_books()","title":""},{"docid":"6af53be12cad589993d2fd84f01ca72c","score":"0.49382204","text":"def get(self):\n courses = list(Course.objects.all())\n return courses","title":""},{"docid":"6c9c566457fe64b0dab5057945228a3c","score":"0.4929902","text":"def get_book():\n # Check if an ID was provided as part of the URL.\n if 'id' in request.args:\n # If ID is provided, assign it to a variable.\n id = int(request.args['id'])\n else:\n # If no ID is provided, display an error in the browser.\n return \"Request error: no id field provided\"\n\n # prepare results variable (empty list)\n results = []\n\n import raw\n for book in raw.get_books():\n if book['id'] == id:\n results.append(book)\n\n # return data in json format\n return jsonify(results)","title":""},{"docid":"b48cade1199a9ddcd0bc3ec12b8a4ef6","score":"0.4925487","text":"def get_projects_by_accession(accession):\n project = Project()\n print(project.get_by_accession(accession))","title":""},{"docid":"09c8ca693e70b4c298d05c01aa7a575d","score":"0.492406","text":"def load_all_chapters(self):\n folder = join(\"/Volumes/SSD01/dialogues\",self.this_Book.txt_file_path)\n\n # self.files = listdir(join(\"data/Kindle-combined-txt\",str(isbn)))\n # no reason to get too complicated here\n self.txtfiles = [x for x in listdir(folder) if \".txt\" in x]\n\n # print(\"a sample of the text files:\")\n # print(self.txtfiles[:10])\n\n rawtext_by_chapter = []\n for fname in self.txtfiles:\n f = open(join(folder,fname),\"r\")\n rawtext_by_chapter.append(f.read())\n f.close()\n # word_lists_by_chapter = [listify(t) for t in rawtext_by_chapter]\n # apply spacy to each of them...\n word_lists_by_chapter = [[str(token) for token in nlp(t) if ((not token.is_punct) and (len(str(token)) != 0))] for t in rawtext_by_chapter]\n self.chapter_ends = cumsum(list(map(len,word_lists_by_chapter)))\n # add a 0 to the start, clip (to get the starts)\n # could just move the above array around too...\n self.chapter_beginnings = cumsum([0]+list(map(len,word_lists_by_chapter[:-1])))\n self.chapter_centers = (self.chapter_ends+self.chapter_beginnings)/2\n# print(list(map(len,self.word_lists_by_chapter)))\n# print(self.chapter_ends)\n# print(self.chapter_beginnings)\n# print(self.chapter_centers)\n# print(len(self.chapter_ends))\n# print(len(self.word_lists_by_chapter))\n self.all_word_list = list(itertools.chain(*word_lists_by_chapter))","title":""},{"docid":"f2cca76f77e96af07bcbb2595db675b9","score":"0.49230716","text":"def get_book():\n soup = Soup(CONTENT)\n title = str(soup.find_all(\"h2\")[0]).replace('<h2>', '').replace('</h2>', '').strip()\n desc = str([entry for entry in soup.find_all('div', class_=\"dotd-main-book-summary float-left\")][0]).split('<div')[4][2:-7].strip()\n img = soup.findAll('img')[5]['src']\n link = soup.find_all('div', class_=\"dotd-main-book-image float-left\")\n\n for div in link:\n link_p = div.find('a')['href']\n return Book(title, desc, img, link)","title":""},{"docid":"5b56f9049b0a5af56044011ccf3ea9a3","score":"0.49147058","text":"def get_all():\n return Doctor.query.all()","title":""},{"docid":"c50746a02d51d94ca427f68de0b57a4c","score":"0.49130303","text":"def get_mine(self):\n\n self._init_dict_chal_keys()\n\n # Gets award informations\n filter_award_name = 'plugin_intermflag_' + str(self.chal_id) + '_%'\n awards_query_result = db.session.query(\n Awards.id.label('award_id'), Awards.date, Awards.name.label('award_name'),\n ).filter(Awards.teamid==self.team_id).filter(Awards.name.like(filter_award_name)).order_by(Awards.date).all()\n\n # Joins award datas and key datas\n awards = [\n (award.award_id, award.date, self._award_infos(award.award_name))\n for award in awards_query_result\n ]\n awards = [\n award\n for award in awards\n if award[-1] is not None\n ]\n\n # Removes unneeded fields\n awards = [\n (award_id, award_date, award_infos['congrat_msg'], award_infos['congrat_img_url'], award_infos['score'])\n for award_id, award_date, award_infos in awards ]\n return awards","title":""},{"docid":"57ba6f0cc57d7662c698b5a07c807065","score":"0.4912252","text":"def get(self):\n all_blogss = get_all_blogss()\n print(\"All blogss=\",all_blogss)\n return all_blogss","title":""},{"docid":"147720b020aedaa97fc349619755f841","score":"0.49097124","text":"def get_authors(venue):\r\n papers=0\r\n results = dblp.search_pub(venue)\r\n\r\n hits = json.loads(results)[\"result\"][\"hits\"]\r\n for i in hits[\"hit\"]:\r\n info = i[\"info\"]\r\n if filter_papers(info, venue):\r\n papers+=1\r\n # print(info)\r\n if 'authors' in info:\r\n if isinstance(info[\"authors\"][\"author\"], list):\r\n for author in info[\"authors\"][\"author\"]:\r\n update_authors(author[\"@pid\"], author[\"text\"], info[\"key\"])\r\n else:\r\n author = info[\"authors\"][\"author\"]\r\n update_authors(author[\"@pid\"], author[\"text\"], info[\"key\"])\r\n\r\n return papers","title":""},{"docid":"87519999fe1b78f5a4f1835d9dfd27a6","score":"0.49049982","text":"def list_csrs():\n cmd = subprocess.Popen([PUPPETCA, '--list'], stdout = subprocess.PIPE, stderr = subprocess.STDOUT)\n out, err = cmd.communicate()\n csrs = out.split('\\n')\n return [r for r in csrs if r]","title":""},{"docid":"d9009a2f36c1438806386d3cfd6803ea","score":"0.4901347","text":"def resolve_labbook_list(self, info):\n return LabbookList(id=\"\")","title":""},{"docid":"16b462d0dbc43003f675551cd3ba7b18","score":"0.48985198","text":"def testimonials(self):\n testimonials = self.context.getRefs(relationship=\"course_testimonial\") \n return testimonials","title":""},{"docid":"ad48868230e24c2b9483e22cdb8e3c83","score":"0.48958287","text":"def springer_bib(year, conf, link):\n html_file = download_to_hash(link)\n soup = BeautifulSoup(open(html_file), 'lxml')\n res = ''\n for paper in soup.select('.chapter-item'):\n meta = paper.select('.content-type-list__meta')[0]\n title = meta.select('div')[0].get_text()\n authors_str = meta.select('div')[1].get_text()\n authors = authors_str2lst(authors_str)\n pdflink_a = paper.select('a.test-book-toc-download-link')\n pdflink = ''\n # some conference may not have a pdflink, e.g.\n # https://link.springer.com//book/10.1007/BFb0015518\n if pdflink_a:\n pdflink = pdflink_a[0]['href']\n if not pdflink.startswith('http'):\n pdflink = 'https://link.springer.com/' + pdflink\n id = gen_id(year, conf, authors, title)\n bib = gen_single_bib(id, title, ' and '.join(authors), pdflink, year, conf)\n res += bib\n return res","title":""},{"docid":"72543b0b93f999ed139508daec9bd423","score":"0.4895037","text":"def test_get_all_quotes_by_book(self):\n # hit the API endpoint\n response = self.client.post(\n reverse(\"quotes-book\", kwargs={\"version\": \"v1\"}), {\"book\": \"Don Quijote de la Mancha\"}\n )\n # fetch the data from db\n book = Book.objects.filter(title=\"Don Quijote de la Mancha\")[:1].get()\n expected = book.quotes\n serialized = QuoteSerializer(expected, many=True)\n self.assertEqual(response.data, serialized.data)\n self.assertEqual(response.status_code, status.HTTP_200_OK)","title":""},{"docid":"67cc7015b51ab776bba4148fff31bcc3","score":"0.4884307","text":"def list():\n return Company.query.all(), HTTPStatus.OK","title":""},{"docid":"b51ba71ff07fffae14bce8c9be190339","score":"0.48839167","text":"def get_CA(self):\n\n\t\treturn [r.Ca for r in self.res]","title":""},{"docid":"c8e49af7ea1ca8720574179626671b90","score":"0.4882444","text":"def getProjectMaintainers(self, project):\n tree = ElementTree.fromstring(''.join(core.show_project_meta(self.apiurl,\n project)))\n maintainers = []\n for person in tree.findall('person'):\n if person.get('role') == \"maintainer\":\n maintainers.append(person.get('userid'))\n return maintainers","title":""},{"docid":"46ce485964905983bcd771a5595c703a","score":"0.4879896","text":"def readBook2(self):\n # summary from https://www.amazon.ca/Essential-Calvin-Hobbes-Bill-\n # Watterson/dp/0836218051/ref=sr_1_1?crid=1YAN87VEA3JWD&keywords=the+\n # essential+calvin+and+hobbes&qid=1579321108&sprefix=the+essential+cal\n # %2Caps%2C181&sr=8-1\n print(form.border(\"\"\"\nTHE ESSENTIAL CALVIN AND HOBBES by Bill Watterson\n Beginning with the day Hobbes sprang into Calvin's tuna fish trap, the\n first two Calvin and Hobbes collections, Calvin and Hobbes and Something\n Under The Bed Is Drooling, are brought together in this treasury.\n Including black-and-white dailies and color Sundays, The Essential Calvin\n and Hobbes also features an original full-color 16-page story.\n \"\"\"))","title":""},{"docid":"0806836cfdeb94bad73bc76a354702ab","score":"0.4878542","text":"def most_client_report_order_books(self):\n\t\tclient_list = self.__borrow_controller.getReverseListClients()\n\t\tclient_list = self.__client_controller.getListFromIdsOrderBooks(client_list)\n\t\tprint(\"\\n\" + '-------=======-------')\n\t\tfor client in client_list:\n\t\t\tprint(client, end='')","title":""},{"docid":"bf2980bb0e099750f2f635458aa2c677","score":"0.4877945","text":"def get_book():\n id = get_input(\"id? \")\n r = requests.get(url = API_ENDPOINT + \"/\" + id)\n\n result_text = r.text\n print(\"\")\n print(\"result: \" + result_text)","title":""},{"docid":"230e1d7187be7c6342949083411ad154","score":"0.4870439","text":"def bid_list():\n # Grab all projects\n projects = request_api(project_path)\n\n # Parse and find \"Schnabel\" project id\n sfc_id = None\n for project in projects:\n if project[\"name\"] == \"Schnabel\":\n sfc_id = project[\"id\"]\n break\n\n # Grab all sections in \"Schnabel\" project\n params = {\"project_id\": sfc_id}\n sections = request_api(section_path, params=params)\n\n # Determine \"Bids\" section id\n bidding_id = None\n for section in sections:\n if section[\"name\"] == \"Bids\":\n bidding_id = section[\"id\"]\n break\n \n # Grab all tasks in \"Schnabel\" project\n params = {\"project_id\": sfc_id}\n tasks = request_api(task_path, params=params)\n\n # Filter tasks based on section id\n bid_tasks = []\n for task in tasks:\n if task[\"section_id\"] == bidding_id:\n bid_tasks.append(task)\n\n # Sort and return bid_tasks\n bid_tasks_sorted = quicksort(bid_tasks)\n return bid_tasks_sorted","title":""},{"docid":"01480a553da0b344569b406ff2d515bc","score":"0.4866259","text":"def getBook(self, _id):\n db = self.getBookDB()\n book=None\n try:\n value = (_id,)\n db.execute('select * from books where _id=?',value)\n book = self.getBookFromList(db.fetchone())\n except Exception as e:\n print(e)\n return book","title":""},{"docid":"7e2fbd99ee5e9dca5b830621f90e17f6","score":"0.48623312","text":"def get_available_cops():\n allIncidents = Incident.get_all()\n cops = []\n \n for i in allIncidents:\n if(inicioAmostragem <= i.reporting_date and i.reporting_date <=terminoAmostragem):\n# cops.append(i['operations_center']['id'])\n#conf\n cops.append(i['operations_center'])\n \n allReports = RelatoDeSituacao.get_all()\n \n for r in allReports:\n if (\n inicioAmostragem <= r.data_hora and \n r.data_hora <=terminoAmostragem and\n 'cop' in r.relator \n# and # todos tem que ter o COP\n#conf 'id' in r.relator['cop'] # todos tem que ter o id \n ):\n# cops.append(r.relator['cop']['id'])\n#conf \n cops.append(r.relator['cop'])\n\n allSincronizations = Sincronizacao.get_all()\n for sinc in allSincronizations:\n for action in sinc.acoes:\n if ( \n ((action.tipo == 'PONTUAL') and (action.inicio >= inicioAmostragem) and (action.inicio <= terminoAmostragem)) or\n ((action.tipo == 'INTERVALO') and (action.inicio >= inicioAmostragem and action.fim <= terminoAmostragem))\n ):\n cops.append(sinc.cop_responsavel['id'])\n \n return set(cops)","title":""},{"docid":"42bd5fff3bf83a8c1d6b707a4b206d95","score":"0.48550457","text":"def get_book(self, request, pk):\n query_set = Library.objects.get(id=pk)\n data = self.serializer_class(query_set, context={'request': request}).data\n return Response({'result': data}, status=status.HTTP_200_OK)","title":""},{"docid":"8243269654818ba0873eeae14ec49e4f","score":"0.48539725","text":"def print_catalog(self):\n print(\"THIS IS THE CATALOG OF ALL OUR BOOKS:\")\n for book, amount in self.books.items():\n print(book.title + ' - rating: ' + str(amount))","title":""},{"docid":"a58870f9f4fa8d5d3502ac32f7e3d318","score":"0.48537493","text":"def get_accounts():","title":""},{"docid":"a58870f9f4fa8d5d3502ac32f7e3d318","score":"0.48537493","text":"def get_accounts():","title":""},{"docid":"229b0e45cd060f50cefc112660732f2e","score":"0.4853242","text":"def extract_books(soup):\n books = {}\n for d in soup.find_all('div', 'bookMain'):\n asin = sub(r'_.*$', '', d['id'])\n title = d.find('span', 'title').text.strip()\n author = sub(r'by ', '', d.find('span', 'author').text.strip())\n author = sub('\\n', '', author)\n\n books[asin] = dict(asin=asin, title=title, author=author)\n\n return books","title":""},{"docid":"31d79332d6e68cd849b57c61ecb12b0d","score":"0.4850605","text":"def getBestBooks(catalog, number):\n books = catalog['books']\n bestbooks = lt.newList()\n for cont in range(1, number+1):\n book = lt.getElement(books, cont)\n lt.addLast(bestbooks, book)\n return bestbooks","title":""},{"docid":"c515019aac9c9b246e05855a0943f385","score":"0.4849605","text":"def get_all_biblio(page=1):\n query = BiblioEntry.query.paginate(page, ITEMS_PER_PAGE, False).items\n bibdat = convert_rows_to_dict(query)\n years = [str(value.year)\n for value in db.session.query(BiblioEntry.year).distinct()]\n tags = [str(value.tag)\n for value in db.session.query(BiblioEntry.tag).distinct()]\n templateVars = format_bibdatabase(bibdat)\n years.sort()\n templateVars[\"years\"] = years[::-1]\n templateVars[\"tags\"] = tags\n templateVars[\"nentries\"] = BiblioEntry.query.count()\n return render_template(\"references.html\", **templateVars)","title":""},{"docid":"0bbdb4c670cd4d67bb669d273d3d2c90","score":"0.48495772","text":"def papers(self):\n if self.all_papers == None:\n name = self.name\n orcid = self.orcid\n if not isinstance(name, str) and name != None:\n papers_name = list(ads.SearchQuery(\n author=name[0], fl=self.fl, **self.kw))\n papers_name_id = [x.id for x in papers_name]\n for kname in range(1, len(name)):\n papers_kname = list(ads.SearchQuery(\n author=name[kname], fl=self.fl, **self.kw))\n for pkn in papers_kname:\n if pkn.id not in papers_name_id:\n papers_name.append(pkn)\n papers_name_id.append(pkn.id)\n elif name != None:\n papers_name = list(ads.SearchQuery(\n author=name, fl=self.fl, **self.kw))\n papers_name_id = [x.id for x in papers_name]\n else:\n papers_name = []\n papers_name_id = []\n\n if orcid != None:\n papers_orcid = list(ads.SearchQuery(\n orcid=orcid, fl=self.fl, **self.kw))\n for porc in papers_orcid:\n if porc.id not in papers_name_id:\n papers_name.append(porc)\n papers_name_id.append(porc.id)\n\n self.ncite_all = [x.citation_count for x in papers_name]\n self.all_papers = PapersManager(papers_name)\n\n return self.all_papers","title":""},{"docid":"43387e866359b532e0f7ae9d8daf3237","score":"0.48454508","text":"def list_bitbucketserver_projects(self, almSettings):","title":""},{"docid":"321584db0441ace5dec0e13bdd715a5f","score":"0.48432878","text":"def test_get_all_audiobook_ok(client):\n\n with app.test_request_context():\n response = client.get(url_for(\"get_all_audio\", audioFileType=\"audiobook\"))\n\n assert response.status_code == 200\n res_data = response.get_json()\n record = list(filter(lambda x: x[\"id\"] == AUDIOBOOK_ID, res_data))\n assert len(record) == 1\n assert record[0][\"title\"] == \"Tale of two cities\"","title":""},{"docid":"e6c1dc6814ec66c490722a9594aee29a","score":"0.4835043","text":"def books_surname(self):\n return [i.author_sur for i in shelf.books_list]","title":""}],"string":"[\n {\n \"docid\": \"edff7cd81254a3f14281af7177212839\",\n \"score\": \"0.6072002\",\n \"text\": \"def getBooks():\\n ret = {}\\n books = []\\n iter = model.getBooks()\\n while iter.next():\\n books.append((iter.id, iter.name))\\n\\n ret['books'] = books\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c20ccd70c0b4dc0f21126c5b35a2add6\",\n \"score\": \"0.60219944\",\n \"text\": \"def get_all_books():\\n\\n # return Book.query.all()\\n return Book.query.order_by(Book.title).all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bf9f7f1f1c9dad337225ac773f549ca\",\n \"score\": \"0.5911421\",\n \"text\": \"def books(self):\\n return self._results_dict['books']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffaac922289e589946ee40ee82d085cd\",\n \"score\": \"0.59017754\",\n \"text\": \"def makeBooksArray():\\n books=[]\\n metadata = metainfo.readmetadata()\\n for key in metadata:\\n if( (len(metadata[key]['subjects']) > 0) and( metadata[key]['subjects'] is not None) and (metadata[key]['downloads']>75) and ( metadata[key]['author'] is not None) and (\\\"en\\\" in metadata[key]['language']) ):\\n #authorNames = extractNames(metadata[key]['author'])\\n genreString = ', '.join(metadata[key]['subjects'])\\n books.append(Book(key,metadata[key]['title'],metadata[key]['author'],genreString,0,0))\\n return books\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd0291c5e68e86d1df97ac53b11c36a3\",\n \"score\": \"0.5664254\",\n \"text\": \"def bookkeeping_all():\\n result = handlers.BookKeeping().retrieve_bookkeeping_all()\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1614d4de7151421f0c9791faa015e5c\",\n \"score\": \"0.5599402\",\n \"text\": \"def get_all_books() -> List[Dict]:\\n with open(BOOKS, 'r') as in_file:\\n return [_parse_from_line(line) for line in in_file]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"237be531bc784d39ef4a4f3921d9d799\",\n \"score\": \"0.5585883\",\n \"text\": \"def get_books():\\n books = Book.query.all()\\n json_books = []\\n for book in books:\\n json_books.append(book.to_json())\\n return jsonify(json_books), 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef68a6b4baab0918e4133a86782e5c38\",\n \"score\": \"0.5565488\",\n \"text\": \"def getClips(book_id):\\n ret = {}\\n clips = []\\n it = model.getClipsByBookId(book_id)\\n while it.next():\\n clips.append((it.id, it.content))\\n pass\\n\\n ret['clips'] = clips\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9822c9b8267f4bbe3be1a2a68d825825\",\n \"score\": \"0.5564752\",\n \"text\": \"def books():\\n import raw\\n return jsonify(raw.get_books())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbc1d921dc9c507e31bd65a8ccb389bf\",\n \"score\": \"0.55092233\",\n \"text\": \"def get_book(self, top_n=None):\\n return {\\n 'asks': self.get_asks(top_n=top_n),\\n 'bids': self.get_bids(top_n=top_n)\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97c3fb06b8fb3ae945e9ec38ceb52e37\",\n \"score\": \"0.5496954\",\n \"text\": \"def get_all_books_alphabetical(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e546d6dc1b8c1cf1b01fb12e3d27bfc\",\n \"score\": \"0.54952437\",\n \"text\": \"def get_all_books_release_year(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3530625f47dfbaac23e3bb2548db518b\",\n \"score\": \"0.5468389\",\n \"text\": \"def getBorrowedBooks(self):\\n return self.__borrowedBooks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9732865d0e328131cc0a5c7aed478c34\",\n \"score\": \"0.546294\",\n \"text\": \"def get_all_books(self, book_type):\\n validate_type(book_type)\\n return [book.serialize() for book in self.list_books if book.type == Type(book_type)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a73881df25c2175e71e8c0915b410a8\",\n \"score\": \"0.5461085\",\n \"text\": \"def get_bible(out):\\n for book in BOOKS:\\n book_chapters = IBook(book).chapter_to_text(version=\\\"basicenglish\\\")\\n with open(Path(out) / f\\\"{book}.json\\\", \\\"w\\\") as out_json:\\n json.dump(book_chapters, out_json)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df876f5293768f809cb674acdd1282ae\",\n \"score\": \"0.5460983\",\n \"text\": \"def get_books(self):\\n files = [f for f in listdir(self.text_dir)]\\n for author, title, period, url in self.json():\\n filename = format_filename(author, title)\\n if not filename in files:\\n logger.debug(\\\"Getting %s\\\" % filename)\\n book = self.extractor.download_book(url, False, author, title, period)\\n else:\\n logger.debug(\\\"%s already downloaded\\\" % filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c25455989ae0ff94927d623ecc64382\",\n \"score\": \"0.54381627\",\n \"text\": \"def getNyTimesBooks():\\n lists = [lst['list_name_encoded'] for lst in requests.get('https://api.nytimes.com/svc/books/v3/lists/names.json?api-key=1bfa24a95061415dbc8d4a4f136329a5').json()['results']]\\n global printout\\n for l in lists[:1]:\\n results = requests.get('https://api.nytimes.com/svc/books/v3/lists.json?list='+l+'&api-key=1bfa24a95061415dbc8d4a4f136329a5').json()\\n for r in results['results']:\\n isbn = r['book_details'][0]['primary_isbn13']\\n list = r['list_name']\\n try:\\n GRBook_req = requests.get('https://www.goodreads.com/book/isbn_to_id/'+isbn+'?key='+API_KEY['GOODREADS'])\\n if(GRBook_req.status_code == 200):\\n getGRBookByID(int(GRBook_req.text), list)\\n except ExpatError as e:\\n print(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48efe85434fc0b68dd59a1906bbfe648\",\n \"score\": \"0.54263264\",\n \"text\": \"def citations(self):\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48ee710991bee60d2ba6ceafc328780f\",\n \"score\": \"0.5416807\",\n \"text\": \"def getLooks(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3cb373aca17c84403726111cf0ef93f\",\n \"score\": \"0.5414597\",\n \"text\": \"def report_books(self):\\n for book in self._books.itervalues():\\n print(book.report())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6304b84bc10f74f192ff44ede1114e7a\",\n \"score\": \"0.5382276\",\n \"text\": \"def get_all_project_details() -> list:\\n\\n list_project = db_connector.collection(Collections.PROJECTS).find(\\n {}, {\\\"_id\\\": 0}).sort(\\\"project_name\\\", pymongo.ASCENDING)\\n list_projects_to_be_send = []\\n for project in list_project:\\n list_projects_to_be_send.append(project)\\n return list_projects_to_be_send\",\n \"title\": \"\"\n },\n {\n \"docid\": \"672c738042ef32362a7eaa5ce36e731a\",\n \"score\": \"0.5363474\",\n \"text\": \"def get_chapters():\\n q = 'project/'\\n chapters = exec_jira_query(q)\\n tcs = []\\n for c in chapters:\\n k = c['key']\\n if k in relevant_chapter_keys:\\n tcs.append(_build_chapter(c))\\n return tcs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ad5bfe68833e5763701cec6d8345a9c\",\n \"score\": \"0.5345248\",\n \"text\": \"def get_books(self, shelf):\\n titles = self.parse_titles(shelf)\\n isbns = self.parse_isbns(shelf)\\n isbn13s = self.parse_isbn13s(shelf)\\n authors = self.parse_author(shelf)\\n\\n books = []\\n for i in range(len(titles)):\\n b = Book(titles[i], isbns[i], isbn13s[i], authors[i])\\n books.append(b)\\n return books\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74cf32b3febfbaa7e8c684f007192f04\",\n \"score\": \"0.53447914\",\n \"text\": \"def books(self):\\n if not hasattr(self, '_books'):\\n self._books = list(Bestseller.select().where(Bestseller.author == self))\\n\\n return self._books\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1a081d8885482958cb5ffbf91794e50\",\n \"score\": \"0.53246725\",\n \"text\": \"def bookshelves_subjects(self, book_id):\\n select = \\\"SELECT DISTINCT ?subject ?bookshelf\\\"\\n pattern = f\\\"\\\"\\\"\\n WHERE {{\\n <{book_id}> dcterms:subject [dcterms:title ?subject];\\n pgterms:bookshelf [dcterms:title ?bookshelf] ;\\n dcterms:type dcmitype:Text. \\n }}\\n ORDER BY ?subject\\n \\\"\\\"\\\"\\n query = self._namespace + select + pattern\\n query = self._get_query_results(query)\\n\\n subjects = [result[\\\"subject\\\"] for result in query]\\n bookshelves = [result[\\\"bookshelf\\\"] for result in query]\\n\\n return {\\\"bookshelves\\\": set(bookshelves), \\\"subjects\\\": set(subjects)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"035f5538e005f363b6d8bec20bd36a8e\",\n \"score\": \"0.5317984\",\n \"text\": \"def book_list(username):\\n reviews=Review.objects.filter(creator__username__contains=username)\\n return {'books_read':[r.book.title for r in reviews]}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd584a6e594699b465eb8376480b73b8\",\n \"score\": \"0.5293692\",\n \"text\": \"def get_all_book_ids(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be9122f82f2ad38b2c9797839d77dae1\",\n \"score\": \"0.5280353\",\n \"text\": \"def test_get_all_company_fundamentals(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1fb51aa792d13daf074d9ca509842b5\",\n \"score\": \"0.5274824\",\n \"text\": \"def get_bibliography(self, id):\\n select = \\\"SELECT DISTINCT ?title\\\"\\n pattern = f\\\"\\\"\\\"\\n WHERE {{\\n ?book_id purl:creator <{id}> .\\n {self._books}\\n }}\\n ORDER BY ?title\\n \\\"\\\"\\\"\\n\\n query = self._namespace + select + pattern\\n return [result[\\\"title\\\"] for result in self._get_query_results(query)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35ed5a246c763eda36d66954f9ac9472\",\n \"score\": \"0.5273621\",\n \"text\": \"def getCaptials():\\n query_strings = request.args.get('query')\\n search_strings = request.args.get('search')\\n book = capital.Capital()\\n if not query_strings and not search_strings:\\n return jsonify(book.fetch_20Capitals()), 200\\n else:\\n result = book.fetch_capitals(query_strings, search_strings)\\n return jsonify(result), 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f94f6c712c4fb595164c705a98aca96a\",\n \"score\": \"0.5262072\",\n \"text\": \"def report_all_books(self):\\n print(\\\"= All Books at {library} =\\\".format(library=self))\\n print(\\\"+ {:^30} | {:^30} | {:^10} +\\\"\\n .format(\\\"Title\\\", \\\"Author\\\", \\\"Copies\\\"))\\n for shelf in self._shelves.itervalues():\\n print(\\\"Shelf - {shelf}\\\".format(shelf=shelf))\\n shelf.report_books()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"852fd0932e249ca1b9b0513846f2beb5\",\n \"score\": \"0.52414143\",\n \"text\": \"def get_all(self):\\n\\n self._init_dict_chal_keys()\\n\\n # REC FUTURE BIG : woups. Seems that underscore is a special char in the LIKE clauses. Will fix that later.\\n # Gets award informations\\n filter_award_name = 'plugin_intermflag_' + str(self.chal_id) + '_%'\\n awards_query_result = db.session.query(\\n Awards.id.label('award_id'), Awards.date, Awards.name.label('award_name'),\\n Teams.id.label('team_id'), Teams.name.label('team_name')\\n ).join(Teams).filter(Awards.name.like(filter_award_name)).order_by(Awards.date).all()\\n\\n # Joins award datas and key datas\\n awards = [\\n (award.award_id, award.date, award.team_id, award.team_name, self._award_infos(award.award_name))\\n for award in awards_query_result\\n ]\\n awards = [\\n award\\n for award in awards\\n if award[-1] is not None\\n ]\\n\\n # Hides descrip, icon and scores that the team should not know.\\n key_ids_of_team = [\\n award_infos['key_id']\\n for award_id, _, award_team_id, __, award_infos in awards\\n if award_team_id == self.team_id ]\\n for award in awards:\\n award_id, _, award_team_id, __, award_infos = award\\n if award_infos['key_id'] not in key_ids_of_team and not award_infos['public']:\\n award_infos['congrat_msg'] = None\\n award_infos['congrat_img_url'] = None\\n award_infos['score'] = None\\n\\n # Removes unneeded fields\\n awards = [\\n (award_id, award_date.strftime('%Y-%m-%d %H:%M:%S'), award_team_name, award_infos['congrat_msg'], award_infos['congrat_img_url'], award_infos['score'])\\n for award_id, award_date, award_team_id, award_team_name, award_infos in awards ]\\n return awards\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a10e9faa2f17a446fabe0eb42492f79a\",\n \"score\": \"0.52363074\",\n \"text\": \"def book(items):\\n list1 = [ ]\\n for i in items:\\n if i[1] == 'book':\\n list1.append(i)\\n \\n\\n #print(list1)\\n for i in list1:\\n print(i[0],i[1],i[2],i[3])\\n \\n #print('\\\\n')\\n #print(\\\"Book Category List: \\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9011e7ed69e422937eb5327e0fd5a057\",\n \"score\": \"0.52341586\",\n \"text\": \"def get_popularAuthors():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bae812189214d0eca9a33a1b8283017\",\n \"score\": \"0.5205308\",\n \"text\": \"def all_books(self, date_format=DATE_FORMAT):\\n return self.execute(queries.ALL_BOOKS, (date_format,)).fetchall()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dab7fefb4df6626bf48f6bf501e8abe\",\n \"score\": \"0.52029043\",\n \"text\": \"def fetch_titles_from_page(page_number, get_books=True) -> ([Book], bool):\\n assert type(page_number) is int\\n assert type(get_books) is bool\\n\\n logger.debug(f\\\"Fetching catalog page #{page_number}... \\\")\\n url = TITLES_URL + f\\\"&search_page={page_number}\\\"\\n result = requests.get(url, headers=_get_scrape_headers())\\n\\n if result.status_code != 200:\\n return [], False\\n\\n json_result = json.loads(result.text)\\n if json_result[\\\"status\\\"] != \\\"SUCCESS\\\":\\n return [], False\\n\\n if json_result[\\\"results\\\"] == \\\"No results\\\":\\n return [], False\\n\\n books = []\\n if get_books:\\n html_results = json_result[\\\"results\\\"]\\n scraper = BeautifulSoup(html_results, 'html.parser')\\n catalog_results = scraper.find_all(\\\"li\\\", class_=\\\"catalog-result\\\")\\n\\n if catalog_results:\\n for catalog_result in catalog_results:\\n book = Book()\\n result_data = catalog_result.find(\\\"div\\\", class_=\\\"result-data\\\")\\n book.title = result_data.a.text.strip()\\n # Remove enclosing \\\" or ' if any\\n if book.title[0] is \\\"\\\\\\\"\\\" or book.title[0] is \\\"'\\\":\\n book.title = book.title[1:]\\n\\n if book.title[-1] is \\\"\\\\\\\"\\\" or book.title[-1] is \\\"'\\\":\\n book.title = book.title[:-1]\\n\\n book.url = result_data.a.attrs[\\\"href\\\"]\\n book_author = result_data.find(class_=\\\"book-author\\\")\\n if book_author and book_author.text:\\n book.author = book_author.text.strip()\\n else:\\n logger.error(f\\\"Failed to scrape the author for book \\\\\\\"{book.url}\\\\\\\" \\\"\\n f\\\"in catalog page #{page_number}\\\")\\n if book_author and book_author.a and book_author.a.attrs[\\\"href\\\"]:\\n book.author_url = book_author.a.attrs[\\\"href\\\"]\\n else:\\n # Books with various authors don't have an author url so this might be fine\\n logger.warn(f\\\"Failed to scrape the author url for book \\\\\\\"{book.url}\\\\\\\" \\\"\\n f\\\"in catalog page #{page_number}\\\")\\n book.download_url = catalog_result.find(class_=\\\"download-btn\\\").a.attrs[\\\"href\\\"]\\n book.size = catalog_result.find(class_=\\\"download-btn\\\").span.text.strip()\\n books.append(book)\\n logger.debug(f\\\"Fetched metadata for {len(books)} books from page #{page_number}\\\")\\n\\n else:\\n logger.warn(f\\\"Failed to fetch books from the catalog page #{page_number}\\\")\\n\\n else:\\n logger.debug(f\\\"Skipping scraping page #{page_number} for book metadata...\\\")\\n\\n return books, True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24177aea31d1974e83f92029e9fdd9e4\",\n \"score\": \"0.51874995\",\n \"text\": \"def get_project_cla_groups() -> List[dict]:\\n return [project.to_dict() for project in get_project_cla_group_instance().all()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20a060a2a5bce25663f6e65a1093082b\",\n \"score\": \"0.51766706\",\n \"text\": \"def get_book_all(self) -> (tuple, list):\\n sql_expr = \\\\\\n '''\\n SELECT * FROM Books;\\n '''\\n sql_result = self._my_connect.execute_sql_read(sql_expr)\\n return sql_result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e5e47b31b5d480b438d8b922ac26c47\",\n \"score\": \"0.5172181\",\n \"text\": \"def allReviews(self,book):\\n return self.query.filter_by(book_id=book.id).all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c34a455b5b6e5dacc98dd48267a7c8db\",\n \"score\": \"0.5162416\",\n \"text\": \"def get_all_obgyn():\\n \\n ##first find where this condition is true for facility for obgyn\\n obgyn_facility_list = Facility.query.filter_by(facility_category_name_english='Obstetrics and Gynecology').all()\\n \\n return obgyn_facility_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36d5129ff7c35b11a63cd60cba04f239\",\n \"score\": \"0.51620466\",\n \"text\": \"def get_bible_books() -> {int: dict}:\\n genres = get_bible_book_genres()\\n\\n with open(KEY_ENGLISH_PATH, 'r') as csvfile:\\n reader = csv.reader(csvfile)\\n headers = next(reader)\\n\\n book_index = headers.index(BOOK_KEY)\\n name_index = headers.index(NAME_KEY)\\n testament_index = headers.index(TESTAMENT_KEY)\\n genre_index = headers.index(GENRE_KEY)\\n dataset_index = headers.index(DATASET_KEY)\\n\\n return {\\n int(book[book_index]): {\\n 'name': book[name_index],\\n 'testament': book[testament_index],\\n 'genre_id': int(book[genre_index]),\\n 'genre': genres[int(book[genre_index])],\\n 'dataset': book[dataset_index]\\n } for book in reader\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1039cac6e05df4019fe627b9cd9b488\",\n \"score\": \"0.5157313\",\n \"text\": \"def harvest_all():\\n db = db_connect()\\n dois = None\\n get_commits()\\n get_citations(db, dois)\\n get_mentions(since_version=None)\\n list_records(dois)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ea38c63d3d310e833fa6010f016c207\",\n \"score\": \"0.5141538\",\n \"text\": \"def cli(ctx, biomaterial_name=\\\"\\\", provider_id=\\\"\\\", biomaterial_id=\\\"\\\", organism_id=\\\"\\\", dbxref_id=\\\"\\\"):\\n return ctx.gi.expression.get_biomaterials(biomaterial_name=biomaterial_name, provider_id=provider_id, biomaterial_id=biomaterial_id, organism_id=organism_id, dbxref_id=dbxref_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0552a8791165f00bd82c35a9076ab387\",\n \"score\": \"0.5130115\",\n \"text\": \"def get_test_bible_book_ids() -> {int}:\\n return {book_id for (book_id, book) in get_bible_books().items() if book['dataset'] == 'test' }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b90c8edc53393d9501476ac214728a50\",\n \"score\": \"0.5111732\",\n \"text\": \"def get_all_books():\\n\\n book_info = db.books.find({}, {\\\"_id\\\": 0})\\n book_info_cur = list(book_info)\\n # print(book_info_cur[0][\\\"rating\\\"])\\n sorted_cur = sorted(book_info_cur, key = lambda i: int(i.get('rating', 0)), reverse=True)\\n if len(book_info_cur) != 0:\\n return json.dumps(sorted_cur, indent=4), 200\\n else:\\n return \\\"Empty book collection in database\\\", 404\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10b924ef88152a9fac406b6d482606bf\",\n \"score\": \"0.51091206\",\n \"text\": \"def get_all_cb_comps():\\n u = urllib2.urlopen('http://api.crunchbase.com/v/1/companies.js')\\n \\n comps = u.read()\\n\\n return comps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37744c1787adacba8b8ce6ee9b38eb8a\",\n \"score\": \"0.5083007\",\n \"text\": \"def fetch_all_books(start_page=1, end_page=MAX_KNOWN_PAGE, need_update_page=False) -> [Book]:\\n assert type(start_page) is int\\n assert type(end_page) is int\\n assert start_page < end_page\\n assert type(need_update_page) is bool\\n\\n while need_update_page:\\n logger.debug(f\\\"Checking if the # of LibriVox catalog pages is larger than {end_page}...\\\")\\n _, need_update_page = fetch_titles_from_page(end_page, False)\\n if need_update_page:\\n end_page += 1\\n logger.debug(f\\\"Found new catalog page #{end_page}\\\")\\n\\n logger.info(f\\\"Fetching LibriVox's book catalog from pages #{start_page} till #{end_page}...\\\")\\n\\n all_books = []\\n \\\"\\\"\\\"\\n # Commenting out multiprocess code for now as it crashes in Python 3.6.2 with the error:\\n # \\\"__NSPlaceholderDate initialize] may have been in progress in another thread when fork() was called.\\\"\\n fetched_titles = []\\n with Pool(NUM_PROCESSES) as pool:\\n fetched_titles = pool.map(fetch_titles_from_page, [n for n in range(start_page, end_page)])\\n for result in fetched_titles:\\n if result and result[1]:\\n all_books.extend(result[0])\\n \\\"\\\"\\\"\\n for page_num in range(start_page, end_page):\\n catalog_page, could_fetch = fetch_titles_from_page(page_num)\\n if could_fetch and catalog_page:\\n all_books.extend(catalog_page)\\n\\n return all_books\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f76e593d6b23f2a302b628d8dc58ba0\",\n \"score\": \"0.5065584\",\n \"text\": \"def listConsents(self):\\n Response = self.make_request()\\n return Response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e6648cba3aa87f3160051c3d7766ec1\",\n \"score\": \"0.5063357\",\n \"text\": \"def get_all(self):\\n return [DocumentCorpus(self.collection, document) for document in self.collection.find()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df84247f23d395f335bc7958cdf6d32f\",\n \"score\": \"0.5062308\",\n \"text\": \"def fetchCompanies():\\n return desk.companies.list_companies()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afb6780e1b14530393c586bdff951aa2\",\n \"score\": \"0.50400996\",\n \"text\": \"def get_citations_needed_report(URL):\\n # print('top report')\\n response = requests.get(URL)\\n content = response.content\\n soup = BeautifulSoup(content, 'html.parser')\\n result = soup.find_all('a', href=\\\"/wiki/Wikipedia:Citation_needed\\\")\\n\\n dictionary = []\\n\\n for link in result:\\n P = link.parent.parent.parent\\n P = P.text.strip()\\n dictionary.append(P)\\n\\n # print('above dictionary')\\n for P in dictionary:\\n print(P, sep='\\\\n')\\n print('\\\\n')\\n\\n return dictionary\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c0aea7c5e824b0c9aecad3c418b9157\",\n \"score\": \"0.5029921\",\n \"text\": \"def books_title(self):\\n return [i.title for i in shelf.books_list]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1ad1e8add187059eb6f82e5671699c0\",\n \"score\": \"0.5021048\",\n \"text\": \"def getBucketlists():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"900b206cfe54379257569656e674a907\",\n \"score\": \"0.5011742\",\n \"text\": \"def test_books_get(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0920b7316709655aa523d2dd5b7398d\",\n \"score\": \"0.49976185\",\n \"text\": \"def fetch_all_chapters(book) -> [Chapter]:\\n logger.debug(f\\\"Downloading info for chapters in book: \\\\\\\"{book.title[:70]}\\\\\\\"...\\\")\\n session = download_session.make_session()\\n try:\\n book_page = session.get(book.url, headers=_get_scrape_headers(), timeout=70)\\n except download_session.get_download_exceptions() as e:\\n logger.error(f\\\"Scraping timed out to download chapters from book \\\\\\\"{book.url}\\\\\\\"\\\")\\n logger.error(e)\\n return []\\n\\n if book_page.status_code != 200:\\n logger.warn(f\\\"Failed to download chapters information for book \\\\\\\"{book.url}\\\\\\\"\\\")\\n return []\\n\\n scraper = BeautifulSoup(book_page.text, \\\"html.parser\\\")\\n _fetch_missing_book_metadata(book, scraper)\\n\\n # Get the chapters information\\n chapters = []\\n chapters_table = scraper.find(\\\"table\\\", class_=\\\"chapter-download\\\")\\n if not chapters_table:\\n logger.error(f\\\"Scraping failed to find the chapters table for book \\\\\\\"{book.url}\\\\\\\"\\\")\\n return chapters\\n\\n chapter_rows = chapters_table.find_all(\\\"tr\\\")[1:] # The first row is the table headers\\n if not chapter_rows:\\n logger.error(f\\\"Scraping failed to find the chapters rows in the chapters table for book \\\\\\\"{book.url}\\\\\\\"\\\")\\n return chapters\\n\\n num_row_elements = len(chapter_rows[0].find_all(\\\"td\\\"))\\n if num_row_elements == 7:\\n for row in chapter_rows:\\n chapter = Chapter(book)\\n chapter.title = row.find(\\\"a\\\", class_=\\\"chapter-name\\\").text\\n chapter.download_url = row.find(\\\"a\\\", class_=\\\"chapter-name\\\").attrs[\\\"href\\\"]\\n row_elements = row.find_all(\\\"td\\\")\\n if not row_elements or not row_elements[0] or not row_elements[0].a:\\n logger.error(f\\\"Scraping failed for chapter metadata of book \\\\\\\"{book.url}\\\\\\\"\\\")\\n break\\n\\n chapter.number = int(row_elements[0].text.replace(row_elements[0].a.text, \\\"\\\").strip())\\n if len(row_elements) > 2:\\n chapter.author = row_elements[2].text.strip()\\n if row_elements[2].a:\\n chapter.author_url = row_elements[2].a.attrs[\\\"href\\\"]\\n\\n if len(row_elements) > 3:\\n chapter.source_text = row_elements[3].text.strip()\\n if row_elements[3].a:\\n chapter.source_text_url = row_elements[3].a.attrs[\\\"href\\\"]\\n\\n if len(row_elements) > 4:\\n chapter.reader_name = row_elements[4].text.strip()\\n if row_elements[4].a:\\n chapter.reader_url = row_elements[4].a.attrs[\\\"href\\\"]\\n\\n if len(row_elements) > 5:\\n chapter.duration = row_elements[5].text.strip()\\n\\n if len(row_elements) > 6:\\n chapter.language_code = row_elements[6].text.strip()\\n # If no language_code was found, we fallback to use the book's language\\n # This is not perfect as the book's language can be \\\"multilingual\\\" and chapters\\n # are supposed to be recorded in only one language\\n if len(row_elements) < 6:\\n logger.warn(f\\\"Found an unexpected number of columns in the chapter row \\\\\\\"{chapter.title}\\\\\\\"\\\")\\n\\n chapters.append(chapter)\\n\\n else:\\n logger.error(f\\\"Scraping failed for chapter metadata of book \\\\\\\"{book.url}\\\\\\\"\\\")\\n\\n elif num_row_elements == 4:\\n for row in chapter_rows:\\n chapter = Chapter(book)\\n chapter.title = row.find(\\\"a\\\", class_=\\\"chapter-name\\\").text\\n chapter.download_url = row.find(\\\"a\\\", class_=\\\"chapter-name\\\").attrs[\\\"href\\\"]\\n row_elements = row.find_all(\\\"td\\\")\\n if not row_elements or not row_elements[0] or not row_elements[0].a:\\n logger.error(f\\\"Scraping failed for chapter metadata of book \\\\\\\"{book.url}\\\\\\\"\\\")\\n break\\n\\n chapter.number = int(row_elements[0].text.replace(row_elements[0].a.text, \\\"\\\").strip())\\n if len(row_elements) > 2:\\n chapter.reader_name = row_elements[2].text.strip()\\n # Chapters read by a group of people don't have a link to the reader's profile\\n if row_elements[2].a:\\n chapter.reader_url = row_elements[2].a.attrs[\\\"href\\\"]\\n\\n if len(row_elements) > 3:\\n chapter.duration = row_elements[3].text.strip()\\n # The chapter's language will be set to the book's language via the chapter's init\\n chapter.author = book.author\\n chapters.append(chapter)\\n\\n else:\\n logger.error(f\\\"Scraping failed for metadata of chapter \\\\\\\"{chapter.title}\\\\\\\"\\\")\\n else:\\n logger.error(f\\\"Found an unknown number ({num_row_elements}) of chapter_rows in \\\"\\n f\\\"the book page of \\\\\\\"{book.url}\\\\\\\"\\\")\\n\\n logger.debug(f\\\"Finished downloading info for {len(chapters)} chapters in book \\\\\\\"{book.title[:50]}\\\\\\\"\\\")\\n return chapters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82f50f6b850e4250535ba4a318356de3\",\n \"score\": \"0.49940458\",\n \"text\": \"def get_campos(self):\\n campos = DBSession.query(Campos).all()\\n return campos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04b04e5dc6586c5a26cd4999c0e5428a\",\n \"score\": \"0.49917156\",\n \"text\": \"def group_by_author(self, author: Author):\\n return list(filter(lambda book: book.author == author, self.books))\\n # return [book for book in self.books if book.author == author]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"575ee14284891d05a646ecd5e8b93d04\",\n \"score\": \"0.49862415\",\n \"text\": \"def get_books(date, list_name):\\n \\n response = requests.get('http://api.nytimes.com/svc/books/v3/lists/%s/%s.json?api-key=%s'%(str(date), list_name, key))\\n response = response.json()\\n\\n num_results = response['num_results']\\n fetched = 20\\n names = [book['title'] for book in response['results']['books']]\\n\\n while num_results > fetched:\\n time.sleep(0.125)\\n response = requests.get('http://api.nytimes.com/svc/books/v3/lists/%s/%s.json?offset=%d&api-key=%s'%(str(date), list_name, fetched, key))\\n response = response.json()\\n names += [book['title'] for book in response['results']['books']]\\n fetched += 20\\n\\n return set(names), datetime.datetime.strptime(response['results']['published_date'], \\\"%Y-%m-%d\\\")\\n\\n import datetime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6754e5ddfdd274ebad8c7dc16e92ecf5\",\n \"score\": \"0.49814323\",\n \"text\": \"def get_books_by_author_by(author_id):\\n\\n return Book.query.filter(Book.author_id == author_id).all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"059da709ae06cf5dc3abb55bfd751068\",\n \"score\": \"0.49789444\",\n \"text\": \"def address_book():\\n return [(beings[k], locales[v]) for k,v in residencies.items()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13ed1135b97bc4d8ad273ec5643edb03\",\n \"score\": \"0.49710968\",\n \"text\": \"def get_books(self, top_k=None, cache=True):\\n # Reset the books saved in the cache if its length is under the threshold\\n if top_k and len(self._books) < top_k:\\n self._books = []\\n # Get the top-k books, ordered from the author's book page\\n books = []\\n for i, book in enumerate(self.books(cache=cache)):\\n book.register_author(self)\\n books.append(book)\\n if top_k and i + 1 >= top_k:\\n break\\n return books\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa7897ea3b6bb81356248b187a8dd014\",\n \"score\": \"0.4967275\",\n \"text\": \"def get_books_contained_by_version(bible_version: dict) -> [int]:\\n table_path = TABLE_DIRECTORY / TABLE_NAME_FORMAT.format(table = bible_version['table'])\\n\\n with open(table_path, 'r', encoding='utf-8') as csvfile:\\n reader = csv.reader(csvfile)\\n\\n headers = next(reader)\\n book_index = headers.index(BOOK_KEY)\\n\\n return sorted({int(verse[book_index]) for verse in reader})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad55ea24f2e82e232ae5f2893b4d496c\",\n \"score\": \"0.49647325\",\n \"text\": \"def get_bills(self):\\n return Bill.objects.filter(club=self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75f7e52d5e64c5f32df2d433bc20aaa9\",\n \"score\": \"0.4963884\",\n \"text\": \"def get_finished_books_by_criteria(search_criteria, patron_id):\\n criteria = '%' + search_criteria + '%'\\n search_result = (db_session.query(Finished_Book,\\n Book,\\n Book_Author,\\n Author)\\n .filter(Finished_Book.book_id == Book.id,\\n Finished_Book.patron_id == patron_id,\\n Book.id == Book_Author.book_id,\\n Book_Author.author_id == Author.id,\\n Book.title.like(criteria) |\\n Author.name.like(criteria))\\n .order_by(Book.title).all()) \\n\\n books_grouped_by_id = itertools.groupby(search_result, lambda x: x[1].id)\\n\\n list_of_books = []\\n for book_id, result_set in books_grouped_by_id:\\n\\n\\n for d in result_set:\\n list_of_books += [(d[1].title, d[1].cover_png, d[0].date, d[3].name,\\n \\\"\\\".join(r[3].name for r in result_set))]\\n #end for\\n #end for\\n\\n return list_of_books\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8eefcef1d4d158585d11392c8d9cf137\",\n \"score\": \"0.49480593\",\n \"text\": \"def html_sample(self,limit=20):\\n books = self.query.order_by(self.isbn).all()\\n books = books[:limit]\\n data = []\\n for book in books:\\n isbn = book.isbn\\n desc = GoodReads.getDescription(isbn)\\n data.append(desc)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9fb78dccddb588333440f822406b273\",\n \"score\": \"0.49452752\",\n \"text\": \"def testBook():\\n\\n # book build\\n books = []\\n books.append(Book(\\\"The Hobbit\\\", \\\"J.R.R. Tolkien\\\", \\\"Doug\\\", [\\\"Wendy\\\"]))\\n books.append(Book(\\\"Greek Myths\\\", \\\"Troy Miller\\\", \\\"Bob\\\", []))\\n books.append(Book(\\\"Python Programming\\\", \\\"Ken Lambert\\\", \\\"Wendy\\\", [\\\"Bob\\\", \\\"Doug\\\"]))\\n books.append(Book(\\\"Introduction to Computers\\\", \\\"Ted Murphy\\\", \\\"Wendy\\\", []))\\n books.append(Book(\\\"P.H.P. Basics\\\", \\\"Michael Smith\\\", \\\"Wendy\\\", [\\\"Bob\\\"]))\\n\\n print(\\\"Current Books:\\\")\\n print()\\n for i in range(len(books)):\\n print(\\\"\\\\tTitle: \\\", books[i].getTitle())\\n print(\\\"\\\\tAuthor: \\\", books[i].getAuthor())\\n print(\\\"\\\\tChecked Out To: \\\", books[i].getCurrentPatron())\\n print(\\\"\\\\tCurrent Waiting List: \\\", books[i].getWaitingList())\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f274280e00eaca4f1c5bad1369dd8c2\",\n \"score\": \"0.49442163\",\n \"text\": \"def books(self, cache=True):\\n if len(self._books) > 0 and cache:\\n yield from self._books\\n else:\\n yield from self._search_books()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6af53be12cad589993d2fd84f01ca72c\",\n \"score\": \"0.49382204\",\n \"text\": \"def get(self):\\n courses = list(Course.objects.all())\\n return courses\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c9c566457fe64b0dab5057945228a3c\",\n \"score\": \"0.4929902\",\n \"text\": \"def get_book():\\n # Check if an ID was provided as part of the URL.\\n if 'id' in request.args:\\n # If ID is provided, assign it to a variable.\\n id = int(request.args['id'])\\n else:\\n # If no ID is provided, display an error in the browser.\\n return \\\"Request error: no id field provided\\\"\\n\\n # prepare results variable (empty list)\\n results = []\\n\\n import raw\\n for book in raw.get_books():\\n if book['id'] == id:\\n results.append(book)\\n\\n # return data in json format\\n return jsonify(results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b48cade1199a9ddcd0bc3ec12b8a4ef6\",\n \"score\": \"0.4925487\",\n \"text\": \"def get_projects_by_accession(accession):\\n project = Project()\\n print(project.get_by_accession(accession))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09c8ca693e70b4c298d05c01aa7a575d\",\n \"score\": \"0.492406\",\n \"text\": \"def load_all_chapters(self):\\n folder = join(\\\"/Volumes/SSD01/dialogues\\\",self.this_Book.txt_file_path)\\n\\n # self.files = listdir(join(\\\"data/Kindle-combined-txt\\\",str(isbn)))\\n # no reason to get too complicated here\\n self.txtfiles = [x for x in listdir(folder) if \\\".txt\\\" in x]\\n\\n # print(\\\"a sample of the text files:\\\")\\n # print(self.txtfiles[:10])\\n\\n rawtext_by_chapter = []\\n for fname in self.txtfiles:\\n f = open(join(folder,fname),\\\"r\\\")\\n rawtext_by_chapter.append(f.read())\\n f.close()\\n # word_lists_by_chapter = [listify(t) for t in rawtext_by_chapter]\\n # apply spacy to each of them...\\n word_lists_by_chapter = [[str(token) for token in nlp(t) if ((not token.is_punct) and (len(str(token)) != 0))] for t in rawtext_by_chapter]\\n self.chapter_ends = cumsum(list(map(len,word_lists_by_chapter)))\\n # add a 0 to the start, clip (to get the starts)\\n # could just move the above array around too...\\n self.chapter_beginnings = cumsum([0]+list(map(len,word_lists_by_chapter[:-1])))\\n self.chapter_centers = (self.chapter_ends+self.chapter_beginnings)/2\\n# print(list(map(len,self.word_lists_by_chapter)))\\n# print(self.chapter_ends)\\n# print(self.chapter_beginnings)\\n# print(self.chapter_centers)\\n# print(len(self.chapter_ends))\\n# print(len(self.word_lists_by_chapter))\\n self.all_word_list = list(itertools.chain(*word_lists_by_chapter))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2cca76f77e96af07bcbb2595db675b9\",\n \"score\": \"0.49230716\",\n \"text\": \"def get_book():\\n soup = Soup(CONTENT)\\n title = str(soup.find_all(\\\"h2\\\")[0]).replace('<h2>', '').replace('</h2>', '').strip()\\n desc = str([entry for entry in soup.find_all('div', class_=\\\"dotd-main-book-summary float-left\\\")][0]).split('<div')[4][2:-7].strip()\\n img = soup.findAll('img')[5]['src']\\n link = soup.find_all('div', class_=\\\"dotd-main-book-image float-left\\\")\\n\\n for div in link:\\n link_p = div.find('a')['href']\\n return Book(title, desc, img, link)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b56f9049b0a5af56044011ccf3ea9a3\",\n \"score\": \"0.49147058\",\n \"text\": \"def get_all():\\n return Doctor.query.all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c50746a02d51d94ca427f68de0b57a4c\",\n \"score\": \"0.49130303\",\n \"text\": \"def get_mine(self):\\n\\n self._init_dict_chal_keys()\\n\\n # Gets award informations\\n filter_award_name = 'plugin_intermflag_' + str(self.chal_id) + '_%'\\n awards_query_result = db.session.query(\\n Awards.id.label('award_id'), Awards.date, Awards.name.label('award_name'),\\n ).filter(Awards.teamid==self.team_id).filter(Awards.name.like(filter_award_name)).order_by(Awards.date).all()\\n\\n # Joins award datas and key datas\\n awards = [\\n (award.award_id, award.date, self._award_infos(award.award_name))\\n for award in awards_query_result\\n ]\\n awards = [\\n award\\n for award in awards\\n if award[-1] is not None\\n ]\\n\\n # Removes unneeded fields\\n awards = [\\n (award_id, award_date, award_infos['congrat_msg'], award_infos['congrat_img_url'], award_infos['score'])\\n for award_id, award_date, award_infos in awards ]\\n return awards\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57ba6f0cc57d7662c698b5a07c807065\",\n \"score\": \"0.4912252\",\n \"text\": \"def get(self):\\n all_blogss = get_all_blogss()\\n print(\\\"All blogss=\\\",all_blogss)\\n return all_blogss\",\n \"title\": \"\"\n },\n {\n \"docid\": \"147720b020aedaa97fc349619755f841\",\n \"score\": \"0.49097124\",\n \"text\": \"def get_authors(venue):\\r\\n papers=0\\r\\n results = dblp.search_pub(venue)\\r\\n\\r\\n hits = json.loads(results)[\\\"result\\\"][\\\"hits\\\"]\\r\\n for i in hits[\\\"hit\\\"]:\\r\\n info = i[\\\"info\\\"]\\r\\n if filter_papers(info, venue):\\r\\n papers+=1\\r\\n # print(info)\\r\\n if 'authors' in info:\\r\\n if isinstance(info[\\\"authors\\\"][\\\"author\\\"], list):\\r\\n for author in info[\\\"authors\\\"][\\\"author\\\"]:\\r\\n update_authors(author[\\\"@pid\\\"], author[\\\"text\\\"], info[\\\"key\\\"])\\r\\n else:\\r\\n author = info[\\\"authors\\\"][\\\"author\\\"]\\r\\n update_authors(author[\\\"@pid\\\"], author[\\\"text\\\"], info[\\\"key\\\"])\\r\\n\\r\\n return papers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87519999fe1b78f5a4f1835d9dfd27a6\",\n \"score\": \"0.49049982\",\n \"text\": \"def list_csrs():\\n cmd = subprocess.Popen([PUPPETCA, '--list'], stdout = subprocess.PIPE, stderr = subprocess.STDOUT)\\n out, err = cmd.communicate()\\n csrs = out.split('\\\\n')\\n return [r for r in csrs if r]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9009a2f36c1438806386d3cfd6803ea\",\n \"score\": \"0.4901347\",\n \"text\": \"def resolve_labbook_list(self, info):\\n return LabbookList(id=\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16b462d0dbc43003f675551cd3ba7b18\",\n \"score\": \"0.48985198\",\n \"text\": \"def testimonials(self):\\n testimonials = self.context.getRefs(relationship=\\\"course_testimonial\\\") \\n return testimonials\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad48868230e24c2b9483e22cdb8e3c83\",\n \"score\": \"0.48958287\",\n \"text\": \"def springer_bib(year, conf, link):\\n html_file = download_to_hash(link)\\n soup = BeautifulSoup(open(html_file), 'lxml')\\n res = ''\\n for paper in soup.select('.chapter-item'):\\n meta = paper.select('.content-type-list__meta')[0]\\n title = meta.select('div')[0].get_text()\\n authors_str = meta.select('div')[1].get_text()\\n authors = authors_str2lst(authors_str)\\n pdflink_a = paper.select('a.test-book-toc-download-link')\\n pdflink = ''\\n # some conference may not have a pdflink, e.g.\\n # https://link.springer.com//book/10.1007/BFb0015518\\n if pdflink_a:\\n pdflink = pdflink_a[0]['href']\\n if not pdflink.startswith('http'):\\n pdflink = 'https://link.springer.com/' + pdflink\\n id = gen_id(year, conf, authors, title)\\n bib = gen_single_bib(id, title, ' and '.join(authors), pdflink, year, conf)\\n res += bib\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72543b0b93f999ed139508daec9bd423\",\n \"score\": \"0.4895037\",\n \"text\": \"def test_get_all_quotes_by_book(self):\\n # hit the API endpoint\\n response = self.client.post(\\n reverse(\\\"quotes-book\\\", kwargs={\\\"version\\\": \\\"v1\\\"}), {\\\"book\\\": \\\"Don Quijote de la Mancha\\\"}\\n )\\n # fetch the data from db\\n book = Book.objects.filter(title=\\\"Don Quijote de la Mancha\\\")[:1].get()\\n expected = book.quotes\\n serialized = QuoteSerializer(expected, many=True)\\n self.assertEqual(response.data, serialized.data)\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67cc7015b51ab776bba4148fff31bcc3\",\n \"score\": \"0.4884307\",\n \"text\": \"def list():\\n return Company.query.all(), HTTPStatus.OK\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b51ba71ff07fffae14bce8c9be190339\",\n \"score\": \"0.48839167\",\n \"text\": \"def get_CA(self):\\n\\n\\t\\treturn [r.Ca for r in self.res]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8e49af7ea1ca8720574179626671b90\",\n \"score\": \"0.4882444\",\n \"text\": \"def getProjectMaintainers(self, project):\\n tree = ElementTree.fromstring(''.join(core.show_project_meta(self.apiurl,\\n project)))\\n maintainers = []\\n for person in tree.findall('person'):\\n if person.get('role') == \\\"maintainer\\\":\\n maintainers.append(person.get('userid'))\\n return maintainers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46ce485964905983bcd771a5595c703a\",\n \"score\": \"0.4879896\",\n \"text\": \"def readBook2(self):\\n # summary from https://www.amazon.ca/Essential-Calvin-Hobbes-Bill-\\n # Watterson/dp/0836218051/ref=sr_1_1?crid=1YAN87VEA3JWD&keywords=the+\\n # essential+calvin+and+hobbes&qid=1579321108&sprefix=the+essential+cal\\n # %2Caps%2C181&sr=8-1\\n print(form.border(\\\"\\\"\\\"\\nTHE ESSENTIAL CALVIN AND HOBBES by Bill Watterson\\n Beginning with the day Hobbes sprang into Calvin's tuna fish trap, the\\n first two Calvin and Hobbes collections, Calvin and Hobbes and Something\\n Under The Bed Is Drooling, are brought together in this treasury.\\n Including black-and-white dailies and color Sundays, The Essential Calvin\\n and Hobbes also features an original full-color 16-page story.\\n \\\"\\\"\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0806836cfdeb94bad73bc76a354702ab\",\n \"score\": \"0.4878542\",\n \"text\": \"def most_client_report_order_books(self):\\n\\t\\tclient_list = self.__borrow_controller.getReverseListClients()\\n\\t\\tclient_list = self.__client_controller.getListFromIdsOrderBooks(client_list)\\n\\t\\tprint(\\\"\\\\n\\\" + '-------=======-------')\\n\\t\\tfor client in client_list:\\n\\t\\t\\tprint(client, end='')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf2980bb0e099750f2f635458aa2c677\",\n \"score\": \"0.4877945\",\n \"text\": \"def get_book():\\n id = get_input(\\\"id? \\\")\\n r = requests.get(url = API_ENDPOINT + \\\"/\\\" + id)\\n\\n result_text = r.text\\n print(\\\"\\\")\\n print(\\\"result: \\\" + result_text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"230e1d7187be7c6342949083411ad154\",\n \"score\": \"0.4870439\",\n \"text\": \"def bid_list():\\n # Grab all projects\\n projects = request_api(project_path)\\n\\n # Parse and find \\\"Schnabel\\\" project id\\n sfc_id = None\\n for project in projects:\\n if project[\\\"name\\\"] == \\\"Schnabel\\\":\\n sfc_id = project[\\\"id\\\"]\\n break\\n\\n # Grab all sections in \\\"Schnabel\\\" project\\n params = {\\\"project_id\\\": sfc_id}\\n sections = request_api(section_path, params=params)\\n\\n # Determine \\\"Bids\\\" section id\\n bidding_id = None\\n for section in sections:\\n if section[\\\"name\\\"] == \\\"Bids\\\":\\n bidding_id = section[\\\"id\\\"]\\n break\\n \\n # Grab all tasks in \\\"Schnabel\\\" project\\n params = {\\\"project_id\\\": sfc_id}\\n tasks = request_api(task_path, params=params)\\n\\n # Filter tasks based on section id\\n bid_tasks = []\\n for task in tasks:\\n if task[\\\"section_id\\\"] == bidding_id:\\n bid_tasks.append(task)\\n\\n # Sort and return bid_tasks\\n bid_tasks_sorted = quicksort(bid_tasks)\\n return bid_tasks_sorted\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01480a553da0b344569b406ff2d515bc\",\n \"score\": \"0.4866259\",\n \"text\": \"def getBook(self, _id):\\n db = self.getBookDB()\\n book=None\\n try:\\n value = (_id,)\\n db.execute('select * from books where _id=?',value)\\n book = self.getBookFromList(db.fetchone())\\n except Exception as e:\\n print(e)\\n return book\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e2fbd99ee5e9dca5b830621f90e17f6\",\n \"score\": \"0.48623312\",\n \"text\": \"def get_available_cops():\\n allIncidents = Incident.get_all()\\n cops = []\\n \\n for i in allIncidents:\\n if(inicioAmostragem <= i.reporting_date and i.reporting_date <=terminoAmostragem):\\n# cops.append(i['operations_center']['id'])\\n#conf\\n cops.append(i['operations_center'])\\n \\n allReports = RelatoDeSituacao.get_all()\\n \\n for r in allReports:\\n if (\\n inicioAmostragem <= r.data_hora and \\n r.data_hora <=terminoAmostragem and\\n 'cop' in r.relator \\n# and # todos tem que ter o COP\\n#conf 'id' in r.relator['cop'] # todos tem que ter o id \\n ):\\n# cops.append(r.relator['cop']['id'])\\n#conf \\n cops.append(r.relator['cop'])\\n\\n allSincronizations = Sincronizacao.get_all()\\n for sinc in allSincronizations:\\n for action in sinc.acoes:\\n if ( \\n ((action.tipo == 'PONTUAL') and (action.inicio >= inicioAmostragem) and (action.inicio <= terminoAmostragem)) or\\n ((action.tipo == 'INTERVALO') and (action.inicio >= inicioAmostragem and action.fim <= terminoAmostragem))\\n ):\\n cops.append(sinc.cop_responsavel['id'])\\n \\n return set(cops)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42bd5fff3bf83a8c1d6b707a4b206d95\",\n \"score\": \"0.48550457\",\n \"text\": \"def get_book(self, request, pk):\\n query_set = Library.objects.get(id=pk)\\n data = self.serializer_class(query_set, context={'request': request}).data\\n return Response({'result': data}, status=status.HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8243269654818ba0873eeae14ec49e4f\",\n \"score\": \"0.48539725\",\n \"text\": \"def print_catalog(self):\\n print(\\\"THIS IS THE CATALOG OF ALL OUR BOOKS:\\\")\\n for book, amount in self.books.items():\\n print(book.title + ' - rating: ' + str(amount))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a58870f9f4fa8d5d3502ac32f7e3d318\",\n \"score\": \"0.48537493\",\n \"text\": \"def get_accounts():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a58870f9f4fa8d5d3502ac32f7e3d318\",\n \"score\": \"0.48537493\",\n \"text\": \"def get_accounts():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"229b0e45cd060f50cefc112660732f2e\",\n \"score\": \"0.4853242\",\n \"text\": \"def extract_books(soup):\\n books = {}\\n for d in soup.find_all('div', 'bookMain'):\\n asin = sub(r'_.*$', '', d['id'])\\n title = d.find('span', 'title').text.strip()\\n author = sub(r'by ', '', d.find('span', 'author').text.strip())\\n author = sub('\\\\n', '', author)\\n\\n books[asin] = dict(asin=asin, title=title, author=author)\\n\\n return books\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31d79332d6e68cd849b57c61ecb12b0d\",\n \"score\": \"0.4850605\",\n \"text\": \"def getBestBooks(catalog, number):\\n books = catalog['books']\\n bestbooks = lt.newList()\\n for cont in range(1, number+1):\\n book = lt.getElement(books, cont)\\n lt.addLast(bestbooks, book)\\n return bestbooks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c515019aac9c9b246e05855a0943f385\",\n \"score\": \"0.4849605\",\n \"text\": \"def get_all_biblio(page=1):\\n query = BiblioEntry.query.paginate(page, ITEMS_PER_PAGE, False).items\\n bibdat = convert_rows_to_dict(query)\\n years = [str(value.year)\\n for value in db.session.query(BiblioEntry.year).distinct()]\\n tags = [str(value.tag)\\n for value in db.session.query(BiblioEntry.tag).distinct()]\\n templateVars = format_bibdatabase(bibdat)\\n years.sort()\\n templateVars[\\\"years\\\"] = years[::-1]\\n templateVars[\\\"tags\\\"] = tags\\n templateVars[\\\"nentries\\\"] = BiblioEntry.query.count()\\n return render_template(\\\"references.html\\\", **templateVars)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bbdb4c670cd4d67bb669d273d3d2c90\",\n \"score\": \"0.48495772\",\n \"text\": \"def papers(self):\\n if self.all_papers == None:\\n name = self.name\\n orcid = self.orcid\\n if not isinstance(name, str) and name != None:\\n papers_name = list(ads.SearchQuery(\\n author=name[0], fl=self.fl, **self.kw))\\n papers_name_id = [x.id for x in papers_name]\\n for kname in range(1, len(name)):\\n papers_kname = list(ads.SearchQuery(\\n author=name[kname], fl=self.fl, **self.kw))\\n for pkn in papers_kname:\\n if pkn.id not in papers_name_id:\\n papers_name.append(pkn)\\n papers_name_id.append(pkn.id)\\n elif name != None:\\n papers_name = list(ads.SearchQuery(\\n author=name, fl=self.fl, **self.kw))\\n papers_name_id = [x.id for x in papers_name]\\n else:\\n papers_name = []\\n papers_name_id = []\\n\\n if orcid != None:\\n papers_orcid = list(ads.SearchQuery(\\n orcid=orcid, fl=self.fl, **self.kw))\\n for porc in papers_orcid:\\n if porc.id not in papers_name_id:\\n papers_name.append(porc)\\n papers_name_id.append(porc.id)\\n\\n self.ncite_all = [x.citation_count for x in papers_name]\\n self.all_papers = PapersManager(papers_name)\\n\\n return self.all_papers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43387e866359b532e0f7ae9d8daf3237\",\n \"score\": \"0.48454508\",\n \"text\": \"def list_bitbucketserver_projects(self, almSettings):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"321584db0441ace5dec0e13bdd715a5f\",\n \"score\": \"0.48432878\",\n \"text\": \"def test_get_all_audiobook_ok(client):\\n\\n with app.test_request_context():\\n response = client.get(url_for(\\\"get_all_audio\\\", audioFileType=\\\"audiobook\\\"))\\n\\n assert response.status_code == 200\\n res_data = response.get_json()\\n record = list(filter(lambda x: x[\\\"id\\\"] == AUDIOBOOK_ID, res_data))\\n assert len(record) == 1\\n assert record[0][\\\"title\\\"] == \\\"Tale of two cities\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6c1dc6814ec66c490722a9594aee29a\",\n \"score\": \"0.4835043\",\n \"text\": \"def books_surname(self):\\n return [i.author_sur for i in shelf.books_list]\",\n \"title\": \"\"\n }\n]"}}},{"rowIdx":799,"cells":{"query_id":{"kind":"string","value":"e048fbeb7c6ddb0b67d7a05a70285c70"},"query":{"kind":"string","value":"Gets the side of the line p is on."},"positive_passages":{"kind":"list like","value":[{"docid":"61392af388f6f7d1f80e07e4e0dd26b4","score":"0.711668","text":"def line_side(pl, p, pr):\n\tv1 = (pr[0]-pl[0], pr[1]-pl[1]) # Vector 1\n\tv2 = (pr[0]-p[0], pr[1]-p[1]) # Vector 1\n\txp = v1[0]*v2[1] - v1[1]*v2[0] # Cross product\n\tif xp > 0:\n\t return 1\n\telif xp < 0:\n\t return -1\n\telse:\n\t return 0","title":""}],"string":"[\n {\n \"docid\": \"61392af388f6f7d1f80e07e4e0dd26b4\",\n \"score\": \"0.711668\",\n \"text\": \"def line_side(pl, p, pr):\\n\\tv1 = (pr[0]-pl[0], pr[1]-pl[1]) # Vector 1\\n\\tv2 = (pr[0]-p[0], pr[1]-p[1]) # Vector 1\\n\\txp = v1[0]*v2[1] - v1[1]*v2[0] # Cross product\\n\\tif xp > 0:\\n\\t return 1\\n\\telif xp < 0:\\n\\t return -1\\n\\telse:\\n\\t return 0\",\n \"title\": \"\"\n }\n]"},"negative_passages":{"kind":"list like","value":[{"docid":"cb2c6c21f616e1864d15179f7c84d096","score":"0.7492487","text":"def get_side(self):\n return self.get_perimeter() / 4","title":""},{"docid":"dc079eec7d6fd53e500597fcb4a05fcb","score":"0.7384879","text":"def point_side_line(p1, p2, p):\n\n # Get vector from p1 to p2\n a = p2 - p1\n # Get vector from p1 to q\n b = p - p1\n # The sign of the cross product determines point side\n s = np.sign(np.cross(a,b))\n\n # s>0 -> LEFT side of the line\n # s=0 -> ON side of the line\n # s<0 -> RIGHT side of the line\n return s","title":""},{"docid":"d9f66d4996016c63cce2787af84ca213","score":"0.731372","text":"def getSide(self):\n return self.side","title":""},{"docid":"c6b22f78f373225ad3d6b028b74f755a","score":"0.7087271","text":"def side(self) -> int:\n return self._side","title":""},{"docid":"546c66e2ae21ff0ed1122999b1f1a98a","score":"0.6932154","text":"def side(self):\n return self._side","title":""},{"docid":"546c66e2ae21ff0ed1122999b1f1a98a","score":"0.6932154","text":"def side(self):\n return self._side","title":""},{"docid":"319b8e8eaec902c8d4a61daea65f714e","score":"0.67851067","text":"def side(self):\n\t\treturn self.get(54)","title":""},{"docid":"99ce10f146becbd285f82f30503daa8d","score":"0.64582276","text":"def get_side_left(self):\n return self.filter_var(self.side_left)","title":""},{"docid":"9bac596a77cc5e3988f13cf4d3cf5c97","score":"0.6424401","text":"def point_line_side(pointx, pointy, x1, y1, x2, y2):\n return (x2-x1) * (pointy-y1) - (y2-y1) * (pointx-x1)","title":""},{"docid":"ca1c895aa5e8ba15ce9142beafd4aa8b","score":"0.6313351","text":"def get_side(self, player):\n if self.__team == player.get_team():\n return \"ALLIES\"\n return \"ENEMIES\"","title":""},{"docid":"388ef22d1cbe686519f8df605e5cceda","score":"0.61742723","text":"def get_side_right(self):\n return self.filter_var(self.side_right)","title":""},{"docid":"5bf47b198470cfb534d7252195d40d25","score":"0.61605984","text":"def side1(self) -> float:\n return self._side1","title":""},{"docid":"c96cab5dc570f59ec86ef0529726dbd8","score":"0.61603254","text":"def get_side(a, b):\n x = cosine_sign(a, b) #computes z component of out of plane component\n if x < 0:\n return LEFT\n elif x > 0: \n return RIGHT\n elif x == 0.:\n return \"zero\"\n else:\n return None","title":""},{"docid":"d44143d085f4d3f1bf40a5ecb61ddcff","score":"0.61546004","text":"def side2(self) -> float:\n return self._side2","title":""},{"docid":"5cb2c8f9f85a895ec83d33b86194efab","score":"0.6146379","text":"def distance_from_line(self, p):\n rp = pt.diff(p, self.start) # make relative to start of line\n rp_along_d = self.direction.copy().scale(rp.dot_product(self.direction)) # Get component in direction of line\n rpr = pt.diff(rp, rp_along_d) # perpendicular\n return rpr.length()","title":""},{"docid":"30bc1e85f3f9f64b5b8e912b0e9b661e","score":"0.6144422","text":"def get_flop(self):\n return self.Flop","title":""},{"docid":"4812ef8c1c078aebffa2a372682cc043","score":"0.60142285","text":"def complementary_side(self):\n if self == PuzzlePieceSide.top:\n return PuzzlePieceSide.bottom\n\n if self == PuzzlePieceSide.right:\n return PuzzlePieceSide.left\n\n if self == PuzzlePieceSide.bottom:\n return PuzzlePieceSide.top\n\n if self == PuzzlePieceSide.left:\n return PuzzlePieceSide.right","title":""},{"docid":"fe908da91ed5367c91c1ecdf043f099e","score":"0.5988404","text":"def _determineSide(self, testPlayer):\n playerList = self._playerOrder(self._playerTurn)\n lastPlayer = playerList[-1]\n if testPlayer < lastPlayer:\n testPlayer += 4\n return testPlayer - lastPlayer - 1","title":""},{"docid":"ac3dc604b14c12375571c029bbfb43ba","score":"0.5897389","text":"def get_side(given_name):\n return get_group(given_name, group='side') or ''","title":""},{"docid":"452b59b17bdf485a91f4f306fc899d97","score":"0.58860266","text":"def point_is_on_leftside_of_line(self, point):\n\n if (point.y < self.point2.y and point.y >= self.point1.y) or (point.y >= self.point2.y and point.y < self.point1.y):\n\n # The equation to determine this comes from here: https://math.stackexchange.com/questions/274712/calculate-on-which-side-of-a-straight-line-is-a-given-point-located\n\n d = ((point.x - self.point1.x)*(self.point2.y - self.point1.y)) - ((point.y - self.point1.y)*(self.point2.x - self.point1.x))\n\n left = (self.point1.y - self.point2.y)\n # If d and left are both positive or both negative, then the point is on the left side of the line\n\n if (d < 0 and left < 0) or (d > 0 and left > 0):\n return True\n\n else:\n return False","title":""},{"docid":"e4811fb638efc5f5d24f044525e886ee","score":"0.5875098","text":"def linha_pos(p):\n\treturn p[0]","title":""},{"docid":"9f2e4c3181ec788edc55e9f45175d7f0","score":"0.5852361","text":"def side(pitch):\n return pitch / SQRT3","title":""},{"docid":"c7ecd08a42dbabcd7f85d608af4e0d73","score":"0.58335465","text":"def getPerimeter(self):\n return self.side * 4","title":""},{"docid":"505dc43b635a26a2b30b27a6ba5dc9e6","score":"0.5772587","text":"def side(self, value):\n if value in (self.LEFT, self.RIGHT):\n self._side = value\n else:\n raise ValueError('Side value must be LEFT (-1) or RIGHT (1)')","title":""},{"docid":"65b7f0c5539dfef34becf6dbd667105f","score":"0.57487196","text":"def left(self):\n return self.corner.x()","title":""},{"docid":"c813cffbd7d40171bb2fb28d280983df","score":"0.5639955","text":"def side_name(self):\n return str(self).split(\".\")[1]","title":""},{"docid":"3adb71702fb9cd2cc0ece1f8045760ba","score":"0.56043947","text":"def get_semi_perimeter(self):\n return (self.side1 + self.side2 + self.side3) / 2","title":""},{"docid":"9a066b8bd9a856bd12319defc923aef4","score":"0.559953","text":"def getSide( name ):\n \n # make sure is not a full dag path\n edit = name.split('|')[-1]\n \n # make sure is lowerCase and return the side\n edit = edit.lower()\n \n if edit.startswith('l_'): return 'l'\n if edit.startswith('r_'): return 'r'\n \n return ''","title":""},{"docid":"5e2991d7d4d56b1bce69d751f18d4576","score":"0.5562073","text":"def side_lengths(self):\n d12 = self.p1.euclidean_distance(self.p2)\n d23 = self.p2.euclidean_distance(self.p3)\n d31 = self.p3.euclidean_distance(self.p1)\n return d12, d23, d31","title":""},{"docid":"e3bd52e2d680737939d8ba267bcddba0","score":"0.5545313","text":"def grating_direction(self, p):\n normal = self.normal(p)\n xaxis = Vec(1, 0, 0)\n a = normal.cross(xaxis)\n rotation = Mat().rotateAxis(90, a)\n return normal.transformDir(rotation).normalize()","title":""},{"docid":"60b97ef5e685d6009ee908fbd9a55829","score":"0.55079645","text":"def getQuarter(self, point):\n # Checks on which side of the bottom-left to top-right diagonal the point\n # is.\n posDiag = point[0] + point[1] > 0\n\n # Checks on which side of the top-left to bottom-right diagonal the point\n # is.\n negDiag = point[1] > point[0]\n\n if posDiag:\n if negDiag:\n return 0\n else:\n return 1\n if negDiag:\n return 3\n return 2","title":""},{"docid":"7cace02a999fdba75f2c696856f3d90b","score":"0.5465378","text":"def get_location_to_move_to(self, side):\n temp = PoseStamped()\n if self.get_domino_direction() == \"L\":\n if side == \"top\":\n temp.pose.position.y += VERT_VERT_OFFSET\n if side == \"bottom\":\n temp.pose.position.y -= VERT_VERT_OFFSET\n if self.get_domino_direction() == \"R\":\n if side == \"top\":\n temp.pose.position.y -= VERT_VERT_OFFSET\n if side == \"bottom\":\n temp.pose.position.y += VERT_VERT_OFFSET\n return temp\n #if \"side\" == \"left\":\n # temp.pose.position.y += HORIZ_VERT_OFFSET\n #if \"side\" == \"right\":\n # temp.pose.position.y -= HORIZ_VERT_OFFSET","title":""},{"docid":"2ba517229a96a0ae281a986f32b12561","score":"0.5462002","text":"def get_perimeter(self):\n return self.side1 + self.side2 + self.side3","title":""},{"docid":"f0c094c6beb4f14393aec743aeed28b9","score":"0.54574496","text":"def get_distance_from_starting_side(img, mode, x_edge_left, x_edge_right):\n if mode == \"left\":\n return img.shape[1] - x_edge_right\n else:\n return x_edge_left","title":""},{"docid":"4f67f669222f5bb0958ed5913d501004","score":"0.5418983","text":"def get_pos(self, x, y):\n return x * self.SIDE_SQUARES + y","title":""},{"docid":"f46217b5d4a3bf9930b48694b85137e4","score":"0.54149246","text":"def get_relative(self, p):\n return (p - self.absolute.bottom_left) / self.absolute.size.to_float()","title":""},{"docid":"feafb19adaf2e87952196d4b826f8166","score":"0.5411935","text":"def direction_ratio(self, point):\n return [(point.x - self.x),(point.y - self.y),(point.z - self.z)]","title":""},{"docid":"39aa45a24134572f06f9aed8b4fcc24a","score":"0.5406185","text":"def area(self):\n return self.side**2","title":""},{"docid":"39aa45a24134572f06f9aed8b4fcc24a","score":"0.5406185","text":"def area(self):\n return self.side**2","title":""},{"docid":"39aa45a24134572f06f9aed8b4fcc24a","score":"0.5406185","text":"def area(self):\n return self.side**2","title":""},{"docid":"eaad10f40fe76b681998e34ee83a1a8c","score":"0.5405452","text":"def get_right(self):\n\t\tif self.computed:\n\t\t\treturn self.dimensions[1]\n\t\treturn 1","title":""},{"docid":"d6e47540c05fecce63ccfae23df225ce","score":"0.53937054","text":"def getPerpendicularLineHomogenous(p1, p2):\n b1 = (p2[1] - p1[1]) / (p2[0] - p1[0]) if p1[0] != p2[0] else float('inf')\n chord_cent = [(p2[0] + p1[0]) / 2, (p2[1] + p1[1]) / 2, 1]\n print(\"Chord_cent1 \", chord_cent)\n if b1 == 0:\n return float('inf'), chord_cent\n if b1 == float('inf'):\n return 0, chord_cent\n return -1 / b1, chord_cent","title":""},{"docid":"ff766003e683a29f7d2c6416c280a635","score":"0.53896916","text":"def getArea(self):\n #return self.side * self.side\n return 2*self.side","title":""},{"docid":"7df14a41fa16a48c86b48839d61fdc82","score":"0.53799415","text":"def midpoint(self, p):\n\n x = (p[0][0] + p[1][0] + p[2][0] + p[3][0]) / 4\n y = (p[0][1] + p[1][1] + p[2][1] + p[3][1]) / 4\n return x, y","title":""},{"docid":"f73dab94634347f82d0dc28547542c25","score":"0.53726375","text":"def getLineWidth(self):\n return self._lineWidth","title":""},{"docid":"06f05f2ccca3c0281d049deaa4f03d8c","score":"0.53612983","text":"def linestyle(self):\n return self._linestyle","title":""},{"docid":"4076cda62c00ab929cf05a1aae6cf65c","score":"0.53612137","text":"def get_offset_right(self):\n\t\treturn self.offset[1]","title":""},{"docid":"f90514e3e77fcadcb9a2c86e09e4e1b1","score":"0.5358349","text":"def getdLine(self):\n return self._dline","title":""},{"docid":"b6fa7a36eb53e525f484638168cc8b5b","score":"0.53573775","text":"def height(self):\r\n return self._side","title":""},{"docid":"1b5f5bd755b344a8a75bc6c57f86475c","score":"0.5345058","text":"def calculate_perimeter(self):\n perimeter = self.__side * 4\n return perimeter","title":""},{"docid":"f87b30c945a5567bfc2240bcd77dd08c","score":"0.5343993","text":"def quadrant(self, pt):\r\n if pt[X] >= self.origin[X]:\r\n if pt[Y] >= self.origin[Y]:\r\n return NE\r\n else:\r\n return SE\r\n else:\r\n if pt[Y] >= self.origin[Y]:\r\n return NW\r\n else:\r\n return SW","title":""},{"docid":"bd23c5c0554d9d9997c69b4457f3c274","score":"0.5329019","text":"def line_from_pts(p1, p2):\n\n ##### STUDENT CODE START #####\n l = np.cross(p1, p2)\n l = l / np.linalg.norm(l[:2], 2)\n\n ##### STUDENT CODE END #####\n \n return l","title":""},{"docid":"21a35e959d4908f30b97bf4848e75ec6","score":"0.5325119","text":"def side_classification(self):\n sides = self.side_lengths()\n\n if isequal(sides[0], sides[1]) and isequal(sides[1], sides[2]) and isequal(sides[2], sides[0]):\n return \"equilateral\" # três lados iguais: equilátero\n\n if isequal(sides[0], sides[1]) or isequal(sides[1], sides[2]) or isequal(sides[2], sides[0]):\n return \"isosceles\" # dois lados iguais, mas não os três: isósceles\n\n return \"scalene\" # lados diferentes: escaleno","title":""},{"docid":"2641821b1ed811817a778bb890aaa36c","score":"0.532211","text":"def get_direction(self):\n if self.is_left_pressed():\n self._current = Point(-1, 0)\n elif self.is_right_pressed():\n self._current = Point(1, 0)\n elif self.is_up_pressed():\n self._current = Point(0, -1)\n elif self.is_down_pressed():\n self._current = Point(0, 1)\n\n return self._current","title":""},{"docid":"be4b4aedcdfc2898a74a38f5ad3bef25","score":"0.5311293","text":"def get_prow_direction(self):\r\n return self.__prow_direction","title":""},{"docid":"4824af83cbfcd0c9b4827977a55c8929","score":"0.5307189","text":"def right(self):\n return self.corner.x() + self.size.width()","title":""},{"docid":"1e76bbc1bed440819b0e30feab13317b","score":"0.52988553","text":"def left_position(self):\n try:\n return self.__left_position\n except AttributeError:\n self.__left_position = self.start_position if self.feature.clockwise \\\n else 2 * self.start_position - self.stop_position\n return self.__left_position","title":""},{"docid":"31b0b084c0645ffe85794cb9c11aaf33","score":"0.52933043","text":"def Direction(self, *args):\n return _Geom2d.Geom2d_Line_Direction(self, *args)","title":""},{"docid":"3c17ae41ecdb1315c0c732d7dacbe2d1","score":"0.52901244","text":"def getLeftQuadraturePosition(self):\r\n return -self.leftTalon.getSensorCollection().getQuadraturePosition()","title":""},{"docid":"508bb380003269349fc110de0c40a413","score":"0.528571","text":"def getEdgeThickness(self):\n front = self[0].point.z + self[0].edgePlane()\n back = self[1].point.z + self[1].edgePlane()\n return back - front","title":""},{"docid":"ab32697a754caf7edf5eef8b455737f6","score":"0.5277734","text":"def signed_dist_to_line(self, p, q) -> float:\n if p.y == q.y:\n return self.y - p.y\n elif p.x == q.x:\n return self.x - p.x\n else:\n a = 1 / (q.x - p.x)\n b = -1 / (q.y - p.y)\n c = p.y / (q.y - p.y) - p.x / (q.x - p.x)\n return (a * self.x + b * self.y + c) / math.sqrt(a ** 2 + b ** 2)","title":""},{"docid":"8e3305c5054dc703ff0d2b23d82d1e5e","score":"0.52753776","text":"def line_orientation(p: Position, q: Position, r: Position) -> OrientationResult:\r\n val = ((q[1] - p[1]) * (r[0] - q[0])) - ((q[0] - p[0]) * (r[1] - q[1]))\r\n\r\n if np.isclose(val, 0.0):\r\n return OrientationResult.COLLINEAR\r\n\r\n if val > 0:\r\n return OrientationResult.CLOCKWISE\r\n return OrientationResult.ANTICLOCKWISE","title":""},{"docid":"dfb13ef9256803cb070b6e95a2d28ecf","score":"0.52742714","text":"def side_func(x):\n y=(window_height-HEIGHT)/2.0 - sqrt(3) * (x-window_width/2.0)\n if (x>=250):\n y=(window_height-HEIGHT)/2.0 + sqrt(3) * (x-window_width/2.0)\n return y","title":""},{"docid":"1086cb68103222f6a4abfcea670b4adb","score":"0.52640045","text":"def perimeter(self) -> float:\n return 2*(self.side1+self.side2)","title":""},{"docid":"87f215dbff347887aad936ea533ed38a","score":"0.5243852","text":"def on_surface(self, point):\n return point[0] - self.rect.x, point[1] - self.rect.y","title":""},{"docid":"064023801890f585ef985bafa019b550","score":"0.5243692","text":"def perpendicular(self, through: Point | Line) -> Line | Plane:\n if self.dim != 3 and isinstance(through, Line):\n raise NotImplementedError(f\"Expected dimension 3 but found dimension {self.dim}.\")\n\n p = self.array[..., :-1]\n p = Point(np.append(p, np.zeros(p.shape[:-1] + (1,), dtype=p.dtype), axis=-1), copy=False)\n return through.join(p)","title":""},{"docid":"b20216c62a07a2c561a93c157149e1ef","score":"0.5235033","text":"def frame_left(self) -> float:\r\n return self.position.x - self.half_width","title":""},{"docid":"028d0164af79b05c3ac256b9c71d08c3","score":"0.522857","text":"def direction(self) -> Point:\n first_vec = self.first.subtract(self.second)\n second_vec = self.first.subtract(self.third)\n return first_vec.vector_product(second_vec)","title":""},{"docid":"8268a4baf77c92f8ebf10101ad82ba1c","score":"0.52246726","text":"def SPL(self):\n return self.S().P().L()","title":""},{"docid":"d8d39586dbd0d67cf187c96d7c2580c5","score":"0.52229756","text":"def lineLength (line):\r\n return math.sqrt((line[1].x - line[0].x) ** 2 + (line[1].y - line[0].y) ** 2)","title":""},{"docid":"69259cea9e83b1ffc23ae212e788078f","score":"0.5217991","text":"def side_length(X1,y1,x2,y2):\n return math.sqrt((X1-X2)**2 + (y1 - y2)**2))","title":""},{"docid":"7b047d858bc17cad65646b9791437d70","score":"0.52150047","text":"def getThickness(self):\n return self[1].point.z - self[0].point.z","title":""},{"docid":"7bf01ad6f6db18d2c958c702f21d424a","score":"0.5213559","text":"def linewidth():","title":""},{"docid":"732448dd60c29e209140f9bca58d8ae5","score":"0.52098674","text":"def frame_right(self) -> float:\r\n return self.position.x + self.half_width","title":""},{"docid":"acc3c505cc6af72b33e15aa92f07b3c7","score":"0.51981115","text":"def get_connected(self, side=None):\n end_side=[]\n if self._orientation==1:\n end_side.append('W')\n return end_side\n elif self._orientation==2:\n end_side.append('N')\n return end_side\n elif self._orientation==3:\n end_side.append('E')\n return end_side\n else:\n end_side.append('S')\n return end_side","title":""},{"docid":"c439f1be62b3408051c86203f5f0318b","score":"0.51950645","text":"def _width_of_current_line(self) -> int:\n return self._tw.width_of_current_line","title":""},{"docid":"c92873d5d9b9e8feafb4a5fe82708e6a","score":"0.5189287","text":"def dof_point(self) -> PointType:\n return self.ref.sub_entity(*self.entity).midpoint()","title":""},{"docid":"e6575bde7e407ed2671b78943f03ad74","score":"0.51873684","text":"def pentagon_perimeter(side: Number) -> Number:\n return 5*side","title":""},{"docid":"de905baaaa716709c4113fc4938a03bb","score":"0.5185853","text":"def get_side_of_head(s:bs4.element.Tag) -> SideOfHead:\n title = s.title.contents[0].lower()\n description = s.description.contents[0].lower()\n if 'rt' in title or 'right' in description:\n return SideOfHead.RIGHT\n elif 'lt' in title or 'left' in description:\n return SideOfHead.LEFT\n else:\n error(f'side of head cannot be determined from:\\n\\ttitle:{title}\\n\\t description:{description}')\n return None","title":""},{"docid":"3d092e967e0ec30a79077167779e0415","score":"0.51793754","text":"def get_offset_left(self):\n\t\treturn self.offset[0]","title":""},{"docid":"a4be2789fba83d16e1bf5707f434fb23","score":"0.517089","text":"def column(self) -> int:\n\n return int(self[-2:])","title":""},{"docid":"5743ed02767f576faa0f5a98e9b8430c","score":"0.51668185","text":"def side_lobe_level():\n return -13.26","title":""},{"docid":"3a8cacc4e6a6e14b4df8722e1f377b38","score":"0.5161773","text":"def get_shape(sides):\n pass","title":""},{"docid":"31be2fda9a74fdd41f7ebe74fe131f23","score":"0.5157701","text":"def get_pin_direction(self, line):\n is_input = self._get_bit_in_register('DIRECTION', line)\n return Directions.IN if is_input else Directions.OUT","title":""},{"docid":"c9ac0edf3447d8db00b4d251ccdc5281","score":"0.51407963","text":"def _get_level(self):\n if not hasattr(self.__p, 'pPr'):\n return 0\n return int(self.__p.pPr.get('lvl', 0))","title":""},{"docid":"18fc0904c9be3ee6edecbd6ba0bd0794","score":"0.51407915","text":"def pointDistance( self, p ):\n disp = self.p2 - self.p1\n segLen = disp.magnitude()\n norm = disp / segLen\n dispP = p - self.p1\n dp = norm.dot( dispP )\n if ( dp < 0 ): \n return (p - self.p1).magnitude()\n elif ( dp > segLen ):\n return ( p - self.p2).magnitude()\n else:\n A = -norm.y\n B = norm.x\n C = -( A * self.p1.x + B * self.p1.y )\n return abs( A * p.x + B * p.y + C )","title":""},{"docid":"79fa7b3b4b53af8196ccbd5dc769c301","score":"0.5140242","text":"def sideinfo(self):\n return self._get_element(*self._side_info_locator)","title":""},{"docid":"a688a18fc0e8075e19decfe1f28a08e0","score":"0.5128","text":"def line(self):\n return self.__line","title":""},{"docid":"5d85367c9d9286952cef6560643372c3","score":"0.5127137","text":"def right(self):\n return self._x + self._width","title":""},{"docid":"6b5e166a12cb1538013848c4b2f848a2","score":"0.51181984","text":"def line(p1, p2):\n A = (p1[1] - p2[1])\n B = (p2[0] - p1[0])\n C = (p1[0]*p2[1] - p2[0]*p1[1])\n return A, B, -C","title":""},{"docid":"611a49d6ca4dc570aaf11508efcccfa2","score":"0.5113819","text":"def perpendicular(self):\n Vperp = Vector2(-self.mData[1], self.mData[0])\n return Vperp","title":""},{"docid":"ae882b581d115c24f67cdfe1172c7eac","score":"0.5109769","text":"def distance_to(self, p):\n\t\treturn (self - p).length()","title":""},{"docid":"ac64bdf0462135e4d1d630ee1a66a143","score":"0.5109033","text":"def float2SideLeft(self):\n self.mLeft = self.getFloatSideLeft()\n return True","title":""},{"docid":"02812c3794e0b2e346c612557edd9d1d","score":"0.5107515","text":"def midPoint( self ):\n try:\n return ( self.p1 + self.p2 ) * 0.5\n except TypeError:\n print type( self.p1 ), type( self.p2 )","title":""},{"docid":"984e3ad3e8d3118d918353047d3d1428","score":"0.510563","text":"def coluna_pos(p):\n\treturn p[1]","title":""},{"docid":"bfae792a2165e1b3e43234a27b076f87","score":"0.51040936","text":"def right(self) -> float:\n return self.x + self.w","title":""},{"docid":"55dd12c453218080573b3268eebd75a4","score":"0.5101551","text":"def right(self):\n return self.left + self.width","title":""},{"docid":"59f689ee7b14e64d8b2fd2cde977e06b","score":"0.50990087","text":"def point_to_line(tower):\n return True, tower","title":""},{"docid":"5388fc94cbc0fdd59754edf92694cbee","score":"0.5096413","text":"def line_width(self):\n return self._line_width","title":""},{"docid":"8175b4c1543f79275f8b383fec11f42c","score":"0.5087364","text":"def perpendicular(self, through):\n if self.contains(through):\n n = self.dim + 1\n\n l = self\n\n if n > 3:\n # additional point is required to determine the exact line\n arr = np.zeros(n)\n for i in range(n):\n arr[-i - 1] = 1\n o = Point(arr)\n if not self.contains(o):\n break\n e = join(self, o)\n basis = e.basis_matrix\n line_pts = basis.dot(self.basis_matrix.T)\n l = Line(np.cross(*line_pts.T))\n\n from .operators import harmonic_set\n p = l.meet(infty)\n q = harmonic_set(I, J, p)\n\n if n > 3:\n q = Point(basis.T.dot(q.array))\n\n return Line(through, q)\n\n return self.mirror(through).join(through)","title":""}],"string":"[\n {\n \"docid\": \"cb2c6c21f616e1864d15179f7c84d096\",\n \"score\": \"0.7492487\",\n \"text\": \"def get_side(self):\\n return self.get_perimeter() / 4\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc079eec7d6fd53e500597fcb4a05fcb\",\n \"score\": \"0.7384879\",\n \"text\": \"def point_side_line(p1, p2, p):\\n\\n # Get vector from p1 to p2\\n a = p2 - p1\\n # Get vector from p1 to q\\n b = p - p1\\n # The sign of the cross product determines point side\\n s = np.sign(np.cross(a,b))\\n\\n # s>0 -> LEFT side of the line\\n # s=0 -> ON side of the line\\n # s<0 -> RIGHT side of the line\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9f66d4996016c63cce2787af84ca213\",\n \"score\": \"0.731372\",\n \"text\": \"def getSide(self):\\n return self.side\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6b22f78f373225ad3d6b028b74f755a\",\n \"score\": \"0.7087271\",\n \"text\": \"def side(self) -> int:\\n return self._side\",\n \"title\": \"\"\n },\n {\n \"docid\": \"546c66e2ae21ff0ed1122999b1f1a98a\",\n \"score\": \"0.6932154\",\n \"text\": \"def side(self):\\n return self._side\",\n \"title\": \"\"\n },\n {\n \"docid\": \"546c66e2ae21ff0ed1122999b1f1a98a\",\n \"score\": \"0.6932154\",\n \"text\": \"def side(self):\\n return self._side\",\n \"title\": \"\"\n },\n {\n \"docid\": \"319b8e8eaec902c8d4a61daea65f714e\",\n \"score\": \"0.67851067\",\n \"text\": \"def side(self):\\n\\t\\treturn self.get(54)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99ce10f146becbd285f82f30503daa8d\",\n \"score\": \"0.64582276\",\n \"text\": \"def get_side_left(self):\\n return self.filter_var(self.side_left)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bac596a77cc5e3988f13cf4d3cf5c97\",\n \"score\": \"0.6424401\",\n \"text\": \"def point_line_side(pointx, pointy, x1, y1, x2, y2):\\n return (x2-x1) * (pointy-y1) - (y2-y1) * (pointx-x1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca1c895aa5e8ba15ce9142beafd4aa8b\",\n \"score\": \"0.6313351\",\n \"text\": \"def get_side(self, player):\\n if self.__team == player.get_team():\\n return \\\"ALLIES\\\"\\n return \\\"ENEMIES\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"388ef22d1cbe686519f8df605e5cceda\",\n \"score\": \"0.61742723\",\n \"text\": \"def get_side_right(self):\\n return self.filter_var(self.side_right)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bf47b198470cfb534d7252195d40d25\",\n \"score\": \"0.61605984\",\n \"text\": \"def side1(self) -> float:\\n return self._side1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c96cab5dc570f59ec86ef0529726dbd8\",\n \"score\": \"0.61603254\",\n \"text\": \"def get_side(a, b):\\n x = cosine_sign(a, b) #computes z component of out of plane component\\n if x < 0:\\n return LEFT\\n elif x > 0: \\n return RIGHT\\n elif x == 0.:\\n return \\\"zero\\\"\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d44143d085f4d3f1bf40a5ecb61ddcff\",\n \"score\": \"0.61546004\",\n \"text\": \"def side2(self) -> float:\\n return self._side2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cb2c8f9f85a895ec83d33b86194efab\",\n \"score\": \"0.6146379\",\n \"text\": \"def distance_from_line(self, p):\\n rp = pt.diff(p, self.start) # make relative to start of line\\n rp_along_d = self.direction.copy().scale(rp.dot_product(self.direction)) # Get component in direction of line\\n rpr = pt.diff(rp, rp_along_d) # perpendicular\\n return rpr.length()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30bc1e85f3f9f64b5b8e912b0e9b661e\",\n \"score\": \"0.6144422\",\n \"text\": \"def get_flop(self):\\n return self.Flop\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4812ef8c1c078aebffa2a372682cc043\",\n \"score\": \"0.60142285\",\n \"text\": \"def complementary_side(self):\\n if self == PuzzlePieceSide.top:\\n return PuzzlePieceSide.bottom\\n\\n if self == PuzzlePieceSide.right:\\n return PuzzlePieceSide.left\\n\\n if self == PuzzlePieceSide.bottom:\\n return PuzzlePieceSide.top\\n\\n if self == PuzzlePieceSide.left:\\n return PuzzlePieceSide.right\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe908da91ed5367c91c1ecdf043f099e\",\n \"score\": \"0.5988404\",\n \"text\": \"def _determineSide(self, testPlayer):\\n playerList = self._playerOrder(self._playerTurn)\\n lastPlayer = playerList[-1]\\n if testPlayer < lastPlayer:\\n testPlayer += 4\\n return testPlayer - lastPlayer - 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac3dc604b14c12375571c029bbfb43ba\",\n \"score\": \"0.5897389\",\n \"text\": \"def get_side(given_name):\\n return get_group(given_name, group='side') or ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"452b59b17bdf485a91f4f306fc899d97\",\n \"score\": \"0.58860266\",\n \"text\": \"def point_is_on_leftside_of_line(self, point):\\n\\n if (point.y < self.point2.y and point.y >= self.point1.y) or (point.y >= self.point2.y and point.y < self.point1.y):\\n\\n # The equation to determine this comes from here: https://math.stackexchange.com/questions/274712/calculate-on-which-side-of-a-straight-line-is-a-given-point-located\\n\\n d = ((point.x - self.point1.x)*(self.point2.y - self.point1.y)) - ((point.y - self.point1.y)*(self.point2.x - self.point1.x))\\n\\n left = (self.point1.y - self.point2.y)\\n # If d and left are both positive or both negative, then the point is on the left side of the line\\n\\n if (d < 0 and left < 0) or (d > 0 and left > 0):\\n return True\\n\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4811fb638efc5f5d24f044525e886ee\",\n \"score\": \"0.5875098\",\n \"text\": \"def linha_pos(p):\\n\\treturn p[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f2e4c3181ec788edc55e9f45175d7f0\",\n \"score\": \"0.5852361\",\n \"text\": \"def side(pitch):\\n return pitch / SQRT3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7ecd08a42dbabcd7f85d608af4e0d73\",\n \"score\": \"0.58335465\",\n \"text\": \"def getPerimeter(self):\\n return self.side * 4\",\n \"title\": \"\"\n },\n {\n \"docid\": \"505dc43b635a26a2b30b27a6ba5dc9e6\",\n \"score\": \"0.5772587\",\n \"text\": \"def side(self, value):\\n if value in (self.LEFT, self.RIGHT):\\n self._side = value\\n else:\\n raise ValueError('Side value must be LEFT (-1) or RIGHT (1)')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65b7f0c5539dfef34becf6dbd667105f\",\n \"score\": \"0.57487196\",\n \"text\": \"def left(self):\\n return self.corner.x()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c813cffbd7d40171bb2fb28d280983df\",\n \"score\": \"0.5639955\",\n \"text\": \"def side_name(self):\\n return str(self).split(\\\".\\\")[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3adb71702fb9cd2cc0ece1f8045760ba\",\n \"score\": \"0.56043947\",\n \"text\": \"def get_semi_perimeter(self):\\n return (self.side1 + self.side2 + self.side3) / 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a066b8bd9a856bd12319defc923aef4\",\n \"score\": \"0.559953\",\n \"text\": \"def getSide( name ):\\n \\n # make sure is not a full dag path\\n edit = name.split('|')[-1]\\n \\n # make sure is lowerCase and return the side\\n edit = edit.lower()\\n \\n if edit.startswith('l_'): return 'l'\\n if edit.startswith('r_'): return 'r'\\n \\n return ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e2991d7d4d56b1bce69d751f18d4576\",\n \"score\": \"0.5562073\",\n \"text\": \"def side_lengths(self):\\n d12 = self.p1.euclidean_distance(self.p2)\\n d23 = self.p2.euclidean_distance(self.p3)\\n d31 = self.p3.euclidean_distance(self.p1)\\n return d12, d23, d31\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3bd52e2d680737939d8ba267bcddba0\",\n \"score\": \"0.5545313\",\n \"text\": \"def grating_direction(self, p):\\n normal = self.normal(p)\\n xaxis = Vec(1, 0, 0)\\n a = normal.cross(xaxis)\\n rotation = Mat().rotateAxis(90, a)\\n return normal.transformDir(rotation).normalize()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60b97ef5e685d6009ee908fbd9a55829\",\n \"score\": \"0.55079645\",\n \"text\": \"def getQuarter(self, point):\\n # Checks on which side of the bottom-left to top-right diagonal the point\\n # is.\\n posDiag = point[0] + point[1] > 0\\n\\n # Checks on which side of the top-left to bottom-right diagonal the point\\n # is.\\n negDiag = point[1] > point[0]\\n\\n if posDiag:\\n if negDiag:\\n return 0\\n else:\\n return 1\\n if negDiag:\\n return 3\\n return 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cace02a999fdba75f2c696856f3d90b\",\n \"score\": \"0.5465378\",\n \"text\": \"def get_location_to_move_to(self, side):\\n temp = PoseStamped()\\n if self.get_domino_direction() == \\\"L\\\":\\n if side == \\\"top\\\":\\n temp.pose.position.y += VERT_VERT_OFFSET\\n if side == \\\"bottom\\\":\\n temp.pose.position.y -= VERT_VERT_OFFSET\\n if self.get_domino_direction() == \\\"R\\\":\\n if side == \\\"top\\\":\\n temp.pose.position.y -= VERT_VERT_OFFSET\\n if side == \\\"bottom\\\":\\n temp.pose.position.y += VERT_VERT_OFFSET\\n return temp\\n #if \\\"side\\\" == \\\"left\\\":\\n # temp.pose.position.y += HORIZ_VERT_OFFSET\\n #if \\\"side\\\" == \\\"right\\\":\\n # temp.pose.position.y -= HORIZ_VERT_OFFSET\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ba517229a96a0ae281a986f32b12561\",\n \"score\": \"0.5462002\",\n \"text\": \"def get_perimeter(self):\\n return self.side1 + self.side2 + self.side3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0c094c6beb4f14393aec743aeed28b9\",\n \"score\": \"0.54574496\",\n \"text\": \"def get_distance_from_starting_side(img, mode, x_edge_left, x_edge_right):\\n if mode == \\\"left\\\":\\n return img.shape[1] - x_edge_right\\n else:\\n return x_edge_left\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f67f669222f5bb0958ed5913d501004\",\n \"score\": \"0.5418983\",\n \"text\": \"def get_pos(self, x, y):\\n return x * self.SIDE_SQUARES + y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f46217b5d4a3bf9930b48694b85137e4\",\n \"score\": \"0.54149246\",\n \"text\": \"def get_relative(self, p):\\n return (p - self.absolute.bottom_left) / self.absolute.size.to_float()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feafb19adaf2e87952196d4b826f8166\",\n \"score\": \"0.5411935\",\n \"text\": \"def direction_ratio(self, point):\\n return [(point.x - self.x),(point.y - self.y),(point.z - self.z)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39aa45a24134572f06f9aed8b4fcc24a\",\n \"score\": \"0.5406185\",\n \"text\": \"def area(self):\\n return self.side**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39aa45a24134572f06f9aed8b4fcc24a\",\n \"score\": \"0.5406185\",\n \"text\": \"def area(self):\\n return self.side**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39aa45a24134572f06f9aed8b4fcc24a\",\n \"score\": \"0.5406185\",\n \"text\": \"def area(self):\\n return self.side**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eaad10f40fe76b681998e34ee83a1a8c\",\n \"score\": \"0.5405452\",\n \"text\": \"def get_right(self):\\n\\t\\tif self.computed:\\n\\t\\t\\treturn self.dimensions[1]\\n\\t\\treturn 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6e47540c05fecce63ccfae23df225ce\",\n \"score\": \"0.53937054\",\n \"text\": \"def getPerpendicularLineHomogenous(p1, p2):\\n b1 = (p2[1] - p1[1]) / (p2[0] - p1[0]) if p1[0] != p2[0] else float('inf')\\n chord_cent = [(p2[0] + p1[0]) / 2, (p2[1] + p1[1]) / 2, 1]\\n print(\\\"Chord_cent1 \\\", chord_cent)\\n if b1 == 0:\\n return float('inf'), chord_cent\\n if b1 == float('inf'):\\n return 0, chord_cent\\n return -1 / b1, chord_cent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff766003e683a29f7d2c6416c280a635\",\n \"score\": \"0.53896916\",\n \"text\": \"def getArea(self):\\n #return self.side * self.side\\n return 2*self.side\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7df14a41fa16a48c86b48839d61fdc82\",\n \"score\": \"0.53799415\",\n \"text\": \"def midpoint(self, p):\\n\\n x = (p[0][0] + p[1][0] + p[2][0] + p[3][0]) / 4\\n y = (p[0][1] + p[1][1] + p[2][1] + p[3][1]) / 4\\n return x, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f73dab94634347f82d0dc28547542c25\",\n \"score\": \"0.53726375\",\n \"text\": \"def getLineWidth(self):\\n return self._lineWidth\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06f05f2ccca3c0281d049deaa4f03d8c\",\n \"score\": \"0.53612983\",\n \"text\": \"def linestyle(self):\\n return self._linestyle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4076cda62c00ab929cf05a1aae6cf65c\",\n \"score\": \"0.53612137\",\n \"text\": \"def get_offset_right(self):\\n\\t\\treturn self.offset[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f90514e3e77fcadcb9a2c86e09e4e1b1\",\n \"score\": \"0.5358349\",\n \"text\": \"def getdLine(self):\\n return self._dline\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6fa7a36eb53e525f484638168cc8b5b\",\n \"score\": \"0.53573775\",\n \"text\": \"def height(self):\\r\\n return self._side\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b5f5bd755b344a8a75bc6c57f86475c\",\n \"score\": \"0.5345058\",\n \"text\": \"def calculate_perimeter(self):\\n perimeter = self.__side * 4\\n return perimeter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f87b30c945a5567bfc2240bcd77dd08c\",\n \"score\": \"0.5343993\",\n \"text\": \"def quadrant(self, pt):\\r\\n if pt[X] >= self.origin[X]:\\r\\n if pt[Y] >= self.origin[Y]:\\r\\n return NE\\r\\n else:\\r\\n return SE\\r\\n else:\\r\\n if pt[Y] >= self.origin[Y]:\\r\\n return NW\\r\\n else:\\r\\n return SW\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd23c5c0554d9d9997c69b4457f3c274\",\n \"score\": \"0.5329019\",\n \"text\": \"def line_from_pts(p1, p2):\\n\\n ##### STUDENT CODE START #####\\n l = np.cross(p1, p2)\\n l = l / np.linalg.norm(l[:2], 2)\\n\\n ##### STUDENT CODE END #####\\n \\n return l\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21a35e959d4908f30b97bf4848e75ec6\",\n \"score\": \"0.5325119\",\n \"text\": \"def side_classification(self):\\n sides = self.side_lengths()\\n\\n if isequal(sides[0], sides[1]) and isequal(sides[1], sides[2]) and isequal(sides[2], sides[0]):\\n return \\\"equilateral\\\" # três lados iguais: equilátero\\n\\n if isequal(sides[0], sides[1]) or isequal(sides[1], sides[2]) or isequal(sides[2], sides[0]):\\n return \\\"isosceles\\\" # dois lados iguais, mas não os três: isósceles\\n\\n return \\\"scalene\\\" # lados diferentes: escaleno\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2641821b1ed811817a778bb890aaa36c\",\n \"score\": \"0.532211\",\n \"text\": \"def get_direction(self):\\n if self.is_left_pressed():\\n self._current = Point(-1, 0)\\n elif self.is_right_pressed():\\n self._current = Point(1, 0)\\n elif self.is_up_pressed():\\n self._current = Point(0, -1)\\n elif self.is_down_pressed():\\n self._current = Point(0, 1)\\n\\n return self._current\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be4b4aedcdfc2898a74a38f5ad3bef25\",\n \"score\": \"0.5311293\",\n \"text\": \"def get_prow_direction(self):\\r\\n return self.__prow_direction\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4824af83cbfcd0c9b4827977a55c8929\",\n \"score\": \"0.5307189\",\n \"text\": \"def right(self):\\n return self.corner.x() + self.size.width()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e76bbc1bed440819b0e30feab13317b\",\n \"score\": \"0.52988553\",\n \"text\": \"def left_position(self):\\n try:\\n return self.__left_position\\n except AttributeError:\\n self.__left_position = self.start_position if self.feature.clockwise \\\\\\n else 2 * self.start_position - self.stop_position\\n return self.__left_position\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b0b084c0645ffe85794cb9c11aaf33\",\n \"score\": \"0.52933043\",\n \"text\": \"def Direction(self, *args):\\n return _Geom2d.Geom2d_Line_Direction(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c17ae41ecdb1315c0c732d7dacbe2d1\",\n \"score\": \"0.52901244\",\n \"text\": \"def getLeftQuadraturePosition(self):\\r\\n return -self.leftTalon.getSensorCollection().getQuadraturePosition()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"508bb380003269349fc110de0c40a413\",\n \"score\": \"0.528571\",\n \"text\": \"def getEdgeThickness(self):\\n front = self[0].point.z + self[0].edgePlane()\\n back = self[1].point.z + self[1].edgePlane()\\n return back - front\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab32697a754caf7edf5eef8b455737f6\",\n \"score\": \"0.5277734\",\n \"text\": \"def signed_dist_to_line(self, p, q) -> float:\\n if p.y == q.y:\\n return self.y - p.y\\n elif p.x == q.x:\\n return self.x - p.x\\n else:\\n a = 1 / (q.x - p.x)\\n b = -1 / (q.y - p.y)\\n c = p.y / (q.y - p.y) - p.x / (q.x - p.x)\\n return (a * self.x + b * self.y + c) / math.sqrt(a ** 2 + b ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e3305c5054dc703ff0d2b23d82d1e5e\",\n \"score\": \"0.52753776\",\n \"text\": \"def line_orientation(p: Position, q: Position, r: Position) -> OrientationResult:\\r\\n val = ((q[1] - p[1]) * (r[0] - q[0])) - ((q[0] - p[0]) * (r[1] - q[1]))\\r\\n\\r\\n if np.isclose(val, 0.0):\\r\\n return OrientationResult.COLLINEAR\\r\\n\\r\\n if val > 0:\\r\\n return OrientationResult.CLOCKWISE\\r\\n return OrientationResult.ANTICLOCKWISE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfb13ef9256803cb070b6e95a2d28ecf\",\n \"score\": \"0.52742714\",\n \"text\": \"def side_func(x):\\n y=(window_height-HEIGHT)/2.0 - sqrt(3) * (x-window_width/2.0)\\n if (x>=250):\\n y=(window_height-HEIGHT)/2.0 + sqrt(3) * (x-window_width/2.0)\\n return y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1086cb68103222f6a4abfcea670b4adb\",\n \"score\": \"0.52640045\",\n \"text\": \"def perimeter(self) -> float:\\n return 2*(self.side1+self.side2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87f215dbff347887aad936ea533ed38a\",\n \"score\": \"0.5243852\",\n \"text\": \"def on_surface(self, point):\\n return point[0] - self.rect.x, point[1] - self.rect.y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"064023801890f585ef985bafa019b550\",\n \"score\": \"0.5243692\",\n \"text\": \"def perpendicular(self, through: Point | Line) -> Line | Plane:\\n if self.dim != 3 and isinstance(through, Line):\\n raise NotImplementedError(f\\\"Expected dimension 3 but found dimension {self.dim}.\\\")\\n\\n p = self.array[..., :-1]\\n p = Point(np.append(p, np.zeros(p.shape[:-1] + (1,), dtype=p.dtype), axis=-1), copy=False)\\n return through.join(p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b20216c62a07a2c561a93c157149e1ef\",\n \"score\": \"0.5235033\",\n \"text\": \"def frame_left(self) -> float:\\r\\n return self.position.x - self.half_width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"028d0164af79b05c3ac256b9c71d08c3\",\n \"score\": \"0.522857\",\n \"text\": \"def direction(self) -> Point:\\n first_vec = self.first.subtract(self.second)\\n second_vec = self.first.subtract(self.third)\\n return first_vec.vector_product(second_vec)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8268a4baf77c92f8ebf10101ad82ba1c\",\n \"score\": \"0.52246726\",\n \"text\": \"def SPL(self):\\n return self.S().P().L()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8d39586dbd0d67cf187c96d7c2580c5\",\n \"score\": \"0.52229756\",\n \"text\": \"def lineLength (line):\\r\\n return math.sqrt((line[1].x - line[0].x) ** 2 + (line[1].y - line[0].y) ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69259cea9e83b1ffc23ae212e788078f\",\n \"score\": \"0.5217991\",\n \"text\": \"def side_length(X1,y1,x2,y2):\\n return math.sqrt((X1-X2)**2 + (y1 - y2)**2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b047d858bc17cad65646b9791437d70\",\n \"score\": \"0.52150047\",\n \"text\": \"def getThickness(self):\\n return self[1].point.z - self[0].point.z\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bf01ad6f6db18d2c958c702f21d424a\",\n \"score\": \"0.5213559\",\n \"text\": \"def linewidth():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"732448dd60c29e209140f9bca58d8ae5\",\n \"score\": \"0.52098674\",\n \"text\": \"def frame_right(self) -> float:\\r\\n return self.position.x + self.half_width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acc3c505cc6af72b33e15aa92f07b3c7\",\n \"score\": \"0.51981115\",\n \"text\": \"def get_connected(self, side=None):\\n end_side=[]\\n if self._orientation==1:\\n end_side.append('W')\\n return end_side\\n elif self._orientation==2:\\n end_side.append('N')\\n return end_side\\n elif self._orientation==3:\\n end_side.append('E')\\n return end_side\\n else:\\n end_side.append('S')\\n return end_side\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c439f1be62b3408051c86203f5f0318b\",\n \"score\": \"0.51950645\",\n \"text\": \"def _width_of_current_line(self) -> int:\\n return self._tw.width_of_current_line\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c92873d5d9b9e8feafb4a5fe82708e6a\",\n \"score\": \"0.5189287\",\n \"text\": \"def dof_point(self) -> PointType:\\n return self.ref.sub_entity(*self.entity).midpoint()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6575bde7e407ed2671b78943f03ad74\",\n \"score\": \"0.51873684\",\n \"text\": \"def pentagon_perimeter(side: Number) -> Number:\\n return 5*side\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de905baaaa716709c4113fc4938a03bb\",\n \"score\": \"0.5185853\",\n \"text\": \"def get_side_of_head(s:bs4.element.Tag) -> SideOfHead:\\n title = s.title.contents[0].lower()\\n description = s.description.contents[0].lower()\\n if 'rt' in title or 'right' in description:\\n return SideOfHead.RIGHT\\n elif 'lt' in title or 'left' in description:\\n return SideOfHead.LEFT\\n else:\\n error(f'side of head cannot be determined from:\\\\n\\\\ttitle:{title}\\\\n\\\\t description:{description}')\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d092e967e0ec30a79077167779e0415\",\n \"score\": \"0.51793754\",\n \"text\": \"def get_offset_left(self):\\n\\t\\treturn self.offset[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4be2789fba83d16e1bf5707f434fb23\",\n \"score\": \"0.517089\",\n \"text\": \"def column(self) -> int:\\n\\n return int(self[-2:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5743ed02767f576faa0f5a98e9b8430c\",\n \"score\": \"0.51668185\",\n \"text\": \"def side_lobe_level():\\n return -13.26\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a8cacc4e6a6e14b4df8722e1f377b38\",\n \"score\": \"0.5161773\",\n \"text\": \"def get_shape(sides):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31be2fda9a74fdd41f7ebe74fe131f23\",\n \"score\": \"0.5157701\",\n \"text\": \"def get_pin_direction(self, line):\\n is_input = self._get_bit_in_register('DIRECTION', line)\\n return Directions.IN if is_input else Directions.OUT\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9ac0edf3447d8db00b4d251ccdc5281\",\n \"score\": \"0.51407963\",\n \"text\": \"def _get_level(self):\\n if not hasattr(self.__p, 'pPr'):\\n return 0\\n return int(self.__p.pPr.get('lvl', 0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18fc0904c9be3ee6edecbd6ba0bd0794\",\n \"score\": \"0.51407915\",\n \"text\": \"def pointDistance( self, p ):\\n disp = self.p2 - self.p1\\n segLen = disp.magnitude()\\n norm = disp / segLen\\n dispP = p - self.p1\\n dp = norm.dot( dispP )\\n if ( dp < 0 ): \\n return (p - self.p1).magnitude()\\n elif ( dp > segLen ):\\n return ( p - self.p2).magnitude()\\n else:\\n A = -norm.y\\n B = norm.x\\n C = -( A * self.p1.x + B * self.p1.y )\\n return abs( A * p.x + B * p.y + C )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79fa7b3b4b53af8196ccbd5dc769c301\",\n \"score\": \"0.5140242\",\n \"text\": \"def sideinfo(self):\\n return self._get_element(*self._side_info_locator)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a688a18fc0e8075e19decfe1f28a08e0\",\n \"score\": \"0.5128\",\n \"text\": \"def line(self):\\n return self.__line\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d85367c9d9286952cef6560643372c3\",\n \"score\": \"0.5127137\",\n \"text\": \"def right(self):\\n return self._x + self._width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b5e166a12cb1538013848c4b2f848a2\",\n \"score\": \"0.51181984\",\n \"text\": \"def line(p1, p2):\\n A = (p1[1] - p2[1])\\n B = (p2[0] - p1[0])\\n C = (p1[0]*p2[1] - p2[0]*p1[1])\\n return A, B, -C\",\n \"title\": \"\"\n },\n {\n \"docid\": \"611a49d6ca4dc570aaf11508efcccfa2\",\n \"score\": \"0.5113819\",\n \"text\": \"def perpendicular(self):\\n Vperp = Vector2(-self.mData[1], self.mData[0])\\n return Vperp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae882b581d115c24f67cdfe1172c7eac\",\n \"score\": \"0.5109769\",\n \"text\": \"def distance_to(self, p):\\n\\t\\treturn (self - p).length()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac64bdf0462135e4d1d630ee1a66a143\",\n \"score\": \"0.5109033\",\n \"text\": \"def float2SideLeft(self):\\n self.mLeft = self.getFloatSideLeft()\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02812c3794e0b2e346c612557edd9d1d\",\n \"score\": \"0.5107515\",\n \"text\": \"def midPoint( self ):\\n try:\\n return ( self.p1 + self.p2 ) * 0.5\\n except TypeError:\\n print type( self.p1 ), type( self.p2 )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"984e3ad3e8d3118d918353047d3d1428\",\n \"score\": \"0.510563\",\n \"text\": \"def coluna_pos(p):\\n\\treturn p[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfae792a2165e1b3e43234a27b076f87\",\n \"score\": \"0.51040936\",\n \"text\": \"def right(self) -> float:\\n return self.x + self.w\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55dd12c453218080573b3268eebd75a4\",\n \"score\": \"0.5101551\",\n \"text\": \"def right(self):\\n return self.left + self.width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59f689ee7b14e64d8b2fd2cde977e06b\",\n \"score\": \"0.50990087\",\n \"text\": \"def point_to_line(tower):\\n return True, tower\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5388fc94cbc0fdd59754edf92694cbee\",\n \"score\": \"0.5096413\",\n \"text\": \"def line_width(self):\\n return self._line_width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8175b4c1543f79275f8b383fec11f42c\",\n \"score\": \"0.5087364\",\n \"text\": \"def perpendicular(self, through):\\n if self.contains(through):\\n n = self.dim + 1\\n\\n l = self\\n\\n if n > 3:\\n # additional point is required to determine the exact line\\n arr = np.zeros(n)\\n for i in range(n):\\n arr[-i - 1] = 1\\n o = Point(arr)\\n if not self.contains(o):\\n break\\n e = join(self, o)\\n basis = e.basis_matrix\\n line_pts = basis.dot(self.basis_matrix.T)\\n l = Line(np.cross(*line_pts.T))\\n\\n from .operators import harmonic_set\\n p = l.meet(infty)\\n q = harmonic_set(I, J, p)\\n\\n if n > 3:\\n q = Point(basis.T.dot(q.array))\\n\\n return Line(through, q)\\n\\n return self.mirror(through).join(through)\",\n \"title\": \"\"\n }\n]"}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":7,"numItemsPerPage":100,"numTotalItems":994482,"offset":700,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc0OTUwODAyMCwic3ViIjoiL2RhdGFzZXRzL250aGFrdXIvY29ybnN0YWNrLXB5dGhvbi12MS10ZXZhdHJvbi0xTSIsImV4cCI6MTc0OTUxMTYyMCwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.cVP4FChx72lcO3nEDEfFKTANBSCkEHPR9IVqc--ko9uFNHBJef3-T2p1RvXtwV4I4Dn_Mz9eM4Qei6U5eytVCg","displayUrls":true},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}"><div><header 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<div class="flex flex-1 flex-col overflow-hidden "><div class="flex flex-1 flex-col overflow-hidden"><div class="flex min-h-0 flex-1"><div class="flex flex-1 flex-col overflow-hidden"><div class="md:shadow-xs dark:border-gray-800 md:my-4 md:ml-4 md:rounded-lg md:border flex min-w-0 flex-wrap "><div class="flex min-w-0 flex-1 flex-wrap"><div class="grid flex-1 grid-cols-1 overflow-hidden text-sm md:grid-cols-2 md:place-content-center md:rounded-lg"><label class="relative block flex-1 px-3 py-2 hover:bg-gray-50 dark:border-gray-850 dark:hover:bg-gray-950 md:border-r md:border-r-0 hidden" title="default"><span class="text-gray-500">Subset (1)</span>
<div class="flex items-center whitespace-nowrap"><span class="truncate">default</span>
<span class="mx-2 text-gray-500">·</span>
<span class="text-gray-500">994k rows</span>
<svg class="ml-auto min-w-6 pl-2" width="1em" height="1em" viewBox="0 0 12 7" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M1 1L6 6L11 1" stroke="currentColor"></path></svg></div>
<select class="absolute inset-0 z-10 w-full cursor-pointer border-0 bg-white text-base opacity-0"><optgroup label="Subset (1)"><option value="default" selected>default (994k rows)</option></optgroup></select></label>
<label class="relative block flex-1 px-3 py-2 hover:bg-gray-50 dark:border-gray-850 dark:hover:bg-gray-900 md:border-r md:border-r" title="train"><div class="text-gray-500">Split (1)</div>
<div class="flex items-center overflow-hidden whitespace-nowrap"><span class="truncate">train</span>
<span class="mx-2 text-gray-500">·</span>
<span class="text-gray-500">994k rows</span>
<svg class="ml-auto min-w-6 pl-2" width="1em" height="1em" viewBox="0 0 12 7" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M1 1L6 6L11 1" stroke="currentColor"></path></svg></div>
<select class="absolute inset-0 z-10 w-full cursor-pointer border-0 bg-white text-base opacity-0"><optgroup label="Split (1)"><option value="train" selected>train (994k rows)</option></optgroup></select></label></div></div>
<div class="hidden flex-none flex-col items-center gap-0.5 border-l px-1 md:flex justify-end">
<span class="inline-block "><span class="contents"><div slot="anchor"><button class="group text-gray-500 hover:text-gray-700" aria-label="Hide sidepanel"><div class="rounded-xs flex size-4 items-center justify-center border border-gray-400 bg-gray-100 hover:border-gray-600 hover:bg-blue-50 dark:border-gray-600 dark:bg-gray-800 dark:hover:bg-gray-700 dark:group-hover:border-gray-400"><div class="float-left h-full w-[65%]"></div>
<div class="float-right h-full w-[35%] bg-gray-400 group-hover:bg-gray-600 dark:bg-gray-600 dark:group-hover:bg-gray-400"></div></div></button></div></span>
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<button class="btn px-0.5 py-0.5 " type="button">
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="p-0.5" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><circle cx="16" cy="7" r="3" fill="currentColor"></circle><circle cx="16" cy="16" r="3" fill="currentColor"></circle><circle cx="16" cy="25" r="3" fill="currentColor"></circle></svg>
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<div class="flex min-h-0 flex-1 flex-col border dark:border-gray-800 md:mb-4 md:ml-4 md:rounded-lg">
<div class="bg-linear-to-r text-smd relative flex items-center dark:border-gray-900 dark:bg-gray-950 false rounded-t-lg [&:has(:focus)]:from-gray-50 [&:has(:focus)]:to-transparent [&:has(:focus)]:to-20% dark:[&:has(:focus)]:from-gray-900"><form class="flex-1"><svg class="absolute left-3 top-1/2 transform -translate-y-1/2 pointer-events-none text-gray-400" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M30 28.59L22.45 21A11 11 0 1 0 21 22.45L28.59 30zM5 14a9 9 0 1 1 9 9a9 9 0 0 1-9-9z" fill="currentColor"></path></svg>
<input disabled class="outline-hidden h-9 w-full border-none bg-transparent px-1 pl-9 pr-3 placeholder:text-gray-400 " placeholder="Search this dataset" dir="auto"></form>
<div class="flex items-center gap-2 px-2 py-1"><button type="button" class="hover:bg-yellow-200/70 flex items-center gap-1 rounded-md border border-yellow-200 bg-yellow-100 pl-0.5 pr-1 text-[.8rem] leading-normal text-gray-700 dark:border-orange-500/25 dark:bg-orange-500/20 dark:text-gray-300 dark:hover:brightness-110 md:hidden"><div class="rounded-sm bg-yellow-300 px-1 font-mono text-[.7rem] font-bold text-black dark:bg-yellow-700 dark:text-gray-200">SQL
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Console
</button></div></div>
<div class="flex flex-1 flex-col overflow-hidden min-h-64 flex w-full flex-col border-t md:rounded-b-lg md:shadow-lg">
<div class="flex-1 relative overflow-auto"><table class="w-full table-auto rounded-lg font-mono text-xs text-gray-900"><thead class="shadow-xs sticky left-0 right-0 top-0 z-1 bg-white align-top"><tr class="space-y-54 h-full min-w-fit divide-x border-b text-left"><th class="h-full max-w-sm p-2 text-left relative w-auto"><div class="flex h-full flex-col flex-nowrap justify-between"><div><div class="flex items-center justify-between">query_id
<form class="flex flex-col"><button id="asc" class="-mr-1 ml-2 h-[0.4rem] w-[0.8rem] transition ease-in-out"><svg class="-rotate-180 transform text-gray-300 hover:text-gray-500" xmlns="http://www.w3.org/2000/svg" viewBox="0 64 256 128" fill="currentColor" aria-hidden="true"><path d="M213.65674,101.657l-80,79.99976a7.99945,7.99945,0,0,1-11.31348,0l-80-79.99976A8,8,0,0,1,48,88H208a8,8,0,0,1,5.65674,13.657Z"></path></svg></button>
<button id="desc" class="-mr-1 ml-2 h-[0.4rem] w-[0.8rem] transition ease-in-out"><svg class="text-gray-300 hover:text-gray-500" xmlns="http://www.w3.org/2000/svg" viewBox="0 64 256 128" fill="currentColor" aria-hidden="true"><path d="M213.65674,101.657l-80,79.99976a7.99945,7.99945,0,0,1-11.31348,0l-80-79.99976A8,8,0,0,1,48,88H208a8,8,0,0,1,5.65674,13.657Z"></path></svg></button></form></div>
<div class="mb-2 whitespace-nowrap text-xs font-normal text-gray-500"><span>string</span><span class="italic text-gray-400 before:mx-1 before:content-['·']">lengths</span></div></div>
<div><div class="" style="height: 40px; padding-top: 2px"><svg width="130" height="28"><g><rect class="fill-gray-400 dark:fill-gray-500/80" rx="2" x="0" y="0" width="130" height="30" fill-opacity="1"></rect></g><rect class="fill-white dark:fill-gray-900" x="0" y="26" width="130" height="2" stroke-opacity="1"></rect><line class="stroke-gray-100 dark:stroke-gray-500/20" x1="0" y1="27.5" x2="130" y2="27.5" stroke-opacity="1"></line><g><rect class="fill-indigo-500 cursor-pointer" x="-1" y="0" width="132" height="30" fill-opacity="0"></rect></g></svg>
<div class="relative font-light text-gray-400" style="height: 10px; width: 130px;"><div class="absolute left-0 overflow-hidden text-ellipsis whitespace-nowrap" style="max-width: 60px">32</div>
<div class="absolute overflow-hidden text-ellipsis whitespace-nowrap" style="right: 0px; max-width: 60px">32</div>
</div></div></div></div>
<div class="absolute right-0 top-0 z-10 h-full w-1 cursor-col-resize hover:bg-indigo-100 active:bg-indigo-500 dark:hover:bg-indigo-800 dark:active:bg-indigo-600/80"><div class="absolute right-0 top-0 h-full w-1"></div>
</div>
</th><th class="h-full max-w-sm p-2 text-left relative w-auto"><div class="flex h-full flex-col flex-nowrap justify-between"><div><div class="flex items-center justify-between">query
<form class="flex flex-col"><button id="asc" class="-mr-1 ml-2 h-[0.4rem] w-[0.8rem] transition ease-in-out"><svg class="-rotate-180 transform text-gray-300 hover:text-gray-500" xmlns="http://www.w3.org/2000/svg" viewBox="0 64 256 128" fill="currentColor" aria-hidden="true"><path d="M213.65674,101.657l-80,79.99976a7.99945,7.99945,0,0,1-11.31348,0l-80-79.99976A8,8,0,0,1,48,88H208a8,8,0,0,1,5.65674,13.657Z"></path></svg></button>
<button id="desc" class="-mr-1 ml-2 h-[0.4rem] w-[0.8rem] transition ease-in-out"><svg class="text-gray-300 hover:text-gray-500" xmlns="http://www.w3.org/2000/svg" viewBox="0 64 256 128" fill="currentColor" aria-hidden="true"><path d="M213.65674,101.657l-80,79.99976a7.99945,7.99945,0,0,1-11.31348,0l-80-79.99976A8,8,0,0,1,48,88H208a8,8,0,0,1,5.65674,13.657Z"></path></svg></button></form></div>
<div class="mb-2 whitespace-nowrap text-xs font-normal text-gray-500"><span>string</span><span class="italic text-gray-400 before:mx-1 before:content-['·']">lengths</span></div></div>
<div><div class="" style="height: 40px; padding-top: 2px"><svg width="130" height="28"><g><rect class="fill-gray-400 dark:fill-gray-500/80" rx="2" x="0" y="0" width="11.2" height="30" fill-opacity="1"></rect><rect class="fill-gray-400 dark:fill-gray-500/80" rx="2" x="13.2" y="25" width="11.2" height="5" fill-opacity="1"></rect><rect class="fill-gray-400 dark:fill-gray-500/80" rx="2" x="26.4" y="25" width="11.2" height="5" fill-opacity="1"></rect><rect class="fill-gray-400 dark:fill-gray-500/80" rx="2" x="39.599999999999994" y="25" width="11.2" height="5" fill-opacity="1"></rect><rect class="fill-gray-400 dark:fill-gray-500/80" rx="2" x="52.8" y="25" width="11.2" height="5" fill-opacity="1"></rect><rect class="fill-gray-400 dark:fill-gray-500/80" rx="2" x="66" y="25" width="11.2" height="5" fill-opacity="1"></rect><rect class="fill-gray-400 dark:fill-gray-500/80" rx="2" x="79.19999999999999" y="25" width="11.2" height="5" fill-opacity="1"></rect><rect class="fill-gray-400 dark:fill-gray-500/80" rx="2" x="92.39999999999999" y="25" width="11.2" height="5" fill-opacity="1"></rect><rect class="fill-gray-400 dark:fill-gray-500/80" rx="2" x="105.6" y="26" width="11.2" height="4" fill-opacity="1"></rect><rect class="fill-gray-400 dark:fill-gray-500/80" rx="2" x="118.8" y="25" width="11.2" height="5" fill-opacity="1"></rect></g><rect class="fill-white dark:fill-gray-900" x="0" y="26" width="130" height="2" stroke-opacity="1"></rect><line class="stroke-gray-100 dark:stroke-gray-500/20" x1="0" y1="27.5" x2="130" y2="27.5" stroke-opacity="1"></line><g><rect class="fill-indigo-500 cursor-pointer" x="-1" y="0" width="13.2" height="30" fill-opacity="0"></rect><rect class="fill-indigo-500 cursor-pointer" x="12.2" y="0" width="13.2" height="30" fill-opacity="0"></rect><rect class="fill-indigo-500 cursor-pointer" x="25.4" y="0" width="13.2" height="30" fill-opacity="0"></rect><rect class="fill-indigo-500 cursor-pointer" x="38.599999999999994" y="0" width="13.2" height="30" fill-opacity="0"></rect><rect class="fill-indigo-500 cursor-pointer" x="51.8" y="0" width="13.2" height="30" fill-opacity="0"></rect><rect class="fill-indigo-500 cursor-pointer" x="65" y="0" width="13.2" height="30" fill-opacity="0"></rect><rect class="fill-indigo-500 cursor-pointer" x="78.19999999999999" y="0" width="13.2" height="30" fill-opacity="0"></rect><rect class="fill-indigo-500 cursor-pointer" x="91.39999999999999" y="0" width="13.2" height="30" fill-opacity="0"></rect><rect class="fill-indigo-500 cursor-pointer" x="104.6" y="0" width="13.2" height="30" fill-opacity="0"></rect><rect class="fill-indigo-500 cursor-pointer" x="117.8" y="0" width="13.2" height="30" fill-opacity="0"></rect></g></svg>
<div class="relative font-light text-gray-400" style="height: 10px; width: 130px;"><div class="absolute left-0 overflow-hidden text-ellipsis whitespace-nowrap" style="max-width: 60px">9</div>
<div class="absolute overflow-hidden text-ellipsis whitespace-nowrap" style="right: 0px; max-width: 60px">4.01k</div>
</div></div></div></div>
<div class="absolute right-0 top-0 z-10 h-full w-1 cursor-col-resize hover:bg-indigo-100 active:bg-indigo-500 dark:hover:bg-indigo-800 dark:active:bg-indigo-600/80"><div class="absolute right-0 top-0 h-full w-1"></div>
</div>
</th><th class="h-full max-w-sm p-2 text-left relative w-auto"><div class="flex h-full flex-col flex-nowrap justify-between"><div><div class="flex items-center justify-between">positive_passages
<form class="flex flex-col"><button id="asc" class="-mr-1 ml-2 h-[0.4rem] w-[0.8rem] transition ease-in-out"><svg class="-rotate-180 transform text-gray-300 hover:text-gray-500" xmlns="http://www.w3.org/2000/svg" viewBox="0 64 256 128" fill="currentColor" aria-hidden="true"><path d="M213.65674,101.657l-80,79.99976a7.99945,7.99945,0,0,1-11.31348,0l-80-79.99976A8,8,0,0,1,48,88H208a8,8,0,0,1,5.65674,13.657Z"></path></svg></button>
<button id="desc" class="-mr-1 ml-2 h-[0.4rem] w-[0.8rem] transition ease-in-out"><svg class="text-gray-300 hover:text-gray-500" xmlns="http://www.w3.org/2000/svg" viewBox="0 64 256 128" fill="currentColor" aria-hidden="true"><path d="M213.65674,101.657l-80,79.99976a7.99945,7.99945,0,0,1-11.31348,0l-80-79.99976A8,8,0,0,1,48,88H208a8,8,0,0,1,5.65674,13.657Z"></path></svg></button></form></div>
<div class="mb-2 whitespace-nowrap text-xs font-normal text-gray-500"><span>list</span><span class="italic text-gray-400 before:mx-1 before:content-['·']">lengths</span></div></div>
<div><div class="" style="height: 40px; padding-top: 2px"><svg width="130" height="28"><g><rect class="fill-gray-400 dark:fill-gray-500/80" rx="2" x="0" y="0" width="130" height="30" fill-opacity="1"></rect></g><rect class="fill-white dark:fill-gray-900" x="0" y="26" width="130" height="2" stroke-opacity="1"></rect><line class="stroke-gray-100 dark:stroke-gray-500/20" x1="0" y1="27.5" x2="130" y2="27.5" stroke-opacity="1"></line><g><rect class="fill-indigo-500 cursor-pointer" x="-1" y="0" width="132" height="30" fill-opacity="0"></rect></g></svg>
<div class="relative font-light text-gray-400" style="height: 10px; width: 130px;"><div class="absolute left-0 overflow-hidden text-ellipsis whitespace-nowrap" style="max-width: 60px">1</div>
<div class="absolute overflow-hidden text-ellipsis whitespace-nowrap" style="right: 0px; max-width: 60px">1</div>
</div></div></div></div>
<div class="absolute right-0 top-0 z-10 h-full w-1 cursor-col-resize hover:bg-indigo-100 active:bg-indigo-500 dark:hover:bg-indigo-800 dark:active:bg-indigo-600/80"><div class="absolute right-0 top-0 h-full w-1"></div>
</div>
</th><th class="h-full max-w-sm p-2 text-left relative w-auto"><div class="flex h-full flex-col flex-nowrap justify-between"><div><div class="flex items-center justify-between">negative_passages
<form class="flex flex-col"><button id="asc" class="-mr-1 ml-2 h-[0.4rem] w-[0.8rem] transition ease-in-out"><svg class="-rotate-180 transform text-gray-300 hover:text-gray-500" xmlns="http://www.w3.org/2000/svg" viewBox="0 64 256 128" fill="currentColor" aria-hidden="true"><path d="M213.65674,101.657l-80,79.99976a7.99945,7.99945,0,0,1-11.31348,0l-80-79.99976A8,8,0,0,1,48,88H208a8,8,0,0,1,5.65674,13.657Z"></path></svg></button>
<button id="desc" class="-mr-1 ml-2 h-[0.4rem] w-[0.8rem] transition ease-in-out"><svg class="text-gray-300 hover:text-gray-500" xmlns="http://www.w3.org/2000/svg" viewBox="0 64 256 128" fill="currentColor" aria-hidden="true"><path d="M213.65674,101.657l-80,79.99976a7.99945,7.99945,0,0,1-11.31348,0l-80-79.99976A8,8,0,0,1,48,88H208a8,8,0,0,1,5.65674,13.657Z"></path></svg></button></form></div>
<div class="mb-2 whitespace-nowrap text-xs font-normal text-gray-500"><span>list</span><span class="italic text-gray-400 before:mx-1 before:content-['·']">lengths</span></div></div>
<div><div class="" style="height: 40px; padding-top: 2px"><svg width="130" height="28"><g><rect class="fill-gray-400 dark:fill-gray-500/80" rx="2" x="0" y="25" width="16.857142857142858" height="5" fill-opacity="1"></rect><rect class="fill-gray-400 dark:fill-gray-500/80" rx="2" x="18.857142857142858" y="25" width="16.857142857142858" height="5" fill-opacity="1"></rect><rect class="fill-gray-400 dark:fill-gray-500/80" rx="2" x="37.714285714285715" y="25" width="16.857142857142858" height="5" fill-opacity="1"></rect><rect class="fill-gray-400 dark:fill-gray-500/80" rx="2" x="56.57142857142857" y="25" width="16.857142857142858" height="5" fill-opacity="1"></rect><rect class="fill-gray-400 dark:fill-gray-500/80" rx="2" x="75.42857142857143" y="25" width="16.857142857142858" height="5" fill-opacity="1"></rect><rect class="fill-gray-400 dark:fill-gray-500/80" rx="2" x="94.28571428571429" y="25" width="16.857142857142858" height="5" fill-opacity="1"></rect><rect class="fill-gray-400 dark:fill-gray-500/80" rx="2" x="113.14285714285714" y="0" width="16.857142857142858" height="30" fill-opacity="1"></rect></g><rect class="fill-white dark:fill-gray-900" x="0" y="26" width="130" height="2" stroke-opacity="1"></rect><line class="stroke-gray-100 dark:stroke-gray-500/20" x1="0" y1="27.5" x2="130" y2="27.5" stroke-opacity="1"></line><g><rect class="fill-indigo-500 cursor-pointer" x="-1" y="0" width="18.857142857142858" height="30" fill-opacity="0"></rect><rect class="fill-indigo-500 cursor-pointer" x="17.857142857142858" y="0" width="18.857142857142858" height="30" fill-opacity="0"></rect><rect class="fill-indigo-500 cursor-pointer" x="36.714285714285715" y="0" width="18.857142857142858" height="30" fill-opacity="0"></rect><rect class="fill-indigo-500 cursor-pointer" x="55.57142857142857" y="0" width="18.857142857142858" height="30" fill-opacity="0"></rect><rect class="fill-indigo-500 cursor-pointer" x="74.42857142857143" y="0" width="18.857142857142858" height="30" fill-opacity="0"></rect><rect class="fill-indigo-500 cursor-pointer" x="93.28571428571429" y="0" width="18.857142857142858" height="30" fill-opacity="0"></rect><rect class="fill-indigo-500 cursor-pointer" x="112.14285714285714" y="0" width="18.857142857142858" height="30" fill-opacity="0"></rect></g></svg>
<div class="relative font-light text-gray-400" style="height: 10px; width: 130px;"><div class="absolute left-0 overflow-hidden text-ellipsis whitespace-nowrap" style="max-width: 60px">88</div>
<div class="absolute overflow-hidden text-ellipsis whitespace-nowrap" style="right: 0px; max-width: 60px">101</div>
</div></div></div></div>
<div class="absolute right-0 top-0 z-10 h-full w-1 cursor-col-resize hover:bg-indigo-100 active:bg-indigo-500 dark:hover:bg-indigo-800 dark:active:bg-indigo-600/80"><div class="absolute right-0 top-0 h-full w-1"></div>
</div>
</th></tr></thead>
<tbody class="h-16 overflow-scroll"><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="700"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">eb85624807aa0c6481b333d5603773be</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Tests the insert method.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "d80cf976fc8e4c59b66f82ca08877bc9",
"score": "0.762313",
"text": "def test_insert(self):\n test_text = 'this is an insert test post'\n doc_id = self.engine.insert({'text': test_text})\n\n result = self.engine.find_by_id(doc_id)\n self.assertEqual(self._get_id([result], 0), doc_id)\n self.assertEqual(self._get_doc([result], 0)['text'], test_text)",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "42e6d55ed9b8167441d0c0db9c55427c",
"score": "0.8176738",
"text": "def test_insert(self):\n\n ids = self._helper.insert('test_table', (\n {'col_b': randint(0, 99)},\n {'col_b': randint(0, 99)},\n {'col_b': randint(0, 99)}\n ))\n self.assertEqual(len(ids), 3)\n self.assertEqual(ids[0] + 1, ids[1])\n self.assertEqual(ids[0] + 2, ids[2])\n last = ids[2]\n ids = self._helper.insert('test_table', {'col_b': randint(0, 99)})\n self.assertEqual(ids[0], last + 1)\n self.assertRaises(\n OperationalError,\n self._helper.insert,\n 'missing_table',\n {'col_b': 0}\n )\n ids = self._helper.insert('test_table', {})\n self.assertEqual(len(ids), 0)",
"title": ""
},
{
"docid": "cc176a976253c30c53d764cc59cfe110",
"score": "0.77671057",
"text": "def insert(self):\n pass",
"title": ""
},
{
"docid": "b75c06c5134539d323631b151538cbe0",
"score": "0.7410205",
"text": "def test_insert(self):\n with self.assertRaises(IndexError):\n self.cdll.insert(-1,\"a\")\n self.cdll.add('d')\n self.cdll.add('c')\n self.cdll.add('b')\n self.cdll.add('a')\n self.assertEqual(self.cdll.getSize(), 4)\n with self.assertRaises(IndexError):\n self.cdll.insert(5,\"a\")\n self.cdll.insert(4,\"z\")\n self.assertEqual(self.cdll.index('z'), 4)\n self.assertEqual(self.cdll.head.getPrevious().getData(),'z')\n self.assertEqual(self.cdll.getSize(), 5)\n self.cdll.insert(0,\"r\")\n self.assertEqual(self.cdll.index('r'), 0)\n self.assertEqual(self.cdll.head.getData(),'r')\n self.assertEqual(self.cdll.getSize(), 6)\n self.cdll.insert(1,\"l\")\n self.assertEqual(self.cdll.index('l'), 1)\n self.assertEqual(self.cdll.head.getNext().getData(),'l')\n self.assertEqual(self.cdll.getSize(), 7)",
"title": ""
},
{
"docid": "d747193e2c6b100cd15665cd82cd26dc",
"score": "0.73941094",
"text": "def test_insert(self):\n student = StudentNode(\"123456\", \"ART\", \"Tosin\", \"Kuye\")\n student1 = StudentNode(\"555555\", \"SCI\", \"Satoshi\", \"Nakamoto\")\n student2 = StudentNode(\"654321\", \"ART\", \"Levi\", \"Ackermann\")\n table = EnrollTable(35)\n\n table.insert(student)\n table.insert(student1)\n table.insert(student2)\n \n self.assertEqual(table.tableSize, 3)\n\n self.assertEqual(table.enrollTable[32].getID(), \"123456\")\n self.assertEqual(table.enrollTable[20].getID(), \"555555\")\n self.assertEqual(table.enrollTable[24].getID(), \"654321\")",
"title": ""
},
{
"docid": "3eea51e063896d1f1088e87d20569ea0",
"score": "0.7334376",
"text": "def test_insert(source, value, expected):\n test_heap = heap.Heap(source)\n\n test_heap.insert(value)\n\n assert test_heap._list == expected",
"title": ""
},
{
"docid": "8301902ca5be3cd8ce5b9134041c1d55",
"score": "0.7293645",
"text": "def test_insert(self):\n parent, nodes = self.BigParent, self.BigName\n self.assertEqual(len(parent), 10)\n parent.insert(3, 5)\n self.assertEqual(len(parent), 11)\n self.assertEqual(parent[3].name, 5)\n self.assertEqual(parent[4].name, \"3\")\n parent.insert(-1, 123)\n self.assertEqual(len(parent), 12)\n self.assertEqual(parent[-1].name, \"9\")\n self.assertEqual(parent[-2].name, 123)",
"title": ""
},
{
"docid": "e521e57559741f7c33fcf8ae4d47179a",
"score": "0.7275155",
"text": "def test_insert(self):\r\n\r\n # Remember, 0 is a place-holder and heap has a 1-based index.\r\n heap = [0]\r\n expected = [0, 1]\r\n\r\n hs.insert(1, heap)\r\n self.assertEqual(heap, expected, \r\n \"insert() failed. Expected: {}, Got {}\".format(expected, heap))\r\n\r\n add = [4, 4, 8, 13, 5, 9, 3]\r\n expected = [0, 1, 3, 4, 4, 13, 5, 9, 8]\r\n for node in add:\r\n hs.insert(node, heap)\r\n\r\n self.assertEqual(heap, expected,\r\n \"insert() failed.\\\r\n \\n\\tExpected:\\t{}\\\r\n \\n\\tGot:\\t\\t{}\".format(expected, heap))",
"title": ""
},
{
"docid": "0f62fe697b898fe10c8ad25c5be3f3be",
"score": "0.7257886",
"text": "def test_insert(super_repo):\n super_repo.insert(super_obj)\n assert super_repo.get(super_id) is super_obj",
"title": ""
},
{
"docid": "cbbd266037963b3ab3fe617e82e22473",
"score": "0.72469956",
"text": "def test_insert_tree():\n pass",
"title": ""
},
{
"docid": "f0ad7857be80ac5edc87eed83266ec09",
"score": "0.72357756",
"text": "def test_insert(empty_tt):\n empty_tt.insert('their')\n assert empty_tt._root == {'t': {'h': {'e': {'i': {'r': {'$': {}}}}}}}",
"title": ""
},
{
"docid": "fd526d8bc4e7886cb4e3cd2c0c2a97ae",
"score": "0.71895224",
"text": "def test_insert(self):\n # [0, 1, 2, 3, 4] --> ['apple', 0, 1, (5, -1), 2, 3, 4, self.arr]\n # Create the array [0, 1, 2, 3, 4]\n arr = DynamicArray(self._GROWTH_FACTOR)\n for i in range(5):\n arr.append(i)\n\n # Insert new values\n arr.insert(0, 'apple')\n arr.insert(3, (5, -1))\n arr.insert(len(arr), self.arr)\n\n self.assertEqual(['apple', 0, 1, (5, -1), 2, 3, 4,\n [i for i in range(self._INITIAL_SIZE)]], arr)",
"title": ""
},
{
"docid": "da1a64862894fe6411d5d38e96171dbb",
"score": "0.7109961",
"text": "def test_insert_IBLT():\n\tt = IBLT(2, 2)\n\ttup = (md5(\"key\"), sha1(\"value\"))\n\tt.insert(tup)\n\t# Check if the entry is inserted\n\tassert not t.is_empty() \n\treturn t",
"title": ""
},
{
"docid": "be412411efc3596a565e9ca3a0c6a4bf",
"score": "0.70875096",
"text": "def test_insert_left_left_child():\n pass",
"title": ""
},
{
"docid": "014b091094a5cac5ddc57054a1757dee",
"score": "0.7075189",
"text": "def insert(self, val):",
"title": ""
},
{
"docid": "62b974e0da2c929377ebb68acf3ebd3d",
"score": "0.699781",
"text": "def fucked_up_insert():\n pass # pragma: no cover",
"title": ""
},
{
"docid": "52c844d08077bb3e1c499b0056056c68",
"score": "0.6991467",
"text": "def insert(self, value):\n raise Exception('Implement me!')",
"title": ""
},
{
"docid": "964f5ee651d9e942a0e15f323f515edb",
"score": "0.698354",
"text": "def test_insert_left_and_right_child():\n pass",
"title": ""
},
{
"docid": "5c036471400b319219beed8bd2215ba3",
"score": "0.6980812",
"text": "def test_insertion_sort():",
"title": ""
},
{
"docid": "23fd017452d5a4c928e231da9fe8d2e3",
"score": "0.6977857",
"text": "def test_insertion(empty_linked_list):\n assert empty_linked_list.head is None\n assert empty_linked_list.insert(1).val == 1\n assert len(empty_linked_list) == 1",
"title": ""
},
{
"docid": "9b08ca2eecd4bdb2e8476dfebf4adb94",
"score": "0.6960217",
"text": "def test_insert_text(self):\n pass",
"title": ""
},
{
"docid": "5bf26d24aeaf85cc36d12f10b3a47e76",
"score": "0.69576687",
"text": "def test_insert_left_child():\n pass",
"title": ""
},
{
"docid": "0792c5b819a5151d05726fd08261afb4",
"score": "0.6956006",
"text": "def testInsertEntity(self):\n TestModel().put()\n self.assertEqual(1, len(TestModel.query().fetch(2)))",
"title": ""
},
{
"docid": "591c671c376824003389e6ba6ddfe0a4",
"score": "0.6929873",
"text": "def insert(self, insert):\n\n self._insert = insert",
"title": ""
},
{
"docid": "a2be0b24a60ac51b9a15f7b70f57275b",
"score": "0.6923426",
"text": "def insert(self, *__args): # real signature unknown; restored from __doc__ with multiple overloads\n pass",
"title": ""
},
{
"docid": "eacf6d19603a98753835efe4f0f940f8",
"score": "0.69191265",
"text": "def test_insert(self):\n heap = MaxHeap(10)\n self.assertTrue(heap.is_empty())\n self.assertFalse(heap.is_full())\n for i in range(10):\n heap.insert(i)\n self.assertTrue(heap.is_full())\n self.assertFalse(heap.is_empty())",
"title": ""
},
{
"docid": "c5fa3d3c1d021666dee1c922a8c28663",
"score": "0.6918459",
"text": "def insert(self, val: int) -> bool:",
"title": ""
},
{
"docid": "5c83b9fd08fb57483e5064eb407f55e5",
"score": "0.6915837",
"text": "def test_insert_nodes():\n b = BinarySearchTree()\n for n in nodes:\n b.insert(n)\n assert b.contains(5)\n assert b.contains(43)\n assert b.contains(2)\n assert b.size() == 9",
"title": ""
},
{
"docid": "565c5708395d0d4cf4bd6cae402badaf",
"score": "0.6914181",
"text": "def test_insert(self):\n table = self.make_table()\n self.query(\"INSERT INTO foobar (id, bar) VALUES ('a', 1), ('b', 2)\")\n items = list(self.dynamo.scan(table))\n self.assertCountEqual(items, [{\"id\": \"a\", \"bar\": 1}, {\"id\": \"b\", \"bar\": 2}])",
"title": ""
},
{
"docid": "fa732882269b00c894d64e6c4afd0692",
"score": "0.68530685",
"text": "def test_insert(self):\n sqltap.start(self.engine)\n\n sess = self.Session()\n sess.add(self.A())\n sess.flush()\n\n stats = sqltap.collect()\n assert len(_startswith(stats, 'INSERT')) == 1",
"title": ""
},
{
"docid": "ab2e229f5892c83c665a5057cb2c6957",
"score": "0.68499327",
"text": "def test_insert_right_left_child():\n pass",
"title": ""
},
{
"docid": "dca4a023373cff9bcc62ac15a04353ee",
"score": "0.68375796",
"text": "def test_trie_tree_insert_using_helper(tt_fixture):\n new_word = tt_fixture.insert('word')\n assert tt_fixture._val_crawler('word') == ['*', 'w', 'o', 'r', 'd', '$']",
"title": ""
},
{
"docid": "167114010f13d60c7789363d044566d7",
"score": "0.6835096",
"text": "def test_insert_at_head(self):\n data = 798\n #fixture list indices go from 0 to 6\n self.assertEqual(7, self.non_empty_list.size)\n \n #insert at the head\n index = 0\n self.assertEqual(0, self.non_empty_list.head.data)\n self.non_empty_list.insert(index, data)\n self.assertEqual(data, self.non_empty_list.head.data)\n self.assertEqual(8, self.non_empty_list.size)",
"title": ""
},
{
"docid": "1f8cea67dafa5f5def3424cb90f48c1a",
"score": "0.6830823",
"text": "def test_trie_tree_insert_size_is_one(tt_fixture):\n new_word = tt_fixture.insert('word')\n assert tt_fixture.size == 1",
"title": ""
},
{
"docid": "4cd61352d81d5a0f942457685df2c75f",
"score": "0.6810696",
"text": "def test_insert_new_page(self):\n pass",
"title": ""
},
{
"docid": "e9e0af298edc72b1e7f8daccfaf99454",
"score": "0.6809443",
"text": "def test_UL_insert(self):\n insert_list = UnorderedList()\n for number in range(1, 10):\n insert_list.add(number)\n insert_list.insert(12, 5)\n self.assertEqual(12, insert_list.pop(6))",
"title": ""
},
{
"docid": "34a6a3db536b71362c88b6320a2b15a0",
"score": "0.67869633",
"text": "def test_insert_not_at_head(self):\n index = 5\n data = 798\n self.assertEqual(5, self.non_empty_list.value_at(index))\n self.non_empty_list.insert(index, data)\n self.assertEqual(8, self.non_empty_list.size)\n self.assertEqual(data, self.non_empty_list.value_at(index))",
"title": ""
},
{
"docid": "b117e4bf0da727698c2e1ac7c191f7f4",
"score": "0.6781791",
"text": "def test_insert_row(self):\n # Add an entry to the database\n name = data.hashstring(str(random()))\n pair_xlate_group.insert_row(name)\n\n # Make sure it exists\n idx_pair_xlate_group = pair_xlate_group.exists(name)\n\n # Verify the index exists\n result = pair_xlate_group.idx_exists(idx_pair_xlate_group)\n self.assertTrue(result)",
"title": ""
},
{
"docid": "6a591c42cfe33e4de4fc0920bf3d8828",
"score": "0.6775705",
"text": "def FindInsert(self):\n\t\tdb = Mongo(0)\n\t\tdb_insert = db.insert({'session': 'FindInsertTest', 'data': 'FindInsertTest Test data'})\n\t\tdb_findinsert = db.find_insert({'session': 'FindInsertTest', 'data': 'FindInsertTest Test data'})\n\t\treturn self.assertEqual('FindInsertTest', db_findinsert['data'])",
"title": ""
},
{
"docid": "742bd06a0b36ffb9576e90b5bcaea2da",
"score": "0.6734819",
"text": "def test_insert():\n doubly_linked = DoublyLinked()\n for val in VALUES:\n doubly_linked.insert(val)\n assert doubly_linked.head.value == val\n assert doubly_linked.head.value == VALUES[-1]",
"title": ""
},
{
"docid": "7895f8ad30634a93d679233989e7a137",
"score": "0.673352",
"text": "def insert(self, pos, data):\n print(\"cannot use insert(pos, data) with OrderedList\")",
"title": ""
},
{
"docid": "023e4375ced536b3c66366f49d99bb36",
"score": "0.6707997",
"text": "def test_insert_right_child():\n pass",
"title": ""
},
{
"docid": "c012ad54b321cfe5d07b7f23508c57de",
"score": "0.6707562",
"text": "def insert_iterative(self, data):",
"title": ""
},
{
"docid": "ff61a6c8dba70cc1bc7eefdf2a834935",
"score": "0.66767776",
"text": "def test_insert_row(self):\n table = EnrollTable(35)\n student = StudentNode(\"999670\", \"ART\", \"Tosin\", \"Kuye\")\n student1 = StudentNode(\"999635\", \"SCI\", \"Satoshi\", \"Nakamoto\")\n student2 = StudentNode(\"999600\", \"ART\", \"Levi\", \"Ackermann\")\n student3 = StudentNode(\"999599\", \"ART\", \"Tosin\", \"Kuye\")\n student4 = StudentNode(\"555555\", \"SCI\", \"Satoshi\", \"Nakamoto\")\n student5 = StudentNode(\"534125\", \"SCI\", \"Satoshi\", \"Monkery\")\n student6 = StudentNode(\"211122\", \"SCI\", \"Satoshi\", \"Masaaas\")\n table.insert(student)\n table.insert(student1)\n table.insert(student2)\n table.insert(student3)\n table.insert(student4)\n table.insert(student5)\n table.insert(student6)\n temp = table.enrollTable[20]\n \n count = 0 \n while temp is not None:\n temp = temp.getNext()\n count += 1\n\n self.assertEqual(count, 5)",
"title": ""
},
{
"docid": "2d7337babadaab5153237ec33b65782c",
"score": "0.6673654",
"text": "def insert(self, *x):\n raise NotImplementedError",
"title": ""
},
{
"docid": "24cca7b754f7787b65a5a254e6455470",
"score": "0.66391873",
"text": "async def insert(self, data):\n\n pass",
"title": ""
},
{
"docid": "ebb13d5af66a87b6e3834bb3424eed29",
"score": "0.6637071",
"text": "def MongoInsert(self):\n\t\tdb = Mongo(0)\n\t\tdb_insert = db.insert({'session': 'MongoTest', 'data': 'Test data'})\n\t\tdb_fetch = db.fetch({'session': 'MongoTest', 'data': 'Test data'})\n\t\treturn self.assertEqual('MongoTest', db_fetch['session'])",
"title": ""
},
{
"docid": "aa8fabad80b815189f78940390f8133b",
"score": "0.66164833",
"text": "def test_insert_at_index_out_of_range_raises_error(self):\n with self.assertRaises(IndexError):\n self.empty_list.insert(0, 234)\n\n #fixture list indices go from 0 to 6\n with self.assertRaises(IndexError):\n self.non_empty_list.insert(8, 67)\n\n #negative indicies aren't allowed\n with self.assertRaises(IndexError):\n self.non_empty_list.insert(-1, 67)",
"title": ""
},
{
"docid": "93dfe3e9bf1c01cdfefba226d2273b66",
"score": "0.6590262",
"text": "def test_insert_empty(empty_bst):\n empty_bst.insert('some random value')\n assert empty_bst.root.val == 'some random value'",
"title": ""
},
{
"docid": "88d893cc4c1544f4ccbcde71d05ec038",
"score": "0.65824074",
"text": "def insert(self, index, p_object): # real signature unknown; restored from __doc__\n pass",
"title": ""
},
{
"docid": "68026eb75ed117560dad3e760efd14ab",
"score": "0.6581847",
"text": "def test_insert_data_teacher(self):\n id = 1\n name = \"Ashis\"\n subject = \"Python\"\n date1 = date.today()\n count = self.input_object.insert_data_teacher(id,name,subject,date1)\n assert count == 1, \"Insertion unsuccessful\"",
"title": ""
},
{
"docid": "2ee8104ccbe5b4ecafd027da4239ba41",
"score": "0.6576692",
"text": "def test_double_insertion():\n q = InitiativeQueue()\n q.add(\"Tasha\", 18)\n q.add(\"Elyn\", 12)\n with pytest.raises(ValueError):\n q.add(\"Tasha\", 15)",
"title": ""
},
{
"docid": "2e9fd6e26fd9dc51c3b9347fa1b4564a",
"score": "0.657096",
"text": "def expect_insert(self, doc_type, ident):\n with patch('regcore.db.es.ElasticSearch') as es:\n yield es.return_value.index\n self.assertEqual(doc_type, es.return_value.index.call_args[0][1])\n self.assertEqual(ident,\n es.return_value.index.call_args[1].get('id'))",
"title": ""
},
{
"docid": "2ab858dc4eba603b2a54de0faa6dc9f4",
"score": "0.6567272",
"text": "def insert(self, index: int, item):",
"title": ""
},
{
"docid": "a833125958f89858bcaba01f1b96998e",
"score": "0.6566097",
"text": "def test_can_insert_single(self):\r\n new_node = node.Node(10, self.tree.root)\r\n self.tree.put(self.tree.root, new_node)\r\n\r\n # Check\r\n assert len(self.tree.root.children) == 1, \\\r\n \"expected: {}, got: {}\".format(1, len(self.tree.root.children))\r\n assert self.tree.root.subtree_value == 10, \\\r\n \"expected: {}, got: {}\".format(10, self.tree.root.subtree_value)",
"title": ""
},
{
"docid": "adcd33d270db61d3dc604bc09d997dee",
"score": "0.6529747",
"text": "def test_insert_data_student(self):\n id = 1\n name = \"Santosh kumar panda\"\n age = 26\n grade = \"Twelveth\"\n date1 = date.today()\n count = self.input_object.insert_data_student(id,name,age,grade,date1)\n assert count == 1, \"Insertion unsuccessful\"",
"title": ""
},
{
"docid": "948c06befa2efa6cf398fda30f568dce",
"score": "0.6513366",
"text": "def test_insert_ok(self, client):\n res = client.post(url_for('transactions_create_wallet'), json=dict(request))\n assert res.status_code == 200\n assert res.json['msg'] == 'insertion ok'\n id = res.json['id']\n check_row(id, dict(request), 'OK')",
"title": ""
},
{
"docid": "5e573ff3fb3e0be4667e04cee3e8abf2",
"score": "0.65062654",
"text": "def test_order_insert_sort_1(self):\n test_1n = insert_sort(list(range(1000))[ : : -1])\n test_2n = insert_sort(list(range(2000))[ : : -1])\n self.assertTrue(self.epsilon_equal((test_2n / test_1n), 4))",
"title": ""
},
{
"docid": "b495111d451119975077934ae8724649",
"score": "0.6489291",
"text": "def insert(self):\n self.getDbRecord().insert()\n\n return",
"title": ""
},
{
"docid": "577cf468bd6c812f7fbabdca0be3042d",
"score": "0.64803016",
"text": "def test_insert_one(self, orion_db):\n item = {\"exp_name\": \"supernaekei\", \"user\": \"tsirif\"}\n count_before = orion_db.count(\"experiments\")\n # call interface\n assert orion_db.write(\"experiments\", item) == 1\n assert orion_db.count(\"experiments\") == count_before + 1\n value = get_db(orion_db)[\"experiments\"].find({\"exp_name\": \"supernaekei\"})[0]\n assert value == item",
"title": ""
},
{
"docid": "32917ef8fca9c8d4c1591bba55265da5",
"score": "0.6458602",
"text": "def sorted_insert(self, value):",
"title": ""
},
{
"docid": "7cb6912e5ca44aeee6e06ae5d47a6d0d",
"score": "0.64065146",
"text": "def test_common_insert(clean_db):\n # Check disabling committing\n u1 = Users(email=\"a\")\n u1.insert(commit=False)\n assert not u1.id\n\n # Insert a new record without disabling committing\n u2 = Users(email=\"b\")\n u2.insert()\n assert u1.id and u1._etag\n assert u2.id and u2._etag\n assert u1._etag != u2._etag\n\n assert Users.find_by_id(u1.id)\n assert Users.find_by_id(u2.id)",
"title": ""
},
{
"docid": "4ac2ab4b8ebbed1658830fd48efc9bcf",
"score": "0.6394129",
"text": "def test_insert_not_string(empty_tt):\n message = 'Please enter a string.'\n with pytest.raises(TypeError, message=message):\n empty_tt.insert(None)",
"title": ""
},
{
"docid": "0c6a744cc93bfb4180fb143943b6f7ce",
"score": "0.63876534",
"text": "def testInsertMusic(self):\n self.data.createTables()\n self.data.insertMusic('test', 'test', 100, '/test')\n self.data._conn.commit()\n res = self.data._conn.execute(\"Select * from music\").fetchall()\n self.assertEqual(res, [(1, 'test', 'test', 100, '/test')])",
"title": ""
},
{
"docid": "f2191d039fa34274df046d25c1b49a7c",
"score": "0.6385286",
"text": "def testInserting(skiplist, tree, data, general):\n\tstart = time.time()\n\tfor i in data:\n\t\tskiplist.insert(i)\n\tlisttime = time.time() - start\n\tgeneral['list_insert'] += listtime\n\n\tstart = time.time()\n\tfor i in data:\n\t\ttree.insert(i)\n\ttreetime = time.time() - start\n\tgeneral['tree_insert'] += treetime\n\n\tprint \"Inserting:\"\n\tprint \"SkipList: \" + str(listtime)\n\tprint \"RedBlackTree: \" + str(treetime)\n\tif listtime < treetime:\n\t\tmsg = \"SkipList\"\n\t\tresult = treetime / listtime * 100\n\telse:\n\t\tmsg = \"RedBlackTree\"\n\t\tresult = listtime / treetime * 100\n\tresult -= 100\n\tmsg += \" is better for about \" + str(int(result)) + \"%\"\n\tprint msg",
"title": ""
},
{
"docid": "ab3a006ece3d125bb7ae678c7aaf00fb",
"score": "0.6360048",
"text": "def test_contains(empty_bst):\n empty_bst.insert(4)\n assert empty_bst.contains(4)",
"title": ""
},
{
"docid": "2112cbb75b1f751f8d31b65272ebdff4",
"score": "0.6352614",
"text": "def test_insert_x():\n print \"Computed:\", \"\\\"\" + insert_x(\"\") + \"\\\"\", \"Expected: \\\"\\\"\"\n print \"Computed:\", \"\\\"\" + insert_x(\"c\") + \"\\\"\", \"Expected: \\\"c\\\"\"\n print \"Computed:\", \"\\\"\" + insert_x(\"pig\") + \"\\\"\", \"Expected: \\\"pxixg\\\"\"\n print \"Computed:\", \"\\\"\" + insert_x(\"catdog\") + \"\\\"\", \"Expected: \\\"cxaxtxdxoxg\\\"\"",
"title": ""
},
{
"docid": "2b4000596f0153182aa567edc35a8506",
"score": "0.6350033",
"text": "def db_insert(self, data):\n print(\"INSERT\", data)",
"title": ""
},
{
"docid": "75d1e65064be2dfa2ad0f25073f1560a",
"score": "0.6345107",
"text": "def insert(self, records, context):",
"title": ""
},
{
"docid": "3785dff5bb9fd66bb877b752b0b37201",
"score": "0.6344903",
"text": "def test_insert_root():\n pass",
"title": ""
},
{
"docid": "ad915cd05171f382130b4b684c166acb",
"score": "0.6325421",
"text": "def test_insert_called(self, index_method):\n blk = ESInsert()\n\n self.configure_block(blk, {\n \"index\": \"index_name\",\n \"doc_type\": \"doc_type_name\"\n })\n blk.start()\n blk.process_signals([Signal({\"field1\": \"1\"})])\n index_method.assert_called_once_with(\n \"index_name\", \"doc_type_name\", {\"field1\": \"1\"})\n blk.stop()",
"title": ""
},
{
"docid": "99d2f787007dc4f391fa184d459e8da4",
"score": "0.6322538",
"text": "def test_orm_insert(self):\n orm_insert(\"34443\", \"44rfdfd\")\n password = orm_password_query(\"34443\")\n self.assertEqual(\"44rfdfd\", password)",
"title": ""
},
{
"docid": "d9623137b98427c1911cd0942a5a5461",
"score": "0.63165146",
"text": "def insert(self):\n self._insert_or_remove(insert=True)",
"title": ""
},
{
"docid": "2681519f69c06131f990e7aae368e5ec",
"score": "0.6309384",
"text": "def insert(self, insert_str, value_str):\n with self.get_cursor() as cur:\n try:\n cur.execute(insert_str, value_str)\n except IntegrityError, i:\n logger.error('Duplicated data. {i}'.format(i=i))\n except Exception, e:\n logger.error(e)",
"title": ""
},
{
"docid": "bf56e5e8532e826f891c64f72fa10dd2",
"score": "0.63060564",
"text": "def test_insert(self):\r\n assert self.session.query(MyModel).count() == 0\r\n self.session.add(MyModel(id=1))\r\n assert self.session.query(MyModel).count() == 1",
"title": ""
},
{
"docid": "e62ef99df5246c1e629392599cafe13c",
"score": "0.6301609",
"text": "def insert(self, arg):\n if arg == '' or arg.lower() == 'help':\n return dbhelp(self, 'insert')\n if not db_ready():\n return\n DB.execute(\"insert \"+arg.replace(\";\", \"\"), db_alias(), list_results = 0)",
"title": ""
},
{
"docid": "c452decd30709ff0fb5103ca2127fb0e",
"score": "0.629494",
"text": "def do_insert(self, args):\n return self.__execute_query(self.__create_beeswax_query(\"insert %s\" % args),\n is_insert=True)",
"title": ""
},
{
"docid": "24171c9678dcc7d0371903a2dd70fd98",
"score": "0.6292291",
"text": "def test_insert_pet():\n\n name = faker.name()\n specie = \"dog\"\n age = faker.random_number(digits=2)\n user_id = faker.random_number(digits=5)\n\n new_pet = pet_repository.insert_pet(name, specie, age, user_id)\n\n engine = db_connect.get_engine()\n query_pet = engine.execute(\"SELECT * FROM pets WHERE id={}\".format(new_pet.id)).fetchone()\n\n assert new_pet.id == query_pet.id\n assert new_pet.name == query_pet.name\n assert new_pet.specie == query_pet.specie\n assert new_pet.age == query_pet.age\n assert new_pet.user_id == query_pet.user_id\n\n engine.execute(\"DELETE FROM pets WHERE id={}\".format(new_pet.id))",
"title": ""
},
{
"docid": "abb4d37c520d8b26e6da5c713fe8d199",
"score": "0.6272923",
"text": "def insert(self, q, value=None):\n pass",
"title": ""
},
{
"docid": "70b7ac90abec7309802a37c44fc1c29f",
"score": "0.6270322",
"text": "def test_binary_insert(self):\r\n self.assertEqual([self.root.key, self.root.value], [6, 'Root'])\r\n self.assertEqual([self.root.left.key, self.root.left.value], [3, 'D'])\r\n self.assertEqual(\r\n [self.root.right.key, self.root.right.value], [7, 'A'])\r\n self.assertEqual(\r\n [self.root.left.left.key, self.root.left.left.value], [2, 'E'])",
"title": ""
},
{
"docid": "b4f4755b385288a17beb32c9cae4d01b",
"score": "0.6263013",
"text": "def test_insertPerson( self ):\n\t\tfrom bioetl.processControllers.personProcessing import processData\n\t\ttestNewPerson = copy.deepcopy( newPersonSeed )\n\t\t\n\t\tnewPersonObj = AsuDwPsPerson( **testNewPerson )\n\t\trecords = self.session.query( People ).all()\n\t\tself.assertListEqual( records, [] )\n\t\trecord = processData( newPersonObj, self.session )\n\t\tself.session.add( record )\n\t\tself.assertIsInstance( record, People )\n\t\tnewRecords = self.session.query( People ).filter( People.emplid == testNewPerson['emplid'] ).all()\n\t\tnewRecords = self.session.query( People ).all()\n\t\tself.assertTrue( newRecords[0].updated_flag )\n\t\tself.recordEqualsTest( newPersonObj, testNewPerson, People )\n\t\t\n\t\tself.assertEquals( newRecords[0].emplid, testNewPerson['emplid'] )\n\t\tself.assertEquals( newRecords[0].asurite_id, testNewPerson['asurite_id'] )\n\t\tself.assertEquals( newRecords[0].asu_id, testNewPerson['asu_id'] )\n\t\tself.assertEquals( newRecords[0].ferpa, testNewPerson['ferpa'] )\n\t\tself.assertEquals( newRecords[0].last_name, testNewPerson['last_name'] )\n\t\tself.assertEquals( newRecords[0].first_name, testNewPerson['first_name'] )\n\t\tself.assertEquals( newRecords[0].middle_name, testNewPerson['middle_name'] )\n\t\tself.assertEquals( newRecords[0].display_name, testNewPerson['display_name'] )\n\t\tself.assertEquals( newRecords[0].preferred_first_name, testNewPerson['preferred_first_name'] )\n\t\tself.assertEquals( newRecords[0].affiliations, testNewPerson['affiliations'] )\n\t\tself.assertEquals( newRecords[0].email_address, testNewPerson['email_address'] )\n\t\tself.assertEquals( newRecords[0].eid, testNewPerson['eid'] )\n\n\t\t# self.assertIsInstance( newRecords[0].birthdate, date )\n\t\t# self.assertIsInstance( newRecords[0].created_at, datetime )\n\t\twith self.assertRaises( ValueError ):\n\t\t\tbadSeed = testNewPerson\n\t\t\tbadPersonObj = AsuDwPsPerson( **badSeed )\n\t\t\tbadPersonObj.emplid = 2147483647L\n\t\t\tprocessData( badPersonObj, self.session )",
"title": ""
},
{
"docid": "dab29e411e2a3ceaf58d46a1a947f620",
"score": "0.62611794",
"text": "def test_inserted_row(import_setup):\n task = Task.objects.get(title=\"Make dinner\", task_list__name=\"Zip\")\n assert task.created_by == get_user_model().objects.get(username=\"u1\")\n assert task.assigned_to == get_user_model().objects.get(username=\"u1\")\n assert not task.completed\n assert task.note == \"This is note one\"\n assert task.priority == 3\n assert task.created_date == datetime.datetime.today().date()",
"title": ""
},
{
"docid": "be252ce3ce36a80a714038914e5e6379",
"score": "0.6245858",
"text": "def test_insert2(self):\n heap = MaxHeap()\n heap.insert(15)\n heap.insert(10)\n heap.insert(12)\n heap.insert(5)\n heap.insert(6)\n heap.insert(7)\n heap.insert(9)\n self.assertEqual(heap.heap_contents(), [15, 10, 12, 5, 6, 7, 9])",
"title": ""
},
{
"docid": "d3804d818a4da943fdf69fbf0d820f1b",
"score": "0.6245371",
"text": "def do_insert(self, **kwargs): # pylint: disable=unused-argument\n _testmethod = RAMSTKTestMethod()\n _testmethod.load_id = kwargs['load_id']\n _error_code, _msg = RAMSTKDataModel.do_insert(\n self, entities=[\n _testmethod,\n ])\n\n if _error_code == 0:\n self.tree.create_node(\n _testmethod.description,\n _testmethod.test_id,\n parent=0,\n data=_testmethod)\n\n # pylint: disable=attribute-defined-outside-init\n # It is defined in RAMSTKDataModel.__init__\n self.last_id = max(self.last_id, _testmethod.test_id)\n\n return _error_code, _msg",
"title": ""
},
{
"docid": "2d2fb14c37c45a12c189865a8a2fa0e2",
"score": "0.6245122",
"text": "def test_insert_keywords(self):\n table = self.make_table(range_key=None)\n self.query(\"INSERT INTO foobar (id='a', bar=1), (id='b', baz=4)\")\n items = list(self.dynamo.scan(table))\n self.assertCountEqual(items, [{\"id\": \"a\", \"bar\": 1}, {\"id\": \"b\", \"baz\": 4}])",
"title": ""
},
{
"docid": "fd6165cde4f0f9b497ae511b9b86ec43",
"score": "0.62342966",
"text": "def testInsertMood(self):\n self.data.createTables()\n self.data.insertMood('test')\n self.data._conn.commit()\n res = self.data._conn.execute(\"Select * from moods\").fetchall()\n self.assertEqual(res, [('test',)])",
"title": ""
},
{
"docid": "d51bab6e364aa0b0ece71524987464b6",
"score": "0.62342876",
"text": "def test_insert_out_of_bounds(self):\n # [0, 1, 2, 3, 4] --> [-1, 0, 1, 2, 3, 5, 4, 6]\n # Create the array [0, 1, 2, 3, 4]\n arr = DynamicArray(self._GROWTH_FACTOR)\n for i in range(5):\n arr.append(i)\n\n arr.insert(-1, 5) # Insert 5 at the front\n arr.insert(100, 6) # Insert 6 at the end\n arr.insert(-10, -1) # Insert -1 at the front\n\n self.assertEqual([-1, 0, 1, 2, 3, 5, 4, 6], arr)",
"title": ""
},
{
"docid": "01a4c67d3b5e692c9085964138b3e8bc",
"score": "0.62195563",
"text": "def test_insertion_sort(self):\r\n self.assertTrue(insertion_sort([7,4,3,2,6])==[2, 3, 4, 6, 7])\r\n self.assertTrue(insertion_sort([7,4,3,2,6])==[1,2,3,4,5])\r\n self.assertTrue(insertion_sort([])==[2, 3, 4, 6, 7])",
"title": ""
},
{
"docid": "1ceb377e759ccdbb1fb55adf731b101f",
"score": "0.62094903",
"text": "def test_empty_val_on_insert(empty_linked_list):\n with pytest.raises(TypeError) as e:\n empty_linked_list.insert(None)\n assert str(e.value) == 'Cannot insert a value of none'",
"title": ""
},
{
"docid": "c4b65951d0e69b1758b99d9c093b7e15",
"score": "0.62080044",
"text": "def insert(self):\n try:\n \n self.cursor.execute(self.get_insert_query(), self.get_values())\n except Exception as e:\n print(e)\n \n self.conn.commit()",
"title": ""
},
{
"docid": "c4b65951d0e69b1758b99d9c093b7e15",
"score": "0.62080044",
"text": "def insert(self):\n try:\n \n self.cursor.execute(self.get_insert_query(), self.get_values())\n except Exception as e:\n print(e)\n \n self.conn.commit()",
"title": ""
},
{
"docid": "c4b65951d0e69b1758b99d9c093b7e15",
"score": "0.62080044",
"text": "def insert(self):\n try:\n \n self.cursor.execute(self.get_insert_query(), self.get_values())\n except Exception as e:\n print(e)\n \n self.conn.commit()",
"title": ""
},
{
"docid": "c4b65951d0e69b1758b99d9c093b7e15",
"score": "0.62080044",
"text": "def insert(self):\n try:\n \n self.cursor.execute(self.get_insert_query(), self.get_values())\n except Exception as e:\n print(e)\n \n self.conn.commit()",
"title": ""
},
{
"docid": "c4b65951d0e69b1758b99d9c093b7e15",
"score": "0.62080044",
"text": "def insert(self):\n try:\n \n self.cursor.execute(self.get_insert_query(), self.get_values())\n except Exception as e:\n print(e)\n \n self.conn.commit()",
"title": ""
},
{
"docid": "490e37592ff0d04f444116e2e5931cdf",
"score": "0.6189212",
"text": "def test_insert_row(mixin_cols):\n t = QTable(mixin_cols)\n t0 = t.copy()\n t[\"m\"].info.description = \"d\"\n idxs = [0, -1, 1, 2, 3]\n if isinstance(\n t[\"m\"], (u.Quantity, Column, time.Time, time.TimeDelta, coordinates.SkyCoord)\n ):\n t.insert_row(1, t[-1])\n\n for name in t.colnames:\n col = t[name]\n if isinstance(col, coordinates.SkyCoord):\n assert skycoord_equal(col, t0[name][idxs])\n else:\n assert np.all(col == t0[name][idxs])\n\n assert t[\"m\"].info.description == \"d\"\n else:\n with pytest.raises(ValueError) as exc:\n t.insert_row(1, t[-1])\n assert \"Unable to insert row\" in str(exc.value)",
"title": ""
},
{
"docid": "012b55bdec8942ac88f6267fadd19267",
"score": "0.61765456",
"text": "def insert(self, item):\n\n # since is list, could append, however that would violate the heap,\n # so need to do an insertion sort type insertion to insert\n pass",
"title": ""
},
{
"docid": "9854a60ec6dc8475b45f959662f697b0",
"score": "0.6163492",
"text": "def test_insert_populated_tree(pop_tt):\n pop_tt.insert('they\\'re')\n assert pop_tt._root == {'t': {'h': {'e': {'y': {'\\'': {'r': {'e': {'$': {}}}}}, 'r': {'e': {'$': {}}}, 'i': {'r': {'$': {}}}}}}}",
"title": ""
},
{
"docid": "d1adb05dab0bb8968e17cc937c72d3dc",
"score": "0.6161037",
"text": "def test_es_insert(es, add_institution):\n\n for i in range(10):\n add_institution('inst'+str(i))\n\n Institution_Index.es_insert()\n\n for inst in Institution.select():\n\n doc = config.es.get(\n index='institution',\n id=inst.id,\n )\n\n assert doc['_source']['name'] == inst.name",
"title": ""
},
{
"docid": "c7262b799aaee5acb02365d1dd9e42f4",
"score": "0.6160727",
"text": "def insert_db(self):\n \n pass",
"title": ""
},
{
"docid": "9dce0fcba19d53a2d4e351fbac25b684",
"score": "0.61571586",
"text": "def test_insert_after_subtract_IBLT():\n t = test_bMinusA_IBLT()\n tup = (md5(\"key\"), sha1(\"value\"))\n t.insert(tup)\n assert t.is_empty()",
"title": ""
},
{
"docid": "067389f886fb47257f7c776aa79f90d2",
"score": "0.61559397",
"text": "def _insert(self, tree):\n tablename = tree.table\n keys = tree.attrs\n count = 0\n with self.connection.batch_write(tablename) as batch:\n for values in tree.data:\n if len(keys) != len(values):\n raise SyntaxError(\"Values '%s' do not match attributes \"\n \"'%s'\" % (values, keys))\n data = dict(zip(keys, map(self.resolve, values)))\n batch.put(data)\n count += 1\n return \"Inserted %d items\" % count",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="701"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">94d0024e99bf646bff8fd64e8a0db8e4</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">generates a new date from person_id preferences</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "b5233295c049c8cc5179ac1568ad534c",
"score": "0.67847145",
"text": "def generate_from_person_id(date_person_id):\r\n\r\n person_prefs = Preference.query.filter(Preference.date_person_id==date_person_id).first()\r\n \r\n # person_prefs.attribute == \r\n print(f'\\n\\n{person_prefs}')\r\n\r\n if person_prefs.is_video == True:\r\n bubble = (DateIdea.is_video == True)\r\n\r\n if person_prefs.is_socially_distant == True:\r\n bubble = (DateIdea.is_socially_distant == True)\r\n\r\n if person_prefs.is_co_quarantined == True:\r\n bubble = (DateIdea.is_co_quarantined == True)\r\n\r\n if person_prefs.is_outside == True and person_prefs.is_at_home == False:\r\n location = (DateIdea.is_outside == True)\r\n\r\n if person_prefs.is_at_home == True and person_prefs.is_outside == False:\r\n location = (DateIdea.is_at_home == True)\r\n\r\n if person_prefs.is_at_home == True and person_prefs.is_outside == True:\r\n location = ((DateIdea.is_at_home == True) | (DateIdea.is_outside == True))\r\n \r\n q = DateIdea.query\r\n\r\n return q.filter(bubble,location).all()\r\n\r\n # date_options = q.filter(DateIdea.is_video == is_video, DateIdea.is_socially_distant == is_socially_distant,\r\n # DateIdea.is_co_quarantined == is_co_quarantined, DateIdea.is_at_home == is_at_home, \r\n # DateIdea.is_outside == is_outside).all()\r",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "a38fa82d82be41e76567c98bb83f0bf3",
"score": "0.60629743",
"text": "def create_date_person(user_id, name, relationship_type):\r\n\r\n date_person = DatePerson(user_id=user_id, name=name, relationship_type=relationship_type)\r\n\r\n db.session.add(date_person)\r\n db.session.commit()\r\n\r\n return date_person",
"title": ""
},
{
"docid": "9403c9a313de8763593aead5490ed1fd",
"score": "0.5744288",
"text": "def gen_date():\n return date.fromordinal(random.randrange(START_DATE_ORD, END_DATE_ORD)).strftime(\"%m/%d/%Y\")",
"title": ""
},
{
"docid": "d762f59e198d2d82fe7ddd83ae426771",
"score": "0.57022244",
"text": "def create_person_preferences(user_id, date_person_id,\r\n is_video, is_socially_distant,\r\n is_co_quarantined, is_outside,\r\n is_at_home):\r\n\r\n person_prefs = Preference(user_id=user_id, date_person_id=date_person_id,\r\n is_video=is_video, is_socially_distant=is_socially_distant,\r\n is_co_quarantined=is_co_quarantined, is_outside=is_outside,\r\n is_at_home=is_at_home)\r\n\r\n db.session.add(person_prefs)\r\n db.session.commit()\r\n\r\n return person_prefs",
"title": ""
},
{
"docid": "2c1bcad3a9f1d01f59fd6275fdaace8b",
"score": "0.5658733",
"text": "def set_release_date_by_prop(fobj):\n #!reldate = pytz.utc.localize(datetime.datetime.combine(\n #! fobj.date_obs.lower\n #! + datetime.timedelta(days=30*fobj.proposal.proprietary_period),\n #! datetime.time(hour=12)\n #! ))\n reldate = (fobj.date_obs.lower\n + datetime.timedelta(days=30*fobj.proposal.proprietary_period))\n fobj.release_date = reldate\n fobj.save()",
"title": ""
},
{
"docid": "371f611e7fd2549e04be4d96c88bfab4",
"score": "0.5449056",
"text": "def create_nice_date():\n now = datetime.datetime.now()\n new_date = \"{0}-{1}-{2}\".format(now.day,now.month,now.year)\n return new_date",
"title": ""
},
{
"docid": "652a47a9252fe01c54a1c9a8d5de332f",
"score": "0.54319876",
"text": "def generate_DateField(self,**kwargs):\n return datetime.datetime.now()",
"title": ""
},
{
"docid": "239ce1c0ba7ba6b1364f9f5c534da93f",
"score": "0.5359402",
"text": "def get_next_gen_date(self):\r\n #get the starting point\r\n if self.last_gen_date is None:\r\n start = self.start_date\r\n else:\r\n start = self.last_gen_date\r\n \r\n # Processing depends on the time unit\r\n #daily is easy - incremeent the day\r\n if self.time_unit == 'day':\r\n new = start + timedelta(days = 1)\r\n\r\n # weekly. if weekly, add 7 days If 2 times per week, alternate 3 and 4 days\r\n elif self.time_unit == 'week':\r\n if self.number_of_times_per_unit == 1:\r\n new = start + timedelta(days = 7)\r\n \r\n elif self.number_of_times_per_unit == 2:\r\n diff = (start - self.start_date).days % 7\r\n if (diff == 0) | (diff == 3): \r\n new = start + timedelta(days = 4)\r\n else: \r\n new = start + timedelta(days = 3)\r\n else:\r\n raise ValueError(\"Invalid recurrance of chore: \"+self.number_of_times_per_unit+\" time(s) per \"+self.time_unit)\r\n \r\n # monthly: if monthly, add a month. If semimonthly, add 2 weeks\r\n elif self.time_unit == 'month':\r\n if self.number_of_times_per_unit == 1:\r\n new = self.add_months(start,1)\r\n else:\r\n #twice per month is really every two weeks\r\n new = start + timedelta(weeks = 2)\r\n \r\n # year, half, or quarter. add a year, 6 months, or 3 months respectively. \r\n elif self.time_unit == 'year':\r\n if self.number_of_times_per_unit == 1:\r\n new = date(start.year+1,start.month,start.day)\r\n elif self.number_of_times_per_unit == 2:\r\n new = self.add_months(start,6)\r\n elif self.number_of_times_per_unit == 4:\r\n new = self.add_months(start,3)\r\n else:\r\n raise ValueError(\"Invalid recurrance of chore: \"+self.number_of_times_per_unit+\" time(s) per \"+self.time_unit)\r\n\r\n else:\r\n raise ValueError(\"Invalid recurrance of chore: \"+self.number_of_times_per_unit+\" time(s) per \"+self.time_unit)\r\n\r\n return new",
"title": ""
},
{
"docid": "1a141eec1d7e91ed105d8f2e35b44d3b",
"score": "0.5338735",
"text": "def goal_create_date(self, goal_create_date):\n\n self._goal_create_date = goal_create_date",
"title": ""
},
{
"docid": "a4ea96b0a099643e7b0256544fe594b8",
"score": "0.53038675",
"text": "def rdate():\r\n start_date = date(2010, 1, 1)\r\n years = 9\r\n end_date = timedelta(years*365)\r\n random_date = (start_date + end_date * random()).strftime('%m/%d/%Y')\r\n return random_date",
"title": ""
},
{
"docid": "6afda1821acca2a33aefe2d5fd6696a5",
"score": "0.52732897",
"text": "def get_date_person_id(user_id, name):\r\n\r\n return DatePerson.query.filter(DatePerson.user_id==user_id,\r\n DatePerson.name==name).first().date_person_id",
"title": ""
},
{
"docid": "b30109e6e15e32e85f39a59b3c9f9fd2",
"score": "0.5232627",
"text": "def fetch(gen, metadata):\n gen.context['gen_date'] = datetime.date.today().isoformat()",
"title": ""
},
{
"docid": "0b4a8ee3fc75bb19930ed3291296d06a",
"score": "0.5198665",
"text": "def bornOn(person):\n if person.dateOfBirth:\n print(\"Date of birth: {}.{}.{}.\".format(person.dateOfBirth.day, person.dateOfBirth.month, person.dateOfBirth.year))\n else:\n print(\"Date of birth: unknown.\")",
"title": ""
},
{
"docid": "9a19886d3325434e988b8257ebcdb919",
"score": "0.5169439",
"text": "def date_gen():\n timestamp = time.time()\n time_local = time.localtime(timestamp)\n res = time.strftime('%Y%m%d', time_local)\n return res[2:]",
"title": ""
},
{
"docid": "866fceee9faf332be98ad81db9ea03b1",
"score": "0.5149602",
"text": "def date(self) -> datetime:",
"title": ""
},
{
"docid": "d5dc684db4e6dc53ab7bce9ee6f19086",
"score": "0.5115773",
"text": "def get_date():\r\n\treturn datetime.datetime.now()",
"title": ""
},
{
"docid": "1d1e1c09f63b97ed9a99eee2f29936ec",
"score": "0.5112372",
"text": "def update_date(self, date_int_val, data_manager):\n self.date_int_val_ = date_int_val\n return",
"title": ""
},
{
"docid": "df4082709c695d3038727babd3df5871",
"score": "0.5097955",
"text": "def _get_period_id_now(self, cr, uid):\n result=[]\n date_now = datetime.today().strftime('%Y-%m-%d')\n date_nows = datetime.strptime(date_now,'%Y-%m-%d').date()\n period = self.pool.get('account.period')\n month = '0'\n if date_nows.month < 10 :\n month = '0'+ str(date_nows.month)\n period_now = month+'/'+str(date_nows.year)\n #raise osv.except_osv(_('Error'), _('period_now = %s')%(period_now))\n period_now = period.search(cr,uid,[('name','=',period_now)])\n if len(period_now)==0:\n raise osv.except_osv(_('Error'), _('Cette periode n\\'existe pas! \\n Veuillez la creer !'))\n result.append(period_now[0])\n return result",
"title": ""
},
{
"docid": "1e1ad3efea924a0355337611dfb2f860",
"score": "0.50798947",
"text": "def update_recurring_date(self):\n pass",
"title": ""
},
{
"docid": "b498ec40bb6ded1929dd185e50be2bee",
"score": "0.5077726",
"text": "def create_date(self, date_dictionary=None, get_isoformat=False):\n return_var = False\n\n if date_dictionary:\n day = date_dictionary.get(\"day\")\n month = date_dictionary.get(\"month\")\n year = date_dictionary.get(\"year\")\n\n # building birthday date\n if (day and month and year):\n if get_isoformat:\n return_var = date(year=int(year), month=int(month), day=int(day)).isoformat()\n else:\n return_var = date(year=int(year), month=int(month), day=int(day))\n\n return return_var",
"title": ""
},
{
"docid": "4f513d830d73a8fa7443d6777bf262f0",
"score": "0.5074996",
"text": "def date(self):",
"title": ""
},
{
"docid": "b0d7b31a35d7fee861f22374f56258d9",
"score": "0.5070288",
"text": "def generate_chronological_id():\n date = datetime.now()\n return '{:04}{:02}{:02}{:02}{:02}{:02}'.format(date.year, date.month, date.day, date.hour, date.minute, date.second)",
"title": ""
},
{
"docid": "57c82de4ac63dc1b963a97cd9cc69eae",
"score": "0.5049633",
"text": "def add_person(people: dict):\n is_valid_name = False\n while not is_valid_name:\n name = input(\"Name: \")\n if name == \"\":\n print(\"Name can't be empty\")\n elif name in people.keys():\n print(\"Name has already existed\")\n else:\n is_valid_name = True\n\n is_valid_birthday = False\n while not is_valid_birthday:\n birthday = input(\"Birthday(DD/MM/YYYY): \")\n if birthday == \"\":\n print(\"Birthday can't be empty\")\n else:\n birthday_details = birthday.split('/')\n if len(birthday_details) != 3:\n print(\"Invalid birthday format\")\n else:\n try:\n birth_date = int(birthday_details[0])\n birth_month = int(birthday_details[1])\n birth_year = int(birthday_details[2])\n except ValueError:\n print(\"Invalid birthday format\")\n continue\n\n today = date.today().strftime(\"%d-%m-%Y\").split('-')\n current_date = int(today[0])\n current_month = int(today[1])\n current_year = int(today[2])\n\n if 0 < birth_year <= current_year:\n month_limit = 12 if birth_year != current_year else current_month\n if 0 < birth_month <= month_limit:\n if birth_month == 2:\n date_limit = 28 + (1 if birth_year % 4 == 0 else 0)\n elif birth_month in [1, 3, 5, 7, 8, 10, 12]:\n date_limit = 31\n else:\n date_limit = 30\n date_limit = date_limit if birth_year != current_year and birth_month != current_month else current_date\n if 0 < birth_date <= date_limit:\n people[name] = (birth_date, birth_month, birth_year)\n is_valid_birthday = True\n else:\n print(\"Invalid date\")\n else:\n print(\"Invalid month\")\n else:\n print(\"Invalid year\")",
"title": ""
},
{
"docid": "10709cf8562dcc0266efc5493dc06389",
"score": "0.5046428",
"text": "def resolve_balance_date(_data, _info):\n return str(datetime.date.today())",
"title": ""
},
{
"docid": "f6d64f7aad327724fac1af6632c0eaa5",
"score": "0.50394523",
"text": "def convert_DateProperty(self, model, prop, kwargs):\r\n if prop._auto_now or prop._auto_now_add:\r\n return None\r\n\r\n return f.DateField(format='%Y-%m-%d', **kwargs)",
"title": ""
},
{
"docid": "63839f5149ca1297fab47cd7b280d095",
"score": "0.502926",
"text": "def get_person_oldest_date(person_id):\n return get_cache_oldest_date('pid:' + str(person_id))",
"title": ""
},
{
"docid": "dc7517b4a565a9ad9898eb3e81492ab5",
"score": "0.50268525",
"text": "def _get_date_for_url(self, days_until: int) -> Tuple[str, date]:\n d = date.today() + timedelta(days=days_until)\n return f'{d.year}-{d.month:02d}-{d.day:02d}', d",
"title": ""
},
{
"docid": "8124a83b3ce7e8fa390fb2793cd04833",
"score": "0.50153536",
"text": "def onchange_recurring(self):\n self.new_date = False\n if self.recurring:\n self.new_date = self.get_date()",
"title": ""
},
{
"docid": "1f52e57338da1ce4ae76c018fa21c03e",
"score": "0.50149435",
"text": "def individual_date_of_birth(self, individual_date_of_birth):\n\n self._individual_date_of_birth = individual_date_of_birth",
"title": ""
},
{
"docid": "3dd6d131c7aaf357489f63ce336d1889",
"score": "0.5003564",
"text": "def get_date(name, age):\n\tprint(\"It's currently\", time.ctime(), name, \". You probably have,\", (80 - age), \"good years left.\")",
"title": ""
},
{
"docid": "258905783fe7ecc6c6ef2f687e49cc44",
"score": "0.5001661",
"text": "def create_date():\n # The funny things you could with Python!\n return '{2}/{1}/{0}'.format(input('Enter the year: '),\n input('Enter the month: '),\n input('Enter the date: '))",
"title": ""
},
{
"docid": "342016cf05f21274048a6d06f32ba88f",
"score": "0.4984347",
"text": "def make_date(day):\n return Date(day.day, day.month, day.year)",
"title": ""
},
{
"docid": "77b52ca7077b55e4fd25a4ade9ce90fc",
"score": "0.49809825",
"text": "def formid_date(sprayformid):\n parts = sprayformid.split(\".\")\n\n return datetime(year=datetime.now().year, month=parts[1], day=parts[0])",
"title": ""
},
{
"docid": "20251cff6b21f7aef2c91f10eac54f6f",
"score": "0.49572805",
"text": "def generate_id(self, person_list):\n current_id = '{}-'.format(self.designation[0].upper()) + ''.join(\n random.choice(string.ascii_uppercase + string.digits)\n for _ in range(5))\n if current_id in self.get_existing_id(person_list):\n generate_id(self, person_list, current_id)\n self.id = current_id",
"title": ""
},
{
"docid": "affe07a9ec293a44d58c16eeea4bd3de",
"score": "0.4951295",
"text": "def _format_next_interest_due_date_30X(self, val):\n if val:\n date_format = '%Y%m%d'\n val = FSwiftWriterUtils.format_date(val, date_format)\n return str(val)",
"title": ""
},
{
"docid": "d19540ee13c6189fd02a91f504e9e837",
"score": "0.49510908",
"text": "def date():\r\n d = dt.datetime(1987, 1, 14)\r\n d = d.today()\r\n return str(d.year) + \"-\" + str(d.month) + \"-\" + str(d.day)",
"title": ""
},
{
"docid": "25af59a3339d51e3d7cd8f11c54a335b",
"score": "0.49492422",
"text": "def PgDate(year, month, day):\n return Date(year, month, day)",
"title": ""
},
{
"docid": "bf1009e2f089a127ef3fe25c8c81cc88",
"score": "0.49489272",
"text": "def _convert_promo2_date(self) -> None:\n # Construct date when participation started\n dates = self.data.loc[:, \"Promo2SinceYear\"] * 100 + (\n self.data.loc[:, \"Promo2SinceWeek\"] - 1\n )\n \n # Construct Promo2 start date\n dates.fillna(0, inplace=True)\n dates = dates.astype(int)\n dates = dates.astype(str) + \"0\"\n dates.replace(\"00\", np.nan, inplace=True)\n self.data.loc[:, \"Promo2Date\"] = pd.to_datetime(dates, format=\"%Y%W%w\")\n\n # Compute difference between current date and Promo2 participation\n lag = (\n self.data.loc[:, \"DateObj\"] - self.data.loc[:, \"Promo2Date\"]\n ) / np.timedelta64(1, \"D\")\n lag[lag < 0] = 0 # encode days before competition (i.e., negative numbers)\n lag.fillna(-1, inplace=True) # impute NaNs\n self.data.loc[:, \"Promo2Lag\"] = lag # create lag feature",
"title": ""
},
{
"docid": "e158556f21cec70ac3a183ca181b5955",
"score": "0.49470103",
"text": "def user_selects_date(date):\n avatarClaimsIntakePage.input_payments_date(date)",
"title": ""
},
{
"docid": "4b269ec71214c2a3c46d1fc91f795499",
"score": "0.4943491",
"text": "def convert_DateProperty(model, prop, kwargs):\r\n if prop.auto_now or prop.auto_now_add:\r\n return None\r\n\r\n return f.DateField(format='%Y-%m-%d', **kwargs)",
"title": ""
},
{
"docid": "ef734b80e0f9eb4203df435cbbd6ecd8",
"score": "0.49400082",
"text": "def create_deals_of_the_day(self):\n\n self.deals_of_the_day = self.book_prices_db_last_date",
"title": ""
},
{
"docid": "40b1bebc463cbc48382ff71988ba0a4d",
"score": "0.49391162",
"text": "def main():\n people = {}\n\n for i in range(5):\n add_person(people)\n\n today = date.today().strftime(\"%d-%m-%Y\").split('-')\n current_date = int(today[0])\n current_month = int(today[1])\n current_year = int(today[2])\n\n for name, birthday in people.items():\n birth_date = birthday[0]\n birth_month = birthday[1]\n birth_year = birthday[2]\n\n age = current_year - birth_year - 1\n if birth_month < current_month:\n age += 1\n elif birth_month == current_month:\n if birth_date < current_date:\n age += 1\n\n print(\"{0} is {1} years old\".format(name,age))",
"title": ""
},
{
"docid": "44a71d1e8a583af6715486eb8b28bf21",
"score": "0.49223408",
"text": "def ordinal_to_date(self, ordinal):",
"title": ""
},
{
"docid": "108774d1a330160f4df5dbe66433b8d9",
"score": "0.49155056",
"text": "def get_formated_date(day_bias: int or float=0) -> str:\n return (datetime.now() + timedelta(\n days=float(day_bias)\n )).strftime(\"%Y.%m.%d\")",
"title": ""
},
{
"docid": "83098829d2c6c9d19d282c0ac06ffa92",
"score": "0.49121267",
"text": "def random_birth_date(category: str = None, age: int = None) -> str:\n\n if age:\n start = start_date(age)\n end = end_date(age + 1)\n elif category == 'young':\n start = start_date(0)\n end = end_date(16)\n elif category == 'adult':\n start = start_date(16)\n end = end_date(60)\n elif category == 'elder':\n start = start_date(60)\n end = end_date(120)\n else:\n return 'Please provide an appropriate category or age'\n return random_date(start, end)",
"title": ""
},
{
"docid": "3f990924320aca407ed1edab11915c59",
"score": "0.49086204",
"text": "def date(self) -> 'Date':",
"title": ""
},
{
"docid": "f6505b17158dab88a54c9696f3820e3d",
"score": "0.49035403",
"text": "def SaveDatesAndStations(self):\n print self.calcDict('ID')\n recID = self.calcDict('ID')",
"title": ""
},
{
"docid": "4111036d7869033e76d2f365c96fca6c",
"score": "0.48988405",
"text": "def _get_date():\n return str(datetime.date.today())",
"title": ""
},
{
"docid": "14f3295f2bafdcf874e67da984890a2b",
"score": "0.4889925",
"text": "def adjust_date(refdate, num, timeunit=\"days\"):\n dd = relativedelta(**{timeunit: int(num)})\n conv_dt = refdate + relativedelta(**{timeunit: int(num)})\n return conv_dt",
"title": ""
},
{
"docid": "6451b433b44893eab619e9ee627411bf",
"score": "0.4875631",
"text": "def get_date():\n return datetime.datetime.now().date()",
"title": ""
},
{
"docid": "c927e742a99d06063748b160a7d29d17",
"score": "0.48754895",
"text": "def _migrate_dates(self, oldobj):\n dates = admin_models.base.ObjectDates()\n dates.save()\n\n dates.creation = oldobj.created\n dates.save()\n return dates",
"title": ""
},
{
"docid": "329468d1f2be3af0151421ba82a3e185",
"score": "0.4866381",
"text": "def incDate(self) -> str:\n self.tradingday_obj += timedelta(days=1)\n self.tradingday = self.tradingday_obj.strftime('%Y%m%d')\n return self.tradingday",
"title": ""
},
{
"docid": "d568f474b058765fe7aae8956ae54025",
"score": "0.48630005",
"text": "def set_date(self):\n new_date = input(' Task Date[{}]: '.format(self.date)) or self.date\n self.date = new_date",
"title": ""
},
{
"docid": "06ee6f36a5401fbdd9aeedeeedcad614",
"score": "0.485052",
"text": "def handle_date(x):\n epoch = random.getrandbits(32)\n date = datetime.datetime.fromtimestamp(epoch, pytz.utc)\n return date.isoformat()",
"title": ""
},
{
"docid": "349e59819954b936e3b642e6ed97feac",
"score": "0.48437932",
"text": "def idfn(val):\n if isinstance(val, (datetime)):\n return val.strftime(\"%Y%m%d\")",
"title": ""
},
{
"docid": "335a45ce0cc9c4697a752d0d91bca662",
"score": "0.484042",
"text": "def get_date (self):\n\t\tself.__time = datetime.datetime.now()\n\t\tadd_offset=datetime.timedelta(hours=self.time_offset)\n\t\treturn (self.__time+add_offset).strftime(self.date_format)",
"title": ""
},
{
"docid": "326c45cbe5bd7552cb3afac8cabe9d06",
"score": "0.48311275",
"text": "def future_date(x: int) -> str:\n timeline: timedelta = timedelta(x)\n today: datetime = datetime.today()\n future: datetime = today + timeline\n the_day: str = future.strftime(\"%B %d, %Y\")\n return(the_day)",
"title": ""
},
{
"docid": "023bf8359cbc1ed93db660293417c9cb",
"score": "0.48292089",
"text": "def dt_new_date(data, is_debug=IS_DEBUG):\n if is_debug:\n print(\"dt_new_date: data=<{}>\".format(data))\n dt = datetime.datetime(data['year'],data['month'],data['day'],data['hour'],data['minute'],data['second'])\n return dt",
"title": ""
},
{
"docid": "aca14e5e65f56beeb4db9ff1026c2191",
"score": "0.48074827",
"text": "def _get_date(self, cr, uid, ids, field_name, arg, context={}):\n res = {}\n if context is None:\n context = {}\n records = self.browse(cr, uid, ids, context)\n for record in records:\n \n # date est de type datetime.date\n # records pointe vers oph.bloc.agenda.line\n # on obtient la date du oph_bloc_agenda avec\n # for record in records:\n # record.bloc_agenda_id.start_date\n # qui est de type string en utc. Il faut la transformer en ?\n # on a aussi record.bloc_agenda_id.name qui donne une str YYYY-MM-DD\n res[record.id] = record.bloc_agenda_id.name\n # là c'est pas en date type pas sur que cela marche.\n return res",
"title": ""
},
{
"docid": "1149207d2b427797f3bc3f1097f50c58",
"score": "0.4799788",
"text": "def add_delta(self, date):",
"title": ""
},
{
"docid": "b147a08c4f71b1974ff0b4f9d022a6cf",
"score": "0.4796338",
"text": "def gen_date_key(date):\n seconds = date.strftime('%012s')\n count = increment('action_%s'%seconds)\n return '%s|%s' % (seconds,count)",
"title": ""
},
{
"docid": "2e63d1f1619f60c351a3f0976aff2cb7",
"score": "0.47932285",
"text": "def random_birthday(start_year=1960, end_year=1990):\n\n date_obj = date(randint(start_year, end_year), randint(1, 12), randint(1, 28))\n return {\n 'day': str(date_obj.day),\n 'month': str(date_obj.month),\n 'year': str(date_obj.year),\n 'date': date_obj,\n }",
"title": ""
},
{
"docid": "a3d9e5e4138fbdd5243be59e24932a2c",
"score": "0.47825187",
"text": "def random_birthday(start_year=1960, end_year=1990):\n\n return date(randint(start_year, end_year), randint(1, 12), randint(1, 28))",
"title": ""
},
{
"docid": "5a69eee09feddb49bff520635b970358",
"score": "0.47821987",
"text": "def _inc_day(year, month, day, net):\n d = date(year, month, day)\n new_d = d + timezone.timedelta(days=net)\n return new_d.year, new_d.month, new_d.day",
"title": ""
},
{
"docid": "da039a9328c598afc56a543573e9e285",
"score": "0.47686967",
"text": "def create_new_dog_day(date_instance):\n new_dog_day = DogDay(date_of_record=date_instance)\n new_dog_day.save()\n return True",
"title": ""
},
{
"docid": "a4921dd57ce069fa94c4ff846dba80fc",
"score": "0.47683728",
"text": "def setDueDate(chat, msg):\n text = ''\n task = Task\n\n if msg != '':\n if len(msg.split(' ', 1)) > 1:\n text = msg.split(' ', 1)[1]\n msg = msg.split(' ', 1)[0]\n\n if not msg.isdigit():\n send_message(\"You have to inform the task id\", chat)\n\n else:\n task_id = int(msg)\n query = db.session.query(Task).filter_by(id=task_id, chat=chat)\n\n try:\n task = query.one()\n\n except sqlalchemy.orm.exc.NoResultFound:\n send_message(\"_404_ Task {} not found x.x\".format(task_id), chat)\n\n if text == '':\n task.duedate = ''\n send_message(\"_Cleared_ due date from task {}\".format(task_id), chat)\n\n else:\n text = text.split(\"/\")\n text.reverse()\n if not (1 <= int(text[2]) <= 31 and 1 <= int(text[1]) <= 12 and 1900 <= int(text[0]) <= 2100):\n send_message(\n \"The due date format is: *DD/MM/YYYY* (Max number day = 31, Max mouth day = 12 and Max number year = 2100 ) )\", chat)\n\n else:\n from datetime import datetime\n task.duedate = datetime.strptime(\" \".join(text), '%Y %m %d')\n send_message(\n \"Task {} has the due date *{}*\".format(task_id, task.duedate), chat)\n\n db.session.commit()",
"title": ""
},
{
"docid": "f75a66cfd0be537ffbc90ff9adb69af8",
"score": "0.47650748",
"text": "def get_date_str(self):\n now = datetime.now()\n return str(now.year) + (\"{:02d}{:02d}\".format(now.month, now.day))",
"title": ""
},
{
"docid": "c50a623926e556fd6911a0590826fa58",
"score": "0.47582534",
"text": "def __cid_dates__(self):\n self.cid_dates = self.pr2[['cohortid', 'date']].\\\n drop_duplicates().\\\n groupby('cohortid').\\\n apply(lambda x : x.date.values)",
"title": ""
},
{
"docid": "118b5ff49e4589eeba765ef5701ebed4",
"score": "0.47548828",
"text": "def personCreated(self, person):\n self.createdPeople.append(person)",
"title": ""
},
{
"docid": "179f1f039a8a2e26693ca2242863c07c",
"score": "0.47471124",
"text": "def __get_date(self):\n if self.datetime is not None:\n self.date = self.datetime.date()\n else:\n self.date = None",
"title": ""
},
{
"docid": "8877953b81b7f97cb84e5b59ce389e75",
"score": "0.4738041",
"text": "def set_date_1(self, date_obj):\n self.date_1 = date_obj\n self.file.get_screen('navi').ids.date_text.text = str(date_obj.strftime(\"%d.%m.%Y\"))",
"title": ""
},
{
"docid": "b5c88801c908821663a046a4f4abf975",
"score": "0.4735387",
"text": "def due_date(self, new_date: datetime):\n self.data = patch(\n self._token, self.url, {\n 'due_on':\n datetime.strftime(new_date, '%Y-%m-%dT%H:%M:%SZ')\n if new_date else None\n })",
"title": ""
},
{
"docid": "e1695f804dc4294c5a3e43410a044147",
"score": "0.47334445",
"text": "def _get_date_string(self, num_days_delta):\n today = datetime.today()\n day = today + timedelta(days=num_days_delta)\n return day.strftime(\"%Y-%m-%d\")",
"title": ""
},
{
"docid": "b367b31cc1f3712825dc4fdbef93d710",
"score": "0.47186068",
"text": "def new_years_day(gyear, observed):\n nyday = gdate_to_rdate(gyear, 1, 1)\n if observed and day_of_week(nyday) == 0:\n nyday += 1\n return nyday",
"title": ""
},
{
"docid": "a135da38691a4b8e79ac980b0b55ec2d",
"score": "0.4714316",
"text": "def _value_calculo(self):\n if self.fecha_nac:\n d1 = datetime.strptime(self.fecha_nac, \"%Y-%m-%d\").date()\n d2 = date.today()\n self.edad = relativedelta(d2, d1).years",
"title": ""
},
{
"docid": "3c5ecb588f40d3eed8cda9b53295a899",
"score": "0.4710137",
"text": "def add_date_liked(user_id, idea_id):\r\n\r\n liked_date = DateLiked(user_id=user_id, idea_id=idea_id)\r\n\r\n db.session.add(liked_date)\r\n db.session.commit()\r\n\r\n return liked_date",
"title": ""
},
{
"docid": "b11cc221b55f553f4660023f76dbb2ac",
"score": "0.4704417",
"text": "def gen_rand_date(year):\n try:\n month = random.randint(1,13)\n day = random.randint(1,31)\n rand_date = datetime.date(year, month, day)\n return rand_date.strftime('%m/%d/%Y')\n except ValueError:\n return gen_rand_date(year)",
"title": ""
},
{
"docid": "fffcafcc21b99591be312c9ee4c16f6c",
"score": "0.46958056",
"text": "def _convert_competition_date(self) -> None:\n # Construct date when competition opened\n self.data.loc[:, \"CompetitionOpenDate\"] = pd.to_datetime(\n dict(\n year=self.data.loc[:, \"CompetitionOpenSinceYear\"],\n month=self.data.loc[:, \"CompetitionOpenSinceMonth\"],\n day=1,\n )\n )\n\n # Create DateObj from Date\n self.data.loc[:, \"DateObj\"] = pd.DatetimeIndex(self.data.loc[:, \"Date\"])\n\n # Compute difference between current date and competition appearance\n lag = (\n self.data.loc[:, \"DateObj\"] - self.data.loc[:, \"CompetitionOpenDate\"]\n ) / np.timedelta64(1, \"D\")\n\n lag[lag < 0] = 0 # encode days before competition (i.e., negative numbers)\n lag.fillna(-1, inplace=True) # impute NaNs\n self.data.loc[:, \"SalesCompetitionLag\"] = lag # create lag feature",
"title": ""
},
{
"docid": "a8eaedb13d05d9b8b46f345848ccfb42",
"score": "0.46897823",
"text": "def _onchange_category_id(self):\n if self.trial_date_start:\n if (self.type_id.name == \"CDI\" and self.category_id.name==\"Employee\"):\n self.trial_date_end = (datetime.strptime(self.trial_date_start,'%Y-%m-%d') + relativedelta(months=6)).strftime('%Y-%m-%d')\n elif (self.type_id.name == \"CDI\" and self.category_id.name==\"Cadre\"):\n self.trial_date_end = (datetime.strptime(self.trial_date_start,'%Y-%m-%d') + relativedelta(months=3)).strftime('%Y-%m-%d')",
"title": ""
},
{
"docid": "4a45767f3ec303b3c98a802cb0c60a7a",
"score": "0.46885738",
"text": "def date_prop(self, date_prop):\n\n self._date_prop = date_prop",
"title": ""
},
{
"docid": "fd9be77c6762f5b34fa964bc8b2011bf",
"score": "0.46821183",
"text": "def get_date():\n return datetime.now().strftime(\"%d/%m/%Y\")",
"title": ""
},
{
"docid": "f46f77e36bf5b7029eeea144f9ec4368",
"score": "0.46818054",
"text": "def grab_date_i(self):\n self.e_f_i_par.delete(0, END)\n self.e_f_i_par.insert(0, self.cal.get_date())\n self.date_win.destroy()",
"title": ""
},
{
"docid": "8d61b7cae2ce074e64f8370ca83cb535",
"score": "0.46773693",
"text": "def compute_CreatedOn(self, tarrif=None):\n return datetime.now()",
"title": ""
},
{
"docid": "b78efda0fcffd1fd354fba793e44bea6",
"score": "0.4665828",
"text": "def _add_card_sent_date(contact_dict, timestamp):\n for jj in range(0,len(contact_dict)):\n contact_dict[jj]['Last Membership Card Sent Date']=timestamp.strftime('%Y-%m-%dT00:00:00')\n return(contact_dict)",
"title": ""
},
{
"docid": "8ad715fb54f94f8e8161fa78901cec68",
"score": "0.4664927",
"text": "def generate_date(gas_price):\n data = dict()\n delta = {'Mon': 0, 'Tue': 1, 'Wed': 2, 'Thu': 3, 'Fri': 4}\n for price_key, price_value in gas_price.iteritems():\n start_date = price_key.strip().split('to')[0].strip()\n try:\n date = arrow.get(start_date, 'YYYY MMM-D')\n except Exception:\n date = arrow.get(start_date, 'YYYY MMM- D')\n for delta_key, delta_value in delta.iteritems():\n modified_date = date.replace(days=delta_value)\n if price_value[delta_key] != '':\n data[modified_date] = float(price_value[delta_key])\n return collections.OrderedDict(sorted(data.items()))",
"title": ""
},
{
"docid": "4195a802c979364ffe3a699bcd2f63ce",
"score": "0.46608964",
"text": "def build_person_add(first_name, last_name, age=None):\n person = {'first': first_name, 'last': last_name}\n if age:\n person['age'] = age\n return person",
"title": ""
},
{
"docid": "69dce4313be1e1bc067e47c073c02e61",
"score": "0.46608505",
"text": "def _person_participant(dataset, event_data_type, foreign_key):\n from remapp.models import PersonParticipant\n from remapp.tools.get_values import get_or_create_cid\n if event_data_type == 'ct_dose_check_alert':\n person = PersonParticipant.objects.create(ct_dose_check_details_alert=foreign_key)\n elif event_data_type == 'ct_dose_check_notification':\n person = PersonParticipant.objects.create(ct_dose_check_details_notification=foreign_key)\n else:\n return\n person.person_name = dataset.PersonName\n for cont in dataset.ContentSequence:\n if cont.ConceptNameCodeSequence[0].CodeMeaning == 'Person Role in Procedure':\n person.person_role_in_procedure_cid = get_or_create_cid(\n cont.ConceptCodeSequence[0].CodeValue, cont.ConceptCodeSequence[0].CodeMeaning)\n elif cont.ConceptNameCodeSequence[0].CodeMeaning == 'Person ID':\n person.person_id = cont.TextValue\n elif cont.ConceptNameCodeSequence[0].CodeMeaning == 'Person ID Issue':\n person.person_id_issuer = cont.TextValue\n elif cont.ConceptNameCodeSequence[0].CodeMeaning == 'Organization Name':\n person.organization_name = cont.TextValue\n elif cont.ConceptNameCodeSequence[0].CodeMeaning == 'Person Role in Organization':\n person.person_role_in_organization_cid = get_or_create_cid(\n cont.ConceptCodeSequence[0].CodeValue, cont.ConceptCodeSequence[0].CodeMeaning)\n person.save()",
"title": ""
},
{
"docid": "d4bffea73a8ddf5d454bf952c8ef5d8f",
"score": "0.46604988",
"text": "def sameDate(numPeople, numSame):\n possibleDates = list(range(366))\n birthdays = [0]*366\n for p in range(numPeople):\n birthDate = choice(possibleDates)\n birthdays[birthDate] += 1\n return max(birthdays) >= numSame",
"title": ""
},
{
"docid": "31c1bc64161c27e7fd58767e9fb8e67a",
"score": "0.4658937",
"text": "def get_deceased_date(dbo, animalid):\n return db.query_date(dbo, \"SELECT DeceasedDate FROM animal WHERE ID = %d\" % animalid)",
"title": ""
},
{
"docid": "baceab3d29b81a0920bf14d700f12f2b",
"score": "0.46491495",
"text": "def date():\n d = dt.datetime(2000, 1, 1)\n d = d.today()\n date_today = \"{}-{}-{}\".format(d.year, d.month, d.day)\n\n return date_today",
"title": ""
},
{
"docid": "43040998fd72ca48fb718197d1bad14d",
"score": "0.46474668",
"text": "def make_default_values_with_new_dates(self, date_values, num=None):\n if num is None:\n num = random.randint(0, 9999999)\n defaut_event_values = {\n 'title': 'Default event test {0}'.format(num),\n 'app_config': self.app_config,\n 'short_description': 'Lorem ipsum blah blah {0}'.format(num),\n }\n defaut_event_values.update(date_values)\n return defaut_event_values",
"title": ""
},
{
"docid": "c0eeca0a996e5dde5fa85397951abff6",
"score": "0.464323",
"text": "def set_date(self, ds=None):\n date_node = ns.date_node(ds) # Get Date (day) node\n ns.create_relation(from_node=self.org_node, rel=\"On\", to_node=date_node)\n return",
"title": ""
},
{
"docid": "4da39a583b6d89ae932645c97906b926",
"score": "0.46423241",
"text": "def start(self, date: datetime) -> None:\r\n self.date = date\r\n self.last_paid = date",
"title": ""
},
{
"docid": "f4c93529c5dc08c3f359d45e20896729",
"score": "0.46359295",
"text": "def __AssignDates(self):\n # How many commands currently exist in the db?\n num_com = self.NumCommands()\n # Generate num_com unique dates starting with today.\n today = date.today()\n date_list = []\n for i in range(num_com):\n d = today + timedelta(days=i)\n date_list.append(d)\n random.shuffle(date_list)\n # Grab the commands that currently exist in the database\n sql = \"\"\"\n SELECT cid\n FROM VimCommands\n \"\"\"\n self.__db_cursor.execute(sql)\n results = self.__db_cursor.fetchall()\n cmd_date_pairs = []\n for i in range(num_com):\n cmd_date_pairs.append({'cid':str(results[i][0]), \n 'ddate':str(date_list[i])})\n # Truncate old VimDates and add the new key-value pairs in\n sql = \"\"\"\n TRUNCATE VimDates\n \"\"\"\n self.__db_cursor.execute(sql)\n for date_pair in cmd_date_pairs:\n sql = \"\"\"\n INSERT INTO VimDates (cid, ddate)\n VALUES('%s', '%s')\n \"\"\" % (date_pair['cid'], date_pair['ddate']) \n self.__db_cursor.execute(sql)",
"title": ""
},
{
"docid": "2acb009c9100b51eeaacd013066169c0",
"score": "0.46341634",
"text": "def get_date_completed(self):\r\n\t\tdate_str = self.widgets[\"date_completed\"][\"cal\"].get()\r\n\t\tdate_info = [int(i) for i in date_str.split(\"/\")]\r\n\t\tmonth, day, year = tuple(date_info)\r\n\t\tyear += 2000\r\n\t\tdate_obj = dt.datetime(year, month, day).date()\r\n\t\treturn date_obj.strftime(\"%m-%d-%y\")",
"title": ""
},
{
"docid": "6d700527219a3b403d78889ded116e60",
"score": "0.46299827",
"text": "def compute_initial_final_date(self):\n for statement in self:\n if statement.line_ids:\n line_list = statement.line_ids.sorted(key=lambda r: r.date)\n statement.initial_date = line_list[0].date\n statement.final_date = line_list[-1].date",
"title": ""
},
{
"docid": "924fa8ac8cd000ad50bad406c88d070f",
"score": "0.46278575",
"text": "def _date(self, query):\n (date, _) = self.cal.parseDT(query)\n return \"{:%Y-%m-%d}\".format(date)",
"title": ""
},
{
"docid": "10b78b91bf091f9ce7eb2c2d6ed34ee9",
"score": "0.46220452",
"text": "def add_date_to_path(path: Path):\n dated_path = path / f'{date.today().year}' / f'{date.today().month:02d}'\n dated_path.mkdir(parents=True, exist_ok=True)\n return dated_path",
"title": ""
},
{
"docid": "d9c91e283c3c2916fca6b871dc0829ce",
"score": "0.46140787",
"text": "def gen_datetimenow():\n return datetime.utcnow().isoformat() + \"Z\"",
"title": ""
},
{
"docid": "589186b3e67afad25e9ccfc9a3764048",
"score": "0.46111202",
"text": "def onchange_date_invoice(self):\n partner = False\n if self.type == 'out_invoice':\n partner = self.partner_id\n elif self.type == 'in_invoice':\n partner = self.company_id.partner_id\n if partner and self.date_invoice:\n exemption_certificate_id = partner.get_certificate(self.date_invoice)[0]\n if exemption_certificate_id:\n self.exemption_certificate_id = exemption_certificate_id\n self.vat_exemption = True\n else:\n self.exemption_certificate_id = False\n self.vat_exemption = False\n self.vat_order = False\n else:\n self.exemption_certificate_id = False\n self.vat_exemption = False\n self.vat_order = False\n self._compute_amount()",
"title": ""
},
{
"docid": "dc10436340831bca5f1657f0f125b068",
"score": "0.4604057",
"text": "def conviction_date(self, conviction_date):\n\n self._conviction_date = conviction_date",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="702"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">1589c5865da94ec1a570d5900ce7fe81</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Create a property package</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "a837c1b42e44072798c7ada49de8282d",
"score": "0.529537",
"text": "def pp_toluene():\n\n return PropertyPackage(phases=1, phase_names=['toluene'])",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "d4becaaefdda3b48b49cdee6a733c8b9",
"score": "0.66099685",
"text": "def create_property(key, value):\n\n prop = manifest.CTD_ANON_4()\n prop.PropertyKey = unicode(key)\n prop.PropertyValue = unicode(value)\n\n return prop",
"title": ""
},
{
"docid": "8f881dd8a34bdb0fe2907bdd9d3a2b49",
"score": "0.6331332",
"text": "def create_package(self, filename, format, params):",
"title": ""
},
{
"docid": "b580e37de721ab17ec7871e7da753fa8",
"score": "0.60891664",
"text": "def _create_property(p_tree, values, name, description):\n def append_value(p, v):\n if v is not None:\n p.values.append(str(v))\n\n # Instantiate.\n p = ComponentProperty()\n p.short_name = p.long_name = name\n p.description = description\n\n # Append each value.\n if values:\n try:\n iter(values)\n except ValueError:\n for v in values:\n append_value(p, v)\n else:\n append_value(p, values)\n\n # Append to tree.\n p_tree.append(p)\n\n return p",
"title": ""
},
{
"docid": "50ebf28e9515da1483a1ffba5197d7e7",
"score": "0.5988291",
"text": "def CreateProperty(self):\n self.__CreateMaterial()\n self.__CreateProfile()\n self.__CreateSection()\n\n self.__SectionAssignment()\n self.__AssignBeamSectionOrientation()",
"title": ""
},
{
"docid": "a36c7fc41f922f8e28830b43ca753b2d",
"score": "0.5944733",
"text": "def __genProp(self):\n code4 = \"\"\"\n\"\"\"\n for line in self.__proplist:\n code4 += \" %s\\n\" % line\n return code4",
"title": ""
},
{
"docid": "e8dd314555fcac310d806f6856df821c",
"score": "0.5940916",
"text": "def PropertyCreate(self,pName,pType,pDataType,pAnimatable,pIsUser,pReferenceSource):\n pass",
"title": ""
},
{
"docid": "2be8a53adc0ca5fe2a7f46a9ec73cbd7",
"score": "0.5931078",
"text": "def __init__(self, properties):\n\t\tself.p = properties",
"title": ""
},
{
"docid": "3ea718035461290dcb31399dea28a1d9",
"score": "0.587499",
"text": "def create_package(PackageName=None, PackageType=None, PackageDescription=None, PackageSource=None):\n pass",
"title": ""
},
{
"docid": "e325e877fc3932c4bfabc7c41e3fb297",
"score": "0.58711326",
"text": "def pp_water():\n\n return PropertyPackage(phases=1, phase_names=['water'])",
"title": ""
},
{
"docid": "4fc3c51ce0e5629fcc174d74ce42f9f0",
"score": "0.5796095",
"text": "def _component_property():\n return {\n 'type' : 'class',\n 'name' : 'component_property',\n 'base' : 'shared.data_source',\n 'abstract' : False,\n 'doc' : 'ComponentProperties include things that a component simulates (ie: pressure, humidity) and things that prescribe that simulation (ie: gravity, choice of advection scheme). Note that this is a specialisation of shared::DataSource. data::DataObject is also a specialisation of shared::DataSource. This allows software::Connections and/or activity::Conformance to refer to either ComponentProperties or DataObjects.',\n 'properties' : [\n ('children', 'software.component_property', '0.N', None),\n ('citations', 'shared.citation', '0.N', None),\n ('description', 'str', '0.1', None),\n # TODO define type\n ('grid', 'str', '0.1', 'A reference to the grid that is used by this component.'),\n ('intent', 'software.component_property_intent_type', '0.1', 'The direction that this property is intended to be coupled: in, out, or inout.'),\n ('is_represented', 'bool', '1.1', 'When set to false, means that this property is not used by the component. Covers the case when, for instance, a modeler chooses not to represent some property in their model. (But still allows meaningful comparisons between components which _do_ model this property.)'),\n ('long_name', 'str', '0.1', None),\n ('short_name', 'str', '1.1', None),\n # TODO define type\n ('standard_names', 'str', '0.N', None),\n ('units', 'shared.unit_type', '0.1', 'The standard name that this property is known as (for example, its CF name).'),\n ('values', 'str', '0.N', 'The value of the property (not applicable to fields).'),\n ],\n 'decodings' : [\n ('children', 'child::cim:componentProperty'),\n ('citations', 'child::cim:citation'),\n ('description', 'child::cim:description'),\n ('grid', None),\n ('intent', 'self::cim:componentProperty/@intent'),\n ('is_represented', 'self::cim:componentProperty/@represented'),\n ('long_name', 'child::cim:longName'),\n ('short_name', 'child::cim:shortName'),\n ('standard_names', 'child::cim:standardName/@value'),\n ('units', 'child::cim:units/@value'),\n ('values', 'child::cim:value'),\n ]\n }",
"title": ""
},
{
"docid": "ecd378c9e825cc702af7033f6df51d0e",
"score": "0.5787132",
"text": "def generate_property_template(self):\n template = {\n \"@id\": \"url or curie of the property\",\n \"@type\": \"rdf:Property\",\n \"rdfs:comment\": \"description of the property\",\n \"rdfs:label\": \"carmel case, should match @id\",\n \"schema:domainIncludes\": {\n \"@id\": \"class which use it as a property, could be list\"\n },\n \"schema:isPartOf\": {\n \"@id\": \"http://schema.biothings.io\"\n },\n \"schema:rangeIncludes\": {\n \"@id\": \"relates a property to a class that constitutes (one of) the expected type(s) for values of the property\"\n }\n }\n return template",
"title": ""
},
{
"docid": "d3ba7b28fb1eed4282b080190a164c62",
"score": "0.57870847",
"text": "def _create_property(*args, **opts):\n global num_properties\n type = args[0]\n\n # For aliases, just return the property you're aliasing\n if type == 'alias':\n return args[1]\n num_properties += 1\n attrvalue = '__%s_%d' % (type, num_properties)\n\n for arg in args[1:]:\n opts[arg] = True\n\n # Get the default value. Maybe this is silly?\n default_val = opts.setdefault('default', None)\n postprocess = opts.setdefault('postprocess', None)\n null = opts.setdefault('null', True)\n if type == 'integer':\n postprocess = int\n if not null:\n default_val = 0\n elif type == 'string':\n postprocess = str\n if not null:\n default_val = ''\n elif type == 'list':\n def postprocess(val):\n if isinstance(val, list):\n return list(val)\n else:\n return [val]\n if not null:\n default_val = []\n else:\n raise 'Unrecognized type %s' % type\n\n def fget(self):\n import types\n try:\n val = getattr(self, attrvalue)\n if hasattr(val, '__call__'):\n val = val(self)\n except AttributeError:\n val = default_val\n if hasattr(val, '__call__'):\n val = val(self)\n if opts.setdefault('sticky_default', False):\n setattr(self, attrvalue, val)\n if postprocess:\n return val\n else:\n return postprocess(val)\n \n def fset(self, value):\n setattr(self, attrvalue, postprocess(value))\n\n newproperty = property(fget, fset)\n return newproperty",
"title": ""
},
{
"docid": "af7a95b2e1392cf2f0a1cc621dc3dcb0",
"score": "0.5723132",
"text": "def __init__(self, module_name:str, property_list, used_named_properties):\n if not isinstance(module_name, str):\n raise ValueError()\n if not isinstance(property_list, PropertyList) and property_list is not None:\n raise ValueError()\n\n self._module_name = module_name\n self._property_list = property_list\n\n self._named_propertie_by_name = dict([(i.naming.class_name, i) for i in used_named_properties])",
"title": ""
},
{
"docid": "3bf4b1a4fb32657be2fcd2fd88524bf6",
"score": "0.56473106",
"text": "def builder(namespace):\n\n def build_property(prop):\n \"\"\"\n Build a single property getter for this class.\n\n :param prop: the property\n \"\"\"\n\n def dbus_func(proxy_object):\n \"\"\"\n The property getter.\n \"\"\"\n return proxy_object.Get(\n spec.INTERFACE_NAME,\n prop.name,\n dbus_interface=dbus.PROPERTIES_IFACE\n )\n\n return dbus_func\n\n for prop in spec.PropertyNames:\n namespace[prop.name] = staticmethod(build_property(prop))",
"title": ""
},
{
"docid": "a502c618c140d30482594269463aa325",
"score": "0.562671",
"text": "def GenerateCode(prop, save_location):\n #make base tag and supply attributes as necessary\n root = ElementTree.Element(Property.GetName().lower(),\n {\"name\": prop.name,\n \"description\": prop.description,\n \"rank\": prop.rank,\n \"display_text\": prop.display_text,\n \"version\": XmlUtils.VERSION})\n #add the tpcl_requirements tag and its text\n tpcl_display_elem = ElementTree.SubElement(root, \"tpcl_display\")\n tpcl_display_elem.text = prop.tpcl_display\n tpcl_requires_elem = ElementTree.SubElement(root, \"tpcl_requires\")\n tpcl_requires_elem.text = prop.tpcl_requires\n #add the categories\n root.append(ElementTree.Comment(\"categories\"))\n for cat in prop.categories:\n cat_elem = ElementTree.SubElement(root, \"category\", {'name': cat})\n et = ElementTree.ElementTree(root)\n XmlUtils.WriteElementTree(et, save_location, indent=True)",
"title": ""
},
{
"docid": "e9df65ba774bd5a27c18c068a63cfb05",
"score": "0.5624782",
"text": "def InitPropTypes(t):\n t[T_VAR] = PropertyType(T_VAR, calcType=INTENSIVE_PROP|CANFLASH_PROP, unitType='Temperature',\n scaleFactor=100.0, minValue=0.0) #Temperature\n\n t[P_VAR] = PropertyType(P_VAR, calcType=INTENSIVE_PROP|CANFLASH_PROP, unitType='Pressure',\n scaleFactor=1000.0, minValue=0.0) #Pressure\n\n t[H_VAR] = PropertyType(H_VAR, calcType=INTENSIVE_PROP|CANFLASH_PROP, unitType='MolarEnthalpy',\n scaleFactor=10000.0) #Enthalpy\n\n t[HMASS_VAR] = PropertyType(HMASS_VAR, calcType=INTENSIVE_PROP, unitType='MassEnthalpy',\n scaleFactor=10000.0) #Enthalpy mass basis\n\n t[MOLARV_VAR] = PropertyType(MOLARV_VAR, calcType=INTENSIVE_PROP,\n unitType='MolarVolume',\n scaleFactor=10.0, minValue=0.0) #MolarVolume\n\n t[STDLIQVOL_VAR] = PropertyType(STDLIQVOL_VAR, calcType=INTENSIVE_PROP,\n unitType='MolarVolume',\n scaleFactor=10.0, minValue=0.0) #StdLiqMolarVolume\n\n t[STDLIQDEN_VAR] = PropertyType(STDLIQDEN_VAR, calcType=INTENSIVE_PROP, unitType='Density',\n scaleFactor=50000.0, minValue=0.0) #StdLiqMassDensity\n\n t[S_VAR] = PropertyType(S_VAR, calcType=INTENSIVE_PROP, unitType='MolarSpecificHeat',\n scaleFactor=1000.0) #Entropy\n\n t[VPFRAC_VAR] = PropertyType(VPFRAC_VAR, calcType=INTENSIVE_PROP,\n scaleFactor=1.0,\n minValue=0.0, maxValue=1.0) #Vapor fraction\n\n t[MASSFLOW_VAR] = PropertyType(MASSFLOW_VAR, calcType=EXTENSIVE_PROP,\n unitType='MassFlow',\n scaleFactor=50000.0) #Mass Flow\n\n t[STDVOLFLOW_VAR] = PropertyType(STDVOLFLOW_VAR, calcType=EXTENSIVE_PROP,\n unitType='VolumetricFlow',\n scaleFactor=10000.0) #standard liquid volumetric Flow\n\n t[VOLFLOW_VAR] = PropertyType(VOLFLOW_VAR, calcType=EXTENSIVE_PROP,\n unitType='VolumetricFlow',\n scaleFactor=10000.0) #actual volumetric Flow\n\n t[MOLEFLOW_VAR] = PropertyType(MOLEFLOW_VAR, calcType=EXTENSIVE_PROP,\n unitType='MoleFlow',\n scaleFactor=1000.0) #Mole Flow\n\n t[ENERGY_VAR] = PropertyType(ENERGY_VAR, calcType=EXTENSIVE_PROP,\n unitType='Power',\n scaleFactor=1000000.0) #Energy\n\n t[FRAC_VAR] = PropertyType(FRAC_VAR, calcType=INTENSIVE_PROP,\n scaleFactor = 1.0,\n minValue=0.0, maxValue=1.0) #Fraction\n\n t[ZFACTOR_VAR] = PropertyType(ZFACTOR_VAR, calcType=INTENSIVE_PROP,\n scaleFactor=1.0)\n t[MOLEWT_VAR] = PropertyType(MOLE_WT, calcType=INTENSIVE_PROP,\n scaleFactor=100.0)\n t[DELTAT_VAR] = PropertyType(DELTAT_VAR, calcType=INTENSIVE_PROP, unitType='DeltaT',\n scaleFactor=100.0, minValue=0.0) #Temperature difference\n\n\n t[DELTAP_VAR] = PropertyType(DELTAP_VAR, calcType=INTENSIVE_PROP, unitType='DeltaP',\n scaleFactor=1000.0, minValue=0.0) #Pressure difference\n\n t[LENGTH_VAR] = PropertyType(LENGTH_VAR, unitType='Length', scaleFactor=10.0)\n t[CMPMOLEFRAC_VAR] = PropertyType(CMPMOLEFRAC_VAR, calcType=INTENSIVE_PROP,\n scaleFactor = 1.0,\n minValue=0.0, maxValue=1.0) #Used for accessing single mole fraction\n\n t[CMPMASSFRAC_VAR] = PropertyType(CMPMASSFRAC_VAR, calcType=INTENSIVE_PROP,\n scaleFactor = 1.0,\n minValue=0.0, maxValue=1.0) #Used for accessing single mass fraction\n\n t[STDVOLFRAC_VAR] = PropertyType(STDVOLFRAC_VAR, calcType=INTENSIVE_PROP,\n scaleFactor = 1.0,\n minValue=0.0, maxValue=1.0) #Used for accessing single vol fraction\n\n\n t[CP_VAR] = PropertyType(CP_VAR, calcType=INTENSIVE_PROP,unitType='MolarSpecificHeat',\n scaleFactor=500.0, minValue=0.0)\n\n t[CV_VAR] = PropertyType(CV_VAR, calcType=INTENSIVE_PROP,unitType='MolarSpecificHeat',\n scaleFactor=500.0, minValue=0.0)\n\n t[DPDVT_VAR] = PropertyType(DPDVT_VAR, calcType=INTENSIVE_PROP,unitType='Pressure/MolarVolume',\n scaleFactor=1.0e6)\n\n t[GIBBSFREEENERGY_VAR] = PropertyType(GIBBSFREEENERGY_VAR, calcType=INTENSIVE_PROP,\n unitType='MolarEnthalpy',\n scaleFactor=1.0e5)\n\n t[HELMHOLTZENERGY_VAR] = PropertyType(HELMHOLTZENERGY_VAR, calcType=INTENSIVE_PROP,\n unitType='MolarEnthalpy', scaleFactor=1.0e5)\n\n t[IDEALGASCP_VAR] = PropertyType(IDEALGASCP_VAR, calcType=INTENSIVE_PROP,unitType='MolarSpecificHeat',\n scaleFactor=500.0, minValue=0.0)\n\n t[IDEALGASENTHALPY_VAR] = PropertyType(IDEALGASENTHALPY_VAR, calcType=INTENSIVE_PROP,\n unitType='MolarEnthalpy', scaleFactor=10000.0)\n\n t[IDEALGASENTROPY_VAR] = PropertyType(IDEALGASENTROPY_VAR, calcType=INTENSIVE_PROP,\n unitType='MolarSpecificHeat', scaleFactor=1000.0)\n\n t[IDEALGASFORMATION_VAR] = PropertyType(IDEALGASFORMATION_VAR, calcType=INTENSIVE_PROP,\n unitType='MolarEnthalpy', scaleFactor=100000.0)\n\n t[IDEALGASGIBBS_VAR] = PropertyType(IDEALGASGIBBS_VAR, calcType=INTENSIVE_PROP,\n unitType='MolarEnthalpy', scaleFactor=100000.0)\n\n t[INTERNALENERGY_VAR] = PropertyType(INTERNALENERGY_VAR, calcType=INTENSIVE_PROP,\n unitType='MolarEnthalpy', scaleFactor=100000.0)\n\n t[ISOTHERMALCOMPRESSIBILITY_VAR] = PropertyType(ISOTHERMALCOMPRESSIBILITY_VAR,\n calcType=INTENSIVE_PROP, scaleFactor=1.0)\n\n t[MASSDEN_VAR] = PropertyType(MASSDEN_VAR, calcType=INTENSIVE_PROP,unitType='Density',\n scaleFactor = 50000.0, minValue=0.0)\n\n t[MECHANICALZFACTOR_VAR] = PropertyType(MECHANICALZFACTOR_VAR,\n calcType=INTENSIVE_PROP, scaleFactor=1.0)\n\n t[PH_VAR] = PropertyType(PH_VAR, calcType=INTENSIVE_PROP, scaleFactor=10.0)\n\n t[RESIDUALCP_VAR] = PropertyType(RESIDUALCP_VAR, calcType=INTENSIVE_PROP,unitType='MolarSpecificHeat',\n scaleFactor=500.0, minValue=0.0)\n\n t[RESIDUALCV_VAR] = PropertyType(RESIDUALCV_VAR, calcType=INTENSIVE_PROP,unitType='MolarSpecificHeat',\n scaleFactor=500.0, minValue=0.0)\n\n t[RESIDUALENTHALPY_VAR] = PropertyType(RESIDUALENTHALPY_VAR, calcType=INTENSIVE_PROP,\n unitType='MolarEnthalpy', scaleFactor=10000.0)\n\n t[RESIDUALENTROPY_VAR] = PropertyType(RESIDUALENTROPY_VAR, calcType=INTENSIVE_PROP,\n unitType='MolarSpecificHeat', scaleFactor=1000.0)\n\n t[RXNBASEH_VAR] = PropertyType(RXNBASEH_VAR, calcType=INTENSIVE_PROP,\n unitType='MolarEnthalpy', scaleFactor=100000.0)\n\n t[SURFACETENSION_VAR] = PropertyType(SURFACETENSION_VAR, calcType=INTENSIVE_PROP,\n unitType='SurfaceTension', scaleFactor=-1.0)\n\n t[SPEEDOFSOUND_VAR] = PropertyType(SPEEDOFSOUND_VAR, calcType=INTENSIVE_PROP,\n unitType='Velocity',\n scaleFactor=-1.0, minValue=0.0)\n\n t[THERMOCONDUCTIVITY_VAR] = PropertyType(THERMOCONDUCTIVITY_VAR, calcType=INTENSIVE_PROP,unitType='ThermalConductivity',\n scaleFactor=-1.0, minValue=0.0)\n\n t[VISCOSITY_VAR] = PropertyType(VISCOSITY_VAR, calcType=INTENSIVE_PROP,unitType='Viscosity',\n scaleFactor=-1.0, minValue=0.0)\n\n t[KINEMATICVISCOSITY_VAR] = PropertyType(KINEMATICVISCOSITY_VAR, calcType=INTENSIVE_PROP,unitType='KinematicViscosity',\n scaleFactor=1.0, minValue=0.0)\n\n t[UA_VAR] = PropertyType(UA_VAR, calcType=EXTENSIVE_PROP, unitType='UA', scaleFactor=100000.0, minValue=0.0)\n\n t[U_VAR] = PropertyType(U_VAR, calcType=EXTENSIVE_PROP, unitType='HeatTransferCoeff', scaleFactor=10.0, minValue=0.0)\n\n t[AREA_VAR] = PropertyType(AREA_VAR, calcType=EXTENSIVE_PROP, unitType='Area', scaleFactor=10.0)\n\n t[TIME_VAR] = PropertyType(TIME_VAR, calcType=EXTENSIVE_PROP, unitType='Time', scaleFactor=10.0)\n\n t[MASS_VAR] = PropertyType(MASS_VAR, calcType=EXTENSIVE_PROP, unitType='Mass', scaleFactor=100.0)\n\n t[VOL_VAR] = PropertyType(VOL_VAR, calcType=EXTENSIVE_PROP, unitType='Volume', scaleFactor=10.0)\n\n t[CONCENTRATION_VAR] = PropertyType(CONCENTRATION_VAR, calcType=EXTENSIVE_PROP,\n unitType='MolarConcentration', scaleFactor=10.0)\n\n t[RATERXNVOL_VAR] = PropertyType(RATERXNVOL_VAR, calcType=INTENSIVE_PROP,\n unitType=RATERXNVOL_VAR, scaleFactor=1.0)\n\n t[RATERXNCAT_VAR] = PropertyType(RATERXNCAT_VAR, calcType=INTENSIVE_PROP,\n unitType=RATERXNCAT_VAR, scaleFactor=1.0)\n\n t['GasConstant'] = PropertyType('GasConstant', calcType=EXTENSIVE_PROP,\n unitType='GasConstant', scaleFactor=1.0)\n\n t[GENERIC_VAR] = PropertyType(GENERIC_VAR, scaleFactor=1.0) #unknown type\n\n t[VELOCITY_VAR] = PropertyType(VELOCITY_VAR, calcType=INTENSIVE_PROP,\n unitType='Velocity', scaleFactor=1000.0)\n\n t[NHVMASS_VAR] = PropertyType(NHVMASS_VAR, calcType=INTENSIVE_PROP, unitType='MassEnthalpy',\n scaleFactor=10000.0) #NHV mass basis\n\n t[GHVMASS_VAR] = PropertyType(GHVMASS_VAR, calcType=INTENSIVE_PROP, unitType='MassEnthalpy',\n scaleFactor=10000.0) #GHV mass basis\n\n t[HVAPCTEP_VAR] = PropertyType(HVAPCTEP_VAR, calcType=INTENSIVE_PROP, unitType='MolarEnthalpy',\n scaleFactor=10000.0) #Enthalpy\n\n t[HVAPCTET_VAR] = PropertyType(HVAPCTET_VAR, calcType=INTENSIVE_PROP, unitType='MolarEnthalpy',\n scaleFactor=10000.0) #Enthalpy\n\n t[PSEUDOTC_VAR] = PropertyType(PSEUDOTC_VAR, calcType=INTENSIVE_PROP, unitType='Temperature',\n scaleFactor=100.0, minValue=0.0) #Temperature\n\n t[PSEUDOPC_VAR] = PropertyType(PSEUDOPC_VAR, calcType=INTENSIVE_PROP, unitType='Pressure',\n scaleFactor=1000.0, minValue=0.0) #Pressure\n\n t[PSEUDOVC_VAR] = PropertyType(PSEUDOVC_VAR, calcType=INTENSIVE_PROP, unitType='MolarVolume',\n scaleFactor=10.0, minValue=0.0) #MolarVolume\n\n t[JT_VAR] = PropertyType(JT_VAR, calcType=INTENSIVE_PROP, unitType='JouleThomson',\n scaleFactor=1.0) #JouleThomson coefficient\n\n t[HUMIDITY_VAR] = PropertyType(HUMIDITY_VAR, calcType=INTENSIVE_PROP, unitType='Humidity', scaleFactor=10000.0)\n\n t[WORK_VAR] = PropertyType(WORK_VAR, calcType=EXTENSIVE_PROP, unitType='Work', scaleFactor=1000000.0)\n\n t[STDGASVOLFLOW_VAR] = PropertyType(STDGASVOLFLOW_VAR, calcType=EXTENSIVE_PROP,\n unitType='StdGasVolumeFlow', scaleFactor=10000.0)\n\n t[HMASS_VAR] = PropertyType(HMASS_VAR, calcType=INTENSIVE_PROP, unitType='MassEnthalpy',\n scaleFactor=10000.0)\n t[CPMASS_VAR] = PropertyType(CPMASS_VAR, calcType=INTENSIVE_PROP, unitType='MassSpecificHeat',\n scaleFactor=500.0)\n t[CVMASS_VAR] = PropertyType(CVMASS_VAR, calcType=INTENSIVE_PROP, unitType='MassSpecificHeat',\n scaleFactor=500.0)\n t[SMASS_VAR] = PropertyType(SMASS_VAR, calcType=INTENSIVE_PROP, unitType='MassSpecificHeat',\n scaleFactor=1000.0)",
"title": ""
},
{
"docid": "0390e4dec7d952015c97de07a9a483ef",
"score": "0.5584408",
"text": "def __init__(self, *args, **kwargs):\n self.packaging_type = UPSPackage.PACKAGING_TYPE_PACKAGE\n\n self.dimensions = [0, 0, 0] # Length, width, height.\n self.dimensions_unit = UPSPackage.DIMENSIONS_UNIT_IN\n\n self.weight = 0.0\n self.weight_unit = UPSPackage.WEIGHT_UNIT_LBS\n\n self.currency = UPSPackage.CURRENCY_USD\n self.value = 0.0\n\n super(UPSPackage, self).__init__(*args, **kwargs)",
"title": ""
},
{
"docid": "5bcec62bbe86bc787dc08c5565c5b213",
"score": "0.557655",
"text": "def properties(cls) -> ComponentProperties:\n ...",
"title": ""
},
{
"docid": "1b020e95fab7b759a67b81e65eda134a",
"score": "0.5522289",
"text": "def _prop_builder(spec):\n\n def builder(namespace):\n \"\"\"\n The property class's namespace.\n\n :param namespace: the class's namespace\n \"\"\"\n\n def build_property(prop):\n \"\"\"\n Build a single property getter for this class.\n\n :param prop: the property\n \"\"\"\n\n def dbus_func(proxy_object):\n \"\"\"\n The property getter.\n \"\"\"\n return proxy_object.Get(\n spec.INTERFACE_NAME,\n prop.name,\n dbus_interface=dbus.PROPERTIES_IFACE\n )\n\n return dbus_func\n\n for prop in spec.PropertyNames:\n namespace[prop.name] = staticmethod(build_property(prop))\n\n return builder",
"title": ""
},
{
"docid": "2b9e1c5830b9d61484fd46bba47da47c",
"score": "0.54672146",
"text": "def _register_props(self):\n\n props = props_assets.register_props()\n props.path = \"\"\n return props",
"title": ""
},
{
"docid": "33be1094b9208b2fe8ceeb35a835b3ee",
"score": "0.54396075",
"text": "def convert_properties(class_):\n\n if bpy.app.version < (2, 80):\n return class_\n\n if not hasattr(class_, '__annotations__'):\n class_.__annotations__ = {}\n\n for name, value in class_.__dict__.items():\n # This is a property definition, add annotation for it.\n if name in (\"eggpath\", \"pydevpath\"):\n class_.__annotations__[name] = value\n\n return class_",
"title": ""
},
{
"docid": "a9163da78ae229b8032dbce3c0905d07",
"score": "0.542754",
"text": "def _coupling_property():\n return {\n 'type' : 'class',\n 'name' : 'coupling_property',\n 'base' : 'shared.property',\n 'abstract' : False,\n 'doc' : 'A CouplingProperty is a name/value pair used to specify OASIS-specific properties.',\n 'properties' : [\n\n ],\n 'decodings' : [\n\n ]\n }",
"title": ""
},
{
"docid": "ed7eb378a7ab1db108d65631a86c3ed7",
"score": "0.54083705",
"text": "async def publish_properties(self):\n nid = self.id\n publish = self.device.publish\n\n # node attributes\n await publish(b\"{}/$name\".format(nid), self.name)\n await publish(b\"{}/$type\".format(nid), self.type)\n\n # property attributes\n props = self._properties\n await publish(\n b\"{}/$properties\".format(nid),\n b\",\".join([p.id.encode() for p in props]),\n )\n\n for p in props:\n t = \"{}/{}\".format(nid, p.id)\n await publish(b\"{}/$name\".format(t), p.name)\n await publish(b\"{}/$datatype\".format(t), p.datatype)\n\n if p.format is not None:\n await publish(b\"{}/$format\".format(t), p.format)\n\n if p.settable is True:\n await publish(b\"{}/$settable\".format(t), TRUE)\n\n if p.retained is False:\n await publish(b\"{}/$retained\".format(t), FALSE)\n\n if p.unit:\n await publish(b\"{}/$unit\".format(t), p.unit)",
"title": ""
},
{
"docid": "7bd5fdd8f8a670811dcc578f425d90a0",
"score": "0.538621",
"text": "def pp_hexane():\n\n return PropertyPackage(phases=1, phase_names=['hexane'])",
"title": ""
},
{
"docid": "b5594c98fee33eba6ab320658ba8afcc",
"score": "0.53813326",
"text": "def add_property(self):\r\n property_type = get_valid_input(\r\n \"What type of property? \",\r\n (\"house\", \"apartment\")).lower()\r\n payment_type = get_valid_input(\r\n \"What payment type? \",\r\n (\"purchase\", \"rental\")).lower()\r\n\r\n PropertyClass = self.type_map[\r\n (property_type, payment_type)]\r\n init_args = PropertyClass.prompt_init()\r\n self.property_list.append(PropertyClass(**init_args))",
"title": ""
},
{
"docid": "0fc2d11d9719351830585eed563f5bfc",
"score": "0.53659284",
"text": "def SetProperties(self, props):",
"title": ""
},
{
"docid": "0fc2d11d9719351830585eed563f5bfc",
"score": "0.53659284",
"text": "def SetProperties(self, props):",
"title": ""
},
{
"docid": "0fc2d11d9719351830585eed563f5bfc",
"score": "0.53659284",
"text": "def SetProperties(self, props):",
"title": ""
},
{
"docid": "0fc2d11d9719351830585eed563f5bfc",
"score": "0.53659284",
"text": "def SetProperties(self, props):",
"title": ""
},
{
"docid": "886f5bcda109ffa29bc8989ba3cb8a36",
"score": "0.53629565",
"text": "def _property_to_html(P, name, classname):\n html = '<div class=\"property\" id=\"%s-%s\">' % (classname, name)\n html += '<p class=\"property-name\">%s</p>' % name\n html += _get_docstr(P) + \"</div>\"\n return html",
"title": ""
},
{
"docid": "0c404ffd86ae97396774de5e403a4c4a",
"score": "0.5362424",
"text": "def create_package_container(self, project, package, disable_build=False):\n dst_meta = '<package name=\"{}\"><title/><description/></package>'\n dst_meta = dst_meta.format(package)\n if disable_build:\n root = ET.fromstring(dst_meta)\n elm = ET.SubElement(root, 'build')\n ET.SubElement(elm, 'disable')\n dst_meta = ET.tostring(root)\n\n url = self.makeurl(['source', project, package, '_meta'])\n http_PUT(url, data=dst_meta)",
"title": ""
},
{
"docid": "b8cdf5db4fc1e4476599f9751ad56ef5",
"score": "0.5361983",
"text": "def set_properties(data):",
"title": ""
},
{
"docid": "c300625a80eac8665c01eb78f3c6d9a4",
"score": "0.53478223",
"text": "def _component_language_property():\n return {\n 'type' : 'class',\n 'name' : 'component_language_property',\n 'base' : 'shared.property',\n 'abstract' : False,\n 'doc' : 'This provides a place to include language-specific information. Every property is basically a name/value pair, where the names are things like: moduleName, reservedUnits, reservedNames (these are all examples of Fortran-specific properties).',\n 'properties' : [\n\n ],\n 'decodings' : [\n\n ]\n }",
"title": ""
},
{
"docid": "6c61f8191f2baa53a5f796d9a11cdb1e",
"score": "0.53412724",
"text": "def construct_props():\n properties = []\n for color in COLORS.keys():\n for _ in range(COLORS[color]):\n properties.append(Property(color))\n for _ in range(2):\n properties.extend([\n WildCard(\"Yellow\", \"Red\"),\n WildCard(\"Orange\", \"Purple\"),\n UberWildCard(),\n UberWildCard()\n ])\n properties.extend([\n WildCard(\"Green\", \"Blue\"),\n WildCard(\"Brown\", \"Light Blue\"),\n WildCard(\"Railroad\", \"Green\"),\n WildCard(\"Railroad\", \"Light Blue\"),\n WildCard(\"Railroad\", \"Utility\"),\n ])\n return properties",
"title": ""
},
{
"docid": "3238fc4cafd821b337d7901724636493",
"score": "0.53294027",
"text": "def register_property(property):\n PROPERTIES.append(property)\n return property",
"title": ""
},
{
"docid": "d8c20ef2067c8ddc80045548b46667fa",
"score": "0.52800715",
"text": "def generate_datapackage(self, *args, **kwargs):\n from bounos.DataPackage import DataPackage\n\n dp = DataPackage(**(self.current_state()))\n return dp",
"title": ""
},
{
"docid": "f94c5a6cf5bfa1dd80a5581f58cb188d",
"score": "0.5257494",
"text": "def _generate_property(\n class_name: str, var_decl: ast_pb2.VarDecl) -> Generator[str, None, None]:\n cpp_get_name = f'&{var_decl.cpp_get.name.cpp_name}'\n cpp_set_name = f'&{var_decl.cpp_set.name.cpp_name}'\n if not var_decl.cpp_get.name.cpp_name:\n cpp_get_name = 'nullptr'\n if not var_decl.cpp_set.name.cpp_name:\n cpp_set_name = 'nullptr'\n if var_decl.HasField('cpp_set'):\n yield (f'{class_name}.def_property(\"{var_decl.name.native}\", '\n f'{cpp_get_name}, {cpp_set_name});')\n else:\n yield (f'{class_name}.def_property_readonly(\"{var_decl.name.native}\", '\n f'&{var_decl.cpp_get.name.cpp_name});')",
"title": ""
},
{
"docid": "b6fff9f88b20d707e0806edec043d4e2",
"score": "0.52507985",
"text": "def create_xml(self, ext_uuid, property_array, title, property_kind, support_uuid = None,\n p_uuid = None, facet_type = None, facet = None, discrete = False,\n time_series_uuid = None, time_index = None, uom = None, null_value = None,\n originator = None, source = None,\n add_as_part = True, add_relationships = True, add_min_max = True,\n min_value = None, max_value = None, realization = None,\n string_lookup_uuid = None, property_kind_uuid = None,\n find_local_property_kinds = True, indexable_element = None,\n count = 1, extra_metadata = {}, const_value = None):\n\n# log.debug('creating property node for ' + title)\n # currently assumes discrete properties to be 32 bit integers and continuous to be 64 bit reals\n # also assumes property_kind is one of the standard resqml property kinds; todo: allow local p kind node as optional arg\n assert self.model is not None\n if support_uuid is None: support_uuid = self.support_uuid\n assert support_uuid is not None\n support_root = self.model.root_for_uuid(support_uuid)\n assert support_root is not None\n\n if ext_uuid is None: ext_uuid = self.model.h5_uuid()\n\n support_type = self.model.type_of_part(self.model.part_for_uuid(support_uuid))\n if indexable_element is None:\n if support_type == 'obj_IjkGridRepresentation': indexable_element = 'cells'\n elif support_type == 'obj_WellboreFrameRepresentation': indexable_element = 'nodes' # note: could be 'intervals'\n elif support_type == 'obj_BlockedWellboreRepresentation': indexable_element = 'cells'# default could be 'intervals'\n else: raise Exception('indexable element unknown for unsupported supporting representation object')\n\n if self.support is not None:\n shape_list = self.supporting_shape(indexable_element = indexable_element)\n if shape_list is not None:\n if count > 1: shape_list.append(count)\n if property_array is not None:\n assert tuple(shape_list) == property_array.shape, 'property array does not have the correct shape'\n # todo: assertions:\n # numpy data type matches discrete flag (and assumptions about precision)\n # uom are valid units for property_kind\n assert property_kind, 'missing property kind when creating xml for property'\n\n if discrete:\n if string_lookup_uuid is None: d_or_c_text = 'Discrete'\n else: d_or_c_text = 'Categorical'\n xsd_type = 'integer'\n hdf5_type = 'IntegerHdf5Array'\n else:\n d_or_c_text = 'Continuous'\n xsd_type = 'double'\n hdf5_type = 'DoubleHdf5Array'\n null_value = None\n\n p_node = self.model.new_obj_node(d_or_c_text + 'Property')\n if p_uuid is None:\n p_uuid = bu.uuid_from_string(p_node.attrib['uuid'])\n else:\n p_node.attrib['uuid'] = str(p_uuid)\n\n self.model.create_citation(root = p_node, title = title, originator = originator)\n\n rqet.create_metadata_xml(node = p_node, extra_metadata = extra_metadata)\n\n if source is not None and len(source) > 0:\n self.model.create_source(source = source, root = p_node)\n\n count_node = rqet.SubElement(p_node, ns['resqml2'] + 'Count')\n count_node.set(ns['xsi'] + 'type', ns['xsd'] + 'positiveInteger')\n count_node.text = str(count)\n\n ie_node = rqet.SubElement(p_node, ns['resqml2'] + 'IndexableElement')\n ie_node.set(ns['xsi'] + 'type', ns['resqml2'] + 'IndexableElements')\n ie_node.text = indexable_element\n\n if realization is not None and realization >= 0:\n ri_node = rqet.SubElement(p_node, ns['resqml2'] + 'RealizationIndex')\n ri_node.set(ns['xsi'] + 'type', ns['xsd'] + 'nonNegativeInteger')\n ri_node.text = str(realization)\n\n if time_series_uuid is None or time_index is None:\n related_time_series_node = None\n else:\n related_time_series_node = self.model.root(uuid = time_series_uuid)\n time_series = rts.TimeSeries(self.model, uuid = time_series_uuid)\n time_series.create_time_index(time_index, root = p_node)\n\n self.model.create_supporting_representation(support_uuid = support_uuid, root = p_node,\n title = rqet.citation_title_for_node(support_root), content_type=support_type)\n\n p_kind_node = rqet.SubElement(p_node, ns['resqml2'] + 'PropertyKind')\n p_kind_node.text = rqet.null_xml_text\n if find_local_property_kinds and property_kind not in supported_property_kind_list:\n if property_kind_uuid is None:\n pk_parts_list = self.model.parts_list_of_type('PropertyKind')\n for part in pk_parts_list:\n if self.model.citation_title_for_part(part) == property_kind:\n property_kind_uuid = self.model.uuid_for_part(part)\n break\n if property_kind_uuid is None:\n # create local property kind object and fetch uuid\n lpk = PropertyKind(self.model, title = property_kind, example_uom = uom,\n parent_property_kind = 'discrete' if discrete else 'continuous')\n lpk.create_xml()\n property_kind_uuid = lpk.uuid\n if property_kind_uuid is None:\n p_kind_node.set(ns['xsi'] + 'type', ns['resqml2'] + 'StandardPropertyKind') # todo: local prop kind ref\n kind_node = rqet.SubElement(p_kind_node, ns['resqml2'] + 'Kind')\n kind_node.set(ns['xsi'] + 'type', ns['resqml2'] + 'ResqmlPropertyKind')\n kind_node.text = property_kind\n else:\n p_kind_node.set(ns['xsi'] + 'type', ns['resqml2'] + 'LocalPropertyKind') # todo: local prop kind ref\n self.model.create_ref_node('LocalPropertyKind', property_kind, property_kind_uuid,\n content_type = 'obj_PropertyKind', root = p_kind_node)\n\n # create patch node\n const_count = None\n if const_value is not None:\n s_shape = self.supporting_shape(indexable_element = indexable_element,\n direction = facet if facet_type == 'direction' else None)\n assert s_shape is not None\n const_count = np.product(np.array(s_shape, dtype = int))\n _ = self.model.create_patch(p_uuid, ext_uuid, root = p_node,\n hdf5_type = hdf5_type,\n xsd_type = xsd_type,\n null_value = null_value,\n const_value = const_value,\n const_count = const_count)\n\n if facet_type is not None and facet is not None:\n facet_node = rqet.SubElement(p_node, ns['resqml2'] + 'Facet')\n facet_node.set(ns['xsi'] + 'type', ns['resqml2'] + 'PropertyKindFacet')\n facet_node.text = rqet.null_xml_text\n facet_type_node = rqet.SubElement(facet_node, ns['resqml2'] + 'Facet')\n facet_type_node.set(ns['xsi'] + 'type', ns['resqml2'] + 'Facet')\n facet_type_node.text = facet_type\n facet_value_node = rqet.SubElement(facet_node, ns['resqml2'] + 'Value')\n facet_value_node.set(ns['xsi'] + 'type', ns['xsd'] + 'string')\n facet_value_node.text = facet\n\n if add_min_max:\n # todo: use active cell mask on numpy min and max operations; exclude null values on discrete min max\n if const_value is not None:\n if (discrete and const_value != null_value) or (not discrete and not np.isnan(const_value)):\n if min_value is None: min_value = const_value\n if max_value is None: max_value = const_value\n elif property_array is not None:\n if discrete:\n if min_value is None:\n try:\n min_value = int(property_array.min())\n except:\n min_value = None\n log.warning('no xml minimum value set for discrete property')\n if max_value is None:\n try:\n max_value = int(property_array.max())\n except:\n max_value = None\n log.warning('no xml maximum value set for discrete property')\n else:\n if min_value is None or max_value is None:\n all_nan = np.all(np.isnan(property_array))\n if min_value is None and not all_nan:\n min_value = np.nanmin(property_array)\n if np.isnan(min_value) or min_value is ma.masked: min_value = None\n if max_value is None and not all_nan:\n max_value = np.nanmax(property_array)\n if np.isnan(max_value) or max_value is ma.masked: max_value = None\n if min_value is not None:\n min_node = rqet.SubElement(p_node, ns['resqml2'] + 'MinimumValue')\n min_node.set(ns['xsi'] + 'type', ns['xsd'] + xsd_type)\n min_node.text = str(min_value)\n if max_value is not None:\n max_node = rqet.SubElement(p_node, ns['resqml2'] + 'MaximumValue')\n max_node.set(ns['xsi'] + 'type', ns['xsd'] + xsd_type)\n max_node.text = str(max_value)\n\n if discrete:\n if string_lookup_uuid is not None:\n sl_root = self.model.root_for_uuid(string_lookup_uuid)\n assert sl_root is not None, 'string table lookup is missing whilst importing categorical property'\n assert rqet.node_type(sl_root) == 'obj_StringTableLookup', 'referenced uuid is not for string table lookup'\n self.model.create_ref_node('Lookup', self.model.title_for_root(sl_root), string_lookup_uuid,\n content_type = 'obj_StringTableLookup', root = p_node)\n else: # continuous\n if not uom:\n uom = guess_uom(property_kind, min_value, max_value, self.support, facet_type = facet_type, facet = facet)\n if not uom:\n uom = 'Euc' # todo: put RESQML base uom for quantity class here, instead of Euc\n log.warning(f'uom set to Euc for property {title} of kind {property_kind}')\n self.model.uom_node(p_node, uom)\n\n if add_as_part:\n self.model.add_part('obj_' + d_or_c_text + 'Property', p_uuid, p_node)\n if add_relationships:\n self.model.create_reciprocal_relationship(p_node, 'destinationObject', support_root, 'sourceObject')\n if property_kind_uuid is not None:\n pk_node = self.model.root_for_uuid(property_kind_uuid)\n if pk_node is not None:\n self.model.create_reciprocal_relationship(p_node, 'destinationObject', pk_node, 'sourceObject')\n if related_time_series_node is not None:\n self.model.create_reciprocal_relationship(p_node, 'destinationObject', related_time_series_node, 'sourceObject')\n if discrete and string_lookup_uuid is not None:\n self.model.create_reciprocal_relationship(p_node, 'destinationObject', sl_root, 'sourceObject')\n# ext_node = self.model.root_for_part(rqet.part_name_for_object('obj_EpcExternalPartReference', ext_uuid, prefixed = True))\n if const_value is None:\n ext_node = self.model.root_for_part(rqet.part_name_for_object('obj_EpcExternalPartReference', ext_uuid, prefixed = False))\n self.model.create_reciprocal_relationship(p_node, 'mlToExternalPartProxy', ext_node, 'externalPartProxyToMl')\n\n return p_node",
"title": ""
},
{
"docid": "54c31106fb5bf81c648f0762e4e22c88",
"score": "0.5226463",
"text": "def set_properties(params):",
"title": ""
},
{
"docid": "1eca8567524bbeedd812bd51b3e1be80",
"score": "0.5225248",
"text": "def property_list():\n return PetscManager()",
"title": ""
},
{
"docid": "97edb1e3c8c02d649d3f9f764c6c4d63",
"score": "0.52034503",
"text": "def create(connection, args):\n\n metadata = sap.adt.ADTCoreData(language='EN', master_language='EN', responsible=connection.user)\n\n package = sap.adt.Package(connection, args.name.upper(), metadata=metadata)\n package.description = args.description\n package.set_package_type('development')\n\n package.set_software_component(args.software_component)\n\n if args.app_component is not None:\n package.set_app_component(args.app_component.upper())\n\n if args.super_package is not None:\n package.super_package.name = args.super_package.upper()\n\n if args.transport_layer is not None:\n package.set_transport_layer(args.transport_layer.upper())\n\n try:\n package.create(corrnr=args.corrnr)\n except ExceptionResourceAlreadyExists as err:\n if not args.no_error_existing:\n raise err\n\n mod_log().info(err.message)",
"title": ""
},
{
"docid": "ef387f3498d89e12ec1c4e91e8d5d1a6",
"score": "0.51921713",
"text": "def _GenerateObjectDefinition(self, properties):\n if not properties: return Code()\n\n c = Code()\n c.Sblock('{')\n first = True\n for field, prop in properties.items():\n # Avoid trailing comma.\n # TODO(devlin): This will be unneeded, if/when\n # https://github.com/google/closure-compiler/issues/796 is fixed.\n if not first:\n c.Append(',', new_line=False)\n first = False\n js_type = self._TypeToJsType(prop.type_)\n if prop.optional:\n js_type = (Code().\n Append('(').\n Concat(js_type, new_line=False).\n Append('|undefined)', new_line=False))\n c.Append('%s: ' % field, strip_right=False)\n c.Concat(js_type, new_line=False)\n\n c.Eblock('}')\n\n return c",
"title": ""
},
{
"docid": "b9d472b175b76445fcc30f03bbf8ed6a",
"score": "0.5190829",
"text": "def __init__(self, properties_file: str):\n self.properties_file = properties_file",
"title": ""
},
{
"docid": "ebc8305dd3c2f6aeeb82f4a4fdad2b4e",
"score": "0.5185833",
"text": "def create_setup():\n global OUTPUTDIR\n if PROJECT_INFO.get('{PLATFORMS}', None) is None:\n PROJECT_INFO['{PLATFORMS}'] = ''\n PROJECT_INFO['{PLATFORMS}'] = \\\n [string.strip() for string in PROJECT_INFO['{PLATFORMS}'].split(',')]\n base = PDE_SETUP\n for key, value in PROJECT_INFO.items():\n base = base.replace(key, str(value))\n\n filename = os.path.join(OUTPUTDIR, 'setup.py')\n ensure_dir(filename)\n open(filename, 'w').write(base)",
"title": ""
},
{
"docid": "198dcfc4543a81529a8f1eb9e84ccf8a",
"score": "0.5164907",
"text": "def generate_property_module(mod, grouped_categories, category_subset):\n # type: (str, Dict[str, List[Tuple[int, int]]], Iterable[str]) -> Iterator[str]\n\n yield \"pub(crate) mod %s {\\n\" % mod\n for cat in sorted(category_subset):\n if cat in (\"Cc\", \"White_Space\"):\n generator = generate_small_bool_trie(\"%s_table\" % cat, grouped_categories[cat])\n else:\n generator = generate_bool_trie(\"%s_table\" % cat, grouped_categories[cat])\n\n for fragment in generator:\n yield fragment\n\n yield \" pub fn %s(c: char) -> bool {\\n\" % cat\n yield \" %s_table.lookup(c)\\n\" % cat\n yield \" }\\n\\n\"\n\n yield \"}\\n\\n\"",
"title": ""
},
{
"docid": "860665f1be340b004f3b35edef9fd52b",
"score": "0.5161001",
"text": "def get_properties():",
"title": ""
},
{
"docid": "c701b538396e23eba5372b332513d6b8",
"score": "0.515814",
"text": "def properties(self):",
"title": ""
},
{
"docid": "c701b538396e23eba5372b332513d6b8",
"score": "0.515814",
"text": "def properties(self):",
"title": ""
},
{
"docid": "c701b538396e23eba5372b332513d6b8",
"score": "0.515814",
"text": "def properties(self):",
"title": ""
},
{
"docid": "c701b538396e23eba5372b332513d6b8",
"score": "0.515814",
"text": "def properties(self):",
"title": ""
},
{
"docid": "c701b538396e23eba5372b332513d6b8",
"score": "0.515814",
"text": "def properties(self):",
"title": ""
},
{
"docid": "49ae4269169b66bc871bd4472ab6e275",
"score": "0.5129711",
"text": "def add_property(self, prop):\n props_node = self._root.find('properties')\n props_node.append(prop.node)\n prop.parent = self",
"title": ""
},
{
"docid": "65e01c8b4026161ca33ea7caa86fa04c",
"score": "0.51233965",
"text": "def _set_standard_properties_descriptive(c, p_tree):\n _create_property(p_tree,\n c.description,\n 'Description',\n 'High-level component description')\n _create_property(p_tree,\n c.short_name,\n 'Short Name',\n 'Abbreviated component name')\n _create_property(p_tree,\n c.long_name,\n 'Long Name',\n 'Full component name')",
"title": ""
},
{
"docid": "d3ab4442385b7ffddbfd6651001d7f64",
"score": "0.51226103",
"text": "def write_properties_app(workbook):\n worksheets_count = len(workbook.worksheets)\n root = Element('Properties', {'xmlns': 'http://schemas.openxmlformats.org/officeDocument/2006/extended-properties',\n 'xmlns:vt': 'http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes'})\n SubElement(root, 'Application').text = 'Microsoft Excel'\n SubElement(root, 'DocSecurity').text = '0'\n SubElement(root, 'ScaleCrop').text = 'false'\n SubElement(root, 'Company')\n SubElement(root, 'LinksUpToDate').text = 'false'\n SubElement(root, 'SharedDoc').text = 'false'\n SubElement(root, 'HyperlinksChanged').text = 'false'\n SubElement(root, 'AppVersion').text = '12.0000'\n\n # heading pairs part\n heading_pairs = SubElement(root, 'HeadingPairs')\n vector = SubElement(heading_pairs, 'vt:vector',\n {'size': '2', 'baseType': 'variant'})\n variant = SubElement(vector, 'vt:variant')\n SubElement(variant, 'vt:lpstr').text = 'Worksheets'\n variant = SubElement(vector, 'vt:variant')\n SubElement(variant, 'vt:i4').text = '%d' % worksheets_count\n\n # title of parts\n title_of_parts = SubElement(root, 'TitlesOfParts')\n vector = SubElement(title_of_parts, 'vt:vector',\n {'size': '%d' % worksheets_count, 'baseType': 'lpstr'})\n for ws in workbook.worksheets:\n SubElement(vector, 'vt:lpstr').text = '%s' % ws.title\n return get_document_content(root)",
"title": ""
},
{
"docid": "c14189d04b75d08c90c805078e0d87d7",
"score": "0.51221704",
"text": "def _make_configurable(pkg_name, name, **kwds):\n kwds['name']=name\n return Configurable(pkg_name=pkg_name, **kwds)",
"title": ""
},
{
"docid": "382223d11a4155186e4eedee03f51969",
"score": "0.51198226",
"text": "def create_prop(self, class_, key, uselist, callable_, typecallable, **kwargs):\n return InstrumentedAttribute(self, key, uselist, callable_, typecallable, **kwargs)",
"title": ""
},
{
"docid": "229ee61386b858ab1912f9cd7fb463df",
"score": "0.5106309",
"text": "def createNewPackage(self):\n domain = q.console.askChoice(q.qp.getDomainNames(), \"Please select a domain\")\n q.qp.getDomainObject(domain)._ensureDomainCanBeUpdated()\n\n name = q.console.askString(\"Please provide a name\")\n version = q.console.askString(\"Please provide a version\",\"1.0\")\n descr = q.console.askString(\"Please provide a description\",\"\")\n supportedPlatforms = None\n while not supportedPlatforms:\n supportedPlatforms = q.console.askChoiceMultiple(q.enumerators.PlatformType.ALL, 'Please enumerate the supported platforms')\n qp = q.qp.createNewQPackage(domain, name, version, descr, supportedPlatforms)\n res = QPackageIObject4(qp)\n self._attachLastPackages([res])\n return res",
"title": ""
},
{
"docid": "e5a7859e4032374ca86651c3dd7b36ea",
"score": "0.51015234",
"text": "def setup_properties(self):\n for i in zip(self.nbhds, self.colors):\n prop = dict.fromkeys(['nbhd', 'color'])\n prop['nbhd'] = i[0]\n prop['color'] = i[1]\n self.properties.append(prop)",
"title": ""
},
{
"docid": "5bb72c817582dd7275d2f3872190b67b",
"score": "0.509846",
"text": "def build_property(prop):\n\n def dbus_func(proxy_object):\n \"\"\"\n The property getter.\n \"\"\"\n return proxy_object.Get(\n spec.INTERFACE_NAME,\n prop.name,\n dbus_interface=dbus.PROPERTIES_IFACE\n )\n\n return dbus_func",
"title": ""
},
{
"docid": "f830f19670bca587727b7ff4daa98719",
"score": "0.5094645",
"text": "def AddProperty(self,pProp):\n pass",
"title": ""
},
{
"docid": "966f20fc400dbcc8366d5d288b22daf0",
"score": "0.5061939",
"text": "def __init__(self,properties):\n\t\tself.processProperties(properties)",
"title": ""
},
{
"docid": "c276dcd16b35f39216b7b0d7e108cf8a",
"score": "0.50554",
"text": "def getProperties():",
"title": ""
},
{
"docid": "4f14595768f7964e640a75ec694cb88e",
"score": "0.5055253",
"text": "def __init__(self, dict=None, port=None, varTypeName=ENERGY_VAR):\n dict.__init__(self, dict)\n\n self[varTypeName] = BasicProperty(varTypeName, port)",
"title": ""
},
{
"docid": "b09952192af0911ebf45a9ed56654be5",
"score": "0.50337636",
"text": "def AddPropertyView(self,pClassName,pPropertyName,pHierarchy):\n pass",
"title": ""
},
{
"docid": "1f7bf81b1c021362bee2456ceea6858f",
"score": "0.503028",
"text": "def create(self, vals):\n if vals.get('package_seq', _('New')) == _('New'):\n vals['package_seq'] = self.env['ir.sequence'].next_by_code('tour.packages.sequence') or _('New')\n result = super(TourPackages, self).create(vals)\n return result",
"title": ""
},
{
"docid": "5a890ea83172836725eb3da09c212ced",
"score": "0.5029791",
"text": "def test_properties():\n inst = CompoundDefinition(\n uid=\"test_uid\",\n name=\"test_name\",\n author=\"test_author\",\n version=\"test_version\",\n path=\"some_path\",\n )\n assert inst.uid == \"test_uid\"\n assert inst.name == \"test_name\"\n assert inst.author == \"test_author\"\n assert inst.version == \"test_version\"\n assert inst.path == \"some_path\"",
"title": ""
},
{
"docid": "4924a3fdc2405cd4e85e4a5b512a6b48",
"score": "0.50284183",
"text": "def __init__(self,\r\n signature_package_formats=None,\r\n pades_settings=None,\r\n additional_properties = {}):\r\n\r\n # Initialize members of the class\r\n self.signature_package_formats = signature_package_formats\r\n self.pades_settings = pades_settings\r\n\r\n # Add additional model properties to the instance\r\n self.additional_properties = additional_properties",
"title": ""
},
{
"docid": "9c35269f746f83dc5fc5b22517397862",
"score": "0.50249684",
"text": "def test_generate_package(self):\n m = {\n # normal module syntax should just work.\n \"caliban.util\":\n u.module_package(\"caliban.util\"),\n\n # This one is controversial, maybe... if something exists as a module\n # if you replace slashes with dots, THEN it will also parse as a\n # module. If it exists as a file in its own right this won't happen.\n #\n # TODO get a test in for this final claim using temp directories.\n \"caliban/util\":\n u.module_package(\"caliban.util\"),\n\n # root scripts or packages should require the entire local directory.\n \"setup\":\n u.module_package(\"setup\"),\n \"cake.py\":\n u.script_package(\"cake.py\", \"python\"),\n\n # This is busted but should still parse.\n \"face.cake.py\":\n u.script_package(\"face.cake.py\", \"python\"),\n\n # Paths into directories should parse properly into modules and include\n # the root as their required package to import.\n \"face/cake.py\":\n u.script_package(\"face/cake.py\", \"python\"),\n\n # Deeper nesting works.\n \"face/cake/cheese.py\":\n u.script_package(\"face/cake/cheese.py\", \"python\"),\n\n # Other executables work.\n \"face/cake/cheese.sh\":\n u.script_package(\"face/cake/cheese.sh\"),\n }\n for k in m:\n self.assertEqual(u.generate_package(k), m[k])",
"title": ""
},
{
"docid": "721636cc0f5fe569b6355e83a7d1168a",
"score": "0.5013457",
"text": "def write_nexus_property_generating_filename(self, part, directory,\n use_title_for_keyword = False, headers = True,\n columns = 20, decimals = 3, # note: decimals only applicable to real numbers\n blank_line_after_i_block = True, blank_line_after_j_block = False,\n space_separated = False, # default is tab separated\n use_binary = False, binary_only = False,\n nan_substitute_value = None):\n\n title = self.citation_title_for_part(part).replace(' ', '_')\n if use_title_for_keyword: keyword = title\n else: keyword = None\n fname = title\n facet_type = self.facet_type_for_part(part)\n if facet_type is not None:\n fname += '_' + facet_type.replace(' ', '_') + '_' + self.facet_for_part(part).replace(' ', '_')\n time_index = self.time_index_for_part(part)\n if time_index is not None:\n fname += '_t_' + str(time_index)\n # could add .dat extension\n self.write_nexus_property(part, os.path.join(directory, fname), keyword = keyword,\n headers = headers, append = False,\n columns = columns, decimals = decimals,\n blank_line_after_i_block = blank_line_after_i_block,\n blank_line_after_j_block = blank_line_after_j_block,\n space_separated = space_separated,\n use_binary = use_binary, binary_only = binary_only,\n nan_substitute_value = nan_substitute_value)",
"title": ""
},
{
"docid": "aab729d8e7d84ad98d70e902563009db",
"score": "0.50016415",
"text": "def add_property(self, prop):\r\n self.properties[prop.name] = prop",
"title": ""
},
{
"docid": "aab729d8e7d84ad98d70e902563009db",
"score": "0.50016415",
"text": "def add_property(self, prop):\r\n self.properties[prop.name] = prop",
"title": ""
},
{
"docid": "be213e282592e2234725a4be1039a5f8",
"score": "0.4998315",
"text": "def test_schemaproperty_class(self):\n # loop through all properties\n for _prop in self.props:\n # test get_property\n sp = self.se.get_property(_prop.name)\n self.assertEqual(sp.prefix, \"schema\")\n # test describe function\n describe = sp.describe()\n sp = self.se.get_property(_prop.name, output_type=\"curie\")\n # test describe function\n describe = sp.describe()\n sp = self.se.get_property(_prop.name, output_type=\"uri\")\n # test describe function\n describe = sp.describe()\n sp = self.se.get_property(_prop.name, output_type=\"label\")\n # test describe function\n describe = sp.describe()\n del describe",
"title": ""
},
{
"docid": "f3a3a27c0a519e3730dc4740843accad",
"score": "0.49915418",
"text": "def add_property(self, **args):\n self._convertargs(args)\n if \"medium_id\" in args:\n try:\n medium = self.tree['media']['children'][args['medium_id']]\n except KeyError:\n self.tree['media']['children'][args['medium_id']] = {\n 'tag': 'medium',\n 'text': '',\n 'attr': {\n 'id': args['medium_id']},\n 'children': {}\n }\n medium = self.tree['media']['children'][args['medium_id']]\n if \"phase_type\" in args:\n if not 'phases' in medium['children']:\n medium['children']['phases'] = {\n 'tag': 'phases',\n 'text': '',\n 'attr': {},\n 'children': {}\n }\n try:\n phase_ = medium['children']['phases']['children'][\n args['phase_type']]\n except KeyError:\n medium['children']['phases']['children'][\n args['phase_type']] = {\n 'tag': 'phase',\n 'text': '',\n 'attr': {},\n 'children': {}\n }\n phase_ = medium['children']['phases']['children'][\n args['phase_type']]\n phase_['children'][args['phase_type']] = {\n 'tag': 'type',\n 'text': args['phase_type'],\n 'attr': {},\n 'children': {}\n }\n phase_['children']['properties'] = {\n 'tag': 'properties',\n 'text': '',\n 'attr': {},\n 'children': {}\n }\n else:\n try:\n _ = medium['children']['properties']\n except KeyError:\n medium['children']['properties'] = {\n 'tag': 'properties',\n 'text': '',\n 'attr': {},\n 'children': {}\n }\n phase_ = medium\n phase = phase_['children']['properties']['children']\n phase[args['name']] = {\n 'tag': 'property',\n 'text': '',\n 'attr': {},\n 'children': {}\n }\n base_property_param = [\"name\", \"type\"]\n for param in base_property_param:\n phase[args['name']]['children'][param] = {\n 'tag': param,\n 'text': args[param],\n 'attr': {},\n 'children': {}\n }\n try:\n if args['type'] == \"Linear\":\n phase[args['name']]['children'].update(self._generate_linear_property(args))\n elif args['type'] == \"Exponential\":\n phase[args['name']]['children'].update(self._generate_exponential_property(args))\n elif args['type'] == \"Function\":\n phase[args['name']]['children'].update(self._generate_function_property(args))\n else:\n phase[args['name']]['children'].update(self._generate_generic_property(args))\n except KeyError:\n print(\"Material property parameters incomplete for\")\n if \"phase_type\" in args:\n print(f\"Medium {args['medium_id']}->{args['phase_type']}->{args['name']}[{args['type']}]\")\n else:\n print(f\"Medium {args['medium_id']}->{args['name']}[{args['type']}]\")",
"title": ""
},
{
"docid": "3b48786122c5cf177683cb81542238b5",
"score": "0.4976444",
"text": "def SetupNetworkVarProp(ent, name, propname):\n cls = ent.__class__\n\n # Skip if the property is already setup\n try:\n p = getattr(cls, name)\n if type(p) == property:\n return\n except AttributeError:\n pass\n\n # Define the property\n getter = lambda self: getattr(self, propname)\n def setter(self, value):\n setattr(self, propname, value)\n p = property(getter, setter, None, '%s networkvar_prop property' % name)\n setattr(cls, name, p)",
"title": ""
},
{
"docid": "15dd14f637879aa8bcaf4ef7b215dd1e",
"score": "0.49705702",
"text": "def CreatePropertyList(self,pObject,pViewType,pName):\n pass",
"title": ""
},
{
"docid": "bbff4866955e207112ee2346bef847be",
"score": "0.49650937",
"text": "def _property_create_dict(header, data):\n prop = dict(zip(header, _merge_last(data, len(header))))\n prop[\"name\"] = _property_normalize_name(prop[\"property\"])\n prop[\"type\"] = _property_detect_type(prop[\"name\"], prop[\"values\"])\n prop[\"edit\"] = from_bool(prop[\"edit\"])\n if \"inherit\" in prop:\n prop[\"inherit\"] = from_bool(prop[\"inherit\"])\n del prop[\"property\"]\n return prop",
"title": ""
},
{
"docid": "2f077c25fd91f392796d892f2794ba42",
"score": "0.49541387",
"text": "def make_value_from_datastore(self, value):\n contents = super(PubspecProperty, self).make_value_from_datastore(value)\n return Pubspec(contents)",
"title": ""
},
{
"docid": "e6059ef4be89cc4c059cd0f3e885f412",
"score": "0.49530232",
"text": "def fill_proj_properties(proj_ext, meta_links, product_meta):\n # Read meta file\n links = meta_links.create_product_asset()\n root = XmlElement.from_file(links[0][1].href)\n\n proj_ext.epsg = 4326\n\n proj_ext.geometry = product_meta.geometry\n\n proj_ext.bbox = product_meta.bbox\n\n x_size = int(root.findall(\".//numberOfSamples\")[0].text)\n y_size = int(root.findall(\".//numberOfLines\")[0].text)\n\n proj_ext.shape = [x_size, y_size]",
"title": ""
},
{
"docid": "89acf1b90493d617c8b82d54bb184c74",
"score": "0.49511984",
"text": "def __init__(self, name, parent, properties):\n self.name = name\n self.parent = parent\n self.properties = [Property(self, 'id', BaseType(None, 'Long'))]\n self.properties.extend(properties)",
"title": ""
},
{
"docid": "8dc75630291b48ce2ec35311c033f7ee",
"score": "0.49475542",
"text": "def test_properties(self):\n self.setUp_distribution_with_bootstrap(\n Bootstrap().get_bootstrap(\"sdl2\", self.ctx)\n )\n distribution = self.ctx.bootstrap.distribution\n self.assertEqual(self.ctx, distribution.ctx)\n expected_repr = (\n \"<Distribution: name test_prj with recipes (python3, kivy)>\"\n )\n self.assertEqual(distribution.__str__(), expected_repr)\n self.assertEqual(distribution.__repr__(), expected_repr)",
"title": ""
},
{
"docid": "4e234013b8fcdbfe3021d430a9c0b315",
"score": "0.49392608",
"text": "def _build_code_property(tf_properties: dict, resource: TFResource) -> Any:\n filename = tf_properties.get(\"filename\")\n if filename:\n return filename\n\n code = {}\n tf_cfn_prop_names = [\n (\"s3_bucket\", \"S3Bucket\"),\n (\"s3_key\", \"S3Key\"),\n (\"image_uri\", \"ImageUri\"),\n (\"s3_object_version\", \"S3ObjectVersion\"),\n ]\n for tf_prop_name, cfn_prop_name in tf_cfn_prop_names:\n tf_prop_value = tf_properties.get(tf_prop_name)\n if tf_prop_value is not None:\n code[cfn_prop_name] = tf_prop_value\n\n package_type = tf_properties.get(\"package_type\", ZIP)\n\n # Get the S3 Bucket details from configuration in case if the customer is creating the S3 bucket in the tf project\n if package_type == ZIP and (\"S3Bucket\" not in code or \"S3Key\" not in code or \"S3ObjectVersion\" not in code):\n s3_bucket_tf_config_value = _resolve_resource_attribute(resource, \"s3_bucket\")\n s3_key_tf_config_value = _resolve_resource_attribute(resource, \"s3_key\")\n s3_object_version_tf_config_value = _resolve_resource_attribute(resource, \"s3_object_version\")\n if \"S3Bucket\" not in code and s3_bucket_tf_config_value:\n code[\"S3Bucket\"] = REMOTE_DUMMY_VALUE\n code[\"S3Bucket_config_value\"] = s3_bucket_tf_config_value\n if \"S3Key\" not in code and s3_key_tf_config_value:\n code[\"S3Key\"] = REMOTE_DUMMY_VALUE\n code[\"S3Key_config_value\"] = s3_key_tf_config_value\n if \"S3ObjectVersion\" not in code and s3_object_version_tf_config_value:\n code[\"S3ObjectVersion\"] = REMOTE_DUMMY_VALUE\n code[\"S3ObjectVersion_config_value\"] = s3_object_version_tf_config_value\n\n # Get the Image URI details from configuration in case if the customer is creating the ecr repo in the tf project\n if package_type == IMAGE and \"ImageUri\" not in code:\n image_uri_tf_config_value = _resolve_resource_attribute(resource, \"image_uri\")\n if image_uri_tf_config_value:\n code[\"ImageUri\"] = REMOTE_DUMMY_VALUE\n\n return code",
"title": ""
},
{
"docid": "d646ad0cde35065c9090543e9d4827ec",
"score": "0.49339727",
"text": "def AddProperty(self,pProperty):\n pass",
"title": ""
},
{
"docid": "4eb37e4b62cb856089a8a76b43d8e6f3",
"score": "0.49304262",
"text": "def define_metadata(cls, obj):\n obj.add_properties(\n {'flow_mol': {'method': None},\n 'mole_frac_comp': {'method': None},\n 'temperature': {'method': None},\n 'pressure': {'method': None},\n 'mw_comp': {'method': None},\n 'dens_mol': {'method': None},\n 'enth_mol': {'method': '_enth_mol'}})\n\n obj.add_default_units({'time': pyunits.s,\n 'length': pyunits.m,\n 'mass': pyunits.kg,\n 'amount': pyunits.mol,\n 'temperature': pyunits.K})",
"title": ""
},
{
"docid": "ac44adb25e62c355dfc963fa28360d0f",
"score": "0.49199343",
"text": "def create_prop_with_polygons(s, inputs, logger):\n dict_prop = inputs.get('prop')\n if dict_prop is None:\n return\n logger.info(\"Processing prop outputs...\")\n expected_items = ('default', 'polygons')\n output_dir = inputs['output_dir']\n for fname, item in dict_prop.items():\n check_and_suggest(item, expected_items)\n polygons = item.get('polygons', [])\n if polygons is not None:\n polygon_items = ('name', 'vertices', 'type', 'attribute')\n for polygon in polygons:\n check_and_suggest(polygon, polygon_items)\n for fname, item in dict_prop.items():\n if fname is None:\n logger.warning(\"No filename is given in one of prop\")\n continue\n fname = ensure_outdir(inputs['output_dir'],fname)\n polygons = item.get('polygons', [])\n default = item.get('default')\n logger.info(\"Creating %s...\" % fname)\n s.create_prop_partitioning(fname, polygons, default)",
"title": ""
},
{
"docid": "a9122106a079dd37e791d9ced9058811",
"score": "0.49177235",
"text": "def internal_property(name, value):\n return Property(name, value, True, True, True, True)",
"title": ""
},
{
"docid": "5a34298c5aa730e1d3dbcf0db07bd4b1",
"score": "0.49145165",
"text": "def MergeProperties(self, properties):",
"title": ""
},
{
"docid": "62b1af29505dd41708cc0c3bc8dcd645",
"score": "0.48948646",
"text": "def make_package_directory(self) -> types.TaskDict:\n task = self.basic_task\n mkdir = directory.Mkdir(directory=self.rootdir).task\n task.update(\n {\n \"name\": self._get_task_name(self.MKDIR_TASK_NAME),\n \"doc\": \"Create directory for {}.\".format(self.name),\n \"title\": mkdir[\"title\"],\n \"actions\": mkdir[\"actions\"],\n \"uptodate\": mkdir[\"uptodate\"],\n \"targets\": mkdir[\"targets\"],\n }\n )\n return task",
"title": ""
},
{
"docid": "3bc83f58e68611d3e0637b691a00573f",
"score": "0.489398",
"text": "def create_setup(self):\r\n vprog_name = self.vpkg_name.replace(\"_\", \"-\") \r\n vsetup_string = self.trim_lines(f\"\"\"\r\n #!/usr/bin/python\r\n from setuptools import setup, find_packages\r\n\r\n setup(name='{self.vpkg_name}',\r\n version='0.0.1',\r\n description='{self.vpkg_desc}',\r\n url='https://github.com/{self.vpkg_git_account}/{self.vpkg_name}',\r\n author='{self.vpkg_author}',\r\n author_email='{self.vpkg_email}',\r\n license='{self.vpkg_license}',\r\n python_requires=\">={self.vpkg_py_version}\",\r\n packages=['{self.vpkg_name}'],\r\n entry_points = {{\r\n 'console_scripts': [\r\n '{vprog_name}={self.vpkg_name}.__main__:main'\r\n ]\r\n }},\r\n install_requires=[])\r\n \"\"\")\r\n vsetup_path = os.path.join(self.vpkg_source, \"setup.py\")\r\n open(vsetup_path, \"w+\").write(vsetup_string)",
"title": ""
},
{
"docid": "189997e436b537b1dff616944b840e34",
"score": "0.48889285",
"text": "def get_properties(self):\n data_format = {\"format\": (\"NetCDF/%s\" % self.convention)}\n return product.Properties(temporal=self.get_temporal(),\n filesystem=self.get_filesystem(self.fpath),\n spatial=self.get_geospatial(),\n data_format=data_format,\n parameters=self.get_parameters())",
"title": ""
},
{
"docid": "2892e3eca10f422f7aacff36d55fcbf1",
"score": "0.48808363",
"text": "def PropertyAdd(self,pProperty):\n pass",
"title": ""
},
{
"docid": "281aae93de4acc8dc99d32c00f7d0bd7",
"score": "0.48765546",
"text": "def create_source_folder(self):\r\n vinit_path = os.path.join(self.vpkg_subsource, \"__init__.py\")\r\n open(vinit_path, \"w+\").write(\"\")\r\n \r\n # Create __main__ entry point.\r\n vmain_path = os.path.join(self.vpkg_subsource, \"__main__.py\")\r\n vclass_name = self.underscore_to_camelcase(self.vpkg_name)\r\n vmain_text = self.trim_lines(f\"\"\"\r\n \\\"\\\"\\\"{self.break_string(self.vpkg_desc, 80)}\\\"\\\"\\\"\"\r\n #!/usr/bin/python\r\n import sys\r\n import argparse\r\n\r\n from {self.vpkg_name}.{vclass_name} import {vclass_name}\r\n\r\n\r\n def main():\r\n \\\"\\\"\\\"Run CLI for {self.vpkg_name.replace(\"_\", \"-\")}.\\\"\\\"\\\"\r\n parser = argparse.ArgumentParser(\r\n prog=\"{self.vpkg_name.replace(\"_\", \"-\")}\",\r\n description=\"{self.vpkg_desc}\"\r\n )\r\n\r\n args = parser.parse_args()\r\n\r\n\r\n if __name__ == \"__main__\":\r\n # Use this entry point to test the package locally.\r\n main()\r\n \"\"\")\r\n open(vmain_path, \"w+\").write(vmain_text)\r\n \r\n # Create main class.\r\n vclass_path = os.path.join(self.vpkg_subsource, vclass_name+\".py\")\r\n vclass_text = self.trim_lines(f\"\"\"\r\n class {vclass_name}:\r\n \\\"\\\"\\\"Enter class description.\\\"\\\"\\\"\r\n pass\r\n \"\"\")\r\n open(vclass_path, \"w+\").write(vclass_text)",
"title": ""
},
{
"docid": "cc9d048f17d36513256ec8c910c405ef",
"score": "0.4872328",
"text": "def GetProperties(self):",
"title": ""
},
{
"docid": "cc9d048f17d36513256ec8c910c405ef",
"score": "0.4872328",
"text": "def GetProperties(self):",
"title": ""
},
{
"docid": "cc9d048f17d36513256ec8c910c405ef",
"score": "0.4872328",
"text": "def GetProperties(self):",
"title": ""
},
{
"docid": "cc9d048f17d36513256ec8c910c405ef",
"score": "0.4872328",
"text": "def GetProperties(self):",
"title": ""
},
{
"docid": "8bf9621b55e753852f82d6b07891df75",
"score": "0.48694023",
"text": "def register_props():\n\n bpy.types.Scene.batchapps_assets = \\\n bpy.props.PointerProperty(type=AssetProps)\n\n bpy.app.handlers.load_post.append(on_load)\n\n return bpy.context.scene.batchapps_assets",
"title": ""
},
{
"docid": "b5aee5b730be1bea3cc1c034e925f50b",
"score": "0.4868495",
"text": "def init(path, package_name):\n package_path = os.path.join(path, package_name)\n return dpm.package.Package.create_on_disk(package_path)",
"title": ""
},
{
"docid": "d80e2a4f57cc8f7679874528eacae639",
"score": "0.48666662",
"text": "def test_write_properties_for_association(filename, element_factory):\n patron = element_factory.create(UML.Class)\n patron.ownedAttribute = element_factory.create(UML.Property)\n a = patron.ownedAttribute[0]\n a.name = \"libraryCard\"\n type_value = a.typeValue = \"Card\"\n a.upperValue = \"1\"\n a.aggregation = \"composite\"\n upper = f\", upper={a.upperValue}\"\n composite = \", composite=True\"\n lower = opposite = \"\"\n\n write_properties(patron, filename)\n\n assert (\n filename.data\n == f'{patron.name}.{a.name} = association(\"{a.name}\", {type_value}{lower}{upper}{composite}{opposite})\\n'\n )",
"title": ""
},
{
"docid": "83c3a5ff0c068cc44a5d1a848a0ef0c7",
"score": "0.48592654",
"text": "def __init__(self, name, properties):\n self.name = name\n self.properties = properties[\"properties\"]\n self.callback = properties[\"callback\"]",
"title": ""
},
{
"docid": "659188562272799497e32d71363f0af5",
"score": "0.48536897",
"text": "def generate_structurelib():",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="703"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">5c1b4edc37cf0c9d7eaae82e09ff2489</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">This method sets self.country_code from given locale</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "09124be737d4905ae4c97bb5cff1c361",
"score": "0.6249225",
"text": "def get_country_code_from_locale(self):\n regex_pattern = re.compile('[-_](.*$)', re.U)\n match = regex_pattern.findall(self.locale)\n if match:\n return match[0].upper()\n else:\n return 'IN'",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "2cd480f0e5b77b98260f1207398ba917",
"score": "0.7170048",
"text": "def country_code(self, country_code):\n\n self._country_code = country_code",
"title": ""
},
{
"docid": "2cd480f0e5b77b98260f1207398ba917",
"score": "0.7170048",
"text": "def country_code(self, country_code):\n\n self._country_code = country_code",
"title": ""
},
{
"docid": "2cd480f0e5b77b98260f1207398ba917",
"score": "0.7170048",
"text": "def country_code(self, country_code):\n\n self._country_code = country_code",
"title": ""
},
{
"docid": "acb2f6564a96522c23325aee007de0e6",
"score": "0.6968256",
"text": "def countrycode(self, countrycode):\n\n self._countrycode = countrycode",
"title": ""
},
{
"docid": "acb2f6564a96522c23325aee007de0e6",
"score": "0.6968256",
"text": "def countrycode(self, countrycode):\n\n self._countrycode = countrycode",
"title": ""
},
{
"docid": "98083a8289dfd6f1a7225d28e49250f5",
"score": "0.66345316",
"text": "def country(self, value):\n assert isinstance(value, str) or value is None\n self._country = value",
"title": ""
},
{
"docid": "e769a967e7744a6ef88d3329a7e6b52b",
"score": "0.66000664",
"text": "def set_country(self, country=None):\n mask_country = self.data['country'] == country\n self.country = self.data[mask_country].copy()\n self.country = self.country.set_index('date').sort_index()",
"title": ""
},
{
"docid": "6e6a8e40b0578397d21b85c04a9a0a64",
"score": "0.6580102",
"text": "def country(self, country):\n\n self._country = country",
"title": ""
},
{
"docid": "6e6a8e40b0578397d21b85c04a9a0a64",
"score": "0.6580102",
"text": "def country(self, country):\n\n self._country = country",
"title": ""
},
{
"docid": "6e6a8e40b0578397d21b85c04a9a0a64",
"score": "0.6580102",
"text": "def country(self, country):\n\n self._country = country",
"title": ""
},
{
"docid": "6e6a8e40b0578397d21b85c04a9a0a64",
"score": "0.6580102",
"text": "def country(self, country):\n\n self._country = country",
"title": ""
},
{
"docid": "6e6a8e40b0578397d21b85c04a9a0a64",
"score": "0.6580102",
"text": "def country(self, country):\n\n self._country = country",
"title": ""
},
{
"docid": "6011a4a49187adc3bdaff2666f6f960f",
"score": "0.65761197",
"text": "def set_country(query):\n chat_id = query.message.chat.id\n country = query.data.split(':')[1]\n db_funcs.set_country(chat_id, country)\n bot.answer_callback_query(query.id, 'Country changed on: ' + country)",
"title": ""
},
{
"docid": "ebaeda20fabd044bb839071411fa6960",
"score": "0.6339169",
"text": "def country_code(self):\n return self.__country_code",
"title": ""
},
{
"docid": "d7719b598269ec478586b369cb16ee4e",
"score": "0.63295424",
"text": "def country_code(self) -> str:\n return pulumi.get(self, \"country_code\")",
"title": ""
},
{
"docid": "de1cf727620bb12dea145f11a46906aa",
"score": "0.6323459",
"text": "def country_code(self) -> str:\n return self.__country_code",
"title": ""
},
{
"docid": "de1cf727620bb12dea145f11a46906aa",
"score": "0.6323459",
"text": "def country_code(self) -> str:\n return self.__country_code",
"title": ""
},
{
"docid": "5080b2d36e9ce470a4a4d1610b0f7cca",
"score": "0.6266199",
"text": "def language_iso_code(self, language_iso_code):\n\n self._language_iso_code = language_iso_code",
"title": ""
},
{
"docid": "91fcce453225bfee5e6abdadc191f103",
"score": "0.62659746",
"text": "def merchant_country_code(self, merchant_country_code):\n\n self._merchant_country_code = merchant_country_code",
"title": ""
},
{
"docid": "b0eba246af7d44e66c6ffd8978cf99c2",
"score": "0.6173593",
"text": "def country_name(self, country_name):\n\n self._country_name = country_name",
"title": ""
},
{
"docid": "b5a92493d89bfe8e1a746e6e3d6be20e",
"score": "0.600749",
"text": "def locale_filter(self, locale_filter):\n\n self._locale_filter = locale_filter",
"title": ""
},
{
"docid": "a552eed9d1a77fcea4865f3904c1765f",
"score": "0.59766155",
"text": "def vat_country_code(self, vat_country_code: Country):\n\n self._vat_country_code = vat_country_code",
"title": ""
},
{
"docid": "0caba582a86227f36637b4bf0179cbfe",
"score": "0.5912412",
"text": "def init(locale):\n global _locale\n _locale = locale",
"title": ""
},
{
"docid": "72a01af9decb0110ebf4f0eb8fbe7ebe",
"score": "0.59068143",
"text": "def country_code(self):\n return self.city.country_code if self.city else None",
"title": ""
},
{
"docid": "d21315d26608e4acb3443317447e46cf",
"score": "0.5865132",
"text": "def inc_country(self, inc_country):\n\n self._inc_country = inc_country",
"title": ""
},
{
"docid": "e1ecc991a7c094242ad21d4178a79e6f",
"score": "0.5863276",
"text": "def country_id(self, country_id):\n\n self._country_id = country_id",
"title": ""
},
{
"docid": "d831d9d733168d2cc380e1ac34919079",
"score": "0.58616495",
"text": "def get_country_code(self):\n\n return self.country_code",
"title": ""
},
{
"docid": "d831d9d733168d2cc380e1ac34919079",
"score": "0.58616495",
"text": "def get_country_code(self):\n\n return self.country_code",
"title": ""
},
{
"docid": "d831d9d733168d2cc380e1ac34919079",
"score": "0.58616495",
"text": "def get_country_code(self):\n\n return self.country_code",
"title": ""
},
{
"docid": "d831d9d733168d2cc380e1ac34919079",
"score": "0.58616495",
"text": "def get_country_code(self):\n\n return self.country_code",
"title": ""
},
{
"docid": "d831d9d733168d2cc380e1ac34919079",
"score": "0.58616495",
"text": "def get_country_code(self):\n\n return self.country_code",
"title": ""
},
{
"docid": "d831d9d733168d2cc380e1ac34919079",
"score": "0.58616495",
"text": "def get_country_code(self):\n\n return self.country_code",
"title": ""
},
{
"docid": "d831d9d733168d2cc380e1ac34919079",
"score": "0.58616495",
"text": "def get_country_code(self):\n\n return self.country_code",
"title": ""
},
{
"docid": "d831d9d733168d2cc380e1ac34919079",
"score": "0.58616495",
"text": "def get_country_code(self):\n\n return self.country_code",
"title": ""
},
{
"docid": "71871310710d48298e10f3640320e90a",
"score": "0.5844202",
"text": "def country_code_alpha2(self, country_code_alpha2):\n\n self._country_code_alpha2 = country_code_alpha2",
"title": ""
},
{
"docid": "a03a8eaf7464e28fcfe4982e526e7146",
"score": "0.5797144",
"text": "def setLocale(self, locale):\n raise SAXNotSupportedException(\"Locale support not implemented\")",
"title": ""
},
{
"docid": "6fadd201c247df791df732c58f50eed5",
"score": "0.5751201",
"text": "def country_code(self, country_code):\n if country_code is None:\n raise ValueError(\"Invalid value for `country_code`, must not be `None`\") # noqa: E501\n\n self._country_code = country_code",
"title": ""
},
{
"docid": "6fadd201c247df791df732c58f50eed5",
"score": "0.5751201",
"text": "def country_code(self, country_code):\n if country_code is None:\n raise ValueError(\"Invalid value for `country_code`, must not be `None`\") # noqa: E501\n\n self._country_code = country_code",
"title": ""
},
{
"docid": "2484171ea043aec4d65e071f780ad06e",
"score": "0.57445806",
"text": "def location_country(self, location_country):\n\n self._location_country = location_country",
"title": ""
},
{
"docid": "f68c7783f46061d7ee455107b626cb18",
"score": "0.57023984",
"text": "def locale_id(self, locale_id: str):\n\n self._locale_id = locale_id",
"title": ""
},
{
"docid": "564d220b5df63bbdb064c3804fb85c6b",
"score": "0.56817794",
"text": "def set_culture(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "7cc6a53b8e4dab80add467c6517dba05",
"score": "0.56801146",
"text": "def _get_country_code(self):\n try:\n return self.data['address']['country_code'].upper()\n except KeyError:\n return ''",
"title": ""
},
{
"docid": "5b412ab5d1810ac8b76fdccfbb12c731",
"score": "0.56788355",
"text": "def _select_country(self, country_list):\n if isinstance(country_list, str):\n self.country_list = [country_list]\n if isinstance(country_list, list):\n self.country_list = country_list",
"title": ""
},
{
"docid": "34b2de25444dacd58b73f1d0711f24cf",
"score": "0.5584461",
"text": "def locale_name(self, locale_name: str):\n\n self._locale_name = locale_name",
"title": ""
},
{
"docid": "fb3d2780919dad8f3115939274c9ef7b",
"score": "0.55731475",
"text": "def langcode(self, langcode):\n\n self._langcode = langcode",
"title": ""
},
{
"docid": "379db04ffd26a223902b7236e73c563b",
"score": "0.5566139",
"text": "def postbox_country(self, postbox_country):\n\n self._postbox_country = postbox_country",
"title": ""
},
{
"docid": "c565f10d1d8f0856b06755bd9fe97f0d",
"score": "0.5543386",
"text": "def update_country(file):\n # https://www.iso.org/iso-3166-country-codes.html\n # https://www.iso.org/obp/ui/#search\n\n codes = {\n # https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2#User-assigned_code_elements\n 'XK': 'Kosovo',\n }\n\n rpc = json.load(file)[0]['rpc'][0]\n offset = int(rpc[0])\n for entry in rpc[3][1]:\n d = entry['d']\n # Clean \"Western Sahara*\", \"United Arab Emirates (the)\", etc.\n codes[d[str(offset + 9)]] = re.sub(r' \\(the\\)|\\*', '', d[str(offset + 13)])\n # The country code appears at offsets 9 and 15. Check that they are always the same.\n assert d[str(offset + 9)] == d[str(offset + 15)]\n\n with open(schemadir / 'codelists' / 'country.csv', 'w') as f:\n writer = csv.writer(f, lineterminator='\\n')\n writer.writerow(['Code', 'Title'])\n for code in sorted(codes):\n writer.writerow([code, codes[code]])",
"title": ""
},
{
"docid": "418a400681107a5c57a33d7ddd162fe0",
"score": "0.5529582",
"text": "def locale_update(ctx, country):\n\n fpath = locale_generate(ctx, country)\n\n api = internal_api.LocaleUpdater()\n curr_ver, err = api.get_version()\n if err :\n message(err)\n exit(-1)\n\n next_ver = curr_ver + 1\n data, err = aws.upload_locale_to_aws(fpath, next_ver)\n if err :\n message(err)\n exit(-1)\n\n ok, err = api.update_version(next_ver, data['hash'])\n if err :\n message(err)\n exit(-1)\n\n message(f\"[success] locale_update - {country} : {curr_ver} => {next_ver}\")",
"title": ""
},
{
"docid": "008ff43c3b3025fdac84f0e236595c0b",
"score": "0.54885334",
"text": "def get_country(self):\n pass",
"title": ""
},
{
"docid": "a91df5f6e9f5daeddf979de75904d3b1",
"score": "0.5466486",
"text": "def setupLocale():",
"title": ""
},
{
"docid": "2f7324f55b8c0049aea67286d316acf2",
"score": "0.54613936",
"text": "def country(self, country):\n if country is None:\n raise ValueError(\"Invalid value for `country`, must not be `None`\") # noqa: E501\n if country is not None and len(country) > 2:\n raise ValueError(\"Invalid value for `country`, length must be less than or equal to `2`\") # noqa: E501\n\n self._country = country",
"title": ""
},
{
"docid": "b6bd6ab6290424ea2b8685ad256c72c6",
"score": "0.5438527",
"text": "def incrementGeoByCode(self,country_code):\n if not iplocation.isValidCountryCode(country_code) :\n return\n code=country_code.lower()\n counterMap=eval(self.countryBracket)\n counterMap[code]=counterMap.setdefault(code,0)+1\n self.countryBracket=str(counterMap)",
"title": ""
},
{
"docid": "b9a8080c4c1ec47271b5d91f00c003e9",
"score": "0.5426335",
"text": "def setlocale(category, value=None):\r\n if value not in (None, '', 'C'):\r\n raise Error, '_locale emulation only supports \"C\" locale'\r\n return 'C'",
"title": ""
},
{
"docid": "ca6b8e33ef09aff950f88fe76d028c66",
"score": "0.5425097",
"text": "def set_locale(ln):\n ctx = _request_ctx_stack.top\n if ctx is None:\n raise RuntimeError(\"Working outside of request context.\")\n new_locale = current_app.extensions[\"babel\"].load_locale(ln)\n old_locale = getattr(ctx, \"babel_locale\", None)\n setattr(ctx, \"babel_locale\", new_locale)\n yield\n setattr(ctx, \"babel_locale\", old_locale)",
"title": ""
},
{
"docid": "e54fa7a88727551a591275c4492caf60",
"score": "0.5420766",
"text": "def set_language(request, lang_code):\n request.method = 'POST'\n request.POST = {'language': lang_code}\n django.views.i18n.set_language(request)\n return django.http.HttpResponse(lang_code, content_type=\"text/plain\")",
"title": ""
},
{
"docid": "e3833acf826019b9579ee65a8281ce70",
"score": "0.5419031",
"text": "def test_locale(self):\n self.context[\"request\"].LANGUAGE_CODE = \"fr\"\n self._get_datetime_result(\"fr\", self.timezone)",
"title": ""
},
{
"docid": "dbf1587b17924ba742a73bc4f9e6035f",
"score": "0.5404355",
"text": "def setlocale(category, locale=None):\r\n if locale and type(locale) is not type(\"\"):\r\n # convert to string\r\n locale = normalize(_build_localename(locale))\r\n return _setlocale(category, locale)",
"title": ""
},
{
"docid": "5e3d373b3cc1adf5467bf47c234fda25",
"score": "0.53988135",
"text": "def country(self) -> str:\n return self.__country",
"title": ""
},
{
"docid": "5e3d373b3cc1adf5467bf47c234fda25",
"score": "0.53988135",
"text": "def country(self) -> str:\n return self.__country",
"title": ""
},
{
"docid": "2d420cc058bf1c86963dbb15f021b08a",
"score": "0.5391806",
"text": "def __setLocale(self, collate=\"C\", encoding=None, variant=None):\n\n success = False\n\n for e in [encoding, \"UTF-8\"]:\n if success:\n break\n for v in [variant, \"\"]:\n localestring = '.'.join([x for x in [collate, e] if x])\n localestring = '@'.join([x for x in [localestring, v] if x])\n try:\n locale.setlocale(locale.LC_COLLATE, localestring)\n success = True\n break\n except locale.Error:\n pass\n #end for\n #end for\n\n if not success:\n msg = \"Warning: cannot set locale '%s'.\" % collate\n sys.stderr.write(msg)",
"title": ""
},
{
"docid": "c22e2309d0df303c363ff2f038ca332d",
"score": "0.53752744",
"text": "def hq_country(self, hq_country):\n\n self._hq_country = hq_country",
"title": ""
},
{
"docid": "33b9d0536136685033bc9a114509127d",
"score": "0.53703326",
"text": "def delivery_country(self, delivery_country):\n\n self._delivery_country = delivery_country",
"title": ""
},
{
"docid": "c3bb754399161d321ddb99c02d57db56",
"score": "0.5354384",
"text": "def validate_locale(form, field):\n try:\n Locale(field.data)\n except UnknownLocaleError:\n raise ValidationError(\n _(\"Please select a valid locale from above.\"))",
"title": ""
},
{
"docid": "8b3445791329a40c945ca43960c2b28f",
"score": "0.5350372",
"text": "def language_code(self, language_code):\n allowed_values = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]\n if language_code not in allowed_values:\n raise ValueError(\n \"Invalid value for `language_code` ({0}), must be one of {1}\"\n .format(language_code, allowed_values)\n )\n\n self._language_code = language_code",
"title": ""
},
{
"docid": "b3f09b4f081134df1e60f284f3726c92",
"score": "0.5349361",
"text": "def countries(self, countries):\n\n self._countries = countries",
"title": ""
},
{
"docid": "ebd2f845dc6b8c1b1ed9db2023624901",
"score": "0.53276014",
"text": "def currency_code(self, currency_code):\n\n self._currency_code = currency_code",
"title": ""
},
{
"docid": "ebd2f845dc6b8c1b1ed9db2023624901",
"score": "0.53276014",
"text": "def currency_code(self, currency_code):\n\n self._currency_code = currency_code",
"title": ""
},
{
"docid": "ebd2f845dc6b8c1b1ed9db2023624901",
"score": "0.53276014",
"text": "def currency_code(self, currency_code):\n\n self._currency_code = currency_code",
"title": ""
},
{
"docid": "ebd2f845dc6b8c1b1ed9db2023624901",
"score": "0.53276014",
"text": "def currency_code(self, currency_code):\n\n self._currency_code = currency_code",
"title": ""
},
{
"docid": "ebd2f845dc6b8c1b1ed9db2023624901",
"score": "0.53276014",
"text": "def currency_code(self, currency_code):\n\n self._currency_code = currency_code",
"title": ""
},
{
"docid": "d1deeed8df30aa1ab95780d4e45ef777",
"score": "0.5317358",
"text": "def setLanguage(self, lang):\n self.language = lang",
"title": ""
},
{
"docid": "906c31a971b1a87b255b2fe48f6b3270",
"score": "0.5313735",
"text": "def currency_code(self, currency_code: str):\n\n self._currency_code = currency_code",
"title": ""
},
{
"docid": "7eeae7f783f7a7ac62944e32643576d0",
"score": "0.52821153",
"text": "def convert_language_code(django_lang):\n lang_and_country = django_lang.split('-')\n try:\n return '_'.join((lang_and_country[0], lang_and_country[1].upper()))\n except IndexError:\n return lang_and_country[0]",
"title": ""
},
{
"docid": "e2c589205ab9a2d564db176d66a526d3",
"score": "0.5257837",
"text": "def set_capital_city(self, capital_country):\n self.capital = capital_country",
"title": ""
},
{
"docid": "5f8a0e3968a96e91e92259c56106036e",
"score": "0.5247793",
"text": "def country(self):\n return self._country",
"title": ""
},
{
"docid": "5f8a0e3968a96e91e92259c56106036e",
"score": "0.5247793",
"text": "def country(self):\n return self._country",
"title": ""
},
{
"docid": "5f8a0e3968a96e91e92259c56106036e",
"score": "0.5247793",
"text": "def country(self):\n return self._country",
"title": ""
},
{
"docid": "8af0ae3f7ea4084b20a94e1239518c7f",
"score": "0.5235966",
"text": "def unify_language_code(language_code):\n if language_code == \"en-gb\":\n return \"en-us\"\n return language_code",
"title": ""
},
{
"docid": "34ea159d4c0c89736bea1845931f3ccf",
"score": "0.5230383",
"text": "def set_locale_cookie(request):\n if request.GET['language']:\n language = request.GET['language']\n response = Response()\n response.set_cookie('_LOCALE_',\n value=language,\n max_age=31536000) # max_age = year\n return HTTPFound(location=request.environ['HTTP_REFERER'],\n headers=response.headers)",
"title": ""
},
{
"docid": "8dff454ee3b175b009728aac9d6c4cd2",
"score": "0.5229261",
"text": "def get_country_code(self):\n name=self.country_name.upper()\n # The following is necessary for compatibility with sql syntax\n name=\"'\"+re.sub(\"'\",\"''\",name)+\"'\"\n ##cursor=self.conn.cursor()\n #The following is not working so I am using .format, but this is not secure\n# cursor.execute(u'SELECT CO_CODE FROM COUNTRY WHERE UPPER(CO_LONG_NAME) IS ?', (name,) )\n country_code = self.read_sql((\"SELECT CO_CODE FROM COUNTRY \"\n \"WHERE UPPER(CO_LONG_NAME) IS {0} limit 1;\".format(name)))\n if(country_code==None):\n self.country_code=0\n else:\n self.country_code=country_code[0][0]",
"title": ""
},
{
"docid": "874d5b21ae49b7244ef43007e61b09a8",
"score": "0.5227609",
"text": "def phone_country_code(self, phone_country_code):\n if phone_country_code is not None and len(phone_country_code) > 20:\n raise ValueError(\n \"Invalid value for `phone_country_code`, \"\n \"length must be less than or equal to `20`\"\n ) # noqa: E501\n\n self._phone_country_code = phone_country_code",
"title": ""
},
{
"docid": "d9e9d83f32213ead1d84535c75efa405",
"score": "0.52136034",
"text": "def setCode(self, c):\n self.code = c",
"title": ""
},
{
"docid": "31e7a4d7ad9875513ee67ba128af0297",
"score": "0.5186201",
"text": "def merchant_country_code(self):\n return self._merchant_country_code",
"title": ""
},
{
"docid": "14a07cff680efc3d412888ff8ae1359c",
"score": "0.5183125",
"text": "def iso3_to_country(self, iso3_code):\n return self.ensure_country_name(iso3_code)",
"title": ""
},
{
"docid": "7805c3265247e489bbf1b9f5e31be382",
"score": "0.5180637",
"text": "def country_changed(self, ar):\n if self.city is not None and self.country != self.city.country:\n self.city = None",
"title": ""
},
{
"docid": "5d909e1d2993d8d79b31d6a52e446d7a",
"score": "0.5174607",
"text": "def language(self, language):\n self._language = language",
"title": ""
},
{
"docid": "03e28eb57296510c2f18f5c2fb37d465",
"score": "0.5150702",
"text": "def country_profile(self, country_profile):\n\n self._country_profile = country_profile",
"title": ""
},
{
"docid": "1aeb3d63ea1c5ae85c79006db257d799",
"score": "0.51501715",
"text": "def location_country_code(tweet):\n result = None\n if tweet[\"place\"]:\n result = tweet[\"place\"][\"country_code\"]\n return result\n return result",
"title": ""
},
{
"docid": "ccf72ea2412ca5fb04a4bf153eb0e611",
"score": "0.513411",
"text": "def _country_code_to_name(country_code :str) -> str:\n # For obsolete version of pycountry\n Log.warning(\"Please update python3-pycountry; apt-get update && apt-get upgrade\")\n\n ret = None\n\n try:\n country = pycountry.countries.get(alpha_2 = country_code.upper())\n ret = country.name\n except KeyError as e:\n Log.warning(\"Unknown country %r\" % country_code)\n\n return ret",
"title": ""
},
{
"docid": "5c1f32f2fe161790e88d0e30fc68a09f",
"score": "0.5101396",
"text": "def get_country_code(country_name):#获得国家代码\n for code,name in COUNTRIES.items():\n if name==country_name:\n return code\n #if the country wasn't found,return none.\n return None",
"title": ""
},
{
"docid": "5729b8c2233084dce9c6e0bf57dbc13d",
"score": "0.5075233",
"text": "def country(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"country\")",
"title": ""
},
{
"docid": "dfa750a9d648244107ecd7bdfe9a8f93",
"score": "0.50717306",
"text": "def get_home_location(self, locale):\n from geo.countries import countries\n if self.country_code and locale in countries:\n country = countries[locale][self.country_code.lower()]\n return '%s, %s' % (self.city_name, country)\n else:\n return ''",
"title": ""
},
{
"docid": "8c0c661c43043c0704d98758ea7f11f4",
"score": "0.5065583",
"text": "def update_country(\n country: country_schema.Country, db: orm.Session = fastapi.Depends(database.get_db)\n):\n return country_service.update_country(country=country, db=db)",
"title": ""
},
{
"docid": "a1c76527540785140f225a3147aebc03",
"score": "0.5065483",
"text": "def country_code(message):\n et, err_list = message\n\n elements = et.findall('//*[@country]')\n for elem in elements:\n value = elem.attrib['country']\n if value not in ISO3166_CODES_SET:\n err = StyleError()\n err.line = elem.sourceline\n err.message = \"Element '%s', attribute country: Invalid country code \\\"%s\\\".\" % (elem.tag, value)\n err_list.append(err)\n\n return message",
"title": ""
},
{
"docid": "d00c997f926abc3fd6c32d710603b14d",
"score": "0.505664",
"text": "def set_language(chat_data, language):\n language = language[:2]\n chat_data['language'] = language\n return",
"title": ""
},
{
"docid": "51f1537b25423a9898d43833415e26ac",
"score": "0.5045897",
"text": "def vat_country_code(self) -> Country:\n return self._vat_country_code",
"title": ""
},
{
"docid": "c00c2ae3a04a7ec921509a6f4d8d33db",
"score": "0.5035399",
"text": "def test_update_country(self):\n pass",
"title": ""
},
{
"docid": "818f7af5c7afa921c60309617bafdd7e",
"score": "0.502244",
"text": "def test_with_locale(self):\n request = self.rf.get('/pa+th', {'a': 'b'})\n request.LANGUAGE_CODE = 'ru'\n response = self.ptsm.process_request(request)\n eq_('/ru/pa%20th?a=b', response['location'])",
"title": ""
},
{
"docid": "63553e6dcc44c26bfec18574b812349c",
"score": "0.5007964",
"text": "def repconf_locale(self, repconf_locale):\n\n self._repconf_locale = repconf_locale",
"title": ""
},
{
"docid": "78e0d848c0df4ec788dc0ecf0ef36b7f",
"score": "0.49977377",
"text": "def set_locale_source(self, source=None):\n # Unregister a previously configure locale source and clear the map.\n if self._locale_source is not None:\n self._locale_source.remove_mixin(self)\n self._locale_map.clear()\n\n if source is None:\n return\n\n # Set the locale source, which subsequently regenerates the cache.\n self._locale_source = source\n self._locale_source.add_mixin(self)\n for locale in self._locale_source.get_all():\n self._add_locale(locale)",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="704"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">a5a14d993f096de597bd893e0ee12422</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Return the Body Mass Index (BMI) for the given weight, height, and measurement system.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "31680e17a6bc9efb79d34c03894346b1",
"score": "0.784507",
"text": "def calculate_bmi(height, weight, system='m'):\n if system == 'm':\n bmi = (weight / (height ** 2))\n else:\n bmi = 703 * (weight / (height ** 2))\n return bmi",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "f5996c4338c038a932b2454500eb8fee",
"score": "0.8331378",
"text": "def body_mass_index(weight, height):\n bmi = weight / (height ** 2) * 10000\n return bmi",
"title": ""
},
{
"docid": "9078e04477ee72b02fc0ca32e01c4981",
"score": "0.7788883",
"text": "def calculate_bmi(weight, height, system='metric'):\n if system=='metric':\n bmi = (weight/(height**2))\n else:\n bmi = 703 * (weight / (height**2))\n return bmi",
"title": ""
},
{
"docid": "ae0a230c4721d29fbcc99f4a58a0adae",
"score": "0.7059137",
"text": "def boer(height: float, weight: float, sex: str) -> float:\r\n \"\"\" Boer P. \"Estimated lean body mass as an index for normalization of body fluid volumes in man.\" Am J Physiol 1984; 247: F632-5\"\"\"\r\n\r\n if sex != \"m\" and sex != \"f\":\r\n raise ValueError(\r\n \"Unknown sex '%s'. This algorithm can only handle 'm' and 'f'. :(\" % sex\r\n )\r\n\r\n if sex == \"m\":\r\n lbm = (0.407 * weight) + (0.267 * height) - 19.2\r\n else:\r\n lbm = (0.252 * weight) + (0.473 * height) - 48.3\r\n\r\n return round(lbm, 1)",
"title": ""
},
{
"docid": "c1601f3ba84b7fff4e46fdd5f11a99ca",
"score": "0.6803907",
"text": "def bmi_calculate(weight, height):\n\t# The equation to compute the body weight index\n\tbmi = weight/(height**2)\n\thealth_cond = \"\"\n\tif (bmi < 18.5):\n\t\thealth_cond = \"underweight\"\n\tif (18.5 <= bmi < 25):\n\t\thealth_cond = \"normal weight\"\n\tif (25 <= bmi < 30):\n\t\thealth_cond = \"overweight\"\n\tif (bmi >= 30):\n\t\thealth_cond = \"obese\"\n\treturn health_cond",
"title": ""
},
{
"docid": "480ef60a2ee18d99c1d778645017d5a8",
"score": "0.6726397",
"text": "def getBMI(weight, height):\n try:\n bmi = weight/(height*height)\n return jsonify({\"BMI\": str(bmi)})\n except Exception as e:\n print(e)\n return throwError(500)",
"title": ""
},
{
"docid": "2f2f5f51f1db6e53b22f1fa31f08c93d",
"score": "0.6270309",
"text": "def idealbodyweight(height: float, sex: str) -> float:\r\n\r\n if sex != \"m\" and sex != \"f\":\r\n raise ValueError(\r\n \"Unknown sex '%s'. This algorithm can only handle 'm' and 'f'. :(\" % sex\r\n )\r\n\r\n if sex == \"m\":\r\n ibm = 50.0 + 0.91 * (height - 152.4)\r\n else:\r\n ibm = 45.5 + 0.91 * (height - 152.4)\r\n\r\n return round(ibm, 1)",
"title": ""
},
{
"docid": "9e0771ce8c5a796d9beeab0d9da93868",
"score": "0.62535685",
"text": "def bmi(weight, height):\n\n value = weight / height ** 2\n if value >= 30.0:\n return \"obese\"\n elif value >= 25.0:\n return \"over\"\n elif value >= 18.5:\n return \"normal\"\n else:\n return \"under\"",
"title": ""
},
{
"docid": "84aad8739d2aa2be29781a41d629ae0f",
"score": "0.61749285",
"text": "def janmahasation(height: float, weight: float, sex: str) -> float:\r\n\r\n if sex != \"m\" and sex != \"f\":\r\n raise ValueError(\r\n \"Unknown sex '%s'. This algorithm can only handle 'm' and 'f'. :(\" % sex\r\n )\r\n bodymass = bmi(height, weight)\r\n\r\n if sex == \"m\":\r\n lbm = (9270 * weight) / (6680 + 216 * bodymass)\r\n else:\r\n lbm = (9270 * weight) / (8780 + 244 * bodymass)\r\n\r\n return round(lbm, 1)",
"title": ""
},
{
"docid": "9992598e82047bf93fedcfb03e97289c",
"score": "0.60606587",
"text": "def hume66(height: float, weight: float, sex: str) -> float:\r\n \"\"\" Hume, R \"Prediction of lean body mass from height and weight.\". J Clin Pathol. 1966 Jul; 19(4):389-91\"\"\"\r\n\r\n if sex != \"m\" and sex != \"f\":\r\n raise ValueError(\r\n \"Unknown sex '%s'. This algorithm can only handle 'm' and 'f'. :(\" % sex\r\n )\r\n\r\n if sex == \"m\":\r\n lbm = (0.32810 * weight) + (0.33929 * height) - 29.5336\r\n else:\r\n lbm = (0.29569 * weight) + (0.41813 * height) - 43.2933\r\n\r\n return round(lbm, 1)",
"title": ""
},
{
"docid": "f924dc5f290a34636dedc7549752d36c",
"score": "0.6035409",
"text": "def calc_mb(**param_dict):\n model = sncosmo.Model(source='salt2',\n effects=[sncosmo.CCM89Dust()],\n effect_names=['mw'],\n effect_frames=['obs'])\n model.set(**param_dict)\n try:\n mb = model.bandmag(band='bessellb', time=param_dict['t0'], magsys='ab')\n except:\n mb = np.nan\n return mb",
"title": ""
},
{
"docid": "216504cda78551ac86514d079a5806ef",
"score": "0.59328145",
"text": "def basal_metabolic_rate(gender, weight, height, age):\n if gender.upper() == \"F\":\n bmr = 447.593 + 9.247 * weight + 3.098 * height - 4.330 * age\n else:\n bmr = 88.362 + 13.397 * weight + 4.799 * height - 5.677 * age\n return bmr",
"title": ""
},
{
"docid": "8f7d1acf8f2c85d89995578a8e45b9ea",
"score": "0.5904466",
"text": "def hume71(height: float, weight: float, sex: str) -> float:\r\n \"\"\" Relationship between total body water and surface area in normal and obese subjects. Hume R, Weyers E J Clin Pathol 24 p234-8 (1971 Apr) \"\"\"\r\n\r\n if sex != \"m\" and sex != \"f\":\r\n raise ValueError(\r\n \"Unknown sex '%s'. This algorithm can only handle 'm' and 'f'. :(\" % sex\r\n )\r\n\r\n if sex == \"m\":\r\n lbm = (0.4066 * weight) + (0.2668 * height) - 19.19\r\n else:\r\n lbm = (0.2518 * weight) + (0.4720 * height) - 48.32\r\n\r\n return round(lbm, 1)",
"title": ""
},
{
"docid": "3e261c9bc14f07692205674b83961602",
"score": "0.5856087",
"text": "def calculate_bmi():\n if session.get(\"user_id\") != None:\n user = User.query.filter_by(user_id=session[\"user_id\"]).one()\n if user.weight and user.height:\n class_type = user.class_type\n bmi = round((user.weight / (user.height ** 2)), 1)\n assessment = assess_bmi(bmi)\n return render_template(\n \"bmi.html\", bmi=bmi, assessment=assessment, class_type=class_type\n )\n else:\n flash(\n \"You must update your weight and height before accessing the BMI calculator.\"\n )\n return redirect(\"/editprofile\")\n else:\n flash(\"You must be logged in to access the BMI calculator.\")\n return redirect(\"/login\")",
"title": ""
},
{
"docid": "86cc89b1e98a0c75d76de1131a105dce",
"score": "0.5814167",
"text": "def adjustedbodyweight(height: float, weight: float, sex: str) -> float:\r\n\r\n ibw = idealbodyweight(height, sex)\r\n abw = ibw + 0.4 * (weight - ibw)\r\n\r\n return round(abw, 1)",
"title": ""
},
{
"docid": "0e0722b147aa369c19b695c9372eedb4",
"score": "0.5662682",
"text": "def get_mw(self):\n\t\taminoacid_mw = {'A': 89.09, 'C': 121.16, 'E': 147.13, 'D': 133.1, 'G': 75.07, 'F': 165.19, 'I': 131.18, 'H': 155.16, 'K': 146.19, 'M': 149.21, \n\t\t'L': 131.18, 'N': 132.12, 'Q': 146.15, 'P': 115.13, 'S': 105.09, 'R': 174.2, 'T': 119.12, 'W': 204.23, 'V': 117.15, 'Y': 181.19}\n\t\t\n\t\tmolecular_weight = sum(aminoacid_mw[aa] for aa in self.sequence)\n\t\treturn molecular_weight",
"title": ""
},
{
"docid": "d0908514260701a1d6345282d2b09d4e",
"score": "0.56420517",
"text": "def is_normal_bmi(weight, height):\n bmi = weight / (height**2)\n return (BMI_THRESHOLD_LOW <= bmi and bmi <= BMI_THRESHOLD_HIGH)",
"title": ""
},
{
"docid": "ace2a4c9bff407708e87b6c95b8d6906",
"score": "0.5590485",
"text": "def get_vitals_bmi(self, patient_id):\n return self.get_obs(patient_id, \"39156-5\")",
"title": ""
},
{
"docid": "408e0bb657522ca7b7447b647586c668",
"score": "0.5580011",
"text": "def bh_mass_from_bulge_mass(bulge_mass):\n prefactor = 0.49*(bulge_mass/100.)\n return prefactor*(bulge_mass/1e11)**0.15",
"title": ""
},
{
"docid": "8e88acf4a488e0c04f4d52859e93b297",
"score": "0.5569449",
"text": "def bmi(height, weight):\r\n user_bmi = round(weight / (height / 100) ** 2, 2)\r\n if user_bmi < 18:\r\n print(str(user_bmi) + \",\" + \" you are underweight\")\r\n elif user_bmi > 25:\r\n print(str(user_bmi) + \",\" + \" you are overweight\")\r\n else:\r\n print(str(user_bmi) + \",\" + \" you are normal\")\r\n return user_bmi",
"title": ""
},
{
"docid": "97e64c0ca381601337b767e95b0f47b8",
"score": "0.54999954",
"text": "def getBaseMass(self):\n return _bullet.btMultiBody_getBaseMass(self)",
"title": ""
},
{
"docid": "cf97bef80ccaf26eaeafddc5e58ab11b",
"score": "0.54411787",
"text": "def getMass(self):\n \n return self.mass",
"title": ""
},
{
"docid": "cfcb8d69942d65c9d77def853ef0c850",
"score": "0.53865093",
"text": "def getGasMass(self, mweight=2.3, rhogas=False):\n\n vol = self.grid.getCellVolume()\n if not rhogas:\n #\n # I'm not sure if this is the right way of doing it but right now I don't have a better idea\n #\n if isinstance(self.grid, radmc3dOctree):\n return (vol * self.ndens_mol[:, 0] * mweight * nc.mp).sum()\n else:\n return (vol * self.ndens_mol[:, :, :, 0] * mweight * nc.mp).sum()\n\n else:\n if isinstance(self.grid, radmc3dOctree):\n return (vol * self.rhogas[:, 0]).sum()\n else:\n return (vol * self.rhogas[:, :, :, 0]).sum()",
"title": ""
},
{
"docid": "b9decdc6308009c53ea5e62f212e1df5",
"score": "0.5366369",
"text": "def body_mass_correction(self):\n self.mb = (1 / 3) * ((self.rho * self.bl) / self.sb) * (self.sn ** 2)",
"title": ""
},
{
"docid": "4dba923a91918554ca4f01b2da2ce736",
"score": "0.53576905",
"text": "def getGasMass(self, mweight=2.3, rhogas=False):\n\n vol = self.grid.getCellVolume()\n gmass = -1.\n if not rhogas:\n #\n # I'm not sure if this is the right way of doing it but right now I don't have a better idea\n #\n if isinstance(self.grid, radmc3dOctree):\n gmass = (vol * self.ndens_mol[:,0] * mweight*mp).sum()\n else:\n gmass = (vol * self.ndens_mol[:,:,:,0] * mweight*mp).sum()\n\n else:\n if isinstance(self.grid, radmc3dOctree):\n gmass = (vol * self.rhogas[:,0]).sum()\n else:\n gmass = (vol * self.rhogas[:,:,:,0]).sum()\n\n return gmass",
"title": ""
},
{
"docid": "f9c0f09c8373738e490a67f05fe36647",
"score": "0.5348666",
"text": "def localbsa(height=1.72, weight=74.43, equation=\"dubois\",):\n _bsa_rate = bsa_rate(height, weight, equation,) # The BSA ratio to the standard body (1.87m2)\n bsa = _BSAst * _bsa_rate\n return bsa",
"title": ""
},
{
"docid": "def75caae61d3d665996e5475d5b6653",
"score": "0.5345217",
"text": "def molecular_weight(self):\n pass",
"title": ""
},
{
"docid": "a551158fa7406366e2c4382cdacfad9c",
"score": "0.5290259",
"text": "def bsa_rate(height=1.72, weight=74.43, equation=\"dubois\",):\n bsa_all = body_surface_area(height, weight, equation,)\n bsa_rate = bsa_all/_BSAst.sum() # The BSA ratio to the standard body (1.87m2)\n return bsa_rate",
"title": ""
},
{
"docid": "0b14a6e526a5f939a81e82f89ca84974",
"score": "0.5280404",
"text": "def Mass(self) -> float:\n #print(' rho=%e volume=%e' % (self.Rho(), self.Volume()))\n return self.Rho() * self.Volume()",
"title": ""
},
{
"docid": "4147f1fd6fe6377b7cade0c62c8c20da",
"score": "0.5271127",
"text": "def bmicalculator(weight, length):\n bmi = round(weight / length ** 2, 2)\n print(\"bmi:\", bmi)\n if bmi < 16:\n print(\"niedowaga\")\n elif 18.5 <= bmi <= 24.99:\n print(\"optimum\")\n elif 25 <= bmi <= 29.99:\n print(\"nadwaga\")\n else:\n print(\"otylosc\")",
"title": ""
},
{
"docid": "7a32acbc74547edcedc278d2f8a41af5",
"score": "0.525462",
"text": "def bm25_weight(data, K1=1.2, B=0.8):\r\n # calculate idf per term (user)\r\n N = float(data.shape[0])\r\n idf = np.log(N / (1 + np.bincount(data.col)))\r\n\r\n # calculate length_norm per document (artist)\r\n row_sums = np.squeeze(np.asarray(data.sum(1)))\r\n average_length = row_sums.sum() / N\r\n length_norm = (1.0 - B) + B * row_sums / average_length\r\n\r\n # weight matrix rows by bm25\r\n ret = coo_matrix(data)\r\n ret.data = ret.data * (K1 + 1.0) / (K1 * length_norm[ret.row] + ret.data) * idf[ret.col]\r\n return ret",
"title": ""
},
{
"docid": "94c22038dec6df920f270c79ea6cccae",
"score": "0.5230618",
"text": "def _get_weight(self) -> float:\r\n return self.weight",
"title": ""
},
{
"docid": "2c3be17e10964ab934e5ff56d898496f",
"score": "0.5212119",
"text": "def get_mass(self):\n return self._data[\"meta\"][\"mass\"]",
"title": ""
},
{
"docid": "fb734a5042b9d1c108d8b1e8d8dedba9",
"score": "0.52085876",
"text": "def _calc_VBM(occupancy, energy):\n _check_partial_occupancy(occupancy)\n energy_occupied = ma.masked_where(occupancy < 0.5, energy)\n return np.amax(energy_occupied)",
"title": ""
},
{
"docid": "846c0073e2ad1b9f8833f019a06f47a0",
"score": "0.5178983",
"text": "def scale_height(temperature: ArrayLike, mass: ArrayLike, gravity: float) -> np.ndarray:\n return Boltzmann * temperature / (mass * gravity)",
"title": ""
},
{
"docid": "973b753f4ccddecbfceacd6f87305a93",
"score": "0.51561546",
"text": "def size_mi_b(self) -> float:\n return pulumi.get(self, \"size_mi_b\")",
"title": ""
},
{
"docid": "821a1228cba1235d219ec2a07bec3448",
"score": "0.51549155",
"text": "def getMass(self, node):\n return _bullet.btSoftBody_getMass(self, node)",
"title": ""
},
{
"docid": "635e2c8e32aa6f2c49a07374843e2ffe",
"score": "0.5152674",
"text": "def unit_cell_mass(self):\n return self._unit_cell_mass",
"title": ""
},
{
"docid": "86789c24a6398347512c43d61261d5b2",
"score": "0.5152492",
"text": "def test_bmi(self):\n task = tasks['NHIS/bmi_pvals']\n meta = task.meta\n\n assert not task.is_classif()\n\n X = task.X\n y = task.y\n\n n_rows = 11247\n\n assert X.shape[0] == n_rows\n assert y.shape == (n_rows,)\n assert not y.isna().any()\n\n assert task._f_y == meta.predict.output_features\n # L1 = list(X.columns)\n # L2 = meta.select.output_features\n # self.assertCountEqual(L1, L2)",
"title": ""
},
{
"docid": "947417c710a90b9494cc6aaab0168bb3",
"score": "0.51491517",
"text": "def bmi_status_child(mass, height):\n bmi = compute_bmi(mass, height)\n\n if bmi < 22:\n status = \"NORMAL\"\n elif bmi < 25:\n status = \"OVERWEIGHT\"\n else:\n status = \"OBESE\"\n\n return status",
"title": ""
},
{
"docid": "1db3657292358d8e37b71003d56641ff",
"score": "0.5145514",
"text": "def calculate_molecular_mass(symbols):\n #initialize weight\n weight= 0\n #sum over all symbols\n for i in symbols:\n weight += atomic_weights[i]\n\n return weight",
"title": ""
},
{
"docid": "654336f546bcdb87cc9bf1d379e3be5b",
"score": "0.5142828",
"text": "def mass(system, state):\n\n total_mass = 0.0 * system.getPrticleMass(0)\n nparticles = system.getNumParticles()\n for i in range(particles):\n total_mass += system.getParticleMass(i)\n return total_mass",
"title": ""
},
{
"docid": "b9305dcef761e8b8028a8b612fe1c160",
"score": "0.51379836",
"text": "def weight_to_mole(sys):\n # Open and read the molar masses for the element in the system \n ppt_file = open('UNARY_PROPERTIES.MTC', 'r')\n ppt_li = ppt_file.readlines()\n ppt_file.close()\n \n MM = dict()\n \n # Read molar masses in file\n for line in ppt_li:\n li = line.split()\n if len(li) > 4 and li[1] in sys.keys():\n MM[li[1]] = float(li[3])\n tot = 0.0\n for i, elt in enumerate(sys):\n tot = tot + float(sys[elt]) / MM[elt]\n mol = dict()\n for i, elt in enumerate(sys):\n mol[elt] = sys[elt] / MM[elt] / tot\n \n return mol",
"title": ""
},
{
"docid": "7008409974eb8024d6a78fa99a87b871",
"score": "0.51312006",
"text": "def get_bm_info(self):\n return self.bm_info",
"title": ""
},
{
"docid": "90fbe84827c1cd695a55ca52776691e5",
"score": "0.5130422",
"text": "def CalculateMolWeight(mol):\n mol = Chem.AddHs(mol)\n MW = round(sum([atom.GetMass() for atom in mol.GetAtoms()]),2)\n return MW",
"title": ""
},
{
"docid": "4a82b79ffb1fcc9816432be5d46e960e",
"score": "0.5126746",
"text": "def getMass(self):\n\n return self.mass",
"title": ""
},
{
"docid": "4a82b79ffb1fcc9816432be5d46e960e",
"score": "0.5126746",
"text": "def getMass(self):\n\n return self.mass",
"title": ""
},
{
"docid": "4a82b79ffb1fcc9816432be5d46e960e",
"score": "0.5126746",
"text": "def getMass(self):\n\n return self.mass",
"title": ""
},
{
"docid": "e14e7c5b75dd7f74b776253984d4af43",
"score": "0.5110824",
"text": "def heightM(self):\n return self.data[\"heightM\"]",
"title": ""
},
{
"docid": "24fa896caad7b47aa21174a886d19d94",
"score": "0.50939417",
"text": "def blackbody(wavelengths, temperature=5777, norm=1):\n bb = blackbody_lambda(wavelengths*10, temperature).value\n bb = bb / bb.max() * norm\n return bb",
"title": ""
},
{
"docid": "11afa4f89165c6b15a2b49ccb512908d",
"score": "0.5091937",
"text": "def get_mass(self):\n return self._mass",
"title": ""
},
{
"docid": "e6242c9f6a32dd01498c3f90412978f3",
"score": "0.50846416",
"text": "def get_db_weight(weight_text):\n weight = -1\n if weight_text.endswith(\"kg\"):\n # convert kg to lbs\n weight = int(float(weight_text[:-2])*2.20462)\n\n elif weight_text.endswith(\"lbs\"):\n weight = weight_text\n\n return weight",
"title": ""
},
{
"docid": "39addcf052fa4af10f4b709e61997f10",
"score": "0.5041964",
"text": "def assess_bmi(bmi):\n if bmi <= 18.5:\n return \"Underweight\"\n elif bmi > 18.5 and bmi <= 24.9:\n return \"Normal\"\n elif bmi >= 25 and bmi < 29.9:\n return \"Overweight\"\n elif bmi >= 30:\n return \"Obese\"\n else:\n return \"Error\"",
"title": ""
},
{
"docid": "63e8adc7ec62ce909478e4596a3cccd3",
"score": "0.5021904",
"text": "def SMHM(self, z, Mh=None, return_mean_only=False, Mbin=0.1):\n\n return self.XMHM(z, field='Ms', Mh=Mh, return_mean_only=return_mean_only,\n Mbin=Mbin)",
"title": ""
},
{
"docid": "f2cc3ca4e6271d17345be1d6f9a2f9a9",
"score": "0.50215304",
"text": "def weight(self) -> int:\n return pulumi.get(self, \"weight\")",
"title": ""
},
{
"docid": "f2cc3ca4e6271d17345be1d6f9a2f9a9",
"score": "0.50215304",
"text": "def weight(self) -> int:\n return pulumi.get(self, \"weight\")",
"title": ""
},
{
"docid": "f2cc3ca4e6271d17345be1d6f9a2f9a9",
"score": "0.50215304",
"text": "def weight(self) -> int:\n return pulumi.get(self, \"weight\")",
"title": ""
},
{
"docid": "f2cc3ca4e6271d17345be1d6f9a2f9a9",
"score": "0.50215304",
"text": "def weight(self) -> int:\n return pulumi.get(self, \"weight\")",
"title": ""
},
{
"docid": "7990fbf55d6623482bb78df854d61873",
"score": "0.5017691",
"text": "def biweight_location(self):\n return self._calculate_stats(biweight_location)",
"title": ""
},
{
"docid": "0a0629b069945fe060bdb68246cf1f55",
"score": "0.50018024",
"text": "def calc_CBM_VBM_from_Fermi(Data, CBMVBM_search_depth=4.0):\n Data.CBM = Data.fermi_energy\n Data.VBM = Data.fermi_energy\n\n Settings = inputs.Settings(extrema_search_depth=CBMVBM_search_depth)\n extrema_indices=find_extrema_indices(Data, Settings)\n\n CBM = min([Data.energies[i][j] for i,j in extrema_indices[1]])\n VBM = max([Data.energies[i][j] for i,j in extrema_indices[0]])\n\n return CBM, VBM",
"title": ""
},
{
"docid": "a85a0123ef974d113adb95d3ec79164f",
"score": "0.4996085",
"text": "def get_atom_weight(atom):\n\n # atomic masses ( in 'g/mol' aka. 'u' - often written 'amu' ) taken from: \n # <http://www.chemicalelements.com/show/mass.html> 7/31/15 by Matt Agee\n dict={'h' : 1.00794, 'he' : 4.002602,'li' : 6.941, 'be' : 9.012182,\n 'b' : 10.811, 'c' : 12.0107, 'n' : 14.00674, 'o' : 15.9994, \n 'f' : 18.9984032,'ne' : 20.1797, 'na' : 22.989770,'mg' : 24.3050, \n 'al' : 26.981538, 'si' : 28.0855, 'p' : 30.973761,'s' : 32.066, \n 'cl' : 35.4527, 'ar' : 39.948, 'k' : 39.0983, 'ca' : 40.078, \n 'sc' : 44.955910, 'ti' : 47.867, 'v' : 50.9415, 'cr' : 51.9961, \n 'mn' : 54.938049, 'fe' : 55.845, 'co' : 58.933200,'ni' : 58.9634, \n 'cu' : 63.546, 'zn' : 65.39, 'ga' : 69.723, 'ge' : 72.61, \n 'as' : 74.92160, 'se' : 78.96, 'br' : 79.904, 'kr' : 83.80}\n \n try:\n return dict[atom]\n except KeyError:\n raise KnownError(\"'\" + atom + \"' isn't in the dictionary yet.\")",
"title": ""
},
{
"docid": "771a5438878fea0a4eabac0de5d7ca3b",
"score": "0.49921966",
"text": "def getweight(self) -> float:\n return self.weight",
"title": ""
},
{
"docid": "83eb10f0f2548854639255de7bb40931",
"score": "0.49878627",
"text": "def weight(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"weight\")",
"title": ""
},
{
"docid": "83eb10f0f2548854639255de7bb40931",
"score": "0.49878627",
"text": "def weight(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"weight\")",
"title": ""
},
{
"docid": "83eb10f0f2548854639255de7bb40931",
"score": "0.49878627",
"text": "def weight(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"weight\")",
"title": ""
},
{
"docid": "83eb10f0f2548854639255de7bb40931",
"score": "0.49878627",
"text": "def weight(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"weight\")",
"title": ""
},
{
"docid": "83eb10f0f2548854639255de7bb40931",
"score": "0.49878627",
"text": "def weight(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"weight\")",
"title": ""
},
{
"docid": "83eb10f0f2548854639255de7bb40931",
"score": "0.49878627",
"text": "def weight(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"weight\")",
"title": ""
},
{
"docid": "83eb10f0f2548854639255de7bb40931",
"score": "0.49878627",
"text": "def weight(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"weight\")",
"title": ""
},
{
"docid": "83eb10f0f2548854639255de7bb40931",
"score": "0.49878627",
"text": "def weight(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"weight\")",
"title": ""
},
{
"docid": "83eb10f0f2548854639255de7bb40931",
"score": "0.49878627",
"text": "def weight(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"weight\")",
"title": ""
},
{
"docid": "a66378034ac796ac55346ff0099bb4ac",
"score": "0.498301",
"text": "def mass(self) -> float:\n result = 0.0\n for symbol in self._elements:\n ele = ELEMENTS[symbol]\n for massnumber, count in self._elements[symbol].items():\n if massnumber:\n result += ele.isotopes[massnumber].mass * count\n else:\n result += ele.mass * count\n return result - ELECTRON.mass * self._charge",
"title": ""
},
{
"docid": "24a6f22d78c2859ac1076a7e78445844",
"score": "0.49798685",
"text": "def mu(self) -> float: # pylint: disable=C0103\n return self.mass / 2",
"title": ""
},
{
"docid": "d329eac653c04991e57205ebab28c5a2",
"score": "0.49610582",
"text": "def base_mass(self) -> float:\n return self._base_mass",
"title": ""
},
{
"docid": "935e437300db318f5281ffcb21a0d88c",
"score": "0.49557632",
"text": "def mass(self):\n return self.skin.mass + sum([s.mass*self.stringers[s] for s in self.stringers])",
"title": ""
},
{
"docid": "df9d8671b7b45a9d29cbb7cf7beb295e",
"score": "0.49535477",
"text": "def body_surface_area(height=1.72, weight=74.43, equation=\"dubois\",):\n\n if equation == \"dubois\":\n bsa = dubois(height, weight)\n elif equation == \"takahira\":\n bsa = takahira(height, weight)\n elif equation == \"fujimoto\":\n bsa = fujimoto(height, weight)\n elif equation == \"kurazumi\":\n bsa = kurazumi(height, weight)\n\n return bsa",
"title": ""
},
{
"docid": "b6372ee623ef29b7d514d824edcb0217",
"score": "0.49321148",
"text": "def weight(self):\n pattern_weight = self.weightmatrix[self.pattern]\n pattern_weight = pattern_weight[self.p_mask]\n weight = np.prod(pattern_weight)\n return weight",
"title": ""
},
{
"docid": "d002630b4f63ba5cb7e761cc586f6ed9",
"score": "0.4905225",
"text": "def get_weight(self):\n return self.weight",
"title": ""
},
{
"docid": "d002630b4f63ba5cb7e761cc586f6ed9",
"score": "0.4905225",
"text": "def get_weight(self):\n return self.weight",
"title": ""
},
{
"docid": "a17ef61e0e711da79873cfab2270d1f1",
"score": "0.4900388",
"text": "def get_weights(self) -> Weights:",
"title": ""
},
{
"docid": "5af1d87b7957c034e0441d45e304b822",
"score": "0.48941106",
"text": "def computeMass(object):\n if object.wrapped.ShapeType == 'Face':\n return object.wrapped.Area\n else:\n return object.wrapped.Mass",
"title": ""
},
{
"docid": "6175bc4fa950e532f4ad65c4b2c87d77",
"score": "0.48902008",
"text": "def monoisotopic_mass(self) -> float:\n return self.isotope.mass",
"title": ""
},
{
"docid": "9c9ab726983e614d17133dcf0b262a3e",
"score": "0.48892072",
"text": "def systolic_bp(bmi, age, gender_male, treatment):\n return (\n 68.15+0.58*bmi+0.65*age+0.94*gender_male+6.44*treatment\n )",
"title": ""
},
{
"docid": "5eb1641246cbd53f9c9b5736cbec4330",
"score": "0.48868227",
"text": "def james(height: float, weight: float, sex: str) -> float:\r\n \"\"\"James, W. \"Research on obesity: a report of the DHSS/MRC group\" HM Stationery Office 1976\"\"\"\r\n\r\n if sex != \"m\" and sex != \"f\":\r\n raise ValueError(\r\n \"Unknown sex '%s'. This algorithm can only handle 'm' and 'f'. :(\" % sex\r\n )\r\n\r\n if sex == \"m\":\r\n return 1.1 * weight - 128 * ((weight / height) * (weight / height))\r\n else:\r\n return 1.07 * weight - 148 * ((weight / height) * (weight / height))",
"title": ""
},
{
"docid": "a0cc0afe78c8032a4d1aeb4942523daa",
"score": "0.48853743",
"text": "def mapmass_h2_thin(flux, temp, d, k, Bnu, hk) :\n\n mass = maps.Map.empty()\n\n ## calculate masses\n ##\n mass.data[0] = flux.data[0] * d * d / k / Bnu / const.M_sun\n\n ## calculate the variances\n ##\n hkt = hk / temp.data[0]\n fct = mass.data[0] / flux.data[0]\n\n term_varT= flux.data[0] * hkt / temp.data[0] / (1. - np.exp(-hkt))\n term_var = flux.data[1] + term_varT * term_varT * temp.data[1]\n\n mass.data[1] = fct * fct * term_var\n\n return mass",
"title": ""
},
{
"docid": "f1fd8a75ea6f2fa602b63f0103590f90",
"score": "0.48773143",
"text": "def get_mass(self):\n\t\ttry:\n\t\t\treturn self.mass_total;\n\t\texcept AttributeError:\n\t\t\tself.mass_total = np.sum(self.get_dm());\n\t\t\treturn self.mass_total;",
"title": ""
},
{
"docid": "67ff3f1f716ed32b53955b0e7706b617",
"score": "0.48769766",
"text": "def get_weight(self):\n pass",
"title": ""
},
{
"docid": "70bba4628bb386c8fe768b6500278ded",
"score": "0.48706424",
"text": "def sample_stellar_mass(self):\n\t\treturn 0.",
"title": ""
},
{
"docid": "8c7704b4bdbcd052c02c8c2c0783db12",
"score": "0.48647454",
"text": "def do_bmi(self, args):\n self.myView.getController().displayBMIGraph()",
"title": ""
},
{
"docid": "d58db9acb827e64b173b261585522337",
"score": "0.4861161",
"text": "def _compute_WMI_BC(self, formula, weights):\n problems = []\n for index, model in enumerate(WMI._model_iterator_base(formula)):\n atom_assignments = {a : model.get_value(a).constant_value()\n for a in formula.get_atoms()}\n problem = self._create_problem(atom_assignments, weights)\n problems.append(problem)\n\n results, cached = self.integrator.integrate_batch(problems, self.cache)\n volume = fsum(results)\n return volume, len(problems)-cached, cached",
"title": ""
},
{
"docid": "fd2e490d438a375a1065b77427dcd303",
"score": "0.485961",
"text": "def computeWeightScale(self):\n graph_model = self.base.extensions[\"graph_model\"]\n K = self.modelParams[\"proc_id_model\", \"K\"]\n b_w = 1\n if isinstance(graph_model,ErdosRenyiModel):\n rho = graph_model.rho_v\n b_w = K * self.params[\"a_w\"] * rho / 0.7\n log.info(\"Set b_w=%f based on number of processes and specified alpha and rho\", b_w)\n \n elif isinstance(graph_model,StochasticBlockModel):\n # Try to approximate the in-degree based on the average \n # prob of connection between blocks\n b0 = graph_model.params[\"b0\"]\n b1 = graph_model.params[\"b1\"]\n rho = graph_model.rho\n \n rho *= b0/ (b0+b1)\n # Set the expected out-degree to mean+1 std\n# stdev = np.sqrt(b0*b1/((b0+b1)**2)/(b0+b1+1))\n# rho += stdev\n \n b_w = K * self.params[\"a_w\"] * rho / 0.7\n log.info(\"Set b_w=%f based on number of processes and specified alpha and SBM params\", b_w)\n else:\n # Default to weights appropriate for a complete graph \n b_w = K * self.params[\"a_w\"] / 0.7\n log.debug(\"Set b_w=%f based on number of processes and specified alpha and complete graph model\", b_w)\n \n return b_w",
"title": ""
},
{
"docid": "de5c34d66750dd8b474b97d769b35369",
"score": "0.4857881",
"text": "def _get_combined_weight_and_bias():\n w_c = np.float(np.random.normal(loc=7.7621, scale=2.5784, size=1))\n b = np.float(np.random.normal(loc=-3.6684, scale=0.8909, size=1))\n\n return w_c, b",
"title": ""
},
{
"docid": "b721a249bf2a4395e14f55eb7ff72b1f",
"score": "0.48529533",
"text": "def weight(self) -> float:\n self.sanitize(raise_exception=False)\n return Descriptors.ExactMolWt(self.rd_mol)",
"title": ""
},
{
"docid": "8653eb7da971590696a66650d2786295",
"score": "0.48492703",
"text": "def internalGetInvMass(self):\n return _bullet.btSolverBody_internalGetInvMass(self)",
"title": ""
},
{
"docid": "dd5b180f6abf031b71de385b78757078",
"score": "0.48431498",
"text": "def get_mw(self):\n if self.__mw is None:\n self.__mw = 0\n for letter in self.__sequence:\n # if the letter of the sequence doesn't match an aminoacid,\n # it is treated as an \"error\" and no weight is added\n try:\n self.__mw += self.weights[letter]\n except:\n pass\n return self.__mw",
"title": ""
},
{
"docid": "47aec77ff06252e47553c13234c05a72",
"score": "0.48337224",
"text": "def getDustMass(self, idust=-1):\n\n vol = self.grid.getCellVolume()\n if idust > 0:\n #\n # I'm not sure if this is the right way of doing it but right now I don't have a better idea\n #\n if isinstance(self.grid, radmc3dOctree):\n dmass = (vol * self.rhodust[:, idust]).sum()\n else:\n dmass = (vol * self.rhodust[:, :, :, idust]).sum()\n else:\n dmass = 0.\n if isinstance(self.grid, radmc3dOctree):\n for i in range(self.rhodust.shape[1]):\n dmass += (vol * self.rhodust[:, i]).sum()\n else:\n for i in range(self.rhodust.shape[3]):\n dmass += (vol * self.rhodust[:, :, :, i]).sum()\n\n return dmass",
"title": ""
},
{
"docid": "295d9c0a9bba0742d188a581a6c8cbb9",
"score": "0.48278084",
"text": "def MassMatrix(im, iwi, r):\n return _bullet.MassMatrix(im, iwi, r)",
"title": ""
},
{
"docid": "686e91e3ea05face2d11b4001a38ef19",
"score": "0.48274502",
"text": "def calculate_protein_mass(seq):\n dic = {\n 'A':71.03711,\n 'C':103.00919,\n 'D':115.02694,\n 'E':129.04259,\n 'F':147.06841,\n 'G':57.02146,\n 'H':137.05891,\n 'I':113.08406,\n 'K':128.09496,\n 'L':113.08406,\n 'M':131.04049,\n 'N':114.04293,\n 'P':97.05276,\n 'Q':128.05858,\n 'R':156.10111,\n 'S':87.03203,\n 'T':101.04768,\n 'V':99.06841,\n 'W':186.07931,\n 'Y':163.06333}\n weight = 0\n for i in seq:\n weight += dic.get(i)\n return weight",
"title": ""
},
{
"docid": "72cc45215c5abf7c1359bd04d098959c",
"score": "0.48260626",
"text": "def total_weight(m):\n if is_weight(m):\n return size(m)\n else:\n assert is_mobile(m), \"must get total weight of a mobile or a weight\"\n return total_weight(end(left(m))) + total_weight(end(right(m)))",
"title": ""
},
{
"docid": "e600709375ec0fc01db7b5c69bef348d",
"score": "0.48254204",
"text": "def _get_bmu(self, sample):\r\n # TODO expose distance function as parameter\r\n loc = np.argmin(((self.K - sample) ** 2).sum(axis=2))\r\n # assumes 2D Kohonen layer\r\n return (np.divide(loc, self.kshape[1]).astype('int'), loc % self.kshape[1])",
"title": ""
},
{
"docid": "e27b3a921c5d3394df0a71eeeeafa17c",
"score": "0.4818563",
"text": "def getDustMass(self, idust=-1):\n\n vol = self.grid.getCellVolume()\n dmass = -1.\n if idust>0:\n #\n # I'm not sure if this is the right way of doing it but right now I don't have a better idea\n #\n if isinstance(self.grid, radmc3dOctree):\n dmass = (vol*self.rhodust[:,idust]).sum()\n else:\n dmass = (vol * self.rhodust[:,:,:,idust]).sum()\n else:\n dmass = 0.\n if isinstance(self.grid, radmc3dOctree):\n for i in range(self.rhodust.shape[1]):\n dmass += (vol * self.rhodust[:,i]).sum()\n else:\n for i in range(self.rhodust.shape[3]):\n dmass += (vol * self.rhodust[:,:,:,i]).sum()\n\n return dmass",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="705"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">81869966304cac1edc9a4a0a291c2a06</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">>>> cover_phone_number('01234 567 890') '01234 '</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "6a782a16b67a0e7bc8c81c9310fd264e",
"score": "0.74416536",
"text": "def cover_phone_number(no):\n result = ''\n for order, digit in enumerate(no):\n if order < 5:\n result = result + digit\n else:\n if order in (5, 8, 11):\n result = result + ' '\n result = result + '*'\n return result",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "10ef34eb9301b5071bdb4651a7f4541b",
"score": "0.7311842",
"text": "def phone(number):\n\treturn",
"title": ""
},
{
"docid": "6973b0b1aa9a6a9c68ae05a6f689a096",
"score": "0.72521627",
"text": "def get_raw_phone_number(phone):\n if not phone:\n return\n if phone.startswith('+'):\n phone = phone.replace('+', '00')\n return ''.join(i for i in phone if i.isdigit())",
"title": ""
},
{
"docid": "c540de7f74e3544e686e0dea82c8b8a3",
"score": "0.7147944",
"text": "def phone(astr_num):\n res = \"\"\n for char in astr_num:\n if char not in \"()\":\n res += char\n return \"+38\"+res",
"title": ""
},
{
"docid": "5c420c5d0c15213d1d8ec64b737a9b59",
"score": "0.7055469",
"text": "def format_phone_1(data):\r\n phone = str(data)\r\n clean_phone = re.sub('^081\\d{7}', '+39{}'.format(phone), phone)\r\n return clean_phone",
"title": ""
},
{
"docid": "fdef1fcbaae89e3ff2c793a94f5c134e",
"score": "0.6807835",
"text": "def format_phone_final(data):\r\n #TODO: possible to craft one function instead of three, but safer approach\r\n phone = str(data)\r\n m = re.search(pattern_phone, phone)\r\n if m is not None:\r\n return m.group(0)",
"title": ""
},
{
"docid": "5ee56a74c588c6460908c333d9590633",
"score": "0.67871296",
"text": "def create_phone_number(n):\n return '({}{}{}) {}{}{}-{}{}{}{}'.format(*n)",
"title": ""
},
{
"docid": "96f4c86f171776b6c4cd4d6cf90b5316",
"score": "0.67333597",
"text": "def phone_better(exchange, number, areacode='415'):",
"title": ""
},
{
"docid": "3668004d2adbc002a2cfd057e4eb22d1",
"score": "0.6663207",
"text": "def format_phone(data):\r\n phone = str(data)\r\n clean_phone = re.sub('\\+39\\s{1}081\\s{1}\\d{7}', phone.replace(' ', ''), phone)\r\n return clean_phone",
"title": ""
},
{
"docid": "bbc326832c4a35168d3e9d6193c50968",
"score": "0.66597974",
"text": "def number(self, number_only=True, pad=15):\n if number_only:\n return self._number\n try: \n l = len(self._number)\n except:\n return ''\n if l==7:\n phone = \"\"\"%s-%s\"\"\" % (self._number[0:3], self._number[3:7])\n elif l==10:\n phone = \"\"\"%s-%s-%s\"\"\" % (self._number[0:3], self._number[3:6], self._number[-4:])\n else:\n phone = self._number\n \n return phone.ljust(pad)",
"title": ""
},
{
"docid": "eb9e40884ae8cf3df959ed75e9302084",
"score": "0.66421884",
"text": "def format_phone_num(self, phone_num):\n\n\t\tregex = \"[0-9]+\"\n\t\tnums = re.findall(regex, phone_num)\n\n\t\t#formats into string \n\t\tnew_num = \"\"\n\n\t\tfor every_num in nums:\n\t\t\tnew_num += every_num\n\n\t\t#introduced the correct formatting to the string of nums \n\t\t\n\t\tformated_num = \"(\" + new_num[0:3] + \")\" + new_num[3:6] + \"_\" + new_num[6:]\n\t\tprint (formated_num)\t\n\t\t\n\t\t# save formated num to contact \n\t\tself.phone_num + formated_num",
"title": ""
},
{
"docid": "2544a4e2392206d13fa6a10e23882ba8",
"score": "0.6625255",
"text": "def mobile_number(text):\n mob_num_regex = mob_num_regex = r'''\\d{3}[-\\.\\s]??\\d{3}[-\\.\\s]??\\d{4}|\\(\\d{3}\\)[-\\.\\s]*\\d{3}[-\\.\\s]??\\d{4}|\\d{3}[-\\.\\s]\\d{3}.\\d{4}|\\(\\d{3}[)-\\.\\s].\\d{3}.\\d{4}'''\n\n phone = re.findall(re.compile(mob_num_regex), text)\n if phone:\n number = ''.join(phone[0])\n\n return str(number)\n else:\n\n return None",
"title": ""
},
{
"docid": "bf22dabe680c963da341261bbc35d110",
"score": "0.6476084",
"text": "def check_phone_number(in_str):\r\n if match(\"\\+[7]\\d{10}\", in_str) and len(in_str) == 12:\r\n return in_str[0:2] + \"-\" + in_str[2:5] + \"-\" + in_str[5:8] + \"-\" + in_str[8:10] + \"-\" + in_str[10:12]\r\n if match(\"\\+[7][-]\\d{3}[-]\\d{3}[-]\\d{2}[-]\\d{2}\", in_str) and len(in_str) == 16:\r\n return in_str\r\n return None",
"title": ""
},
{
"docid": "2adb7853b8d2a01b91b56a6077857ad5",
"score": "0.6450726",
"text": "def format(self, number):\n\n number = number.replace(\" \", \"\") # Remove all white spaces\n\n # Use regular expressions to match numbers starting with either\n # 07, +447, or 447 followed by 9 other digits to have a total\n # of 11 digits\n match = re.compile(r'^(07(\\d{9})|\\+?447(\\d{9}))$').search(number)\n\n # Return the following message if no match was found\n if not match:\n return(\"Invalid number.\")\n \n # Construct the 9 digits without the areacode. group(2) matches\n # 07 case, while group(3) matches +447 or 447 cases\n remainingDigits = match.group(2) if match.group(2) else match.group(3)\n return \"+447\" + remainingDigits",
"title": ""
},
{
"docid": "ae8afe2d49989999f7c5df9a47e92052",
"score": "0.6412868",
"text": "def validate_phone_numbers(phone_number):\n\n all_matches = phone_number_re.findall(phone_number)\n for match in all_matches[0]:\n if len(match) > 2:\n phone_number = str(match)\n\n return phone_number",
"title": ""
},
{
"docid": "daf1f9ce747ca94f6fa40f1c144f3b23",
"score": "0.63802403",
"text": "def retain_msisdn(msisdn):\n if msisdn is None:\n return u\"None\"\n\n if msisdn == u\"unknown\":\n return u\"\"\n\n # msisdn a regular phone number\n elif msisdn[0:3] == u\"+27\" and len(msisdn) == 12:\n return msisdn\n\n # msisdn a mxit phone number\n elif msisdn[0] == u'm':\n return msisdn\n\n elif msisdn[0:2] == u'07' or msisdn[0:2] == u'27' and len(msisdn) == 10:\n return u'27' + msisdn[1:]\n\n elif msisdn[0:2] == u'27' and len(msisdn) == 11:\n return msisdn\n\n # some other type of number we don't know about\n\n elif msisdn[0:2] != '27' and msisdn[0:2] != '07' and msisdn[0:3] != '+27':\n return msisdn",
"title": ""
},
{
"docid": "ab876932aae05eb7a6ff7da7c495e0e7",
"score": "0.6372481",
"text": "def generate_number():\n\n number = []\n for i in range(3):\n for i in range(3):\n number.append(str(random.randint(0, 9)))\n number.append(' ')\n number = (''.join(number)).strip(' ')\n print('telephone: ', number)\n return number",
"title": ""
},
{
"docid": "72f4581627a69f0c55dd45402274f582",
"score": "0.6355498",
"text": "def phone_number(self, mask: str = \"\", placeholder: str = \"#\") -> str:\n if not mask:\n code = self.random.choice(CALLING_CODES)\n default = f\"{code}-(###)-###-####\"\n masks = self.extract([\"telephone_fmt\"], default=[default])\n mask = self.random.choice(masks)\n\n return self.random.custom_code(mask=mask, digit=placeholder)",
"title": ""
},
{
"docid": "0984ab83a905bc038cf88a29a27b0092",
"score": "0.635545",
"text": "def get_phone_number_of_contact():\n contact_phone_number = input(\"Phone Number | Enter Your Contact\\'s 10 Digit Phone Number, ex: 1235551212, Then Press Enter: \") # Phone Number\n length_of_contact_phone_number = len(contact_phone_number)\n if contact_phone_number.isdigit() == True and length_of_contact_phone_number == 10:\n pass\n else:\n while contact_phone_number.isdigit() == False or length_of_contact_phone_number != 10:\n contact_phone_number = input(\"\\n---> Invalid Input - Value Must Include 10 Digits -- Please Enter Your Contact's Age, Then Press Enter: \")\n length_of_contact_phone_number = len(contact_phone_number)\n return contact_phone_number",
"title": ""
},
{
"docid": "d13265b8f8133548e63433b63f976ce0",
"score": "0.63490164",
"text": "def phonenumber_from_raw_contact(raw_phone_string):\n phonenumber = unicodedata.normalize(\"NFKC\", raw_phone_string).strip()\n # Some phone numbers are prefixed with \"(cell)\", get rid of that\n phonenumber = phonenumber.strip(\"(cell)\") \\\n .strip(\" – \")\n return phonenumber",
"title": ""
},
{
"docid": "54663442ecc8150753e0bc9556ffcd2b",
"score": "0.6342517",
"text": "def improve_phone_number(phone):\n phone_text = phone.encode('ascii', 'ignore') # encode to ascii\n phone_text = phone_text.lower()\n phone_text = phone_text.strip()\n extension_digits = None\n #\n # strip off US international country code\n #\n if phone_text.find('+1 ') == 0:\n phone_text = phone_text[3:]\n if phone_text.find('+1-') == 0:\n phone_text = phone_text[3:]\n if phone_text.find('(1)') == 0:\n phone_text = phone_text[3:]\n digits = []\n for c in list(phone_text):\n if c in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']:\n digits.append(c)\n if len(digits) > 10 or phone_text.rfind('x') > -1:\n # pull off the extension\n i = phone_text.rfind(' ') # last blank\n if i > 0:\n extension = phone_text[i + 1:]\n extension_digits = []\n for c in list(extension):\n if c in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']:\n extension_digits.append(c)\n digits = [] # reset the digits\n for c in list(phone_text[:i + 1]):\n if c in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']:\n digits.append(c)\n elif phone_text.rfind('x') > 0:\n i = phone_text.rfind('x')\n extension = phone_text[i + 1:]\n extension_digits = []\n for c in list(extension):\n if c in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']:\n extension_digits.append(c)\n digits = [] # recalculate the digits\n for c in list(phone_text[:i + 1]):\n if c in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']:\n digits.append(c)\n else:\n extension_digits = digits[10:]\n digits = digits[:10]\n if len(digits) == 7:\n if phone[0:5] == '352392':\n updated_phone = '' # Damaged UF phone number, nothing to repair\n extension_digits = None\n elif phone[0:5] == '352273':\n updated_phone = '' # Another damaged phone number, not to repair\n extension_digits = None\n else:\n updated_phone = '(352) ' + \"\".join(digits[0:3]) + '-' + \"\".join(digits[3:7])\n elif len(digits) == 10:\n updated_phone = '(' + \"\".join(digits[0:3]) + ') ' + \"\".join(digits[3:6]) + \\\n '-' + \"\".join(digits[6:10])\n elif len(digits) == 5 and digits[0] == '2': # UF special\n updated_phone = '(352) 392-' + \"\".join(digits[1:5])\n elif len(digits) == 5 and digits[0] == '3': # another UF special\n updated_phone = '(352) 273-' + \"\".join(digits[1:5])\n else:\n updated_phone = '' # no repair\n extension_digits = None\n if extension_digits is not None and len(extension_digits) > 0:\n updated_phone = updated_phone + ' ext. ' + \"\".join(extension_digits)\n return updated_phone",
"title": ""
},
{
"docid": "aa5348cd5b4fdbdb4f4e001297be3832",
"score": "0.63419574",
"text": "def get_phone_num(self, id_inp):\n try:\n if not self.check_existed_id(\"Person\", id_inp):\n return \"\"\n cursor = self.func\n cursor.execute(\"select phone_num from Person where id = ?\", id_inp)\n ans = \"\"\n for c in cursor:\n ans = c[0]\n break\n cursor.commit()\n return ans\n except Exception as ex:\n print(\"----Error in get_phone_num----\")\n print(ex)\n return \"\"",
"title": ""
},
{
"docid": "ac48aba9f42da888d61750508b766f11",
"score": "0.6256835",
"text": "def phone_number(self) -> str:\n return pulumi.get(self, \"phone_number\")",
"title": ""
},
{
"docid": "962df087c2ea3ea8a4ee6587a9b4d20b",
"score": "0.62413645",
"text": "def phone_num(self) -> str:\n return pulumi.get(self, \"phone_num\")",
"title": ""
},
{
"docid": "af92bbc65d1804b8ae266304c5236d7f",
"score": "0.6234645",
"text": "def get_number(self, number, inv_type, max_size):\n if not number:\n return '0'\n result = ''\n for i in number:\n if inv_type == 'vou_number' and i.isdigit():\n if len(result) < max_size:\n result = i + result\n elif i.isalnum():\n if len(result) < max_size:\n result = i + result\n return result[::-1].strip()",
"title": ""
},
{
"docid": "aa59ddc12f189f8a61037150bfdd6a75",
"score": "0.6197003",
"text": "def phonenumber(anon, obj, field, val):\n return anon.faker.phone_number(field=field)",
"title": ""
},
{
"docid": "eeef633cb154b75f665476ccb8535713",
"score": "0.61963105",
"text": "def _normalize_phone_num(phonestr, region=None):\n onlynum = re.sub(\"[^0-9]+\", \"\", phonestr)\n if len(onlynum) == 10 and region == \"US\":\n onlynum = \"1\" + onlynum\n return \"+\" + onlynum",
"title": ""
},
{
"docid": "538f36356eeb9556dbd007b1459842a6",
"score": "0.6190672",
"text": "def formatPhone(self, phone):\n return re.sub(r'[^a-zA-Z0-9]', '', phone)",
"title": ""
},
{
"docid": "39d1a464d31d791e019233c52c8ef048",
"score": "0.6154872",
"text": "def extract_numbers(string: str, phone_first00=False) -> str:\n regex = r\"\\D\"\n return (\"+\" + re.sub(regex, \"\", string)) if phone_first00 is True and string[0] == \"+\" \\\n else re.sub(regex, \"\", string)",
"title": ""
},
{
"docid": "0f6b76255ee544de93f1c77d9e3d3bc8",
"score": "0.61408556",
"text": "def parse_phone(value):\n code = ''\n number = ''\n if value != None and value != '':\n pos = 0\n value.strip()\n if value[0] == '(':\n pos = value.find(')')\n if pos > 1:\n code = value[1:pos]\n # Skip closing bracket and a space\n pos += 1\n if value[pos] == ' ':\n pos += 1\n # Slice the remaining part (without the code, if it's present)\n number = value[pos:]\n return code, number",
"title": ""
},
{
"docid": "96e03f196df3d3d8d6f77dd67322d81c",
"score": "0.6137079",
"text": "def simplify_phoneme(phoneme):\n return re.sub(r\"(\\d+|_.$)\", \"\", phoneme)",
"title": ""
},
{
"docid": "0b83b155af082a062d89a247202719cf",
"score": "0.61245936",
"text": "def get_user_phone_number(self,info):\n self.number = info.split(\" \")\n return self.number[1]",
"title": ""
},
{
"docid": "7a3c5eaa86c29ce1905207d18712fb71",
"score": "0.6104285",
"text": "def validate_telephone(ctx, param, value: str) -> str:\n return ''.join(filter(str.isdigit, value))",
"title": ""
},
{
"docid": "56330b2123987fab982f90f32b3cdd52",
"score": "0.6059106",
"text": "def get_phone(number_format=\"{area_code}-{exchange_code}-{line_number}\", **kwargs):\n kwargs.setdefault(\"country_code\", kwargs.pop(\"country\", \"1\"))\n kwargs.setdefault(\"area_code\", kwargs.pop(\"area\", get_digits(3)))\n kwargs.setdefault(\"exchange_code\", kwargs.pop(\"exchange\", get_digits(3)))\n kwargs.setdefault(\"line_number\", kwargs.pop(\"line\", get_digits(4)))\n return number_format.format(**kwargs)",
"title": ""
},
{
"docid": "eddc9db5dbee12676ec3aa0fdf712470",
"score": "0.5981601",
"text": "def rgi_num(num):\n \n if len(str(num))==1:\n num='0'+str(num)\n else: \n num=str(num) \n return num",
"title": ""
},
{
"docid": "16d399da4e86a9025a99d27d01994cee",
"score": "0.5975799",
"text": "def update_phone(phoneNumber):\n\trePhone = re.compile(r'(\\d{3})(\\D*)(\\d{3})(\\D*)(\\d{4})')\n\tm = rePhone.search(phoneNumber)\n\tphone = None\n\tif m:\n\t\tphone = m.group()\n\t\tphone = phone.replace(' ', '').replace('-','').replace(')','')\n\t\tphone = phone[0:3] + '-' + phone[3:6] + '-' + phone[6:]\n\treturn phone",
"title": ""
},
{
"docid": "0d811a5bf9f7525502982a542680aa80",
"score": "0.5970193",
"text": "def check_phone(value, reg=PHONNB): \n numb = reg.search(value)\n if numb:\n group = numb.group()\n if '.' in value:\n group = ''.join(group.split('.')) \n if '-' in value:\n group = ''.join(group.split('-'))\n group = ''.join(group.split(' '))\n group = '+33' + group\n return group\n else:\n return value",
"title": ""
},
{
"docid": "4abd8709631d51bbbcdb106545a393c5",
"score": "0.59478885",
"text": "def phone(string: str, number: str) -> str:\n if number not in string:\n return f\"Error => Not found: {number}\"\n if string.count(number) > 1:\n return f\"Error => Too many people: {number}\"\n for sub in string.splitlines(): # type: str\n if number in sub:\n name: str = (\n re.search(r\"<[ a-zA-z']+>\", sub).group().lstrip(\"<\").rstrip(\">\")\n )\n address: str = \" \".join(\n filter(\n lambda item: item,\n re.split(f\"{number}|{name}|[^A-Za-z0-9-.]\", sub),\n )\n )\n return f\"Phone => {number}, Name => {name}, Address => {address}\"",
"title": ""
},
{
"docid": "6c36a9b74925e286f47f2599dfc8f019",
"score": "0.59295356",
"text": "def audit_phone_format(phone, phoneDict):\n phoneLen = len(phone)\n tmp = \"\"\n for i in range(0, phoneLen):\n if phone[i].isdigit():\n tmp = tmp + phone[i]\n phoneDict[phone] = tmp[-11:]",
"title": ""
},
{
"docid": "c58c0386b0eb09ea8f34f6d9d11cf2e1",
"score": "0.59252465",
"text": "def pick_phone():\n phone = get_string(\"What is your phone number: -> \")\n\n # this removes anything that is not a number\n phone = re.sub(r\"\\D\", \"\", phone)\n\n if not validate_phone(phone):\n # if number doesn't appear valid, print message\n print(\"This doesn't appear to be a valid phone number\")\n # try again\n return pick_phone()\n\n return phone",
"title": ""
},
{
"docid": "18514c0af97d071f443182543128bfb1",
"score": "0.5915917",
"text": "def clean_phone(phone_number, drop_invalid=False, area_code='406'):\n \n # Removes non-digit characters\n clean_number = digit_only(phone_number)\n\n # Removes US country code if present\n if len(clean_number) == 11 and clean_number[0] == '1': \n clean_number = clean_number[1:]\n\n # Adds area code if missing\n elif len(clean_number) == 7:\n clean_number = area_code + clean_number\n\n # If drop_invalid is true, returns empty string for still too long/short numbers\n elif drop_invalid == True and len(clean_number)!=10:\n clean_number = ''\n\n return clean_number",
"title": ""
},
{
"docid": "0a32bebd30f1d515fcf719651719b301",
"score": "0.5904773",
"text": "def NativeDigits(self) -> _n_0_t_1[str]:",
"title": ""
},
{
"docid": "a7a5a87defe393c864a5b9e765b785b4",
"score": "0.58889395",
"text": "def get_phone_intl_format(self, prefix='+88'):\n phone_intl = f'{prefix}{self.phone}' if self.phone else None\n logger.debug( # prints class and function name\n f\"{self.__class__.__name__}.{_getframe().f_code.co_name} \"\n f\"Returning phone number in international format: {phone_intl}\"\n )\n return phone_intl",
"title": ""
},
{
"docid": "3c09e19a9a19f0f59a92df8f6eadbe43",
"score": "0.5885409",
"text": "def clean_number(number):\n \n if number.startswith('0'):\n number = '49' + number[1:]\n number = number.replace(' ', '').replace('-', '').replace('+', '')\n return number",
"title": ""
},
{
"docid": "8211f49037c02a1e714950254ec03d0c",
"score": "0.5881193",
"text": "def compact(number):\n return clean(number, ' ').upper().strip()",
"title": ""
},
{
"docid": "ca6def6357d2eb5d5d7778ee5469348a",
"score": "0.586726",
"text": "def random_phone_number():\n return random.randint(100_000_0000, 1_000_000_0000)",
"title": ""
},
{
"docid": "92ededbd385da99c09bec51f144c4d15",
"score": "0.5855704",
"text": "def phone_number(self) -> Optional[str]:\n return pulumi.get(self, \"phone_number\")",
"title": ""
},
{
"docid": "3aba84fc4642885d005c25b697d6766a",
"score": "0.58416986",
"text": "def clean_phone(self):\n return ''.join([l for l in self.cleaned_data.get('phone', '') if l.isdigit()])",
"title": ""
},
{
"docid": "b69f0bd882ac4b6b8d52d2003e121f10",
"score": "0.5836177",
"text": "def compact(number):\n return clean(number, ' ').strip()",
"title": ""
},
{
"docid": "241374516c19a8b6697f439f24a5521d",
"score": "0.5778223",
"text": "def format_phone_number(number):\n if not isinstance(number, phonenumbers.PhoneNumber):\n number = phonenumbers.parse(number)\n return phonenumbers.format_number(number, phonenumbers.PhoneNumberFormat.INTERNATIONAL)",
"title": ""
},
{
"docid": "047feb35ee6a8246c61c69c9fb9da777",
"score": "0.5759929",
"text": "def compact(number):\n return clean(number, ' -.').strip()",
"title": ""
},
{
"docid": "aa88d8010126649d2f0fb298d24af6e5",
"score": "0.57577205",
"text": "def make_pretty(phonenumber):\n\tif phonenumber is None or phonenumber == \"\":\n\t\treturn \"\"\n\n\tphonenumber = normalize_number(phonenumber)\n\n\tif phonenumber == \"\":\n\t\treturn \"\"\n\telif phonenumber[0] == \"+\":\n\t\tprettynumber = _make_pretty_international(phonenumber[1:])\n\t\tif not prettynumber.startswith(\"+\"):\n\t\t\tprettynumber = \"+\"+prettynumber\n\telif 8 < len(phonenumber) and phonenumber[0] in (\"1\", ):\n\t\tprettynumber = _make_pretty_international(phonenumber)\n\telif 7 < len(phonenumber):\n\t\tprettynumber = _make_pretty_with_areacode(phonenumber)\n\telif 3 < len(phonenumber):\n\t\tprettynumber = _make_pretty_local(phonenumber)\n\telse:\n\t\tprettynumber = phonenumber\n\treturn prettynumber.strip()",
"title": ""
},
{
"docid": "b7e9857930f762b95b0a5254333756b5",
"score": "0.57410794",
"text": "def phone(user: dict, context, area_code: bool = True) -> str: # type: ignore\n if area_code:\n return user[\"area_code\"] + \"-\" + user[\"phone\"]\n else:\n return user[\"phone\"]",
"title": ""
},
{
"docid": "e7c524a01eaea07011ee08f878380e12",
"score": "0.57352614",
"text": "def coerce_input(value: str) -> str:\n return _check_phone_number(value)",
"title": ""
},
{
"docid": "656e8cb957cc73392a3a34e6307828ec",
"score": "0.57312644",
"text": "def Luhn_digit(digits):\n return str(Generation.Luhn(digits) * 9)[-1]",
"title": ""
},
{
"docid": "32a4aad82f479db10a537c269f118895",
"score": "0.5722507",
"text": "def func(theinput):\n num, digFrom, digTo = theinput.split()\n\n return ''",
"title": ""
},
{
"docid": "2720f139e4fe3fa1598f4c380f2453fb",
"score": "0.57144773",
"text": "def mask_phone_number(number):\n if isinstance(number, phonenumbers.PhoneNumber):\n number = format_phone_number(number)\n return phone_mask.sub('*', number)",
"title": ""
},
{
"docid": "a6a8ccc6d8565eaec639824d9b13b415",
"score": "0.5714444",
"text": "def s3_phone_represent(value):\n\n if not value:\n return current.messages[\"NONE\"]\n return s3_str(\"%s%s\" % (chr(8206), s3_str(value)))",
"title": ""
},
{
"docid": "c3e72617a45ef7b95a842421abaf507e",
"score": "0.57090396",
"text": "def getNumber (self):\r\n return '000'",
"title": ""
},
{
"docid": "c3e72617a45ef7b95a842421abaf507e",
"score": "0.57090396",
"text": "def getNumber (self):\r\n return '000'",
"title": ""
},
{
"docid": "e9eebc458cb62e4d4e7ef41a99b57600",
"score": "0.56968695",
"text": "def phone_number(phone):\n phone = str(phone)\n if(len(phone) == 10 and phone[0] in \"6789\" and user.check_int(phone)):\n #number should be 10 digits and start with 6 or 7 or 8 or 9\n return True\n else:\n return False",
"title": ""
},
{
"docid": "3818b5313a0143056be650b3c95d1fae",
"score": "0.5655635",
"text": "def coerce_output(value: str) -> str:\n return _check_phone_number(value)",
"title": ""
},
{
"docid": "6c62ac3c57c7a5b54953beb7fb89f3c7",
"score": "0.5652095",
"text": "def phoneword(phonenumber):\n\n digit_to_chars = {\n '2': 'abc', '3': 'def', '4': 'ghi', '5': 'jkl',\n '6': 'mno', '7': 'pqrs', '8': 'tuv', '9': 'wxyz'\n }\n\n size = len(phonenumber)\n\n def phoneword_rec(previous_results, cursor):\n if cursor == size:\n return previous_results\n digit = phonenumber[cursor]\n results = []\n for char in digit_to_chars[digit]:\n results.extend(prev_result + char for prev_result in previous_results)\n return phoneword_rec(results, cursor + 1)\n\n return phoneword_rec([''], 0)",
"title": ""
},
{
"docid": "bb3c15cf8ac6bc648a7799250f5754f9",
"score": "0.56437474",
"text": "def format(number):\n number = compact(number)\n return number[:3] + '.' + number[3:6] + '.' + number[6:-2] + '-' + number[-2:]",
"title": ""
},
{
"docid": "980f60c935c75de420fc19d6654251bb",
"score": "0.56429577",
"text": "def _make_standard_rus(number_11):\n number_11 = f\"+7 ({number_11[1:4]}) {number_11[4:]}\"\n return number_11",
"title": ""
},
{
"docid": "a21bdccd0d52f0c61a39de948a1a946d",
"score": "0.56424457",
"text": "def get_agency_no(val):\n return val.replace(' ', '').replace('-', '')[:7]",
"title": ""
},
{
"docid": "799af46666bdffea989c7693afbe00e1",
"score": "0.56422794",
"text": "def clean_phone_number(field_name):\n\n @check_field_is_empty(field_name)\n def wrapper(self):\n \"\"\"Decorator wrapped method.\n \"\"\"\n\n value = self.cleaned_data.get(field_name)\n\n # allow for a '+' prefix which means '00'\n if value[0] == '+':\n value = '00' + value[1:]\n\n if not value.isdigit():\n raise forms.ValidationError(\"Only numerical characters are allowed\")\n\n return value\n return wrapper",
"title": ""
},
{
"docid": "f2488ad2977f92ba363b747bfd00eeea",
"score": "0.56392336",
"text": "def validate_mobile(num):\n import re\n\n PREFIX = (\"070\", \"072\", \"073\", \"076\", \"079\")\n if len(num) == 13:\n if num.find(\"-\") == 3: #bindestreck\n lst1 = num.split(\"-\")\n if lst1[0] in PREFIX: #Matches prefix\n if re.search('[^0-9 ]+', lst1[1]) is None:\n suffix = lst1[1].split(\" \")\n if len(suffix) == 3 and len(suffix[0]) == 3 and len(suffix[1]) == 2 and len(suffix[2]) == 2:\n return True\n\n return False",
"title": ""
},
{
"docid": "2ce5ab7e8787f973560335b4ac69cc0f",
"score": "0.5634245",
"text": "def getNumber (self):\r\n self.number = '000'",
"title": ""
},
{
"docid": "f320489c0b8d23c66f9487b024e2acd9",
"score": "0.56286573",
"text": "def hex(number):\n return ''",
"title": ""
},
{
"docid": "c42180ad381df3ef705c35a25aa6dd7a",
"score": "0.5615288",
"text": "def _oir_number_transform(self,number):\n if (len(number) != 10):\n raise AssertionError(\"Please enter the correct number, which is expected to have 10 digits.\")\n else:\n self.expected_number = number[3:]\n return self.expected_number",
"title": ""
},
{
"docid": "f946e435c7aa2e1360f14c3164b549d3",
"score": "0.55846435",
"text": "def phone(self) -> str:\n return self._phone",
"title": ""
},
{
"docid": "9bc922327fde2e746080e31db0e5761c",
"score": "0.55753875",
"text": "def make_short_address(address: str) -> str:\n results = address.replace(\"-\", \":\").split(\":\")\n return f\"{results[-2].upper()}{results[-1].upper()}\"[-4:]",
"title": ""
},
{
"docid": "41a9d347785bf891044b7a914506d235",
"score": "0.55690897",
"text": "def create_phone_text(self) -> str:\n reserve: Reserve = self.state_manager.data\n return (f\"{self.create_book_text(reserve)}\\n\"\n f\"{self.strings.phone_message}\")",
"title": ""
},
{
"docid": "f5034cefd81537f94d47daa8e9ba14a8",
"score": "0.55684197",
"text": "def oct(number):\n return ''",
"title": ""
},
{
"docid": "60b724f0d64cdf881e45113d939f72b4",
"score": "0.5566166",
"text": "def _format_account_number(account):\n\n if account.shred_no:\n return \"{}-{}-{}\".format(account.account_no,account.sub_no, account.shred_no)\n\n elif account.sub_no:\n return \"{}-{}\".format(account.account_no,account.sub_no)\n\n else:\n return str(account.account_no)",
"title": ""
},
{
"docid": "3fee29b63fe687dfc8d47735cd60e001",
"score": "0.55589783",
"text": "def phone_number(self):\n return self._phone_number",
"title": ""
},
{
"docid": "5ef1540aae4231efcdda8c699160835e",
"score": "0.5547124",
"text": "def normalize(number, country_code=None):\n try:\n parsed_number = phonenumbers.parse(number, country_code)\n return format(parsed_number)\n except Exception:\n return number",
"title": ""
},
{
"docid": "ac731cee0b6888bd5423e35a0fa2770e",
"score": "0.5516838",
"text": "def _format_phone(self, formatStr, phone):\n return '\"' + formatStr.format(phone) + '\"'",
"title": ""
},
{
"docid": "714289675503b2ce415408b9b4121093",
"score": "0.54969287",
"text": "def get_number():\r\n number = input(\"Enter 2 or 3 digit number : \")\r\n number = int(number)\r\n return number",
"title": ""
},
{
"docid": "5a20a77c42c7e2a422401e58eae498ae",
"score": "0.54743314",
"text": "def clean_telephone_number(self):\n\n organization = Organization.objects.get(org_id=self.org_id)\n mobile_number = self.cleaned_data.get('telephone_number')\n if organization.in_trial_mode:\n if not is_mobile_number_unique_for_trial_account(organization, mobile_number):\n self._errors['telephone_number'] = self.error_class(\n [_(u\"Sorry, this number has already been used for a different DataWinners Basic account.\")])\n return mobile_number",
"title": ""
},
{
"docid": "d6fa42c7a0d3423e5d447d8b7c73e064",
"score": "0.5465157",
"text": "def short_address(address: str) -> str:\n results = address.replace(\"-\", \":\").split(\":\")\n return f\"{results[0].upper()}{results[1].upper()}\"[0:4]",
"title": ""
},
{
"docid": "0d476d4f9e5683dcb5e9e3da364a8ca7",
"score": "0.5457714",
"text": "def getFull(num):\n i = 0\n try:\n i = int(num)\n except ValueError as verr: pass\n except Exception as ex: pass\n\n if i in NAME_BY_NUM.keys(): return '{0}'.format(NAME_BY_NUM[i])\n else: raise ValueError('\"num\" should be in the range of \"1 - 12\"')",
"title": ""
},
{
"docid": "854d583653fac06be59581ff98771cf9",
"score": "0.54516506",
"text": "def clean(self, value):\n# print \"-----------------|%s|------------------!!!\" % value\n super(RU_PhoneNumberField, self).clean(value)\n if value in EMPTY_VALUES:\n return u''\n value = re.sub('(\\s+)', '', smart_unicode(value))\n# print value\n for pd_re in phone_digits_re:\n m = pd_re.search(value)\n if m:\n value = u'(%s) %s-%s-%s' % (m.group(1), m.group(2), m.group(3), m.group(4))\n return value\n raise ValidationError(self.error_messages['invalid'])",
"title": ""
},
{
"docid": "b8a5b603a13991c37c433d342ad3dbd9",
"score": "0.54470193",
"text": "def format_msisdn(msisdn=None):\n assert msisdn is not None\n num = phonenumbers.parse(msisdn, getattr(settings, 'COUNTRY', 'UG'))\n is_valid = phonenumbers.is_valid_number(num)\n if not is_valid:\n return None\n return phonenumbers.format_number(\n num, phonenumbers.PhoneNumberFormat.E164).replace('+', '')",
"title": ""
},
{
"docid": "53ea24b2c648186b271026ea116f519c",
"score": "0.54454726",
"text": "def insert_cnpj(num):\r\n cnpj = num[:2]+'.'+num[2:5]+'.'+num[5:8]+r'/'+num[8:12]+'-'+num[12:]\r\n return cnpj",
"title": ""
},
{
"docid": "0088e94481730b429c0a68364be2b413",
"score": "0.54335546",
"text": "def add_num(str_):\n if not re.search('\\d*$',str_).group():\n str_ += '_00'\n return str_",
"title": ""
},
{
"docid": "64c5925eb86b968bc1c3794259d6d087",
"score": "0.5428749",
"text": "def matchFormat(n: int, source: int) -> str:\n nstr: str = str(n)\n return \"0\" * (len(str(source)) - len(nstr)) + nstr",
"title": ""
},
{
"docid": "c67ff223d6b286ab4563eb67b806f6e1",
"score": "0.5427244",
"text": "def validate_bd_phone_number(phone_number):\n\tphone_number = re.sub(r'\\D', '', phone_number)\n\tsearch = re.search(\n\t\tr'^(008801|8801|01)(?P<local_number>[1|5-9]{1}(\\d){8})$',\n\t\tphone_number\n\t)\n\tif not search:\n\t\traise ValidationError(\n\t\t\t'Must be a valid Bangaldeshi phone number.'\n\t\t)\n\telse:\n\t\tphone_number = '+8801' + search.group('local_number')\n\treturn phone_number",
"title": ""
},
{
"docid": "6df0da0f26e4ee9fe717e3f1d5050575",
"score": "0.542557",
"text": "def phone(self):\n return self.created_by.userprofile.phone",
"title": ""
},
{
"docid": "240c4b4876c0460fefe174c3467d9cae",
"score": "0.5425534",
"text": "def get_client_phone_num(client_phone_num):\r\n\r\n return Client.query.filter_by(client_phone_num=client_phone_num).first()",
"title": ""
},
{
"docid": "a3ce015196c37da06c0f8db2bc49623c",
"score": "0.5417503",
"text": "def get_numero_cadena(val):\n\tif val == None: return None\n\ttxt = \"%s\" % val\n\treturn txt if not txt.endswith('.0') else txt[:-2]",
"title": ""
},
{
"docid": "cc5027c5d76bc8ea9b122eca4240da64",
"score": "0.5388741",
"text": "def serial(self, number: int) -> str:\n return \"{:04d}\".format(number) + \"{:08d}\".format(\n self.signature(number))[-self.size:]",
"title": ""
},
{
"docid": "fa5f52e7054c0c1d901ded6c6325e67a",
"score": "0.53713566",
"text": "def improve_deptid(s):\n import re\n deptid_pattern = re.compile('([0-9]{1,8})')\n match_object = deptid_pattern.match(s)\n if match_object:\n return match_object.group(1).rjust(8, '0')\n else:\n raise InvalidDataException(s + ' is not a valid deptid')",
"title": ""
},
{
"docid": "540cdd0e7fdcd7eba98600a482ac079b",
"score": "0.5366706",
"text": "def ValidPhoneNumber(phone):\n if (len(phone)) != 12:\n return False\n if(phone[3]!='-' or phone[7]!='-'):\n return False\n Phone = phone.replace('-','')\n return Phone.isnumeric()",
"title": ""
},
{
"docid": "4656aaaaa7dec3fec88f159d13d7226b",
"score": "0.53626645",
"text": "def get_zero_string(n):\n zero_str = ''\n\n if n < 10:\n zero_str = \"00\" + str(n)\n elif n < 100:\n zero_str = \"0\" + str(n)\n elif n < 1000:\n zero_str = str(n)\n\n return zero_str",
"title": ""
},
{
"docid": "ed5ee457793607e26961bd52fa7e63d7",
"score": "0.5357467",
"text": "def sms(self, phone, message=\"The PIN is <pin>\", digits=None):\n params = {\n \"phone\": phone,\n \"message\": message,\n }\n if digits is not None:\n params[\"digits\"] = str(int(digits))\n response = self.json_api_call(\"POST\", \"/verify/v1/sms\", params)\n return response[\"pin\"]",
"title": ""
},
{
"docid": "b0007c46b2fea23d5284a5b43d2e05c4",
"score": "0.53431636",
"text": "def phone_basic(exchange, number, areacode):\n\n # REPLACE THIS LINE AND THE pass BELOW WITH YOUR OWN CODE.\n\n pass",
"title": ""
},
{
"docid": "96d5378538a6e169fc4df9088452c5f5",
"score": "0.53272444",
"text": "def premise_id():\n return '1234567890'",
"title": ""
},
{
"docid": "cfc44728e1bccec20be87fcca4dffb83",
"score": "0.5325004",
"text": "def unsip(n):\r\n n = n.split(\"/\",1)[-1]\r\n# print(\"unsip\", repr(n))\r\n if len(n)==11 and n[0]==\"7\":\r\n n=\"+\"+n\r\n return n",
"title": ""
},
{
"docid": "2c526f5bbf556b6401f8065394ae47b5",
"score": "0.53228533",
"text": "def tell1_formatting(tell1_number):\n if tell1_number < 10:\n tell1_name = \"00{}\".format(tell1_number)\n elif 10 <= tell1_number < 100:\n tell1_name = \"0{}\".format(tell1_number)\n else:\n tell1_name = str(tell1_number)\n\n return tell1_name",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="706"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">fcec292034e2345fb9fb6face4ca9787</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Initializes an empty graph that will store</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "78ac43c9ec2bf6ad7fc97f1d39a653c3",
"score": "0.80484927",
"text": "def __init__(self):\n self.graph = {}",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "f9edf2b2716187983888b6916bdc16e5",
"score": "0.8202905",
"text": "def __init__(self):\n super(Graph, self).__init__()\n self.vertex = []\n self.edges = {}",
"title": ""
},
{
"docid": "cd62b0588bbf781e12dcf50f6b2377a3",
"score": "0.8162918",
"text": "def __init__(self):\n\t\tself.graph = defaultdict(list)",
"title": ""
},
{
"docid": "cf767ba873412feb1404d22bfa23c571",
"score": "0.8070309",
"text": "def __init__(self):\n self.graph = defaultdict(list)",
"title": ""
},
{
"docid": "b0a6e1ed489fcd39263876511d3b62b6",
"score": "0.7927307",
"text": "def __init__(self):\r\n self.graph = defaultdict(list)",
"title": ""
},
{
"docid": "68e736c363419b4006c9b0121176f146",
"score": "0.7683507",
"text": "def __init__(self):\n self.graph_dict = {}",
"title": ""
},
{
"docid": "b3a2db850723a629fe457d71c950748b",
"score": "0.7679049",
"text": "def __init__(self):\n self.graph_dict = {}\n self.size = 0",
"title": ""
},
{
"docid": "1b0370f2f86203838e426aa7316d11fa",
"score": "0.7658616",
"text": "def __init__(self):\n self.graph = collections.defaultdict(list)",
"title": ""
},
{
"docid": "f8afd651b4d97b68cc69a350f8357f5e",
"score": "0.76434314",
"text": "def initialize_graphs_info(self):\r\n self.first_graph = Graph(None)\r\n self.second_graph = Graph(None)\r\n self.file_name = None",
"title": ""
},
{
"docid": "fd3aed43cc63b8c79cacd9f6cd2d8100",
"score": "0.761873",
"text": "def __init__(self):\n self.__G = nx.Graph()",
"title": ""
},
{
"docid": "fb1a849c7b1690e4d30cf941929c5667",
"score": "0.7574053",
"text": "def __init__(self):\n #create an empty directed graph\n self.G = nx.DiGraph()",
"title": ""
},
{
"docid": "4a8c7d51eea693d5aab3c64256f57990",
"score": "0.75702345",
"text": "def init_graph(self, g: GraphInterface) -> None:\n self.g = g",
"title": ""
},
{
"docid": "7d4b994f7cf499f8c9e3b7d5911a0e43",
"score": "0.7564386",
"text": "def __init__(self, edges=set()):\n self._graph = dict()\n self._edges = set()\n self.add_edges(edges)",
"title": ""
},
{
"docid": "053ada760a87c8609325ac428e5a756a",
"score": "0.75465536",
"text": "def __init__(self, size: int = 0) -> None:\n self._graph = _graph.Graph(size)",
"title": ""
},
{
"docid": "2462540ec4c8812d72a7506ba8782fc2",
"score": "0.7514672",
"text": "def _init_(self):\r\n self.edges = defaultdict(list)\r\n self.weights = {}",
"title": ""
},
{
"docid": "06abbaa6f8e043d2f934bf0a77802d15",
"score": "0.7452306",
"text": "def initgraph(self) -> None:\n for i in self.g.Nodes.values():\n i.settag(0)\n i.setweight(math.inf)",
"title": ""
},
{
"docid": "ad672a10ce5cf7b012e6e6851ab6da8b",
"score": "0.7431094",
"text": "def __init__ (self, graph_dict = None):\n if graph_dict == None:\n graph_dict = {}\n self.__graph_dict = graph_dict",
"title": ""
},
{
"docid": "eaf4d0783c57ddedf639c2ea3e565438",
"score": "0.7414546",
"text": "def test_init_creates_graph(graph):\n assert graph.nodes() == []",
"title": ""
},
{
"docid": "e5e8b5efe0363b4fdbfa5ee1aaadbb4e",
"score": "0.73800164",
"text": "def __init__(self, graph_dict={}):\n self.__graph_dict = graph_dict",
"title": ""
},
{
"docid": "e5e8b5efe0363b4fdbfa5ee1aaadbb4e",
"score": "0.73800164",
"text": "def __init__(self, graph_dict={}):\n self.__graph_dict = graph_dict",
"title": ""
},
{
"docid": "b7180dc9e65e952071cedc9eb8e80c8a",
"score": "0.73784816",
"text": "def __init__(self, graph_dict=None):\n if graph_dict == None:\n graph_dict = {}\n self.__graph_dict = graph_dict",
"title": ""
},
{
"docid": "b7180dc9e65e952071cedc9eb8e80c8a",
"score": "0.73784816",
"text": "def __init__(self, graph_dict=None):\n if graph_dict == None:\n graph_dict = {}\n self.__graph_dict = graph_dict",
"title": ""
},
{
"docid": "b7180dc9e65e952071cedc9eb8e80c8a",
"score": "0.73784816",
"text": "def __init__(self, graph_dict=None):\n if graph_dict == None:\n graph_dict = {}\n self.__graph_dict = graph_dict",
"title": ""
},
{
"docid": "b7180dc9e65e952071cedc9eb8e80c8a",
"score": "0.73784816",
"text": "def __init__(self, graph_dict=None):\n if graph_dict == None:\n graph_dict = {}\n self.__graph_dict = graph_dict",
"title": ""
},
{
"docid": "b7180dc9e65e952071cedc9eb8e80c8a",
"score": "0.73784816",
"text": "def __init__(self, graph_dict=None):\n if graph_dict == None:\n graph_dict = {}\n self.__graph_dict = graph_dict",
"title": ""
},
{
"docid": "09e6236d2913af93cb92e3dcbb71d8d6",
"score": "0.7362689",
"text": "def __init__(self):\n self.tree = nx.Graph()\n self.bags = {}",
"title": ""
},
{
"docid": "8b6692d7982250a52187cb40fbee2d5e",
"score": "0.7316179",
"text": "def __init__(self, graph_dict=None):\n self.__graph_dict = graph_dict or dict()",
"title": ""
},
{
"docid": "76221bc698222ea21104176d85e84009",
"score": "0.72934085",
"text": "def init_graph(num_nodes):\n\n # Init graph structure.\n graph = dict()\n\n # Handle the error case case num_nodes is bogus.\n if (num_nodes > 0):\n \n # Add nodes to graph.\n for idx in range(num_nodes):\n graph[idx] = set([])\n\n return graph",
"title": ""
},
{
"docid": "dd2bc6c7d3ba19039d13223c90a69940",
"score": "0.7249726",
"text": "def __init__(self):\n self.edges = dict()",
"title": ""
},
{
"docid": "69445e2eb2768d3bbba3a0eb7f9c41f9",
"score": "0.7229909",
"text": "def __init__(self, nodes = [], edges = []):\n self._graph = {}\n self._graph_reverse = {}\n for node in nodes:\n self.add_node(node)\n for edge in edges:\n self.add_edge(*edge)",
"title": ""
},
{
"docid": "2febee812204bc8dff8745a4ccc5fa46",
"score": "0.7225623",
"text": "def __init__(self):\n try:\n self._adjacency_list = {}\n except Exception as err:\n print(f\"There is an error in init as {err}\")",
"title": ""
},
{
"docid": "0b1963f7a6a703d03ab9bc91100ed14a",
"score": "0.7158139",
"text": "def __init__(self):\n self.nodes = {}\n self.inEdges = {}\n self.outEdges = {}\n self.MC = 0\n self.edgesNum = 0",
"title": ""
},
{
"docid": "a4ccab8c24444c3374feed14ceeb0a02",
"score": "0.7146363",
"text": "def __init__(self):\n self.adjacency_list = {}",
"title": ""
},
{
"docid": "11480007ead7c784d88ba7315e0a0972",
"score": "0.713131",
"text": "def __init__(self):\n self.edges = defaultdict(list)\n self.weights = {}",
"title": ""
},
{
"docid": "11480007ead7c784d88ba7315e0a0972",
"score": "0.713131",
"text": "def __init__(self):\n self.edges = defaultdict(list)\n self.weights = {}",
"title": ""
},
{
"docid": "aad6ae99f47db6e6b54725fa19281e53",
"score": "0.7129956",
"text": "def __init__(self):\n self.varMap = {}\n self.edges = []\n self.rootNodes = []",
"title": ""
},
{
"docid": "2ddb702bed988c2be11b0a5d5be272fc",
"score": "0.71244496",
"text": "def __init__(self):\n\t\tself.graphFile = None",
"title": ""
},
{
"docid": "ada87a071b463ddc369f6d4386a39b48",
"score": "0.69956553",
"text": "def __init__(self):\n self.__nodes = dict()",
"title": ""
},
{
"docid": "a534297a6631dbb232521f25b24fb71b",
"score": "0.6950278",
"text": "def __init__(self, graphfile=None):\n abstract_graph.__init__(self)\n self.vhash = {}\n if graphfile is not None:\n self.load_graphfile(graphfile)",
"title": ""
},
{
"docid": "5f03e6c28999e7ee691942200cef5a00",
"score": "0.69338286",
"text": "def model_init(self):\r\n\t\tt = time()\r\n\t\twith self.graph.as_default():\r\n\t\t\tself.HG.create_model()\r\n\t\tprint('Graph Generated in ', int(time() - t), ' sec.')",
"title": ""
},
{
"docid": "082514023b492130995d7b7a3aa17d82",
"score": "0.69260126",
"text": "def __init__(self, g: Digraph):\n self.g = g\n self.visited = [False] * self.g.get_size()\n self.vertex_group = [0] * self.g.get_size()",
"title": ""
},
{
"docid": "a2ed55b76dfd3ec1059e7b236661472b",
"score": "0.6902682",
"text": "def __init__(self, is_directed=True):\n self.vertex_dict = {}\n self.is_directed = is_directed",
"title": ""
},
{
"docid": "80b62ebfd384e2c26395d2de8173089e",
"score": "0.6896673",
"text": "def __init__(self, graph=None):\n super(Graph, self).__init__()\n # Privates\n self._thread_to_graph_mapping = {}\n self._creator_thread = threading.get_ident()\n self._creator_pid = mp.current_process().pid\n # Publics\n if graph is not None:\n self.graph = graph\n else:\n self.graph = NNGraph()",
"title": ""
},
{
"docid": "9832cbe22e25a0165e352c725b8fdffb",
"score": "0.6891614",
"text": "def __init__(\n self,\n graph: Optional[Dict[str, Set[str]]] = None,\n edges: Optional[Dict[Tuple[str, str], float]] = None,\n vertices: Optional[Dict[str, float]] = None,\n ) -> None:\n self._graph = {}\n self._edges = {}\n self._vertices = {}\n\n if graph is not None:\n for vertex, connections in graph.items():\n if len(connections) == 0:\n if not self.has_vertex(vertex):\n self.add_vertex(vertex)\n else:\n for connection in connections:\n edge = vertex, connection\n\n if self.has_edge(edge):\n continue\n\n self.add_edge(edge)\n\n if edges is not None:\n for edge, weight in edges.items():\n if not self.has_edge(edge):\n self.add_edge(edge)\n\n self.set_edge_weight(edge, weight)\n\n if vertices is not None:\n for vertex, weight in vertices.items():\n if not self.has_vertex(vertex):\n self.add_vertex(vertex)\n\n self.set_vertex_weight(vertex, weight)",
"title": ""
},
{
"docid": "7d9a7433f27f2e7c704337940408cea1",
"score": "0.6859393",
"text": "def __init__(self, graph):\n self.graph = graph\n self.clusters = {}",
"title": ""
},
{
"docid": "cddfe4ece2e67429f8416c9785734d78",
"score": "0.6843422",
"text": "def __init__(self):\n self.nodes = []\n self.head = self._add_node()",
"title": ""
},
{
"docid": "05550b3caab6a44a771e874e8d3031a2",
"score": "0.684217",
"text": "def __init__(self, ):\n self.node = {}\n self={}",
"title": ""
},
{
"docid": "7d2eee82383a29d5fcf41c7e7554862d",
"score": "0.68346035",
"text": "def __init__(self, graph_dict=None):\n if graph_dict == None:\n graph_dict = {}\n self.__graph_dict = graph_dict\n \n \n self.visited = {}\n for key in self.__graph_dict:\n self.visited[key] = False\n \n \n\tself.pnum = 0 \n\tself.prenum = {}",
"title": ""
},
{
"docid": "149466d2da9ac9ab90ac0a88c98bae0e",
"score": "0.68164694",
"text": "def __init__(self):\n self.vertices = {}\n self.adj_matrix = np.zeros((375, 375))\n self.num_vertices = 0",
"title": ""
},
{
"docid": "1f0a1752af70f633c297972d50c7e75b",
"score": "0.6811163",
"text": "def __init__(self, start_edges=None):\n self.v_count = 0\n self.adj_matrix = []#[[0,0,0],[0,0,0],[0,0,0]]\n\n # populate graph with initial vertices and edges (if provided)\n # before using, implement add_vertex() and add_edge() methods\n if start_edges is not None:\n v_count = 0\n for u, v, _ in start_edges:\n v_count = max(v_count, u, v)\n for _ in range(v_count + 1):\n self.add_vertex()\n for u, v, weight in start_edges:\n self.add_edge(u, v, weight)",
"title": ""
},
{
"docid": "585a90f99807ef7e8a3f4f8ffa3a1609",
"score": "0.67989635",
"text": "def empty(self, *args):\n return _ida_graph.mutable_graph_t_empty(self, *args)",
"title": ""
},
{
"docid": "ecd19f876c00313ced96168bee26101e",
"score": "0.6798854",
"text": "def __init__(self):\n\t\tself.nodes = None\n\t\tself.head = None\n\t\tself.next = None",
"title": ""
},
{
"docid": "4a63b15ee176ec8c686fd431fc9002c7",
"score": "0.6792604",
"text": "def __init__(self, graphDict = None):\n\n\t\tif graphDict is not None:\n\t\t\tself.graphDict = graphDict\n\t\t\tself.generateEdges()\n\n\t\telse:\n\t\t\tself.graphDict = {}\n\n\t\tself.testBidirectional()",
"title": ""
},
{
"docid": "9d70f0984d1b07150b965fe1764c46da",
"score": "0.6791503",
"text": "def initialize(self):\n self.runnable = Queue.Queue()\n self.createEdges()\n self.computeGraphHeads()",
"title": ""
},
{
"docid": "9d70f0984d1b07150b965fe1764c46da",
"score": "0.6791503",
"text": "def initialize(self):\n self.runnable = Queue.Queue()\n self.createEdges()\n self.computeGraphHeads()",
"title": ""
},
{
"docid": "509f5a5031cae5aef6537a3e73bc55be",
"score": "0.67795837",
"text": "def __init__(self, pygraph):\n self.nodes = {} #dictionary node_name -> node\n self.agents = {} #dictionary agent -> node_name\n self.graph = pygraph\n for node in self.graph.nodes():\n self.nodes[node] = Node(node)",
"title": ""
},
{
"docid": "a265c3204263bd28321efc7110d36e06",
"score": "0.6779533",
"text": "def new_graph(self, graph_id):\n self.log.debug('Graph {}: Initializing'.format(graph_id))\n self._graphs[graph_id] = core.Graph(graph_id, initialize=False)",
"title": ""
},
{
"docid": "2687b05ace9218306bdeb3c811269da4",
"score": "0.67756087",
"text": "def __init__(self):\n self.edges = {}\n self.FeatExtract = analyse.ExtractFeatures()\n self.graph = PUNGraph.New()",
"title": ""
},
{
"docid": "ae411b75ab6cd6b835bce4d4e46bdd54",
"score": "0.67715937",
"text": "def _build_graph(self):\n self.g = tf.Graph()\n with self.g.as_default():\n self._placeholders()\n self._policy_nn()\n self._logprob()\n self._kl_entropy()\n self._sample()\n self._loss_train_op()\n self.init = tf.global_variables_initializer()",
"title": ""
},
{
"docid": "3a541f8875d9b22c64d81447ad6a6876",
"score": "0.6759665",
"text": "def __init__(self, graph, root):\n self._graph = tuple([tuple([tuple(idy) for idy in idx]) \\\n for idx in graph])\n self.root_index = root\n self._coverage = self.get_coverage()\n self.leafs = self.gen_leafs()",
"title": ""
},
{
"docid": "a77fed8f48c57b97ca32b1a12b342395",
"score": "0.67586744",
"text": "def _initialize_graph(self):\n change_sets_add = self._data_manager.get_initial_window()\n\n self._number_of_files_in_project = change_sets_add[-1].num_files_in_project\n self._add_change_sets(change_sets_add)",
"title": ""
},
{
"docid": "1c47bb74b8c2c51332b6950087ab289e",
"score": "0.6751077",
"text": "def __init__(self):\n super(IncidenceMatrix, self).__init__()\n self.vertex_count = 0\n self.edges = set()",
"title": ""
},
{
"docid": "9d0a48c9792bb6980a57d71ce45aff05",
"score": "0.67494875",
"text": "def __init__(self):\n\n self.nodes = set()",
"title": ""
},
{
"docid": "9ae365997cdc555c5224d8e0ae899f4c",
"score": "0.6734918",
"text": "def __init__(self, graph, root, *args):\n apply(WGraph.__init__, (self, graph) + args)\n self._graph = graph\n self.root_index = root",
"title": ""
},
{
"docid": "63eacc779fed79f41bc154816c836d51",
"score": "0.6731706",
"text": "def __init__(self):\n self.nodes = [None]*10000",
"title": ""
},
{
"docid": "bc05c224ef30b43739610cd8628727d8",
"score": "0.6730572",
"text": "def empty(n: int, type: str = None):\n if type is None:\n type = \"simple\"\n\n graph_dict = dict()\n for i in range(n):\n if type == \"multiple\":\n graph_dict[Vertex(i)] = []\n else:\n graph_dict[Vertex(i)] = set()\n if type == \"oriented\":\n return OrientedGraph(graph_dict)\n elif type == \"multiple\":\n return MultiGraph(graph_dict)\n return Graph(graph_dict)",
"title": ""
},
{
"docid": "b0fc850019f3a4288cf8ddd900160afe",
"score": "0.67276436",
"text": "def given_a_graph(self):\n\n self.graph = Graph()\n self.vertexA = Vertex(element=\"Vertex A\")\n self.vertexB = Vertex(element=\"Vertex B\")\n self.vertexC = Vertex(element=\"Vertex C\")\n\n self.edgeAB = Edge(self.vertexA, self.vertexB, element=1)\n self.edgeBC = Edge(self.vertexB, self.vertexC, element=1)\n self.edgeCA = Edge(self.vertexC, self.vertexA, element=1)\n\n self.graph.add_edge(self.edgeAB)\n self.graph.add_edge(self.edgeBC)\n self.graph.add_edge(self.edgeCA)",
"title": ""
},
{
"docid": "1cf65169a8a19c5e786f35e00dce100a",
"score": "0.67162365",
"text": "def get_default_graph() -> Graph:\n graph = Graph()\n graph.set_distance('s', {'A': 3, 'B': 6})\n graph.set_distance('A', {'B': 1, 't': 4})\n graph.set_distance('B', {'t': 2})\n graph.set_distance('t', {})\n return graph",
"title": ""
},
{
"docid": "b309d847fd6a7de042c126d8381b54a2",
"score": "0.6713974",
"text": "def __init__(self):\n\n self.weights = {}\n self.parents = {}",
"title": ""
},
{
"docid": "1f40dd26e1d0cac4634dd7bff4949978",
"score": "0.6711702",
"text": "def __init__(self):\n self.vertices = {}",
"title": ""
},
{
"docid": "0442fe509a52689c52000c59e36fc397",
"score": "0.6709711",
"text": "def _initialize_graph(self):\n\n change_sets_add = self._data_manager.get_initial_window()\n\n self._number_of_files_in_project = change_sets_add[-1].num_files_in_project\n self._add_change_sets(change_sets_add)",
"title": ""
},
{
"docid": "c30b5bf670d1bd6593cf3d8e0694e3da",
"score": "0.67038345",
"text": "def model_init(self):\r\n\t\tt = time()\r\n\t\twith self.graph.as_default():\r\n\t\t\tself.HG.generate_model()\r\n\t\tprint('Graph Generated in ', int(time() - t), ' sec.')",
"title": ""
},
{
"docid": "215fc2eb76417ff25e1cf7baed10b63e",
"score": "0.66998535",
"text": "def __init__(self):\n self.nodes = []",
"title": ""
},
{
"docid": "1bd0e3487d8c5c21633355785001c185",
"score": "0.6697795",
"text": "def setup_graph_structures(self):\n\n self.G = nx.Graph(self.paths())\n self.spl = dict(nx.all_pairs_shortest_path_length(self.G))\n self.diam = nx.diameter(self.G)",
"title": ""
},
{
"docid": "c347f43e42d6e1d1854de579301aed30",
"score": "0.6692148",
"text": "def __init__(self, is_directed=True):\n self.__vertex_dict = {} # id -> object\n self.__is_directed = is_directed",
"title": ""
},
{
"docid": "c347f43e42d6e1d1854de579301aed30",
"score": "0.6692148",
"text": "def __init__(self, is_directed=True):\n self.__vertex_dict = {} # id -> object\n self.__is_directed = is_directed",
"title": ""
},
{
"docid": "0a91f37e9a75bc13f32fd50bad9131cc",
"score": "0.6687692",
"text": "def __init__(self, start_edges=None):\n self.v_count = 0\n self.adj_matrix = []\n\n # populate graph with initial vertices and edges (if provided)\n # before using, implement add_vertex() and add_edge() methods\n if start_edges is not None:\n v_count = 0\n for u, v, _ in start_edges:\n v_count = max(v_count, u, v)\n for _ in range(v_count + 1):\n self.add_vertex()\n for u, v, weight in start_edges:\n self.add_edge(u, v, weight)",
"title": ""
},
{
"docid": "0a91f37e9a75bc13f32fd50bad9131cc",
"score": "0.6687692",
"text": "def __init__(self, start_edges=None):\n self.v_count = 0\n self.adj_matrix = []\n\n # populate graph with initial vertices and edges (if provided)\n # before using, implement add_vertex() and add_edge() methods\n if start_edges is not None:\n v_count = 0\n for u, v, _ in start_edges:\n v_count = max(v_count, u, v)\n for _ in range(v_count + 1):\n self.add_vertex()\n for u, v, weight in start_edges:\n self.add_edge(u, v, weight)",
"title": ""
},
{
"docid": "0a91f37e9a75bc13f32fd50bad9131cc",
"score": "0.6687692",
"text": "def __init__(self, start_edges=None):\n self.v_count = 0\n self.adj_matrix = []\n\n # populate graph with initial vertices and edges (if provided)\n # before using, implement add_vertex() and add_edge() methods\n if start_edges is not None:\n v_count = 0\n for u, v, _ in start_edges:\n v_count = max(v_count, u, v)\n for _ in range(v_count + 1):\n self.add_vertex()\n for u, v, weight in start_edges:\n self.add_edge(u, v, weight)",
"title": ""
},
{
"docid": "0a91f37e9a75bc13f32fd50bad9131cc",
"score": "0.6687692",
"text": "def __init__(self, start_edges=None):\n self.v_count = 0\n self.adj_matrix = []\n\n # populate graph with initial vertices and edges (if provided)\n # before using, implement add_vertex() and add_edge() methods\n if start_edges is not None:\n v_count = 0\n for u, v, _ in start_edges:\n v_count = max(v_count, u, v)\n for _ in range(v_count + 1):\n self.add_vertex()\n for u, v, weight in start_edges:\n self.add_edge(u, v, weight)",
"title": ""
},
{
"docid": "1c85bd4d0fd372b7b09621621b4cd5d0",
"score": "0.66692746",
"text": "def init_graph_stuff(self):\n # Create several queue that holds the number for each line in every graph\n self.balloon_acc_xQ = queue.Queue()\n self.balloon_acc_yQ = queue.Queue()\n self.balloon_acc_zQ = queue.Queue()\n self.balloon_gyro_xQ = queue.Queue()\n self.balloon_gyro_yQ = queue.Queue()\n self.balloon_gyro_zQ = queue.Queue()\n self.rocket_acc_xQ = queue.Queue()\n self.rocket_acc_yQ = queue.Queue()\n self.rocket_acc_zQ = queue.Queue()\n self.rocket_gyro_xQ = queue.Queue()\n self.rocket_gyro_yQ = queue.Queue()\n self.rocket_gyro_zQ = queue.Queue()\n self.altitudeQ = queue.Queue()\n\n amount_of_point_to_graph = 20\n for i in range(0, amount_of_point_to_graph):\n self.balloon_acc_xQ.put(0)\n self.balloon_acc_yQ.put(0)\n self.balloon_acc_zQ.put(0)\n self.balloon_gyro_xQ.put(0)\n self.balloon_gyro_yQ.put(0)\n self.balloon_gyro_zQ.put(0)\n self.rocket_acc_xQ.put(0)\n self.rocket_acc_yQ.put(0)\n self.rocket_acc_zQ.put(0)\n self.rocket_gyro_xQ.put(0)\n self.rocket_gyro_yQ.put(0)\n self.rocket_gyro_zQ.put(0)\n self.altitudeQ.put(0)\n\n self.altitude_graph = None\n self.acc_gyro_graphs = None",
"title": ""
},
{
"docid": "107f651fde1d4e6879ceca94b87b9ff0",
"score": "0.6666746",
"text": "def given_a_graph(self):\n\n self.graph = Graph()\n self.vertexA = Vertex(element=\"Vertex A\")\n self.vertexB = Vertex(element=\"Vertex B\")\n self.vertexC = Vertex(element=\"Vertex C\")\n self.vertexD = Vertex(element=\"Vertex D\")\n\n self.edgeAB = Edge(self.vertexA, self.vertexB, element=3)\n self.edgeBC = Edge(self.vertexB, self.vertexC, element=1)\n self.edgeDC = Edge(self.vertexD, self.vertexC, element=1)\n self.edgeDB = Edge(self.vertexD, self.vertexB, element=1)\n\n self.graph.add_edge(self.edgeAB)\n self.graph.add_edge(self.edgeBC)\n self.graph.add_edge(self.edgeDC)\n self.graph.add_edge(self.edgeDB)",
"title": ""
},
{
"docid": "5a8e01608ff89f89b9e01013ab48f572",
"score": "0.66625726",
"text": "def __init__(self, graph):\n self._graph = tuple([tuple([tuple(idy) for idy in idx]) \\\n for idx in graph])\n self._coverage = None\n self._reverse = None\n self._edge_count = None\n self._weight_count = 0.0\n self.d = None # dist matrix\n self.PT = None # path matrix",
"title": ""
},
{
"docid": "9aa71a3aa55097b908dda7d09e09a9f1",
"score": "0.6660175",
"text": "def given_a_graph(self):\n\n self.graph = Graph()\n self.vertexA = Vertex(element=\"Vertex A\")\n self.vertexB = Vertex(element=\"Vertex B\")\n self.vertexC = Vertex(element=\"Vertex C\")\n self.vertexD = Vertex(element=\"Vertex D\")\n\n self.edgeAB = Edge(self.vertexA, self.vertexB, element=3)\n self.edgeBC = Edge(self.vertexB, self.vertexC, element=1)\n self.edgeCD = Edge(self.vertexC, self.vertexD, element=1)\n self.edgeBD = Edge(self.vertexB, self.vertexD, element=1)\n\n self.graph.add_edge(self.edgeAB)\n self.graph.add_edge(self.edgeBC)\n self.graph.add_edge(self.edgeCD)\n self.graph.add_edge(self.edgeBD)",
"title": ""
},
{
"docid": "411aa71465d1d982c282c8deb974c8ce",
"score": "0.665039",
"text": "def init(self):\n self.visited = False\n print (\"inicializo algoritmo\")",
"title": ""
},
{
"docid": "5c86a4d68c84ab74105a46047fc4ef1e",
"score": "0.6646679",
"text": "def _build_graph(self):\n self.g = tf.Graph()\n with self.g.as_default():\n self._placeholders()\n self._oppo_nn()\n self._policy_nn()\n self._logprob_self()\n self._logprob_oppo()\n self._kl_entropy_self()\n self._kl_entropy_oppo()\n self._sample_action()\n self._sample_intent()\n self._loss_train_op()\n self.init = tf.global_variables_initializer()",
"title": ""
},
{
"docid": "cefbbe9e14e4f41eb0372052c0f25c9e",
"score": "0.6646633",
"text": "def __init__(self):\n self.weights = {}\n self.parents = {}",
"title": ""
},
{
"docid": "8fe5c2544fdcdbb031cc43cbf01fd56b",
"score": "0.66424656",
"text": "def __init__(self, vertices = set(), edges = list()):\n\n self._alist = dict()\n self._flows = dict()\n self._weights = dict()\n self._capacities = dict()\n\n for v in vertices:\n self.add_vertex(v)\n for e in edges:\n self.add_edge(e[0],e[1],e[2],e[3])",
"title": ""
},
{
"docid": "574c2691ac14008d621937ff8c1ee731",
"score": "0.66345924",
"text": "def __init__(self, start_edges=None):\n self.adj_list = dict()\n\n # populate graph with initial vertices and edges (if provided)\n # before using, implement add_vertex() and add_edge() methods\n if start_edges is not None:\n for u, v in start_edges:\n self.add_edge(u, v)",
"title": ""
},
{
"docid": "574c2691ac14008d621937ff8c1ee731",
"score": "0.66345924",
"text": "def __init__(self, start_edges=None):\n self.adj_list = dict()\n\n # populate graph with initial vertices and edges (if provided)\n # before using, implement add_vertex() and add_edge() methods\n if start_edges is not None:\n for u, v in start_edges:\n self.add_edge(u, v)",
"title": ""
},
{
"docid": "1cc9f7438c39e71b2f92ce5d6a2acd29",
"score": "0.66131747",
"text": "def __init__(self, graph_node):\n self.graph_node = graph_node\n self.H = 0\n self.G = 0\n self.F = 0",
"title": ""
},
{
"docid": "d2a7855731c3b85f59643ff425dc8640",
"score": "0.65948254",
"text": "def build_graph(self) -> None:\n raise NotImplementedError()",
"title": ""
},
{
"docid": "d2a7855731c3b85f59643ff425dc8640",
"score": "0.65948254",
"text": "def build_graph(self) -> None:\n raise NotImplementedError()",
"title": ""
},
{
"docid": "af3db8cc2966e4432a7f48c14bae6ee9",
"score": "0.65808153",
"text": "def __init__(self, gw=None):\n self._nodes = { }\n self._gw = gw",
"title": ""
},
{
"docid": "0808101ef575b27eefc7196bcfd54070",
"score": "0.6577423",
"text": "def from_graph(self, graph):",
"title": ""
},
{
"docid": "b5b48f3862a4306dcef6b5aa64b06a59",
"score": "0.6567787",
"text": "def __init__(self):\n self.root = None\n self.ontologies = set()\n self.index = dict()\n self.wordnet = None",
"title": ""
},
{
"docid": "639df1c4753435161d2d92935ff6c503",
"score": "0.6563561",
"text": "def __init__(self, *args):\n _snap.TNGraph_swiginit(self, _snap.new_TNGraph(*args))",
"title": ""
},
{
"docid": "6610c9e2a2ccf59f843b283fd4500074",
"score": "0.656265",
"text": "def __init__(self, digraph=None, file_path=None, init_data=False):\n self.graph = None\n self.root = None\n\n if file_path:\n graph = GraphAdapter()\n graph.load_dot(file_path)\n self.graph = graph\n elif digraph:\n graph_adapter = GraphAdapter()\n graph_adapter.set_graph(digraph)\n self.graph = graph_adapter\n else:\n self.graph = GraphAdapter()\n\n if init_data:\n self.init_proof_graph_data()",
"title": ""
},
{
"docid": "6838f550ea331e75436d7174882bed50",
"score": "0.6553883",
"text": "def build_graph(self):\n for i in range(param.LIMIT):\n for j in range(param.LIMIT):\n self.graph[i, j] = Node(i, j)",
"title": ""
},
{
"docid": "8b42b32d8632fa7ceac280ce9cc82f7c",
"score": "0.6538376",
"text": "def init():\n \n global EX_GRAPH0, EX_GRAPH1, EX_GRAPH2\n\n # create the graphs and nodes.\n EX_GRAPH0 = init_graph(3)\n EX_GRAPH1 = init_graph(7)\n EX_GRAPH2 = init_graph(10)\n\n # Add edges to EX_GRAPH0.\n EX_GRAPH0[0].add(1)\n EX_GRAPH0[0].add(2)\n\n # Add edges to EX_GRAPH1.\n EX_GRAPH1[0].add(1)\n EX_GRAPH1[1].add(2)\n EX_GRAPH1[2].add(3)\n EX_GRAPH1[3].add(0)\n EX_GRAPH1[0].add(4)\n EX_GRAPH1[4].add(1)\n EX_GRAPH1[0].add(5)\n EX_GRAPH1[5].add(2)\n EX_GRAPH1[1].add(6)\n \n # Add edges to EX_GRAPH2.\n EX_GRAPH2[0].add(1)\n EX_GRAPH2[1].add(2)\n EX_GRAPH2[2].add(3)\n EX_GRAPH2[8].add(1)\n EX_GRAPH2[8].add(2)\n EX_GRAPH2[0].add(4)\n EX_GRAPH2[4].add(1)\n EX_GRAPH2[0].add(5)\n EX_GRAPH2[5].add(2)\n EX_GRAPH2[1].add(6)\n EX_GRAPH2[2].add(7)\n EX_GRAPH2[7].add(3)\n EX_GRAPH2[3].add(7)\n EX_GRAPH2[9].add(4)\n EX_GRAPH2[9].add(5)\n EX_GRAPH2[9].add(6)\n EX_GRAPH2[9].add(7)\n EX_GRAPH2[9].add(0)\n EX_GRAPH2[9].add(3)\n \n return",
"title": ""
},
{
"docid": "afc937914d3dda356fca4b4d5e7b1d79",
"score": "0.65371037",
"text": "def __init__(self, edges, directed=False):\n self.adj = defaultdict(set)\n self.directed = directed\n self.add_edge(edges)",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="707"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">d0a2a8fec99873f3f23741f91d1b5447</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Get appliance OSPF interfaces configuration</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "fae438d4fd00c57de550c3c278de6b0b",
"score": "0.77904",
"text": "def get_appliance_ospf_interfaces_config(\n self,\n ne_id: str,\n) -> dict:\n return self._get(\"/ospf/config/interfaces/{}\".format(ne_id))",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "3234037c36bb52ad62356d0e92c5e305",
"score": "0.6962521",
"text": "def config_if(self):\n\n resource = \"/ip/address\"\n\n for intf in self.interfaces:\n\n response = self.request_put(resource, intf)\n #print(response)",
"title": ""
},
{
"docid": "93393cfed99a67bcb7984363edfb26a4",
"score": "0.673463",
"text": "def get_appliance_ospf_config(\n self,\n ne_id: str,\n) -> dict:\n return self._get(\"/ospf/config/system/{}\".format(ne_id))",
"title": ""
},
{
"docid": "33a5bcfa831877047386bb6dad1e2174",
"score": "0.6622687",
"text": "def _get_interface_ospfv3_conf(self):\n return self.__interface_ospfv3_conf",
"title": ""
},
{
"docid": "3e938282f7cbb594377777f074e508a9",
"score": "0.6426761",
"text": "def get_ospf_interfaces(host):\n command = \"net show ospf interface json\"\n\n output = ssh_command(host, command)\n\n if \"interfaces\" not in output:\n print 'Error'\n exit(1)\n else:\n return output[\"interfaces\"].keys()",
"title": ""
},
{
"docid": "09ab37227ddf7b569e413192c0163fb9",
"score": "0.6297937",
"text": "def get_interfaces(cls):\n i_dict = {}\n\n open_files = subprocess.Popen(IFCONFIG, shell=True,\n stdout=subprocess.PIPE).communicate()[0]\n lines = open_files.split('\\n')\n interface = ''\n\n for line in lines:\n if not line:\n continue\n\n ls = line.split(' ')\n\n if ls[0].strip():\n interface = ls[0].strip()\n i_dict[interface] = {'i_ip': '', 'i_mac': '', 'i_mask': ''}\n\n # Get MAC address.\n if 'HWaddr' in ls:\n i_dict[interface]['i_mac'] = ls[ls.index('HWaddr') + 1].lower()\n\n # Get IP address and netmask.\n if 'inet' in ls:\n inet = ls[ls.index('inet') + 1]\n if ':' in inet:\n i_dict[interface]['i_ip'] = inet.split(':')[1]\n else:\n i_dict[interface]['i_ip'] = inet\n\n if ':' in ls[-1]:\n i_dict[interface]['i_mask'] = ls[-1].split(':')[1]\n else:\n i_dict[interface]['i_mask'] = ls[-1]\n\n return i_dict",
"title": ""
},
{
"docid": "729edeefa1b739f5b6b454069662b36a",
"score": "0.6228722",
"text": "def get_appliance_ospf_interfaces_state(\n self,\n ne_id: str,\n) -> dict:\n return self._get(\"/ospf/state/interfaces/{}\".format(ne_id))",
"title": ""
},
{
"docid": "ee45eb1126169780ad8361f83103b08e",
"score": "0.6159336",
"text": "def get_interface_ips(self):\n pass",
"title": ""
},
{
"docid": "b4023e94c8750e3599a29e51bad99114",
"score": "0.6096761",
"text": "def get_interfaces_ip(self):\n interfaces_ip = {}\n ipv4_command = \"show ip interface vrf all\"\n ipv6_command = \"show ipv6 interface vrf all\"\n output_v4 = self._send_command(ipv4_command)\n output_v6 = self._send_command(ipv6_command)\n\n v4_interfaces = {}\n for line in output_v4.splitlines():\n # Ethernet2/2, Interface status: protocol-up/link-up/admin-up, iod: 38,\n # IP address: 2.2.2.2, IP subnet: 2.2.2.0/27 route-preference: 0, tag: 0\n # IP address: 3.3.3.3, IP subnet: 3.3.3.0/25 secondary route-preference: 0, tag: 0\n if \"Interface status\" in line:\n interface = line.split(\",\")[0]\n continue\n if \"IP address\" in line:\n ip_address = line.split(\",\")[0].split()[2]\n try:\n prefix_len = int(line.split()[5].split(\"/\")[1])\n except (ValueError, IndexError):\n prefix_len = \"N/A\"\n\n if ip_address == \"none\":\n v4_interfaces.setdefault(interface, {})\n else:\n val = {\"prefix_length\": prefix_len}\n v4_interfaces.setdefault(interface, {})[ip_address] = val\n\n v6_interfaces = {}\n for line in output_v6.splitlines():\n # Ethernet2/4, Interface status: protocol-up/link-up/admin-up, iod: 40\n # IPv6 address:\n # 2001:11:2233::a1/24 [VALID]\n # 2001:cc11:22bb:0:2ec2:60ff:fe4f:feb2/64 [VALID]\n # IPv6 subnet: 2001::/24\n # IPv6 link-local address: fe80::2ec2:60ff:fe4f:feb2 (default) [VALID]\n # IPv6 address: fe80::a293:51ff:fe5f:5ce9 [VALID]\n if \"Interface status\" in line:\n interface = line.split(\",\")[0]\n continue\n if \"VALID\" in line:\n line = line.strip()\n if \"link-local address\" in line:\n # match the following format:\n # IPv6 link-local address: fe80::2ec2:60ff:fe4f:feb2 (default) [VALID]\n ip_address = line.split()[3]\n prefix_len = \"64\"\n elif \"IPv6 address\" in line:\n # match the following format:\n # IPv6 address: fe80::a293:51ff:fe5f:5ce9 [VALID]\n ip_address = line.split()[2]\n prefix_len = \"64\"\n else:\n ip_address, prefix_len = line.split()[0].split(\"/\")\n prefix_len = int(prefix_len)\n val = {\"prefix_length\": prefix_len}\n v6_interfaces.setdefault(interface, {})[ip_address] = val\n else:\n # match the following format:\n # IPv6 address: none\n v6_interfaces.setdefault(interface, {})\n\n # Join data from intermediate dictionaries.\n for interface, data in v4_interfaces.items():\n interfaces_ip.setdefault(interface, {\"ipv4\": {}})[\"ipv4\"] = data\n\n for interface, data in v6_interfaces.items():\n interfaces_ip.setdefault(interface, {\"ipv6\": {}})[\"ipv6\"] = data\n\n return interfaces_ip",
"title": ""
},
{
"docid": "2980b837cb00ec02d44b11d6b3e183ee",
"score": "0.60876936",
"text": "def interfaces(self):\r\n return self._data.get(\"interfaces\", [])",
"title": ""
},
{
"docid": "863fba99314cf013b2c76cd26070f9ad",
"score": "0.6036056",
"text": "def extcap_config(interface):\n print(\"arg {number=0}{call=--only-advertising}{display=Only advertising packets}\"\n \"{tooltip=The sniffer will only capture advertising packets from the selected device}{type=boolflag}{save=true}\")\n print(\"arg {number=1}{call=--only-legacy-advertising}{display=Only legacy advertising packets}\"\n \"{tooltip=The sniffer will only capture legacy advertising packets from the selected device}{type=boolflag}{save=true}\")\n print(\"arg {number=2}{call=--scan-follow-rsp}{display=Find scan response data}\"\n \"{tooltip=The sniffer will follow scan requests and scan responses in scan mode}{type=boolflag}{default=true}{save=true}\")\n print(\"arg {number=3}{call=--scan-follow-aux}{display=Find auxiliary pointer data}\"\n \"{tooltip=The sniffer will follow aux pointers in scan mode}{type=boolflag}{default=true}{save=true}\")\n print(\"arg {number=3}{call=--coded}{display=Scan and follow devices on LE Coded PHY}\"\n \"{tooltip=Scan for devices and follow advertiser on LE Coded PHY}{type=boolflag}{default=false}{save=true}\")",
"title": ""
},
{
"docid": "cb2a89510fc55a2dbf3620f5f6c5144e",
"score": "0.6019435",
"text": "def netinf():\n co = subprocess.run(['/sbin/ifconfig'], capture_output=True, text=True)\n ifaces={}\n alines=co.stdout.split('\\n')\n def lineget():\n if alines:\n return alines.pop(0)+'\\n'\n else:\n return ''\n aline=lineget()\n while aline:\n if aline[0] in (' ','\\n'):\n print('unexpected line:', aline)\n aline=lineget()\n else:\n iname, rest = aline.split(':', maxsplit=1)\n ifaceinfo={}\n ifaces[iname] = ifaceinfo\n aline=lineget()\n while aline and aline[0] == ' ':\n lparts = [p.strip() for p in aline.strip().split(' ') if not p.strip() == '']\n if lparts[0]=='inet':\n _sectadd(ifaceinfo,'IP4',_ip4parse(lparts))\n elif lparts[0]=='inet6':\n pass\n elif lparts[0] == 'ether':\n _sectadd(ifaceinfo, 'mac_addr', lparts[1])\n elif lparts[0] in ('loop', 'RX', 'TX'):\n pass\n else:\n print('???', lparts)\n print(lparts[0])\n aline=lineget()\n if len(aline) == 0:\n pass # loop will exit - we're done\n else:\n while aline and aline[0]== '\\n':\n aline=lineget() # skip to next interface\n alines=co.stderr.split('\\n')\n for aline in alines:\n if len(aline) > 1:\n print('-x->', aline)\n return ifaces",
"title": ""
},
{
"docid": "ce80d00fa1534894e347007ed35ec95f",
"score": "0.6015132",
"text": "def ifconfig_get_ip(iface):\n\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n try:\n return socket.inet_ntoa(fcntl.ioctl(s.fileno(), 0x8915, struct.pack('256s', iface[:15].encode('utf-8')))[20:24])\n except IOError:\n return None",
"title": ""
},
{
"docid": "2f25cf5df38a404f30d7bc1c7e8cea63",
"score": "0.59995764",
"text": "def get_interfaces(self):\n\n return self.get('Interfaces')",
"title": ""
},
{
"docid": "2478927886ce64b094f64e0cba9fe51f",
"score": "0.59676665",
"text": "def _get_open_interfaces(self):\n return []",
"title": ""
},
{
"docid": "f5636e3c99199db926cf8abe42638a25",
"score": "0.5942025",
"text": "def get_interfaces_ip(self):\n interfaces_ip = {}\n output_v4 = self.device.send_command('display ip interface')\n output_v6 = self.device.send_command('display ipv6 interface')\n\n v4_interfaces = {}\n separator = r\"(^(?!Line protocol).*current state.*$)\"\n new_v4_interfaces = self._separate_section(separator, output_v4)\n for interface in new_v4_interfaces:\n re_intf_name_state = r\"^(?!Line protocol)(?P<intf_name>\\S+).+current state\\W+(?P<intf_state>.+)$\"\n re_intf_ip = r\"Internet Address is\\s+(?P<ip_address>\\d+.\\d+.\\d+.\\d+)\\/(?P<prefix_length>\\d+)\"\n\n match_intf = re.search(re_intf_name_state, interface, flags=re.M)\n if match_intf is None:\n msg = \"Unexpected interface format: {}\".format(interface)\n raise ValueError(msg)\n intf_name = match_intf.group('intf_name')\n # v4_interfaces[intf_name] = {}\n match_ip = re.findall(re_intf_ip, interface, flags=re.M)\n\n for ip_info in match_ip:\n val = {'prefix_length': int(ip_info[1])}\n # v4_interfaces[intf_name][ip_info[0]] = val\n v4_interfaces.setdefault(intf_name, {})[ip_info[0]] = val\n\n v6_interfaces = {}\n separator = r\"(^(?!IPv6 protocol).*current state.*$)\"\n new_v6_interfaces = self._separate_section(separator, output_v6)\n for interface in new_v6_interfaces:\n re_intf_name_state = r\"^(?!IPv6 protocol)(?P<intf_name>\\S+).+current state\\W+(?P<intf_state>.+)$\"\n re_intf_ip = r\"(?P<ip_address>\\S+), subnet is.+\\/(?P<prefix_length>\\d+)\"\n\n match_intf = re.search(re_intf_name_state, interface, flags=re.M)\n if match_intf is None:\n msg = \"Unexpected interface format: {}\".format(interface)\n raise ValueError(msg)\n intf_name = match_intf.group('intf_name')\n match_ip = re.findall(re_intf_ip, interface, flags=re.M)\n\n for ip_info in match_ip:\n val = {'prefix_length': int(ip_info[1])}\n v6_interfaces.setdefault(intf_name, {})[ip_info[0]] = val\n\n # Join data from intermediate dictionaries.\n for interface, data in v4_interfaces.items():\n interfaces_ip.setdefault(interface, {'ipv4': {}})['ipv4'] = data\n\n for interface, data in v6_interfaces.items():\n interfaces_ip.setdefault(interface, {'ipv6': {}})['ipv6'] = data\n\n return interfaces_ip",
"title": ""
},
{
"docid": "9dec9d78c606d863e6cf0445dea90260",
"score": "0.5927479",
"text": "def get_all_interfaces():\n _is_64bits = sys.maxsize > 2**32\n struct_size = 40 if _is_64bits else 32\n\n if_buffer = array.array(\"B\", b\"\\0\" * MAX_INTERFACES * struct_size)\n if_pointer, buffer_size = if_buffer.buffer_info()\n\n _socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n try:\n fh = _socket.fileno()\n ifaces_bytes = fcntl.ioctl(\n fh, 0x8912, struct.pack(\"iL\", buffer_size, if_pointer) # SIOCGIFCONF\n )\n if_buffer_size, _pointer = struct.unpack(\"iL\", ifaces_bytes)\n\n finally:\n _socket.close()\n\n try:\n # tostring() was renamed to tobytes() in Python3.2\n ifaces = if_buffer.tobytes()\n except AttributeError:\n ifaces = if_buffer.tostring()\n\n interfaces = []\n for offset in range(0, if_buffer_size, struct_size):\n _iface = ifaces[offset : offset + struct_size]\n name, _, _ = _iface.partition(b\"\\0\")\n ip = socket.inet_ntoa(_iface[20:24]) # struct sockaddr ifr_hwaddr;\n if not PY3:\n ip = ip.decode(\"utf-8\")\n interfaces.append((name.decode(\"ascii\", \"replace\"), ip))\n\n return interfaces",
"title": ""
},
{
"docid": "523529b21a810cf4e6f698c6fa9498e6",
"score": "0.5900946",
"text": "def get_vpp_interfaces(self):\n ifargs = ['show', 'interface']\n output = self.run_vppctl(ifargs)\n ifaces = output[0].split('\\n')\n pifaces = []\n vifaces = []\n for iface in ifaces:\n name = iface.split()[0]\n if 'Name' in name or 'local' in name:\n continue\n if 'Ethernet' in name:\n pifaces.append(name)\n if 'memif' in name:\n vifaces.append(name)\n assert len(vifaces) == 2 or len(vifaces) == 4\n assert len(pifaces) == 2\n assert pifaces[0][:-1] in pifaces[1][:-1]\n return pifaces, vifaces",
"title": ""
},
{
"docid": "669bb2a78bd2d863e9fe8a87172663fd",
"score": "0.5878599",
"text": "def _get_interfaces(self, namespaces=False):\n paths = []\n seq = SequenceSearchDef(start=SearchDef(IP_IFACE_NAME),\n body=SearchDef([IP_IFACE_V4_ADDR,\n IP_IFACE_V6_ADDR,\n IP_IFACE_HW_ADDR,\n IP_IFACE_VXLAN_INFO]),\n tag='ip_addr_show')\n search_obj = FileSearcher()\n if namespaces:\n for ns in self.cli.ip_netns():\n ns_name = ns.partition(\" \")[0]\n ip_addr = self.cli.ns_ip_addr(namespace=ns_name)\n path = mktemp_dump('\\n'.join(ip_addr))\n search_obj.add_search_term(seq, path)\n paths.append(path)\n else:\n paths = [self.ip_addr_dump]\n search_obj.add_search_term(seq, self.ip_addr_dump)\n\n r = search_obj.search()\n interfaces = []\n for path in paths:\n sections = r.find_sequence_sections(seq, path).values()\n for section in sections:\n addrs = []\n encap_info = None\n hwaddr = None\n name = None\n state = None\n for result in section:\n if result.tag == seq.start_tag:\n name = result.get(1)\n state = result.get(2)\n elif result.tag == seq.body_tag:\n if result.get(1) in ['inet', 'inet6']:\n addrs.append(result.get(2))\n elif result.get(1) in ['vxlan']:\n encap_info = {result.get(1): {\n 'id': result.get(2),\n 'local_ip': result.get(3),\n 'dev': result.get(4)}}\n else:\n hwaddr = result.get(2)\n\n interfaces.append(NetworkPort(name, addrs, hwaddr, state,\n encap_info))\n\n return interfaces",
"title": ""
},
{
"docid": "42434afa3fcfcd2018e2c353696dbfa1",
"score": "0.5877286",
"text": "def network_interfaces():\n # NETWORK INTERFACES DETAILS\n network_interfaces_var = {}\n try:\n if_addrs = psutil.net_if_addrs()\n for interface_name, interface_addresses in if_addrs.items():\n for address in interface_addresses:\n if str(address.family) == 'AddressFamily.AF_INET':\n network_interfaces_var[interface_name] = {\n 'ip': address.address,\n 'netmask': address.netmask,\n 'broadcast_ip': address.broadcast\n }\n elif str(address.family) == 'AddressFamily.AF_PACKET':\n network_interfaces_var[interface_name] = {\n 'mac': address.address,\n 'netmask': address.nNetmask,\n 'broadcast_mac': address.broadcast\n }\n except:\n network_interfaces_var = {'status': 'error'}\n return network_interfaces_var",
"title": ""
},
{
"docid": "e0e53fb273110adff395a656560d04f2",
"score": "0.58443636",
"text": "def defined_virtual_interfaces(self):\n cmd = \"sudo ip link show\"\n out = CmdExecutor.run_and_return_result(cmd=cmd)\n interfaces = []\n rows = out.split(\"\\n\")\n relevant_row = True\n for row in rows:\n if len(row) < 2:\n break\n if relevant_row:\n relevant_row = False\n words = row.split(\" \")\n iface = words[1]\n iface = iface[:-1]\n iface_type = iface[0:3]\n if iface_type == \"ifb\":\n interfaces.append(iface)\n else:\n relevant_row = True\n return interfaces",
"title": ""
},
{
"docid": "60ea633f5cb4bd373217185c4c681e1e",
"score": "0.58099663",
"text": "def all_interfaces():\n import netifaces\n res = {}\n for ifc in netifaces.interfaces():\n try:\n ip_list = netifaces.ifaddresses(ifc)[netifaces.AF_INET]\n if len(ip_list) > 1:\n raise NotImplemented(\"Only one IP per interface supported\")\n res[ifc] = ip_list[0][\"addr\"] #IP info also available: \"mask\" \"broadcast\"\n except KeyError:\n #interface is not supporting IP address\n pass\n return res",
"title": ""
},
{
"docid": "a8d7bde219d2fe83e28f3c285a34d6af",
"score": "0.5784064",
"text": "def interface(self):\n return self.options['interface']",
"title": ""
},
{
"docid": "a398bed65dbf6f198390da666416400f",
"score": "0.5771881",
"text": "def get_ospf_interface_and_area(device):\n try:\n out = device.parse(\"show ospf interface brief\")\n except SchemaEmptyParserError as spe:\n raise SchemaEmptyParserError(\n \"Could not parse output for\" \" command 'show ospf interface brief'\"\n ) from spe\n\n key_val = {}\n\n try:\n interface_dict = out[\"instance\"][\"master\"][\"areas\"]\n for k, v in interface_dict.items():\n for interface in v[\"interfaces\"].keys():\n key_val.update({interface: k})\n except KeyError as ke:\n raise KeyError(\"Key issue with exception: {}\".format(str(ke))) from ke\n return key_val",
"title": ""
},
{
"docid": "9b8e18a44978fd4eb1e209c2c287ec51",
"score": "0.5759131",
"text": "def Interfaces(self):\n return self._get_attribute('interfaces')",
"title": ""
},
{
"docid": "8782955801456b96ead4e5296dc8df35",
"score": "0.5745409",
"text": "def get(self):\n try:\n interfaces = interfaceManager.getAllInterfaces()\n if interfaces is None:\n returnValue({})\n marshal_fields = {\n 'type': fields.String(attribute='__class__.__name__'),\n 'id': fields.Integer(attribute='appinterface.id'),\n 'name': fields.String\n }\n data = {}\n for i,interface in enumerate(interfaces):\n data[i] = marshal(interface, marshal_fields)\n returnValue(data)\n yield\n \n except TimeoutError:\n log.error(\"REST API timeout retrieving application interfaces\")",
"title": ""
},
{
"docid": "bab2ffa39092eeaa4fd3d773d0df9ee3",
"score": "0.57431656",
"text": "def getall(self):\n interfaces_re = re.compile(r'(?<=^interface\\s)(.+)$', re.M)\n\n response = dict()\n for name in interfaces_re.findall(self.config):\n interface = self.get(name)\n if interface:\n response[name] = interface\n return response",
"title": ""
},
{
"docid": "e78ee7ee1de559256d118e4f37cc97ea",
"score": "0.57280314",
"text": "def get_interfaces_on_firewall(self, firewall_id):\n\n # TODO(anirudh): Have not thought of a better approach then to\n # query the netconf server for the interfaces.\n output = self.fwaas_driver[firewall_id].executeCommands('sget /vyatta-interfaces')\n fields = re.split(r'[\\t\\n\\s,{}]\\s*',output)\n indicies = [i for i,x in enumerate(fields) if x == \"interface\"]\n names = []\n for index in indicies:\n names.append(fields[index+1])\n return names",
"title": ""
},
{
"docid": "d8c9f75cb990e83acfe786652f44870e",
"score": "0.57157207",
"text": "def show_interfaces(self, vlan='', host=None, netmask=None, format=None):\n if self.mode == 'passive':\n return None\n ifcs = []\n vlan = str(vlan)\n found = False\n ava_ifc, fnd_ifc, cnt = 0, 0, 0\n ifc_info = self.send_cmd(self.SHOW_INTERFACE.format(''))\n if format is None:\n return ifc_info\n ifc_info = ifc_info.splitlines()\n for line in ifc_info:\n match = re.search(r'^(\\d+):\\s+IP4\\s+(\\w+.\\w+.\\w+.\\w+)\\s+(\\w+.\\w+.\\w+.\\w+)'\n r'\\s+\\w+.\\w+.\\w+.\\w+,\\s+vlan\\s(\\d+),', line)\n if match:\n cnt += 1\n ifcs.append(\n [match.group(4), match.group(2), match.group(3), match.group(1)])\n if [vlan, host, netmask, match.group(1)] in ifcs:\n fnd_ifc = match.group(1)\n found = True\n if cnt != int(match.group(1)) and ava_ifc == 0:\n ava_ifc = cnt\n ifcs.append(\n [{'configured': found,\n 'avail ifc': str(ava_ifc),\n 'found ifc': str(fnd_ifc)}])\n return ifcs",
"title": ""
},
{
"docid": "9f212e1725a68a93f23737ce4220e1b5",
"score": "0.57091683",
"text": "def get_win_ifaddrs():\n import ctypes\n import struct\n import ipaddress\n import ctypes.wintypes\n from ctypes.wintypes import DWORD, WCHAR, BYTE, BOOL\n from socket import AF_INET, AF_UNSPEC, AF_INET6\n \n # from iptypes.h\n MAX_ADAPTER_ADDRESS_LENGTH = 8\n MAX_DHCPV6_DUID_LENGTH = 130\n\n GAA_FLAG_INCLUDE_PREFIX = ctypes.c_ulong(0x0010)\n \n class SOCKADDR(ctypes.Structure):\n _fields_ = [\n ('family', ctypes.c_ushort),\n ('data', ctypes.c_byte*14),\n ]\n LPSOCKADDR = ctypes.POINTER(SOCKADDR)\n \n class IN6_ADDR(ctypes.Structure):\n _fields_ = [\n ('byte', ctypes.c_byte*16),\n ('word', ctypes.c_byte*16), #this should be changed\n ]\n \n class SOCKADDR_IN6(ctypes.Structure):\n _fields_ = [\n ('family', ctypes.c_short),\n ('port', ctypes.c_ushort),\n ('flowinfo', ctypes.c_ulong),\n ('addr', IN6_ADDR),\n ('scope_id', ctypes.c_ulong),\n ]\n LPSOCKADDR_IN6 = ctypes.POINTER(SOCKADDR_IN6)\n \n\n # NB: It's not true mapping of `sockaddr_storage` structure!\n class SOCKADDR_STORAGE(ctypes.Union):\n _fields_ = (('v4', LPSOCKADDR), ('v6', LPSOCKADDR_IN6))\n\n class SOCKET_ADDRESS(ctypes.Structure):\n _fields_ = [\n #('address', LPSOCKADDR),\n ('address', SOCKADDR_STORAGE),\n ('length', ctypes.c_int),\n ]\n\n class _IP_ADAPTER_ADDRESSES_METRIC(ctypes.Structure):\n _fields_ = [\n ('length', ctypes.c_ulong),\n ('interface_index', DWORD),\n ]\n\n class _IP_ADAPTER_ADDRESSES_U1(ctypes.Union):\n _fields_ = [\n ('alignment', ctypes.c_ulonglong),\n ('metric', _IP_ADAPTER_ADDRESSES_METRIC),\n ]\n\n class IP_ADAPTER_UNICAST_ADDRESS(ctypes.Structure):\n pass\n PIP_ADAPTER_UNICAST_ADDRESS = ctypes.POINTER(IP_ADAPTER_UNICAST_ADDRESS)\n IP_ADAPTER_UNICAST_ADDRESS._fields_ = [\n (\"length\", ctypes.c_ulong),\n (\"flags\", ctypes.wintypes.DWORD),\n (\"next\", PIP_ADAPTER_UNICAST_ADDRESS),\n (\"address\", SOCKET_ADDRESS),\n (\"prefix_origin\", ctypes.c_int),\n (\"suffix_origin\", ctypes.c_int),\n (\"dad_state\", ctypes.c_int),\n (\"valid_lifetime\", ctypes.c_ulong),\n (\"preferred_lifetime\", ctypes.c_ulong),\n (\"lease_lifetime\", ctypes.c_ulong),\n (\"on_link_prefix_length\", ctypes.c_ubyte)\n ]\n\n # it crashes when retrieving prefix data :(\n class IP_ADAPTER_PREFIX(ctypes.Structure):\n pass\n PIP_ADAPTER_PREFIX = ctypes.POINTER(IP_ADAPTER_PREFIX)\n IP_ADAPTER_PREFIX._fields_ = [\n (\"alignment\", ctypes.c_ulonglong),\n (\"next\", PIP_ADAPTER_PREFIX),\n (\"address\", SOCKET_ADDRESS),\n (\"prefix_length\", ctypes.c_ulong)\n ]\n\n class IP_ADAPTER_ADDRESSES(ctypes.Structure):\n pass\n LP_IP_ADAPTER_ADDRESSES = ctypes.POINTER(IP_ADAPTER_ADDRESSES)\n \n # for now, just use void * for pointers to unused structures\n PIP_ADAPTER_ANYCAST_ADDRESS = ctypes.c_void_p\n PIP_ADAPTER_MULTICAST_ADDRESS = ctypes.c_void_p\n PIP_ADAPTER_DNS_SERVER_ADDRESS = ctypes.c_void_p\n #PIP_ADAPTER_PREFIX = ctypes.c_void_p\n PIP_ADAPTER_WINS_SERVER_ADDRESS_LH = ctypes.c_void_p\n PIP_ADAPTER_GATEWAY_ADDRESS_LH = ctypes.c_void_p\n PIP_ADAPTER_DNS_SUFFIX = ctypes.c_void_p\n\n IF_OPER_STATUS = ctypes.c_uint # this is an enum, consider http://code.activestate.com/recipes/576415/\n IF_LUID = ctypes.c_uint64\n\n NET_IF_COMPARTMENT_ID = ctypes.c_uint32\n GUID = ctypes.c_byte*16\n NET_IF_NETWORK_GUID = GUID\n NET_IF_CONNECTION_TYPE = ctypes.c_uint # enum\n TUNNEL_TYPE = ctypes.c_uint # enum\n\n IP_ADAPTER_ADDRESSES._fields_ = [\n #('u', _IP_ADAPTER_ADDRESSES_U1),\n ('length', ctypes.c_ulong),\n ('interface_index', DWORD),\n ('next', LP_IP_ADAPTER_ADDRESSES),\n ('adapter_name', ctypes.c_char_p),\n ('first_unicast_address', PIP_ADAPTER_UNICAST_ADDRESS),\n ('first_anycast_address', PIP_ADAPTER_ANYCAST_ADDRESS),\n ('first_multicast_address', PIP_ADAPTER_MULTICAST_ADDRESS),\n ('first_dns_server_address', PIP_ADAPTER_DNS_SERVER_ADDRESS),\n ('dns_suffix', ctypes.c_wchar_p),\n ('description', ctypes.c_wchar_p),\n ('friendly_name', ctypes.c_wchar_p),\n ('byte', BYTE*MAX_ADAPTER_ADDRESS_LENGTH),\n ('physical_address_length', DWORD),\n ('flags', DWORD),\n ('mtu', DWORD),\n ('interface_type', DWORD),\n ('oper_status', IF_OPER_STATUS),\n ('ipv6_interface_index', DWORD),\n ('zone_indices', DWORD),\n ('first_prefix', PIP_ADAPTER_PREFIX),\n ('transmit_link_speed', ctypes.c_uint64),\n ('receive_link_speed', ctypes.c_uint64),\n ('first_wins_server_address', PIP_ADAPTER_WINS_SERVER_ADDRESS_LH),\n ('first_gateway_address', PIP_ADAPTER_GATEWAY_ADDRESS_LH),\n ('ipv4_metric', ctypes.c_ulong),\n ('ipv6_metric', ctypes.c_ulong),\n ('luid', IF_LUID),\n ('dhcpv4_server', SOCKET_ADDRESS),\n ('compartment_id', NET_IF_COMPARTMENT_ID),\n ('network_guid', NET_IF_NETWORK_GUID),\n ('connection_type', NET_IF_CONNECTION_TYPE),\n ('tunnel_type', TUNNEL_TYPE),\n ('dhcpv6_server', SOCKET_ADDRESS),\n ('dhcpv6_client_duid', ctypes.c_byte*MAX_DHCPV6_DUID_LENGTH),\n ('dhcpv6_client_duid_length', ctypes.c_ulong),\n ('dhcpv6_iaid', ctypes.c_ulong),\n ('first_dns_suffix', PIP_ADAPTER_DNS_SUFFIX),\n ]\n\n def GetAdaptersAddresses():\n \"\"\"\n Returns an iteratable list of adapters\n \"\"\" \n size = ctypes.c_ulong()\n GetAdaptersAddresses = ctypes.windll.iphlpapi.GetAdaptersAddresses\n GetAdaptersAddresses.argtypes = [\n ctypes.c_ulong,\n ctypes.c_ulong,\n ctypes.c_void_p,\n ctypes.POINTER(IP_ADAPTER_ADDRESSES),\n ctypes.POINTER(ctypes.c_ulong),\n ]\n GetAdaptersAddresses.restype = ctypes.c_ulong\n #res = GetAdaptersAddresses(AF_INET,0,None, None,size)\n res = GetAdaptersAddresses(AF_UNSPEC,0,None, None,size)\n if res != 0x6f: # BUFFER OVERFLOW\n raise RuntimeError(\"Error getting structure length (%d)\" % res)\n pointer_type = ctypes.POINTER(IP_ADAPTER_ADDRESSES)\n size.value = 15000\n buffer = ctypes.create_string_buffer(size.value)\n struct_p = ctypes.cast(buffer, pointer_type)\n #res = GetAdaptersAddresses(AF_INET,0,None, struct_p, size)\n res = GetAdaptersAddresses(AF_UNSPEC,0,None, struct_p, size)\n if res != 0x0: # NO_ERROR:\n raise RuntimeError(\"Error retrieving table (%d)\" % res)\n while struct_p:\n yield struct_p.contents\n struct_p = struct_p.contents.next\n\n interfaced = {}\n for i in GetAdaptersAddresses():\n result = NetworkInterface()\n result.ifname = i.description\n result.ifindex = i.zone_indices #zone_indices in windows\n \n \n addresses = i.first_unicast_address\n result.IPv4 = []\n result.IPv6 = []\n \n while addresses:\n \n fu = addresses.contents\n \n ipversion = fu.address.address.v4.contents.family \n if ipversion == AF_INET:\n ad = fu.address.address.v4.contents\n #print(\"\\tfamily: {0}\".format(ad.family))\n ip_int = struct.unpack('>2xI8x', ad.data)[0]\n ip = ipaddress.IPv4Address(ip_int)\n #print(ip)\n result.IPv4.append(ip)\n elif ipversion == AF_INET6:\n ad = fu.address.address.v6.contents\n ip_int = struct.unpack('!QQ', ad.addr.byte)[0]\n ip = ipaddress.IPv6Address(ip_int)\n result.IPv6.append(ip)\n \n addresses = addresses.contents.next\n \n interfaced[result.ifname] = result\n return interfaced",
"title": ""
},
{
"docid": "7d12df82b5ff0022e26f68f6000f5fb5",
"score": "0.56958973",
"text": "def get_interfaces(self):\n interfaces = {}\n output = self.device.send_command('display interface')\n if not output:\n return {}\n\n separator = r\"(^(?!Line protocol).*current state.*$)\"\n re_intf_name_state = r\"^(?!Line protocol)(?P<intf_name>\\S+).+current state\\W+(?P<intf_state>.+)$\"\n re_protocol = r\"Line protocol current state\\W+(?P<protocol>.+)$\"\n re_mac = r\"Hardware address is\\W+(?P<mac_address>\\S+)\"\n re_speed = r\"^Speed\\W+(?P<speed>\\d+|\\w+)\"\n re_description = r\"^Description:(?P<description>.*)$\"\n re_mtu = r\"(Maximum Transmit Unit|Maximum Frame Length) is (?P<mtu>\\d+)\"\n\n new_interfaces = self._separate_section(separator, output)\n for interface in new_interfaces:\n interface = interface.strip()\n match_intf = re.search(re_intf_name_state, interface, flags=re.M)\n match_proto = re.search(re_protocol, interface, flags=re.M)\n\n if match_intf is None or match_proto is None:\n msg = \"Unexpected interface format: {}\".format(interface)\n raise ValueError(msg)\n intf_name = match_intf.group('intf_name')\n intf_state = match_intf.group('intf_state')\n is_enabled = bool('up' in intf_state.lower())\n\n protocol = match_proto.group('protocol')\n is_up = bool('up' in protocol.lower())\n\n match_mac = re.search(re_mac, interface, flags=re.M)\n if match_mac:\n mac_address = match_mac.group('mac_address')\n mac_address = napalm.base.helpers.mac(mac_address)\n else:\n mac_address = \"\"\n\n speed = mtu = 0\n match_speed = re.search(re_speed, interface, flags=re.M)\n if match_speed:\n speed = match_speed.group('speed')\n if speed.isdigit():\n speed = int(speed)\n\n match_mtu = re.search(re_mtu, interface, flags=re.M)\n if match_mtu:\n mtu = match_mtu.group('mtu')\n if mtu.isdigit():\n mtu = int(mtu)\n\n description = ''\n match = re.search(re_description, interface, flags=re.M)\n if match:\n description = match.group('description').strip()\n\n interfaces.update({\n intf_name: {\n 'description': description,\n 'is_enabled': is_enabled,\n 'is_up': is_up,\n 'last_flapped': -1.0,\n 'mac_address': mac_address,\n 'speed': speed,\n 'mtu': mtu\n }\n })\n return interfaces",
"title": ""
},
{
"docid": "0c7e4423b5e65397f96de1774fba1bec",
"score": "0.56787306",
"text": "def _get_available_interfaces(self):\n return []",
"title": ""
},
{
"docid": "581bc04be23047f3b680870dbff420a6",
"score": "0.5676502",
"text": "def get_interfaces(family=4):\n d = subprocess.check_output('ip -%s -o addr' % str(family), shell=True)\n ifs = re.findall(r'^\\S+:\\s+(\\S+)\\s+inet[6]?\\s+([^\\s/]+)', d, re.MULTILINE)\n return [i for i in ifs if i[0] != 'lo']",
"title": ""
},
{
"docid": "581bc04be23047f3b680870dbff420a6",
"score": "0.5676502",
"text": "def get_interfaces(family=4):\n d = subprocess.check_output('ip -%s -o addr' % str(family), shell=True)\n ifs = re.findall(r'^\\S+:\\s+(\\S+)\\s+inet[6]?\\s+([^\\s/]+)', d, re.MULTILINE)\n return [i for i in ifs if i[0] != 'lo']",
"title": ""
},
{
"docid": "c6148367c23527d069bf634d0dc2132d",
"score": "0.56709194",
"text": "def get_interfaces(cls):\n\n if cls.interfaces:\n # if we already populated the interfaces array, gtfo\n return cls.interfaces\n\n ifnr = re.compile(r\"\\d[:]\")\n\n # first round: find the interfaces\n ip_out = subprocess.check_output(['/bin/ip', 'addr'], shell=False,\n universal_newlines=True)\n\n for row in ip_out.splitlines():\n words = row.split(' ')\n if ifnr.match(words[0]):\n # first line of new interface definition\n # create new cInterface object, take the if name and ignore\n # the rest\n\n # cut off the \":\" at the end of if name\n current = Interface(words[1][:-1])\n\n cls.interfaces.append(current)\n else:\n # additional line to parse for the current interface\n index = 0\n\n while index < len(words):\n if words[index] != \"\": # skip empty\n if words[index][0:5] == \"link/\":\n # if it starts with \"link/\" then we got the\n # interface type and next IS hw address\n current.type = words[index][5:]\n index = index + 1\n current.hwaddress = words[index]\n\n elif words[index] == \"inet\":\n # in that case, next one is ipv4 address\n index = index + 1\n current.ipv4.append(words[index])\n\n elif words[index] == \"inet6\":\n # Houston, we got an IPv6 address next step\n index = index + 1\n current.ipv6.append(words[index])\n\n index = index + 1\n\n return Interface.interfaces",
"title": ""
},
{
"docid": "770a64c0ce0cb3f9af89b6ef8abb1371",
"score": "0.5647698",
"text": "def _get_interfaces(self):\n return self.__interfaces",
"title": ""
},
{
"docid": "770a64c0ce0cb3f9af89b6ef8abb1371",
"score": "0.5647698",
"text": "def _get_interfaces(self):\n return self.__interfaces",
"title": ""
},
{
"docid": "770a64c0ce0cb3f9af89b6ef8abb1371",
"score": "0.5647698",
"text": "def _get_interfaces(self):\n return self.__interfaces",
"title": ""
},
{
"docid": "95d3cfdc4b581b30d0a509cf248d71b1",
"score": "0.5638045",
"text": "def get_vrf_interface(device, vrf):\n log.info(\"Getting the interfaces under vrf {vrf}\".format(vrf=vrf))\n\n try:\n out = device.parse(\"show vrf {vrf}\".format(vrf=vrf))\n except SchemaEmptyParserError:\n return None\n\n if out and \"vrf\" in out and vrf in out[\"vrf\"]:\n return out[\"vrf\"][vrf].get(\"interfaces\", None)",
"title": ""
},
{
"docid": "24dfb1dfc2f954dd90d236feff73b0f8",
"score": "0.55968195",
"text": "def network_interfaces(self) -> Optional[Sequence['outputs.IpAddressResponse']]:\n return pulumi.get(self, \"network_interfaces\")",
"title": ""
},
{
"docid": "0f877a6cb5db8157d04f8205514ce901",
"score": "0.5594478",
"text": "def _get_interface_phy_dhcp_conf(self):\n return self.__interface_phy_dhcp_conf",
"title": ""
},
{
"docid": "af3016fd6620dba2f0db2a41586ad12d",
"score": "0.55908054",
"text": "def get_interfaces(prop):\n ret_val = []\n for interface in prop.configManager.networkSystem.networkInfo.pnic:\n name = interface.device\n mac = interface.mac\n driver = interface.driver\n try:\n linkSpeed = interface.linkSpeed.speedMb\n except:\n try:\n linkSpeed = interface.spec.linkSpeed.speedMb\n except:\n linkSpeed = 0\n ret_val.append({'name': name,\n 'mac': mac,\n 'driver': driver,\n 'linkSpeed': linkSpeed})\n\n return ret_val",
"title": ""
},
{
"docid": "53a3bd2b476374e157bd607794e7da50",
"score": "0.55594534",
"text": "def get_active_interfaces() -> List[Interface]:\n results: List[Interface] = list()\n interfaces = netifaces.interfaces()\n for interface in interfaces:\n if netifaces.ifaddresses(interface).get(netifaces.AF_INET) is not None or netifaces.ifaddresses(interface).get(\n netifaces.AF_INET6) is not None:\n iface = Interface(interface)\n for af_type, af_list in netifaces.ifaddresses(interface).items():\n if af_type == netifaces.AF_INET:\n for af_dict in af_list:\n cidr = calculateIPv4CIDR(af_dict.get(\"netmask\"))\n iface.ipv4_interfaces.append(IPv4Interface(\"{0}/{1}\".format(af_dict.get(\"addr\"), cidr)))\n if af_type == netifaces.AF_INET6:\n for af_dict in af_list:\n cidr = af_dict.get(\"netmask\").split(\"/\")[1]\n iface.ipv6_interfaces.append(\n IPv6Interface(\"{0}/{1}\".format(af_dict.get(\"addr\").split('%')[0], cidr))\n )\n if af_type == netifaces.AF_LINK:\n for af_dict in af_list:\n iface.mac += af_list[0].get(\"addr\")\n\n gateway_list: List[tuple] = list()\n for gateway in netifaces.gateways()[2]:\n if gateway[1] == iface.name:\n gateway_list.append(gateway)\n if len(gateway_list) != 0:\n iface.gateways = gateway_list\n\n results.append(iface)\n\n return results if len(results) > 1 else None",
"title": ""
},
{
"docid": "0247f3be3a2eeca3255806ea2c4c22bf",
"score": "0.55493075",
"text": "def show_intf(self):\n dev = self.connect()\n res = dev.rpc.get_interface_information({\"format\": \"json\"}, terse=True).json\n return res",
"title": ""
},
{
"docid": "2a8e418f309a6c0b6c65a1c1eb9a7e06",
"score": "0.5527731",
"text": "def GetInterfaces(self, request, context):\n interfaces = []\n for interface in os.listdir(\"/sys/class/net\"):\n if (\n interface.startswith(\"b.\")\n or interface.startswith(\"veth\")\n or interface == \"lo\"\n ):\n continue\n interfaces.append(interface)\n return core_pb2.GetInterfacesResponse(interfaces=interfaces)",
"title": ""
},
{
"docid": "5a14dc65ff9eb26a149d8e41a87df7be",
"score": "0.55269325",
"text": "def show_addresses(self):\n print(\"Interfaces:\")\n for address in self.addresses:\n print(f'{address} - {self.addresses[address]}')",
"title": ""
},
{
"docid": "435b0f1fc6c9a2d3ff9409b1f7fc0688",
"score": "0.5508248",
"text": "def getIface(self, interface_name=''):\n theinterface = \"\"\n wecontinue = False\n\n if os.name == \"posix\":\n co = subprocess.Popen(self.defaultProg, stdout = subprocess.PIPE)\n ifconfig = co.stdout.read()\n thechoices = []\n \n if not interface_name:\n print \"\\nPick an interface to mess with:\\n\"\n\n if self.getRunningPlatform() == DARWIN:\n #thechoices = re.findall(r'^([\\w]*):? [\\w=<,>\\s]*(([a-f\\d]{1,2}\\:){5}[a-f\\d]{1,2})', ifconfig, re.MULTILINE)\n thechoices = re.findall(r'^([\\w]*):? [\\w=<,>\\s]*(([0-9a-fA-F]{2}:?){6})', ifconfig, re.MULTILINE)\n if DEBUG:\n print thechoices\n '''\n this regex was tested with Fedora and Debian ...\n not sure if it will actually work with every flavor of Linux\n \n this MAC regex: ([0-9a-fA-F]{2}:?){6} seemed good\n but gave way too many false positives on Linux\n '''\n if self.getRunningPlatform() == LINUX:\n if self.getRunningPlatformFlavor() == 'fedora':\n thechoices = re.findall(r'^([\\w]*): [\\w=<,>:.\\s]* ([a-f\\d]{1,2}(?::[a-f\\d]{1,2}){5})', ifconfig, re.MULTILINE)\n else:\n thechoices = re.findall(r'^([\\w]*):? [\\w=<,>:.\\s]*(([a-f\\d]{1,2}\\:){5}[a-f\\d]{1,2})', ifconfig, re.MULTILINE)\n #thechoices = re.findall(r'^([\\w]*):? [\\w=<,>:.\\s]*(([0-9a-fA-F]{2}:?){6})', ifconfig, re.MULTILINE)\n \n '''\n if no interfaces are discovered then\n there is no need to go any further\n '''\n if thechoices != None:\n if not interface_name:\n for f in thechoices:\n if not f[1].endswith(\":\"):\n print \"%s %s\" % (f[0], f[1])\n else:\n print \"No interfaces discovered, help us out and send us this data via email ...\"\n print \"\\n\\n###########################\"\n print ifconfig\n print \"###########################\\n\"\n print \"mail to: <support [at] neurofuzzsecurity dot com>\\n\\n\"\n sys.exit(1)\n\n # interfaces discovered, get a choice \n try:\n if not interface_name:\n var = raw_input(\"\\nYour choice: \")\n else:\n var = interface_name\n \n # ensure choice is in range\n for f in thechoices:\n if var == f[0]:\n var = f\n wecontinue = True\n break\n \n if wecontinue:\n self.set_interface(iface=var[0])\n self.setOriginalMacAddress(mac=var[1])\n if WRITEDAT:\n self.persist_data()\n # check to see if DHCP is used\n #self.dhcpUsed = self.isDhcpUsed()\n self.dhcpUsed = self.get_dhcp_used()\n else:\n shut_down(\"Choice out of range\")\n except ValueError, e:\n print e\n shut_down(\"Invalid input\")\n except IndexError, e:\n print e\n shut_down(\"Invalid input\")\n else:\n shut_down(\"Sorry but this is written to run on *nix platforms, grow up\")",
"title": ""
},
{
"docid": "471039f6df7ae7b56498307ec0fbca1d",
"score": "0.55069494",
"text": "def GetDot1xInterfaces():\n interfaces = []\n for interface in GetNetworkInterfaces():\n if interface['type'] == 'IEEE80211' or interface['type'] == 'Ethernet':\n if (interface['builtin'] and\n 'AppleThunderboltIPPort' not in interface['bus']):\n interfaces.append(interface)\n return interfaces",
"title": ""
},
{
"docid": "15e06128c015d7974dd70282bc340c37",
"score": "0.549582",
"text": "def get_dns_config(interface=\"Local Area Connection\"):\n # remove any escape characters\n interface = interface.split(\"\\\\\")\n interface = \"\".join(interface)\n\n with salt.utils.winapi.Com():\n c = wmi.WMI()\n for iface in c.Win32_NetworkAdapterConfiguration(IPEnabled=1):\n if interface == iface.Description:\n return iface.DHCPEnabled",
"title": ""
},
{
"docid": "6c72effc6a766c54eb38a763749e763f",
"score": "0.5495333",
"text": "def get_interfaces(client):\n return client.call('get_interfaces')",
"title": ""
},
{
"docid": "de400573c91a14c7ff89b0677d17f2db",
"score": "0.54889125",
"text": "def parse_ospf(self, conf, attrib=None):\n\n cfg_dict = self.parse_attrib(conf, \"ospf\", match=attrib)\n return cfg_dict",
"title": ""
},
{
"docid": "4dce0cadba0f9ecc22995f619d4d2e2e",
"score": "0.54861444",
"text": "def get_interfaces(self):\n interfaces = {}\n command = \"show interface\"\n output = self._send_command(command)\n if not output:\n return {}\n\n # Break output into per-interface sections (note, separator text is retained)\n separator1 = r\"^\\S+\\s+is \\S+.*\\nadmin state is.*$\"\n separator2 = r\"^.* is .*, line protocol is .*$\"\n separator3 = r\"^.* is (?:down|up).*$\"\n separators = r\"({}|{}|{})\".format(separator1, separator2, separator3)\n interface_lines = re.split(separators, output, flags=re.M)\n\n if len(interface_lines) == 1:\n msg = \"Unexpected output data in '{}':\\n\\n{}\".format(\n command, interface_lines\n )\n raise ValueError(msg)\n\n # Get rid of the blank data at the beginning\n interface_lines.pop(0)\n\n # Must be pairs of data (the separator and section corresponding to it)\n if len(interface_lines) % 2 != 0:\n msg = \"Unexpected output data in '{}':\\n\\n{}\".format(\n command, interface_lines\n )\n raise ValueError(msg)\n\n # Combine the separator and section into one string\n intf_iter = iter(interface_lines)\n try:\n new_interfaces = [line + next(intf_iter, \"\") for line in intf_iter]\n except TypeError:\n raise ValueError()\n\n for entry in new_interfaces:\n interfaces.update(parse_intf_section(entry))\n\n return interfaces",
"title": ""
},
{
"docid": "1ee507fa4aaa2e3d0387e97a6369518e",
"score": "0.54791784",
"text": "def get_addresses_of_all_interfaces():\n ipv6_addresses = [addr[4][0] for addr in socket.getaddrinfo(socket.gethostname(), None, family=socket.AF_INET6)]\n ipv4_addresses = [addr[4][0] for addr in socket.getaddrinfo(socket.gethostname(), None, family=socket.AF_INET)]\n return ipv6_addresses, ipv4_addresses",
"title": ""
},
{
"docid": "2ecc611864f4b1ab05659fc522e50367",
"score": "0.54783976",
"text": "def connected_ifaces(self):\n return {nif.infname:self.routed_subnet[-1].ip4network[i+2] for i, nif in enumerate(self.net_iface_routed_by)}",
"title": ""
},
{
"docid": "906d47f29a6c204c7b8eb1bbbdfdf2f0",
"score": "0.5477794",
"text": "def get_interfaces(self):\n interfaces = {}\n switches = self._get_switches_dict()\n for switch in switches['switches'].values():\n for interface_id, interface in switch['interfaces'].items():\n interfaces[interface_id] = interface\n\n return jsonify({'interfaces': interfaces})",
"title": ""
},
{
"docid": "86c9a789e9a51d5b6f796fe7a43c65aa",
"score": "0.5470988",
"text": "def get_beacon_iface(self, ifaces):\n for s in ifaces:\n if \"bss\" not in s:\n continue\n v = self.cli(\"get bss %s detail\" % s[\"bss\"])\n for block in v.split(\"\\n\\n\"):\n data = dict(\n line.split(None, 1)\n for line in block.splitlines()\n if len(line.split(None, 1)) == 2\n )\n if \"status\" not in data:\n continue\n s[\"status\"] = data[\"status\"]\n s[\"radio\"] = data[\"radio\"]",
"title": ""
},
{
"docid": "c89898c4ced8fd9325db916e73695bab",
"score": "0.54550695",
"text": "def getNetworkInterfacesInfo():\n interfaces = dict()\n adapters = GetAdaptersAddresses()\n adapter = adapters.contents\n while True:\n interfaces[adapter.AdapterName] = dict()\n mac_address_lst = list()\n for idx in range(0, adapter.PhysicalAddressLength):\n mac_address_lst.append(\"%02X\" % adapter.PhysicalAddress[idx])\n interfaces[adapter.AdapterName]['description'] = adapter.Description\n interfaces[adapter.AdapterName]['friendly name'] = adapter.FriendlyName\n if mac_address_lst:\n interfaces[adapter.AdapterName]['mac address'] = \":\".join(mac_address_lst)\n try:\n adapter = adapter.Next.contents\n except ValueError:\n break\n return interfaces",
"title": ""
},
{
"docid": "701d627c5dfbeb9ebf8680e6ed88ba5b",
"score": "0.54530305",
"text": "def get_firewall_sections(self):\n return self.nsxlib.firewall_section.list()",
"title": ""
},
{
"docid": "87a84990109acd8a959eebac9fbb832d",
"score": "0.5449623",
"text": "def List_Interfaces():\n iface_list = []\n f = open('/proc/net/dev','r')\n ifacelist = f.read().split('\\n') \n f.close()\n\n # remove 2 lines header\n ifacelist.pop(0)\n ifacelist.pop(0)\n\n # loop to check each line\n for line in ifacelist:\n ifacedata = line.replace(' ','').split(':')\n # check the data have 2 elements\n if len(ifacedata) == 2:\n iface_list.append(ifacedata[0])\n return iface_list",
"title": ""
},
{
"docid": "0d6b48fcd18971f683e56b6469209f43",
"score": "0.5437187",
"text": "def get_network_info():\n interfaces = []\n\n validate_interface = re.compile(\"^\\\\s*(\\\\S+):(.*)$\")\n split_values = re.compile(\"(\\\\S+)\")\n\n for line in open(\"/proc/net/dev\", \"r\"):\n interface = validate_interface.match(line)\n if interface:\n ifname = interface.groups()[0]\n values = map(int, split_values.findall(interface.groups()[1]))\n interfaces.append({\n 'name' : ifname,\n 'ipv4' : get_ipv4_address(ifname),\n 'hwaddr' : get_hardware_address(ifname),\n 'receive' : {\n 'bytes' : values[ 0],\n 'packets' : values[ 1],\n 'drop' : values[ 2],\n 'errs' : values[ 3],\n 'fifo' : values[ 4],\n 'frame' : values[ 5],\n 'compressed' : values[ 6],\n 'multicast' : values[ 7]\n },\n 'transmit' : {\n 'bytes' : values[ 8],\n 'packets' : values[ 9],\n 'errs' : values[10],\n 'drop' : values[11],\n 'fifo' : values[12],\n 'frame' : values[13],\n 'compressed' : values[14],\n 'multicast' : values[15]\n }\n })\n\n return interfaces",
"title": ""
},
{
"docid": "581ee9e7a69165c36e9c71ad8221d19a",
"score": "0.5417731",
"text": "def GetIwconfig(self):\n return self.wiface.GetIwconfig()",
"title": ""
},
{
"docid": "55dc11ca25847c79397d4adbc1aaea5d",
"score": "0.5414103",
"text": "def get_ext_ip():\n headers = {'Accept': 'application/json'}\n resp = requests.get('http://ifconfig.co', headers=headers)\n return resp.json()['ip']",
"title": ""
},
{
"docid": "4c78f427f1cf1817cdac7839fc59f97d",
"score": "0.54004794",
"text": "def interface(self) -> str:\n return self.get(\"interface\") # type: ignore",
"title": ""
},
{
"docid": "3ea7757fbd34f760f5373a92c467ba09",
"score": "0.5391717",
"text": "def ifc(choices):\n if len(x) > 1:\n os.system('ifconfig {0}'.format(x[1]))\n else:\n os.system('ifconfig eth0')",
"title": ""
},
{
"docid": "b6686cab06e9c4cfc1d109bde3d3e9fe",
"score": "0.53872263",
"text": "def get_open_interfaces(self, c):\n open_interfaces = self._get_open_interfaces()\n return open_interfaces",
"title": ""
},
{
"docid": "b170391ce112bf96194be19ce144b48b",
"score": "0.5387187",
"text": "def get_appliance_ospf_neighbors_state(\n self,\n ne_id: str,\n) -> dict:\n return self._get(\"/ospf/state/interfaces/{}\".format(ne_id))",
"title": ""
},
{
"docid": "cfc9376fd8340577c3833a04e575bd5a",
"score": "0.5374884",
"text": "def populate_facts(self, connection, ansible_facts, data=None):\n facts = {}\n objs = []\n\n if not data:\n data = self.get_device_data(connection)\n\n # parse native config using the Ospf_interfaces template\n ospf_interfaces_facts = []\n resources = self.get_config_set(data)\n for resource in resources:\n ospf_interfaces_parser = Ospf_interfacesTemplate(\n lines=resource.split(\"\\n\")\n )\n objs = ospf_interfaces_parser.parse()\n for key, sortv in [(\"address_family\", \"afi\")]:\n if key in objs and objs[key]:\n objs[key] = list(objs[key].values())\n ospf_interfaces_facts.append(objs)\n\n ansible_facts[\"ansible_network_resources\"].pop(\"ospf_interfaces\", None)\n facts = {\"ospf_interfaces\": []}\n params = utils.remove_empties(\n utils.validate_config(\n self.argument_spec, {\"config\": ospf_interfaces_facts}\n )\n )\n if params.get(\"config\"):\n for cfg in params[\"config\"]:\n facts[\"ospf_interfaces\"].append(utils.remove_empties(cfg))\n ansible_facts[\"ansible_network_resources\"].update(facts)\n\n return ansible_facts",
"title": ""
},
{
"docid": "5f70bcec92c1de3f5e5595e914aedc65",
"score": "0.5360476",
"text": "def get_network_interfaces():\n mac_address = \"\"\n cmd_string = (\n ipaddr\n + \" addr show | \"\n + egrep\n + \" 'eth0:|wlan0:' | \"\n + awk\n + \" '{print $2}' | \"\n + cut\n + \" -d':' -f1\"\n )\n out = subprocess.Popen(\n cmd_string, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT\n )\n stdout, _ = out.communicate()\n iface_names = stdout.decode(\"utf-8\").split()\n\n # loop over 'iface_names' and build OrderedDict 'interfaces'\n interfaces = OrderedDict()\n for idx in iface_names:\n interface = OrderedDict()\n # get IP4 address\n cmd_IP = (\n ipaddr\n + \" -4 addr show \"\n + idx\n + \" | \"\n + egrep\n + \" inet | \"\n + awk\n + \" '{print $2}' | \"\n + cut\n + \" -d'/' -f1\"\n )\n out = subprocess.Popen(\n cmd_IP, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT\n )\n stdout, _ = out.communicate()\n ip = stdout.decode(\"utf-8\").strip()\n if not ip == \"\":\n interface[\"IP\"] = ip\n\n # get MAC address\n cmd_MAC = (\n ipaddr\n + \" link show \"\n + idx\n + \" | \"\n + egrep\n + \" ether | \"\n + awk\n + \" '{print $2}'\"\n )\n out = subprocess.Popen(\n cmd_MAC, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT\n )\n stdout, _ = out.communicate()\n mac = stdout.decode(\"utf-8\").upper().strip()\n if not mac == \"\":\n interface[\"MAC\"] = mac\n if mac_address == \"\":\n mac_address = mac.lower()\n\n interfaces[idx] = interface\n return (interfaces, mac_address)",
"title": ""
},
{
"docid": "77750fc45ecc3bb84c78e1338695784c",
"score": "0.5358643",
"text": "def config_interface(interface_configurations, address):\n # configure IPv4 loopback\n interface_configuration = interface_configurations.InterfaceConfiguration()\n interface_configuration.active = \"act\"\n interface_configuration.interface_name = \"Loopback99\"\n interface_configuration.interface_virtual = Empty()\n interface_configuration.description = \"ADDITIONAL ROUTER LOOPBACK\"\n primary = interface_configuration.ipv4_network.addresses.Primary()\n primary.address = address\n primary.netmask = \"255.255.255.255\"\n interface_configuration.ipv4_network.addresses.primary = primary\n interface_configurations.interface_configuration.append(interface_configuration)",
"title": ""
},
{
"docid": "81feeb5f8a1b0b048eb1db393959a1b7",
"score": "0.53541017",
"text": "def _get_igp_ospf(self):\n return self.__igp_ospf",
"title": ""
},
{
"docid": "d336f8e10a4fa1cae83ed2a4a3c05a4a",
"score": "0.5353517",
"text": "def get_interfaces(self):\n interfaces = self.create_obj_list('INTERFACES')\n return interfaces",
"title": ""
},
{
"docid": "f4aefc61f61ace2c498ceff5de9dab3e",
"score": "0.53469646",
"text": "def getInterfaces(self):\n return self.interfaces[:]",
"title": ""
},
{
"docid": "669c2e254e6411f8f84dd9dc3537047f",
"score": "0.5327269",
"text": "def enumerate_ip_choices():\r\n adapters = ifaddr.get_adapters()\r\n rtn = []\r\n for adapter in adapters:\r\n for ip in adapter.ips:\r\n if isinstance(ip.ip, str): # IPv4\r\n rtn.append({\"addr\": ipaddress.IPv4Address(ip.ip),\r\n \"name\": adapter.nice_name,\r\n \"prefix\": ip.network_prefix})\r\n else: # IPv6\r\n rtn.append({\"addr\": ipaddress.IPv6Address(ip.ip[0]),\r\n \"name\": adapter.nice_name,\r\n \"prefix\": ip.network_prefix})\r\n return rtn",
"title": ""
},
{
"docid": "660154244831be224fcb9bf5dada0950",
"score": "0.53218424",
"text": "def extcap_interfaces():\n print(\"extcap {version=%s}{display=nRF Sniffer for Bluetooth LE}\"\n \"{help=https://www.nordicsemi.com/Software-and-Tools/Development-Tools/nRF-Sniffer-for-Bluetooth-LE}\"\n % Sniffer.VERSION_STRING)\n\n for interface_port in get_interfaces():\n if sys.platform == 'win32':\n print(\"interface {value=%s-%s}{display=nRF Sniffer for Bluetooth LE %s}\" % (interface_port, extcap_version, interface_port))\n else:\n print(\"interface {value=%s-%s}{display=nRF Sniffer for Bluetooth LE}\" % (interface_port, extcap_version))\n\n print(\"control {number=%d}{type=selector}{display=Device}{tooltip=Device list}\" % CTRL_ARG_DEVICE)\n print(\"control {number=%d}{type=selector}{display=Key}{tooltip=}\" % CTRL_ARG_KEY_TYPE)\n print(\"control {number=%d}{type=string}{display=Value}\"\n \"{tooltip=6 digit passkey or 16 or 32 bytes encryption key in hexadecimal starting with '0x', big endian format.\"\n \"If the entered key is shorter than 16 or 32 bytes, it will be zero-padded in front'}\"\n \"{validation=\\\\b^(([0-9]{6})|(0x[0-9a-fA-F]{1,64})|([0-9A-Fa-f]{2}[:-]){5}([0-9A-Fa-f]{2}) (public|random))$\\\\b}\" % CTRL_ARG_KEY_VAL)\n print(\"control {number=%d}{type=string}{display=Adv Hop}\"\n \"{default=37,38,39}\"\n \"{tooltip=Advertising channel hop sequence. \"\n \"Change the order in which the sniffer switches advertising channels. \"\n \"Valid channels are 37, 38 and 39 separated by comma.}\"\n \"{validation=^\\s*((37|38|39)\\s*,\\s*){0,2}(37|38|39){1}\\s*$}{required=true}\" % CTRL_ARG_ADVHOP)\n print(\"control {number=%d}{type=button}{display=Clear}{tooltop=Clear or remove device from Device list}\" % CTRL_ARG_DEVICE_CLEAR)\n print(\"control {number=%d}{type=button}{role=help}{display=Help}{tooltip=Access user guide (launches browser)}\" % CTRL_ARG_HELP)\n print(\"control {number=%d}{type=button}{role=restore}{display=Defaults}{tooltip=Resets the user interface and clears the log file}\" % CTRL_ARG_RESTORE)\n print(\"control {number=%d}{type=button}{role=logger}{display=Log}{tooltip=Log per interface}\" % CTRL_ARG_LOG)\n\n print(\"value {control=%d}{value= }{display=All advertising devices}{default=true}\" % CTRL_ARG_DEVICE)\n print(\"value {control=%d}{value=%s}{display=Follow IRK}\" % (CTRL_ARG_DEVICE, zero_addr))\n\n print(\"value {control=%d}{value=%d}{display=Legacy Passkey}{default=true}\" % (CTRL_ARG_KEY_TYPE, CTRL_KEY_TYPE_PASSKEY))\n print(\"value {control=%d}{value=%d}{display=Legacy OOB data}\" % (CTRL_ARG_KEY_TYPE, CTRL_KEY_TYPE_OOB))\n print(\"value {control=%d}{value=%d}{display=Legacy LTK}\" % (CTRL_ARG_KEY_TYPE, CTRL_KEY_TYPE_LEGACY_LTK))\n print(\"value {control=%d}{value=%d}{display=SC LTK}\" % (CTRL_ARG_KEY_TYPE, CTRL_KEY_TYPE_SC_LTK))\n print(\"value {control=%d}{value=%d}{display=SC Private Key}\" % (CTRL_ARG_KEY_TYPE, CTRL_KEY_TYPE_DH_PRIVATE_KEY))\n print(\"value {control=%d}{value=%d}{display=IRK}\" % (CTRL_ARG_KEY_TYPE, CTRL_KEY_TYPE_IRK))\n print(\"value {control=%d}{value=%d}{display=Add LE address}\" % (CTRL_ARG_KEY_TYPE, CTRL_KEY_TYPE_ADD_ADDR))\n print(\"value {control=%d}{value=%d}{display=Follow LE address}\" % (CTRL_ARG_KEY_TYPE, CTRL_KEY_TYPE_FOLLOW_ADDR))",
"title": ""
},
{
"docid": "2e2da0e9ceb7fb11f6545eb58ec2df11",
"score": "0.5319251",
"text": "def get_interface(self):\n self.data[\"interface\"] = parse_api(\n data=self.data[\"interface\"],\n source=self.api.query(\"/interface\"),\n key=\"default-name\",\n key_secondary=\"name\",\n vals=[\n {\"name\": \"default-name\"},\n {\"name\": \".id\"},\n {\"name\": \"name\", \"default_val\": \"default-name\"},\n {\"name\": \"type\", \"default\": \"unknown\"},\n {\"name\": \"running\", \"type\": \"bool\"},\n {\n \"name\": \"enabled\",\n \"source\": \"disabled\",\n \"type\": \"bool\",\n \"reverse\": True,\n },\n {\"name\": \"port-mac-address\", \"source\": \"mac-address\"},\n {\"name\": \"comment\"},\n {\"name\": \"last-link-down-time\"},\n {\"name\": \"last-link-up-time\"},\n {\"name\": \"link-downs\"},\n {\"name\": \"tx-queue-drop\"},\n {\"name\": \"actual-mtu\"},\n {\"name\": \"about\", \"source\": \".about\", \"default\": \"\"},\n {\"name\": \"rx-current\", \"source\": \"rx-byte\", \"default\": 0.0},\n {\"name\": \"tx-current\", \"source\": \"tx-byte\", \"default\": 0.0},\n ],\n ensure_vals=[\n {\"name\": \"client-ip-address\"},\n {\"name\": \"client-mac-address\"},\n {\"name\": \"rx-previous\", \"default\": 0.0},\n {\"name\": \"tx-previous\", \"default\": 0.0},\n {\"name\": \"rx\", \"default\": 0.0},\n {\"name\": \"tx\", \"default\": 0.0},\n {\"name\": \"rx-total\", \"default\": 0.0},\n {\"name\": \"tx-total\", \"default\": 0.0},\n ],\n skip=[\n {\"name\": \"type\", \"value\": \"bridge\"},\n {\"name\": \"type\", \"value\": \"ppp-in\"},\n {\"name\": \"type\", \"value\": \"pptp-in\"},\n {\"name\": \"type\", \"value\": \"sstp-in\"},\n {\"name\": \"type\", \"value\": \"l2tp-in\"},\n {\"name\": \"type\", \"value\": \"pppoe-in\"},\n {\"name\": \"type\", \"value\": \"ovpn-in\"},\n ],\n )\n\n if self.option_sensor_port_traffic:\n uom_type, uom_div = self._get_unit_of_measurement()\n for uid, vals in self.data[\"interface\"].items():\n self.data[\"interface\"][uid][\"rx-attr\"] = uom_type\n self.data[\"interface\"][uid][\"tx-attr\"] = uom_type\n\n current_tx = vals[\"tx-current\"]\n previous_tx = vals[\"tx-previous\"]\n if not previous_tx:\n previous_tx = current_tx\n\n delta_tx = max(0, current_tx - previous_tx) * 8\n self.data[\"interface\"][uid][\"tx\"] = round(\n delta_tx / self.option_scan_interval.seconds * uom_div, 2\n )\n self.data[\"interface\"][uid][\"tx-previous\"] = current_tx\n\n current_rx = vals[\"rx-current\"]\n previous_rx = vals[\"rx-previous\"]\n if not previous_rx:\n previous_rx = current_rx\n\n delta_rx = max(0, current_rx - previous_rx) * 8\n self.data[\"interface\"][uid][\"rx\"] = round(\n delta_rx / self.option_scan_interval.seconds * uom_div, 2\n )\n self.data[\"interface\"][uid][\"rx-previous\"] = current_rx\n\n self.data[\"interface\"][uid][\"tx-total\"] = current_tx\n self.data[\"interface\"][uid][\"rx-total\"] = current_rx\n\n self.data[\"interface\"] = parse_api(\n data=self.data[\"interface\"],\n source=self.api.query(\"/interface/ethernet\"),\n key=\"default-name\",\n key_secondary=\"name\",\n vals=[\n {\"name\": \"default-name\"},\n {\"name\": \"name\", \"default_val\": \"default-name\"},\n {\"name\": \"poe-out\", \"default\": \"N/A\"},\n {\"name\": \"sfp-shutdown-temperature\", \"default\": \"\"},\n ],\n skip=[\n {\"name\": \"type\", \"value\": \"bridge\"},\n {\"name\": \"type\", \"value\": \"ppp-in\"},\n {\"name\": \"type\", \"value\": \"pptp-in\"},\n {\"name\": \"type\", \"value\": \"sstp-in\"},\n {\"name\": \"type\", \"value\": \"l2tp-in\"},\n {\"name\": \"type\", \"value\": \"pppoe-in\"},\n {\"name\": \"type\", \"value\": \"ovpn-in\"},\n ],\n )\n\n # Udpate virtual interfaces\n for uid, vals in self.data[\"interface\"].items():\n self.data[\"interface\"][uid][\"comment\"] = str(\n self.data[\"interface\"][uid][\"comment\"]\n )\n\n if vals[\"default-name\"] == \"\":\n self.data[\"interface\"][uid][\"default-name\"] = vals[\"name\"]\n self.data[\"interface\"][uid][\n \"port-mac-address\"\n ] = f\"{vals['port-mac-address']}-{vals['name']}\"\n\n if self.data[\"interface\"][uid][\"type\"] == \"ether\":\n if (\n \"sfp-shutdown-temperature\" in vals\n and vals[\"sfp-shutdown-temperature\"] != \"\"\n ):\n self.data[\"interface\"] = parse_api(\n data=self.data[\"interface\"],\n source=self.api.query(\n \"/interface/ethernet\",\n command=\"monitor\",\n args={\".id\": vals[\".id\"], \"once\": True},\n ),\n key_search=\"name\",\n vals=[\n {\"name\": \"status\", \"default\": \"unknown\"},\n {\"name\": \"auto-negotiation\", \"default\": \"unknown\"},\n {\"name\": \"advertising\", \"default\": \"unknown\"},\n {\"name\": \"link-partner-advertising\", \"default\": \"unknown\"},\n {\"name\": \"sfp-temperature\", \"default\": \"unknown\"},\n {\"name\": \"sfp-supply-voltage\", \"default\": \"unknown\"},\n {\"name\": \"sfp-module-present\", \"default\": \"unknown\"},\n {\"name\": \"sfp-tx-bias-current\", \"default\": \"unknown\"},\n {\"name\": \"sfp-tx-power\", \"default\": \"unknown\"},\n {\"name\": \"sfp-rx-power\", \"default\": \"unknown\"},\n {\"name\": \"sfp-rx-loss\", \"default\": \"unknown\"},\n {\"name\": \"sfp-tx-fault\", \"default\": \"unknown\"},\n {\"name\": \"sfp-type\", \"default\": \"unknown\"},\n {\"name\": \"sfp-connector-type\", \"default\": \"unknown\"},\n {\"name\": \"sfp-vendor-name\", \"default\": \"unknown\"},\n {\"name\": \"sfp-vendor-part-number\", \"default\": \"unknown\"},\n {\"name\": \"sfp-vendor-revision\", \"default\": \"unknown\"},\n {\"name\": \"sfp-vendor-serial\", \"default\": \"unknown\"},\n {\"name\": \"sfp-manufacturing-date\", \"default\": \"unknown\"},\n {\"name\": \"eeprom-checksum\", \"default\": \"unknown\"},\n ],\n )\n else:\n self.data[\"interface\"] = parse_api(\n data=self.data[\"interface\"],\n source=self.api.query(\n \"/interface/ethernet\",\n command=\"monitor\",\n args={\".id\": vals[\".id\"], \"once\": True},\n ),\n key_search=\"name\",\n vals=[\n {\"name\": \"status\", \"default\": \"unknown\"},\n {\"name\": \"rate\", \"default\": \"unknown\"},\n {\"name\": \"full-duplex\", \"default\": \"unknown\"},\n {\"name\": \"auto-negotiation\", \"default\": \"unknown\"},\n ],\n )",
"title": ""
},
{
"docid": "7cf7d96207b311d764a245c0a60faaae",
"score": "0.5309424",
"text": "def get_epic_games_iap_config(\n namespace: Optional[str] = None,\n x_additional_headers: Optional[Dict[str, str]] = None,\n **kwargs\n):\n if namespace is None:\n namespace, error = get_services_namespace()\n if error:\n return None, error\n request = GetEpicGamesIAPConfig.create(\n namespace=namespace,\n )\n return run_request(request, additional_headers=x_additional_headers, **kwargs)",
"title": ""
},
{
"docid": "52cf02fe8e0824c0f6d94cf06ddba057",
"score": "0.52994",
"text": "def get_interfaces(self):\n ifaces = []\n try:\n rc, ifaces = self.request(\"storage-systems/%s/interfaces?channelType=hostside\" % self.ssid)\n except Exception as err:\n self.module.fail_json(msg=\"Failed to retrieve defined host interfaces. Array Id [%s]. Error [%s].\" % (self.ssid, to_native(err)))\n\n # Filter out non-ib-iser interfaces\n ib_iser_ifaces = []\n for iface in ifaces:\n if ((iface[\"ioInterfaceTypeData\"][\"interfaceType\"] == \"iscsi\" and\n iface[\"ioInterfaceTypeData\"][\"iscsi\"][\"interfaceData\"][\"type\"] == \"infiniband\" and\n iface[\"ioInterfaceTypeData\"][\"iscsi\"][\"interfaceData\"][\"infinibandData\"][\"isIser\"]) or\n (iface[\"ioInterfaceTypeData\"][\"interfaceType\"] == \"ib\" and\n iface[\"ioInterfaceTypeData\"][\"ib\"][\"isISERSupported\"])):\n ib_iser_ifaces.append(iface)\n\n if not ib_iser_ifaces:\n self.module.fail_json(msg=\"Failed to detect any InfiniBand iSER interfaces! Array [%s] - %s.\" % self.ssid)\n\n return ib_iser_ifaces",
"title": ""
},
{
"docid": "f27577247dbb3423dd94fd50bf8e62a4",
"score": "0.5298453",
"text": "def getOptions(self):\n return {\"interface\": self.iface,\n \"proxies\": self.getProxies(),\n \"ipv6\": self.core.config[\"download\"][\"ipv6\"]}",
"title": ""
},
{
"docid": "a042c338d36abf591093e924a695e5fe",
"score": "0.52803713",
"text": "def network_interfaces(self) -> pulumi.Output[Sequence['outputs.EndpointNetworkInterface']]:\n return pulumi.get(self, \"network_interfaces\")",
"title": ""
},
{
"docid": "9b985ff9355a5a30fdcaf9ceb343e40c",
"score": "0.5278764",
"text": "def option_track_iface_clients(self):\n return self.config_entry.options.get(\n CONF_TRACK_IFACE_CLIENTS, DEFAULT_TRACK_IFACE_CLIENTS\n )",
"title": ""
},
{
"docid": "0150396bf08107e83e004ee2945bd93e",
"score": "0.52785546",
"text": "def get(self, name):\n config = self.get_block('^interface %s' % name)\n\n if not config:\n return None\n\n response = super(EthernetInterface, self).get(name)\n response.update(dict(name=name, type='ethernet'))\n\n response['sflow'] = SFLOW_RE.search(config) is None\n\n flowc_tx = FLOWC_TX_RE.search(config)\n response['flowcontrol_send'] = self.value(flowc_tx, 'off')\n\n flowc_rx = FLOWC_RX_RE.search(config)\n response['flowcontrol_receive'] = self.value(flowc_rx, 'off')\n\n return response",
"title": ""
},
{
"docid": "7a266a97239e2fdd90414a233937c2b7",
"score": "0.5276274",
"text": "def _get_ipv6_config(self):\n return self.__ipv6_config",
"title": ""
},
{
"docid": "9c4ec9bf8341fec14f7b6c2764dcfe37",
"score": "0.52759916",
"text": "def get_l2vpn_interface_under_service_instance(device, service_instance_id):\n interfaces = None\n try:\n out = device.parse(\"show ethernet service instance\")\n except SchemaEmptyParserError as e:\n return interfaces\n try:\n interfaces = list(\n out[\"service_instance\"][service_instance_id][\"interfaces\"]\n )\n except KeyError as e:\n return interfaces\n\n return interfaces",
"title": ""
},
{
"docid": "dd9fc2ddd4214b3ee92b91bd69985f2d",
"score": "0.5275693",
"text": "def get_ips(iface='eth1', hostglob='*'):\n ip_list = []\n caller = salt.client.Caller()\n ipinfo = caller.function('publish.publish', hostglob, 'grains.item', 'ip_interfaces')\n for host in ipinfo:\n ip_list.append(ipinfo[host]['ip_interfaces'][iface][0])\n return ip_list",
"title": ""
},
{
"docid": "cf37fe7ce0c8eabc66fb8e670ff92da4",
"score": "0.5273463",
"text": "def get(isamAppliance, check_mode=False, force=False):\n return isamAppliance.invoke_get(\"Retrieve MMFA endpoint details\",\n \"/iam/access/v8/mmfa-config\")",
"title": ""
},
{
"docid": "563c78bdaff4a76172a59421f5ec3d14",
"score": "0.5260508",
"text": "def _get_interfaces_with_vlan(self):\n output = CommandTemplateExecutor(\n self._cli_service, vlan_template.SHOW_SUB_INTERFACES\n ).execute_command()\n\n pattern = r\"^(\\S+).+?{}:\\d+(?:\\.\\*)?$\".format(self._port_name)\n\n return re.findall(pattern, output, re.MULTILINE)",
"title": ""
},
{
"docid": "b19fbd9bd18502888fb62da1e9cab730",
"score": "0.52591914",
"text": "def getInterfaces(remoteIp=None, username=None, password=None):\n ints = {}\n cmd = \"\\\"/mnt/c/Windows/System32/WindowsPowerShell/v1.0/powershell.exe 'Get-WmiObject win32_networkadapterconfiguration | Select-Object -Property InterfaceIndex , MacAddress, serviceName, dhcpEnabled, ipaddress | format-table -autosize | out-string -width 512'\\\"\"\n if remoteIp:\n ssh = SshClient(remoteIp, username, password)\n output = ssh.sshCmd(cmd)\n else:\n output = subprocess.check_output(cmd, shell=True, stderr=subprocess.PIPE)\n output = output.split('\\r\\n') \n for line in output:\n found = re.search(\"^[\\s]*([\\d]+)[\\s]+([\\S]+)[\\s]+([\\S]+)[\\s]+([\\S]+)[\\s]+\\{(.*)\\}.*$\",line)\n if found:\n ips = found.group(5).split(',')\n ints[found.group(1)] = {\"mac\":found.group(2), \"service\":found.group(3), \"dhcp\":bool(found.group(4)), \"ips\":ips}\n for intId in ints.keys():\n name = getInterfaceName(intId, remoteIp, username, password)\n ints[intId]['name'] = name\n return ints",
"title": ""
},
{
"docid": "4eb1dda1d0c3e11ce7a3f4dcd4006a7c",
"score": "0.52570254",
"text": "def get_config_set(self, data):\n interface_list = []\n config_set = []\n int_string = \"\"\n for config_line in data.splitlines():\n ospf_int = re.search(r\"set interfaces \\S+ (\\S+) .*\", config_line)\n if ospf_int:\n if ospf_int.group(1) not in interface_list:\n if int_string:\n config_set.append(int_string)\n interface_list.append(ospf_int.group(1))\n int_string = \"\"\n int_string = int_string + config_line + \"\\n\"\n if int_string:\n config_set.append(int_string)\n return config_set",
"title": ""
},
{
"docid": "ade9143d548d6a4c6730afa94ef007ef",
"score": "0.5255472",
"text": "def get_config(self):\n\n return self.fc",
"title": ""
},
{
"docid": "b7a9b69b78364c7b08813c2b1fc7411b",
"score": "0.52531207",
"text": "def ip_configurations(self) -> Sequence['outputs.GetVirtualNetworkGatewayIpConfigurationResult']:\n return pulumi.get(self, \"ip_configurations\")",
"title": ""
},
{
"docid": "e52868ad806e8df5168cfddd79d3c30c",
"score": "0.5246077",
"text": "def apiconfig():\n return lod_api.CONFIG",
"title": ""
},
{
"docid": "453cf5eee7395df3ebe34648e0ba6404",
"score": "0.5245864",
"text": "def ifconfig_get_up(iface):\n\n if subprocess.call(['ifconfig', iface]) != 0:\n return False\n return b'UP' in subprocess.check_output(['ifconfig', iface])",
"title": ""
},
{
"docid": "7b8be4a168e4e6569c026d27b5f959fb",
"score": "0.52359074",
"text": "def get_ospf(task):\n\n ospf_url = f\"https://{task.host.hostname}:443/restconf/data/ospf-oper-data\"\n ospf_send = requests.get(\n url=ospf_url,\n headers=headers,\n auth=(f\"{task.host.username}\", f\"{task.host.password}\"),\n verify=False,\n )\n\n inter_url = f\"https://{task.host.hostname}:443/restconf/data/native/interface\"\n inter_send = requests.get(\n url=inter_url,\n headers=headers,\n auth=(f\"{task.host.username}\", f\"{task.host.password}\"),\n verify=False,\n )\n\n task.host[\"ospf-facts\"] = ospf_send.json()\n task.host[\"ospf-config\"] = inter_send.json()\n\n config_interfaces = task.host[\"ospf-config\"][\"Cisco-IOS-XE-native:interface\"]\n for inter_type in config_interfaces:\n interfaces = config_interfaces[inter_type]\n for intf in interfaces:\n try:\n name = intf[\"name\"]\n intlink = inter_type + str(name)\n ip = intf[\"ip\"][\"address\"][\"primary\"][\"address\"]\n mask = intf[\"ip\"][\"address\"][\"primary\"][\"mask\"]\n config_dict[f\"{task.host}\"][intlink] = [ip, mask]\n\n except KeyError:\n pass\n\n instances = task.host[\"ospf-facts\"][\"Cisco-IOS-XE-ospf-oper:ospf-oper-data\"][\n \"ospf-state\"\n ][\"ospf-instance\"]\n for instance in instances:\n areas = instance[\"ospf-area\"]\n for area in areas:\n try:\n area_id = area[\"area-id\"]\n ospf_interfaces = area[\"ospf-interface\"]\n for ospf_interface in ospf_interfaces:\n name = ospf_interface[\"name\"]\n dead = ospf_interface[\"dead-interval\"]\n hello = ospf_interface[\"hello-interval\"]\n facts_dict[f\"{task.host}\"][name] = [dead, hello, area_id]\n\n except KeyError:\n pass",
"title": ""
},
{
"docid": "1ad18e812f7969fdc2f85c6e6fe2ec22",
"score": "0.5224885",
"text": "def getInterfacesList():\n listOfInterfaces = \\\n [\n \"Blogger_5\",\"gmail\",\"Blogger_2\",\"FB_photo\",\"gmail_4\",\"Blogger_7\",\"Blogger\",\"FB_post\",\n \"Blogger_6\",\"gmail_1\",\"Blogger_9\",\"gmail_8\",\"DB_diary_8\",\"Blogger_8\",\"gmail_4\",\"Blogger_10\",\n \"GitHub_8\",\"Blogger_1\",\"Amazon_review\",\"Blogger_11\",\"gmail_8\",\"Blogger_3\",\"GitHub\",\n \"gmail_1\",\"Blogger_4\",\"Tumblr_link\"\n ]\n return listOfInterfaces",
"title": ""
},
{
"docid": "5c9549f5cc4e2a2d84ebcc979a699876",
"score": "0.5197816",
"text": "def ifconfig(code):\n try:\n answer_req = requests.get(req_link).json()\n json_answer = json.dumps(answer_req[code], indent=4).strip('\"')\n json_answer_user_agent = json.dumps(\n answer_req[\"user_agent\"][\"raw_value\"], indent=4\n ).strip('\"')\n return json_answer, json_answer_user_agent\n\n except ConnectionError as req_error:\n return req_error\n\n except requests.exceptions.ConnectionError as con_error:\n return con_error",
"title": ""
},
{
"docid": "b8bc0fe8574de6966ebb385a06f10065",
"score": "0.51965487",
"text": "def network_interfaces(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['NetworkInterfaceArgs']]]]:\n return pulumi.get(self, \"network_interfaces\")",
"title": ""
},
{
"docid": "1bc4d32bf69cabafb3cdd4353cc8fcaf",
"score": "0.5193007",
"text": "def configuration(self):\n return self._read_register(_ADT7410_CONFIG)[0]",
"title": ""
},
{
"docid": "99890d81b63cf7698c0eac6c7de80099",
"score": "0.5190292",
"text": "def Ospf(self):\n\t\tfrom ixnetwork_restpy.testplatform.sessions.ixnetwork.vport.protocols.ospf_l3zwb3j0l3byb3rvy29scy9vc3bm import Ospf\n\t\treturn Ospf(self)._select()",
"title": ""
},
{
"docid": "33d025c3d152f4ed9f6d915cfdc5e7b4",
"score": "0.51647055",
"text": "def get_iapps(self):\n return self._iapps",
"title": ""
},
{
"docid": "c68c5edf29a3172ddb240da8b2dec69e",
"score": "0.51407033",
"text": "def discover_all(verbose=2):\n # Use AF_INET because HL2 only supports IPv4 and not IPv6 \n PROTO = netifaces.AF_INET \n # Fetch list of network interfaces, remove 'lo' if present\n ifaces = [iface for iface in netifaces.interfaces() if iface != 'lo']\n # Get (address, name) tuples for all addresses for each remaining interface\n if_addrs = [(netifaces.ifaddresses(iface), iface) for iface in ifaces]\n # Keep interfaces with IPv4 addresses, drop others\n if_addrs = [(t[0][PROTO], t[1]) for t in if_addrs if PROTO in t[0]]\n # Keep the value of the 'addr' field from interfaces that have them\n iface_addrs = [(d['addr'], t[1]) for t in if_addrs for d in t[0] \\\n if 'addr' in d]\n # Keep interfaces that do not have 127.0.0.1 as an address (loopback)\n iface_addrs = [ t for t in iface_addrs if t[0] != '127.0.0.1']\n # Do discovery on all remaining interfaces that have IP addresses\n responses = []\n for ifa in iface_addrs: \n if verbose >= 2: \n print(\"\\nPerforming discovery: interface %s, IP %s\" % (ifa[1],ifa[0]))\n d = discover(ifaddr=ifa[0],verbose=verbose)\n if d != []:\n for r in d:\n if verbose >= 1: \n print('Discovered radio: IP %s MAC %s GW %s #RX %d' % \n (r[0][0],r[1].mac,r[1].gateware,r[1].receivers))\n responses.append(r)\n return responses",
"title": ""
},
{
"docid": "888a01d0974f8220d6a06939063054c8",
"score": "0.5137317",
"text": "def Configure(self):\n self.ifaceHostapd = self.conf.get(\"accesspoint\", \"interface\")\n self.DHCP = self.getDHCPConfig()\n self.SettingsAP = {\n \"interface\": [\n \"ifconfig %s up\" % (self.ifaceHostapd),\n \"ifconfig %s %s netmask %s\"\n % (self.ifaceHostapd, self.DHCP[\"router\"], self.DHCP[\"netmask\"]),\n \"ifconfig %s mtu 1400\" % (self.ifaceHostapd),\n \"route add -net %s netmask %s gw %s\"\n % (self.DHCP[\"subnet\"], self.DHCP[\"netmask\"], self.DHCP[\"router\"]),\n ],\n \"kill\": [\n \"{} -w --flush\".format(self.getIptablesPath),\n \"{} -w --table nat --flush\".format(self.getIptablesPath),\n \"{} -w --delete-chain\".format(self.getIptablesPath),\n \"{} -w --table nat --delete-chain\".format(self.getIptablesPath),\n \"killall dhpcd 2>/dev/null\",\n \"ifconfig {} down\".format(self.ifaceHostapd),\n \"ifconfig {} up\".format(self.ifaceHostapd),\n \"ifconfig {} 0\".format(self.ifaceHostapd),\n ],\n \"hostapd\": [\n \"interface={}\\n\".format(self.ifaceHostapd),\n \"ssid={}\\n\".format(self.conf.get(\"accesspoint\", \"ssid\")),\n \"channel={}\\n\".format(self.conf.get(\"accesspoint\", \"channel\")),\n \"bssid={}\\n\".format(self.conf.get(\"accesspoint\", \"bssid\")),\n ],\n \"dhcp-server\": [\n \"subnet %s netmask %s {\\n\"\n % (self.DHCP[\"subnet\"], self.DHCP[\"netmask\"]),\n \"authoritative;\\n\",\n \"default-lease-time {};\\n\".format(self.DHCP[\"leasetimeDef\"]),\n \"max-lease-time {};\\n\".format(self.DHCP[\"leasetimeMax\"]),\n \"option routers {};\\n\".format(self.DHCP[\"router\"]),\n \"option subnet-mask {};\\n\".format(self.DHCP[\"netmask\"]),\n \"option broadcast-address {};\\n\".format(self.DHCP[\"broadcast\"]),\n 'option domain-name \"%s\";\\n' % (self.conf.get(\"accesspoint\", \"ssid\")),\n \"option domain-name-servers {};\\n\".format(\"8.8.8.8\"),\n \"range {};\\n\".format(self.DHCP[\"range\"].replace(\"/\", \" \")),\n \"}\",\n ],\n }\n print(display_messages(\"enable forwarding in iptables...\", sucess=True))\n Linux.set_ip_forward(1)\n # clean iptables settings\n for line in self.SettingsAP[\"kill\"]:\n exec_bash(line)\n # set interface using ifconfig\n for line in self.SettingsAP[\"interface\"]:\n exec_bash(line)\n # check if dhcp option is enabled.\n if self.conf.get(\"accesspoint\", \"dhcpd_server\", format=bool):\n self.apply_dhcp_config_leases_config(self.SettingsAP)",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="708"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">7495509b729bd83921a95fa42410dfce</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">check input, extract data from csv file find copynumber files created by varscan</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "dd3b2cc955a7893deea1dfc1c0bbed64",
"score": "0.5857006",
"text": "def main(): # run analyses of copynumber files using functions defined above\n # initialize lists\n individual_samples = set()\n ancestor_clone_pairs = set()\n missing_pairs = set()\n analyzed_pairs = set()\n\n input_eval() # check input\n individual_samples, ancestor_clone_pairs = read_csv_file()\n missing_pairs, analyzed_pairs, cn_dict = find_cn_files(ancestor_clone_pairs)\n\n if missing_pairs:\n print 'Varscan copynumber analysis files missing for pairs '+', '.join(str(f) for f in missing_pairs)\n print \"Run copynumber.py script to generate varscan files or script 1 to generate pileup files.\"\n else: print 'All varscan copynumber analysis files have been found.'\n\n for input_file in cn_dict.itervalues(): # cn_dict = {'Anc_vs_Evo': /Anc_vs_Evo.copynumber}\n plot_copynumber(input_file, output_dir)\n\n print 'Plots are complete.'\n sys.exit(0)",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "712e9b347ffc2d5821cf6bec6902a509",
"score": "0.65452796",
"text": "def read_csv_file():\n\n csv_open = open(csv_file, 'rU')\n csv_reader = csv.reader(csv_open)\n row1 = next(csv_reader)\n\n clone_regex = re.compile('(Clone).*')\n clones = ([c.group(0) for cell in row1 for c in [clone_regex.search(cell)] if c])\n num_clones = len(clones)\n\n ancestor_index = row1.index('Ancestor')\n clone_indices = []\n for c in clones:\n clone_indices.append(row1.index(c))\n\n # make sets to pass to other functions\n individual_samples = set() # set of all samples\n ancestor_clone_pairs = set() # pairs to find copynumber files for\n for row in csv_reader:\n ancestor = row[ancestor_index]\n individual_samples.add(ancestor)\n list_of_clones = []\n for c in clone_indices:\n try:\n clone = row[c]\n if clone:\n individual_samples.add(clone)\n ancestor_clone_pairs.add((ancestor, clone))\n list_of_clones.append(clone)\n except IndexError: # no more clones in row\n break\n\n print \"Received directions to analyze \" + str(len(individual_samples)) + \\\n \" unique samples and \" + str(len(ancestor_clone_pairs)) + \" anc-clone pairs. \"\n\n return individual_samples, ancestor_clone_pairs",
"title": ""
},
{
"docid": "56f3c69f49c39ed158b9112d80015434",
"score": "0.62659967",
"text": "def read_csv_file(csv_file):\n# problem: can't read csv file with uneven numbers of entries in rows\n# hack: format all empty cells as text in excel before saving as csv\n\n csv_open = open(csv_file, 'rU')\n csv_reader = csv.reader(csv_open)\n row1 = next(csv_reader)\n\n # get number of pools\n clone_regex = re.compile('(Clone).*')\n clones = ([c.group(0) for cell in row1 for c in [clone_regex.search(cell)] if c])\n num_clones = len(clones)\n pool_regex = re.compile('(Pool).*')\n pools = ([p.group(0) for cell in row1 for p in [pool_regex.search(cell)] if p])\n if pools: print \"Use csv file specifying all comparisons to ancestor as clones.\"\n\n # get indices for ancestor, clone, pool(s)\n ancestor_index = row1.index('Ancestor')\n clone_indices = []\n for c in clones:\n clone_indices.append(row1.index(c))\n\n # make sets to pass to other functions\n individual_samples = set() # set of samples to make pileups of\n ancestor_clone_pairs = set() # pairs for varscan analysis\n for row in csv_reader:\n ancestor = row[ancestor_index]\n individual_samples.add(ancestor)\n list_of_clones = []\n for c in clone_indices:\n clone = row[c]\n if clone:\n individual_samples.add(clone)\n ancestor_clone_pairs.add((ancestor, clone))\n list_of_clones.append(clone)\n \n print \"Received directions to analyze \" + str(len(individual_samples)) + \\\n \" unique samples and \" + str(len(ancestor_clone_pairs)) + \" anc-clone pairs. \" \\\n\n return individual_samples, ancestor_clone_pairs",
"title": ""
},
{
"docid": "451be54b228f16b7d48edb28edb49c86",
"score": "0.57335585",
"text": "def main() -> None:\n\n args = get_args()\n variables = get_variables(args.variables)\n writer = csv.DictWriter(args.outfile,\n fieldnames=[\n 'source', 'unit', 'location_name',\n 'location_type', 'variable_name',\n 'variable_desc', 'collected_on', 'medium',\n 'value'\n ])\n writer.writeheader()\n num_exported = 0\n\n for fh in args.files:\n reader = csv.DictReader(fh, delimiter=',')\n\n # Cannot normalize names b/c some have \"Name\" and \"NAME\" fields!\n # reader.fieldnames = list(map(normalize, reader.fieldnames))\n\n if 'Name' not in reader.fieldnames:\n print(f'\"{fh.name}\" missing county \"Name\" field', file=sys.stderr)\n print(\"\\n\".join(map(lambda f: f'\"{f}\"', reader.fieldnames)))\n print(reader.fieldnames)\n continue\n\n counties = [\n 'Apache', 'Cochise', 'Coconino', 'Gila', 'Graham', 'Greenlee',\n 'La Paz', 'Maricopa', 'Mohave', 'Navajo', 'Pima', 'Pinal',\n 'Santa Cruz', 'Yavapai', 'Yuma'\n ]\n\n basename = os.path.basename(fh.name)\n for rec in reader:\n loc_name = ' '.join(map(str.title, rec.get('Name', '').split()))\n if args.location_type == 'county' and loc_name not in counties:\n print(f'{basename}: unknown county \"{loc_name}\"',\n file=sys.stderr)\n continue\n\n indicator_name = rec.get('indicatorName')\n if not indicator_name:\n print(f'{basename}: missing indicator name', file=sys.stderr)\n continue\n\n vars_ = variables.get(normalize(indicator_name))\n if not vars_:\n print(f'{basename}: unknown indicator \"{indicator_name}\"',\n file=sys.stderr)\n continue\n\n if len(vars_) > 1:\n print(f'{basename}: multiple variables for \"{indicator_name}\"',\n file=sys.stderr)\n continue\n\n variable = vars_[0]\n\n value = rec.get('Value')\n if not value:\n print(f'{basename}: missing value', file=sys.stderr)\n continue\n\n num_exported += 1\n writer.writerow({\n 'source':\n args.source,\n 'unit':\n args.units,\n 'location_name':\n loc_name,\n 'location_type':\n args.location_type,\n 'variable_name':\n variable['Code'],\n 'variable_desc':\n '{}: {}'.format(indicator_name, variable['Measure']),\n 'medium':\n args.medium,\n 'collected_on':\n rec.get('Year') or '',\n 'value':\n value,\n })\n\n print(f'Done, exported {num_exported:,} to \"{args.outfile.name}\"')",
"title": ""
},
{
"docid": "993f3016098d219f957b798b243fb7d5",
"score": "0.5662192",
"text": "def get_candidate_queries(num_candidate, file_path,type):\n try:\n L=[]\n path_of_file = type+'_'+file_path+'_'+num_candidate+'.txt'\n with open(path_of_file) as csvfile :\n for line in csvfile :\n L.append(line)\n return L\n\n except IOError:\n print ('erreur dans la fonction')",
"title": ""
},
{
"docid": "f5b4d42bbe3207642ea86c6a4a6e0167",
"score": "0.565908",
"text": "def recfromcsv_func():\n print(\"Start of recfromcsv\")\n filename = \"C:\\\\Users\\mdjuk\\\\repos\\\\q_python_scripts\\\\titanic.csv\"\n\n #d = np.recfromcsv(filename, delimiter=',', names=True, dtype=None)\n d = np.genfromtxt(filename, delimiter=',', names=True, dtype=None)\n\n #\n # print first 3 records\n #\n #print(\"recs from recfromcsv_func-->%s\" %(d[:3]))\n for i in d['Survived'] :\n if i == 1 :\n print(\"data from titanic.csv-->%s\" %(i))",
"title": ""
},
{
"docid": "9e170dd010464c3dff9ca4c98291c403",
"score": "0.56229925",
"text": "def input_eval(directory, csv_file):\n try:\n os.path.isdir(directory)\n except:\n print directory + \" is not a directory\"\n sys.exit(1)\n## try:\n## csv_open = open(csv_file, 'rU')\n## csv_reader = csv.reader(csv_open)\n## #some other function here to check file?\n## except:\n## print csv_file + \" is not readable csv file.\"\n## return 1\n\n print \"Now working on files in \" + directory",
"title": ""
},
{
"docid": "215b153038a2f8422466f88d3c751f04",
"score": "0.55368936",
"text": "def interpretPattern(read_csv=True, file_location=\"Stream_Input.csv\"):\r\n if(read_csv):\r\n try: \r\n csv_data = pd.read_csv(file_location, skiprows=[0], header=None, names = [\"SID\", \"Size\", \"Hex\"])\r\n except Exception,e:\r\n print \"Failed to read file with exception\" + str(e)\r\n return None, 1\r\n \r\n return csv_data, 0",
"title": ""
},
{
"docid": "681553a110eeb23ec0249945aac06bed",
"score": "0.55192345",
"text": "def get_csv_ids(csv_in, out_txt_name):\n f_in = open(csv_in, 'r+')\n f_out = open(out_txt_name, 'w')\n reader = csv.reader(f_in)\n header = next(reader)\n # print(header)\n stat_names = ['work_id', 'title', 'rating', 'category', 'fandom', 'relationship', 'character', 'additional tags',\n 'language',\n 'published', 'status', 'status date', 'words', 'chapters', 'comments', 'kudos', 'bookmarks', 'hits',\n 'body']\n header = {stat: stat_names.index(stat) for stat in stat_names}\n seen = []\n for row in reader:\n word_str = row[header['words']]\n if word_str == 'null' or word_str == '' or word_str == 0:\n continue\n elif word_str == 'words':\n print(\"Row contains 'words'\")\n continue\n work_id = row[header[\"work_id\"]]\n if work_id in seen:\n print(out_txt_name, work_id)\n else:\n f_out.write(work_id)\n f_out.write(\"\\n\")\n f_in.close()",
"title": ""
},
{
"docid": "1ef11eee563f0176584cd9a0a41bed08",
"score": "0.54852676",
"text": "def validate_input(csv, download_dir, max_processes) -> bool:\n if not os.path.isfile(csv):\n return False\n elif not os.path.isdir(download_dir):\n return False\n elif not max_processes.isdigit():\n return False\n elif not 1 <= int(max_processes) < 30:\n return False\n\n return True",
"title": ""
},
{
"docid": "6602950c453d20174b4454b5402a678c",
"score": "0.54012734",
"text": "def bmistat(filepath):\n fopen = open(filepath,mode='r+')\n fread = fopen.readlines()\n x = '# rsid\tchromosome\tposition\tgenotype'\n n = 0\n for line in fread:\n n += 1\n if x in line:\n break\n df = pd.read_csv (filepath,'\\s+', skiprows=n, names=['rsid','chromosome','position','genotype'])\n #df = df.replace('--', pd.NaT) # need to correct this on the data extract file\n #df = df.dropna\n testfile = df[(df['rsid'] == 'rs9939609') | \n (df['rsid'] =='rs6548238') |\n (df['rsid'] == 'rs17782313') |\n (df['rsid'] == 'rs10938397') | \n (df['rsid'] == 'rs7498665') | \n (df['rsid'] == 'rs10838738') | \n (df['rsid'] == 'rs11084753') |\n (df['rsid'] == 'rs2815752')]\n testlist = []\n for i in range(0, len(testfile.index)-1):\n rsid = testfile.iloc[i,0]\n genotype = testfile.iloc[i,3]\n i = (rsid, genotype) # tuples of one rsid with genotype\n testlist.append(i) # a list of tuples\n gendata = pd.read_csv('Genetic Data.csv')\n gendata['effect'] = pd.to_numeric(gendata['effect'])\n total = 0\n for i in testlist:\n snp = gendata[(gendata['rsid'] == i[0]) & (gendata['genotype'] == i[1])]\n effect = snp.iloc[0,4]\n total += effect\n if total < 4:\n return (25.4, 3.1)\n elif total == 4:\n return (25.7, 3.4)\n elif total == 5:\n return (25.9, 3.8)\n elif total == 6:\n return (26.2, 3.7)\n elif total == 7:\n return (26.2, 3.6)\n elif total == 8:\n return (26.3, 3.7)\n elif total == 9:\n return (26.5, 3.7)\n elif total == 10:\n return (26.6, 3.9)\n elif total == 11:\n return (26.8, 4.2)\n elif total == 12:\n return (27, 4)\n else:\n return (26.8, 3.8)",
"title": ""
},
{
"docid": "847db80f98a606660a931b2ebbac74b1",
"score": "0.5397004",
"text": "def load(csv_file):",
"title": ""
},
{
"docid": "4b478c709f299721f6435fca504b7eae",
"score": "0.5391488",
"text": "def import_distancia_2010(survey, infile):\n\n with open(infile, 'rb') as csvfile:\n datareader = csv.DictReader(csvfile, delimiter=',')\n plot_counter=[]\n for row in datareader:\n ID = row['nombre_pm']\n if ID in survey.plots.keys():\n if ID not in plot_counter:\n plot_counter.append(ID)\n position = 1\n censo = 1\n\n # Adding the distance infomation carretera vecinal\n if row.has_key('carreteraCaminoVecinal') and row['carreteraCaminoVecinal'] not in ['', ' ']:\n carreteraVecinal = class_lib.Distance(ID)\n carreteraVecinal.parcela_pm_censo = censo\n carreteraVecinal.distancia_position = position\n carreteraVecinal.distancia_kilometros_unit_name = 'kilometros'\n carreteraVecinal.distancia_categoria = 1\n carreteraVecinal.distancia_camino_estado = '-'\n carreteraVecinal.distancia_kilometros = \\\n tools_lib.import_variable(row, 'carreteraCaminoVecinal', 'int', ID)\n\n # Adding the distance infomation camino vecinal\n if row.has_key('caminoVecinalCaminoAcceso') and row['caminoVecinalCaminoAcceso'] not in ['', ' ']:\n caminoVecinal = class_lib.Distance(ID)\n caminoVecinal.parcela_pm_censo = censo\n caminoVecinal.distancia_position = position\n caminoVecinal.distancia_kilometros_unit_name = 'kilometros'\n caminoVecinal.distancia_categoria = 2\n caminoVecinal.distancia_camino_estado = '-'\n caminoVecinal.distancia_kilometros =\\\n tools_lib.import_variable(row, 'caminoVecinalCaminoAcceso', 'int', ID)\n\n # Adding the distance infomation camino accesso\n if row.has_key('caminoAccesoPuntoGPS') and row['caminoAccesoPuntoGPS'] not in ['', ' ']:\n caminoAccesso = class_lib.Distance(ID)\n caminoAccesso.parcela_pm_censo = censo\n caminoAccesso.distancia_position = position\n caminoAccesso.distancia_kilometros_unit_name = 'kilometros'\n caminoAccesso.distancia_categoria = 3\n caminoAccesso.distancia_camino_estado = '-'\n caminoAccesso.distancia_kilometros = \\\n tools_lib.import_variable(row, 'caminoAccesoPuntoGPS', 'int', ID)\n\n # Adding the distance infomation PuntoGPSCentroParcella\n if row.has_key('rumboCaminoCentroParcela') and row['rumboCaminoCentroParcela'] not in ['', ' ']:\n puntoGPSCentroParcella = class_lib.Distance(ID)\n puntoGPSCentroParcella.parcela_pm_censo = censo\n puntoGPSCentroParcella.distancia_position = position\n puntoGPSCentroParcella.distancia_categoria = 4\n puntoGPSCentroParcella.distancia_kilometros_unit_name = 'kilometros'\n puntoGPSCentroParcella.distancia_camino_estado = '-'\n puntoGPSCentroParcella.rumbo_punto_gps_centro =\\\n tools_lib.import_variable(row, 'rumboCaminoCentroParcela', 'int', ID)\n\n if row.has_key('puntoGPSCentroParcela') and row['puntoGPSCentroParcela'] not in ['', ' ']:\n puntoGPSCentroParcella.distancia_kilometros = \\\n tools_lib.import_variable(row, 'puntoGPSCentroParcela', 'int', ID)\n\n #Adding the distance instances to the survey\n try:\n survey.plots[ID].distances['1'] = carreteraVecinal\n except:\n warn_msg = 'Could not find information on distance \"carreteraVecinal\" on plot: {plotid}.' \\\n .format( plotid=ID)\n logging.warning(warn_msg)\n try:\n survey.plots[ID].distances['2'] = caminoVecinal\n except:\n warn_msg = 'Could not find information on distance \"caminoVecinal\" on plot: {plotid}.' \\\n .format( plotid=ID)\n logging.warning(warn_msg)\n try:\n survey.plots[ID].distances['3'] = caminoAccesso\n except:\n warn_msg = 'Could not find information on distance \"caminoAcceso\" on plot: {plotid}.' \\\n .format(plotid=ID)\n logging.warning(warn_msg)\n try:\n survey.plots[ID].distances['4'] = puntoGPSCentroParcella\n except:\n warn_msg = 'Could not find information on distance \"puntoGPSCentroParcella\" on plot: {plotid}.' \\\n .format( plotid=ID)\n logging.warning(warn_msg)\n\n info_msg = \"Updated the distance table for {nplots} plots from the file: {file}\" \\\n .format(nplots=plot_counter.__len__(), file=os.path.basename(infile))\n logging.info(info_msg)\n print(info_msg)",
"title": ""
},
{
"docid": "461d9d9a76b62ecef4ea10abe466f63c",
"score": "0.5387985",
"text": "def csv_reader_with_check(root_path, csv_file_path):\r\n with open(csv_file_path, 'r') as file:\r\n reader = csv.reader(file)\r\n\r\n actual_images = []\r\n\r\n for row in reader:\r\n csv_old_path = row[0]\r\n age = row[1]\r\n\r\n new_path= root_path+\"/\"+csv_old_path\r\n image = pathlib.Path(new_path)\r\n\r\n if image.exists() and not row[1] in (None,\"\"):\r\n\r\n actual_images.append([csv_old_path, age])\r\n\r\n return actual_images",
"title": ""
},
{
"docid": "51cd2f8c0ef53438089dd9a0e718e923",
"score": "0.53835934",
"text": "def website_query():\n http = urllib3.PoolManager()\n r = http.request('GET', URL)\n links = re.findall('[0-9][0-9]-[0-9][0-9]-[0-9][0-9][0-9][0-9].csv', str(r.data))\n print(\"Link: {}\".format(URL))\n links = list(set(links))\n links.sort()\n\n for file_name in links:\n print(file_name)\n\n file_structure_01 = False\n file_structure_02 = False\n file_structure_03 = False\n header = True\n\n each_csv = requests.get(SITE_BASE + file_name)\n\n # each_csv = requests.get(SITE_BASE + \"03-22-2020.csv\")\n # each_csv = requests.get(SITE_BASE + \"03-23-2020.csv\")\n #\n # each_csv = requests.get(SITE_BASE + \"01-22-2020.csv\")\n # each_csv = requests.get(SITE_BASE + \"03-19-2020.csv\")\n # each_csv = requests.get(SITE_BASE + \"03-29-2020.csv\")\n # print(each_csv.status)\n open('temp_file.csv', 'wb').write(each_csv.content)\n\n with open('temp_file.csv.aux', \"w\") as stage_file:\n for line in open('temp_file.csv'):\n line = line.rstrip()\n # print(line)\n stage_file.write(line + '\\n')\n os.rename('temp_file.csv.aux', 'temp_file.csv')\n\n with open('temp_file.csv') as fp:\n ref_file = csv.reader(fp)\n for row in ref_file:\n # print(row)\n if (row[0] == \"FIPS\") or \\\n (row[0] == \"\\ufeffFIPS\") and \\\n (row[1] == \"Admin2\") and \\\n (row[2] == \"Province_State\") and \\\n (row[3] == \"Country_Region\") and \\\n (row[4] == \"Last_Update\") and \\\n (row[5] == \"Lat\") and \\\n (row[6] == \"Long_\") and \\\n (row[7] == \"Confirmed\") and \\\n (row[8] == \"Deaths\") and \\\n (row[9] == \"Recovered\") and \\\n (row[10] == \"Active\") and \\\n (row[11] == \"Combined_Key\"):\n file_structure_01 = True\n header = True\n elif (row[0] == \"\\ufeffProvince/State\") or \\\n (row[0] == \"Province/State\") and \\\n (row[1] == \"Country/Region\") and \\\n (row[2] == \"Last Update\") and \\\n (row[3] == \"Confirmed\") and \\\n (row[4] == \"Deaths\") and \\\n (row[5] == \"Recovered\"):\n file_structure_02 = True\n header = True\n elif (row[0] == \"Province/State\") and \\\n (row[1] == \"Country/Region\") and \\\n (row[2] == \"Last Update\") and \\\n (row[3] == \"Confirmed\") and \\\n (row[4] == \"Deaths\") and \\\n (row[5] == \"Recovered\") and \\\n (row[6] == \"Latitude\") and \\\n (row[7] == \"Longitude\"):\n file_structure_03 = True\n header = True\n else:\n header = False\n if file_structure_01 and not header:\n row.insert(0, file_name.split(\".\")[0])\n final_list.append(row)\n elif file_structure_02 and not header:\n row.insert(0, file_name.split(\".\")[0])\n row.insert(1, \"\")\n row.insert(1, \"\")\n row.insert(6, \"\")\n row.insert(6, \"\")\n row.insert(11, \"\")\n row.insert(11, \"\")\n final_list.append(row)\n elif file_structure_03 and not header:\n aux = []\n row.insert(0, file_name.split(\".\")[0])\n row.insert(1, \"\")\n row.insert(1, \"\")\n row.insert(11, \"\")\n row.insert(11, \"\")\n aux.insert(0, row[0])\n aux.insert(1, row[1])\n aux.insert(2, row[2])\n aux.insert(3, row[3])\n aux.insert(4, row[4])\n aux.insert(5, row[5])\n aux.insert(6, row[9])\n aux.insert(7, row[10])\n aux.insert(8, row[6])\n aux.insert(9, row[7])\n aux.insert(10, row[8])\n aux.insert(11, row[11])\n aux.insert(12, row[12])\n row = aux\n final_list.append(row)\n else:\n print(\"CHECK NEW STRUCTURE {}\".format(ref_file))",
"title": ""
},
{
"docid": "464aa2f71b8638ba6740fbff6d4d8593",
"score": "0.5361135",
"text": "def load_data(file_name):\n read_data = []\n warnings = []\n # print('File name = ' + str(file_name))\n # Check that file exists\n valid_file = False\n while valid_file is False:\n try:\n file = open(file_name + '.csv', 'r')\n except IOError:\n print('The file does not exist. Check file name.')\n file_name = input('What is the name of the file? ')\n else:\n file.readline()\n reader = csv.reader(file, delimiter=',', quotechar='\"')\n for row in reader:\n if row[0] not in (None, ''):\n read_data.append(row)\n file.close()\n # Check data is correct\n for item in read_data:\n if len(item[0].strip()) != 9:\n warnings.append(item)\n valid_file = True\n # print('Check loaded data:')\n # debug_list(read_data)\n # print('Length Load Data warnings: ' + str(len(warnings)))\n if len(warnings) > 0:\n return read_data, True, warnings\n else:\n return read_data, False, warnings",
"title": ""
},
{
"docid": "58d3465a106ce0320284f4023d2a3d42",
"score": "0.5360267",
"text": "def _readBookList(self):\n with open(input_file, \"r\") as csv_file:\n csv_reader = csv.reader(csv_file)\n for row in csv_reader:\n if any(val in (None, \"\") for val in row):\n output_handle.write(f\"\\nValue Missing for {row}\")\n continue\n if self.rootNode is None:\n self.rootNode = BookNode(int(row[0]), int(row[1]))\n else:\n self.rootNode.insertBook(int(row[0]), int(row[1]))",
"title": ""
},
{
"docid": "d777e0121ec034b112405deac6b61bdd",
"score": "0.53429115",
"text": "def main(): # run analyses of pileup files using functions defined above\n\n # initialize lists\n samples = set(); pairs = set()\n fastq_pairs = []\n pileups_not_found = set()\n\n # set default exit statuses\n var_cn_status = 0; new_cn_file = 0\n\n input_eval(directory, csv_file) # check input\n individual_samples, ancestor_clone_pairs = read_csv_file(csv_file)\n\n # empty sets or lists initialized above evaluate to False; if any have members created by previous function, \"if\" statement below is True and dependent function is executed\n samples, pairs = check_cn_files(ancestor_clone_pairs)\n\n if not samples:\n print \"All varscan copynumber analyses have been performed.\"\n sys.exit(0)\n\n paired_pileups, pileups_not_found = find_pileups(samples, pairs)\n\n if pileups_not_found:\n print \"Not all pileup files were found. Run script 1 to generate pileups.\"\n sys.exit(1) # pileup files need to be created\n\n # now all required pileup files should exist - create snp, indel files if needed\n if paired_pileups:\n new_cn_file = 1\n var_cn_status = batch_varscan_copynumber(paired_pileups)\n if var_cn_status !=0: # error while making varscan files; status is 0 if function not performed\n print \"Error encountered while making copynumber files. Check error log for varscan jobs.\"\n sys.exit(1)\n\n if new_cn_file == 1: # if varscan function was run\n print \"Varscan analyses being run.\"\n print \"To receive an email when varscan files have been completed, confirm that your email address is in line 8 of a copy of script email_notification.sh. Then type the following into the command line, substituting in the sbatch job ID(s) and full path to script:\"\n print \"sbatch --dependency=afterok:<jobid1>:<jobid2> </your/path/to/email_notification.sh>\"\n print \"To check job status, type squeue -u <username> -j<jobid> into the command line.\"\n sys.exit(0)\n else:\n print \"Error encountered while executing varscan function.\"\n sys.exit(1)",
"title": ""
},
{
"docid": "b80f6337aed65e5302a153bd32e67e30",
"score": "0.53414214",
"text": "def check_input_LOO(p_sources:str, m:str, t:str, plot:str, output:str, mem:str)->tuple:\n\n # Verify output directory does not exist already\n if os.path.isdir(output):\n print_error(\"Output directory already exists\")\n return 1\n #Check if map.csv is correctly formatted:\n if os.path.isfile(m):\n\n if m[-4:] != \".csv\":\n print_error(\"File extension of map is not .csv\")\n return 1 \n try:\n m_file=pd.read_csv(m) \n except Exception as e:\n print_error(e)\n print_error(\"your map.csv file is corrupted or not properly formatted\")\n return 1\n \n nrows_m = open(m).read().splitlines()\n if len(nrows_m) == 0:\n print_error(\"Your map.csv file is empty.\")\n return 1\n\n true_cols={\"Env\",\"SampleID\"}\n user_cols=set(m_file.columns) \n \n if true_cols!=user_cols:\n print_error(\"Your map.csv file is not properly formatted. Columns of map.csv file should be Env and SampleID only.\")\n return 1\n\n elif m_file.duplicated(subset=\"SampleID\").any():\n print_error(\"Your map.csv file contains duplicates in the column called SampleID. Please remove them.\")\n \n if m_file.isnull().values.any():\n print_error(\"Your map.csv file contains NaNs. Please remove them.\")\n return 1\n \n classes=set(m_file.Env)\n for c in classes:\n if m_file[m_file[\"Env\"]==c].shape[0]==1:\n print_error(\"You need at least two samples belonging to each source environment, and there is only one sample coming from environment \"\\\n +c+\".\")\n return 1\n else:\n print_error(\"map.csv file does not exist or path is incorrect\") \n return 1\n \n # Add / to output directory if not added by the user\n if output[-1] != \"/\":\n output = output + \"/\"\n\n if p_sources[-1] != \"/\":\n p_sources = p_sources + \"/\"\n\n #Check folder with sources\n if not os.path.isdir(p_sources):\n print_error(\"Path to matrix of sources is incorrect or you have not created your matrix of sources.\")\n return 1\n \n try:\n fof=pd.read_csv(p_sources+\"kmtricks.fof\",sep=\"\\s:\\s\",engine=\"python\", names=[\"SampleID\",\"Path\"])\n samples_m_file=set(m_file[\"SampleID\"])\n samples_fof=set(fof[\"SampleID\"])\n if samples_m_file!=samples_fof:\n print_error(\"The samples in the kmtricks.fof of your p_sources/ folder are different from the samples in your map.csv file.\")\n return 1\n if not os.path.isfile(p_sources+\"/matrices/matrix.pa\"):\n print_error(\"File matrix.pa does not exist. Make sure a binary pa version of your matrix of sources is present in the folder p_sources/matrices/\")\n return 1\n if not os.path.isfile(p_sources+\"/matrices/matrix.pa.txt\"):\n print_error(\"File matrix.pa.txt does not exist. Make sure a text pa version of your matrix of sources is present in the folder p_sources/matrices/\")\n return 1\n except:\n print_error(\"p_sources folder is corrupted. Make sure you follow the instructions to create the matrix of sources as described in the README file and make sure you did not remove any file inside output the folder.\")\n \n # Check number of processes\n if int(t) <= 0:\n t = 5\n print_warning(\"-t parameter has been set to 5\")\n\n # Check if plot variable was set correctly\n if plot not in [\"True\", \"False\"]:\n plot = \"True\"\n print_warning(\"-plot parameter has been set to True\")\n\n # Verify input for mem is an integer\n try:\n int(mem[0:-2])\n except:\n print_error(\"-mem parameter is incorrectly set. It should be a positive number followed by GB. Ex: 10GB\")\n return 1\n\n # Verify the user assigned some GB to the process\n if mem[-2:] != \"GB\" or int(mem[0:-2]) <= 0:\n print_error(\"-mem parameter is incorrectly set. It should be a positive number followed by GB. Ex: 10GB\")\n return 1\n\n return p_sources, m,t, plot, output, mem",
"title": ""
},
{
"docid": "ef9ea86d02e0a7d5c8d3e8d623ba6b9a",
"score": "0.5328471",
"text": "def test_import_data_success(self):\n actual = import_data('csvfiles', 'products.csv', 'customers.csv', 'rentals.csv')\n self.assertEqual(actual, ((4, 4, 5), (0, 0, 0)))",
"title": ""
},
{
"docid": "321fa5158e132bc3172aecc32c2d30f9",
"score": "0.5328178",
"text": "def main(input_file=False, output_file=False):\n\n # Read input part list.\n partlist = []\n reader = csv.reader(input_file)\n for row in reader:\n if len(row) > 0:\n partlist.append(row[0].rstrip())\n\n # Process part numbers.\n s = scottopart.Scottopart()\n results = s.match_by_mpn(partlist)\n\n # Write output.\n writer = csv.DictWriter(output_file, dialect='excel-tab', extrasaction='ignore', fieldnames=scottopart.dblib_conventions.get_tablefields(results[0]['table_name']))\n writer.writeheader()\n for r in results:\n if r['success']:\n writer.writerow(r['table_row'])",
"title": ""
},
{
"docid": "dc0b8812157dc2429a342b58b476acf2",
"score": "0.5322487",
"text": "def pickAssembly():\n #manual = raw_input(\"would you like to manually enter the parts to assemble? (y/n)\")\n\n manual = \"n\"\n if(manual == \"n\"):\n print(\"searching for compatible input files...\")\n time.sleep(1)\n pllist = findExpts(\".\")\n #print pllist\n pickedlist = ''\n if(len(pllist) <=0):\n print(\"could not find any assembly files\")\n else:\n print(\"OK! I found\")\n print()\n for el in range(len(pllist)):\n print(\"[{}] {}\".format(el,pllist[el][1]))\n print()\n if(len(pllist)==1):\n pickedlist = pllist[0][0]\n print(\"picked the only one in the list!\")\n else:\n userpick = int(input(\"type the number of your favorite! \"))\n pickedlist = pllist[userpick][0]\n openlist = pd.read_csv(pickedlist)\n print(\"===================================\")\n return openlist,pickedlist\n else:\n print(\"sorry I haven't implemented this yet\")\n pickAssembly()\n return pd.read_csv(aslist),aslist",
"title": ""
},
{
"docid": "61c66bd6e8f770b82ee3393edc2dbd6d",
"score": "0.53209865",
"text": "def read_csv_file(file_location, input_timestamp_format=COMMON_TIMESTAMP_FORMAT,\n output_timestamp_format=COMMON_TIMESTAMP_FORMAT, station_list=None):\n\n with open(file_location) as csvfile:\n # Read the scv file. will get an array of arrays.\n read_csv = csv.reader(csvfile, delimiter=',')\n # Destructuring the read_csv array to separate meta-data and data.\n meta_data, *data_matrix = read_csv\n\n if not isinstance(meta_data, list) or not isinstance(data_matrix, list):\n print(\"Invalid csv file; \\nmeta_data: %s\" % meta_data)\n return None\n\n extract_index_list = [] # to collect indices that should be extracted, when station_list is given.\n output_meta_data = [] # to collect meta_data corresponding to the given station_list.\n if station_list is not None:\n output_meta_data.append(meta_data[0])\n for station in station_list:\n if station in meta_data:\n extract_index_list.append(meta_data.index(station))\n output_meta_data.append(station)\n\n if station_list is not None:\n # If the station_list is given, then extract_index_list and corresponding output_meta_data\n # should not be empty.\n if len(extract_index_list) > 0 and len(output_meta_data) > 0:\n print(\"Extracting data of the given station list. Meta data list: \", output_meta_data)\n\n output_data_matrix = []\n for row in data_matrix:\n\n # Format the timestamp (row[0]) to comply with :param output_timestamp_format.\n data_row = [_format_timestamp(row[0], input_timestamp_format, output_timestamp_format)]\n for item_index in extract_index_list:\n data_row.append(row[item_index])\n\n output_data_matrix.append(data_row)\n\n return output_meta_data, output_data_matrix\n else:\n print(\"No matching station can be found corresponding to the given station_list. \\n\" +\n \"Given station-List: %s \\n Meta data of csv: %s \\n\" % (station_list, meta_data))\n return None\n\n else:\n print(\"Extracting data of all the available stations. Meta data list: \", meta_data)\n\n # Iterate all the rows of the data_matrix and format the timestamp (row[0]) to comply with\n # :param output_timestamp_format.\n for row in data_matrix:\n row[0] = _format_timestamp(row[0], input_timestamp_format, output_timestamp_format)\n\n return meta_data, data_matrix",
"title": ""
},
{
"docid": "b5ea00e068adda12f0d6c920ff382646",
"score": "0.5297411",
"text": "def read_csv(csv_file):\r\n global csv_list, header\r\n\r\n#Open assign dictionaries to reader\r\n with open(csv_file, 'rt') as sf:\r\n reader = csv.reader(sf)\r\n\r\n #INIT VARIABLES\r\n csv_list = to_sequence(reader)\r\n header = csv_list[0]\r\n tot_col = len(header)\r\n tot_rows = len(csv_list) - 1 # Assumes Header\r\n\r\n ## PRINT ROWS\r\n # try:\r\n # for row in csv_list:\r\n # print(row)\r\n # except csv.Error as e:\r\n # sys.exit('file %s, line %d: %s' % (csv_file, reader.line_num, e))\r\n\r\n #Add if statement here to change analysis type\r\n p_csv = stats.Analysis(csv_list, tot_col, tot_rows)\r\n p_csv.full_analysis()\r\n create_file(p_csv.get_results())",
"title": ""
},
{
"docid": "80f93e1a383ac4be43d67ce7a1c32438",
"score": "0.5283439",
"text": "def import_all_from_csv(csv_file, lecturer):\n csv_data = []\n ifile = csv_file.read().decode(\"utf-8\")\n for row in ifile.split('\\n'):\n csv_data.append(row.split(','))\n \n result_objects = []\n print(csv_data)\n # Check if headers exists. Skip the first entry if true.\n header_check = ['student', 'course', 'assignment_score', 'quiz_score', 'exam_score', 'level', 'semester', 'session']\n first_row = [i.lower().strip() for i in csv_data[0]]\n if all(i in first_row for i in header_check):\n csv_data = csv_data[1:]\n \n new_value = 0 # To get the number of records entered\n update_value = 0\n\n for row in csv_data:\n # Let's do an extra check to make sure the row is not empty.\n if row:\n student = Student.objects.get(reg_number=row[0])\n course = Course.objects.get(course_code = str(row[1]).upper(), lecturers=lecturer)\n exiting = Result.objects.filter(student=student, course=course,level=row[5],semester=row[6])\n if exiting.count() > 0:\n if existing[0].quiz_score == 0.0:\n \texiting[0].quiz_score = row[3]\n if existing[0].exam_score == 0.0:\n \texisting[0].exam_score = row[4]\n if existing[0].assignment_score == 0.0:\n \texisting[0].assignment_score = row[2]\n existing[0].save()\n update_value+=1\n else:\n Result.objects.create(\n student=student,\n course=course,\n assignment_score=row[2],\n quiz_score=row[3],\n exam_score=row[4],\n level=row[5],\n semester=row[6],\n session=row[7],\n )\n new_value+=1\t\t\t\n return new_value",
"title": ""
},
{
"docid": "e74047bce3447604dc6de9d954e8d2d6",
"score": "0.52819943",
"text": "def check_cn_files(pairs):\n # find all snp and indel files in directory tree:\n cn_files = []\n for r, d, f in os.walk(directory):\n for item in f:\n if fnmatch.fnmatch(item, '*.copynumber'):\n cn_files.append(os.path.abspath(os.path.join(r, item)))\n\n if not cn_files: # no files yet\n samples = set() # initialize set of individual samples to find pileups for\n for pair in pairs:\n for sample in pair: samples.add(sample)\n print 'Now finding pileup files for ' + str(len(samples)) + ' samples'\n return samples, pairs\n\n # below script should only run if any copynumber files were found\n\n # make dictionary of (key=anc_vs_clone, value=(ancestor, clone))\n # anc_vs_clone name is used to search copynumber files\n anc_clone_dict = {}\n for pair in pairs: # ancestor_clone_pairs is a set of tuples\n anc_clone_name = str(pair[0] + '_vs_' + pair[1]) # name of analysis\n anc_clone_dict[anc_clone_name] = pair\n\n # create sets of pairs, pools not already analyzed\n filtered_pairs = set()\n analyzed_pairs = set()\n for key, pair in anc_clone_dict.iteritems():\n key_pattern = '*'+key+'*'\n matched_cn = fnmatch.filter(cn_files, key_pattern)\n if not matched_cn: filtered_pairs.add(pair) # missing file(s)\n else: analyzed_pairs.add(pair) # both files exist\n\n filtered_samples = set() # initialize set of individual samples to find pileups for\n for pair in filtered_pairs:\n for sample in pair: filtered_samples.add(sample)\n\n return filtered_samples, filtered_pairs",
"title": ""
},
{
"docid": "e02217ad4574ed5b0cca4ece1f7e3920",
"score": "0.5271668",
"text": "def fromcsv(filename, sourcecol=\"source\", targetcol=\"target\"):\n MatchListItem=[]\n expected=[]\n n=789\n with open(filename,\"r\") as csvfile:\n reader=csv.DictReader(csvfile,delimiter=';')\n for row in reader:\n n+=1\n MatchListItem.append({\"id\":\"source\"+str(n),\"content\":row[sourcecol], \"source\": True})\n MatchListItem.append({\"id\":\"target\"+str(n),\"content\":row[targetcol], \"target\": True})\n expected.append({ \"source\": \"source\"+str(n), \"target\": \"target\"+str(n) })\n return MatchListItem,expected",
"title": ""
},
{
"docid": "8e4c480eb869359037b9b3b405fd73e5",
"score": "0.5264599",
"text": "def read_file(self):\n index = 0\n try:\n f = open(self.filename, 'rb')\n with f:\n reader = csv.reader(f)\n for row in reader:\n if index == 0:\n self.create_ds(row)\n index += 1\n else:\n if len(row) != self.length_per_row:\n print \"File is malformed\"\n self.error_flag = True\n self.max_share_companies = \"File is malformed\"\n break\n else: \n self.process_row(row)\n except IOError:\n print \"Could not read file:\", self.filename\n self.error_flag = True\n self.max_share_companies = \"File not found exception\"\n #sys.exit()",
"title": ""
},
{
"docid": "082745ca5c7d93c8f9bfc9b4697f0248",
"score": "0.52639425",
"text": "def read_unstructured_data():\n #\n # Assign the filename: file\n #\n filename = \"C:\\\\Users\\mdjuk\\\\repos\\\\q_python_scripts\\\\titanic.csv\"\n\n data = np.genfromtxt(filename, delimiter=',', names=True, dtype=None) \n\n for i in data['Survived'] :\n if i == 1 :\n print(\"data from titanic.csv-->%s\" %(i))",
"title": ""
},
{
"docid": "0a342fb990d4bd5d3cae8dd100b0969d",
"score": "0.52628005",
"text": "def readInData(filename):\n data_entries = []\n\n with open(filename, 'rb') as csvfile:\n reader = csv.reader(csvfile, delimiter=',', quotechar='|')\n for row in reader:\n if row[5] == 'amount': #skip the header row\n continue\n\n \"\"\"(self, booking_date, issuer_country_code, tx_variant_code, bin, amount, currency_code, shopper_country_code, shopper_interaction,\n simple_journal, card_ver_code_supplied, cvc_response_code, creation_date, account_code, mail_id, ip_id, card_id)\"\"\"\n\n booking_date = row[1]\n issuer_country_code = row[2]\n tx_variant_code = (row[3])\n bin = row[4]\n amount = float(row[5])\n currency_code = row[6]\n shopper_country_code = row[7]\n shopper_interaction = row[8]\n simple_journal = row[9]\n card_ver_code_supplied = row[10]\n cvc_response_code = row[11]\n creation_date = row[12]\n account_code = row[13]\n mail_id = row[14]\n ip_id = row[15]\n card_id = row[16]\n\n data_entry = DataEntry(booking_date, issuer_country_code, tx_variant_code, bin, amount, currency_code, shopper_country_code, shopper_interaction,\n simple_journal, card_ver_code_supplied, cvc_response_code, creation_date, account_code, mail_id, ip_id, card_id)\n\n data_entries.append(data_entry)\n\n return data_entries",
"title": ""
},
{
"docid": "24e38be4199d494ffa50a7e1f4878452",
"score": "0.52532953",
"text": "def test_import_data_fails(self):\n actual = import_data('csvfiles', 'product.csv', 'customer.csv', 'rental.csv')\n self.assertEqual(actual, ((0, 0, 0), (1, 1, 1)))",
"title": ""
},
{
"docid": "4db730637351f424363a06ad6d33923f",
"score": "0.5243099",
"text": "def supported_microarray_csv2list(csv_file):\n count = 0\n ans = []\n with open(csv_file) as csvfile:\n readCSV = csv.reader(csvfile, delimiter=',')\n for row in readCSV:\n #print(row)\n ans.append(row[1])\n\n return ans[1:]",
"title": ""
},
{
"docid": "cb87f2d91af294b307a40382e3a3d87f",
"score": "0.5236165",
"text": "def extract_n_lines(input_dir, nb_lines):\n\n for csv_path in get_all_csvs_underdir(input_dir):\n current_line = 0\n print(\"Dealing with : %s \" % (csv_path))\n\n with open(csv_path, \"r\") as inputs:\n output_file = csv_path[:-4] + \"_extract_%s.csv\" % (nb_lines)\n with open(output_file, \"w\") as output:\n for line in inputs:\n output.write(line)\n current_line += 1\n if current_line == nb_lines:\n break",
"title": ""
},
{
"docid": "d9befaab4050e41999358492237fb8b9",
"score": "0.5234767",
"text": "def load_data(file_name, file_path):\n\n #file_path = '/home/kolan/mycode/python/dektak/data/'\n # filename = '/home/kolan/mycode/python/dektak/data/t10_1_3_normal.csv'\n #filename = '/home/kolan/mycode/python/dektak/data/t10_1_6_normal.csv'\n #filename = '/home/kolan/mycode/python/dektak/data/t10_1_7_normal.csv' #first peak very good 18thPositive peak short\n #filename = '/home/kolan/mycode/python/dektak/data/t10_1_12_normal.csv' #abottom IndexError: list index out of range\n #filename = '/home/kolan/mycode/python/dektak/data/t10_1_15_normal.csv' #abottom IndexError: list index out of range\n #filename = '/home/kolan/mycode/python/dektak/data/t10_1_19_normal.csv'\n #filename = '/home/kolan/mycode/python/dektak/data/t10_1_21_normal.csv' #no top & bottom\n #filename = '/home/kolan/mycode/python/dektak/data/t10_1_24_normal.csv' #no top & bottom\n #filename = '/home/kolan/mycode/python/dektak/data/t10_1_3_parallel.csv' #no top & bottom\n #filename = '/home/kolan/mycode/python/dektak/data/t10_1_15_parallel.csv' #abottom IndexError: list index out of range\n #filename = '/home/kolan/mycode/python/dektak/data/t10_1_19_parallel.csv' #0.035 too low 0.04 ok BADabottom\n #filename = '/home/kolan/mycode/python/dektak/data/t10_1_24_parallel.csv' #first peak very good\n #filename = '/home/kolan/mycode/python/dektak/data/t10_3_1_normal.csv'\n #filename = '/home/kolan/mycode/python/dektak/data/t10_3_3_normal.csv'\n #filename = '/home/kolan/mycode/python/dektak/data/t10_3_6_normal.csv'\n #filename = '/home/kolan/mycode/python/dektak/data/t10_3_7_normal.csv' #short peak\n #filename = '/home/kolan/mycode/python/dektak/data/t10_3_15_normal.csv'\n #filename = '/home/kolan/mycode/python/dektak/data/t10_3_19_normal.csv'\n\n file_name_and_path = file_path + file_name\n\n x, y = np.loadtxt(file_name_and_path, dtype=float, delimiter=',', skiprows=FindHeaderLength(file_path, file_name),\n usecols=(0, 1), unpack=True)\n return x, y",
"title": ""
},
{
"docid": "1cbd775d197c249b37bd1cf5073f6aa0",
"score": "0.52246624",
"text": "def read_in_subset_from(subset_csv, cli_args, gp_demo_str, sub_ID_var):\n result = None\n subset_df = pd.read_csv(subset_csv)\n if (\"1\" in subset_df and \"2\" in subset_df \n and len(subset_df.index) in cli_args.subset_size):\n result = get_gp_subsets_demo(subset_df, cli_args,\n gp_demo_str, sub_ID_var)\n result[\"subset_size\"] = len(subset_df.index)\n return result",
"title": ""
},
{
"docid": "00682289eca6e6ce215c46652efcf74e",
"score": "0.5222414",
"text": "def batch_varscan_copynumber(paired_pileups): # paired_pileups is a list of tuples (anc.pileup, clone.pileup, anc_clone_name, output_dir)\n array_num = len(paired_pileups)-1\n bash_file = os.path.join(script_directory, 'array_scripts', 'copynumber.sh')\n diag_dir = os.path.join(directory, 'diagnostic')\n if not os.path.exists(diag_dir): os.makedirs(diag_dir)\n output = os.path.join(diag_dir, 'varscan_copynumber_%A-%a.out')\n error = os.path.join(diag_dir, 'varscan_copynumber_%A-%a.err')\n print \"Now creating varscan files for \" + str(len(paired_pileups)) + \" ancestor-clone pairs\"\n arg_list = []\n for pair in paired_pileups:\n arg_list.append(' '.join(pair))\n subcall_list = ['sbatch', '--array=0-'+str(array_num), '--error='+str(error), '--output='+str(output), bash_file, script_directory]\n subcall_list.extend(arg_list)\n returncode = subprocess.call(subcall_list)\n print \"sbatch copynumber.sh executed, return code is \" + str(returncode)\n return returncode",
"title": ""
},
{
"docid": "7309b604ebd8d1ba4b5dad3e720b8391",
"score": "0.52107877",
"text": "def get_numwords_ids(csv_in, out_txt_name, min_words, max_words):\n f_in = open(csv_in, 'r+')\n f_out = open(out_txt_name, 'a+')\n reader = csv.reader(f_in)\n header = next(reader)\n # print(header)\n header = {stat: header.index(stat) for stat in header}\n seen = []\n for row in reader:\n work_id = row[header[\"work_id\"]]\n if work_id in stop_dict[csv_in[csv_in.index(\"CSV/\")+4:].replace(\"_edit.csv\", \"\").replace(\"_\", \" \")]:\n print(\"work_id in stop dict\", csv_in, work_id)\n try:\n word_count = int(row[header[\"words\"]])\n except ValueError:\n continue\n if word_count == 'null' or word_count == '':\n continue\n if min_words <= word_count <= max_words:\n f_out.write(work_id)\n f_out.write(\"\\n\")\n seen.append(work_id)",
"title": ""
},
{
"docid": "826de19aa1a49c9413a381081edfa489",
"score": "0.5207679",
"text": "def load_spec_list_from_cvs(folder_base_path=f\"{os.getcwd()}\"):\n data_list = []\n with open(f'{folder_base_path}\\\\spec_list.csv', 'r') as csvfile:\n reader = csv.reader(csvfile, skipinitialspace=True)\n for on, off in reader:\n data_list.append((on, off))\n return data_list",
"title": ""
},
{
"docid": "b4b6195c3718333a4b0ae6155831181f",
"score": "0.5198604",
"text": "def test_csv(self):\n \n good_file = 'df.csv'\n bad_file = 'df.tsv'\n\n # return no error\n assert good_file[-3:] == 'csv', \"input_file must be a .csv file\"\n\n # raise type error for invalid file format\n with pytest.raises(AttributeError):\n subset_rows(bad_file)",
"title": ""
},
{
"docid": "b422711271ff5ba6d6979df81b02f538",
"score": "0.5197998",
"text": "def import_assignment_from_csv(csv_file, lecturer):\n csv_data = []\n ifile = csv_file.read().decode(\"utf-8\")\n for row in ifile.split('\\n'):\n csv_data.append(row.split(','))\n \n result_objects = []\n print(csv_data)\n # Check if headers exists. Skip the first entry if true.\n header_check = ['student', 'course', 'score', 'level', 'semester', 'session']\n first_row = [i.lower().strip() for i in csv_data[0]]\n if all(i in first_row for i in header_check):\n csv_data = csv_data[1:]\n \n count_value = 0 # To get the number of records entered\n\n for row in csv_data:\n # Let's do an extra check to make sure the row is not empty.\n try:\n if row:\n student = Student.objects.get(reg_number=row[0])\n course = Course.objects.get(course_code = str(row[1]).upper(), lecturers=lecturer)\n assignment = AssignmentScore.objects.filter(student=student, assignment__course=course)[0]\n if assignment:\n if assignment.status == 'M':\n \tpass\n elif assignment.status == 'S':\n \tassignment.score = row[2]\n \tassignment.status = 'M'\n \tassignment.save()\n \t count_value+=1\t\n except Exception as e:\n pass\t\n return count_value",
"title": ""
},
{
"docid": "c917bf3ce7dea996e873680ded219124",
"score": "0.5194387",
"text": "def find_scan_info_from_input_file(local_path, input_filename):\n scan_trsh = \"\\n\"\n pivot = None\n local_input_path = os.path.join(local_path, input_filename)\n with open(local_input_path, \"r\") as f:\n for line in f.readlines():\n if re.search(\"D[\\s\\d]+[\\s\\d]+[\\s\\d]+[\\s\\d]+[\\s]+S\", line.strip()):\n pivot = [int(line.split(\" \")[2]), int(line.split(\" \")[3])]\n scan = [int(line.split(\" \")[1]),int(line.split(\" \")[2]), int(line.split(\" \")[3]), int(line.split(\" \")[4])]\n scan_res = float(line.split(\" \")[-1])\n if re.search(\"D[\\s\\d]+[\\s\\d]+[\\s\\d]+[\\s\\d]+[\\s]+F\", line.strip()):\n scan_trsh += re.search(\"D[\\s\\d]+[\\s\\d]+[\\s\\d]+[\\s\\d]+[\\s]+F\", line.strip()).group() + '\\n'\n if scan_trsh == \"\\n\":\n scan_trsh = ''\n if pivot:\n return (pivot, scan, scan_res, scan_trsh)\n else:\n return (None, None, None, None)",
"title": ""
},
{
"docid": "0469afb415e8f720a02591fc05a7fbc4",
"score": "0.5192612",
"text": "def loop_csv_file(source_csv):\n import csv\n\n file_data = []\n with open(source_csv, \"rb\") as csvfile:\n file_reader = csv.reader(csvfile, delimiter=\",\", quotechar='\"')\n for row in file_reader:\n file_data.append(row)\n return file_data",
"title": ""
},
{
"docid": "60cf0d4c13c594db043c102f01bd2e19",
"score": "0.5179791",
"text": "def import_input_targ_csv(fname_inp, fname_targ, nn):\n nn = int(nn)\n targets_y = pd.read_csv(fname_targ)\n input_x = pd.read_csv(fname_inp)\n\n input_x_nn = input_x.iloc[:,0:nn*3]\n\n return input_x_nn.values, targets_y.values",
"title": ""
},
{
"docid": "68a08306b1e985619c028a41a444d389",
"score": "0.51794136",
"text": "def __init__(self, input_filepath, has_header, uri_column, filename_column):\n\n # User must pass a valid csv file as the input_filepath argument as type str\n if input_filepath != \"\" and input_filepath != None and isinstance(input_filepath, str):\n if os.path.isfile(input_filepath):\n self.input_filepath = input_filepath\n else:\n raise FileNotFoundError(\"input_filepath %s is not a file!\" % input_filepath)\n elif input_filepath.lower().endswith(\".csv\") != True:\n raise Exception(\"input_filepath must be a valid *.csv file\")\n else: \n raise TypeError(\"input_filepath must be of type (str) and cannot be empty or None\")\n\n # Check if file has a header or not and represent with bool\n # TODO: Use csv.Sniffer().has_header as a fallback method if chosen\n if has_header is True or has_header is False:\n self.has_header = has_header\n else:\n raise TypeError(\"has_header must be of type (bool)\")\n\n # Allow users to pass the name of the column or the index of the column that contains the uri list, else raise exception\n # TODO: Add regex for detecting valid URI\n if uri_column != \"\" and uri_column != None and isinstance(uri_column, str):\n self.uri_column = self._translate_column_to_index(uri_column)\n elif isinstance(uri_column, int):\n self.uri_column = uri_column\n else:\n raise TypeError(\"uri_column must be either column name of type (str) or index of column of type (int)\")\n\n # Check if filename column is given, if empty or None, then assume that the filename is included in the uri\n if filename_column != \"\" and filename_column != None and isinstance(filename_column, str):\n self.filename_column = self._translate_column_to_index(filename_column)\n elif isinstance(filename_column, int):\n self.filename_column = filename_column\n else:\n self.filename_column = None\n\n # Create an empty dict\n self.uri_dict = {}",
"title": ""
},
{
"docid": "21ee39eabd101a8469fc9e8d4520c002",
"score": "0.5179176",
"text": "def read_csv(csv_filename,datapath): \n file_list, file_label =[],[]\n with open(csv_filename) as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n for row in csv_reader: \n file_list.append([datapath + str(row[0]),np.array(row[1:],dtype=int)])\n return file_list",
"title": ""
},
{
"docid": "d18592b6aea60f40b469a94849df1978",
"score": "0.51775193",
"text": "def test_import_data(self):\n clear_collections()\n prod_file = \"files/products.csv\"\n cust_file = \"files/customers.csv\"\n rent_file = \"files/rentals.csv\"\n records, errors = import_data(prod_file, cust_file, rent_file)\n\n self.assertEqual(records, (7, 4, 6))\n self.assertEqual(errors, (0, 0, 0))",
"title": ""
},
{
"docid": "d710a7c4d6fe23f4ef50422543bf8f9f",
"score": "0.51736283",
"text": "def mod2(fileName):\n rowNum = 1\n with open(fileName, 'rt') as csvfile:\n configreader = csv.reader(csvfile)\n for row in configreader:\n if str(row[0]) == 'H1':\n continue\n modFile1.mod1(rowNum, int(row[1].lstrip()), int(row[2].lstrip()), int(row[3].lstrip()), int(row[4].lstrip()), int(row[5].lstrip()), int(row[6].lstrip()), int(row[7].lstrip()), int(row[8].lstrip()), str(row[9]).lstrip())\n print(\"\")\n rowNum = rowNum + 1",
"title": ""
},
{
"docid": "d3cfd50c3d882cf9ddee7f77ceefc3d8",
"score": "0.5165516",
"text": "def extract_sim_data():\n#\n#--- find the time of the last entry from the sim_data.out\n#\n sfile = outdir + 'sim_data.out'\n f = open(sfile, 'r')\n data = [line.strip() for line in f.readlines()]\n f.close()\n#\n#--- cleaning up the data; drop the data which the date starts from \":\" e.g. :2014\n#\n pdata = []\n for ent in data:\n if re.search('^:', ent):\n continue\n else:\n pdata.append(ent)\n\n#\n#--- the last entiry values\n#\n if len(pdata) > 0:\n atemp = re.split('\\s+', pdata[len(pdata)-1])\n ltime = tcnv.axTimeMTA(atemp[0]) #--- converting time to sec from 1998.1.1\n time_2 = atemp[0]\n col1_2 = atemp[1]\n col2_2 = atemp[2]\n col3_2 = atemp[3]\n else:\n ltime = 0\n time_2 = 0\n col1_2 = ''\n col2_2 = ''\n col3_2 = ''\n#\n#--- check whether input files exists \n#\n cmd = 'ls -rt ' + dumpdir + 'PRIMARYCCDM_*.*.tl >' + zspace\n os.system(cmd)\n\n f = open(zspace, 'r')\n data = [line.strip() for line in f.readlines()]\n f.close()\n cmd = 'rm ' + zspace\n os.system(cmd)\n\n dlen = len(data)\n\n if dlen < 1:\n exit(1)\n\n#\n#--- files exist. read the data from the last 10 files\n#\n tlist = data[dlen-40:]\n\n for ent in tlist:\n cmd = 'cat ' +ent + ' >> ' + zspace\n os.system(cmd)\n\n f = open(zspace, 'r')\n data = [line.strip() for line in f.readlines()]\n f.close()\n cmd = 'rm ' + zspace\n os.system(cmd)\n\n prev = ''\n fo = open('./temp_save', 'w')\n#\n#--- go though each data line\n#\n for ent in data:\n try:\n#\n#--- expect the first letter of the data line is numeric (e.g. 2014).\n#\n val = float(ent[0]) \n except:\n continue\n#\n#--- only data with \"FMT\" format will be used\n#\n mc = re.search('FMT', ent)\n if mc is None:\n continue\n\n atemp = re.split('\\t+', ent)\n#\n#--- if there are less than 20 entries, something wrong; skip it\n#\n if len(atemp) < 20: \n continue\n#\n#--- convert time format\n#\n time = atemp[0]\n time = time.strip();\n time = time.replace(' ', ':')\n time = time.replace(':::', ':00')\n time = time.replace('::', ':0')\n#\n#--- if the time is exactly same as one before, skip it\n#\n if time == time_2:\n continue\n#\n#--- if the time is already in the database, keip it\n#\n stime = tcnv.axTimeMTA(time)\n if stime <= ltime:\n continue\n#\n#--- use only data which tscpos and fapos have numeric values\n#\n tscpos = atemp[4].strip()\n fapos = atemp[5].strip()\n\n if tscpos == \"\" or fapos == \"\":\n continue\n else:\n tscpos = int(float(tscpos))\n fapos = int(float(fapos))\n\n# aopc = atemp[11].strip()\n# if aopc == '':\n# aopc = '0'\n\n mpwm = atemp[12].strip()\n if mcf.chkNumeric(mpwm):\n mpwm = int(float(mpwm))\n mpwm = str(mpwm)\n else:\n mpwm = '0'\n\n\n#\n#--- we want to print only beginning and ending of the same data entries.\n#--- skip the line if all three entiries are same as one before, except the last one\n#\n if col1_2 == tscpos and col2_2 == fapos and col3_2 == mpwm:\n time_2 = time\n continue\n\n line = time + '\\t' + str(tscpos) + '\\t' + str(fapos) + '\\t' + mpwm + '\\n'\n if line == prev:\n continue\n else:\n pline = time_2 + '\\t' + str(col1_2) + '\\t' + str(col2_2) + '\\t' + str(col3_2) + '\\n'\n fo.write(pline)\n fo.write(line)\n prev = line\n time_2 = time\n col1_2 = tscpos\n col2_2 = fapos\n col3_2 = mpwm\n\n fo.close()\n\n sfile2 = sfile + '~'\n cmd = 'cp ' + sfile + ' ' + sfile2\n os.system(cmd)\n cmd = 'cat ./temp_save >> ' + sfile\n os.system(cmd)",
"title": ""
},
{
"docid": "06000c5ef3a1736894ece98b6b7bc1f7",
"score": "0.5164312",
"text": "def data_load_info(self, name_cup):\n\n name_file = open('data_cup.csv')\n name, height, width, volume=np.loadtxt('data_cup.csv',\n delimiter=';',\n unpack=True,\n dtype='str')\n\n for i in range(len(name_file.readlines())):\n if (name[i] == name_cup):\n # print(\"Name: \" + name[i], \"\\nHeight: \" + height[i], \"\\nWidth: \" + width[i], \"\\nVolume: \" + volume[i])\n return name[i], float(height[i]), float(width[i]), int(volume[i])",
"title": ""
},
{
"docid": "84d7265b04345b2371d10ed02e26aa58",
"score": "0.5155537",
"text": "def check_order(input_dir):\n with os.scandir(input_dir) as entries:\n for entry in entries:\n entry_name = entry.name\n if entry.is_file() and entry_name.endswith(\".csv\"):\n with open(input_dir + entry_name, \"r\", encoding='utf-8') as csv_in:\n print(\"\\n\", entry_name)\n cr = csv.reader(csv_in)\n next(cr, None) # skip header\n time_prev = 0\n pre_row = []\n for row in cr:\n time = float(row[-2])\n if time < time_prev:\n print(\"=== Outlier Founded ===\")\n print(pre_row)\n print(row)\n time_prev = time\n pre_row = row",
"title": ""
},
{
"docid": "5cdb8552112cb26f8c2f61a5aca2deb9",
"score": "0.5153172",
"text": "def mapper():\n filepath = os.environ[\"map_input_file\"] #get multiple input\n filename = os.path.split(filepath)[-1]\n # filename = 'cacleaned.csv'\n for line in sys.stdin:\n # Clean input and split it\n parts = line.strip().split(\",\")\n\n # Check that the line is of the correct format\n # If line is malformed, we ignore the line and continue to the next line\n # if len(parts) != 6:\n # continue\n if not parts[2].isdigit() or len(parts[1])!=11:\n continue\n\n v_id = parts[1].strip()\n c_id = parts[2].strip()\n if filename == 'cacleaned.csv':\n country = 'ca'\n if filename == 'uscleaned.csv':\n country = 'us'\n\n # In hadoop streaming, the output is sys.stdout, thus the print command\n # Do not use or add return\n print(\"{}\\t{}\\t{}\".format(c_id,v_id,country))",
"title": ""
},
{
"docid": "a8fad6b273e9cbe7c31360d298abe5d2",
"score": "0.5146471",
"text": "def extract_file(value):\n global records\n global csv_cols\n global fsize\n fsize = 0\n csv_cols = 0\n records = 0\n os.chdir(src_dir)\n src = (src_dir + os.sep + value)\n dest_dir = (csv_dir)\n dest_name = os.path.join(dest_dir, value[:-3])\n qt1 = time.time()\n\n # extract the .zip / .gz file\n print(\"* Extracting Archive ..: {}\".format(value))\n with gzip.open(src, 'rb') as infile, open(dest_name, 'wb') as outfile:\n for line in infile:\n outfile.write(line)\n\n infile.close()\n outfile.close()\n qt2 = (time.time() - qt1)\n\n # get the number of columns and records\n # use mmap for speed increa, could also use multi-core processing\n qt3 = time.time()\n\n # get record count\n with open(dest_name, \"r\") as f:\n reader = csv.reader(f, delimiter=' ', skipinitialspace=True)\n first_row = next(reader)\n data = list(reader)\n records = len(data)\n f.close()\n\n # get column cound\n with open(dest_name) as f:\n reader = csv.reader(f, delimiter=',', skipinitialspace=True)\n first_row = next(reader)\n num_cols = len(first_row)\n f.close()\n qt4 = (time.time() - qt3)\n\n # get the file size before deleting\n fsize = os.stat(dest_name).st_size\n print(\"* CSV File Name .......: {}\".format(value[:-3]))\n print(\"* CSV Size ............: {:,} bytes\".format(fsize))\n print(\"* CSV Columns .........: {}\".format(num_cols))\n print(\"* CSV Record Count ....: {:,}\".format(records))\n print(\"* Extraction Time .....: %.2f seconds\" % qt2)\n print(\"* Record Query Time ...: %.2f seconds\" % qt4)\n return fsize, csv_cols, records\n clean_csv_dir()",
"title": ""
},
{
"docid": "37f20f066c55bf68d8a2908e79190e90",
"score": "0.51431715",
"text": "def verify_file_data_count(input_config_file):\n line_no = 0\n config_file = open(input_config_file, 'r')\n config_file_reader = csv.reader(config_file, delimiter=',')\n for line in config_file_reader:\n line_no += 1\n if len(line) < 3 or len(line) > 4:\n raise SystemExit(\"Oops...data missing in config file at line no: %s <format needed:key,type,encoding>\" % str(line_no))\n\n config_file.close()",
"title": ""
},
{
"docid": "4cc9da9b5e2ef692f702d4ceefb29465",
"score": "0.51370144",
"text": "def getCSVdata():\n csvfile = scribus.fileDialog(\"csv2table :: open file\", \"*.csv\")\n if csvfile != \"\":\n try:\n reader = csv.reader(file(csvfile))\n datalist=[]\n for row in reader:\n rowlist=[]\n for col in row:\n rowlist.append(col)\n datalist.append(rowlist)\n return datalist\n except Exception, e:\n scribus.messageBox(\"csv2table\", \"Could not open file %s\"%e)\n else:\n sys.exit",
"title": ""
},
{
"docid": "0a5125422cc66ec6fbb9a1c942638b1c",
"score": "0.513395",
"text": "def load_source(csv_path):\n # parse csv file\n try: \n with open(csv_path, 'r') as csv_file: \n # skips over the header information in the first row\n contents = pd.read_csv(csv_file, skiprows=[0])\n print(contents)\n except FileNotFoundError as e: \n print(\"Cannot read in contents of source csv file\")\n raise\n\n # format for entry into source sql table \n\n\n # enter into source plate table\n\n\n # Todo: finish sql command\n # sql = \"select * from source plate\"\n # cursor = cnx.cursor()\n # results = cursor.fetchall()\n # for each in results: \n # print(each)",
"title": ""
},
{
"docid": "50203404a9ebbab5259dba70a62da3a3",
"score": "0.5127628",
"text": "def read_market_file(path,input_file):\n _file = open(\"%s\\input\\%s\" % (path,input_file), \"r\")\n content_list = csv.reader(_file)\n return content_list,_file",
"title": ""
},
{
"docid": "c1ab5d2aa30e4451bd02c86066b9a2b6",
"score": "0.5126011",
"text": "def collect_codes_for_import(args: Arguments):\n return [\n name[3:7]\n for name in os.listdir(with_context(args).predictions_dir())\n if name.endswith(\"_predictions.csv\")\n ]",
"title": ""
},
{
"docid": "de172dd2218ba8003c039a36a16ff5ee",
"score": "0.5124222",
"text": "def read_out_6(lissst, ordernum):\n for index in range((len(lissst))):\n print(index)\n a = lissst[index][0]\n b = lissst[index][1]\n c = lissst[index][2]\n d = lissst[index][3]\n e = lissst[index][4]\n f = lissst[index][5]\n\n color_1 = f\"VDP_{index + 1}\"\n color_2 = f\"{index}b\"\n\n file_1 = pd.read_csv(f\"vdps/{a}\", \";\")\n file_2 = pd.read_csv(f\"vdps/{b}\", \";\")\n\n file_3 = pd.read_csv(f\"vdps/{c}\", \";\")\n file_4 = pd.read_csv(f\"vdps/{d}\", \";\")\n\n file_5 = pd.read_csv(f\"vdps/{d}\", \";\")\n file_6 = pd.read_csv(f\"vdps/{d}\", \";\")\n\n samengevoeg_4 = pd.concat(\n [file_1, file_2, file_3, file_4, file_5, file_6], axis=1\n )\n\n samengevoeg_4.columns = [\n \"barcode_1\",\n \"omschrijving_1\",\n \"pdf_1\",\n \"barcode_2\",\n \"omschrijving_2\",\n \"pdf_2\",\n \"barcode_3\",\n \"omschrijving_3\",\n \"pdf_3\",\n \"barcode_4\",\n \"omschrijving_4\",\n \"pdf_4\",\n \"barcode_5\",\n \"omschrijving_5\",\n \"pdf_5\",\n \"barcode_6\",\n \"omschrijving_6\",\n \"pdf_6\",\n ]\n\n samengevoeg_4.fillna(\n {\n \"pdf_1\": \"stans.pdf\",\n \"pdf_2\": \"stans.pdf\",\n \"pdf_3\": \"stans.pdf\",\n \"pdf_4\": \"stans.pdf\",\n \"pdf_5\": \"stans.pdf\",\n \"pdf_6\": \"stans.pdf\",\n },\n inplace=True,\n )\n\n samengevoeg_4.to_csv(f\"VDP_map/{ordernum}_{color_1}.csv\", \";\")",
"title": ""
},
{
"docid": "bc4c36e263374c9b30b9edc99fa4d1f3",
"score": "0.51216763",
"text": "def read_csv_results(file_name):\n data = []\n values = set()\n\n # open the csv file\n with open(file_name, 'r') as csvfile:\n readCSV = csv.reader(csvfile, delimiter=',')\n # manipulate the data for each row based on the variable type\n for row in readCSV:\n row[0] = int(row[0])\n row[1] = int(row[1])\n row[2] = float(row[2])\n row[3] = int(row[3])\n row[4] = bool(row[4])\n # remove the last row that holds the model and we do not need it for the calculations\n row = row[:-1]\n # append to the end of the list the m/n value\n row.append(row[0] / row[1])\n # print(row)\n values.add(row[5])\n\n data.append(row)\n\n return values, data",
"title": ""
},
{
"docid": "112db4fdf360256e4ff53f9c863eb6d9",
"score": "0.51202554",
"text": "def readDataFromCsv():\r\n # exception handling try except to catch if file is missing or can not open.\r\n try:\r\n # define an empty array\r\n dataArr = []\r\n # if file exists, open file\r\n with open('E:/CP-2019W/CST8333_351ProgrammingLanguageResearch/data.csv') as file:\r\n # read csv file\r\n reader =csv.reader(file)\r\n # variable line_count\r\n line_count = 0\r\n # for loop\r\n for row in reader:\r\n if line_count == 0 or line_count == 1:\r\n # ignore header lines (line1 & line2)\r\n pass\r\n elif line_count < 12:\r\n dataArr.append(row)\r\n else:\r\n break;\r\n line_count += 1\r\n # close file\r\n file.close()\r\n except IOError:\r\n print(\"Something went wrong when open the file\")\r\n return dataArr",
"title": ""
},
{
"docid": "151977d03967180d438aabb727b37002",
"score": "0.51184493",
"text": "def test_function_should_return_data_set():\n assert func.function_to_import_data(\"data_2019.csv\").shape[0] == 156",
"title": ""
},
{
"docid": "ba6b27535ba2256a6219053bbd2c8842",
"score": "0.51125306",
"text": "def process_sample_sheet(index_length, dir_name, input_csv, logger):\n out_csv_path = os.path.join(\n dir_name, 'processedSampleSheet%s.csv' % index_length\n )\n if os.path.exists(out_csv_path):\n logger.warn(\n 'Found already-existing processed sample sheet(s) at %s. '\n '(Directory was already run?)' % out_csv_path\n )\n in_csv_f = open(input_csv)\n with open(input_csv) as in_csv_f:\n with open(out_csv_path, 'w') as out_csv_f:\n first_line = in_csv_f.readline().strip('\\n').strip('\\r')\n if first_line.startswith('[Data]'):\n header = in_csv_f.readline().strip('\\n').strip('\\r')\n else:\n header = first_line\n # Assumes index col is called 'index' and\n # dual index second index col is called 'index2'\n index_col = header.split(',').index('index')\n index2_col = -1\n if 'index2' in header.split(','):\n index2_col = header.split(',').index('index2')\n out_csv_f.write('[Data]\\n%s\\n' % header)\n index_length_is_present = False\n for row in in_csv_f:\n if not row.strip():\n continue\n # Change periods and spaces to _ so bcl2fastq doesn't choke.\n row = re.sub(r'[. ]', '_', row.strip())\n # Removed so that dual indexing works.\n # row = row.replace('-', '_')\n cols = row.strip('\\n').strip('\\r').split(',')\n curr_index_len = len(\n [z for z in cols[index_col] if z not in '-_']\n )\n if index2_col != -1:\n curr_index_len += len(\n [z for z in cols[index2_col] if z not in '-_']\n )\n if curr_index_len == index_length:\n index_length_is_present = True\n out_csv_f.write('%s\\n' % ','.join(cols))\n if index_length_is_present:\n logger.info(\n 'Created processed sample sheet %s (index length %d).' %\n (out_csv_path, index_length)\n )\n return out_csv_path\n else:\n os.remove(out_csv_path)\n logger.info('No indices present of length %d.' % index_length)\n return None",
"title": ""
},
{
"docid": "7a9a33c4f2a44f765f2f293be45df441",
"score": "0.5112135",
"text": "def fileHandlerCSV(file, request):\n\n filepath = file.temporary_file_path()\n\n company = request.user.company.pk\n\n listVuln = []\n bad_vuln = []\n array = []\n\n # The counters for the dynamic reader\n\n counter = 0 - 1\n hostAddress_counter = 0 - 1\n hostName_counter = 0 - 1\n port_counter = 0 - 1\n ssl = 'none'\n vulnID_counter = 0 - 1\n vulnName_counter = 0 - 1\n severity_counter = 0 - 1\n desc_counter = 0 - 1\n path = 'none'\n sol_counter = 0 - 1\n ref_counter = 0 - 1\n date_counter = 0 - 1\n cvss_counter = 0 - 1\n\n nexposeTrigger = False\n nessusTrigger = False\n\n\n with open(filepath) as file:\n readCSV = csv.reader(file, delimiter=',')\n for row in readCSV:\n for elements in row:\n counter = counter + 1\n if elements == 'Host':\n hostAddress_counter = counter\n nessusTrigger = True\n\n elif elements == 'Asset IP Address':\n hostAddress_counter = counter\n nexposeTrigger = True\n\n if hostName_counter == 'MAC Address':\n hostName_counter = counter\n elif elements == 'Asset MAC Addresses':\n hostName_counter = counter\n\n if elements == 'Port':\n port_counter = counter\n elif elements == 'Service Port':\n port_counter = counter\n\n if elements == 'Plugin ID':\n vulnID_counter = counter\n elif elements == 'Vulnerability ID':\n vulnID_counter = counter\n\n if elements == 'Name':\n vulnName_counter = counter\n elif elements == 'Vulnerability Title':\n vulnName_counter = counter\n\n if elements == 'Risk':\n severity_counter = counter\n elif elements == 'Vulnerability Severity Level':\n severity_counter = counter\n\n if elements == 'Description':\n desc_counter = counter\n elif elements == 'Vulnerability Description':\n desc_counter = counter\n\n if elements == 'Solution':\n sol_counter = counter\n elif elements == 'Vulnerability Solution':\n sol_counter = counter\n\n if elements == 'See Also':\n ref_counter = counter\n elif elements == 'Vulnerability CVE IDs':\n ref_counter = counter\n\n if elements == 'Date':\n date_counter = counter\n\n if elements == 'CVSS':\n cvss_counter = counter\n elif elements == 'Vulnerability CVSS Score':\n cvss_counter = counter\n\n if nessusTrigger or nexposeTrigger == True:\n if hostAddress_counter > -1:\n hostAddress = row[hostAddress_counter]\n else:\n hostAddress = 'none'\n\n if hostName_counter > -1:\n hostName = row[hostName_counter]\n else:\n hostName = 'none'\n\n if port_counter > -1:\n port = row[port_counter]\n else:\n port = 'none'\n\n if vulnID_counter > -1:\n vulnID = row[vulnID_counter]\n else:\n vulnID = 'none'\n\n if vulnName_counter > -1:\n vulnName = row[vulnName_counter]\n else:\n vulnName = 'none'\n\n if severity_counter > -1:\n severity = row[severity_counter]\n if severity == 'Critical':\n severity = '4'\n elif severity == 'High':\n severity = '3'\n elif severity == 'Medium':\n severity = '2'\n elif severity == 'Low':\n severity = '1'\n elif severity == 'None':\n severity = '0'\n elif severity == 'Vulnerability Severity Level':\n severity = '0'\n\n else:\n severity = 'none'\n\n if desc_counter > -1:\n desc = row[desc_counter]\n else:\n desc = 'none'\n\n if sol_counter > -1:\n sol = row[sol_counter]\n else:\n sol = 'none'\n\n if ref_counter > -1:\n ref = row[ref_counter]\n else:\n ref = 'none'\n\n if date_counter > -1:\n date = row[date_counter]\n else:\n # SET TO CURRENT DATE IF CSV FILE HAS NO DATE TO BE READ\n today = datetime.now()\n if nessusTrigger == True:\n date = today.strftime(\"%a %b %d %H:%M:%S %Y\")\n elif nexposeTrigger == True:\n date = today.strftime(\"%Y%m%dT%H%M%s\")\n\n if cvss_counter > -1:\n cvss = row[cvss_counter]\n else:\n cvss = 'none'\n else:\n return Response(\"Error: Not a supported CSV File is being read\")\n\n # if hostAddress != 'Host':\n vulnerability = TempVulnerability(hostAddress, hostName, port, ssl, vulnID, vulnName, severity, desc, path, sol,\n ref, date, cvss)\n\n listVuln.append(vulnerability)\n\n if vulnerability is None:\n return Response(\" Error: Not a supported CSV File is being read\")\n\n # Push Object into the end of the list.\n # vulnerability.print()\n\n if nessusTrigger == True:\n tempArray = []\n for i in listVuln:\n if i.host != 'Host':\n tempArray.append(i)\n elif i.host == 'Host':\n bad_vuln.append(i)\n\n for i in tempArray:\n if valid_import_vuln_nes(i):\n array.append(i)\n else:\n bad_vuln.append(i)\n\n response = build_vulns('Nes', company, array)\n\n elif nexposeTrigger == True:\n tempArray = []\n for i in listVuln:\n # print(i.host)\n if i.host != 'Asset IP Address':\n tempArray.append(i)\n elif i.host == 'Asset IP Address':\n bad_vuln.append(i)\n else:\n return Response(\"Not a supported CSV File is being read\")\n\n for i in tempArray:\n if valid_import_vuln_nex(i):\n array.append(i)\n else:\n bad_vuln.append(i)\n\n response = build_vulns('Nex', company, array)\n else:\n return Response(\"Not a supported CSV File is being read\")\n\n # This is a print method to print out the vulnerability -- For TESTING ONLY\n '''for i in array:\n i.print()'''\n\n return response",
"title": ""
},
{
"docid": "cde662e13be1e2a02b20d8398f343593",
"score": "0.50991607",
"text": "def test_csv_mismatch(self):\n self.assertTrue(execute([\n ('read|CSVFile', identifier, [\n ('file', [('File', self._test_dir + '/test.csv')]),\n ]),\n ('ExtractColumn', identifier, [\n ('column_index', [('Integer', '0')]), # index is wrong\n ('column_name', [('String', 'col 2')]),\n ]),\n ],\n [\n (0, 'value', 1, 'table'),\n ]))",
"title": ""
},
{
"docid": "bd8c106843ecee1c5da1d1a2b14b41a9",
"score": "0.5097735",
"text": "def parse(csvfilename):\n table = []\n with open(csvfilename, \"r\") as csvfile:\n for line in csvfile:\n line = line.rstrip()\n columns = line.split(',')\n table.append(columns)\n return 0",
"title": ""
},
{
"docid": "d67ba29368395de64e5279552bab1af3",
"score": "0.50931704",
"text": "def scan_interim_report(int_report, ref_code):\n global debug\n requirement_artefact_list = []\n try:\n with open(int_report, 'r') as csvfile:\n interim_reader = csv.reader(csvfile, dialect='excel')\n for row in interim_reader:\n references = extract_reference(ref_code, row[3]+' '+row[4]) # To do: Change 3 and 4 to the actual \"response\" column index\n requirement = row[0] # To do: Change 0 to the actual \"requirement\" column index\n requirement_artefact_list.append([requirement, references])\n return requirement_artefact_list\n except csv.Error as e:\n sys.exit(\"[!] Error reading interim report file\")",
"title": ""
},
{
"docid": "220fa676399be99377c764234c935389",
"score": "0.5088325",
"text": "def import_csv(directory_name, file_name):\n LOGGER.info(\"Importing CSV\")\n directory_name = Path(\"./\" + directory_name)\n file_field = file_name + \".csv\"\n file_path = directory_name / file_field\n LOGGER.info(\"Importing Product CSV file: %s\", file_path)\n error_cnt = 0\n\n return_data = []\n try:\n with open(file_path, \"r\") as csvfile:\n csv_data = csv.reader(csvfile, delimiter=\",\")\n\n for idx, row in enumerate(csv_data):\n LOGGER.debug(\"ICSV Header: %s\", row)\n if idx == 0:\n header = row\n LOGGER.debug(\"%s\", file_name)\n LOGGER.debug(\"Validating headers: %s\", header)\n else:\n LOGGER.debug(\"ICSV Data: %s\", row)\n temp_product = _file_parser(row, header)\n return_data.append(temp_product)\n except IndexError as err:\n LOGGER.error(\"Index error in import_csv\")\n LOGGER.error(err)\n error_cnt = 1\n LOGGER.info(return_data)\n LOGGER.info(\"Error count = %d\", error_cnt)\n return (error_cnt, return_data)",
"title": ""
},
{
"docid": "6ef0aa6f563257a681986c708e5f9b0c",
"score": "0.5087115",
"text": "def _parse_file(self, data_file):\n if not self.__check_oko(data_file):\n return super(AccountBankStatementImport, self)._parse_file(data_file) \n \n \n transactions=[]\n mindate=\"9999-99-99\"\n maxdate=\"0000-00-00\"\n total_amt = Decimal(0) \n header_skipped=False\n linenum=1\n for row in data_file.split(\"\\n\"):\n if header_skipped:\n row=row.strip().decode(\"iso8859-15\").encode(\"utf-8\")\n fields=row.split(\";\")\n if row=='':\n continue;\n if (len(fields)!=10):\n raise UserError(_('OKO Bank CSV file (tositetiliote) included wrong number of fields. Expected 10 got %d\\nLine %d:%s') % (len(fields),linenum,row))\n accdate,valuedate,amountEUR,transtype,transdescr,other_part,transaccount_and_bic,referencecode,memo,archiveid=fields\n d,m,y=accdate.split(\".\")\n amountEUR=float(amountEUR.replace(\",\",\".\"))\n accdate=\"%04d-%02d-%02d\"%(int(y),int(m),int(d))\n if accdate<mindate:\n mindate=accdate\n if accdate>maxdate:\n maxdate=accdate\n\n #Mikulta\n # The last part is just the bank identifier\n identifier = transaccount_and_bic.rfind(' ')\n acc_num=transaccount_and_bic[:identifier]\n if len(memo.strip())==0:\n memo='-'\n if len(other_part.strip())==0:\n other_part=''\n hashMD5 = hashlib.md5() #known security issues with md5 but simple enough to provide uniqueness for OKO bank archiveid duplicates\n if sys.version_info.major>2:\n hashMD5.update(bytes(row),\"iso8859-15\")\n else:\n hashMD5.update(row)\n extra_uniqueness= hashMD5.hexdigest()\n oneval={\n 'sequence': linenum, # added for sequence?\n 'name':other_part,\n 'date':accdate,\n 'amount': amountEUR,\n 'unique_import_id':archiveid+\"-\"+extra_uniqueness,\n 'account_number':acc_num,\n 'note':memo,\n 'partner_name':other_part,\n 'ref':referencecode,\n }\n transactions.append(oneval)\n \n total_amt = total_amt + Decimal(amountEUR)\n linenum=linenum+1 # advance sequence \n else:\n header_skipped=True\n # OKO csv does not include account number so we get it from journal\n journal_obj = self.env['account.journal']\n journal = journal_obj.browse(self.env.context.get('journal_id', []))\n balance_end=Decimal(self.balance_start)+total_amt\n \n account=journal.bank_account_id.sanitized_acc_number \n vals_bank_statement = {\n 'balance_start': self.balance_start,\n 'balance_end_real': balance_end,\n 'date': self.bank_statement_date if self.bank_statement_date else maxdate,\n 'transactions': transactions\n } \n return (\"EUR\",account,[vals_bank_statement,])",
"title": ""
},
{
"docid": "396e97c556f816d041a7473da3d9a464",
"score": "0.5086517",
"text": "def selectionfromcsv(filename, number=4, sourcecol=\"source\", targetcol=\"target\"):\n l=[]\n with open(filename,\"r\") as csvfile:\n reader=csv.DictReader(csvfile,delimiter=';')\n for row in reader:\n l.append((row[sourcecol],row[targetcol]))\n if len(l)<number:\n number = len(l)\n random.shuffle(l)\n MatchListItem=[]\n expected=[]\n for n in range(number):\n MatchListItem.append({\"id\":\"source\"+str(n),\"content\":l[n][0], \"source\": True})\n MatchListItem.append({\"id\":\"target\"+str(n),\"content\":l[n][1], \"target\": True})\n expected.append({ \"source\": \"source\"+str(n), \"target\": \"target\"+str(n) })\n return MatchListItem,expected",
"title": ""
},
{
"docid": "0a90eccfa06813ad7dc051d4581707cb",
"score": "0.50837284",
"text": "def handle_masterfile(writer: object) -> True:\n # For each URL:\n print('open master csv file')\n session = Session()\n with open(INPUT_CSV_FILENAME, encoding=\"utf-8\") as doc_list:\n csv_reader = csv.reader(doc_list)\n next(csv_reader) # Skip first row of csv file\n for counter, row in enumerate(csv_reader):\n web_address = row[8]\n file_type = row[2]\n doc_id = row[0]\n expected_author = row[5] \n print(f'Working on: {web_address}')\n s = session.get(web_address)\n if file_type.upper() == 'EXCEL':\n logging.info(web_address)\n data = extract_xlsx_info(\"temp.xlsx\", doc_id, web_address, s.content) \n elif file_type.upper() == 'PDF':\n logging.info(web_address)\n data = extract_pdf_info(\"temp.pdf\", doc_id, web_address, s.content) \n else:\n data = empty_dict(doc_id, web_address)\n data['Error']: f\"NOT PDF OR EXCEL [{file_type}]\"\n \n data['Author'] = f\"{expected_author} - ({data['Author']})\" \n data[\"Row\"] = counter\n print_data(data, os.path.basename(web_address))\n writer.writerow(data) # write data into csv file\n print(\"...done\")\n return True",
"title": ""
},
{
"docid": "7305f659b8b6211dbdc0fa4f028f1578",
"score": "0.5077962",
"text": "def read_data(name):\r\n inputs=[]\r\n with open(name) as csvfile:\r\n readCSV = csv.reader(csvfile, delimiter=',')\r\n for row in readCSV:\r\n \r\n row[0] = float(row[0])\r\n row[1] = float(row[1])\r\n row[2] = int(row[2])\r\n \r\n inputs.append(row)\r\n \r\n return inputs",
"title": ""
},
{
"docid": "94d451b129a6980e4023ebb9369fae4d",
"score": "0.50744087",
"text": "def process_one(csv_file: str, out_folder: str, win_size: int,\r\n filter_blacklist: list = [], filter_key: str = 'epc',\r\n align_key: str = 'rssi', group_key_len: int = g_group_key_len):\r\n print(f'Processing file `{csv_file}`')\r\n\r\n global g_group_key_len\r\n g_group_key_len = group_key_len\r\n\r\n if not filter_blacklist:\r\n filter_blacklist = [\r\n 'F01000310F30010011711560',\r\n 'E2003066701700620960B90B'\r\n ]\r\n\r\n pd_data = read_csv(csv_file)\r\n pd_data_dict = split_table_by(pd_data, filter_key)\r\n pd_data_dict = filter_table(pd_data_dict, filter_blacklist)\r\n for key, data in pd_data_dict.items():\r\n data = fresh_index(data)\r\n sample_data, len_pad = sample_at_the_valley(data, win_size, align_key)\r\n save_data_to(sample_data, csv_file, out_folder, key, len_pad, win_size)",
"title": ""
},
{
"docid": "8dd1b6e0abc772ba8bbc2a8d568b6a67",
"score": "0.5069883",
"text": "def load_data(self):\n try:\n with open(self._in_cvs, 'r', encoding=self._in_enc) as read_file:\n reader = csv.DictReader(read_file, delimiter=';')\n for row in reader:\n self._information.load(row['name1'], row['name2'], row['group'], row['whom'],\n row['course'], row['data'], row['when'], row['num'], row['kind'], row['aud'])\n self._is_load_data_done = True\n except OSError:\n self._information.clear_data()\n raise ReadCvsError()\n except LoadError as e:\n self._information.clear_data()\n raise InputCvsError(str(e))",
"title": ""
},
{
"docid": "f267c9b7e1058a3547cff5b6f717df6a",
"score": "0.5068198",
"text": "def scan_csv_list(url):\n\n # Issue request: r => requests.models.Response\n r = requests.get(url)\n\n # Extract text: html_doc => str\n html_doc = r.text\n\n # Parse the HTML: soup => bs4.BeautifulSoup\n soup = BeautifulSoup(html_doc, 'html.parser')\n\n # Find all 'a' tags (which define hyperlinks): a_tags => bs4.element.ResultSet\n a_tags = soup.find_all('a')\n\n # Store a list of urls ending in .csv: urls => list\n urls = ['https://raw.githubusercontent.com'+re.sub('/blob', '', link.get('href'))\n for link in a_tags if '.csv' in link.get('href')]\n\n # Store a list of file names\n list_names = [url.split('.csv')[0].split('/')[url.count('/')] for url in urls]\n\n return urls, list_names",
"title": ""
},
{
"docid": "d7c48a2293e6d4cb8089ff5585eed295",
"score": "0.506819",
"text": "def parse(self, input_file):",
"title": ""
},
{
"docid": "3e1ae8623360dbabac91b69b06697b7b",
"score": "0.5061252",
"text": "def _ReadCsv(path):\n ret = []\n with open(path) as f:\n for line in f:\n parts = line.rstrip().split(',')\n if len(parts) == 2 and parts[0] != 'revision':\n ret.append((int(parts[0]), int(float(parts[1]))))\n return ret",
"title": ""
},
{
"docid": "35641eb163eb78be41f26c297b8b24f1",
"score": "0.505761",
"text": "def import_data():\n return 1",
"title": ""
},
{
"docid": "8a303d3b5189641f2e411ddd056e6c6a",
"score": "0.50552815",
"text": "def read_input_split_data():\n print('reading data...')\n # train = pd.read_csv('train.csv')\n # all_data = pd.read_csv('train.csv')\n all_data = pd.read_csv('out.csv')\n all_data[\"date_time\"] = pd.to_datetime(all_data[\"date_time\"])\n all_data[\"year\"] = all_data[\"date_time\"].dt.year\n all_data[\"month\"] = all_data[\"date_time\"].dt.month\n all_data[\"date_time\"] = all_data[\"date_time\"].dt.day\n all_data[\"srch_ci\"] = pd.to_datetime(all_data[\"srch_ci\"])\n all_data[\"srch_ci_year\"] = all_data[\"srch_ci\"].dt.year\n all_data[\"srch_ci_month\"] = all_data[\"srch_ci\"].dt.month\n all_data[\"srch_ci\"] = all_data[\"srch_ci\"].dt.day\n\n all_data[\"srch_co\"] = pd.to_datetime(all_data[\"srch_co\"])\n all_data[\"srch_co_year\"] = all_data[\"srch_co\"].dt.year\n all_data[\"srch_co_month\"] = all_data[\"srch_co\"].dt.month\n all_data[\"srch_co\"] = all_data[\"srch_co\"].dt.day\n print('Reading Done')\n for key in all_data.keys():\n all_data = all_data[pd.notnull(all_data[key])]\n\n # print(all.keys())\n hotel_id_set = set(all_data['hotel_cluster'])\n train = None\n test = None\n # all_data = all_data[all_data['is_booking'] == 1]\n # total = 0\n for hotel_id in hotel_id_set:\n flt = all_data[all_data['hotel_cluster'] == hotel_id]\n flt = shuffle(flt)\n l = len(flt)\n train_rows = int(l * 0.7)\n if train is None:\n train = flt[:train_rows]\n test = flt[train_rows:]\n else:\n train = pd.concat([train, flt[:train_rows]])\n test = pd.concat([test, flt[train_rows:]])\n print(train.shape)\n print(test.shape)\n print(all_data.shape)\n train.to_csv('train_naive.csv', index=False)\n test.to_csv('test_naive.csv', index=False)\n print(\"csv files written to train_naive.csv, test_naive.csv'\")",
"title": ""
},
{
"docid": "6f2df4b2153560b454573bcf66560275",
"score": "0.50450814",
"text": "def readCsvHistData(csvFileName): \n h2dd = HistData()\n peaksPerStddFileName, rgnLblPerStddFileName = getPerStddFileNames(csvFileName)\n hpd = readCsvPeaksPerStdd(peaksPerStddFileName, rgnLblPerStddFileName)\n h2dd.peaksData = hpd\n\n lines = file(csvFileName, 'r').readlines()\n lineIdx = 0\n edgesRegEx = re.compile('\\\\s*(bin-pts-[0-9]*)(,\\\\s*bin-pts-[0-9]*)*(.*)')\n datasetsRegEx = re.compile('.*data-file-[0-9]*=\"(.*)\"\\\\s*,\\\\s*data-file-[0-9]*=\"(.*)\".*\\\\s*,\\\\s*runId=\"(.*)\"(.*)')\n foundEdgesLine = False\n while ((not foundEdgesLine) and (lineIdx < len(lines))):\n line = lines[lineIdx].strip()\n lineIdx += 1\n foundEdgesLine = ((edgesRegEx.match(line)) != None)\n datasetsMtch = datasetsRegEx.match(line)\n if (datasetsMtch != None):\n h2dd.fName0 = datasetsMtch.group(1)\n h2dd.fName1 = datasetsMtch.group(2)\n h2dd.runId = datasetsMtch.group(3)\n rootLogger.info(\"CSV file = %s\" % csvFileName)\n rootLogger.info(\"CSV dataset0 = %s\" % h2dd.fName0)\n rootLogger.info(\"CSV dataset1 = %s\" % h2dd.fName1)\n rootLogger.info(\"CSV runId = %s\\n\" % h2dd.runId)\n \n if (foundEdgesLine):\n edges = [[],[]]\n line = lines[lineIdx].strip()\n pairRegEx = re.compile(\"\\\\s*([^,]*)\\\\s*,\\\\s*([^,]*)((,.*)*)\")\n while ((len(line) > 0) and (lineIdx < len(lines))):\n mtch = pairRegEx.match(line)\n if (mtch != None):\n g1 = mtch.group(1).strip()\n g2 = mtch.group(2).strip()\n if (len(g1) > 0):\n edges[0].append(float(g1))\n if (len(g2) > 0):\n edges[1].append(float(g2))\n lineIdx += 1\n if (lineIdx < len(lines)):\n line = lines[lineIdx].strip()\n h2dd.edges = [sp.array(edges[0], dtype=\"float64\"), sp.array(edges[1], dtype=\"float64\")]\n h2dd.x = (h2dd.edges[0][1:] + h2dd.edges[0][0:-1])/2.0\n h2dd.y = (h2dd.edges[1][1:] + h2dd.edges[1][0:-1])/2.0\n\n foundCountsLine = False\n countsRegEx = re.compile('\\\\s*bin-[0-9]*-idx\\\\s*,\\\\s*bin-[0-9]*-idx\\\\s*,\\\\s*count')\n while ((not foundCountsLine) and (lineIdx < len(lines))):\n line = lines[lineIdx].strip()\n lineIdx += 1\n foundCountsLine = ((countsRegEx.match(line)) != None)\n\n if (foundCountsLine):\n h2dd.hist2dData = sp.zeros((h2dd.x.size, h2dd.y.size), dtype=\"float64\")\n if (lineIdx < len(lines)):\n line = lines[lineIdx].strip()\n tripleRegEx = re.compile(\"\\\\s*([^,]*)\\\\s*,\\\\s*([^,]*),\\\\s*([^,]*)\")\n while (lineIdx < len(lines)):\n mtch = tripleRegEx.match(line)\n if (mtch != None):\n triple = [int(mtch.group(1).strip()), int(mtch.group(2).strip()), int(mtch.group(3).strip())]\n h2dd.hist2dData[triple[0], triple[1]] = triple[2]\n lineIdx += 1\n if (lineIdx < len(lines)):\n line = lines[lineIdx].strip()\n\n h2dd.hist1dData0 = sp.sum(h2dd.hist2dData, axis=0)\n h2dd.hist1dData1 = sp.sum(h2dd.hist2dData, axis=1)\n\n else:\n raise RuntimeError(\"Could not find bin-counts header line in file '%s'\" % csvFileName)\n else:\n raise RuntimeError(\"Could not find bin-pts header line in file '%s'\" % csvFileName)\n\n # transpose everything for plotting\n tmp = h2dd\n h2dd = copy.copy(h2dd)\n h2dd.x = tmp.y\n h2dd.y = tmp.x\n h2dd.edges = [tmp.edges[1], tmp.edges[0]]\n h2dd.hist2dData = tmp.hist2dData.transpose()\n h2dd.hist1dData0 = tmp.hist1dData1\n h2dd.hist1dData1 = tmp.hist1dData0\n\n return h2dd",
"title": ""
},
{
"docid": "30769e11023382b2edbd29ce793d3008",
"score": "0.50385654",
"text": "def test_command_parsefile(self):\n self.assertEqual(CitiesData.objects.count(), 0)\n\n correct_file = '2CuNPefD.csv'\n call_command('parsefile', correct_file)\n\n self.assertNotEqual(CitiesData.objects.count(), 0)",
"title": ""
},
{
"docid": "6492dbc11d88d244f58f391ae0867c16",
"score": "0.50274134",
"text": "def read_from_multiple_csv(cfg, extension, cfg_elastic):\n if extension == 'csv':\n host, port, username, password, pattern, sep, chunksize, fild_to_convert, directory, dir_arch = [*cfg['csv'].values()]\n all_file = glob.glob(directory+'*')\n print(directory)\n print(len(all_file))\n for filename in all_file:\n print(filename)\n if chunksize != None:\n read_file_by_chunk(filename, sep, chunksize, cfg_elastic, fild_to_convert)\n os.remove(filename)\n else:\n df = return_dataframe(filename, sep)\n elastic.send_to_elastic(df, cfg_elastic)\n #shutil.move(file, dir_arch)\n elif extension == 'xml':\n print('Wait comming next')\n else:\n raise Warning('this ext not reconize')\n print('*********Success!!!!!!!!!!!!!!!!!!!************')",
"title": ""
},
{
"docid": "f70f0441cc9efadf32c9e6e0c320b97f",
"score": "0.5023401",
"text": "def getCSVdata(csvfile):\n try:\n with open(defaultresourcedirectory + \"analyzed_0.7.csv\") as ff:\n reader = csv.reader(ff)\n datalist=[]\n for row in reader:\n rowlist=[]\n for col in row:\n rowlist.append(col)\n datalist.append(rowlist)\n return datalist\n except:\n print(\"Could not open file {}\".format(csvfile))",
"title": ""
},
{
"docid": "4b42c6a1f6996b0b8aaf680999d26c35",
"score": "0.5023002",
"text": "def get_file_data(insert_limit):\n last_timestamp = time.time()\n # Read formatted data from csv file\n file_resolver = FileResolver(get_file_from_data_dir())\n data = file_resolver.get_parsed_zip_csv(insert_limit)\n get_update_last_timestamp(last_timestamp, 'Data parsed from file')\n\n return data",
"title": ""
},
{
"docid": "0e5089b24fe433f8147ea7414e144644",
"score": "0.5019136",
"text": "def load_bank_data(try_attempt = 0):\n ''' Sample Input\n .csv: data/daily_rate_sheet.csv\n '''\n\n # Ask for the .csv file from where to load the bank data\n csvpath = questionary.text(\"Enter a file path to a rate-sheet (.csv):\").ask()\n # check if the csv file path name is valid\n if check_csvpath_name(csvpath, try_attempt):\n # the path name is valid, so check if the path exists. \n csvpath = Path(csvpath)\n if not csvpath.exists():\n sys.exit(f\"Oops! Can't find this path: {csvpath}\")\n # the path exists, load the bank data from the csv file.\n return load_csv(csvpath)\n else:\n # the path name is invalid. Retry for a maximum of max_input_tries to obtain a valid path name\n return load_bank_data(try_attempt+1)",
"title": ""
},
{
"docid": "162a437029709a1c54d81e7d191b3af9",
"score": "0.5018811",
"text": "def csv(args):\n from data_acquisition.ingest_csv import ingest_data\n ingest_data.main(args=args)",
"title": ""
},
{
"docid": "0a5345d904422c7d9ecc0244faa4cfa6",
"score": "0.50186163",
"text": "def parse_test_results():\n os.chdir(spec_output_dir)\n\n try:\n for csv_result in csv_results:\n for item in spec_tests:\n with open(csv_result, 'rb') as f:\n csv_handler = csv.reader(f)\n for row in csv_handler:\n if item in row:\n results[\"%s_base_copies\" % item] = row[1] if row[1] is not None else ''\n results[\"%s_base_runtime\" % item] = row[2] if row[2] is not None else ''\n results[\"%s_base_rate\" % item] = row[3] if row[3] is not None else ''\n results[\"%s_peak_copies\" % item] = row[6] if row[6] is not None else ''\n results[\"%s_peak_runtime\" % item] = row[7] if row[7] is not None else ''\n results[\"%s_peak_rate\" % item] = row[8] if row[8] is not None else ''\n break\n return\n except Exception as e:\n raise e",
"title": ""
},
{
"docid": "ed314e2ab0ac36c751f3cbc87dc62fa4",
"score": "0.501628",
"text": "def read_out_4(lissst, ordernum):\n for index in range((len(lissst))):\n print(index)\n a = lissst[index][0]\n b = lissst[index][1]\n c = lissst[index][2]\n d = lissst[index][3]\n\n color_1 = f\"VDP_{index + 1}\"\n color_2 = f\"{index}b\"\n\n file_1 = pd.read_csv(f\"vdps/{a}\", \";\")\n file_2 = pd.read_csv(f\"vdps/{b}\", \";\")\n\n file_3 = pd.read_csv(f\"vdps/{c}\", \";\")\n file_4 = pd.read_csv(f\"vdps/{d}\", \";\")\n\n samengevoeg_4 = pd.concat([file_1, file_2, file_3, file_4], axis=1)\n\n samengevoeg_4.columns = [\n \"barcode_1\",\n \"omschrijving_1\",\n \"pdf_1\",\n \"barcode_2\",\n \"omschrijving_2\",\n \"pdf_2\",\n \"barcode_3\",\n \"omschrijving_3\",\n \"pdf_3\",\n \"barcode_4\",\n \"omschrijving_4\",\n \"pdf_4\",\n ]\n\n samengevoeg_4.fillna(\n {\n \"pdf_1\": \"stans.pdf\",\n \"pdf_2\": \"stans.pdf\",\n \"pdf_3\": \"stans.pdf\",\n \"pdf_4\": \"stans.pdf\",\n },\n inplace=True,\n )\n\n samengevoeg_4.to_csv(f\"VDP_map/{ordernum}_{color_1}.csv\", \";\")",
"title": ""
},
{
"docid": "9afb0c0c1a138fbb6134cca3ffd06e72",
"score": "0.5014216",
"text": "def validate_1_2(submission_filepath, validHLA):\n\t#VAR_ID have to check out with first file\n\tprint(\"VALIDATING %s\" % submission_filepath)\n\tbasename = os.path.basename(submission_filepath)\n\trequired_cols = pd.Series([\"RANK\",\"VAR_ID\",\"PROT_POS\",\"HLA_ALLELE\",\"HLA_ALLELE_MUT\",\"HLA_ALT_BINDING\",\"HLA_REF_BINDING\",\"PEP_LEN\",\"ALT_EPI_SEQ\",\"REF_EPI_SEQ\",\"RANK_METRICS\",\"RANK_DESC\",\"ADDN_INFO\",\"SCORE\",'REF_ALLELE_EXP','ALT_ALLELE_EXP'])\n\n\tsubmission = pd.read_csv(submission_filepath,na_values=\"n/a\")\n\t#CHECK: Required headers must exist in submission\n\tassert all(required_cols.isin(submission.columns)), \"%s: These column headers are missing: %s\" % (basename,\", \".join(required_cols[~required_cols.isin(submission.columns)]))\n\tsubmission['VAR_ID'] = submission['VAR_ID'].astype(str)\n\tinteger_cols = ['PEP_LEN',\"RANK\"]\n\tstring_cols = ['HLA_ALLELE','ALT_EPI_SEQ','REF_EPI_SEQ','RANK_METRICS','VAR_ID']\n\tcheckType(submission, integer_cols, int, basename)\n\t#CHECK: RANK must be ordered from 1 to nrows\n\tassert all(submission.RANK == range(1, len(submission)+1)), \"%s: RANK column must be sequencial and must start from 1 to the length of the data\" % basename\n\t#CHECK: integer, string and float columns are correct types\n\tcheckType(submission, string_cols, str, basename)\n\tsubmission['RANK_DESC'] = submission['RANK_DESC'].fillna('').apply(str)\n\tcheckType(submission, ['HLA_ALLELE_MUT',\"RANK_DESC\",\"ADDN_INFO\"], str, basename, optional=True)\n\tcheckType(submission, ['HLA_ALT_BINDING','HLA_REF_BINDING','SCORE','REF_ALLELE_EXP','ALT_ALLELE_EXP'], float, basename, optional=True)\n\tcheckDelimiter(submission, ['RANK_METRICS'], basename,allowed=[';',':',\".\",\"_\",\"-\"])\n\tintSemiColonListCheck(submission, basename, 'PROT_POS')\n\n\tassert all(submission[['PEP_LEN','REF_EPI_SEQ']].apply(lambda x: len(x['REF_EPI_SEQ']) == x['PEP_LEN'], axis=1)), \"%s: Length of REF_EPI_SEQ values must be equal to the PEP_LEN\" % basename\n\tassert all(submission[['PEP_LEN','ALT_EPI_SEQ']].apply(lambda x: len(x['ALT_EPI_SEQ']) == x['PEP_LEN'], axis=1)), \"%s: Length of ALT_EPI_SEQ values must be equal to the PEP_LEN\" % basename\n\tassert all(submission['HLA_ALLELE'].apply(lambda x: configureHLA(x) in validHLA)), \"%s: HLA_ALLELE must be part of this list for this patient: %s\" % (basename,\", \".join(validHLA))\n\treturn(True,\"Passed Validation!\")",
"title": ""
},
{
"docid": "7dbf24c38f33f26f91073c3f7599cea1",
"score": "0.501317",
"text": "def read_file(self):\r\n # train = np.array(list(csv.reader(open(self.source_data_file, \"rb\"), delimiter=','))) # .astype('float')\r\n tmp = []\r\n try:\r\n with open(self.source_data_file, 'rb') as csvfile:\r\n spam_reader = csv.reader(csvfile, delimiter=',', quotechar='|')\r\n for row in spam_reader:\r\n # tmp.append(', '.join(row))\r\n tmp.append(row)\r\n except:\r\n print '\\nFile Not found.\\nThe fine named {} is not found at the root directory, ' \\\r\n 'please make sure it is located at the correct location and try again.'.format(self.source_data_file)\r\n exit()\r\n\r\n def read_cell(cel):\r\n \"\"\"\r\n Read data and specify if string or numeric\r\n @param cel: data cell\r\n @return: float of string value\r\n \"\"\"\r\n try: # if is is a number\r\n return float(cel)\r\n except: # otherwise return a trimmed string (with no spaces on either directions\r\n return cel.strip()\r\n # return x\r\n\r\n # creating titles and separating data from them\r\n\r\n var_count = len(tmp[0])\r\n self.num_inputs = var_count - self.num_outputs\r\n if self.has_titles and self.has_briefs:\r\n # remove white spaces if any (trim)\r\n tmp[0] = map(lambda x: x.strip(), tmp[0])\r\n tmp[1] = map(lambda x: x.strip(), tmp[1])\r\n self.titles = tmp[0]\r\n self.briefs = tmp[1]\r\n tmp = tmp[2:]\r\n elif self.has_titles:\r\n # if it only has full titles, we will initiate a brief title\r\n tmp[0] = map(lambda x: x.strip(), tmp[0])\r\n self.titles = tmp[0]\r\n self.briefs = ['In' + str(x) if x < self.num_inputs\r\n else 'Ot' + str(x - self.num_inputs) for x in range(var_count)]\r\n tmp = tmp[1:]\r\n elif self.has_briefs:\r\n # if it only has briefs we will consider them as full titles as well\r\n tmp[0] = map(lambda x: x.strip(), tmp[0])\r\n self.briefs = tmp[0]\r\n self.titles = tmp[0]\r\n tmp = tmp[1:]\r\n else: # no titles provided\r\n self.titles = ['Input variable {' + str(x + 1) + '}' if x < self.num_inputs\r\n else 'Output variable {' + str(x - self.num_inputs + 1) + '}' for x in range(var_count)]\r\n self.briefs = ['In' + str(x + 1) if x < self.num_inputs\r\n else 'Ot' + str(x - self.num_inputs + 1) for x in range(var_count)]\r\n\r\n data_ok = []\r\n for line in tmp:\r\n lll = []\r\n for cell in line:\r\n lll.append(read_cell(cell))\r\n data_ok.append(lll)\r\n return data_ok",
"title": ""
},
{
"docid": "bd13d199d90f6357d1b9bbffd9dbae8d",
"score": "0.5008058",
"text": "def file_reader(source):\n\n frame = pd.read_csv(f'../data/scraped/{source}_bitcoin.csv')\n frame = frame.drop(['Unnamed: 0', 'Unnamed: 0.1'], axis=1)\n return frame",
"title": ""
},
{
"docid": "32875568bc78f29f57ee231c12169892",
"score": "0.5003466",
"text": "def test_ALL_THE_CSVS(self):\n for csv in os.listdir(\"testfiles\"):\n main(os.path.join(\"testfiles\", csv))",
"title": ""
},
{
"docid": "9c0ddf7fdbc423bfc50bcf2c0a695cb4",
"score": "0.50032234",
"text": "def _parse_csv(self):\n if self.gcs_path:\n if isinstance(self.csv_path, list):\n for index, path in enumerate(self.csv_path):\n parse_result = urlparse(path)\n bucket = parse_result.hostname\n csv_name = parse_result.path\n self._download_csv(\n bucket,\n csv_name,\n path_name='/tmp/data_' +\n str(index) +\n '.csv')\n csv_path = '/tmp/data_*.csv'\n else:\n parse_result = urlparse(self.csv_path)\n bucket = parse_result.hostname\n csv_name = parse_result.path\n self._download_csv(bucket, csv_name)\n csv_path = '/tmp/data.csv'\n else:\n csv_path = self.csv_path\n\n if self.column_names:\n header = None\n else:\n header = 'infer'\n\n try:\n df = dd.read_csv(\n csv_path,\n names=self.column_names,\n header=header,\n na_values=self.na_values,\n sample=12800000,\n dtype=self.data_type)\n if isinstance(csv_path, list):\n len(df) # Checks whether schema is consistent throughout the data\n except Exception:\n raise AssertionError(\n 'Data types given are inconsistent with data provided')\n\n if self.to_drop is not None:\n drop_column_names = self.to_drop\n drop_column_names = [\n name for name in drop_column_names if name in df.columns]\n df = self.drop_cols(df, drop_column_names)\n tf.logging.info('Dropping the columns : %s', drop_column_names)\n\n return df, list(df.columns)",
"title": ""
},
{
"docid": "c69722a2bd0dcdfbe85405742fc91c4f",
"score": "0.50023586",
"text": "def import_data():\n\n try:\n data = pd.read_csv(\"data/covid19.csv\",\n usecols=['pruid', 'prname', 'prnameFR', 'date',\n 'numconf', 'numprob', 'numdeaths',\n 'numtotal', 'numtoday', 'ratetotal'])\n df = data.fillna(0)\n return df\n\n except FileNotFoundError:\n print(\"File not found, try again\")",
"title": ""
},
{
"docid": "6d0a5c11501d50abf2551a173010ad3d",
"score": "0.4999105",
"text": "def read_smi_file(file_path, ignore_invalid=True, num=-1):\n return map(lambda fields: fields[0], read_csv_file(file_path, ignore_invalid, num))",
"title": ""
},
{
"docid": "e35d72418efa2b26e7eed9043abccb5c",
"score": "0.49984846",
"text": "def filter_and_copy(row, extractedvars, newmetadata):\n # Now copy the files on disk to a new file structure. But first...\n # filter out files on disk that we don't want to reupload:\n # empty files (corrupted to, in or from Research Hub) and .DS_Store files.\n # NOTE that folders, like empty files, have a size of 0, and yet we must copy folders\n # to keep the integrity of the tree, so we can't just filter out all items with size = 0.\n # NOTE, too, that for dev purposes we copy all the files -- we pass the ones we want\n # to a 'reupthese-files' directory and the others to a 'dontreupthese-files' directory.\n localpath, name, mimetype, size = extractedvars\n newpath, newname = newmetadata\n # TODO: Combine if and else into one code block?\n if (size != '0' and newname != '.DS_Store') or mimetype == 'FOLDER':\n # Eventually, this will be the path on the Piction servers, passed in as argument.\n # During dev, use 'reupthese-files'\n reupbasepath = sys.argv[3] + rhbasepath\n # Replace local path, i.e. '../working-files', '../working-files-test-set', etc.\n newpath = reupbasepath + newpath.replace(sys.argv[2], '')\n #logging.info('\\tchanged: %s\\t%s' % (localpath, newpath))\n row[0] = newpath\n row[2] = newname\n # Output modified metadata to new .csv file\n outputwriter.writerow(row)\n # Copy file to new destination, i.e., re-upload it to Piction servers.\n try:\n ensure_dir(newpath)\n except:\n #pass\n logging.info('\\tCould not ensure directory at %s' % newpath)\n try:\n shutil.copy2(localpath, newpath)\n logging.info('\\treupload - copy succeeded %s' % newpath)\n except:\n #pass\n logging.info('\\tcould not copy %s' % newpath)\n\n else:\n # Directory to catch files that failed to upload - passed in as argument\n dontreupbasepath = sys.argv[4] + rhbasepath\n # Replace local path ../working-files', '../working-files-test-set', etc.\n newpath = dontreupbasepath + newpath.replace(sys.argv[2], '')\n #logging.info('\\tchanged: %s\\t%s' % (localpath, newpath))\n row[0] = newpath\n row[2] = newname\n # Output modified metadata to new .csv file even if file has failed re-upload.\n outputwriter.writerow(row)\n # Copy file to catch-basin for files that failed re-upload.\n try:\n ensure_dir(newpath)\n except:\n #pass\n logging.info('\\tCould not ensure directory at %s' % newpath)\n try:\n shutil.copy2(localpath, newpath)\n logging.info('\\tdont reupload - copy succeeded %s' % newpath)\n except:\n #pass\n logging.info('\\tcould not copy %s' % newpath)",
"title": ""
},
{
"docid": "f38ba96be09acdcc5ca164312c25d646",
"score": "0.49970576",
"text": "def test_read_input_no_header(self):\n c_finder = CombinationsFinder()\n with open(test_inputs_dir + '/read_test_no_header.csv') as test_file:\n c_finder.read_input_and_get_combinations(test_file, False, 4)\n self.assertTrue('u1' in c_finder.graph_nodes)",
"title": ""
},
{
"docid": "6c9baa2511003977275b011f7b06a223",
"score": "0.4991876",
"text": "def create_random_sample(input_dir, output_dir, nb_lines, max_line):\n import numpy as np\n onlycsv = get_all_csvs_underdir(input_dir)\n len_input_dir = len(input_dir)\n # 1) USER INFO : print( \"-\"*50)print( \" \"* 20, \"Creating random extract\")\n print(\"-\" * 50)\n print(\"input dir = %s\" % (input_dir))\n print(\"%s files : [%s]\" % (len(onlycsv), \",\".join(onlycsv)))\n print(\"output dir = %s\" % (output_dir))\n print(\"Selecting %s lines within the %s first.\" % (nb_lines, max_line))\n # 2) READING FILES\n for csv_path in onlycsv:\n random_lines = np.random.randint(1, max_line, size=nb_lines) # SELECTING RANDOM LINES\n current_line = 0\n nb_written_lines = 0\n to_print = \"\"\n print(\"Dealing with : %s \" % (csv_path))\n with open(csv_path, \"r\") as inputs:\n output_file = csv_path[:-4] + \"_random_%s_lines.csv\" % (nb_lines)\n with open(output_file, \"w\") as output:\n for line in inputs:\n current_line += 1\n\n if current_line == 1: # WE SAVE THE HEADERS\n to_print = line\n continue\n\n if current_line in random_lines: # WE WRITE OUT THE RANDOM LINES\n to_print += line\n nb_written_lines += 1\n\n if current_line % 100000 == 0:\n output.write(to_print)\n to_print = \"\"\n print(\"Line %s : wrote %s lines out of %s wanted. (%.2f pct) \" % (\n current_line, nb_written_lines, nb_lines, nb_written_lines / nb_lines))\n if nb_written_lines >= nb_lines:\n break\n if current_line >= max_line:\n break\n output.write(to_print)",
"title": ""
},
{
"docid": "bb4cdf36309005987068296eb73e2af0",
"score": "0.49881783",
"text": "def csv2pd3(csvFile,top):",
"title": ""
},
{
"docid": "372fbac03fa2ba2f91335cd6ddc18607",
"score": "0.49871847",
"text": "def check_file():\n try:\n with open(\"stock.csv\", \"r\") as file:\n print(\"Items loaded from stock.csv\\n\")\n reader = csv.reader(file)\n for row in reader:\n ST.item_list.append(SIA.StockItem(row[0], row[1], row[2]))\n except IOError:\n print(\"Stock file not found! A new file will be created at end of session...\\n\")",
"title": ""
},
{
"docid": "96a629a0bb50dfb5d50b0242e9f7e445",
"score": "0.4981551",
"text": "def import_data(directory_name, product_file, customer_file, rentals_file):\n directory_name = Path(directory_name)\n record_count = [0, 0, 0]\n \n try:\n with open(directory_name / product_file) as csvfile:\n csv_header = csv.reader(csvfile, delimiter=',')\n except IOError:\n LOGGER.error('Invalid product file name %s', product_file)\n except IndexError:\n LOGGER.error('Mismatched data and header length')\n LOGGER.error('Header: %s', csv_header)\n LOGGER.error('Data:%s', csv_data)",
"title": ""
},
{
"docid": "2f90bebba7de44d0fa8045ea60a05e75",
"score": "0.49792862",
"text": "def import_bom(csvFileName = \"arduino_bom.csv\", toTest = False):\n\t\n\timport csv\n\n\tcsv_file = open(csvFileName, \"r\")\n\tcsv_reader = csv.DictReader(csv_file)\n\tline_items = []\n\tqueries = []\n\tfor line_item in csv_reader:\n\n\t\t# Skip line items without part numbers and manufacturers\n\t\tif not line_item['Part Number'] or not line_item['Manufacturer']:\n\t\t\tcontinue\n\t\tline_items.append(line_item)\n\t\t# if toTest:\n\t\t\t# print \"toTest == True, line item is:\", line_item, \"\\n\"\n\t\tqueries.append({'mpn': line_item['Part Number'],\n\t\t\t\t\t\t'brand': line_item['Manufacturer'],\n\t\t\t\t\t\t'reference': len(line_items) - 1})\n\t\t\n\tif toTest:\n\t\tprint (\"\\n\\ntoTest = True in import_bom\")\n\t\t# return only a subset \n\t\tline_items = line_items[:1]\n\t\tqueries = queries[:1]\n\t\tprint (\"\\tline_items:\", line_items)\n\t\tprint (\"\\tqueries:\", queries)\n\t\t#assert False\n\n\treturn line_items, queries",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="709"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">6d7c635ef6190c251b027ad60d043f1b</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Encode image file into string using base64.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "083775d02d746f13cdbf4045f2c8b572",
"score": "0.84103495",
"text": "def encodeImage(imageFile):\n encoded_string = ''\n if not os.path.exist(imageFile):\n print(\"File does not exist\",imageFile)\n with open(imgFile, \"rb\") as image_file:\n encoded_string = base64.b64encode(image_file.read())\n return encoded_string",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "e3eb3d717edbcc83c69c8e8a7f2f63ec",
"score": "0.8400944",
"text": "def encode_image(filename):\n with open(filename, \"rb\") as image_file:\n encoded_string = base64.b64encode(image_file.read())\n return encoded_string.decode(\"utf-8\")",
"title": ""
},
{
"docid": "20a0d79762c867b28bb65d39f712ee7e",
"score": "0.8246751",
"text": "def encode_to_base64(filename):\n imgstr = \"\"\n with open(filename, 'rb') as file:\n imgstr = base64.b64encode(file.read())\n return imgstr",
"title": ""
},
{
"docid": "f2bb8d800050116a0ec8484aa1ed6aef",
"score": "0.81332844",
"text": "def image_to_base64(img_path):\n with open(img_path, \"rb\") as image_file:\n encoded_string = base64.b64encode(image_file.read())\n\n return encoded_string",
"title": ""
},
{
"docid": "b16ec9039e87407833ba5706ae793277",
"score": "0.8112049",
"text": "def encode_image(image_file):\n encoded = base64.b64encode(open(image_file, 'rb').read())\n return 'data:image/png;base64,{}'.format(encoded.decode())",
"title": ""
},
{
"docid": "3283625051e2cbca427bb8969754cccd",
"score": "0.7972864",
"text": "def _encode(img_path):\n with open(img_path, \"rb\") as img:\n encoded_img = base64.b64encode(img.read()).decode()\n encoded_img = \"data:image/png;base64,\" + encoded_img\n return encoded_img",
"title": ""
},
{
"docid": "f01b60c7f9ce5249df481b9484014803",
"score": "0.7936824",
"text": "def encode_image(image):\n base64_img = base64.b64encode(image).decode('ascii')\n return base64_img",
"title": ""
},
{
"docid": "bc34bc59a89779ff751f3a947d7b1b5a",
"score": "0.764582",
"text": "def encode_image_base64(pillow_image):\n byteIOBuffer = BytesIO()\n # write image in JPEG format to a byte buffer\n pillow_image.save(byteIOBuffer, format='JPEG')\n # flush IO buffer to an array\n byte_array = byteIOBuffer.getvalue()\n\n base64_encoded = b64encode(byte_array)\n # use string utf-8 representation for usage convenience\n return str(base64_encoded.decode(\"utf-8\"))",
"title": ""
},
{
"docid": "c969bc8500696ed2da458c735a107a23",
"score": "0.7623925",
"text": "def _img_to_str_base64(image):\n img_encode = cv2.imencode('.jpg', image)[1]\n img_base64 = base64.b64encode(img_encode)\n return img_base64",
"title": ""
},
{
"docid": "324f34341954c5b7e5c7853323b0f066",
"score": "0.7565268",
"text": "def img_encode(img_path):\n # If the img_path is not provided as a parameter.\n if not img_path:\n return \"\"\n\n if os.path.isfile(img_path):\n # Local image\n with open(img_path, \"rb\") as f:\n img_str = str(b64encode(f.read()))[2:-1]\n img_type = img_path[-3:]\n img_str = f\"data:image/{img_type};base64,{img_str}\"\n else:\n # Remote image\n logging.info(\"Downloading {img_path}\")\n img_str = img_path\n\n return img_str",
"title": ""
},
{
"docid": "a479c5032134e623de3407dea8d09784",
"score": "0.7477123",
"text": "def image_file_to_b64(image_file: io.BytesIO) -> bytes:\n return base64.b64encode(image_file.getvalue())",
"title": ""
},
{
"docid": "78e011ad835afb4d71c171e0172f1e5f",
"score": "0.740808",
"text": "def encode(image):\n\n file_name = \"temp.png\"\n base64_message = \"\"\n \n cv.imwrite(file_name, image)\n\n with open(file_name, 'rb') as binary_file:\n binary_file_data = binary_file.read()\n base64_encoded_data = base64.b64encode(binary_file_data)\n base64_message = base64_encoded_data.decode('utf-8')\n \n if os.path.isfile(file_name):\n print(\"Delete temp file\")\n os.remove(file_name)\n \n return base64_message",
"title": ""
},
{
"docid": "0c734974f5d03fe2de54200831a35300",
"score": "0.733938",
"text": "def encode_file(self):\n return base64.b64encode(self.file.encode()).decode()",
"title": ""
},
{
"docid": "87cf1b5539becf74617e80c273092105",
"score": "0.73183435",
"text": "def file_encode(filepath):\r\n with open(filepath, 'rb') as f:\r\n return base64.b64encode(f.read()).decode('utf-8')",
"title": ""
},
{
"docid": "e512c5cdcf214d9758645d5eec8d70a9",
"score": "0.72599095",
"text": "def base64_data_to_image(image: str) -> str:\n target = settings.UPLOAD_FOLDER\n date = datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M-%S')\n base64_to_image = Image.open(BytesIO(base64.b64decode(image)))\n image_path = f'{target}/{date}.jpg'\n base64_to_image.save(image_path)\n return image_path",
"title": ""
},
{
"docid": "b7b987d72794f07f3d28f239c5e9d510",
"score": "0.72097194",
"text": "def img_to_base64(img_path):\n s = ''\n with Image.open(img_path) as img:\n with io.BytesIO() as buffered:\n img.save(buffered, format=\"JPEG\")\n s += base64.b64encode(buffered.getvalue()).decode(\"ascii\")\n return f'\"data:image/jpeg;base64,{s}\"'",
"title": ""
},
{
"docid": "1b123c62937ae940833fdab64b64e9d6",
"score": "0.7137357",
"text": "def base64encode(self, data):\n return base64.b64encode(data)",
"title": ""
},
{
"docid": "674d1288b2091f7ebf74731a13092e4f",
"score": "0.7073156",
"text": "def base64_encode(file_name, extension):\n \n output_file = '%s.%s' % (file_name, extension)\n with open(file_name, 'rb') as in_file:\n with open(output_file, 'w') as out_file:\n base64.encode(in_file, out_file)\n\n return output_file",
"title": ""
},
{
"docid": "8422acc1a9f7ee914045a4489c4448b6",
"score": "0.7010187",
"text": "def f_base64_encode(self):\n return base64.b64encode(self.input)",
"title": ""
},
{
"docid": "7ef4af3dcab7f99e5b81f3320a62e1b2",
"score": "0.70015097",
"text": "def data_encode_image(name, content):\n return u'data:{0};base64,{1}'.format(\n mimetypes.guess_type(name)[0],\n base64.standard_b64encode(content))",
"title": ""
},
{
"docid": "c3ca128c1df3538ab8deb245a66ff8b5",
"score": "0.6950856",
"text": "def stored_image_base64(value):\n infos, content = get_info_content(value)\n infos = infos.split(';')\n data_type = infos[0]\n data_path = infos[1].strip('name=')\n storage = get_storage()\n with storage.open(data_path, 'rb') as data_file:\n file_bytes = data_file.read()\n file_b64 = base64.encodebytes(file_bytes)\n result = f\"{data_type};base64,\" + file_b64.decode()\n return result",
"title": ""
},
{
"docid": "d0c0eadbc41f07b942fc3766131125ed",
"score": "0.6892301",
"text": "def base64_encode(path):\n if os.path.isfile(path):\n encoded_content = StringIO.StringIO()\n\n try:\n with open(path) as read_file:\n base64.encode(read_file, encoded_content)\n\n return encoded_content.getvalue().strip()\n except IOError:\n raise CommandError(\"Cannot read file \"\n \"'{0}'.\".format(path))\n else:\n raise CommandError(\"Provided path does not exists or is not a file: \"\n \"{0}.\".format(path))",
"title": ""
},
{
"docid": "24fe2860b69078b473660b9a6dafc451",
"score": "0.6887465",
"text": "def base64_encodestring(data):\n return base64.encodebytes(data).decode()",
"title": ""
},
{
"docid": "5c7b53d71a46e2fa56482f4964b7e734",
"score": "0.6796265",
"text": "def _get_base64(data: str) -> str:\n ebytes = base64.b64encode(data.encode(\"utf-8\"))\n estring = str(ebytes, \"utf-8\")\n return estring",
"title": ""
},
{
"docid": "63eed1bcd68aee9b805dde2d498d87fd",
"score": "0.67883563",
"text": "def file_to_base64(filepath_input):\n with open(filepath_input, 'rb') as f:\n b64_string = base64.b64encode(f.read()).decode('utf-8')\n return b64_string",
"title": ""
},
{
"docid": "e2e6127083bf8152248fbd8f0f55302e",
"score": "0.67882335",
"text": "def encodeImage(np_img_array):\n _, img_buffer = cv2.imencode(\".tiff\", np_img_array)\n img_buffer_enc64 = base64.b64encode(img_buffer)\n str_img_buffer_enc64 = str(img_buffer_enc64, encoding='utf-8')\n return str_img_buffer_enc64",
"title": ""
},
{
"docid": "cc19f4b0b6c6e8ff7c50fece35f89bea",
"score": "0.67395896",
"text": "def base64_encode_image(frame):\n if frame.ndim == 2:\n frame = np.expand_dims(frame, axis=2)\n assert frame.ndim == 3 and frame.shape[2] == 1\n elif frame.ndim == 3:\n if frame.shape[2] == 1:\n pass # all good\n elif frame.shape[2] == 3:\n # cv2.imencode expects a BGR image:\n frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)\n assert frame.ndim == 3 and frame.shape[2] == 3\n else:\n raise Exception(\"invalid number of channels\")\n else:\n raise Exception(\"invalid frame ndarray ndim\")\n if IS_VIRTUAL:\n png_img = cv2.imencode('.png', frame, [cv2.IMWRITE_PNG_COMPRESSION, 6])[1].tobytes()\n base64_img = 'data:image/png;base64,' + base64.b64encode(png_img).decode('ascii')\n else:\n jpg_img = cv2.imencode('.jpg', frame, [cv2.IMWRITE_JPEG_QUALITY, 50])[1].tobytes()\n base64_img = 'data:image/jpeg;base64,' + base64.b64encode(jpg_img).decode('ascii')\n return base64_img",
"title": ""
},
{
"docid": "b1f1661a8f6ee4b1e89a32f1d9f3218a",
"score": "0.6735111",
"text": "def png_img_to_base64(img: Image) -> str:\n with io.BytesIO() as buffer:\n img.save(buffer, \"png\")\n buffer.seek(0)\n result = base64.b64encode(buffer.getvalue()).decode()\n\n return result",
"title": ""
},
{
"docid": "c5abeff7df80ba2438a83dc7ea88a42d",
"score": "0.670934",
"text": "def convert_numpy_to_base64_image_string(image_np):\n \n image_pil = Image.fromarray(image_np)\n \n buffer = BytesIO()\n image_pil.save(buffer, format=\"JPEG\")\n base64_img_string = base64.b64encode(buffer.getvalue())\n \n base64_img_string_with_metadata = \"data:image/jpg;base64,{}\".format(base64_img_string)\n \n return base64_img_string_with_metadata",
"title": ""
},
{
"docid": "1c00cff249aac84b12aeadf79f060923",
"score": "0.67056197",
"text": "def get_base_64_string():\n return string.ascii_uppercase + string.ascii_lowercase + string.digits + \"+/\"",
"title": ""
},
{
"docid": "1234039c523aa4d8ba7cd46b0e7d9354",
"score": "0.6654407",
"text": "def encode_png(png_file):\n return \"data:image/png;base64,%s\" % \\\n PNGBase64Encoder().encodePNG(png_file)",
"title": ""
},
{
"docid": "29601eece4390e9330c87bc987dc1c1a",
"score": "0.66154486",
"text": "def base64encode(to_encode: bytes) -> str:\n\n return base64.b64encode(to_encode).decode(\"utf-8\")",
"title": ""
},
{
"docid": "bbe9b41cafd776130c2f296741039ac6",
"score": "0.65875196",
"text": "def base64_encodestring(instr):\n return salt.utils.stringutils.to_unicode(\n base64.encodebytes(salt.utils.stringutils.to_bytes(instr)),\n encoding=\"utf8\" if salt.utils.platform.is_windows() else None,\n )",
"title": ""
},
{
"docid": "3cb15e45c38741abd17b8573c806d839",
"score": "0.65831876",
"text": "def _base64_encode(message):\n\n return base64.b64encode(message.encode())",
"title": ""
},
{
"docid": "bbf5dd3befbb82f85057f9cfe2b4de8a",
"score": "0.6579827",
"text": "def get_base64_encoded_screen_shot(self, file_name):\n base = Base()\n abs_file_path = os.path.join(base.get_project_path(), 'ScreenShots', file_name + '.png')\n self.objDriver.get_screenshot_as_file(abs_file_path)\n with open(abs_file_path, \"rb\") as image_file:\n encoded_string = base64.b64encode(image_file.read())\n img_element = 'b64EncodedStart{0}b64EncodedEnd'.format(encoded_string)\n return img_element",
"title": ""
},
{
"docid": "85c237575b7eeb5369cfc07c3d63e7fe",
"score": "0.6570295",
"text": "def encode_base64(input_bytes: bytes) -> str:\n output_bytes = base64.b64encode(input_bytes)\n output_string = output_bytes.decode(\"ascii\")\n return output_string.rstrip(\"=\")",
"title": ""
},
{
"docid": "574287a6c7aaa95b137ad46020423ee0",
"score": "0.65550715",
"text": "def b64_encode(data):\n encodedBytes = base64.b64encode(data.encode('utf-8'))\n return encodedBytes.decode('utf-8')",
"title": ""
},
{
"docid": "71b81cacfd1291e180294bd67c0c5842",
"score": "0.65271384",
"text": "def encode_data(data):\n data_bytes = data.encode(\"ascii\")\n data_encoded = base64.b64encode(data_bytes)\n return data_encoded",
"title": ""
},
{
"docid": "3bc7125ddfb29699c91ea54f02114a82",
"score": "0.65151894",
"text": "def base64_to_binary_for_cv2(image_64_encoded):\n img_base64_binary = image_64_encoded.encode(\"utf-8\")\n img_binary = base64.b64decode(img_base64_binary)\n image = cv2.imdecode(np.frombuffer(img_binary, np.uint8), cv2.IMREAD_COLOR)\n return image",
"title": ""
},
{
"docid": "3bc7125ddfb29699c91ea54f02114a82",
"score": "0.65151894",
"text": "def base64_to_binary_for_cv2(image_64_encoded):\n img_base64_binary = image_64_encoded.encode(\"utf-8\")\n img_binary = base64.b64decode(img_base64_binary)\n image = cv2.imdecode(np.frombuffer(img_binary, np.uint8), cv2.IMREAD_COLOR)\n return image",
"title": ""
},
{
"docid": "935ccea16fee12755b06a1b7116a201c",
"score": "0.6502948",
"text": "def image_to_base64(file_or_path, close=False):\n pil_parser = PillowImageFile.Parser()\n if hasattr(file_or_path, \"read\"):\n file = file_or_path\n if file.closed and hasattr(file, \"open\"):\n file_or_path.open()\n file_pos = file.tell()\n file.seek(0)\n else:\n try:\n # pylint: disable=consider-using-with\n file = open(file_or_path, \"rb\")\n except OSError:\n return \"\"\n close = True\n\n try:\n image_data = file.read()\n if not image_data:\n return \"\"\n pil_parser.feed(image_data)\n if pil_parser.image:\n mime_type = pil_parser.image.get_format_mimetype()\n encoded_string = base64.b64encode(image_data)\n return f\"data:{mime_type:s};base64, {encoded_string.decode('utf-8'):s}\"\n return \"\"\n finally:\n if close:\n file.close()\n else:\n file.seek(file_pos)",
"title": ""
},
{
"docid": "886a5043229299291f4c0ca183197209",
"score": "0.64724314",
"text": "def test_01_image_to_base64(self):\n image = Image.new('RGB', (1, 1))\n image_base64 = tools.image_to_base64(image, 'PNG')\n self.assertEqual(image_base64, self.base64_1x1_png)",
"title": ""
},
{
"docid": "bd36175d927102d8e9003a93b6c48007",
"score": "0.6425317",
"text": "def img_to_base64(imgarray, for_html=True, image_format='jpeg'):\n input_image_buff = BytesIO()\n Image.fromarray(imgarray).save(input_image_buff, image_format,\n quality=99, optimize=True, progressive=True)\n res = base64.b64encode(input_image_buff.getvalue()).decode('ascii')\n if for_html:\n return 'data:image/' + image_format + ';base64,' + res\n else:\n return res",
"title": ""
},
{
"docid": "effaafd73d4fcac9c94b21f79b370950",
"score": "0.6397844",
"text": "def base64_b64encode(instr):\n return salt.utils.stringutils.to_unicode(\n base64.b64encode(salt.utils.stringutils.to_bytes(instr)),\n encoding=\"utf8\" if salt.utils.platform.is_windows() else None,\n )",
"title": ""
},
{
"docid": "bc3a960515a94d70535971d275fa1a18",
"score": "0.6341614",
"text": "def base64(b: bytes) -> str:\n b64 = b64encode(b)\n s = b64.decode() # as a string\n return s",
"title": ""
},
{
"docid": "488ba3e030b65efc10961079e585f5e5",
"score": "0.63166",
"text": "def to_internal_value(self, data):\r\n if isinstance(data, six.string_types):\r\n # base64 encoding means that we decode it\r\n if 'data:' in data and ';base64,' in data:\r\n header, data = data.split(';base64,')\r\n\r\n try:\r\n decoded_file = base64.b64decode(data)\r\n except TypeError:\r\n self.fail('invalid_image')\r\n\r\n fn = 'filename='\r\n # assume filename is last\r\n if fn in header:\r\n i = header.index(fn)\r\n file_name = header[i + len(fn):]\r\n complete_file_name = file_name\r\n else:\r\n # 12 characters are more than enough.\r\n file_name = str(uuid.uuid4())[:12]\r\n file_extension = self.get_file_extension(\r\n file_name, decoded_file)\r\n complete_file_name = \"%s.%s\" % (\r\n file_name, file_extension,)\r\n data = ContentFile(decoded_file, name=complete_file_name)\r\n\r\n return super(Base64ImageField, self).to_internal_value(data)",
"title": ""
},
{
"docid": "68a7f1ec89cfb9d19d5071f92cce08f4",
"score": "0.6304009",
"text": "def encode_ndarray_as_b64str(array: np.ndarray):\n raw_img = Image.fromarray(np.uint8(np.array(array)), 'RGB')\n buffer = io.BytesIO()\n raw_img.save(buffer, format='jpeg')\n return base64.b64encode(buffer.getvalue()).decode('utf-8')",
"title": ""
},
{
"docid": "3bb4d34e6a3533a9c7b51f8a221bc2a7",
"score": "0.62982506",
"text": "def base64_encode_as_string(obj): # noqa\n # type: (any) -> str\n return str(base64.b64encode(obj), 'ascii')",
"title": ""
},
{
"docid": "c5f8ae1aa8ac53dbe5f82ebc695ee940",
"score": "0.62971944",
"text": "def _base64_encode(s):\n if 3 <= sys.version_info.major:\n return (base64.b64encode(s.encode(\"utf-8\"))).decode(\"ascii\")\n else:\n if isinstance(s, str) or isinstance(s, unicode):\n return base64.b64encode(s)\n else:\n return binascii.b2a_base64(s)",
"title": ""
},
{
"docid": "2dd994d8315f9dafdbcd06ea3623a6d9",
"score": "0.628934",
"text": "def b64encode(source):\n # type: (Union[str, bytes]) -> str\n return base64.b64encode(bytearray(source)).decode(\"utf-8\", \"ignore\")",
"title": ""
},
{
"docid": "be7f054a5b9e181a2a50a98646bbf841",
"score": "0.6242175",
"text": "def test_00_base64_to_image(self):\n image = tools.base64_to_image(self.base64_1x1_png)\n self.assertEqual(type(image), PngImagePlugin.PngImageFile, \"base64 as bytes, correct format\")\n self.assertEqual(image.size, (1, 1), \"base64 as bytes, correct size\")\n\n image = tools.base64_to_image(self.base64_1x1_png.decode('ASCII'))\n self.assertEqual(type(image), PngImagePlugin.PngImageFile, \"base64 as string, correct format\")\n self.assertEqual(image.size, (1, 1), \"base64 as string, correct size\")\n\n with self.assertRaises(UserError, msg=\"This file could not be decoded as an image file. Please try with a different file.\"):\n image = tools.base64_to_image(b'oazdazpodazdpok')\n\n with self.assertRaises(UserError, msg=\"This file could not be decoded as an image file. Please try with a different file.\"):\n image = tools.base64_to_image(b'oazdazpodazdpokd')",
"title": ""
},
{
"docid": "f008bbc81b7828b4a23c86ebdf64a16c",
"score": "0.6176172",
"text": "def __base64_url_encode(self, string):\n encoded = base64.urlsafe_b64encode(string.encode('utf-8')).decode('utf-8')\n return encoded.rstrip(\"=\")",
"title": ""
},
{
"docid": "aa96fa30b471d608fb98390bc2e5a212",
"score": "0.6109599",
"text": "def b64_encode(source):\n if isinstance(source, bytes):\n encoded = base64.urlsafe_b64encode(source)\n else:\n encoded = base64.urlsafe_b64encode(bytearray(source, \"utf-8\"))\n return bytearray(encoded).decode().rstrip(\"=\")",
"title": ""
},
{
"docid": "ee9a51fe996461210191eb9880f96294",
"score": "0.61062694",
"text": "def stringToBase64(s):\n return base64.b64encode(bytes(s, 'utf-8'))",
"title": ""
},
{
"docid": "d2982f214c44a2741e05a2b98e20b3af",
"score": "0.60734934",
"text": "def open_file(file_name):\n sample_dir = app.config[\"samples_dir\"]\n file = open(os.path.join(sample_dir, file_name), 'r')\n stream = file.read()\n encoded_stream = base64.b64encode(stream)\n return encoded_stream",
"title": ""
},
{
"docid": "38e3ffe7338478b576d275a64c8180c2",
"score": "0.6055671",
"text": "def bytes_base64(x):\n if six.PY2:\n return base64.encodestring(x).replace('\\n', '')\n return base64.encodebytes(raw(x)).replace(b'\\n', b'')",
"title": ""
},
{
"docid": "e653328ae534ac322a389647bd5177d9",
"score": "0.60467917",
"text": "def image_data(img, fmt='PNG'):\n buffered = BytesIO()\n img.save(buffered, format=fmt)\n\n img_str = base64.b64encode(buffered.getvalue())\n\n return f\"data:image/{fmt.lower()};charset=utf-8;base64,\" + img_str.decode()",
"title": ""
},
{
"docid": "06034d5c7ea7b1ac764d1acbce543cfb",
"score": "0.6035246",
"text": "def base64_encode(cls, s):\n # urlsafe_b64encode() returns six.binary_type so need to convert to\n # six.text_type, might as well do it before stripping.\n return base64.urlsafe_b64encode(s).decode('utf-8').rstrip('=')",
"title": ""
},
{
"docid": "5ba202894bbe958739ceebab9de209ad",
"score": "0.6025212",
"text": "def toBase64(self):\n return NotImplementedError()",
"title": ""
},
{
"docid": "a3dc4d790de83605be158f7ab9a228e4",
"score": "0.6009925",
"text": "def encode_frame(frame):\n\n ret, jpeg = cv2.imencode('.jpg', frame)\n frame = base64.b64encode(jpeg).decode('utf-8')\n return \"data:image/jpeg;base64,{}\".format(frame)",
"title": ""
},
{
"docid": "5665f0b33e3a36f9313fa9dbcdeb586b",
"score": "0.59844047",
"text": "def np_to_base64(img_np):\n img = Image.fromarray(img_np.astype('uint8'), 'RGB')\n buffered = BytesIO()\n img.save(buffered, format=\"PNG\")\n return u\"data:image/png;base64,\" + base64.b64encode(buffered.getvalue()).decode(\"ascii\")",
"title": ""
},
{
"docid": "1f7e4cfa4a8c9affad95638da393893b",
"score": "0.5951814",
"text": "def send_base_64(sock, message):\n send_bytes(sock, base64.b64encode(message.encode(\"ASCII\")))",
"title": ""
},
{
"docid": "c84b4673f270c11efe1a5879662eb864",
"score": "0.59459597",
"text": "def encode(self, data, key):\n try:\n databyte = data.encode()\n except:\n databyte = data\n finally:\n encoded = base64.b64encode(self.encrypt(self.compress(databyte), key))\n print(f\"Original size {len(data)}\")\n print(f\"Encoded size {len(encoded)}\")\n return encoded.decode('ascii')",
"title": ""
},
{
"docid": "356ca8b0413a1bd24a7a596d62d10f47",
"score": "0.59256965",
"text": "def cv2_image_to_string(cv2_image):\n # 1 - Transform the image to byte\n _, buffer = cv2.imencode('.jpg', cv2_image)\n # 2 - convert to byte 64 and trasform to string remove the b' symbol\n return str(base64.b64encode(buffer))[2:]",
"title": ""
},
{
"docid": "41e7623b1025442821e2ca09f3524dc0",
"score": "0.58864576",
"text": "def _png_to_web_base64(fn, fliplr=False, crop_to=None):\n web_image_prefix = 'data:image/png;base64,'\n\n image = imageio.imread(fn)\n if fliplr:\n image = np.fliplr(image)\n tmp_file = BytesIO()\n\n if crop_to is not None:\n assert len(crop_to) == 2\n h, w = image.shape\n image = image[h//2-crop_to[0]//2:h//2+crop_to[0]//2,\n w//2-crop_to[1]//2:w//2+crop_to[1]//2]\n\n imageio.imwrite(tmp_file, image, format='png')\n tmp_file.seek(0)\n tmp = tmp_file.read()\n\n tmp = base64.b64encode(tmp).decode('ascii')\n return web_image_prefix + tmp",
"title": ""
},
{
"docid": "2ed2cc372373a86e95fbc8cd908dde30",
"score": "0.5880162",
"text": "def encode(self) -> bytes:\n json_data = {\n \"data\": self._data,\n \"metadata\": self.metadata,\n }\n return base64.b64encode(bytes(json.dumps(json_data), encoding='utf8'))",
"title": ""
},
{
"docid": "9dc236069c22c28bea53cea75b5ff931",
"score": "0.58666885",
"text": "def base64encode(payload, **kwargs):\n return base64.b64encode(payload.encode(UNICODE_ENCODING)) if payload else payload",
"title": ""
},
{
"docid": "255447bb80f412a624d8e15bc54517eb",
"score": "0.5852593",
"text": "def decode_image_base64(base64_string):\n bytes_decoded = b64decode(base64_string)\n # load image from IO buffer filled from base64 decoding\n decoded_image = Image.open(BytesIO(bytes_decoded))\n return decoded_image",
"title": ""
},
{
"docid": "4f47e8dc83a102815ca8c63d48e6a226",
"score": "0.5835479",
"text": "def save(encoded_data, filename):\n nparr = np.fromstring(base64.b64decode(encoded_data), np.uint8)\n img = cv2.imdecode(nparr, cv2.IMREAD_ANYCOLOR)\n return cv2.imwrite(filename, img)",
"title": ""
},
{
"docid": "738e08775c7cbe1a382002bb7c3eb128",
"score": "0.5834993",
"text": "def img_to_data(path):\n if not os.path.exists(path):\n raise FileNotFoundError\n mime, _ = mimetypes.guess_type(path)\n with open(path, 'rb') as fp:\n data = fp.read()\n data64 = u''.join(base64.encodestring(data).splitlines())\n return u'data:%s;base64,%s' % (mime, data64)",
"title": ""
},
{
"docid": "9d0bf5b8b7343fc6280f3487d094c044",
"score": "0.5834197",
"text": "def b64encode(value, *args, **kwargs):\n return base64.b64encode(encode(value, *args, **kwargs))",
"title": ""
},
{
"docid": "b3beb3533fc968ae96135c4854ed4381",
"score": "0.5833389",
"text": "def certificate_blob_base64(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"certificate_blob_base64\")",
"title": ""
},
{
"docid": "1deccce336b09afd3e928f5d05ae2dd4",
"score": "0.58270884",
"text": "def create_file_base64(contents, file_name):\n if contents == None:\n return None\n file = open(file_name, 'w')\n file.write(base64.b64decode(contents).decode('utf-8'))\n file.close()\n return file_name",
"title": ""
},
{
"docid": "1b8267a619e41561539c66553d753e58",
"score": "0.58257186",
"text": "def compress(string, method=zlib):\r\n data = method.compress(string)\r\n return base64.encodestring(data)",
"title": ""
},
{
"docid": "c2f6e0e69be2829160238556b64cfdb0",
"score": "0.58121365",
"text": "def Base64FromUrl(url):\n return base64.b64encode(urllib.urlopen(url).read())",
"title": ""
},
{
"docid": "d4d55d9f6e8890ce435474856b123e0b",
"score": "0.5807078",
"text": "def encode(self, data):\n s = json.dumps(data, cls=PBBJSONEncoder).encode('utf8')\n return base64.b64encode(zlib.compress(s))",
"title": ""
},
{
"docid": "19423e85ba24d244ad89d7fe1a9eb754",
"score": "0.5797129",
"text": "def b64encode(data: Union[str, bytes], urlsafe: bool = False) -> str:\n if not isinstance(data, bytes):\n data = data.encode(\"ascii\")\n if urlsafe:\n b64 = base64.urlsafe_b64encode(data)\n return b64.decode(\"ascii\")\n b64 = base64.b64encode(data)\n return b64.decode(\"ascii\")",
"title": ""
},
{
"docid": "0cf6d9d032113fd9cf1435d27e43ae50",
"score": "0.5796232",
"text": "def imencode(arr: np.ndarray, ext='.png') -> str:\n return cv2.imencode(ext, arr[:, :, ::-1])[1].tostring()",
"title": ""
},
{
"docid": "bc378031eb040fa6a1372b9c1cebbd1d",
"score": "0.57877254",
"text": "def encrypt_file(filepath):\n try:\n # if it starts with ~\n # os.path.expanduser\n with open(filepath) as inf:\n file_contents = inf.read()\n return file_contents.encode('base64')\n except IOError:\n return filepath",
"title": ""
},
{
"docid": "87c445a00a091df749c4079b5e7a757d",
"score": "0.5786043",
"text": "def encode64(s):\r\n b = base64.b64encode(s.encode(\"utf-8\"),b\"?!\")\r\n if b.endswith(b'='): b=b[:-1]\r\n if b.endswith(b'='): return b[:-1].decode(\"utf-8\")\r\n return b.decode(\"utf-8\")",
"title": ""
},
{
"docid": "435ecc2f5d28978f2096fb7cbc1bda35",
"score": "0.577856",
"text": "def read_base64_file(filename):\n with open(filename, \"rb\") as f:\n return f.read().replace(b\"\\r\\n\", b\"\")",
"title": ""
},
{
"docid": "54a966d3d14435cd88f4fedf97310ba5",
"score": "0.57772326",
"text": "def export_images_as_base64(self):\n return self.container['export_images_as_base64']",
"title": ""
},
{
"docid": "07c58baaec2b9ddcbccb401c172446ba",
"score": "0.5740073",
"text": "def decode_base64(base64_str):\n starter = base64_str.find(',')\n image_data = base64_str[starter + 1:]\n image_data = bytes(image_data, encoding=\"ascii\")\n image = Image.open(BytesIO(base64.b64decode(image_data)))\n if image.mode != \"RGB\":\n image = image.convert(\"RGB\")\n return image",
"title": ""
},
{
"docid": "ceae958bc992090be1d1db57626d36dc",
"score": "0.57165796",
"text": "def encode(self):\r\n if self.data is None:\r\n return \"\"\r\n elif not self.data:\r\n return \"=\"\r\n else:\r\n ret = standard_b64encode(self.data)\r\n return ret.decode(\"us-ascii\")",
"title": ""
},
{
"docid": "6afa1c0121553b8866297bea23aa24b1",
"score": "0.57121426",
"text": "def send_msg_as_base64(sock, msg):\n sock.sendall(b64encode(msg) + '\\n')",
"title": ""
},
{
"docid": "0166734779577d14ea1bce24379eab07",
"score": "0.56818706",
"text": "def export_images_as_base64(self, export_images_as_base64):\n\n self.container['export_images_as_base64'] = export_images_as_base64",
"title": ""
},
{
"docid": "f9c9e43a83084ac0867d3c12f5c7824d",
"score": "0.5653945",
"text": "def render_image_b64(self, from_date, to_date, dashboard_uid, connector):\r\n return b64encode(connector.get_image_panel(dashboard_uid, self.id, from_date, to_date, 75*self.col_width, 75*self.height))",
"title": ""
},
{
"docid": "7ee8494d337ae8d5fcaa409a0474ed58",
"score": "0.5653614",
"text": "def blobEncode(self, s):\r\n return s",
"title": ""
},
{
"docid": "86489ed00b6a1d357c173e3b912b6c0b",
"score": "0.5651095",
"text": "def _write_image(self):\n return \" \".join(str(i) for i in self.image)",
"title": ""
},
{
"docid": "ca8a7186a238833821d0cab8c12d86cc",
"score": "0.5647665",
"text": "async def fetch_image_b64(self, client: httpx.AsyncClient, url: str) -> str:\n resp = await client.get(url)\n if (\n resp.status_code == 200\n and resp.headers.get(\"Content-Type\") in web_images._value2member_map_\n ):\n return bytes.decode(b64encode(resp.content))",
"title": ""
},
{
"docid": "cdcf0c7b09db0e671a3bf99ba123d6be",
"score": "0.564148",
"text": "def byte_to_base64(b):\n return base64.b64encode(b)",
"title": ""
},
{
"docid": "76bc38f8ee7f79b93eda9de910aa9b0a",
"score": "0.5635365",
"text": "def certificate_blob_base64(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"certificate_blob_base64\")",
"title": ""
},
{
"docid": "067efdf7b4eb95924180b66c18dbf490",
"score": "0.5634131",
"text": "def json_b64encode(value):\n return base64.b64encode(json_encode(value))",
"title": ""
},
{
"docid": "e52baae31fc44feb2d6832af641ea5c5",
"score": "0.5606726",
"text": "def encode_image_array_as_png_str(image):\n image_pil = Image.fromarray(np.uint8(image))\n output = six.BytesIO()\n image_pil.save(output, format='PNG')\n png_string = output.getvalue()\n output.close()\n return png_string",
"title": ""
},
{
"docid": "94ce033d527ba091b4f544687b69124b",
"score": "0.5583294",
"text": "def toBase64(s):\r\n return binascii.b2a_base64(s)[:-1]",
"title": ""
},
{
"docid": "5d1667b16897d80a5f1a82c220c94803",
"score": "0.55643564",
"text": "def get_img_from_base64(\n codec\n):\n\n # data:image/png;base64\n base64_data = re.sub(b'^data:image/.+;base64,', b'', codec)\n byte_data = base64.b64decode(base64_data)\n image_data = BytesIO(byte_data)\n img = Image.open(image_data)\n return img",
"title": ""
},
{
"docid": "0377d3be45bec44cb2b66953d2d651f3",
"score": "0.5529833",
"text": "def to_base64(self, subformat=\"json\", encoding=\"utf-8\", **kwargs):\n kwargs[\"subformat\"] = subformat\n kwargs[\"encoding\"] = encoding\n return self._encode(self.dict(), \"base64\", **kwargs)",
"title": ""
},
{
"docid": "d2f46fb202606beac18136ed8dee3e35",
"score": "0.55254406",
"text": "def convert_into_image(self, request):\n\n im_b64 = request.json[\"image\"]\n img_bytes = base64.b64decode(im_b64.encode(\"utf-8\"))\n \n return Image.open(io.BytesIO(img_bytes))",
"title": ""
},
{
"docid": "7f8166ed0b406df9fd3121c2305448da",
"score": "0.55203444",
"text": "def save_file(name, content):\n data = content.encode(\"utf8\").split(b\";base64,\")[1]\n with open(os.path.join(UPLOAD_DIRECTORY, name), \"wb\") as fp:\n fp.write(base64.decodebytes(data))\n return name",
"title": ""
},
{
"docid": "16b4f689984461b288d81f370cb7b668",
"score": "0.5519325",
"text": "def toBase64(s):\n if isinstance(s, str):\n s = s.encode(\"utf-8\")\n return binascii.b2a_base64(s)[:-1]",
"title": ""
},
{
"docid": "dedfdae4a60c8d5ee647616bf8ebc6c9",
"score": "0.5510415",
"text": "def sign_certificate_base64_string(self):\n return self._sign_certificate_base64_string",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="710"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">7b760be8345a0f6b5f9b92938d3443f2</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Insert a new tray by adding tray to database.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "34cdcc6da48271164ffd28891aa06cdc",
"score": "0.8211067",
"text": "def insert_tray(tray: NewTrayInsert):\n with get_session() as sess:\n tray = Tray(**tray.dict())\n sess.add(tray)",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "986f26d9d9e21b32706b815174baa564",
"score": "0.60956424",
"text": "def insert(self):\n db.session.add(self)\n db.session.commit()",
"title": ""
},
{
"docid": "1333b9854ffb8e3522d004fb384a98e6",
"score": "0.6006402",
"text": "def insert_transaction(self, item, info, table):\n\n self.db.begin()\n try:\n table.insert(info)\n self.db.commit()\n self.redis_conn.publish(self.publish_name, info)\n except:\n self.db.rollback()\n raise Exception('Insert Transaction Failed: Rolling Back....')",
"title": ""
},
{
"docid": "1e33c409c2422380c51451309b1decc8",
"score": "0.58826476",
"text": "def insert(self, iotree):\r\n self.commit()\r\n self.prepended_children.append(iotree)",
"title": ""
},
{
"docid": "4272319aba3b0dbcac3cdb82f4f18586",
"score": "0.5824656",
"text": "def insert(self, *args, **kwargs):\n pass",
"title": ""
},
{
"docid": "cd1199c0667f5d83fab15456cd8b172a",
"score": "0.57908386",
"text": "def insert(self, obj):\n self.__session.add(obj)\n self.commit()",
"title": ""
},
{
"docid": "4a7fa602681fc46cad87c0755af0fa5f",
"score": "0.57412905",
"text": "def insertDay(self):\n path = '/work_sample/DiaryPrinter/database/db_days' \n try:\n conn = sqlite3.connect(path)\n c = conn.cursor()\n sql = \"insert into days (sleepingTimeHours,\"\n sql += \"sleepingTimeMinutes,wakeupTimeHours,\"\n sql += \"wakeupTimeMinutes,night,breakfast,\"\n sql += \"morning,lunch,afternoon,\"\n sql += \"dinner,evening,id)\"\n sql += \" values (?,?,?,?,?,?,?,?,?,?,?,?)\"\n c.executemany(sql, self.dayToSave())\n conn.commit()\n c.close()\n conn.close()\n except sqlite3.Error as e:\n messagebox.showerror(\"Virhe tietojen syötössä \", e.args[0])",
"title": ""
},
{
"docid": "7b715448e39058a757816b891875ca33",
"score": "0.57293093",
"text": "def insert_to_db(self) -> None:\n query = '''INSERT INTO Transactions(Date, Amount, Card_Type, Merchant, Description, User_id)\n VALUES(?,?,?,?,?,?);'''\n self.db.commit(query, values=self.to_tuple())",
"title": ""
},
{
"docid": "f29d8dc6493ffb7652d80cb1fc5b8329",
"score": "0.56700045",
"text": "def insert_data(self, data):\n try:\n db_session = DBSession()\n # Check if whatif is in database, if so update else create\n try:\n whatif = db_session.query(Whatif).filter(Whatif.whatif_id == data.get('whatif_id')).one()\n except NoResultFound:\n whatif = Whatif()\n\n whatif.title = data.get('title')\n whatif.question = data.get('question')\n whatif.whatif_id = data.get('whatif_id')\n whatif.saved_file_location = data.get('saved_file_location')\n whatif.posted_at = data.get('posted_at')\n whatif.time_collected = data.get('time_collected')\n\n db_session.add(whatif)\n db_session.commit()\n\n except Exception:\n db_session.rollback()\n logger.exception(\"Error adding to db {data}\".format(data=data))",
"title": ""
},
{
"docid": "14a8b36e9aed9fad0ce71e26845c079a",
"score": "0.55927306",
"text": "def add(self, entry):\n #TODO some verification\n\n # store entry in database\n with self.lock:\n _DBTransaction_store(id(self), entry)",
"title": ""
},
{
"docid": "b910d739a14f8ebc763c7c62053445c8",
"score": "0.5526059",
"text": "def add_task(data: dict) -> None:\n \n with closing(sqlite3.connect(\"./taskmanager/data/tasks.db\")) as conn:\n with closing(conn.cursor()) as curs:\n fields = str(tuple(data.keys())).replace(\"'\", \"\")\n values = tuple(data.values())\n insert = \"(\" + (\"?,\"*len(values))[:-1] + \")\"\n\n sql = \"INSERT INTO tasks %s VALUES %s\" % (fields, insert)\n\n curs.execute(sql, values)\n conn.commit()",
"title": ""
},
{
"docid": "7346843acb715fb9da5ca7729333421c",
"score": "0.5505085",
"text": "def insert(self):\n self.update()",
"title": ""
},
{
"docid": "8213c02c52b7139b12ee295d2de6dc11",
"score": "0.54815143",
"text": "def insert_track(self, track):\n\n sql = (\n \"INSERT INTO `track` (`name`, `artist`, `album`, `path`, `modified`) \"\n \"VALUES (%s, %s, %s, %s, %s)\"\n )\n\n try:\n connection = self.get_connection()\n with connection.cursor() as cursor:\n cursor.execute(\n sql,\n (\n track['track'],\n track['artist'],\n track['album'],\n track['path'],\n utils.get_cur_datetime()\n )\n )\n connection.commit()\n finally:\n connection.close()",
"title": ""
},
{
"docid": "27d87a0d1322e6c145dd6c29432711fa",
"score": "0.5481088",
"text": "def test_tax_insert(self):\n data = {\n 'name': 'Small Mac',\n 'tax_code': 1,\n 'price': 500\n }\n result = self.tax.insert(data)\n self.assertEqual(result, {'status': 1, 'result': 'Success'})",
"title": ""
},
{
"docid": "3d4566816263ee8825385de3fb5ad09f",
"score": "0.5474228",
"text": "def add(self, chat_id, cause, cur_time):\n with Connection() as conn:\n try:\n sql_query = \"\"\"insert into log(chat_id, cause, cur_time) values(%s, %s, %s);\"\"\"\n send_query = (\n chat_id,\n cause,\n cur_time,\n )\n print(\"log\" + str((chat_id, cause, cur_time)))\n conn.cursor.execute(sql_query, send_query)\n conn.connection.commit()\n except Exception as error:\n print(\"Error with PostgreSQL in Log.add\", error)",
"title": ""
},
{
"docid": "b3b1c4e6b5998f15bfe014102efcc047",
"score": "0.5460815",
"text": "def add_item_to_database(database, **data_dict):\n with database.atomic():\n item = Item.create(\n name=data_dict['name'],\n price=data_dict['price'],\n date=data_dict['date'],\n category=data_dict['category'],\n )\n print('Dodano do bazy produkt: {0}, cena: {1},kategoria: {2} '.format(\n item.name, item.price, item.category,\n ))",
"title": ""
},
{
"docid": "993fe4ab64b7363ac2d567a6c2e6c40f",
"score": "0.54395086",
"text": "def guardar(self,tinyDB):\n tinyDB.insert(self.toDict())",
"title": ""
},
{
"docid": "67cef3bc02472fae9618390bc9f85255",
"score": "0.5416892",
"text": "def _insert(self):\n with sqlite3.connect(self.dbpath) as connection: \n cursor = connection.cursor()\n INSERTSQL = \"\"\"INSERT INTO positions(ticker, shares, account_id) \n VALUES (:ticker, :shares, :account_id); \"\"\"\n values = {\n \"ticker\": self.ticker,\n \"shares\" : self.shares, \n \"account_id\" : self.account_id \n }\n try: \n cursor.execute(INSERTSQL, values)\n self.id = cursor.lastrowid \n except sqlite3.IntegrityError:\n raise ValueError(\"ticker not set or a position for this ticker already exists\")",
"title": ""
},
{
"docid": "5ca0bdc9aba561776bb7e5f4a0a9755d",
"score": "0.5414293",
"text": "def insert(self, sql: str):\n self.cursor.execute(sql)\n if self.do_commits:\n self.conn.commit()",
"title": ""
},
{
"docid": "5cc57f4ec193a87624e8259e621a85c9",
"score": "0.5409777",
"text": "def insertFact(fact, tre):\n dbclass = getDbClass(fact, tre)\n if fact not in dbclass.facts:\n if tre.debugging:\n print(tre, 'Inserting',fact,'into database.')\n dbclass.facts.append(fact)\n return True\n return False",
"title": ""
},
{
"docid": "e7864bd4eaf8650efa34f3a87ab23521",
"score": "0.5409522",
"text": "def addTask(self, name, description, date, type, workHours, subject, topic):\n\t\tsql = \"INSERT INTO tasks(name, description, date, type, work_hours, subject, topic) VALUES(?,?,?,?,?,?,?)\"\n\n\t\tself.openConnection()\n\n\t\tself.con.execute(sql, (name, description, date, type, workHours, subject, topic))\n\n\t\tself.con.commit()\n\t\tself.closeConnection()",
"title": ""
},
{
"docid": "bd29d192667bd4de5bc092b25f2af9ad",
"score": "0.5406874",
"text": "def insert_tienda(id):\n direccion = random.choice(addressList)\n nombre = random.choice(storeNameList)\n query = f\"INSERT INTO tienda VALUES({id}, '{nombre}', '{direccion}')\"\n cursor.execute(query)\n return query",
"title": ""
},
{
"docid": "0c8342bc330a64cf24b3fd911021e239",
"score": "0.539565",
"text": "def add_task(self, context):\n id = self._get_greatest_id() + 1\n cursor = self.insert(self.table_name, id=id, context=context, completed = False)\n cursor.close()",
"title": ""
},
{
"docid": "c130b3ac00450baea974d6983752ac9b",
"score": "0.5393323",
"text": "def insert(self): \n db = Db('db/{}'.format(DBNAME))\n return db.append(self.key, self)",
"title": ""
},
{
"docid": "785c13b6f3496033e0c9529427ec8caa",
"score": "0.5390801",
"text": "def insert(self, table, payload, **kwargs):\n\n r = self._legacy_request(\"POST\", table, **kwargs)\n return r.insert(payload)",
"title": ""
},
{
"docid": "d4aa037272d1cbe699fb6417027a7a6a",
"score": "0.5385296",
"text": "def _insert_event(self, game, event_type):\n insert_sql = sql.INSERT_ONE_EVENT.format(tbl=myscoresettings.MSC_EVENT_TABLE)\n params = (*game.get_game_params(), event_type, datetime.now())\n run_sql(insert_sql, params)\n print('New event {}'.format(params))",
"title": ""
},
{
"docid": "9551c7fd2258698c07927d43fb0f5b38",
"score": "0.5358222",
"text": "def insert_item(self, shop_item):",
"title": ""
},
{
"docid": "28493fbe035c4474383d848f0b404bef",
"score": "0.5357478",
"text": "def insert(cls, env, col1, db=None):\n if not db:\n db = env.get_db_cnx()\n handle_ta = True\n else:\n handle_ta = False\n\n cursor = db.cursor()\n cursor.execute(\"INSERT INTO ticketlog_store (col1, ) VALUES (%s,);\",\n (col1, ))\n id = db.get_last_id(cursor, 'ticketlog_store')\n\n if handle_ta:\n db.commit()\n\n return id",
"title": ""
},
{
"docid": "f5d8b5d3d3d18edfcc93a4c1d531096e",
"score": "0.5357442",
"text": "def AddTicket(self, tik):\n\t\ttik.dumpFile(self.f + \"/tmp/title.tik\")\n\t\tself.ticketadded = 1",
"title": ""
},
{
"docid": "3e567d25eda908c7a9053e48ca80a160",
"score": "0.5339413",
"text": "def save_to_db(self):\n db.session.add(self)\n db.session.commit()\n logger.info(\"Item properly saved to the database\")",
"title": ""
},
{
"docid": "4e01dc8985af04d454011cbf2a8f1613",
"score": "0.5328786",
"text": "def insertDb(data, db):\n \n try:\n cursor = db.cursor()\n \n add_weather = (\" INSERT INTO DarkSky_hourly_weather_prediction \"\n \"(timestamp, time, day_of_week, description, temp, icon, precip_intensity, hour, month, date, dow) \"\n \"VALUES (CURRENT_TIMESTAMP, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)\")\n \n cursor.execute(add_weather, data)\n db.commit()\n \n except Exception as e: \n template = \"While trying to insert into the table, an exception of type {0} occurred. Arguments:\\n{1!r}\"\n message = template.format(type(e).__name__, e.args)\n print(message)",
"title": ""
},
{
"docid": "bc842c0b3b0f2d736dec1f128a19e52b",
"score": "0.5327449",
"text": "def add(self, item):\n\n # Make sure that the item is a derived type,\n # rather than base type so that item can be\n # properly placed into database.\n if not isinstance(item, Pizza) and \\\n not isinstance(item, Breadstick) and \\\n not isinstance(item, Drink):\n return\n\n # The generic item information needs to be\n # added into database first. Then, specific\n # item type comes second.\n executed = self.database.execute(\n \"INSERT INTO Items (\"\n \" Name, ItemType, Description, Price\"\n \") VALUES\"\n \" ('{0}','{1}','{2}',{3})\".format(\n item.getName(), item.getItemType(), \n item.getDescription(), item.getPrice()))\n\n # If an IntegrityError is raised, that means we\n # were unable to create due to UNIQUE constraints.\n # If executed isnt this, add the item in other tables.\n if not isinstance(executed, IntegrityError):\n # We created a new item and need to retrieve id now\n new_item_id = self.getItemId(item)[0][0]\n database_string = self._getItemTableString(item, new_item_id)\n item_table = self.database.execute(database_string)\n\n if (item.getItemType() == \"Pizza\"):\n pizza_id = self.database.execute(\n \"SELECT p.PizzaId \"\n \"FROM Pizzas p \"\n \"INNER JOIN Items i \"\n \"ON p.ItemId = i.ItemId \"\n \"WHERE i.Name='{0}' AND i.Description='{1}' \"\n \"AND i.Price={2}\".format(\n item.getName(), item.getDescription(),\n item.getPrice()))[0][0]\n\n # If we found the pizza, add toppings.\n for topping in item.getToppings():\n topping_id = self.database.execute(\n \"SELECT ToppingId \"\n \"FROM Toppings \"\n \"WHERE ToppingName='{0}' AND ToppingPrice={1}\"\n .format(\n topping.getName(),\n topping.getPrice()))[0][0]\n self.database.execute(\n \"INSERT INTO PizzaToppings (\"\n \" PizzaId, ToppingId \"\n \") VALUES \"\n \" ({0},{1})\".format(\n pizza_id, topping_id))\n return new_item_id",
"title": ""
},
{
"docid": "6f8af0f08456da9eee5717092cb34d34",
"score": "0.52998954",
"text": "def insert_table(self,index:int, table:Table)->None:\n if isinstance(table, Table):\n log.debug(f\"Adding table {table.name} to the submission\")\n self.tables.insert(index,table)\n self._add_tab_to_dict_safely(table)\n else:\n raise TypeError(\"Unknown object type: {0}\".format(str(type(table))))",
"title": ""
},
{
"docid": "383369625e11f064dc646798cc1c07e6",
"score": "0.52996475",
"text": "def add(self):\n\t\tdb.session.add(self)\n\t\tdb.session.commit()",
"title": ""
},
{
"docid": "66c0ed8dbf111f4549d1759eb03c11a7",
"score": "0.52848494",
"text": "def add(cls, instance):\n Database.add(instance)",
"title": ""
},
{
"docid": "de54d2ae6d0ea3fc369d574cfa1f7dba",
"score": "0.528093",
"text": "def insert_trainer(self, trainer):\n self.db_base._trainer_collection.insert_one(trainer.encode())",
"title": ""
},
{
"docid": "cdd00ea394406323798d2c09b30fe52f",
"score": "0.52782315",
"text": "def insert(self, table: str, data: dict) -> bool:\n pass",
"title": ""
},
{
"docid": "8df25dbf546c923a0990324a387f3ad2",
"score": "0.52718866",
"text": "def _create_entry(self, key, value):\n new_entry = self.Entry(key=key, value=value)\n self.db.session.add(new_entry)\n self.db.session.commit()",
"title": ""
},
{
"docid": "11fd235fa9ccdd3069abfb30a2989d3f",
"score": "0.5271284",
"text": "def insert(collections, data):\n Database.DATABASE[collections].insert(data)",
"title": ""
},
{
"docid": "48f477d4f2198d3ed50f30fd5d7a9857",
"score": "0.5259693",
"text": "def insert(self, subject, predicate, obj):\n # do warmup if necessary, then start a new transaction\n self.open()\n transaction = self._manager.start_transaction()\n if subject is not None and predicate is not None and obj is not None:\n insert_query = get_insert_query(self._table_name)\n transaction.execute(insert_query, (subject, predicate, obj))\n self._manager.commit()",
"title": ""
},
{
"docid": "69373cc0c641afd946d6b9aaa8a525ad",
"score": "0.5255674",
"text": "def insert_tour(request):\n\n for line in globals.all_lines:\n new_entry = Tour.objects.using('lorchidee').create(\n nurse=Nurse.objects.using('lorchidee').get(id=int(line[7])),\n jour=globals.tours.date_tour,\n heure=line[0],\n patient=line[1],\n addrTel=line[2],\n cotation=line[3],\n assure=line[4],\n honoraire=line[5],\n finTraitement=line[6],\n commentaires=\"\",\n nomTournee=line[8]\n )\n message = \"Enregistrement de ces tournées effectué avec succès !\"\n return render(request, \"l_orchidee/display_detailed_form.html\", context={'recorded': message})",
"title": ""
},
{
"docid": "3504d5267618c5255474f53cf1b70a57",
"score": "0.5251778",
"text": "def insertRow(self, fila): \n try:\n self.openSQL()\n self.cursor.execute(f\"INSERT INTO tabla (region, city, language, time) VALUES (?, ?, ? ,?)\", fila)\n self.con.commit()\n except Exception as error:\n print (\"Error insertando fila\", error)\n finally:\n self.closeSQL()",
"title": ""
},
{
"docid": "e4d5b3b91af4ca28bdaae7915854e5aa",
"score": "0.5250777",
"text": "def insert(self, value): \n if self.root is None:\n self.root = TreeNode(value)\n else:\n self._insert_helper(self.root, value)\n self.balance_tree()",
"title": ""
},
{
"docid": "0914ce6af81458dfe03d275589d8fae3",
"score": "0.524728",
"text": "def add_task(self,task):\n \n self.task = task\n try:\n self.connection = lite.connect('kanban.db')\n self.cursor = self.connection.cursor()\n self.cursor.execute('INSERT INTO todo (task_name,valid) VALUES (?,?)',(self.task,1))\n self.connection.commit() \n \n print('\\n\\t',self.task ,' was added successfully')\n \n except Exception as e:\n # rollback in case of errors\n self.connection.rollback() \n \n print(\"\\nPlease try again\")\n\n finally:\n self.connection.close()",
"title": ""
},
{
"docid": "7d4e2546b785202dc43cf53b5d2163d3",
"score": "0.52445984",
"text": "def insert(self, tableName, datadic):\n table = self.tableObj(tableName)\n table.insert(datadic)",
"title": ""
},
{
"docid": "d258a7fb93b40be6a6c55f1e0bd91067",
"score": "0.5243442",
"text": "def insert(self, row):\n data = self._convert_value(row)\n self._service.InsertRow(data, self._ss.id, self.id)",
"title": ""
},
{
"docid": "f7b0fba5ab267604f8c409f9261a2814",
"score": "0.5241722",
"text": "def insert(self, table, values_types):\n params = self.get_table_params(table)\n index = self.get_table_last_id(table) + 1\n script = 'INSERT INTO %s (id, ' % table\n idx = 1\n for param in params:\n script += param[0]\n if idx < len(params):\n script += ', '\n idx += 1\n script += ') VALUES (%d, ' % index\n idx = 1\n for value, typ in values_types:\n if 'TEXT' == typ:\n script += '\"%s\"' % value\n elif 'INTEGER' == typ:\n script += '%d' % value\n if idx < len(values_types):\n script += ', '\n idx += 1\n script += ')'\n self.__db.put(script)",
"title": ""
},
{
"docid": "e4cb23a3aea4d698a33b3d58d51238ed",
"score": "0.523641",
"text": "def add(self, tn):\n heappush(self.transactions, (tn.timestamp, tn.payer))",
"title": ""
},
{
"docid": "94cffba157a1bd9393090d333b39beb6",
"score": "0.52262366",
"text": "def insert_new_instrument(instrumentid: int, instrument_type: str, brand: str) -> None:\r\n\r\n conn = db.connect()\r\n query = 'INSERT INTO Instruments VALUES ({}, \"{}\", \"{}\");'.format(instrumentid, brand, instrument_type)\r\n conn.execute(query)\r\n conn.close()",
"title": ""
},
{
"docid": "4f31e9b66820a8a98ef9a5ff916fc822",
"score": "0.5215591",
"text": "def add(self):\n db.session.add(self)\n db.session.commit()",
"title": ""
},
{
"docid": "bf316f5451bab73ef8752797eadc45e3",
"score": "0.52139705",
"text": "def insert(self, dd_type):\n \n self._type_map[dd_type.drag_type] = dd_type\n self._app_id_map[dd_type.app_id] = dd_type",
"title": ""
},
{
"docid": "801490505b2b066b1996938538ebc03b",
"score": "0.5211285",
"text": "def addtransaction(user_id, symbol, shares, share_price, order_type):\n return db.execute(\"INSERT INTO transactions ('user_id', 'stock', 'shares', 'share_price', 'order_type') \\\n VALUES (:user_id, :symbol, :shares, :share_price, :order_type)\",\n user_id=user_id, symbol=symbol, shares=shares, share_price=share_price, order_type=order_type)",
"title": ""
},
{
"docid": "b53362bd6833a6ec2314389b1008e756",
"score": "0.52038",
"text": "def add_record(self, data):\n\n if not self.already_exists(data, 'event_type'):\n sql = 'INSERT INTO events ('\n\n keys = list(data)\n values = list(data.values())\n values = list(map(lambda x: \"\\'{0}\\'\".format(x), values))\n\n sql += ', '.join(keys) + ') VALUES ('\n sql += ', '.join(values) + ');'\n\n self.c.execute(sql)\n self.conn.commit()",
"title": ""
},
{
"docid": "4696b93d845f0aa4c68fc174a98106ed",
"score": "0.519021",
"text": "def perform_insertion():\n pass",
"title": ""
},
{
"docid": "78ffcc124e8f5a66a779c7bbaa8802b2",
"score": "0.5174085",
"text": "def insertPNNs(self, pnns):\n addAction = self.wmbsDAOFactory(classname=\"Locations.AddPNNs\")\n addAction.execute(pnns=pnns, conn=self.getDBConn(),\n transaction=self.existingTransaction())\n return",
"title": ""
},
{
"docid": "925b3263bba3c6d73cd2f53069e8cbc2",
"score": "0.5168372",
"text": "def insert_entry(self, src_id, user, start_time, end_time, duration, tags,\n comments, task_id):\n\n if not user:\n return\n\n cursor = self.__conn.cursor()\n\n cursor.execute(\n \"\"\"\n INSERT INTO times\n SET src_id = %s,\n user = %s,\n start_time = %s,\n end_time = %s,\n duration = %s,\n tags = %s,\n comments = %s,\n task_id = %s\n \"\"\",\n (src_id, user, self._format_date(start_time),\n self._format_date(end_time), duration, tags, comments,\n task_id))\n\n cursor.close()\n self.__conn.commit()",
"title": ""
},
{
"docid": "493348a647fa92ac90ba49763fe39171",
"score": "0.51681834",
"text": "def add_entry(self, name, expiredate, desc, cat):\n self.__cursor.execute(\n \"INSERT INTO main(name,expiredate,desc,cat) VALUES(?,?,?,?)\", (name, expiredate, desc, cat))\n self.__conn.commit()",
"title": ""
},
{
"docid": "9eb8d6788a1f82b866fcf9c719326dca",
"score": "0.51645213",
"text": "def insert(self, entity):\n self.dao.do_insert(entity)\n return entity",
"title": ""
},
{
"docid": "3026f884a679df7041c92af6e7353062",
"score": "0.51628304",
"text": "def insert(self, value):\n self.save_state()\n self._model.insert(value)\n self._render.display()",
"title": ""
},
{
"docid": "399277985a5d2d281d880c7e5218387d",
"score": "0.5161641",
"text": "def insert(self, value):\n\n self.insert_bst(self.root, value)",
"title": ""
},
{
"docid": "4a7e57bb963e1563de85f68b1731dcff",
"score": "0.51610875",
"text": "def insert_tweet(self, raw_tweet):\n try:\n self.insert(raw_tweet)\n except DuplicateKeyError:\n # self.logger.warning(f'Trying to insert a duplicated tweet {raw_tweet[\"user_id\"]}.')\n raise DuplicatedTweetError",
"title": ""
},
{
"docid": "f31b48be0fed23a4954005479c77a127",
"score": "0.51495516",
"text": "def insert_new_maintenance(instrumentid: int, send_date: str, return_date: str, maintenance_location:str, cost:int, maintenance_id:int) -> None:\r\n\r\n conn = db.connect()\r\n query = 'INSERT INTO Maintenance VALUES ({}, \"{}\", \"{}\", \"{}\", {}, {});'.format(instrumentid, send_date, return_date, maintenance_location, cost, maintenance_id)\r\n print(query)\r\n conn.execute(query)\r\n conn.close()",
"title": ""
},
{
"docid": "18675ac524634e6073009e51a0885208",
"score": "0.51481545",
"text": "def insert(self: 'BST', item: object) -> None:\r\n self.root = BST._insert(self.root, item)",
"title": ""
},
{
"docid": "171ae57b0383aa562efd77dcdd8ca12a",
"score": "0.51448774",
"text": "def insert(runner, qry, params=()):\n return runner.runInteraction(_insert, qry, params)",
"title": ""
},
{
"docid": "2e403408a11a5334fdf5096829bb784d",
"score": "0.51418245",
"text": "def insert_et(self, input_dict, database):\n insert_record = StatusRecord(\n data_date=input_dict['data_date'],\n et_status=\"started\",\n table_versions=input_dict['table_versions']\n )\n insert_record.set_updated_at()\n return self.psql.run_sql(\n get_insert_string(insert_record.minimized_record()),\n database,\n 'insert',\n params=insert_record.minimized_record()\n )",
"title": ""
},
{
"docid": "5d38ef88bb224ddf77d94dd6a78450e0",
"score": "0.5127909",
"text": "def add_to_database(self) -> None:\n log.debug(\n f\"Adding infraction {self.type} to {self.user_id} by {self.actor_id}, reason: {self.reason} ; {self.str_start} [{self.duration}]\")\n\n # In order to prevent SQL Injections use `?` as placeholder and let SQLite handle the input\n sql_write_command = \"\"\"INSERT INTO infractions VALUES(?, ?, ?, ?, ?, ?, ?);\"\"\"\n sql_write_args = (self.user_id, self.type, self.reason, self.actor_id,\n self.str_start, self.duration, int(self.is_active))\n\n sql_find_command = \"\"\"SELECT rowid FROM infractions WHERE(\n UID=? and Type=? and Reason=? and ActorID=? and Start=? and Duration=? and Active=?\n );\"\"\"\n sql_find_args = (self.user_id, self.type, self.reason, self.actor_id,\n self.str_start, self.duration, int(self.is_active))\n\n db = SQLite()\n db.execute(sql_write_command, sql_write_args)\n db.execute(sql_find_command, sql_find_args)\n self.id = db.cur.fetchone()[0]\n db.close()",
"title": ""
},
{
"docid": "4b25f406ed65627f883313d2697d30af",
"score": "0.51255774",
"text": "def insert_to_db():\n\n mydb = mysql.connector.connect(\n host=\"db\",\n user=\"root\",\n password=\"root\",\n database=\"cluster\",\n )\n myc = mydb.cursor()\n val = datetime.datetime.now()\n sql = f\"INSERT INTO reqs (req_id) VALUES ('{val}')\"\n myc.execute(sql)\n\n mydb.commit()\n mydb.close()\n myc.close()",
"title": ""
},
{
"docid": "d0e47a5db2e75cd8fa814bfeb87aa493",
"score": "0.5121899",
"text": "def insert(self, target, data=None):\n data = data or self._data\n return self.execute('airy.ui.insert(\"%s\", %s);' % (target, json_encode(data)))",
"title": ""
},
{
"docid": "d5b938b002fcce525be8becd9b43ad1d",
"score": "0.51168203",
"text": "def insert_memo_bills(entry: MemoBill) -> None:\n\n # Open a new connection\n db, cursor = db_connector.cursor()\n\n sql = \"INSERT INTO memo_bills (memo_id, bill_number, type, amount) \" \\\n \"VALUES (%s, %s, %s, %s)\"\n val = (entry.memo_id, entry.memo_number, entry.type, entry.amount)\n\n cursor.execute(sql, val)\n db_connector.add_stack_val(sql, val)\n db.commit()\n db.disconnect()\n db_connector.update()",
"title": ""
},
{
"docid": "d1927608c3d37994fab5af9e694efcf7",
"score": "0.51161486",
"text": "def insert_new_rental(rental_id: int, instrument_id: int, net_id: str, date_out: str, date_in: str) -> None:\r\n\r\n conn = db.connect()\r\n query = 'INSERT INTO Rentals VALUES ({}, {}, \"{}\", \"{}\", \"{}\");'.format(rental_id, instrument_id, net_id, date_out, date_in)\r\n print(query)\r\n conn.execute(query)\r\n conn.close()",
"title": ""
},
{
"docid": "858ad70dea07dd9ca1f22c80ded6a57b",
"score": "0.51124793",
"text": "def save_to_db(self) -> None:\n db.session.add(self)\n db.session.commit()",
"title": ""
},
{
"docid": "858ad70dea07dd9ca1f22c80ded6a57b",
"score": "0.51124793",
"text": "def save_to_db(self) -> None:\n db.session.add(self)\n db.session.commit()",
"title": ""
},
{
"docid": "93e4303b082f4fe0cecc91a0256ff94a",
"score": "0.51090705",
"text": "def insert(self, event):\n heapq.heappush(self.events, event)\n logger.info('%s inserted.', event)",
"title": ""
},
{
"docid": "82c26db38d7a12f84d5981ef6f41855a",
"score": "0.5107812",
"text": "def insert(self, tablename, *args):",
"title": ""
},
{
"docid": "c52083b8e0e58bf79295ade33c95697a",
"score": "0.5107311",
"text": "def test_tax_insert_error_tax_code(self):\n data = {\n 'name': 'Small Mac',\n 'tax_code': 4,\n 'price': 500\n }\n result = self.tax.insert(data)\n self.assertEqual(result, {'message': 'Sorry your tax code wrong', 'status': 0})",
"title": ""
},
{
"docid": "15ca99ffa2e409033ce359c9a5aa4f16",
"score": "0.5102485",
"text": "def insert(self, pos, item):\n self.insertChild(pos, item)",
"title": ""
},
{
"docid": "f82d3974b9aad8b5b1071169dd30f80e",
"score": "0.5102094",
"text": "def add_to_db(self, event):\n self.session.add(event)",
"title": ""
},
{
"docid": "106175d9bb4251ce20e9d8f56441123e",
"score": "0.5096324",
"text": "def insert(self, locality):\n l = locality\n cursor = self.conn.cursor()\n cursor.execute('INSERT INTO zipcodes (onrp, type, zip, '\n 'short_name, long_name, canton)'\n 'VALUES (?, ?, ?, ?, ?, ?)',\n (l._onrp, l._zip_type_number, l.zip,\n l.short_name, l.long_name, l.canton))\n self.conn.commit()\n cursor.close()",
"title": ""
},
{
"docid": "81086f735a55d9e4765c9853687bacc6",
"score": "0.5095775",
"text": "def add_to_db(datatypes):\n try:\n iter(datatypes)\n except TypeError:\n datatypes = [datatypes]\n if len(datatypes) == 0:\n return\n db = connect_to_db()\n cls = datatypes[0].__class__\n table_name = table_dict[cls][0]\n db_coloumns = table_dict[cls][1]\n new = get_not_already_in_db(datatypes, table_name)\n db.executemany(\n \"INSERT INTO \" + table_name + \" VALUES \"\n + db_coloumns, [cls.get_database_row() for cls in new])\n db.commit()",
"title": ""
},
{
"docid": "a5db0e0a5b03b59f35c69dbfc45f1942",
"score": "0.5092959",
"text": "def new_record(self, data):\n sql = \"SELECT catID FROM categories WHERE name = ?;\"\n catID = self.conn.execute(sql, (data[0],)).fetchone()\n insert_data = catID + data[1:]\n sql = \"\"\"INSERT INTO items (catID, name, cost, costp, purchase_date)\n VALUES (?, ?, ?, ?, ?);\"\"\"\n self.conn.execute(sql, insert_data)\n self.conn.commit()",
"title": ""
},
{
"docid": "b80a8ea7180a26bccc8100da7cc5a1c0",
"score": "0.5092889",
"text": "def save_to_db(self):\n db.session.add(self)\n try:\n db.session.commit()\n except Exception as e:\n logging.debug('save_to_db.commit() Exception: ', e)\n return e",
"title": ""
},
{
"docid": "bc9b3ff26c3e50edd8650810957a0431",
"score": "0.5088971",
"text": "def add(title, author, quantity, rating, root):\n\n cnx = db.connect(host=\"localhost\", user=\"root\", password=\"\", database=\"Library\")\n cur = cnx.cursor()\n\n sql = \"INSERT INTO Books(title, author, quantity, rating) VALUES (%s, %s, %s, %s)\"\n val = (title, author, quantity, rating)\n try:\n # Executing Insert query\n cur.execute(sql, val)\n cnx.commit()\n except Exception as e:\n print(e)\n cnx.rollback()\n\n cnx.close()\n if root:\n root.destroy()",
"title": ""
},
{
"docid": "7ad9ea72614e153791a9b1986674e837",
"score": "0.5088903",
"text": "def insert(self, dict):\n collection = self.db[self.collection]\n return collection.insert_one(dict)",
"title": ""
},
{
"docid": "2c875002e5728662130de716f2b42006",
"score": "0.5088663",
"text": "def save_to_db(self):\n db.session.add(self)\n db.session.commit()",
"title": ""
},
{
"docid": "2c875002e5728662130de716f2b42006",
"score": "0.5088663",
"text": "def save_to_db(self):\n db.session.add(self)\n db.session.commit()",
"title": ""
},
{
"docid": "ff356be6059585a01f6f84ee999c9b15",
"score": "0.50874627",
"text": "def Insert(self, parent, key, text, values, icon=...):\n ...",
"title": ""
},
{
"docid": "67f4f874952d24618a3b5d879eb0f93a",
"score": "0.50857913",
"text": "def insert(self, atom, proofs=None):\n return self.modify(Event(formula=atom, insert=True, proofs=proofs))",
"title": ""
},
{
"docid": "68707d595314699d7f88d01cd461318b",
"score": "0.5084248",
"text": "def save(self):\n nc = self.conn.cursor()\n # Save a new task?\n if not self.id:\n nc.execute(\"INSERT INTO `tasks` (`type`, `category`, `enabled`, `muted`, `payload`, `name`) VALUES (?, ?, ?, ?, ?, ?)\",\n (self.type, self.category, 1 if self.enabled else 0, 1 if self.muted else 0, json.dumps(self.payload), self.name, )\n )\n # Update existing task?\n else:\n nc.execute(\"UPDATE `tasks` SET `type` = ?, `category` = ?, `enabled` = ?, `muted` = ?, `payload` = ?, `name` = ? WHERE `id` = ? LIMIT 1\",\n (self.type, self.category, 1 if self.enabled else 0, 1 if self.muted else 0, json.dumps(self.payload), self.name, self.id, )\n )\n self.conn.commit()",
"title": ""
},
{
"docid": "1b13449c9dff27f27cb7f3ce554af0b2",
"score": "0.5071464",
"text": "def add_object(value):\n return DATABASE_ENTRIES.insert(value)",
"title": ""
},
{
"docid": "16a8c75f84cc60f88063c07ce9615685",
"score": "0.50713104",
"text": "def _store_notification(type: NotificationType, user_id: int, data: typing.Dict[str, typing.Any]) -> None:\n # ensure the user exists\n logic.users.check_user_exists(user_id)\n notification = notifications.Notification(\n type=type,\n user_id=user_id,\n data=data,\n utc_datetime=datetime.datetime.utcnow()\n )\n db.session.add(notification)\n db.session.commit()",
"title": ""
},
{
"docid": "a59bb4faa11428902e12578fe81cdfb5",
"score": "0.5059275",
"text": "def addEntry(self, time, value):",
"title": ""
},
{
"docid": "671aa2067296981b4fce715eecf12dd3",
"score": "0.5059054",
"text": "def add_data(self, data):\n insert = self.format_for_insert(data)\n sql = f\"\"\"INSERT INTO weather({','.join(self.headers.keys())})\n VALUES(?,?,?,?,?,?,?,?,?,?,?,?)\"\"\"\n\n self.c.execute(sql, insert)",
"title": ""
},
{
"docid": "6d8845cdb2c00762074a93fecf9e81f5",
"score": "0.5054523",
"text": "def new_task(db, title, task_user_id):\n time_in = datetime.now()\n c = db.cursor()\n query = \"\"\"INSERT INTO Tasks Values (NULL, ?, ?, NULL, ?)\"\"\"\n result = c.execute(query, (title, time_in, task_user_id))\n db.commit()\n return result.lastrowid",
"title": ""
},
{
"docid": "faac1969e8c98789833663bba2d8beef",
"score": "0.5049881",
"text": "def insert(self, _test=False):\n changed = self._changed_values()\n if self._primary_key in changed:\n del changed[self._primary_key]\n return web.insert(self._table, _test=_test, **changed)",
"title": ""
},
{
"docid": "6f3dd00449bace8c83cdae0971126f99",
"score": "0.5042004",
"text": "def insertPost(self, newPost):\n\t\tdate = datetime.date.today()\n\t\ttry:\n\t\t\tp = post(\n\t\t\t\tguid=newPost['guid'], \n\t\t\t\ttitle=newPost['title'], \n\t\t\t\tdesc=newPost['desc'],\n\t\t\t\temail=newPost['email'],\n\t\t\t\tprice=newPost['price'],\n\t\t\t\tzip=newPost['zip'],\n\t\t\t\tphone=newPost['phone'],\n\t\t\t\tdate=date)\n\t\t\tsesh = Session()\n\t\t\tsesh.add(p)\n\t\t\tsesh.commit()\n\t\texcept Exception, e:\n\t\t\tprint \"Error %s\" % e\n\t\t\traise e",
"title": ""
},
{
"docid": "5ae4ea3e94cd8d8cfd7381ce6b17a8c4",
"score": "0.50412273",
"text": "def record(self, op, completed) -> None:\n current_datetime = datetime.datetime.now()\n self.db.cursor().execute(\n self.INSERT_QUERY, (op, completed, current_datetime)\n )\n self.db.commit()",
"title": ""
},
{
"docid": "396a52117dfc805b25131a513bad0199",
"score": "0.5038122",
"text": "def insert(self, time, actor):\n\n if time < self.current_time:\n interface.add_debug_text(\"{} added to timer {} at current time {}\".format(actor, time, self.current_time))\n return\n elif time == self.current_time:\n self.current_actors.append(actor)\n else:\n self.queue.setdefault(time, []).append(actor)",
"title": ""
},
{
"docid": "92d1218e8ed68db43d396f0d2ea04cd6",
"score": "0.50302094",
"text": "def insert_to_database(self, db):\n # Create INSERT statement\n sql = 'INSERT INTO %s VALUES (' % self.__class__._table_name\n args = []\n\n # Add values\n formats = ['%s'] * len(self.__class__._cols)\n sql += ', '.join(formats)\n args.extend([self.get(col) for col in self.__class__._cols])\n\n # End values\n sql += ')'\n\n # Try to insert it to database\n try:\n return db.execute_sql(sql, args) == 1\n except DatabaseIntegerityError:\n return False",
"title": ""
},
{
"docid": "b435ddfda33459bc9dc43e3376659a40",
"score": "0.50296885",
"text": "def insert(self, sql = None):\n\n if sql == None:\n return None\n\n cursor = self.conn.cursor()\n\n #set attr\n try: \n cursor.execute(sql)\n #cursor.commit()\n self.conn.commit()\n\n cursor.close\n\n except cx_Oracle.DatabaseError as e:\n raise db_Error(e, sql)",
"title": ""
},
{
"docid": "755993fa56c01fce58fc10b1ed035e48",
"score": "0.5027297",
"text": "def insert_table(person):\r\n # Note: The amount of ? correspond to the number of fields in the table.\r\n con = sqlite3.connect(\"database.db\")\r\n cur = con.cursor()\r\n cur.execute('INSERT INTO person VALUES (?,?,?,?,?,?)', person)\r\n con.commit()\r\n con.close()",
"title": ""
},
{
"docid": "d0e8d01c2124cc553aeeee3714331621",
"score": "0.5025787",
"text": "def insert():\n alias = request.json[\"alias\"]\n if alias not in databases:\n return {\"error\": \"database not found\"}, 400\n database = databases[alias]\n resource = request.json[\"resource\"]\n status = database.insert_resource(resource)\n database._add_to_stack({\"operation\": \"insert\", \"resource\": resource})\n return status",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="711"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">60b1d7c2af15251da1f18ba1fdaec554</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">show the stdout and stderr logs from a container</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "d2edbb196b20ba3bf704ef2110636600",
"score": "0.68574196",
"text": "def logs(docker, container):\n info = docker.inspect_container(container)\n real_user = os.environ['SUDO_USER']\n if check_contenair_user(info, real_user):\n print docker.logs(container, stdout=True, stderr=True, tail=\"all\")\n else:\n print >> sys.stderr, \"not you're own container\"\n return 0",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "18b4643ddd8f6a3602c022aca55b9799",
"score": "0.6842843",
"text": "def app_logs():\n run(f'docker logs {service_container_name}')",
"title": ""
},
{
"docid": "318a9b46d67867f3bdb8d0e98f9815cf",
"score": "0.6645457",
"text": "def logs(c):\n docker_compose(c, 'logs -f --tail=150')",
"title": ""
},
{
"docid": "770fa18049b1383eaa640eb1f215a989",
"score": "0.62123865",
"text": "def capture_stdout(self):\n sys.stdout = StreamToLogger(logging.getLogger(\n 'sys.stdout'), logging.INFO)\n sys.stderr = StreamToLogger(logging.getLogger(\n 'sys.stderr'), logging.ERROR)",
"title": ""
},
{
"docid": "770fa18049b1383eaa640eb1f215a989",
"score": "0.62123865",
"text": "def capture_stdout(self):\n sys.stdout = StreamToLogger(logging.getLogger(\n 'sys.stdout'), logging.INFO)\n sys.stderr = StreamToLogger(logging.getLogger(\n 'sys.stderr'), logging.ERROR)",
"title": ""
},
{
"docid": "16f81c4b338226cb93fd41d86df8ef4f",
"score": "0.61980397",
"text": "def get_errors(self):\n if self.container is not None:\n # 1. I output both STDOUT and STDERR because some programs may not use STDERR for errors\n # and STDOUT might help with providing the context for the errors\n logs = self.container.logs(stdout=True, stderr=True)\n if logs is None or len(logs) == 0:\n logs = \"No logs yet\"\n else:\n logs = logs.replace(b\";\", b\"<br>\")\n return logs\n return \"No container running!\"",
"title": ""
},
{
"docid": "43d301880cc9c88f0d90c663c1a982fa",
"score": "0.6124342",
"text": "def tail(docker, container):\n info = docker.inspect_container(container)\n real_user = os.environ['SUDO_USER']\n if check_contenair_user(info, real_user):\n for l in docker.logs(container, stream=True, stdout=True, stderr=True, tail=5):\n print l,\n else:\n print >> sys.stderr, \"not you're own container\"\n return 0",
"title": ""
},
{
"docid": "26e7ada17c210d5e49da78b72f93fd49",
"score": "0.5970343",
"text": "def log_to_stdout() -> None:\n logging.basicConfig(stream=sys.stdout)",
"title": ""
},
{
"docid": "024c38c28bc042021eacebe93c57e438",
"score": "0.5914121",
"text": "def logs(service, follow):\n docker_compose(['logs'] + (['--follow'] if follow else []) + list(service))",
"title": ""
},
{
"docid": "af39fe16f5c4bcb11f67bb0e43e6d044",
"score": "0.58811706",
"text": "def container_logs(self, token, container_id):\n path = \"/logs\"\n job_info = self._get_job_info()\n token_file = self._get_token_file(job_info[\"home\"],\n job_info['job_id'])\n token = token_parse(token, token_file)\n parameters = {\"token\": token, \"container_id\": container_id}\n results = self.control.execute_get(path=path, parameters=parameters)\n return results",
"title": ""
},
{
"docid": "7d432dc3b30af61d4edf86101753df95",
"score": "0.58356893",
"text": "def _list():\n containers = get_running_containers()\n if len(containers) == 0:\n print('No running containers!')\n else:\n print('Running containers:')\n for container in containers:\n print(' {}'.format(container.name[len(CUAUV_CONTAINER_PREFIX):]))",
"title": ""
},
{
"docid": "944671191dff397cc7219a7524a090cb",
"score": "0.5786295",
"text": "def cli():\n logging.basicConfig(level=logging.DEBUG, format=\"%(message)s\")",
"title": ""
},
{
"docid": "d29dd9dbc1ac13dd8cd9b40edb498373",
"score": "0.5747788",
"text": "def info(c):\n c.run(f\"{IN_DOCKER} sls info\")",
"title": ""
},
{
"docid": "daea9b5a8849e5ccb72b02d336621bbf",
"score": "0.57327265",
"text": "def _ShowEmulatorLog(self):\n with self._EmulatorLogFile('r') as f:\n self._LogFileContent('Emulator log', f)\n\n for log in ('watchdog.out', 'watchdog.err'):\n name = os.path.join(self._emulator_tmp_dir, 'watchdog', log)\n if os.path.exists(name):\n with open(name) as f:\n self._LogFileContent(log, f)\n else:\n logging.info('cannot show log %s: file does not exist', log)",
"title": ""
},
{
"docid": "398e2b19234d9aebccc560f36883f946",
"score": "0.56150866",
"text": "def get_output(self):\n if self.container != '':\n return self.container.logs()\n\n return 'No Container found'",
"title": ""
},
{
"docid": "0c00ab1506bf71a3f3a4d8f73069b267",
"score": "0.56147516",
"text": "def Logs(self, namespace, log_target):\n return self._RunKubectl(['logs', '-n', namespace, log_target])",
"title": ""
},
{
"docid": "f430356de202d19ffe682421bbb759fd",
"score": "0.56013334",
"text": "def main(args=None):\n logging.basicConfig(stream=sys.stderr, level=logging.INFO)",
"title": ""
},
{
"docid": "103a05bb3d3cd0a02f57627efccb2317",
"score": "0.55545455",
"text": "def print_errors(self):\n out = f\"process standard output:\\n{self.stdout_str}\"\n err = f\"process error output:\\n{self.stderr_str}\"\n print(out, file=sys.stderr)\n print(err, file=sys.stderr)",
"title": ""
},
{
"docid": "8147a1c417b6f2642ce0b368f8610a0f",
"score": "0.5545747",
"text": "def display_logs(context):\n print(\"request id is : \", context.aws_request_id)\n print(\"mem. limits(MB):\", context.memory_limit_in_mb)\n print(\"log stream name is : \", context.log_stream_name)\n print(\"millis is : \",context.get_remaining_time_in_millis())\n print(\"log group name is : \", context.log_group_name)\n print(\"name of function invoked is : \", context.function_name)",
"title": ""
},
{
"docid": "46813840894a48a9968ce6892ecb6326",
"score": "0.55244267",
"text": "def testDockerModeStdErrStdOut(self):\n\n task = {\n 'mode': 'docker',\n 'docker_image': TEST_IMAGE,\n 'pull_image': True,\n 'container_args': ['$input{test_mode}', '-m', '$input{message}'],\n 'inputs': [{\n 'id': 'test_mode',\n 'name': '',\n 'format': 'string',\n 'type': 'string'\n }, {\n 'id': 'message',\n 'name': '',\n 'format': 'string',\n 'type': 'string'\n }],\n 'outputs': [{\n 'id': '_stdout',\n 'format': 'string',\n 'type': 'string'\n }, {\n 'id': '_stderr',\n 'format': 'string',\n 'type': 'string'\n }]\n }\n\n inputs = {\n 'test_mode': {\n 'format': 'string',\n 'data': 'stdout_stderr'\n },\n 'message': {\n 'format': 'string',\n 'data': self._test_message\n }\n }\n\n out = run(\n task, inputs=inputs, _tempdir=self._tmp, cleanup=True, validate=False,\n auto_convert=False)\n\n self.assertEqual(out['_stdout']['data'], 'this is stdout data\\n')\n self.assertEqual(out['_stderr']['data'], 'this is stderr data\\n')",
"title": ""
},
{
"docid": "dc77b5f6ea2782b1b6888b6de7f2b9f4",
"score": "0.54720044",
"text": "def docker_container_list(self):\n containers = Container.objects()\n if len(containers) == 0:\n print(\"No containers exist\")\n return\n\n print(\"Name\\t\\tStatus\")\n for container in containers:\n print(container.containerName + \"\\t\\t\" + container.containerStatus)",
"title": ""
},
{
"docid": "7fd6345324e2b0299817704589dc9c9a",
"score": "0.5436721",
"text": "def save_component_container_logs(self, component, container):\n awk_script = textwrap.dedent('''\\\n BEGIN {\n n = 2000\n }\n NR <= n {\n print\n }\n NR > n {\n buf[(NR - 1)%n + 1] = $0\n }\n END {\n if (NR <= n)\n exit\n\n if (NR > 2*n)\n print \"...\"\n\n for (i = NR >= 2*n ? 0 : n - NR%n; i < n; ++i)\n print buf[(NR + i)%n + 1]\n }''')\n out = self.read_command_output(' '.join(['/usr/local/bin/master-logs',\n component, container, '2>&1',\n '|', '/bin/awk',\n \"'%s'\" % awk_script]))\n self.register_file('master-logs_%s_%s' % (component, container), out)",
"title": ""
},
{
"docid": "4690151c1d0f4389c90dffb1ed50ef50",
"score": "0.54226226",
"text": "def logShow():\n # find pod\n name = request.form.get(\"name\")\n pod = getPod(name)\n app.logger.info(\"LOG: \" + name)\n\n log = \"\"\n result = {}\n if pod.status.phase not in [\"Pending\", \"Unknown\"]:\n log = v1.read_namespaced_pod_log(name, ns),\n # why tuple\n if isinstance(log, tuple):\n log = log[0]\n if pod.status.container_statuses[0].state.terminated:\n result = pod.status.container_statuses[0].state.terminated\n\n # phase: Pending Running Succeeded Failed Unknown\n return Ok({\n 'log': log,\n 'times': [result.started_at.timestamp(), result.finished_at.timestamp()] if result else [],\n 'status': pod.status.phase\n })",
"title": ""
},
{
"docid": "3b0cef7aed85fa227c409c5d5460e9e6",
"score": "0.5392628",
"text": "def stderr_logger():\n stderr_info_logger = logging.getLogger(\"stderr_logger\")\n stderr_info_logger.setLevel(logging.INFO)\n stderr_info_logger_handler = logging.StreamHandler(sys.stderr)\n stderr_info_logger_handler.setLevel(logging.INFO)\n stderr_info_logger.addHandler(stderr_info_logger_handler)\n stderr_info_logger_handler.setFormatter(PLAIN_FORMATTER)\n return stderr_info_logger",
"title": ""
},
{
"docid": "f7ffb135e92a963a697f3e9fc5947352",
"score": "0.53830796",
"text": "def display_output(run, verbose):\n out, err = run.communicate()\n if verbose:\n print(out.strip())\n if err:\n print(err.strip())",
"title": ""
},
{
"docid": "1aaab9b7b64ef473c90ceb5a90d4114d",
"score": "0.5375945",
"text": "def test_cmd_logging(self):\n\n # The first two lines are RE patterns because the log entries\n # will contain the CWD path.\n expected = [\n \"INFO:root:Executing:cmd_gather \\[[^\\]]+\\]: \\['echo', 'hello'\\]\",\n \"INFO:root:Process \\[[^\\]]+\\]: \\['echo', 'hello'\\]: exited with: 0\",\n \"stdout>>hello\",\n \"<<\",\n \"stderr>><<\",\n \"\",\n \"\"\n ]\n\n c0 = container.DockerContainer(\"test/image\")\n c0._cmd(\"echo hello\")\n\n actual = self.stream.getvalue()\n lines = string.split(actual, \"\\n\")\n self.assertEqual(len(lines), 7)\n\n # check that the first and second lines match the expected patterns.\n self.assertTrue(\n re.match(expected[0], lines[0]),\n \"process exit line does not match: \\n actual: {}\\n expected {}\".\n format(expected[1], lines[1])\n )\n self.assertTrue(\n re.match(expected[1], lines[1]),\n \"process exit line does not match: \\n actual: {}\\n expected {}\".\n format(expected[1], lines[1])\n )\n\n # The remainder of the output must match verbatim\n self.assertListEqual(lines[2:], expected[2:])",
"title": ""
},
{
"docid": "28e6add650379d8bdfc7d0342d7643a3",
"score": "0.5368965",
"text": "def cli(debug):\n logging_format = '%(asctime)s - %(name)-12s - %(levelname)s - %(message)s'\n logging.basicConfig(format=logging_format, level=logging.DEBUG if debug else logging.INFO)",
"title": ""
},
{
"docid": "af59013851f13b3914890c5cc654a9d7",
"score": "0.5342533",
"text": "def log_output(additional_flags=''):\n output = adb.run_command('logcat -d -v brief %s *:V' % additional_flags)\n return filter_log_output(output)",
"title": ""
},
{
"docid": "c9e7e4235cbe24c0776cea513260063e",
"score": "0.5328318",
"text": "def Process(self) -> None:\n logs_containers = self.GetContainers(containers.GCPLogs)\n for logs_container in logs_containers:\n self._ProcessLogContainer(logs_container)",
"title": ""
},
{
"docid": "99d6d108e669d31461c3c887c7f2590b",
"score": "0.53181",
"text": "def run_kubectl_logs_cmd(self, pod_name, container_name=None, cmd_params=\"\", namespace=DEFAULT_NAMESPACE):\n\n # cmd_params can be used to pass in \"-n nuvoloso-cluster\"\n cmd = \"%s -n %s\" % (KUBECTL_LOGS % (pod_name, self.context), namespace)\n if container_name:\n cmd += \" -c %s \" % container_name\n cmd += cmd_params\n logger.info(\"cmd: %s\", cmd)\n result = self.nuvoloso_helper.run_check_output(cmd)\n if result:\n logger.info(result)\n return result\n else:\n raise Exception(\"No output when running cmd: %s\" % cmd)",
"title": ""
},
{
"docid": "71408aab6e6f584edff8e96eb24b67ea",
"score": "0.53133947",
"text": "def logs(env: Optional[str], config: str, seconds: Optional[int]) -> None:\n\n check_opta_file_exists(config)\n # Configure kubectl\n layer = Layer.load_from_yaml(config, env)\n amplitude_client.send_event(\n amplitude_client.SHELL_EVENT,\n event_properties={\"org_name\": layer.org_name, \"layer_name\": layer.name},\n )\n layer.verify_cloud_credentials()\n gen_all(layer)\n configure_kubectl(layer)\n load_kube_config()\n if layer.cloud == \"aws\":\n modules = layer.get_module_by_type(\"k8s-service\")\n elif layer.cloud == \"google\":\n modules = layer.get_module_by_type(\"gcp-k8s-service\")\n else:\n raise Exception(f\"Currently not handling logs for cloud {layer.cloud}\")\n if len(modules) == 0:\n raise UserErrors(\"No module of type (gcp-)k8s-service in the yaml file\")\n elif len(modules) > 1:\n raise UserErrors(\n \"Don't put more than one (gcp-)k8s-service module file per opta file\"\n )\n module_name = modules[0].name\n tail_module_log(layer, module_name, seconds)",
"title": ""
},
{
"docid": "7a093d4ecde9d802b88d901dd169c8dc",
"score": "0.5291549",
"text": "def list_contenair(docker):\n real_user = os.environ['SUDO_USER']\n for container in docker.containers(all=True):\n info = docker.inspect_container(container)\n if check_contenair_user(info, real_user):\n print \"%s %s %s\" % (info['Name'][1:], info['Config']['Hostname'], container['Status'])\n return 0",
"title": ""
},
{
"docid": "e1d6597b1626080fc2443f1752c2ed91",
"score": "0.52884656",
"text": "def run():\n logger.info(\"hello world\")\n logger.debug(\"outro teste\")",
"title": ""
},
{
"docid": "d7cab4445fd1e9d76a21d1e935166271",
"score": "0.5262767",
"text": "def main(ctx, verbose):\n verbosity_levels = (logging.WARNING, logging.INFO, logging.DEBUG)\n level = verbosity_levels[verbose]\n fmt = '%(asctime)s %(name)s %(levelname)s %(message)s'\n coloredlogs.install(level=level, fmt=fmt)",
"title": ""
},
{
"docid": "b63114db41afcad7f2452718874bf99f",
"score": "0.5261177",
"text": "def export_logs(self, **args):\n logging.info(\"Get image %s build log.\", self.image_id)\n command = [\"pcluster\", \"export-image-logs\", \"--region\", self.region, \"--image-id\", self.image_id]\n for k, val in args.items():\n command.extend([f\"--{kebab_case(k)}\", str(val)])\n result = run_pcluster_command(command)\n return json.loads(result.stdout)",
"title": ""
},
{
"docid": "94e7da56780515f07fd64abba049d6da",
"score": "0.5260877",
"text": "def log(*args, **kwargs):\n if not DEBUG:\n return\n kwargs[\"file\"] = sys.stderr\n print(*args, **kwargs)",
"title": ""
},
{
"docid": "14f6d24110429988a31066c57329cee7",
"score": "0.52557796",
"text": "def redirect_stdout_stderr():\n stdout_logger = logging.getLogger('STDOUT')\n sys.stdout = StreamToLogger(stdout_logger, logging.INFO)\n\n stderr_logger = logging.getLogger('STDERR')\n sys.stderr = StreamToLogger(stderr_logger, logging.ERROR)",
"title": ""
},
{
"docid": "6137a542d9ebbd1c41702d59f23a2e9f",
"score": "0.5254996",
"text": "def _stdout_path():\n return opts.proj.dirs.logs / \"app.log\"",
"title": ""
},
{
"docid": "1687dfe16f2e3b5788775982659eeac3",
"score": "0.52434945",
"text": "def setup_logging(self, options):\n logging.basicConfig(filename=options['logfile'],\n level = getattr(logging, options['loglevel'].upper()),\n format=\"%(asctime)s %(levelname)s %(message)s\")\n\n # Add stdout to logging, useful for short query sets.\n if 'stdout' in options:\n formatter = logging.root.handlers[0].formatter\n sh = logging.StreamHandler(sys.stdout)\n sh.formatter = formatter\n logging.root.addHandler( sh )",
"title": ""
},
{
"docid": "7cfa314136c4cf8a3aa5dfdf67b80699",
"score": "0.5212591",
"text": "def setupLogging():\n global logger\n \n logger = logging.getLogger(__name__) #name logger after module\n logger.setLevel(logging.DEBUG)\n \n basicConsoleHandler = logging.StreamHandler() #sys.stderr\n basicformatter = logging.Formatter('%(message)s') #standard format\n basicConsoleHandler.setFormatter(basicformatter)\n logger.addHandler(basicConsoleHandler)\n logger.propagate = False",
"title": ""
},
{
"docid": "d0a4c16112aeae1efb12449486cd8b81",
"score": "0.5178063",
"text": "def QA_util_log_debug(logs, ui_log=None, ui_progress=None):\r\n logging.debug(logs)",
"title": ""
},
{
"docid": "e9b84e673ca3cf7795caa4a1ee6fc951",
"score": "0.5164871",
"text": "def _cli_request(command, logpath):\n os.chdir(os.path.dirname(logpath))\n print(\"Logging stdout/stderror to:\\n\" + logpath + \"\\n\")\n\n with Popen(command, shell=True, stdout=PIPE, stderr=STDOUT) as process, \\\n open(file=logpath, mode='wt') as logfile:\n for line in io.TextIOWrapper(process.stdout, newline=''):\n sys.stdout.write(line)\n logfile.write(line)\n logfile.flush()",
"title": ""
},
{
"docid": "f804eb1940f8a2c52215da82b9fdcae4",
"score": "0.51576024",
"text": "def setup_logging(loglevel=logging.INFO):\n root = logging.getLogger(__package__)\n root.setLevel(loglevel)\n ch = logging.StreamHandler(sys.stderr)\n ch.setLevel(loglevel)\n formatter = logging.Formatter('[%(asctime)s] %(name)s %(levelname)s - %(message)s')\n ch.setFormatter(formatter)\n root.addHandler(ch)",
"title": ""
},
{
"docid": "103993b005c36e059d3ea680e65e21d0",
"score": "0.51484054",
"text": "def test_docker(self):\n # use docker image inspect to see that the image is installed and tagged as otter-grader\n inspect = subprocess.run([\"docker\", \"image\", \"inspect\", \"otter-test\"], stdout=PIPE, stderr=PIPE)\n\n # assert that it didn't fail, it will fail if it is not installed\n self.assertEqual(len(inspect.stderr), 0, inspect.stderr.decode(\"utf-8\"))",
"title": ""
},
{
"docid": "2b0411852488fa48a0d7b920e365a991",
"score": "0.51450586",
"text": "def logging_process(arguments):\n p = subprocess.Popen(arguments, stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n stdout, stderr = p.communicate()\n if stdout:\n logger.debug(\"%s\" % stdout)\n if stderr:\n logger.debug(\"%s\" % stderr)",
"title": ""
},
{
"docid": "6119714750ea2db07bafe24d6c98f7ca",
"score": "0.5138049",
"text": "def start_container(iden, *params):\n\n container = docker_output('run', '-d', iden, *params).strip()\n return container",
"title": ""
},
{
"docid": "d509ce5314c9b23e65d23a4685d13b65",
"score": "0.51373345",
"text": "def setup(\n debug=lfps['debug'],\n debug_log_logging_level=logging.DEBUG,\n # content log aka info:\n content=lfps['content'],\n content_log_logging_level=logging.INFO,\n # timings log\n timings=lfps['timings'],\n timings_log_logging_level=8,\n # log file parameters:\n log_file_format=None,\n log_file_time_format=None,\n # shell logging parameters:\n stdout_logging_level=logging.INFO,\n stdout_logging_format=None):\n # Add logging level below logging.Debug to log timings:\n add_logging_level_timings()\n\n # Get root logger\n root_logger = logging.getLogger()\n\n # Set logging level to the lowest (1) to let handles assign levels\n root_logger.setLevel(1)\n\n # File handles:\n debug_file_handle = logging.FileHandler(debug, mode='w') # w for write\n debug_file_handle.setLevel(debug_log_logging_level)\n\n content_file_handle = logging.FileHandler(content, mode='w') # w for write\n content_file_handle.setLevel(content_log_logging_level)\n\n timings_file_handle = logging.FileHandler(timings, mode='w') # w for write\n timings_file_handle.setLevel(timings_log_logging_level)\n # Add filter to only allow messages between 11 and 20\n timings_file_handle.addFilter(TimingsFilter())\n\n # Stream handles:\n stdout_handler = logging.StreamHandler()\n stdout_handler.setLevel(logging.INFO)\n\n # File formatters:\n if not log_file_format: # loging msg structure\n log_file_format = '[{levelname} at {asctime}] {msg}'\n if not log_file_time_format: # time stemp format:\n log_file_time_format = '%Y-%m-%d %H:%M:%S'\n file_formatter = logging.Formatter(\n log_file_format, datefmt=log_file_time_format, style='{')\n\n # Stream formatter\n if not stdout_logging_format: # logging msg structure\n stdout_logging_format = '[{levelname} at {asctime}] {msg}'\n stdout_formatter = logging.Formatter(\n stdout_logging_format, datefmt=log_file_time_format, style='{')\n\n # 4.) Add formatters to handlers:\n debug_file_handle.setFormatter(file_formatter)\n content_file_handle.setFormatter(file_formatter)\n timings_file_handle.setFormatter(file_formatter)\n stdout_handler.setFormatter(stdout_formatter)\n\n # 5.) Add handles to root logger if not done already:\n if not root_logger.hasHandlers():\n root_logger.addHandler(debug_file_handle)\n root_logger.addHandler(content_file_handle)\n root_logger.addHandler(timings_file_handle)\n root_logger.addHandler(stdout_handler)",
"title": ""
},
{
"docid": "03fafa8d43b834b3c720bffb0735fc9a",
"score": "0.5133093",
"text": "def hello():\n return \"This is my app inside a Docker container!\\n\"",
"title": ""
},
{
"docid": "5103552e82799017421b69b165b68f18",
"score": "0.5132405",
"text": "def generate_docker_run(self):\n example = []\n example.append(\"docker run -it\")\n for key in sorted(list(self.spec.keys())):\n if self.spec[key]['type'] in (dict, list):\n value = f\"\\'{json.dumps(self.spec[key].get('example', ''))}\\'\"\n else:\n value = f\"{self.spec[key].get('example', '')}\"\n string = f\" -e {self.env_prefix}_{key.upper()}={value}\"\n example.append(string)\n example.append(\" <container-name>\")\n print(\" \\\\\\n\".join(example))",
"title": ""
},
{
"docid": "d62608e175d2767cb62b53399e797655",
"score": "0.5128768",
"text": "def capture(display=True):\n try:\n stdout_buff = io.StringIO()\n stderr_buff = io.StringIO()\n with contextlib.redirect_stdout(stdout_buff):\n with contextlib.redirect_stderr(stderr_buff):\n yield (stdout_buff, stderr_buff)\n finally:\n stdout_buff.seek(0,0)\n stderr_buff.seek(0,0)\n if display:\n print(stderr_buff.read(), end='', file=sys.stderr)\n stderr_buff.seek(0,0)\n print(stdout_buff.read(), end='')\n stdout_buff.seek(0,0)",
"title": ""
},
{
"docid": "032bd8e701d89151141981a9b1d46520",
"score": "0.5127353",
"text": "def setup_logger(file_name='neurokernel.log', screen=True, port=None):\n\n if file_name:\n file_output = \\\n twiggy.outputs.FileOutput(file_name, twiggy.formats.line_format, 'w')\n twiggy.addEmitters(('file', twiggy.levels.DEBUG, None, file_output))\n\n if screen:\n screen_output = \\\n twiggy.outputs.StreamOutput(twiggy.formats.line_format,\n stream=sys.stdout)\n twiggy.addEmitters(('screen', twiggy.levels.DEBUG, None, screen_output))\n\n if port:\n port_output = ZMQOutput('tcp://*:%i' % port,\n twiggy.formats.line_format)\n twiggy.addEmitters(('port', twiggy.levels.DEBUG, None, port_output))\n\n return twiggy.log.name(('{name:%s}' % 12).format(name='main'))",
"title": ""
},
{
"docid": "95751a13f46efa04ccf5cf2c71903ce2",
"score": "0.51155597",
"text": "def get_console_output(DryRun=None, InstanceId=None):\n pass",
"title": ""
},
{
"docid": "be26e51a6b6d0ac769befbb973918fd4",
"score": "0.51140624",
"text": "def logToStdout(timestamp=False):\n logs['console'] = MyLogObserver(sys.stdout)\n if not timestamp:\n logs['console'].timeFormat = \"\" #get rid of that\n sys.stdout = StdioKabob(0)\n sys.stderr = StdioKabob(1)",
"title": ""
},
{
"docid": "9efd9125b86c11a2d5953b0720b17043",
"score": "0.5112107",
"text": "def print_logging():\n logger_names = LOGGER_NAMES.values()\n for logger_name in logger_names:\n logger = logging.getLogger(logger_name)\n print_logger(logger)",
"title": ""
},
{
"docid": "ca6ed08cc589b04b9ffe278d316b19d4",
"score": "0.5107147",
"text": "def cookietemple_cli(ctx, verbose, log_file):\n # Set the base logger to output DEBUG\n log.setLevel(logging.DEBUG)\n\n # Set up logs to the console\n log.addHandler(\n rich.logging.RichHandler(\n level=logging.DEBUG if verbose else logging.INFO,\n console=rich.console.Console(file=sys.stderr),\n show_time=True,\n markup=True,\n )\n )\n\n # Set up logs to a file if we asked for one\n if log_file:\n log_fh = logging.FileHandler(log_file, encoding=\"utf-8\")\n log_fh.setLevel(logging.DEBUG)\n log_fh.setFormatter(logging.Formatter(\"[%(asctime)s] %(name)-20s [%(levelname)-7s] %(message)s\"))\n log.addHandler(log_fh)",
"title": ""
},
{
"docid": "c06dbc264cc0ef66b9c42c789e1978cb",
"score": "0.51024085",
"text": "def start_logging(self):\n if self.args['logfile'] is None:\n logfile = path.splitext(sys.argv[0])[0] + '.log'\n logfile = path.join(getcwd(), path.basename(logfile))\n else:\n logfile = path.join(getcwd(), self.args['logfile'])\n\n if not self.args['nolog']:\n sys.stdout = Logger(logfile = logfile)\n self.loghandle = sys.stdout.log\n self.log = AppendLogger(self.loghandle)",
"title": ""
},
{
"docid": "e41e9f0a64a15257a32df8faa1c8ca71",
"score": "0.510215",
"text": "def _print_container(ctr, depth=0):\n message = \"[cluster_id={cluster_id}] N_children: {N_children} N_samples: {N_document}\"\\\n .format(cluster_id=ctr['cluster_id'],\n N_children=len(ctr.children),\n N_document=len(ctr['cluster_id_accumulated']))\n\n print(''.join(['> ' * depth, message]))\n\n for child in ctr.children:\n _print_container(child, depth + 1)",
"title": ""
},
{
"docid": "8f3d4024ea45926a869590aa9680f250",
"score": "0.5083537",
"text": "def run_and_display(args, build_folder, msg=''):\n os.makedirs(build_folder, exist_ok=True)\n try:\n proc = subprocess.run(args, cwd=build_folder, stdout=subprocess.PIPE, check=True)\n if pykeops.config.verbose:\n print(proc.stdout.decode('utf-8'))\n\n except subprocess.CalledProcessError as e:\n print('\\n--------------------- ' + msg + ' DEBUG -----------------')\n print(e)\n print(e.stdout.decode('utf-8'))\n print('--------------------- ----------- -----------------')",
"title": ""
},
{
"docid": "f699e1aabfbe033190e8a22eaa77b35d",
"score": "0.50776666",
"text": "def setup_logging(verbose=0, name=None):\n root_logger = logging.getLogger(name)\n root_logger.setLevel(logging.DEBUG if verbose > 0 else logging.INFO)\n formatter = logging.Formatter(\"%(asctime)s (PID:%(process)s) [%(levelname)s]: %(message)s\")\n\n handler_stdout = logging.StreamHandler(sys.stdout)\n handler_stdout.setFormatter(formatter)\n handler_stdout.setLevel(logging.DEBUG)\n handler_stdout.addFilter(type('', (logging.Filter,), {'filter': staticmethod(lambda r: r.levelno <= logging.INFO)}))\n root_logger.addHandler(handler_stdout)\n\n handler_stderr = logging.StreamHandler(sys.stderr)\n handler_stderr.setFormatter(formatter)\n handler_stderr.setLevel(logging.WARNING)\n root_logger.addHandler(handler_stderr)",
"title": ""
},
{
"docid": "b5498bbab12ac9f33501fb80ead5a5ea",
"score": "0.507605",
"text": "def list_log_streams(self):\n logging.info(\"Get image %s build log streams.\", self.image_id)\n command = [\"pcluster\", \"list-image-log-streams\", \"--region\", self.region, \"--image-id\", self.image_id]\n result = run_pcluster_command(command).stdout\n response = json.loads(result)\n return response",
"title": ""
},
{
"docid": "778edaa81e8cea89042309110fd2f809",
"score": "0.50713056",
"text": "def _docker(args, cwd=None, env=os.environ, capture_output=False, print_stdout=True):\n\n cmd = ['docker'] + args\n env = dict(env)\n\n if capture_output:\n try:\n output = subprocess.check_output(\n cmd, env=env, cwd=cwd, stderr=subprocess.STDOUT)\n except subprocess.CalledProcessError as e:\n output = e.output\n return output\n else:\n retcode = subprocess.call(cmd, env=env, cwd=cwd, stderr=subprocess.STDOUT,\n stdout=(None if print_stdout else open('/dev/null', 'w')))\n return retcode",
"title": ""
},
{
"docid": "11af3e98f83ed6ff061594dbc3dbb861",
"score": "0.5070068",
"text": "def start_logging():\n stream.push_application()\n file.push_application()",
"title": ""
},
{
"docid": "9da6a8b226c066b33bfd9ab983b78808",
"score": "0.5067027",
"text": "def show_debug_output() -> widgets.Output:\n return debug_out",
"title": ""
},
{
"docid": "85ebbfe34b9de6938754cd466386d818",
"score": "0.5053717",
"text": "def logs(self) -> str:\n try:\n logs = self.container.logs().decode()\n except APIError as e:\n logs = str(e)\n\n return logs",
"title": ""
},
{
"docid": "e00ad7bda4ad1775691883f9f7914898",
"score": "0.50508595",
"text": "def validate_docker_is_runnig():\n process = subprocess.Popen(\n [\"docker\", \"info\"],\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n universal_newlines=True,\n )\n _, err = process.communicate()\n if process.returncode != 0:\n logging.error(err)\n sys.exit()",
"title": ""
},
{
"docid": "3a8881d5f3001d632867e44fa077433b",
"score": "0.5050822",
"text": "def run(self, cmd, **args):\n client = docker.from_env()\n container = client.containers.run(self.img_tag, cmd, detach=True,\n auto_remove=False, **args)\n self.id = container.short_id\n self.running = True\n\n # t = Thread(target=self.out_listener)\n # t.daemon = True\n # t.start()",
"title": ""
},
{
"docid": "2d71bc1372cf475ceddb7f5fb00424ca",
"score": "0.5045002",
"text": "def _containers_down(\n cls,\n docker_project: Project,\n docker_containers: typing.Iterable[Container],\n ) -> None:\n # Send container logs to stdout, so that they get included in\n # the test report.\n # https://docs.pytest.org/en/latest/capture.html\n for container in sorted(docker_containers, key=lambda c: c.name):\n header = \"Logs from {name}:\".format(name=container.name)\n print(header)\n print(\"=\" * len(header))\n print(\n container.logs().decode(\"utf-8\", errors=\"replace\") or\n \"(no logs)\"\n )\n print()\n\n docker_project.down(ImageType.none, False)",
"title": ""
},
{
"docid": "8768d42e701ea976c3242be24c853d8f",
"score": "0.5043432",
"text": "def test_base_runs(base_image): \n client = docker.from_env()\n \n output = client.containers.run(base_image, \n command= '1000.0', \n stdout=True, stderr=False)\n\n result = json.loads(output)\n assert 'membrane' in result",
"title": ""
},
{
"docid": "8d4cc9e2da1e4c11b828bc2db509a088",
"score": "0.50405943",
"text": "def setup_logging(logname):\n log = logging.getLogger(logname)\n log.setLevel(logging.INFO)\n ch = logging.StreamHandler(sys.stdout)\n ch.setLevel(logging.INFO)\n formatter = logging.Formatter('%(asctime)s - %(threadName)s - %(name)s - line %(lineno)d - %(levelname)s - %(message)s')\n ch.setFormatter(formatter)\n log.addHandler(ch)\n return log",
"title": ""
},
{
"docid": "339dc8fce88b5ea770064f3cd7782270",
"score": "0.50389725",
"text": "def aws_logs_command(self, args: Namespace, extra_args: List[str], argv: List[str]) -> None:\n scheduler = self.get_scheduler(args)\n executors = [\n executor\n for executor in scheduler.executors.values()\n if isinstance(executor, AWSBatchExecutor)\n ]\n aws_region2executor = {executor.aws_region: executor for executor in executors}\n statuses = args.status.split(\",\") if args.status else None\n default_aws_region = list(aws_region2executor.keys())[0]\n\n # Build up query of log streams.\n query: List[Tuple[dict, Optional[Job], str, str]] = []\n if args.all:\n for executor in aws_region2executor.values():\n for batch_job in executor.get_jobs(statuses=statuses):\n args.batch_job.append(batch_job[\"jobId\"])\n\n for job_id in args.job:\n job = self.infer_id(job_id)\n if isinstance(job, Job):\n for tag in job.tags:\n if tag.key == \"aws_log_stream\":\n query.append(({}, job, tag.value, default_aws_region))\n\n if args.batch_job:\n for aws_region in aws_region2executor.keys():\n for batch_job in aws_describe_jobs(args.batch_job, aws_region=aws_region):\n log_stream = batch_job[\"container\"].get(\"logStreamName\")\n query.append((batch_job, None, log_stream, aws_region))\n\n if args.log_stream:\n query.extend(\n [({}, None, log_stream, default_aws_region) for log_stream in args.log_stream]\n )\n\n query.sort(key=lambda query: query[0].get(\"createdAt\", 0), reverse=True)\n\n # Display logs\n for batch_job, job, log_stream, aws_region in query:\n # Display header.\n if job:\n self.display(f\"# redun Job {job.id}\")\n if batch_job:\n self.display(f\"# AWS Batch job {batch_job['jobId']}\")\n if log_stream:\n self.display(f\"# AWS Log Stream {log_stream}\")\n\n # Display events.\n events = iter_log_stream(\n log_group_name=BATCH_LOG_GROUP,\n log_stream=log_stream,\n aws_region=aws_region,\n )\n for event in events:\n self.display(format_log_stream_event(event))\n\n self.display()",
"title": ""
},
{
"docid": "ab1e7560bd05f9a2f4f429de3697339f",
"score": "0.5031479",
"text": "def slurp_dataplane_logs(self):\n\n result = self.kubectl_helper.get_pods(namespace=NUVOLOSO_CLUSTER, output='name')\n if not result:\n raise Exception(\"Failed to get pods for namespace %s. Can't get logs. Exiting..\" % NUVOLOSO_CLUSTER)\n logging.info(result)\n nuvo_pod_list = [x.split('/')[-1] for x in result.splitlines()]\n logging.info(nuvo_pod_list)\n\n # Get logs for clusterd containers\n self.args.pod = 'clusterd-0'\n self.args.namespace = 'nuvoloso-cluster'\n for container in CLUSTERD_CONTAINERS:\n logging.info('Getting logs for container %s', container)\n try:\n self.args.container = container\n logging.info(\"Getting logs for %s\", self.args.container)\n\n self.args.previous = False\n private_host, c_id = self.get_host_and_container(self.args)\n public_host = self.get_aws_host(private_host)\n self.slurp_logs(public_host, c_id, self.args.pod, self.args.container)\n\n # previous\n self.args.previous = True\n private_host, c_id = self.get_host_and_container(self.args)\n if private_host != '':\n logging.info('Feching previous logs for %s', container)\n public_host = self.get_aws_host(private_host)\n self.slurp_logs(public_host, c_id, self.args.pod, self.args.container)\n except subprocess.CalledProcessError as err:\n if err.output:\n logging.error(err.output)\n if \"previous terminated container\" in err.output and \\\n \"not found\" in err.output:\n logging.error(\"Container: %s in pod: %s has no previous \"\n \"container logs. Will move ahead to collect \"\n \"other logs\", container, \"clusterd\")\n else:\n logging.error(\"Failed to collect logs for pod: %s \"\n \"container: %s . Will move ahead to collect \"\n \"other logs\", \"clusterd\", container)\n\n # Logs and crash files for agents/nuvo\n # list /var/crash on each node\n # For each node (except 'clusterd') get logs of agentd and nuvo\n # skip 0th since we just collected its logs (clusterd-0)\n for i in range(1, len(nuvo_pod_list)):\n self.args.pod = nuvo_pod_list[i]\n for j, container in enumerate(NUVO_CONTAINERS):\n try:\n self.args.container = container\n logging.info(\"Getting logs for %s\", self.args.container)\n\n self.args.previous = False\n private_host, c_id = self.get_host_and_container(self.args)\n public_host = self.get_aws_host(private_host)\n self.slurp_logs(public_host, c_id, self.args.pod, self.args.container)\n\n # previous\n self.args.previous = True\n private_host, c_id = self.get_host_and_container(self.args)\n if private_host != '':\n logging.info('Feching previous logs for %s', container)\n public_host = self.get_aws_host(private_host)\n self.slurp_logs(public_host, c_id, self.args.pod, self.args.container)\n\n # ls -lrt /var/crash for nuvo containers\n if NUVO_CONTAINERS[j] == \"nuvo\":\n logging.info(\"Checking for presence of core dumps in /var/crash\")\n result = self.kubectl_helper.run_kubectl_exec_cmd(\"ls -lrt /var/crash\", nuvo_pod_list[i], container_name=\"nuvo\", namespace=NUVOLOSO_CLUSTER)\n if result:\n logging.info(result)\n except subprocess.CalledProcessError as err:\n if err.output:\n logging.error(err.output)\n if \"previous terminated container\" in err.output and \\\n \"not found\" in err.output:\n logging.error(\"Container: %s in pod: %s has no previous \"\n \"container logs. Will move ahead to collect \"\n \"other logs\", NUVO_CONTAINERS[j], nuvo_pod_list[i])\n else:\n logging.error(\"Failed to collect logs for pod: %s \"\n \"container: %s . Will move ahead to collect \"\n \"other logs\", nuvo_pod_list[i], NUVO_CONTAINERS[j])\n logging.info(\"Done collecting logs.\")",
"title": ""
},
{
"docid": "d4e8fab77d9d4b0547c7cb44a1c3defd",
"score": "0.50274473",
"text": "def _docker_compose_up():\n cmd = [\n \"docker-compose\",\n \"-f\",\n os.path.join(TEMP_FOLDER, DOCKER_COMPOSE_FILE),\n \"up\",\n \"--build\",\n \"--no-color\",\n \"--abort-on-container-exit\",\n ]\n with open(\"nodes.log\", \"w\") as outfile:\n subprocess.call(cmd, stdout=outfile)",
"title": ""
},
{
"docid": "424051936cf1f56e64d8b567fe9e29e1",
"score": "0.50158614",
"text": "def main(debug: bool, namespace: str) -> None:\n if debug:\n coloredlogs.set_level(\"DEBUG\")\n try:\n config.load_incluster_config()\n except config.config_exception.ConfigException:\n config.load_kube_config()\n\n images = get_top_level_resources(namespace)\n\n notifications = []\n\n for resource, containers in sorted(images.items()):\n logger.info(\n \"Considering {kind}: {name} ({container_count} running containers)\".format(\n kind=resource.kind, name=resource.name, container_count=len(containers)\n )\n )\n notifications.extend(check_resouce_for_new_image_tags(resource))\n notifications.extend(\n check_resource_containers_for_updated_image_digests(resource, containers)\n )\n\n log_notifications(notifications)",
"title": ""
},
{
"docid": "5f2c615d09a3ba58dd714a5d4e3a8c19",
"score": "0.5015689",
"text": "def slurp_controlplane_logs(self):\n\n try:\n self.args.namespace = 'nuvoloso-management'\n\n result = self.kubectl_helper.get_pods(namespace=NUVOLOSO_MANAGEMENT, output='name')\n if not result:\n raise Exception(\"Get pods from %s failed. Can't get logs. Exiting..\" % NUVOLOSO_MANAGEMENT)\n logging.info(result)\n controller_pod_list = [x.split('/')[-1] for x in result.splitlines()]\n logging.info(controller_pod_list)\n\n for k_pod in controller_pod_list:\n logging.info('Looking for containers in %s', k_pod)\n if CONFIGDB in k_pod or METRICSDB in k_pod:\n self.args.pod = k_pod\n if 'metricsdb' in k_pod:\n self.args.container = 'db'\n elif 'configdb' in k_pod:\n self.args.container = 'mongo'\n logging.info(\"Getting logs for %s\", self.args.container)\n\n self.args.previous = False\n private_host, c_id = self.get_host_and_container(self.args)\n public_host = self.get_aws_host(private_host)\n self.slurp_logs(public_host, c_id, self.args.pod, self.args.container)\n\n # previous\n self.args.previous = True\n private_host, c_id = self.get_host_and_container(self.args)\n if private_host != '':\n logging.info('Feching previous logs for %s', self.args.container)\n public_host = self.get_aws_host(private_host)\n self.slurp_logs(public_host, c_id, self.args.pod, self.args.container)\n elif SERVICES in k_pod:\n # there are multiple containers in this pod\n for kon_container in KON_CONTAINERS:\n logging.info('Getting logs for container: %s', kon_container)\n self.args.pod = k_pod\n self.args.container = kon_container\n logging.info(\"Getting logs for %s\", self.args.container)\n\n self.args.previous = False\n private_host, c_id = self.get_host_and_container(self.args)\n public_host = self.get_aws_host(private_host)\n self.slurp_logs(public_host, c_id, self.args.pod, self.args.container)\n\n # previous\n self.args.previous = True\n private_host, c_id = self.get_host_and_container(self.args)\n if private_host != '':\n logging.info('Feching previous logs for %s', self.args.container)\n public_host = self.get_aws_host(private_host)\n self.slurp_logs(public_host, c_id, self.args.pod, self.args.container)\n except subprocess.CalledProcessError as err:\n if err.output:\n logging.error(err.output)\n except:\n logging.error(\"Cannot collect logs for control plane\")\n raise",
"title": ""
},
{
"docid": "8f2c0ae202040c8cb86f6e4c9d3c1950",
"score": "0.501256",
"text": "def SetupToolLogging(debug, verbose, threadname=False,\n _root_logger=None, _stream=None):\n if _root_logger is None:\n root_logger = logging.getLogger(\"\")\n else:\n root_logger = _root_logger\n\n fmt = StringIO()\n fmt.write(\"%(asctime)s:\")\n\n if threadname:\n fmt.write(\" %(threadName)s\")\n\n if debug or verbose:\n fmt.write(\" %(levelname)s\")\n\n fmt.write(\" %(message)s\")\n\n formatter = logging.Formatter(fmt.getvalue())\n\n stderr_handler = logging.StreamHandler(_stream)\n stderr_handler.setFormatter(formatter)\n if debug:\n stderr_handler.setLevel(logging.NOTSET)\n elif verbose:\n stderr_handler.setLevel(logging.INFO)\n else:\n stderr_handler.setLevel(logging.WARNING)\n\n root_logger.setLevel(logging.NOTSET)\n root_logger.addHandler(stderr_handler)",
"title": ""
},
{
"docid": "b926b4d887f6e7d5f404024dfbaad7e5",
"score": "0.5010223",
"text": "def setup_logging():\n debug = os.environ.get(\"DEBUG\", \"0\")\n print(\"DEBUG: %s\" % debug)\n if debug == \"1\":\n level = logging.DEBUG\n else:\n level = logging.INFO\n logging.basicConfig(stream=sys.stdout,\n level=level,\n format='%(name)s - %(levelname)s - %(message)s',\n datefmt='%m/%d/%Y %I:%M:%S')\n logging.getLogger('googleapiclient').setLevel(logging.ERROR)",
"title": ""
},
{
"docid": "9def34b7e3a99b2a63bce9ef215106ae",
"score": "0.5002494",
"text": "def setup(stream: IO[str],\n debug: bool) -> Tuple[logging.Logger, Tuple[str, int, int]]:\n logging_server = LogServer()\n logging_server.start(True)\n\n if stream is None:\n stream = sys.stdout\n level = logging.DEBUG if debug else logging.INFO\n # Capture dask.distributed\n _config_logger(stream, level=level, name=\"distributed\")\n return _config_logger(\n stream,\n level=level,\n name=pathlib.Path(__file__).absolute().parent.name), (\n logging_server.ip, logging_server.port, level)",
"title": ""
},
{
"docid": "496dd70625b756822e0f756a93ecf30d",
"score": "0.49980557",
"text": "def getDockerOutput(args):\r\n return subprocess.check_output([getDockerExecutable()]+args)",
"title": ""
},
{
"docid": "175cbecc9b4e393deab42a0206c9d874",
"score": "0.49835387",
"text": "def _exec_dockerps():\n if docker is None:\n raise ImportError('Please install the Docker python client.')\n\n client = docker.Client(base_url='unix://var/run/docker.sock')\n containers = client.containers()\n inspect_arr = []\n for container in containers: # docker ps\n inspect = client.inspect_container(container['Id'])\n _reformat_inspect(inspect)\n inspect_arr.append(inspect)\n\n # Is this needed?\n del client\n\n return inspect_arr",
"title": ""
},
{
"docid": "96a055e0f01c37eed4ea1139f01ea8dd",
"score": "0.49828857",
"text": "def main(verbose, uv):\n if uv:\n uvloop.install()\n\n if verbose:\n level = 'INFO' if verbose == 1 else 'DEBUG'\n coloredlogs.install(level=level, fmt=LOGGING_FORMAT)",
"title": ""
},
{
"docid": "2144a957006db081bfd4fdffc6012a09",
"score": "0.4982557",
"text": "def main(unused_argv):\n FORMAT = '%(asctime)-15s %(message)s'\n stream1 = sys.stdout\n stream2 = file('stream2.log', 'w+')\n\n split_stream = SplitStream(stream1, stream2)\n logging.basicConfig(format=FORMAT, level=logging.DEBUG, stream=split_stream)\n\n logging.info('1')\n logging.info('2')\n logging.info('3')\n\n stream3 = file('stream3.log', 'a+')\n split_stream.SetStreams(stream1, stream3)\n stream2.close()\n\n logging.info('4')\n logging.info('5')\n logging.info('6')\n\n stream4 = file('stream4.log', 'w')\n split_stream.AddStream(stream4)\n logging.info('7')\n split_stream.RemoveStream(stream3)\n stream3.close()\n\n logging.info('8')\n logging.info('9')\n\n logging.shutdown()\n split_stream.flush()\n stream4.close()",
"title": ""
},
{
"docid": "255812840e9e46adba17d311ee3ee63a",
"score": "0.49772352",
"text": "def executeShellLog(self):\n\n return subprocess.check_output([self.SHELL_PATH + '/logs.sh',\n self.GITLAB_GROUP,\n self.PLAIN_PROJECT,\n self.ROOT_PATH])",
"title": ""
},
{
"docid": "d22c8494e3934e8665829aa383743461",
"score": "0.49721032",
"text": "def slurp_logs(self, aws_host, c_id, pod, container):\n\n # the logs are readable only by root, copy, chown and chmod them so they can be downloaded\n tmp_log_dir = \"docker_logs_%s\" % os.getpid()\n cmd = 'sudo sh -c \"mkdir -p %s && cp /var/lib/docker/containers/%s/%s-json.log* %s/\" && ' \\\n 'sudo sh -c \"chown %s %s/%s-json.log* && chmod 600 %s/%s-json.log*\"' % \\\n (tmp_log_dir, c_id, c_id, tmp_log_dir, DEFAULT_LOGIN, tmp_log_dir, c_id, tmp_log_dir, c_id)\n\n logging.info('About to execute %s on host %s', cmd, aws_host)\n cmd_output = self.connect_ssh.execute_cmd_remote(aws_host, cmd)\n logging.info('Output is %s', cmd_output)\n\n if self.args.previous:\n dir_name = '%s/%s/%s-previous' % (self.args.log_dirpath, pod, container)\n else:\n dir_name = '%s/%s/%s' % (self.args.log_dirpath, pod, container)\n cmd = ['mkdir', '-p', dir_name]\n retcode = subprocess.call(cmd)\n if retcode:\n sys.exit(1)\n\n # scp prints status of the the copy, so no need for additional messages on\n # success or failure\n remote_file_list = '%s/%s-json.log*' % (tmp_log_dir, c_id)\n logging.info('About to scp files from host %s: %s to %s',\n aws_host, remote_file_list, dir_name)\n self.connect_ssh.scp_copy_file(aws_host, remote_file_list, dir_name)\n logging.info('Output is %s', cmd_output)\n\n # delete the copy\n cmd = 'sudo sh -c \"rm -rf %s\"' % tmp_log_dir\n logging.info('About to execute %s on host %s', cmd, aws_host)\n cmd_output = self.connect_ssh.execute_cmd_remote(aws_host, cmd)\n logging.info('Output is %s', cmd_output)",
"title": ""
},
{
"docid": "934b41be26b374b00832a98580dd1854",
"score": "0.49719992",
"text": "def setup_loggers(verbose):\n\n stderr_handler = logging.StreamHandler(sys.stderr)\n\n class ColoredFormatter(logging.Formatter):\n \"\"\"\n Formatter that allows coloring logs via Clint library.\n \"\"\"\n def format(self, record):\n msg = record.getMessage()\n\n out_msg = '{}:{}:{}'.format(\n str(record.levelname),\n record.name,\n str(msg)\n )\n\n if hasattr(record.msg, 'color'):\n color = record.msg.color\n\n colored_msg = str(ColoredString(color, str(out_msg)))\n return colored_msg\n\n return out_msg\n\n if args.verbose:\n stderr_handler.setLevel(logging.DEBUG)\n else:\n stderr_handler.setLevel(logging.INFO)\n stderr_handler.setFormatter(ColoredFormatter())\n\n formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n\n # Logging to file as well\n file_handler = logging.FileHandler('attack-graph.log')\n file_handler.setLevel(logging.DEBUG)\n file_handler.setFormatter(formatter)\n\n logging.basicConfig(\n level=logging.DEBUG,\n handlers=[stderr_handler, file_handler]\n )",
"title": ""
},
{
"docid": "45fdaf25941abf335301dc453ce778fb",
"score": "0.49636096",
"text": "def thread_stdout(self):\n logger.debug(\"Threading stdout\")\n thread = Thread(target=self.read_stdout)\n thread.daemon = True\n thread.start()\n logger.debug(\"Threaded stdout\")",
"title": ""
},
{
"docid": "ef8f7bcac1bb980cf9faac11f3a0df14",
"score": "0.49541613",
"text": "def debug(cluster, deployment, project=\"kubeflow-ci-deployment\"):\n\n client = google_logging.Client()\n #log_name=f\"projects/{project}/logs/events\"\n #logger = client.logger()\n\n # Get the kubernetes events for this deployment\n namespace, name = deployment.split(\".\", 1)\n\n\n\n # Use a stack to recourse down the list of involved objects\n seen = set()\n involved_objects = [name]\n\n pod_name = None # pylint: disable=unused-variable\n while involved_objects:\n name = involved_objects.pop()\n seen.add(name)\n\n events_filter = f\"\"\"resource.labels.cluster_name=\"{cluster}\"\n logName=\"projects/{project}/logs/events\"\n jsonPayload.involvedObject.name=\"{name}\"\n jsonPayload.involvedObject.namespace=\"{namespace}\"\n \"\"\"\n # TODO(jlewi): This seems very slow; maybe we need to add a timestamp filter?\n # What if we add a timestamp filter like timestamp>=\"2020-01-28T18:54:58.453-0800\"\n logging.info(f\"Getting events for {name}\")\n for entry in client.list_entries(projects=[project], filter_=events_filter):\n logging.info(f\"Found event {entry.payload.get('message')}\")\n if entry.payload.get(\"reason\") == \"ScalingReplicaSet\":\n m = re.match(\"Scaled up replica set ([^ ]*) .*\",\n entry.payload.get(\"message\"))\n if not m:\n logging.info(\"Could not get replica set from message\")\n continue\n new_name = m.group(1)\n\n m = re.match(\"Created pod: ([^ ]*)\", entry.payload.get(\"message\"))\n if m:\n new_name = m.group(1)\n pod_name = new_name\n\n if not new_name in seen:\n involved_objects.insert(0, new_name)\n\n # TODO(jlewi): Fetch container logs. if the container started",
"title": ""
},
{
"docid": "24f89ffc52085e95b845f3171084d007",
"score": "0.49534097",
"text": "def setup_console_logging(log_level=None):\n\n formatter = logging.Formatter(\"[%(asctime)s] [%(name)s %(funcName)s:%(lineno)d] %(message)s\") # same as default\n\n # setup `RainbowLoggingHandler`\n # and quiet some logs for the test output\n handler = RainbowLoggingHandler(sys.stdout)\n handler.setFormatter(formatter)\n logger = logging.getLogger()\n logger.handlers = [handler]\n\n env_level = os.environ.get(\"LOG_LEVEL\", \"info\")\n log_level = log_level or getattr(logging, env_level.upper())\n logger.setLevel(log_level)\n\n # logger = logging.getLogger(\"requests.packages.urllib3.connectionpool\")\n # logger.setLevel(logging.ERROR)\n\n # # SQL Alchemy transactions\n # logger = logging.getLogger(\"txn\")\n # logger.setLevel(logging.ERROR)",
"title": ""
},
{
"docid": "d616ee253e630576989683a23050c5fd",
"score": "0.49478838",
"text": "def test_logs_build_url():\n with app.test_client() as c:\n req = c.get(\"/stacks-api/outputs/logs?logname={1}\".format(\n os.environ.get(\"STACK\"), os.environ.get(\"LOG_GROUP\")\n ))\n assert req.status_code == 200\n assert json.loads(req.data.decode())[\"log-url\"] == \"https://us-west-2.console.aws.amazon.com/cloudwatch/home?region=us-west-2#logStream:group={0}\".format(os.environ.get(\"LOG_GROUP\"))",
"title": ""
},
{
"docid": "55b5869999097d5d2518068abd0798dc",
"score": "0.49417403",
"text": "def start_logging():\n log = logging.getLogger('jiracli')\n log.setLevel(logging.DEBUG)\n # create console handler with a higher log level\n console_h = logging.StreamHandler()\n console_h.setLevel(logging.DEBUG)\n # create file handler which logs even debug messages\n fh = logging.FileHandler('jiracli.log')\n fh.setLevel(logging.DEBUG)\n # create formatter and add it to the handlers\n formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n fh.setFormatter(formatter)\n # console_h.setFormatter(formatter)\n # add the handlers to the logger\n log.addHandler(fh)\n log.addHandler(console_h)\n return log",
"title": ""
},
{
"docid": "405b522138fc68575e1021f2d2de5b5a",
"score": "0.49378666",
"text": "def echo(self,*args):\n log.debug('?'*30)\n return {'retcode':0,'stdout':args}",
"title": ""
},
{
"docid": "ef27e42a36cfa242f9a85df7c350733a",
"score": "0.49357757",
"text": "def enable_logging() -> None:\n module_name = os.path.splitext(os.path.basename(__file__))[0]\n log_file = '../output/' + module_name + \".log\"\n\n tee = subprocess.Popen([\"tee\", \"-a\", log_file], stdin=subprocess.PIPE)\n os.dup2(tee.stdin.fileno(), sys.stdout.fileno())",
"title": ""
},
{
"docid": "321679e7fe7c6abb91377d615cc1a0dc",
"score": "0.49353",
"text": "def collect_dataplane_logs(self):\n\n result = self.kubectl_helper.get_pods(namespace=NUVOLOSO_CLUSTER)\n if not result:\n raise Exception(\"Failed to get list of pods from namespace %s. Can't get logs. Exiting..\" % NUVOLOSO_CLUSTER)\n logging.info(result)\n nuvo_pod_list = [x.split('/')[-1] for x in result.splitlines()]\n logging.info(nuvo_pod_list)\n\n for container in CLUSTERD_CONTAINERS:\n try:\n # get container log file\n logging.info(\"Collecting logs for container %s in %s\", container, CLUSTERD)\n with open(self.args.log_dirpath + \"/\" + container + \"-\" + \\\n CLUSTERD + \".log\", \"w\") as outfile:\n outfile.write(self.kubectl_helper.run_kubectl_logs_cmd(CLUSTERD, container_name=container, namespace=NUVOLOSO_CLUSTER))\n\n # get previous logs in case container restarted\n logging.info(\"Collecting previous logs for container %s in %s\", container, CLUSTERD)\n with open(self.args.log_dirpath + \"/\" + container + \"-\" + \\\n CLUSTERD + \"-previous.log\", \"w\") as outfile:\n outfile.write(self.kubectl_helper.run_kubectl_logs_cmd(CLUSTERD, container_name=container, namespace=NUVOLOSO_CLUSTER, cmd_params=\"-p\"))\n except subprocess.CalledProcessError as err:\n if err.output:\n logging.error(err.output)\n if \"previous terminated container\" in err.output and \\\n \"not found\" in err.output:\n logging.error(\"Container: %s in pod: %s has no previous \"\n \"container logs. Will move ahead to collect \"\n \"other logs\", container, \"clusterd\")\n else:\n logging.error(\"Failed to collect logs for pod: %s \"\n \"container: %s . Will move ahead to collect \"\n \"other logs\", \"clusterd\", container)\n\n # list /var/crash on each node\n # For each node (except 'clusterd') get logs of agentd and nuvo\n # skip 0th since we just collected its logs (clusterd-0)\n for i in range(1, len(nuvo_pod_list)):\n for container in NUVO_CONTAINERS:\n try:\n # get container log file\n logging.info(\"Collecting logs for container %s in %s \", container, nuvo_pod_list[i])\n with open(self.args.log_dirpath + \"/\" + container + \"-\" + \\\n nuvo_pod_list[i] + \".log\", \"w\") as outfile:\n outfile.write(self.kubectl_helper.run_kubectl_logs_cmd(nuvo_pod_list[i], container_name=container, namespace=NUVOLOSO_CLUSTER))\n\n # get previous logs in case container restarted\n logging.info(\"Collecting previous logs for container %s in %s \", container, nuvo_pod_list[i])\n with open(self.args.log_dirpath + \"/\" + container + \"-\" + \\\n nuvo_pod_list[i] + \"-previous.log\", \"w\") as outfile:\n outfile.write(self.kubectl_helper.run_kubectl_logs_cmd(nuvo_pod_list[i], container_name=container, namespace=NUVOLOSO_CLUSTER, cmd_params='-p'))\n\n # ls -lrt /var/crash for nuvo containers\n if container == \"nuvo\":\n logging.info(\"Checking for presence of core dumps in /var/crash\")\n result = self.kubectl_helper.run_kubectl_exec_cmd(\"ls -lrt /var/crash\", nuvo_pod_list[i], container_name=\"nuvo\", namespace=NUVOLOSO_CLUSTER)\n if result:\n logging.info(result)\n except subprocess.CalledProcessError as err:\n if err.output:\n logging.error(err.output)\n if \"previous terminated container\" in err.output and \\\n \"not found\" in err.output:\n logging.error(\"Container: %s in pod: %s has no previous \"\n \"container logs. Will move ahead to collect \"\n \"other logs\", container, nuvo_pod_list[i])\n else:\n logging.error(\"Failed to collect logs for pod: %s \"\n \"container: %s . Will move ahead to collect \"\n \"other logs\", nuvo_pod_list[i], container)\n logging.info(\"Done collecting logs.\")",
"title": ""
},
{
"docid": "258f1a681e5c87b151fffd70eb52a5b6",
"score": "0.49328884",
"text": "def configure_logging():\n logger = logging.getLogger(\"coverview_\")\n\n formatter = logging.Formatter(\"%(asctime)s - %(levelname)s - %(filename)s - Line %(lineno)s - %(message)s\")\n\n stream_handler = logging.StreamHandler()\n stream_handler.setFormatter(formatter)\n stream_handler.setLevel(logging.INFO)\n\n logger.addHandler(stream_handler)\n logger.setLevel(logging.INFO)",
"title": ""
},
{
"docid": "0a30467697329a478f8c048b53e0254f",
"score": "0.4932864",
"text": "def output(args, config, cf_conn):\n print(\"Describing CloudFormation Stack %s...\" % config['stack_name'])\n resp = conn.describe_stacks(\n config['stack_name']\n )\n print('---');\n print('region: %s' % args['--region'])\n for output in resp[0].outputs:\n print(\"%s: %s\" % (output.description, output.value))",
"title": ""
},
{
"docid": "f6cafd0f2896b10b1ee72d09dd942f97",
"score": "0.49246496",
"text": "def run_dev(\n qserv_root: str,\n test_container: str,\n qserv_image: str,\n bind: List[str],\n project: str,\n dry: bool,\n) -> str:\n args = [\n \"docker\",\n \"run\",\n \"--init\",\n \"--rm\",\n \"--name\",\n test_container,\n \"-it\",\n ]\n if bind:\n args.extend(bind_args(qserv_root=qserv_root, bind_names=bind))\n add_network_option(args, project)\n args.extend([qserv_image, \"/bin/bash\"])\n if dry:\n print(\" \".join(args))\n else:\n _log.debug('Running \"%s\"', \" \".join(args))\n subprocess.run(args)\n return test_container",
"title": ""
},
{
"docid": "b2412e8204afde9e8cdb2c8fbe908dcc",
"score": "0.49199986",
"text": "def get_console_errors():\n logs = seleniumExtended._current_browser().get_log('browser')\n seleniumExtended._log(logs)\n seleniumExtended.capture_page_screenshot()",
"title": ""
},
{
"docid": "d4df340b1320e91bcb2eec365d2169c5",
"score": "0.4917765",
"text": "def print_log_overview(self):\n if self.src_msg and self.src_events:\n print \"*** Information extract from Source log file:\"\n print \"\\t%d events and %d log messages:\" % (len(self.src_events),\n len(self.src_msg))\n print \"\\tsimulation start: %s\" % self.src_simulation_start\n print \"\\tsimulation end: %s\" % self.src_simulation_end\n print \"\\tsimulation duration: %s\" % self.src_simulation_duration\n print \"\\tno bootstrap events: %d\" % len(self.src_bootstrap_events)\n print \"\\tno simulation events: %d\" % len(self.src_simulation_events)\n if self.dst_msg and self.dst_events:\n print \"*** Information extract from Destimnation log file:\"\n print \"\\t%d events and %d log messages.\" % (len(self.dst_events),\n len(self.dst_msg))\n print \"\\tsimulation start: %s\" % self.dst_simulation_start\n print \"\\tsimulation end: %s\" % self.dst_simulation_end\n print \"\\tsimulation duration: %s\" % self.dst_simulation_duration",
"title": ""
},
{
"docid": "4e0a7420d2244659d303b41711f7df55",
"score": "0.49175334",
"text": "def _result_show(self, result):\n details = \"\"\n if result[5] != \"NA\":\n details += \"--install: \" + result[5] + \"\\n\"\n if result[3] != \"NA\":\n details += \"--instance: \" + result[3]\n if result[4] != \"NA\":\n details += \" --database: \" + result[4] + \" --\"\n\n self.gLogging.info(\"--------\" + result[1] + \" \" + self.hostDict[result[1]] + \"--------\")\n self.gLogging.info(details)\n #self.gLogging.show(\"\")\n for line in result[0].splitlines():\n if len(line.decode(\"utf-8\")) > 0:\n self.gLogging.info(line.decode(\"utf-8\"))\n self.gLogging.show(\"\")",
"title": ""
},
{
"docid": "8b95bc274f3c249cb4461ff739c54d43",
"score": "0.49152526",
"text": "def _setup_log(self):\n self._log = logging.getLogger(__name__)\n self._log.handlers = []\n stdout_handler = logging.StreamHandler(sys.stdout)\n stdout_handler.setFormatter(\n logging.Formatter(\"%(asctime)s - %(name)s | %(levelname)8s: %(message)s\")\n )\n self._log.addHandler(stdout_handler)\n if self.verbosity == 0:\n self._log.setLevel(logging.CRITICAL)\n elif self.verbosity == 1:\n self._log.setLevel(logging.INFO)\n else:\n self._log.setLevel(logging.DEBUG)",
"title": ""
},
{
"docid": "d37e49f5aa771990a71a2a83c9eef449",
"score": "0.49026302",
"text": "def testDockerModeStdio(self):\n\n task = {\n 'mode': 'docker',\n 'docker_image': TEST_IMAGE,\n 'pull_image': True,\n 'container_args': ['$input{test_mode}', '-m', '$input{message}'],\n 'inputs': [{\n 'id': 'test_mode',\n 'name': '',\n 'format': 'string',\n 'type': 'string'\n }, {\n 'id': 'message',\n 'name': '',\n 'format': 'string',\n 'type': 'string'\n }],\n 'outputs': []\n }\n\n inputs = {\n 'test_mode': {\n 'format': 'string',\n 'data': 'stdio'\n },\n 'message': {\n 'format': 'string',\n 'data': self._test_message\n }\n }\n celery_task = mock.MagicMock()\n celery_task.canceled = False\n\n _old = sys.stdout\n stdout_captor = six.StringIO()\n sys.stdout = stdout_captor\n run(\n task, inputs=inputs, _tempdir=self._tmp, cleanup=True, validate=False,\n auto_convert=False, _celery_task=celery_task)\n sys.stdout = _old\n lines = stdout_captor.getvalue().splitlines()\n self.assertEqual(lines[-1], self._test_message)\n\n task = {\n 'mode': 'docker',\n 'docker_image': TEST_IMAGE,\n 'pull_image': True,\n 'container_args': ['$input{test_mode}', '-m', '$input{message}'],\n 'inputs': [{\n 'id': 'test_mode',\n 'name': '',\n 'format': 'string',\n 'type': 'string'\n }, {\n 'id': 'message',\n 'name': '',\n 'format': 'string',\n 'type': 'string'\n }],\n 'outputs': []\n }\n _old = sys.stdout\n stdout_captor = six.StringIO()\n sys.stdout = stdout_captor\n run(\n task, inputs=inputs, cleanup=True, validate=False,\n auto_convert=False, _celery_task=celery_task)\n sys.stdout = _old\n\n lines = stdout_captor.getvalue().splitlines()\n self.assertEqual(lines[-1], self._test_message)\n\n # Test _stdout\n task['outputs'] = [{\n 'id': '_stdout',\n 'format': 'string',\n 'type': 'string'\n }]\n\n _old = sys.stdout\n stdout_captor = six.StringIO()\n sys.stdout = stdout_captor\n out = run(\n task, inputs=inputs, cleanup=False, validate=False,\n auto_convert=False, _celery_task=celery_task)\n sys.stdout = _old\n\n lines = stdout_captor.getvalue().splitlines()\n message = '%s\\n' % self._test_message\n self.assertTrue(message not in lines)\n self.assertEqual(out['_stdout']['data'], message)",
"title": ""
},
{
"docid": "9e9df9ffb366ef9695660c4c84f3cb2e",
"score": "0.48980942",
"text": "def test(image, config):\n c = APIClient(base_url='unix://var/run/docker.sock')\n env = {\"TEST_USER\": config['test_user'], \"TEST_TOKEN\": config['test_token'],\n \"TEST_WSURL\": config['test_wsurl']}\n container = c.create_container(image=image, command=\"test\", environment=env)\n id = container.get('Id')\n response = c.start(container=id)\n status = dict()\n status['Running'] = True\n while status['Running'] == True:\n status = c.inspect_container(id)['State']\n time.sleep(1)\n c.remove_container(container=id)\n if status['Running'] == False:\n print(\"Exited with %d\" % (status['ExitCode']))\n sys.exit(status['ExitCode'])\n return",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="712"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">84c47483d857f890fe9beeda7750176d</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">returns the color of the drawn circle on the screen it can be different from the mass radius depending on mass.z</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "352421a22a01d4800f90a2cba9f68d4d",
"score": "0.6111481",
"text": "def show_color(self):\n if not self.bound:\n return self.color\n r, g, b = self.color\n x = (WIND + self.z) / (2 * WIND)\n r = int(abs(r * x))\n g = int(abs(g * x))\n b = int(abs(b * x))\n return r, g, b",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "7a7d93903e0a47e10ecb16bf8d297757",
"score": "0.707346",
"text": "def _drawcircle(self,x,y,rad):\n color=\"red\"\n return self.c.create_oval(x-rad,y-rad,x+rad,y+rad,width=rad/5,fill=color,outline='black')",
"title": ""
},
{
"docid": "5251198223462dcdfb636d832cd01f64",
"score": "0.7000268",
"text": "def _drawcircle(self, x, y, rad):\n color=\"red\"\n return self.c.create_oval(x-rad, y-rad, x+rad, y+rad, width=rad/5, fill=color, outline='black')",
"title": ""
},
{
"docid": "56c1d4297634838fb68ceee8e3a21c8b",
"score": "0.6699024",
"text": "def color(screen_to_display, position, turn, columns, r_last):\n\n global cl, rows_lib\n if turn == 1:\n cl = (255, 0, 0)\n elif turn == 2:\n cl = (255, 255, 0)\n pygame.draw.circle(screen_to_display, cl, (int(position * 500 / columns + 500 / (columns * 2)),\n int(r_last * 500 / columns + 500 / rows_lib + 500 / (columns * 2))),\n int(500 / (2.5 * columns)))\n pygame.display.update()",
"title": ""
},
{
"docid": "5757a87a6fe44b5fe0af4618869fddb1",
"score": "0.65900064",
"text": "def radial(radius, startcolor, endcolor):\r\n bigSurf = pygame.Surface((2*radius, 2*radius)).convert_alpha() \r\n bigSurf.fill((0,0,0,0))\r\n dd = -1.0/radius\r\n sr, sg, sb, sa = endcolor\r\n er, eg, eb, ea = startcolor\r\n rm = (er-sr)*dd\r\n gm = (eg-sg)*dd\r\n bm = (eb-sb)*dd\r\n am = (ea-sa)*dd\r\n \r\n draw_circle = pygame.draw.circle\r\n for rad in range(radius, 0, -1):\r\n draw_circle(bigSurf, (er + int(rm*rad),\r\n eg + int(gm*rad),\r\n eb + int(bm*rad),\r\n ea + int(am*rad)), (radius, radius), rad)\r\n return bigSurf",
"title": ""
},
{
"docid": "ea04c09852183b2e84ece000daaf8ecf",
"score": "0.6582159",
"text": "def draw_circle(self, color, center, radius, width=1):\n _c = self.T.itrans(center)\n pg.draw.circle(self.screen, color, _c(), radius, width)",
"title": ""
},
{
"docid": "837e1eebb336ebde3628b9451bfceea2",
"score": "0.63453174",
"text": "def draw_circle(self, position, radius, color, tag):\n return self.canvas.create_oval(\n util.get_circle_points(position, radius),\n fill=color,\n outline='black',\n tags=tag\n )",
"title": ""
},
{
"docid": "25e50973d0ef0c9411f43ad798ecb581",
"score": "0.6336504",
"text": "def makeCircle(c, r, color):\n\n circ = Circle(c, r)\n circ.setOutline(\"black\")\n circ.setFill(color)\n return circ",
"title": ""
},
{
"docid": "96c3aa9884eebfd6baf7c8eee1c4036d",
"score": "0.63314706",
"text": "def drawCircle():\n\tglobal d\n\tx = randint(0, d.getWidth())\n\ty = randint(0, d.getHeight())\n\tradius = randint(5, 40)\n\tred = randint(100, 255)\n\tblue = randint(0, 100)\n\tcolor = Color(red, 0, blue)\n\tc = Circle(x, y, radius, color, True)\n\td.add(c)\n\tpitch = mapScale(255 - red + blue, 0, 255, C4, C6, MAJOR_SCALE)\n\tdynamic = mapValue(radius, 5, 40, 20, 127)\n\tPlay.note(pitch, 0, 5000, dynamic)",
"title": ""
},
{
"docid": "07fb51ab78ad760b2849aab2629c7d1b",
"score": "0.61972344",
"text": "def circle(x, y, radius, fill_color=\"\", stroke_color=\"\", stroke_width=-1):\n raise NotImplementedError(\"circle() not implemented\")",
"title": ""
},
{
"docid": "a1d513b0491c20dfce4c731832dd92f3",
"score": "0.6189247",
"text": "def circle_square(R):\n import math\n return math.pi*R*R",
"title": ""
},
{
"docid": "2b55dc90d50ccc0e258f014af7bcee3e",
"score": "0.618901",
"text": "def circle(r):\r\n import math\r\n area= math.pi*(r*r)\r\n print('The area of circle is ',area)",
"title": ""
},
{
"docid": "eefd48da5acc24151ce9385cffb0ed8a",
"score": "0.61442167",
"text": "def color(ray):\n sphere_center = Vec3(0, 0, -1)\n t = hit_sphere(sphere_center, 0.5, r)\n if t > 0:\n norm = make_unit_vector(ray.point_at_parameter(t) - sphere_center)\n return 0.5*Vec3(norm.x()+1, norm.y()+1, norm.z()+1)\n unit_direction = make_unit_vector(ray.direction())\n t = 0.5*(unit_direction.y() + 1.0)\n return (1.0 - t)*Vec3(1.0, 1.0, 1.0) + t*Vec3(0.5, 0.7, 1.0)",
"title": ""
},
{
"docid": "212b3690a5b650a72d865af3f9ed4085",
"score": "0.60957396",
"text": "def draw(self):\n pg.draw.circle(screen, self.color, list(map(int, self.coord)), self.rad)",
"title": ""
},
{
"docid": "3d38951b76fa5a9a3955fd34563dcae0",
"score": "0.6094328",
"text": "def circlesDraw():",
"title": ""
},
{
"docid": "0ba02fa7aad2d49feb4416cd36594e91",
"score": "0.60907704",
"text": "def circle(shape, radius, shift=(0, 0)):\n x, y = mesh(shape)\n r = np.sqrt(np.square(x - shift[1]) + np.square(y - shift[0]))\n return np.clip(radius + 0.5 - r, 0.0, 1.0)",
"title": ""
},
{
"docid": "50b250127246fc0ca9cd70cc4ff0b5f1",
"score": "0.6080311",
"text": "def circle_circumference(radius):\n return 2*pi*radius",
"title": ""
},
{
"docid": "c032309ac4e606137b73be5b1ff44439",
"score": "0.60733724",
"text": "def circleInfo(r):\n c = 2 * 3.14159 * r\n a = 3.14159 * r * r\n return (c, a)",
"title": ""
},
{
"docid": "a2b31ba47a5dc1154d1d57a0c968dc7b",
"score": "0.60430205",
"text": "def draw(self, screen):\n\t\tpygame.draw.circle(screen, self.color, (self.x, self.y), self.radius)",
"title": ""
},
{
"docid": "e3fffe431c6f0bec5da270ba383aa454",
"score": "0.60300267",
"text": "def renderCircle(image, center, radius, color, thickness):\r\n return cv2.circle(image, center, radius, color, thickness)",
"title": ""
},
{
"docid": "e3da518ba0b7575f0579d8138e1a3dfc",
"score": "0.60197145",
"text": "def circle(x, y, r):\n ws = _factorX(2*r)\n hs = _factorY(2*r)\n #If the radius is too small, then simply draw a pixel\n if (ws <= 1) and (hs <= 1):\n _pixel(x, y)\n else:\n xs = _scaleX(x)\n ys = _scaleY(y)\n pygame.draw.ellipse(_surface,\n _pygameColor(_penColor),\n pygame.Rect(int(round(xs-ws/2)),\n int(round(ys-hs/2)), \n int(round(ws)),\n int(round(hs))),\n int(round(_penRadius)))",
"title": ""
},
{
"docid": "b5196b0c5c7efce1afedcc697c26bb81",
"score": "0.6018193",
"text": "def Circle(self,name, rad=1.): \n #put the apropriate code here\n circle=None\n #set name and rad for the circle\n return circle",
"title": ""
},
{
"docid": "9a30fc5c8b3cc38c6c5a060e5b32d6df",
"score": "0.59935087",
"text": "def circle(self, x_cen, y_cen, r):\n x1 = x_cen - r\n y1 = y_cen - r\n width = height = 2*r\n self.ellipse(x1, y1, width, height)",
"title": ""
},
{
"docid": "2313e41496a6fbd9546968c3913731e4",
"score": "0.59905297",
"text": "def create_circle(image, *, center=(0, 0), radius=10, color=None):\n color = np.random.rand(3) if color is None else color\n\n x0, y0 = center\n left, right = x0 - radius, x0 + radius\n top, bottom = y0 - radius, y0 + radius\n\n f = 1 - radius\n dx = 0\n dy = -2 * radius\n x, y = 0, radius\n while x < y:\n if f >= 0:\n y -= 1\n dy += 2\n f += dy\n\n x += 1\n dx += 2\n f += dx + 1\n image[y0-x:y0+x+1, x0-y:x0+y+1] = color\n image[y0-y:y0+y+1, x0-x:x0+x+1] = color\n\n return left, top, right, bottom",
"title": ""
},
{
"docid": "840c170c1eb8b6e557f789381921a0af",
"score": "0.59889185",
"text": "def draw_circle(self, center: point_like, radius: float) -> Point:\n if not radius > EPSILON:\n raise ValueError(\"radius must be postive\")\n center = Point(center)\n p1 = center - (radius, 0)\n return self.draw_sector(center, p1, 360, fullSector=False)",
"title": ""
},
{
"docid": "9c46b4c32e8e587a8b490979b37bd9e9",
"score": "0.59777117",
"text": "def drawCircle(self, x0, y0, z, radius, blockType):\n \n f = 1 - radius\n ddf_x = 1\n ddf_y = -2 * radius\n x = 0\n y = radius\n self.drawPoint3d(x0, y0 + radius, z, blockType)\n self.drawPoint3d(x0, y0 - radius, z, blockType)\n self.drawPoint3d(x0 + radius, y0, z, blockType)\n self.drawPoint3d(x0 - radius, y0, z, blockType)\n \n while x < y:\n if f >= 0:\n y -= 1\n ddf_y += 2\n f += ddf_y\n x += 1\n ddf_x += 2\n f += ddf_x \n self.drawPoint3d(x0 + x, y0 + y, z, blockType)\n self.drawPoint3d(x0 - x, y0 + y, z, blockType)\n self.drawPoint3d(x0 + x, y0 - y, z, blockType)\n self.drawPoint3d(x0 - x, y0 - y, z, blockType)\n self.drawPoint3d(x0 + y, y0 + x, z, blockType)\n self.drawPoint3d(x0 - y, y0 + x, z, blockType)\n self.drawPoint3d(x0 + y, y0 - x, z, blockType)\n self.drawPoint3d(x0 - y, y0 - x, z, blockType)",
"title": ""
},
{
"docid": "1c62bb863f4704ddc8266150f374fe5c",
"score": "0.597261",
"text": "def DrawSolidCircle(*args):\n return _Box2D2.b2DebugDraw_DrawSolidCircle(*args)",
"title": ""
},
{
"docid": "816974f84098f7b333f5679482105fa5",
"score": "0.5957713",
"text": "def DrawCircle(*args):\n return _Box2D2.b2DebugDraw_DrawCircle(*args)",
"title": ""
},
{
"docid": "6ec6365698f8bb5e3d706375a25aab4f",
"score": "0.59549505",
"text": "def get_color(self):\n cur_color = self.color\n # cur_color = self.cline.get_color()\n return cur_color",
"title": ""
},
{
"docid": "0814960ba4442d026173f128296bcb54",
"score": "0.5944981",
"text": "def circle(q):\n\trcirc = roche.rcirc(q)\n\tangle = np.linspace(0,2*np.pi,200)\n\t\n\tx = rcirc * np.sin(angle)\n\ty = rcirc * np.cos(angle)\n\t\n\treturn x, y",
"title": ""
},
{
"docid": "124d7d72ff75c6aa882fffe1cbc1b56a",
"score": "0.59304154",
"text": "def circle():\n\n size = 20\n X1 = np.linspace(-1, 1, size)\n X2 = np.sqrt(1-(X1**2))\n\n return X1, X2",
"title": ""
},
{
"docid": "99ad0ab44df41c91394d6efdc40f3049",
"score": "0.59261954",
"text": "def get_color(self):\n\n\t\treturn self.color",
"title": ""
},
{
"docid": "99ad0ab44df41c91394d6efdc40f3049",
"score": "0.59261954",
"text": "def get_color(self):\n\n\t\treturn self.color",
"title": ""
},
{
"docid": "3cad4a721f8a3369c357b4c19896eb7e",
"score": "0.5901064",
"text": "def filledCircle(x, y, r):\n ws = _factorX(2*r)\n hs = _factorY(2*r)\n #If the radius is too small, then simply draw a pixel\n if (ws <= 1) and (hs <= 1):\n _pixel(x, y)\n else:\n xs = _scaleX(x)\n ys = _scaleY(y)\n pygame.draw.ellipse(_surface,\n _pygameColor(_penColor),\n pygame.Rect(int(round(xs-ws/2)),\n int(round(ys-hs/2)),\n int(round(ws)),\n int(round(hs))),\n 0)",
"title": ""
},
{
"docid": "e896fb23e40b60e4172149675f3cc3c5",
"score": "0.58955777",
"text": "def mycircle(radius):\n if radius < 20:\n sides = 10\n elif radius < 100:\n sides = 30\n else:\n sides = 50\n polygon(sides, 6.28*radius/sides)",
"title": ""
},
{
"docid": "63340e6f2084247be9da3ae2f4e74f6e",
"score": "0.58921075",
"text": "def init_circle(self, circle):\n circle[\"color\"] = np.random.randint(-255, 256)\n circle[\"radius\"] = np.random.randint(20, max(self.height, self.width))\n circle[\"center\"][\"x\"] = np.random.randint(0, self.width)\n circle[\"center\"][\"y\"] = np.random.randint(0, self.height)",
"title": ""
},
{
"docid": "66f83094d94de096c90ee213fce3bae5",
"score": "0.58761346",
"text": "def __init__(self, centre, radius, colour=None):\r\n\r\n if colour is None:\r\n self._colour = [0, 0, 0]\r\n else:\r\n self._colour = colour\r\n\r\n Circle.__init__(self, centre, radius)",
"title": ""
},
{
"docid": "0e17ce180ec96f1f0e4ac88a10f0404c",
"score": "0.58727765",
"text": "def balls_draw(colour: tuple, xpos, ypos, r):\n ball = pygame.draw.circle(screen, colour, (int(xpos), int(ypos)), r)\n return ball",
"title": ""
},
{
"docid": "6d2a0998441c38487334a669311822c4",
"score": "0.5847786",
"text": "def get_color(self):\n return self.color",
"title": ""
},
{
"docid": "6d2a0998441c38487334a669311822c4",
"score": "0.5847786",
"text": "def get_color(self):\n return self.color",
"title": ""
},
{
"docid": "6d2a0998441c38487334a669311822c4",
"score": "0.5847786",
"text": "def get_color(self):\n return self.color",
"title": ""
},
{
"docid": "106a7442ced0b8401c06e7a4e5db0d86",
"score": "0.58423674",
"text": "def filledCircle(x, y, r):\n global _surface\n ws = stddraw._factorX(2*r)\n hs = stddraw._factorY(2*r)\n if (ws <= 1) and (hs <= 1):\n stddraw._pixel(x, y)\n else:\n xs = stddraw._scaleX(x)\n ys = stddraw._scaleY(y)\n pygame.draw.ellipse(_surface,\n stddrawpygameColor(stddraw.BLACK),\n pygame.Rect(xs-ws/2, ys-hs/2, ws, hs),\n 0)\n _draw()",
"title": ""
},
{
"docid": "d1d97c051049a21e84927778dce8151b",
"score": "0.5822862",
"text": "def get_color(self):\n return self.__color",
"title": ""
},
{
"docid": "ead096af2f10a4f6e596792e2555376f",
"score": "0.582256",
"text": "def radius(self):\r\n return self.__rayon",
"title": ""
},
{
"docid": "3acd36c2db462fd607e176652335f7f6",
"score": "0.5820885",
"text": "def drawCenters():\n \n radius = int(0.1 * CELLSIZE)\n for X, Y in product(X_GRID, Y_GRID):\n x, y = X + CELLSIZE//2, Y + CELLSIZE//2\n pygame.draw.circle(screen, color_GRIDS, (int(x), int(y)), radius)",
"title": ""
},
{
"docid": "c85cbfe4f8a655c408cfbde9a7164d39",
"score": "0.58185273",
"text": "def radius_gyration(mdl, cx, cy, cz, totalmass):\n rgyr = 0.\n for a in mdl.atoms:\n rgyr += ((a.x - cx) ** 2 + (a.y - cy) ** 2 + (a.z - cz) ** 2) * a.mass\n rgyr /= totalmass\n return math.sqrt(rgyr)",
"title": ""
},
{
"docid": "06e18067b764bac28e3c6900f53b3ae5",
"score": "0.5811671",
"text": "def circle(x, y, r):\n # sqrt((X-x)**2 + (Y-y)**2) - r\n r = abs(r)\n return Shape(\n '-r+q%sq%sf%g' % (('-Xf%g' % x) if x else 'X',\n ('-Yf%g' % y) if y else 'Y', r),\n x - r, y - r, x + r, y + r)",
"title": ""
},
{
"docid": "70231004cea2559ba2bd2271af52b680",
"score": "0.5810398",
"text": "def r(self):\n return self.__colour.x",
"title": ""
},
{
"docid": "b4b7686119f8340db1594ca84f2bf0dc",
"score": "0.5787844",
"text": "def draw_circle(\n page: Page,\n center: point_like,\n radius: float,\n color: OptSeq = None,\n fill: OptSeq = None,\n morph: OptSeq = None,\n dashes: OptStr = None,\n width: float = 1,\n lineCap: int = 0,\n lineJoin: int = 0,\n overlay: bool = True,\n stroke_opacity: float = 1,\n fill_opacity: float = 1,\n oc: int = 0,\n) -> Point:\n img = page.new_shape()\n Q = img.draw_circle(Point(center), radius)\n img.finish(\n color=color,\n fill=fill,\n dashes=dashes,\n width=width,\n lineCap=lineCap,\n lineJoin=lineJoin,\n morph=morph,\n stroke_opacity=stroke_opacity,\n fill_opacity=fill_opacity,\n oc=oc,\n )\n img.commit(overlay)\n return Q",
"title": ""
},
{
"docid": "5cdaa3645efc637a6caa600a9fbec0ec",
"score": "0.57755595",
"text": "def draw(self, screen, position_y):\n\t\tself.center_y = position_y\n\t\treturn pygame.draw.circle(screen, self.color, (self.center_x, self.center_y), self.radius)",
"title": ""
},
{
"docid": "f56f24cd8319b03fec730d59ac29b7a0",
"score": "0.5772547",
"text": "def circle_shape(x_center: float, y_center: float, width: float, height: float, color: str = '#636efa'):\n color = get_rgb_tuple(color)\n fillcolor = f'rgb{color}'\n linecolor = [x-50 if x-50 >= 0 else 0 for x in color]\n linecolor = f'rgb{tuple(linecolor)}'\n x0 = x_center - 0.5 * width\n x1 = x_center + 0.5 * width\n y0 = y_center - 0.5 * height\n y1 = y_center + 0.5 * height\n shape = dict(type='circle', x0=x0, x1=x1, y0=y0, y1=y1, fillcolor=fillcolor, line=dict(color=linecolor, width=1))\n\n return shape",
"title": ""
},
{
"docid": "ddc8e5751c230dc727a9433d5a420e31",
"score": "0.5755852",
"text": "def draw(self):\n coordinates = (int(self.cx), int(self.cy))\n draw.circle(screen, self.color, coordinates, self.radius)",
"title": ""
},
{
"docid": "54bd1ff0c4df025fec2e626d0dbda8ef",
"score": "0.57509494",
"text": "def _get_ccolor(self):\n if self._ccolor is None:\n self._ccolor = np.tile( [1.,0.,0,1.], (len(self),1)).astype(np.float32) \n return self._ccolor",
"title": ""
},
{
"docid": "064aeb152418d6dd1cde7c86e1cfcd70",
"score": "0.57481354",
"text": "def get_color(self):\n return self._color",
"title": ""
},
{
"docid": "74d5e983770892204f3f0346d353f911",
"score": "0.57418436",
"text": "def getColor(self):\n return _osg.ShapeDrawable_getColor(self)",
"title": ""
},
{
"docid": "b042df3c61c8189d027c9193b49e0cf8",
"score": "0.57308495",
"text": "def drawCircle(self, xC, yC, rad, color):\n raise NotImplementedError(\"PictureDrawer: attempted to call abstract method\")",
"title": ""
},
{
"docid": "96e3c6a35f844615392d860ec44fa6ee",
"score": "0.5713039",
"text": "def visualize_robot(current_position, robot_radius, color='m'):\n robot = plt.Circle((current_position[0], current_position[1]), robot_radius, color=color)\n ax = plt.gca()\n ax.add_artist(robot)\n\n return robot",
"title": ""
},
{
"docid": "a68fa905cb7db19552b012a668f14cff",
"score": "0.5708645",
"text": "def drawCircle (centerpoint, radius):\r\n (x,y) = centerpoint\r\n turtle.up()\r\n turtle.setpos(x,y)\r\n turtle.down()\r\n turtle.circle(radius)\r\n #making the print statements for the calculations of circumference\r\n print(\"The radius of the circle is\", radius)\r\n print(\"The circumference of the circle is\", (2.0 * 3.14 * radius))",
"title": ""
},
{
"docid": "e6abb0ad8ba75fdbf243cf65051deb47",
"score": "0.57073915",
"text": "def specularColor(self):\n return core.Color()",
"title": ""
},
{
"docid": "80e136817b21f8450b7f1fe15695c391",
"score": "0.57019496",
"text": "def get_color(self):\n\n if self.state == 2:\n # Black\n color = 0, 0, 0\n elif self.state == 1:\n # Flame orange\n color = 230, 41, 44\n elif self.veg == 'nov':\n # Blue\n color = 0, 105, 148\n elif self.dens == 'nov':\n # Gray\n color = 188, 188, 188\n elif self.veg == 'for':\n # Forest green\n if self.dens == 'den':\n color = 13, 72, 13\n elif self.dens == 'nor':\n color = 59, 128, 59\n else:\n color = 149, 193, 149\n elif self.veg == 'agr':\n # Wheat yellow\n if self.dens == 'den':\n color = 242, 210, 50\n elif self.dens == 'nor':\n color = 236, 221, 147\n else:\n color = 210, 205, 178\n elif self.veg == 'shr':\n # Shrubland green\n if self.dens == 'den':\n color = 105, 200, 105\n elif self.dens == 'nor':\n color = 162, 220, 162\n else:\n color = 192, 220, 192\n else:\n raise ValueError(\"Invalid vegetation type\")\n\n # Convert to 0-1\n color = tuple([c / 255 for c in color])\n\n return color",
"title": ""
},
{
"docid": "2c269af4fe4e6917a196da8440e8af42",
"score": "0.5695652",
"text": "def area_circle(r):\n return r * r * pi",
"title": ""
},
{
"docid": "5486bf303935da8e35250b37e4302691",
"score": "0.5687823",
"text": "def circAp(totalSize,radius):\n x = np.linspace(-1,1,totalSize)\n y = np.linspace(1,-1,totalSize)\n xx, yy = np.meshgrid(x,y)\n \n r = np.abs(xx + 1j*yy)\n theta = np.angle(xx + 1j*yy)\n \n mask = r<radius\n \n return mask,r,theta",
"title": ""
},
{
"docid": "6309777f51e31ebe750da4b3ab0be1d2",
"score": "0.5685132",
"text": "def color(self):\r\n return self._color",
"title": ""
},
{
"docid": "11acdc83d926e6d3a5521ee60b6dff05",
"score": "0.5679463",
"text": "def _pygameColor(c):\n r = c.getRed()\n g = c.getGreen()\n b = c.getBlue()\n return pygame.Color(r, g, b)",
"title": ""
},
{
"docid": "8967b238c425cc1736144c0ac551077a",
"score": "0.5676356",
"text": "def get_outline(self):\r\n return 2 * 3.14 * self.radius",
"title": ""
},
{
"docid": "d8141391d4082a743cf53a0a2db714de",
"score": "0.5674283",
"text": "def circle_len(R):\n import math\n return 2*math.pi*R",
"title": ""
},
{
"docid": "28c96489a198c387073aa70de29b6f27",
"score": "0.5672668",
"text": "def draw(self,canvas):\n x,y=self.pos # can be float values\n botColor=getColorByName(self.color)\n pygame.draw.circle(canvas, botColor,(int(x),int(y)), self.size, 0)\n self.pixelRing.draw(self.pos,canvas)\n self.distSensor.draw(canvas,self)",
"title": ""
},
{
"docid": "af1ca057223dc28c535d6384d96de789",
"score": "0.5671056",
"text": "def getColor(self):\n return self.color",
"title": ""
},
{
"docid": "633d1e13adf33125b6a397de68775c86",
"score": "0.56589866",
"text": "def create_circle():\n Circle(get_random_int(), get_random_int(), 300 * random.random())",
"title": ""
},
{
"docid": "4450149a609e035e79be2e3ed44ca78f",
"score": "0.5642762",
"text": "def getColor(self):\n return self.color",
"title": ""
},
{
"docid": "182a3576e47b10b48b93b9f5498a9f17",
"score": "0.5639062",
"text": "def cylinder_radius(self):\n return self.__cyradius",
"title": ""
},
{
"docid": "182a3576e47b10b48b93b9f5498a9f17",
"score": "0.5639062",
"text": "def cylinder_radius(self):\n return self.__cyradius",
"title": ""
},
{
"docid": "7ff05de67eb4cdef98d9b767b278178b",
"score": "0.56377923",
"text": "def color(self):\n return self._color",
"title": ""
},
{
"docid": "7ff05de67eb4cdef98d9b767b278178b",
"score": "0.56377923",
"text": "def color(self):\n return self._color",
"title": ""
},
{
"docid": "7ff05de67eb4cdef98d9b767b278178b",
"score": "0.56377923",
"text": "def color(self):\n return self._color",
"title": ""
},
{
"docid": "50dcd494502da62121093f50b79a826e",
"score": "0.5637753",
"text": "def circle():\n radius = checkPos(\"Enter the circle's radius: \")\n printCircumference(2 * pi * radius)\n printArea(pi * radius * radius)",
"title": ""
},
{
"docid": "d27f0392b1d83ce446551a4726e0a8b1",
"score": "0.56336576",
"text": "def draw(self, surface):\n pygame.draw.circle(surface, self.colour, (self.x, self.y), self.radius)",
"title": ""
},
{
"docid": "ba8ef1dc67a5541ddbf59d61e214ff61",
"score": "0.56323326",
"text": "def color(self):\n return core.Color()",
"title": ""
},
{
"docid": "ba8ef1dc67a5541ddbf59d61e214ff61",
"score": "0.563153",
"text": "def color(self):\n return core.Color()",
"title": ""
},
{
"docid": "296ccec21e46be4a92979493bb1a0fa4",
"score": "0.5630054",
"text": "def DrawCircle(r, dx, dy):\n\n for x in range(0, r):\n if x/(sqrt(r**2-x**2)) < 1:\n y = round(sqrt(r**2-x**2))\n\n lcd.dot(dx + x, dy - y) #1\n lcd.dot(dx + y, dy - x) #2\n lcd.dot(dx + y, dy + x) #3\n lcd.dot(dx + x, dy + y) #4\n lcd.dot(dx - x, dy + y) #5\n lcd.dot(dx - y, dy + x) #6\n lcd.dot(dx - y, dy - x) #7\n lcd.dot(dx - x, dy - y) #8",
"title": ""
},
{
"docid": "48074d2a9e0a35c12f5be982f218c3dc",
"score": "0.5624318",
"text": "def build_circle_image():\n\n # Create a 500x500 black image with a circle inside:\n img = np.zeros((500, 500, 3), dtype=\"uint8\")\n cv2.circle(img, (250, 250), 200, (255, 255, 255), 1)\n\n return img",
"title": ""
},
{
"docid": "08fec6c5772014d4ebb4d732e821652d",
"score": "0.56205523",
"text": "def colour(self):\n try:\n colour = self._material.Color\n except AttributeError:\n colour = None\n return colour",
"title": ""
},
{
"docid": "8ca7cf32339db43ffc27d6282f94ae47",
"score": "0.56061536",
"text": "def get_colour(self):\n return self.colour",
"title": ""
},
{
"docid": "3f92ead57087abe82255017a2df42e14",
"score": "0.5602003",
"text": "def getcolor(self):\r\n if self.roulette_number in [1,3,5,7,9,12,14,16,18,19,21,23,25,27,30,32,34,36]:\r\n return 'red'\r\n elif self.roulette_number == 0:\r\n return 'No color [Zero]'\r\n else:\r\n return 'black'",
"title": ""
},
{
"docid": "639da7337639d5e5ff79282a9c980e58",
"score": "0.55994433",
"text": "def circle(a, b, c):\n # following http://mathworld.wolfram.com/Circle.html\n A = det([[a[0], a[1], 1],\n [b[0], b[1], 1],\n [c[0], c[1], 1]])\n if abs(A) < 10**-8:\n return 0, 0, 0\n sa = a[0]**2 + a[1]**2\n sb = b[0]**2 + b[1]**2\n sc = c[0]**2 + c[1]**2\n D = -det([[sa, a[1], 1],\n [sb, b[1], 1],\n [sc, c[1], 1]])\n E = det([[sa, a[0], 1],\n [sb, b[0], 1],\n [sc, c[0], 1]])\n F = -det([[sa, a[0], a[1]],\n [sb, b[0], b[1]],\n [sc, c[0], c[1]]])\n x = -D / (2 * A)\n y = -E / (2 * A)\n G = (D**2 + E**2) / (4 * (A**2))\n r = np.sqrt(G - (F / A))\n if r > 10**12:\n return 0, 0, 0\n return float(r), float(x), float(y)",
"title": ""
},
{
"docid": "50d3518ba5d99da4a0026ac1f862bc1b",
"score": "0.5597814",
"text": "def color(self):\n return CustomGraphicsColorEffect()",
"title": ""
},
{
"docid": "a1569a371e6c29ef951f4951561592cf",
"score": "0.55977625",
"text": "def get_color(self) -> int:\n return abs(self.dxf.color)",
"title": ""
},
{
"docid": "e1ec3a7857b2f9ff1ae184f59af15861",
"score": "0.55902296",
"text": "def _circleEquation(pt1, pt2, pt3):\n x, y, z = complex(*pt1), complex(*pt2), complex(*pt3)\n w = z - x\n w /= y - x\n c = (x - y) * (w - abs(w) ** 2) / 2j / w.imag - x\n return numpy.array((-c.real, -c.imag)), abs(c + x)",
"title": ""
},
{
"docid": "e9783799ad74fff79c5c4f8fbc048866",
"score": "0.55893755",
"text": "def radius(self, rad):\r\n \r\n self.__rayon = rad",
"title": ""
},
{
"docid": "3e34ca7560d84d58c326cce2f088cec5",
"score": "0.5588928",
"text": "def get_color(self):\n\n return self._color",
"title": ""
},
{
"docid": "be1c96e55337a069eb7d19c07a1a773d",
"score": "0.5581548",
"text": "def get_radius(self):\r\n return self.radius",
"title": ""
},
{
"docid": "45fd122be47353b27b3c10ee7eadbfb2",
"score": "0.5579749",
"text": "def circle(radius=1.0, theta=10.0, xc=0.0, yc=0.0):\r\n angles = np.deg2rad(np.arange(180.0, -180.0-theta, step=-theta))\r\n x_s = radius*np.cos(angles) + xc # X values\r\n y_s = radius*np.sin(angles) + yc # Y values\r\n pnts = np.c_[x_s, y_s]\r\n return pnts",
"title": ""
},
{
"docid": "78cec25d1f5e1e8a7289e26832cd1695",
"score": "0.5572761",
"text": "def solid_angle_cone(radius):\n return 4 * np.pi * np.sin(radius * np.pi / 180 / 2) ** 2",
"title": ""
},
{
"docid": "95108a1bac9887e335106e35dfb63f37",
"score": "0.5570925",
"text": "def circle():\n speed = Twist()\n speed.linear.x = 1\n speed.linear.y = 0\n speed.linear.z = 0\n speed.angular.x = 0\n speed.angular.y = 0\n speed.angular.z = 1\n cmd_vel_pub.publish(speed)",
"title": ""
},
{
"docid": "cf43ea31db904c68749f6dc093e8aa36",
"score": "0.5553893",
"text": "def Create_Circle(self, x1, y1, rad, Da_or_UDa, Colo):\n if Da_or_UDa == 1:\n self.cnvs.create_oval(x1-rad, y1-rad, x1+rad, y1+rad, dash=(4, 2), outline=Colo)\n elif Da_or_UDa == 2:\n self.cnvs.create_oval(x1-rad, y1-rad, x1+rad, y1+rad, outline=Colo)",
"title": ""
},
{
"docid": "05333ab8ab42d0609b1be3c3a3a31781",
"score": "0.5553658",
"text": "def color(self) -> int:\n return self.get_color()",
"title": ""
},
{
"docid": "90abb06e85160c54dc629bf0ef3c1974",
"score": "0.55483633",
"text": "def is_circle(self):\n return self.__shape == 'Ball'",
"title": ""
},
{
"docid": "318ee983bd3c2aa3ecb8f24d6dd9a067",
"score": "0.55423564",
"text": "def colour(self):\r\n\r\n return self._colour",
"title": ""
},
{
"docid": "318ee983bd3c2aa3ecb8f24d6dd9a067",
"score": "0.55423564",
"text": "def colour(self):\r\n\r\n return self._colour",
"title": ""
},
{
"docid": "839118fa5b7374cf49b75e4347266ae5",
"score": "0.55417085",
"text": "def getConstantColor(self):\n return _osg.TexEnvCombine_getConstantColor(self)",
"title": ""
},
{
"docid": "99c7eeec916621eae033c437bfbae0a3",
"score": "0.55392396",
"text": "def circle_area(radius):\n return math.pi * radius ** 2",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="713"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">7fba9ede4b0716e61c11ec7b2329ca94</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Generates a random character in [AZaz09].</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "c2cee4d2d87f3a14e49abaa85bf7e0d1",
"score": "0.75228137",
"text": "def random_alpha_num_char():\n num = random.randint(0, 26 + 26 + 10)\n if num < 26:\n return chr(num + 65)\n num -= 26\n if num < 26:\n return chr(num + 97)\n return chr(num + 48)",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "b331f1497ca3a7668249f633afa29201",
"score": "0.7927985",
"text": "def random_charachter() -> chr:\r\n return chr(int(random.randrange(32, 126, 1)))",
"title": ""
},
{
"docid": "1e58f69b2bcd64cf7c038a4f1183e9af",
"score": "0.79179096",
"text": "def random_char(alph):\n char = alph[rand_generator.randrange(len(alph))]\n return char",
"title": ""
},
{
"docid": "3a9cf46a3666f7bee2a150f87055a174",
"score": "0.78053504",
"text": "def random_char():\n return chr(random.randrange(32, 126, 1))",
"title": ""
},
{
"docid": "0ae2f03f91761eb9f3a2421721add1ce",
"score": "0.77989227",
"text": "def RandomAlphaNumChar():\n num = random.randint(0, 26 + 26 + 10)\n if num < 26:\n return chr(num + 65)\n num -= 26\n if num < 26:\n return chr(num + 97)\n return chr(num + 48)",
"title": ""
},
{
"docid": "7d68fbb59f2d1c68148be84348a608c1",
"score": "0.7693868",
"text": "def get_random_alphabetic_string():\n return get_random_string(char_choice=string.ascii_letters)",
"title": ""
},
{
"docid": "4e34b109ff63d12b1f224545e58d95e8",
"score": "0.7666537",
"text": "def rand_string():\n out = ''\n for _ in range(24):\n out += choice(ascii_letters)\n return out",
"title": ""
},
{
"docid": "94ff29e3374ffc82d2c19a957732bcdc",
"score": "0.7624199",
"text": "def get_random_char():\n return chr(randint(97, 122))",
"title": ""
},
{
"docid": "b98a51d696452c64eb43a9cc82a1dda2",
"score": "0.7483066",
"text": "def get_random_char(letters: str) -> str:\n return letters[random.randint(0, len(letters) - 1)]",
"title": ""
},
{
"docid": "a5c715ce492f457dd2453774f68240f3",
"score": "0.7399742",
"text": "def gen_random_char_string(n, base_s=\"\"):\n if n == 0:\n return base_s\n \n c = random.choice(string.ascii_letters)\n return gen_random_char_string(n-1, base_s + c)",
"title": ""
},
{
"docid": "840ad228bdea9b17080e53e2dc018c20",
"score": "0.73987985",
"text": "def __getRandChar(self):\n return self.letterbag[random.randint(0,25)]",
"title": ""
},
{
"docid": "7e56e92440c4ef2d654738416ad8dcb8",
"score": "0.73835564",
"text": "def random_string():\n return \"\".join(random.choice(string.ascii_letters) for i in range(6))",
"title": ""
},
{
"docid": "af24f72695dbb294a0aa1da29347880a",
"score": "0.73753226",
"text": "def create_random_code(chars=AVAIABLE_CHARS):\n return \"\".join(\n [choice(chars) for _ in range(SIZE)]\n )",
"title": ""
},
{
"docid": "0ddfbe1e65bebbe638bee0913dcaeb86",
"score": "0.7268345",
"text": "def random_string() -> str:\n letters = string.ascii_lowercase\n return ''.join(random.choice(letters) for i in range(8))",
"title": ""
},
{
"docid": "5b7c0a7fb7a79804d57009c88a012164",
"score": "0.72070974",
"text": "def random_alpha(n=8):\n return \"\".join(random.SystemRandom().choice(string.ascii_letters) for _ in range(n))",
"title": ""
},
{
"docid": "fcb98bf8749fa0405de9ff700275b18c",
"score": "0.7206695",
"text": "def get_random_alphanumeric_string():\n return get_random_string(char_choice=string.ascii_letters + string.digits)",
"title": ""
},
{
"docid": "6d829b00ba9ab8fcece2dd8aae4a753d",
"score": "0.718114",
"text": "def create_secret_code():\n characters = string.ascii_uppercase + string.digits\n size = 6\n return ''.join(random.choice(characters) for _ in range(size))",
"title": ""
},
{
"docid": "7f4c3f6b58e1a83c4b1c8a2e0c2168ca",
"score": "0.713891",
"text": "def generate(length):\n alpha = map(chr, range(97, 123))\n alpha.append(' ')\n result = \"\"\n for x in range(length):\n result += alpha[random.randrange(0,27)]\n return result",
"title": ""
},
{
"docid": "fd0e8d3e98c34525dae75137af7e4f0c",
"score": "0.7099082",
"text": "def generate_rnd_msg() -> str:\n\n char_num = random.randint(8,20)\n i = 0\n s = \"\"\n for n in range(char_num):\n if i == char_num:\n break\n rnd_char = random.randint(0, len(string.ascii_lowercase) - 1)\n s += string.ascii_lowercase[rnd_char]\n i += 1\n\n return s",
"title": ""
},
{
"docid": "f2e2c4f1697d5cc9e2df2cbc76055038",
"score": "0.7097231",
"text": "def gen_code():\n return ''.join([random.choice(string.ascii_uppercase + string.digits) for _ in range(10)])",
"title": ""
},
{
"docid": "9f01dddb1116f83c4ca1aac6b6055247",
"score": "0.7079096",
"text": "def random_letter(letters):\n return random.choice(letters)",
"title": ""
},
{
"docid": "274f9e76b2f875161e26770ff54fd5b8",
"score": "0.7075379",
"text": "def genrate_letters():\n lt = []\n letter = \"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ\"\n for l in letter:\n lt.append(l)\n text_signature = ''.join([random.choice(lt) for x in range(26)])\n return text_signature",
"title": ""
},
{
"docid": "9f6d9830f31aa877dde335c27c4f5590",
"score": "0.69970953",
"text": "def random_string(length=25):\n return ''.join(random.choice(string.ascii_letters) for i in range(25))",
"title": ""
},
{
"docid": "e03a8eb5874ac64b1d9c253012acbf74",
"score": "0.698281",
"text": "def gen_rand_str(n):\n return \"\".join(random.choice(string.ascii_letters) for _ in range(n))",
"title": ""
},
{
"docid": "0b335256276e074bccc6a6d1651effc1",
"score": "0.69729125",
"text": "def password_alphabetical(i):\r\n\r\n return ''.join(_random.choice(string.ascii_letters) for x in\r\n range(i))",
"title": ""
},
{
"docid": "f75209a88f6e6cf434e2ea7e9c5b84ac",
"score": "0.6943582",
"text": "def generate_code(_=None):\n chars = \"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789\"\n\n rand = random.SystemRandom()\n return \"\".join(rand.choice(chars) for x in range(30))",
"title": ""
},
{
"docid": "6f8dc3192d7369d75adf7ad9ba1488fd",
"score": "0.6924147",
"text": "def gen_alphabet():\n for x in list(xrange(ord('a'),ord('z')+1)):\n yield chr(x)",
"title": ""
},
{
"docid": "5788763cf93c3e5e5d61da020d08be7e",
"score": "0.6877694",
"text": "def gen_char(filename):\n random.seed()\n with open(filename, \"w\") as f:\n for i in range(1000):\n a=random.randint(33,122)\n c=chr(a)\n f.write(c)\n f.write(\" \")",
"title": ""
},
{
"docid": "eafe1e9ec158db282754dd047644eab3",
"score": "0.68717074",
"text": "def random_word ():\n letters = [ chr(i) for i in range(ord('a'),ord('z')+1) ] + [ chr(i) for i in range(ord('A'),ord('Z')+1) ]\n length = 4 + random.randint(0,4)\n str = \"\"\n for i in range(length):\n str = str + random.choice(letters)\n return str",
"title": ""
},
{
"docid": "4cc2d4122484c980e6f79cdc6dfe74cf",
"score": "0.68578136",
"text": "def random_string(strlen=10):\n return \"\".join([random.choice(string.ascii_letters) for _ in range(strlen)])",
"title": ""
},
{
"docid": "6deb948a4fcd08334415611a1ab9741d",
"score": "0.68509054",
"text": "def characters():\n\n letter = \"a b c d e f g h i j k l m n o p q r s t u v w x y z\".split()\n sc = \"! @ # $ % ^ & * ( ) _ - + = ? : ;\".split()\n\n\n chars = []\n chars.append(random.choice(letter))\n chars.append(random.choice(letter).upper())\n chars.append(str(random.randint(0,9)))\n chars.append(random.choice(sc))\n\n return chars",
"title": ""
},
{
"docid": "e53f1fa5674439352d08d720ef9ac4b1",
"score": "0.68415403",
"text": "def GenerateRandomName():\n buf = cStringIO.StringIO()\n buf.write(random.choice(_BEGIN_ALPHABET))\n for _ in xrange(_LENGTH - 1):\n buf.write(random.choice(_ALPHABET))\n return buf.getvalue()",
"title": ""
},
{
"docid": "dcec96af631e435731418a165a0902a8",
"score": "0.6838262",
"text": "def random_character(latin_chance=0.6):\n if random.random() < latin_chance:\n return random.choice(LATIN) + random.choice(LATIN)\n else:\n return random.choice(NON_LATIN)",
"title": ""
},
{
"docid": "c481473e4167cebd64f692a3c9dcf907",
"score": "0.6827478",
"text": "def gen_random_chars(n: int = 10) -> Text:\n if n < 1:\n raise Exception('Number of random chars to generate has to be > 0')\n\n return ''.join(choice(ascii_lowercase + '-_')\n for i in range(n))",
"title": ""
},
{
"docid": "ff3367361e8ae31898ca5543aefff915",
"score": "0.6808887",
"text": "def get_random(self,num):\n return ''.join(sample('abcdefghijklmnopqrstuvwxyz1234567890!',8))",
"title": ""
},
{
"docid": "47a5690913c27c124b164521affb9686",
"score": "0.6808044",
"text": "def generate_hks():\n return ''.join((random.choice(string.ascii_letters) for x in range(26)))",
"title": ""
},
{
"docid": "ee35b41851e65dde76b6df5d7f78f925",
"score": "0.679494",
"text": "def symbol(string: str) -> str:\n L = len(string)\n P1 = random.randint(1, L-1, 2)\n chars = []\n for char in string:\n chars.append(char)\n chars[P1[0]] = chars[P1[0]].upper()\n chars[P1[1]] = chars[P1[1]].upper()\n return ''.join(x for x in chars if x.isupper())+str(random.randint(9))",
"title": ""
},
{
"docid": "94b6a132ec017137b43eae4c30802534",
"score": "0.67712796",
"text": "def gen_key():\n key = []\n chars = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789'\n for i in xrange(20):\n key.append(random.choice(chars))\n return ''.join(key)",
"title": ""
},
{
"docid": "5fb5c245010bc9d0dff6e93dd9ab52e0",
"score": "0.67706585",
"text": "def random_characters(alpha, numeric_percent_chance=20):\n\n random.seed()\n string_length = len(alpha)\n alphanumeric = ''\n\n for i in range(0, string_length):\n check_int = random.randrange(1, 100)\n\n if check_int <= numeric_percent_chance:\n alphanumeric += str(alpha_to_leet(alpha[i]))\n else:\n alphanumeric += alpha[i]\n\n return alphanumeric",
"title": ""
},
{
"docid": "185d84f67dd6bc26beaea733a20dac4b",
"score": "0.676798",
"text": "def randomString():\n randInt = random.randint(0, 10)\n randStr = ''.join(random.choice(\n string.ascii_letters) for _ in range(randInt))\n return randStr",
"title": ""
},
{
"docid": "2a826dab4ddfc98cc0f0b773910ffa20",
"score": "0.67654943",
"text": "def getGeneLetter():\n iRand = random.randint(0, 3)\n if iRand == 0:\n return 'A'\n elif iRand == 1:\n return 'C'\n elif iRand == 2:\n return 'G'\n elif iRand == 3:\n return 'T'\n return '';",
"title": ""
},
{
"docid": "c1fc9e8415e000aa3c7e5084eb2147b7",
"score": "0.67638427",
"text": "def generate_random_string():\n return \"\".join(random.choice(string.ascii_lowercase + string.digits) for _ in range(16)) # nosec",
"title": ""
},
{
"docid": "d0b130c610ead68f392494016836555f",
"score": "0.67493063",
"text": "def random_string(length=8, chars=string.ascii_letters + string.digits):\n return ''.join([chars[random.randint(0, len(chars) - 1)] for i in range(length)])",
"title": ""
},
{
"docid": "c27716251f43a8f5e189705325ab1939",
"score": "0.6735017",
"text": "def random_name(symbols=6):\n name = ''\n for i in range(symbols):\n name += random.choice(random.choice(string.ascii_letters))\n return name",
"title": ""
},
{
"docid": "c259c18de59f3947b182592dff3f22fb",
"score": "0.67286575",
"text": "def generate_key():\r\n\t\treturn ''.join(random.SystemRandom().choice(string.ascii_lowercase) for _ in range(123))",
"title": ""
},
{
"docid": "b437f8467b3d8d6d6510f24f4c449578",
"score": "0.67241436",
"text": "def createGene(self):\n # Beginning and end of the alphabet for random gene generation\n Astart = 97\n Zend = 122\n return \"\".join(map(lambda i: chr(random.randint(Astart, Zend)), range(random.randint(4, 8)))).upper()",
"title": ""
},
{
"docid": "a9da0e8e68470b7028847c0e8d871bde",
"score": "0.6721791",
"text": "def random_string():\n\n k = random.randint(5, 10)\n return ''.join(random.choices(string.ascii_letters + string.digits, k=k))",
"title": ""
},
{
"docid": "726f3f48b66ff94871ffe2500af374cc",
"score": "0.6716837",
"text": "def random_str(length=8, letters=string.ascii_letters + string.digits):\r\n return \"\".join(random.choice(letters) for x in range(length))",
"title": ""
},
{
"docid": "9f1cfb455172daca9fc91862bd2bcbc1",
"score": "0.6693836",
"text": "def GenRandom(length = 10, chars=string.letters+string.digits):\n return ''.join([random.choice(chars) for i in range(length)])",
"title": ""
},
{
"docid": "8e176d8d4251b9214993e2f9356bd07a",
"score": "0.66812474",
"text": "def generate_randomkey(length):\n chars = string.letters + string.digits\n return ''.join([choice(chars) for i in range(length)])",
"title": ""
},
{
"docid": "c0886647aa6cb25819a57802c5e90bd9",
"score": "0.6678761",
"text": "def random_string() -> str:\n\n k = random.randint(5, 10)\n return ''.join(random.choices(string.ascii_letters + string.digits, k=k))",
"title": ""
},
{
"docid": "1db698604887fd67d2da4cbba3e78fed",
"score": "0.6678393",
"text": "def __generate_random_string():\n return uuid4().hex[:6].upper()",
"title": ""
},
{
"docid": "09904ef8d88f5d35dc439883c4fb5963",
"score": "0.667244",
"text": "def generate_anki_guid() -> str:\n\n def base62(num: int, extra: str = \"\") -> str:\n s = string\n table = s.ascii_letters + s.digits + extra\n buf = \"\"\n while num:\n num, i = divmod(num, len(table))\n buf = table[i] + buf\n return buf\n\n _base91_extra_chars = \"!#$%&()*+,-./:;<=>?@[]^_`{|}~\"\n\n def base91(num: int) -> str:\n # all printable characters minus quotes, backslash and separators\n return base62(num, _base91_extra_chars)\n\n return base91(random.randint(0, 2 ** 64 - 1))",
"title": ""
},
{
"docid": "197c94db56dea67147b7098ed121316a",
"score": "0.66656965",
"text": "def gen_chars(length, character):\n return ''.join([character for i in range(length)])",
"title": ""
},
{
"docid": "52378775b6e7f92e50e5f6fae7836fb3",
"score": "0.6652644",
"text": "def random_string(i):\r\n\r\n return ''.join(_random.choice(string.ascii_letters) for x in\r\n xrange(i))",
"title": ""
},
{
"docid": "430c62fffe8636247954ba62a8014711",
"score": "0.66412127",
"text": "def random_character(self, script):\n if script in self.script_characters['Script'].values:\n\n script_index = list(self.script_characters['Script'].values).index(script)\n\n character_set = list(self.script_characters['Characters'][script_index].decode(\"utf-8\"))\n\n # selecting random character from all characters within script\n characters_in_script = len(character_set)\n character = character_set[np.random.randint(0, characters_in_script)]\n\n # TODO find a more elegant solution to double width characters\n # to deal with hanzi characters being twice the width of other unicode characters, simply delete half of\n # them\n # if script == 'hanzi':\n # if np.random.randint(0,2) > 0:\n # character = ''\n\n # print(character)\n\n return character\n\n raise ValueError('unsupported script')",
"title": ""
},
{
"docid": "20338b08236a3f7b8192a1b59ad7778a",
"score": "0.6631996",
"text": "def generate_vowel():\n return random.sample(['a', 'e', 'i', 'o', 'u', 'y'], 1)",
"title": ""
},
{
"docid": "4fb6767220bef3c7d34220e377df3934",
"score": "0.6625665",
"text": "def create_random_string(chars=string.ascii_letters + string.digits, length=16):\n return \"\".join([random.choice(chars) for _ in range(int(length))])",
"title": ""
},
{
"docid": "17f8c8be46be39221d5ed72ceb5bb3f4",
"score": "0.6610807",
"text": "def random_string(self, length):\n return \"\".join(\n SystemRandom().choice(string.ascii_letters) for _ in range(length)\n )",
"title": ""
},
{
"docid": "293dc03be0a7daaf80d94c9c4475ff98",
"score": "0.6598015",
"text": "def generate_token(length: int = 30, chars: str = UNICODE_ASCII_CHARACTER_SET) -> str:\n rand = random.SystemRandom()\n return \"\".join(rand.choice(chars) for _ in range(length))",
"title": ""
},
{
"docid": "cea50b4d30a5d34ae5fd2c315df0e0ee",
"score": "0.65972984",
"text": "def create_random_string(total_character):\n feed=string.printable\n words=\"\"\n i=0\n while i < total_character:\n words += feed[random.randrange(0,len(feed)-1)]\n i+=1\n return words",
"title": ""
},
{
"docid": "a0ba21ca241e1d5578c206cbdf06c523",
"score": "0.65953577",
"text": "def rng_string(alphabet, length):\n bitList = []\n for _ in range(0, length):\n bitList.append(str(randint(0, alphabet)))\n return ''.join(bitList)",
"title": ""
},
{
"docid": "03c4e370153a1c9b91632d1e43cd6004",
"score": "0.6595256",
"text": "def name_generator(size=8, chars=string.ascii_uppercase + string.digits):\n return ''.join(random.choice(chars) for _ in range(size))",
"title": ""
},
{
"docid": "03919078b56c38c86fb8c7c652ab02b4",
"score": "0.6577124",
"text": "def string(self, string_length=10):\n letters = string.ascii_letters\n return ''.join(random.choice(letters) for i in range(string_length))",
"title": ""
},
{
"docid": "b6f8466b6645e721090b7758eb4f4315",
"score": "0.6572131",
"text": "def generate_token(length=30, chars=UNICODE_ASCII_CHARACTER_SET):\n rand = random.SystemRandom()\n return ''.join(rand.choice(chars) for x in range(length))",
"title": ""
},
{
"docid": "b6f8466b6645e721090b7758eb4f4315",
"score": "0.6572131",
"text": "def generate_token(length=30, chars=UNICODE_ASCII_CHARACTER_SET):\n rand = random.SystemRandom()\n return ''.join(rand.choice(chars) for x in range(length))",
"title": ""
},
{
"docid": "d265b3735dfabe98d0edffa7b4c2c180",
"score": "0.65518975",
"text": "def generate_char(model, history, prev_chars):\n history = history[-prev_chars:]\n distr = model.get(history)\n rnd = random()\n # randomly sample most probable chars\n for char, prob in distr:\n rnd -= prob # gradually subtract until good candidate\n if rnd <= 0:\n return char",
"title": ""
},
{
"docid": "081d436fd3d17acd3f35f3e69967205d",
"score": "0.6550215",
"text": "def activation_code_generaor(size=6,candidate_chars=upper_case+lower_case+digits):\n\tcode = ''.join([random.choice(candidate_chars) for i in xrange(size)])#random.choice(list) picks an element from list randomly\n\treturn code",
"title": ""
},
{
"docid": "6f226a4c660cbd18f6c35113da23d332",
"score": "0.65499705",
"text": "def gen_secret() -> str:\n r = random.randrange(0, 255) # INSECURE, just for demo\n r = hex(r)[2:]\n if len(r) == 1:\n return f'0{r}'\n return r",
"title": ""
},
{
"docid": "5022c9c0f8f92c41b29bffd2e9954927",
"score": "0.6535925",
"text": "def random_seed(chars, nb_chars):\n s = ''\n for i in range(nb_chars):\n s += chars[random.randint(0, len(chars) - 1)]\n\n return s",
"title": ""
},
{
"docid": "574f81323f1fb9232967d4054e847e20",
"score": "0.65335643",
"text": "def generate_random_alphanumeric(length):\n return ''.join(random.SystemRandom().choice(string.ascii_letters + string.digits) \\\n for _ in range(length))",
"title": ""
},
{
"docid": "4b163894fc6ab5d72b4469326180c94c",
"score": "0.65329033",
"text": "def random_string(length=10):\n\n\tletters = string.ascii_lowercase\n\n\treturn ''.join(random.choice(letters) for i in xrange(length))",
"title": ""
},
{
"docid": "f8a0d07b7e8fa82164166de5e4ddebc2",
"score": "0.65299815",
"text": "def random_string(length=8):\n return \"\".join([random.choice(string.letters + string.digits) for x in range(length)])",
"title": ""
},
{
"docid": "921d6171fb91331e535e6bd3886bf642",
"score": "0.65296054",
"text": "def _gen_rand_name(n=10):\n # Ensure the name starts with a letter.\n return ''.join([random.choice(LETTER_LIST)]\n + random.choices(CHAR_LIST, k=n-1))",
"title": ""
},
{
"docid": "6ab75befdee12c3f0944782be030d350",
"score": "0.6527909",
"text": "def random_name(size=6):\r\n chars = string.ascii_uppercase + string.digits\r\n return 'test-' + ''.join(random.choice(chars) for x in range(size))",
"title": ""
},
{
"docid": "a04dab3d72a0a672ea4f6e32357479e2",
"score": "0.6525919",
"text": "def RandomAlphaNumWord(length):\n return ''.join([RandomAlphaNumChar() for _ in range(0, length)])",
"title": ""
},
{
"docid": "dbd466e958a8d355cae4163d07d5000c",
"score": "0.6521238",
"text": "def single_temp() -> str:\n return '36.' + str(random.randint(1, 5))",
"title": ""
},
{
"docid": "128bb00721fa2457f42032b2f6a424fb",
"score": "0.65183663",
"text": "def random_mutate(dna: str) -> str:\r\n result = \"\"\r\n for c in range(DNA_SIZE):\r\n if random.randrange(0, 100, 1) == 1:\r\n result += random_charachter()\r\n else:\r\n result += dna[c]\r\n return result",
"title": ""
},
{
"docid": "8ab8bed9926a965262512112d1485267",
"score": "0.6512125",
"text": "def generate_random_key(self):\n self.key = ''.join(choice(ascii_letters + digits) for i in range(300))",
"title": ""
},
{
"docid": "b16d08d84e3270b9a0fcb7fa8268e009",
"score": "0.6511942",
"text": "def random_string(n, alphabet=string.ascii_lowercase):\n return \"\".join(random.choice(alphabet) for _ in range(n))",
"title": ""
},
{
"docid": "49f5aaa16914ca4b2561775d707cca39",
"score": "0.65081567",
"text": "def random_string(stringLength=10):\n letters = string.ascii_lowercase\n return ''.join(choice(letters) for i in range(stringLength))",
"title": ""
},
{
"docid": "b11ed6b7bc141f3b47cf5b8eb9929ab0",
"score": "0.6507416",
"text": "def generate_raiz():\n\treturn os.urandom(12)",
"title": ""
},
{
"docid": "52a78f9553b98badef9a3476f246ef9c",
"score": "0.650601",
"text": "def random_string(length=12):\n\n return ''.join(\n [random.choice(string.ascii_letters) for _ in range(length)])",
"title": ""
},
{
"docid": "77a1f61cee91a3186c0bd2750217b5db",
"score": "0.6504291",
"text": "def randompassword():\n characters = string.ascii_uppercase + string.ascii_lowercase + string.digits\n size = random.randint(8, 12)\n return ''.join(random.choice(characters) for x in range(size))",
"title": ""
},
{
"docid": "0c8b28c859687a07dbe7e2b551e5c76a",
"score": "0.6503354",
"text": "def _generateSecretKey():\n return ''.join(SystemRandom().choice(string.ascii_letters + string.digits) for _ in range(20))",
"title": ""
},
{
"docid": "c3d5e327e7432a50bcfa1f711a4109eb",
"score": "0.65006065",
"text": "def randkey():\n return binascii.b2a_hex(os.urandom(15))",
"title": ""
},
{
"docid": "a4d0e06b1eb9e942db3b87f6350b6e46",
"score": "0.65003353",
"text": "def random_chars(char_set, length):\n s = \"\"\n sz = len(char_set)-1\n r = random.SystemRandom()\n for i in range(0, length+1):\n n = r.randint(0, sz)\n s = s + char_set[n]\n return s",
"title": ""
},
{
"docid": "535ee3906c6b1129b08c16d7b1ab752d",
"score": "0.6498842",
"text": "def generate_username():\n return ''.join(choice(ascii_letters + digits) for _ in range(15))",
"title": ""
},
{
"docid": "42ceef7f41b3981dcaa0237a1597df1a",
"score": "0.6495837",
"text": "def genRandString(dl = 10):\n ret = ''\n for i in range(dl) :\n ret += random.choice(string.ascii_letters + string.digits)\n return ret",
"title": ""
},
{
"docid": "ba168d9dd6c24ea17a7ec7af75638bb9",
"score": "0.6492322",
"text": "def random_string_alphanumeric(size):\n\t# requirements = random, string\n\treturn ''.join(random.choice(string.ascii_letters + string.digits) for x in range(size))",
"title": ""
},
{
"docid": "b79a5703d57fd8b896dd09647f5aa881",
"score": "0.6490289",
"text": "def stringGen(size, chars=string.ascii_uppercase + string.digits):\n\treturn ''.join(random.choice(chars) for _ in range(size))",
"title": ""
},
{
"docid": "9de24f96329e3ad87fbf21808fe763ed",
"score": "0.64817077",
"text": "def generate_name(self):\n letters = string.ascii_letters\n random_name = ''.join(random.choice(letters) for _ in range(10))\n assert isinstance(random_name, str)\n return random_name",
"title": ""
},
{
"docid": "7e31b1ca4917e3d83644a6b932bdeb1c",
"score": "0.6480228",
"text": "def get_random_string(self, length):\n letters = string.ascii_lowercase\n return ''.join(random.choice(letters) for i in range(length))",
"title": ""
},
{
"docid": "87a4981d0917ba485d8c2d3cfd7d40e0",
"score": "0.64736986",
"text": "def pwgen(length=16, ichars=string.ascii_letters+string.digits):\n return ''.join(random.choice(ichars) for i in range(length))",
"title": ""
},
{
"docid": "1490d08916d90850f18d8f2e1d7b7028",
"score": "0.6465734",
"text": "def gen_randomkey(length):\n chars = string.letters + string.digits + string.punctuation\n return ''.join([choice(chars) for _ in xrange(int(str(length)))])",
"title": ""
},
{
"docid": "8d6527a4b4ca8069515b15acd5757a3f",
"score": "0.6455166",
"text": "def random_string():\n\n return ''.join(random.choices(string.ascii_uppercase + string.digits, k=5))",
"title": ""
},
{
"docid": "1a7e30928c39a1298198d25875b25c82",
"score": "0.6453122",
"text": "def randstr(n):\n alphabets = string.digits + string.letters\n return ''.join(random.choice(alphabets) for i in xrange(n))",
"title": ""
},
{
"docid": "5232d99bcd3a6efb7d6ad6f91ef1f0fa",
"score": "0.644686",
"text": "def password_alphanumeric(i):\r\n\r\n chars = string.ascii_letters + string.digits\r\n return ''.join(_random.choice(chars) for x in range(i))",
"title": ""
},
{
"docid": "f564522c71e6e6459cadc9d3a078566a",
"score": "0.6442831",
"text": "def randstr(chars=string.ascii_lowercase + string.digits, len=16) -> str:\n return ''.join(random.choices(chars, k=len))",
"title": ""
},
{
"docid": "2ccffade708d2bbaa87a612e8f3ef59d",
"score": "0.6441663",
"text": "def generator(size=6, chars=string.ascii_uppercase + string.digits):\n return ''.join(random.choice(chars) for _ in range(size))",
"title": ""
},
{
"docid": "dd8fd3ae94683f7d3aeea488c82d773d",
"score": "0.6440562",
"text": "def generate_random_key():\n return '%030x' % (random.randrange(256**15),)",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="714"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">480ab2652d0d43eb768f1e1958a2b2af</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Sets the volume of the player.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "5188a5b1a0c43f191664c6fbf30dc6f7",
"score": "0.71377057",
"text": "async def _volume(self, ctx: commands.Context, *, volume: int):\n\n await ctx.message.delete(delay=5)\n if not ctx.voice_state.is_playing:\n return await ctx.send('Nothing being played at the moment.', delete_after=5)\n\n if 0 > volume > 100:\n return await ctx.send('Volume must be between 0 and 100', delete_after=5)\n\n ctx.voice_state.volume = volume / 100\n await ctx.send('Volume of the player set to {}%'.format(volume))",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "0605d5ecd7672b195973cb02635dd7d5",
"score": "0.8093429",
"text": "async def volume(self, ctx, volume: int=None):\n player = self.bot.lavalink.players.get(ctx.guild.id)\n\n if not volume:\n return await ctx.send(f'🔈 **{player.volume}%**')\n\n await player.set_volume(volume)\n await ctx.send(f'🔈 **Set to {player.volume}%**')",
"title": ""
},
{
"docid": "a0819431df8a60fef963ea11e9ba4792",
"score": "0.7906841",
"text": "async def volume(self, ctx, volume: int):\n\n if ctx.voice_client is None:\n return await ctx.send(\"Not connected to a voice channel.\")\n\n ctx.voice_client.source.volume = volume / 100\n await ctx.send(\"Changed volume to {}%\".format(volume))",
"title": ""
},
{
"docid": "38f05cc8721e4baa82b29f9f0e606fc5",
"score": "0.7906002",
"text": "async def volume(self, ctx, volume: int):\n\n if ctx.voice_client is None:\n return await ctx.send(\"Not connected to a voice channel.\")\n\n ctx.voice_client.source.volume = volume / 100\n await ctx.send(f\"Changed volume to {volume}%\")",
"title": ""
},
{
"docid": "97793cbfe10f7a4c40fee64903e34cff",
"score": "0.7815204",
"text": "async def volume(self, ctx, volume: int):\n\n # set certains guild volume\n state = self.get_guild_state(ctx.guild.id)\n state.volume = float(volume / 100.0)\n\n if ctx.voice_client is None:\n return await ctx.send(\"Not connected to a voice channel.\")\n\n ctx.voice_client.source.volume = state.volume\n await ctx.send(\"Changed volume to {}%\".format(volume))",
"title": ""
},
{
"docid": "e1a19142ef34ed1a865282a4e4c3da25",
"score": "0.77753854",
"text": "def set_volume(self, volume: float):\n self.initialize()\n\n # Clamp volume to range witrhin 0.0 and 1.0\n volume = clamp(volume, 0.0, 1.0)\n\n # Send message\n self.client.send_message(self.address, volume)",
"title": ""
},
{
"docid": "33b593494e12d10cae0829c7e8460e74",
"score": "0.7771511",
"text": "async def volume(self, ctx, volume: int = None):\n player = self.bot.lavalink.player_manager.get(ctx.guild.id)\n\n if not volume:\n embed = discord.Embed(\n color=self.bot.embed_color,\n title=\"🔊 Current Volume!\",\n description=f\"• Volume: **{player.volume}%**\"\n )\n return await ctx.send(embed=embed)\n\n await player.set_volume(volume) # Lavalink will automatically cap values between, or equal to 0-1000.\n\n embed = discord.Embed(\n color=self.bot.embed_color,\n title=\" Volume Updated!\",\n description=f\"• Volume set to: **{player.volume}%**\"\n )\n\n await ctx.send(embed=embed)",
"title": ""
},
{
"docid": "2ce478de7a9e47d3f41be6d4b1cd9abb",
"score": "0.77147394",
"text": "def set_volume_level(self, volume):\n # API expects string int value within 0..100 range.\n api_volume = str(int(round(volume * 100)))\n self.bravia_req_json(\n \"sony/audio\",\n self._jdata_build(\n \"setAudioVolume\", {\"target\": \"speaker\", \"volume\": api_volume}\n ),\n )",
"title": ""
},
{
"docid": "820b5033be7ba49a84e8c2a1feeab916",
"score": "0.7674969",
"text": "async def volume(self, ctx: commands.Context, volume: int) -> None:\n if volume < 0:\n volume = 0\n elif volume > 100:\n volume = 100\n if ctx.voice_client is None:\n await self.bot.message_guild(\n 'not connected to a voice channel.', ctx.channel)\n elif ctx.voice_client.source is not None:\n ctx.voice_client.source.volume = volume / 100\n await self.bot.message_guild(\n 'changed volume to {}%'.format(volume), ctx.channel)",
"title": ""
},
{
"docid": "7b13c434dc743bb65e5ac9491be54eb3",
"score": "0.76571465",
"text": "async def volume(self, ctx, volume: int):\n state = self.get_state(ctx.guild)\n\n # make sure volume is nonnegative\n if volume < 0:\n volume = 0\n\n max_vol = self.config[\"max_volume\"]\n if max_vol > -1: # check if max volume is set\n # clamp volume to [0, max_vol]\n if volume > max_vol:\n volume = max_vol\n\n client = ctx.guild.voice_client\n\n state.volume = float(volume) / 100.0\n client.source.volume = state.volume # update the AudioSource's volume to match",
"title": ""
},
{
"docid": "29ef092ab39b48854f21368b769a563f",
"score": "0.76449764",
"text": "def _set_volume(self, vol=5, way=\"down\"):\n if isinstance(vol, (int, str)):\n if not str(vol).isnumeric():\n return\n\n vol = int(vol)\n min_vol = 0\n max_vol = 100\n try:\n current_volume = int(self._volumeLevel)\n if way == \"down\":\n if current_volume - vol < min_vol:\n vol = min_vol\n else:\n vol = current_volume - vol\n elif way == \"up\":\n if current_volume + vol > max_vol:\n vol = max_vol\n else:\n vol = current_volume + vol\n elif way == \"exact\":\n vol = 0 if vol < min_vol else vol\n vol = 100 if vol > max_vol else vol\n Print(\"volume: %s\" % vol)\n except:\n pass\n self._player.audio_set_volume(vol)",
"title": ""
},
{
"docid": "d5b5d2d762c000ce4fc56e23cfbbe89e",
"score": "0.76440585",
"text": "def set_volume(self, value, scope=Scope.SELF):\n assert isinstance(value, int) and -100 <= value <= 100\n self._conn.send_command('SET', scope, 'VOLUME', value)",
"title": ""
},
{
"docid": "bbb1e7d6cbe290edcd5f5719cf2c9f05",
"score": "0.76016015",
"text": "def SetVolume(volume):\n safe_volume = 0\n if volume < 0:\n safe_volume = 0\n elif volume > 300:\n safe_volume = 300\n else:\n safe_volume = volume\n payload = {'command':'volume'}\n payload['val'] = safe_volume\n current_status = PlayerStatus(payload)\n set_volume = current_status['volume']\n if set_volume == volume:\n return True\n else:\n return False",
"title": ""
},
{
"docid": "85020a794ef428b5ab62258eacc87d11",
"score": "0.7591813",
"text": "def volume(self, volume):\n self._volume = volume",
"title": ""
},
{
"docid": "a7149e4c042a691c5123f20a5ea8a747",
"score": "0.7576295",
"text": "def set_volume(self, target):\n self.media.set_volume(target)\n self.system.notify(f\"Jarvis::Volume has been set to: {self.media.get_volume()['volume']}%\")",
"title": ""
},
{
"docid": "df64fa17ee1eaee5fc9624164c9b0e69",
"score": "0.7503202",
"text": "async def change_volume(self, ctx, *, vol: float):\r\n vc = ctx.voice_client\r\n\r\n if not vc or not vc.is_connected():\r\n return await ctx.send('Eu não estou conectado a um canal de voz!', )\r\n\r\n if not 0 < vol < 101:\r\n return await ctx.send('Por favor, diga um valor entre 1 e 100.')\r\n\r\n player = self.get_player(ctx)\r\n\r\n if vc.source:\r\n vc.source.volume = vol / 100\r\n\r\n player.volume = vol / 100\r\n await ctx.send(f'**`{ctx.author}`**: Deixou o volume em **{vol}%**')",
"title": ""
},
{
"docid": "5588905da9b947cc52e36d60490f7954",
"score": "0.74728423",
"text": "def set_volume(self, level):\n return self._set_value(level, self.__volume, self.volume_up,\n self.volume_down, -90, 0)",
"title": ""
},
{
"docid": "7931b7a3015e6dd8b03407f77bf404ec",
"score": "0.74494815",
"text": "def setVolume(self, *args):\n return _osg.ImageStream_setVolume(self, *args)",
"title": ""
},
{
"docid": "1b312b46ea10c26e6deefbc0259d6da4",
"score": "0.7434436",
"text": "async def async_set_volume_level(self, volume: float) -> None:\n\n volume_int = int(volume * 100)\n await self.device.set_speaker_volume(volume_int)",
"title": ""
},
{
"docid": "a366fd54aa7aac194071af36e50be9a1",
"score": "0.7411519",
"text": "async def set_volume(self, value):\r\n return (await self.handle_set(self.API_CALLS.get('volume'), value))\r\n #TODO maybe do the same hack with 0\r",
"title": ""
},
{
"docid": "0313fd1655cd5dc1f3ec87a18e253547",
"score": "0.73584867",
"text": "def change_volume(self):\n if self.video:\n if self.video.volume >= 0.8:\n self.video.volume = 0.5\n self.volume_button.source = self.half_volume_icon\n elif self.video.volume >= 0.5:\n self.video.volume = 0.25\n self.volume_button.source = self.small_volume_icon\n elif self.video.volume > 0:\n self.video.volume = 0\n self.volume_button.source = self.mute_icon\n else:\n self.video.volume = 1\n self.volume_button.source = self.full_volume_icon",
"title": ""
},
{
"docid": "3bb73bebd4ab8d5ce0dbbf9d8deee800",
"score": "0.7349233",
"text": "def set_volume(volume):\n\n logging.debug(\"Setting volume to %d%%\" % (volume))\n subprocess.call([\"amixer\",\"-q\",\"sset\",\"PCM,0\",\"%d%%\" % (volume)]) #Set audio_volume_string to 96 for 100%",
"title": ""
},
{
"docid": "877325b701a7488c747809a4d4a48551",
"score": "0.72983634",
"text": "def set_volume(self, volume, track='master', **kwargs):\n del kwargs\n\n try:\n volume = int(volume)\n except ValueError:\n self.log.warning(\"Received invalid volume setting: '%s'\", volume)\n return\n\n try:\n if volume > self.machine.config['volume']['steps']:\n volume = self.machine.config['volume']['steps']\n elif volume < 0:\n volume = 0\n\n self.track_volumes[track] = volume\n volume_float = round(volume / float(self.machine.config['volume']['steps']), 2)\n send_kwargs = {'volume_' + track: volume_float}\n self.send('config', **send_kwargs)\n except KeyError:\n self.log.warning('Received volume for unknown track \"%s\"', track)",
"title": ""
},
{
"docid": "e8d9e7f7a7974e1b2c0ae6b4294c7fcd",
"score": "0.72470576",
"text": "async def volume(self, ctx, volume: float):\n if not 0 <= volume <= 32:\n await ctx.send(f\"{config.emojis['warning']} Volume must be between 0 and 32.\")\n return\n await improcess(ctx, improcessing.volume, [[\"VIDEO\", \"AUDIO\"]], volume)",
"title": ""
},
{
"docid": "563b5cb4b5c7c53d387c40a24488fa62",
"score": "0.7246438",
"text": "async def _volume(self, ctx: commands.Context, *, volume: int):\r\n\r\n if not ctx.voice_state.is_playing:\r\n return await ctx.send('Сейчас музыка не играет. Можете включить.')\r\n\r\n if 0 > volume > 100:\r\n return await ctx.send('Volume must be between 0 and 100')\r\n\r\n ctx.voice_state.volume = volume / 100\r\n await ctx.send('Громкость изменена на {}%'.format(volume))",
"title": ""
},
{
"docid": "4e667c8b6010f53dd2d3b1d189b79645",
"score": "0.7236398",
"text": "def set_volume(self, value):\n subprocess.Popen([\"amixer\", \"set\", \"'PCM'\", str(value)], shell=False)",
"title": ""
},
{
"docid": "a2163911829482ec19cb2c2799089584",
"score": "0.7213829",
"text": "def adjust_volume(self, volume):",
"title": ""
},
{
"docid": "c6b17d57ebb1191f8a720473e33ba81b",
"score": "0.714063",
"text": "def test(self):\n self.player.change_volume(-10)",
"title": ""
},
{
"docid": "ac49b7c2d4c3019668d0c6fb8dd6337e",
"score": "0.7131812",
"text": "def __set_current_volume(volume):\n if volume > 100:\n Key.__current_volume = 100\n elif volume < 0:\n Key.__current_volume = 0\n else:\n Key.__current_volume = volume",
"title": ""
},
{
"docid": "1190afebfc9e9172d8852e51b6c30a9a",
"score": "0.7059594",
"text": "def volume(self, vol=None):\n if vol is None:\n return\n self._set_volume(vol, way=\"exact\")",
"title": ""
},
{
"docid": "55503fca027cbe6ec9364f698c578467",
"score": "0.7047693",
"text": "async def async_set_volume_level(self, volume):\n await self._volumio.set_volume_level(int(volume * 100))",
"title": ""
},
{
"docid": "c0560fb06e4fd9c97e1ab86748abc7ee",
"score": "0.7022057",
"text": "def setVolume(newvol):\n log.debug(\"AudioPlayer.set_volume(%d) called\", newvol)\n maxvol = int(config[\"max_volume\"])\n with _lock:\n if newvol > maxvol:\n log.warning(\"New volume above max, setting to max\")\n pmm.set_volume(maxvol/100)\n _player[\"volume\"] = maxvol\n elif newvol < 0:\n log.warning(\"New volume below 0%, setting to min\")\n pmm.set_volume(0)\n _player[\"volume\"] = 0\n else: \n pmm.set_volume(newvol/100)\n _player[\"volume\"] = newvol\n log.info(\"Volume adjusted to %d\", _player[\"volume\"])\n return _player[\"volume\"]",
"title": ""
},
{
"docid": "f767fb10079cca3b85f162aa228a371e",
"score": "0.70212835",
"text": "def aumentar_vol(self):\r\n Player_Musica.volume = Player_Musica.volume + 0.05\r\n if Player_Musica.volume > 1:\r\n Player_Musica.volume = 1\r\n mixer_music.set_volume(Player_Musica.volume)\r\n print('volume: ', str(Player_Musica.volume))\r\n else:\r\n mixer_music.set_volume(Player_Musica.volume)\r\n print('volume: ', str(Player_Musica.volume))",
"title": ""
},
{
"docid": "a125d364b238f8c2ae32160bf4491213",
"score": "0.6997204",
"text": "def set_volume_level(self, volume):\n # 60dB max\n if self._zone == \"Main\":\n _LOGGER.debug(\"Set volume to \"+str(volume) \\\n +\", so to \"+str(round(volume * MAX_VOLUME)).zfill(3)+\"VL\")\n self.telnet_command(str(round(volume * MAX_VOLUME)).zfill(3) + \"VL\")\n elif self._zone == \"Zone2\":\n _LOGGER.debug(\"Set Zone2 volume to \"+str(volume) \\\n +\", so to ZV\"+str(round(volume * MAX_ZONE_VOLUME)).zfill(2))\n self.telnet_command(str(round(volume * MAX_ZONE_VOLUME)).zfill(2) + \"ZV\")\n elif self._zone == \"HDZone\":\n _LOGGER.debug(\"Set HDZone volume to \"+str(volume) \\\n +\", so to \"+str(round(volume * MAX_ZONE_VOLUME)).zfill(2)+\"HZV\")\n self.telnet_command(str(round(volume * MAX_ZONE_VOLUME)).zfill(2) + \"HZV\")",
"title": ""
},
{
"docid": "ae6a75ad7103c88dd5a37a11f669e249",
"score": "0.69833344",
"text": "def volume_set(amount):\n Key.__track()\n\n if Key.current_volume() > amount:\n for i in range(0, int((Key.current_volume() - amount) / 2)):\n Key.volume_down()\n else:\n for i in range(0, int((amount - Key.current_volume()) / 2)):\n Key.volume_up()",
"title": ""
},
{
"docid": "38609044c5ae8f7146489ec995abe868",
"score": "0.6972628",
"text": "async def volume(self, ctx, volume : int):\n\t\tawait ctx.message.delete()\n\t\tif self.voice_client is not None and self.voice_client.is_playing():\n\t\t\tif volume > 100 or volume < 0:\n\t\t\t\tawait ctx.send(\"Tu dois spécifier un niveau de volume entre 0 et 100.\", delete_after=10)\n\t\t\telse:\n\t\t\t\tself.voice_client.source.volume = volume/100\n\t\t\t\tawait ctx.send(f\"Volume fixé à {self.voice_client.source.volume*100}%.\", delete_after=10)\n\t\telse:\n\t\t\tawait ctx.send(\"Il n'y a pas de musique actuellement en cours d'écoute.\", delete_after=10)",
"title": ""
},
{
"docid": "0ad415960909d1ceeeb6f26e2d39dd5e",
"score": "0.69690764",
"text": "def setVolume(self, iVolumeLevel):\n return self.usercommand(122, iVolumeLevel)",
"title": ""
},
{
"docid": "448972a531151c9c5f6f8e27b5a72bed",
"score": "0.6954816",
"text": "def change_volume(step):\n cur_volume = player.volume\n if cur_volume == None: return\n volume = cur_volume + step\n if volume > 100: volume = 100\n if volume < 0: volume = 0\n player.volume = volume\n return volume",
"title": ""
},
{
"docid": "0bdf0b0d52040488db379570534b5c59",
"score": "0.6930333",
"text": "def volume(self, volume_value: float) -> None:\n self.tune = self.frequency, volume_value",
"title": ""
},
{
"docid": "4e728a6fc0a72ff36e8f78998e870d55",
"score": "0.6756375",
"text": "def abaixar_vol(self):\r\n Player_Musica.volume = Player_Musica.volume - 0.05\r\n if Player_Musica.volume < 0:\r\n Player_Musicavolume = 0\r\n mixer_music.set_volume(Player_Musica.volume)\r\n print('volume: ', str(Player_Musica.volume))\r\n else:\r\n vol = mixer.music.set_volume(Player_Musica.volume)\r\n print('volume: ', str(Player_Musica.volume))",
"title": ""
},
{
"docid": "f85edb654b001d46424f1e664ddc8f7e",
"score": "0.6746452",
"text": "def __set_volume(self, percentage):\n volume_arg = int(percentage / 100.0 * 65536)\n volume_arg = str(volume_arg)\n subprocess.call([\"pactl\", \"set-sink-volume\", \"0\", volume_arg])",
"title": ""
},
{
"docid": "76fbfb52a05064489a24eeb8db1014dd",
"score": "0.66921914",
"text": "def volume(self):\n if not self._client.is_playing():\n self._beep.play()",
"title": ""
},
{
"docid": "2d5040c2211a2db3bc754f342599494d",
"score": "0.6682706",
"text": "def volume(self, value):\n self._put(\"VOL\", number_to_string_with_stepsize(value, 1, 0.5))",
"title": ""
},
{
"docid": "e44870be580c1fc99d4d26b9dfb4fbe6",
"score": "0.66754204",
"text": "def update_volume(self, sentence):\n myid = self.model.rate(sentence)\n modifier = \"\"\n num = 10\n\n if myid == 0:\n if self.get_current_volume() != 0:\n self.volume_before_mute = self.get_current_volume()\n num = 0\n elif myid == 1:\n modifier = \"-\"\n elif myid == 2:\n modifier = \"+\"\n elif myid == 3:\n modifier = \"\"\n num = self.volume_before_mute\n\n if sys.platform.startswith(constants.MAC_OS_X_IDENTIFIER) and myid != 3:\n modifier = \"\"\n # Find the current volume and calculates a lower and higher volume\n num = self.get_current_volume()\n # Janky Mac OS: Get volume gives 0-100, but set volume must be\n # between 0 and 10???? wtf Apple?\n num = (num / 10)\n if myid == 0:\n num = 0\n elif myid == 1:\n num -= 1\n elif myid == 2:\n num += 1\n\n os.system(VOLUME_CONTROL_COMMAND % (num, modifier))",
"title": ""
},
{
"docid": "008fcec3e31a7e518a9962510761bf74",
"score": "0.6614298",
"text": "def set_volume(self, volume):\n if self._software_mixing:\n self._playbin.set_property('volume', volume / 100.0)\n return True\n\n if self._mixer is None:\n return False\n\n old_scale = (0, 100)\n new_scale = (self._mixer_track.min_volume, self._mixer_track.max_volume)\n\n volume = utils.rescale(volume, old=old_scale, new=new_scale)\n\n volumes = (volume,) * self._mixer_track.num_channels\n self._mixer.set_volume(self._mixer_track, volumes)\n\n return self._mixer.get_volume(self._mixer_track) == volumes",
"title": ""
},
{
"docid": "26f44984e5e6a3d0366cfa2494d126e4",
"score": "0.6583904",
"text": "def set_volume(percent):\n subprocess.run([\"pactl\", \"set-sink-mute\", \"0\", \"0\"])\n subprocess.run([\"pactl\", \"set-sink-volume\", \"0\", str(int(percent * 100)) + \"%\"])",
"title": ""
},
{
"docid": "202e99ea7576d66225725e2f2ece6b93",
"score": "0.65635335",
"text": "def mute():\n Key.__track()\n Key.__is_muted = (not Key.__is_muted)\n Keyboard.key(Keyboard.VK_VOLUME_MUTE)",
"title": ""
},
{
"docid": "59571118f9961a939012f8d0d19cae4f",
"score": "0.65571684",
"text": "async def volume_up(self):\n volume = round(self._volume * 100)\n if volume < 100:\n volume = volume + 1\n result = await self._try_command(\n \"Turning the Gateway volume failed.\", self._device.send,\n 'set_fm_volume', [volume])\n if result[0] == \"ok\":\n self.async_schedule_update_ha_state()",
"title": ""
},
{
"docid": "d9bdb7661532b0dd04045f8a6f0c2730",
"score": "0.65190023",
"text": "def change_volume(self, increase):\n if increase:\n self.music_volume += 0.05\n self.effect_volume += 0.1\n else:\n self.music_volume -= 0.05\n self.effect_volume -= 0.1\n\n #If the volume isn't greater than 0 and less than 1, fix it\n self.music_volume = min(1, max(0, self.music_volume))\n self.effect_volume = min(1, max(0, self.effect_volume))\n\n #Update the volume for effects and music\n pygame.mixer.music.set_volume(self.music_volume)\n self.menu_select.set_volume(self.effect_volume)\n self.passive_food_collected.set_volume(self.effect_volume)\n self.hunting_food_collected.set_volume(self.effect_volume)\n self.demon_move.set_volume(.4*self.effect_volume)",
"title": ""
},
{
"docid": "22f72e55af7ccfa0ed0b94d665e1ada2",
"score": "0.649691",
"text": "def set_output_volume(self, volume):\n if self.version >= '2.0.0':\n return soundio.outstream_set_volume(volume)\n else:\n raise NotImplementedError('Not implemented in < 2.0.0')",
"title": ""
},
{
"docid": "13a8f969c1890e9489c3abfb2417ca39",
"score": "0.64544",
"text": "def _music(self):\r\n pygame.mixer.music.set_volume(1.0)\r\n pygame.mixer.music.play(-1)",
"title": ""
},
{
"docid": "bbce902f130293ff3adf6f5ef7ea8eb3",
"score": "0.64088696",
"text": "def set_volume_level(self, volume, method=\"absolute\"):\n for output in self.zone_details()[\"outputs\"]:\n output_id = output[\"output_id\"]\n cur_vol = output[\"volume\"][\"value\"]\n if method == \"up\":\n volume = cur_vol + volume\n elif method == \"down\":\n volume = cur_vol - volume\n self.send_request(\"change_volume\", {\"volume\":volume, \"output\": output_id} )",
"title": ""
},
{
"docid": "c2740f2df043b0d361831c62ff097da5",
"score": "0.6384435",
"text": "def setvolume(level):\r\n\tosax('aevtstvl', {'----':level})",
"title": ""
},
{
"docid": "c2740f2df043b0d361831c62ff097da5",
"score": "0.6384435",
"text": "def setvolume(level):\r\n\tosax('aevtstvl', {'----':level})",
"title": ""
},
{
"docid": "08be88c1453990e75493e9a5d3f789a4",
"score": "0.6382532",
"text": "def volume(self):\n volume = (4/3) * (math.pi) * (self.r ** 2)\n print(\"Volume = \", volume)",
"title": ""
},
{
"docid": "b3ddb08a1edea19609284e72ec59f28b",
"score": "0.6349086",
"text": "def coin_volume(self, coin_volume):\n if coin_volume is None:\n raise ValueError(\"Invalid value for `coin_volume`, must not be `None`\")\n\n self._coin_volume = coin_volume",
"title": ""
},
{
"docid": "93f9e475b87e0298cc0259103dc76246",
"score": "0.63384914",
"text": "def winmm_midiOutSetVolume(jitter):\n ret_ad, args = jitter.func_args_stdcall([\"hmo\", \"dwVolume\"])\n raise RuntimeError('API not implemented')\n jitter.func_ret_stdcall(ret_ad, ret_value)",
"title": ""
},
{
"docid": "237ff89a06fbcd5aa86639f053f78645",
"score": "0.6310048",
"text": "def root_volume(self, root_volume):\n self._root_volume = root_volume",
"title": ""
},
{
"docid": "42ac4daa580e386fa825bd85fff720df",
"score": "0.6266388",
"text": "def volumeUp(self, vol=5):\n self._set_volume(vol, way=\"up\")",
"title": ""
},
{
"docid": "829c32cdf61a7b9f0f68005e2fd3882f",
"score": "0.62645364",
"text": "def increase_volume(self):\n for _ in range(10):\n self.media.volume_up()\n self.system.notify(f\"Jarvis::Increased Volume: {self.media.get_volume()['volume']}%\")",
"title": ""
},
{
"docid": "f198109cd7fb145212816401af0b31c5",
"score": "0.61455864",
"text": "def volume():\n if request.method == 'POST':\n volume = request.form['volume']\n print (\"Volume =\", volume)\n jc.volume = volume\n jc.speaker.set_volume(volume)\n return jsonify({'success': True})\n else:\n return jsonify({'success': True, 'volume': jc.speaker.volume})",
"title": ""
},
{
"docid": "9584a7d88d9db7813bd88fc0a7a9e7db",
"score": "0.6112581",
"text": "def data_volume(self, data_volume):\n self._data_volume = data_volume",
"title": ""
},
{
"docid": "18a749c335dc0f1a4505dc22c9ed911c",
"score": "0.61056215",
"text": "def volume(self):\n self.volume = (4 / 3) * math.pi * (self.radius ** 3)\n return self.volume",
"title": ""
},
{
"docid": "74821ede7f1103719fb2fc0e8501bff1",
"score": "0.60982186",
"text": "def _set_volume_idx(self):\n idx = self._volume_index_spinbox.value()\n self._model.set_time_point(idx)",
"title": ""
},
{
"docid": "7e6bbea626e311bcfcf4a410c3e23854",
"score": "0.60937685",
"text": "def volume_up():\n Key.__track()\n Key.__set_current_volume(Key.current_volume() + 2)\n Keyboard.key(Keyboard.VK_VOLUME_UP)",
"title": ""
},
{
"docid": "35f78eb68e6eab0f905655f75955f2a8",
"score": "0.6086422",
"text": "def set_vol(val):\n\twith open(os.devnull,\"w\") as fnull:\n\t\tres = subprocess.Popen([MIXER,MIXER_STR,val],stdout = fnull, stderr = fnull)\n\t\tos.waitpid(res.pid,0)",
"title": ""
},
{
"docid": "d778833fd44db0215ff3c5df08463ed9",
"score": "0.60847294",
"text": "def winmm_waveOutSetVolume(jitter):\n ret_ad, args = jitter.func_args_stdcall([\"hwo\", \"dwVolume\"])\n raise RuntimeError('API not implemented')\n jitter.func_ret_stdcall(ret_ad, ret_value)",
"title": ""
},
{
"docid": "4b8c92cf20eb3730dba67e9d79f09286",
"score": "0.6074624",
"text": "async def async_mute_volume(self, mute):\n if mute:\n await self._volumio.mute()\n else:\n await self._volumio.unmute()",
"title": ""
},
{
"docid": "b49118e82ab146dd73f1db0f5101e8c0",
"score": "0.6067763",
"text": "def change_volume(original_sound):\n\n pass # replace this line with your code",
"title": ""
},
{
"docid": "f719ceb9b6545fb36d91d94d7bc3f5fc",
"score": "0.6066487",
"text": "async def async_mute_volume(self, mute):\n await self._mute_script.async_run(context=self._context)",
"title": ""
},
{
"docid": "ee261eb94e61e82384b80db21cd5a667",
"score": "0.6062422",
"text": "def volume_min():\n Key.volume_set(0)",
"title": ""
},
{
"docid": "244bf1777c6842aa66b5ac45699d8983",
"score": "0.602695",
"text": "def setSanVolume(self, sanVolume):\n self['sanVolume'] = sanVolume",
"title": ""
},
{
"docid": "1e5a1a18cd0700c13f4500fa5f38c702",
"score": "0.6014075",
"text": "def winmm_auxSetVolume(jitter):\n ret_ad, args = jitter.func_args_stdcall([\"uDeviceID\", \"dwVolume\"])\n raise RuntimeError('API not implemented')\n jitter.func_ret_stdcall(ret_ad, ret_value)",
"title": ""
},
{
"docid": "fd7e7d36387be2937b193bd45584934b",
"score": "0.6000937",
"text": "def volume():\n pass",
"title": ""
},
{
"docid": "c1d11dee50f6b647c726d8b30da0c91e",
"score": "0.5971329",
"text": "def volume(self) -> float:\n return self._volume",
"title": ""
},
{
"docid": "4c9b236bffc300b52bb1b24139badaa5",
"score": "0.5932977",
"text": "def volume_level(self):\n volume = self._state.get(\"volume\", None)\n if volume is not None and volume != \"\":\n volume = int(volume) / 100\n return volume",
"title": ""
},
{
"docid": "80ebbd42a611819112333fdc140e0d5c",
"score": "0.5921186",
"text": "def setPlayer(self, player):\n self._player = player",
"title": ""
},
{
"docid": "292a1008f9c60bf3801e765915a0819e",
"score": "0.5900649",
"text": "def test_set_volume(self):\n self.receiver_mock.add_mock('volumeset', MockResponse(responses=[\n (200, VolumeCase.VOLUME_STATUS)\n ], path='/goform/formiPhoneAppVolume.xml'))\n\n code, payload = self.open_jrpc('Application.SetVolume', {'volume': 66})\n\n self.assertEqual(code, 200)\n self.assertPayloadEqual(payload, 75)\n self.assertEqual(self.receiver_mock.queries[0].payload, '1+-40.0')",
"title": ""
},
{
"docid": "4e439025d15144a002918c423ba7cf62",
"score": "0.58919716",
"text": "def computeVolume(self):\n radius = self.radiusField.getNumber()\n volume = 4/3 * radius ** 3 * math.pi\n self.outputField.setNumber(volume)\n self.outputLabel[\"text\"] = \"Volume\"",
"title": ""
},
{
"docid": "19ae4327089bc5c70d9613c0c3bc8e63",
"score": "0.5878599",
"text": "def volume(self, volume):\n\n if type(volume) is not np.ndarray:\n raise ValueError('Volume is not a Numpy array')\n if volume.ndim != 3:\n raise ValueError('Volume is not a 3D Numpy array')\n\n self._volume = volume\n self._ax.volume = self._volume\n\n if self._ax.slice_index > self._ax.volume.shape[0]:\n self._ax.slice_index = self._ax.volume.shape[0] - 1\n self.slice_index = self._ax.slice_index\n\n self._ax.images[0].set_array(self._ax.volume[self._ax.slice_index])\n self._ax.set_title('{}/{}'.format(\n self._ax.slice_index, self._ax.volume.shape[0] - 1))\n self.figure.canvas.draw()",
"title": ""
},
{
"docid": "8863718f13c895e0c79196d9ebae3deb",
"score": "0.5868048",
"text": "def remote_volume(self, remote_volume):\n\n self._remote_volume = remote_volume",
"title": ""
},
{
"docid": "d625799d455f6cd86d72c46fa7bf6df2",
"score": "0.5856352",
"text": "def __track():\n if Key.__current_volume == None:\n Key.__current_volume = 0\n for i in range(0, 50):\n Key.volume_up()",
"title": ""
},
{
"docid": "c26f0ab061cec121ef4d4f49f154c568",
"score": "0.58523816",
"text": "def master_volume(self, master_volume):\n\n self._master_volume = master_volume",
"title": ""
},
{
"docid": "26712d4e46cd6eb793ef79549521ba19",
"score": "0.5845805",
"text": "def increase_volume(self, track='master', **kwargs):\n del kwargs\n\n try:\n self.track_volumes[track] += 1\n self.set_volume(self.track_volumes[track], track)\n except KeyError:\n self.log.warning('Received volume increase request for unknown '\n 'track \"%s\"', track)",
"title": ""
},
{
"docid": "b3eb1e4516f3111a677ac62a4a43bda5",
"score": "0.5823873",
"text": "def mute_volume(self, mute):\n if self._zone == \"Main\":\n self.telnet_command(\"MO\" if mute else \"MF\")\n elif self._zone == \"Zone2\":\n self.telnet_command(\"Z2MO\" if mute else \"Z2MF\")\n elif self._zone == \"HDZone\":\n self.telnet_command(\"HZMO\" if mute else \"HZMF\")",
"title": ""
},
{
"docid": "1ebeb436ce478e7544a4c8c13485fdef",
"score": "0.58155483",
"text": "def volume_up(self):\n # pylint: disable=invalid-name\n self._send_command('VolumeUp')",
"title": ""
},
{
"docid": "76266e52f436956c7b6fbf0ca52144ea",
"score": "0.580382",
"text": "def volume_up(self):\n self.send_req_ircc(self.get_command_code(\"VolumeUp\"))",
"title": ""
},
{
"docid": "6f51f29c946dbd227e8256cd20614885",
"score": "0.57763475",
"text": "def volume(self):\n return self._volume",
"title": ""
},
{
"docid": "6f51f29c946dbd227e8256cd20614885",
"score": "0.57763475",
"text": "def volume(self):\n return self._volume",
"title": ""
},
{
"docid": "6f51f29c946dbd227e8256cd20614885",
"score": "0.57763475",
"text": "def volume(self):\n return self._volume",
"title": ""
},
{
"docid": "6f51f29c946dbd227e8256cd20614885",
"score": "0.57763475",
"text": "def volume(self):\n return self._volume",
"title": ""
},
{
"docid": "fee87d7d9198a7e981387512cdc19579",
"score": "0.5775483",
"text": "def show_volume(self, volume):\n self.raise_top()\n\n if self._hide_timeout_key is not None:\n gobject.source_remove(self._hide_timeout_key)\n self._hide_timeout_key = gobject.timeout_add(2000,\n self.animate_out)\n\n for index, bars in enumerate(self._bars):\n if index >= volume:\n bars[0].set_opacity(0)\n bars[1].set_opacity(255)\n else:\n bars[0].set_opacity(255)\n bars[1].set_opacity(0)\n\n if self.visible == True:\n return\n\n self.visible = True\n self.behaviour.set_bounds(0, 255)\n self.timeline.start()",
"title": ""
},
{
"docid": "89264c769422d7654cc30114944de37d",
"score": "0.57617307",
"text": "def max_volume(self, value):\n self._max_volume = value",
"title": ""
},
{
"docid": "371187c1697a3db899e1079fd6f5c4f9",
"score": "0.575617",
"text": "def volume_up(self, **kwargs):\n try:\n vol_step = kwargs['vol_step']\n self.controls.volume_up(vol_step)\n except KeyError:\n self.controls.volume_up()",
"title": ""
},
{
"docid": "4c8ba507db3807ca12df58824827e93a",
"score": "0.5728093",
"text": "def volume_max():\n Key.volume_set(100)",
"title": ""
},
{
"docid": "ddc051a6013aabfeaa3d3d0e3568affb",
"score": "0.5722892",
"text": "def volumeDown(self, vol=5):\n self._set_volume(vol, way=\"down\")",
"title": ""
},
{
"docid": "c2a1f50781e785e6e8db4d59bab16b42",
"score": "0.5710245",
"text": "def volumes(self, volumes):\n\n self._volumes = volumes",
"title": ""
},
{
"docid": "04e9c7fb31e19045a0c4ff73bff39b0f",
"score": "0.567529",
"text": "async def volume_down(self):\n volume = round(self._volume * 100)\n if volume > 0:\n volume = volume - 1\n result = await self._try_command(\n \"Turning the Gateway volume failed.\", self._device.send,\n 'set_fm_volume', [volume])\n if result[0] == \"ok\":\n self.async_schedule_update_ha_state()",
"title": ""
},
{
"docid": "27bff84f1e1710d0499a3bb2d90ad4d4",
"score": "0.5659065",
"text": "def volume(self):\r\n volume = 1\r\n for length in self.system.box:\r\n volume *= length\r\n return volume",
"title": ""
},
{
"docid": "8f1337bccb609407bf657d97f3d25cee",
"score": "0.55999136",
"text": "def mute(self):\n if isinstance( self.vlc_player, MediaPlayer ):\n self.vlc_player.audio_toggle_mute()",
"title": ""
},
{
"docid": "bf9df189170a7465cdf3df73d400d145",
"score": "0.5575025",
"text": "def volume_down():\n Key.__track()\n Key.__set_current_volume(Key.current_volume() - 2)\n Keyboard.key(Keyboard.VK_VOLUME_DOWN)",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="715"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">48396caa299375cb51992b31e43c3e59</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Takes key delimiter to avoid false collisions</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "7dcf97ff70b889f82261618afbdd3bbb",
"score": "0.0",
"text": "def __init__(self, delim):\n \n self.__headers__= []\n self.__keys__ = []\n self.__delim__= delim",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "034355288292464d0dae3d826be50cda",
"score": "0.6467016",
"text": "def key_to_path(self, key):\n assert type(key) == str\n if self.config['separator'] in key:\n return key.split(self.config['separator'])\n else:\n return [key]",
"title": ""
},
{
"docid": "e75ff18dbdf5790644ca266f31e188b3",
"score": "0.6331727",
"text": "def _replace_special_keys(key):\n if key.lower() == \"plus\":\n return \"+\"\n if key.lower() == \"comma\":\n return \",\"\n if key.lower().startswith(\"delay\"):\n return key.lower()\n return key",
"title": ""
},
{
"docid": "6826a6645b3d9c52106433fd78880296",
"score": "0.6183432",
"text": "def elongateKey( self, string, key):\r\n key2 = [key[i % len(key)] for i in range(len(string))]\r\n return key2",
"title": ""
},
{
"docid": "62df7f12ec5b72f268622a12eb91781e",
"score": "0.6129091",
"text": "def _split_key(self, key):\n key = key.lower()\n\n try:\n section, option = key.split('_', 1)\n except (ValueError, IndexError):\n section = self._default_section\n option = key\n\n if not self._parser.has_section(section):\n self._parser.add_section(section)\n\n return section, option",
"title": ""
},
{
"docid": "efe1646bf2846d4691b0bcc82d41e017",
"score": "0.6113278",
"text": "def _pass_kv_split(line):\n if ': ' in line:\n return line.split(': ', 1)\n return None,line",
"title": ""
},
{
"docid": "e43cc358b3a9df2a3813db2b55ff0327",
"score": "0.61107",
"text": "def _parse_key(self) -> Key:\n self.mark()\n while self._current.is_spaces() and self.inc():\n # Skip any leading whitespace\n pass\n if self._current in \"\\\"'\":\n return self._parse_quoted_key()\n else:\n return self._parse_bare_key()",
"title": ""
},
{
"docid": "d7606df5f18746efb643ebd098b15ad4",
"score": "0.60920435",
"text": "def split(value, key):\n return value.split(key)",
"title": ""
},
{
"docid": "63c7ec7c1146127a4ece159263829f54",
"score": "0.6059463",
"text": "def clean_key(key):\n prefix, new_key = key.split('.', 1)\n return new_key",
"title": ""
},
{
"docid": "52c181b9dcac9998fe634ddb39256f0e",
"score": "0.6025939",
"text": "def get_index(key):\n return key.split(':')[1].replace('\\'', '')",
"title": ""
},
{
"docid": "376894758480b4cc0aca5f221246fa8e",
"score": "0.59145176",
"text": "def _parse_quoted_key(self) -> Key:\n # Extract the leading whitespace\n original = self.extract()\n quote_style = self._current\n key_type = next((t for t in KeyType if t.value == quote_style), None)\n\n if key_type is None:\n raise RuntimeError(\"Should not have entered _parse_quoted_key()\")\n\n key_str = self._parse_string(\n StringType.SLB if key_type == KeyType.Basic else StringType.SLL\n )\n if key_str._t.is_multiline():\n raise self.parse_error(UnexpectedCharError, key_str._t.value)\n original += key_str.as_string()\n self.mark()\n while self._current.is_spaces() and self.inc():\n pass\n original += self.extract()\n key = SingleKey(str(key_str), t=key_type, sep=\"\", original=original)\n if self._current == \".\":\n self.inc()\n key = key.concat(self._parse_key())\n\n return key",
"title": ""
},
{
"docid": "230679da1b0349112c5a0a1fd73d12a2",
"score": "0.5904374",
"text": "def is_getting_key(self, key):\n return re.sub('\\[.*\\]', '', key)",
"title": ""
},
{
"docid": "5e59a6e355842759475bd6a385e8ee94",
"score": "0.58796895",
"text": "def _parse_key(self, content, offset):\n if offset >= len(content):\n return None, offset\n if content[offset] == '\"':\n collected, offset = self._parse_string(content, offset+1)\n return collected, offset+1\n\n collected = content[offset:content.find(':', offset)]\n\n return collected, offset+len(collected)+1",
"title": ""
},
{
"docid": "8452d55f149d8f5414c6f4dbbd94cd4e",
"score": "0.58653104",
"text": "def assign(line):\r\n further = False\r\n first = False\r\n name = \"\"\r\n key = \"\"\r\n for a in line:\r\n if a==\"|\":\r\n name=name[:-1]\r\n further=True\r\n first=True\r\n elif not further:\r\n name+=a\r\n elif further and first:\r\n first=False\r\n elif a==\"\\n\":\r\n continue\r\n else:\r\n key+=a\r\n return name, key",
"title": ""
},
{
"docid": "5c9989140c86cbcafd11913108ec11e8",
"score": "0.58534926",
"text": "def split(value, key):\n return value.strip().split(key)",
"title": ""
},
{
"docid": "11bd244fd0dc9a0cfbf4ddca6db7bfb5",
"score": "0.58389837",
"text": "def _parse_bare_key(self) -> Key:\n while (\n self._current.is_bare_key_char() or self._current.is_spaces()\n ) and self.inc():\n pass\n\n original = self.extract()\n key = original.strip()\n if not key:\n # Empty key\n raise self.parse_error(EmptyKeyError)\n\n if \" \" in key:\n # Bare key with spaces in it\n raise self.parse_error(ParseError, f'Invalid key \"{key}\"')\n\n key = SingleKey(key, KeyType.Bare, \"\", original)\n\n if self._current == \".\":\n self.inc()\n key = key.concat(self._parse_key())\n\n return key",
"title": ""
},
{
"docid": "cbd47e5da54940327f5d5e095a026c7c",
"score": "0.58355224",
"text": "def parse_key(key: str) -> str:\n if key.startswith(\"specific_data.data.\"):\n # specific_data.data.hostname\n # -> aggregated_hostname\n key = strip_left(obj=key, fix=\"specific_data.data.\")\n key = f\"aggregated_{key}\"\n if key.startswith(\"adapters_data.\"):\n # adapters_data.aws_adapter.hostname\n # -> aws_adapter_hostname\n key = strip_left(obj=key, fix=\"adapters_data.\")\n key = key.replace(\".\", \"_\")\n return key",
"title": ""
},
{
"docid": "221c89095a8e67ca350c789d7aeca00d",
"score": "0.5829833",
"text": "def reverse_key(key):\n return key.split(':', 3)[3]",
"title": ""
},
{
"docid": "035fc173247960156b8f4300727494ba",
"score": "0.5827784",
"text": "def is_key(line):\n if re.match(':::[^:].*', line):\n if len(line[3:]) > 0:\n return True\n else:\n return False",
"title": ""
},
{
"docid": "732d409374aaac7b18ecad34687054af",
"score": "0.57868135",
"text": "def MakeKey(self, string, string_1):\n ...",
"title": ""
},
{
"docid": "68de1cb54411b1e19cf60e074c37fcee",
"score": "0.5762134",
"text": "def __clean_key(key):\n try:\n key_cleaner = str(key)\n key_cleaner = key_cleaner.lower()\n key_cleaner = re.sub(config.PUNCTUATION_MARK, config.EMPTY_STRING, key_cleaner.strip())\n if re.match(config.WORD_REGEX, key_cleaner):\n # adding underscore between two words (for SQL columns' names)\n key_cleaner = re.sub(config.SPACE_STRING, config.UNDERLINE, key_cleaner)\n except:\n return config.NULL_VALUE\n return key_cleaner",
"title": ""
},
{
"docid": "205b603663566e5b91e8fed68fc6909a",
"score": "0.57603425",
"text": "def get_key(line):\n return line.strip().split()[0].lower()",
"title": ""
},
{
"docid": "f2d7cc082453ac3889675b99727193c6",
"score": "0.57399035",
"text": "def is_getting_key(self, key):\n return re.sub('[\\[\\]()]', '', key)",
"title": ""
},
{
"docid": "1ff9a253869b3a9de559bf686d2c7829",
"score": "0.57262975",
"text": "def key_iterator(key):\n unfinished_line = \"\"\n for byte in key:\n byte = unfinished_line + byte\n lines = byte.split(\"\\n\")\n unfinished_line = lines.pop()\n for line in lines:\n yield line",
"title": ""
},
{
"docid": "6f29886a62e73d5f82e53589c1420c6c",
"score": "0.5719982",
"text": "def tidy_key(key):\n key = key[:-1].replace(\" \", \"-\")\n key = key.replace(\".\", \"-\")\n key = key.replace(\"_\", \"-\")\n return key.strip()",
"title": ""
},
{
"docid": "50f566500ebce43222c13c56a6119110",
"score": "0.56917596",
"text": "def add_delimiter_to_dict(dict, symbol):\n i = 0\n while i < len(symbol):\n c = symbol[i]\n i += 1\n if c == '=': continue\n add_to_dictionary(dict, c)",
"title": ""
},
{
"docid": "f60ca0acb14c0426480f1ad05cec2a7a",
"score": "0.5651557",
"text": "def generate_key(*args):\n values = []\n for arg in args:\n if ESCAPE_CHARACTER in arg:\n arg = arg.replace(ESCAPE_CHARACTER, ESCAPE_CHARACTER * 2)\n if DELIMITER in arg:\n arg = arg.replace(DELIMITER, ESCAPE_CHARACTER + DELIMITER)\n values.append(arg)\n return hash_string(DELIMITER.join(values))",
"title": ""
},
{
"docid": "d9ac2c22886208f2eb49059d6dfcab7a",
"score": "0.5610847",
"text": "def resolve_keypath(keypath, split=\"/\"):\r\n if isinstance(keypath, STRINGS):\r\n if split:\r\n keypath = keypath.split(split)\r\n else:\r\n keypath = [keypath]\r\n return keypath",
"title": ""
},
{
"docid": "ff07a9cfc16dff6efa0d267ca2c4029c",
"score": "0.56092364",
"text": "def properSpacing(key, param):\n\n delta=len(key)-len(param)\n\n if (len(param) < len(key)):\n param=(\" \"*delta)+param\n\n return param",
"title": ""
},
{
"docid": "2cde876f7996830c1ed483c8b240d892",
"score": "0.5604598",
"text": "def assemble_key(pieces):\n return ':'.join([str(p) for p in pieces if p])",
"title": ""
},
{
"docid": "b7c666f85e172eec0b87d77b9574fb09",
"score": "0.5586938",
"text": "def _setkey(self, val):\n\n if isinstance(val, str):\n val = val.strip()\n if len(val) <= 8:\n val = val.upper()\n if val == 'END':\n raise ValueError, \"keyword 'END' not allowed\"\n self._checkKey(val)\n else:\n if val[:8].upper() == 'HIERARCH':\n val = val[8:].strip()\n self.__class__ = _Hierarch\n else:\n raise ValueError, 'keyword name %s is too long (> 8), use HIERARCH.' % val\n else:\n raise ValueError, 'keyword name %s is not a string' % val\n self.__dict__['key'] = val",
"title": ""
},
{
"docid": "a836d44f14b3d28098f22bc7a5b3d894",
"score": "0.5581438",
"text": "def floorKey(self, key):",
"title": ""
},
{
"docid": "70fdfc1155aa5199271e69fc322dd676",
"score": "0.5575774",
"text": "def key_split(key, eval=False):\n signatures = [signature for signature in key.split(conf.SEP)]\n if eval:\n return [signature_eval(signature) for signature in signatures]\n else:\n return signatures",
"title": ""
},
{
"docid": "ddbc87f2f9e24d802f6068c4f3417454",
"score": "0.5564317",
"text": "def parse_key(key: str) -> Key:\n if not key:\n return Key(key=None, modifier=KeyModifier.NO)\n\n modifiers = collections.OrderedDict([\n ('ctrl+alt+', KeyModifier.CTRL | KeyModifier.ALT),\n ('ctrl+', KeyModifier.CTRL),\n ('alt+', KeyModifier.ALT),\n ('_none', KeyModifier.NO),\n ])\n\n modifier = '_none'\n\n for m in modifiers:\n if m in key:\n key = key.replace(m, '')\n modifier = m\n break\n\n return Key(key=key, modifier=modifiers[modifier])",
"title": ""
},
{
"docid": "3bb0e4471a6ab5511325226164f18502",
"score": "0.5563135",
"text": "def key_before_get(self, key):\n return key",
"title": ""
},
{
"docid": "7b157ea4f3acb8cc51dd19c9667f0656",
"score": "0.55530316",
"text": "def key_before_set(self, key):\n return key",
"title": ""
},
{
"docid": "eba0e303bc7e9f89a705faf432af5819",
"score": "0.5544635",
"text": "def validate_one(cls, keystr):\n if re.match(r\"\\w+(?:\\.\\w+)*$\", keystr) is None:\n raise cls.Bad(\"Bad key syntax for: %s. Should be: key1.key2...\" % (keystr))",
"title": ""
},
{
"docid": "36f02211caadd7173965d11e7e191a30",
"score": "0.5537486",
"text": "def safe_routing_key(routing_key):\n return reduce(lambda r_key, kv: r_key.replace(*kv),\n [('*', 's'), ('#', 'h')], routing_key)",
"title": ""
},
{
"docid": "fa3df6c2fa631743d5df7b1b2463776a",
"score": "0.5536218",
"text": "def _init_new_key() -> str:\n separator = random.choice([\" \", \"_\", \"-\", \"\"])\n separator = \"\"\n return utils.get_random_string() + separator + str(random.randrange(10**3, 10**6))",
"title": ""
},
{
"docid": "d64d6be9aa81c549d80cb8a69e58a300",
"score": "0.5503533",
"text": "def genkey(value):\n return CLEAN_RE.sub('', value)",
"title": ""
},
{
"docid": "4d47e8601e675b04fad60f2158977103",
"score": "0.5496635",
"text": "def fileLineParse (line, kvfun):\n try:\n v = line.split ('=\"')\n key = v[0].split (\" \")[1]\n for s in v[1:]:\n val, key2 = s.split ('\" ')\n kvfun (key, val)\n key = key2\n except ValueError:\n pass",
"title": ""
},
{
"docid": "faf67243fc3ebf4a56508dcb9859a3d4",
"score": "0.5494946",
"text": "def _to_dot_key(cls, section, key=None):\n if key:\n return (NON_ALPHA_NUM.sub('_', section.lower()), NON_ALPHA_NUM.sub('_', key.lower()))\n else:\n return NON_ALPHA_NUM.sub('_', section.lower())",
"title": ""
},
{
"docid": "f3575a48e36d8d0ec3eff9101db16115",
"score": "0.54938185",
"text": "def __getKeyWord(self, line):\n return line.split('=')[0].strip()",
"title": ""
},
{
"docid": "42e4a720601056500df9e72ddc7c5684",
"score": "0.54793257",
"text": "def _extractKey(self):\n head = self._getKeyString()\n if isinstance(self, _Hierarch):\n self.__dict__['key'] = head.strip()\n else:\n self.__dict__['key'] = head.strip().upper()",
"title": ""
},
{
"docid": "2c830b309052d560908d36a7262ba6cb",
"score": "0.54749703",
"text": "def split_key(key):\n regex = re.compile(r\"([^\\W\\d_]+)(\\d+)\")\n key_dict = {part: int(number) for part, number in regex.findall(key)}\n assert sorted(list(key_dict.keys())) == ['ind', 'it', 'locus', 'pop']\n return key_dict",
"title": ""
},
{
"docid": "52c6230020c3b53bf0a29d9c41e3a951",
"score": "0.5463771",
"text": "def _decode_key(self, key):\n return key",
"title": ""
},
{
"docid": "3bbee3ddee70c9cd8e394aefa7d08a0a",
"score": "0.5460108",
"text": "def step_key(key, seen=None):\n if seen is None:\n seen = set()\n choices=list(itertools.product(range(len(key)), range(len(key))))\n seen.add(key)\n new_key = key\n while new_key in seen:\n if len(choices) == 0:\n return False\n result = list(key)\n choice_idx = random.choice(range(len(choices)))\n first_idx, second_idx = choices.pop(choice_idx)\n result[first_idx], result[second_idx] = result[second_idx], key[first_idx]\n new_key = \"\".join(result)\n return new_key",
"title": ""
},
{
"docid": "437a317b5d796f123a890b06d71f08c5",
"score": "0.5450355",
"text": "def naming_key(self, key):\r\n if '(' in key:\r\n key = key.split('(')[0].strip()\r\n\r\n return self.strip_text(key.lower().replace(' ', '_'))",
"title": ""
},
{
"docid": "2d01d61337bcee4a93345378122853ca",
"score": "0.5437761",
"text": "def _omas_key_dict_preprocessor(key):\n if not isinstance(key, (list, tuple)):\n key = str(key)\n key = re.sub('\\]', '', re.sub('\\[', '.', key)).split('.')\n else:\n key = list(map(str, key))\n for k,item in enumerate(key):\n try:\n key[k] = int(item)\n except ValueError:\n pass\n return key",
"title": ""
},
{
"docid": "c25e966a4059d048475757e0ae006335",
"score": "0.5422775",
"text": "def _format_key(key: str) -> str:\n return env_prefix + key.upper().lstrip('_')",
"title": ""
},
{
"docid": "4dc460d20c676523d205f40c5e3c92cf",
"score": "0.5415723",
"text": "def shorter_name(key):\n key_short = key\n for sep in ['#', '/']:\n ind = key_short.rfind(sep)\n if ind is not None:\n key_short = key_short[ind+1:]\n else:\n key_short = key_short\n return key_short.replace('-', '_').replace('.', '_')",
"title": ""
},
{
"docid": "61ff7a47c54c185e907184a7eca26fc2",
"score": "0.54149926",
"text": "def normalize_keys(key):\n key = key.replace(\"-\", \"_\")\n return key",
"title": ""
},
{
"docid": "362d2119cc86ba76f582a865ad40467a",
"score": "0.5411692",
"text": "def _concatKey(str1,str2):\n return concat(concat(str1, '_'), str2)",
"title": ""
},
{
"docid": "f3fcb921aca94ffb21709afeb731c7aa",
"score": "0.5410922",
"text": "def _valid_key(key: str) -> bool:\n return key not in used_keys and len(key) <= 24",
"title": ""
},
{
"docid": "04abf61f7df5c719d7cd8a8d24711883",
"score": "0.5397115",
"text": "def test_empty_key_on_explode(self):\n list1 = calliope.preprocess.nodes.explode_nodes(\"1--3\")\n list2 = calliope.preprocess.nodes.explode_nodes(\"1,2,3\")\n\n assert list1 == list2 == [\"1\", \"2\", \"3\"]",
"title": ""
},
{
"docid": "72ee337815420de9c809fa2656cd88ae",
"score": "0.5393815",
"text": "def _force_key_as_list(self, key):\r\n return [key] if isinstance(key, str) else key",
"title": ""
},
{
"docid": "adde59f6d8f9e5d04240aa5ea0006998",
"score": "0.53931993",
"text": "def split_key_value(kv_pair):\n kv = kv_pair.split(\"=\")\n return kv[0], kv[1]",
"title": ""
},
{
"docid": "30277b383426bf238d6f98f00df04047",
"score": "0.5377292",
"text": "def get_key_for(unique_key):",
"title": ""
},
{
"docid": "b4c0ed8d758bc23b90aa4867f4ca3389",
"score": "0.5373789",
"text": "def _makeNoKey(key):\n return NO_KEY_PREFIX + key",
"title": ""
},
{
"docid": "883cce89c5c5bb95b72ab859f3867c1c",
"score": "0.536577",
"text": "def parse_key(key, data):\n\n value = findall(\"%s:(.*?)\\n\" % key.upper(), data)\n if value and len(value) > 0:\n value = value[0]\n else:\n value = \"UNKNOWN\"\n\n return value",
"title": ""
},
{
"docid": "6922d9a3092e3acb55ceeb90e00890de",
"score": "0.5363615",
"text": "def set_delimiter(self, delimiter):\n self._delimiter = delimiter\n for key in self._values.keys():\n if isinstance(self._values[key], FlatDict):\n self._values[key].set_delimiter(delimiter)",
"title": ""
},
{
"docid": "2be803cb3346a85a278aed0f3d47a711",
"score": "0.53617436",
"text": "def split_record(key, value, out):\n # splitting keys in dict through recursive calls.\n key, *rest = key.split(separator, 1)\n if rest:\n split_record(rest[0], value, out.setdefault(key, {}))\n else:\n out[key] = value",
"title": ""
},
{
"docid": "394ec4b09b40cd3190b88c73721e6f7f",
"score": "0.53598344",
"text": "def _key_prefix(key):\n # For instance, foo_agl2344321 and foo_agl5252133 will share a prefix.\n return _KEY_PREFIX_RE.sub(lambda m: m.group(1) + '...', key)",
"title": ""
},
{
"docid": "c9a377dfd2e6b92e13150be56d63cfea",
"score": "0.5351437",
"text": "def extract_key(self, comp):\n pass",
"title": ""
},
{
"docid": "6962dafbb92edfad52d0a8133e0dc9a4",
"score": "0.53459877",
"text": "def makeIdFromKey(key):",
"title": ""
},
{
"docid": "ab9a9ec8e5b3e8f4f9ef5bfda122b44b",
"score": "0.5338418",
"text": "def _key_func(x):\n return int(x.stem[len('gen_') : ])",
"title": ""
},
{
"docid": "01cbddf53c6c04ca4eb14bb8da2673e0",
"score": "0.5336328",
"text": "def test_tag_delimiter_mixed(self):\n pass",
"title": ""
},
{
"docid": "fd6996075336a732cb66fb458baecc5d",
"score": "0.5327979",
"text": "def _parse_first_key(self, content, offset):\n\n length = len(content) - offset\n is_class_name = False\n if content[offset] == '\"':\n collected, offset = self._parse_string(content, offset+1)\n return collected, offset+1, is_class_name\n\n i = 0\n while i < length:\n if content[offset+i] == '@':\n is_class_name = True\n break\n elif content[offset+i] == ':':\n break\n i += 1\n\n return content[offset:offset+i], offset+i+1, is_class_name",
"title": ""
},
{
"docid": "eed9eb69792b8e360265f9e0c7b5ab0b",
"score": "0.53227675",
"text": "def convert_key(key: str) -> str:\n string = re.sub(r\"[\\-\\.\\s]\", \"_\", str(key))\n return (string[0]).lower() + re.sub(\n r\"[A-Z]\",\n lambda matched: f\"_{matched.group(0).lower()}\", # type:ignore[str-bytes-safe]\n string[1:],\n )",
"title": ""
},
{
"docid": "4ba7e96a308857440b39e1523336e33f",
"score": "0.5317386",
"text": "def _normalize(key):\n return key.lower().replace(' ', '_')",
"title": ""
},
{
"docid": "1ee51d82c7627b56816b5b4018af119e",
"score": "0.5316606",
"text": "def __validate_key(self, key):\n is_match = self.__rexp.match(key)\n if not is_match or is_match.group() is not key:\n raise KeyError('\"%s\" is an invalid key as it does not match with the following regular expression, %s'%(key, self.__key_rexp))\n return key",
"title": ""
},
{
"docid": "4392d46b7f00f69cfe5540e67f1ddefd",
"score": "0.53031886",
"text": "def _key_to_partition_and_row(key):\n partition, _, row = key.partition('_')\n return partition, row",
"title": ""
},
{
"docid": "9b0019d5fe88522b6973d752fb55b857",
"score": "0.52970326",
"text": "def __init__(self, env, var, item_sep=os.pathsep, key_sep='='):\n\n # Initialize the superclass and store the separators\n super(SpecialDict, self).__init__(env, var)\n self._item_sep = item_sep\n self._key_sep = key_sep\n\n # Try to interpret the current value\n try:\n self._value = self._split(self.raw)\n except KeyError:\n # Not set\n self._value = {}",
"title": ""
},
{
"docid": "1668a729a32a14c9cac51a801e514b58",
"score": "0.5293978",
"text": "def _handle_single_key(self, key):\n try:\n len(key)\n return [self._handle_distance(k) for k in key]\n except TypeError:\n if isinstance(key, slice):\n return self._handle_slice(key)\n else:\n return [self._handle_distance(key)]\n return key",
"title": ""
},
{
"docid": "777cc44557c23592d78f7a5b0cf5a20b",
"score": "0.5290928",
"text": "def test_match_dict_keys(self):\n delims = \" \\t\\n`!@#$^&*()=+[{]}\\\\|;:'\\\",<>?\"\n\n def match(*args, **kwargs):\n quote, offset, matches = match_dict_keys(*args, delims=delims, **kwargs)\n return quote, offset, list(matches)\n\n keys = [\"foo\", b\"far\"]\n assert match(keys, \"b'\") == (\"'\", 2, [\"far\"])\n assert match(keys, \"b'f\") == (\"'\", 2, [\"far\"])\n assert match(keys, 'b\"') == ('\"', 2, [\"far\"])\n assert match(keys, 'b\"f') == ('\"', 2, [\"far\"])\n\n assert match(keys, \"'\") == (\"'\", 1, [\"foo\"])\n assert match(keys, \"'f\") == (\"'\", 1, [\"foo\"])\n assert match(keys, '\"') == ('\"', 1, [\"foo\"])\n assert match(keys, '\"f') == ('\"', 1, [\"foo\"])\n\n # Completion on first item of tuple\n keys = [(\"foo\", 1111), (\"foo\", 2222), (3333, \"bar\"), (3333, \"test\")]\n assert match(keys, \"'f\") == (\"'\", 1, [\"foo\"])\n assert match(keys, \"33\") == (\"\", 0, [\"3333\"])\n\n # Completion on numbers\n keys = [\n 0xDEADBEEF,\n 1111,\n 1234,\n \"1999\",\n 0b10101,\n 22,\n ] # 0xDEADBEEF = 3735928559; 0b10101 = 21\n assert match(keys, \"0xdead\") == (\"\", 0, [\"0xdeadbeef\"])\n assert match(keys, \"1\") == (\"\", 0, [\"1111\", \"1234\"])\n assert match(keys, \"2\") == (\"\", 0, [\"21\", \"22\"])\n assert match(keys, \"0b101\") == (\"\", 0, [\"0b10101\", \"0b10110\"])\n\n # Should yield on variables\n assert match(keys, \"a_variable\") == (\"\", 0, [])\n\n # Should pass over invalid literals\n assert match(keys, \"'' ''\") == (\"\", 0, [])",
"title": ""
},
{
"docid": "6107df2b73336e4bfce5aa55a0c81bde",
"score": "0.528245",
"text": "def _de_normalize(key):\n return key.replace('_', ' ')",
"title": ""
},
{
"docid": "b7528522f283ec881bd2d46f778215c2",
"score": "0.5276917",
"text": "def parse(self,char_read):\n\n char_read = char_read.decode(\"utf-8\")\n\n # do nothing if space\n if char_read == ' ':\n pass\n\n # do nothing if newline\n elif char_read == '\\n':\n pass\n\n # if it is alpha ie letter, should log in the corresponding key list\n elif char_read.isalpha():\n # if already such a key, just use it as the next key and add a\n # comma since starting to log new value\n if char_read in self.dict_grabbed:\n self.current_key = char_read\n self.dict_grabbed[self.current_key].append(',')\n\n # otherwise, create key and use it as next key\n else:\n # create empty new list for this key\n self.dict_grabbed[char_read] = []\n self.current_key = char_read\n\n # otherwise number or decimal point: put content in current key\n else:\n self.dict_grabbed[self.current_key].append(char_read)",
"title": ""
},
{
"docid": "5e9ae114ef2dd26d82b0da84b8efc4f8",
"score": "0.527643",
"text": "def regex_key(key):\n convert = lambda text: int(text) if text.isdigit() else text.lower()\n return [convert(c) for c in re.split('([0-9]+)', key)]",
"title": ""
},
{
"docid": "c97cea33abfff60e3836aefb8c670276",
"score": "0.52688646",
"text": "def _split(self, value):\n\n result = {}\n for item in value.split(self._item_sep):\n key, sep, value = item.partition(self._key_sep)\n result[key] = value if sep else None\n return result",
"title": ""
},
{
"docid": "2a4e56cb26508ce0bdb60c26b151f4f3",
"score": "0.5257343",
"text": "def _split(self, value):\n\n result = collections.OrderedDict()\n for item in value.split(self._item_sep):\n key, sep, value = item.partition(self._key_sep)\n result[key] = value if sep else None\n return result",
"title": ""
},
{
"docid": "0ec8dc3659158337852005f8b77b1981",
"score": "0.5246174",
"text": "def test_repeated_nonstandard_keys(self):\n self.assertTrue('good_cookie' in parse_cookie('a,=b; a,=c; good_cookie=yes').keys())",
"title": ""
},
{
"docid": "d6081d9777fa76dca40a5ec5f70c354a",
"score": "0.52456886",
"text": "def make_key(self, key, version=None):\n key = super(ConsulCache, self).make_key(key, version)\n\n return re.sub(r'\\$|\\.', '', key)",
"title": ""
},
{
"docid": "ed927bf0ae6cbb2c13acd5f28563cc10",
"score": "0.523241",
"text": "def parseKeyVarArray(string: str, splitter: str) -> RobDict:\r\n\r\n arrayFirstSplit: List[str] = string.split(splitter)\r\n\r\n finalDict: Dict[str, str] = {}\r\n\r\n for index, value in enumerate(arrayFirstSplit):\r\n # if odd then we on index\r\n if index % 2 == 0:\r\n finalDict[arrayFirstSplit[index]] = arrayFirstSplit[index + 1]\r\n\r\n return RobDict(finalDict)",
"title": ""
},
{
"docid": "c819b68b6c9df85ce9e48498b912dd7b",
"score": "0.52313",
"text": "def do_keysequence(self, line):\n\n args = line.split(\" \")\n\n if args[0] == \"counter\":\n newline = line.replace(\"counter\", \"\")\n self.handle_property(newline, SPINEL.PROP_NET_KEY_SEQUENCE_COUNTER,\n 'L')\n\n elif args[0] == \"guardtime\":\n newline = line.replace(\"guardtime\", \"\")\n self.handle_property(newline, SPINEL.PROP_NET_KEY_SWITCH_GUARDTIME,\n 'L')",
"title": ""
},
{
"docid": "291b9675c7a30020cbca39359913d1a6",
"score": "0.5219385",
"text": "def key_to_trackingkey(key):\n ((a, _), (_, B)) = key\n return (a, B)",
"title": ""
},
{
"docid": "9a280207af9af064da113e4ec1a441c9",
"score": "0.52193516",
"text": "def path_to_key(self, path):\n assert type(path) == list\n return self.config['separator'].join(path)",
"title": ""
},
{
"docid": "eb2414fce341ab150288afd1cf280188",
"score": "0.5215471",
"text": "def getKey(move):\n if move == 0:\n return \"'i'\"\n if move == 1:\n return \"'o'\"\n if move == 2:\n return \"'k'\"\n if move == 3:\n return \"'l'\"\n if move == 4:\n return \"','\"\n if move == 5:\n return \"'.'\"",
"title": ""
},
{
"docid": "a07c8e6ea5326cca1a1ce6b2a8ce76f9",
"score": "0.5214945",
"text": "def __init__(self, env, var, item_sep=os.pathsep, key_sep='='):\n\n # Initialize the superclass and store the separators\n super(SpecialOrderedDict, self).__init__(env, var)\n self._item_sep = item_sep\n self._key_sep = key_sep\n\n # Try to interpret the current value\n try:\n self._value = self._split(self.raw)\n except KeyError:\n # Not set\n self._value = collections.OrderedDict()",
"title": ""
},
{
"docid": "0bf7e2436ad211051da2d1d379f8a0b9",
"score": "0.52071416",
"text": "def fnv1(self, key):",
"title": ""
},
{
"docid": "12b2f139ee898df6c9027f9dd57f1e07",
"score": "0.52000636",
"text": "def tokenize_record_key(record): # type: (ImportRecord) -> Iterator[str]\n record_type = record.type or \"\"\n yield f'$type:{record_type}'\n yield f'$title:{record.title or \"\"}'\n yield f'$login:{record.login or \"\"}'\n yield f'$url:{record.login_url or \"\"}'\n\n if record_type in {'', 'login'}:\n return\n\n excluded = {x for x in RECORD_FIELD_TYPES}\n excluded.update(IGNORABLE_FIELD_TYPES)\n fields = {x.name_key(): x.value for x in record.fields if x.type and x.type not in excluded}\n if record.type == 'bankCard':\n if '$paymentcard' in fields:\n payment_card = fields.pop('$paymentcard')\n else:\n payment_card = {}\n yield '$paymentcard:' + value_to_token(payment_card.get('cardNumber'))\n elif record.type == 'bankAccount':\n yield value_to_token(fields.get('bankAccount'))\n yield value_to_token(fields.get('name'))\n elif record.type == 'address':\n yield value_to_token(fields.get('address'))\n else:\n fields = [x for x in record.fields if x.type not in excluded]\n fields.sort(key=ImportRecordField.name_key, reverse=False)\n for field in fields:\n hash_value = field.hash_key()\n if hash_value:\n yield hash_value",
"title": ""
},
{
"docid": "9ee18a8f77fd6fab2b4a999e3bec6a02",
"score": "0.5197637",
"text": "def __missing__(self, key):\n return str(key).join(\"{}\")",
"title": ""
},
{
"docid": "4d26407025e4110984ea88c9760cc4aa",
"score": "0.51959735",
"text": "def getlinekey(line, strict=True):\n if not Engine.LineParser.linehaskey(line=line, strict=strict): key = None\n elif \":\" in line[:line.find(\"=\")]: key = line.split(\":\", 1)[0].strip()\n else: key = line.split(\"=\", 1)[0].strip()\n return key",
"title": ""
},
{
"docid": "10104693ca3cb8d9981e394db78814f0",
"score": "0.51946425",
"text": "def remove_extra_keys():\n if pygcurse.K_SEMICOLON in Interface.key_dictionary:\n del Interface.key_dictionary[pygcurse.K_SEMICOLON]\n if pygcurse.K_QUOTE in Interface.key_dictionary:\n del Interface.key_dictionary[pygcurse.K_QUOTE]\n if pygcurse.K_LEFTBRACKET in Interface.key_dictionary:\n del Interface.key_dictionary[pygcurse.K_LEFTBRACKET]\n if pygcurse.K_RIGHTBRACKET in Interface.key_dictionary:\n del Interface.key_dictionary[pygcurse.K_RIGHTBRACKET]",
"title": ""
},
{
"docid": "f057617696785913423697fb84d12d03",
"score": "0.5194423",
"text": "def _key(self, name, group=\"\"):\n return group + \"::\" + name",
"title": ""
},
{
"docid": "4b654f313171287412fb4a6323e67f81",
"score": "0.5182694",
"text": "def test_procure_key(self):\n pass",
"title": ""
},
{
"docid": "7f9b63541ce29303191146ce600d8db5",
"score": "0.51803315",
"text": "def _update(self):\n\n super(SpecialDict, self).set(\n self._item_sep.join(\n '%s%s%s' % (\n key,\n '' if value is None else self._key_sep,\n '' if value is None else value\n ) for key, value in\n sorted(self._value.items(), key=lambda x: x[0])\n )\n )",
"title": ""
},
{
"docid": "a0322622af4e7ae77a9e53de172b4032",
"score": "0.517399",
"text": "def checkpair(self, key, value):\n if key in self:\n if '*' in str(value):\n return fnmatch.fnmatch(self[key], value)\n elif self[key] == str(value):\n return True\n else:\n return False\n\n else:\n return False",
"title": ""
},
{
"docid": "535a7f8c86d7b5ed4bd357623bf5b664",
"score": "0.5172586",
"text": "def set_key(cls, key: str, values: dict[str, str]) -> str:\n return key if key else values[\"name\"].lower().replace(\" \", \"_\")",
"title": ""
},
{
"docid": "fea8f3897d6b97785079294315778fac",
"score": "0.5172436",
"text": "def get_string_split(line, index, key, val):\n params = \"\"\n for element in line.split(\"|\")[index].split(\",\"):\n params += val + element.split(\"=\")[key] + \",\"\n return params",
"title": ""
},
{
"docid": "8e3e5d17974966434f94c83827a28392",
"score": "0.5170691",
"text": "def test_match_dict_keys_tuple(self):\n delims = \" \\t\\n`!@#$^&*()=+[{]}\\\\|;:'\\\",<>?\"\n\n keys = [(\"foo\", \"bar\"), (\"foo\", \"oof\"), (\"foo\", b\"bar\"), ('other', 'test')]\n\n def match(*args, extra=None, **kwargs):\n quote, offset, matches = match_dict_keys(\n *args, delims=delims, extra_prefix=extra, **kwargs\n )\n return quote, offset, list(matches)\n\n # Completion on first key == \"foo\"\n assert match(keys, \"'\", extra=(\"foo\",)) == (\"'\", 1, [\"bar\", \"oof\"])\n assert match(keys, '\"', extra=(\"foo\",)) == ('\"', 1, [\"bar\", \"oof\"])\n assert match(keys, \"'o\", extra=(\"foo\",)) == (\"'\", 1, [\"oof\"])\n assert match(keys, '\"o', extra=(\"foo\",)) == ('\"', 1, [\"oof\"])\n assert match(keys, \"b'\", extra=(\"foo\",)) == (\"'\", 2, [\"bar\"])\n assert match(keys, 'b\"', extra=(\"foo\",)) == ('\"', 2, [\"bar\"])\n assert match(keys, \"b'b\", extra=(\"foo\",)) == (\"'\", 2, [\"bar\"])\n assert match(keys, 'b\"b', extra=(\"foo\",)) == ('\"', 2, [\"bar\"])\n\n # No Completion\n assert match(keys, \"'\", extra=(\"no_foo\",)) == (\"'\", 1, [])\n assert match(keys, \"'\", extra=(\"fo\",)) == (\"'\", 1, [])\n\n keys = [(\"foo1\", \"foo2\", \"foo3\", \"foo4\"), (\"foo1\", \"foo2\", \"bar\", \"foo4\")]\n assert match(keys, \"'foo\", extra=(\"foo1\",)) == (\"'\", 1, [\"foo2\"])\n assert match(keys, \"'foo\", extra=(\"foo1\", \"foo2\")) == (\"'\", 1, [\"foo3\"])\n assert match(keys, \"'foo\", extra=(\"foo1\", \"foo2\", \"foo3\")) == (\"'\", 1, [\"foo4\"])\n assert match(keys, \"'foo\", extra=(\"foo1\", \"foo2\", \"foo3\", \"foo4\")) == (\n \"'\",\n 1,\n [],\n )\n\n keys = [(\"foo\", 1111), (\"foo\", \"2222\"), (3333, \"bar\"), (3333, 4444)]\n assert match(keys, \"'\", extra=(\"foo\",)) == (\"'\", 1, [\"2222\"])\n assert match(keys, \"\", extra=(\"foo\",)) == (\"\", 0, [\"1111\", \"'2222'\"])\n assert match(keys, \"'\", extra=(3333,)) == (\"'\", 1, [\"bar\"])\n assert match(keys, \"\", extra=(3333,)) == (\"\", 0, [\"'bar'\", \"4444\"])\n assert match(keys, \"'\", extra=(\"3333\",)) == (\"'\", 1, [])\n assert match(keys, \"33\") == (\"\", 0, [\"3333\"])",
"title": ""
},
{
"docid": "f246722b53d2f2ea135bcff6dfdcd767",
"score": "0.5162745",
"text": "def is_key_valid(self,key):\n if not key or any(map(lambda s: s in key,space_chars))\\\n or any(map(lambda s: s in key,bad_chars)):\n return False \n return True",
"title": ""
},
{
"docid": "b847673e00b1033bcb73f2ece4c78e5d",
"score": "0.51618147",
"text": "def read_interventions(self, key):",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="716"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">d2c6b52742fafc7dd50224580b53e7da</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Register an endpoint with this client.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "b4e161884b7e6e24370777782bd9d197",
"score": "0.0",
"text": "def register(self, name, cls, routes):\n self.__dict__[name] = cls(self, routes[0], routes[1])",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "8e9c617398c09504bf9c8a7a7e5c2be9",
"score": "0.74871415",
"text": "def register(self, endpoint):\n endpoint = endpoint if not self.prefix else self.prefix + endpoint\n self.routes.append((endpoint, self.handler))",
"title": ""
},
{
"docid": "05b66432fa3d0bd4403cee00d45c8bd7",
"score": "0.7370913",
"text": "def _add_endpoint(self, endpoint=None, endpoint_name=None, handler=None, methods=['GET', 'POST']):\n raise NotImplementedError",
"title": ""
},
{
"docid": "e8ec182b957f43364ea4f968d587f9d4",
"score": "0.7358815",
"text": "def register(self, endpoint):\n method, path, func = self._extract_data(endpoint)\n\n if path not in self.endpoints:\n log.debug(f\"registering new endpoint: {path}\")\n self.endpoints[path] = {}\n\n if method in self.endpoints[path]:\n log.warn(f\"overriding existing method {method} {path}\")\n\n log.debug(f\"registering method {method} {path}\")\n self.endpoints[path][method] = func",
"title": ""
},
{
"docid": "73b2e252ac0acb9641d367f71a0aa6c6",
"score": "0.7015975",
"text": "def add_endpoint(self, name, operation, url):\n endpoint = Endpoint(name, operation, url)\n self._endpoints[endpoint.name] = endpoint",
"title": ""
},
{
"docid": "c757b1448dcdc3778298733f63ba47b0",
"score": "0.6977591",
"text": "def add(service, endpoint):\n try:\n endpoints = get_endpoint()\n except Exception:\n endpoints = {}\n\n if service in endpoints:\n raise ValueError(f\"'{service}' service already exists.\")\n endpoints[service] = endpoint\n set_endpoint(endpoints)\n click.echo(f\"'{service}: {endpoint}' endpoint has been added.\")",
"title": ""
},
{
"docid": "8c45dd541d3d36491f14c584c855d440",
"score": "0.69187",
"text": "def register_endpoint(funcx_client, endpoint_name, endpoint_uuid, endpoint_dir):\n logger.debug(\"Attempting registration\")\n logger.debug(f\"Trying with eid : {endpoint_uuid}\")\n from funcx_endpoint.endpoint.version import VERSION as ENDPOINT_VERSION\n reg_info = funcx_client.register_endpoint(endpoint_name,\n endpoint_uuid,\n endpoint_version=ENDPOINT_VERSION)\n\n with open(os.path.join(endpoint_dir, 'endpoint.json'), 'w+') as fp:\n json.dump(reg_info, fp)\n logger.debug(\"Registration info written to {}/endpoint.json\".format(endpoint_dir))\n\n return reg_info",
"title": ""
},
{
"docid": "5c316fe61a15435e34a8868b5915c0a7",
"score": "0.68949014",
"text": "def endpoint_register(*, endpoint_key: str = None):\n\n def decorator(function):\n nonlocal endpoint_key\n\n if not endpoint_key:\n endpoint_key = function.__name__\n\n if endpoint_key in _endpoints_mapping:\n raise Exception(f\"Endpoint {endpoint_key} already registered.\")\n\n _endpoints_mapping[endpoint_key] = function\n\n def wrapper(*args, **kwargs):\n # Both sync and async support.\n async_function = asyncio.coroutine(function)\n loop = asyncio.get_event_loop()\n loop.run_until_complete(async_function(*args, **kwargs))\n\n return wrapper\n return decorator",
"title": ""
},
{
"docid": "4c025ea9a27ae2c64b7f2ec9ec855565",
"score": "0.66386026",
"text": "def add_endpoint(self, point: _ComplexEndpoint) -> None:\n self._endpoints.append(point)",
"title": ""
},
{
"docid": "57e57bbd9d78685061ab040cdf3f8519",
"score": "0.6634472",
"text": "def addEndpoint(self, connection, name, type, headers=[]):\n self.endpoints.append((connection, Queue.Queue(), name, type, headers))",
"title": ""
},
{
"docid": "ebee99d69be41a18ec222f41906ff9e9",
"score": "0.66084176",
"text": "def endpoint(self, endpoint):\n\n self._endpoint = endpoint",
"title": ""
},
{
"docid": "ebee99d69be41a18ec222f41906ff9e9",
"score": "0.66084176",
"text": "def endpoint(self, endpoint):\n\n self._endpoint = endpoint",
"title": ""
},
{
"docid": "a84a7e0bce265abb38eeedb280a1cf05",
"score": "0.6372093",
"text": "def addEndpoints(self, endpoints):\n self.endpoints.extend(endpoints)\n self._connectOrBind(endpoints)",
"title": ""
},
{
"docid": "e17fda11f87f458e56e58c8468662dd9",
"score": "0.6294592",
"text": "def add(self, endpoint):\n with self.__lock:\n # Check framework UID (avoid to import our own services)\n if endpoint.framework == self._fw_uid:\n return False\n\n # Check if the end point already exists\n if endpoint.uid in self._registry:\n # Already known end point: do nothing\n _logger.debug(\"Already known endpoint\")\n return False\n\n # Store the end point\n self._registry[endpoint.uid] = endpoint\n if endpoint.framework:\n self._frameworks.setdefault(endpoint.framework, []).append(\n endpoint\n )\n\n # Notify listeners (out of lock)\n if self._listeners:\n for listener in self._listeners[:]:\n try:\n listener.endpoint_added(endpoint)\n except Exception as ex:\n _logger.exception(\"Error calling listener: %s\", ex)\n return True",
"title": ""
},
{
"docid": "b8f8abb3826fc6741df59114246f5fc4",
"score": "0.6184897",
"text": "def create_endpoint(self, context, endpoint_values):",
"title": ""
},
{
"docid": "d75f47b7a6cae161060bc4b403deadad",
"score": "0.61295336",
"text": "def test_create_endpoint_with_region(self):\n ref = self.new_endpoint_ref(service_id=self.service_id)\n ref[\"region\"] = uuid.uuid4().hex\n ref.pop('region_id')\n self.post('/endpoints', body={'endpoint': ref}, expected_status=201)\n # Make sure the region is created\n self.get('/regions/%(region_id)s' % {\n 'region_id': ref[\"region\"]})",
"title": ""
},
{
"docid": "d96245673b7a58926552247583d5d313",
"score": "0.61165506",
"text": "def _add_new_endpoint(self):\n # Assign IP addresses using the given IPAM plugin.\n _log.info(\"Configuring a new Endpoint\")\n ipv4, ipv6, ipam_result = self._assign_ips(self.ipam_env)\n\n # Filter out addresses that didn't get assigned.\n ip_list = [ip for ip in [ipv4, ipv6] if ip is not None]\n\n # Create the Calico endpoint object.\n endpoint = self._create_endpoint(ip_list)\n\n # Provision the veth for this endpoint.\n endpoint = self._provision_veth(endpoint)\n\n # Provision / apply profile on the created endpoint.\n try:\n self.policy_driver.apply_profile(endpoint)\n except PolicyException as e:\n _log.error(\"Failed to apply profile to endpoint %s\",\n endpoint.name)\n self._remove_veth(endpoint)\n self._remove_workload()\n self.ipam_env[CNI_COMMAND_ENV] = CNI_CMD_DELETE\n self._release_ip(self.ipam_env)\n print_cni_error(ERR_CODE_GENERIC, e.message, e.details)\n sys.exit(ERR_CODE_GENERIC)\n\n # Return the IPAM plugin's result.\n return ipam_result",
"title": ""
},
{
"docid": "69428f94e15a8a09cf473c2570d7669e",
"score": "0.6020522",
"text": "def register(self, endpoint: Union[Callable, Any], procedure: Optional[str] = None,\n options: Optional[RegisterOptions] = None, prefix: Optional[str] = None,\n check_types: Optional[bool] = None) -> Union[Registration, List[Registration]]:",
"title": ""
},
{
"docid": "9eb43294a8b2cd14b0549362f48776af",
"score": "0.5959905",
"text": "def create_endpoint(self, context, endpoint):\n self.assert_admin(context)\n\n # according to the v2 spec publicurl is mandatory\n self._require_attribute(endpoint, 'publicurl')\n # service_id is necessary\n self._require_attribute(endpoint, 'service_id')\n\n if endpoint.get('region') is not None:\n try:\n self.catalog_api.get_region(endpoint['region'])\n except exception.RegionNotFound:\n region = dict(id=endpoint['region'])\n self.catalog_api.create_region(region)\n\n legacy_endpoint_ref = endpoint.copy()\n\n urls = {}\n for i in INTERFACES:\n # remove all urls so they aren't persisted them more than once\n url = '%surl' % i\n if endpoint.get(url):\n # valid urls need to be persisted\n urls[i] = endpoint.pop(url)\n elif url in endpoint:\n # null or empty urls can be discarded\n endpoint.pop(url)\n legacy_endpoint_ref.pop(url)\n\n legacy_endpoint_id = uuid.uuid4().hex\n for interface, url in six.iteritems(urls):\n endpoint_ref = endpoint.copy()\n endpoint_ref['id'] = uuid.uuid4().hex\n endpoint_ref['legacy_endpoint_id'] = legacy_endpoint_id\n endpoint_ref['interface'] = interface\n endpoint_ref['url'] = url\n endpoint_ref['region_id'] = endpoint_ref.pop('region')\n\n self.catalog_api.create_endpoint(endpoint_ref['id'], endpoint_ref)\n\n legacy_endpoint_ref['id'] = legacy_endpoint_id\n return {'endpoint': legacy_endpoint_ref}",
"title": ""
},
{
"docid": "0a51ea7123370fa4649c62aec5632c84",
"score": "0.5916628",
"text": "def endpoint(self, endpoint):\n\n def decorator(f):\n self.view_functions[endpoint] = f\n return f\n\n return decorator",
"title": ""
},
{
"docid": "a401f0072d1c60dd133c1645743e240d",
"score": "0.586833",
"text": "def __init__(self, endpoints=[]):\n self.endpoints = {}\n for endpoint in endpoints:\n self.register(endpoint)",
"title": ""
},
{
"docid": "db00086746e50f4629c675ebe3a5fa41",
"score": "0.58420277",
"text": "def register_end_point(self, name, socket_type, end_point):\n socket = self.__zmq_context.socket(socket_type)\n self.__end_points[name] = socket\n if socket_type in [zmq.PULL, zmq.REP]:\n socket.bind(end_point)\n elif socket_type == zmq.PUSH:\n socket.connect(end_point)\n else:\n raise ValueError(\"Unknown socket type {0}\".format(socket_type))",
"title": ""
},
{
"docid": "43984254519be61407b4e095ca6fa51a",
"score": "0.5820334",
"text": "def setup_endpoint_connection(keystone):\n with charm.provide_charm_instance() as instance:\n keystone.register_endpoints(instance.service_type,\n instance.region,\n instance.public_url,\n instance.internal_url,\n instance.admin_url,\n requested_roles=octavia.OCTAVIA_ROLES,\n add_role_to_admin=octavia.OCTAVIA_ROLES)\n instance.assess_status()",
"title": ""
},
{
"docid": "39bdb34d7405a1bb5689cc5136946046",
"score": "0.58197355",
"text": "def connect(__endpoint: Endpoint | int, *, access_token: str | None = None) -> Endpoint:",
"title": ""
},
{
"docid": "df5ce045a42b7373a7d21a04322072e1",
"score": "0.58151716",
"text": "def _add_existing_endpoint(self, endpoint):\n # Get the already existing IP information for this Endpoint.\n try:\n ip4 = next(iter(endpoint.ipv4_nets))\n except StopIteration:\n # No IPv4 address on this endpoint.\n _log.warning(\"No IPV4 address attached to existing endpoint\")\n ip4 = IPNetwork(\"0.0.0.0/32\")\n\n try:\n ip6 = next(iter(endpoint.ipv6_nets))\n except StopIteration:\n # No IPv6 address on this endpoint.\n _log.warning(\"No IPV6 address attached to existing endpoint\")\n ip6 = IPNetwork(\"::/128\")\n\n # Apply a new profile to this endpoint.\n try:\n self.policy_driver.apply_profile(endpoint)\n except PolicyException as e:\n # Hit an exception applying the profile. We haven't configured\n # anything, so we don't need to clean anything up. Just exit.\n _log.error(\"Failed to apply profile to endpoint %s\",\n endpoint.name)\n print_cni_error(ERR_CODE_GENERIC, e.message)\n sys.exit(ERR_CODE_GENERIC)\n\n return {\"ip4\": {\"ip\": str(ip4.cidr)},\n \"ip6\": {\"ip\": str(ip6.cidr)}}",
"title": ""
},
{
"docid": "994c07f1186504e3e9c35676aaa897cd",
"score": "0.5791213",
"text": "def add_instance(name, endpoint, client_id, client_secret, username, password, timeout):\n try:\n # parse the endpoint\n endpoint = urlparse(endpoint).geturl().strip('/')\n except:\n raise ApiError(\"Endpoint is not well formatted.\")\n\n # delete extra white space\n name = name.strip()\n\n # Request the remote\n r = post_request_token(endpoint, client_id, client_secret,\n timeout, username, password)\n\n if r.status_code == 200:\n # create the instance from a request\n instance = _create_instance_object_from_request_response(name,\n endpoint,\n r.content)\n\n # upsert the instance\n return upsert(instance)\n else:\n raise ApiError(\"Unable to get access to the remote instance using these parameters.\")",
"title": ""
},
{
"docid": "5f53c4d50fd40f9094fc77ae6336537a",
"score": "0.5751848",
"text": "def create_endpoint(endpoint_id, endpoint_name, ip, username, password, endpoint_type):\n endpoint_data = {\n \"id\": endpoint_id,\n \"name\": endpoint_name,\n \"ip\": ip,\n \"username\": username,\n \"password\": password,\n \"type\": endpoint_type\n }\n return endpoint_data",
"title": ""
},
{
"docid": "54436dc3bd14f7996ba1e3188f708662",
"score": "0.5743613",
"text": "def test_create_endpoint_with_no_region(self):\n ref = self.new_endpoint_ref(service_id=self.service_id)\n ref.pop('region_id')\n self.post('/endpoints', body={'endpoint': ref}, expected_status=201)",
"title": ""
},
{
"docid": "9d57718adcb346817fed4655e2c5c269",
"score": "0.5712974",
"text": "def add_view(self, endpoint, callback):\n self.views[endpoint] = callback",
"title": ""
},
{
"docid": "5550ded1935e19bfc1b7f3fcd592c7de",
"score": "0.57079",
"text": "def tcpservice_register(self, name, domain, endpoint, user_session=None):\n j.tools.tf_gateway.tcpservice_register(name, domain, endpoint)\n return True",
"title": ""
},
{
"docid": "4325774b88092abf82ffd8c0f7109fad",
"score": "0.5703129",
"text": "def register_with_hub(endpoint_uuid, endpoint_dir, address,\n redis_host='funcx-redis.wtgh6h.0001.use1.cache.amazonaws.com'):\n print(\"Picking source as a mock site\")\n sites = ['128.135.112.73', '140.221.69.24',\n '52.86.208.63', '129.114.63.99',\n '128.219.134.72', '134.79.129.79']\n ip_addr = random.choice(sites)\n try:\n r = requests.post(address + '/register',\n json={'endpoint_id': endpoint_uuid,\n 'endpoint_addr': ip_addr,\n 'redis_address': redis_host})\n except requests.exceptions.ConnectionError:\n logger.critical(\"Unable to reach the funcX hub at {}\".format(address))\n exit(-1)\n\n if r.status_code != 200:\n print(dir(r))\n print(r)\n raise RegistrationError(r.reason)\n\n with open(os.path.join(endpoint_dir, 'endpoint.json'), 'w+') as fp:\n json.dump(r.json(), fp)\n logger.debug(\"Registration info written to {}/endpoint.json\".format(endpoint_dir))\n\n return r.json()",
"title": ""
},
{
"docid": "0910a396362fd16c5b3013772d059619",
"score": "0.5694868",
"text": "def register(uri):\r\n def decorate(f):\r\n assert(callable(f))\r\n if not hasattr(f, '_wampuris'):\r\n f._wampuris = []\r\n f._wampuris.append(Pattern(six.u(uri), Pattern.URI_TARGET_ENDPOINT))\r\n return f\r\n return decorate",
"title": ""
},
{
"docid": "f818ec944a653ea63032046a77ce3880",
"score": "0.5586393",
"text": "def register_route(self, router, path=\"/pubsub\"):\n self.endpoint.register_route(router, path)",
"title": ""
},
{
"docid": "6da228f5c76acf6583757668d649c1ed",
"score": "0.55728674",
"text": "def post(self, request):\n LOG.debug('***************call create endpoint************')\n new_endpoint = oasis.endpoint_create(request, **request.DATA)\n return rest_utils.CreatedResponse(\n '/api/oasis/endpoints/%s' % new_endpoint.id,\n new_endpoint.to_dict())",
"title": ""
},
{
"docid": "c445f7a46bd2f73ec45b38cd9a153e38",
"score": "0.557214",
"text": "def create_endpoint(client, uuid, host, location):\n try:\n Endpoint_V1 = client.get_model('endpoint-v1')\n ep = Endpoint_V1(hostname=host, locationName=location)\n\n res = (\n client.ui_facing\n .createConfigurationOverlayEndpoint(endpoint=ep, uuid=uuid)\n .response()\n .result\n )\n\n _log.info(res)\n _log.info(\"endpoint created\")\n except Exception as e:\n raise Exception(\n \"Failed to create endpoint for location '%s': %s\" % (location, e))",
"title": ""
},
{
"docid": "d6489564c242b5b0545e8b1543d197f2",
"score": "0.55653006",
"text": "def create_endpoints():\n api.add_resource(Foo, '/foo')\n api.add_resource(PdnList, '/pdn')\n api.add_resource(PdnResource, '/pdn/<int:id>')",
"title": ""
},
{
"docid": "ed50431fb1e7a0fcb78e4474aab32fca",
"score": "0.5541515",
"text": "def api_endpoint(self, value):\n self._data[API_ENDPOINT] = value",
"title": ""
},
{
"docid": "4fe6f14ef18af9e79fc76ae2beb0c525",
"score": "0.5539246",
"text": "def _add_resource_cls(self, route_str, endpoint=None, thing=None, route_kwargs=None):\n route_kwargs = route_kwargs if route_kwargs is not None else {}\n if endpoint not in self._instances:\n self._instances[endpoint] = thing()\n self.add_resource(route_str, endpoint=endpoint, thing=self._instances[endpoint], route_kwargs=route_kwargs)",
"title": ""
},
{
"docid": "6d20fddc2189219f5437df90815e5f49",
"score": "0.5408151",
"text": "def register(cls, app):\n url = cls.get_api_url()\n view_func = cls.as_view(cls.endpoint)\n app.add_url_rule(url, view_func=view_func, methods=['GET', \"POST\", \"PUT\", \"DELETE\"])\n url = cls.get_api_url('<command>')\n app.add_url_rule(url, view_func=view_func, methods=[\"GET\", \"POST\", \"PUT\", \"DELETE\"])",
"title": ""
},
{
"docid": "174f446c802f0d546dd801cfa2a560b9",
"score": "0.53995687",
"text": "def register(self):\n self.zeroconf.register_service(self.info)\n log.debug(\"Registered {} on {}:{}\".format(self.info.name,\n self.ip,\n self.info.port))",
"title": ""
},
{
"docid": "bfc79036b99a6b25733fdef92d4b2b00",
"score": "0.537764",
"text": "def save_satellite_endpoint(endpoint):\r\n pass",
"title": ""
},
{
"docid": "11b3a8f51b7accc97b90d0091e7e6f6a",
"score": "0.5368624",
"text": "def endpoint_type(self, endpoint_type):\n\n self._endpoint_type = endpoint_type",
"title": ""
},
{
"docid": "8109fd81e4092ac1b3313a74a53237a3",
"score": "0.5364883",
"text": "def registerControl(self, control):\n if self._control:\n raise InternalError('There is already a control for the '\n 'communication registered.')\n \n verifyObject(IEndpointControl, control)\n self._control = control",
"title": ""
},
{
"docid": "671b171ba7220dc86cabc29751a13f62",
"score": "0.53400975",
"text": "def _set_subendpoint(self, klass):\n assert(issubclass(klass, Endpoint))\n subendpoint = klass(self._context, self._path)\n subendpoint = self._context.prepare_endpoint(subendpoint)\n subendpoint_name = klass._name\n setattr(self, subendpoint_name, subendpoint)",
"title": ""
},
{
"docid": "671b171ba7220dc86cabc29751a13f62",
"score": "0.53400975",
"text": "def _set_subendpoint(self, klass):\n assert(issubclass(klass, Endpoint))\n subendpoint = klass(self._context, self._path)\n subendpoint = self._context.prepare_endpoint(subendpoint)\n subendpoint_name = klass._name\n setattr(self, subendpoint_name, subendpoint)",
"title": ""
},
{
"docid": "37d38d75877374ae3e47e44b593795b9",
"score": "0.5337835",
"text": "def register_endpoints(self, backend_names):\n url_map = []\n\n if self.enable_metadata_reload():\n url_map.append(\n (\"^%s/%s$\" % (self.name, \"reload-metadata\"), self._reload_metadata))\n\n self.idp_config = self._build_idp_config_endpoints(\n self.config[self.KEY_IDP_CONFIG], backend_names)\n # Create the idp\n idp_config = IdPConfig().load(copy.deepcopy(self.idp_config))\n self.idp = Server(config=idp_config)\n return self._register_endpoints(backend_names) + url_map",
"title": ""
},
{
"docid": "3afdf9232c2c15ed5934b9c07217720b",
"score": "0.5287526",
"text": "def __init__(self, connector, identity, redfish_version=None):\n super(Endpoint, self).__init__(connector, identity,\n redfish_version)",
"title": ""
},
{
"docid": "861ca67619066f8e0ab2f9a20d55f31d",
"score": "0.5262404",
"text": "def _create_endpoint(self, ip_list):\n _log.debug(\"Creating Calico endpoint with workload_id=%s\",\n self.workload_id)\n try:\n endpoint = self._client.create_endpoint(self.hostname,\n self.orchestrator_id,\n self.workload_id,\n ip_list)\n except (AddrFormatError, KeyError) as e:\n # AddrFormatError: Raised when an IP address type is not\n # compatible with the node.\n # KeyError: Raised when BGP config for host is not found.\n _log.exception(\"Failed to create Calico endpoint.\")\n self.ipam_env[CNI_COMMAND_ENV] = CNI_CMD_DELETE\n self._release_ip(self.ipam_env)\n print_cni_error(ERR_CODE_GENERIC, e.message)\n sys.exit(ERR_CODE_GENERIC)\n\n _log.info(\"Created Calico endpoint with IP address(es) %s\", ip_list)\n return endpoint",
"title": ""
},
{
"docid": "bcb9513a7ad38dea06398a91bbadfc2c",
"score": "0.524253",
"text": "def create_endpoint(self,\n service_model,\n explicit_endpoint_url,\n scoped_config,\n region,\n response_parser_factory,\n tls_verification,\n timeout,\n retry_handler):\n endpoint_url = \\\n self._endpoint_resolver.resolve(explicit_endpoint_url=explicit_endpoint_url,\n config=scoped_config,\n region=region,\n service_name=service_model.endpoint_name,\n prefix=service_model.endpoint_prefix,\n products=service_model.products,\n scheme='https',\n port=443)\n proxies = self._get_proxies(endpoint_url)\n return Endpoint(\n endpoint_url,\n proxies=proxies,\n tls_verification=tls_verification,\n timeout=timeout,\n response_parser_factory=response_parser_factory,\n retry_handler=retry_handler)",
"title": ""
},
{
"docid": "77e8d407d5225ccccde1c46cc077f252",
"score": "0.5240231",
"text": "def listen(\n __endpoint: Endpoint | int, *, in_process_debug_adapter: bool = False\n) -> Endpoint:",
"title": ""
},
{
"docid": "e89744275a8599cdf56161b6ebcabe36",
"score": "0.5219208",
"text": "def register(cls, app):\n view_func = cls.as_view(cls.endpoint)\n\n url = cls.get_api_url('<command>')\n app.add_url_rule(url, view_func=view_func, methods=[\"GET\", \"POST\", \"PUT\", \"DELETE\"])",
"title": ""
},
{
"docid": "d99689b8bcd3a83f840c0cb2dc1f5490",
"score": "0.5206404",
"text": "def cmd_connect(self, endpoint):\n d = self.hub.connect(endpoint)\n def cb(_, self, endpoint):\n self.sendLine('Connected to %s' % endpoint)\n d.addCallback(cb, self, endpoint)",
"title": ""
},
{
"docid": "c510201ccb71d8326fb8583843ffb1fe",
"score": "0.51889944",
"text": "def set_up_endpoint(ip, hostname, orchestrator_id, workload_id, cpid, next_hop_ips,\n veth_name=VETH_NAME,\n proc_alias=PROC_ALIAS,\n mac=None):\n assert isinstance(ip, IPAddress)\n\n # Generate a new endpoint ID.\n ep_id = uuid.uuid1().hex\n\n iface = IF_PREFIX + ep_id[:11]\n iface_tmp = \"tmp\" + ep_id[:11]\n\n # Provision the networking. We create a temporary link from the proc\n # alias to the /var/run/netns to provide a named namespace. If we don't\n # do this, when run from the calico-node container the PID of the\n # container process is not recognised by `ip link set <if> netns <pid>`\n # command because that uses /proc rather than the proc alias to\n # dereference the PID.\n with NamedNamespace(cpid, proc=proc_alias) as ns:\n # Create the veth pair and move one end into container:\n check_call(\"ip link add %s type veth peer name %s\" %\n (iface, iface_tmp),\n shell=True)\n check_call(\"ip link set %s up\" % iface, shell=True)\n check_call(\"ip link set %s netns %s\" % (iface_tmp, ns.name),\n shell=True)\n\n if mac:\n ns.check_call(\"ip link set dev %s name %s address %s\" %\n (iface_tmp, veth_name, str(mac)),\n shell=True)\n else:\n ns.check_call(\"ip link set dev %s name %s\" %\n (iface_tmp, veth_name),\n shell=True)\n ns.check_call(\"ip link set %s up\" % veth_name, shell=True)\n\n # Add an IP address.\n add_ip_to_interface(cpid, ip, veth_name, proc_alias=proc_alias)\n\n with NamedNamespace(cpid, proc=proc_alias) as ns:\n # Connected route to next hop & default route.\n next_hop = next_hop_ips[ip.version]\n ns.check_call(\"ip -%(version)s route replace\"\n \" %(next_hop)s dev %(device)s\" %\n {\"version\": ip.version,\n \"device\": veth_name,\n \"next_hop\": next_hop},\n shell=True)\n ns.check_call(\"ip -%(version)s route replace\"\n \" default via %(next_hop)s dev %(device)s\" %\n {\"version\": ip.version,\n \"device\": veth_name,\n \"next_hop\": next_hop},\n shell=True)\n\n # Get the MAC address.\n mac = ns.check_output(\n \"ip link show %s | grep ether | awk '{print $2}'\" %\n (veth_name), shell=True).strip()\n\n # Return an Endpoint.\n network = IPNetwork(IPAddress(ip))\n ep = Endpoint(hostname=hostname,\n orchestrator_id=orchestrator_id,\n workload_id=workload_id,\n endpoint_id=ep_id,\n state=\"active\",\n mac=mac)\n ep.if_name = veth_name\n if network.version == 4:\n ep.ipv4_nets.add(network)\n ep.ipv4_gateway = next_hop\n else:\n ep.ipv6_nets.add(network)\n ep.ipv6_gateway = next_hop\n return ep",
"title": ""
},
{
"docid": "e714fdc3fc87d47549bcfc40369ddb61",
"score": "0.51611775",
"text": "def register(self, service_type, service_url):\n service_registry.register_instance(ProxyController(service_type,\n service_url))\n log.debug (\"Registered remote %s %s\" % (service_type, service_url))",
"title": ""
},
{
"docid": "825349eb9bd9aed5cab791f159e8f280",
"score": "0.51518494",
"text": "def configure_endpoint(\n name: str = typer.Argument(\"default\", help=\"endpoint name\", autocompletion=complete_endpoint_name),\n endpoint_config: str = typer.Option(None, \"--endpoint-config\", help=\"endpoint config file\")\n):\n endpoint_dir = os.path.join(State.FUNCX_DIR, name)\n new_config_file = os.path.join(endpoint_dir, FUNCX_CONFIG_FILE_NAME)\n\n if not os.path.exists(endpoint_dir):\n init_endpoint_dir(name, endpoint_config=endpoint_config)\n print('''A default profile has been create for <{}> at {}\nConfigure this file and try restarting with:\n $ funcx-endpoint start {}'''.format(name,\n new_config_file,\n name))\n return",
"title": ""
},
{
"docid": "962a46b03c91a0e89c14d384e7f631a0",
"score": "0.51473373",
"text": "def register_service(self, service):\n self.services.append(service)",
"title": ""
},
{
"docid": "5aa25116edbd7c3fc7a169eb3fe4343f",
"score": "0.5145041",
"text": "def update_endpoint(mediapackage, create_endpoint, event, context):\n endpoint_id = event[\"PhysicalResourceId\"]\n try:\n result = delete_endpoint(mediapackage, event, context)\n if result['Status'] == 'SUCCESS':\n result = create_endpoint(mediapackage, event, context, False)\n except Exception as ex:\n print(ex)\n result = {'Status': 'FAILED', 'Data': {\"Exception\": str(ex)}, 'ResourceId': endpoint_id}\n return result",
"title": ""
},
{
"docid": "9f685857a5e4295baacff6a0b90be24d",
"score": "0.5138805",
"text": "def add_servicepoint(self, identifier, callback):\n self._services[identifier] = callback",
"title": ""
},
{
"docid": "4324d6e217ba104edf29f58110bc8e2f",
"score": "0.51311016",
"text": "def ensure_endpoint(self, variables):\n self._authenticate()\n required_vars = [\n 'region_name',\n 'service_name',\n 'service_type',\n 'endpoint_list'\n ]\n variables_dict = self._get_vars(variables, required=required_vars)\n\n service_name = variables_dict.pop('service_name')\n service_type = variables_dict.pop('service_type')\n region = variables_dict.pop('region_name')\n endpoint_list = variables_dict.pop('endpoint_list')\n\n service = self._get_service(name=service_name, srv_type=service_type)\n if service is None:\n self.failure(\n error='service [ %s ] was not found.' % service_name,\n rc=2,\n msg='Service was not found, does it exist?'\n )\n\n endpoints = {}\n for endpoint_dict in endpoint_list:\n url = endpoint_dict.pop('url')\n interface = endpoint_dict.pop('interface')\n endpoint = self._get_endpoint(\n region=region,\n url=url,\n interface=interface\n )\n if endpoint is None:\n self.state_change = True\n endpoint = self.keystone.endpoints.create(\n region=region,\n service=service,\n url=url,\n interface=interface\n )\n endpoints[interface] = endpoint\n\n return self._facts(\n facts={'%sid' % interface: endpoint.id\n for interface, endpoint in endpoints.items()})",
"title": ""
},
{
"docid": "b37c9cb78840bdc2dbe0e371f05f068e",
"score": "0.5128284",
"text": "def accept(tkt_spool_dir, port, appname, endpoint):\n if port == 0:\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n sock.bind(('0.0.0.0', 0))\n port = sock.getsockname()[1]\n sock.close()\n\n hostname = sysinfo.hostname()\n hostport = '%s:%s' % (hostname, port)\n\n endpoint_proid_path = z.path.endpoint_proid(appname)\n _LOGGER.info('Ensuring %s exists with ACL %r',\n endpoint_proid_path, _SERVERS_ACL)\n zkutils.ensure_exists(context.GLOBAL.zk.conn, endpoint_proid_path,\n acl=[_SERVERS_ACL])\n\n endpoint_path = z.path.endpoint(appname, 'tcp', endpoint)\n _LOGGER.info('Registering %s %s', endpoint_path, hostport)\n\n # Need to delete/create endpoints for the disovery to pick it up in\n # case of master restart.\n #\n # Unlile typical endpoint, we cannot make the node ephemeral as we\n # exec into tkt-recv.\n zkutils.ensure_deleted(context.GLOBAL.zk.conn, endpoint_path)\n time.sleep(5)\n zkutils.put(context.GLOBAL.zk.conn, endpoint_path, hostport)\n\n context.GLOBAL.zk.conn.stop()\n\n # Exec into tickets acceptor. If race condition will not allow it to\n # bind to the provided port, it will exit and registration will\n # happen again.\n subproc.safe_exec(['tkt-recv', 'tcp://*:%s' % port, tkt_spool_dir])",
"title": ""
},
{
"docid": "9666353651ae71fe4f4116885d345ffb",
"score": "0.51067746",
"text": "async def put(self, endpoint, **kwargs):\n return await self.request('PUT', endpoint, **kwargs)",
"title": ""
},
{
"docid": "5113a0af19ef0338863cfc38b66d1895",
"score": "0.5095605",
"text": "def register_connector(self,Connector,*connector_args,**connector_kwargs):\n connector_instance = Connector(*connector_args,**connector_kwargs)\n self.connectors.append(connector_instance)",
"title": ""
},
{
"docid": "28210007692aed2d1efac4f6c799e503",
"score": "0.5088647",
"text": "def register_service(self):\r\n self._zeroconf.register_service(self.get_service_info())\r\n logger.info(\"Service registered!\")\r\n print(\"Service registered!\")",
"title": ""
},
{
"docid": "26bb4790d843d40f8b762d698bebcfaf",
"score": "0.5055061",
"text": "def register(self, func, **params):\n def decorator(f):\n \"\"\"Saves in url_dict the Methods and the function to run from a path\"\"\"\n if f is None:\n raise WebbyException(\"Function is Invalid\")\n\n path = func.lstrip('/')\n if 'methods' in params:\n urls_dict[path] ={'function': f, 'request_methods': params['methods']}\n else:\n urls_dict[path] ={'function': f, 'request_methods': {\"GET\", \"POST\", 'PUT', 'DELETE'}}\n return f\n\n return decorator",
"title": ""
},
{
"docid": "562b3a1745f085364732af540784df1e",
"score": "0.50322723",
"text": "async def async_add_endpoint_to_group(\n self, endpoint_id: int, group_id: int\n ) -> None:\n try:\n await self._zigpy_device.endpoints[endpoint_id].add_to_group(\n group_id, name=f\"0x{group_id:04X}\"\n )\n except (zigpy.exceptions.ZigbeeException, asyncio.TimeoutError) as ex:\n self.debug(\n \"Failed to add endpoint: %s for device: '%s' to group: 0x%04x ex: %s\",\n endpoint_id,\n self._zigpy_device.ieee,\n group_id,\n str(ex),\n )",
"title": ""
},
{
"docid": "7659954d65019677daa4e74cf4af6629",
"score": "0.50210065",
"text": "def test_registered_endpoints(self):\n url = reverse('django-numerics-index')\n\n self.create_user()\n api.register('label_endpoint',\n self.label_endpoint,\n responses.LabelResponse)\n\n api.register('number_endpoint',\n self.number_endpoint,\n responses.NumberResponse)\n\n self.client.login(username=self.USERNAME, password=self.PASSWORD)\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n endpoint_urls = sorted(response.context[0].get('endpoints'),\n key=itemgetter('name'))\n expected_urls = [{'help_url': '/help/username/label_endpoint',\n 'name': 'label_endpoint',\n 'url': '/username/label_endpoint'},\n {'help_url': '/help/username/number_endpoint',\n 'name': 'number_endpoint',\n 'url': '/username/number_endpoint'}]\n self.assertListEqual(endpoint_urls, expected_urls)",
"title": ""
},
{
"docid": "91ac60b418f96f1f06590b91bc2481d5",
"score": "0.50111115",
"text": "def add_template(endpoint_template): # pragma:nocover",
"title": ""
},
{
"docid": "823eff95fe1b99f47dc55f93a86e725f",
"score": "0.5009521",
"text": "def seed_endpoints(service, endpoints, keystone):\n logging.debug(\"seeding endpoints %s %s\" % (service.name, endpoints))\n\n for endpoint in endpoints:\n endpoint = sanitize(endpoint, (\n 'interface', 'region', 'url', 'enabled', 'name'))\n if 'interface' not in endpoint or not endpoint['interface']:\n logging.warn(\n \"skipping endpoint '%s/%s', since it is misconfigured\" % (\n service['name'], endpoint))\n continue\n\n if 'url' not in endpoint or not endpoint['url']:\n logging.warn(\n \"skipping endpoint '%s/%s', since it has no URL configured\" % (\n service.name, endpoint['interface']))\n continue\n try:\n parsed = urlparse(endpoint['url'])\n if not parsed.scheme or not parsed.netloc:\n logging.warn(\n \"skipping endpoint '%s/%s', since its URL is misconfigured\" % (\n service.name, endpoint['interface']))\n continue\n except Exception:\n logging.warn(\n \"skipping endpoint '%s/%s', since its URL is misconfigured\" % (\n service.name, endpoint['interface']))\n continue\n\n region = None\n if 'region' in endpoint:\n region = endpoint['region']\n if not region or not region.strip():\n logging.warn(\n \"skipping endpoint '%s/%s', since its region is misconfigured\" % (\n service.name, endpoint['interface']))\n continue\n\n result = keystone.endpoints.list(service=service.id,\n interface=endpoint[\n 'interface'],\n region_id=region)\n if not result:\n logging.info(\"create endpoint '%s/%s'\" % (\n service.name, endpoint['interface']))\n keystone.endpoints.create(service.id, **endpoint)\n else:\n resource = result[0]\n for attr in list(endpoint.keys()):\n if endpoint[attr] != resource._info.get(attr, ''):\n logging.info(\"%s differs. update endpoint '%s/%s'\" %\n (attr, service.name,\n endpoint['interface']))\n keystone.endpoints.update(resource.id, **endpoint)\n break",
"title": ""
},
{
"docid": "1c5343a287fcdbd5812812cc28c895b8",
"score": "0.50045365",
"text": "async def update_endpoint_for_did(\n self,\n did: str,\n endpoint: str,\n endpoint_type: EndpointType = EndpointType.ENDPOINT,\n ) -> bool:",
"title": ""
},
{
"docid": "78cf34e41fd40d4f2fde55128bab4aa9",
"score": "0.5000329",
"text": "def test_create_endpoint(self):\n endpoint = 'http://lxd'\n expected = 'http://lxd/1.0'\n\n an_client = client.Client(endpoint=endpoint)\n\n self.assertEqual(expected, an_client.api._api_endpoint)",
"title": ""
},
{
"docid": "9c1b11f18f7abef0417311507cbab70e",
"score": "0.49994105",
"text": "def post(self, *args):\n\n try:\n\n if len(args) > 2 or len(args) < 1:\n raise ValueError(\"Invalid url\")\n\n request = tornado.escape.json_decode(self.request.body)\n\n if \"version\" not in request:\n raise ValueError(\"missing version element\")\n\n if \"endpoint_name\" not in request:\n raise ValueError(\"missing endpoint_name element\")\n\n if \"dpid\" not in request:\n raise ValueError(\"missing dpid element\")\n\n if \"ports\" not in request:\n raise ValueError(\"missing ports element\")\n\n if not isinstance(request[\"ports\"], dict):\n raise ValueError(\"ports is not a dictionary\")\n\n for port in request[\"ports\"].values():\n\n if \"port_id\" not in port:\n raise ValueError(\"missing port_id element\")\n\n tenant_id = UUID(args[0])\n\n if tenant_id not in RUNTIME.tenants:\n raise KeyError(tenant_id)\n\n tenant = RUNTIME.tenants[tenant_id]\n\n if len(args) == 1:\n endpoint_id = uuid4()\n else:\n endpoint_id = UUID(args[1])\n\n dpid = DPID(request[\"dpid\"])\n datapath = RUNTIME.datapaths[dpid]\n\n tenant.add_endpoint(endpoint_id=endpoint_id,\n endpoint_name=request[\"endpoint_name\"],\n datapath=datapath,\n ports=request[\"ports\"])\n\n url = \"/api/v1/tenants/%s/eps/%s\" % (tenant_id, endpoint_id)\n self.set_header(\"Location\", url)\n\n except ValueError as ex:\n self.send_error(400, message=ex)\n\n except RuntimeError as ex:\n self.send_error(400, message=ex)\n\n except KeyError as ex:\n self.send_error(404, message=ex)\n\n self.set_status(201, None)",
"title": ""
},
{
"docid": "a15927760de4d05dfbbc17a2930f98bb",
"score": "0.49956372",
"text": "def get_endpoint_by_id(self, context, endpoint_id):",
"title": ""
},
{
"docid": "5684265b28e6fec35c1f0f0f8f53a1b8",
"score": "0.49767846",
"text": "def register_service(self, service):\n self._services.append(service)",
"title": ""
},
{
"docid": "37a4a135b0e7460b2876962913390ded",
"score": "0.49757084",
"text": "def endpoint(self) -> str:\n error(\"endpoint\")",
"title": ""
},
{
"docid": "032506f5f4f7f5b275d2b3de97d1683e",
"score": "0.49526525",
"text": "def get_endpoint(self, endpoint):\r\n if endpoint in self.__endpoints__:\r\n return self.__endpoints__[endpoint]",
"title": ""
},
{
"docid": "9c3392a24e82a061db8088866e9825f5",
"score": "0.49390578",
"text": "def cmd_start(self, endpoint):\n factory = self.hub.getPBServerFactory()\n d = self.hub.startServer(factory, endpoint)\n \n def cb(_, self, endpoint):\n self.sendLine('Server started: %s' % endpoint)\n d.addCallback(cb, self, endpoint)",
"title": ""
},
{
"docid": "57ce573203005405e8e366b3e0e0cb34",
"score": "0.4936089",
"text": "def register(cls, schema, resolver):\n cls(schema, 2)(resolver)",
"title": ""
},
{
"docid": "b98ea4fc85b950883bfd41ed6cc1ee17",
"score": "0.49347898",
"text": "def enable_endpoint_for_tenant(tenant_id, template_id): # pragma:nocover",
"title": ""
},
{
"docid": "b3dd3b8f62495c081b01523f72505d22",
"score": "0.493285",
"text": "def connect_endpoint(self, url):\r\n urldata = urlparse.urlparse(url)\r\n\r\n endpoint = urldata.path\r\n\r\n conn = self.endpoints.get(endpoint, None)\r\n if conn is None:\r\n conn_class = self.conn.get_endpoint(endpoint)\r\n if conn_class is None:\r\n logger.error('There is no handler for endpoint %s' % endpoint)\r\n return\r\n\r\n conn = conn_class(self, endpoint)\r\n self.endpoints[endpoint] = conn\r\n\r\n self.send_message(proto.connect(endpoint))\r\n\r\n if conn.on_open(self.info) == False:\r\n self.disconnect_endpoint(endpoint)",
"title": ""
},
{
"docid": "aa4daaa5168eb19118a06936880fddf5",
"score": "0.49279287",
"text": "def send_to_endpoint(self, endpoint_name, destination='', value=0):\n endpoint_idx = self.endpoints[endpoint_name]\n try:\n h.helicsEndpointSendEventRaw(endpoint_idx, destination, str(56.7), self.current_time)\n except h._helics.HelicsException as e:\n _log.exception(\"Error sending endpoint message to HELICS {}\".format(e))",
"title": ""
},
{
"docid": "586fa9209d2e9e779034772bc01ae80c",
"score": "0.4924516",
"text": "def register_route(url_path, name, fn, methods=['GET']):\n app.add_url_rule(url_path, name, fn, methods=methods)",
"title": ""
},
{
"docid": "a4d23d00da4e53d0dda272a89304d973",
"score": "0.4923314",
"text": "def register(cls):\n pass",
"title": ""
},
{
"docid": "4dd08aafdb072b8d790614f95289fda2",
"score": "0.49232405",
"text": "def create(self, endpoint, **urlopen_kw):\n body = json.dumps(urlopen_kw.pop(\"body\", dict()))\n return self._request(\"POST\", endpoint, body=body, **urlopen_kw)",
"title": ""
},
{
"docid": "8144e3a2b0fc4bb2c2c6f96f69bc872f",
"score": "0.49041396",
"text": "def __init__(self, endpoint_resolver):\n self._endpoint_resolver = endpoint_resolver",
"title": ""
},
{
"docid": "18dde6c61464b1d2e5ce648c8119781d",
"score": "0.4903121",
"text": "def exposed_add_ep(self, epname, ce_ip, ce_port):\n try:\n CLIENT.add_ep(self, epname, ce_ip, ce_port)\n return True\n except Exception:\n trace = traceback.format_exc()[34:].strip()\n logPrint('ClientService: Error on Add EP: `{}`.'.format(trace))\n return False",
"title": ""
},
{
"docid": "22099a13d95fd8b75a4b90d62e0ddf9c",
"score": "0.48952067",
"text": "def cloud_endpoint(self, cloud_endpoint):\n\n self._cloud_endpoint = cloud_endpoint",
"title": ""
},
{
"docid": "4f2e184931023f7889395dadd3990fa6",
"score": "0.48930597",
"text": "def test_registered_endpoints(self):\n User = get_user_model()\n User.objects.create_user('test', 'test@test.com', 'test')\n user = User.objects.get()\n\n api.register('label_endpoint',\n self.label_endpoint,\n responses.LabelResponse)\n\n url = api.get_endpoints(user)[0]['url']\n\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n resp_json = json.loads(response.content.decode('utf-8'))\n expected_json = {'data': {'value': 'label_val'},\n 'postfix': 'postfix_label'}\n self.assertEqual(resp_json, expected_json)",
"title": ""
},
{
"docid": "d28e448fc91fae706588d863ec124af0",
"score": "0.48799276",
"text": "def __init__(self, endpoint):",
"title": ""
},
{
"docid": "faafe33645a2cd1cd66884e8f26d8702",
"score": "0.48622587",
"text": "def register(self, alias, uri):\n # TODO: do we want to constrain the valid alias names.\n if isinstance(uri, dict):\n id = uri.get('id', alias)\n self.resolver.store[id] = uri\n uri = id\n self.aliases[alias] = uri",
"title": ""
},
{
"docid": "e2576492206b13d477982c5f044a39cf",
"score": "0.48544192",
"text": "def _publish_handler(self):\n\n try:\n self._token = service_proto.register(self.ec, self.service_name, self.service_addr)\n except etcd.EtcdConnectionFailed:\n log.warn('connect to etcd failed.')\n # TODO complete it.\n raise\n else:\n log.info('publish service(%s: %s) to etcd SUCCESS.', self.service_name, self.service_addr)",
"title": ""
},
{
"docid": "ffb1ca845a0225a7ad96bd6dd6542c33",
"score": "0.48538986",
"text": "def add_resource(self, name, handler):\n \n setattr(self, name, handler(self))",
"title": ""
},
{
"docid": "ed5020ba5c84078f9100b4191d5b25ad",
"score": "0.4847497",
"text": "def test_register_endpoints(self, idp_conf):\n\n def get_path_from_url(url):\n return urlparse(url).path.lstrip(\"/\")\n\n config = {\"idp_config\": idp_conf, \"endpoints\": ENDPOINTS}\n\n base_url = self.construct_base_url_from_entity_id(idp_conf[\"entityid\"])\n samlfrontend = SAMLFrontend(lambda context, internal_req: (context, internal_req),\n INTERNAL_ATTRIBUTES, config, base_url, \"saml_frontend\")\n\n providers = [\"foo\", \"bar\"]\n url_map = samlfrontend.register_endpoints(providers)\n all_idp_endpoints = [get_path_from_url(v[0][0]) for v in idp_conf[\"service\"][\"idp\"][\"endpoints\"].values()]\n compiled_regex = [re.compile(regex) for regex, _ in url_map]\n for endp in all_idp_endpoints:\n assert any(p.match(endp) for p in compiled_regex)",
"title": ""
},
{
"docid": "293c51a2960aa383fe19f7fc3ecdf20f",
"score": "0.48461595",
"text": "def _substitute_custom_endpoint(self, endpoint_url, service_name, prefix, products):\n # For an explicit endpoint URL, swap in the service name (Altus) or prefix\n # (CDP) if there is a spot for it, and then return it.\n if endpoint_url.count('%s') == 1:\n if products == ['CDP']:\n return endpoint_url % prefix\n else:\n return endpoint_url % service_name\n else:\n return endpoint_url",
"title": ""
},
{
"docid": "ccadf0300b6aded689e0ba8b3366f123",
"score": "0.48428246",
"text": "def RegisterGraph(self, request, timeout, metadata=None, with_call=False, protocol_options=None):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "68ab1caa9cbba3c090e93fffb9f7e22c",
"score": "0.48406544",
"text": "def register_serializer(cls, name, method):\n cls._serializers[name] = method",
"title": ""
},
{
"docid": "32fdd1a56740bc0923e2739d0b11f6af",
"score": "0.48368427",
"text": "def test_one_registered_endpoint_no_user(self):\n url = reverse('django-numerics-index')\n\n api.register('label_endpoint',\n self.label_endpoint,\n responses.LabelResponse)\n\n api.register('number_endpoint',\n self.number_endpoint,\n responses.NumberResponse)\n\n response = self.client.get(url)\n self.assertEqual(response.status_code, 404)",
"title": ""
},
{
"docid": "6d6fb69bd2b9c418ce6f718ce5715015",
"score": "0.48198417",
"text": "def _setup_endpoint(self, region_string: str) -> str:\n region_code = region_map.get(region_string)\n\n if region_code:\n endpoint = f\"https://{region_code}.api.insight.rapid7.com/graphql\"\n else:\n # It's an enum, hopefully this never happens.\n raise PluginException(\n cause=\"Region not found.\",\n assistance=\"Region code was not found for selected region. Available regions are: United States, \"\n \"Europe, Canada, Australia, Japan\",\n )\n\n return endpoint",
"title": ""
},
{
"docid": "560ff1568d3e1017306117e9196c139a",
"score": "0.48071843",
"text": "def register(self, fn, owner=None):\n\n pass",
"title": ""
},
{
"docid": "90f45844d29f449339664b2a1ee9823b",
"score": "0.47949094",
"text": "def register(self):\n self.send_message({'uuid' : self.uuid, 'action' : 'register', 'type' : self._type, 'name' : self.name})\n return",
"title": ""
},
{
"docid": "1c618e2b47356fdbb1968528f48138bd",
"score": "0.47904924",
"text": "def register_protocol(\n self, peer: Tuple[str, int], prot: \"SBSServerProtocol\"\n ) -> None:\n self.protocols[peer] = prot",
"title": ""
},
{
"docid": "31ce08daca89e6612a9ae007b8b169fb",
"score": "0.47881892",
"text": "def connect(endpoint=None):\r\n return u'1::%s' % (\r\n endpoint or ''\r\n )",
"title": ""
},
{
"docid": "b986d3081845e8d086cd2fcdd1e7f2a3",
"score": "0.47865677",
"text": "def register_service(self, name, url, *args, **kwargs):\n data = dict(kwargs)\n data['name'] = sanitize(name)\n data['url'] = url\n try:\n service = self.store.save_service(**data)\n except Exception:\n LOGGER.exception('register service failed')\n return {}\n return service.json()",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="717"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">df15ea22cd573caba7e021139fb6add6</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Updates configuration to continue parsing from the last state.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "fe75c85fbfa26e293115d1958ec091d3",
"score": "0.0",
"text": "def configureLastEnv(self, lastBlock):\n\t\tself.lastOffset = self.db.getLastOffset(lastBlock.fileName)\n\t\tself.setDatFileList(lastBlock.fileName)\n\t\tself.blockCount = lastBlock.blockNumber + 1",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "8c847d2d8c5d8e067e9b9c4cec258a93",
"score": "0.67347",
"text": "def config_reloaded(self):\n self.normalize_config()\n self.rebuild_alias_maps()",
"title": ""
},
{
"docid": "ac27db10d59c79d20bbbffb7541fc7c3",
"score": "0.657165",
"text": "def reload(self):\n while 1:\n try:\n yield from self.reload_event.wait()\n self.log.info(\"Reloading configuration...\")\n except asyncio.CancelledError:\n return\n finally:\n self.reload_event.clear()\n try:\n config = Config(self, self.filename, self.verbose)\n self.log.debug(\"New config instance %s\" % config)\n yield from config.validate()\n self.config = config\n self.log.info(\"Done\")\n except asyncio.CancelledError:\n return\n except Exception:\n self.log.exception(\"Error loading new config\")",
"title": ""
},
{
"docid": "eed1ddeaed0da24f6da31353ca292aee",
"score": "0.6394397",
"text": "def reload(self):\n self._config.read(constants.CONFIGURATION_FILE_PATH)",
"title": ""
},
{
"docid": "f703eed69b45232157fdd38cce4de913",
"score": "0.636677",
"text": "def reconfigure(self, config={}):\n if config:\n self.config = config",
"title": ""
},
{
"docid": "f2c7ca415dcf028296cee288acd1ea89",
"score": "0.6329764",
"text": "def config_changed(self):\r\n self.config_modified = True",
"title": ""
},
{
"docid": "bc5536bc4953c34ef7e178183fedd504",
"score": "0.62842447",
"text": "def update_config(self, new_config):\n # TODO(hotload) unload/reloading of modules, maybe that will help with hot code reloading?\n # * Admin user could invoke a module reload?\n # * It's assumed that the current in-memory state goes away, so anything you need should be stored\n # * For things like wordbot, you need a database to keep its state persistent between disconnects\n # Go through all modules, ask them to update\n assert hasattr(self, 'modules'), 'JaykChatbot does not have modules member; are you sure it derives from jayk.chatbot.Chatbot?'\n\n # Make sure that no disabled modules are included\n new_config = util.AttrDict(new_config).infect() # make sure that this is an attrdict because it's how we'll be accessing it\n new_config.modules = util.AttrDict({\n name: mod for name, mod in new_config.modules.items() if mod.enabled\n })\n\n # Create the new desired state, and get it to match\n self.config = new_config\n self.desired_state = JaykState.from_config(self.config.modules)\n self.match_desired_state()",
"title": ""
},
{
"docid": "ac602ba8df222779018a92612366588a",
"score": "0.6273174",
"text": "def reload_config_file(self):\n self.config.reload()\n self.load_dialog_state_from_config_memory()",
"title": ""
},
{
"docid": "a2c9dfe3fdfa4fd10368cace42609927",
"score": "0.62500644",
"text": "def configure(self):\n for cfgfile in self.possible_config_files:\n if os.path.isfile(cfgfile):\n with open(cfgfile, 'r') as handle:\n self.config = PARSE(handle.read())",
"title": ""
},
{
"docid": "5647d1d278af63662d56273bbdab5d53",
"score": "0.62427884",
"text": "def _parse_config(self):\n # logger\n tmp_logger = logger_utils.make_logger(base_logger, token=self.get_pid(), method_name='_parse_config')\n tmp_logger.debug('start')\n # parse config json\n with open(self.config_file, 'r') as _f:\n raw_dict = json.load(_f)\n self._mb_servers_dict = raw_dict['mb_servers']\n self._queues_dict = raw_dict['queues']\n self._processors_dict = raw_dict.get('processors', {})\n # set self optional attributes\n if raw_dict.get('guard_period') is not None:\n self.guard_period = raw_dict['guard_period']\n tmp_logger.debug('done')",
"title": ""
},
{
"docid": "7b67958f3561bc8844c0dbe6eaa18ece",
"score": "0.6226193",
"text": "def update(self):\n self.parse_data()",
"title": ""
},
{
"docid": "5f7dfb969e9431d660629fb6d70535df",
"score": "0.6226095",
"text": "def update_config_dict(self):\n logger.info(\"Updating config dict with uploaded json file.\")\n new_config_dict = json.loads(self.file_input.value)\n try:\n # Validating new config file by instantiating WorkflowEngineAuto\n _ = WorkflowEngineAuto(new_config_dict)\n self.config_dict = new_config_dict\n except Exception:\n logger.error(\"Could not update config file. Reverting to previous config.\")",
"title": ""
},
{
"docid": "b2c43af00d1f293568cec9742655d87d",
"score": "0.62168473",
"text": "def update_config(self, new_config: dict):\n self._config = self.update(self._config, new_config)",
"title": ""
},
{
"docid": "aa8c8158d05adc2db6a090ff47509690",
"score": "0.6213664",
"text": "def process_config(self, config):\n self.config = config",
"title": ""
},
{
"docid": "aa8c8158d05adc2db6a090ff47509690",
"score": "0.6213664",
"text": "def process_config(self, config):\n self.config = config",
"title": ""
},
{
"docid": "ad1cc2cc16f99111247eba9792a34f99",
"score": "0.6178818",
"text": "def reread_config(self):\n\n assoc = yield self._parent._dbpool.getCurrentServiceConfig(self._name)\n if not assoc:\n info(\"Found no configuration\")\n defer.returnValue(-1)\n\n self._config = assoc\n\n if not assoc.has_key(\"version\") or not assoc.has_key(\"flavorName\"):\n error(\"netboot: Oops, configuration is broken!\")\n defer.returnValue(-1)\n\n defer.returnValue(0)",
"title": ""
},
{
"docid": "36c1c3c02eee0e972dee092bcd3548ab",
"score": "0.611292",
"text": "def end_of_config(self):\n pass",
"title": ""
},
{
"docid": "caee13cda172d9231da5d9afd2af6640",
"score": "0.61015254",
"text": "def reload_config(self):\n self.config.read(\"config.ini\")\n\n # Discord login token\n self.token = self.get_config_value(\"client\", \"token\")\n\n # Bot nickname\n self.nickName = self.get_config_value(\"client\", \"nick\")\n\n # Command prefix\n self.commandPrefix = self.get_config_value(\"client\", \"prefix\")",
"title": ""
},
{
"docid": "3295fba32ae172aea110c50179d73b61",
"score": "0.6098233",
"text": "def onConfigurationChanged(self, newConfig):\n pass",
"title": ""
},
{
"docid": "f9cb119160a6ebe990a222427b814dd4",
"score": "0.60800326",
"text": "def _fill(self):\n self.data = Config(self._fill_part(self._last_state,0,1))",
"title": ""
},
{
"docid": "31768534a5777d8fbeb143b1cfd30cec",
"score": "0.6052823",
"text": "def _update_config(self, config_val):\n # Check if our configuration is about to change.\n old_url = self.config.url\n old_loadbalancer = self.config.loadbalancer\n new_config = EndpointConfig(values=config_val)\n new_scaling = ScalingConfig(obj=new_config)\n new_url = new_config.url\n new_loadbalancer = new_config.loadbalancer\n\n # NOTE: We used to take action on old static\n # addresses. This is no longer done, because it's\n # an easy attack vector for different endpoints.\n # Now we don't really allow static addresses to\n # influence any actions wrt. to registered IPs.\n\n # Remove all old instances from loadbalancer,\n # (Only necessary if we've changed the endpoint URL).\n if old_url != new_url or old_loadbalancer != new_loadbalancer:\n self.reload(exclude=True)\n\n # Reload the configuration.\n self.config = new_config\n self.scaling = new_scaling\n\n # We can't really know if loadbalancer settings\n # have changed in the backend, so we really need\n # to *always* do a loadbalancer reload.\n self.reload()",
"title": ""
},
{
"docid": "88618dbd677efe4b38eb4011c007b10e",
"score": "0.6048801",
"text": "def next_config(self):\n (tape, start, end, current, state) = self.config\n tape = list(tape) # copy the tape\n table = self.next_state_dict\n symbol = tape[current]\n if (state, symbol) in table:\n (newstate, newsymbol, direction) = table[(state, symbol)]\n tape[current] = newsymbol\n newcurrent = min(max(current + direction, 0), 20000 - 1) # keep it on our finite tape\n if current < start and tape[current] != ' ':\n newstart = current\n else:\n newstart = start\n if current > end and tape[current] != ' ':\n newend = current\n else:\n newend = end\n elif state >= 0:\n newstate = -2\n newcurrent = current\n newstart = start\n newend = end\n else:\n newstate = state\n newcurrent = current\n newstart = start\n newend = end\n newconfig = (tape, newstart, newend, newcurrent, newstate)\n self.config_list.append(newconfig)\n self.config = newconfig\n self.step += 1\n\n return self.config",
"title": ""
},
{
"docid": "e528e5b2f16cfc4adaf102e258324da1",
"score": "0.60170996",
"text": "def update_config(self, config):\r\n old_config = self.config\r\n self.config = config\r\n errors = self.validate_config()\r\n if errors:\r\n for error in errors:\r\n log.critical(\"[%s] %s\", error.json_pointer, error.message)\r\n log.debug('invalid config, rolling back')\r\n self.config = old_config\r\n raise ValueError('Config did not pass schema validation')\r\n log.debug('New config data loaded.')\r\n fire_event('manager.config_updated', self)",
"title": ""
},
{
"docid": "b2e50877ffa9ccf4eeede03a63876b7c",
"score": "0.60118866",
"text": "def run(self):\n self.__readConfigInfo()\n self.__upgradeInstanceConf()",
"title": ""
},
{
"docid": "f7f9f5f7a47c0cae0d67b90fe75e4e24",
"score": "0.59885883",
"text": "def refresh(self):\n responses = self.api_request('GET', 'config/')\n self._state = responses\n for attr in self._state:\n if hasattr(self, attr):\n setattr(self, attr, hue_decode(self._state[attr]))\n\n self.refresh_lights()\n self.refresh_groups()\n self.refresh_rules()\n self.refresh_scenes()\n self.refresh_schedules()\n self.refresh_sensors()\n\n if self.all_lights:\n self.all_lights.refresh()\n else:\n self.all_lights = Group(self, 0)",
"title": ""
},
{
"docid": "2a935b3540392355b621fb6dc3055e3b",
"score": "0.5965942",
"text": "def load_config(self):\n print(\"[Counter] Loading config from {}\".format(self.config_filename))\n self.cfg = MultiplePeopleCounterConfig(self.config_filename)\n self.map_config()\n self.next_reset.replace(hour=self.reset_hour.hour,\n minute=self.reset_hour.minute)",
"title": ""
},
{
"docid": "4a9a44f570ac4b73116406247e691db9",
"score": "0.5964807",
"text": "def _process_config():\n # Allow to specify name-based loglevel values in arc.conf and replace them by numeric to be used by ARC services\n str_loglevels = {'DEBUG': '5', 'VERBOSE': '4', 'INFO': '3', 'WARNING': '2', 'ERROR': '1', 'FATAL': '0'}\n for block in __parsed_blocks:\n if 'loglevel' in __parsed_config[block]['__options']:\n loglevel_idx = __parsed_config[block]['__options'].index('loglevel')\n loglevel_value = __parsed_config[block]['__values'][loglevel_idx]\n if loglevel_value in str_loglevels.keys():\n loglevel_num_value = str_loglevels[loglevel_value]\n __logger.debug('Replacing loglevel %s with numeric value %s in [%s].',\n loglevel_value, loglevel_num_value, block)\n __parsed_config[block]['__values'][loglevel_idx] = loglevel_num_value",
"title": ""
},
{
"docid": "06cb9735b018bf6400a2671ad43365ce",
"score": "0.5953147",
"text": "def update_config(self):\n self._config = self._pomodoro_service.get_config(self.name)\n self._apikey = self._config[\"apikey\"]\n self._secret = self._config[\"secret\"]\n self._token = self._config[\"token\"]\n self._task_filter = self._config[\"filter\"]\n self._rtm = rtm.createRTM(self._apikey, self._secret, self._token)\n\n if self._token is None:\n # TODO invoke xdg-open, browser, get authentication\n # save token into config file...\n pass\n self._task_filter = 'dueBefore:tomorrow AND status:incomplete'\n return",
"title": ""
},
{
"docid": "5bb951048540fdf630ba056bb67135a8",
"score": "0.59369916",
"text": "def updateConfig(self,new_config):\n\t\tself.config=new_config\n\t\tself.thermocline = thermocline(new_config)\n\t\tself.DCL = DCL(new_config)\n\t\tself.features = None",
"title": ""
},
{
"docid": "04f82eca9c3f27255ed95eff1eb1d87e",
"score": "0.59355783",
"text": "def finalise_config(self):\n if not self.external_config_checked:\n self.load_external_config()\n \n self._configure_logging(self.app)\n self.config_finalised = True",
"title": ""
},
{
"docid": "d0e909047e6d9d1ab291bde4d054dea7",
"score": "0.5921247",
"text": "def update(self):\r\n if self._config_updates:\r\n cmd = self._config_updates.pop()\r\n self._handle_config_command(cmd)\r\n if self._daytime_updates:\r\n cmd = self._daytime_updates.pop()\r\n self._handle_daytime_command(cmd)",
"title": ""
},
{
"docid": "f546f3af35ce5cd90611c3ca5c48b6a3",
"score": "0.5918024",
"text": "def reload(self):\n filename = self.get('config_file', DEFAULT_CONFIG)\n self.load_from_file(filename)",
"title": ""
},
{
"docid": "d41b66c704b39b149da22b9fc7e96267",
"score": "0.5912324",
"text": "def _update_state(self, new_state):\n # print(\",\".join(new_state.keys()))\n\n if \"model\" in new_state:\n self.module.load_state_dict(new_state.pop(\"model\"))\n\n if \"optimizer\" in new_state and new_state[\"optimizer\"]:\n optim_state = new_state.pop(\"optimizer\")\n for key in self.optimizers.keys():\n self.optimizers[key].load_state_dict(\n optim_state[key])\n\n if \"epoch\" in new_state:\n self.start_epoch = new_state.pop(\"epoch\")\n\n return super()._update_state(new_state)",
"title": ""
},
{
"docid": "8fb7405571ac806f1cb98d38b9bda8a9",
"score": "0.5902198",
"text": "def config_file_update():\n log.debug('config_file_update() Started....')\n if os.path.isfile(skel_config_file):\n with open(config_file, 'r') as current_config:\n current_config = yaml.safe_load(current_config)\n with open(skel_config_file, 'r') as temp_config:\n temp_config = yaml.safe_load(temp_config)\n temp_current_config = flatten(current_config)\n temp_temp_config = flatten(temp_config)\n updates = (dict((k, v) for k, v in temp_temp_config.items() if k not in temp_current_config))\n if updates != {}:\n copyfile(skel_config_file, (str(Path.home()) + '/.config/plot_manager/Config_Instructions.yaml'))\n copyfile(config_file, (str(Path.home()) + f'/.config/plot_manager/plot_manager.yaml.{current_military_time}'))\n temp_current_config.update(updates)\n new_config = (dict((k, v) for k, v in temp_current_config.items() if k in temp_temp_config))\n else:\n new_config = (dict((k, v) for k, v in temp_current_config.items() if k not in temp_temp_config))\n if new_config != {}:\n new_config = (dict((k, v) for k, v in temp_current_config.items() if k in temp_temp_config))\n current_config = unflatten(new_config)\n current_config.update({'configured': False})\n with open((str(Path.home()) + '/.config/plot_manager/plot_manager.yaml'), 'w') as f:\n yaml.safe_dump(current_config, f)\n log.debug(f'Config File: {config_file} updated. Update as necessary to run this script.')\n exit()\n else:\n log.debug('No config file changes necessary! No changes made.')\n else:\n log.debug('New configuration file not found. No changes made.')",
"title": ""
},
{
"docid": "c5d45161db2008d291b04fa3e88042ed",
"score": "0.5899105",
"text": "def restoreConfiguration(state):\n _configs.clear()\n _configs.update(state)",
"title": ""
},
{
"docid": "1bbadc8bd34f4913178c62b9e9e43cc5",
"score": "0.58968765",
"text": "def cmd_init(self):\n with open(self.params['config'], \"rt\") as config_fd:\n self.config = yaml.safe_load(config_fd)\n logger.debug(\"Config before merge [%s]\", self.config)\n self.config = config_tree_to_graph(self.config)\n logger.debug(\"Config before merge [%s]\", self.config)\n self._merge_rootfs_params()\n logger.debug(\"Final config [%s]\", self.config)\n # Write the final config\n with open(self.config_json_file_name, \"wt\") as fd:\n json.dump(self.config, fd)\n logger.info(\"Wrote final block device config to [%s]\",\n self.config_json_file_name)",
"title": ""
},
{
"docid": "c590dd57ef0377648b2951dfe15ab4c2",
"score": "0.5849958",
"text": "def update_config(self):\n try:\n with open(self.config_file, \"w\") as f:\n json.dump(self.config, f, indent=4)\n return True\n except:\n import traceback\n traceback.print_exc()\n return False",
"title": ""
},
{
"docid": "a41bda926a37ac287ea4c5de570ea499",
"score": "0.5841515",
"text": "def finalize_config(self):\n # Close files and save config\n if self.__config_bad:\n logger.info('Regenerating config with missing values...')\n self.__config_file.close()\n self.__config_file = open(self.__config_file_path, 'w')\n config_text = json.dumps(self.__config, sort_keys=True, indent=4)\n config_text = config_text.replace(self.__config_location, '$(AUGUR)')\n self.__config_file.write(config_text)\n self.__config_file.close()",
"title": ""
},
{
"docid": "3a779b615c7514ab480bdb3b46d04234",
"score": "0.5834858",
"text": "def complete_config(self, config: Configuration, result: ActionResult) -> None:\n self._completed_configs[config] = result\n if config in self._pending_configs:\n del self._pending_configs[config]",
"title": ""
},
{
"docid": "64a12dfa41fe1a29cc207f8c2a98583b",
"score": "0.58164585",
"text": "def update_config(self, subsection=None):\r\n if self.cfg_file is not None:\r\n if self.section in self.cfg_file:\r\n if subsection is not None:\r\n config_section = self.cfg_file[self.section].get(subsection)\r\n else:\r\n config_section = self.cfg_file.get(self.section)\r\n\r\n # update attributes from config class\r\n if config_section is not None:\r\n for k, v in config_section.items():\r\n if hasattr(self, k):\r\n setattr(self, k, v)\r\n else:\r\n print(f\"Warning: Processing config section:{config_section}; provided key: {k} does not exist in config\")",
"title": ""
},
{
"docid": "4c1d3870b202330787ae9a486dd9caae",
"score": "0.5800773",
"text": "def config_changed():",
"title": ""
},
{
"docid": "e25676f3f2f41a10f843d42e355b8122",
"score": "0.57964855",
"text": "def update_config(self, config):\n return self._update_config(\"config\", config)",
"title": ""
},
{
"docid": "e25676f3f2f41a10f843d42e355b8122",
"score": "0.57964855",
"text": "def update_config(self, config):\n return self._update_config(\"config\", config)",
"title": ""
},
{
"docid": "d8ed6d19f85dab8c53a69f62571f68b6",
"score": "0.57896394",
"text": "def configure(self, config: dict):\n self.config.update(config)",
"title": ""
},
{
"docid": "11e46072a271d2adf4156e43521f6359",
"score": "0.5788456",
"text": "def options_parsed_hook():\n read_config()",
"title": ""
},
{
"docid": "8fef852e7b591a46762c132ac8a8804f",
"score": "0.5772193",
"text": "def handle_log_config_updated(self, event: Event) -> None:\n event.data[\"log_config\"] = self.log_config = LogConfig.from_dict(\n event.data[\"config\"]\n )",
"title": ""
},
{
"docid": "37539c90faab03b05674a12efb2ea869",
"score": "0.57659566",
"text": "def updateStatusAndConfig(self):\n\n if self.dconfig.rereadIfUpdated():\n self.setVarsFromConfig()\n self.info(\"config file reread\")\n\n if self.status == status.STOPPED:\n self.info(\"stop requested in config\")\n\n elif Daemon.weWereSignalled(\"USR1\"):\n self.info(\"stop requested by signal\")\n self.status = status.STOPPED",
"title": ""
},
{
"docid": "db7bd59aed622d009d47ee8e8ff7ea32",
"score": "0.57435006",
"text": "def onLoadConfig(self):\n self._update_config_file = self.getSetting('settings', 'update_config_file', b3.BOOL, self._update_config_file)",
"title": ""
},
{
"docid": "de815779b8a6f3077a4f34e13d702d20",
"score": "0.57375485",
"text": "async def reload(self):\n self.cfg.load(self.get_default_config(), create=True)\n self.storage.load()",
"title": ""
},
{
"docid": "b541865bd66dd77f06fde8964246b1d6",
"score": "0.572685",
"text": "def _migrate_config(self):\n old_options = dict(self._config[\"options\"])\n self._create_new_config(old_options)",
"title": ""
},
{
"docid": "462cceff96882044274040905f3c1728",
"score": "0.57253313",
"text": "def next_config(self):\n\n (tapes, starts, ends, currents, state) = self.config\n (t1, t2) = tapes\n t1 = list(t1) # copy the lists\n t2 = list(t2)\n (s1, s2) = starts\n (e1, e2) = ends\n (c1, c2) = currents\n table = self.next_state_dict\n symbol1 = t1[c1]\n symbol2 = t2[c2]\n symbols = (symbol1, symbol2)\n if (state, symbols) in table:\n (newstate, newsymbols, directions) = table[(state, symbols)]\n t1[c1] = newsymbols[0]\n t2[c2] = newsymbols[1]\n (d1, d2) = directions\n newcurrent1 = min(max(c1 + d1, 0), 20000 - 1) # keep it on our finite tape\n newcurrent2 = min(max(c2 + d2, 0), 20000 - 1)\n\n if c1 < s1 and t1[c1] != ' ':\n newstart1 = c1\n else:\n newstart1 = s1\n if c1 > e1 and t1[c1] != ' ':\n newend1 = c1\n else:\n newend1 = e1\n\n if c2 < s2 and t2[c2] != ' ':\n newstart2 = c2\n else:\n newstart2 = s2\n if c2 > e2 and t2[c2] != ' ':\n newend2 = c2\n else:\n newend2 = e2\n\n elif state >= 0:\n newstate = -2\n newcurrent1 = c1\n newcurrent2 = c2\n newstart1 = s1\n newstart2 = s2\n newend1 = e1\n newend2 = e2\n else:\n newstate = state\n newcurrent1 = c1\n newcurrent2 = c2\n newstart1 = s1\n newstart2 = s2\n newend1 = e1\n newend2 = e2\n\n newconfig = ((t1, t2), (newstart1, newstart2), (newend1, newend2), (newcurrent1, newcurrent2), newstate)\n self.config_list.append(newconfig)\n self.config = newconfig\n self.step += 1\n\n return self.config",
"title": ""
},
{
"docid": "7d132c5b48084c0b43de9ef46b8ca4f3",
"score": "0.57191867",
"text": "def configurate(self, config):\n\t\tfor key in self.state.keys():\n\t\t\tif key in config:\n\t\t\t\tself.state[key] = config[key] or False\n\t\tself.reapply_templates()",
"title": ""
},
{
"docid": "9efde4754099f00c354e4eab49cea0cf",
"score": "0.5697344",
"text": "def reload_config(self):\n # TODO: check envvar LABELORD_CONFIG and reload the config\n # Because there are problems with reimporting the app with\n # different configuration, this method will be called in\n # order to reload configuration file. Check if everything\n # is correctly set-up\n \n conffile = configparser.ConfigParser()\n conffile.optionxform = str\n \n if 'LABELORD_CONFIG' in os.environ:\n config = os.environ['LABELORD_CONFIG']\n conffile.read(config) \n else:\n if os.path.isfile('./config.cfg') == True:\n conffile.read('./config.cfg')\n config = './config.cfg'\n \n if os.path.isfile('./config.cfg') == False and 'github' not in conffile:\n print('No GitHub token has been provided', file=sys.stderr)\n sys.exit(3)\n if 'github' in conffile and 'token' not in conffile['github']:\n print('No GitHub token has been provided', file=sys.stderr)\n sys.exit(3)\n else: self.token = conffile['github']['token']\n \n self.session = setup(self.session, self.token)\n \n if 'github' in conffile and 'webhook_secret' not in conffile['github']:\n print('No webhook secret has been provided', file=sys.stderr)\n sys.exit(8)\n else: self.secret = conffile['github']['webhook_secret']\n \n self.repos = []\n if not 'repos' in conffile:\n print('No repositories specification has been found', file=sys.stderr)\n sys.exit(7) \n for repo in conffile['repos']:\n if conffile.getboolean('repos', repo):\n self.repos.append(repo) \n \n self.labels = {}\n if 'labels' in conffile:\n for label in conffile['labels']:\n self.labels[label] = conffile['labels'][label]\n \n self.ename = ''\n self.dname = ''",
"title": ""
},
{
"docid": "3897e5872faf1a8ec753456947e20a7d",
"score": "0.56883097",
"text": "def _parse_config(self):\n if not access(self._configpath, os.F_OK):\n # If the file does not exist, we create it empty and exit\n with open(self._configpath, \"wt\") as outstream:\n outstream.write(\"\")\n elif not access(self._configpath, os.R_OK):\n raise OSError(\"%s has no read permissions\" % self._configpath)\n else:\n with open(self._configpath) as instream:\n for l in instream:\n if l and not l.isspace() and not l.startswith(\"#\"):\n key, value = l.split(\"=\", 1)\n\n self._config[key.strip()] = value.strip()",
"title": ""
},
{
"docid": "d0b8d7639be24f69082ae0da2a90b30c",
"score": "0.5667512",
"text": "def parse(self) -> Dict:\n args_raw, cmdline_args_raw = self.parser.parse_known_args()\n self.logger.debug(f\"args_raw are : {args_raw}, cmdline_args_raw are: {cmdline_args_raw}\")\n cmdline_args_list = [cmd_arg for cmd_arg in cmdline_args_raw if cmd_arg.startswith('--')]\n args, _ = self.parser.parse_known_args()\n self._parse_config(args.config_path)\n\n self._add_arguments(cmdline_args_list)\n args, unknown_args = self.parser.parse_known_args()\n self.logger.debug(f\"parsed args are : {args}, unknown args are: {unknown_args}\")\n cmdline_args = vars(args)\n params_dict = None\n for cmd_arg in cmdline_args.keys():\n if cmd_arg == 'params':\n params_dict = json.loads(cmdline_args[cmd_arg])\n continue\n self._parse_arg_and_update_config(cmd_arg, cmdline_args)\n self.logger.debug(f\"params_dict is: {params_dict}\")\n self._update_from_params_dict(params_dict)\n return self._config",
"title": ""
},
{
"docid": "8792364fc9223b019024daca076623fa",
"score": "0.566537",
"text": "def reload_settings(self):\n self.load_settings(self.cfg_file)",
"title": ""
},
{
"docid": "7f31558374af1822327d96418881eb83",
"score": "0.5658991",
"text": "def parseConfig(self):\n with open(self.config) as user:\n user_settings = yaml.load(user, yaml.SafeLoader)\n\n for k, v in list(user_settings.items()):\n for k2, v2 in list(v.items()):\n self.options[k][k2] = v2",
"title": ""
},
{
"docid": "a18df587561b509d8facba4d4d4afd7b",
"score": "0.565867",
"text": "def update(self, flat_config: Dict[str, object]) -> None:\n ns_config = self._flat_config_to_namespace_configs(flat_config)\n # merge all namespace configs\n for namespace, ns_conf in ns_config.items():\n self._NAMESPACE_CONFIGS.setdefault(namespace, {}).update(ns_conf)\n self._reconfigure_all()",
"title": ""
},
{
"docid": "8d88eeb80971ce3a78f76b8614925818",
"score": "0.5649476",
"text": "def _process_structural_config(self, faucet_config):\n with self._lock:\n self._structural_faucet_config = copy.deepcopy(faucet_config)\n self._acl_configs.clear()\n\n self._next_cookie = 1\n behavioral_include = []\n new_watched_include_files = []\n\n for include_file_name in self._structural_faucet_config.get('include', []):\n include_file_path = os.path.join(self._forch_config_dir, include_file_name)\n self.reload_include_file(include_file_path)\n behavioral_include.append(self._augment_include_file_name(include_file_name))\n new_watched_include_files.append(include_file_path)\n\n structural_acls_config = copy.deepcopy(self._structural_faucet_config.get('acls'))\n self._augment_acls_config(structural_acls_config, self._structural_config_file, )\n\n tail_acl_name = self._config.tail_acl\n if tail_acl_name and not self._has_acl(tail_acl_name):\n raise Exception('No ACL is defined for tail ACL %s' % tail_acl_name)\n\n self._behavioral_include = behavioral_include\n\n if not self._config.faucetize_interval_sec and self._reregister_include_file_handlers:\n self._reregister_include_file_handlers(\n self._watched_include_files, new_watched_include_files)\n self._watched_include_files = new_watched_include_files\n\n self.flush_behavioral_config()",
"title": ""
},
{
"docid": "464f6e47753eda0335fdfcda3df472ad",
"score": "0.5636518",
"text": "def update(self):\n try:\n if self.nextUpdate <= time.time(): # only update when needed\n self._get_data()\n conf = get_config()\n self.nextUpdate = time.time() + conf['api']['oebb']['updateInterval']\n except Exception as err:\n import sys\n self.exc_info = sys.exc_info()",
"title": ""
},
{
"docid": "1c82b1b8c75c1f698976077d604d68de",
"score": "0.5625364",
"text": "def __reload(self):\n self.__configs = dotenv_values(self.__env_location)",
"title": ""
},
{
"docid": "114b5b4847e7514e453ff6fc7669195e",
"score": "0.5617127",
"text": "def configure(self, updated: typing.Set[str]):",
"title": ""
},
{
"docid": "2039cd91f856c27ee4c2e6dc80157cf1",
"score": "0.56137335",
"text": "def reset(self):\n\t\tself.config.add_section('extension')\n\t\tself.config.set('extension', 'file', json.dumps([\".mp3\", \".ogg\", \".flac\"]))\n\t\tself.config.set('extension', 'tag', json.dumps([\"artist\", \"album\", \"title\", \"date\", \"tracknumber\", \"genre\"]))\n\t\tself.config.set('extension', 'unk', 'unknown')\n\n\t\tself.config.add_section('server')\n\t\tself.config.set('server', 'port', '1664')\n\t\tself.config.set('server', 'name', 'localhost')\n\n\t\tself.config.add_section('time')\n\t\tself.config.set('time', 'check', '1.0')\n\t\tself.config.set('time', 'anticipateCheck', '1.1')\n\n\t\tself.config.add_section('constante')\n\t\tself.config.set('constante', 'normal', '0')\n\t\tself.config.set('constante', 'random', '1')\n\t\tself.config.set('constante', 'playlist', '2')\n\n\t\tself.config.add_section('pruning')\n\t\tself.config.set('pruning', 'constante', '0.001')\n\n\t\tself.config.add_section('location')\n\t\tself.config.set('location', 'rootLoc', rootAd)\n \tself.config.set('location', 'userLoc', userAd)\n\t\tself.config.set('location', 'DbLoc', '%(userLoc)s')\n\t\tself.config.set('location', 'Markov', 'dbMarkov.ghk')\n\t\tself.config.set('location', 'DbFile', 'db.xml')",
"title": ""
},
{
"docid": "0c90f458cb390d663cf9ab78369bb062",
"score": "0.5613222",
"text": "def resolve_configuration(self, configuration):\n changed = True\n while changed:\n unaliased_options = configobj.ConfigObj()\n for key, value in configuration.options.items():\n alias = self(key)\n if alias != key:\n unaliased_options[alias] = value\n try:\n del configuration.options[key]\n except KeyError: # pragma: no cover\n pass\n\n changed = len(unaliased_options) > 0\n if changed:\n configuration.apply_configuration_options(unaliased_options)",
"title": ""
},
{
"docid": "c232fd6fad054390d6183b80f723ee5c",
"score": "0.5613005",
"text": "def _update_sami_config(self, raw_str):\r\n log.debug(\"UUUUUUUUUUUUUUUUUUUpdate _update_sami_config()\")\r\n if( self._sami_config.set_config_str(raw_str) is True):\r\n # Valid configuration string.\r\n log.debug(\"GOODDDDD Config String\")\r\n # Initialize the \"new\" raw string if it has not been built yet!\r\n if(self._sami_new_config_str is None):\r\n self._sami_new_config_str = raw_str\r\n self._sami_new_config_valid = True\r\n\r\n log.debug(\"Sys Config: \" + self._sami_config.get_config_str())\r\n log.debug(\"New Config: \" + self._sami_new_config_str)\r\n else:\r\n log.debug(\"BADDDDD Config String\")\r\n\r\n log.debug(\"UUUUUUUUUUUUUUUUUU Update _update_sami_config() Done **********\")",
"title": ""
},
{
"docid": "ca1b8b89564f58d8329be6ae02558d32",
"score": "0.5607919",
"text": "def __setstate__(self, state):\n import StringIO\n self.restricted_to = None\n self.default_config = ConfigParser.SafeConfigParser()\n self.default_config.read(DEFAULT_CONFIG)\n self.user_config = ConfigParser.SafeConfigParser()\n self.user_config.readfp(StringIO.StringIO(state))\n self.sections = {section_name: Section(section_name, config=self)\n for section_name in self.default_config.sections()}",
"title": ""
},
{
"docid": "0bb4fc66124c54c6a82bff97ecf515d6",
"score": "0.5606354",
"text": "def reset_config(self):\n self.config = None\n self.config_list = None\n if self.two_way:\n table = [' '] * 10000 + list(self.inputstring) + [' '] * (10000 - len(self.inputstring))\n self.config = (table, 10000, 10000 + len(self.inputstring) - 1, 10000, 0)\n else:\n table = list(self.inputstring) + [' '] * (20000 - len(self.inputstring))\n self.config = (table, 0, len(self.inputstring) - 1, 0, 0)\n self.step = 0\n\n self.config_list = [self.config]\n return self.config",
"title": ""
},
{
"docid": "b4c29bbf9cbdf856724832d490b12def",
"score": "0.5602283",
"text": "def reload_configuration(self):\n path = os.path.join(self.path, \"lambentlight.json\")\n\n # If the file is there, load it\n if os.path.isfile(path):\n newconfig = {}\n with open(path) as file:\n loaded = json.load(file)\n for key, item in default.items():\n if key in loaded:\n newconfig[key] = loaded[key]\n else:\n newconfig[key] = item\n self.config = newconfig\n # Otherwise, use the default values\n else:\n logger.warning(f\"Data Folder {self.name} does not has a LambentLight Configuration File\")\n self.config = default",
"title": ""
},
{
"docid": "e84fb137dbad986fa5b2bba70720dca9",
"score": "0.56008255",
"text": "def init_new_config(self):\n pass",
"title": ""
},
{
"docid": "d68199b35ec7a1459619e46933bed705",
"score": "0.5594922",
"text": "def _update_configuration(self, configuration):\n super(MicrophoneSelectiveListener, self)._update_configuration(configuration)\n options = ['-{} {}'.format(key, value) for key, value in self.get_configuration('options', {}).iteritems()]\n self._configuration['options'] = ' '.join(options)\n\n if 'silence' in configuration:\n silence = configuration['silence']\n for when in ('pre', 'post'):\n if when in silence:\n when_config = silence[when]\n for what in ('level', 'trim', 'duration'):\n if what in when_config:\n self._configuration['silence_{}_{}'.format(when, what)] = when_config[what]",
"title": ""
},
{
"docid": "265da66fc7bd59b1d20349716e5ea0fe",
"score": "0.55877215",
"text": "def edit_in_conf_file(self):\n try:\n replacements = {}\n if 'gateway' in self.component:\n replacements['APPVIEWX_GATEWAY_HTTPS'] = self.state\n if 'web' in self.component:\n replacements['APPVIEWX_WEB_HTTPS'] = self.state\n if 'plugins' in self.component:\n replacements['VM_HTTPS'] = self.state\n replacements['ENABLE_CLIENT_CERT'] = self.state\n with open(self.conf_file) as conf_file:\n conf_content = conf_file.readlines()\n out_content = []\n for line in conf_content:\n if '=' in line and not line.startswith('#'):\n key, value = line.split('=')\n for dic_key, dic_value in replacements.items():\n if key.strip() == dic_key:\n line = dic_key + '=' + dic_value + '\\n'\n lggr.debug('New line added in conf file: ' + line)\n out_content.append(line)\n with open(self.conf_file, 'w+') as conf_file:\n lggr.debug('Writing new values to conf_file')\n conf_file.writelines(out_content)\n except Exception as e:\n print(e)\n lggr.error(e)",
"title": ""
},
{
"docid": "518f914be2fc6fbf2423b805140c72e1",
"score": "0.55872196",
"text": "def reload_config(self, sighup: bool = False, local: Optional[bool] = False) -> None:\n try:\n super(Patroni, self).reload_config(sighup, local)\n if local:\n self._tags = self._get_tags()\n self.request.reload_config(self.config)\n if local or sighup and self.api.reload_local_certificate():\n self.api.reload_config(self.config['restapi'])\n self.watchdog.reload_config(self.config)\n self.postgresql.reload_config(self.config['postgresql'], sighup)\n self.dcs.reload_config(self.config)\n except Exception:\n logger.exception('Failed to reload config_file=%s', self.config.config_file)",
"title": ""
},
{
"docid": "1c2e169a272853167d79f020e51e2274",
"score": "0.5578542",
"text": "def commit_config(self):\r\n if self.replace_config is False and self.merge_config is False:\r\n print(\"Please replace or merge a configuration \")\r\n return -1 # returns failure\r\n\r\n if self.replace_config is not False:\r\n replace_list = list()\r\n\r\n diff_in_config = FastIronDriver.compare_config(self)\r\n my_temp = FastIronDriver.__creates_list_of_nlines(diff_in_config)\r\n\r\n for sentence in my_temp:\r\n\r\n if sentence[0] == '-':\r\n sentence = sentence[1:len(sentence)]\r\n elif sentence[0] == '+':\r\n sentence = 'no' + sentence[1:len(sentence)]\r\n replace_list.append(sentence)\r\n\r\n self.device.config_mode()\r\n self.device.send_config_set(replace_list)\r\n\r\n return True\r\n\r\n if self.merge_config is not False: # merges candidate configuration with existing config\r\n self.device.config_mode()\r\n self.device.send_config_set(self.config_merge)\r\n\r\n return True # returns success\r",
"title": ""
},
{
"docid": "b40359cb5b0cb2d094f596db8a094479",
"score": "0.55715173",
"text": "def recurrent_config_parse(configs_to_parse: list, configs_parsed: list, abs_config_path: str):\n # Terminal condition.\n while len(configs_to_parse) > 0:\n\n # Get config.\n config = configs_to_parse.pop(0)\n\n # Skip empty names (after lose comas).\n if config == '':\n continue\n print(\"Info: Parsing the {} configuration file\".format(config))\n\n # Check if it was already loaded.\n if config in configs_parsed:\n print('Warning: Configuration file {} already parsed - skipping'.format(config))\n continue\n\n # Check if file exists.\n if not os.path.isfile(config):\n print('Error: Configuration file {} does not exist'.format(config))\n exit(-1)\n\n try:\n # Open file and get parameter dictionary.\n with open(config, 'r') as stream:\n param_dict = yaml.safe_load(stream)\n except yaml.YAMLError as e:\n print(\"Error: Couldn't properly parse the {} configuration file\".format(config))\n print('yaml.YAMLERROR:', e)\n exit(-1)\n\n # Remember that we loaded that config.\n configs_parsed.append(config)\n\n # Check if there are any default configs to load.\n if 'default_configs' in param_dict:\n default_configs_to_parse = param_dict['default_configs'].replace(\" \", \"\").split(',')\n # If there are - expand them to absolute paths.\n abs_default_configs_to_parse = [os.path.join(abs_config_path,config) for config in default_configs_to_parse]\n # Recursion!\n configs_parsed = recurrent_config_parse(abs_default_configs_to_parse, configs_parsed, abs_config_path)\n\n # Done, return list of loaded configs.\n return configs_parsed",
"title": ""
},
{
"docid": "ad86b4651803bfec31eac3ad7fff9425",
"score": "0.5571344",
"text": "def reload(self) -> Self:\n\n if self._BASE_CONFIG_FILE is not None:\n self._BASE = self._parse_config(self._BASE_CONFIG_FILE, use_base=False)\n\n self.load(self._CONFIG_FILE or dict(self))\n\n return self",
"title": ""
},
{
"docid": "c18aff4db147474ac8b515e3e0414f64",
"score": "0.557097",
"text": "def reload_config():\n cfg = getattr(cache_config()(config), 'cfg')\n setattr(config, 'cfg', cfg)",
"title": ""
},
{
"docid": "275c2766edb9c077f6b81ac83ad44c37",
"score": "0.5559656",
"text": "def apply_configuration(self):\n self.manage_appends()",
"title": ""
},
{
"docid": "f7cc3db4270e5329d206d539f3c67da7",
"score": "0.5545605",
"text": "def load(self):\n try:\n self.update(yaml.load(self.config.read())) # B\n except py.error.ENOENT:\n pass",
"title": ""
},
{
"docid": "85f59546990d91ec9891897a195d7201",
"score": "0.554447",
"text": "def _on_config_changed(self, event: ops.framework.EventBase) -> None:\n self._update_files(event)",
"title": ""
},
{
"docid": "4c9063a1beb44436fd63704649ea00d1",
"score": "0.5534494",
"text": "def reset(self):\n callbacks = []\n for key in self._config.values.keys():\n if key in self._first_config.values and self._config.values[key] != self._first_config.values[key]:\n callbacks.append((key, self._config.values[key], self._first_config.values[key]))\n\n with self._lock:\n self._version = self._first_version\n self._config = self._first_config\n\n self._config.callbacks_queue.extend(callbacks)\n self._config.call_pending_callbacks()",
"title": ""
},
{
"docid": "34eb0e68a009047d60d587893df84a1d",
"score": "0.552875",
"text": "def _reconfigure_all(self) -> None:\n global_config = self._NAMESPACE_CONFIGS.get(Config.GLOBAL_NAMESPACE, {})\n for path, configurable in self._CONFIGURABLES.items():\n ns_config = self._NAMESPACE_CONFIGS.get(configurable.namespace, {})\n configurable.reconfigure(ns_config, global_config)",
"title": ""
},
{
"docid": "6d5e1e711bb0eca06e09f760bf025df9",
"score": "0.551731",
"text": "def _merge_base_config(self, config):\n\n for gk, gv in config.items():\n for ok, ov in gv.items():\n for kk, kv in ov.items():\n if gk != '' and ok != '' and kk != '':\n if gk not in self._config:\n self._config[gk] = {}\n if ok not in self._config[gk]:\n self._config[gk][ok] = {}\n # update in memory configuration\n self._config[gk][ok][kk] = kv",
"title": ""
},
{
"docid": "8aa8192bce1be86821d19c9241888915",
"score": "0.55138206",
"text": "def config_changed(self):\r\n from flexget.task import config_changed\r\n for task in self.tasks:\r\n config_changed(task)",
"title": ""
},
{
"docid": "f999b66b4bc60225fba91439f03dc347",
"score": "0.550875",
"text": "def config_changed(self):\n for task in self.tasks.values():\n task.config_changed()",
"title": ""
},
{
"docid": "80c7acaa54395297b931505e1e20728b",
"score": "0.55005944",
"text": "def on_update(self, new_config: dict) -> bool:\n pass",
"title": ""
},
{
"docid": "1f5b0927c02be70645934cd507ba7a24",
"score": "0.5492927",
"text": "def parse(self, config):\n if isinstance(config, Config):\n return config.config\n new_config = dict()\n for key, value in config.items():\n if isinstance(value, dict):\n value = self.parse(value)\n self.put(key, value, new_config)\n return new_config",
"title": ""
},
{
"docid": "eb799d0ff704f0e12cc9890e5dcc3930",
"score": "0.5490577",
"text": "def reload_config() -> None:\n config.read(os.path.join(SOURCE_PATH, \"config.ini\"))",
"title": ""
},
{
"docid": "5bdcfb1b2923ef1d2d4170e16af17e31",
"score": "0.5487313",
"text": "def substitute_config(self):\n for rank in range(len(self.loops)):\n section = self.loops[rank][0]\n var = self.loops[rank][1]\n self.das.config[section][var] = str(self.state[rank])\n\n return self",
"title": ""
},
{
"docid": "237edf7b01b3444a3b89b9dd47144e29",
"score": "0.54754186",
"text": "def parse_config_file(self):\n\n if self.config_file is not None:\n\n if self.verbose:\n print ('Parsing config file')\n\n with open(self.config_file) as data_file:\n config_data = json.load(data_file)\n\n if \"start_date\" in config_data:\n self.start_date = int(config_data[\"start_date\"])\n\n if \"end_date\" in config_data:\n self.end_date = int(config_data[\"end_date\"])\n\n if \"start_hour\" in config_data:\n self.start_hour = int(config_data[\"start_hour\"])\n\n if \"end_hour\" in config_data:\n self.end_hour = int(config_data[\"end_hour\"])\n\n if \"ubications\" in config_data:\n self.ubications = config_data[\"ubications\"]\n\n if \"original_folder\" in config_data:\n self.orig_folder = config_data[\"original_folder\"]\n\n if \"destination_folder\" in config_data:\n self.dest_folder = config_data[\"destination_folder\"]",
"title": ""
},
{
"docid": "15c9adf6e1e4a2f8e804227f68f7d793",
"score": "0.54751676",
"text": "def _on_config_changed(self, event):\n try:\n self._configure()\n self.unit.status = ActiveStatus()\n except ConfigMissingException as e:\n self.unit.status = BlockedStatus(f\"Config missing: {e}\")\n event.defer()",
"title": ""
},
{
"docid": "9ea3e208feaae3f96ec1c60c03743828",
"score": "0.5474706",
"text": "def finish(self):\r\n for key in [x for x in dir(self.document.config) if not x.startswith(\"_\")]:\r\n self.set_option(key)",
"title": ""
},
{
"docid": "33fc99c72cae73ab150c70333834dfad",
"score": "0.54687136",
"text": "def _update_params(self, *args, **kwargs):\n # Get old param dict config.\n old_config = self._param_dict.get_config()\n\n # Issue display commands and parse results.\n timeout = kwargs.get('timeout', TIMEOUT)\n\n log.debug(\"Run status command: %s\" % InstrumentCommands.GET_STATUS_DATA)\n response = self._do_cmd_resp(InstrumentCommands.GET_STATUS_DATA, timeout=timeout)\n for line in response.split(NEWLINE):\n self._param_dict.update(line)\n log.debug(\"status command response: %r\" % response)\n\n log.debug(\"Run configure command: %s\" % InstrumentCommands.GET_CONFIGURATION_DATA)\n response = self._do_cmd_resp(InstrumentCommands.GET_CONFIGURATION_DATA, timeout=timeout)\n for line in response.split(NEWLINE):\n self._param_dict.update(line)\n log.debug(\"configure command response: %r\" % response)\n\n # Get new param dict config. If it differs from the old config,\n # tell driver superclass to publish a config change event.\n new_config = self._param_dict.get_config()\n\n if new_config != old_config:\n self._driver_event(DriverAsyncEvent.CONFIG_CHANGE)",
"title": ""
},
{
"docid": "35a8681e066f5c43f155938a699b0928",
"score": "0.5465704",
"text": "def _reload_status(self):\n # TODO Add to vdev info error counts and such\n s = self.status()\n\n #for k in ['status', 'errors', 'scan', 'see', 'state', 'action', 'config']:\n #for k in ['errors', 'scan', 'see', 'state', 'action', 'config']:\n # setattr(self, k, s[k])\n #self.pool_status = s['status']\n\n for k, v in s['config'].items():\n if k == self.name:\n self.state = v['state']\n else:\n vdev = self.guess_vdev_by_basename(k)\n #logging.info(\"k=%s v=%s vdev=%s\", k, v, vdev)\n if vdev:\n #logging.info(\"Got vdev=%s\", vdev)\n vdev.state = v['state']\n\n if vdev._changed_fields:\n vdev.save()",
"title": ""
},
{
"docid": "3cb164f6c7a1e56ff87bbbe15dc63706",
"score": "0.54635584",
"text": "def configure(self):\n\n self.configuration = configparser.ConfigParser(interpolation=configparser.ExtendedInterpolation())\n\n if not self.config_file:\n self.logger.info('No configuration file specified. Running with defaults')\n return\n\n if not os.path.exists(self.config_file):\n print(\"Configuration file {} does not exist. Exiting\".format(self.config_file))\n raise SystemExit(1)\n\n self.configuration.read(self.config_file)\n\n if self.configuration.has_section('main'):\n if not self.username:\n if 'username' in self.configuration['main']:\n self.username = self.configuration.get('main', 'username')\n\n loglevel = self.configuration.get('main', 'loglevel', fallback='INFO')\n loglevel = getattr(logging, loglevel.upper())\n self.logger.setLevel(loglevel)\n\n if self.configuration.has_section('scope'):\n self.scope_radius = self.configuration.getint('scope', 'radius', fallback=60)\n self.scope_brightness = self.configuration.getfloat('scope', 'scope_brightness', fallback=0.5)\n self.airport_brightness = self.configuration.getfloat('scope', 'airport_brightness', fallback=0.5)\n self.scope_rotation = self.configuration.getint('scope', 'rotation', fallback=0)\n\n if self.configuration.has_section('ADSB'):\n self.adsb_host = self.configuration.get('ADSB', 'adsb_host', fallback='localhost')\n self.receiverurl = self.configuration.get('ADSB', 'receiver_url',\n fallback='http://{}/dump1090-fa/data/receiver.json'.format(\n self.adsb_host\n ))\n self.aircrafturl = self.configuration.get('ADSB', 'aircraft_url',\n fallback='http://{}/dump1090-fa/data/aircraft.json'.format(\n self.adsb_host\n ))\n\n if self.configuration.has_section('airports'):\n for airport in self.configuration.items(section='airports'):\n icao_code = airport[0]\n coordinates = airport[1].strip().split(',')\n self.add_airport(icao_code, float(coordinates[0]), float(coordinates[1]))",
"title": ""
},
{
"docid": "e772c8d00aa76e0d4845dd84bb8b7efe",
"score": "0.5450018",
"text": "def load_config(self):\n self.config = [200]\n try:\n with open(self.config_file, \"r\") as f:\n f.readline()\n self.config = [int(x) for x in f.readline().split(\",\")]\n except FileNotFoundError:\n print_command(\"Configuration file not found, using default configuration\")\n except ValueError:\n print_command(\"Configuration file incorrect, using default configuration\")",
"title": ""
},
{
"docid": "4f69e5159cb48837336412a6fb3b52e8",
"score": "0.5443276",
"text": "def getConfig(self):\n for i in range(self.options.configtries):\n try:\n self.loadConfig()\n return\n except Exception:\n if self.validConfig():\n self.log.exception(\n \"configuration load exception using previous configuration\")\n return\n else:\n self.log.exception('config load failed')\n if i <= (self.options.configtries - 2):\n self.log.warn(\n \"initial config load failed will retry\")\n time.sleep(self.options.configsleep)\n else:\n self.log.critical(\n \"initial config load failed %d times exiting\",\n self.options.configtries)\n sys.exit(2)",
"title": ""
},
{
"docid": "7aff14dc89938ce528fb9a3d202ceb12",
"score": "0.5439321",
"text": "def parse(self, conf):\n for line in conf:\n self.lineno += 1\n\n line = line.rstrip()\n if not line:\n continue\n if line[0] == '[':\n self._check_section(line)\n\n elif line[0] in '#;':\n continue\n else:\n key, value = self._split_key_value(line)\n if not key:\n raise ParseError('Key cannot be empty', self.lineno, line)\n self.assignment(key, value)",
"title": ""
},
{
"docid": "449bc53917ae76f61b8cf4167d73347c",
"score": "0.54392457",
"text": "def parse_config(self):\n top.Config.parse_config(self)\n\n kwargs = [{'section': 'dirs',\n 'option': 'comms',\n 'var': 'comms_dir',\n 'is_required': True},\n {'section': 'timeout',\n 'option': 'reminder_loop',\n 'cast_type': 'int'},\n {'section': 'reminder',\n 'option': 'notification_delay',\n 'cast_type': 'int'},\n {'section': 'reminder',\n 'option': 'start_date'},\n {'section': 'reminder',\n 'option': 'hold_period',\n 'cast_type': 'int'}]\n for kw in kwargs:\n self.parse_scalar_config(**kw)",
"title": ""
},
{
"docid": "67deed171d2e7f2822031ba79686f742",
"score": "0.5438431",
"text": "def substitute_config(self):\n\n for rank in range(len(self.loops)):\n section = self.loops[rank][0]\n var = self.loops[rank][1]\n self.das.config[section][var] = str(self.state[rank])\n\n return self",
"title": ""
},
{
"docid": "31d26a3531494b8f699b9e34909ea2b8",
"score": "0.5435037",
"text": "def reload(self):\r\n if not isinstance(self.filename, basestring):\r\n raise ReloadError()\r\n\r\n filename = self.filename\r\n current_options = {}\r\n for entry in OPTION_DEFAULTS:\r\n if entry == 'configspec':\r\n continue\r\n current_options[entry] = getattr(self, entry)\r\n\r\n configspec = self._original_configspec\r\n current_options['configspec'] = configspec\r\n\r\n self.clear()\r\n self._initialise(current_options)\r\n self._load(filename, configspec)",
"title": ""
},
{
"docid": "77d96dce3df5d29901430f54824df5f3",
"score": "0.5425455",
"text": "def __upgradeInstanceConf(self):\n pass",
"title": ""
},
{
"docid": "f7bf60d77e5679ad2747ad5b6e9096da",
"score": "0.54170007",
"text": "def renku_op(self):\n update_config_command = update_multiple_config().with_commit_message(self.ctx[\"commit_message\"]).build()\n update_config_command.execute(self.ctx[\"config\"])\n\n return self.context",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="718"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">cdbfad92f73dfb254a59e293ab82bb5c</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">The ID of the basic GA instance.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "10168b545ae147fb3b30235bea337f67",
"score": "0.0",
"text": "def accelerator_id(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"accelerator_id\")",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "623bf3ecf10b26f32a0e22e015278b41",
"score": "0.7669954",
"text": "def analytics_id(self):\r\n return self.__analytics_id",
"title": ""
},
{
"docid": "748ad9b2fba9e34c2af90a9740a955f9",
"score": "0.71005523",
"text": "def unique_id(self):\n return f\"{self.hass.data[DOMAIN]['instance']}#{self._name}\"",
"title": ""
},
{
"docid": "e90560bc42bc0c73b9b115e4216ae2f9",
"score": "0.69561553",
"text": "def id(self):\n pass",
"title": ""
},
{
"docid": "d216611b83455d81f0266f7e6b93c5c2",
"score": "0.6955818",
"text": "def unique_id(self):\n return self.grill_id",
"title": ""
},
{
"docid": "05819748a0503af728dffd5b6a614cb5",
"score": "0.69480073",
"text": "def getID(self):\n return id(self)",
"title": ""
},
{
"docid": "9cae8f5fb37809e2609601c3177ee0a9",
"score": "0.6892058",
"text": "def unique_id(self) -> str:\n return self._ais_id",
"title": ""
},
{
"docid": "1b2cc1f61b97e15c39b79aeff814d2a0",
"score": "0.68178034",
"text": "def get_ga_tracking_id(self, request):\n return settings.GA_TRACKING_ID",
"title": ""
},
{
"docid": "f10c69ab70b3641ddf1dd6d60b84e901",
"score": "0.6814841",
"text": "def id(self):\n return self.__identifier__",
"title": ""
},
{
"docid": "7795bfbdde0ed15295aed7390102390b",
"score": "0.6785262",
"text": "def get_global_id(self):\n return self.admin_socket(['mds_sessions'])['id']",
"title": ""
},
{
"docid": "2cfb24babf8832b2094f9c89a9b366bf",
"score": "0.67849165",
"text": "def unique_id(self) -> str:\n return f\"{self._inst.lower()}-{self._sensor.lower()}-{self._data['default-name'].lower()}\"",
"title": ""
},
{
"docid": "a9e9eb3785c0f387ebe2ce53200b5e8e",
"score": "0.6770514",
"text": "def unique_id(self):\n pass",
"title": ""
},
{
"docid": "0df9fa672b80db6f9e24eb6073c73ac7",
"score": "0.6749059",
"text": "def analytics_id(cls):\n raise NotImplementedError(\"Must specify an id to enable course tool eventing.\")",
"title": ""
},
{
"docid": "cfc9c744225fa9445a1b954b946be5e3",
"score": "0.67379814",
"text": "def id(self) -> str:\n return self.__id",
"title": ""
},
{
"docid": "cfc9c744225fa9445a1b954b946be5e3",
"score": "0.67379814",
"text": "def id(self) -> str:\n return self.__id",
"title": ""
},
{
"docid": "c208ea57951cf399694dc931b0c206fa",
"score": "0.6730028",
"text": "def id(cls):\n return cls._class_id",
"title": ""
},
{
"docid": "c7508bf7597a018cc9418dd0c6116a11",
"score": "0.67178243",
"text": "def id(self):\r\n return self.__id",
"title": ""
},
{
"docid": "84614b02f00bc03d045aec0e7c890ff0",
"score": "0.6713938",
"text": "def unique_id(self):\n return self._name",
"title": ""
},
{
"docid": "84614b02f00bc03d045aec0e7c890ff0",
"score": "0.6713938",
"text": "def unique_id(self):\n return self._name",
"title": ""
},
{
"docid": "4618abfeefe8411b8a2c566ed2d581b6",
"score": "0.6662385",
"text": "def id(self):\n return self.__id",
"title": ""
},
{
"docid": "4618abfeefe8411b8a2c566ed2d581b6",
"score": "0.6662385",
"text": "def id(self):\n return self.__id",
"title": ""
},
{
"docid": "4618abfeefe8411b8a2c566ed2d581b6",
"score": "0.6662385",
"text": "def id(self):\n return self.__id",
"title": ""
},
{
"docid": "dcda5bd87a1f3823ab41ab93bbed5903",
"score": "0.6659144",
"text": "def get_id(self) -> str:\n return self.__id",
"title": ""
},
{
"docid": "534282cc28cc0ad4cea3860d0bdf0cf7",
"score": "0.6636909",
"text": "def get_id(self):\n return 1",
"title": ""
},
{
"docid": "1a72b11d47169ade2963468de44e6808",
"score": "0.66163516",
"text": "def id(self):\n return self.get_id()",
"title": ""
},
{
"docid": "f36672ad2ce85197a3e1d50cc8abf278",
"score": "0.6613727",
"text": "def __getId(self):\r\n\r\n\r\n if self.id is None:\r\n self.id = self.__createId()\r\n return self.id",
"title": ""
},
{
"docid": "f652cfda46cb3cca853a390f38497a5a",
"score": "0.6611902",
"text": "def unique_id(self) -> str:\n return f\"{self._inst.lower()}-{self._sensor.lower()}\"",
"title": ""
},
{
"docid": "740771278413d22786ffeeaae93e2d5e",
"score": "0.66042936",
"text": "def id(self) -> str:\n return self._id",
"title": ""
},
{
"docid": "740771278413d22786ffeeaae93e2d5e",
"score": "0.66042936",
"text": "def id(self) -> str:\n return self._id",
"title": ""
},
{
"docid": "740771278413d22786ffeeaae93e2d5e",
"score": "0.66042936",
"text": "def id(self) -> str:\n return self._id",
"title": ""
},
{
"docid": "740771278413d22786ffeeaae93e2d5e",
"score": "0.66042936",
"text": "def id(self) -> str:\n return self._id",
"title": ""
},
{
"docid": "740771278413d22786ffeeaae93e2d5e",
"score": "0.66042936",
"text": "def id(self) -> str:\n return self._id",
"title": ""
},
{
"docid": "740771278413d22786ffeeaae93e2d5e",
"score": "0.66042936",
"text": "def id(self) -> str:\n return self._id",
"title": ""
},
{
"docid": "740771278413d22786ffeeaae93e2d5e",
"score": "0.66042936",
"text": "def id(self) -> str:\n return self._id",
"title": ""
},
{
"docid": "740771278413d22786ffeeaae93e2d5e",
"score": "0.66042936",
"text": "def id(self) -> str:\n return self._id",
"title": ""
},
{
"docid": "7aad3817ae1e626a7b714b1710521a62",
"score": "0.6595385",
"text": "def get_id(self): # pragma: no cover\n pass",
"title": ""
},
{
"docid": "973b72a1fe0d858ff0a6ae4d610f240e",
"score": "0.65942246",
"text": "def get_id(self):\n\n\t\treturn self.__id",
"title": ""
},
{
"docid": "973b72a1fe0d858ff0a6ae4d610f240e",
"score": "0.65942246",
"text": "def get_id(self):\n\n\t\treturn self.__id",
"title": ""
},
{
"docid": "973b72a1fe0d858ff0a6ae4d610f240e",
"score": "0.65942246",
"text": "def get_id(self):\n\n\t\treturn self.__id",
"title": ""
},
{
"docid": "771bbd77969b25f1ea8eb762e51d2ea9",
"score": "0.65795785",
"text": "def getId(self):\n pass",
"title": ""
},
{
"docid": "c597cdcad2db55f865e4b21b35c8b9d2",
"score": "0.6573876",
"text": "def instance_id(self) -> str:\n return self._instance_id",
"title": ""
},
{
"docid": "90ec7864dec7bcc798975c81b3da747c",
"score": "0.6570885",
"text": "def get_id(self):\n return self.__id",
"title": ""
},
{
"docid": "cc0a97369156e6e99b8a9e414b756380",
"score": "0.65678895",
"text": "def get_id(self):\n return self.server.get_id()",
"title": ""
},
{
"docid": "0b7875b2ed0f7dad029102d2fd7f2150",
"score": "0.656159",
"text": "def get_id(self) -> str:\n return self._id",
"title": ""
},
{
"docid": "023466aef50ce7b28ba64e6448f57d96",
"score": "0.65567774",
"text": "def getID(self):\n return self._id",
"title": ""
},
{
"docid": "ff90bb6308d8af96eeec6a1b95ee6a15",
"score": "0.6550907",
"text": "def getId(self):\n # XXX-Aurel : this must be based on the GID definition\n # As GID in TioSafe case is unique, it must be used to get\n # the last ID of an inserted object (usefull for cases where\n # transactionnal operation is not provided like with prestashop)\n #raise ValueError, self.last_id\n return TioSafeBrain.getId(self)",
"title": ""
},
{
"docid": "4c4fe8ce42b09f9e0892849c9a1d7edd",
"score": "0.6548028",
"text": "def instance_id(self) -> str:\n return pulumi.get(self, \"instance_id\")",
"title": ""
},
{
"docid": "4c4fe8ce42b09f9e0892849c9a1d7edd",
"score": "0.6548028",
"text": "def instance_id(self) -> str:\n return pulumi.get(self, \"instance_id\")",
"title": ""
},
{
"docid": "4c4fe8ce42b09f9e0892849c9a1d7edd",
"score": "0.6548028",
"text": "def instance_id(self) -> str:\n return pulumi.get(self, \"instance_id\")",
"title": ""
},
{
"docid": "4c4fe8ce42b09f9e0892849c9a1d7edd",
"score": "0.6548028",
"text": "def instance_id(self) -> str:\n return pulumi.get(self, \"instance_id\")",
"title": ""
},
{
"docid": "4c4fe8ce42b09f9e0892849c9a1d7edd",
"score": "0.6548028",
"text": "def instance_id(self) -> str:\n return pulumi.get(self, \"instance_id\")",
"title": ""
},
{
"docid": "4c4fe8ce42b09f9e0892849c9a1d7edd",
"score": "0.6548028",
"text": "def instance_id(self) -> str:\n return pulumi.get(self, \"instance_id\")",
"title": ""
},
{
"docid": "4c4fe8ce42b09f9e0892849c9a1d7edd",
"score": "0.6548028",
"text": "def instance_id(self) -> str:\n return pulumi.get(self, \"instance_id\")",
"title": ""
},
{
"docid": "4c4fe8ce42b09f9e0892849c9a1d7edd",
"score": "0.6548028",
"text": "def instance_id(self) -> str:\n return pulumi.get(self, \"instance_id\")",
"title": ""
},
{
"docid": "4c4fe8ce42b09f9e0892849c9a1d7edd",
"score": "0.6548028",
"text": "def instance_id(self) -> str:\n return pulumi.get(self, \"instance_id\")",
"title": ""
},
{
"docid": "4c4fe8ce42b09f9e0892849c9a1d7edd",
"score": "0.6548028",
"text": "def instance_id(self) -> str:\n return pulumi.get(self, \"instance_id\")",
"title": ""
},
{
"docid": "ab07dcca5ad0b1c5092479172174bc3d",
"score": "0.654532",
"text": "def _get_id(self):\n return self.__id",
"title": ""
},
{
"docid": "ab07dcca5ad0b1c5092479172174bc3d",
"score": "0.654532",
"text": "def _get_id(self):\n return self.__id",
"title": ""
},
{
"docid": "ab07dcca5ad0b1c5092479172174bc3d",
"score": "0.654532",
"text": "def _get_id(self):\n return self.__id",
"title": ""
},
{
"docid": "ab07dcca5ad0b1c5092479172174bc3d",
"score": "0.654532",
"text": "def _get_id(self):\n return self.__id",
"title": ""
},
{
"docid": "ab07dcca5ad0b1c5092479172174bc3d",
"score": "0.654532",
"text": "def _get_id(self):\n return self.__id",
"title": ""
},
{
"docid": "ab07dcca5ad0b1c5092479172174bc3d",
"score": "0.654532",
"text": "def _get_id(self):\n return self.__id",
"title": ""
},
{
"docid": "ab07dcca5ad0b1c5092479172174bc3d",
"score": "0.654532",
"text": "def _get_id(self):\n return self.__id",
"title": ""
},
{
"docid": "ab07dcca5ad0b1c5092479172174bc3d",
"score": "0.654532",
"text": "def _get_id(self):\n return self.__id",
"title": ""
},
{
"docid": "ab07dcca5ad0b1c5092479172174bc3d",
"score": "0.654532",
"text": "def _get_id(self):\n return self.__id",
"title": ""
},
{
"docid": "ab07dcca5ad0b1c5092479172174bc3d",
"score": "0.654532",
"text": "def _get_id(self):\n return self.__id",
"title": ""
},
{
"docid": "ab07dcca5ad0b1c5092479172174bc3d",
"score": "0.654532",
"text": "def _get_id(self):\n return self.__id",
"title": ""
},
{
"docid": "ab07dcca5ad0b1c5092479172174bc3d",
"score": "0.654532",
"text": "def _get_id(self):\n return self.__id",
"title": ""
},
{
"docid": "ab07dcca5ad0b1c5092479172174bc3d",
"score": "0.654532",
"text": "def _get_id(self):\n return self.__id",
"title": ""
},
{
"docid": "aac6f4bc2681c0f35cc71a5387066412",
"score": "0.6543381",
"text": "def id( self ) :\n if (self._id is None) :\n self._id = hashlib.sha1( self.title() ).hexdigest()\n return self._id",
"title": ""
},
{
"docid": "4432dfe631d1f239ccc9916d70e23ee7",
"score": "0.65310925",
"text": "def id(self):\n # type: () -> string_types\n return self._id",
"title": ""
},
{
"docid": "977574db26aaf884c00247865cfda88e",
"score": "0.6531038",
"text": "def id(self):\n # type: () -> int\n return self._id",
"title": ""
},
{
"docid": "f091bf7a47675004a8b9c696c0e332c9",
"score": "0.65304905",
"text": "def amgid(self):\n return self._amgid",
"title": ""
},
{
"docid": "56137587f6cdf2596d7833c3c8944cf6",
"score": "0.6526596",
"text": "def unique_id(self):\r\n return self.config.get(\"unique_id\", None)",
"title": ""
},
{
"docid": "217a36c105b48fc3b71202a49fe27b7b",
"score": "0.6518757",
"text": "def unique_id(self):\n return self._sensor.id",
"title": ""
},
{
"docid": "b29dd2e84e6de2dc75092e98e40b0b3e",
"score": "0.6511629",
"text": "def id(self):\r\n return self._id",
"title": ""
},
{
"docid": "b29dd2e84e6de2dc75092e98e40b0b3e",
"score": "0.6511629",
"text": "def id(self):\r\n return self._id",
"title": ""
},
{
"docid": "66ef645ce680a88b0535b56fa0060ae0",
"score": "0.6504359",
"text": "def id(self):\n return getattr(self, '_id', None)",
"title": ""
},
{
"docid": "7954129953c436dfa82e989b708edabe",
"score": "0.6501305",
"text": "def ID(self):\n agent = self.agent\n return agent.ID if agent else \"\"",
"title": ""
},
{
"docid": "40f74158cd16c004f4df16e8ce9bffe8",
"score": "0.64984375",
"text": "def get_id(self):\n return self.ID",
"title": ""
},
{
"docid": "c49b35f3e666cc0ee60179a17970c2b7",
"score": "0.64969516",
"text": "def unique_id(self):\n return f\"point.{self._id}-{self.device_class}\"",
"title": ""
},
{
"docid": "0bbe1731a4898555f6bcb16e26425813",
"score": "0.64935565",
"text": "def unique_id(self) -> str:\n return f\"{DOMAIN}_{self.devicename}\"",
"title": ""
},
{
"docid": "048678c7611871a745d4880874fc3455",
"score": "0.64929646",
"text": "def unique_id(self):\n return self._handle('unique_id')",
"title": ""
},
{
"docid": "fac089f51c0ab6e1feec3724d8a39699",
"score": "0.6489685",
"text": "def get_id(self):\n return \"\"",
"title": ""
},
{
"docid": "88ada2ce674ff43c072c360e78f35f15",
"score": "0.6484145",
"text": "def unique_id(self) -> str:\n return pulumi.get(self, \"unique_id\")",
"title": ""
},
{
"docid": "88ada2ce674ff43c072c360e78f35f15",
"score": "0.6484145",
"text": "def unique_id(self) -> str:\n return pulumi.get(self, \"unique_id\")",
"title": ""
},
{
"docid": "41982ae673d6d1dfc94752114f0a4e08",
"score": "0.6482204",
"text": "def getId():",
"title": ""
},
{
"docid": "440671bb7fb47f673c292154e177f137",
"score": "0.64736664",
"text": "def ID(self):\n return self._ID",
"title": ""
},
{
"docid": "07aecf0f338ae85fda36ed08e49f7603",
"score": "0.64710706",
"text": "def get_instance_id(self) -> str:\n return self._instance_id",
"title": ""
},
{
"docid": "dea664011e23d1d7cb68c55eb136d325",
"score": "0.64567006",
"text": "def id(self) -> int:\n return self.__id",
"title": ""
},
{
"docid": "13406af85aab3bc4c63327efefca4d67",
"score": "0.6456149",
"text": "def unique_id(self) -> str:\n return f\"{self._inst.lower()}-{self._sid_data['sid']}-{self._data[self._sid_data['sid_ref']]}\"",
"title": ""
},
{
"docid": "1b6952ef430d3131fafb25e5dfc2c7ab",
"score": "0.64553696",
"text": "def unique_id(self) -> str:\n return self._unique_id",
"title": ""
},
{
"docid": "1b6952ef430d3131fafb25e5dfc2c7ab",
"score": "0.64553696",
"text": "def unique_id(self) -> str:\n return self._unique_id",
"title": ""
},
{
"docid": "1b6952ef430d3131fafb25e5dfc2c7ab",
"score": "0.64553696",
"text": "def unique_id(self) -> str:\n return self._unique_id",
"title": ""
},
{
"docid": "1b6952ef430d3131fafb25e5dfc2c7ab",
"score": "0.64553696",
"text": "def unique_id(self) -> str:\n return self._unique_id",
"title": ""
},
{
"docid": "1b6952ef430d3131fafb25e5dfc2c7ab",
"score": "0.64553696",
"text": "def unique_id(self) -> str:\n return self._unique_id",
"title": ""
},
{
"docid": "1b6952ef430d3131fafb25e5dfc2c7ab",
"score": "0.64553696",
"text": "def unique_id(self) -> str:\n return self._unique_id",
"title": ""
},
{
"docid": "74b210bc3acbf501f4211a4265d48ccf",
"score": "0.64542544",
"text": "def id(self):",
"title": ""
},
{
"docid": "2188d6e2d07a176653d1fbda5bd6922c",
"score": "0.645362",
"text": "def unique_id(self):\n return f\"{self._api.service.id}-{self._sensor_type}\"",
"title": ""
},
{
"docid": "539564a5f40d37326075eec41551c5e5",
"score": "0.64532506",
"text": "def get_site_id(self):\n return # osid.id.Id",
"title": ""
},
{
"docid": "539564a5f40d37326075eec41551c5e5",
"score": "0.64532506",
"text": "def get_site_id(self):\n return # osid.id.Id",
"title": ""
},
{
"docid": "539564a5f40d37326075eec41551c5e5",
"score": "0.64532506",
"text": "def get_site_id(self):\n return # osid.id.Id",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="719"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">7751137b5ef6a8be9faa1f2ea7c6c57b</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Encodes a Prensor into a tuple of lists of Tensors.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "45a99029ad517ac5a66a7d00aa3e38fc",
"score": "0.62981755",
"text": "def _to_components(self, value: \"Prensor\") -> List[tf.Tensor]:\n result = []\n self._append_to_components(value, result)\n return result",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "ffa4a990d841e423269f90aac54c0873",
"score": "0.59007865",
"text": "def encode(self, X: Tensor) -> List[Tensor]:\n return self.encoder(X)",
"title": ""
},
{
"docid": "615d83ebd482ecaa56c904bb4eba76ba",
"score": "0.5785119",
"text": "def encode_tensor(obj: Tensor) -> List:\n return obj.tolist()",
"title": ""
},
{
"docid": "c65934fc70824c5f5e2fddf9b91fabf7",
"score": "0.56351596",
"text": "def encode(self, input: Tensor) -> List[Tensor]:\n result = self.encoder(input)\n result = self.encoder_output(result)\n return result",
"title": ""
},
{
"docid": "d8d10289c450f527e0dfbcb5cbdf5561",
"score": "0.5530792",
"text": "def _from_components(self, components: List[tf.Tensor]) -> \"Prensor\":\n return self._from_component_iter(iter(components))",
"title": ""
},
{
"docid": "5dd22fdd7fdfa4178039d523322195b2",
"score": "0.54519945",
"text": "def create_list_encoding(xs, ys, ts, ps):\n\n return torch.stack([ts, ys, xs, ps])",
"title": ""
},
{
"docid": "84ad19adf3d057a856d3c09343cbeaf4",
"score": "0.53503287",
"text": "def preprocess_tensors(L):\n if hasattr(L, \"shape\"):\n L = [L]\n else:\n L = list(L)\n return L",
"title": ""
},
{
"docid": "17eeff3e7b0cb6267ecc541b5c343178",
"score": "0.53475845",
"text": "def encodeState(state):\n elements = [tuple(elem) for elem in state]\n return tuple(elements)",
"title": ""
},
{
"docid": "6e71fd5b8452a4bbef94057e77c9b7a4",
"score": "0.5333051",
"text": "def tupleInputC(self):\n return tuple(map(lambda c: self.codeC(c, name+str(c))['input'], xrange(self.channels())))",
"title": ""
},
{
"docid": "091c44d53052a120f7496f58cc64329b",
"score": "0.5326877",
"text": "def bulk_encode_question(self, questions: list):\n return self.question_encoder(input=tf.constant(np.asarray(questions)))[\"outputs\"].numpy().tolist()",
"title": ""
},
{
"docid": "f41bb54f42f4a2e9ab91e7c861220243",
"score": "0.5274953",
"text": "def _encode(self, value):\n\n return tuple(value)",
"title": ""
},
{
"docid": "36e83e2c525902828f1a8a43921a933c",
"score": "0.52729815",
"text": "def encode(self):\n xyz = self.wp.get_free_xyzs(self.position)\n #print(self.wp.ops[0].rotation_matrix, self.wp.get_frozen_axis(), self.wp.get_dof())\n #print([self.specie, self.wp.index] + list(xyz))\n return [self.specie, self.wp.index] + list(xyz)",
"title": ""
},
{
"docid": "d404e9be0b91e49f083f122999a4bda1",
"score": "0.5248446",
"text": "def pack(self, tensors):\n with ops.device(self.name):\n return tpu_ops.tpu_replicated_input(inputs=tensors)",
"title": ""
},
{
"docid": "6ec51e446b10868ff46e63097e2f48dc",
"score": "0.5211542",
"text": "def encode(self, values: Sequence[Any]) -> Sequence[T_out]:\n ...",
"title": ""
},
{
"docid": "9af9dd997e312d58b583a16a8fcaf177",
"score": "0.5180512",
"text": "def outputs(self) -> Tuple[Tensor, ...]:\n return tuple(\n Tensor._from_pb_tensor(t) for t in self._op.getOutputTensors())",
"title": ""
},
{
"docid": "ec56603ace4ec0307735efb7d958ad31",
"score": "0.5169466",
"text": "def pack_sequence(self,sequence):\n #return tf.transpose(tf.pack(sequence), perm=[1, 0, 2])\n return tf.pack(sequence,axis=1)",
"title": ""
},
{
"docid": "a76b76949242373b55016646f41afa4a",
"score": "0.51641595",
"text": "def encode(self):\n if len(self.molecule.mol)>1:\n xyz, _ = self._get_coords_and_species(absolute=True, first=True)\n #if len(xyz)==3: print(\"encode: \\n\", self.molecule.mol.cart_coords)\n rotor = self.molecule.get_torsion_angles(xyz)\n ori, _, reflect = self.molecule.get_orientation(xyz)\n return list(self.position) + list(ori) + rotor + [reflect]\n else:\n return list(self.position) + [0]",
"title": ""
},
{
"docid": "60f5692c54877fced0ad5c67c57d965a",
"score": "0.51609933",
"text": "def encode(self, input: Tensor) ->List[Tensor]:\n result = self.encoder(input)\n result = torch.flatten(result, start_dim=1)\n mu = self.fc_mu(result)\n log_var = self.fc_var(result)\n return [mu, log_var]",
"title": ""
},
{
"docid": "2d6f30a7a4d33e4f99ff442e127438f8",
"score": "0.51045364",
"text": "def pack(self, seq):\n def _pack(value, typ, out):\n if isinstance(typ, ty.TensorType):\n out.append(value)\n elif isinstance(typ, ty.TupleType):\n tuple_out = []\n for i, field_ty in enumerate(typ.fields):\n _pack(value[i], field_ty, tuple_out)\n out.append(expr.Tuple(tuple_out))\n else:\n raise Exception(\"unsupported Relay type: {0}\".format(typ))\n\n if len(seq) == 1:\n return seq[0]\n else:\n out = []\n _pack(seq, self.typ, out)\n assert len(out) == 1, \"must return fully packed type\"\n return out[0]",
"title": ""
},
{
"docid": "50fcf7bca9a175faa0de9ca619f913ef",
"score": "0.5058586",
"text": "def encode(self, values: Sequence[Any]) -> Sequence[Tuple[int,...]]:\n\n if not self.is_fit:\n raise Exception(\"This encoder must be fit before it can be used.\")\n\n try:\n return [ self._onehots[value] for value in values ]\n except KeyError as e:\n raise Exception(f\"We were unable to find {e} in {self._onehots.keys()}\")",
"title": ""
},
{
"docid": "6c1f10ef370d90cc9757198dcaa0989b",
"score": "0.50567687",
"text": "def as_tuple(self):\n return (self.code_ptr, self.mem_ptr, self.input_ptr,\n self.mem, self.inputs, self.output_str)",
"title": ""
},
{
"docid": "23e16ec14c41629d042b0c7491fb2fbe",
"score": "0.5040932",
"text": "def to_tuple(self) -> Tuple[Tensor, Tensor, Tuple[int, ...]]:\n return self.indices, self.values, self.shape",
"title": ""
},
{
"docid": "2bef4fa2e0ea2b0a34ee7a51f791a3b9",
"score": "0.50405407",
"text": "def inputs(self) -> Tuple[Tensor, ...]:\n return tuple(\n Tensor._from_pb_tensor(t) for t in self._op.getInputTensors())",
"title": ""
},
{
"docid": "2f985240e8aefdef7dfaee4f67cfbb36",
"score": "0.502292",
"text": "def to_tuple(self) -> Tuple[Tensor, Tensor, Tensor, Tuple[int, ...]]:\n return self.indptr, self.indices, self.values, self.shape",
"title": ""
},
{
"docid": "7a1c7aa6b17ba095c8a8d4abd2ac5f56",
"score": "0.5003012",
"text": "def encode(self) -> tuple[Ty, list[tuple[Box, int]]]:\n return self.dom, list(zip(self.boxes, self.offsets))",
"title": ""
},
{
"docid": "5e10f04257cbeebe90098ee9659dcfbd",
"score": "0.4997728",
"text": "def encode_input(self, values: Union[List, Tuple]) -> Any:\n\n return [self._encode_input(ipt, v) for ipt, v in zip(self.abi.inputs, values)]",
"title": ""
},
{
"docid": "24f66e7358061cc803b08ee9c797d0b3",
"score": "0.49874863",
"text": "def __encode_tuple(t: tuple) -> None:\n l = [i for i in t]\n __encode_list(l)",
"title": ""
},
{
"docid": "00cfa667098293ff6a8797702130f6cc",
"score": "0.49758407",
"text": "def convert(self):\n nodes = [\n onnx.helper.make_node(\n self.__opr_type__,\n self._get_inputs(),\n self._get_outputs(),\n name=self._opr.out_tensors[0].name,\n **self._get_attrs(),\n )\n ]\n return nodes, self._net_sources, self._parameters",
"title": ""
},
{
"docid": "699da6f85423eb542c7f51ac14a60445",
"score": "0.4970261",
"text": "def pack(parameter_values: Iterable[np.ndarray]) -> np.ndarray:\n\n return np.concatenate(tuple(param_value.ravel() for param_value in parameter_values))",
"title": ""
},
{
"docid": "6ea7bd5fd9d05be164ccdf26c9480909",
"score": "0.4964442",
"text": "def encode(self, inputs):\n return np.array(\n [self._char_indices[char] for char in inputs] + [self.eos_id])",
"title": ""
},
{
"docid": "666a2033a51faaa40508d2885eeabbd1",
"score": "0.49506542",
"text": "def map(self) -> Tuple:\n sensor_map = []\n for thing in self.things:\n sensor_map.append((thing.position.to_tuple(), \"Dirt\" if isinstance(thing, Dirt) else \"Jewel\"))\n if self.agent:\n return tuple([self.agent.position.to_tuple()] + sensor_map)\n return tuple(sensor_map)",
"title": ""
},
{
"docid": "a154ade0d3b1f34253c1070ae700eab7",
"score": "0.4932052",
"text": "def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:\n z = self.encode(x)\n recon_x = self.decode(z)\n return z, recon_x",
"title": ""
},
{
"docid": "328e4ace99a9964693b38fd0ae1e49c5",
"score": "0.49036682",
"text": "def recouche(self):\n self.plateau_couche = tuple(zip(*self.plateau))",
"title": ""
},
{
"docid": "8ea33fa27ea784ec4751badad5be76c0",
"score": "0.48907453",
"text": "def _tuplify(self):\n return tuple(v for v in self._w)",
"title": ""
},
{
"docid": "b57b80fa78feba5ea912599e68561259",
"score": "0.48851186",
"text": "def encode_output_code_seq(vocab, batch, cuda, volatile=False):\n codes = prepare_code_sequence(batch)\n return lists_padding_to_tensor(codes, vocab.stoi, cuda, volatile)",
"title": ""
},
{
"docid": "29d5c70e92eb7dae66b7bda644e1111c",
"score": "0.48785698",
"text": "def encode_question(self, question: str):\n return self.question_encoder(input=tf.constant(np.asarray([question])))[\"outputs\"][0].numpy().tolist()",
"title": ""
},
{
"docid": "2fab3079118f996276d84124f296949d",
"score": "0.48631483",
"text": "def encode(self, input: Tensor) -> List[Tensor]:\n result = self.encoder(input)\n result = torch.flatten(result, start_dim=1)\n\n # Split the result into mu and var components\n # of the latent Gaussian distribution\n mu = self.fc_mu(result)\n log_var = self.fc_var(result)\n\n return [mu, log_var]",
"title": ""
},
{
"docid": "50ce33448ae9796e2514e36dff19b076",
"score": "0.48628634",
"text": "def pack_inputs(inputs):\n inputs = tf.nest.flatten(inputs)\n outputs = []\n for x in inputs:\n if x is None:\n outputs.append(tf.constant(0, shape=[], dtype=tf.int32))\n else:\n outputs.append(x)\n return tuple(outputs)",
"title": ""
},
{
"docid": "17dcc01b1045ffb378af2bc37368a1b9",
"score": "0.48481578",
"text": "def ptseriestoarray(ser):\n return np.stack([x.coords for x in ser], axis=-1).squeeze()",
"title": ""
},
{
"docid": "4287637ab3724ae80134b651c96fee84",
"score": "0.483832",
"text": "def puff_array(self):\n return np.array([tuple(puff) for puff in self.puffs])",
"title": ""
},
{
"docid": "f9d057bf565b8fa988045a27bb953f2c",
"score": "0.4824168",
"text": "def encode_input(X: T1) -> TensorType:\n return X",
"title": ""
},
{
"docid": "263de7cb47086677a6aa338a10c410a4",
"score": "0.48210853",
"text": "def tensors(self):\n ts = []\n for op in self.nodes:\n ts += op.outputs\n return ts",
"title": ""
},
{
"docid": "3adcc85595ce28a1883f2e125189700d",
"score": "0.48166436",
"text": "def __call__(self, X) -> tf.Tensor:\n return tf.stack(tuple(s(X) for s in self._singles), axis=1)",
"title": ""
},
{
"docid": "eedc590ef4b571896d36d963ba28153a",
"score": "0.48156798",
"text": "def encode(self, X: Tensor) -> List[Tensor]:\n Z = self.encoder(X)\n Z = torch.flatten(Z, start_dim=1)\n\n # Split the result into mu and var components\n # of the latent Gaussian distribution\n mu = self.fc_mu(Z)\n log_var = self.fc_var(Z)\n\n return [mu, log_var]",
"title": ""
},
{
"docid": "b51034bd542fb38ff38ca61f5377e69e",
"score": "0.48147786",
"text": "def stimulus_encoding(self):\n\t\tself.latent = tf.nn.relu(self.stim_pl @ self.var_dict['W_enc'] + self.var_dict['b_enc'])\n\t\tself.stim_hat = self.latent @ self.var_dict['W_dec']\n\n\t\t\"\"\"\n\t\tProject the encoded stimulus into the Hopfield network, retrieve predicted action/values\n\t\t\"\"\"\n\t\tx_mapped = self.latent @ self.W_stim_read\n\t\tx_read = self.read_hopfield(x_mapped)\n\t\tx_read = tf.reshape(x_read, [par['batch_size'], (len(par['rewards']) + 1)*par['n_pol'], \\\n\t\t\tpar['hopf_multiplier']])\n\t\tself.x_read = tf.reduce_sum(x_read, axis = 2)\n\t\t#print(x_read)\n\t\t#print(self.latent)\n\t\tself.encoding_out = tf.concat([self.x_read, self.latent], axis = 1) # main output",
"title": ""
},
{
"docid": "48607cd9e046d60e91a5bab34f7a2921",
"score": "0.48023015",
"text": "def _to_tensor(self, *args, device: str = \"cpu\") -> List[torch.Tensor]:\n return [self._convert(input_data=arg, device=device) for arg in args]",
"title": ""
},
{
"docid": "5061823e8acca92d0201ae3c23a52e40",
"score": "0.47937217",
"text": "def pack(tensors: List[torch.Tensor]) -> Tuple[torch.Tensor, List[torch.Size]]:\n buffer = torch.cat([t.view(-1) for t in tensors]) # copies\n shapes = [tensor.shape for tensor in tensors]\n return buffer, shapes",
"title": ""
},
{
"docid": "0da1eb815ca3a31fdb4e91efed48abbd",
"score": "0.47924724",
"text": "def encode(self, input: Tensor) -> List[Tensor]:\n result = self.encoder(input)\n result = torch.flatten(result, start_dim=1)\n\n # Split the result into mu and var components\n # of the latent Gaussian distribution\n z = self.fc_z(result)\n z = z.view(-1, self.latent_dim, self.categorical_dim)\n return [z]",
"title": ""
},
{
"docid": "b3b18c19fc5a2717e4da4b0654583346",
"score": "0.47863746",
"text": "def encode_input_code_seq(vocab, batch, cuda, volatile=False):\n codes = prepare_code_sequence(batch)\n return lists_to_packed_sequence(codes, vocab.stoi, cuda, volatile)",
"title": ""
},
{
"docid": "a75778e935a2ebc0fb0ce409e6d67a58",
"score": "0.47824377",
"text": "def encode(self, inputs):\r\n \r\n \r\n \r\n with tf.variable_scope('encoder', reuse=tf.AUTO_REUSE):\r\n encoder_rnn_input = tf.layers.conv2d(\r\n inputs=inputs, filters=ENCODER_INPUT_DIM, kernel_size=[3, 3], strides=(2, 2), padding='same', name='encoder_rnn_input')\r\n \r\n \r\n \r\n self.hiddens1 = rnn_conv('encoder_rnn_conv_1',\r\n encoder_rnn_input, self.hiddens1, ENCODER1_DIM, [3, 3], (2, 2))\r\n \r\n \r\n \r\n self.hiddens2 = rnn_conv('encoder_rnn_conv_2',\r\n self.hiddens1[0], self.hiddens2, ENCODER2_DIM, [3, 3], (2, 2))\r\n self.hiddens3 = rnn_conv('encoder_rnn_conv_3',\r\n self.hiddens2[0], self.hiddens3, ENCODER3_DIM, [3, 3], (2, 2))\r\n code = self.binarizer(self.hiddens3[0])\r\n return code",
"title": ""
},
{
"docid": "7396dca2c8d3377005e0917b9c8b7523",
"score": "0.4777754",
"text": "def encode_features(self, state: StateT) -> Tuple[Tuple[int, ...], ...]:\n return tuple(tuple(np.digitize(state, bins)) for bins in self.bin_groups)",
"title": ""
},
{
"docid": "1af608604db9d5a84b08538e7f1ec8cb",
"score": "0.47769567",
"text": "def translate(self, pts): \r\n for i in range(0,len(pts),2):\r\n pts[i] = pts[i] + self.x\r\n pts[i+1] = pts[i+1] + self.y\r\n return tuple(pts)",
"title": ""
},
{
"docid": "a5cc6247322bb59225e260fa384f5350",
"score": "0.4769639",
"text": "def get_bprop_list_to_tensor(self):\n\n def bprop(x, dtype, out, dout):\n tuple_type = F.typeof(x)\n dout = P.Cast()(dout, tuple_type)\n d_x = seq.TensorToList()(dout)\n return (d_x, zeros_like(dtype))\n\n return bprop",
"title": ""
},
{
"docid": "4ce68253d9ad3e600f41e01c1ab2c8c0",
"score": "0.4757618",
"text": "def encode(self, tokenizer: BertTokenizer) -> Tuple[np.ndarray, ...]:\n inputs = tokenizer.encode_plus(\n self.question, self.answer, add_special_tokens=True, max_length=MAX_SEQUENCE_LENGTH, pad_to_max_length=True\n )\n\n label = np.array(self.label)\n input_ids = np.array(inputs.input_ids)\n attention_mask = np.array(inputs.attention_mask)\n token_type_ids = np.array(inputs.token_type_ids)\n\n return label, input_ids, attention_mask, token_type_ids",
"title": ""
},
{
"docid": "381ef19d2ad0619ae2bec50b08436367",
"score": "0.475716",
"text": "def convert(self):\n nodes = [\n onnx.helper.make_node(\n self.__opr_type__,\n self._get_inputs(),\n self._get_outputs(),\n **self._get_attrs(),\n )\n ]\n return nodes, self._net_sources, self._parameters",
"title": ""
},
{
"docid": "f54cb45e1ce9a33022cc3fff1d4cb575",
"score": "0.4756497",
"text": "def sor_process(pc):\n N = len(pc)\n batch_size = 32\n sor_pc = []\n sor_defense = SORDefense(k=args.sor_k, alpha=args.sor_alpha)\n for i in range(0, N, batch_size):\n input_pc = pc[i:i + batch_size] # [B, K, 3]\n input_pc = torch.from_numpy(input_pc).float().cuda()\n output_pc = sor_defense(input_pc)\n # to np array list\n output_pc = [\n one_pc.detach().cpu().numpy().\n astype(np.float32) for one_pc in output_pc\n ]\n sor_pc.append(output_pc)\n pc = []\n for i in range(len(sor_pc)):\n pc += sor_pc[i] # list of [k, 3]\n assert len(pc[0].shape) == 2 and pc[0].shape[1] == 3\n return pc",
"title": ""
},
{
"docid": "f54cb45e1ce9a33022cc3fff1d4cb575",
"score": "0.4756497",
"text": "def sor_process(pc):\n N = len(pc)\n batch_size = 32\n sor_pc = []\n sor_defense = SORDefense(k=args.sor_k, alpha=args.sor_alpha)\n for i in range(0, N, batch_size):\n input_pc = pc[i:i + batch_size] # [B, K, 3]\n input_pc = torch.from_numpy(input_pc).float().cuda()\n output_pc = sor_defense(input_pc)\n # to np array list\n output_pc = [\n one_pc.detach().cpu().numpy().\n astype(np.float32) for one_pc in output_pc\n ]\n sor_pc.append(output_pc)\n pc = []\n for i in range(len(sor_pc)):\n pc += sor_pc[i] # list of [k, 3]\n assert len(pc[0].shape) == 2 and pc[0].shape[1] == 3\n return pc",
"title": ""
},
{
"docid": "9f72a9a85470d85976cde2f731d48ae9",
"score": "0.4752273",
"text": "def lists2onehottensors(lists, dim, onehot_encoder): \r\n tensor_list = []\r\n \r\n for sample_idx in range(0, len(lists)): # for every sample in the batch\r\n sample = lists[sample_idx]\r\n sample = np.array(sample) # convert to np.array\r\n sample = sample.reshape(len(sample), 1)\r\n encoded_sample = torch.tensor(onehot_encoder.fit_transform(sample)).float().cuda()\r\n tensor_list.append(encoded_sample) # shape(seq_len, dim)\r\n \r\n tensors = torch.stack(tensor_list) # shape(batch_size, seq_len, dim)\r\n # reshape to (seq_len, batch_size, dim)\r\n tensors = tensors.transpose(dim0=0, dim1=1)\r\n \r\n return tensors",
"title": ""
},
{
"docid": "9f72a9a85470d85976cde2f731d48ae9",
"score": "0.4752273",
"text": "def lists2onehottensors(lists, dim, onehot_encoder): \r\n tensor_list = []\r\n \r\n for sample_idx in range(0, len(lists)): # for every sample in the batch\r\n sample = lists[sample_idx]\r\n sample = np.array(sample) # convert to np.array\r\n sample = sample.reshape(len(sample), 1)\r\n encoded_sample = torch.tensor(onehot_encoder.fit_transform(sample)).float().cuda()\r\n tensor_list.append(encoded_sample) # shape(seq_len, dim)\r\n \r\n tensors = torch.stack(tensor_list) # shape(batch_size, seq_len, dim)\r\n # reshape to (seq_len, batch_size, dim)\r\n tensors = tensors.transpose(dim0=0, dim1=1)\r\n \r\n return tensors",
"title": ""
},
{
"docid": "2ae5d8937b854a0fc72e65ea5487e9af",
"score": "0.47499806",
"text": "def tensor_to_string_list(inputs):\n list_outputs = tensor_to_list(inputs)\n return _decode_strings_to_utf8(list_outputs)",
"title": ""
},
{
"docid": "5054962ffcc86926d4a1a2573265bc1c",
"score": "0.47462454",
"text": "def to_components(self):\n return (type(self),) + self._to_components()",
"title": ""
},
{
"docid": "69774f4f32b634a164c18cbcbe3f978b",
"score": "0.47461587",
"text": "def encode(self, obj: Union[str, List]) -> Tensor:\n if isinstance(obj, str):\n return self._encode(obj)\n elif isinstance(obj, list):\n return torch.stack([self._encode(s) for s in obj], dim=0)\n else:\n raise NotImplementedError",
"title": ""
},
{
"docid": "896393c140fef65f73833a4c59431e91",
"score": "0.4743676",
"text": "def tensorToPylist(tensor_list):\n\n\tnp_list = np.around(np.array(tensor_list.tolist()),2)\n\tpy_list = list(np_list)\n\n\treturn py_list",
"title": ""
},
{
"docid": "256596bfe2917c26f5101b1628bfc0f7",
"score": "0.47403726",
"text": "def _to_tensor(self, *args):\n numpy_args = []\n variable_args = []\n tmp = 0.0\n\n for arg in args:\n if not isinstance(arg, (float, list, tuple, np.ndarray, Variable)):\n raise TypeError(\n \"Type of input args must be float, list, tuple, numpy.ndarray or Tensor, but received type {}\".format(\n type(arg)\n )\n )\n\n arg_np = np.array(arg)\n arg_dtype = arg_np.dtype\n if str(arg_dtype) != 'float32':\n if str(arg_dtype) != 'float64':\n # \"assign\" op doesn't support float64. if dtype is float64, float32 variable will be generated\n # and converted to float64 later using \"cast\".\n warnings.warn(\n \"data type of argument only support float32 and float64, your argument will be convert to float32.\"\n )\n arg_np = arg_np.astype('float32')\n # tmp is used to support broadcast, it summarizes shapes of all the args and get the mixed shape.\n tmp = tmp + arg_np\n numpy_args.append(arg_np)\n\n dtype = tmp.dtype\n for arg in numpy_args:\n arg_broadcasted, _ = np.broadcast_arrays(arg, tmp)\n arg_variable = paddle.tensor.create_tensor(dtype=dtype)\n paddle.assign(arg_broadcasted, arg_variable)\n variable_args.append(arg_variable)\n\n return tuple(variable_args)",
"title": ""
},
{
"docid": "1ef9de3fd5fdda69abee3e2d28a98a0c",
"score": "0.4730972",
"text": "def PS(self, X, r):\n\n # Main OP that you can arbitrarily use in you tensorflow code\n Xc = tf.split(X, 3, 3)\n if self.is_train:\n \n # Does concat RGB\n X = tf.concat([self._phase_shift(x, r) for x in Xc], 3) \n else:\n \n # Does concat RGB\n X = tf.concat([self._phase_shift_test(x, r) for x in Xc], 3) \n return X",
"title": ""
},
{
"docid": "c5c38d2378ae7f9993ac4b025ff2be10",
"score": "0.47296348",
"text": "def _autopacking_helper(list_or_tuple, dtype, name):\n if context.executing_eagerly():\n # NOTE: Fast path when all the items are tensors, this doesn't do any type\n # checking.\n if all(isinstance(elem, core.Tensor) for elem in list_or_tuple):\n return gen_array_ops.pack(list_or_tuple, name=name)\n must_pack = False\n converted_elems = []\n with ops.name_scope(name) as scope:\n for i, elem in enumerate(list_or_tuple):\n if isinstance(elem, core.Tensor):\n if dtype is not None and elem.dtype.base_dtype != dtype:\n raise TypeError(f\"Cannot convert a list containing a tensor of dtype \"\n f\"{elem.dtype} to {dtype} (Tensor is: {elem!r})\")\n converted_elems.append(elem)\n must_pack = True\n elif isinstance(elem, (list, tuple)):\n converted_elem = _autopacking_helper(elem, dtype, str(i))\n if isinstance(converted_elem, core.Tensor):\n must_pack = True\n converted_elems.append(converted_elem)\n else:\n converted_elems.append(elem)\n if must_pack:\n elems_as_tensors = []\n for i, elem in enumerate(converted_elems):\n if isinstance(elem, core.Tensor):\n elems_as_tensors.append(elem)\n else:\n # NOTE(mrry): This is inefficient, but it enables us to\n # handle the case where the list arguments are other\n # convertible-to-tensor types, such as numpy arrays.\n elems_as_tensors.append(\n constant_op.constant(elem, dtype=dtype, name=str(i)))\n return gen_array_ops.pack(elems_as_tensors, name=scope)\n else:\n return converted_elems",
"title": ""
},
{
"docid": "3db92087eba26bae8338bff760fc4b96",
"score": "0.47202918",
"text": "def organise_tensor_data(inputs, res):\n if res == []:\n return inputs\n batch, _, step = inputs.shape\n res = np.array(res)\n res = np.array(res)\n res01 = res[:, 0:2, :]\n inputs_np = inputs.detach().numpy()\n s3 = inputs_np[:, 3, :]\n s4 = inputs_np[:, 4, :]\n res2 = s4-10*s3\n res2 = res2.reshape((batch, 1, step))\n res_data = np.concatenate((res01, res2), 1)\n res_data = torch.Tensor(res_data)\n data = torch.cat((inputs, res_data), 1)\n return data",
"title": ""
},
{
"docid": "0825dab58a4bbf9a9ee906955cecdcea",
"score": "0.47159466",
"text": "def constant_to_list(node):\n tensor = node.attribute[0].t\n # 1. check data type\n # 2. get data from raw or data\n # 3. get shape from dim\n if tensor.data_type == onnx.helper.TensorProto.INT32:\n if len(tensor.int32_data) != 0:\n data = list(tensor.int32_data)\n else:\n data = [i[0] for i in struct.iter_unpack('i', tensor.raw_data)]\n elif tensor.data_type == onnx.helper.TensorProto.INT64:\n if len(tensor.int64_data) != 0:\n data = list(tensor.int64_data)\n else:\n data = [i[0] for i in struct.iter_unpack('q', tensor.raw_data)]\n elif tensor.data_type == onnx.helper.TensorProto.FLOAT:\n if len(tensor.float_data) != 0:\n data = list(tensor.float_data)\n else:\n data = [i[0] for i in struct.iter_unpack('f', tensor.raw_data)]\n elif tensor.data_type == onnx.helper.TensorProto.DOUBLE:\n if len(tensor.double_data) != 0:\n data = list(tensor.double_data)\n else:\n data = [i[0] for i in struct.iter_unpack('d', tensor.raw_data)]\n else:\n print(\"Not supported data type {}\".format(tensor.data_type))\n raise RuntimeError\n if len(tensor.dims) == 0:\n shape = len(data)\n else:\n shape = list(tensor.dims)\n return shape, data",
"title": ""
},
{
"docid": "a8eed4179097034d5c4bdf44310d9e81",
"score": "0.47068608",
"text": "def _build_encoder_inputs(self):\n x = Input(shape=self.inputShape_)\n return [x]",
"title": ""
},
{
"docid": "8e770c0b74dd09c0b2fe471351e201a9",
"score": "0.47054562",
"text": "def encode_list(input_set,ancilla):\r\n b=[]\r\n for j in range(len(input_set)):\r\n b.append(np.array([math.cos(input_set[j]*0.5),math.sin(input_set[j]*0.5)]))\r\n for k in range(ancilla):\r\n b.append(np.array([1,0]))\r\n dm=full_tensor(b)\r\n dm=np.reshape(np.kron(np.transpose(dm),dm),(2**(len(input_set)+ancilla),2**(len(input_set)+ancilla)))\r\n return dm",
"title": ""
},
{
"docid": "8d8552c2299fe46afc31cc489154ba86",
"score": "0.47037175",
"text": "def decode_output(y: Iterable[TensorType]) -> T2:\n return y",
"title": ""
},
{
"docid": "e44963f67bb9c9c861b267c32ec4d946",
"score": "0.46982256",
"text": "def _mapped_obs(self):\n return tf.nest.map_structure(lambda *args: np.array(args), *self._obs)",
"title": ""
},
{
"docid": "65e5b530ec7bc2e9f703aa63df649aa8",
"score": "0.46961543",
"text": "def _create_encoder(step: Tensorflow2ModelStep, encoder_inputs: Input) -> (tf.Tensor, List[tf.Tensor]):\n encoder = RNN(cell=_create_stacked_rnn_cells(step), return_sequences=False, return_state=True)\n\n last_encoder_outputs_and_states = encoder(encoder_inputs)\n # last_encoder_outputs shape: (batch_size, hidden_dim)\n # last_encoder_states shape: (layers_stacked_count, batch_size, hidden_dim)\n\n # refer to: https://www.tensorflow.org/api_docs/python/tf/keras/layers/RNN?version=stable#output_shape_2\n #ASK AFONSO (o que o *)\n last_encoder_outputs, *last_encoders_states = last_encoder_outputs_and_states\n return last_encoder_outputs, last_encoders_states",
"title": ""
},
{
"docid": "697eecc00261dbc8414af3b00baeb974",
"score": "0.46899474",
"text": "def publishListfromTouple(location_list):\n ret_list = []\n for i in location_list:\n x, y = i\n ret_list.append(makePoint(x, y, 0))\n\n return ret_list",
"title": ""
},
{
"docid": "87210be443327d27b00c1c9737021337",
"score": "0.468825",
"text": "def encode(self, input_seqs:torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n input_embeddings = self.embedding(input_seqs)\n if self.enc_rnn_type == \"gru\":\n _, hidden = self.encoder_rnn(input_embeddings)\n else:\n _, (hidden, _) = self.encoder_rnn(input_embeddings)\n # flatten RNN output\n hidden = hidden.view(-1, self.enc_hidden_factor * self.hidden_dim)\n # reparametrize (compute posterior distribution params)\n mean = self.hidden2mean(hidden)\n logv = self.hidden2logv(hidden)\n stdev = torch.exp(logv / 2)\n return mean, logv, stdev",
"title": ""
},
{
"docid": "57c38960fc3f44fd99d8e9dc64872cf5",
"score": "0.4684306",
"text": "def translate_shape(shp):\n shp_temp = []\n for i in range(0,len(shp)):\n if type(shp[i]) == tuple:\n shp_temp.append(shp[i][0]*shp[i][1])\n else:\n shp_temp.append(shp[i])\n return shp_temp",
"title": ""
},
{
"docid": "dadb08499d7127b4504602bcdefd4eec",
"score": "0.468288",
"text": "def _encode(self, data): # Look at save/load from numpy\n\n if isinstance(data, np.ndarray):\n return data.tolist()\n\n if isinstance(data, np.int64):\n return int(data)\n\n if isinstance(data, Layer):\n return {attribute: self._encode(getattr(data, attribute)) for attribute in data.__dict__}",
"title": ""
},
{
"docid": "e8220046a3974f06e7b011073d3743d4",
"score": "0.4681328",
"text": "def positional_encoding(positions, d):\n # START CODE HERE\n # initialize a matrix angle_rads of all the angles\n angle_rads = get_angles(np.arange(positions)[:, np.newaxis],\n np.arange(d)[np.newaxis,:],\n d)\n\n # apply sin to even indices in the array; 2i\n angle_rads[:, 0::2] = np.sin(angle_rads[:, 0::2])\n\n # apply cos to odd indices in the array; 2i+1\n angle_rads[:, 1::2] = np.cos(angle_rads[:, 1::2])\n # END CODE HERE\n\n pos_encoding = angle_rads[np.newaxis, ...]\n\n return tf.cast(pos_encoding, dtype=tf.float32)",
"title": ""
},
{
"docid": "ca2cc8ed8727dd19b45549b834ab3ca0",
"score": "0.46725932",
"text": "def forward(self, X: Tensor) -> List[Tensor]:\n mu, log_var = self.encode(X)\n Z = self.reparameterize(mu, log_var)\n return [self.decode(Z), X, mu, log_var]",
"title": ""
},
{
"docid": "4329a82757ffadbee995823116f95e00",
"score": "0.46660677",
"text": "def coords_to_tuples_list(coords):\r\n op = []\r\n for lst in coords:\r\n op.append(tuple(lst))\r\n return op",
"title": ""
},
{
"docid": "77ec77d0fd98df88b85a7fe38a3e17d0",
"score": "0.46652594",
"text": "def outputs(self):\n return tuple(output for output in self._outputs)",
"title": ""
},
{
"docid": "139a2c47e4021cb34d00a1459f8d213c",
"score": "0.4663016",
"text": "def encode(self, inputs, masks, encoder_state_input):\n\n\n\n return",
"title": ""
},
{
"docid": "8b710af6c756b3f2250c5dcc5bfc1482",
"score": "0.46616256",
"text": "def raw_data(self):\n pointer1 = b''.join([pack('<I', _) for _ in self._p1_list])\n pointer2 = b''.join([pack('<II', *_) for _ in self._p2_list])\n return self._data + pointer1 + pointer2 + self._labels",
"title": ""
},
{
"docid": "30e63c7d6fc35ee5a23adeddfdb7ad04",
"score": "0.46610737",
"text": "def tf_encode(self, pt, en):\n tf_pt, tf_en = tf.py_function(\n self.encode, inp=[\n pt, en], Tout=[\n tf.int64, tf.int64])\n tf_pt.set_shape([None])\n tf_en.set_shape([None])\n return tf_pt, tf_en",
"title": ""
},
{
"docid": "0e09756d1426f1729ff02b8c111b3c6c",
"score": "0.46448642",
"text": "def encode(self, state):\n state_vector = torch.zeros(len(self.idx2state))\n state = state.astype(np.int)\n state_st = \"\".join([str(i) for i in state])\n index = self.state2idx[state_st]\n state_vector[index] = 1\n return state_vector, index",
"title": ""
},
{
"docid": "ad757c70b70fb630a888fe9bd67a12c0",
"score": "0.46434593",
"text": "def _encode_tuple(self, values):\n # TODO: refactor this into a utility function and update jobs\n # to always UTF8 encode mapper keys.\n if len(values) > 1:\n return tuple([value.encode('utf8') for value in values])\n else:\n return values[0].encode('utf8')",
"title": ""
},
{
"docid": "dd68f10dafe80085efaae3134936741b",
"score": "0.46430463",
"text": "def tolist(self):\n return np.array(self.raw).reshape(self.shape).tolist()",
"title": ""
},
{
"docid": "92767f853eb94c5008c5e62dea9fbdff",
"score": "0.4626147",
"text": "def morton_decode(self, code: int) -> tuple:\n return tuple([self.compact_func(code >> i) for i in range(self.dim)])",
"title": ""
},
{
"docid": "e16607e245c48207f6d812c7092bc47d",
"score": "0.4623388",
"text": "def peptides_to_network_input(self, peptides):\n encoder = EncodableSequences.create(peptides)\n if self.hyperparameters['use_embedding']:\n encoded = encoder.variable_length_to_fixed_length_categorical(\n max_length=self.hyperparameters['kmer_size'],\n **self.input_encoding_hyperparameter_defaults.subselect(\n self.hyperparameters))\n else:\n encoded = encoder.variable_length_to_fixed_length_one_hot(\n max_length=self.hyperparameters['kmer_size'],\n **self.input_encoding_hyperparameter_defaults.subselect(\n self.hyperparameters))\n assert len(encoded) == len(peptides)\n return encoded",
"title": ""
},
{
"docid": "5da8028d094ff01c68a4727cdbe47adc",
"score": "0.46232504",
"text": "def _pack(self, state) -> np.ndarray:\n inputs = []\n for name in RandomForest.inputs:\n quantity = state[name]\n compute_domain = quantity.view[:]\n inputs.append(self._to_feature_array(compute_domain, quantity.dims))\n return np.concatenate(inputs, axis=1)",
"title": ""
},
{
"docid": "e57f737566fd398536e6b0f9fbbd1f7e",
"score": "0.46153072",
"text": "def preprocess(self):\n # Separate data for joystick\n self.left_joys = []\n self.right_joys = []\n self.data = []\n for joy in self.joys:\n self.left_joys.append(joy[0])\n self.right_joys.append(joy[1])\n # Normalize data with time changes.\n for i in range(1, len(self.times)):\n t = self.times[i] - self.times[i - 1]\n t = round(float(t), 3)\n self.delta_gyro[i] = int(round(float(self.delta_gyro[i] / t), 3))\n self.delta_accelX[i] = round(float(self.delta_accelX[i] / t), 3)\n self.delta_accelY[i] = round(float(self.delta_accelY[i] / t), 3)\n for g, x, y in zip(self.delta_gyro, self.delta_accelX, self.delta_accelY):\n self.data.append([g, x, y])\n return self.joys, self.left_joys, self.right_joys, self.data, self.delta_gyro, self.delta_accelX, self.delta_accelY, self.times",
"title": ""
},
{
"docid": "fa4673918dc92e9661eec084e752753b",
"score": "0.46094987",
"text": "def get_outputs(self):\n sequence = ep.astensors(self._sequence)\n # sequence = self._sequence\n\n def _stack(x):\n if isinstance(x[0], (pd.DataFrame, pd.Series)):\n return pd.concat(x, axis=0)\n elif isinstance(x[0], (xr.Dataset, xr.DataArray)):\n return xr.concat(x, dim=x[0].dims[0])\n else:\n return ep.stack(x, axis=0)\n\n sequence = list(map(_stack, sequence))\n sequence = tree_unflatten(self._treedef, sequence)\n sequence = ep.as_raw_tensors(sequence)\n return sequence",
"title": ""
},
{
"docid": "057936f6cdd8faef1617054c16ba2018",
"score": "0.46090093",
"text": "def read_encoder_values(self):\n enc_msg = JointState()\n enc_msg.header.stamp = rospy.Time.now()\n for motor_name, properties in self.roboclaw_mapping.iteritems():\n enc_msg.name.append(motor_name)\n position = self.read_encoder_position(properties[\"address\"], properties[\"channel\"])\n velocity = self.read_encoder_velocity(properties[\"address\"], properties[\"channel\"])\n current = self.read_encoder_current(properties[\"address\"], properties[\"channel\"])\n enc_msg.position.append(self.tick2position(position,\n self.encoder_limits[motor_name][0],\n self.encoder_limits[motor_name][1],\n properties['ticks_per_rev'],\n properties['gear_ratio']))\n enc_msg.velocity.append(self.qpps2velocity(velocity,\n properties['ticks_per_rev'],\n properties['gear_ratio']))\n enc_msg.effort.append(current)\n\n self.current_enc_vals = enc_msg",
"title": ""
},
{
"docid": "751bff9d8bcf223aba09a4009d0bf5fb",
"score": "0.46087733",
"text": "def _inputs_to_list(self, inputs: InputsType) -> list:\n if not isinstance(inputs, (list, tuple)):\n inputs = [inputs]\n\n return list(inputs)",
"title": ""
},
{
"docid": "c1e1674f5f9c83d502e8726fdac9435a",
"score": "0.46001142",
"text": "def reconstruction(self):\n output = utt.combine_tensor_list(self._buffer, shape=self._shape,\n strides=self._strides,\n margin0=self._margin0,\n margin1=self._margin1,\n check_all=True)\n self._recon = output\n self._buffer = []\n return output",
"title": ""
},
{
"docid": "6750b799fc123429678998216cc6df85",
"score": "0.45985913",
"text": "def _as_tensor(x: Tuple[int, int]) -> torch.Tensor:\n if torch.jit.is_scripting():\n return torch.as_tensor(x)\n if isinstance(x, (list, tuple)) and all([isinstance(t, torch.Tensor) for t in x]):\n return torch.stack(x)\n return torch.as_tensor(x)",
"title": ""
},
{
"docid": "cec7a937f400688f8f1d90a747c051b3",
"score": "0.45920086",
"text": "def sensors(self):\n return tuple(sensor for sensor in self._sensors)",
"title": ""
},
{
"docid": "f1fcafe73604c5330b680069d8ea2911",
"score": "0.45781422",
"text": "def tensorize(test_data):\n res = []\n for input in test_data:\n res.append(torch.tensor(input).to(self.device))\n return res",
"title": ""
},
{
"docid": "7d653df8e6e048709cfadb3247908817",
"score": "0.45774147",
"text": "def __call__(self, x):\n\n # x[level] should be a list of placeholders\n ylist = []\n\n for level in self.encoders.keys():\n inputs = x[level]\n ylocallist = [self.encoders[level](xin) for xin in inputs]\n ylist += ylocallist\n\n y = tf.concat(ylist, -1)\n\n if not self.trainable_weights:\n self.trainable_weights = self.weights()\n\n return y",
"title": ""
},
{
"docid": "d8bd5e1bfed87c4a7ce08b537aadb0a1",
"score": "0.45742887",
"text": "def encode_output(rvalue, classes):\n i = classes.index(rvalue)\n values = []\n for j in range(len(classes)):\n if j != i:\n values.append(0)\n else:\n values.append(1)\n return values",
"title": ""
},
{
"docid": "92d3135ceaf7f63ac9cdd120cbe8930b",
"score": "0.45713633",
"text": "def preprocess(self, x):\n\n input_transform = transforms.Compose([\n transforms.ToTensor()\n ])\n\n if type(x) == list:\n out_tensor = None\n for elem in x:\n out = input_transform(elem).unsqueeze(0)\n if out_tensor is not None:\n torch.cat((out_tensor, out), 0)\n else:\n out_tensor = out\n else:\n out_tensor = input_transform(x).unsqueeze(0)\n\n return out_tensor",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="720"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">d9c067aea9f5fa263434023f4e0cdca0</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Stub method, no functionality so that calling ``.get_input_entity()`` from ``.resolve()`` doesn't fail.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "caf590479c1c5e4deb68e1ad9f005bca",
"score": "0.54916525",
"text": "def get_input_entity(self, peer):\n return peer",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "c6776fd5951f50f77a98f2827ca3787d",
"score": "0.53908837",
"text": "def _get_input(self):\n return self.__input",
"title": ""
},
{
"docid": "c6776fd5951f50f77a98f2827ca3787d",
"score": "0.53908837",
"text": "def _get_input(self):\n return self.__input",
"title": ""
},
{
"docid": "c6776fd5951f50f77a98f2827ca3787d",
"score": "0.53905433",
"text": "def _get_input(self):\n return self.__input",
"title": ""
},
{
"docid": "c6776fd5951f50f77a98f2827ca3787d",
"score": "0.53905433",
"text": "def _get_input(self):\n return self.__input",
"title": ""
},
{
"docid": "c6776fd5951f50f77a98f2827ca3787d",
"score": "0.53905433",
"text": "def _get_input(self):\n return self.__input",
"title": ""
},
{
"docid": "c6776fd5951f50f77a98f2827ca3787d",
"score": "0.53905433",
"text": "def _get_input(self):\n return self.__input",
"title": ""
},
{
"docid": "c6776fd5951f50f77a98f2827ca3787d",
"score": "0.53905433",
"text": "def _get_input(self):\n return self.__input",
"title": ""
},
{
"docid": "c6776fd5951f50f77a98f2827ca3787d",
"score": "0.53905433",
"text": "def _get_input(self):\n return self.__input",
"title": ""
},
{
"docid": "c6776fd5951f50f77a98f2827ca3787d",
"score": "0.53905433",
"text": "def _get_input(self):\n return self.__input",
"title": ""
},
{
"docid": "c6776fd5951f50f77a98f2827ca3787d",
"score": "0.53905433",
"text": "def _get_input(self):\n return self.__input",
"title": ""
},
{
"docid": "c6776fd5951f50f77a98f2827ca3787d",
"score": "0.53905433",
"text": "def _get_input(self):\n return self.__input",
"title": ""
},
{
"docid": "924c5e3be7d82ea05ab01fdc9e47c6cb",
"score": "0.53091484",
"text": "def post_process(self, input: typing.Any, context: \"IResolveContext\") -> typing.Any:\n ...",
"title": ""
},
{
"docid": "6e6e24e8806b9ebc53cecf7f814e51e0",
"score": "0.5208128",
"text": "async def test_input_text_context(opp, opp_admin_user):\n assert await async_setup_component(\n opp, \"input_text\", {\"input_text\": {\"t1\": {\"initial\": \"bla\"}}}\n )\n\n state = opp.states.get(\"input_text.t1\")\n assert state is not None\n\n await opp.services.async_call(\n \"input_text\",\n \"set_value\",\n {\"entity_id\": state.entity_id, \"value\": \"new_value\"},\n True,\n Context(user_id=opp_admin_user.id),\n )\n\n state2 = opp.states.get(\"input_text.t1\")\n assert state2 is not None\n assert state.state != state2.state\n assert state2.context.user_id == opp_admin_user.id",
"title": ""
},
{
"docid": "fd04840e0eb0a2c5b5540fa8b749f5e9",
"score": "0.517621",
"text": "def mock_input_yes(monkeypatch):\n mock_input = mock.Mock()\n mock_input.return_value = \"y\"\n monkeypatch.setattr(\"builtins.input\", mock_input)",
"title": ""
},
{
"docid": "239b160bf09da820785ca6dc3af59222",
"score": "0.5163373",
"text": "def handleInput(self, spec):\n super().handleInput(spec)",
"title": ""
},
{
"docid": "0a159acfc730a0270bde797f4cb62c78",
"score": "0.5070567",
"text": "def raw_input_mock_enter(prompt):\n return \"\"",
"title": ""
},
{
"docid": "1da5ceaac4f16e69339ea8e0be47697e",
"score": "0.5047981",
"text": "def mock_validate_input():\n info = {\n \"serial_number\": DISCOVERY_INFO.properties[\"SN\"],\n \"title\": DISCOVERY_INFO.properties[\"Product\"],\n }\n\n with patch(\n \"homeassistant.components.devolo_home_network.config_flow.validate_input\",\n return_value=info,\n ):\n yield info",
"title": ""
},
{
"docid": "1f9f2cf1e7e93cad87980c34353b7e14",
"score": "0.50277984",
"text": "def resolve(self, arg) -> None:\n raise NotImplementedError(f\"Unexpected type provided.\")",
"title": ""
},
{
"docid": "230af86a15fcdfaae43b4a0c9bb7a64b",
"score": "0.50132805",
"text": "def test_field_model_indicator_entity_input(\n self,\n input_value: str,\n expected: str,\n optional: bool,\n fail_test: bool,\n playbook_app: Callable[..., MockApp],\n ):\n\n class PytestModelRequired(BaseModel):\n \"\"\"Test Model for Inputs\"\"\"\n\n my_data: IndicatorEntity\n\n class PytestModelOptional(BaseModel):\n \"\"\"Test Model for Inputs\"\"\"\n\n my_data: IndicatorEntity | None\n\n pytest_model = PytestModelOptional\n if optional is False:\n pytest_model = PytestModelRequired\n\n self._type_validation(\n pytest_model,\n input_name='my_data',\n input_value=input_value,\n input_type='TCEntity',\n expected=expected,\n fail_test=fail_test,\n playbook_app=playbook_app,\n )",
"title": ""
},
{
"docid": "5bc341612978467f8e34417df8e3593a",
"score": "0.50025976",
"text": "def mock_input_no(monkeypatch):\n mock_input_n = mock.Mock()\n mock_input_n.return_value = \"n\"\n monkeypatch.setattr(\"builtins.input\", mock_input_n)",
"title": ""
},
{
"docid": "b16b8e70dbc84aec32cc2d424fdeb292",
"score": "0.49997956",
"text": "def test_get_input(self):\n scope = self.get_scope()\n with nn.variable_scope(scope):\n input_0 = nn.Input(shape=[], name='input_a')\n input_1 = nn.get_input('input_a')\n\n self.assertIs(input_0, input_1)\n\n with self.assertRaises(ValueError):\n nn.get_input('input_b')\n\n input_2 = nn.get_input('{}/input_a'.format(scope))\n self.assertIs(input_0, input_2)\n\n with self.assertRaises(ValueError):\n nn.get_input('{}/input_b'.format(scope))",
"title": ""
},
{
"docid": "8b195e15cc4bf994b7d089b83d391407",
"score": "0.4886713",
"text": "def test_field_model_tc_entity_input(\n self,\n input_value: str,\n expected: str,\n optional: bool,\n fail_test: bool,\n playbook_app: Callable[..., MockApp],\n ):\n\n class PytestModelRequired(BaseModel):\n \"\"\"Test Model for Inputs\"\"\"\n\n my_data: TCEntity\n\n class PytestModelOptional(BaseModel):\n \"\"\"Test Model for Inputs\"\"\"\n\n my_data: TCEntity | None\n\n pytest_model = PytestModelOptional\n if optional is False:\n pytest_model = PytestModelRequired\n\n self._type_validation(\n pytest_model,\n input_name='my_data',\n input_value=input_value,\n input_type='TCEntity',\n expected=expected,\n fail_test=fail_test,\n playbook_app=playbook_app,\n )",
"title": ""
},
{
"docid": "baf3a3a451c40890f5f567d4d9f3eaa0",
"score": "0.48554653",
"text": "def test_input_exists(self):\n\t\twith patch(\"builtins.input\", return_value=\"1\") as input_call:\n\t\t\timport attempt\n\t\t\tself.assertEqual(3, input_call.call_count)",
"title": ""
},
{
"docid": "efdc4c07c76fd300a834bf0025b9870e",
"score": "0.48310557",
"text": "def handleInput(self, specs):\n pass",
"title": ""
},
{
"docid": "ee396b2e7f91789e88aac2d22eb3778f",
"score": "0.48290864",
"text": "def resolve_successful(self):\n\t\traise NotImplementedError()",
"title": ""
},
{
"docid": "fe2006918093825fa18611c572a08cc0",
"score": "0.4818933",
"text": "def resolve(self, context: \"IResolveContext\") -> typing.Any:\n ...",
"title": ""
},
{
"docid": "539c943b44e966f1a44396a11e857cdc",
"score": "0.48080158",
"text": "def Try(self, entity):\n raise NotImplementedError",
"title": ""
},
{
"docid": "6ddca7f31e3ead1abf889386c0eda9ed",
"score": "0.4761278",
"text": "def test_input2():\n return Input.from_data(\"\"\"\"\"\")",
"title": ""
},
{
"docid": "fbbb7f124735c0a16120d60f4c576939",
"score": "0.47429407",
"text": "def test_incomplete(self):\n with self.assertRaises(Exception):\n interact(Protocol(), Protocol(), Deferred())",
"title": ""
},
{
"docid": "6d7a5b517f8352881d584ebecf96633c",
"score": "0.4733582",
"text": "def mock_import(self, args, stdin_data=\"\\n\\n\"):\n return mock_import(args, stdin_data, self.repo.path)",
"title": ""
},
{
"docid": "6e03ed20ab169aa9e39a092f29345ace",
"score": "0.47205588",
"text": "def test_choose_emotion():\n image = Image.open(\"src/tests/datavalidation/storm_surprise.jpg\")\n with mock.patch.object(builtins, 'input', lambda _: 'surprise'):\n assert choose_emotion(image) == 'surprise'",
"title": ""
},
{
"docid": "2c33bc67f2206ff0b81c1bf402455c89",
"score": "0.46997407",
"text": "def get_result(self, inp):\n return self.model(inp)",
"title": ""
},
{
"docid": "e019e9bdf2e77b55bdff1785f13c4c0d",
"score": "0.46838814",
"text": "def post_process(self, input: typing.Any, context: \"IResolveContext\") -> typing.Any:\n return jsii.invoke(self, \"postProcess\", [input, context])",
"title": ""
},
{
"docid": "ee9ffc45f153bd693d3d556cb595a749",
"score": "0.46733084",
"text": "def __call__(self, seed_input):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "570094093de5ae4f589317ea5e15ff65",
"score": "0.46620223",
"text": "def mock_prompt_thunk_worker(env, cont):\n\n pycketconfig = env.toplevel_env()._pycketconfig\n from pycket.interpreter import return_value\n\n stdout_port.write(\"> \")\n\n from pycket.racket_entry import get_primitive\n rs = get_primitive(\"read-syntax\")\n obj_name = values.W_Symbol.make(\"readline-input\")\n\n return rs.call([obj_name, stdin_port], env, cont)",
"title": ""
},
{
"docid": "07587abc53f6167e0b6ee80000b02a88",
"score": "0.4656296",
"text": "def test_person_input(self, mock_inputs):\n result = mailroom4.person_input()\n assert result == person_input_tester\n return",
"title": ""
},
{
"docid": "4506fbae31b47e0174e4104a9573da72",
"score": "0.46553066",
"text": "def getInputSpecification(cls):\n spec = super().getInputSpecification()\n return spec",
"title": ""
},
{
"docid": "60f0f85c417b6d16d75ad8b68a2b7f6c",
"score": "0.46507943",
"text": "def mock_import(args, stdin_data=\"\\n\\n\", cwd=None):\n old_cwd = os.path.abspath(os.path.curdir)\n if cwd:\n os.chdir(cwd)\n\n # Create stub file with mock data\n mock_stdin = StringIO()\n mock_stdin.write(stdin_data)\n mock_stdin.seek(0)\n\n # Call import-orig-rpm with mock data\n sys.stdin = mock_stdin\n ret = import_orig_rpm(['arg0'] + args)\n sys.stdin = sys.__stdin__\n mock_stdin.close()\n\n # Return to original working directory\n if cwd:\n os.chdir(old_cwd)\n return ret",
"title": ""
},
{
"docid": "63f9c52d7629c2517cf8953cd43d6c75",
"score": "0.46504498",
"text": "def _resolve_cmd(cmd: Optional[_CommandlineArgumentType]) -> Optional[str]:\n if cmd is None:\n return None\n elif isinstance(cmd, (str, float, int)):\n return str(cmd)\n elif isinstance(cmd, _structures.InputValuePlaceholder):\n return _input_parameter_placeholder(cmd.input_name)\n elif isinstance(cmd, _structures.InputPathPlaceholder):\n return _input_artifact_path_placeholder(cmd.input_name)\n elif isinstance(cmd, _structures.InputUriPlaceholder):\n return _input_artifact_uri_placeholder(cmd.input_name)\n elif isinstance(cmd, _structures.OutputPathPlaceholder):\n if is_output_parameter(cmd.output_name):\n return _output_parameter_path_placeholder(cmd.output_name)\n else:\n return _output_artifact_path_placeholder(cmd.output_name)\n elif isinstance(cmd, _structures.OutputUriPlaceholder):\n return _output_artifact_uri_placeholder(cmd.output_name)\n elif isinstance(cmd, _structures.ExecutorInputPlaceholder):\n return _executor_input_placeholder()\n else:\n raise TypeError('Got unexpected placeholder type for %s' % cmd)",
"title": ""
},
{
"docid": "dc6012a7383bcb2311c0799c94144b73",
"score": "0.46430486",
"text": "def test_shell_cmd_inputspec_typeval_2():\n cmd_exec = \"echo\"\n\n my_input_spec = SpecInfo(\n name=\"Input\",\n fields=[(\"text\", int, {\"position\": 1, \"argstr\": \"\", \"help_string\": \"text\"})],\n bases=(ShellSpec,),\n )\n\n with pytest.raises(TypeError):\n ShellCommandTask(executable=cmd_exec, text=\"hello\", input_spec=my_input_spec)",
"title": ""
},
{
"docid": "592ab03c8f664c3359bbb87df7128578",
"score": "0.4640911",
"text": "def test_shell_cmd_inputspec_typeval_1():\n cmd_exec = \"echo\"\n\n my_input_spec = SpecInfo(\n name=\"Input\",\n fields=[\n (\n \"text\",\n attr.ib(\n type=int,\n metadata={\"position\": 1, \"argstr\": \"\", \"help_string\": \"text\"},\n ),\n )\n ],\n bases=(ShellSpec,),\n )\n\n with pytest.raises(TypeError):\n ShellCommandTask(executable=cmd_exec, text=\"hello\", input_spec=my_input_spec)",
"title": ""
},
{
"docid": "600a88e87551740baa9d0921e5c86a4e",
"score": "0.46215796",
"text": "def test_raw_input(self):\r\n before = \"\"\"\r\n from io import BytesIO\r\n def greet(name):\r\n print \"Hello, {0}!\".format(name)\r\n print \"What's your name?\"\r\n import sys\r\n oldstdin = sys.stdin\r\n\r\n sys.stdin = BytesIO(b'Ed\\\\n')\r\n name = raw_input()\r\n greet(name.decode())\r\n\r\n sys.stdin = oldstdin\r\n assert name == b'Ed'\r\n \"\"\"\r\n desired = \"\"\"\r\n from io import BytesIO\r\n def greet(name):\r\n print(\"Hello, {0}!\".format(name))\r\n print(\"What's your name?\")\r\n import sys\r\n oldstdin = sys.stdin\r\n\r\n sys.stdin = BytesIO(b'Ed\\\\n')\r\n name = input()\r\n greet(name.decode())\r\n\r\n sys.stdin = oldstdin\r\n assert name == b'Ed'\r\n \"\"\"\r\n self.convert_check(before, desired, run=False)\r\n\r\n for interpreter in self.interpreters:\r\n p1 = Popen([interpreter, self.tempdir + 'mytestscript.py'],\r\n stdout=PIPE, stdin=PIPE, stderr=PIPE, env=self.env)\r\n (stdout, stderr) = p1.communicate(b'Ed')\r\n self.assertEqual(stderr, b'')\r\n self.assertEqual(stdout, b\"What's your name?\\nHello, Ed!\\n\")",
"title": ""
},
{
"docid": "202e4fa9596e3dbae813f319a505d526",
"score": "0.4615517",
"text": "def interpret_input(self):\n pass",
"title": ""
},
{
"docid": "8bc137198db2f8cd0e307e3697f29d68",
"score": "0.46137264",
"text": "def test_user_input():\n with unittest.mock.patch('builtins.input', return_value='4'):\n assert get_user_input() == '4'",
"title": ""
},
{
"docid": "71bb36cc280c9f114cc3fa35bcc0f989",
"score": "0.46135813",
"text": "def test_shell_cmd_inputspec_state_1(plugin, results_function, tmp_path):\n cmd_exec = \"echo\"\n hello = [\"HELLO\", \"hi\"]\n my_input_spec = SpecInfo(\n name=\"Input\",\n fields=[\n (\n \"text\",\n attr.ib(\n type=str,\n metadata={\n \"position\": 1,\n \"help_string\": \"text\",\n \"mandatory\": True,\n \"argstr\": \"\",\n },\n ),\n )\n ],\n bases=(ShellSpec,),\n )\n\n # separate command into exec + args\n shelly = ShellCommandTask(\n name=\"shelly\",\n executable=cmd_exec,\n input_spec=my_input_spec,\n cache_dir=tmp_path,\n ).split(\"text\", text=hello)\n assert shelly.inputs.executable == cmd_exec\n # todo: this doesn't work when state\n # assert shelly.cmdline == \"echo HELLO\"\n res = results_function(shelly, plugin)\n assert res[0].output.stdout == \"HELLO\\n\"\n assert res[1].output.stdout == \"hi\\n\"",
"title": ""
},
{
"docid": "5316e082879122cd17740342ccf6cbec",
"score": "0.4611729",
"text": "def resolve ():",
"title": ""
},
{
"docid": "c33d6da4817b01032aecf4a3959567df",
"score": "0.46089825",
"text": "def post_execute(self):\n result = super(Transformer, self).post_execute()\n if result is None:\n self._input = None\n return result",
"title": ""
},
{
"docid": "0b1b7f50eda706bd3510cc3b0efce7bc",
"score": "0.46035737",
"text": "def _resolve_cmd(cmd: Optional[_CommandlineArgumentType]) -> Optional[str]:\n if cmd is None:\n return None\n elif isinstance(cmd, (str, float, int)):\n return str(cmd)\n elif isinstance(cmd, _structures.InputValuePlaceholder):\n return _input_parameter_placeholder(cmd.input_name)\n elif isinstance(cmd, _structures.InputPathPlaceholder):\n return _input_artifact_path_placeholder(cmd.input_name)\n elif isinstance(cmd, _structures.InputUriPlaceholder):\n return _input_artifact_uri_placeholder(cmd.input_name)\n elif isinstance(cmd, _structures.OutputPathPlaceholder):\n return _resolve_output_path_placeholder(cmd.output_name)\n elif isinstance(cmd, _structures.OutputUriPlaceholder):\n return _output_artifact_uri_placeholder(cmd.output_name)\n else:\n raise TypeError('Got unexpected placeholder type for %s' % cmd)",
"title": ""
},
{
"docid": "d9124afda9db2d3861cc170374600e36",
"score": "0.459983",
"text": "def get_input(prompt):\n return input(prompt)",
"title": ""
},
{
"docid": "3a8e90a22c6b5a1de19a3b1110cef137",
"score": "0.45976242",
"text": "def raw_input_mock_n(prompt):\n return \"n\"",
"title": ""
},
{
"docid": "b103113134af73c7e6f2742cdb8ec276",
"score": "0.45958614",
"text": "def get_input(self, name):\n # Loop through each server class for the entity\n for server_class in self.server_classes:\n\n # Does the current server class contain the input?\n if name in server_class.inputs:\n\n # Return the InputFunction instance for the given input name\n return getattr(\n make_object(server_class._inputs, self.pointer), name)\n\n # If no server class contains the input, raise an error\n raise ValueError(\n 'Unknown input \"{0}\" for entity type \"{1}\".'.format(\n name, self.classname))",
"title": ""
},
{
"docid": "49d77ebadea2e856c23db76320733ac0",
"score": "0.45799717",
"text": "def _handleInput(self, paramInput):\n super()._handleInput(paramInput)",
"title": ""
},
{
"docid": "ee5d0133a45712a1fd3ac5d0328c748f",
"score": "0.4571968",
"text": "def test_resolve_request(self):\n\n login = self.client.execute(admin_login_mutation_token)\n CommonTestCases.token_assert_equal(\n self,\n login['data']['loginUser']['token'],\n resolve_request_mutation,\n resolve_request_response\n )",
"title": ""
},
{
"docid": "35ad7e0370e3b258c2d39d5b7a7001fb",
"score": "0.45714217",
"text": "def test_repository_request():\n repository = Mock()\n endpoint = Mock()\n endpoint.post.return_value = make_future({'data': [\n {'a': 'something'},\n {'b': 'other thing'},\n ]})\n data = [\n {'source_id': 1, 'source_id_type': 'a'},\n {'source_id': 2, 'source_id_type': 'b'},\n ]\n\n result = yield repository_request(endpoint, repository, data)\n\n assert result == [\n {'a': 'something', 'repository': repository},\n {'b': 'other thing', 'repository': repository},\n ]\n # check request body was compatible with repo service\n assert endpoint.prepare_request.call_args[1]['body'] == json.dumps([\n {'source_id': 1, 'source_id_type': 'a'},\n {'source_id': 2, 'source_id_type': 'b'},\n ])",
"title": ""
},
{
"docid": "55913ed972fd6c7d9861af2bacd2efce",
"score": "0.4569453",
"text": "def test_resolve_self(self):\n actual = self.container.resolve(Container)\n\n assert actual == self.container",
"title": ""
},
{
"docid": "0db2b626b06a8bba0793b7186702673a",
"score": "0.4566815",
"text": "def get_input(self):\n return self.__input",
"title": ""
},
{
"docid": "dd2406d06febc142b6a294f079a40034",
"score": "0.45609975",
"text": "def resolve(self, x: typing.Any) -> typing.Any:\n ...",
"title": ""
},
{
"docid": "79004cf8d1ad3b7993b2e7683bf516da",
"score": "0.4560916",
"text": "def resolve_system_entity(query, entity_type, span):\n msg = \"resolve_system_entity is deprecated in favor \" \\\n \"of DucklingRecognizer.resolve_system_entity.\"\n warnings.warn(msg)\n return DucklingRecognizer.get_instance().resolve_system_entity(query, entity_type, span)",
"title": ""
},
{
"docid": "1ff7bfcea809d125847e6cc6a616f82f",
"score": "0.45608568",
"text": "def rlinput(prompt, prefill=\"\"):\n readline.set_startup_hook(lambda: readline.insert_text(prefill))\n try:\n return input(prompt)\n finally:\n readline.set_startup_hook()",
"title": ""
},
{
"docid": "2fee4160838055de4921c17d62335585",
"score": "0.45567566",
"text": "def resolve(self, _context: \"IResolveContext\") -> typing.Any:\n return jsii.invoke(self, \"resolve\", [_context])",
"title": ""
},
{
"docid": "54b17b3474c740c16f57f3ebd796043d",
"score": "0.45377222",
"text": "def input_spec(self):\n pass",
"title": ""
},
{
"docid": "cc117bdf97750a835a52897e13713a11",
"score": "0.45357585",
"text": "def test_wrappedCallRemote(self):\n f = FakeReference()\n a = RemoteHub(f)\n d = a.wrappedCallRemote('foo')\n self.assertTrue(isinstance(d, defer.Deferred))\n f.result.callback(None)\n return d",
"title": ""
},
{
"docid": "535ba12eb844f3e9a67a31db476e42da",
"score": "0.4525822",
"text": "async def test_form(hass: HomeAssistant, hosts: str, mock_get_source_ip) -> None:\n\n result = await hass.config_entries.flow.async_init(\n DOMAIN, context={\"source\": config_entries.SOURCE_USER}\n )\n assert result[\"type\"] == \"form\"\n assert result[\"errors\"] == {}\n\n schema_defaults = result[\"data_schema\"]({})\n assert CONF_SCAN_INTERVAL not in schema_defaults\n\n with patch(\n \"homeassistant.components.nmap_tracker.async_setup_entry\",\n return_value=True,\n ) as mock_setup_entry:\n result2 = await hass.config_entries.flow.async_configure(\n result[\"flow_id\"],\n {\n CONF_HOSTS: hosts,\n CONF_HOME_INTERVAL: 3,\n CONF_OPTIONS: DEFAULT_OPTIONS,\n CONF_EXCLUDE: \"4.4.4.4\",\n },\n )\n await hass.async_block_till_done()\n\n assert result2[\"type\"] == \"create_entry\"\n assert result2[\"title\"] == f\"Nmap Tracker {hosts}\"\n assert result2[\"data\"] == {}\n assert result2[\"options\"] == {\n CONF_HOSTS: hosts,\n CONF_HOME_INTERVAL: 3,\n CONF_OPTIONS: DEFAULT_OPTIONS,\n CONF_EXCLUDE: \"4.4.4.4\",\n }\n assert len(mock_setup_entry.mock_calls) == 1",
"title": ""
},
{
"docid": "5c341d3b01622d6007b4e9d7846d885c",
"score": "0.4522516",
"text": "def test_item_info_not_found(self, mock_stdout):\n with patch('builtins.input', return_value='1234'):\n item_info()\n self.assertEqual(mock_stdout.getvalue(), 'Item not found in inventory\\n')",
"title": ""
},
{
"docid": "3a4004ae8a5bd16954debeb1bbce856a",
"score": "0.45185375",
"text": "def input(prompt=\"\"):\n \n if __host__ is widget:\n return None\n \n NSBundle = ObjCClass(\"NSBundle\")\n if NSBundle.mainBundle.bundlePath.pathExtension == \"appex\":\n return None\n \n if __platform__ is iOS:\n if not __isREPLAskingForInput__ and ConsoleViewController.visible != None:\n ConsoleViewController.visible.suggestions = []\n ConsoleViewController.visible.completions = []\n \n __PyInputHelper__.userInput = None\n \n __PyInputHelper__.showAlertWithPrompt(prompt)\n \n while __PyInputHelper__.userInput == None or threading.currentThread() in ignoredThreads:\n continue\n \n userInput = __PyInputHelper__.userInput\n __PyInputHelper__.userInput = None\n \n return str(userInput)",
"title": ""
},
{
"docid": "8f3c7fd08dbf43e716db62f97cf58501",
"score": "0.45126247",
"text": "def resolve(self, match_entity=None):\n\n # PRE-PROCESSING STEP :\n # each person name is parsed using human name parser\n # each time we succeed to associate a human_name to an entity, we will remove it from this list\n human_name_list = [(idx, self.name_preprocessing(person_name))\n for idx, person_name in enumerate(self.person_name_list)]\n\n # some name will contain just a title. For instance 'Sir' alone. It will be detected as a character name\n # by BERT NER but we won't try to associate it with an entity.\n # by default, we will associate such terms with a unique \"NONE\" entity\n remaining_list = []\n empty_entity = Entity(HumanName(\"NONE\"))\n for idx, human_name in human_name_list:\n if human_name.first == \"\" and human_name.last == \"\":\n self.entities_match[idx] = empty_entity\n else:\n remaining_list.append((idx, human_name))\n if human_name.first == \"``\":\n human_name.first = \"\"\n self.entities_match[idx] = human_name\n human_name_list = remaining_list\n\n # STEP 1 :\n # for each human_name that are complets ie: that contains a title, a first name and last name\n # -> for instance: Miss Elizabeth Bennet\n # if there already exists an entity which has this first and last name: associate the human_name to this entity\n # else : create a new entity\n print(\"Co-ref step 1 : associate character name that have title, first name and last name to entity\")\n remaining_list = [] # to store the human name we have not succeed to bind to an entity\n for idx, human_name in tqdm(human_name_list):\n if human_name.title != \"\" and human_name.first != \"\" and human_name.last != \"\":\n try:\n match_entity = [entity for entity in self.entity_set\n if human_name.first == entity.human_name.first and\n human_name.last == entity.human_name.last][0]\n except IndexError:\n match_entity = None\n\n if match_entity is None:\n self.create_entity(idx, human_name)\n else:\n self.entities_match[idx] = match_entity\n else:\n remaining_list.append((idx, human_name))\n human_name_list = remaining_list\n\n # STEP 2 :\n # for each remaining human_names that contain at least first name and last name\n # -> for instance : Elizabeth Bennet\n # if there already exists an entity which has this first and last name: associate the human_name to this entity\n # else : create a new entity\n print(\"Co-ref step 2 : associate character name that have just first name and last name to entity\")\n remaining_list = []\n for idx, human_name in tqdm(human_name_list):\n if human_name.first != \"\" and human_name.last != \"\":\n try:\n match_entity = [entity for entity in self.entity_set\n if human_name.first == entity.human_name.first and\n human_name.last == entity.human_name.last][0]\n except IndexError:\n match_entity = None\n\n if match_entity is None:\n self.create_entity(idx, human_name)\n else:\n self.entities_match[idx] = match_entity\n else:\n remaining_list.append((idx, human_name))\n human_name_list = remaining_list\n\n\n # STEP 3 :\n # for each remaining human_names that contain a title and first name\n # -> for instance : Miss Bennet\n # if there already exists entities which contains this first name and has the same genre (ie: Elizabeth Bennet)\n # associate the human_name to the most common entity among those entities\n # else : create a new entity\n print(\"Co-ref step 3 : associate character name that have just title and first name to entity\")\n remaining_list = []\n for idx, human_name in tqdm(human_name_list):\n if human_name.title != \"\" and human_name.first != \"\":\n possible_entities = []\n for entity in self.entity_set:\n if entity.human_name.first == human_name.first:\n if self.genre_of(human_name) == Genre.UKN or entity.genre == Genre.UKN:\n possible_entities.append(entity)\n else:\n if entity.genre == self.genre_of(human_name):\n possible_entities.append(entity)\n\n match_entity = self.most_frequent_entity(possible_entities)\n if match_entity is None:\n self.create_entity(idx, human_name)\n else:\n self.entities_match[idx] = match_entity\n else:\n remaining_list.append((idx, human_name))\n human_name_list = remaining_list\n\n # STEP 4 :\n # for each remaining human_names that contain a title and last name\n # -> for instance : Mrs. Bennet\n # if there already exists entities which contains this last name and has the same genre (ie: Elizabeth Bennet)\n # associate the human_name to the most common entity among those entities\n # else : create a new entity\n print(\"Co-ref step 4 : associate character name that have just title and last name to entity\")\n remaining_list = []\n for idx, human_name in tqdm(human_name_list):\n if human_name.title != \"\" and human_name.last != \"\":\n possible_entities = []\n for entity in self.entity_set:\n if entity.human_name.last == human_name.last:\n if self.genre_of(human_name) == Genre.UKN or entity.genre == Genre.UKN:\n possible_entities.append(entity)\n else:\n if entity.genre == self.genre_of(human_name):\n possible_entities.append(entity)\n match_entity = self.most_frequent_entity(possible_entities)\n\n if match_entity is None:\n self.create_entity(idx, human_name)\n else:\n self.entities_match[idx] = match_entity\n else:\n remaining_list.append((idx, human_name))\n human_name_list = remaining_list\n\n # STEP 5 :\n # At this step, the human_name_list only contain first name\n # Note that this first could also corresponding to last_name, indeed both Duval or Alexandre will be parsed as\n # HumanName(first='Duval') , HumanName(first='Alexandre') by the HumanParser\n #\n # so for each of this human_name we look in the list of entities for the most common entities which contain\n print(\"Co-ref step 5 : associate character name that have just first name or last name to entity\")\n for idx, human_name in tqdm(human_name_list):\n if human_name.first == \"\":\n possible_entities = [entity for entity in self.entity_set\n if entity.human_name.last == human_name.last or\n entity.human_name.first == human_name.last]\n if human_name.last == \"\":\n possible_entities = [entity for entity in self.entity_set\n if entity.human_name.first == human_name.first or\n entity.human_name.last == human_name.first]\n\n match_entity = self.most_frequent_entity(possible_entities)\n if match_entity is None:\n self.create_entity(idx, human_name)\n else:\n self.entities_match[idx] = match_entity\n\n return self.entities_match",
"title": ""
},
{
"docid": "4ed39eb5b800f00f0b3c0345d248942f",
"score": "0.45065755",
"text": "def _resolve_returned_value(self, returned_value):\n return returned_value",
"title": ""
},
{
"docid": "1882acbc063440945c0924b174681a70",
"score": "0.45039085",
"text": "def test_raw_input2(self):\n suitcase.utils.raw_input = self.raw_input_mock_enter\n answer = get_user_input('Is this awesome or what?', [\"y\", \"n\"], \"n\")\n self.assert_equal(answer, 'n')",
"title": ""
},
{
"docid": "0d19b507de9234a29110001e181bed95",
"score": "0.4487778",
"text": "def input_spec(self):",
"title": ""
},
{
"docid": "755a70d5910228805b3d50178624b734",
"score": "0.44843027",
"text": "async def test_form(hass: HomeAssistant) -> None:\n\n result = await hass.config_entries.flow.async_init(\n DOMAIN, context={\"source\": config_entries.SOURCE_USER}\n )\n assert result[\"type\"] == data_entry_flow.FlowResultType.FORM\n assert result[\"errors\"] == {}\n\n with patch(\n \"homeassistant.components.nuki.config_flow.NukiBridge.info\",\n return_value=MOCK_INFO,\n ), patch(\n \"homeassistant.components.nuki.async_setup_entry\",\n return_value=True,\n ) as mock_setup_entry:\n result2 = await hass.config_entries.flow.async_configure(\n result[\"flow_id\"],\n {\n \"host\": \"1.1.1.1\",\n \"port\": 8080,\n \"token\": \"test-token\",\n },\n )\n await hass.async_block_till_done()\n\n assert result2[\"type\"] == data_entry_flow.FlowResultType.CREATE_ENTRY\n assert result2[\"title\"] == \"75BCD15\"\n assert result2[\"data\"] == {\n \"host\": \"1.1.1.1\",\n \"port\": 8080,\n \"token\": \"test-token\",\n }\n assert len(mock_setup_entry.mock_calls) == 1",
"title": ""
},
{
"docid": "0e2ae5115005cee1c78da290dec15640",
"score": "0.4476791",
"text": "def test_raw_input(self):\n suitcase.utils.raw_input = self.raw_input_mock_n\n answer = get_user_input('Is this awesome or what?', [\"y\", \"n\"])\n self.assert_equal(answer, 'n')",
"title": ""
},
{
"docid": "7260b8fee50512fb07bac6504e861bdc",
"score": "0.44749644",
"text": "def do_execute(self):\n conv = self.config[\"setup\"].shallow_copy()\n conv.input = self._input.payload\n result = conv.convert()\n if result is None:\n if conv.output is not None:\n self._output.append(Token(conv.output))\n return None",
"title": ""
},
{
"docid": "ddb678e84d9d95dac813856dd3ab75bd",
"score": "0.44740915",
"text": "def resolve_token(self, t: \"IResolvable\", context: \"IResolveContext\", post_processor: \"IPostProcessor\") -> typing.Any:\n ...",
"title": ""
},
{
"docid": "11400122b773414118c563cab5601baf",
"score": "0.44725645",
"text": "def test_field_model_custom_indicator_entity_input(\n self,\n input_value: str,\n expected: str,\n indicator_types: list[str],\n optional: bool,\n fail_test: bool,\n playbook_app: Callable[..., MockApp],\n ):\n\n class PytestModelRequired(AppPlaybookModel):\n \"\"\"Test Model for Inputs\"\"\"\n\n my_data: indicator_entity(indicator_types=indicator_types) # type: ignore\n\n class PytestModelOptional(AppPlaybookModel):\n \"\"\"Test Model for Inputs\"\"\"\n\n my_data: indicator_entity(indicator_types=indicator_types) | None # type: ignore\n\n pytest_model = PytestModelOptional\n if optional is False:\n pytest_model = PytestModelRequired\n\n self._type_validation(\n pytest_model,\n input_name='my_data',\n input_value=input_value,\n input_type='TCEntity',\n expected=expected,\n fail_test=fail_test,\n playbook_app=playbook_app,\n )",
"title": ""
},
{
"docid": "d544d130e737fc72236da7253ee6f845",
"score": "0.44598043",
"text": "def _handleInput(self, paramInput):\n Model._handleInput(self, paramInput)",
"title": ""
},
{
"docid": "e6f70475ebeadcc3174194e19308204e",
"score": "0.44457087",
"text": "def call_input(self, name, *args, **kwargs):\n self.get_input(name)(*args, **kwargs)",
"title": ""
},
{
"docid": "ab7f574b946bb7203555f9424687e77a",
"score": "0.44213519",
"text": "def test_abstract_handle(self) -> None:\n with pytest.raises(NotImplementedError):\n LookupHandler.handle(None, None) # type: ignore",
"title": ""
},
{
"docid": "a77e8d661d469366035f7d7b9ac95531",
"score": "0.4421009",
"text": "async def test_form_username(hass):\n await setup.async_setup_component(hass, \"persistent_notification\", {})\n result = await hass.config_entries.flow.async_init(\n DOMAIN, context={CONF_SOURCE: SOURCE_USER}, data={FLOW_TYPE: FLOW_NET}\n )\n assert result[\"type\"] == RESULT_TYPE_FORM\n assert result[\"errors\"] == {}\n\n with patch(\n \"homeassistant.components.plugwise.config_flow.Smile\",\n ) as smile_mock, patch(\n \"homeassistant.components.plugwise.async_setup_entry\",\n return_value=True,\n ) as mock_setup_entry:\n smile_mock.return_value.connect.side_effect = AsyncMock(return_value=True)\n smile_mock.return_value.gateway_id = \"abcdefgh12345678\"\n smile_mock.return_value.smile_hostname = TEST_HOST\n smile_mock.return_value.smile_name = \"Adam\"\n\n result2 = await hass.config_entries.flow.async_configure(\n result[\"flow_id\"],\n user_input={\n CONF_HOST: TEST_HOST,\n CONF_PASSWORD: TEST_PASSWORD,\n CONF_USERNAME: TEST_USERNAME2,\n },\n )\n\n await hass.async_block_till_done()\n\n assert result2[\"type\"] == RESULT_TYPE_CREATE_ENTRY\n assert result2[\"data\"] == {\n CONF_HOST: TEST_HOST,\n CONF_PASSWORD: TEST_PASSWORD,\n CONF_PORT: DEFAULT_PORT,\n CONF_USERNAME: TEST_USERNAME2,\n PW_TYPE: API,\n }\n\n assert len(mock_setup_entry.mock_calls) == 1\n\n result3 = await hass.config_entries.flow.async_init(\n DOMAIN,\n context={CONF_SOURCE: SOURCE_ZEROCONF},\n data=TEST_DISCOVERY,\n )\n assert result3[\"type\"] == RESULT_TYPE_FORM\n\n with patch(\n \"homeassistant.components.plugwise.config_flow.Smile\",\n ) as smile_mock, patch(\n \"homeassistant.components.plugwise.async_setup_entry\",\n return_value=True,\n ) as mock_setup_entry:\n smile_mock.return_value.side_effect = AsyncMock(return_value=True)\n smile_mock.return_value.connect.side_effect = AsyncMock(return_value=True)\n smile_mock.return_value.gateway_id = \"abcdefgh12345678\"\n smile_mock.return_value.smile_hostname = TEST_HOST\n smile_mock.return_value.smile_name = \"Adam\"\n\n result4 = await hass.config_entries.flow.async_configure(\n result3[\"flow_id\"],\n user_input={CONF_PASSWORD: TEST_PASSWORD},\n )\n\n await hass.async_block_till_done()\n\n assert result4[\"type\"] == \"abort\"\n assert result4[\"reason\"] == \"already_configured\"",
"title": ""
},
{
"docid": "28fae360493c2e75ace0c4a969962aee",
"score": "0.4418516",
"text": "async def real_resolve(self, request):\n alsoProvides(request, IRequest)\n alsoProvides(request, IDefaultLayer)\n\n request._futures = {}\n\n request.security = IInteraction(request)\n\n method = app_settings['http_methods'][request.method]\n\n language = language_negotiation(request)\n language_object = language(request)\n\n try:\n resource, tail = await self.traverse(request)\n except Exception as _exc:\n request.resource = request.tail = None\n request.exc = _exc\n # XXX should only should traceback if in some sort of dev mode?\n raise HTTPBadRequest(text=json.dumps({\n 'success': False,\n 'exception_message': str(_exc),\n 'exception_type': getattr(type(_exc), '__name__', str(type(_exc))), # noqa\n 'traceback': traceback.format_exc()\n }))\n\n request.resource = resource\n request.tail = tail\n\n if request.resource is None:\n raise HTTPBadRequest(text='Resource not found')\n\n traverse_to = None\n if tail and len(tail) == 1:\n view_name = tail[0]\n elif tail is None or len(tail) == 0:\n view_name = ''\n else:\n view_name = tail[0]\n traverse_to = tail[1:]\n\n await self.apply_authorization(request)\n\n translator = queryMultiAdapter(\n (language_object, resource, request),\n ITranslated)\n if translator is not None:\n resource = translator.translate()\n\n # Add anonymous participation\n if len(request.security.participations) == 0:\n # logger.info(\"Anonymous User\")\n request.security.add(AnonymousParticipation(request))\n\n # Site registry lookup\n try:\n view = queryMultiAdapter(\n (resource, request), method, name=view_name)\n except AttributeError:\n view = None\n\n # Traverse view if its needed\n if traverse_to is not None and view is not None:\n if not ITraversableView.providedBy(view):\n return None\n else:\n try:\n view = view.publishTraverse(traverse_to)\n except Exception as e:\n logger.error(\n \"Exception on view execution\",\n exc_info=e)\n return None\n\n permission = getUtility(IPermission, name='plone.AccessContent')\n\n allowed = IInteraction(request).check_permission(permission.id, resource)\n\n if not allowed:\n # Check if its a CORS call:\n if IOPTIONS != method or not app_settings['cors']:\n # Check if the view has permissions explicit\n if view is None or not view.__allow_access__:\n logger.warn(\"No access content {content} with {auths}\".format(\n content=resource,\n auths=str([x.principal.id\n for x in request.security.participations])))\n raise HTTPUnauthorized()\n\n if view is None and method == IOPTIONS:\n view = DefaultOPTIONS(resource, request)\n\n checker = getCheckerForInstancesOf(view.__class__)\n if checker is not None:\n view = ProxyFactory(view, checker)\n # We want to check for the content negotiation\n\n renderer = content_type_negotiation(request, resource, view)\n renderer_object = renderer(request)\n\n rendered = queryMultiAdapter(\n (renderer_object, view, request), IRendered)\n\n if rendered is not None:\n return MatchInfo(resource, request, view, rendered)\n else:\n return None",
"title": ""
},
{
"docid": "a91cf050435ade9f5e5462dc7a3eb9f1",
"score": "0.44184604",
"text": "def call_create_input_source(\n input: Union[HTTPFileInfo, Path],\n source_param: Optional[SourceParam] = None,\n # source_slot: SourceSlot,\n public_id: Optional[str] = None,\n location_param: Optional[LocationParam] = None,\n file_param: Optional[FileParam] = None,\n data_param: Optional[DataParam] = None,\n format: Optional[str] = None,\n) -> Generator[InputSource, None, None]:\n\n logging.debug(\n \"source_param = %s, location_param = %s, file_param = %s, data_param = %s\",\n source_param,\n location_param,\n file_param,\n data_param,\n )\n\n source: Optional[SourceParamType] = None\n location: Optional[str] = None\n file: Optional[FileParamType] = None\n data: Optional[DataParamType] = None\n\n input_url = None\n if isinstance(input, HTTPFileInfo):\n input_path = input.path\n input_url = input.request_url\n else:\n input_path = input\n\n with ExitStack() as xstack:\n if source_param is not None:\n source = xstack.enter_context(source_param.from_path(input_path))\n if location_param is not None:\n location = xstack.enter_context(\n location_param.from_path(input_path, input_url)\n )\n if file_param is not None:\n file = xstack.enter_context(file_param.from_path(input_path))\n if data_param is not None:\n data = xstack.enter_context(data_param.from_path(input_path))\n\n logging.debug(\n \"source = %s/%r, location = %s/%r, file = %s/..., data = %s/...\",\n type(source),\n source,\n type(location),\n location,\n type(file),\n type(data),\n )\n input_source = create_input_source(\n source=source,\n publicID=public_id,\n location=location,\n file=file,\n data=data,\n format=format,\n )\n yield input_source",
"title": ""
},
{
"docid": "dd4fb77496cefd344990bf6c87cd4699",
"score": "0.44098395",
"text": "def initial_input(self) -> global___Relation:",
"title": ""
},
{
"docid": "ff36706e8f3fe78dae31013de80df8b7",
"score": "0.44064236",
"text": "def handle_request(self, input):\r\n return \"Function not implemented\"",
"title": ""
},
{
"docid": "032edb913b1c4672b83bc207548d9ca7",
"score": "0.440434",
"text": "def testGetPasswordInteractivelyVisible(self):\n input_fn = self.mox.CreateMock(raw_input)\n self.StubSetup()\n input_fn('Password: ').AndReturn('password')\n self.mox.ReplayAll()\n self.assertEqual('password',\n getauth._GetPasswordInteractively(hidden=False,\n input_fn=input_fn))\n self.mox.VerifyAll()",
"title": ""
},
{
"docid": "29deb1a26a55cc86af6a7f8272e2d6bd",
"score": "0.4401555",
"text": "def handle_entityref(self, ref):\r\n data = None\r\n if self.convertHTMLEntities:\r\n try:\r\n data = unichr(name2codepoint[ref])\r\n except KeyError:\r\n pass\r\n\r\n if not data and self.convertXMLEntities:\r\n data = self.XML_ENTITIES_TO_SPECIAL_CHARS.get(ref)\r\n\r\n if not data and self.convertHTMLEntities and \\\r\n not self.XML_ENTITIES_TO_SPECIAL_CHARS.get(ref):\r\n # TODO: We've got a problem here. We're told this is\r\n # an entity reference, but it's not an XML entity\r\n # reference or an HTML entity reference. Nonetheless,\r\n # the logical thing to do is to pass it through as an\r\n # unrecognized entity reference.\r\n #\r\n # Except: when the input is \"&carol;\" this function\r\n # will be called with input \"carol\". When the input is\r\n # \"AT&T\", this function will be called with input\r\n # \"T\". We have no way of knowing whether a semicolon\r\n # was present originally, so we don't know whether\r\n # this is an unknown entity or just a misplaced\r\n # ampersand.\r\n #\r\n # The more common case is a misplaced ampersand, so I\r\n # escape the ampersand and omit the trailing semicolon.\r\n data = \"&%s\" % ref\r\n if not data:\r\n # This case is different from the one above, because we\r\n # haven't already gone through a supposedly comprehensive\r\n # mapping of entities to Unicode characters. We might not\r\n # have gone through any mapping at all. So the chances are\r\n # very high that this is a real entity, and not a\r\n # misplaced ampersand.\r\n data = \"&%s;\" % ref\r\n self.handle_data(data)",
"title": ""
},
{
"docid": "29deb1a26a55cc86af6a7f8272e2d6bd",
"score": "0.4401555",
"text": "def handle_entityref(self, ref):\r\n data = None\r\n if self.convertHTMLEntities:\r\n try:\r\n data = unichr(name2codepoint[ref])\r\n except KeyError:\r\n pass\r\n\r\n if not data and self.convertXMLEntities:\r\n data = self.XML_ENTITIES_TO_SPECIAL_CHARS.get(ref)\r\n\r\n if not data and self.convertHTMLEntities and \\\r\n not self.XML_ENTITIES_TO_SPECIAL_CHARS.get(ref):\r\n # TODO: We've got a problem here. We're told this is\r\n # an entity reference, but it's not an XML entity\r\n # reference or an HTML entity reference. Nonetheless,\r\n # the logical thing to do is to pass it through as an\r\n # unrecognized entity reference.\r\n #\r\n # Except: when the input is \"&carol;\" this function\r\n # will be called with input \"carol\". When the input is\r\n # \"AT&T\", this function will be called with input\r\n # \"T\". We have no way of knowing whether a semicolon\r\n # was present originally, so we don't know whether\r\n # this is an unknown entity or just a misplaced\r\n # ampersand.\r\n #\r\n # The more common case is a misplaced ampersand, so I\r\n # escape the ampersand and omit the trailing semicolon.\r\n data = \"&%s\" % ref\r\n if not data:\r\n # This case is different from the one above, because we\r\n # haven't already gone through a supposedly comprehensive\r\n # mapping of entities to Unicode characters. We might not\r\n # have gone through any mapping at all. So the chances are\r\n # very high that this is a real entity, and not a\r\n # misplaced ampersand.\r\n data = \"&%s;\" % ref\r\n self.handle_data(data)",
"title": ""
},
{
"docid": "a8c0e039ee8e734726cce733b29a59d9",
"score": "0.4399129",
"text": "def resolve(self, context: \"IResolveContext\") -> typing.Any:\n return jsii.invoke(self, \"resolve\", [context])",
"title": ""
},
{
"docid": "fb41fcc828cbdf444d52f61a21105641",
"score": "0.43936762",
"text": "def _setup_input(self, input_component):\n pass",
"title": ""
},
{
"docid": "68a9d020240a410deb5526755731f21a",
"score": "0.43909204",
"text": "async def test_form(hass):\n\n result = await hass.config_entries.flow.async_init(\n glances.DOMAIN, context={\"source\": config_entries.SOURCE_USER}\n )\n assert result[\"type\"] == data_entry_flow.RESULT_TYPE_FORM\n assert result[\"step_id\"] == \"user\"\n\n with patch(\"glances_api.Glances\"), patch.object(\n Glances, \"get_data\", return_value=mock_coro()\n ):\n\n result = await hass.config_entries.flow.async_configure(\n result[\"flow_id\"], user_input=DEMO_USER_INPUT\n )\n\n assert result[\"type\"] == \"create_entry\"\n assert result[\"title\"] == NAME\n assert result[\"data\"] == DEMO_USER_INPUT",
"title": ""
},
{
"docid": "07c9450f126a586e74711553f817b2da",
"score": "0.43895432",
"text": "def test_field_model_tc_entity_union_input(\n self,\n input_value: str,\n expected: str,\n input_type: str,\n optional: bool,\n fail_test: bool,\n playbook_app: Callable[..., MockApp],\n ):\n\n class PytestModelRequired(BaseModel):\n \"\"\"Test Model for Inputs\"\"\"\n\n my_data: TCEntity | list[TCEntity]\n\n _always_array = validator('my_data', allow_reuse=True)(always_array())\n\n class PytestModelOptional(BaseModel):\n \"\"\"Test Model for Inputs\"\"\"\n\n my_data: TCEntity | list[TCEntity] | None\n\n _always_array = validator('my_data', allow_reuse=True)(always_array())\n\n pytest_model = PytestModelOptional\n if optional is False:\n pytest_model = PytestModelRequired\n\n self._type_validation(\n pytest_model,\n input_name='my_data',\n input_value=input_value,\n input_type=input_type,\n expected=expected,\n fail_test=fail_test,\n playbook_app=playbook_app,\n )",
"title": ""
},
{
"docid": "5fb2049a7b1c044d1e7d5a146936346d",
"score": "0.43864307",
"text": "def request_input(self):\n error = None\n if self.socket is not None:\n try:\n self._send_message('SEND ' + str(GETINPUT))\n except Exception as exc:\n error = exc\n finally:\n self.socket = None\n if error is not None:\n raise error",
"title": ""
},
{
"docid": "c47f32ae38f8743c932e2fd4d67e719d",
"score": "0.4373041",
"text": "def test_shell_cmd_inputspec_4b(plugin, results_function, tmp_path):\n cmd_exec = \"echo\"\n my_input_spec = SpecInfo(\n name=\"Input\",\n fields=[\n (\n \"text\",\n attr.ib(\n type=str,\n default=\"Hi\",\n metadata={\"position\": 1, \"help_string\": \"text\", \"argstr\": \"\"},\n ),\n )\n ],\n bases=(ShellSpec,),\n )\n\n # separate command into exec + args\n shelly = ShellCommandTask(\n name=\"shelly\", executable=cmd_exec, input_spec=my_input_spec, cache_dir=tmp_path\n )\n\n assert shelly.inputs.executable == cmd_exec\n assert shelly.cmdline == \"echo Hi\"\n\n res = results_function(shelly, plugin)\n assert res.output.stdout == \"Hi\\n\"",
"title": ""
},
{
"docid": "6b570318c3fddc668825350d0da8b9ec",
"score": "0.43665478",
"text": "def get_input(self, decoder_fun, opts = \"\"):\n result = None\n while result == None:\n raw = \"\"\n if opts == \"\":\n raw = self.raw_input(\"input: \")\n else:\n raw = self.raw_input(\"input [%s]: \" % opts)\n try:\n result = decoder_fun(raw)\n except:\n self.narrate(\"I don't quite catch your meaning?\")\n result = None\n return result",
"title": ""
},
{
"docid": "86ddb0f7e624dc70ff794e6b29569208",
"score": "0.43628573",
"text": "def input_entity_type(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"input_entity_type\")",
"title": ""
},
{
"docid": "2e54e57f08e6e82c34458157066bf32a",
"score": "0.43583274",
"text": "def test_shell_cmd_inputspec_state_1a(plugin, results_function, tmp_path):\n cmd_exec = \"echo\"\n my_input_spec = SpecInfo(\n name=\"Input\",\n fields=[\n (\n \"text\",\n str,\n {\"position\": 1, \"help_string\": \"text\", \"mandatory\": True, \"argstr\": \"\"},\n )\n ],\n bases=(ShellSpec,),\n )\n\n # separate command into exec + args\n shelly = ShellCommandTask(\n name=\"shelly\",\n executable=cmd_exec,\n input_spec=my_input_spec,\n cache_dir=tmp_path,\n ).split(text=[\"HELLO\", \"hi\"])\n assert shelly.inputs.executable == cmd_exec\n\n res = results_function(shelly, plugin)\n assert res[0].output.stdout == \"HELLO\\n\"\n assert res[1].output.stdout == \"hi\\n\"",
"title": ""
},
{
"docid": "d89acb2a2fea27d4d9f79cf946827d06",
"score": "0.43506303",
"text": "async def test_move_to_well_implementation(mock_handlers: AsyncMock) -> None:\n mock_handlers.movement.move_to_well.return_value = None\n\n request = MoveToWellRequest(\n pipetteId=\"abc\",\n labwareId=\"123\",\n wellName=\"A3\",\n )\n\n impl = request.get_implementation()\n result = await impl.execute(mock_handlers)\n\n assert result == MoveToWellResult()\n mock_handlers.movement.move_to_well.assert_called_with(\n pipette_id=\"abc\",\n labware_id=\"123\",\n well_name=\"A3\",\n )",
"title": ""
},
{
"docid": "8cef2b95153bf1f9c99a01f14f33bfd0",
"score": "0.43499893",
"text": "def test_shell_cmd_inputspec_4(plugin, results_function, tmp_path):\n cmd_exec = \"echo\"\n my_input_spec = SpecInfo(\n name=\"Input\",\n fields=[\n (\n \"text\",\n attr.ib(\n type=str,\n default=\"Hello\",\n metadata={\"position\": 1, \"help_string\": \"text\", \"argstr\": \"\"},\n ),\n )\n ],\n bases=(ShellSpec,),\n )\n\n # separate command into exec + args\n shelly = ShellCommandTask(\n name=\"shelly\", executable=cmd_exec, input_spec=my_input_spec, cache_dir=tmp_path\n )\n\n assert shelly.inputs.executable == cmd_exec\n assert shelly.cmdline == \"echo Hello\"\n\n res = results_function(shelly, plugin)\n assert res.output.stdout == \"Hello\\n\"",
"title": ""
},
{
"docid": "a79a4c35446125a799bfcde4878cf1e6",
"score": "0.4348709",
"text": "def read_input(self):\n raise ImplementationError(\n \"read_input() needs to be implemented by each subclass\"\n )",
"title": ""
},
{
"docid": "0e7ef7850c83d5b4174948db6f7441ba",
"score": "0.4347266",
"text": "def resolve( self, resolver ):\n raise NotImplementedError",
"title": ""
},
{
"docid": "b7fd69de3a37ad1a2941845f1faac017",
"score": "0.4342854",
"text": "def input_fn():\n schema = my_metadata.read_schema(schema_file)\n raw_feature_spec = my_metadata.get_raw_feature_spec(schema)\n # Remove label since it is not available during serving.\n raw_feature_spec.pop(my_metadata.LABEL_KEY)\n\n raw_input_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(\n raw_feature_spec, default_batch_size=None)\n serving_input_receiver = raw_input_fn()\n\n transformed_features = tft_output.transform_raw_features(serving_input_receiver.features)\n\n return tf.estimator.export.ServingInputReceiver(\n transformed_features, serving_input_receiver.receiver_tensors)",
"title": ""
},
{
"docid": "8101cdefa02552858fb6306eb624fb44",
"score": "0.43376032",
"text": "def request_entity(self):\n return self._request_entity",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="721"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">d25728fa22bded19bc0c2391aeb198a2</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">If data is identical but at a different level in the tree, strip_template() will not find it.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "20e0b7836f784a80bbc2864de19aabf5",
"score": "0.47186264",
"text": "def test_different_level(self):\r\n self.assertStrips(\r\n '<div><p>Foo</p><p>Bar</p></div>',\r\n '<p>Foo</p><p>Bar</p>',\r\n '<div><p>Foo</p><p>Bar</p></div>',\r\n 0,\r\n )",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "0db6bc4ac2ff1dfc3d0d454d25159543",
"score": "0.6451072",
"text": "def strip_template(tree1, tree2, check_ids=True, debug=False):\r\n # TODO:\r\n # Solve the sidebar problem -- delete them\r\n\r\n # Assemble a list of trees to compare. Obviously, first we just compare the\r\n # given trees -- but if check_ids is True, then we also compare the\r\n # subtrees containing \"id\" attributes.\r\n tree_pairs = [(tree1, tree2)]\r\n if check_ids:\r\n ids2 = dict([(el.get('id'), el) for el in elements_with_ids(tree2)])\r\n other_pairs = [(el.getparent(), ids2[el.get('id')].getparent()) for el in elements_with_ids(tree1) if el.get('id') in ids2]\r\n tree_pairs.extend(other_pairs)\r\n\r\n # Run the algorithm multiple times until no similarities remain. This is\r\n # sort of inelegant, but it works.\r\n num_removed = 0\r\n for tree1, tree2 in tree_pairs:\r\n if debug:\r\n print 'NEW TREE PAIR:\\n %r\\n %r' % (tree1, tree2)\r\n while 1:\r\n if debug:\r\n print 'New round'\r\n if tree1 is None and tree2 is None:\r\n break\r\n result = identical_elements(list(tree1), list(tree2), debug)\r\n if debug:\r\n print \"strip_template() result:\\n%r\" % result\r\n if not result:\r\n break\r\n for drops, tail_removals in result:\r\n for removal in tail_removals:\r\n removal.tail = ''\r\n for drop in drops:\r\n drop.drop_tree()\r\n num_removed += len(result)\r\n return num_removed",
"title": ""
},
{
"docid": "0db6bc4ac2ff1dfc3d0d454d25159543",
"score": "0.6451072",
"text": "def strip_template(tree1, tree2, check_ids=True, debug=False):\r\n # TODO:\r\n # Solve the sidebar problem -- delete them\r\n\r\n # Assemble a list of trees to compare. Obviously, first we just compare the\r\n # given trees -- but if check_ids is True, then we also compare the\r\n # subtrees containing \"id\" attributes.\r\n tree_pairs = [(tree1, tree2)]\r\n if check_ids:\r\n ids2 = dict([(el.get('id'), el) for el in elements_with_ids(tree2)])\r\n other_pairs = [(el.getparent(), ids2[el.get('id')].getparent()) for el in elements_with_ids(tree1) if el.get('id') in ids2]\r\n tree_pairs.extend(other_pairs)\r\n\r\n # Run the algorithm multiple times until no similarities remain. This is\r\n # sort of inelegant, but it works.\r\n num_removed = 0\r\n for tree1, tree2 in tree_pairs:\r\n if debug:\r\n print 'NEW TREE PAIR:\\n %r\\n %r' % (tree1, tree2)\r\n while 1:\r\n if debug:\r\n print 'New round'\r\n if tree1 is None and tree2 is None:\r\n break\r\n result = identical_elements(list(tree1), list(tree2), debug)\r\n if debug:\r\n print \"strip_template() result:\\n%r\" % result\r\n if not result:\r\n break\r\n for drops, tail_removals in result:\r\n for removal in tail_removals:\r\n removal.tail = ''\r\n for drop in drops:\r\n drop.drop_tree()\r\n num_removed += len(result)\r\n return num_removed",
"title": ""
},
{
"docid": "6c912f79a5cf4bc4b1c3ec1fc7afc091",
"score": "0.5949641",
"text": "def clean_page(html, other_page):\r\n tree1 = make_tree_and_preprocess(html)\r\n tree2 = make_tree_and_preprocess(other_page)\r\n strip_template(tree1, tree2)\r\n # drop_useless_tags(tree1)\r\n # remove_empty_tags(tree1, ('div', 'span', 'td', 'tr', 'table'))\r\n return etree.tostring(tree1, method='html'), etree.tostring(tree2, method='html')",
"title": ""
},
{
"docid": "6c912f79a5cf4bc4b1c3ec1fc7afc091",
"score": "0.5949641",
"text": "def clean_page(html, other_page):\r\n tree1 = make_tree_and_preprocess(html)\r\n tree2 = make_tree_and_preprocess(other_page)\r\n strip_template(tree1, tree2)\r\n # drop_useless_tags(tree1)\r\n # remove_empty_tags(tree1, ('div', 'span', 'td', 'tr', 'table'))\r\n return etree.tostring(tree1, method='html'), etree.tostring(tree2, method='html')",
"title": ""
},
{
"docid": "0c1e32bc0b70df9cfe1c575fd105b8da",
"score": "0.5686683",
"text": "def testTemplateWithNoTreelog(self, mock):\n xml = BEAST2XML(template='filename')\n error = (\n r'^Could not find '\n r'\"\\./run/logger\\[@id=\\'treelog\\.t:alignment\\'\\]\" '\n r'tag in XML template$')\n assertRaisesRegex(self, ValueError, error, xml.toString)",
"title": ""
},
{
"docid": "4b3bdcdbe6c25a7791c0038b69177039",
"score": "0.5564394",
"text": "def testTemplateWithNoData(self, mock):\n xml = BEAST2XML(template='filename')\n error = \"^Could not find 'data' tag in XML template$\"\n assertRaisesRegex(self, ValueError, error, xml.toString)",
"title": ""
},
{
"docid": "936e3c005c2a8f38104e480a2f6f6c29",
"score": "0.5550495",
"text": "def filter_template(self):\n\n filtered_lines = []\n for line in self.template_lines:\n # Find loadpdb line, replace pdb file if necessary, set unit name\n if re.search(\"loadpdb\", line):\n words = line.rstrip().replace(\"=\", \" \").split()\n if self.loadpdb_file is None:\n self.loadpdb_file = words[2]\n self.unit = words[0]\n filtered_lines.append(\n \"{} = loadpdb {}\".format(self.unit, self.loadpdb_file)\n )\n # Remove any included solvation and ionization commands if pbc_type\n # is not None\n elif self.pbc_type is not None:\n if not re.search(\n r\"^\\s*(addions|addions2|addionsrand|desc|quit|solvate|save)\",\n line,\n re.IGNORECASE,\n ):\n filtered_lines.append(line)\n else:\n filtered_lines.append(line)\n\n self.template_lines = filtered_lines",
"title": ""
},
{
"docid": "3e2d2a42d5a2282e34bd34dd72114185",
"score": "0.53874993",
"text": "def render_tree(self, data):\n # TODO: find a way to make this localization aware...\n # because ATM it formats texts using French style numbers...\n # best way would be to let the user inject its own vars...\n # but this would not work on fusion servers...\n # so we must find a way to localize this a bit... or remove it and\n # consider our caller must pre - render its variables to the desired\n # locale...?\n new_data = dict(\n decimal=decimal,\n format_float=(\n lambda val: (\n isinstance(\n val, decimal.Decimal\n ) or isinstance(\n val, float\n )\n ) and str(val).replace('.', ',') or val\n ),\n format_percentage=(\n lambda val: (\"%0.2f %%\" % val).replace('.', ',')\n )\n )\n\n # Soft page breaks are hints for applications for rendering a page\n # break. Soft page breaks in for loops may compromise the paragraph\n # formatting especially the margins. Open-/LibreOffice will regenerate\n # the page breaks when displaying the document. Therefore it is save to\n # remove them.\n self.remove_soft_breaks()\n\n # first we need to transform the py3o template into a valid\n # Genshi template.\n starting_tags, closing_tags = self.handle_instructions(\n self.content_trees,\n self.namespaces\n )\n parents = [tag[0].getparent() for tag in starting_tags]\n linknum = len(parents)\n parentnum = len(set(parents))\n if not linknum == parentnum:\n raise TemplateException(\n \"Every py3o link instruction should be on its own line\"\n )\n\n for link, py3o_base in starting_tags:\n self.handle_link(\n link,\n py3o_base,\n closing_tags[id(link)]\n )\n\n self.__prepare_userfield_decl()\n self.__prepare_usertexts()\n\n self.__replace_image_links()\n self.__add_images_to_manifest()\n\n for fnum, content_tree in enumerate(self.content_trees):\n content = lxml.etree.tostring(content_tree.getroot())\n if self.ignore_undefined_variables:\n template = MarkupTemplate(content, lookup='lenient')\n else:\n template = MarkupTemplate(content)\n\n # then we need to render the genshi template itself by\n # providing the data to genshi\n\n template_dict = {}\n template_dict.update(data.items())\n template_dict.update(new_data.items())\n\n self.output_streams.append(\n (\n self.templated_files[fnum],\n template.generate(**template_dict)\n )\n )",
"title": ""
},
{
"docid": "46e0392d21a63b534d9f41fe9e2e0adf",
"score": "0.52444315",
"text": "def render_stripped(template, context={}):\n ret = render(template, context)\n return ret.replace('\\n', '').replace('\\t', '')",
"title": ""
},
{
"docid": "5478bd2fc32294cefeea7dc0e587e3fb",
"score": "0.521798",
"text": "def _strip_template_attributes(self, xe):\n for x in xe.getiterator():\n leavekeys = [\"name\", \"parse_method\", \"is_array\", \"extension\"]\n keys_to_del = [k for k in x.attrib.iterkeys() if k not in leavekeys]\n for k in keys_to_del:\n try:\n del x.attrib[k]\n except:\n pass",
"title": ""
},
{
"docid": "18ebdd80f29eec0ff9237e0b3a91cc08",
"score": "0.520845",
"text": "def test_format_dict_empty_leaf_template():\n dummy_dict_with_leafs = {1: {2: {}}, 2: {}, 3: {1: 3, 2: {}}}\n lines = format_dict(dummy_dict_with_leafs).split(\"\\n\")\n assert len(lines) == 6\n # 1:\n assert lines[1].lstrip(\" \") == \"2\"\n assert lines[2].lstrip(\" \") == \"2\"\n # 3:\n # 1: 3\n assert lines[5].lstrip(\" \") == \"2\"",
"title": ""
},
{
"docid": "d7e0fed61651f1bc8d42f6c34d7831ed",
"score": "0.5185541",
"text": "def cleanup(self, tree: ast.AST) -> ast.AST:\n new_tree = self.visit(tree)\n ast.fix_missing_locations(new_tree)\n return new_tree",
"title": ""
},
{
"docid": "492208b8d7724a73a6c84392a4fcaadc",
"score": "0.5101476",
"text": "def removeTemplates(self, text):\n\treturn re.sub('{{[\\S\\s]*?}}', ' ', text)",
"title": ""
},
{
"docid": "e830dc212aff2e99005c86ac4db6fd1b",
"score": "0.5090104",
"text": "def _cleanRawData(self):\n # try to apply the template to the rawData. may want to make this its own thing, not on init?\n # check for headers \n if not self._cleaned: \n if self._bank_template.header_rows > 0:\n # check if the first row still exists, drop if it does\n for i in range(self._bank_template.header_rows):\n if i in self._raw_data.index:\n self._raw_data.drop([i], inplace=True) \n\n # for the future - do some validations - number of columns matches template, \n # Map the row headers from the template\n colmap = dict(self._bank_template.templateCols)\n # check if the number of columns are the same. if not, raise an error\n lrc = len(self._raw_data.columns)\n if len(colmap) != lrc:\n raise Exception(f\"\"\"The supplied template has a {len(colmap)} columns whereas the uploaded file has {lrc} columns. Please make sure that the correct template was chosen\"\"\")\n # rename dataframe cols to integers, \n self._raw_data.columns = range(lrc)\n # then apply map (in case wrong template applied before)\n self._raw_data.rename(columns=colmap, inplace=True) \n\n if 'amount' in self._raw_data.columns: \n self._raw_data['amount'] = self._raw_data['amount'].str.replace('--', '', regex=False)\n try:\n self._raw_data['amount'] = pd.to_numeric(self._raw_data['amount']) \n except ValueError():\n print(\"Cannot set the column 'Amount' as numeric, proably the wrong template\")\n raise ValueError()\n\n if 'description1' in colmap.values() and 'description2' in colmap.values():\n self._raw_data['description'] = self._raw_data[['description1', 'description2']].apply(lambda x: ' - '.join(x.dropna()), axis=1)\n \n self._raw_data['description'] = self._raw_data['description'].str.replace('~+.*', '', regex=True)\n self._raw_data['description'] = self._raw_data['description'].str.replace(r'\\s\\s+', ' ', regex=True)\n\n if 'amount_neg' in colmap.values() and 'amount_pos' in colmap.values():\n # amt_cols = self._raw_data[['amount_neg', 'amount_pos']]\n self._raw_data[['amount_neg', 'amount_pos']] = self._raw_data[['amount_neg', 'amount_pos']].replace('[\\$,]', '', regex=True).astype(float)\n self._raw_data['amount_neg'] = self._raw_data['amount_neg'].multiply(-1)\n self._raw_data['amount'] = self._raw_data[['amount_neg', 'amount_pos']].sum(axis=1)\n\n self._raw_data['transaction_date'] = pd.to_datetime(self._raw_data['transaction_date'], format=self._bank_template.date_format)\n if 'post_date' in self._raw_data.columns: \n self._raw_data['post_date'] = pd.to_datetime(self._raw_data['post_date'], format=self._bank_template.date_format)\n q = \"\"\"\n SELECT column_name\n FROM information_schema.columns\n WHERE table_schema = 'finance'\n AND table_name in ('import_transaction_stg')\n AND column_name not in ('stg_id','charge_category_id','account_id','batch_id','charge_type_id','note','charge_tracking_type_id')\n \"\"\"\n query = read_query(q, self.conn)\n valid_cols = [item for t in query for item in t]\n\n for n in self._raw_data.columns:\n if n not in valid_cols:\n # is it right to ignore errors here? it breaks if say, 2 unused cols need to be dropped\n self._raw_data = self._raw_data.drop(columns=n, axis=1, errors='ignore')\n # fixes: the weird -- thing for amounts in usaa export\n # fix the description in the weird USAA import. add in a column to the mapping table, make this work dynamically:\n # look for a sequence starting with one or more ~s (the plus), followed by any characters. replace with ''\n # fix weird space issue? \n \n\n # set datatypes. Could the column assignments be more dynamic? Storing the type in the table?\n\n\n self._cleaned = True\n self._stageRawData()\n else:\n print('Cleaned flag has already been set to True')",
"title": ""
},
{
"docid": "6b1d6d1ef63dcf2aaffa5ddcd582e4c7",
"score": "0.508527",
"text": "def test_detach_template_from_inode(self):\n pass",
"title": ""
},
{
"docid": "96d2ec06d02be20d820d7a2da13ed39b",
"score": "0.5069302",
"text": "def _remove_extra_templates(self):\n\n\t\tapps_location = os.path.join(self.folder_location,\"apps\")\n\t\ttemplates_location = os.path.join(self.folder_location,\"templates\")\n\t\textra_template = lambda app,template: os.path.join(self.folder_location,\"templates\",app,template)\n\t\tapps_pages = self._get_apps_and_pages(apps_location)\n\t\ttemplate_apps = self._get_apps_and_pages(templates_location)\n\n\t\tfor app,pages in template_apps.items():\n\t\t\tfor page in pages:\n\t\t\t\tpage = page.replace(\".html\",\"\")\n\t\t\t\tif page not in apps_pages[app]:\n\t\t\t\t\tos.remove(extra_template(app,page + \".html\"))",
"title": ""
},
{
"docid": "3a077f0fe0b6eee04bff85df56cab1b3",
"score": "0.505272",
"text": "def test_format_dict_empty_leaf_template_custom():\n template = \"{key} is a leaf\\n\"\n dummy_dict_with_leafs = {1: {2: {}}, 2: {}, 3: {1: 3, 2: {}}}\n lines = format_dict(\n dummy_dict_with_leafs, templates={\"empty_leaf\": template}\n ).split(\"\\n\")\n assert len(lines) == 6\n # 1:\n assert lines[1] == \"2 is a leaf\"\n assert lines[2] == \"2 is a leaf\"\n # 3:\n # 1: 3\n assert lines[5] == \"2 is a leaf\"",
"title": ""
},
{
"docid": "eac12e4074e5dc6172ef299688555b47",
"score": "0.5033361",
"text": "def filter_markup(t):\r\n if t.startswith('#REDIRECIONAMENTO') or t.startswith('#REDIRECIONAMENTO') \\\r\n or t.startswith(u'{{desambiguação'):\r\n # it is a redirect page, or a disambiguation one, and we don't want it.\r\n return ''\r\n \r\n # the order of the sub patterns is important!!!\r\n \r\n t = t.replace(' ', ' ')\r\n t = t.replace(u'–', '-')\r\n t = t.replace(u'—', '-')\r\n \r\n t = re.sub('(?s)<!--.*?-->', \"\", t) # force removing comments\r\n t = re.sub(\"(\\n\\[\\[[a-z][a-z][\\w-]*:[^:\\]]+\\]\\])+($|\\n)\",\"\", t) # force remove last (=languages) list\r\n t = re.sub('(?i)\\[\\[:?Categoria:(.*?)\\]\\]', '', t)\r\n \r\n # Removes everything in the sections Ver também, Bibliografia, Links Externos\r\n t = re.sub(u'(?is)\\n(=+)\\s*(?:{{)?Ver também(?:}})?\\s*\\\\1.*?(\\n\\\\1\\s|$)', '\\\\2', t)\r\n t = re.sub(u'(?is)\\n(=+)\\s*(?:{{)?Bibliografia(?:}})?\\s*\\\\1.*?(\\n\\\\1\\s|$)', '\\\\2', t)\r\n t = re.sub(u'(?is)\\n(=+)\\s*(?:{{)?Ligações Externas(?:}})?\\s*\\\\1.*?(\\n\\\\1\\s|$)', '\\\\2', t)\r\n \r\n # Replaces mathematical formulae with __MATH__. It is important to remove these early on\r\n # because they often have curly brackets (using Latex notation), which can mess with the parse\r\n t = re.sub('(?s)<math(\\s[^>]*)?>.*?</math>', '__MATH__', t)\r\n \r\n # some formatting options appear as {{{width|200}}}\r\n t = re.sub(\"{{{[^}{]*}}}\", '', t)\r\n \r\n # Replaces all templates not treated before ( marked between {{ and }} ) with __TEMPLATE__\r\n # The regexp is applied until no more changes are made, so nested templates are taken care of\r\n # (e.g., {{ aaa {{ bbb {{ ccc }} }} }} \r\n check = True\r\n while check:\r\n t, check = re.subn(r'{{[^}{]*}}', '__TEMPLATE__', t)\r\n \r\n # Replaces *what I think that are* templates between {| and |}. Similar to the last block.\r\n check = True\r\n while check:\r\n t, check = re.subn(r'''(?sx)\r\n {\\| # open {|\r\n .*\r\n \\|} # close |}\r\n ''', '__TEMPLATE__', t)\r\n \r\n # Removes some tags and their contents\r\n t = re.sub(r'''(?isux)\r\n <\r\n (small|sup|gallery|noinclude|ol|\r\n includeonly|onlyinclude|timeline|\r\n table|ref|code|source|t[rdh])\r\n \\s*[-$!%@&_#\\w=.,:;\\'\" ]*\r\n >\r\n .*?\r\n </\\1\\s*>\r\n ''', '', t)\r\n \r\n # Treats section titles\r\n t = re.sub(r\"(^|\\n)(=+) *[^\\n]*\\2 *(?=\\n)\", '', t )\r\n \r\n # bold and italics markup\r\n t = t.replace(\"'''\", \"\")\r\n t = t.replace(\"''\", \"\")\r\n \r\n # I'm not sure if this order below could make any problem. It is meant to solve nested links as\r\n # [[Image: blabla [[bla]] bla]]\r\n t = re.sub(\"(?s)\\[\\[([^][|]*?)\\]\\]\", link, t)\r\n t = re.sub(\"(?s)\\[\\[([Aa]nexo:[^]|[:]*)\\|([^][]*)\\]\\]\", link, t)\r\n t = re.sub(\"(?s)\\[\\[[Mm]ultim.dia:(.*?)\\]\\]\", '__FILE__', t)\r\n t = re.sub(\"(?s)\\[\\[(:?[Ii]mage:[^][:]*)\\|([^][]*)\\]\\]\", '', t)\r\n t = re.sub(\"(?s)\\[\\[([^][|]*)\\|([^][|]*)\\]\\]\", link, t)\r\n \r\n # external links\r\n t = re.sub('\\[(?:https?|ftp)://[^][\\s]+?\\s+(.+?)\\]', '\\\\1', t)\r\n t = re.sub('\\[?(?:https?|ftp)://[^][\\s]+\\]?', '__LINK__', t)\r\n \r\n t = re.sub(\"\"\"(?msx)\\[\\[(?:\r\n [Aa]rquivo|\r\n [Ii]magem?|\r\n [Ff]icheiro|\r\n [Ff]ile)\r\n :.*?\\]\\]\"\"\", '', t)\r\n \r\n # Ignore tables\r\n t = re.sub('\\{\\|(?P<head>[^!|}]+)(?P<caption>(\\|\\+.*)?)(?P<body>(.*\\n)+?)\\|\\}', '', t)\r\n\r\n # replace <br> and <hr> for line breaks\r\n t = re.sub(r'</?[hb]r\\s*[-#\\w=.,:;\\'\" ]*/?>', r'\\n', t)\r\n \r\n # removes some tags, but don't touch the text\r\n # we don't check for matching opening and closing tags for two reasons:\r\n # 1) efficiency 2) to cope with badly formed tags\r\n t = re.sub(r'''(?isxu)\r\n </? # opening or closing tag\r\n (blockquote|tt|b|u|s|p|i| # any of these tags\r\n sub|span|big|font|poem|\r\n nowiki|strong|cite|div|\r\n center|ref|references|\r\n em|var|li|\r\n noinclude|gallery) # sometimes, a stray element like <noinclude> remains here\r\n \\s* # white space\r\n [-?$#%@\\w/&=().,:;\\'\" ]* # xml attributes \r\n /?> # close tag bracket\r\n ''', '', t)\r\n \r\n # lists \r\n # a trailing newline is appended to the text to deal with lists as the last item in a page\r\n t += '\\n'\r\n t = re.sub(\"\\n(([#*:;]+[^\\n]+\\n)+)\", '\\n', t)\r\n \r\n t = t.replace('--', '-')\r\n \r\n return t",
"title": ""
},
{
"docid": "d2b87305b2256b16db7c78b61076e2df",
"score": "0.5018003",
"text": "def test_format_dict_node_template():\n dummy_dict_with_leafs = {1: {2: {}}, 2: {}, 3: {1: {4: 3}, 2: {}}}\n lines = format_dict(dummy_dict_with_leafs).split(\"\\n\")\n assert len(lines) == 7\n assert lines[0] == \"1:\"\n # 2\n # 2\n assert lines[3] == \"3:\"\n assert lines[4].lstrip(\" \") == \"1:\"\n # 4: 3\n # 2",
"title": ""
},
{
"docid": "2351ac54bd6cdbb96607ba81b727d244",
"score": "0.49926606",
"text": "def test_mergeDocument_5():\n etree_1 = etree.XML(\"\"\"\n <preference>\n <tag name=\"test1\" value=\"test1_old\"/>\n <tag name=\"test2\" value=\"test2_old\"/>\n <tag name=\"first_level\">\n <tag name=\"test1\" value=\"test1_old\"/>\n <tag name=\"test2\" value=\"test2_old\">\n <template>\n <tag name=\"template_param_1\" value=\"template_config_1\"/>\n <tag name=\"template_param_2\" value=\"template_config_2\"/>\n </template>\n </tag>\n <tag name=\"second_level\">\n <tag name=\"test1\" value=\"test1_old\"/>\n <tag name=\"test2\" value=\"test2_old\"/>\n </tag>\n </tag>\n </preference>\n \"\"\", parser=XMLPARSER)\n \n etree_2 = etree.XML(\"\"\"\n <preference>\n <tag name=\"test2\" value=\"test2_new\"/>\n <tag name=\"first_level\">\n <tag name=\"test1\" value=\"test1_new\">\n <template>\n <tag name=\"template_param_1\" value=\"template_config_1\"/>\n <tag name=\"template_param_2\" value=\"template_config_2\"/>\n </template> \n </tag>\n <tag name=\"test2\" value=\"test2_new\"/>\n <tag name=\"second_level\">\n <tag name=\"test1\" value=\"test1_new\"/>\n </tag>\n </tag>\n </preference>\n \"\"\", parser=XMLPARSER)\n \n answer = etree.XML(\"\"\"\n <preference>\n <tag name=\"test1\" value=\"test1_old\"/>\n <tag name=\"test2\" value=\"test2_new\"/>\n <tag name=\"first_level\">\n <tag name=\"test1\" value=\"test1_new\"/>\n <tag name=\"test2\" value=\"test2_new\">\n <template>\n <tag name=\"template_param_1\" value=\"template_config_1\"/>\n <tag name=\"template_param_2\" value=\"template_config_2\"/>\n </template>\n </tag>\n <tag name=\"second_level\">\n <tag name=\"test1\" value=\"test1_new\"/>\n <tag name=\"test2\" value=\"test2_old\"/>\n </tag>\n </tag>\n </preference>\n \"\"\", parser=XMLPARSER)\n \n result = mergeDocuments(etree_1, etree_2)\n compare_etree(answer, result)",
"title": ""
},
{
"docid": "680128069f67470d07de205db594db04",
"score": "0.4980161",
"text": "def remove_from_page(self):\n # we are not using wikitext.remove(node) because we want to clean up newlines later\n self.current_template: Template\n params = []\n for param in self.current_template.params:\n params.append(param.name.strip())\n for param in params:\n self.current_template.remove(param)\n\n self.current_template.name = '@@@DELETE@@@'\n self.check_deletion_marks = True",
"title": ""
},
{
"docid": "98343c2f8afca9c706c00313f43b7588",
"score": "0.4974894",
"text": "def test_format_dict_leaf_template():\n dummy_dict_with_leafs = {1: {2: {}}, 2: {}, 3: {1: 3, 2: {}}}\n lines = format_dict(dummy_dict_with_leafs).split(\"\\n\")\n assert len(lines) == 6\n # 1:\n # 2\n # 2\n # 3:\n assert lines[4].lstrip(\" \") == \"1: 3\"",
"title": ""
},
{
"docid": "e1f3276227e2550dc2d598281e3e7a30",
"score": "0.495989",
"text": "def strip_tags(root):\n for aff in list(root):\n last_item = None\n\n for item in list(aff):\n\n # Expand children\n for item2 in list(item):\n my_text = getstr(item2)\n item.text = addstr(item.text, my_text)\n for item3 in list(item2):\n raise Exception(\"To deep (level 3)\")\n item.remove(item2)\n\n if item.tag not in keep_tags:\n my_text = None\n if (last_item is None and is_label(item)):\n # We don't need the text content\n my_text = item.tail\n else:\n my_text = getstr(item)\n\n if last_item is not None:\n last_item.tail = addstr(last_item.tail, my_text)\n else:\n aff.text = addstr(aff.text, my_text)\n\n aff.remove(item)\n\n else:\n last_item = item",
"title": ""
},
{
"docid": "bd45915918760f94690fbc3362c56ac2",
"score": "0.4944671",
"text": "def remove_metadata(current_template, new_template):\n if 'Metadata' in current_template.keys()\\\n and TOPLEVEL_METADATA_KEY in current_template['Metadata'].keys():\n del current_template['Metadata'][TOPLEVEL_METADATA_KEY]\n if 'Metadata' in new_template.keys()\\\n and TOPLEVEL_METADATA_KEY in new_template['Metadata'].keys():\n del new_template['Metadata'][TOPLEVEL_METADATA_KEY]\n\n # If the existing template has no Metadata sections, the empty Metadata\n # section after removal of the Jetstream subsection would proc an update\n # due to the difference in Metadata section present versus lack of.\n # To avoid this Metadata section also gets removed if Jetstream subsection\n # was the only part of it.\n if 'Metadata' not in current_template.keys():\n if 'Metadata' in new_template.keys() and \\\n not new_template['Metadata'].keys():\n del new_template['Metadata']",
"title": ""
},
{
"docid": "1ff9567ab8b528759934380402f792bb",
"score": "0.49443173",
"text": "def remove_template(template_id): # pragma:nocover",
"title": ""
},
{
"docid": "b2b9ca7dd89b805a790df25e16872d62",
"score": "0.49340352",
"text": "def prunetree(tree, taxlst):\n \n treetaxa = gettaxa(tree)\n t = string2tree(tree)\n \n for taxon in treetaxa:\n if taxon not in taxlst:\n t.removenode(taxon)\n \n return str(t)",
"title": ""
},
{
"docid": "dd6258b3ddd5a73e6db1e558f42babd0",
"score": "0.49065545",
"text": "def template_fixup(self, tmp_name, tmp_list):\r\n return",
"title": ""
},
{
"docid": "af862d9d33984b004aa5435044a85393",
"score": "0.49060553",
"text": "def stripFacsimileNodes(tree, repository):\n parent = tree.xpath('/record')[0]\n for match in tree.xpath('//datafield[@tag=\"530\"]'):\n sfnode = match.xpath('./subfield[@code=\"a\"]')[0]\n if sfnode.text is None:\n pass\n else:\n sftext = sfnode.text.strip()\n sftext = sftext.lower()\n sftext = sftext.encode('ascii', 'xmlcharrefreplace')\n if 'electronic' in sftext:\n if sftext == aFieldStrings[repository].strip().lower():\n parent.remove(match)\n return tree",
"title": ""
},
{
"docid": "7500649af3fd652532b943e54736587d",
"score": "0.48939326",
"text": "def visit_TemplateNode(self, node):\n resolved_children = {key: self._resolve(value) for key, value in node._children.items()}\n resolved_txt = node._template.safe_substitute(resolved_children)\n indented_text = (\"\\n\" + self._tab()).join(resolved_txt.splitlines())\n return indented_text",
"title": ""
},
{
"docid": "6544a0262cf6a7e23f5aafe2521bbc05",
"score": "0.48872128",
"text": "def interpolate(node, data):\n\tfor subnode in node:\n\t\tif subnode.nodename in data:\n\t\t\tsubnode.text = data[subnode.nodename]\n\t\telse:\n\t\t\tsubnode.omit()",
"title": ""
},
{
"docid": "e702ab8ac1a058f828df49557b701da2",
"score": "0.48722604",
"text": "def test_mergeDocument_4():\n etree_1 = etree.XML(\"\"\"\n <preference>\n <template>\n <tag name=\"name\" value=\"template_name_old\"/>\n <tag name=\"type\" value=\"template_type_old\"/>\n </template>\n <tag name=\"first_level\">\n <template>\n <tag name=\"name\" value=\"template_name\"/>\n <tag name=\"type\" value=\"template_type\"/>\n </template>\n <tag name= \"node1\" value=\"value1_old\"/>\n <tag name= \"node2\" value=\"value2_old\"/>\n </tag>\n </preference>\n \"\"\", parser=XMLPARSER)\n \n etree_2 = etree.XML(\"\"\"\n <preference>\n <template>\n <tag name=\"name\" value=\"template_name_new\"/>\n <tag name=\"type\" value=\"template_type_new\"/>\n </template>\n <tag name=\"first_level\">\n <template>\n <tag name=\"name\" value=\"template_name_new\"/>\n <tag name=\"type\" value=\"template_type_new\"/>\n </template>\n <tag name= \"node1\" value=\"value1_new\"/>\n </tag>\n </preference>\n \"\"\", parser=XMLPARSER)\n \n answer = etree.XML(\"\"\"\n <preference>\n <template>\n <tag name=\"name\" value=\"template_name_old\"/>\n <tag name=\"type\" value=\"template_type_old\"/>\n </template>\n <tag name=\"first_level\">\n <template>\n <tag name=\"name\" value=\"template_name\"/>\n <tag name=\"type\" value=\"template_type\"/>\n </template>\n <tag name= \"node1\" value=\"value1_new\"/>\n <tag name= \"node2\" value=\"value2_old\"/>\n </tag>\n </preference>\n \"\"\", parser=XMLPARSER)\n \n result = mergeDocuments(etree_1, etree_2)\n compare_etree(answer, result)",
"title": ""
},
{
"docid": "3a2c0fee4382ecbfdf69b9429724799a",
"score": "0.48480752",
"text": "def test_format_dict_node_template_custom():\n template = \"{key} is a dict:\\n{value}\\n\"\n dummy_dict_with_leafs = {1: {2: {}}, 2: {}, 3: {1: {4: 3}, 2: {}}}\n lines = format_dict(dummy_dict_with_leafs, templates={\"dict_node\": template}).split(\n \"\\n\"\n )\n assert len(lines) == 7\n assert lines[0] == \"1 is a dict:\"\n # 2\n # 2\n assert lines[3] == \"3 is a dict:\"\n assert lines[4] == \"1 is a dict:\"\n # 4: 3\n # 2",
"title": ""
},
{
"docid": "3c3487af806fe48e7be8ff6d24becb72",
"score": "0.48455608",
"text": "def testTemplateWithNoTracelog(self, mock):\n xml = BEAST2XML(template='filename')\n error = (r'^Could not find \"\\./run/logger\\[@id=\\'tracelog\\'\\]\" tag '\n r'in XML template$')\n assertRaisesRegex(self, ValueError, error, xml.toString)",
"title": ""
},
{
"docid": "4c36b62e8faadcb0751935fd6dfe8c1c",
"score": "0.48377553",
"text": "def test_delete_implant_template(self):\n pass",
"title": ""
},
{
"docid": "295665408a44767be986396360bfc213",
"score": "0.48347276",
"text": "def test_no_render_side_effect(self):\n engine = Engine(app_dirs=True, libraries=LIBRARIES)\n template = engine.from_string('{% load inclusion %}{% inclusion_no_params %}')\n count = template.nodelist.get_nodes_by_type(Node)\n template.render(Context({}))\n self.assertEqual(template.nodelist.get_nodes_by_type(Node), count)",
"title": ""
},
{
"docid": "0964e805486b7961c39f9d5213ef2338",
"score": "0.48273683",
"text": "def verify_tags(self, template_data):\n diff = set(template_data) - self.template_tags\n if diff:\n logger.warning(\n \"Template data contains tags that aren't in the template: %s\", diff)\n return False\n else:\n return True",
"title": ""
},
{
"docid": "be790340d95a08c100632e0fba36f92f",
"score": "0.48270077",
"text": "def cleantree(tree):\n tree = string.strip(tree)\n regexp = re.compile(r':\\d+.\\d+')\n tree = regexp.sub('', tree)\n \n regexp = re.compile(r'\\)\\d+') # internal node support values\n tree = regexp.sub(')', tree)\n \n return tree",
"title": ""
},
{
"docid": "f983254d2a545d722b79b7bfd27e2fe0",
"score": "0.48223487",
"text": "def parseWithTreeTemplate(self,mytemplate,lElts,bReplace=False):\n\n PARTIALMATCH_TH = 1.0\n dMtoSingleFeature = {}\n mvPattern = self.treePatternToMV(mytemplate.getPattern(),dMtoSingleFeature, PARTIALMATCH_TH)\n #need to optimize this double call \n for elt in lElts:\n try: elt.setSequenceOfFeatures(elt.lFeatureForParsing[:])\n except TypeError: pass\n try:\n lf= elt.getCanonicalFeatures()[:]\n except: lf=[]\n# print elt, lf \n elt.resetFeatures()\n elt.setFeatureFunction(elt.getSetOfMutliValuedFeatures,TH = PARTIALMATCH_TH, lFeatureList = lf )\n elt.computeSetofFeatures()\n\n # what is missing is the correspondence between rule name (sX) and template element\n ## provide a template to seqGen ? instead if recursive list (mvpatern)\n \n# for e in lElts:\n# print \"wwww\",e, e.getSetofFeatures()\n lNewSequence = lElts[:]\n# parsingRes = self.parseSequence(mvPattern,multiValueFeatureObject,lElts)\n parsingRes = self.parseSequence(mvPattern,None,lElts)\n\n isKleenePlus = False\n nbSeq = 0.0\n nbKleeneInSeq= 0.0 \n if parsingRes is not None:\n# print (mytemplate)\n self.populateElementWithTreeParsing(mytemplate,parsingRes,dMtoSingleFeature)\n _,_, lParse = parsingRes\n for lElts, rootNode in lParse:\n# rootNode.print_(0)\n xx = rootNode.convertToObject(self.getObjectLevel())\n nbKleeneInSeq += rootNode.searchForRule(\"s0\")\n nbSeq+=1\n if bReplace:\n lNewSequence = self.replaceEltsByParsing(lNewSequence,lElts,xx,mytemplate.getPattern()) \n isKleenePlus = nbSeq > 0 and nbKleeneInSeq / nbSeq >= self.getKleenePlusTH()\n if isKleenePlus: print( mytemplate, nbSeq, nbKleeneInSeq, nbKleeneInSeq / nbSeq)\n ### retrun nbKleeneInSeq / nbSeq and let decide at smpcomponent level (if pattern is mirrored: keep it if nbKleeneInSeq / nbSeq near 1.66 )\n# if self.bDebug:print( mytemplate, nbSeq, nbKleeneInSeq, nbKleeneInSeq / nbSeq)\n return isKleenePlus,parsingRes, lNewSequence",
"title": ""
},
{
"docid": "14494e943e393efca7ed9f405214e71f",
"score": "0.48207375",
"text": "def trim_redundant_placeholders(data):\n for msg in data.values():\n for key, settings in list(msg.get('placeholders', {}).items()):\n if (re.match(r'^[0-9]+$', key) and\n re.match(r'^[$][0-9]+$', settings['content'])):\n msg['placeholders'].pop(key)\n\n # Remove the placeholders setting if it's empty now.\n placeholders = msg.get('placeholders', {})\n if not placeholders:\n msg.pop('placeholders', None)",
"title": ""
},
{
"docid": "eb806d45aad446772a84bff375c68b47",
"score": "0.48133153",
"text": "def test_format_dict_leaf_template_custom():\n template = \"value of {key} is {value}\\n\"\n dummy_dict_with_leafs = {1: {2: {}}, 2: {}, 3: {1: 3, 2: {}}}\n lines = format_dict(dummy_dict_with_leafs, templates={\"leaf\": template}).split(\"\\n\")\n assert len(lines) == 6\n # 1:\n # 2\n # 2\n # 3:\n assert lines[4] == \"value of 1 is 3\"",
"title": ""
},
{
"docid": "463b129c273f8a46e2e97e7530947263",
"score": "0.4776075",
"text": "def remove_html_tags(data):\n p = re.compile(r'<.*?>')\n return p.sub('', data)",
"title": ""
},
{
"docid": "b1751c13dce74bf50b699c36398ff9a3",
"score": "0.47676453",
"text": "def cleanNode(self, node):\n if not DEBUG:\n if node.attributes.has_key('model'):\n del node.attributes['model']\n if node.attributes.has_key('view'):\n del node.attributes['view']\n if node.attributes.has_key('controller'):\n del node.attributes['controller']\n return node",
"title": ""
},
{
"docid": "17acd890f7c2b2fd42de4592224e12dc",
"score": "0.47656474",
"text": "def remove_tree(self, path):\n raise NotImplementedError(\n \"Abstract method `Transport.remove_tree()` called - \"\n \"this should have been defined in a derived class.\")",
"title": ""
},
{
"docid": "d8b5f5d726b2f0e4e0dda16f2ed2e347",
"score": "0.474382",
"text": "def clean(self, data, initial=None):\n single_file_clean = super().clean\n if isinstance(data, (list, tuple)):\n result = [single_file_clean(d, initial) for d in data]\n else:\n result = single_file_clean(data, initial)\n return result",
"title": ""
},
{
"docid": "1c1ec7d6aac79c8d8952a7621141a395",
"score": "0.47328585",
"text": "def cleanup(self, data):\n for line in data:\n for key, value in line.items():\n if isinstance(value, str):\n line[key] = re.sub(r'\\s*[,;]\\s*', ',', value.strip()) # rm whitespace\n line[key] = re.sub(r',+', ',', line[key]) # collapse commas\n line[key] = line[key].rstrip(',') # rm trailing commas\n if not self.leave_na:\n if line[key] in ('#NA', 'NA', '#N/A'):\n line[key] = ''\n elif value is False:\n line[key] = ''\n else:\n line[key] = str(line[key])\n line = self.remove_hgnc_prefix(line)\n yield line",
"title": ""
},
{
"docid": "f35bf8b9f7962bff78853a79c39b0ac2",
"score": "0.47321907",
"text": "def clean_data(self, info, config):\n yield info",
"title": ""
},
{
"docid": "40c6119d5f748c0c8f0cb5b34e18bba7",
"score": "0.47242412",
"text": "def test_untag_backwards_compatibility(self):\n fields_to_test = {\n 'title@': 'foo',\n 'nested': {\n 'list@': [\n 'value1',\n ],\n },\n 'list@': [\n 'top-value1',\n 'top-value2',\n 'top-value3',\n ],\n }\n fields = copy.deepcopy(fields_to_test)\n self.assertDictEqual({\n 'title': 'foo',\n 'list': [\n 'top-value1',\n 'top-value2',\n 'top-value3',\n ],\n 'list@': [\n 'top-value1',\n 'top-value2',\n 'top-value3',\n ],\n 'nested': {\n 'list': [\n 'value1',\n ],\n 'list@': [\n 'value1',\n ],\n },\n }, untag.Untag.untag(fields))",
"title": ""
},
{
"docid": "ab480975bfae73e8e212cb2ce6b44e72",
"score": "0.4721887",
"text": "def remove_nodes(self, pattern, adict):\n mydict = self._filetree if adict is None else adict\n\n if isinstance(mydict, dict):\n for nom in mydict.keys():\n if isinstance(mydict[nom], dict):\n matchs = filter_list(mydict[nom], pattern)\n for nom in matchs:\n mydict = self.remove_nodes(pattern, mydict[nom])\n mydict.pop(nom)\n else:\n mydict[nom] = filter_list(mydict[nom], pattern)\n else:\n matchs = set(filter_list(mydict, pattern))\n mydict = set(mydict) - matchs\n\n return mydict",
"title": ""
},
{
"docid": "8056f7b89cfcd60599f0adcf67979619",
"score": "0.47214475",
"text": "def remove_empty_tags(tree, ignore_tags):\r\n ignore_tags += ('body', 'html')\r\n child_removed = False\r\n for element in tree:\r\n # The \"element.getparent() is not None\" check ensures that we don't\r\n # cause the AssertionError in drop_tree().\r\n if element.tag not in ignore_tags and (element.text is None or not element.text.strip()) \\\r\n and not list(element) and element.getparent() is not None:\r\n element.drop_tree()\r\n child_removed = True\r\n else:\r\n remove_empty_tags(element, ignore_tags)\r\n if child_removed:\r\n parent = tree.getparent()\r\n if parent is not None:\r\n remove_empty_tags(parent, ignore_tags)",
"title": ""
},
{
"docid": "8056f7b89cfcd60599f0adcf67979619",
"score": "0.47214475",
"text": "def remove_empty_tags(tree, ignore_tags):\r\n ignore_tags += ('body', 'html')\r\n child_removed = False\r\n for element in tree:\r\n # The \"element.getparent() is not None\" check ensures that we don't\r\n # cause the AssertionError in drop_tree().\r\n if element.tag not in ignore_tags and (element.text is None or not element.text.strip()) \\\r\n and not list(element) and element.getparent() is not None:\r\n element.drop_tree()\r\n child_removed = True\r\n else:\r\n remove_empty_tags(element, ignore_tags)\r\n if child_removed:\r\n parent = tree.getparent()\r\n if parent is not None:\r\n remove_empty_tags(parent, ignore_tags)",
"title": ""
},
{
"docid": "5a619736ae74d12fb622908521a88fe2",
"score": "0.47124115",
"text": "def removeAltmodels(self):\n self.stripChildren((0,), 'ne', 'id', forgiving=False)",
"title": ""
},
{
"docid": "e73d12edefea2cd3a874bf312018066a",
"score": "0.4703938",
"text": "def test_minimal_tree_mismatches(self):\n ps_tree = PsTree(PsRow(1, 0, 'root', 'tini -- true'))\n\n matcher = MatchesPsTree('tuber', 'tini -- true')\n mismatch = matcher.match(ps_tree)\n assert \"'root' != 'tuber': ruser\" in mismatch.describe()\n\n matcher = MatchesPsTree('root', 'tini -- false')\n mismatch = matcher.match(ps_tree)\n assert \"'tini -- true' != 'tini -- false': args\" in mismatch.describe()\n\n matcher = MatchesPsTree('tuber', 'tini -- true', pid=7)\n mismatch = matcher.match(ps_tree)\n assert \"1 != 7: pid\" in mismatch.describe()\n\n matcher = MatchesPsTree('tuber', 'tini -- true', ppid=7)\n mismatch = matcher.match(ps_tree)\n assert \"0 != 7: ppid\" in mismatch.describe()",
"title": ""
},
{
"docid": "4335f04b9b4e11e8767f1d717e2116e8",
"score": "0.46962738",
"text": "def test_leaf_nodes_without_text():\n html = \"\"\"\n <div>\n <p>Some text</p>\n <p></p>\n <p>Some more text</p>\n </div>\n \"\"\".strip()\n soup = BeautifulSoup(html, 'html.parser')\n text_blocks = [plain_text_leaf_node(paragraph) for paragraph in soup.find_all(\"p\")]\n assert text_blocks == [{'text': 'Some text'}, {'text': None}, {'text': 'Some more text'}]",
"title": ""
},
{
"docid": "168cf85b3a6b7eaaecaff9726a236f87",
"score": "0.46959066",
"text": "def test_blocks_and_data_not_operated_on_intact(self):\n\n altered_raw_data = apply_changes_to_raw_data(\n raw_data=self.raw_data,\n block_path_str=\"nestedlist_struct.item\",\n operation=RenameStructChildrenOperation(\n old_name=\"char1\", new_name=\"renamed1\"\n ),\n streamfield=models.SampleModel.content,\n )\n\n self.assertEqual(altered_raw_data[0], self.raw_data[0])\n self.assertEqual(altered_raw_data[3], self.raw_data[3])\n\n self.assertEqual(altered_raw_data[1][\"type\"], self.raw_data[1][\"type\"])\n self.assertEqual(altered_raw_data[2][\"type\"], self.raw_data[2][\"type\"])\n self.assertEqual(altered_raw_data[1][\"id\"], self.raw_data[1][\"id\"])\n self.assertEqual(altered_raw_data[2][\"id\"], self.raw_data[2][\"id\"])\n\n self.assertEqual(\n altered_raw_data[1][\"value\"][0][\"id\"], self.raw_data[1][\"value\"][0][\"id\"]\n )\n self.assertEqual(\n altered_raw_data[1][\"value\"][1][\"id\"], self.raw_data[1][\"value\"][1][\"id\"]\n )\n self.assertEqual(\n altered_raw_data[2][\"value\"][0][\"id\"], self.raw_data[2][\"value\"][0][\"id\"]\n )",
"title": ""
},
{
"docid": "fd0b59ad4e6da4487274b89c95d9fdc0",
"score": "0.46949935",
"text": "def _remove_one(self, node):\n replacement = node.left or node.right\n if node.parent:\n if AvlTree._is_left(node):\n node.parent.left = replacement\n else:\n node.parent.right = replacement\n replacement.parent = node.parent\n node.parent = None\n else:\n self._tree = replacement\n replacement.parent = None\n node.left = None\n node.right = None\n node.parent = None\n self._rebalance(replacement)",
"title": ""
},
{
"docid": "ad45121ec8fe540cfe3357fbecc73c7f",
"score": "0.46869975",
"text": "def test_basic_nested_structure_without_lists(self):\n\n self.assertEqual(\n flatten(self.data),\n {\n 'foo.bar': 'baa',\n 'foo.baz.foo': 'bar',\n 'bar': 'baz'\n }\n )",
"title": ""
},
{
"docid": "a2dd711387cf4e1c1ded05888e1ac2ad",
"score": "0.46857622",
"text": "def testTreeKleeneageTemplates(self,dTemplatesCnd,lElts,iterMax=15):\n \n \"\"\"\n resulting parsing can be used as prior for hmm/viterbi? yes\n but need to apply seqrule for completion or relax parsing\n \n \"\"\"\n lFullPattern=[]\n lFullTemplates=[]\n dScoreFullTemplate={}\n lTemplates = []\n dScoredTemplates = {}\n for templateType in dTemplatesCnd.keys():\n iCpt=0\n iFound = 0\n lenMax = len(dTemplatesCnd[templateType])\n while iCpt < lenMax and iFound < iterMax:\n# for _,_, mytemplate in dTemplatesCnd[templateType][:4]:\n _,_, mytemplate = dTemplatesCnd[templateType][iCpt]\n ## need to test kleene+: if not discard?\n isKleenePlus ,parseRes,lseq = self.parseWithTreeTemplate(mytemplate,lElts,bReplace=False)\n ## assess and keep if kleeneplus\n if isKleenePlus:\n ### if len=2 and [b,a] already exists: skip it !!\n ### also if a bigger pattern contains it ??? (but not TA!!)\n if len(mytemplate.getPattern())==2 and [mytemplate.getPattern()[1],mytemplate.getPattern()[0]] in lFullPattern:\n if self.bDebug:print( 'mirrored already in', mytemplate)\n pass\n else:\n lFullTemplates.append(mytemplate)\n lFullPattern.append(mytemplate.getPattern())\n iFound+= 1 \n dScoreFullTemplate[mytemplate]=len(parseRes[1]) \n if self.bDebug:print (iCpt,'add kleenedPlus template', mytemplate,len(parseRes[1]))\n# print(mytemplate.print_())\n ## traverse the tree template to list the termnimal pattern\n lterm= mytemplate.getTerminalTemplates()\n for template in lterm:\n if template not in lTemplates:\n lTemplates.append(template)\n dScoredTemplates[template] = len(parseRes[1])\n iCpt += 1\n \n lFullTemplates.sort(key=lambda x:dScoreFullTemplate[x],reverse=True)\n\n\n # transition matrix: look at for template in page.getVerticalTemplates():\n N = len(lTemplates) + 1\n transProb = np.zeros((N,N), dtype = np.float16)\n \n dTemplateIndex = {}\n for i,template in enumerate(lTemplates):\n# print i, template\n dTemplateIndex[template]=i\n \n \n for i,elt in enumerate(lElts):\n # only takes the best ?\n ltmpTemplates = elt.getTemplates()\n if ltmpTemplates is None: ltmpTemplates=[]\n for template in ltmpTemplates:\n try:\n nextElt=lElts[i+1]\n lnextTemplates = nextElt.getTemplates()\n# print (elt, nextElt,template,lnextTemplates)\n if lnextTemplates is None: lnextTemplates=[]\n for nextT in lnextTemplates:\n ## not one: but the frequency of the template\n try:\n w = dScoredTemplates[template]\n dTemplateIndex[nextT]\n except KeyError:\n #template from previous iteration: ignore\n w= None\n ## kleene \n if w is not None:\n if nextT is template:\n w += dScoredTemplates[template]\n ##\n if (nextT == template) or (nextT.getParent() == template.getParent()):\n # 1 or the score of the matching !!!!! 03/09/2018\n _,_,score = template.registration(elt)\n transProb[dTemplateIndex[template],dTemplateIndex[nextT]] += score\n# transProb[dTemplateIndex[template],dTemplateIndex[nextT]] = 1\n\n# print (elt,template,score) \n if np.isinf(transProb[dTemplateIndex[template],dTemplateIndex[nextT]]):\n transProb[dTemplateIndex[template],dTemplateIndex[nextT]] = 64000\n except IndexError:pass\n \n transProb[:,-1] = 0.10 #/totalSum\n #last: None: to all\n transProb[-1,:] = 0.10 #/totalSum\n mmax = np.amax(transProb)\n if np.isinf(mmax):\n mmax=64000\n transProb = transProb / len(lElts) * 2\n# print (transProb)\n return lFullTemplates,lTemplates,transProb / mmax",
"title": ""
},
{
"docid": "e83525e18df1da2d4eb787c04d0e5390",
"score": "0.46856058",
"text": "def testTemplateWithNoTrait(self, mock):\n xml = BEAST2XML(template='filename')\n error = (r\"^Could not find '\\./run/state/tree/trait' tag in XML \"\n r\"template$\")\n assertRaisesRegex(self, ValueError, error, xml.toString)",
"title": ""
},
{
"docid": "109220b27e8386ee47b764ce68f1866c",
"score": "0.46781483",
"text": "def test_untag_nested(self):\n fields_to_test = {\n 'nested': {\n 'nested': 'nested-base',\n 'nested@fr': 'nested-fr',\n },\n }\n fields = copy.deepcopy(fields_to_test)\n self.assertDictEqual({\n 'nested': {\n 'nested': 'nested-fr',\n },\n }, untag.Untag.untag(fields, locale_identifier='fr'))\n fields = copy.deepcopy(fields_to_test)\n self.assertDictEqual({\n 'nested': {\n 'nested': 'nested-base',\n },\n }, untag.Untag.untag(fields, locale_identifier='de'))",
"title": ""
},
{
"docid": "600de69cbda51dfb689cfe526d7a4f5b",
"score": "0.4670865",
"text": "def test_parse_remove(self):\n pass",
"title": ""
},
{
"docid": "7ffa9ad6ca0d5cacaf09f4d739c33c79",
"score": "0.46708617",
"text": "def clean_data(self):",
"title": ""
},
{
"docid": "f521df3e1082dbc968fb2ceaf746419e",
"score": "0.4670782",
"text": "def _fill_template(self, template, triple):\n template = template.replace(\"<subject>\", triple.subj) \\\n .replace(\"<predicate>\", triple.pred) \\\n .replace(\"<object>\", triple.obj)\n return template",
"title": ""
},
{
"docid": "d626b928af69695411b9f1703617b5e7",
"score": "0.46702787",
"text": "def gen_general_differences(comparison_file, template_dict,\n audit_config, audit_hierarc_config):\n\n with open(comparison_file, 'a') as res:\n\n non_compl_items = list(set(audit_config) - set(template_dict['glob']))\n print('################ Non compliant General items:', file=res)\n for item in sorted(non_compl_items):\n words = item.split()\n if words[0].lstrip() != 'no':\n print('no ' + item, file=res)\n else:\n print(' '.join(words[1:]), file=res)\n print('!', file=res)\n print('################ Missing general template items:', file=res)\n for item in template_dict['glob']:\n if item not in audit_config:\n print(item, file=res)\n\n print('################ Non compliant hierarchical items:', file=res)\n global_hierarc_templ_items = []\n for item in template_dict['global_hierarc']:\n # First line of all lists\n global_hierarc_templ_items.append(item[0])\n\n global_hier_config_items = []\n for item in audit_hierarc_config:\n # First line of all lists\n global_hier_config_items.append(item[0])\n\n print('!', file=res)\n diff = set(global_hierarc_templ_items) - set(global_hier_config_items)\n if diff:\n fmt = ('Difference(s) found in hierarchical config sections. '\n 'Present in template, not in config:')\n print(fmt, file=res)\n for item in diff:\n for item1 in template_dict['global_hierarc']:\n if item1[0] == item:\n for item in item1:\n print(item, file=res)\n print('!', file=res)\n\n # Remove list not found in config\n for index, item in enumerate(template_dict['global_hierarc']):\n if item[0] in diff:\n template_dict['global_hierarc'].pop(index)\n\n template_dict['global_hierarc'] = sorted(template_dict['global_hierarc'],\n key=lambda x: x[0])\n\n audit_hierarc_config = sorted(audit_hierarc_config, key=lambda x: x[0])\n\n #print(template_dict['global_hierarc'])\n\n for hierconf_part, hierconf_configpart \\\n in zip(template_dict['global_hierarc'], audit_hierarc_config):\n if hierconf_part != hierconf_configpart:\n print('Difference found in {}.'.format(\n hierconf_part[0]), file=res)\n print('!', file=res)\n print('Template configuration is:', file=res)\n for item in hierconf_part:\n print(item, file=res)\n print('!', file=res)\n print('Switch configuration is:', file=res)\n for item in hierconf_configpart:\n print(item, file=res)\n print('!', file=res)\n print('!', file=res)\n print('!', file=res)",
"title": ""
},
{
"docid": "e7c878735b95163f98772b056277cec1",
"score": "0.46670908",
"text": "def assertTemplateEqual(left, right): # noqa: N802 pylint: disable=invalid-name\n return to_template_dict(left) == to_template_dict(right)",
"title": ""
},
{
"docid": "e73a8afa8d1754d03248e9e9b5fd175f",
"score": "0.4664194",
"text": "def visit_FileTemplate(self, node):\n return self.visit_TemplateNode(node)",
"title": ""
},
{
"docid": "b7ec92ca9619fb59741a8b7381b1fc46",
"score": "0.46612525",
"text": "def pruneTree(self, root):\n # Recursion comes here\n self.prune(root)\n return root",
"title": ""
},
{
"docid": "47910575bfedc7740efe39fc1de7c248",
"score": "0.46497604",
"text": "def template_nodes(self):\n\n\t\tif self.is_valid_pair():\n\t\t\tif hasattr(self.node_template, 'children'):\n\t\t\t\treturn [e for e in self.node_template.children if not isinstance(e,(NavigableString, Comment))]\n\n\t\treturn []",
"title": ""
},
{
"docid": "37e1cf0c1508308493cee55221fd0a7d",
"score": "0.4642599",
"text": "def apply_template(self):\n template_lines = [re.sub(\"\\n\", \"\", line) for line in self.template.split(\"\\n\")]\n self.output = []\n for line in template_lines:\n if line.strip().upper() == \"@@CONTENTS\":\n self.output.extend(self.input)\n else:\n self.output.append(line)",
"title": ""
},
{
"docid": "19207d4ae05df47e60b258f3b3823f95",
"score": "0.46403223",
"text": "def test_unfiltered(self):\n \n # this is a test case for django\n engine.setDefaultEngine() \n self.createContent()\n self.portal.folder.easy_template.setText(\"Foobar\")\n self.portal.folder.easy_template.setUnfilteredTemplate(\"{{ explore(context) }}\")\n output = self.portal.folder.easy_template.getTemplatedText()\n \n messages = IStatusMessage(self.portal.REQUEST).showStatusMessages() \n for m in messages: print str(m.message)\n \n self.assertEqual(len(messages), 0)\n \n # Should have variables dumped \n self.assertTrue(\"easy_template\" in output)",
"title": ""
},
{
"docid": "c2421b7a75dda20aac00ada0461b0cdc",
"score": "0.46377864",
"text": "def cleanup(self):\n if self.is_templatized:\n # This check is needed for the case of a symlinked file and its\n # source being processed inside a single group (locale dir);\n # removing either of those two removes both.\n if os.path.exists(self.work_path):\n os.unlink(self.work_path)",
"title": ""
},
{
"docid": "cd1aa703c49cc49cd3f1653aa4590e5a",
"score": "0.46360525",
"text": "def prune(self):\n #traverse tree\n for node in self.traverse(self_after=True,self_before=False):\n #save current parent\n curr_parent=node.Parent\n #If not at the root\n if curr_parent is not None:\n #if current node only has 1 child\n if len(node.Children) == 1:\n #save child\n child=node.Children[0]\n #remove current node by setting parent to None\n node.Parent=None\n #Connect child to current node's parent\n child.Parent=curr_parent",
"title": ""
},
{
"docid": "d82f2180cd32441ce738e6408bb5eeb8",
"score": "0.46150008",
"text": "def replace(self, t_data):\n child1 = self.mutate(t_data[0][0])\n child2 = self.mutate(t_data[1][0])\n\n self.population[t_data[-1][0]] = child1\n self.population[t_data[-2][0]] = child2\n # check to see if any of the children/parents are roots\n for i in (0,1,-1,-2):\n x = float(self.population[t_data[i][0]])\n result = eval(self.equation)\n if -self.ERROR <= result <= self.ERROR:\n self.check_root(t_data[i][0])\n self.v_print(('\\nnew population:', self.population))",
"title": ""
},
{
"docid": "8acf2379c418b3d6398f5100607ce497",
"score": "0.46126303",
"text": "def post_cleanup(ce_inst):\r\n parse_tags = ['p']\r\n if ce_inst.config.parse_lists:\r\n parse_tags.extend(['ul', 'ol'])\r\n if ce_inst.config.parse_headers:\r\n parse_tags.extend(['h1', 'h2', 'h3', 'h4', 'h5', 'h6'])\r\n\r\n target_node = ce_inst.article.top_node\r\n node = ce_inst.add_siblings(target_node)\r\n for elm in ce_inst.parser.getChildren(node):\r\n e_tag = ce_inst.parser.getTag(elm)\r\n if e_tag not in parse_tags:\r\n if ce_inst.is_highlink_density(elm) or ce_inst.is_table_and_no_para_exist(elm):\r\n ce_inst.parser.remove(elm)\r\n return node",
"title": ""
},
{
"docid": "fb42bdd06cf1244a09a65eaab2a8c80b",
"score": "0.46083608",
"text": "def _strip_virtualizer_topology (virtualizer):\n for node in virtualizer.nodes:\n # Remove NFs\n log.debug(\"Removing NFs: %s from %s\" % (node.NF_instances.node.keys(),\n node.id.get_value()))\n node.NF_instances.node._data.clear()\n # Remove flowrules\n log.debug(\n \"Removing Flowrules: %s from %s\" % (node.flowtable.flowentry.keys(),\n node.id.get_value()))\n node.flowtable.flowentry._data.clear()\n return virtualizer",
"title": ""
},
{
"docid": "774b7ec5eb1372b37550b03a28f351b4",
"score": "0.46057153",
"text": "def sanatizeHtml(html, templateRegex):\n #remove empty links\n linkRegex = r'<a[^>]*href=\"\"[^>]*>'\n while re.search(linkRegex,html):\n match = re.search(linkRegex,html)\n s = match.start(0)\n e = match.end(0)\n s2 = e + html[e:].find('</a>')\n html = html[:s] + html[e:s2] + html[s2+4:]\n\n #remove template comments\n while re.search(templateRegex,html):\n match = re.search(templateRegex,html)\n s = match.start(0)\n e = match.end(0)\n html = html[:s] + html[e:]\n #remove wrong commas\n html = re.sub(\",\\s+<\", \"<\", html)\n wrongPunctuationRegex = r'[\\,][^\\s]'\n while re.search(wrongPunctuationRegex, html):\n match = re.search(wrongPunctuationRegex, html)\n s = match.start(0)\n e = match.end(0)\n html = html[:s+1] + ' ' + html[s+1:e] + html[e:]\n #first letter is capitalized\n tagStartRegex = r'<[td,p,b][^>]*>[ ,\\n]*[a-z]'\n while re.search(tagStartRegex, html):\n match = re.search(tagStartRegex,html)\n s = match.start(0)\n e = match.end(0)\n html = html[:e-1] + html[e-1:e].upper() + html[e:]\n #capitalize all words in title\n headerTag = r'<h[0-9][^>]*>[^<]+</h[0-9]>'\n for match in re.finditer(headerTag,html):\n s = match.start(0)\n e = match.end(0)\n match = html[s:e]\n s = s + match.find('>')+1\n match = html[s:e]\n e = s + match.find('<')\n html = html[:s] + html[s:e].title() + html[e:]\n #undo some capitalization\n html = html.replace(\"'S\", \"'s\")\n html = html.replace(\"Http\", \"http\")\n return html",
"title": ""
},
{
"docid": "21c00405ef8c26fafcd76ecd0c31d7b8",
"score": "0.4601219",
"text": "def trim_tree(absenteeList, TreeFile):\n print \"\\nReading the Tree...\"\n #parse the tree using Phylo\n tree = Phylo.read(TreeFile, 'newick')\n print \"Here is the starting tree:\"\n Phylo.draw_ascii(tree)\n terminals = tree.get_terminals()\n print \"\\nFound the following {} taxa in the tree:\".format(len(terminals))\n print terminals\n #prune away taxa that are not included for this sequence file\n for taxon in absenteeList:\n print(\"Removing absent\")\n tree.prune(taxon)\n print \"\\nPruned away these species:\"\n print absenteeList\n print \"\\nHere is the tree with the missing taxa pruned away:\\n\"\n Phylo.draw_ascii(tree)\n\n\n #unless you have a clock, PAML requires that your tree is unrooted, ie has a trifurcation at first node. So do that here\n # ROOT = tree.get_nonterminals()[0]\n # if ROOT.is_bifurcating() == True:\n # firstNode = tree.get_nonterminals()[1]\n # tree.collapse(firstNode)\n \n \n #if RunMode is not 2 just output the pruned tree as is\n print \"\\nOutputting the following revised tree for the species content of the sequence file\"\n print \"it should have a trifurcation at the base unless you are using a clock\\n\"\n Phylo.draw_ascii(tree)\n # if tree.rooted == False:\n # print \"The revised tree is an unrooted tree (regardless of how the sketch above looks)\"\n # if tree.rooted == True:\n # print \"Hmm, the tree is rooted. This may not be right for PAML input. You should check.\"\n Phylo.write(tree, TreeOutFileName, \"newick\")",
"title": ""
},
{
"docid": "d7a89ac456dafa674ebefdfb02a61931",
"score": "0.459926",
"text": "def remove(self, data):\n self.__remove(self.root, data)",
"title": ""
},
{
"docid": "d4e9078aba84556f2c38ab702591a756",
"score": "0.45929077",
"text": "def clean_node(self):\n pho = Pho(copy.deepcopy(self._node))\n if not pho:\n return None\n for item in pho._node.iter():\n keys = []\n for key in item.attrib:\n keys.append(key)\n for key in keys:\n etree.strip_attributes(item, key)\n return pho",
"title": ""
},
{
"docid": "7dd26e458a8d05c869ecc1a5ad7ad291",
"score": "0.45922574",
"text": "def _clean_hierarchy(cls, lib, chip_only_top, chip_only_top_layer,\n diff_pad_cell_layer, hold_all_pads_cell):\n hold_name = chip_only_top_layer.name\n lib.remove(hold_name)\n lib.rename_cell(diff_pad_cell_layer, hold_name)\n chip_only_top.add(gdspy.CellReference(diff_pad_cell_layer))\n # remove the sub libs before removing hold_all_pads_cells\n for _, value in enumerate(hold_all_pads_cell.references):\n lib.remove(value.ref_cell.name)\n lib.remove(hold_all_pads_cell)",
"title": ""
},
{
"docid": "5bfd18895fdc752eaad04d481e9660a1",
"score": "0.4587415",
"text": "def filtereddata():\n return render_template(\"data.html\")",
"title": ""
},
{
"docid": "19f7ffc67040363d19a9553a23dde09f",
"score": "0.45675695",
"text": "def ancestry_cleaner(row, field):\r\n free_text = re.sub(r'(\\d+),?([\\d+]?)', r'\\1\\2', row[field])\r\n free_text = re.sub(r'(\\d+)', r'; \\1', free_text)\r\n free_text = punctuation_cleaner(free_text)\r\n free_text = remove_lower(free_text)\r\n free_text = remove_lower(free_text)\r\n free_text = free_text.replace(' ', ' ')\r\n free_text = free_text.replace(' ', ' ')\r\n free_text = list_remover(free_text)\r\n free_text = dict_replace(free_text)\r\n try:\r\n if free_text[-1] == ';':\r\n free_text = free_text[:-1]\r\n except ValueError:\r\n pass\r\n cleaned = []\r\n for ancestry in free_text[1:].split(';'):\r\n if \" and\" in ancestry.strip()[-4:]:\r\n cleaned.append(ancestry.replace(' and', '').strip())\r\n elif \" or\" in ancestry.strip()[-4:]:\r\n cleaned.append(ancestry.replace(' or', '').strip())\r\n else:\r\n cleaned.append(ancestry.strip())\r\n cleaned = ';'.join(cleaned)\r\n cleaned = cleaned.replace(';', ' ; ')\r\n for word in cleaned.split(' '):\r\n if (word.isalpha()) and (len(word) < 3) and word != \"or\":\r\n cleaned = cleaned.replace(word, '')\r\n cleaned = re.sub(r';\\s+;', ';', cleaned)\r\n return cleaned",
"title": ""
},
{
"docid": "62a3857a1159f64a5f9f642dbdd6398e",
"score": "0.45584258",
"text": "def deidentify_template(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"deidentify_template\")",
"title": ""
},
{
"docid": "fe026acb4feee7fe5088ce15e45d56ba",
"score": "0.45570886",
"text": "def precompute_node_type_templates(cls):\n cls._find_templates('tree', cls.node_type_tpls)",
"title": ""
},
{
"docid": "f45032d02d23c633f72bc70fc0b2ece1",
"score": "0.4557022",
"text": "def handle_data(self, data):\n \n if (self.depth <= 1):\n return\n xmldata.NodeGenerator.handle_data(self, data)",
"title": ""
},
{
"docid": "a3c17ce4513119b007b755b3e21d8b8a",
"score": "0.4556832",
"text": "def test_empty_html(self):\n expected_xml_file_path = str(self.xmlFileDir / 'EmptyHtmlGpSummaryUpdate.xml')\n hash_file_path = str(self.hashFileDir / 'emptyHtmlHash.json')\n\n expected_string = FileUtilities.get_file_string(expected_xml_file_path)\n render = self.summaryCareRecord.populate_template_with_file(hash_file_path)\n XmlUtilities.assert_xml_equal(expected_string, render)",
"title": ""
},
{
"docid": "e75a87cda53c24397698236b5377d6c9",
"score": "0.45549202",
"text": "def sanitation_bypass_replace_tran(self):\n if \"../\" in self.payload and not self.filter:\n self.payload = self.payload.replace(\"../\", \"....//\")\n self.replace_tran = True\n return True\n return False",
"title": ""
},
{
"docid": "3a93268c9a81c919272ef770a3c63e90",
"score": "0.45521766",
"text": "def test_build_whole_regtree_missing_interp(self):\n text = \"PART 200-Regulation Q\\n\"\n text += u\"§ 200.1 First section.\\n\"\n text += \"Section content\\n\"\n text += \"Appendix A to Part 200 - Appendix Title\\n\"\n text += \"Appendix content\"\n\n node200_1 = Node(\"\\nSection content\\n\", label=['200', '1'],\n title=u\"§ 200.1 First section.\", children=[],\n node_type=Node.REGTEXT)\n nodeA = Node(\"\\nAppendix content\", label=[\"200\", \"A\"],\n title=\"Appendix A to Part 200 - Appendix Title\",\n children=[], node_type=Node.APPENDIX)\n nodeEP = Node('', label=['200', 'Subpart'], title='',\n children=[node200_1], node_type=Node.EMPTYPART)\n\n # Convert to JSON so we can ignore some unicode issues\n enc = NodeEncoder(sort_keys=True)\n self.assertEqual(\n enc.encode(build.build_whole_regtree(text)),\n enc.encode(Node(\"\\n\", label=[\"200\"], title=\"PART 200-Regulation Q\",\n children=[nodeEP, nodeA]))\n )",
"title": ""
},
{
"docid": "8b7e634c8c1d5658a14598682e01f292",
"score": "0.4551368",
"text": "def delete(self,data):\n #@param data nodes content to delete \n node,parent = self.search_tree(data)\n\n if node is not None : \n children_count = node.children_count()\n if children_count == 0 : \n # node has no childredn just remove it \n if parent: \n if parent.left is node: \n parent.left = None\n else: \n parent.right = None\n del node \n else : \n self.data = None \n elif children_count == 1 : \n # if node has 1 child replace node with its child\n if node.left:\n n = node.left\n else: \n n = node.right\n if parent: \n if parent.left is node: \n parent.left = n \n else: \n parent.right = n \n else: \n self.left = n.left \n self.right = n.right \n self.data = n.data\n\n else: \n # if node has two children \n parent = none \n sucessor = node.right \n while sucessor.left: \n parent = sucessor\n sucessor = sucessor.left \n #replace node data by its successor data \n node.data = sucessor.data\n #fix sucessors parent child \n if (parent.left == sucessor): \n parent.left = sucessor.right\n else: \n parent.right = sucessor.right",
"title": ""
},
{
"docid": "eac0b3100cae567ae0fb9a253744d780",
"score": "0.4550477",
"text": "def filter_tree(t, fn):\n if not fn(t.label) and not t.is_leaf():\n t.branches = []\n else:\n t_branches_copy = t.branches[:]\n for b in t_branches_copy:\n if not fn(b.label):\n t.branches.remove(b)\n print(b.label)\n else:\n filter_tree(b, fn)",
"title": ""
},
{
"docid": "a4b16767acd3c02caa0654c1e95ea35b",
"score": "0.45477253",
"text": "def test_render_context_is_cleared(self):\n engine = Engine(app_dirs=True, libraries=LIBRARIES)\n template = engine.from_string('{% load inclusion %}{% inclusion_extends1 %}{% inclusion_extends2 %}')\n self.assertEqual(template.render(Context({})).strip(), 'one\\ntwo')",
"title": ""
},
{
"docid": "9bd36717c46f7f0667ac42b92be37e59",
"score": "0.45471904",
"text": "def test_render_template(self):\n template = self.block.meta.template\n self.assertEqual(template, 'common/blocks/flickr.html', 'The templates are not the same')",
"title": ""
},
{
"docid": "d87bde21ad281aa930ed83d0a39e5cfc",
"score": "0.45442235",
"text": "def _cleanupUlinkNode(self):\n if self.cur.xml_qname == u\"ulink\" and len(self.cur.xml_children) == 1 \\\n and isinstance(self.cur.xml_first_child, tree.text) \\\n and self.cur.xml_first_child.xml_value.strip() == self.cur.xml_attributes[None, u'url'].strip():\n self.cur.xml_remove(self.cur.xml_first_child)",
"title": ""
},
{
"docid": "f1587b87f6e4f03325cc88b3a0affc8c",
"score": "0.45429832",
"text": "def removeEmbeddedIgnores(self, dataCell):\n # need to figure out how to remove non\n\n\n #LOGGER.debug(\"--------- REMOVE EMBED IGNORES ---------\")\n if isinstance(dataCell.struct, dict):\n for objProperty in dataCell.struct:\n #LOGGER.debug(f\"objProperty: {objProperty}\")\n newCell = dataCell.generateNewCell(objProperty, self.transConf)\n newCell = self.removeEmbeddedIgnores(newCell)\n dataCell.copyChanges(newCell)\n elif isinstance(dataCell.struct, list):\n positions2Remove = []\n for listPos in range(0, len(dataCell.struct)):\n #LOGGER.debug(f\"listPos: {listPos} - {dataCell.struct[listPos]}\")\n newCell = dataCell.generateNewCell(listPos, self.transConf)\n newCell = self.removeEmbeddedIgnores(newCell)\n if not newCell.include:\n positions2Remove.append(listPos)\n #LOGGER.debug(\"adding value: {listPos} to remove\")\n #LOGGER.debug(f\"include value: {dataCell.include}\")\n LOGGER.debug(f\"removing positions: {positions2Remove}\")\n dataCell.deleteIndexes(positions2Remove)\n #LOGGER.debug(f'returning... {dataCell.struct}, {dataCell.include}')\n #LOGGER.debug(f\"ignore struct: {self.transConf.transConf['users']['ignore_list']}\")\n return dataCell",
"title": ""
},
{
"docid": "a8fe4eb064bab4bca130e8ffee363f59",
"score": "0.45428017",
"text": "def loopUnroling(text,\n data,\n templateLoopRegex,\n templateEndLoopHeadRegex,\n templateRegexHead,\n templateRegexTail,\n endTag,\n templateId):\n while re.search(templateLoopRegex,text):\n match1 = re.search(templateLoopRegex,text)\n s1 = match1.start(0)\n e1 = match1.end(0)\n match1 = text[s1:e1]\n var = match1[match1.find('\"')+1:]\n var = var[:var.find('\"')]\n match1 = match1[match1.find('[')+1:match1.rfind(']')].split('][')\n regexEndLoop = templateEndLoopHeadRegex + '\"' + var + '\"' + templateRegexTail\n match2 = re.search(regexEndLoop,text[e1:])\n s2 = match2.start(0)+e1\n e2 = match2.end(0)+e1\n match2 = text[s2:e2]\n loopBody = text[e1:s2]\n loopBody = re.sub(\"^\\s+|\\s+$\", \"\", loopBody)\n\n loopEle = data\n for ele in match1:\n if('\"' in ele):\n loopEle = loopEle[ele[1:-1]]\n else:\n loopEle = loopEle[int(ele)]\n nItr = len(loopEle)\n stitchedText = \"\"\n for i in range(nItr):\n templateVariableRegex = templateRegexHead + r'[^' + endTag +']*\\[' + var + r'\\][^' + endTag +']*' + templateRegexTail\n ithLoopBody = loopBody\n while re.search(templateVariableRegex,ithLoopBody):\n match = re.search(templateVariableRegex,ithLoopBody)\n s = match.start(0)\n e = match.end(0)\n match = ithLoopBody[s:e]\n match = match.replace('[' + var + ']', '[' + str(i) + ']')\n ithLoopBody = ithLoopBody[:s] + match + ithLoopBody[e:]\n stitchedText += ithLoopBody\n\n textHead = text[:s1]\n openTag = text[s1:e1].replace(templateId + 'LOOP ', templateId + 'LOOPUNROLED ')\n textTail = text[s2:]\n text = textHead + openTag + stitchedText + textTail\n\n return text",
"title": ""
},
{
"docid": "b92c5c878763ad3048a4e61fa9dbe89d",
"score": "0.45383564",
"text": "def test_render_no_name_closing_tag(self):\n t = Template(\n '{% load bb_ember %}' # load the tag library\n '{% block_verbatim test %}'\n '{{verbatim node}}'\n '{% endblock_verbatim %}'\n )\n rendered = t.render(Context())\n\n self.assertEqual(rendered, u'{{verbatim node}}')",
"title": ""
},
{
"docid": "ecff4bc7e51bef50a698c86bc8a4547f",
"score": "0.4537813",
"text": "def unprune_leaf(self):\r\n name = self.prune_hold[0]\r\n sub = self.prune_hold[1]\r\n\r\n self.children[name] = sub",
"title": ""
},
{
"docid": "f0bb4a1e80d91fac58f66260f87350db",
"score": "0.45366606",
"text": "def fix_nested(template):\n def func(row):\n return tuple(\n IterData(col, child) if isinstance(child, SequenceType)\n else col\n for col, child in zip(row, template.children()))\n return func",
"title": ""
},
{
"docid": "2d3e83dde8bb83653321261a41c46368",
"score": "0.45269534",
"text": "def test_recurse_template(file, tmp_path, grail):\n name = tmp_path / \"dest-dir\"\n template_string = \"TEMPLATE TEST STRING\"\n ret = file.recurse(\n name=str(name),\n source=\"salt://grail\",\n template=\"jinja\",\n defaults={\"spam\": template_string},\n )\n assert ret.result is True\n\n scene_src = grail / \"scene33\"\n scene_dst = name / \"scene33\"\n assert scene_dst.is_file()\n assert scene_src.read_text() != scene_dst.read_text()\n assert template_string in scene_dst.read_text()",
"title": ""
},
{
"docid": "f507048a4f9d471858038b307a0a835d",
"score": "0.45235646",
"text": "def test_untag_none(self):\n untag_func = untag.Untag.untag\n fields_to_test = {\n 'foo': 'base',\n 'foo@env.prod': None,\n }\n fields = copy.deepcopy(fields_to_test)\n self.assertDictEqual({\n 'foo': 'base',\n }, untag_func(fields, locale_identifier=None, params={\n 'env': untag.UntagParamRegex(None),\n }))\n self.assertDictEqual({\n 'foo': None,\n }, untag_func(fields, locale_identifier=None, params={\n 'env': untag.UntagParamRegex('prod'),\n }))\n\n fields_to_test = {\n 'nested': {\n 'foo': 'nested-base',\n },\n 'nested@de': {\n 'foo': 'nested-de-base',\n 'foo@env.prod': None,\n }\n }\n fields = copy.deepcopy(fields_to_test)\n self.assertDictEqual({\n 'nested': {\n 'foo': 'nested-base',\n },\n }, untag_func(fields, locale_identifier=None, params={\n 'env': untag.UntagParamRegex(None),\n }))\n self.assertDictEqual({\n 'nested': {\n 'foo': 'nested-base',\n },\n }, untag_func(fields, locale_identifier=None, params={\n 'env': untag.UntagParamRegex('dev'),\n }))\n self.assertDictEqual({\n 'nested': {\n 'foo': None,\n },\n }, untag_func(fields, locale_identifier='de', params={\n 'env': untag.UntagParamRegex('prod'),\n }))",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="722"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">f1b046b956850e508fcf55a6df4b6b7e</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Collate function for PGL dataloader.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "8178101d28df8f18d5dfdd9f1325e9e4",
"score": "0.0",
"text": "def __init__(self, graph_wrapper, label_name='Log10_Kd', is_inference=False):\n assert label_name in ['Log10_Kd', 'Log10_Ki', 'KIBA']\n super(DTACollateFunc, self).__init__()\n self.graph_wrapper = graph_wrapper\n self.is_inference = is_inference\n self.label_name = label_name",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "d92a08224e9af357bb87b0169edadf67",
"score": "0.6742523",
"text": "def collater(self, samples):\n\t\treturn collate(\n\t\t\tsamples, self.src_embedding, self.tgt_embedding, self.src_dict, self.sql_dict, \n\t\t\tpad_idx=self.src_dict.pad(), eos_idx=self.src_dict.eos(), \n\t\t\tunk_idx=self.src_dict.unk(), \n\t\t\tleft_pad_source=self.left_pad_source, left_pad_target=self.left_pad_target,\n\t\t\tinput_feeding=self.input_feeding, eot_symbol=self.eot_symbol, mapping_dict=self.mapping_dict, \n\t\t\tlen_sql_dict=self.eov_symbol + 1\n\t\t)",
"title": ""
},
{
"docid": "e72165ffd0345bb3ec3070ed5cc2b524",
"score": "0.6705718",
"text": "def collate_fn(batch):\n return batch",
"title": ""
},
{
"docid": "b15374ab8245d84c447e311ca253d523",
"score": "0.6654697",
"text": "def collate_fn(data):\n # Sort a data list by caption length (descending order).\n data.sort(key=lambda x: len(x[1]), reverse=True)\n images, captions, label_seqs, location_seqs = zip(*data)\n assert len(label_seqs) > 0\n assert len(label_seqs) == len(location_seqs)\n\n # Merge images (from tuple of 3D tensor to 4D tensor).\n images = torch.stack(images, 0)\n\n # Merge captions (from tuple of 1D tensor to 2D tensor).\n lengths = [len(cap) for cap in captions]\n targets = torch.zeros(len(captions), max(lengths)).long()\n for i, cap in enumerate(captions):\n end = lengths[i]\n targets[i, :end] = cap[:end]\n label_seq_lengths = [len(label_seq) for label_seq in label_seqs]\n label_seq_data = torch.zeros(len(label_seqs), max(label_seq_lengths)).long()\n for i, label_seq in enumerate(label_seqs):\n label_seq_data[i, :len(label_seq)] = torch.LongTensor(label_seq[:len(label_seq)])\n\n location_seq_data = torch.zeros(len(location_seqs), max(label_seq_lengths), 4)\n for i, location_seq in enumerate(location_seqs):\n for j in range(len(location_seq)):\n coords = decode_location(location_seq[j])\n location_seq_data[i, j] = coords\n\n return images, targets, lengths, label_seq_data, location_seq_data, label_seq_lengths",
"title": ""
},
{
"docid": "1b527961bb1ad951077cac918f75192e",
"score": "0.66100025",
"text": "def collate_fn(data):\n images, captions = zip(*data)\n\n # Merge images (from tuple of 3D tensor to 4D tensor).\n images = torch.stack(images, 0).to(device)\n\n return images, captions",
"title": ""
},
{
"docid": "34413ba3bde16415a7b96e2eb341f786",
"score": "0.65992975",
"text": "def collate(self, samples: List):\n ...",
"title": ""
},
{
"docid": "abc16882b6481a476669fe4970dec659",
"score": "0.6473057",
"text": "def collate_fn(data):\n # Sort a data list by caption length (descending order).\n #data.sort(key=lambda x: len(x[1]), reverse=True)\n images, captions = zip(*data)\n\n # Merge images (from tuple of 3D tensor to 4D tensor).\n images = torch.stack(images, 0).to(device)\n\n # Merge captions (from tuple of 1D tensor to 2D tensor).\n lengths = [len(cap) for cap in captions]\n targets = torch.zeros(len(captions), max(lengths), dtype=torch.long)\n for i, cap in enumerate(captions):\n targets[i, :len(cap)] = torch.tensor(cap, dtype=torch.long)\n targets = targets.to(device)\n #targets = pack_padded_sequence(targets, lengths, batch_first=True)[0]\n return images, targets, lengths",
"title": ""
},
{
"docid": "29e2a6a403b92b110c1b38eb58425135",
"score": "0.6448023",
"text": "def collate_fn(data):\n\t# Sort a data list by caption length (descending order).\n\tdata.sort(key=lambda x: len(x[1]), reverse=True)\n\timages, captions = zip(*data)\n\n\t# Merge images (from tuple of 3D tensor to 4D tensor).\n\timages = torch.stack(images, 0)\n\n\t# Merge captions (from tuple of 1D tensor to 2D tensor).\n\tlengths = [len(cap) for cap in captions]\n\ttargets = torch.zeros(len(captions), max(lengths)).long()\n\tfor i, cap in enumerate(captions):\n\t\tend = lengths[i]\n\t\ttargets[i, :end] = cap[:end] \n\treturn images, targets, lengths",
"title": ""
},
{
"docid": "e95965f5062a240f45519dd08c88c016",
"score": "0.643085",
"text": "def collate_fn(data):\n # Sort a data list by caption length (descending order).\n data = list(filter(lambda x: type(x[1]) != int, data))\n data.sort(key=lambda x: len(x[1]), reverse=True)\n images, captions = zip(*data)\n\n # Merge images (from tuple of 3D tensor to 4D tensor).\n images = torch.stack(images, 0)\n\n # Merge captions (from tuple of 1D tensor to 2D tensor).\n lengths = [len(cap) for cap in captions]\n targets = torch.zeros(len(captions), max(lengths)).long()\n for i, cap in enumerate(captions):\n end = lengths[i]\n targets[i, :end] = cap[:end] \n return images, targets, lengths",
"title": ""
},
{
"docid": "d96f71eb0861afb850b1148dbe4d52c7",
"score": "0.64183503",
"text": "def collate_func(batch):\n batch = list(filter(lambda x: x is not None, batch))\n return torch.utils.data.dataloader.default_collate(batch)",
"title": ""
},
{
"docid": "ef929ac32c47a5525e52bb20709185e0",
"score": "0.6373691",
"text": "def collate_fn(self, input_examples):\n pass",
"title": ""
},
{
"docid": "8a175c6f5f40b5f542ffb9ad311b3bc9",
"score": "0.6364051",
"text": "def get_collate_fn(self):\n \n def collate_fn(samples):\n # samples is a list of dictionaries of the form returned by __get_item__()\n # YOUR CODE HERE: Create separate lists for each element by unpacking\n\n # YOUR CODE HERE: Pad 'img_feat' and 'img_pos_feat' tensors using pad_sequence\n\n # Tokenize and pad text\n if self.text_padding is not None:\n texts = self.text_padding(texts)\n \n # YOUR CODE HERE: Stack labels and data_ids into tensors (list --> tensor)\n\n # Text input\n input_ids = texts['input_ids']\n text_len = texts['length'].tolist()\n token_type_ids = texts['token_type_ids'] if 'token_type_ids' in texts else None\n position_ids = torch.arange(0, input_ids.shape[1], device=input_ids.device).unsqueeze(0).repeat(input_ids.shape[0], 1)\n\n # Attention mask\n if self.compact_batch:\n img_len = [i.size(0) for i in img_feat]\n attn_mask = get_attention_mask(text_len, img_len)\n else:\n text_mask = texts['attention_mask']\n img_len = [i.size(0) for i in img_feat]\n zero_text_len = [0] * len(text_len)\n img_mask = get_attention_mask(zero_text_len, img_len)\n attn_mask = torch.cat((text_mask, img_mask), dim=1)\n\n # Gather index\n out_size = attn_mask.shape[1]\n batch_size = attn_mask.shape[0]\n max_text_len = input_ids.shape[1]\n gather_index = get_gather_index(text_len, img_len, batch_size, max_text_len, out_size)\n \n batch = {'input_ids': input_ids,\n 'position_ids': position_ids,\n 'img_feat': img_feat,\n 'img_pos_feat': img_pos_feat,\n 'token_type_ids': token_type_ids,\n 'attn_mask': attn_mask,\n 'gather_index': gather_index,\n 'labels': labels,\n 'ids' : data_ids}\n \n return batch \n return collate_fn",
"title": ""
},
{
"docid": "dd66a6dc35592ee0e149eb6384967e99",
"score": "0.6358811",
"text": "def collate_fn(data):\r\n # Sort a data list by caption length (descending order).\r\n data.sort(key=lambda x: len(x[1]), reverse=True)\r\n images, captions = zip(*data)\r\n\r\n # Merge images (from tuple of 3D tensor to 4D tensor).\r\n images = torch.stack(images, 0)\r\n\r\n # Merge captions (from tuple of 1D tensor to 2D tensor).\r\n lengths = [len(cap) for cap in captions]\r\n targets = torch.zeros(len(captions), max(lengths)).long()\r\n for i, cap in enumerate(captions):\r\n end = lengths[i]\r\n targets[i, :end] = cap[:end] \r\n return images, targets, lengths",
"title": ""
},
{
"docid": "6d97b6659fa4bde638a9c32cc71b0b7a",
"score": "0.63261974",
"text": "def collate_fn(data):\n # Sort a data list by caption length (descending order).\n data.sort(key=lambda x: len(x[1]), reverse=True)\n images, captions = zip(*data)\n\n # Merge images (from tuple of 3D tensor to 4D tensor).\n images = torch.stack(images, 0)\n\n # Merge captions (from tuple of 1D tensor to 2D tensor).\n lengths = [len(cap) for cap in captions]\n targets = torch.zeros(len(captions), max(lengths)).long()\n for i, cap in enumerate(captions):\n end = lengths[i]\n targets[i, :end] = cap[:end] \n return images, targets, lengths",
"title": ""
},
{
"docid": "5abc09bbc87bf8fab005e09a33ec7b9c",
"score": "0.63062286",
"text": "def collate_fn(self, batch: Any) -> Any:\n\n data_list, label_list = [], []\n for _data, _label in batch:\n data_list.append(_data)\n label_list.append(_label) \n return data_list, label_list",
"title": ""
},
{
"docid": "4dea4b45f3ba26cce23b38d0290185a5",
"score": "0.62971747",
"text": "def collate_fn(batch):\n batch = list(filter(lambda x: x is not None, batch))\n return torch.utils.data.dataloader.default_collate(batch)",
"title": ""
},
{
"docid": "af4b70bb48f7517ce7e31a8d5d6e8d9d",
"score": "0.6190462",
"text": "def collater(self, samples):\n return collate(\n samples, pad_idx=self.tgt_dict.pad(), eos_idx=self.tgt_dict.eos(),\n left_pad_source=self.left_pad_source, left_pad_target=self.left_pad_target,\n input_feeding=self.input_feeding,\n multilv_args=self.multilv_args\n )",
"title": ""
},
{
"docid": "3cab1e5f2838fe1dcf3e294ebd1b4708",
"score": "0.61665225",
"text": "def collate_fn(data):\n # Sort by conversation length (descending order) to use 'pack_padded_sequence'\n data.sort(key=lambda x: x[1], reverse=True)\n # Separate\n sentences, conversation_length, sentence_length, images, conv_img_length = zip(*data)\n # return sentences, conversation_length, sentence_length.tolist()\n return sentences, conversation_length, sentence_length, images, conv_img_length",
"title": ""
},
{
"docid": "ea9b63909e565f9302f8abefed8c5a6c",
"score": "0.6151658",
"text": "def collate_fn(self, samples: Any) -> Any:\n samples = self.before_collate(samples)\n batch = self.collate(samples)\n batch = self.after_collate(batch)\n return batch",
"title": ""
},
{
"docid": "639d9b9435c54892db97b24c255ba9ee",
"score": "0.6082922",
"text": "def collate_fn(data):\n def merge(sequences):\n lengths = [len(seq) for seq in sequences]\n padded_seqs = torch.zeros(len(sequences), max(lengths)).long()\n for i, seq in enumerate(sequences):\n end = lengths[i]\n if end:\n padded_seqs[i, :end] = torch.LongTensor(seq[:end])\n return padded_seqs, lengths\n \n data.sort(key=lambda x: len(x[0]), reverse=True)\n src_seqs, trg_seqs, pos_seqs, form_seqs, key_seqs, bert_seqs = zip(*data)\n\n # merge sequences (from tuple of 1D tensor to 2D tensor)\n src_seqs, src_lengths = merge(src_seqs)\n trg_seqs, trg_lengths = merge(trg_seqs)\n form_seqs, form_lengths = merge(form_seqs)\n\n return src_seqs, src_lengths, trg_seqs, trg_lengths, pos_seqs, (form_seqs, form_lengths), key_seqs, bert_seqs",
"title": ""
},
{
"docid": "532090b3a08e7e1ef99f56738d6e83b0",
"score": "0.6055044",
"text": "def collate_fn(batch):\n pos, out_input, out_label, weights, weight_factor = zip(*batch)\n out_input = torch.stack(out_input, 0)\n out_label = torch.stack(out_label, 0)\n weights = torch.stack(weights, 0)\n weight_factor = np.stack(weight_factor, 0)\n\n return pos, out_input, out_label, weights, weight_factor",
"title": ""
},
{
"docid": "37589e28c1128914fb1fc418d3cfbf3c",
"score": "0.59103",
"text": "def collate_fn(data):\n # Sort a data list by text length\n data.sort(key=lambda x: len(x[1]), reverse=True)\n images, texts, hashtag_sets, users, locations, times, ids = zip(*data)\n\n # Merge images and users (convert tuple of 3D tensor to 4D tensor)\n images = images if isinstance(images[0], str) else torch.stack(images, 0)\n users = torch.stack(users, 0)\n\n def set_up_targets(tweet_texts, lengths):\n if len(lengths) > 0:\n targets = torch.zeros(len(tweet_texts), max(lengths)).long()\n\n for i, text in enumerate(tweet_texts):\n end = lengths[i]\n targets[i, :end] = text[:end]\n else:\n return torch.zeros(0)\n\n return targets\n\n # Merge texts (convert tuple of 1D tensor to 2D tensor)\n text_lengths = [len(text) for text in texts]\n text_targets = set_up_targets(texts, text_lengths)\n loc_lengths = [[len(text) for text in l_texts] for l_texts in locations]\n time_lengths = [[len(text) for text in t_texts] for t_texts in times]\n loc_targets, time_targets = [], []\n\n for i, l_texts in enumerate(locations):\n loc_targets.append(set_up_targets(l_texts, loc_lengths[i]))\n\n for i, t_texts in enumerate(times):\n time_targets.append(set_up_targets(t_texts, time_lengths[i]))\n\n return images, text_targets, hashtag_sets, users, loc_targets, \\\n time_targets, text_lengths, loc_lengths, time_lengths, ids",
"title": ""
},
{
"docid": "bfba456902ed97f9dbe446f831ec8c62",
"score": "0.5886282",
"text": "def collate_func(self, batch):\n src_sequences, src_sequence_lengths = ParallelTextDataSet.pad_tokenized_sequence(\n [src for src, _ in batch], self.src_max_length)\n tgt_sequences, tgt_sequence_lengths = ParallelTextDataSet.pad_tokenized_sequence(\n [tgt for _, tgt in batch], self.tgt_max_length)\n src_sequence_lengths, sorted_idx = src_sequence_lengths.sort(descending=True)\n\n src_sequences = src_sequences[sorted_idx].contiguous()\n tgt_sequences = tgt_sequences[sorted_idx].contiguous()\n tgt_sequence_lengths = tgt_sequence_lengths[sorted_idx].contiguous()\n\n return src_sequences.long(), src_sequence_lengths.int(), \\\n tgt_sequences.long(), tgt_sequence_lengths.int()",
"title": ""
},
{
"docid": "216452966faa6054463000f3abf6a222",
"score": "0.5826128",
"text": "def collate_fn(dataset):\n # transpose dict\n batch_by_columns = {}\n for key in dataset[0].keys():\n batch_by_columns[key] = list(map(lambda d: d[key], dataset))\n\n output = {}\n output['input_ids'] = pad_sequence(batch_by_columns[\"input_ids\"], batch_first=True)\n output['labels'] = pad_sequence(batch_by_columns[\"labels\"], batch_first=True).to(float)\n output['sentence_lens'] = batch_by_columns['sentence_lens']\n return output",
"title": ""
},
{
"docid": "67e5b911c2339873b552d2e16a781762",
"score": "0.5801349",
"text": "def collate_fn(self, batch):\n return pack_sequence([torch.tensor(pair[0]) for pair in batch], enforce_sorted=False), torch.tensor(\n [pair[1] for pair in batch])",
"title": ""
},
{
"docid": "8809c272cbb483273f4cf1483c923962",
"score": "0.57579595",
"text": "def __init__(self, *args, **kwargs):\n super(AudioDataLoader, self).__init__(*args, **kwargs)\n self.collate_fn = _collate_fn",
"title": ""
},
{
"docid": "8809c272cbb483273f4cf1483c923962",
"score": "0.57579595",
"text": "def __init__(self, *args, **kwargs):\n super(AudioDataLoader, self).__init__(*args, **kwargs)\n self.collate_fn = _collate_fn",
"title": ""
},
{
"docid": "b16a82ae21a7eb867796c1e2a9e3cdee",
"score": "0.5752136",
"text": "def __init__(self, *args, **kwargs):\n super(NaturalAudioDataLoader, self).__init__(*args, **kwargs)\n self.collate_fn = _collate_fn",
"title": ""
},
{
"docid": "ecac50ba33e7a4bffab3a8e5cac66420",
"score": "0.57023185",
"text": "def my_collate(batch):\n # from torch.utils.data.dataloader import de\n targets = []\n imgs = []\n for sample in batch:\n imgs.append(sample[0])\n targets.append(torch.FloatTensor(sample[1]))\n # return (torch.stack(imgs, 0),\n # torch.stack([i[..., :4] for i in targets], 0),\n # torch.stack([i[..., 4] for i in targets], 0).long())\n # return imgs, targets\n return torch.stack(imgs, 0), targets",
"title": ""
},
{
"docid": "f7f5e67652e31f0652a38fa09b76509c",
"score": "0.57017714",
"text": "def collate_fn(batch):\n\n return list(zip(*batch))",
"title": ""
},
{
"docid": "e668b89123a841e1003a5e5544bfd037",
"score": "0.56758326",
"text": "def collate_fn(self, batch):\n images = list()\n boxes = list()\n labels = list()\n\n for b in batch:\n images.append(b[0])\n boxes.append(b[1])\n labels.append(b[2])\n\n images = torch.stack(images, dim=0)\n\n # return a tensor (N, 3, 300, 300), 3 lists of N tesnors each\n return images, boxes, labels",
"title": ""
},
{
"docid": "02f37d9a5c9cf3c958073d13cecfd932",
"score": "0.5674368",
"text": "def collate_fn(batch):\n x, yu = zip(*batch)\n batch_x = pack_sequence(x, enforce_sorted=False)\n batch_y = pack_sequence(yu, enforce_sorted=False)\n return batch_x, batch_y",
"title": ""
},
{
"docid": "2bcfb19918ab56cde2e336cf354622ee",
"score": "0.5647454",
"text": "def collate_fn(batch):\n return tuple(zip(*batch))",
"title": ""
},
{
"docid": "ef2a5fc026959bac3b9246839c277f5f",
"score": "0.56213045",
"text": "def collate_fn(self, batch):\n if self.policy_container:\n if self.just_aug:\n imgs, targets, imgs_aug, targets_aug, imgs_names = list(zip(*batch))\n imgs_names_list = []\n for idx in range(len(imgs)):\n img_name = imgs_names[idx][0:len(imgs_names[idx])-4]\n file_format = imgs_names[idx][len(imgs_names[idx])-4:]\n imgs_names_list.append(img_name+'_aug'+file_format)\n return imgs_aug, targets_aug, imgs_names_list\n else:\n #---------------------------------------------\n imgs, targets, imgs_aug, targets_aug, imgs_names = list(zip(*batch))\n #---------------------------------------------\n images_list = []\n annots_list = []\n imgs_names_list = []\n for idx in range(len(imgs)):\n img_name = imgs_names[idx][0:len(imgs_names[idx])-4]\n file_format = imgs_names[idx][len(imgs_names[idx])-4:]\n\n images_list.append(imgs[idx])\n annots_list.append(targets[idx])\n imgs_names_list.append(img_name+file_format)\n\n images_list.append(imgs_aug[idx])\n annots_list.append(targets_aug[idx])\n imgs_names_list.append(img_name+'_aug'+file_format)\n\n #---------------------------------------------\n return images_list, annots_list, imgs_names_list\n #---------------------------------------------",
"title": ""
},
{
"docid": "180a582c17e632a2dbc0fc2c67eb3907",
"score": "0.5594906",
"text": "def collate_fn(batch, train=True):\n premise_batch, _ = stack_and_pad_tensors([row['premise'] for row in batch])\n hypothesis_batch, _ = stack_and_pad_tensors([row['hypothesis'] for row in batch])\n label_batch = torch.stack([row['label'] for row in batch])\n\n # PyTorch RNN requires batches to be transposed for speed and integration with CUDA\n transpose = (lambda b: b.t_().squeeze(0).contiguous())\n\n return (transpose(premise_batch), transpose(hypothesis_batch), transpose(label_batch))",
"title": ""
},
{
"docid": "4b89e783b038faa3821f9b967a5e75a5",
"score": "0.556889",
"text": "def visit_collate_fn(batch): \n\n\tmax_length = batch[0][0].shape[0]\n\tnum_features= batch[0][0].shape[1]\n\tl1 = []\n\tl2 = []\n\tl3 = []\n\tfor i in range (len(batch)): \n\t\tl1.append(batch[i][0].toarray())\n\t\tl2.append(batch[i][0].shape[0])\n\t\tl3.append(int (batch[i][1]) )\n\n\tseqs_tensor = torch.FloatTensor(l1)\n\tlengths_tensor = torch.LongTensor(l2)\n\tlabels_tensor = torch.LongTensor(l3)\n\n\treturn (seqs_tensor, lengths_tensor), labels_tensor",
"title": ""
},
{
"docid": "12c0cbebe3f9c671e050fa6142998b12",
"score": "0.55163985",
"text": "def collate_fn(self, batch):\n\n images = list()\n targets = list()\n\n for b in batch:\n images.append(b[0])\n targets.append(b[1])\n \n images = torch.stack(images, dim=0)\n\n return images, targets",
"title": ""
},
{
"docid": "e9af9d2f808f4186d8b701098aee7d20",
"score": "0.5510315",
"text": "def collater(self, samples):\n batch = super().collater(samples)\n collate_score(samples, batch)\n return batch",
"title": ""
},
{
"docid": "083d365520866b1181ccbff6c5cc997d",
"score": "0.5443418",
"text": "def collate_pool(dataset_list):\n batch_atom_fea, batch_nbr_fea, batch_nbr_fea_idx, batch_nbr_fea_offset = [], [], [], []\n batch_fixed_atom_idx, batch_atom_pos = [], []\n fixed_atom_mask, batch_atom_pos_final = [], []\n crystal_cell = []\n batch_target = []\n batch_ads_tag = []\n base_idx = 0\n \n for i, ((atom_fea, nbr_fea, nbr_fea_idx, nbr_fea_offset, atom_pos, nbr_pos, atom_pos_idx, cells, ads_tag, fixed_base, free_atom_idx, atom_pos_final), target)\\\n in enumerate(dataset_list):\n \n n_i = atom_fea.shape[0] # number of atoms for this crystal\n batch_atom_fea.append(atom_fea)\n batch_atom_pos.append(atom_pos)\n batch_nbr_fea.append(nbr_fea)\n batch_nbr_fea_offset.append(nbr_fea_offset)\n batch_nbr_fea_idx.append(nbr_fea_idx+base_idx)\n \n new_idx = torch.LongTensor(np.arange(n_i)+base_idx)\n crystal_cell.append(cells)\n\n fixed_atom_mask.append(torch.LongTensor(fixed_base))\n batch_ads_tag.append(torch.LongTensor(ads_tag))\n \n if type(target) is not float:\n batch_target.append(torch.Tensor(target[0]).view(-1,3))\n else:\n batch_target.append(torch.Tensor([0]))\n batch_atom_pos_final.append(atom_pos_final.view(-1,3)) \n \n base_idx += n_i\n\n return {'node_fea':torch.cat(batch_atom_fea, dim=0), \n 'edge_fea':torch.cat(batch_nbr_fea, dim=0), \n 'edge_idx':torch.cat(batch_nbr_fea_idx, dim=0), \n 'nbr_offset':torch.cat(batch_nbr_fea_offset, dim=0),\n 'atom_pos':torch.cat(batch_atom_pos, dim=0),\n 'cells': torch.cat(crystal_cell, dim=0),\n 'ads_tag_base': torch.cat(batch_ads_tag),\n 'fixed_atom_mask': torch.cat(fixed_atom_mask),\n 'atom_pos_final': torch.cat(batch_atom_pos_final)}, torch.cat(batch_target)",
"title": ""
},
{
"docid": "84d59bf59145a2f761964703a20f8d1f",
"score": "0.5435466",
"text": "def get_dataloader_collate_fn(self) -> Optional[Callable[[Any], Any]]:\n return None",
"title": ""
},
{
"docid": "1421a764c0fc939cf21416f4fcc23158",
"score": "0.53921825",
"text": "def data_collate_func(batch):\n adjacency_list, distance_list, features_list, protein_list = [], [], [], []\n labels = []\n max_size = 0\n\n for node_features, adjacency_matrix, distance_matrix, protein_feat, label \\\n in batch:\n labels.append(label)\n if adjacency_matrix.shape[0] > max_size:\n max_size = adjacency_matrix.shape[0]\n\n for node_features, adjacency_matrix, distance_matrix, protein_feat, label \\\n in batch:\n adjacency_list.append(\n pad_array(adjacency_matrix, (max_size, max_size)))\n distance_list.append(\n pad_array(distance_matrix, (max_size, max_size)))\n features_list.append(\n pad_array(node_features, (max_size, node_features.shape[1])))\n protein_list.append(protein_feat)\n\n return torch.Tensor(adjacency_list), torch.Tensor(features_list), \\\n torch.Tensor(distance_list), torch.stack(protein_list), \\\n torch.Tensor(labels)",
"title": ""
},
{
"docid": "ab3d4f38f3a9c698395045768b8a5c49",
"score": "0.53836644",
"text": "def collate_fn(self, batch):\n\n if self.split in ('train', 'valid'):\n comments, targets = zip(*batch)\n else:\n comments = batch\n\n lengths = [len(c) for c in comments]\n maxlen = max(lengths)\n padded_comments = []\n for i, c in enumerate(comments):\n padded_comments.append([0]*(maxlen - lengths[i])+c)\n\n if self.split in ('train', 'valid'):\n return torch.LongTensor(padded_comments), torch.stack(targets)\n else:\n return torch.LongTensor(padded_comments)",
"title": ""
},
{
"docid": "4fb6b959ecc72e94da8ee6a8b51eb1a2",
"score": "0.5352717",
"text": "def collate_fn(dataset_list):\n\n def _batch():\n batch_atom_fea.append(atom_fea)\n batch_nbr_fea.append(nbr_fea)\n batch_nbr_fea_idx.append(nbr_fea_idx + base_idx)\n new_idx = torch.LongTensor(np.arange(n_i) + base_idx)\n crystal_atom_idx.append(new_idx)\n\n batch_atom_fea, batch_nbr_fea, batch_nbr_fea_idx = [], [], []\n crystal_atom_idx, batch_target = [], []\n base_idx = 0\n\n if len(dataset_list[0]) == 2:\n for i, ((atom_fea, nbr_fea, nbr_fea_idx), target) in enumerate(dataset_list):\n n_i = atom_fea.shape[0] # number of atoms for this crystal\n _batch()\n base_idx += n_i\n batch_target.append(target)\n return (torch.cat(batch_atom_fea, dim=0),\n torch.cat(batch_nbr_fea, dim=0),\n torch.cat(batch_nbr_fea_idx, dim=0),\n crystal_atom_idx), torch.stack(batch_target, dim=0)\n\n else:\n for i, (atom_fea, nbr_fea, nbr_fea_idx) in enumerate(dataset_list):\n n_i = atom_fea.shape[0] # number of atoms for this crystal\n _batch()\n base_idx += n_i\n return (torch.cat(batch_atom_fea, dim=0),\n torch.cat(batch_nbr_fea, dim=0),\n torch.cat(batch_nbr_fea_idx, dim=0),\n crystal_atom_idx)",
"title": ""
},
{
"docid": "3295525b76d9ec82c7c78758f862c9b9",
"score": "0.53169525",
"text": "def collate_fn(self, features):\r\n return TupleMiniBatch(\r\n [\r\n pad_sequences(\r\n [f.input_ids for f in features],\r\n self.pad_token,\r\n self.pad_on_left,\r\n ),\r\n attention_masks(\r\n [len(f.input_ids) for f in features],\r\n self.mask_padding_with_zero,\r\n self.pad_on_left,\r\n ),\r\n pad_sequences(\r\n [f.token_type_ids for f in features],\r\n self.pad_token_segment_id,\r\n self.pad_on_left,\r\n ),\r\n th.stack([f.label for f in features])\r\n ],\r\n attributes={\r\n k: [f.attributes[k] for f in features]\r\n for k in features[0].attributes\r\n }\r\n )",
"title": ""
},
{
"docid": "e823503fbdc78b14f15f813e603df519",
"score": "0.5310294",
"text": "def transform(self, data):",
"title": ""
},
{
"docid": "3355938521e0ae457953433d7df6d842",
"score": "0.53091234",
"text": "def data_preprocess(self):\n pass",
"title": ""
},
{
"docid": "f925952eef05ee7193b5db2218950017",
"score": "0.52647406",
"text": "def __init__(self, *args, **kwargs):\n super(SpectrogramDataLoader, self).__init__(*args, **kwargs)\n self.collate_fn = _collate2_fn",
"title": ""
},
{
"docid": "de2ca275405a1a940a3696f1c63e941e",
"score": "0.52551496",
"text": "def collate_fn(dataset_items: List[dict]):\n result_batch = {\n 'spectrogram' : [],\n 'spectrogram_length' : [],\n 'text_encoded' : [],\n 'text_encoded_length' : [],\n 'text' : [],\n 'audio' : []\n }\n if len(dataset_items) == 0: return {}\n \n for i in range(len(dataset_items)):\n result_batch['spectrogram'].append(dataset_items[i]['spectrogram'].T)\n result_batch[\"spectrogram_length\"].append(result_batch['spectrogram'][-1].shape[0])\n result_batch['text_encoded'].append(dataset_items[i]['text_encoded'].T)\n result_batch['text_encoded_length'].append(result_batch['text_encoded'][-1].shape[0])\n result_batch['text'].append(re.sub(r'[^\\w\\s]','',dataset_items[i]['text']))\n result_batch['audio'].append(dataset_items[i]['audio'])\n \n result_batch['spectrogram'] = pad_sequence(result_batch['spectrogram'], padding_value=0, batch_first=True)\n result_batch['spectrogram'] = result_batch['spectrogram'][:,:,:,0]\n result_batch['spectrogram_length'] = torch.tensor(result_batch['spectrogram_length'])\n result_batch['text_encoded'] = pad_sequence(result_batch['text_encoded'], padding_value=0, batch_first=True).long()\n result_batch['text_encoded'] = result_batch['text_encoded'][:,:,0]\n \n result_batch['text_encoded_length'] = torch.tensor(result_batch['text_encoded_length'])\n \n\n\n return result_batch",
"title": ""
},
{
"docid": "18e0c5ac1f6e06e9b26be921a8b6fa3d",
"score": "0.5254237",
"text": "def transform(self, dataloader):\n result = []\n for _, (bx, by) in enumerate(dataloader):\n transformed_cols = []\n for ci, c in enumerate(bx):\n col_len = self.feature_lens[ci]\n transformed_rc = []\n for r in c:\n transformed_rc.append(self._transform_text(r, col_len))\n transformed_rc = torch.Tensor(transformed_rc).long()\n transformed_cols.append(transformed_rc)\n result.append([transformed_cols, by])\n\n return result",
"title": ""
},
{
"docid": "7dcd3958f99e452f9b496bc21fe5b778",
"score": "0.5241326",
"text": "def collate_fn(self, batches: List[Dict[str, np.ndarray]]) -> Dict[str, torch.Tensor]:\n batch = {k: torch.as_tensor(v) for k, v in batches[0].items()}\n batch['segment_ids'] = batch['segment_ids'].int()\n batch['punct_labels'] = batch['punct_labels'].long()\n batch['capit_labels'] = batch['capit_labels'].long()\n if self.use_audio:\n batch['features'] = batch['features'].to(torch.float32)\n return batch",
"title": ""
},
{
"docid": "a6db58893d9413e6e2c8b6488bd1ec79",
"score": "0.52333975",
"text": "def own_collate(batch):\n\n error_msg = \"batch must contain tensors, numbers, dicts or lists; found {}\"\n elem_type = type(batch[0])\n img = []\n gt_boxes = []\n texts = []\n for per_batch in batch:\n img.append(per_batch[0])\n gt_boxes.append(per_batch[1])\n texts.append(per_batch[2])\n\n return torch.stack(img, 0), gt_boxes, texts",
"title": ""
},
{
"docid": "0187bcdd1811f57abd8ca68ed94e3b25",
"score": "0.521656",
"text": "def collate_fn_ocr(batch):\n images, seqs, seq_lens, texts = [], [], [], []\n for sample in batch:\n images.append(sample[\"img\"])\n seqs.extend(sample[\"seq\"])\n seq_lens.append(sample[\"seq_len\"])\n texts.append(sample[\"text\"])\n images = torch.stack(images)\n seqs = torch.Tensor(seqs).int()\n seq_lens = torch.Tensor(seq_lens).int()\n batch = {\"image\": images, \"seq\": seqs, \"seq_len\": seq_lens, \"text\": texts}\n return batch",
"title": ""
},
{
"docid": "dc8e9b2014e2fc9cc179787dc3656554",
"score": "0.5202133",
"text": "def collate_fn(batch):\n return [\n batch[index] for index in map(\n lambda t: t[0],\n sorted(\n enumerate(batch), key=lambda t: t[1].shape[0], reverse=True\n )\n )\n ]",
"title": ""
},
{
"docid": "153ab6e9e6d0e44d7d0c569eb6feba71",
"score": "0.5159008",
"text": "def collate_fn(\n batch,\n) -> Tuple[torch.tensor, torch.tensor, Sequence[str], Sequence[str]]:\n\n def handle_dict_features(t: Dict[str, torch.tensor]) -> torch.tensor:\n ## Hotfix for the models to work with dict style data\n t = torch.stack(tuple(t.values()))\n ## Handles the case for forecasting input as it has history in it\n ## TODO: Come up with an efficient solution instead of if condition\n if len(t.size()) == 4:\n return torch.transpose(t, 0, 1)\n return t\n\n ## As a hotfix inp is just stacking input and constants data\n ## via {**inp_data, **const_data} i.e. merging both of them unto one dict\n inp = torch.stack(\n [\n handle_dict_features({**batch[i][0], **batch[i][2]})\n for i in range(len(batch))\n ]\n )\n out = torch.stack([handle_dict_features(batch[i][1]) for i in range(len(batch))])\n variables = list(batch[0][0].keys()) + list(batch[0][2].keys())\n out_variables = list(batch[0][1].keys())\n return inp, out, variables, out_variables",
"title": ""
},
{
"docid": "7ff7c2127629abaf468a441c7b42ac7d",
"score": "0.5143986",
"text": "def __init__(self, *args, **kwargs):\n super(NaturalAudioDataLoader_cut, self).__init__(*args, **kwargs)\n self.collate_fn = _collate_fn_cut",
"title": ""
},
{
"docid": "f63b59a8d34f5f8b3d510ff47a19b195",
"score": "0.5136441",
"text": "def preprocess_data(self):\n pass",
"title": ""
},
{
"docid": "1704df89f912967662ee66a4e0f0fc2d",
"score": "0.51283216",
"text": "def _pre_compress(self):\n pass",
"title": ""
},
{
"docid": "3794e959a09b0d74506428ee6ee98e4c",
"score": "0.51170605",
"text": "def deflate(self):\n return [fl[1](fl[0] @ self.vect) for fl in self.deflations]",
"title": ""
},
{
"docid": "07e32d6314f7c11e28d53edabeb76232",
"score": "0.5110846",
"text": "def chowder_collate(data):\n resnet_features, targets = zip(*data)\n targets = torch.FloatTensor(targets)\n\n # `lengths` are calculated as P * {max # of tiles in batch}\n lengths = [features.shape[1] for features in resnet_features]\n chowder_features = torch.zeros(len(targets), 1, max(lengths)) \n for i, features in enumerate(resnet_features):\n feature_length = lengths[i]\n chowder_features[i, 0, :feature_length] = features[:feature_length]\n return chowder_features, targets",
"title": ""
},
{
"docid": "88dd66a5773e5c1bf6695a27e389c416",
"score": "0.5107349",
"text": "def fix_decomp():",
"title": ""
},
{
"docid": "1d8cb7a8bdbc01b217908b06647b6dc5",
"score": "0.50683296",
"text": "def collate_function(batch):\n\n elem = batch[0]\n elem_type = type(elem)\n if isinstance(elem, torch.Tensor):\n out = None\n # TODO: support pytorch < 1.3\n # if torch.utils.data.get_worker_info() is not None:\n # # If we're in a background process, concatenate directly into a\n # # shared memory tensor to avoid an extra copy\n # numel = sum([x.numel() for x in batch])\n # storage = elem.storage()._new_shared(numel)\n # out = elem.new(storage)\n return torch.stack(batch, 0, out=out)\n elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \\\n and elem_type.__name__ != 'string_':\n elem = batch[0]\n if elem_type.__name__ == 'ndarray':\n # array of string classes and object\n if np_str_obj_array_pattern.search(elem.dtype.str) is not None:\n raise TypeError(default_collate_err_msg_format.format(elem.dtype))\n\n # return collate_function([torch.as_tensor(b) for b in batch])\n return batch\n elif elem.shape == (): # scalars\n # return torch.as_tensor(batch)\n return batch\n elif isinstance(elem, float):\n return torch.tensor(batch, dtype=torch.float64)\n elif isinstance(elem, int_classes):\n return torch.tensor(batch)\n elif isinstance(elem, string_classes):\n return batch\n elif isinstance(elem, container_abcs.Mapping):\n return {key: collate_function([d[key] for d in batch]) for key in elem}\n elif isinstance(elem, tuple) and hasattr(elem, '_fields'): # namedtuple\n return elem_type(*(collate_function(samples) for samples in zip(*batch)))\n elif isinstance(elem, container_abcs.Sequence):\n transposed = zip(*batch)\n return [collate_function(samples) for samples in transposed]\n\n raise TypeError(default_collate_err_msg_format.format(elem_type))",
"title": ""
},
{
"docid": "4d1e80c39dbda27a87ee9bb0511ea0a0",
"score": "0.50437105",
"text": "def collate_func(batch):\n steps_dict = defaultdict(list)\n label_dict = defaultdict(list)\n length_dict = defaultdict(list)\n max_sent_len = []\n for datum in batch:\n for idx, task_label in enumerate(datum[-1]):\n label_dict[idx].append(task_label)\n for i in range(6):\n length_dict[i].append(datum[1][i])\n \n # padding\n for i in range(6):\n max_sent_len.append(max(length_dict[i]))\n \n for datum in batch:\n for i, step in enumerate(datum[0]):\n padded_vec = np.pad(np.array(step), \n pad_width=((0, max_sent_len[i]-datum[1][i])), \n mode=\"constant\", constant_values=0)\n steps_dict[i].append(padded_vec)\n \n for key in length_dict.keys():\n length_dict[key] = torch.LongTensor(length_dict[key])\n steps_dict[key] = torch.from_numpy(np.array(steps_dict[key]).astype(np.int))\n for key in label_dict.keys():\n label_dict[key] = torch.LongTensor(label_dict[key])\n \n return [steps_dict, length_dict, label_dict]",
"title": ""
},
{
"docid": "42b4a39a289a171cafd253c5bcc65731",
"score": "0.49908772",
"text": "def load_comp3d():",
"title": ""
},
{
"docid": "038604813e74746f400c023f6575ecbf",
"score": "0.49901792",
"text": "def glCompressedTexImage1D(target, level, internalformat, width, border, imageSize, data):\n return _gl.glCompressedTexImage1D(target, level, internalformat, width, border, imageSize, data)",
"title": ""
},
{
"docid": "c57578fa6945bf40fc24f1f37cd7ca72",
"score": "0.498788",
"text": "def collate(list_of_samples):\n src_seqs = pad_sequence([sample[0] for sample in list_of_samples],padding_value=PADDING_VALUE)\n src_mask= (src_seqs == PADDING_VALUE)\n tgt_seqs = pad_sequence([sample[1] for sample in list_of_samples],padding_value=PADDING_VALUE)\n SOS = torch.zeros([1,tgt_seqs.shape[1]]).type(torch.LongTensor)\n tgt_seqs = torch.cat([SOS,tgt_seqs],dim = 0)\n return src_seqs,src_mask,tgt_seqs",
"title": ""
},
{
"docid": "52450780cda329d304d7d603ffe853d9",
"score": "0.49781",
"text": "def custom_collate(batch):\n\n data = [item[0] for item in batch]\n target = [item[1] for item in batch]\n\n min_size = float('inf')\n\n for d in data:\n assert len(d.shape) == 3\n if min_size > d.shape[2]:\n min_size = d.shape[2]\n\n # avg = int(total / len(data))\n # target_shape = (data[0].shape[0], data[0].shape[1], min_size)\n\n for i in range(len(data)):\n data[i] = crop_3d(data[i], min_size)\n data[i] = np.stack([data[i], data[i], data[i]], 0)\n\n target[i] = crop_3d(target[i], min_size)\n target[i] = np.stack([target[i], target[i], target[i]], 0)\n \n # for i in range(len(data)):\n # data[i] = zoom(data[i], target_shape)\n # data[i] = np.float32(np.stack([data[i], data[i], data[i]], 0))\n \n # for i in range(len(target)):\n # target[i] = zoom(target[i], target_shape, )\n # target[i] = np.float32(np.stack([target[i], target[i], target[i]], 0))\n \n # resampled_data = [sITK_resample(d, target_shape) for d in data]\n # resampled_target = [sITK_resample(d, target_shape) for d in target]\n\n # copy data to three channels\n # resampled_data = [np.stack([d, d, d],0) for d in resampled_data]\n # resampled_target = [np.stack([d, d, d],0) for d in resampled_target]\n\n # img_out = torch.from_numpy(np.stack(resampled_data)).float()\n # msk_out = torch.from_numpy(np.stack(resampled_target)).float()\n\n img_out = torch.from_numpy(np.stack(data)).float()\n msk_out = torch.from_numpy(np.stack(target)).float()\n\n return [img_out, msk_out]",
"title": ""
},
{
"docid": "17c970d70d10e0df8c874944ec157795",
"score": "0.49761528",
"text": "def collate(data_list):\n\n keys = data_list[0].keys\n data = data_list[0].__class__()\n\n for key in keys:\n data[key] = []\n slices = {key: [0] for key in keys}\n\n for item, key in product(data_list, keys):\n data[key].append(item[key])\n if torch.is_tensor(item[key]):\n s = slices[key][-1] + item[key].size(\n item.__cat_dim__(key, item[key]))\n else:\n s = slices[key][-1] + 1\n slices[key].append(s)\n\n if hasattr(data_list[0], '__num_nodes__'):\n data.__num_nodes__ = []\n for item in data_list:\n data.__num_nodes__.append(item.num_nodes)\n\n for key in keys:\n item = data_list[0][key]\n if torch.is_tensor(item):\n data[key] = torch.cat(data[key],\n dim=data.__cat_dim__(key, item))\n elif isinstance(item, int) or isinstance(item, float):\n data[key] = torch.tensor(data[key])\n\n slices[key] = torch.tensor(slices[key], dtype=torch.long)\n\n return data, slices",
"title": ""
},
{
"docid": "1be8d1982de56502c4640fdbdd0f4bf6",
"score": "0.4974466",
"text": "def uncollate_fn(self, batch: Any) -> Any:\n batch = self.before_uncollate(batch)\n samples = self.uncollate(batch)\n samples = self.after_uncollate(samples)\n return samples",
"title": ""
},
{
"docid": "a4a9f02cb454bcd0b734cf5d1fb56e93",
"score": "0.4972509",
"text": "def glCompressedTexImage3D(target, level, internalformat, width, height, depth, border, imageSize, data):\n return _gl.glCompressedTexImage3D(target, level, internalformat, width, height, depth, border, imageSize, data)",
"title": ""
},
{
"docid": "d27dab3b1fb59e31c783e285931483c4",
"score": "0.49626803",
"text": "def transform(self, raw_data, use_dali=False):\n raise NotImplementedError('Should be implemented in derived class!')",
"title": ""
},
{
"docid": "dadfc80bcc61d6cf5df29572f9353ae7",
"score": "0.49333307",
"text": "def collate_fn(self, image_column_names: Optional[List] = None, per_gpu_batch_size: Optional[int] = None) -> Dict:\n\n fn = {}\n if self.requires_column_info:\n assert image_column_names, \"Empty image column names.\"\n for col_name in image_column_names:\n fn[f\"{self.image_column_prefix}_{col_name}\"] = StackCollator()\n\n fn.update(\n {\n self.image_key: self.collate_func(samples_per_gpu=per_gpu_batch_size),\n }\n )\n\n return fn",
"title": ""
},
{
"docid": "c75ee1fe35abe9ff48f087977acccfb4",
"score": "0.49323395",
"text": "def glCompressedTexImage2D(target, level, internalformat, width, height, border, imageSize, data):\n return _gl.glCompressedTexImage2D(target, level, internalformat, width, height, border, imageSize, data)",
"title": ""
},
{
"docid": "699eb3362863df0bd4be273e7a34101c",
"score": "0.48884428",
"text": "def load_gl(filename):\n gl = gdal.Open(filename)\n \n # define my own labels for lc_prop2\n lc2label = {3: 'water',\n 255: 'unclassified',\n 9: 'urban',\n 25:'crop',\n 35: 'crop',\n 36: 'crop',\n 10:'forest',\n 20:'forest',\n 40: 'shrubland'}\n \n # define my own labels for lc_type5\n lc5label = {0: 'water',\n -1: 'unclassified',\n 255: 'unclassified',\n 9: 'urban',\n 8:'crop',\n 7: 'crop',\n 4:'forest',\n 3: 'forest',\n 2: 'forest',\n 1: 'forest',\n 6: 'shrubland',\n 11:'shrubland',\n 5: 'shrubland'}\n # define my own labels for lc_type1\n lc1label = {17: 'water',\n -1: 'unclassified',\n 255: 'unclassified',\n 13: 'urban',\n 12:'crop',\n 14: 'crop',\n 5:'forest',\n 4:'forest',\n 3: 'forest',\n 2: 'forest',\n 1: 'forest',\n 8: 'forest',\n 10: 'shrubland',\n 9: 'shrubland',\n 16: 'shrubland',\n 6: 'shrubland',\n 7: 'shrubland'}\n \n # build lc_dict to link the name to the index \n lc_dict = {}\n label_list = []\n for i, item in enumerate(gl.GetSubDatasets()):\n k = item[0].split(':')[-1]\n lc_dict[k] = i\n \n if i == 2:\n label_list.append(lc2label)\n elif i == 6: \n label_list.append(lc1label)\n elif i == 10: \n label_list.append(lc5label) \n else:\n label_list.append({})\n \n return (gl, lc_dict, label_list)",
"title": ""
},
{
"docid": "462803265d361681ad3c38c893e52163",
"score": "0.48838603",
"text": "def packed_clevr_collate_fn(vocab, batch):\n # batch is a list, and each element is (image, objs, boxes, triplets)\n all_imgs, all_boxes, all_triplets, all_triplet_type, all_conv_counts = [], [], [], [], []\n all_objs = []\n all_masks = None\n all_image_ids = []\n\n max_triplets = 0\n max_objects = 0\n for i, (img, objs, boxes, triplets, conv_counts, triplet_type, _, _) in enumerate(batch):\n O = boxes.size(0)\n T = triplets.size(0)\n\n if max_objects < O:\n max_objects = O\n\n if max_triplets < T:\n max_triplets = T\n\n for i, (img, objs, boxes, triplets, conv_counts, triplet_type, _, image_id) in enumerate(batch):\n all_imgs.append(img[None])\n O, T = boxes.size(0), triplets.size(0)\n\n # Padded objs\n attributes = list(objs.keys())\n sorted(attributes)\n attributes_to_index = {attributes[i]: i for i in range(len(attributes))}\n attributes_objects = torch.zeros(len(attributes), max_objects, dtype=torch.long)\n\n for k, v in objs.items():\n # Padded objects\n if max_objects - O > 0:\n zeros_v = torch.zeros(max_objects - O, dtype=torch.long)\n padd_v = torch.cat([v, zeros_v])\n else:\n padd_v = v\n attributes_objects[attributes_to_index[k], :] = padd_v\n attributes_objects = attributes_objects.transpose(1, 0)\n\n # Padded boxes\n if max_objects - O > 0:\n padded_boxes = torch.FloatTensor([[-1, -1, -1, -1]]).repeat(max_objects - O, 1)\n boxes = torch.cat([boxes, padded_boxes])\n\n # Padded triplets\n if max_triplets - T > 0:\n padded_triplets = torch.LongTensor([[0, vocab[\"pred_name_to_idx\"][\"__padding__\"], 0]]).repeat(\n max_triplets - T, 1)\n triplets = torch.cat([triplets, padded_triplets])\n triplet_type = torch.cat([triplet_type, torch.LongTensor([0] * (max_triplets - T))])\n\n all_objs.append(attributes_objects)\n all_boxes.append(boxes)\n all_triplets.append(triplets)\n all_triplet_type.append(triplet_type)\n all_conv_counts.append(conv_counts)\n all_image_ids.append(image_id)\n\n all_imgs = torch.cat(all_imgs)\n all_objs = torch.stack(all_objs, dim=0)\n all_boxes = torch.stack(all_boxes, dim=0)\n all_triplets = torch.stack(all_triplets, dim=0)\n all_triplet_type = torch.stack(all_triplet_type, dim=0)\n all_conv_counts = torch.stack(all_conv_counts, dim=0).to(torch.float32)\n all_image_ids = torch.LongTensor(all_image_ids)\n\n out = (all_imgs, all_objs, all_boxes, all_triplets, all_conv_counts, all_triplet_type, all_masks, all_image_ids)\n return out",
"title": ""
},
{
"docid": "232253124c2f85b176c8b0094f389e56",
"score": "0.4872281",
"text": "def clevr_collate(batch):\n transposed = list(zip(*batch))\n question_batch = default_collate(transposed[0])\n image_batch = transposed[1]\n if any(img is not None for img in image_batch):\n image_batch = default_collate(image_batch)\n feat_batch = transposed[2]\n if any(f is not None for f in feat_batch):\n feat_batch = default_collate(feat_batch)\n answer_batch = default_collate(transposed[3]) if transposed[3][0] is not None else None\n program_seq_batch = transposed[4]\n if transposed[4][0] is not None:\n program_seq_batch = default_collate(transposed[4])\n return [question_batch, image_batch, feat_batch, answer_batch, program_seq_batch]",
"title": ""
},
{
"docid": "dc7a357e9c6ca013e92c67540b9009a7",
"score": "0.4870297",
"text": "def SNLI_collate_func(batch):\n data_list_1 = []\n data_list_2 = []\n label_list = []\n length_list_1 = []\n length_list_2 = []\n\n for datum in batch:\n label_list.append(datum[4])\n length_list_1.append(datum[1])\n length_list_2.append(datum[3])\n # padding\n for datum in batch:\n #print (MAX_WORD_LENGTH_1-datum[1])\n padded_vec_1 = np.pad(np.array(datum[0]),\n pad_width=((0,MAX_WORD_LENGTH_1-datum[1])),\n mode=\"constant\", constant_values=0)\n \n padded_vec_2 = np.pad(np.array(datum[2]),\n pad_width=((0,MAX_WORD_LENGTH_2-datum[3])),\n mode=\"constant\", constant_values=0)\n \n data_list_1.append(padded_vec_1)\n \n data_list_2.append(padded_vec_2)\n \n# ind_dec_order = np.argsort(length_list_)[::-1]\n# data_list = np.array(data_list)[ind_dec_order]\n# length_list = np.array(length_list)[ind_dec_order]\n# label_list = np.array(label_list)[ind_dec_order]\n \n# print (type(torch.from_numpy(np.array(data_list_1))))\n# print (type(torch.LongTensor(length_list_1)))\n# print (data_list_2)\n# print (type(torch.from_numpy(np.array(data_list_2))))\n# print (type(torch.LongTensor(length_list_2)))\n# print (type(torch.LongTensor(label_list)))\n \n return [torch.from_numpy(np.array(data_list_1)), torch.LongTensor(length_list_1), torch.from_numpy(np.array(data_list_2)), torch.LongTensor(length_list_2), torch.LongTensor(label_list)]",
"title": ""
},
{
"docid": "4d8d6e8133cc59452044ff39e12a8a56",
"score": "0.48539707",
"text": "def ltr_collate(batch):\n\n error_msg = \"batch must contain tensors, numbers, dicts or lists; found {}\"\n elem_type = type(batch[0])\n if isinstance(batch[0], torch.Tensor):\n out = None\n if torch.utils.data._utils.collate._use_shared_memory:\n # If we're in a background process, concatenate directly into a\n # shared memory tensor to avoid an extra copy\n numel = sum([x.numel() for x in batch])\n storage = batch[0].storage()._new_shared(numel)\n out = batch[0].new(storage)\n return torch.stack(batch, 0, out=out)\n # if batch[0].dim() < 4:\n # return torch.stack(batch, 0, out=out)\n # return torch.cat(batch, 0, out=out)\n elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \\\n and elem_type.__name__ != 'string_':\n elem = batch[0]\n if elem_type.__name__ == 'ndarray':\n # array of string classes and object\n if torch.utils.data.dataloader.re.search('[SaUO]', elem.dtype.str) is not None:\n raise TypeError(error_msg.format(elem.dtype))\n\n return torch.stack([torch.from_numpy(b) for b in batch], 0)\n if elem.shape == (): # scalars\n py_type = float if elem.dtype.name.startswith('float') else int\n return torch.utils.data.dataloader.numpy_type_map[elem.dtype.name](list(map(py_type, batch)))\n elif isinstance(batch[0], int_classes):\n return torch.LongTensor(batch)\n elif isinstance(batch[0], float):\n return torch.DoubleTensor(batch)\n elif isinstance(batch[0], string_classes):\n return batch\n elif isinstance(batch[0], TensorDict):\n return TensorDict({key: ltr_collate([d[key] for d in batch]) for key in batch[0]})\n elif isinstance(batch[0], collections.Mapping):\n return {key: ltr_collate([d[key] for d in batch]) for key in batch[0]}\n elif isinstance(batch[0], TensorList):\n transposed = zip(*batch)\n return TensorList([ltr_collate(samples) for samples in transposed])\n elif isinstance(batch[0], collections.Sequence):\n transposed = zip(*batch)\n return [ltr_collate(samples) for samples in transposed]\n elif batch[0] is None:\n return batch\n\n raise TypeError((error_msg.format(type(batch[0]))))",
"title": ""
},
{
"docid": "99b913eff1e5810de523185b66704a87",
"score": "0.48532876",
"text": "def collate(self, samples):\n def merge(key):\n return torch.stack([s[key] for s in samples], dim=0)\n \n keys = ['feature', 'position', 'target', 'index', 'mask']\n res = {k:merge(k) for k in keys}\n \n return res",
"title": ""
},
{
"docid": "ed615071ebede881ac38cde6d0e2c796",
"score": "0.48499623",
"text": "def collater(self, samples: List[Dict]):\r\n return self._collate(samples, self.vocab.pad(), self.vocab.eos())",
"title": ""
},
{
"docid": "5b163080d4a5097329b9ead225e8d36d",
"score": "0.48484528",
"text": "def _blosc_compress(self, data):\n\t\t# return blosc.compress(data, typesize=8, cname='blosclz')\n\t\treturn blosc.compress(data, typesize=8, clevel=self._compression_level, cname='blosclz')",
"title": ""
},
{
"docid": "9b72502f6f7b69ac00b84919d2849c63",
"score": "0.4846957",
"text": "def __create_l_pyramid_features(C1, C2, C3, C4, feature_size=64):\n\n # upsample C4 to get P4 from the FPN paper\n L_P4 = keras.layers.Conv2D(feature_size, kernel_size=1, strides=1, padding='same', name='L_C4_reduced')(C4)\n L_P4_upsampled = layers.UpsampleLike(name='L_P4_upsampled')([L_P4, C3])\n L_P4 = keras.layers.Conv2D(feature_size, kernel_size=3, strides=1, padding='same', name='L_P4')(L_P4)\n\n # add P4 elementwise to C3\n L_P3 = keras.layers.Conv2D(feature_size, kernel_size=1, strides=1, padding='same', name='L_C3_reduced')(C3)\n L_P3 = keras.layers.Add(name='L_P3_merged')([L_P4_upsampled, L_P3])\n L_P3_upsampled = layers.UpsampleLike(name='L_P3_upsampled')([L_P3, C2])\n L_P3 = keras.layers.Conv2D(feature_size, kernel_size=3, strides=1, padding='same', name='L_P3')(L_P3)\n\n # add P3 elementwise to C2\n L_P2 = keras.layers.Conv2D(feature_size, kernel_size=1, strides=1, padding='same', name='L_C2_reduced')(C2)\n L_P2 = keras.layers.Add(name='L_P2_merged')([L_P3_upsampled, L_P2])\n L_P2_upsampled = layers.UpsampleLike(name='L_P2_upsampled')([L_P2, C1])\n L_P2 = keras.layers.Conv2D(feature_size, kernel_size=3, strides=1, padding='same', name='L_P2')(L_P2)\n\n # add P2 elementwise to C1\n L_P1 = keras.layers.Conv2D(feature_size, kernel_size=1, strides=1, padding='same', name='L_C1_reduced')(C1)\n L_P1 = keras.layers.Add(name='L_P1_merged')([L_P2_upsampled, L_P1])\n L_P1 = keras.layers.Conv2D(feature_size, kernel_size=3, strides=1, padding='same', name='L_P1')(L_P1)\n\n return [L_P1, L_P2, L_P3, L_P4]",
"title": ""
},
{
"docid": "4f4bcd1568ec22c5314111bf1ec24556",
"score": "0.48366684",
"text": "def from_raw(self, data, width, height, x_offset=0, y_offset=0, pal=None):\n pal = pal or omg.palette.default\n trans = chr(pal.tran_index)\n # First pass: extract pixel data in column+post format\n columns_in = [data[n:width*height:width] for n in range(width)]\n columns_out = []\n for column in columns_in:\n # Split into chunks of continuous non-transparent pixels\n postdata = filter(None, column.split(trans))\n # Find the y position where each chunk starts\n start_rows = []\n in_trans = True\n for y in range(height):\n if column[y] == trans:\n in_trans = True\n elif in_trans:\n start_rows.append(y)\n in_trans = False\n columns_out.append(zip(start_rows, postdata))\n # Second pass: compile column+post data, adding pointers\n data = []\n columnptrs = []\n pointer = 4*width + 8\n for column in columns_out:\n columnptrs.append(pack('l', pointer))\n for row, pixels in column:\n data.append(\"%c%c\\x00%s\\x00\" % (row, len(pixels), pixels))\n pointer += 4 + len(pixels)\n data.append('\\xff')\n pointer += 1\n # Merge everything together\n self.data = ''.join([pack('4h', width, height, x_offset, y_offset),\n ''.join(columnptrs), ''.join(data)])",
"title": ""
},
{
"docid": "100d9ac437f3f05f7fa6bed6a51984a9",
"score": "0.4826564",
"text": "def fast_collate(memory_format, batch):\n imgs = [img[0] for img in batch]\n targets = torch.tensor([target[1] for target in batch], dtype=torch.int64)\n w = imgs[0].size[0]\n h = imgs[0].size[1]\n tensor = torch.zeros((len(imgs), 3, h, w), dtype=torch.uint8).contiguous(\n memory_format=memory_format\n )\n for i, img in enumerate(imgs):\n nump_array = np.asarray(img, dtype=np.uint8)\n if nump_array.ndim < 3:\n nump_array = np.expand_dims(nump_array, axis=-1)\n nump_array = np.rollaxis(nump_array, 2)\n\n tensor[i] += torch.from_numpy(nump_array)\n\n return tensor, targets",
"title": ""
},
{
"docid": "a06b9a83f868817d55b6a992d205366b",
"score": "0.4822682",
"text": "def glGetCompressedTexImage(target, lod, img):\n return _gl.glGetCompressedTexImage(target, lod, img)",
"title": ""
},
{
"docid": "3d4711e3a6d090b41a1d5b3afc66204a",
"score": "0.481967",
"text": "def _collate_fn(batch, min_num_frames, max_num_frames):\n def get_min_num_frames(batch):\n return min([sample[0].size(0) for sample in batch])\n\n def get_subsample(feature, num_frames):\n length = feature.size(0)\n if length < num_frames:\n msg = 'Sample is too short'\n raise ValueError(msg)\n elif length == num_frames:\n return feature\n else:\n start = np.random.randint(0, length - num_frames)\n return feature[start:start + num_frames]\n\n min_num_frames_batch = get_min_num_frames(batch)\n num_frames = np.random.randint(min_num_frames, max_num_frames)\n num_frames = min(num_frames, min_num_frames_batch)\n\n X = []\n y = []\n for item in batch:\n feature = item[0]\n X.append(get_subsample(feature, num_frames).unsqueeze(0))\n y.append(item[1])\n return {\n 'X': torch.cat(X),\n 'y': torch.tensor(y, dtype=torch.int64)\n }",
"title": ""
},
{
"docid": "a23e700edff15d186066df2b2ed4dd53",
"score": "0.48014867",
"text": "def collate(self, samples: Any) -> Any:\n if not isinstance(samples, Tensor):\n return default_collate(samples)\n return samples",
"title": ""
},
{
"docid": "9561f3b6371939c1cccb3f4a54b0282b",
"score": "0.4800892",
"text": "def _feature_collate_fn(batch):\n packed_batch = list(zip(*batch))\n if len(packed_batch) == 5:\n _, feat_lengths, _, labels_lengths, sample_ids = packed_batch\n elif len(packed_batch) == 4:\n sample_ids = None\n _, feat_lengths, _, labels_lengths = packed_batch\n else:\n raise ValueError(\"Expects 4 or 5 tensors in the batch!\")\n\n features, labels = [], []\n for b in batch:\n feat_i, labels_i = b[0], b[2]\n features.append(feat_i)\n labels.append(labels_i)\n\n features = torch.stack(features)\n feat_lengths = torch.stack(feat_lengths)\n\n labels = torch.stack(labels)\n labels_lengths = torch.stack(labels_lengths)\n\n if sample_ids is None:\n return features, feat_lengths, labels, labels_lengths\n else:\n sample_ids = torch.tensor(sample_ids, dtype=torch.int32)\n return features, feat_lengths, labels, labels_lengths, sample_ids",
"title": ""
},
{
"docid": "87280128992c52afa7edec3bf39da818",
"score": "0.47969347",
"text": "def transform(self):",
"title": ""
},
{
"docid": "1872d41e05835c317b8f448a9f858603",
"score": "0.478985",
"text": "def packed_sync_clevr_collate_fn(vocab, batch):\n # batch is a list, and each element is (image, objs, boxes, triplets)\n all_imgs, all_boxes, all_triplets, all_triplet_type, all_source_edges = [], [], [], [], []\n all_objs = []\n all_image_ids = []\n\n max_triplets = 0\n max_objects = 0\n for i, (objs, boxes, triplets, triplet_type, source_edges, sg) in enumerate(batch):\n O = boxes.size(0)\n T = triplets.size(0)\n\n if max_objects < O:\n max_objects = O\n\n if max_triplets < T:\n max_triplets = T\n\n for i, (objs, boxes, triplets, triplet_type, source_edges, sg) in enumerate(batch):\n all_image_ids.append(int(re.findall(r'\\d+', sg['image_index'])[0]))\n O, T = boxes.size(0), triplets.size(0)\n\n # Padded objs\n attributes = list(objs.keys())\n sorted(attributes)\n attributes_to_index = {attributes[i]: i for i in range(len(attributes))}\n attributes_objects = torch.zeros(len(attributes), max_objects, dtype=torch.long)\n\n for k, v in objs.items():\n # Padded objects\n if max_objects - O > 0:\n zeros_v = torch.zeros(max_objects - O, dtype=torch.long)\n padd_v = torch.cat([v, zeros_v])\n else:\n padd_v = v\n attributes_objects[attributes_to_index[k], :] = padd_v\n attributes_objects = attributes_objects.transpose(1, 0)\n\n # Padded boxes\n if max_objects - O > 0:\n padded_boxes = torch.FloatTensor([[-1, -1, -1, -1]]).repeat(max_objects - O, 1)\n boxes = torch.cat([boxes, padded_boxes])\n\n # Padded triplets\n if max_triplets - T > 0:\n padded_triplets = torch.LongTensor([[0, vocab[\"pred_name_to_idx\"][\"__padding__\"], 0]]).repeat(\n max_triplets - T, 1)\n triplets = torch.cat([triplets, padded_triplets])\n triplet_type = torch.cat([triplet_type, torch.LongTensor([0] * (max_triplets - T))])\n source_edges = torch.cat(\n [source_edges, torch.LongTensor([vocab[\"pred_name_to_idx\"][\"__padding__\"]] * (max_triplets - T))])\n\n all_objs.append(attributes_objects)\n all_boxes.append(boxes)\n all_triplets.append(triplets)\n all_triplet_type.append(triplet_type)\n all_source_edges.append(source_edges)\n\n all_objs = torch.stack(all_objs, dim=0)\n all_boxes = torch.stack(all_boxes, dim=0)\n all_triplets = torch.stack(all_triplets, dim=0)\n all_triplet_type = torch.stack(all_triplet_type, dim=0)\n all_source_edges = torch.stack(all_source_edges, dim=0)\n all_image_ids = torch.LongTensor(all_image_ids)\n\n out = (all_objs, all_boxes, all_triplets, all_triplet_type, all_source_edges, all_image_ids)\n return out",
"title": ""
},
{
"docid": "86ebd999bf3049127f57acb9879afed0",
"score": "0.47711384",
"text": "def __data_prep__(self, turbine_id, buffer):\n new_buffer = []\n for data in buffer:\n roll,pitch,yaw = self.__euler_from_quaternion__(\n data[self.feature_ids[0]],data[self.feature_ids[1]],\n data[self.feature_ids[2]],data[self.feature_ids[3]]\n )\n row = [roll,pitch,yaw, data[self.feature_ids[4]],data[self.feature_ids[5]], data[self.feature_ids[6]]]\n new_buffer.append(row)\n return np.array(new_buffer)",
"title": ""
},
{
"docid": "a59937c84391a6d0d4f70053e309d563",
"score": "0.4766008",
"text": "def collate_fn(generator):\n users, items, item_attributes, num_attributes = zip(*generator)\n # increment attribute index by one (index zero is reserved for the padding index)\n item_attr = [torch.tensor(x + 1, dtype=torch.long) for x in item_attributes]\n item_attr = torch.nn.utils.rnn.pad_sequence(item_attr, batch_first=True, padding_value=0)\n users = torch.tensor(users, dtype=torch.long)\n items = torch.tensor(items, dtype=torch.long)\n num_attributes = torch.tensor(num_attributes, dtype=torch.float)\n return users, items, item_attr, num_attributes",
"title": ""
},
{
"docid": "ba190ced21794cc2c971076697db3b9f",
"score": "0.47650406",
"text": "def prepare_augmentation(self, data):\r\n vertical_flip = tf.image.flip_left_right\r\n rotation = lambda x: tf.keras.preprocessing.image.ImageDataGenerator(rotation_range=x)\r\n\r\n images = vertical_flip(data)\r\n data = np.concatenate((data, images))\r\n\r\n for i in range(-5, 5, 3):\r\n images = rotation(i).flow(data, shuffle=False)\r\n images = np.concatenate([images[i] for i in range(len(images))])\r\n data = np.concatenate((data, images))\r\n\r\n return data",
"title": ""
},
{
"docid": "793e85e29f30a2efdad917cda48141aa",
"score": "0.4764226",
"text": "def decode(self, latents):",
"title": ""
},
{
"docid": "bac992a7152b491a36354fbe9f3ddc20",
"score": "0.47602552",
"text": "def collate_s2t_data(batch: List[Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]:\n\n batch_size = len(batch)\n max_pieces = max(x[\"token_ids\"].size(0) for x in batch)\n max_words = max(x[\"pos_tags\"].size(0) for x in batch)\n\n output = {}\n\n for field in [\"parent_idxs\", \"parent_tags\"]: #, \"is_subtree\"]:\n output[field] = torch.cat([x[field] for x in batch])\n\n for field in [\"token_ids\", \"type_ids\", \"wordpiece_mask\"]:\n pad_output = torch.full([batch_size, max_pieces], 0, dtype=batch[0][field].dtype)\n for sample_idx in range(batch_size):\n data = batch[sample_idx][field]\n pad_output[sample_idx][: data.shape[0]] = data\n output[field] = pad_output\n\n for field in [\"pos_tags\", \"word_mask\", \"mrc_mask\"]:\n pad_output = torch.full([batch_size, max_words], 0, dtype=batch[0][field].dtype)\n for sample_idx in range(batch_size):\n data = batch[sample_idx][field]\n pad_output[sample_idx][: data.shape[0]] = data\n output[field] = pad_output\n\n for field in [\"offsets\", ]:\n fill_value = 0\n pad_output = torch.full([batch_size, max_words, 2], fill_value, dtype=torch.long)\n for sample_idx in range(batch_size):\n data = batch[sample_idx][field]\n pad_output[sample_idx][: data.shape[0]] = data\n output[field] = pad_output\n\n meta_fields = batch[0][\"meta_data\"].keys()\n output[\"meta_data\"] = {field: [x[\"meta_data\"][field] for x in batch] for field in meta_fields}\n\n return output",
"title": ""
},
{
"docid": "e7b7d432409ade54d96f0f434e538b54",
"score": "0.47581473",
"text": "def collate_dataset_variables(self):\n\n self.log('----------------------------------->>>', True)\n self.log('Collating Dataset Structure/Crystal Variables', True)\n\n for d in self.datasets.all():\n # Resolution info\n self.tables.dataset_info.set_value(d.tag, 'high_resolution', numpy.round(d.mtz_summary.high_res,3))\n self.tables.dataset_info.set_value(d.tag, 'low_resolution', numpy.round(d.mtz_summary.low_res,3))\n # Unit cell info\n self.tables.dataset_info.set_value(d.tag, ['uc_a','uc_b','uc_c','uc_alpha','uc_beta','uc_gamma'], numpy.round(d.mtz_summary.unit_cell.parameters(),3))\n self.tables.dataset_info.set_value(d.tag, 'uc_vol', numpy.round(d.mtz_summary.unit_cell.volume()),3)\n # Spacegroup info\n self.tables.dataset_info.set_value(d.tag, 'space_group', d.mtz_summary.space_group.info().type().lookup_symbol())\n # Quality info\n self.tables.dataset_info.set_value(d.tag, 'r_work', round_no_fail(d.input().get_r_rfree_sigma().r_work,3))\n self.tables.dataset_info.set_value(d.tag, 'r_free', round_no_fail(d.input().get_r_rfree_sigma().r_free,3))",
"title": ""
},
{
"docid": "4bf588b56dfc149f5d565ab096009f09",
"score": "0.4744867",
"text": "def transform(self, pzl):\n raise NotImplementedError",
"title": ""
},
{
"docid": "0c859bc72961c158e80a4f7962e1fdb4",
"score": "0.4738956",
"text": "def _concatenate_data(self):",
"title": ""
},
{
"docid": "5d297075ca2820d0804dcab14d1265f3",
"score": "0.47317752",
"text": "def create_lat_cat(lat_name, srcid_name, out_name, cpt_cats):\n\t# Get LAT source catalogue HDU\n\thdu_lat = get_fits_cat(lat_name, catname='LAT_POINT_SOURCE_CATALOG')\n\tnrows = hdu_lat.data.shape[0]\n\t\n\t# Get srcid result catalogue\n\thdu_srcid = get_fits_cat(srcid_name, catname='LAT_POINT_SOURCE_CATALOG')\n\t\n\t# Build empty ID list\n\tidlist = [[] for row in range(nrows)]\n\t\n\t# Build identification list\n\tmax_cpt = 0\n\tfor column in hdu_srcid.columns.names:\n\t\tif (column.find('ID_') != -1 and column.find('_NAME_') != -1):\n\t\t\t\n\t\t\t# Set column names to read\n\t\t\tcol_name = column\n\t\t\tcol_prob = column.replace('_NAME_', '_PROB_')\n\t\t\tcol_ra = column.replace('_NAME_', '_RA___')\n\t\t\tcol_dec = column.replace('_NAME_', '_DEC__')\n\t\t\tcol_sep = column.replace('_NAME_', '_ASEP_')\n\t\t\t\n\t\t\t# Read columns\n\t\t\tnames = hdu_srcid.data.field(col_name)\n\t\t\tprobs = hdu_srcid.data.field(col_prob)\n\t\t\tra = hdu_srcid.data.field(col_ra)\n\t\t\tdec = hdu_srcid.data.field(col_dec)\n\t\t\tsep = hdu_srcid.data.field(col_sep)\n\t\t\t\n\t\t\t# Loop over all rows and extract information\n\t\t\t#for i, name in enumerate(names):\n\t\t\t#\tif (name != ''):\n\t\t\tfor i, prob in enumerate(probs):\n\t\t\t\tif (prob > 0.0):\n\t\t\t\t\t# Set name\n\t\t\t\t\tname = names[i]\n\t\t\t\t\tif name == '':\n\t\t\t\t\t\tname = 'NoSrcName'\n\t\t\t\t\t\n\t\t\t\t\t# Search catalogue number\n\t\t\t\t\tcatid = 0\n\t\t\t\t\tfor cat in cpt_cats:\n\t\t\t\t\t\tpattern = '_'+cat['label']+'_'\n\t\t\t\t\t\tif (col_name.find(pattern) != -1):\n\t\t\t\t\t\t\tcatid = cat['number']\n\t\t\t\t\t# Build entry\n\t\t\t\t\tentry = {'name': names[i], 'prob': probs[i], 'ra': ra[i], \\\n\t\t\t\t\t 'dec': dec[i], 'angsep': sep[i], 'cat': catid}\n\t\t\t\t\tidlist[i].append(entry)\n\t\t\t\t\tif len(idlist[i]) > max_cpt:\n\t\t\t\t\t\tmax_cpt = len(idlist[i])\n\t\n\t# Stop if there were no counterparts\n\tif max_cpt == 0:\n\t\tprint 'WARNING: No counterparts found, no LAT format compatible catalogue built.'\n\t\treturn\n\n\t\n\t# Set column format strings\n\tnum_cpt = max_cpt\n\tfmt_name = '%d' % (num_cpt*25) + 'A25'\n\tfmt_prob = '%d' % num_cpt + 'E'\n\tfmt_cat = '%d' % num_cpt + 'I'\n\t\n\t# Define new table columns\n\tcolumn_number = pyfits.Column(name='ID_Number', format='I')\n\tcolumn_name = pyfits.Column(name='ID_Name', format=fmt_name)\n\tcolumn_prob = pyfits.Column(name='ID_Probability', format=fmt_prob)\n\tcolumn_ra = pyfits.Column(name='ID_RA', format=fmt_prob)\n\tcolumn_dec = pyfits.Column(name='ID_DEC', format=fmt_prob)\n\tcolumn_sep = pyfits.Column(name='ID_Angsep', format=fmt_prob)\n\tcolumn_cat = pyfits.Column(name='ID_Catalog', format=fmt_cat)\n\t\n\t# Collect all columns\n\tcolumns = [column_number, column_name, column_prob, column_ra, column_dec, \\\n\t column_sep, column_cat]\n\t\n\t# Build new table, append it to LAT catalogue and create new table of combined columns\n\thdu_new = pyfits.new_table(columns)\n\tcol_new = hdu_lat.columns + hdu_new.columns\n\thdu_new = pyfits.new_table(col_new)\n\t\n\t# Define catalogue table columns\n\tncats = len(cpt_cats)\n\tarray = numpy.zeros(ncats)\n\tcolumn_cat = pyfits.Column(name='ID_Catalog', format='I', array=array)\n\tcolumn_name = pyfits.Column(name='Name', format='A50')\n\tcolumn_ref = pyfits.Column(name='Reference', format='A255')\n\tcolumn_url = pyfits.Column(name='URL', format='A255')\n\t\n\t# Collect columns\n\tcolumns = [column_cat, column_name, column_ref, column_url]\n\t\n\t# Build new table\n\thdu_cat = pyfits.new_table(columns)\n\t\n\t# Fill new columns\n\tdata_number = hdu_new.data.field('ID_Number')\n\tdata_name = hdu_new.data.field('ID_Name')\n\tdata_prob = hdu_new.data.field('ID_Probability')\n\tdata_ra = hdu_new.data.field('ID_RA')\n\tdata_dec = hdu_new.data.field('ID_DEC')\n\tdata_sep = hdu_new.data.field('ID_Angsep')\n\tdata_cat = hdu_new.data.field('ID_Catalog')\n\tfor i, row in enumerate(idlist):\n\t\t\n\t\t# Determine the number of counterparts\n\t\tnids = len(row)\n\t\tdata_number[i] = nids\n\t\t\n\t\t# Sort list\n\t\trow.sort(compare_by('prob'), reverse=True)\n\t\t\n\t\t# Loop over all entries\n\t\tnames = ''\n\t\tfor k, cpt in enumerate(row):\n\t\t\tname = cpt['name']\n\t\t\twhile (len(name) < 25):\n\t\t\t\tname = name + ' '\n\t\t\tnames = names + name\n#\t\t\tif nids > 1:\n\t\t\tif num_cpt > 1:\n\t\t\t\tdata_prob[i][k] = cpt['prob']\n\t\t\t\tdata_ra[i][k] = cpt['ra']\n\t\t\t\tdata_dec[i][k] = cpt['dec']\n\t\t\t\tdata_sep[i][k] = cpt['angsep']\n\t\t\t\tdata_cat[i][k] = cpt['cat']\n\t\t\telse:\n\t\t\t\tdata_prob[i] = cpt['prob']\n\t\t\t\tdata_ra[i] = cpt['ra']\n\t\t\t\tdata_dec[i] = cpt['dec']\n\t\t\t\tdata_sep[i] = cpt['angsep']\n\t\t\t\tdata_cat[i] = cpt['cat']\n\t\tdata_name[i] = names\n\t\n\t# Copy over LAT catalogue keywords (except basic keywords)\n\tbasic_keys = ['XTENSION', 'BITPIX', 'NAXIS', 'NAXIS1', 'NAXIS2', 'PCOUNT', \\\n\t 'GCOUNT', 'TFIELDS']\n\tfor card in hdu_lat.header.ascardlist():\n\t\tif card.key not in basic_keys:\n\t\t\tif card.key.count('TBCOL') or card.key.count('TTYPE') or \\\n\t\t\t card.key.count('TFORM'):\n\t\t\t\tpass\n\t\t\telse:\n\t\t\t\thdu_new.header.update(card.key, card.value, card.comment)\n\t\n\t# Reformat the 'ID_Name' column\n\tcards = hdu_new.header.ascardlist()\n\tfor card in cards:\n\t\tif card.value == 'ID_Name':\n\t\t\tkey = card.key.replace('TTYPE','TFORM')\n\t\t\tvalue = fmt_name\n\t\t\thdu_new.header.update(key, value)\n\t\n\t# Fill catalogue table\n\tdata_cat = hdu_cat.data.field('ID_Catalog')\n\tdata_name = hdu_cat.data.field('Name')\n\tdata_ref = hdu_cat.data.field('Reference')\n\tdata_url = hdu_cat.data.field('URL')\n\tfor i, cat in enumerate(cpt_cats):\n\t\tdata_cat[i] = cat['number']\n\t\tdata_name[i] = cat['name']\n\t\tdata_ref[i] = cat['ref']\n\t\tdata_url[i] = cat['url']\n\t\n\t# Add keywords to catalogue table\n\thdu_cat.header.update('EXTNAME', 'ID_CAT_REFERENCE')\n\t\n\t# Build result catalogue HDU list. If additional HDUs were present in the input\n\t# catalogue then add them at the end ...\n\thdu_list = [pyfits.PrimaryHDU(), hdu_new, hdu_cat]\n\tlatcat = pyfits.open(lat_name)\n\tif len(latcat) > 2:\n\t\tfor hdu in latcat[2:]:\n\t\t\thdu_list.append(hdu)\n\t\n\t# Save LAT catalogue with attached columns\n\thdulist = pyfits.HDUList(hdu_list)\n\thdulist.writeto(out_name, clobber=True)\n\tlatcat.close()\n\t\n\t# Get Source information column names\n\tcards = hdu_lat.header.ascardlist()\n\tcolname = 'none'\n\tcolra = 'none'\n\tcoldec = 'none'\n\tcolmaj = 'none'\n\tcolmin = 'none'\n\tcolpos = 'none'\n\tfor card in cards:\n\t\t\n\t\t# Search for Name column\n\t\tif card.key[:5] == 'TBUCD' and (card.value == 'ID_MAIN' or \\\n\t\t card.value == 'ID_IDENTIFIER'):\n\t\t\tkey = 'TTYPE' + card.key[5:7]\n\t\t\tcolname = cards[key].value\n\t\telif (str(card.value).upper() == 'ID' or str(card.value).upper() == 'NAME' or \\\n\t\t str(card.value).upper() == 'NICKNAME'):\n\t\t\tkey = 'TTYPE' + card.key[5:7]\n\t\t\tcolname = cards[key].value\n\t\telif (str(card.value).upper() == 'RA'):\n\t\t\tkey = 'TTYPE' + card.key[5:7]\n\t\t\tcolra = cards[key].value\n\t\telif (str(card.value).upper() == 'DEC'):\n\t\t\tkey = 'TTYPE' + card.key[5:7]\n\t\t\tcoldec = cards[key].value\n\t\telif (str(card.value).upper() == 'POSERR95'):\n\t\t\tkey = 'TTYPE' + card.key[5:7]\n\t\t\tcolmaj = cards[key].value\n\t\telif (str(card.value).upper() == 'CONF_95_SEMIMAJOR'):\n\t\t\tkey = 'TTYPE' + card.key[5:7]\n\t\t\tcolmaj = cards[key].value\n\t\telif (str(card.value).upper() == 'CONF_95_SEMIMINOR'):\n\t\t\tkey = 'TTYPE' + card.key[5:7]\n\t\t\tcolmin = cards[key].value\n\t\telif (str(card.value).upper() == 'CONF_95_POSANG'):\n\t\t\tkey = 'TTYPE' + card.key[5:7]\n\t\t\tcolpos = cards[key].value\n\t\n\t# Create region file\n\tregfile = open(\"srcid.reg\", \"w\")\n\t\n\t# Write header\n\tregfile.write('# Region file format: DS9 version 4.0\\n')\n\tregfile.write('# Created by srcid.py\\n')\n\tregfile.write('# Filename: srcid-lat.fits\\n')\n\tregfile.write('global ')\n\tregfile.write('color=blue ')\n\tregfile.write('point=diamond ')\n\tregfile.write('font=\"helvetica 9 normal\" ')\n\tregfile.write('select=1 ')\n\tregfile.write('highlite=1 ')\n\tregfile.write('edit=1 ')\n\tregfile.write('move=0 ')\n\tregfile.write('delete=1 ')\n\tregfile.write('include=1 ')\n\tregfile.write('fixed=0 ')\n\tregfile.write('source ')\n\tregfile.write('\\n')\n\tregfile.write('fk5\\n')\n\t\n\t# Write entry for each LAT source\n\tfor i, row in enumerate(idlist):\n\t\t\n\t\t# Initialise empty region\n\t\tlat_region = ''\n\t\t\n\t\t# Get LAT source information\n\t\tlat_name = 'unknown'\n\t\tif (colname != 'none'):\n\t\t\tlat_name = hdu_lat.data.field(colname)[i]\n\t\tif (colra != 'none' and coldec != 'none'):\n\t\t\tra = '%8.4f' % hdu_lat.data.field(colra)[i]\n\t\t\tdec = '%8.4f' % hdu_lat.data.field(coldec)[i]\n\t\t\tif (colmaj != 'none'):\n\t\t\t\tif (colmin != 'none' and colpos != 'none'):\n\t\t\t\t\tvmaj = hdu_lat.data.field(colmaj)[i]\n\t\t\t\t\tvmin = hdu_lat.data.field(colmin)[i]\n\t\t\t\t\tvang = hdu_lat.data.field(colpos)[i] + 90.0\n\t\t\t\t\tif vang >= 360.0:\n\t\t\t\t\t\tvang = vang - 360.0\n\t\t\t\t\tif abs(vmaj) == float(\"inf\"):\n\t\t\t\t\t\tvmaj = 1.0 / 60.0\n\t\t\t\t\tif abs(vmin) == float(\"inf\"):\n\t\t\t\t\t\tvmin = 1.0 / 60.0\n\t\t\t\t\tif str(vmaj) == \"nan\":\n\t\t\t\t\t\tvmaj = 1.0 / 60.0\n\t\t\t\t\tif str(vmin) == \"nan\":\n\t\t\t\t\t\tvmin = 1.0 / 60.0\n\t\t\t\t\tsmaj = '%8.5f' % vmaj\n\t\t\t\t\tsmin = '%8.5f' % vmin\n\t\t\t\t\tpang = '%8.5f' % vang\n\t\t\t\t\tlat_region = 'ellipse('+ra+','+dec+','+ \\\n\t\t\t\t\t smaj+','+smin+','+pang+')'\n\t\t\t\telse:\n\t\t\t\t\trad = '%8.5f' % hdu_lat.data.field(colmaj)[i]\n\t\t\t\t\tlat_region = 'circle('+ra+','+dec+','+rad+')'\n\t\t\n\t\t# Write LAT source header\n\t\tregfile.write('#\\n')\n\t\tregfile.write('# '+lat_name+'\\n')\n\t\t\n\t\t# Determine the number of counterparts\n\t\tnids = len(row)\n\t\tdata_number[i] = nids\n\t\t\n\t\t# Sort list\n\t\t#row.sort(compare_by('prob'), reverse=True)\n\t\t\n\t\t# Loop over all counterparts\n\t\tfor k, cpt in enumerate(row):\n\t\t\t\n\t\t\t# Gather counterpart information\n\t\t\tcpt_name = cpt['name']\n\t\t\tcpt_ra = '%8.4f' % cpt['ra']\n\t\t\tcpt_dec = '%8.4f' % cpt['dec']\n\t\t\t\n\t\t\t# Write entry\n\t\t\tregfile.write('point('+cpt_ra+','+cpt_dec+') ')\n\t\t\tregfile.write('# ')\n\t\t\tregfile.write('text={'+cpt_name+'}\\n')\n\t\t\n\t\t# Write LAT entry\n\t\tregfile.write(lat_region)\n\t\tregfile.write(' #')\n\t\tregfile.write(' color=green')\n\t\tregfile.write(' font=\"helvetica 11 normal\"')\n\t\tregfile.write(' text={'+lat_name+'}\\n')\n\n\t# Close region file\n\tregfile.close()",
"title": ""
},
{
"docid": "8ac8f3d9f107b28044f21304d60c2b8d",
"score": "0.47292796",
"text": "def collater(self, samples):\n if len(samples) == 0:\n return {}\n\n def merge(key, pad, eos, left_pad, move_eos_to_beginning=False):\n return data_utils.collate_tokens(\n [s[key] for s in samples],\n pad, eos, left_pad, move_eos_to_beginning,\n )\n\n id = torch.LongTensor([s['id'] for s in samples])\n src_tokens = merge('source', self.src_dict.pad(),\n self.src_dict.eos(),\n left_pad=self.left_pad_source)\n\n # sort by descending source length\n src_lengths = torch.LongTensor([s['source'].numel() for s in samples])\n src_lengths, sort_order = src_lengths.sort(descending=True)\n id = id.index_select(0, sort_order)\n src_tokens = src_tokens.index_select(0, sort_order)\n\n prev_output_tokens = None\n target = None\n tgt_lengths = None\n ok_target = None\n if samples[0].get('target', None) is not None:\n\n target = merge('target', self.tgt_dict.pad(),\n self.tgt_dict.eos(),\n left_pad=self.left_pad_source)\n ok_target = merge('target', self.tgt_dict.pad(),\n self.tgt_dict.eos(),\n left_pad=self.left_pad_target)\n\n tgt_lengths = torch.LongTensor(\n [s['target'].numel() for s in samples])\n target = target.index_select(0, sort_order)\n ok_target = ok_target.index_select(0, sort_order)\n tgt_lengths = tgt_lengths.index_select(0, sort_order)\n ntokens = sum(len(s['target']) for s in samples)\n\n if self.input_feeding:\n # we create a shifted version of targets for feeding the\n # previous output token(s) into the next decoder step\n prev_output_tokens = merge(\n 'target', self.tgt_dict.pad(),\n self.tgt_dict.eos(),\n left_pad=self.left_pad_target,\n move_eos_to_beginning=True,\n )\n prev_output_tokens = prev_output_tokens.index_select(0,\n sort_order)\n else:\n ntokens = sum(len(s['source']) for s in samples)\n\n # p = 0.5\n # mask = torch.distributions.Bernoulli(torch.Tensor([p]))\n # mask_tensor = None\n #\n # if samples[0].get('target', None) is not None:\n # mask_tensor = mask.sample(target.size())[:, :, 0]\n #\n # target[(target != self.tgt_dict.pad()) & (\n # mask_tensor.byte())] = self.tgt_dict.index(\"<MASK>\")\n # mask_tensor[(target == self.tgt_dict.pad())] = 0\n\n batch = {\n 'id': id,\n 'nsentences': len(samples),\n 'ntokens': ntokens,\n 'net_input': {\n 'src_tokens': src_tokens,\n 'src_lengths': src_lengths,\n # 'masked_tgt': target,\n 'tgt_lengths': tgt_lengths,\n },\n 'target': ok_target,\n # 'masks': mask_tensor\n }\n\n if prev_output_tokens is not None:\n batch['net_input']['prev_output_tokens'] = prev_output_tokens\n return batch",
"title": ""
},
{
"docid": "5abeff0d742c949745f8147002ce1cff",
"score": "0.47169727",
"text": "def generate_laplace(in_file, out_file):\n\n print(f'Generating Laplacian texture: {out_file}')\n\n img = gdal.Open(in_file)\n ds = img.GetGeoTransform()\n ulx, xres, xskew, uly, yskew, yres = ds\n nx = img.RasterXSize\n ny = img.RasterYSize\n\n driver = gdal.GetDriverByName(\"GTiff\")\n outdata = driver.Create(out_file, nx, ny, 1, gdal.GDT_Float32)\n outdata.SetGeoTransform(img.GetGeoTransform()) ##sets same geotransform as input\n outdata.SetProjection(img.GetProjection()) ##sets same projection as input\n\n in_band = img.GetRasterBand(1)\n in_array = in_band.ReadAsArray()\n nodata_val = in_band.GetNoDataValue()\n print('Filtering')\n array = ndi.laplace(in_array)\n\n print('Writing')\n outdata.GetRasterBand(1).WriteArray(array)\n outdata.GetRasterBand(1).SetNoDataValue(nodata_val) ##if you want these values transparent\n outdata.FlushCache() ##saves to disk!!\n outdata = None\n band = None\n ds = None\n\n print('Laplacian successfully generated')",
"title": ""
},
{
"docid": "2c8b3a1990d54dccd8a51bff4fa58e8c",
"score": "0.47110167",
"text": "def collateral(self, collateral):\n\n self._collateral = collateral",
"title": ""
},
{
"docid": "cc1e5fff8f6f10f3815205018831fe25",
"score": "0.47107804",
"text": "def init_data_map(self):\n\n if not self.data_map:\n message = 'data_map is None.'\n raise PDProcessorError(message)\n self.source_cols = list(set(\n [source_col for (final_col, source_col, formatter) in self.data_map]))\n self.final_cols = [final_col for (final_col, source_col, formatter) in\n self.data_map]\n data_map = []\n for final_col, source_col, formatter in self.data_map:\n if not formatter:\n formatter = '_format_none'\n try:\n data_map.append((final_col, source_col, getattr(self, formatter)))\n except AttributeError:\n message = \"Formatter '{formatter}' is not defined.\".format(formatter = formatter)\n raise PDProcessorError(message)\n self.data_map = data_map",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="723"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">1b765e94847150c9962d5efd934b7ad4</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Check that the GRIB2 URL exist and is of useful length.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "576189b698c80c0c60c6cfe0bfefe259",
"score": "0.76307017",
"text": "def _check_grib(self, url):\n head = requests.head(url)\n check_exists = head.ok\n if check_exists:\n check_content = int(head.raw.info()[\"Content-Length\"]) > 1_000_000\n return check_exists and check_content\n else:\n return False",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "204ea2128c5700896e1ad6969fecb6de",
"score": "0.6853001",
"text": "def checkUrlExists(window,url):\n\tcon = None\n\ttry:\n\t\tcon=db.connect('dlList.db')\n\t\tcursor = con.cursor()\n\t\tsql = \"SELECT * FROM downloads WHERE fileurl='\"+url+\"'\"\n\t\tcursor.execute(sql)\n\t\trows = cursor.fetchall()\n\t\tif len(rows)>0:\n\t\t\treturn 1\n\t\telse:\n\t\t\treturn 0\n\texcept IOError as e:\n\t\thandleError(window,e,Gtk.MessageType.ERROR)",
"title": ""
},
{
"docid": "f22c48e37e054319d400ecface591914",
"score": "0.673813",
"text": "def checking_url():\n return (\"ok\")",
"title": ""
},
{
"docid": "d8995d8f4eef52bb77cddf734f593ea7",
"score": "0.66416883",
"text": "def _url_exists(self, url):\n response = requests.get(url)\n if response.status_code == 200:\n return True\n\n return False",
"title": ""
},
{
"docid": "340879250c9021bc33d70e9a57224e1f",
"score": "0.6585988",
"text": "def url_check(url):\n #Description\n try:\n site_ping = requests.head(url)\n if site_ping.status_code < 400:\n # To view the return status code, type this : **print(site.ping.status_code)** \n return True\n else:\n return False\n except Exception:\n return False",
"title": ""
},
{
"docid": "c50d04e05ed1ea1122afa64d1654d722",
"score": "0.6573698",
"text": "def url_exists(url):\n return True\n validator = URLValidator(True)\n try:\n validator(url)\n return True\n except ValidationError:\n return False",
"title": ""
},
{
"docid": "8e07913a571593d93dde75f277324eb6",
"score": "0.6561532",
"text": "def check_url(url):\n client = http_client()\n response = client.head(url)\n\n if response.status_code == requests.codes.not_implemented:\n response = client.get(url)\n\n if response.status_code == requests.codes.ok:\n return url",
"title": ""
},
{
"docid": "461ad962633b45ce919fb41a72da180c",
"score": "0.651559",
"text": "def is_valid_url(url):\n # Split the url into host and path to the page.\n host, path = urlparse.urlsplit(url)[1:3]\n conn = httplib.HTTPConnection(host)\n # Read the header and not the entire content to save network traffic.\n conn.request(\"HEAD\", path)\n # Check if the response if valid.\n if conn.getresponse().status >= 400:\n print 'link may be invalid'\n conn.close()",
"title": ""
},
{
"docid": "f938ee0985cb332bf6524174e5d81bc0",
"score": "0.6491921",
"text": "def uri_check(uri):\n pass",
"title": ""
},
{
"docid": "2692611e713281f7964b9df9756251f7",
"score": "0.6476068",
"text": "def verify_url(self) -> bool:\n try:\n req = request.Request(self._url)\n req.get_method = lambda: 'HEAD'\n request.urlopen(req)\n logger.info(f\"URL passed verification: {self._url}\")\n return True\n except (HTTPError, ValueError):\n logger.info(f\"URL failed verification: {self._url}\")\n return False",
"title": ""
},
{
"docid": "498706cc225316d059732c8d9adda40a",
"score": "0.64337736",
"text": "def url_exist(url, timeout=20):\r\n print '[testing]', url\r\n \r\n if url is None:\r\n return False\r\n \r\n if os.path.isfile(url):\r\n return True\r\n # for now we cannot determine existence of url in rtmp or mms protocol\r\n if url.startswith(('rtmp', 'rtsp', 'rtp', 'mms')):\r\n return None\r\n \r\n if not url.startswith('http'):\r\n return False\r\n \r\n if url == '' or url.find(' ') != -1:\r\n return False \r\n \r\n scheme, netloc, path, query, fragment = urlsplit(url)\r\n #print 'scheme:', scheme\r\n #print 'netloc:', netloc\r\n #print 'path:', path\r\n #print 'query:', query\r\n #print 'fragment:', fragment\r\n \r\n if netloc == '':\r\n return False\r\n \r\n site = netloc\r\n \r\n if query != '':\r\n query = '?' + query \r\n path = path + query\r\n print site, path\r\n \r\n conn = None\r\n try:\r\n conn = httplib.HTTPConnection(site, timeout=timeout)\r\n conn.request('HEAD', path)\r\n response = conn.getresponse()\r\n #print response.getheaders()\r\n print response.getheader('accept-ranges')\r\n except Exception:\r\n print traceback.print_exc()\r\n return False\r\n finally:\r\n if conn: conn.close()\r\n return response.status in (200, 301, 302)",
"title": ""
},
{
"docid": "c71860889634b2c05fba63f540014d19",
"score": "0.641286",
"text": "def is_available(self, relurl):\n try:\n stat = self.head(relurl)[0]\n return stat >= 200 and stat < 300\n except DistribServerError as ex:\n return False",
"title": ""
},
{
"docid": "921378043f6aaa4b9ee7f6151c0dacb6",
"score": "0.63896143",
"text": "def _checkInternetSpec(self):\n try:\n online = urllib2.urlopen(spec_url,timeout=1)\n return True\n except urllib2.URLError as err:\n return False\n return False",
"title": ""
},
{
"docid": "18b296c2d7e15a5dc816f74788d191ca",
"score": "0.6369461",
"text": "def check(self):\n if len(self._urls) < 1 and not self.mediaid:\n raise Errors.RepoError, \\\n 'Cannot find a valid baseurl for repo: %s' % self.ui_id",
"title": ""
},
{
"docid": "c03f3a06a95ee14adf583600bc891c1c",
"score": "0.6266908",
"text": "def remote_file_exists(self, url):\n return requests.head(url).headers['Location'].find('error') == -1",
"title": ""
},
{
"docid": "082aa341199409c74d61e8394f91b3da",
"score": "0.6252521",
"text": "def url_check(url):\n # getting url request\n print('Connecting to ', url)\n\n req_check = requests.get(url)\n\n # checking request status\n print('url request status = [{}]'.format(req_check.status_code))\n\n if req_check.status_code == requests.codes.ok:\n print('request OK')\n logging.info('url request status = [{}]'.format(req_check.status_code))\n else:\n print('request error')\n logging.warning('Url request from {} Failed, '.format(url))\n\n return",
"title": ""
},
{
"docid": "a7c31dd9c79c685351c25b9a87a52554",
"score": "0.62205017",
"text": "def verify_url(url):\n request = requests.get(url)\n return request.status_code == 200",
"title": ""
},
{
"docid": "fa91de0bd5c29f403c0482bf1eadf30f",
"score": "0.6168059",
"text": "def valid(self, url):\n if validators.url(url):\n return True\n return False",
"title": ""
},
{
"docid": "d7fec6ce59696e6a7dee9f8bbc1e311c",
"score": "0.6159225",
"text": "def check_host(self, target, port):\n url = target+':'+port\n test_strings = ['/sjf_hdid','/s_a?jghjf/','/']\n response = 0\n errors = 0\n for test in test_strings:\n try:\n conn = httplib2.Http(disable_ssl_certificate_validation=True)\n if port == '443':\n try:\n resp, content = conn.request('https://' + url + test, 'GET')\n if resp['status'] == '200':\n response += 1\n except:\n pass\n else:\n resp, content = conn.request('http://' + url + test, 'HEAD')\n\n if resp['status'] == '200':\n response += 1\n\n except ConnectionError as e:\n errors += 1\n logging.debug('Error: '+str(e))\n\n if errors == 3:\n logging.debug(R+'Error limit reached for host %s:%s' %(target,port)+W)\n return False\n\n elif response == 3:\n logging.warning(R+'Ambiguous response from web server on %s:%s. All URIs return status 200' %(target, port)+W)\n return False\n\n return True",
"title": ""
},
{
"docid": "bdca40fa700e7e0a552f8aa3d01ae329",
"score": "0.6116731",
"text": "def url_is_valid(url):\n try:\n o = requests.head(url, allow_redirects=True)\n if o.status_code == requests.codes.ok:\n util_logger.info(str(url) + \" responded with \" + str(o.status_code))\n return True\n else:\n return False\n except Exception:\n util_logger.warning(\"url \" + str(url) + \" did not respond with a 200\")\n return False",
"title": ""
},
{
"docid": "bdca40fa700e7e0a552f8aa3d01ae329",
"score": "0.6116731",
"text": "def url_is_valid(url):\n try:\n o = requests.head(url, allow_redirects=True)\n if o.status_code == requests.codes.ok:\n util_logger.info(str(url) + \" responded with \" + str(o.status_code))\n return True\n else:\n return False\n except Exception:\n util_logger.warning(\"url \" + str(url) + \" did not respond with a 200\")\n return False",
"title": ""
},
{
"docid": "0b732083a81ccf617510d5932f06f3a7",
"score": "0.6109237",
"text": "def url_is_alive(url):\r\n \r\n\r\n try:\r\n request = urllib.request.Request(url)\r\n request.get_method = lambda: 'HEAD'\r\n urllib.request.urlopen(request)\r\n return True\r\n except ValueError:\r\n return False\r\n except urllib.request.HTTPError:\r\n return False",
"title": ""
},
{
"docid": "aee5d167b626f98242f4126753bffde0",
"score": "0.61060935",
"text": "def valid_url(self, var):\n\n if re.match(r\"https?:\\/\\/(www\\.)?[-a-zA-Z0-9@:%._\\+~#=]{2,256}\\.[a-z]{2,6}\\b([-a-zA-Z0-9@:%_\\+.~#?&//=]*)\", var):\n return True\n return False",
"title": ""
},
{
"docid": "246c16cfc7566cac1b9c47a02d2f8aef",
"score": "0.6078562",
"text": "def full_url(self, url) -> bool:\n return self.filter(full_url=url).exists()",
"title": ""
},
{
"docid": "d589a573d088296e195415bb36dd98ff",
"score": "0.6072215",
"text": "def test_uri(base, config_file_exists, capsys):\n if config_file_exists:\n assert bool(re.match(r\"gs://.*\", base.uri))\n else:\n out, err = capsys.readouterr()\n assert \"Apparently, that is the first time that you are using\" in out",
"title": ""
},
{
"docid": "437d78bf1331f7fe173f900af71fbcd8",
"score": "0.6059337",
"text": "def validate_url(url: str) -> bool:\n prepared_request = PreparedRequest()\n try:\n prepared_request.prepare_url(url, None)\n return True\n except MissingSchema as e:\n return False",
"title": ""
},
{
"docid": "19498ebe24fc3c491ebe9b544ca79c93",
"score": "0.60559255",
"text": "def url_is_alive(url):\n request = urllib.request.Request(url)\n request.get_method = lambda: 'HEAD'\n\n try:\n urllib.request.urlopen(request)\n return True\n except urllib.request.HTTPError:\n return False",
"title": ""
},
{
"docid": "8472aaebece8339c9957079376eef21b",
"score": "0.605163",
"text": "def exists(self, url):\r\n try:\r\n self._connection.stat(self._get_filename(url))\r\n except IOError:\r\n return False\r\n else:\r\n return True",
"title": ""
},
{
"docid": "602b4123d88472a216a9c3783f981e1d",
"score": "0.6028184",
"text": "def validate_api_url(url):\n if url is None:\n raise GTmetrixAPIUrlIsNone\n\n if url != settings.good_url:\n raise GTmetrixBadAPIUrl\n\n return True",
"title": ""
},
{
"docid": "5b21cb48f2162f2d547db6b378f03689",
"score": "0.6020513",
"text": "def _validate_fetch(self):\n for url, file_size, filename in self.fetch_entries():\n # fetch_entries will raise a BagError for unsafe filenames\n # so at this point we will check only that the URL is minimally\n # well formed:\n parsed_url = urlparse(url)\n\n # only check for a scheme component since per the spec the URL field is actually a URI per\n # RFC3986 (https://tools.ietf.org/html/rfc3986)\n if not all(parsed_url.scheme):\n raise BagError(_('Malformed URL in fetch.txt: %s') % url)",
"title": ""
},
{
"docid": "11bfd267cec489764e2b356be26f20b9",
"score": "0.60191405",
"text": "def is_invalid(url):\r\n return not validators.url(url)",
"title": ""
},
{
"docid": "968cef9bff51d373c12237a9bf9323bc",
"score": "0.6016218",
"text": "def validate(): # runs before any app.route()\n if request.path == '/shorts': # if the server request is to '/shorts' ...\n shortpath = str(request.form.get('shortpath')).strip() # grab the shortpath\n if shortpath == \"\": \n pass # if there's no path, no worries\n else:\n if Path.query.filter_by(path=shortpath).first():\n app.logger.debug(\"made it here\")\n flash(\"already taken!\")\n\n inputURL = str(request.form.get('url')).lower().strip() # grab the URL\n if inputURL == None or inputURL == \"\": # if it's not there ...\n abort(412) # throw the 412",
"title": ""
},
{
"docid": "09f7bdda97c7af127ae14d84dc437a6c",
"score": "0.60038006",
"text": "def file_path_validation(url):\n file_path = get_file_path(url)\n\n return os.path.isfile(file_path)",
"title": ""
},
{
"docid": "ee1c526fadfca527a5dc89da42137beb",
"score": "0.59763384",
"text": "def is_valid(self, url):\r\n parsed = urlparse(url)\r\n path = parsed.path.lower()\r\n query = parsed.query.lower()\r\n\r\n if parsed.scheme not in set([\"http\", \"https\"]):\r\n return False\r\n\r\n if len(url) > 300:\r\n return False\r\n\r\n #measuring crawler progress\r\n base = url.split('?', 1)[0]\r\n if self.frequency[\"url\"] != base:\r\n self.frequency[\"url\"] = base\r\n self.frequency[\"count\"] = 0\r\n if self.frequency[\"url\"] == base:\r\n self.frequency[\"count\"] += 1\r\n if self.frequency[\"count\"] > 500:\r\n return False\r\n\r\n if \"calendar\" in query:\r\n return False\r\n\r\n if self.trim_scheme(url) in self.trimmed: #same url but with http/https\r\n return False\r\n\r\n parameters = url.split('=') #long hex strings in parameters\r\n for p in parameters:\r\n if re.match(r\"^[a-zA-Z0-9]{30,}$\",p):\r\n return False\r\n\r\n #repeated directories\r\n #https://support.archive-it.org/hc/en-us/articles/208332963-Modify-crawl-scope-with-a-Regular-Expression#RepeatingDirectories\r\n if re.match(r\"^.*?(/.+?/).*?\\1.*$|^.*?/(.+?/)\\2.*$\", path):\r\n return False\r\n\r\n try:\r\n return \".ics.uci.edu\" in parsed.hostname \\\r\n and not re.match(\".*\\.(css|js|bmp|gif|jpe?g|ico\" + \"|png|tiff?|mid|mp2|mp3|mp4\" \\\r\n + \"|wav|avi|mov|mpeg|ram|m4v|mkv|ogg|ogv|pdf\" \\\r\n + \"|ps|eps|tex|ppt|pptx|doc|docx|xls|xlsx|names|data|dat|exe|bz2|tar|msi|bin|7z|psd|dmg|iso|epub|dll|cnf|tgz|sha1\" \\\r\n + \"|thmx|mso|arff|rtf|jar|csv\" \\\r\n + \"|rm|smil|wmv|swf|wma|zip|rar|gz|pdf)$\", parsed.path.lower())\r\n\r\n except TypeError:\r\n print(\"TypeError for \", parsed)\r\n return False",
"title": ""
},
{
"docid": "488d06bccc8d994d8ea08025669d6120",
"score": "0.5973636",
"text": "def _url_check(url):\n\n if \".\" not in url:\n exc_message = \"'{url}' is not a vaild URL.\".format(url=url)\n raise URLError(exc_message)",
"title": ""
},
{
"docid": "ebeb03e7328c90c36f9315cf8802d41e",
"score": "0.59244484",
"text": "def _is_exist_url(self, selenium_elements: list, url: str):\n pass",
"title": ""
},
{
"docid": "a7f2d9adf1185cba8dce9f63195fc35a",
"score": "0.5913329",
"text": "def check_url(url):\n result_ok = False\n try:\n result = urllib.parse.urlparse(url)\n result_ok = all([result.scheme, result.netloc])\n except ValueError:\n result_ok = False\n\n if not result_ok:\n print(f'WARN: {url} is NOT valid')\n\n return result_ok",
"title": ""
},
{
"docid": "90fcbcbe3e5bb563c58094bc2cabe120",
"score": "0.5903844",
"text": "def exists(self, url):\r\n return os.path.exists(self._get_filename(url))",
"title": ""
},
{
"docid": "5568305da004cf0d2f4769f265328eee",
"score": "0.5903483",
"text": "def check_connection(url):\n resp = requests.head(url + POP_PATH)\n\n if resp.status_code != 200:\n print(\"Unable to find backend at:\", url,\n file=sys.stderr)\n exit(4)",
"title": ""
},
{
"docid": "9293fe1c961445775acaf16f8fa18667",
"score": "0.5902758",
"text": "def test_is_url_get_not_base(self):\n\n expected = False\n PyFunceble.CONFIGURATION.idna_conversion = False\n\n for domain in self.not_valid_domain:\n to_check = \"http://{0}/hello_world\".format(domain)\n\n actual = Check(to_check).is_url(return_base=True)\n\n self.assertEqual(expected, actual)",
"title": ""
},
{
"docid": "46dd128eb08ff43221c21407d46f4f97",
"score": "0.589285",
"text": "def is_url(url):\n return (URL_RP.fullmatch(url) is not None)",
"title": ""
},
{
"docid": "01faf716f6d8905b8a3d17130463cf1c",
"score": "0.5891136",
"text": "def match_url(cls, repo):\r\n\r\n return re.search('^https?://bitbucket.org/([^/]+/[^/]+)/?$', repo) != None",
"title": ""
},
{
"docid": "027810dd922cde2e919cc8b844a8b354",
"score": "0.5870757",
"text": "def check_server_url(url_to_test):\n try:\n resp = _get(\"{0}/_api/v2/info\".format(url_to_test))\n return 'Faraday Server' in resp\n except Exception as ex:\n logger.exception(ex)\n test_okey = False\n return test_okey",
"title": ""
},
{
"docid": "ec70da7e9529d6414e8fd1932dc87667",
"score": "0.58705425",
"text": "def verify_url(self, actual_url, expected_name, expected_pixels, expected_version):\n assert actual_url == 'http://example-storage.com/profile-images/{name}_{size}.jpg?v={version}'\\\n .format(name=expected_name, size=expected_pixels, version=expected_version)",
"title": ""
},
{
"docid": "c82e73ce1114de78f0fe4d83434cf98a",
"score": "0.5856844",
"text": "def _host_verify(self, host):\n if host == '':\n raise SwdsError('The variable host needs an URL value')\n\n try:\n import urllib\n urllib.request.urlopen(host)\n except (ValueError, URLError) as e:\n raise SwdsError('The variable host is not a valid URL e.g: \"http://apidomain.com\"')",
"title": ""
},
{
"docid": "936df488db7feeaa5da6b56fd16ed24d",
"score": "0.5850592",
"text": "def doesUrlExist ( url ):\n\terrLines = []\n\tmsgLines = []\n\toutputMsgLines = []\n\n\t_dbx( 'Cheking Url %s' %( url) )\n\tsvnRc, msgLines, errLinesFromSvn = svnQuery ( ['info', url] )\n\n\tif svnRc != 0 :\n\t\tmyRc = False\n\t\toutputMsgLines.append ( \"Got error code %d from svn.\" % ( svnRc ) )\n\t\tif len( errLinesFromSvn ) > 0:\n\t\t\t# for errLine in errLinesFromSvn: errLines.append ( errLine ) \n\t\t\toutputMsgLines.append ( errLinesFromSvn )\n\telse:\n\t\tif len ( msgLines ) > 0:\n\t\t\tmyRc = True\n\t\telse :\n\t\t\tmyRc = False\n\n\t# print(\"test exit\") ; sys.exit(1)\n\n\treturn myRc, outputMsgLines",
"title": ""
},
{
"docid": "3bdb423761a83df6d5d8b3bc69382aba",
"score": "0.584873",
"text": "def short_url(self, url) -> bool:\n return self.filter(short_url=url).exists()",
"title": ""
},
{
"docid": "de905f59a0451ae0f8ec15fc2aed8607",
"score": "0.58455056",
"text": "def website_has_been_scanned(self, url):\n\n with sqlite3.connect(self.db_path) as conn:\n cursor = conn.cursor()\n\n website_id = self.website_exists(url)\n\n if website_id:\n cursor.execute(\"SELECT COUNT(Path.id) FROM Website \"\n \"INNER JOIN WebsitePath Path on Website.id = Path.website_id \"\n \"WHERE Website.id = ?\", (website_id, ))\n return cursor.fetchone()[0] > 0\n return None",
"title": ""
},
{
"docid": "aea0525ad4b466cbec68f3753aff2155",
"score": "0.5794466",
"text": "def exists(self, url):\r\n return self._get_filename(url) in self._filesystem",
"title": ""
},
{
"docid": "5d8ceedbd51cfe7cf8f03bf960fd14dc",
"score": "0.5784438",
"text": "def _url_is_not_world_readable(self, url):\n resp = requests.get(url)\n self.assertTrue(resp.status_code > 399, resp.status_code)",
"title": ""
},
{
"docid": "d547698d058b003a9b16c525131de247",
"score": "0.57837147",
"text": "def check_size(url):\n try:\n response = requests.get(url)\n img = Image.open(BytesIO(response.content))\n except (OSError, IOError) as e:\n log_out('error', '{}: {}'.format(type(e).__name__, e))\n log_out('error', 'Image URL = {}'.format(url))\n return False\n\n # Here's where we use the right logic.\n w, h = img.size\n return op(w >= min_width, h >= min_height)",
"title": ""
},
{
"docid": "7e390bed8fd3c063ad1854975a8cff8f",
"score": "0.5765759",
"text": "def _path_exists(path):\n if path_is_remote(path):\n try:\n urlopen(path).info()\n return True\n except HTTPError as e:\n if e.code == 404:\n return False\n else:\n raise\n else:\n return os.path.exists(path)",
"title": ""
},
{
"docid": "3798824c60e333560a78c242bba08e9c",
"score": "0.5764236",
"text": "def url_ok(url, port):\n\n try:\n conn = HTTPConnection(url, port)\n conn.request(\"GET\", \"/\")\n except ConnectionRefusedError:\n logger.exception(\"Server not started\")\n return False\n\n try:\n r = conn.getresponse()\n return r.status == 200\n except:\n logger.exception(\"Server error\")",
"title": ""
},
{
"docid": "187dc5a917749091514b9ba3f8372cc6",
"score": "0.57458836",
"text": "def validate_jpeg_URI(self, jpeg_URI):\n valid = (jpeg_URI.find(self.URI_header) == 0)\n return valid",
"title": ""
},
{
"docid": "5a932c5e29953aab372c3955de1697ed",
"score": "0.5744392",
"text": "def is_valid_url(self, url):\n return page_utils.is_valid_url(url)",
"title": ""
},
{
"docid": "ffd511fd46fbd874c5defb441b0ed64b",
"score": "0.57322854",
"text": "def _verify_contents(self):\n try:\n client = httplib2.Http()\n (resp_headers, content) = client.request('http://localhost:' + self.port(),\n 'GET',\n headers={'cache-control':'no-cache'})\n str_content = content.decode('utf-8', 'replace')\n except IOError:\n return False\n\n # First check for HTTP 200 OK\n if resp_headers['status'] != '200':\n return False\n\n # Then verify that the unique string exists in the content\n if self._verify_contents_string not in str_content:\n return False\n\n return True",
"title": ""
},
{
"docid": "67cedf11a3472b361b408c73ceb138c8",
"score": "0.57196045",
"text": "def is_valid_address_url(self) -> str:\n pass",
"title": ""
},
{
"docid": "f4b4329e3b1bfcdec1ab38eed5c95429",
"score": "0.5714455",
"text": "def ValidateURL(url, profileURLS, profilesQueued, visitedUsers):\n\n return url not in profileURLS and url not in profilesQueued and \"/in/\" in url and \"connections\" not in url and \"skills\" not in url and url not in visitedUsers",
"title": ""
},
{
"docid": "8876656c2945fd26a547d9295438399e",
"score": "0.5711544",
"text": "def is_valid_url(variable):\n\n if re.match(r\"https?:\\/\\/(www\\.)?[-a-zA-Z0-9@:%._\\+~#=]{2,256}\\.[a-z]{2,6}\\b([-a-zA-Z0-9@:%_\\+.~#?&//=]*)\",\n variable):\n return True\n return False",
"title": ""
},
{
"docid": "d03e3e1988ab64e2d5765d8c7947172f",
"score": "0.5705326",
"text": "def is_candidate_url(url):\n if url is None or url == '':\n return False\n scheme, netloc, path, params, query, fragment = urllib.parse.urlparse(url)\n if scheme not in ('http', 'https'):\n return False\n if path == '' and params == '' and query == '' and fragment == '':\n return False\n return True",
"title": ""
},
{
"docid": "c88adc0973828e1a39babbf84a5f4002",
"score": "0.5705193",
"text": "def is_old_style_link(url):\n return url.count(\"://\") == 2",
"title": ""
},
{
"docid": "eec2596319fbedb0eb0dfeda268c8bb9",
"score": "0.57043463",
"text": "def validate_snap_url(self, snap_url):\n self.assertIn(self.url_base, snap_url)\n self.assertIn(\"tar.gz\", snap_url)\n self.assertNotIn(\"release\", snap_url)\n self.assertNotIn(\"public\", snap_url)",
"title": ""
},
{
"docid": "55e3e3a071088892574b9c63565eafe6",
"score": "0.5703396",
"text": "def validate_url(wiki_url: str) -> (bool, str):\n try:\n response = requests.get(wiki_url)\n except requests.ConnectionError:\n return False, None\n except Exception:\n raise\n return wiki_url.split(\".\")[1] == \"wikipedia\" or wiki_url.startswith(\"91.198.174.192\"), response.text",
"title": ""
},
{
"docid": "72c27d6e4a4e4e0e68796bb97913a4ab",
"score": "0.5703395",
"text": "def _validate_url(self, url):\n if url:\n if url.startswith(\"https://conan.io/center\"):\n raise ConanException(\"Wrong ConanCenter remote URL. You are adding the web \"\n \"https://conan.io/center the correct remote API is \"\n \"https://center.conan.io\")\n address = urlparse(url)\n if not all([address.scheme, address.netloc]):\n self._output.warning(\"The URL '%s' is invalid. It must contain scheme and hostname.\"\n % url)\n else:\n self._output.warning(\"The URL is empty. It must contain scheme and hostname.\")",
"title": ""
},
{
"docid": "998d5051cea3493139f7ae61c3c0cc88",
"score": "0.5702971",
"text": "def test_not_book_url_validate(self):\n url = \"https://www.goodreads.com/\"\n self.assertEqual(main.validate_book_page(url), False)",
"title": ""
},
{
"docid": "1bb8eb62409146feaf122f629bf1bdeb",
"score": "0.5700468",
"text": "def is_valid(self, url):\n parsed = urlparse(url)\n return bool(parsed.netloc) and bool(parsed.scheme)",
"title": ""
},
{
"docid": "fed6f4a2996427f72ae597f875b82a6f",
"score": "0.56977874",
"text": "def check(self, url):\n response = self.client.get(url, follow=True)\n self.ignore.append(url)\n # check if we're a 200\n if response.status_code != 200:\n self.success = False\n self.report(response.status_code, url, \"URL Failed\")\n return\n self.succeeded += 1\n html = response.content\n if response.get('Content-Type', '').startswith('text/html'):\n self.scan(html, url)",
"title": ""
},
{
"docid": "70361b3bce7bd1525e7e01959c12a58b",
"score": "0.56853604",
"text": "def url_check_function(string, web_page_url):\r\n try:\r\n content = requests.get(web_page_url) # Python requests.Response Object\r\n except Exception as e:\r\n print(f'Inputted URL \" {web_page_url} \" isn\\'t match'\r\n f' any URL in the WEB.' + '\\n' + str(e))\r\n return False\r\n detected: bool = string in content.text\r\n content.close() # Closes the connection to the server.\r\n return detected",
"title": ""
},
{
"docid": "887604d6f6990b3a29b3e8bfeefb9ee7",
"score": "0.5684574",
"text": "def is_valid_short(url):\n return not (not re_short.match(url))",
"title": ""
},
{
"docid": "b19d98ff525750b2ce9b447d0cf54d19",
"score": "0.56581974",
"text": "def url_valid(url):\n return re.match(regex, url) is not None",
"title": ""
},
{
"docid": "4c94760adcdafd4ed31c784324c18657",
"score": "0.56520087",
"text": "def test_restricted_url_exists(self):\n url = ''\n\n try:\n url = reverse('main:account')\n except:\n pass\n \n self.assertEqual(url, '/main/account/', f\"{FAILURE_HEADER}Have you created the rango:restricted URL mapping correctly?{FAILURE_FOOTER}\")",
"title": ""
},
{
"docid": "a677ab68c35fc7e281327b1a469a45cb",
"score": "0.5638797",
"text": "def test(url):\n if int(url.split('/')[7]) > time.time():\n r = 'ok'\n else:\n r = 'fail'\n return r",
"title": ""
},
{
"docid": "02b729268c4fde8ae8c3d908dbb49de5",
"score": "0.56385946",
"text": "def grasps(path: any) -> bool:\n parts = urlparse(str(path))\n if parts.scheme not in ['http', 'https']:\n return False\n return True",
"title": ""
},
{
"docid": "70d8ab7e02d4cf4ddb65de784285909c",
"score": "0.56277114",
"text": "def validate_image_url(url):\n is_valid = True\n\n # Test: URL uses http(s)\n if not url.startswith('http'):\n is_valid = False\n flash('Image URL: URL must start with \"http\" or \"https\"')\n else:\n headers = {'user-agent': 'instrument-catalog'}\n try:\n response = requests.head(url, headers=headers, timeout=2.0,\n allow_redirects=True)\n # Test: Image host server responds\n except (Timeout, ConnectionError):\n is_valid = False\n flash('Image URL: The image server could not be reached.'\n ' Double check the URL or try a different one.')\n else:\n # Test: Image server returns successful response\n if response.status_code is not 200:\n is_valid = False\n flash('Image URL: A request for the image failed'\n ' with status code {}.'.format(response.status_code))\n\n # Test: Image has a supported filetype\n elif response.headers.get('Content-Type').lower() not in (\n 'image/jpeg', 'image/png', 'image/gif'):\n is_valid = False\n flash('Image URL: The image must be a jpg, png, or gif.')\n\n else:\n # Test: Content-Length exists and is smaller than 300 KB\n content_length = int(response.headers.get('Content-Length', 0))\n\n if not 0 < content_length < 1024 * 300:\n is_valid = False\n flash('Image URL: The image must be under 300 KB.')\n\n # Update the URL in case there was a redirect (we waited to\n # confirm that the URL was valid before doing this)\n url = response.url\n\n return url, is_valid",
"title": ""
},
{
"docid": "eddaebcddd8484bbf9854a43084a075a",
"score": "0.56248254",
"text": "def test_default_length(self, type_: str, url: str) -> None:\n assert Enclosure(type=type_, url=url).length == 0",
"title": ""
},
{
"docid": "19c19a97d3c9fc4f6038519b36f95bfd",
"score": "0.56216097",
"text": "def validate_url(url):\n if url_regex.match(url): return True\n else: return False",
"title": ""
},
{
"docid": "477488dab8f771c3a5154b256423203a",
"score": "0.5617295",
"text": "def validateUrl(url):\n\ttry:\n\t\tregex = re.compile(r'^(?:http|ftp)s?://' # http:// or https://\n\t\tr'(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\\.)+(?:[A-Z]{2,6}\\.?|[A-Z0-9-]{2,}\\.?)|' #domain...\n\t\tr'localhost|' #localhost...\n\t\tr'\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3})' # ...or ip\n\t\tr'(?::\\d+)?' # optional port\n\t\tr'(?:/?|[/?]\\S+)$', re.IGNORECASE)\n\t\tmatch = regex.match(url)\n\t\tif match:\n\t\t\treturn 1\n\t\telse:\n\t\t\treturn 0\n\texcept IOError:\n\t\treturn 0",
"title": ""
},
{
"docid": "b22a01fa9b7cba404b40c42db1616b06",
"score": "0.56159806",
"text": "def test_invalid_url(self, URL):\n page = requests.get(URL)\n result = json.loads(page.text)\n assert \"Invalid URL\" in result['message']['url']",
"title": ""
},
{
"docid": "4c5a6c2a9af1f305a56a19d34d487002",
"score": "0.5609977",
"text": "def _page_exists(md5_string, conn):\n\n cur = conn.cursor()\n\n cur.execute(rf\"\"\"SELECT url FROM crawldb.page WHERE html_content_hash='{md5_string}'\"\"\")\n front = cur.fetchone()\n\n cur.close()\n\n exists = front is not None\n url = front\n return exists, url",
"title": ""
},
{
"docid": "4ec8eaa291098d8b0fde997758f07161",
"score": "0.56095773",
"text": "def check_file_exists(download_file_fullpath, remote_file_size):\n\tprint('-> file name= %s ' %(download_file_fullpath))\n\tprint('-> size should be= %s' %remote_file_size)\n\t# check if file exists from previous downloadds\n\tprint('-> check if file exists on your machine...')\n\tif os.path.isfile(download_file_fullpath):\n\t\t# check the file size to make sure not empty\n\t\tsize_available_file = os.path.getsize(download_file_fullpath)\n\t\tprint('-> YES, local size is= %s' %size_available_file)\n\t\tif (size_available_file == int(remote_file_size)): # in bytes\n\t\t\tprint('-> file AVAILABLE on your machine, size looks OK, we skip downloading it!')\n\t\t\treturn True # True is boolean keyword\n\t\telse:\n\t\t\tprint('-> file is there, but size NOT matched!')\n\t\t\treturn False\n\telse:\n\t\tprint('-> file NOT AVAILABLE on your machine')\n\t\treturn False",
"title": ""
},
{
"docid": "a0157f05f0806fd8d84080f78a96b39b",
"score": "0.56072646",
"text": "def _validate_event_listener_url(self, url):\n # TODO implement it; for now always returning True\n return True",
"title": ""
},
{
"docid": "3933ed61334727b1359fd5734c281306",
"score": "0.5600772",
"text": "def _check_size(item1,length,message=\"Length mismatch\"):\n if item1 is None:\n return None\n else:\n if len(item1)==length:\n return True\n else:\n raise Exception(message)",
"title": ""
},
{
"docid": "07e733d106a5bdeba9a1fde02385fd04",
"score": "0.55958295",
"text": "def _validate_url(url):\n result = parse.urlparse(url)\n if result.scheme and result.netloc:\n return True",
"title": ""
},
{
"docid": "e023b32298317a2deb50d6a36434056c",
"score": "0.5592674",
"text": "def __test(url):\n good_codes = (200, 202)\n bad_thumbnails = ('217206377fdc22b9ae48a08e819ec18f', 'e3e2234fa4fcfbbf1bdf1cd52b9a3524')\n r = requests.get(url.replace('digital', 'utils/getthumbnail').replace('cdm/ref', 'utils/getthumbnail'))\n if r.status_code in good_codes:\n with open('thumbnails.log', 'a') as thumbnails_log:\n hasher = hashlib.md5()\n hasher.update(r.content)\n thumbnails_log.write(f'{url}: {hasher.hexdigest()}\\n')\n if hasher.hexdigest() in bad_thumbnails:\n return False\n else:\n return True\n else:\n return False",
"title": ""
},
{
"docid": "3c9db0ccd385e74a169f944c74e48d81",
"score": "0.5592381",
"text": "def has_download_url(self):\n pass",
"title": ""
},
{
"docid": "5eea7df58414d39da7e49343e950a4e8",
"score": "0.55902696",
"text": "def IS_URL(data):\n b = r_url.match(data)\n if not b:\n return _('The input value is not a valid url')",
"title": ""
},
{
"docid": "5bbf51fa3f07a3df2487f1926cc85364",
"score": "0.5581744",
"text": "def check_quota():\n resp = requests.request('GET', 'https://www.random.org/quota/?format=plain')\n\n if resp.status_code != 200 or int(resp.text) <= 0:\n return False\n return True",
"title": ""
},
{
"docid": "b7a82930b5a4d0c57de33908b7b2a211",
"score": "0.55706674",
"text": "def check_url(url: StarletteURL):\n base_url = get_base_url(url)\n optimade_path = f\"{url.scheme}://{url.netloc}{url.path}\"[len(base_url) :]\n match = re.match(r\"^(?P<version>/v[0-9]+(\\.[0-9]+){0,2}).*\", optimade_path)\n if match is not None:\n if match.group(\"version\") not in BASE_URL_PREFIXES.values():\n raise VersionNotSupported(\n detail=(\n f\"The parsed versioned base URL {match.group('version')!r} from \"\n f\"{url} is not supported by this implementation. \"\n f\"Supported versioned base URLs are: {', '.join(BASE_URL_PREFIXES.values())}\"\n )\n )",
"title": ""
},
{
"docid": "6f922c946c9c9191e6106b5924fc8998",
"score": "0.5563622",
"text": "def test_is_url_not_valid(self):\n\n expected = False\n\n for domain in self.not_valid_domain:\n to_check = \"https://{0}/hello_world\".format(domain)\n actual = Check(to_check).is_url()\n\n self.assertEqual(expected, actual)",
"title": ""
},
{
"docid": "78081f33d16fd2f9b5071a5b42173297",
"score": "0.5560961",
"text": "def is_valid_url(url):\n validate = URLValidator()\n try:\n validate(url)\n return True\n except ValidationError:\n return False",
"title": ""
},
{
"docid": "7d4ac26cd3762d1756a32a8b3e906cb8",
"score": "0.5550716",
"text": "def check_space(url):\n # Fetch response\n try:\n response = requests.get(url, verify=False, timeout=TIMEOUT_SECONDS)\n except requests.exceptions.ConnectTimeout:\n return False, 'Connection timeout (%ds)' % TIMEOUT_SECONDS\n except requests.exceptions.ReadTimeout:\n return False, 'Read timeout (%ds)' % TIMEOUT_SECONDS\n except requests.exceptions.ConnectionError:\n return False, 'Connection error'\n except Exception as e:\n global has_error\n has_error = True\n return False, 'Error: %s' % e\n\n # Verify status code\n if response.status_code != 200:\n return False, 'Status: HTTP %s (%s)' % (response.status_code, response.reason)\n\n # Verify JSON format\n try:\n data = response.json()\n except ValueError:\n return False, 'Invalid JSON'\n\n # Verify that data at least looks like a valid SpaceAPI response\n if 'api' not in data:\n return False, 'Invalid SpaceAPI response: \"api\" key missing'\n if 'space' not in data:\n return False, 'Invalid SpaceAPI response: \"space\" key missing'\n\n return True, None",
"title": ""
},
{
"docid": "052eebaeb66e46599b3341b22ce7c7c4",
"score": "0.5548607",
"text": "def checkURL(cls, trust_root, url):\n tr = cls.parse(trust_root)\n return tr is not None and tr.validateURL(url)",
"title": ""
},
{
"docid": "6c159b921f5872fda2d0715362ea6afc",
"score": "0.5546457",
"text": "def is_valid_url(self, url: str):\n if url is None or url == '': # proceed only if url is not empty\n return False\n url = urlparse(url)\n # list of rules for a valid url, general rule: our url's path must be a branch of base path\n if self.base_url.path in url.path and (\n url.scheme == \"\" or url.scheme == \"http\" or url.scheme == \"https\") and (\n url.netloc == \"\" or # rule1: empty netloc means same host, hence valid\n url.netloc == self.base_url.netloc): # rule2: if same base path that's also valid\n return True\n else:\n # every thing else is an invalid url\n return False",
"title": ""
},
{
"docid": "346efe7b6379bde366921f864f0128fd",
"score": "0.5541498",
"text": "def validate_long_url(value):\n long_url = check_and_update_url_schema(value)\n if len(long_url) >= 500:\n raise ValidationError(\"Length of URL should be below 500 chars\")\n URLValidator()(long_url)\n return long_url",
"title": ""
},
{
"docid": "76754517b34ef6f583033722cb0260e4",
"score": "0.5539448",
"text": "def is_url(self):\n return (self._url is not None)",
"title": ""
},
{
"docid": "1798edec24b16e5cfebe0289f70d10d1",
"score": "0.5536526",
"text": "def testNoConnectionExists(self):\n access_checker = (\n connection_view.NoConnectionExistsAccessChecker(urls.UrlNames))\n access_checker.checkAccess(self.data, None)",
"title": ""
},
{
"docid": "fb5dc0392339fc42b8891f57f726cca2",
"score": "0.55360556",
"text": "def ReleaseCandidateExists(platform):\n try:\n GetReleaseCandidateUrl(platform)\n return True\n except AssertionError:\n return False",
"title": ""
},
{
"docid": "89a929ca20b84fc9f7e8bc6a34eb4679",
"score": "0.5530852",
"text": "def is_valid_destination(self, url):\n if re.match(self.__regex, url):\n return True\n elif self.is_valid_ipv4_address(url):\n return True\n else:\n return self.is_valid_ipv6_address(url)",
"title": ""
},
{
"docid": "83a918e70445e3aba1062ce66636446e",
"score": "0.55275935",
"text": "def is_downloadable(url):\n# print(yearb2+monthb2+dayb2)\n# print(year0+month0+day0)\n\n h = requests.get(url)\n time.sleep(1.5)\n# print(h.text)\n# print(len(h.text))\n# print(\"test\")\n if len(h.text) < 650:\n time.sleep(1.5)\n print(year0+month0+day0+\"@holiday\")\n #print('holiday')\n return False\n return True",
"title": ""
},
{
"docid": "9dfe4ecd8b8e1088b4bac45aba691477",
"score": "0.5524315",
"text": "def __check_absolute_url(url):\n return re.fullmatch(RegexProperties.LinkFinder.ABSOLUTE_URL, url)",
"title": ""
},
{
"docid": "71ae1b67a2f1ea0b36d50e28079f539d",
"score": "0.55182165",
"text": "def process_id_check_only(self, file_id):\n\n if Downloader.json_query_errored(self, Downloader.gfycat_client_fetcher(self, file_id)):\n return False\n # print(BColors.FAIL + \"source is: \" + \\\n # str(GLOBAL_LIST_OBJECT['source']) + BColors.ENDC)\n\n if GLOBAL_LIST_OBJECT['source'] is None:\n return True\n\n elif \"http\" not in GLOBAL_LIST_OBJECT['source']:\n print(BColors.FAIL + \"ERROR: no valid http link in: \" + \\\n str(GLOBAL_LIST_OBJECT['source']) + BColors.ENDC)\n return False\n\n return True",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="724"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">7e8618d600b26c25ea152421e904908e</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Initiate training in the framework.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "3107dd14c7a1b9bc7e0d61ff6ac437e3",
"score": "0.0",
"text": "def train(self, nncfg=None):\n for nnpatch in self.nnpatches:\n for nnmodel in nnpatch.nnmodels:\n\n # If `nncfg` is already provided at NNModel creation, prioritize !\n if ((nnmodel.nncfgs is not None) and\n (NNModelPhase.TRAIN in nnmodel.nncfgs)):\n tmp = nnmodel.nncfgs[NNModelPhase.TRAIN]\n if tmp is not None and nncfg is not None:\n warning('NNModel already contains a model configuration `nncfg`. '\n 'Discarding `nncfg` provided to train(...) method')\n nncfg = tmp\n\n nnmodel.train(nncfg)",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "c04dac0951e3d7f0955f14e704fab09e",
"score": "0.8627881",
"text": "def start_training(self):\n self.__init__()",
"title": ""
},
{
"docid": "3dfadb6fb2382607383e18917e51ef57",
"score": "0.8184445",
"text": "def train(self):\n self.module.start_training()\n self.module.start_evaluation()",
"title": ""
},
{
"docid": "7149bd3f5f89d18e36acb6e8719cf093",
"score": "0.80330867",
"text": "def start_training(self):\n self.training = Training(self)\n self.training.start()",
"title": ""
},
{
"docid": "7f3163f80f2b22cb04d261a35a61a5d7",
"score": "0.7903856",
"text": "def setup_training(self):\n\n pass",
"title": ""
},
{
"docid": "9d7723f80f247d07702a56b09cf971d5",
"score": "0.7854833",
"text": "def _do_training(self):\n pass",
"title": ""
},
{
"docid": "45a0ae60720803e340f35a7f37d4f5fd",
"score": "0.7844114",
"text": "def _on_training_start(self) -> None:\n pass",
"title": ""
},
{
"docid": "45a0ae60720803e340f35a7f37d4f5fd",
"score": "0.7844114",
"text": "def _on_training_start(self) -> None:\n pass",
"title": ""
},
{
"docid": "45a0ae60720803e340f35a7f37d4f5fd",
"score": "0.7844114",
"text": "def _on_training_start(self) -> None:\n pass",
"title": ""
},
{
"docid": "106c0aec5b7f6bb470e56a03771d2b14",
"score": "0.7762692",
"text": "def run(self):\r\n # initialize\r\n self.initialize()\r\n\r\n # model\r\n self.train()",
"title": ""
},
{
"docid": "8106f83f3e244d277a86e2a32e8447d9",
"score": "0.7751877",
"text": "def train(self) -> None:\n pass",
"title": ""
},
{
"docid": "874f7251e1b64cf10440f243b50a7f04",
"score": "0.7705913",
"text": "def training(self):\n self.config.is_train = True",
"title": ""
},
{
"docid": "3cfc73980dad18a00b154ad1778b29af",
"score": "0.7650954",
"text": "def train(self):\n self.training = True\n self.actor.train()",
"title": ""
},
{
"docid": "f46323c287a7ccf666a79b92564cc795",
"score": "0.75967205",
"text": "def pre_training(self):\n pass",
"title": ""
},
{
"docid": "dddf38ee94e81707050f4e4cdd46b76c",
"score": "0.75898606",
"text": "def train():\n print(\"* Full training *\")\n model = create_model()\n train_from_model(model)",
"title": ""
},
{
"docid": "ae67845edc270d491e89f175362e942c",
"score": "0.75679344",
"text": "def init() -> None:\n # get input arguments\n args = get_args()\n # get static config information\n config = process_config()\n # combine both into dictionary\n config = {**config, **args}\n\n # create your data generators for each mode\n train_data = TFRecordDataLoader(config, mode=\"train\")\n val_data = TFRecordDataLoader(config, mode=\"val\")\n ## test_data = TFRecordDataLoader(config, mode=\"test\")\n\n # initialise model\n ## model = RawModel(config)\n model = HanModel(config)\n\n # initialise the estimator\n ## trainer = RawTrainer(config, model, train_data, val_data, test_data)\n trainer = HanTrainer(config, model, train_data, val_data, val_data)\n\n # start training\n trainer.run()",
"title": ""
},
{
"docid": "5ef40074073748a373c03135c05022da",
"score": "0.75327957",
"text": "def train_start(self):\r\n self.Eiters = 0\r\n self.img_enc.train()\r\n self.txt_enc.train()\r\n self.word_embed.train()\r\n self.logger.reset()",
"title": ""
},
{
"docid": "d5d483db76ced009eafdb2483b6d55d0",
"score": "0.7523074",
"text": "def train(self):\n pass",
"title": ""
},
{
"docid": "d5d483db76ced009eafdb2483b6d55d0",
"score": "0.7523074",
"text": "def train(self):\n pass",
"title": ""
},
{
"docid": "d5d483db76ced009eafdb2483b6d55d0",
"score": "0.7523074",
"text": "def train(self):\n pass",
"title": ""
},
{
"docid": "d5d483db76ced009eafdb2483b6d55d0",
"score": "0.7523074",
"text": "def train(self):\n pass",
"title": ""
},
{
"docid": "f8429e93c1145e33ee3344f1cd7e8077",
"score": "0.75190544",
"text": "def train(self):\n self.classifier.train()",
"title": ""
},
{
"docid": "5c25d4d5dbcbfd8656e1877bae9ec983",
"score": "0.7516849",
"text": "def train(self, cfg=None): \n pass",
"title": ""
},
{
"docid": "818fdd6528c8f8a4eb9f2c43ad24a402",
"score": "0.7493314",
"text": "def backgroundTraining(self):\n thread_gm = Thread(target=self.initTraining)\n thread_gm.start()\n pass",
"title": ""
},
{
"docid": "161633736a64add160f63c5bb2675d07",
"score": "0.7490362",
"text": "def train(\n self,\n ) -> None:\n raise NotImplementedError(\"Please Implement this method\")",
"title": ""
},
{
"docid": "fe4aec1fe6c5eed273fd99076fe34228",
"score": "0.74805343",
"text": "def train(self, env):\n raise NotImplementedError(\"Override me!\")",
"title": ""
},
{
"docid": "fe4aec1fe6c5eed273fd99076fe34228",
"score": "0.74805343",
"text": "def train(self, env):\n raise NotImplementedError(\"Override me!\")",
"title": ""
},
{
"docid": "935c6b7d5143a5cbe1eb916930f75a5c",
"score": "0.74757636",
"text": "def train(self):\n self.runSVM()\n self.runKNN()\n self.runKMeans()\n self.runGMM()",
"title": ""
},
{
"docid": "40763bb0661bc2b652d1ffec155f23f8",
"score": "0.74606746",
"text": "def train(self):\n\n self._train(self._env, self._policy, self._initial_exploration_policy, self._pool)",
"title": ""
},
{
"docid": "e04b20a57bc87306dfe5df97bebcf806",
"score": "0.74566317",
"text": "def train(self):\n\t\tpass",
"title": ""
},
{
"docid": "6739932132a0b7a026a6da46f727118c",
"score": "0.745262",
"text": "def train():\n pass",
"title": ""
},
{
"docid": "ab709c226575498bbc71c9ebd1464c91",
"score": "0.7434302",
"text": "def training(self):\n self.training = True",
"title": ""
},
{
"docid": "06c592ccbe443f966dca72293edc2d9d",
"score": "0.74147934",
"text": "def _on_train_begin(self):\n pass",
"title": ""
},
{
"docid": "48ad7d939ff98e0751efa7fc951bbc11",
"score": "0.74113744",
"text": "def train(self, verbose=True):\n pass",
"title": ""
},
{
"docid": "b127aa910ac22e104c089e9a9e9a16c1",
"score": "0.7409331",
"text": "def train(self, training_path):\n pass",
"title": ""
},
{
"docid": "e96a0431d02a699dd8dac87f7fc06b05",
"score": "0.7406957",
"text": "def train(self):\n self.mode = \"train\"\n self.model.train()",
"title": ""
},
{
"docid": "c550ac8c6681ebb219da6a2352f38242",
"score": "0.7375539",
"text": "def _train(self):\n pass",
"title": ""
},
{
"docid": "d60be20e4b746c3f03437bcb58dfb4eb",
"score": "0.7374302",
"text": "def train_model(self):\n pass",
"title": ""
},
{
"docid": "1c912a9e32ea3ee3a206139b5d593ced",
"score": "0.73721844",
"text": "def train(self):\n\n raise NotImplementedError",
"title": ""
},
{
"docid": "ad7053f06c6f65a2e94c03265a7b3bd5",
"score": "0.73688495",
"text": "def before_train(self, runner) -> None:",
"title": ""
},
{
"docid": "be6e70543b199756dd629416eb16761d",
"score": "0.7368538",
"text": "def train_start(self):\n self.img_enc.train()\n self.txt_enc.train()",
"title": ""
},
{
"docid": "be6e70543b199756dd629416eb16761d",
"score": "0.7368538",
"text": "def train_start(self):\n self.img_enc.train()\n self.txt_enc.train()",
"title": ""
},
{
"docid": "6b56d31dfa1e07a0dc5750f052465e03",
"score": "0.7355875",
"text": "def on_train_begin(self):\n pass",
"title": ""
},
{
"docid": "aec87d15c925ee613ece33aaab288139",
"score": "0.7355342",
"text": "def train(cls, **cfg):\n super().train(**cfg)",
"title": ""
},
{
"docid": "e746fc3de8b2c29f59ce56f9c45fb681",
"score": "0.73348165",
"text": "def train_emulator(self):\n pass",
"title": ""
},
{
"docid": "ea11d6a3cf84643b863c7c9a01b408d8",
"score": "0.7330311",
"text": "def train(self):\n\n raise NotImplementedError()",
"title": ""
},
{
"docid": "e95ea2a4e9ca7e1aa11c72ddfc5a3ce4",
"score": "0.7313868",
"text": "def exposed_init_training(self, cfg):\n cfg = pickle.loads(cfg)\n tstart = time.time()\n log_info('Initializing training...')\n self.seq2seq = Seq2SeqGen(cfg)\n log_info('Training initialized. Time taken: %f secs.' % (time.time() - tstart))",
"title": ""
},
{
"docid": "92647f3be991cd3ab7ea0b7113444787",
"score": "0.7305981",
"text": "def run_train(self, env):\n self.state = self.make_state(env)\n raise NotImplementedError",
"title": ""
},
{
"docid": "2428380a3e585c0e4a9df839814e56fd",
"score": "0.72873425",
"text": "def train(self, **kwargs):\n pass",
"title": ""
},
{
"docid": "38252b82366ffe3debd82d19f3dd408d",
"score": "0.726473",
"text": "def set_train(self):\n self.nets.train()",
"title": ""
},
{
"docid": "372c4cecde7b933591325234c65d6044",
"score": "0.72488135",
"text": "def train_start(self):\n self.img_enc.train()\n #########################################\n self.txt_enc.train()\n #########################################",
"title": ""
},
{
"docid": "27cba51149897cda8fc90d8c42df25b1",
"score": "0.7235211",
"text": "def train(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "27cba51149897cda8fc90d8c42df25b1",
"score": "0.7235211",
"text": "def train(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "27cba51149897cda8fc90d8c42df25b1",
"score": "0.7235211",
"text": "def train(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "f3e985e8f0c1afb9493151ee1cb015f1",
"score": "0.7230325",
"text": "def _train(self):\r\n self._mode = 'train'",
"title": ""
},
{
"docid": "402bf43dce50f65059028462fca781e7",
"score": "0.72197676",
"text": "def on_train_start(self, context: Context) -> None:\n self.on_event(Event.ON_TRAIN_START, context)",
"title": ""
},
{
"docid": "0c39eaf236583042aea3c7722ac34ff7",
"score": "0.72072035",
"text": "def _train(self, model_config: Config):\n cfg = self._build_model_config(model_config)\n self.built_configs.append(cfg)\n trainer = Trainer(cfg)\n trainer.resume_or_load(resume=False)\n trainer.train()",
"title": ""
},
{
"docid": "80668bfba3f8f1bda2717a80cbd00076",
"score": "0.71924746",
"text": "def train(self):\n estimator = self.get_estimator()\n hooks = [self._input_fn]\n estimator.train(\n input_fn=self._input_fn, hooks=hooks, max_steps=self._num_iterations)",
"title": ""
},
{
"docid": "e1d4be306e4f577b8143e32724542ccc",
"score": "0.71891063",
"text": "def train(self):\n self._preprocess()\n self._build_pipeline()\n self._fit()",
"title": ""
},
{
"docid": "ac7112eae3253f3a024bd465d23285a2",
"score": "0.7187902",
"text": "def pretrain(self, *args, **kwargs):\n pass",
"title": ""
},
{
"docid": "e60e1cf98e79e8e45e0dd8f631d39dde",
"score": "0.7187652",
"text": "def train(self):\n\n # fit the model\n self.model.fit(self.train_loader, epochs=self.cnf.epochs, validation_data=self.val_loader,\n callbacks=self.callbacks, use_multiprocessing=True, workers=self.cnf.n_workers)",
"title": ""
},
{
"docid": "79b30daf198a494b117cd33912aaf254",
"score": "0.71795076",
"text": "def train_model(self):\n training_iterations = self.hyperparams.get('training_steps', 1)\n for step in range(training_iterations):\n self.execute_training_step()",
"title": ""
},
{
"docid": "c4e52bb81c4652704d59f6f5fc092023",
"score": "0.71729606",
"text": "def train(self, training_data):\n pass",
"title": ""
},
{
"docid": "dd917fe43834e3388b271c9914c06e5a",
"score": "0.7168456",
"text": "def before_train(self):\n pass",
"title": ""
},
{
"docid": "dd917fe43834e3388b271c9914c06e5a",
"score": "0.7168456",
"text": "def before_train(self):\n pass",
"title": ""
},
{
"docid": "e218ea6f5f2e03db667e1206a22239d0",
"score": "0.71634716",
"text": "def train(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "e0ab608bbcb3df9bf44bbb50768b6759",
"score": "0.7161345",
"text": "def train(self):\n self.classifier.fit(self.train_features(), self.train_targets())",
"title": ""
},
{
"docid": "e22bfb5e96b3d3d82d4351e155fb4cde",
"score": "0.715795",
"text": "def setup_trainer(self):\n \n optimizer = Adam()\n loss = 'mean_squared_error'\n self.model.compile(optimizer = optimizer, loss = loss, metrics = ['accuracy'])\n \n def train_step(videos, prices, learning_rate, epochs):\n K.set_value(self.model.optimizer.lr, learning_rate)\n self.model.fit(x = videos,\n y = prices,\n batch_size = len(videos),\n epochs = epochs,\n verbose = 1)\n self.train_step = train_step",
"title": ""
},
{
"docid": "4f7cb131b6ea178b4851ccde72faab10",
"score": "0.7157163",
"text": "def train(self):\n\t\traise NotImplementedError",
"title": ""
},
{
"docid": "a9eeb1273254760b77636d148f9a732d",
"score": "0.71563256",
"text": "def _init_trainer(self, train_dataset, dev_dataset):\n self._metric = load_metric(\"seqeval\", zero_division=0)\n\n # We can now specify the model\n self._trainer = TrainerTokenClassif(\n model_init=self._get_model,\n args=self._training_args,\n train_dataset=train_dataset,\n eval_dataset=dev_dataset,\n compute_metrics=self._compute_metrics,\n loss=self._loss\n )",
"title": ""
},
{
"docid": "15125cfd1b95fc6e9e38c107e28e688c",
"score": "0.7139888",
"text": "def trainModel(self):\n train = Train()\n train.trainModel()",
"title": ""
},
{
"docid": "50ef3dd2e9897f85f9ed8f9824489260",
"score": "0.71354693",
"text": "def run_train(self, *args, **kwargs):\n return Runner.train(self, *args, **kwargs)",
"title": ""
},
{
"docid": "7902ec10106bae56dc63a537568c318b",
"score": "0.7134637",
"text": "def main():\n print('STARTING TRAINING')\n train_util()\n return None",
"title": ""
},
{
"docid": "48fd81b798e177629fd0f08578f9bae9",
"score": "0.7115965",
"text": "def _train(self):\n logger.info('Starting training')\n self.score = 0\n\n last_checkpoint = 0 - Config.eval_interval\n\n # Main loop, execute this while T < T_max\n self._prepare_episode()\n while self.T < Config.T_max:\n # A new batch begins\n self._prepare_for_batch()\n terminal = self._do_batch()\n if terminal:\n self._prepare_episode()\n if self.n_batches % Config.stat_interval == 0 and self.name == 'Agent1':\n self._show_efficiency()\n\n if self._evaluating and self.T - last_checkpoint > Config.eval_interval:\n self.eval()\n self._store_parameters()\n last_checkpoint += Config.eval_interval",
"title": ""
},
{
"docid": "e67be3b5d964f486f5ec2de60f03fab4",
"score": "0.70964617",
"text": "def train(self):\n url = self._api_url + \"/train?nocache=true\"\n requests.post(url, None)",
"title": ""
},
{
"docid": "50324c9f05dc9d472a5db94e29d6da51",
"score": "0.7085656",
"text": "def train(self):\n with self._net.get_tf_graph().as_default():\n self._train()",
"title": ""
},
{
"docid": "169877bff6f734e13ee438b231fce668",
"score": "0.7081039",
"text": "def _before_train(self):",
"title": ""
},
{
"docid": "03878a38e1d552d621ee8ada2a608884",
"score": "0.7071845",
"text": "def train(self):\n self.training = True\n for _, m in self._models.items():\n m.train()",
"title": ""
},
{
"docid": "86b495ac46e0ed6928ca004619d390bf",
"score": "0.70640135",
"text": "def train(self, args):\n return",
"title": ""
},
{
"docid": "239cbe6c3e3c6b0a5c3bac72a1a32415",
"score": "0.7058315",
"text": "def _do_training(self):\n imgs, actions, rewards, terminals = \\\n self.data_set.random_batch(\n self.network.batch_size)\n return self.network.train(imgs, actions, rewards, terminals)",
"title": ""
},
{
"docid": "06740a0a1986772488626ff7ecc4cb87",
"score": "0.70219284",
"text": "def train_model(self):\n self.st = self.State()\n self._create_model()\n print('\\nTRAINING:')\n self.st.exec_mode = ExecMode.TRAIN\n self.st.out_type = OutputType.NONE\n self._train()",
"title": ""
},
{
"docid": "8f8877327b6b52346ee65eebf4135563",
"score": "0.7016987",
"text": "def train_mode(self):\n self.model.train()",
"title": ""
},
{
"docid": "dd05a8b1128b1439a8103938fa994894",
"score": "0.7016346",
"text": "def run(self):\n dataset = self.create_dataset(self.config.dataset_config)\n\n dataset.prepare()\n\n self.model.train_model(\n train_dataset=dataset,\n epochs=self.config.epochs,\n model_directory=self.config.model_directory,\n save_epochs=self.config.save_epochs,\n resume_model=self.config.resume_model,\n run_id=self.id,\n iterations_log=self.config.iterations_log,\n metrics=self.model.metrics,\n )",
"title": ""
},
{
"docid": "0aa8f6207c10605ed1abc4bc9315c621",
"score": "0.7006398",
"text": "def train_start(self):\n self.encoder.train()",
"title": ""
},
{
"docid": "587551c6dd4112bbab38039c1bbdd982",
"score": "0.7005622",
"text": "def train(self,training_data):\n\t\tprint(\"Method not implemented!\")",
"title": ""
},
{
"docid": "bc22a63a8ed969626f8608ea4c49191a",
"score": "0.6998267",
"text": "def train(self, training_data, config, **kwargs):\n # type: (TrainingData, RasaNLUConfig, **Any) -> None\n pass",
"title": ""
},
{
"docid": "01c3e666c0ca186ef3d7e3757e09f6ad",
"score": "0.6997939",
"text": "def train(self):\n\n if not self.cv_check:\n self.train_algs()\n else:\n self.train_algs_cv()",
"title": ""
},
{
"docid": "07a574440461f61bae27be9d7e48147c",
"score": "0.69886583",
"text": "def train_start(self):\r\n self.rnn1.train()\r\n self.cls1.train()\r\n self.rnn2.train()\r\n self.cls2.train()\r\n self.cnn.train()\r\n self.cls.train()\r\n\r\n for param in self.params:\r\n param.requires_grad = True",
"title": ""
},
{
"docid": "d3f88677c5a50a328b2c4dc7ec5843a9",
"score": "0.6980286",
"text": "def train(self):\n if self._multitask_network != None:\n self._multitask_network.train()",
"title": ""
},
{
"docid": "414f07fbf4a9852f8eb728b70341e103",
"score": "0.69678885",
"text": "def on_train_begin(self, logs=None):\n\n pass",
"title": ""
},
{
"docid": "c868546366af1dcb30f17dda0f7a307f",
"score": "0.6964784",
"text": "def _train(self, game: Game) -> None:\n pass",
"title": ""
},
{
"docid": "c097df984cd832bebcc708b8629c17ff",
"score": "0.6958127",
"text": "def train(self):\n\n self.global_epoch = self.current_nn.train(self.storage.big_bag,\n initial_epoch=self.global_epoch,\n callbacks=self.train_callbacks)",
"title": ""
},
{
"docid": "fa2e48c50990b7da6f7f26fe9b82d4e7",
"score": "0.6950833",
"text": "def train(self):\n print('>>> Training classifier')\n\n print('Aligning')\n align = \"TERM=vt100; ~/openface/util/align-dlib.py ~/openface/training-images/ align outerEyesAndNose \" \\\n \"~/openface/aligned-images/ --size 96\"\n subprocess.Popen(align, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT).communicate()\n\n # Representations from aligned images\n print('Representing')\n rep = \"TERM=vt100; ~/openface/batch-represent/main.lua -outDir ~/openface/generated-embeddings/ \" \\\n \"-data ~/openface/aligned-images/\"\n subprocess.Popen(rep, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT).communicate()\n\n # Train classifier\n print('Training')\n train = \"TERM=vt100; ~/openface/demos/classifier.py train ~/openface/generated-embeddings\"\n subprocess.Popen(train, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT).communicate()\n\n print('Finished Training')",
"title": ""
},
{
"docid": "f9540a8fc539c5a015a8fd4a178be167",
"score": "0.695064",
"text": "def train(self, train_data):\n print(\" ===Training=== \")\n\n # Training method selection\n if self.learning == constants.PERCEPTRON:\n self._perceptron_train(train_data)\n elif self.learning == constants.WINNOW:\n self._winnow_train(train_data)",
"title": ""
},
{
"docid": "d11a057e1c4ae5b17be414e4fb9e0b08",
"score": "0.6950245",
"text": "def on_train_begin(self, logs=None):",
"title": ""
},
{
"docid": "d11a057e1c4ae5b17be414e4fb9e0b08",
"score": "0.6950245",
"text": "def on_train_begin(self, logs=None):",
"title": ""
},
{
"docid": "5fceb73881aca3a0e0e739e001cc93e6",
"score": "0.69499326",
"text": "def __call__(self):\n self.__trainModel()",
"title": ""
},
{
"docid": "1aa90c3379e4209a629bdc20d5430460",
"score": "0.69400525",
"text": "def _training_start(self, state: State, logger: Logger) -> None:\n del state, logger # unused\n pass",
"title": ""
},
{
"docid": "e9cf2ae4b48a2513710fd8fba1cfdc49",
"score": "0.69391906",
"text": "def train_step(self):\n pass",
"title": ""
},
{
"docid": "86730bf5d0983c4800f04bad5750ab97",
"score": "0.6935854",
"text": "def __init__(self, train=True):\n logging.basicConfig(level=logging.INFO)\n self.create_bot(train)",
"title": ""
},
{
"docid": "e4c7f581812b740f75f25d34bb2040f1",
"score": "0.69347745",
"text": "def train():\n if not os.path.exists('./train.py'):\n click.echo(\"You gotta have an src/train.py file\")\n else:\n os.system('python ./train.py')\n print(\"\\nModel trained. For MLFlow logging control, type:\\nmlflow ui\\nand visit http://localhost:5000/\")",
"title": ""
},
{
"docid": "04b59456ea9646210d92d1dc7169fc50",
"score": "0.6933494",
"text": "def _pre_train():\n for callback in lgb._mlrun_callbacks:\n callback.on_train_begin()",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="725"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">f176e2207ac746f2af84b220abbd5d6d</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">ez_as_py.Window.get_rect() Returns the dimensions of the window.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "b3f19e2eba6ae3e7aa3944f36429696d",
"score": "0.8090245",
"text": "def get_rect(self):\n\n return self._window.get_rect()",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "a8725c2b8728a3ac539d861550ecbb31",
"score": "0.80813",
"text": "def get_window_rect(self):\n return self._selenium_web_driver().get_window_rect()",
"title": ""
},
{
"docid": "dc06c84eba3e7d9bdbfdbf195fcfd780",
"score": "0.7673315",
"text": "def get_window_rect(window_id):\n\tattrs = execute_shell_cmd(f'xwininfo -id {window_id} -stats')\n\tx, y, w, h = -1, -1, -1, -1\n\tfor line in attrs:\n\t\tline = line.strip()\n\t\tif line.startswith('Absolute upper-left X:'):\n\t\t\tx = int(line[len('Absolute upper-left X:'):].strip())\n\t\tif line.startswith('Absolute upper-left Y:'):\n\t\t\ty = int(line[len('Absolute upper-left Y:'):].strip())\n\t\tif line.startswith('Width'):\n\t\t\tw = int(line[len('Width:'):].strip())\n\t\tif line.startswith(\"Height\"):\n\t\t\th = int(line[len(\"Height:\"):].strip())\n\treturn x, y, w, h",
"title": ""
},
{
"docid": "c1da1f78fc628a061df4bf7b3191a6ee",
"score": "0.75707364",
"text": "def get_window_rect(self):\n rect = win32gui.GetWindowRect(self._handle)\n return [rect[0], rect[1], rect[2] - rect[0], rect[3] - rect[1]]",
"title": ""
},
{
"docid": "093d8993246daabfe055ed83c4f8b955",
"score": "0.7559407",
"text": "def window_size(self):\n tree = self.message(Sway.IPC_GET_TREE)\n current = Sway._find_current(tree)\n if current and 'rect' in current:\n rect = current['rect']\n width = rect.get('width', 0)\n height = rect.get('height', 0)\n return (width, height)\n return (0, 0)",
"title": ""
},
{
"docid": "6f3d26f21893e5320f3dfd399f1a8ca1",
"score": "0.72941685",
"text": "def screen_rect(self):\n return self.__surface.get_rect()",
"title": ""
},
{
"docid": "ec1958b4babc2425fde873209a7e440d",
"score": "0.7252241",
"text": "def get_window_geometry(self, window: xlib.Window) -> Tuple[int, int, int, int]:\n root_ret = xlib.ffi.new(\"Window *\")\n x = xlib.ffi.new(\"int *\")\n y = xlib.ffi.new(\"int *\")\n w = xlib.ffi.new(\"unsigned int *\")\n h = xlib.ffi.new(\"unsigned int *\")\n border_width = xlib.ffi.new(\"unsigned int *\")\n depth = xlib.ffi.new(\"unsigned int *\")\n xlib.lib.XGetGeometry(self.dpy, window, root_ret, x, y, w, h, border_width, depth)\n return x[0], y[0], w[0], h[0]",
"title": ""
},
{
"docid": "877c6629936abd4e4f9e2449d61f92f0",
"score": "0.71699834",
"text": "def window_size() -> Vec2:\n return Vec2(_canvas.GetSize())",
"title": ""
},
{
"docid": "7c1fc1e4521b5566f23504180a27f3f7",
"score": "0.7156099",
"text": "def rect(self):\n\t\treturn self.surface.get_rect()",
"title": ""
},
{
"docid": "39b4143cf05b427684698f97b58a5ac6",
"score": "0.7124331",
"text": "def get_window_rect(hwnd):\n\n rect = win32gui.GetWindowRect(hwnd)\n\n # Left = 8 pixels of border\n # Top = 30 pixels of border\n # Right = 8 pixels of border\n # Bottom = 8 pixels of border\n rect = (8, 30, rect[2] - rect[0] - 8, rect[3] - rect[1] - 8)\n\n return rect",
"title": ""
},
{
"docid": "cfe2d2777154e73a8f5c9d970c2f1bff",
"score": "0.707009",
"text": "def get_window_size(self):\n return self.window_size",
"title": ""
},
{
"docid": "30462c5704c408fc212b5e14eb5073af",
"score": "0.7056512",
"text": "def get_rect(self):\n return self._surface.get_rect()",
"title": ""
},
{
"docid": "1c5b3ba3f74fd832a476481921ff76b3",
"score": "0.70123404",
"text": "def HWND_client_rect(HWND):\n return HWND.client_area_rect()",
"title": ""
},
{
"docid": "6c6595e9b223d04d57c4fd2ec4c82995",
"score": "0.6998765",
"text": "def get_screen_size(self) -> Tuple[int, int]:\n return self.get_window_geometry(window=self.root)[2:]",
"title": ""
},
{
"docid": "50fe8b0e9fd1ab389712a8c63c37bf32",
"score": "0.6979988",
"text": "def get_window_size(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "3d1720a2801c32fb82c9f71c481681ac",
"score": "0.6930246",
"text": "def get_window_size(driver):\n res = driver.get_window_size()\n\n return int(res['height']), int(res['width'])",
"title": ""
},
{
"docid": "029f468b999595438d919fb370c08fd4",
"score": "0.692287",
"text": "def getVisibleRect(self):\n stx, sty = self.GetViewStart()\n stx = max(stx, 0)\n sty = max(sty, 0)\n self.Scroll((stx, sty))\n sz = self.GetClientSize()\n scale = self.parent.zoom\n ppu = self.GetScrollPixelsPerUnit()\n stx *= ppu[0] # convert start to pixels\n sty *= ppu[1]\n stx = round(stx / scale) # origin and size in bitmap units\n sty = round(sty / scale)\n szx = round(sz[0] / scale)\n szy = round(sz[1] / scale)\n return wx.Rect(stx, sty, szx, szy)",
"title": ""
},
{
"docid": "c9b2cd2cd312991b4312911c3e802d15",
"score": "0.6862324",
"text": "def canvas_rect(self):\n if self.canvas_origin is not None and self.canvas_size is not None:\n return Geometry.IntRect(self.canvas_origin, self.canvas_size)\n return None",
"title": ""
},
{
"docid": "6766aa1fa581e0c91bafa0487b2f6081",
"score": "0.68523186",
"text": "def get_screen_dimensions():\n return (pygame.display.Info().current_h, pygame.display.Info().current_w)",
"title": ""
},
{
"docid": "b3d9529a09766fb512cbb058cac151a2",
"score": "0.68349284",
"text": "def user32_GetWindowRect(jitter):\n ret_ad, args = jitter.func_args_stdcall([\"hWnd\", \"lpRect\"])\n raise RuntimeError('API not implemented')\n jitter.func_ret_stdcall(ret_ad, ret_value)",
"title": ""
},
{
"docid": "63dc282a6e9ca23bec4ab23067832a13",
"score": "0.6804246",
"text": "def _get_display_geometry(self):\n desktop = QtWidgets.QApplication.instance().desktop()\n available_geometry = desktop.screenGeometry(QtWidgets.QCursor().pos())\n x = available_geometry.x()\n y = (available_geometry.height()/2) - (self.height()/2)\n w = available_geometry.width()\n h = self.height()\n return QtCore.QRect(x, y, w, h)",
"title": ""
},
{
"docid": "6ff88214fc55ade992df90a67621d0d0",
"score": "0.6771913",
"text": "def getScreenSize(*args):\n return _coin.SoShape_getScreenSize(*args)",
"title": ""
},
{
"docid": "b670ca164a0f391a249b03837ec3ca03",
"score": "0.6711674",
"text": "def rect(self):\n return self._rect",
"title": ""
},
{
"docid": "b670ca164a0f391a249b03837ec3ca03",
"score": "0.6711674",
"text": "def rect(self):\n return self._rect",
"title": ""
},
{
"docid": "5c2445abdea680a22e8bd44969339ea4",
"score": "0.66904795",
"text": "def size(self):\n return self.__renderer.screen_size()",
"title": ""
},
{
"docid": "eadc018e6575be708e31e31b02c18383",
"score": "0.6685616",
"text": "def SoShape_getScreenSize(*args):\n return _coin.SoShape_getScreenSize(*args)",
"title": ""
},
{
"docid": "cff9319b11770d1509a6576c4b735139",
"score": "0.6685585",
"text": "def canvas_bounds(self):\n if self.canvas_size is not None:\n return Geometry.IntRect((0, 0), self.canvas_size)\n return None",
"title": ""
},
{
"docid": "75feed368478bff13eef2d1944e7d8b1",
"score": "0.6664501",
"text": "def GetWindowSize(*args):\n return _wingdi.MainWnd_GetWindowSize(*args)",
"title": ""
},
{
"docid": "0ab62ad0dd49cab99e63cb94eaac87c8",
"score": "0.666002",
"text": "def get_screen_size():\n screen = pygame.display.get_surface()\n return screen.get_size()",
"title": ""
},
{
"docid": "0ab62ad0dd49cab99e63cb94eaac87c8",
"score": "0.666002",
"text": "def get_screen_size():\n screen = pygame.display.get_surface()\n return screen.get_size()",
"title": ""
},
{
"docid": "60e04ca99bea22f5071863ea23d699d9",
"score": "0.658953",
"text": "def canvas_size(self):\n self.height = self.winfo_reqheight()\n self.width = self.winfo_reqwidth()\n return self.height, self.width",
"title": ""
},
{
"docid": "7155b6fdba10a8e183e2e04c000cc54a",
"score": "0.65835035",
"text": "def screen_rect(self):\n pass",
"title": ""
},
{
"docid": "09eaaa83a20cfa42eeff70544b65f0ea",
"score": "0.6561547",
"text": "def get_win_size():\n return pyautogui.size()",
"title": ""
},
{
"docid": "5601c0affa540e9b3717edd47de6b309",
"score": "0.6554296",
"text": "def screen_size(self):\n return self.__surface.get_size()",
"title": ""
},
{
"docid": "ccf59524fac725fba30d97b32f6e7a26",
"score": "0.6547755",
"text": "def __size(self):\n # get screen size\n screenWidth = self.__win.winfo_screenwidth() # get screen width\n screenHeight = self.__win.winfo_screenheight() # get screen height\n\n # set window size to be within screen\n if screenWidth < screenHeight:\n # for portrait mode, take over entire screen\n self.__gui.width = screenWidth # win width\n self.__gui.height = screenHeight # win height\n\n # format to string that for geometry call\n return \"{}x{}\".format(self.__gui.width, self.__gui.height)",
"title": ""
},
{
"docid": "ecf947bf0929a795e7f7ba6a96aa8b12",
"score": "0.6521926",
"text": "def get_height(self):\n return self.rect.height",
"title": ""
},
{
"docid": "29055f594e542b1d50ae9eec8fafb636",
"score": "0.6499519",
"text": "def _getSize(self):\n return LVecBase2i(\n self.showbase.win.getXSize(),\n self.showbase.win.getYSize())",
"title": ""
},
{
"docid": "808e9201851d3dc8a891c61150103c36",
"score": "0.64888334",
"text": "def rect(self):\n return QtCore.QRect(0, 0, self.width(), self.height())",
"title": ""
},
{
"docid": "55cd5986e23bb94a779c552f95e41f60",
"score": "0.64780253",
"text": "def get_rect(self):\n return self.img.get_rect()",
"title": ""
},
{
"docid": "80b8e8e6e12a00de5a3f5b13e4343abd",
"score": "0.6470359",
"text": "def CurrentBoundingRectangle(self):\n rect = self.IUIAutomationElement.CurrentBoundingRectangle\n return rect.left, rect.top, rect.right, rect.bottom",
"title": ""
},
{
"docid": "591d405bda5e13423916b8ffc3f33647",
"score": "0.6470217",
"text": "def get_rect(self):\n return self.square_rect",
"title": ""
},
{
"docid": "247781fd8d2858f330af79de70b586ba",
"score": "0.6469508",
"text": "def get_window_size(self):\n raise RenderError(\"subclass should override this method!\")",
"title": ""
},
{
"docid": "1ca5b174101a239ce71c3d6cef98657e",
"score": "0.64681864",
"text": "def absolute_bounds(self):\n self.update_idletasks()\n return (self.winfo_rootx(), self.winfo_rooty(),\n self.winfo_rootx() + self.width, self.winfo_rooty() + self.height)",
"title": ""
},
{
"docid": "5a3d7428ac8463ab095c31ef1f1c5669",
"score": "0.6449336",
"text": "def rect(self):\n return pg.Rect(self.pos, self.size)",
"title": ""
},
{
"docid": "ed5bcaafd4a8fdd8cb4bfd5f879daa83",
"score": "0.6437246",
"text": "def get_size(self):\n return self._surface.get_width(), self._surface.get_height()",
"title": ""
},
{
"docid": "5f1a419963f0c50038a0699b2f2ba64b",
"score": "0.6430671",
"text": "def dimensions(self):\n try:\n call = fcntl.ioctl(self.termfd, termios.TIOCGWINSZ, \"\\000\" * 8)\n except IOError:\n return (79, 40)\n else:\n height, width = struct.unpack(\"hhhh\", call)[:2]\n return (width, height)",
"title": ""
},
{
"docid": "120ba840c76daf540756bc48189e60e0",
"score": "0.641645",
"text": "def user32_GetClientRect(jitter):\n ret_ad, args = jitter.func_args_stdcall([\"hWnd\", \"lpRect\"])\n raise RuntimeError('API not implemented')\n jitter.func_ret_stdcall(ret_ad, ret_value)",
"title": ""
},
{
"docid": "d7efd9053005cac58eb24a2f970a2b3c",
"score": "0.6368735",
"text": "def get_rect(self) -> dict:\n try:\n try:\n return self._selenium_element().rect\n except (NoSuchElementException, SeleniumStaleElementReferenceException):\n self.wait_for().exists()\n return self._selenium_element().rect\n except SeleniumWebDriverException as wde:\n raise EasyiumException(wde.msg, self)",
"title": ""
},
{
"docid": "edf0cb86de2c0e0ed5730a1dd2bde25e",
"score": "0.6354094",
"text": "def getWindowImageRect(winname) -> retval:\n ...",
"title": ""
},
{
"docid": "4cf8bbeb6d8e8760801109a855c54b56",
"score": "0.6342849",
"text": "def get_dimensions(self):\t\t\n\t\t\n\t\treturn (self.x, self.y, self.w, self.h)",
"title": ""
},
{
"docid": "2a27adbcb39f101c821ad370d294478a",
"score": "0.63385797",
"text": "def rectangle(self):\n return self.element_info.rectangle",
"title": ""
},
{
"docid": "bceeb84810b95bff62adb9af1688db34",
"score": "0.6328443",
"text": "def height(self):\n return self.WINDOW.getmaxyx()[0] - sum(self.OFFSET_Y)",
"title": ""
},
{
"docid": "55af4bd0f88585b5d06eb208eba38eec",
"score": "0.6321186",
"text": "def boundingRect(self):\n rect = QtCore.QRectF(self.x,\n self.y,\n self.w,\n self.h)\n return rect",
"title": ""
},
{
"docid": "fb57b7d572bd65ee4e47aae84e3badd9",
"score": "0.63210076",
"text": "def get_window_size(self) -> Tuple[Optional[int], Optional[int]]:\n return None, None",
"title": ""
},
{
"docid": "5e910e628271582e1eeccc129fd0fcc4",
"score": "0.63067406",
"text": "def rect(self) -> Rect:\n min_x, min_y, max_x, max_y = sys.maxsize, sys.maxsize, 0, 0\n\n for point in self.left_eyebrow:\n if point.x < min_x:\n min_x = point.x\n\n if point.y < min_y:\n min_y = point.y\n\n for point in self.right_eyebrow:\n if point.x > max_x:\n max_x = point.x\n\n if point.y < min_y:\n min_y = point.y\n\n for point in self.chin:\n if point.x > max_x:\n max_x = point.x\n\n if point.x < min_x:\n min_x = point.x\n\n if point.y > max_y:\n max_y = point.y\n\n return Rect(min_x, min_y, max_x - min_x, max_y - min_y)",
"title": ""
},
{
"docid": "edbdce77bf07631c75848f08de11806e",
"score": "0.6279243",
"text": "def size(self, rect):\n return (0, 0)",
"title": ""
},
{
"docid": "c6f03683ee42e82c333a96f5add2b847",
"score": "0.6240113",
"text": "async def get_canvas_size(self) -> tuple[int, int]:\n data = await self.request('GET', 'get_size')\n return data['width'], data['height']",
"title": ""
},
{
"docid": "1c0816447a6970f3f4617a843e2c1ac2",
"score": "0.62258565",
"text": "def get_geometry():\n\n # Get screen resolution in pixels\n screen_width = GetSystemMetrics(0)\n screen_height = GetSystemMetrics(1)\n\n # Get geometry relative to 1920 x 1080 screen\n x = 450 / 1920 * screen_width\n y = 125 / 1080 * screen_height\n width = 700 / 1920 * screen_width\n height = 600 / 1080 * screen_height\n\n return x, y, width, height",
"title": ""
},
{
"docid": "56e5b26b7c89399f3bb4693488e723ef",
"score": "0.62055326",
"text": "def _get_end_geometry(self):\n desktop = QtWidgets.QApplication.instance().desktop()\n available_geometry = desktop.screenGeometry(QtWidgets.QCursor().pos())\n x = available_geometry.x() + available_geometry.width()\n y = (available_geometry.height()/2) - (self.height()/2)\n w = 0\n h = self.height()\n return QtCore.QRect(x, y, w, h)",
"title": ""
},
{
"docid": "91e1596adce6400c15e8568abc6ec80f",
"score": "0.62010115",
"text": "def rectangle(self):\n return self._rectangle",
"title": ""
},
{
"docid": "c1e7e47ec3b7c2e784eb9cd4a141360a",
"score": "0.61942446",
"text": "def user32_GetUpdateRect(jitter):\n ret_ad, args = jitter.func_args_stdcall([\"hWnd\", \"lpRect\", \"bErase\"])\n raise RuntimeError('API not implemented')\n jitter.func_ret_stdcall(ret_ad, ret_value)",
"title": ""
},
{
"docid": "15229523e3f0c5e0d82f6e13e9667ccc",
"score": "0.61860645",
"text": "def bounds(self) -> ZRect:\n return ZRect((0, 0), (self.width, self.height))",
"title": ""
},
{
"docid": "dba38e042e6a8223bf270d3f91bfaa78",
"score": "0.61839575",
"text": "def _get_plot_dimensions(self) -> Tuple[int, int]:\n return self._width - AXIS_SPACE_PX, self._height - AXIS_SPACE_PX",
"title": ""
},
{
"docid": "222f89c1af65bed6638a8c7b1cd136b3",
"score": "0.61784655",
"text": "def get_size(self) -> int:\n return len([i for i in self.window if i is not None])",
"title": ""
},
{
"docid": "867d9bef54ab73b6a6b47bbab1ca3d95",
"score": "0.61704135",
"text": "def _get_height(self) -> \"int\" :\n return _core.Viewport__get_height(self)",
"title": ""
},
{
"docid": "7bf46239c98f42c2c47e41a8c25b697e",
"score": "0.61554515",
"text": "def getSpan(self):\n if self.slot == pyui.locals.DOCK_LEFT or self.slot == pyui.locals.DOCK_RIGHT:\n return self.width\n else:\n return self.height",
"title": ""
},
{
"docid": "f2ff20274792ba688d4dc98ac6cfdc1f",
"score": "0.61394024",
"text": "def get_width(self):\n return self.rect.width",
"title": ""
},
{
"docid": "7996e6aba965d7cd2e6883127a37e612",
"score": "0.6134051",
"text": "def get_window_size(self, window_reference=\"current\"):\n return self._selenium_web_driver().get_window_size(window_reference)",
"title": ""
},
{
"docid": "c85bd051132e02a2a677dd290756e568",
"score": "0.61335224",
"text": "def getDimensions(self):\n return self.width, self.height",
"title": ""
},
{
"docid": "d6d3a988cfd8450106b909b5c645661a",
"score": "0.6106202",
"text": "def getHeight(self):\n return self.dimensions.height",
"title": ""
},
{
"docid": "d61554928da36d9119e205b59cbfb9fb",
"score": "0.608991",
"text": "def layout(self) -> dict[str, Value]:\n return sublime_api.window_get_layout(self.window_id)",
"title": ""
},
{
"docid": "a458126980b6ed25c047ab4df1d12fce",
"score": "0.6085277",
"text": "def bbox(self) -> pygame.Rect:\n return pygame.Rect(self.bbox_xmin, self.bbox_ymax, self.bbox_xmax-self.bbox_xmin, self.bbox_ymax-self.bbox_ymin)",
"title": ""
},
{
"docid": "5f591c3bb8fa379a6ee5061b513f06b1",
"score": "0.60730046",
"text": "def windowHeight(self):\n return None",
"title": ""
},
{
"docid": "d3507d0fac59e59e5d0a1718816cb7c2",
"score": "0.60668",
"text": "def height(self):\n return self.root.height()",
"title": ""
},
{
"docid": "a7ed211540fa8445a0b228d9e7861d97",
"score": "0.6063533",
"text": "def resolution(self):\n return self._screen.get_size()",
"title": ""
},
{
"docid": "8ec81f97ded9f1de6e4b7275b2aadac4",
"score": "0.6053491",
"text": "def size(self):\r\n\t\treturn self.widgets[0].size()",
"title": ""
},
{
"docid": "fc6d9b1904a361307ce003c3740dcd54",
"score": "0.6026859",
"text": "def height(self):\n rect = self.get_transformed_rect()\n return rect.height",
"title": ""
},
{
"docid": "e225d2654f2d09f92d4d22517cc25194",
"score": "0.6016413",
"text": "def GetSize(self):\n return self._width, self._height",
"title": ""
},
{
"docid": "d9b5a4b3cd109f666b4e8f6f30019824",
"score": "0.6012506",
"text": "def get_rect(self):\n x = list(map(lambda x: x[0], self.polygon.points))\n y = list(map(lambda y: y[1], self.polygon.points))\n\n return pygame.Rect(min(x), min(y), max(x) - min(x), max(y) - min(y))",
"title": ""
},
{
"docid": "dca92f0a4a414bee24d106825df96f57",
"score": "0.5986024",
"text": "def selectionRect(self):\n return self.rect",
"title": ""
},
{
"docid": "73359e31a0b1c580772ff9cab0dda80b",
"score": "0.598312",
"text": "def canvas_bbox(self):\n cbb = self.canvas.bbox(TAG_IMAGE)\n if cbb is None:\n return 0, 0\n canvas_width = cbb[2] - cbb[0]\n canvas_height = cbb[3] - cbb[1]\n return canvas_width, canvas_height",
"title": ""
},
{
"docid": "ef194b4649fac1c363f85e68fb3635ce",
"score": "0.598259",
"text": "def getBounds(self):\n if 'bounds' in self.attributes:\n return self.attributes['bounds']\n else:\n return self.getCoords()",
"title": ""
},
{
"docid": "af02e0b26d0b1eb72d671a84833237fb",
"score": "0.5972635",
"text": "def bounds(self):\n return self.left, self.bottom, self.right, self.top",
"title": ""
},
{
"docid": "36cb104394c2e6494bd81e7c7b409223",
"score": "0.59606963",
"text": "def get_viewport_focus(self):\n return self.width, self.height, self.width, self.height",
"title": ""
},
{
"docid": "478285f1dd1a01a8b28343f65f9bbd33",
"score": "0.5958774",
"text": "def _get_start_geometry(self):\n desktop = QtWidgets.QApplication.instance().desktop()\n available_geometry = desktop.screenGeometry(QtWidgets.QCursor().pos())\n x = available_geometry.x() - self.width()\n y = (available_geometry.height()/2) - (self.height()/2)\n w = self.width()\n h = self.height()\n return QtCore.QRect(x, y, w, h)",
"title": ""
},
{
"docid": "0e516caf6fb76172f796e037cb1d7545",
"score": "0.59565926",
"text": "def height(self):\n return self.dimensions[1]",
"title": ""
},
{
"docid": "ce2cd3c386510956dbff539d5555dd69",
"score": "0.59497577",
"text": "def get_size(self):\n return self.size().width() or 1, self.size().height() or 1",
"title": ""
},
{
"docid": "ab5de5adc08902f01f363543f67df760",
"score": "0.5949193",
"text": "def height(self) -> int:\n return self._widget._mgui_get_height()",
"title": ""
},
{
"docid": "80fe2635afb94979cd032e8788e8b36b",
"score": "0.59409004",
"text": "def getPositionAndSize(self):\n (x, y) = self.getXY()\n w = self.getWidth()\n h = self.getHeight()\n return x, y, w, h",
"title": ""
},
{
"docid": "d7459662add41c8bce990debf3068abb",
"score": "0.5940562",
"text": "def _bbox(self, obj):\n renderer = self._fig.canvas.get_renderer()\n return obj.get_window_extent(renderer=renderer).transformed(\n self._fig.dpi_scale_trans.inverted()\n )",
"title": ""
},
{
"docid": "97095ff76307bbcdebea38300ca4649c",
"score": "0.5938102",
"text": "def get_win_size():\n if 'TIOCGWINSZ' in dir(termios):\n TIOCGWINSZ = termios.TIOCGWINSZ\n else:\n TIOCGWINSZ = 1074295912L # Assume\n s = struct.pack('HHHH', 0, 0, 0, 0)\n x = fcntl.ioctl(sys.stdout.fileno(), TIOCGWINSZ, s)\n return struct.unpack('HHHH', x)[0:2]",
"title": ""
},
{
"docid": "5275211db3e89f60e98aded5af9c9df3",
"score": "0.59318066",
"text": "def rectangle(self):\r\n stats = self.doseDistribution.rectangle_stats(self.ui.xCenter.value(),\r\n self.ui.yCenter.value(),\r\n self.ui.width.value()/2.0, \r\n self.ui.height.value()/2.0,\r\n self.ui.angle.value())\r\n logging.debug(stats) \r\n logging.info(\"### Statistics for rectangle ###\")\r\n logging.info(\"sum: {:.4e} Gy*cm^2\".format(stats[0]/self.doseDistribution.DPC**2))\r\n logging.info(\"average: {:.4e} Gy\".format(stats[1]))\r\n logging.info(\"standard deviation: {:.4e} Gy\".format(stats[2]))\r\n logging.info(\"minimum: {:.4e} Gy\".format(stats[3])) \r\n logging.info(\"maximum: {:.4e} Gy\".format(stats[4]))\r\n logging.info(\"--------------------------------------------------------------\")\r\n return stats",
"title": ""
},
{
"docid": "bda03fc05c7e9571fddd8bd6bcd382ff",
"score": "0.592939",
"text": "def get_sprite_rect(self):\n return self.__spriteRect",
"title": ""
},
{
"docid": "b731a116d23440f8259a0915c8ba8de5",
"score": "0.59274554",
"text": "def frame_size(self):\n\n return self.display.width, self.display.height",
"title": ""
},
{
"docid": "a8cc85826ba658d28ffce5901960b6f4",
"score": "0.5922263",
"text": "def get_dimensions(self):\n\n return self._rows, self._cols",
"title": ""
},
{
"docid": "687fc677a05b55ac72c6399c6999e270",
"score": "0.59142756",
"text": "def height(self) -> int:\n return self.winfo_height()",
"title": ""
},
{
"docid": "8ccefded3d58bd8506ddb5dffc5771b7",
"score": "0.59118605",
"text": "def get_position_and_size(self): # XXX buffer size on windows :/\n info = CONSOLE_SCREEN_BUFFER_INFO()\n ctypes.windll.kernel32.GetConsoleScreenBufferInfo(self.handle, ctypes.byref(info))\n # print('getpos', info.dwCursorPosition.X, info.dwCursorPosition.Y, info.dwSize.X, info.dwSize.Y)\n return info.dwCursorPosition.X, info.dwCursorPosition.Y, info.dwSize.X, info.dwSize.Y",
"title": ""
},
{
"docid": "4b9780c6a24fe27bf99aecff8db7e423",
"score": "0.5906604",
"text": "def get_viewport_size(driver):\n # noinspection PyBroadException\n try:\n width = extract_viewport_width(driver)\n height = extract_viewport_height(driver)\n return {'width': width, 'height': height}\n except:\n logger.info('Failed to get viewport size. Only window size is available')\n browser_size = driver.get_window_size()\n return {'width': browser_size['width'], 'height': browser_size['height']}",
"title": ""
},
{
"docid": "6dda3f0486165bc788e2436e2262b242",
"score": "0.5905312",
"text": "def rectByCenter(center, width, height):\n return wxRect(center[0] - width // 2, center[1] - height // 2, width, height)",
"title": ""
},
{
"docid": "68070314d60d14bcaf77c9c6e357767e",
"score": "0.5896272",
"text": "def get_max_yx(self) -> Tuple[int, int]:\n return self.h, self.w",
"title": ""
},
{
"docid": "d7a5411265afb1f66a6dd080ec76d0fb",
"score": "0.5893418",
"text": "def get_screen_size(self):\n return (self.screen_width, self.screen_height)",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="726"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">126ed6e8cc263f3169177ded98785fb3</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Stops the mojo shell.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "fb731e4dd192d39fac66ae02a8ddbee4",
"score": "0.6821146",
"text": "def StopShell(self):\n subprocess.check_call(self._CreateADBCommand(['shell',\n 'am',\n 'force-stop',\n self.target_package]))",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "deb13e56e09dd66c4b83baf4edb8bbf6",
"score": "0.70763576",
"text": "def stop(self):\n self.execute('pl_stop')",
"title": ""
},
{
"docid": "54870459170defb80c47588d993b09ec",
"score": "0.69280404",
"text": "def stop(self):\n\t\tPopen([\"screen\", \"-S\", self.name, \"-X\", \"quit\"])",
"title": ""
},
{
"docid": "3296b4ddf88eaf18c2cefb9973a765ff",
"score": "0.6822533",
"text": "def Stop(self):\n if self._process is not None:\n self._process.stdin.write('exit\\n')\n self._process = None",
"title": ""
},
{
"docid": "b915e94bd6e1e04bc6d42367b0feb28c",
"score": "0.6795999",
"text": "def shellQuit(self):\n self._restoreSettings()\n if self.stop_reactor_on_quit:\n from twisted.internet import reactor\n reactor.stop()\n else:\n os.write(self.fd, 'Shell exited. Press Ctrl-c to stop process\\n')",
"title": ""
},
{
"docid": "c2c7c7fa9986c7ad69fc4e90ee5b0e36",
"score": "0.6778751",
"text": "def finish(self):\n self.env.runstop()",
"title": ""
},
{
"docid": "8f094d52067934d148a38b48cf071772",
"score": "0.67556435",
"text": "def stop(self):\n self._call(\"stop\")",
"title": ""
},
{
"docid": "cad55ba44c2a0b9db1219876108eb54e",
"score": "0.66975486",
"text": "def stop(self):\r\n if self.state() != self.NotRunning:\r\n self.close()\r\n self.kill()\r\n self.waitForFinished()",
"title": ""
},
{
"docid": "dc6e265d96ac437b45c964367bc5bce3",
"score": "0.66451365",
"text": "def stop(self):\r\n self.io.stop_manager()",
"title": ""
},
{
"docid": "deeb8d030eb29e9013899af8d00b8e1c",
"score": "0.65377164",
"text": "def stop():\n sys.stdout.logfile.close()\n sys.stdout = sys.stdout.terminal",
"title": ""
},
{
"docid": "09e9a3fb4257f1d02eb21e3824ef25d6",
"score": "0.64847034",
"text": "def stop(self) -> None:\n self.proc.terminate()",
"title": ""
},
{
"docid": "b9697d217d76ceae38041b79dcd98a17",
"score": "0.64509284",
"text": "def stop(self) -> None:\n self.action = PLUGIN_ACTION_STOP",
"title": ""
},
{
"docid": "bfbc435f6a155cdb990ff1590fcbd9e4",
"score": "0.64305073",
"text": "def stop(self):\n self._run_flag = False\n self.wait()",
"title": ""
},
{
"docid": "bfbc435f6a155cdb990ff1590fcbd9e4",
"score": "0.64305073",
"text": "def stop(self):\n self._run_flag = False\n self.wait()",
"title": ""
},
{
"docid": "b693336e4533807a80dd384a52870d49",
"score": "0.6415913",
"text": "def stop():\n agent.stop_running()",
"title": ""
},
{
"docid": "2fb599fffaef94d9eba7366e9a48270e",
"score": "0.6411535",
"text": "def do_stop(self, arg):\n self._format_response(self.hackeeg.stop())",
"title": ""
},
{
"docid": "f14c96f839ffa8275258bee2cf626f40",
"score": "0.640417",
"text": "def stop(self):\n os.kill(self.pid, EXIT_SIGNAL)",
"title": ""
},
{
"docid": "bdd17fdbe50b9cbeeefc663d9e946394",
"score": "0.63840836",
"text": "def disconnect(self):\n if self.shell:\n self.shell.close()\n self.shell = None",
"title": ""
},
{
"docid": "a20f9dfeb928684a359a01deca14c981",
"score": "0.6365927",
"text": "def stop(self):\r\n self._run_flag = False\r\n self.wait()",
"title": ""
},
{
"docid": "a20f9dfeb928684a359a01deca14c981",
"score": "0.6365927",
"text": "def stop(self):\r\n self._run_flag = False\r\n self.wait()",
"title": ""
},
{
"docid": "029cea04a96e480b6c24d03c30c5ebe2",
"score": "0.63597316",
"text": "def stop(self):\n # self.p.terminate()\n self._stop_event.set()",
"title": ""
},
{
"docid": "8bf7f0f27fb0aeac0e7307ed6f16f10f",
"score": "0.6359386",
"text": "def stop(self):\n return self.__exit__(None, None, None)",
"title": ""
},
{
"docid": "8bf7f0f27fb0aeac0e7307ed6f16f10f",
"score": "0.6359386",
"text": "def stop(self):\n return self.__exit__(None, None, None)",
"title": ""
},
{
"docid": "23887e04d90694b74cf3ca6e38f31bec",
"score": "0.6355952",
"text": "def stop(self):\n self.running = False\n self.close()",
"title": ""
},
{
"docid": "86301dddbad297294933d3865da00925",
"score": "0.63528085",
"text": "def quit(self):\n self.player.stop()\n exit()",
"title": ""
},
{
"docid": "eff4a409d11877d7ef5b33d6f0728011",
"score": "0.63445896",
"text": "def stop(self):\n _stop_result = self._swigobj.stop()\n return _stop_result",
"title": ""
},
{
"docid": "8e2d4521fb1b02fbb739a8b907b5479d",
"score": "0.6337171",
"text": "def stop(self) -> None:\n ...",
"title": ""
},
{
"docid": "8e2d4521fb1b02fbb739a8b907b5479d",
"score": "0.6337171",
"text": "def stop(self) -> None:\n ...",
"title": ""
},
{
"docid": "8e2d4521fb1b02fbb739a8b907b5479d",
"score": "0.6337171",
"text": "def stop(self) -> None:\n ...",
"title": ""
},
{
"docid": "eeac00b28ee6d8bbd44aba6ad9aa8769",
"score": "0.6334855",
"text": "def stop(self):\n self.close()",
"title": ""
},
{
"docid": "9b8c9614f071847cfef252049fab184d",
"score": "0.63348246",
"text": "def stop(self):\n self.terminate = True",
"title": ""
},
{
"docid": "3da750b9fba668e8b125975670282527",
"score": "0.6334031",
"text": "def stop(self):\n self.alive = False\n self.events.put(None)\n self.responses.put('<exit>')",
"title": ""
},
{
"docid": "0e7f353a8b6b6c05555eaacd530e105f",
"score": "0.6316723",
"text": "def deactivate(self, shell):\n self.main.deactivate(shell)",
"title": ""
},
{
"docid": "665a54f8f980e47254b99a74bcc25531",
"score": "0.6316371",
"text": "def stop(self):\n self.has_exit = True",
"title": ""
},
{
"docid": "526af59978d2d57aa319e7f09ae5acfa",
"score": "0.63051504",
"text": "def stop(self):\n if self.popen:\n try:\n self.popen.kill()\n except ProcessLookupError:\n pass \n self.running = False",
"title": ""
},
{
"docid": "75d1f1ae65ae5c08e8f1beb02c4c5b0e",
"score": "0.630116",
"text": "def on_stop(self):\n executed_command = LeapGui.StoredArgs().load().stored_args[EXECUTED_COMMAND]\n if executed_command == ACTION_RECORD:\n return self.clientRunner.stop_leap()\n self.clientRunner.stop()",
"title": ""
},
{
"docid": "f8725df467e6665e50ac81e3f257103f",
"score": "0.62777483",
"text": "def quit(self):\n self.session_handler.stop(quiet=True)",
"title": ""
},
{
"docid": "e011cc5dbebc9719d4f7684b2d733bb9",
"score": "0.6272812",
"text": "def close_control_shell(self):\n if self._control_shell is None:\n return\n shell = self._control_shell\n self._control_shell = None\n shell.__exit__(None, None, None)",
"title": ""
},
{
"docid": "0fd0c47e23a68caea15836e3b441b755",
"score": "0.6263153",
"text": "async def stop(self) -> None:\n if self.was_started():\n await self._protocol_engine.stop()\n else:\n await self._protocol_engine.finish(\n drop_tips_and_home=False,\n set_run_status=False,\n )",
"title": ""
},
{
"docid": "897338cd0a983909992778c2a4a5e85f",
"score": "0.6258148",
"text": "def stop(self):\n Multipass.stop(self.name)",
"title": ""
},
{
"docid": "63aa799cf88199457a213a7f7757a10d",
"score": "0.62577504",
"text": "def shutdown(self):\n\n self.running = False\n self.session.close()\n print('Bye!')",
"title": ""
},
{
"docid": "e8f7d5b1260bb714cd940775cf401c3d",
"score": "0.6234445",
"text": "def Stop(self):\n self.SendCommand(\"Stop\\n\")\n return None",
"title": ""
},
{
"docid": "293dc4d3a0d1993aa492b8cceeca333a",
"score": "0.6233429",
"text": "def stop(self):\n self._stop()",
"title": ""
},
{
"docid": "d0a2da1df8837d726f02362baf19a604",
"score": "0.62294877",
"text": "def __exit__(self, *args):\n self.backend.stop()",
"title": ""
},
{
"docid": "d71def15d44971e9f4156184e224e273",
"score": "0.62159896",
"text": "def execute(self, env, args):\n\n env.task.stop()",
"title": ""
},
{
"docid": "263ad9a213fe0b4934e1d727a058e1b6",
"score": "0.6213432",
"text": "def stop(self) -> None:\n pass",
"title": ""
},
{
"docid": "769f7951a46b8e38d5b80b390f96ce50",
"score": "0.62085027",
"text": "def stop(self):\n # TODO\n pass",
"title": ""
},
{
"docid": "05a87e529a31e2f19f534867d7475909",
"score": "0.6202586",
"text": "def stop(self):\n\t\t# TO-DO",
"title": ""
},
{
"docid": "4f4d1b10c187513611415d0f4b33bfaf",
"score": "0.62002784",
"text": "def stop(self):\n try:\n atexit.unregister(self.stop)\n except:\n pass\n\n try:\n self._th_out.join(0)\n except:\n pass\n try:\n self._th_err.join(0)\n except:\n pass\n try:\n self.proc.stdin.close()\n except:\n pass\n try:\n self.proc.terminate()\n except:\n pass\n self._th_out = None\n self._th_err = None\n self.proc = None\n self.process = None\n return self",
"title": ""
},
{
"docid": "0b508507e2f79fd241724001e2ef13ab",
"score": "0.618949",
"text": "def stop(self):\n self.running = False",
"title": ""
},
{
"docid": "0b508507e2f79fd241724001e2ef13ab",
"score": "0.618949",
"text": "def stop(self):\n self.running = False",
"title": ""
},
{
"docid": "0b508507e2f79fd241724001e2ef13ab",
"score": "0.618949",
"text": "def stop(self):\n self.running = False",
"title": ""
},
{
"docid": "0b508507e2f79fd241724001e2ef13ab",
"score": "0.618949",
"text": "def stop(self):\n self.running = False",
"title": ""
},
{
"docid": "ad08463af6262f7551e852d7278a8379",
"score": "0.61821026",
"text": "def stop(self):\n pass",
"title": ""
},
{
"docid": "ad08463af6262f7551e852d7278a8379",
"score": "0.61821026",
"text": "def stop(self):\n pass",
"title": ""
},
{
"docid": "ad08463af6262f7551e852d7278a8379",
"score": "0.61821026",
"text": "def stop(self):\n pass",
"title": ""
},
{
"docid": "ad08463af6262f7551e852d7278a8379",
"score": "0.61821026",
"text": "def stop(self):\n pass",
"title": ""
},
{
"docid": "ad08463af6262f7551e852d7278a8379",
"score": "0.61821026",
"text": "def stop(self):\n pass",
"title": ""
},
{
"docid": "ad08463af6262f7551e852d7278a8379",
"score": "0.61821026",
"text": "def stop(self):\n pass",
"title": ""
},
{
"docid": "ad08463af6262f7551e852d7278a8379",
"score": "0.61821026",
"text": "def stop(self):\n pass",
"title": ""
},
{
"docid": "ad08463af6262f7551e852d7278a8379",
"score": "0.61821026",
"text": "def stop(self):\n pass",
"title": ""
},
{
"docid": "ad08463af6262f7551e852d7278a8379",
"score": "0.61821026",
"text": "def stop(self):\n pass",
"title": ""
},
{
"docid": "ad08463af6262f7551e852d7278a8379",
"score": "0.61821026",
"text": "def stop(self):\n pass",
"title": ""
},
{
"docid": "ad08463af6262f7551e852d7278a8379",
"score": "0.61821026",
"text": "def stop(self):\n pass",
"title": ""
},
{
"docid": "ad08463af6262f7551e852d7278a8379",
"score": "0.61821026",
"text": "def stop(self):\n pass",
"title": ""
},
{
"docid": "e53cd8689fbad2498dfb1c339e7b6289",
"score": "0.61770207",
"text": "def stop (self):\n pass",
"title": ""
},
{
"docid": "bc22e8aa14d6d66a2421ffe3a3c13241",
"score": "0.61741656",
"text": "def stop():\n raise NotImplementedError(\"Contribute on github.com/alej0varas/pybolator\")",
"title": ""
},
{
"docid": "0eae4d388519c2fa0297a592e397a57b",
"score": "0.617034",
"text": "def stop():\n require('environment', provided_by=('staging', 'demo', 'production'))\n _supervisor_command('stop %(environment)s:*' % env)",
"title": ""
},
{
"docid": "1d1f4d5358374c0dc6e2303f6ddef425",
"score": "0.6167614",
"text": "def stop(self):\n log.info('Stopping...')\n self.disconnect()\n log.info('Exiting...')",
"title": ""
},
{
"docid": "155e1f60c00e9e420f8ad474d440557c",
"score": "0.6164179",
"text": "def stop(self):\n self._is_running = False\n self._cmd_thread = False",
"title": ""
},
{
"docid": "7594886b93781d9084534ac043c187c6",
"score": "0.61637276",
"text": "def stop(self) -> None:\n self._disconnect()",
"title": ""
},
{
"docid": "1e041b1f72132d990680a97719ee8628",
"score": "0.61599725",
"text": "def stop(self):\n self.stop_backend()\n self.started = False",
"title": ""
},
{
"docid": "9536920ba7e6f7a4ce7dbbf2eac8ac70",
"score": "0.6140143",
"text": "def shutdown(self):\n exit()",
"title": ""
},
{
"docid": "9536920ba7e6f7a4ce7dbbf2eac8ac70",
"score": "0.6140143",
"text": "def shutdown(self):\n exit()",
"title": ""
},
{
"docid": "9536920ba7e6f7a4ce7dbbf2eac8ac70",
"score": "0.6140143",
"text": "def shutdown(self):\n exit()",
"title": ""
},
{
"docid": "c8c581e1ed20552b350b7ee76c7c52e1",
"score": "0.6137799",
"text": "def stop(self) -> None:\n self.running = False",
"title": ""
},
{
"docid": "21ac1f613677ee256e82a12c245877b7",
"score": "0.61365163",
"text": "def stop(self):\n\n self._running = False",
"title": ""
},
{
"docid": "e8370c7decb0451fece689df140984c6",
"score": "0.6134309",
"text": "def stop(self):\n self.solo_controller.abortProcess = True\n self.stop_instruments()",
"title": ""
},
{
"docid": "3507e840471ad7a463f65bba6925bffc",
"score": "0.6129646",
"text": "def stop(self) -> None:\r\n try:\r\n self.window.destroy()\r\n except tk.TclError:\r\n pass",
"title": ""
},
{
"docid": "7a78d23ebda33fd999995d89c19baaa9",
"score": "0.6129438",
"text": "def stop(self):\n self._send(STOP_SEQUENCE)",
"title": ""
},
{
"docid": "e5985d0df65bb1445d1e1bca8b440759",
"score": "0.61217594",
"text": "def stop(self):\n self.started = False\n self._closing = True\n self._close()",
"title": ""
},
{
"docid": "f110d2753f95475bd9f427b0b577949b",
"score": "0.61215925",
"text": "def stop(self) -> None:\n self._handle.cancel()",
"title": ""
},
{
"docid": "2beea8b1c96cf22073eefb3026be8201",
"score": "0.6119767",
"text": "def stopProcess(self) -> None:\n ...",
"title": ""
},
{
"docid": "31e7d6263a197def580f7583bccb624f",
"score": "0.6114032",
"text": "def stop(self, signal):\n self.manager.close()",
"title": ""
},
{
"docid": "21b8a528b000d6f5fe6c7e8fdd0bb96a",
"score": "0.61100227",
"text": "def kill(self):\n\n self.running = False\n\n try:\n # teardown robot\n self.strategy.teardown()\n except Exception:\n # method not implemented by strategy\n pass",
"title": ""
},
{
"docid": "00d571f65e07e2a035d0a15d599fb6be",
"score": "0.61083126",
"text": "def stop(self):\n\t\tself.stopped = True",
"title": ""
},
{
"docid": "257baeb1d16d71d7bc9b03abf67c1b2e",
"score": "0.6093659",
"text": "def music_plugin_stop(self):\r\n\r\n print('Bye,', self.username)\r\n self.soco.stop()",
"title": ""
},
{
"docid": "66f44af591e57a781aef375c6a15a729",
"score": "0.60920554",
"text": "def stop(self):\n self._running = False",
"title": ""
},
{
"docid": "c079593ca98af76356975f2e837d895c",
"score": "0.6086656",
"text": "def stop(self):\r\n self.is_running = False",
"title": ""
},
{
"docid": "0a7104cc55adfc2e9a471f7528d3f4e9",
"score": "0.60852224",
"text": "def stop(self):\n self.stopped = True",
"title": ""
},
{
"docid": "0a7104cc55adfc2e9a471f7528d3f4e9",
"score": "0.60852224",
"text": "def stop(self):\n self.stopped = True",
"title": ""
},
{
"docid": "0a7104cc55adfc2e9a471f7528d3f4e9",
"score": "0.60852224",
"text": "def stop(self):\n self.stopped = True",
"title": ""
},
{
"docid": "0a7104cc55adfc2e9a471f7528d3f4e9",
"score": "0.60852224",
"text": "def stop(self):\n self.stopped = True",
"title": ""
},
{
"docid": "0a7104cc55adfc2e9a471f7528d3f4e9",
"score": "0.60852224",
"text": "def stop(self):\n self.stopped = True",
"title": ""
},
{
"docid": "7a0b49c614bb0ea1b2aea8fdcb7ba023",
"score": "0.60821193",
"text": "def stop_term(self):\n try:\n self.term.close()\n except AttributeError:\n # Not initialized\n pass\n except ProcessLookupError:\n self.logger.warning(\"Process already stopped\")\n except pexpect.ExceptionPexpect:\n # Not sure how to cover this in a test\n # 'make term' is not killed by 'term.close()'\n self.logger.critical(\"Could not close make term\")",
"title": ""
},
{
"docid": "bc04aeda59084e55ce1d3d3628824776",
"score": "0.6076939",
"text": "def stop(self):\n\n # Make Sure We Are Actually Running\n if self._p is None:\n raise Exception(\"Paramdb Process Not Running\")\n\n # Kill Process\n self._p.kill()\n self._p = None\n pass",
"title": ""
},
{
"docid": "9aaab47b1293e3191234f97226f4b617",
"score": "0.60731184",
"text": "def stop(self):\n remove_agent_state(self.params[\"expected_version_id\"])\n try:\n self.process.terminate()\n self.process.wait()\n return True\n except Exception:\n raise",
"title": ""
},
{
"docid": "7431082461671062d106d88148980bd7",
"score": "0.60727423",
"text": "def _stop(self):\n return self.terminate()",
"title": ""
},
{
"docid": "778ec6533f06b79cb7db1e1a1fe137c0",
"score": "0.6071122",
"text": "def stop(self):\n self.emu.emu_stop()\n\n del self.emu\n\n self.emu = None\n self.is_init = False",
"title": ""
},
{
"docid": "72ed7a2df7884948f88983a85e3377ab",
"score": "0.6066715",
"text": "def stop_integration(self):\n self.command('INTEG:STOP')\n self.logger.info('Integration stopped')",
"title": ""
},
{
"docid": "6fa44f027e557dff350eea1a01fcd2f5",
"score": "0.60653687",
"text": "def stop(self):\n if self.is_interactive is False:\n return False\n\n success = self.send_msg(self.engine, eng_messages.ENG_STOP, ())",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="727"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">05a5d5586961a6e30bf575dacccdff7a</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">The name of the Log Store.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "851950395880187338a3968561079308",
"score": "0.739993",
"text": "def sls_logstore(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"sls_logstore\")",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "e9740687d067a0cf19e6252460c0df26",
"score": "0.8303079",
"text": "def store_name(self) -> str:\n return pulumi.get(self, \"store_name\")",
"title": ""
},
{
"docid": "5010d70b0c4cdfe1aba9bb172116dba0",
"score": "0.8030705",
"text": "def store_name(self):\n\n return self._store_name",
"title": ""
},
{
"docid": "6fb0beccb8ca989bea88ba7ec6e6d7f9",
"score": "0.74562675",
"text": "def logstore(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"logstore\")",
"title": ""
},
{
"docid": "703e7f55cf04a954d150722ba99dbf2d",
"score": "0.73786885",
"text": "def target_data_store_name(self) -> str:\n return pulumi.get(self, \"target_data_store_name\")",
"title": ""
},
{
"docid": "084eae219df232409da88c0e9c589b43",
"score": "0.71805894",
"text": "def __str__(self):\n\n return \"Store '\" + self.db + \"'\"",
"title": ""
},
{
"docid": "a16b89a714159683017246382ce533f0",
"score": "0.71618",
"text": "def logstore(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"logstore\")",
"title": ""
},
{
"docid": "06d03b9f1d989d5ad5f2effad6e52c46",
"score": "0.71157223",
"text": "def logstore(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"logstore\")",
"title": ""
},
{
"docid": "e0254eb6241d1d49dc960500be53d602",
"score": "0.69002163",
"text": "def plasma_store_socket_name(self):\n return self._plasma_store_socket_name",
"title": ""
},
{
"docid": "55e9cdae46101f326666bbd35f27bc22",
"score": "0.6505491",
"text": "def shelfname(self) -> str:\n\n return cast(str, self._contexts['shelf_name'][1])",
"title": ""
},
{
"docid": "6883c99708e294a27ed28c537b10f366",
"score": "0.64601296",
"text": "def object_store_role_name(self):\n return self.get(\"object_store_role_name\")",
"title": ""
},
{
"docid": "aea542cd48090ddf8e529645361cd317",
"score": "0.6375478",
"text": "def get_store_name(self, store_id):\n try:\n cursor_instance = self.connection_instance.cursor()\n sql_statement = \"SELECT name FROM Store WHERE id='\"+store_id+\"'\"\n cursor_instance.execute(sql_statement)\n result = cursor_instance.fetchone()\n\n return result['name']\n\n except Exception as e:\n print(\"Exeception occured:{}\".format(e))",
"title": ""
},
{
"docid": "bb9d6eb9d3bc07aec35d51d7ca5edd43",
"score": "0.6326221",
"text": "def service_name_log(self):\n return self.service_name",
"title": ""
},
{
"docid": "83a671f886bb36eb776ee35811dce911",
"score": "0.6284975",
"text": "def store_id(self):\n\n return self._store_id",
"title": ""
},
{
"docid": "56dc7556f2e19e88d4fa804aa9aee680",
"score": "0.62842065",
"text": "def name(self):\n return self.production.name",
"title": ""
},
{
"docid": "c98beca2bcde2214de542f923e1316e1",
"score": "0.62385994",
"text": "def event_log_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"event_log_name\")",
"title": ""
},
{
"docid": "57473e03274000920d8623e8dc194bad",
"score": "0.6168097",
"text": "def event_log_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"event_log_name\")",
"title": ""
},
{
"docid": "483f51aaeec2f4a7f2d840279bb08b26",
"score": "0.6137032",
"text": "def storage_type_name(self):\n return self.name",
"title": ""
},
{
"docid": "2796ea6689d1ec80f2a9430c92197fd5",
"score": "0.6114268",
"text": "def event_log_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"event_log_name\")",
"title": ""
},
{
"docid": "ac884c537218fff2ce982a7af5394fa0",
"score": "0.6099567",
"text": "def name(self):\n return SENSOR_PREFIX + self._name",
"title": ""
},
{
"docid": "0901dc3e1fe2cd3988bc728c18fe07da",
"score": "0.60994023",
"text": "def name(self):\n return SENSOR_NAME.format(self._name, self._name_suffix)",
"title": ""
},
{
"docid": "88958f1eb6d7911f931310567e214bdf",
"score": "0.60489285",
"text": "def logPrefix(self):\n return '{}-Factory'.format(NAME)",
"title": ""
},
{
"docid": "05320e01b4810f1ede86422a2cd0de8b",
"score": "0.59692925",
"text": "def log_stream_name(self) -> Optional[str]:\n return pulumi.get(self, \"log_stream_name\")",
"title": ""
},
{
"docid": "05320e01b4810f1ede86422a2cd0de8b",
"score": "0.59692925",
"text": "def log_stream_name(self) -> Optional[str]:\n return pulumi.get(self, \"log_stream_name\")",
"title": ""
},
{
"docid": "05320e01b4810f1ede86422a2cd0de8b",
"score": "0.59692925",
"text": "def log_stream_name(self) -> Optional[str]:\n return pulumi.get(self, \"log_stream_name\")",
"title": ""
},
{
"docid": "05320e01b4810f1ede86422a2cd0de8b",
"score": "0.59692925",
"text": "def log_stream_name(self) -> Optional[str]:\n return pulumi.get(self, \"log_stream_name\")",
"title": ""
},
{
"docid": "05320e01b4810f1ede86422a2cd0de8b",
"score": "0.59692925",
"text": "def log_stream_name(self) -> Optional[str]:\n return pulumi.get(self, \"log_stream_name\")",
"title": ""
},
{
"docid": "05320e01b4810f1ede86422a2cd0de8b",
"score": "0.59692925",
"text": "def log_stream_name(self) -> Optional[str]:\n return pulumi.get(self, \"log_stream_name\")",
"title": ""
},
{
"docid": "05320e01b4810f1ede86422a2cd0de8b",
"score": "0.59692925",
"text": "def log_stream_name(self) -> Optional[str]:\n return pulumi.get(self, \"log_stream_name\")",
"title": ""
},
{
"docid": "05320e01b4810f1ede86422a2cd0de8b",
"score": "0.59692925",
"text": "def log_stream_name(self) -> Optional[str]:\n return pulumi.get(self, \"log_stream_name\")",
"title": ""
},
{
"docid": "05320e01b4810f1ede86422a2cd0de8b",
"score": "0.59692925",
"text": "def log_stream_name(self) -> Optional[str]:\n return pulumi.get(self, \"log_stream_name\")",
"title": ""
},
{
"docid": "c08a18b466b9c14aec47c64c09ae7da9",
"score": "0.59652793",
"text": "def name(self):\n return \"{} {} ({})\".format(self._base_name, SENSOR_TYPES[self._type].get('name'), self._player.player_id)",
"title": ""
},
{
"docid": "abaa3885de902ed60e69ff89d496bc22",
"score": "0.5961325",
"text": "def get_name(self):\n return self.system['name']",
"title": ""
},
{
"docid": "faad116568350f46a768ed7718b78e90",
"score": "0.5953339",
"text": "def track_name(self) -> str:\n return pulumi.get(self, \"track_name\")",
"title": ""
},
{
"docid": "e3d5965b0236ff03b1ec6a5a405f6071",
"score": "0.5941732",
"text": "def get_name(self) -> str:\n return self._soil_monitor.get_name()",
"title": ""
},
{
"docid": "99a6f393c2ea188e9b7cb2ef14036d2f",
"score": "0.59222263",
"text": "def name(self):\n return self._station_name",
"title": ""
},
{
"docid": "c186759683bda9ad7aa845c7d554f1ce",
"score": "0.58858645",
"text": "def logname(self, suffix=None):\n #return None if not self.savelogfile else\\\n # '%s/%s_log_%s.txt' % (self.dir_logs_year(), self.tstamp, str(suffix))\n if suffix is not None: self.logsuffix = suffix\n s = '%s/%s_log_%s.txt' % (self.makedir_logs_year(), self.tstamp, self.logsuffix)\n if self.logname_tmp is None:\n self.logname_tmp = s\n return s",
"title": ""
},
{
"docid": "8a6b84ab2b8b19bd7703cafa0fb24c43",
"score": "0.5885667",
"text": "def name(cls):\n return cls._NAME",
"title": ""
},
{
"docid": "1ed4409b6a3d899ede0d14191abc1adf",
"score": "0.58723557",
"text": "def get_name(self):\n return type(self).__name__",
"title": ""
},
{
"docid": "00817c1b51adb24875230fe2fa2fbc76",
"score": "0.58635783",
"text": "def log_group_name(self) -> str:\n return pulumi.get(self, \"log_group_name\")",
"title": ""
},
{
"docid": "00817c1b51adb24875230fe2fa2fbc76",
"score": "0.58635783",
"text": "def log_group_name(self) -> str:\n return pulumi.get(self, \"log_group_name\")",
"title": ""
},
{
"docid": "8f8b93e9f2ec6bd25b3883b02f77604d",
"score": "0.5863292",
"text": "def name(self):\n return f\"{self._inst} {self._data[self._sid_data['sid_name']]}\"",
"title": ""
},
{
"docid": "8f8b93e9f2ec6bd25b3883b02f77604d",
"score": "0.5863292",
"text": "def name(self):\n return f\"{self._inst} {self._data[self._sid_data['sid_name']]}\"",
"title": ""
},
{
"docid": "5260d510ab51b434b2a95d32e8ef4b88",
"score": "0.58520716",
"text": "def logPrefix(self):\n return self.versionName",
"title": ""
},
{
"docid": "a3f7aebdcfe7309f2d5c3daf396fc2e3",
"score": "0.58466876",
"text": "def name(self):\n\n\t\treturn self.__name",
"title": ""
},
{
"docid": "0c6f244f4fb41c8a5632201ae7603637",
"score": "0.58431023",
"text": "def storage_id(self):\n return self.NAME.lower()",
"title": ""
},
{
"docid": "e5134c6cd36ab659170901c3741ae1ba",
"score": "0.58427083",
"text": "def name(self):\n return self.lg_name",
"title": ""
},
{
"docid": "bd0dd3912489fa66de733582ce9bd9d4",
"score": "0.58406305",
"text": "def name(self):\n return f\"{self._name}\"",
"title": ""
},
{
"docid": "cf47d42a7bbc25852078b801ecd4905e",
"score": "0.58397406",
"text": "def log_stream_name(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"log_stream_name\")",
"title": ""
},
{
"docid": "bf7eb7be504a8fcf4ffc5ca25c1bce1c",
"score": "0.5837989",
"text": "def database_name(self) -> str:\n return pulumi.get(self, \"database_name\")",
"title": ""
},
{
"docid": "bf7eb7be504a8fcf4ffc5ca25c1bce1c",
"score": "0.5837989",
"text": "def database_name(self) -> str:\n return pulumi.get(self, \"database_name\")",
"title": ""
},
{
"docid": "0d869b8058c30ea0753ef15b17acafe3",
"score": "0.5828854",
"text": "def name ( self ) :\n return self.__name",
"title": ""
},
{
"docid": "66b5a05d3c16f343391ffa962fea884a",
"score": "0.5808251",
"text": "def log_stream_name(self):\n\n log_stream_name = None\n try:\n log_stream_name = self.__log_stream_name\n except AttributeError:\n # If user has specified a log_group_name and the log stream\n # hasnt been created, create one.\n if self.log_group_name:\n new_log_stream_name = (\n '{0}-{1}-{2}'.\n format(\n self.mongo_name,\n self.instance_id,\n int((time.time() + 0.5) * 1000)\n )\n )\n self.logs_client.create_log_stream(\n logGroupName=self.log_group_name,\n logStreamName=new_log_stream_name\n )\n self.log_stream_name = new_log_stream_name\n log_stream_name = self.log_stream_name\n self.log(\n \"Creating CloudWatch log stream [log_group={0}, \"\n \"log_stream={1}].\".\n format(self.log_group_name, self.log_stream_name)\n )\n\n return log_stream_name",
"title": ""
},
{
"docid": "30aa946071e5b8d8c7d4e736567b11c7",
"score": "0.58058757",
"text": "def name(self):\n\t\treturn self.__name",
"title": ""
},
{
"docid": "30aa946071e5b8d8c7d4e736567b11c7",
"score": "0.58058757",
"text": "def name(self):\n\t\treturn self.__name",
"title": ""
},
{
"docid": "d9318ee65d7286ff6d088307952bed74",
"score": "0.5803082",
"text": "def name(cls):\n return cls.__name__",
"title": ""
},
{
"docid": "d9318ee65d7286ff6d088307952bed74",
"score": "0.5803082",
"text": "def name(cls):\n return cls.__name__",
"title": ""
},
{
"docid": "bf5ae75a5b01cdbdd614721d225db209",
"score": "0.5802922",
"text": "def default_data_store(self) -> str:\n return self[\"run_metadata.write_data_store\"]",
"title": ""
},
{
"docid": "7cf1d6c716debc9fc1923b589759508c",
"score": "0.57890546",
"text": "def name(self):\n return self.__name__",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
},
{
"docid": "dab9232825e5690c39a1715767d2679e",
"score": "0.5788929",
"text": "def name(self) -> str:\n return pulumi.get(self, \"name\")",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="728"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">095c89e054db19a35f4b9d6db87667c9</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Try to get data about the board by parameter 'board_url'. Function is called upon GET request, checks request parameters and get data from DB if record exists</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "04915bb04d226f57f3da84c20d9eb072",
"score": "0.7731758",
"text": "def get_board(data):\n # validate all required fields exists\n for required_field in [\"board_url\"]:\n if required_field not in data.keys():\n err = f\"No '{required_field}' specified\"\n return send_error_response(400, err)\n\n try:\n board_url = str(data[\"board_url\"])\n except Exception as err:\n return send_error_response(400, str(err))\n\n try:\n board_obj = Board.query.filter_by(url=board_url).one()\n except NoResultFound:\n err = f\"Didn't find board with url '{board_url}'\"\n return send_error_response(404, err)\n\n board = {\"board_url\": board_obj.url, \"board_data\": board_obj.data}\n\n return send_success_response(board)",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "73ccee6729593ec4a71baee0ddf10528",
"score": "0.58711916",
"text": "def get_boards(self) -> Optional[List[Board]]:\n http_method = 'GET'\n detail = 'boards'\n values = {}\n url_postfix = 'boards'\n response = None\n try:\n response = make_request(platform_access=self.__platform_access,\n http_method=http_method,\n url_postfix=url_postfix,\n detail=detail,\n values=values)\n response.raise_for_status()\n response_data = response.json()\n boards = self.__create_boards_from_dict(response_data)\n return boards\n except HTTPError as http_error:\n logger.exception(OWN_ADAPTER_NAME, f'Could not retrieve boards data: {http_error}',\n response)\n return None\n except ConnectionError as conect_error:\n logger.exception(OWN_ADAPTER_NAME,\n f'Could not get boards list. Exception message: {conect_error}',\n response)\n return None",
"title": ""
},
{
"docid": "9d74e053ccf46e13152f79417aee2e49",
"score": "0.57054204",
"text": "def delete_board(data: dict):\n # validate all required fields exists\n for required_field in [\"board_url\"]:\n if required_field not in data.keys():\n err = f\"No '{required_field}' specified\"\n return send_error_response(400, err)\n\n try:\n board_url = str(data[\"board_url\"])\n except Exception as err:\n return send_error_response(400, str(err))\n\n try:\n # checks if record with such 'board_url' exists in DB\n if not Board.delete({\"url\": board_url}):\n err = f\"Didn't find board with url '{board_url}'\"\n return send_error_response(404, err)\n except Exception as err:\n return send_error_response(400, str(err))\n\n return send_success_response()",
"title": ""
},
{
"docid": "abe2ec9c6aa1d7b4d69111d1f2ff028e",
"score": "0.56166536",
"text": "def fetch_records(board_file):\n data = None\n with open(board_file) as f:\n data = json.load(f)\n return data",
"title": ""
},
{
"docid": "ae7bc215f7d92254cb3502152eae13c9",
"score": "0.56109196",
"text": "def get_board(matching: Callable) -> Board:\n database_mode = _get_database_mode()\n\n if database_mode == _DatabaseMode.OFFLINE:\n logger.info(\"Using the offline database (only) to identify boards.\")\n return Boards.from_offline_database().get_board(matching)\n\n if database_mode == _DatabaseMode.ONLINE:\n logger.info(\"Using the online database (only) to identify boards.\")\n return Boards.from_online_database().get_board(matching)\n try:\n logger.info(\"Using the offline database to identify boards.\")\n return Boards.from_offline_database().get_board(matching)\n except UnknownBoard:\n logger.info(\"Unable to identify a board using the offline database, trying the online database.\")\n return Boards.from_online_database().get_board(matching)",
"title": ""
},
{
"docid": "f8121170186e406578f8a65a58c02b97",
"score": "0.5596751",
"text": "def get_board():\n\treturn board",
"title": ""
},
{
"docid": "fa374110cc9318e195c18fc1f1d10143",
"score": "0.5549775",
"text": "def __create_boards_from_dict(self, data: Dict) -> Optional[List[Board]]:\n boards_data = data.get('boards', None)\n if not boards_data:\n return None\n\n boards = []\n for board in boards_data:\n href = board.get('href', '')\n identifier = href.split('/')[-1] if href else None\n name = board.get('rel', None)\n\n boards.append(Board(self.__platform_access, name=name, identifier=identifier))\n return boards",
"title": ""
},
{
"docid": "2383ffa7358c335afdc991a8365e3c19",
"score": "0.553904",
"text": "def get_board_by_online_id(slug: str, target_type: str) -> Board:\n matched_slug = slug.casefold()\n return get_board(lambda board: board.slug.casefold() == matched_slug and board.target_type == target_type)",
"title": ""
},
{
"docid": "2383ffa7358c335afdc991a8365e3c19",
"score": "0.553904",
"text": "def get_board_by_online_id(slug: str, target_type: str) -> Board:\n matched_slug = slug.casefold()\n return get_board(lambda board: board.slug.casefold() == matched_slug and board.target_type == target_type)",
"title": ""
},
{
"docid": "9ac5d3ee29ec10be22da341c969b105d",
"score": "0.552963",
"text": "def get_boards(self):\n response = requests.get(\n url=self.url_dict[\"boards_url\"], \n params=self.params_key_and_token, \n data=self.arguments\n )\n\n return json.loads(response.text)",
"title": ""
},
{
"docid": "355ae5ec9129e4dcd62d6472ba5abff2",
"score": "0.55151546",
"text": "def get_board_id(url):\n return base.get_structure_id(url)",
"title": ""
},
{
"docid": "fb8bf553e003be6ec6b6860ccace14ed",
"score": "0.5508816",
"text": "def get_board(matching: Callable) -> Board:\n database_mode = _get_database_mode()\n\n if database_mode == _DatabaseMode.OFFLINE:\n logger.info(\"Using the offline database (only) to identify boards.\")\n return Boards.from_offline_database().get_board(matching)\n\n if database_mode == _DatabaseMode.ONLINE:\n logger.info(\"Using the online database (only) to identify boards.\")\n return Boards.from_online_database().get_board(matching)\n try:\n logger.info(\"Using the offline database to identify boards.\")\n return Boards.from_offline_database().get_board(matching)\n except UnknownBoard:\n logger.info(\"Unable to identify a board using the offline database, trying the online database.\")\n try:\n return Boards.from_online_database().get_board(matching)\n except BoardDatabaseError:\n logger.error(\"Unable to access the online database to identify a board.\")\n raise UnknownBoard()",
"title": ""
},
{
"docid": "0e1c12e02684049b0ea4cfbf3fc1ec05",
"score": "0.5485171",
"text": "async def showboard(self, ctx, *, board_type: str = \"donation\"):\n if ctx.message.channel_mentions:\n channel = ctx.message.channel_mentions[0]\n board_type = board_type.replace(channel.mention, \"\").strip()\n\n else:\n channel = ctx.channel\n\n if board_type not in (\"donation\", \"trophy\", \"legend\", \"war\"):\n board_type = \"donation\"\n\n query = \"\"\"\n SELECT DISTINCT boards.*\n FROM boards\n INNER JOIN clans\n ON clans.channel_id = boards.channel_id\n WHERE clans.clan_tag IN (SELECT clan_tag FROM clans WHERE channel_id = $1)\n AND boards.guild_id = $2\n AND type = $3\n \"\"\"\n fetch = await ctx.db.fetch(query, channel.id, ctx.guild.id, board_type)\n if not fetch:\n exists = await ctx.db.fetch(\"SELECT 1 FROM clans WHERE channel_id = $1\", channel.id)\n if not exists:\n return await ctx.send(\"I couldn't find any clans added to this channel.\")\n\n fake_record = {\n \"guild_id\": ctx.guild.id,\n \"channel_id\": ctx.channel.id,\n \"icon_url\": None,\n \"title\": None,\n \"sort_by\": default_sort_by[board_type],\n \"toggle\": True,\n \"type\": board_type,\n \"in_event\": False,\n \"message_id\": None,\n \"per_page\": 0,\n \"page\": 1,\n \"season_id\": None,\n }\n\n configs = [BoardConfig(bot=self.bot, record=fake_record)]\n else:\n configs = [BoardConfig(bot=self.bot, record=row) for row in fetch]\n\n async with ctx.typing():\n for config in configs:\n await self.update_board(None, config, divert_to=ctx.channel.id)",
"title": ""
},
{
"docid": "0675d72e849a7f5c7122f1297668cf17",
"score": "0.54708606",
"text": "def get_boards():\n try:\n boards = BoardModel.objects.all()\n res = [BoardSerializer(board).data for board in boards]\n return True, res\n\n except Exception as e:\n print(e)\n return False, \"Error\"",
"title": ""
},
{
"docid": "c62b3d893bd7c092111b96e27b57bfbe",
"score": "0.54312325",
"text": "def update_board(data):\n if \"action\" not in data.keys():\n err = \"No 'action' specified\"\n return send_error_response(400, err)\n\n action = data[\"action\"]\n\n if action == \"BOARD_ADD_PIC\":\n # validate all required fields exists for this action\n for required_field in [\"board_url\", \"data_delta\", \"key\", \"mode\"]:\n if required_field not in data.keys():\n err = f\"No '{required_field}' specified\"\n return send_error_response(400, err)\n\n try:\n board_url = str(data[\"board_url\"])\n data_delta = loads(data[\"data_delta\"])\n key = str(data[\"key\"])\n mode = str(data[\"mode\"])\n except Exception as err:\n return send_error_response(400, str(err))\n\n if mode == \"TOOLBAR_MODE_DRAW\":\n # validate ket for draw\n if key.count(\".#\") != 1:\n err = \"Invalid DRAW key given\"\n return send_error_response(400, err)\n elif mode == \"TOOLBAR_MODE_ERASE\":\n if len(key) != 2:\n err = \"Invalid ERASE key given\"\n return send_error_response(400, err)\n else:\n err = \"Invalid mode given\"\n return send_error_response(400, err)\n\n if not data_delta:\n err = \"No empty delta allowed\"\n return send_error_response(400, err)\n\n # todo: how to validate input delta for correct structure?\n\n try:\n board_obj = Board.query.filter_by(url=board_url).one()\n except NoResultFound:\n err = f\"Didn't find board with url '{board_url}'\"\n return send_error_response(404, err)\n\n new_data: list = deepcopy(board_obj.data)\n\n # create first pic if empty or if last pic mode isn't equal to given\n if len(new_data) == 0 or new_data[-1][\"mode\"] != mode:\n new_data.append({\"mode\": mode, \"data\": {}})\n\n # assume key is correct\n if key not in new_data[-1][\"data\"]:\n new_data[-1][\"data\"][key] = []\n\n new_data[-1][\"data\"][key].append(data_delta)\n\n try:\n updated_obj = Board.update(\n {\"url\": board_url}, **{\"data\": new_data}\n )\n except Exception as err:\n return send_error_response(400, str(err))\n\n elif action == \"BOARD_CLEAR\":\n # validate all required fields exists for this action\n for required_field in [\"board_url\"]:\n if required_field not in data.keys():\n err = f\"No '{required_field}' specified\"\n return send_error_response(400, err)\n\n try:\n board_url = str(data[\"board_url\"])\n except Exception as err:\n return send_error_response(400, str(err))\n\n try:\n Board.query.filter_by(url=board_url).one()\n except NoResultFound:\n err = f\"Didn't find board with url '{board_url}'\"\n return send_error_response(404, err)\n\n try:\n updated_obj = Board.update({\"url\": board_url}, **{\"data\": []})\n except Exception as err:\n return send_error_response(400, str(err))\n\n elif action == \"BOARD_INIT_POINTS\":\n pass\n\n else:\n err = (\n \"Invalid action type, choose from \"\n + \"['BOARD_ADD_PIC', 'BOARD_CLEAR', 'BOARD_INIT_POINTS']\"\n )\n\n return send_error_response(400, err)\n\n return send_success_response(\n {\"board_url\": updated_obj.url, \"board_data\": updated_obj.data}\n )",
"title": ""
},
{
"docid": "f53118a944283c9b1ef380951c2e844f",
"score": "0.5411482",
"text": "def get_valid_scoreboard_base_endpoint(l):\n scoreboards = l.client.get(SCOREBOARDS_ENDPOINT).json()\n groups = l.client.get(GROUPS_ENDPOINT).json()\n\n # Load the initial page of one of the scoreboards\n possible_boards = []\n for board in scoreboards:\n possible_boards.append((\"scoreboard\", board[\"sid\"]))\n for group in groups:\n possible_boards.append((\"group\", group[\"gid\"]))\n\n board = random.choice(possible_boards)\n if board[0] == \"scoreboard\":\n endpoint = SCOREBOARDS_ENDPOINT + \"/\" + board[1]\n call_label = SCOREBOARDS_ENDPOINT + \"/[sid]\"\n else:\n endpoint = GROUPS_ENDPOINT + \"/\" + board[1]\n call_label = GROUPS_ENDPOINT + \"/[gid]\"\n return (endpoint, call_label)",
"title": ""
},
{
"docid": "cec9f1d34ded374749c21e24c1d4cc6a",
"score": "0.5409592",
"text": "def retrieve(self, request, pk=None):\n try:\n note_board = NoteBoard.objects.get(pk=pk)\n serializer = NoteBoardsSerializer(note_board, context={\"request\": request})\n return Response(serializer.data)\n except Exception as ex:\n return HttpResponseServerError(ex)",
"title": ""
},
{
"docid": "dde2e743b9835b8dc16763bd35d5663c",
"score": "0.53965753",
"text": "def get(self, request, *args, **kwargs):\n #input parameter club, week and validation #todo\n club = int(request.GET.get('club',0))\n showWeek = int(request.GET.get('week',0))\n\n if club == 0:\n return Response(\"Data not found\", status=status.HTTP_204_NO_CONTENT)\n sessionTable = sessionGenerateFull(club, showWeek)\n if sessionTable == 0:\n return Response(\"Data not found\", status=status.HTTP_204_NO_CONTENT)\n serializer = cellSerializer(sessionTable, many=True)\n return Response(serializer.data)",
"title": ""
},
{
"docid": "ea14b6a06e1856557588d7b212b7a375",
"score": "0.53111696",
"text": "def get_organization_boards():\n return requests.get(url=get_boards_organization).json()",
"title": ""
},
{
"docid": "fafeedad5ad821b890654f1fd3b9c8a7",
"score": "0.530711",
"text": "def get_board_lists(id_board):\n endpoint = \"1/boards/{}/lists\".format(id_board)\n params = {\n 'key': APIKEY,\n 'token': TOKEN\n }\n\n response = requests.get(url=URL + endpoint, params=params)\n assert response.status_code == 200\n assert response.reason == \"OK\"\n assert response.ok\n response_json = response.json()\n # print(response_json)\n return response_json",
"title": ""
},
{
"docid": "bbded08a4e495af8983e424878fea5ed",
"score": "0.52971476",
"text": "def _pull_team_page(self, url):\n try:\n return pq(url)\n except HTTPError:\n return None",
"title": ""
},
{
"docid": "081f4e6d13f0857ba805e9a533df0e01",
"score": "0.529433",
"text": "def verify_board_exists(board_tuple):\n if board_tuple[0] == \"id\":\n print(\"---Verifying by ID\")\n return BoardModel.objects.filter(board_id=board_tuple[1]).exists()\n elif board_tuple[0] == \"name\":\n print(\"---Verifying by NAME\") \n return BoardModel.objects.filter(board_name=board_tuple[1]).exists()\n else:\n print(\"Invalid input\")\n return False",
"title": ""
},
{
"docid": "10ad0fe127a84647d2ab1ffb56a401c8",
"score": "0.52762336",
"text": "def get_cards_for_board(board_id: int):\n return data_handler.get_cards_for_board_from_SQL(board_id)",
"title": ""
},
{
"docid": "7dab86e59bab230c5b7f470a14fd27d1",
"score": "0.5259264",
"text": "def __load_board(self):\n # // Value access.\n path: str = self.__path_entry.get()\n subgrid_rows: str = self.__subgrid_row_entry.get()\n subgrid_cols: str = self.__subgrid_column_entry.get()\n safe_pass = True\n\n # // Error logic.\n if subgrid_rows.isdigit():\n subgrid_rows = int(subgrid_rows)\n else:\n self.__subgrid_row_entry.delete(0, tk.END)\n self.__subgrid_row_entry.insert(0,\"Invalid\")\n safe_pass = False\n\n if subgrid_cols.isdigit():\n subgrid_cols = int(subgrid_cols)\n else:\n self.__subgrid_column_entry.delete(0, tk.END)\n self.__subgrid_column_entry.insert(0,\"Invalid\") \n safe_pass = False\n\n # // Board creation.\n if safe_pass:\n loaded: bool = self.__sudoku.load_board_csv(path, subgrid_rows, subgrid_cols)\n if loaded: \n solved: bool = self.__sudoku.backtrack()\n if solved:\n self.__frame_grid.destroy()\n self.__frame_grid = tk.Frame(self.__window, borderwidth=0, relief=\"solid\")\n self.__frame_grid.pack()\n self.__setup_grid()\n self.__setup_separators()\n else:\n self.__path_entry.delete(0, tk.END)\n self.__path_entry.insert(0,\"Unsolvable\")\n else:\n self.__path_entry.delete(0, tk.END)\n self.__path_entry.insert(0,\"Loading failure\")",
"title": ""
},
{
"docid": "52a565ade96e81088cf05cb30b66ea5e",
"score": "0.52560854",
"text": "def get_scoreboard(date):\n day = date.split('-')\n url = 'https://stats.ncaa.org/season_divisions/' + str(seasons.loc[seasons['season'] == int(day[2]),'id'].item()) + '/scoreboards?utf8=%E2%9C%93&game_date='+ day[0] +'%2F'+ day[1] + '%2F' + day[2]\n # url = \"https://stats.ncaa.org/season_divisions/17126/scoreboards?utf8=%E2%9C%93&season_division_id=&game_date=02%2F25%2F2020&conference_id=0&tournament_id=&commit=Submit\"\n page = requests.get(url)\n doc = lh.fromstring(page.content)\n matchups = []\n game = []\n dates = []\n away = []\n home = []\n links = []\n\n #get elements in td index 3 (away team names and home final scores)\n a_teams = doc.xpath(\"//div[@id='contentarea']/table/tbody/tr/td[3]\")\n\n #\n for a in range(len(a_teams)):\n if not 'totalcol' in [x for x in a_teams[a].classes]:\n away.append(a_teams[a][0].text if not len(a_teams[a]) < 1 else a_teams[a].text.replace('\\n', '').replace(' ', '').replace(' ', ''))\n\n #get elements in td index 2 (away team logos, home team names and blank element below attendance)\n h_teams = doc.xpath(\"//div[@id='contentarea']/table/tbody/tr/td[2]\")\n for h in range(len(h_teams)):\n if not 'img' in [a.tag for a in h_teams[h]]:\n if not len([a.text for a in h_teams[h]]) > 0:\n test = h_teams[h].text\n if not test is None:\n team = h_teams[h].text.replace('\\n', '').replace(' ', '').replace(' ', '')\n if not team == '':\n home.append(team)\n else:\n home.append(h_teams[h][0].text)\n\n l = doc.xpath(\"//div[@id='contentarea']/table/tbody/tr/td[1]\")\n na = []\n for i in range(round(len(l)/3)):\n e = l[(i)*3+2]\n if len(e) == 0:\n na.append(i)\n else:\n links.append(e[0].attrib['href'])\n\n deleted = 0\n for i in na:\n del away[i-deleted]\n del home[i-deleted]\n deleted += 1\n\n # Remove rankings and leading spaces\n for i in range(0,len(away)):\n if '#' in away[i]:\n away[i] = away[i][away[i].index(' ')+1:]\n else:\n away[i] = away[i][1:]\n\n if '#' in home[i]:\n home[i] = home[i][home[i].index(' ')+1:]\n else:\n home[i] = home[i][1:]\n # Check for doubleheaders\n m = away[i] + ' ' + home[i]\n if m in matchups:\n game[matchups.index(m)] = 1\n game.append(2)\n else:\n game.append(0)\n matchups.append(m)\n\n for j in range(len(away)):\n # Remove records\n record_check = re.search(r'([0-9]{1,2}-[0-9]{1,2})', home[j])\n if not record_check is None:\n home[j] = home[j].replace(' (' + home[j].split(' (')[-1], '')\n away[j] = away[j].replace(' (' + away[j].split(' (')[-1], '')\n dates.append(day[2] + day[0] + day[1])\n\n return pd.DataFrame({'away': away, 'home': home, 'game': game, 'link': links, 'date': dates})",
"title": ""
},
{
"docid": "eb763f13a54afe25a63f67b6807510fb",
"score": "0.52556825",
"text": "def fetch_data_and_send_to_db():\n\n # Grabbing last record from the database,\n record = crop_conditions_kansas.query.order_by(\n crop_conditions_kansas.week_ending.desc()\n ).first()\n\n if record is None:\n # Makes request if no record is found\n query = build_request_query('year__GE', '2012')\n request = requests.get(format_request_url(query))\n\n send_to_db(request)\n else:\n # If record is found checks to add \n query = build_request_query('week_ending__GT', record.week_ending)\n request = requests.get(format_request_url(query))\n send_to_db(request)",
"title": ""
},
{
"docid": "a1f7e639052de51a775204f7d8e6b865",
"score": "0.5229157",
"text": "def get_user_boards():\n endpoint = \"1/members/me/boards\"\n params = {\n 'key': APIKEY,\n 'token': TOKEN\n }\n\n response = requests.get(url=URL + endpoint, params=params)\n assert response.status_code == 200\n assert response.reason == \"OK\"\n assert response.ok\n response_json = response.json()\n # print(response_json)\n return response_json",
"title": ""
},
{
"docid": "b28eaa3ad0787f1cbb55b49c82dc99d2",
"score": "0.52106273",
"text": "def board_exists(khoros_object, board_id=None, board_url=None):\n return base.structure_exists(khoros_object, 'board', board_id, board_url)",
"title": ""
},
{
"docid": "528df28a3b85d01b740b50908eb15c8c",
"score": "0.51737565",
"text": "def displayBoard():\n return render_template('board.html',\n clubs=clubs)",
"title": ""
},
{
"docid": "5decf980f807ff9924d680d1c531f7a6",
"score": "0.51653767",
"text": "def call_trello_api(self):\n params = {'key': self.trello_key, 'token': self.trello_token}\n response = requests.get('https://api.trello.com/1/boards/{}/cards'.format(self.trello_board), params=params)\n return response.json()",
"title": ""
},
{
"docid": "79f5828d671092954b063cde5920d7a8",
"score": "0.5153368",
"text": "def fetch(self):\n fetched_boards = self.trello_service.boards()\n persisted_boards = Board.query.all()\n\n self._update_or_delete_boards(fetched_boards, persisted_boards)\n self._create_boards(fetched_boards, persisted_boards)\n\n # Persist the changes to the boards and lists\n db.session.commit()",
"title": ""
},
{
"docid": "cbe546be98869594524db8be5df1b2cd",
"score": "0.5150962",
"text": "def create_board(data):\n # validate all required fields exists\n for required_field in [\"points\"]:\n if required_field not in data.keys():\n err = f\"No '{required_field}' specified\"\n return send_error_response(400, err)\n\n try:\n points = loads(data[\"points\"])\n except Exception as err:\n return send_error_response(400, str(err))\n\n # todo: how to validate input points for correct structure?\n\n # todo: fix case when generating 'board_url'\n # at the same time and with empty data\n\n # generate new board_url\n first = sha256(str(time()).encode()).hexdigest()\n second = sha256(str(data).encode()).hexdigest()\n board_url = f\"{first[:4]}{second[:8]}{first[-4:]}\"\n\n try:\n obj = Board.create(**{\"data\": points, \"url\": board_url})\n except Exception as err:\n return send_error_response(400, str(err))\n\n return send_success_response({\"board_url\": obj.url})",
"title": ""
},
{
"docid": "82618171746ead0cdb822d3ce078f0f5",
"score": "0.5143932",
"text": "def get_boards(cls, board_type=EDITORIAL, cursor=None):\n query = cdrdb.Query(\"query_term n\", \"n.doc_id\", \"n.value\").unique()\n query.join(\"query_term t\", \"t.doc_id = n.doc_id\")\n query.join(\"active_doc a\", \"a.id = n.doc_id\")\n query.where(query.Condition(\"n.path\", cls.NAME_PATH))\n query.where(query.Condition(\"t.path\", cls.ORG_TYPE_PATH))\n if board_type in cls.BOARD_TYPES:\n query.where(query.Condition(\"t.value\", f\"PDQ {board_type} Board\"))\n else:\n types = [f\"PDQ {bt} Board\" for bt in cls.BOARD_TYPES]\n query.where(query.Condition(\"t.value\", types, \"IN\"))\n boards = collections.OrderedDict()\n for board_id, board_name in query.order(\"n.value\").execute():\n boards[board_id] = cls(board_id, name=board_name)\n return boards",
"title": ""
},
{
"docid": "f1031140befee44c36082fa6a80ae74d",
"score": "0.51219845",
"text": "def list_boards():\n boards = Board.objects.all()\n return boards",
"title": ""
},
{
"docid": "c30725801d968595c7a931701a173a86",
"score": "0.5116459",
"text": "def get_board_id(name):\n id = None\n response = get_user_boards()\n for board in response:\n if board['name'] == name:\n id = board['id']\n break\n\n if id is None:\n print(\"No board found with the name: {}\".format(name))\n return id",
"title": ""
},
{
"docid": "dc6d9e0fed2519bded71bbfee79bc1b6",
"score": "0.5109778",
"text": "def _fetch(self, url):\n if self.class_helper_bugpage is None:\n raise NotImplementedError, 'this method must be implemented by a concrete subclass'\n try:\n bugpage = self.class_helper_bugpage(url,\n connection=self.__connection, all_tasks=self.__all_tasks)\n except LaunchpadError:\n bugpage = None\n if not self:\n raise\n return bugpage",
"title": ""
},
{
"docid": "13f69cdafcfa23667384d9e12f097344",
"score": "0.5077229",
"text": "def fetch(self):\n try:\n self.__genre = 'review'\n self.__baseuri = 'http://www.livestrong.com'\n self.__setSoupForCurrentUri()\n if '/viewforum.php?f' in self.currenturi:\n return self.__createTasksForThreads()\n else:\n #==> Pickup the List of Question and Answer\n return self.__addThreadAndPosts()\n except:\n log.exception(self.log_msg('Exception in fetch for the url %s'%self.currenturi))\n return False",
"title": ""
},
{
"docid": "59490580bf7804dc71ac14e146baee26",
"score": "0.506132",
"text": "def _get( self, url, params = {} ):\n\n\t\t# The only data we send is the api_key\n\t\tif params:\n\t\t\tdata = params\n\t\telse:\n\t\t\tdata = { 'api_key' : self.api_key }\n\n\t\t# Return the request object\n\t\t# Catch the bad requests because they are probably valid requests just no data for them\n\t\t# I.E. GetSingle( 53 ) when the user does not have a task with id of 53 which returns a 404 page that is\n\t\t# not parseable by json\n\t\ttry:\n\t\t\ttask = self._send_request( url, data, 'GET' )\n\t\texcept ValueError, e:\n\t\t\treturn []\n\n\t\treturn self._decodeJson( task )",
"title": ""
},
{
"docid": "741afba7cd1eb5b1e93e3ca72997c118",
"score": "0.5048703",
"text": "def fetch_by_url(self, url):\n raise NotImplementedError",
"title": ""
},
{
"docid": "d25316c2c824b849413d14b36fde5303",
"score": "0.50458586",
"text": "def get(self, request, *args, **kwargs):\n club= int(request.GET.get('club','-1'))\n week= int(request.GET.get('week','-1'))\n id = int(request.GET.get('cellid','-1'))\n scheduleTable = sessionGenerateFull(club, week)\n cell = filter(lambda x: x.cellid == id, scheduleTable)\n if not cell:\n return Response(\"No cell exist.\",status=status.HTTP_400_BAD_REQUEST)\n\n enrolledInst = enrolledProgramSession.objects.filter(date = cell[0].date).filter(sessionTimeBegin = cell[0].begin).filter(sessionTimeEnd = cell[0].end)\n return Response(enrollProgramSerializer(enrolledInst, many=True).data)",
"title": ""
},
{
"docid": "2baf4dd9ac08a7d95f40e409edb1ce83",
"score": "0.50450927",
"text": "def test_get_game_info(self):\n top_stream_id, featured_li = twitch.views.get_game_info(\"adwkjaabd \\\n dakwwjdas dasxwd\") #bad input, assuming no one used this\n self.assertEqual(top_stream_id, \"\")\n self.assertEqual(featured_li, [])\n top_stream_id, featured_li = twitch.views.get_game_info(\"league\") \n #should be good input as long as there is one stream\n self.assertNotEqual(featured_li, []) \n #this indicates that there is good data stored\n top_stream_id = \"\"\n featured_li = []\n search_q = u'神魔' #foreign input\n if not (search_q == \"\"):\n if re.match(\"^[A-Za-z0-9_ ]*$\", search_q): #should be blocked here\n top_stream_id, featured_li = twitch.views.\\\n get_game_info(search_q) \n #if passed this function should crash\n self.assertEqual(top_stream_id, \"\")\n self.assertEqual(featured_li, [])",
"title": ""
},
{
"docid": "9c8f154478aef97067ff5014283a464f",
"score": "0.50448525",
"text": "def load_scoreboard_pages(l):\n username = login(l)\n if not username:\n l.interrupt()\n simulate_loading_scoreboard_page(l)\n\n endpoint, call_label = get_valid_scoreboard_base_endpoint(l)\n l.client.get(\n endpoint + \"/score_progressions\",\n name=(call_label + \"/score_progressions\"),\n )\n initial_page_res = l.client.get(\n endpoint + \"/scoreboard\", name=(call_label + \"/scoreboard\")\n ).json()\n for i in range(0, random.randrange(1, 10)):\n p = random.randrange(1, initial_page_res[\"total_pages\"] + 1)\n l.client.get(\n endpoint + \"/scoreboard?page=\" + str(p),\n name=(call_label + \"/scoreboard?page=[p]\"),\n )\n logout(l)\n release_user(username)\n l.interrupt()",
"title": ""
},
{
"docid": "f56c7ac25f7253fb7741ba11578e25b3",
"score": "0.502587",
"text": "def get(self, url):\n ...",
"title": ""
},
{
"docid": "ee7c92572cf64f3b43ec3871e52fd1a2",
"score": "0.50135213",
"text": "def fetch_data(redfishobj, url, component):\n response_url = redfishobj.get(url, None)\n if response_url.status == 200:\n return response_url.dict\n\n sys.stdout.write(f\"{component} data could not be fetched\\n\")\n redfishobj.logout()\n sys.exit(1)",
"title": ""
},
{
"docid": "f358790c7c38a0a9b0cd96ab985a8005",
"score": "0.5009813",
"text": "def load_filtered_scoreboard_pages(l):\n username = login(l)\n if not username:\n l.interrupt()\n simulate_loading_scoreboard_page(l)\n\n endpoint, call_label = get_valid_scoreboard_base_endpoint(l)\n l.client.get(\n endpoint + \"/score_progressions\",\n name=(call_label + \"/score_progressions\"),\n )\n initial_page_res = l.client.get(\n endpoint + \"/scoreboard\", name=(call_label + \"/scoreboard\")\n ).json()\n search_endpoint = endpoint + \"/scoreboard?search=\" + get_affiliation()\n for i in range(0, random.randrange(1, 10)):\n p = random.randrange(1, initial_page_res[\"total_pages\"] + 1)\n l.client.get(\n search_endpoint + \"&page=\" + str(p),\n name=(call_label + \"/scoreboard?search=[q]&page=[p]\"),\n )\n logout(l)\n release_user(username)\n l.interrupt()",
"title": ""
},
{
"docid": "12ea96899765995fdcfeb315b1034b03",
"score": "0.49891797",
"text": "def get_board_by_jlink_slug(slug: str) -> Board:\n matched_slug = slug.casefold()\n return get_board(\n lambda board: any(matched_slug == c.casefold() for c in [board.slug, board.board_name, board.board_type])\n )",
"title": ""
},
{
"docid": "2d47d2993364c5af02c3b9d638accca4",
"score": "0.4987027",
"text": "def find_board(self, board):\n for b in self.boards():\n if b.match(board): return b\n return None",
"title": ""
},
{
"docid": "2009f0188d42e5c47f4063e8309469e9",
"score": "0.49552694",
"text": "def start_requests(self):\n board_name = input('Please enter which board you want to archive!:\\n')\n urls = [\n f'https://boards.4channel.org/{board_name}/{i}'\n for i in range(2,11)\n ]\n urls.append('https://boards.4channel.org/{board_name}/')\n for url in urls:\n yield scrapy.Request(url=url, callback=self.parse)",
"title": ""
},
{
"docid": "5f155b8047dbf3a5c508d3cf4e10ac2d",
"score": "0.49394265",
"text": "def test_view_invalid_board(self):\n self.newboard()\n self.logout()\n self.register('un1', 'email@test.com')\n self.login('un1', 'P&ssw0rd')\n rv = self.app.get('/1', follow_redirects=True)\n self.assertIn(b'This is not your board', rv.data)",
"title": ""
},
{
"docid": "e8aa0229b94f607e8937df40c3f05e73",
"score": "0.49367392",
"text": "def get_cards_for_board(board_id: int):\n return data_handler.get_cards_for_board(board_id)",
"title": ""
},
{
"docid": "4b67615d52d721d5486a133f4ae0018e",
"score": "0.49213415",
"text": "def load_data():\n page_num = 1\n # if type(num) == int:\n # url = \"https://refugerestrooms.org:443/api/v1/restrooms.json?page=1&per_page=\" + str(page_num)\n # else:\n # pass\n\n while True:\n url = \"https://www.refugerestrooms.org:443/api/v1/restrooms.json?per_page=100&page=\" + str(page_num)\n results = []\n response = requests.get(url)\n if response.status_code == 200:\n results = response.json()\n page_num += 1\n # loop thru json data\n for v in results:\n # add bathroom and location\n b = Bathroom(name=v['name'], unisex=v['unisex'],\n accessible=v['accessible'],\n changing_table=v['changing_table'])\n db.session.add(b)\n db.session.commit()\n # add location\n if v['latitude'] == None or v['longitude'] == None:\n v['latitude'] = 0.00\n v['longitude'] = 0.00\n\n l = Location(bathroom_id=b.bathroom_id,street=v['street'],\n city=v['city'], state=v['state'], \\\n country=v['country'], latitude=v['latitude'],\n longitude=v['longitude'], directions=v['directions'])\n db.session.add(l)\n db.session.commit()\n # add comment\n if len(v['comment']) > 1:\n c = Comment(comment=v['comment'],\n bathroom_id=b.bathroom_id,\n user_id=0)\n db.session.add(c)\n db.session.commit()\n # add ratings\n if v['downvote'] == 1:\n r = Rating(bathroom_id=b.bathroom_id,\n user_id= 0,\n score=2)\n db.session.add(r)\n db.session.commit()\n elif v['upvote'] == 1:\n r = Rating(bathroom_id=b.bathroom_id,\n user_id= 0,\n score=5)\n db.session.add(r)\n db.session.commit()\n\n time.sleep(1)\n else:\n break\n\n return \"finished loading data\"",
"title": ""
},
{
"docid": "66a941496fabb4fe3ff913e9fb94f62f",
"score": "0.49148974",
"text": "def get_cards_by_board_id(board_id: int):\n return data_handler.get_cards_by_board_id(board_id)",
"title": ""
},
{
"docid": "6f5f01faa8c3fbe80de35bac4909a858",
"score": "0.48930612",
"text": "def get_lists(user=None, board_id=None):\n url = f\"https://api.trello.com/1/boards/{board_id}/lists\"\n response = perform_request(\n method=\"GET\",\n url=url,\n user=user\n )\n print(f\"Get lists status: {response.status_code}\")\n board_lists = list(map(get_needed_data, response.json()))\n return board_lists",
"title": ""
},
{
"docid": "ac1ca25877b2d83158e3fd1def84a069",
"score": "0.4884325",
"text": "def getRoom(admin, roomNumber):\n s = db.get_db().get_session()\n try:\n return dict(Chambre.find(s, roomNumber)), 200\n except RoomNotFound:\n return NoContent, 404",
"title": ""
},
{
"docid": "b17bc6a0dd07a537e0b7160b24d558a3",
"score": "0.48783943",
"text": "def get(self, url):\n # YOUR CODE HERE",
"title": ""
},
{
"docid": "9a0bd49f928fa8295344681083886360",
"score": "0.4874402",
"text": "def get_random_post(self, board=None):\n #choose a random board if you didn't specify any\n if board is None:\n board = random.choice(self.config[\"boards\"])\n\n data = []\n \"this code gets all the threads in the specified board\"\n with urllib.request.urlopen(\"http://a.{}/{}/threads.json\".format(self.config[\"chanDomain\"], board)) as page:\n data = json.loads(page.read().decode(\"utf-8\"))\n\n post_numbers = []\n for page in data:\n for thread in page[\"threads\"]:\n post_numbers.append(thread[\"no\"])\n\n #chooses a random thread\n no = random.choice(post_numbers)\n with urllib.request.urlopen(\"http://a.{}/{}/thread/{}.json\".format(self.config[\"chanDomain\"], board, no)) as page:\n data = json.loads(page.read().decode(\"utf-8\"))\n\n #gets a random post from the random thread\n chosen_one = []\n\n #A post without an image is boring\n while len(data[\"posts\"]) > 0:\n chosen_one = random.choice(data[\"posts\"])\n data[\"posts\"].remove(chosen_one)\n if \"filename\" in chosen_one:\n break\n\n com = \"\"\n if \"com\" in chosen_one:\n com = chosen_one[\"com\"]\n\n #downloads the image in memory\n self.logger.debug(\"PostSifter: {}\".format(chosen_one))\n self.logger.info(\"PostSifter: http://i.{}/{}/{}{}\".format(self.config[\"chanDomain\"], board, chosen_one[\"tim\"], chosen_one[\"ext\"]))\n\n with urllib.request.urlopen(\"http://i.{}/{}/{}{}\".format(self.config[\"chanDomain\"], board, chosen_one[\"tim\"], chosen_one[\"ext\"])) as page:\n return Post(page.read(), com)",
"title": ""
},
{
"docid": "e2fa51e59644604fa5450460c23d5600",
"score": "0.48722157",
"text": "def _pull_folo_report(params):\n\n resp = requests.get(\"%(url)s/api/folo/admin/%(id)s/record\" % params)\n if resp.status_code == 200:\n return resp.json()\n elif resp.status_code == 404:\n return None\n else:\n resp.raise_for_status()",
"title": ""
},
{
"docid": "ecf09209763485763795be5647b3113b",
"score": "0.48715168",
"text": "def get(self):\n\n # mandatory\n game_id = self.urlvars.get('game_id', None)\n\n # for getting arbitrary flights\n flight_number = self.urlvars.get('flight_number', None)\n flight_id = self.urlvars.get('flight_id', None)\n fleet_type_id = self.urlvars.get('fleet_type_id', None)\n if (fleet_type_id is not None):\n fleet_type_id = re.split('[;,]', fleet_type_id)\n\n # for getting data from a specific base\n base_airport_iata = self.urlvars.get('base_airport_iata', None)\n base_airport_icao = self.urlvars.get('base_airport_icao', None)\n\n dest_airport_iata = self.urlvars.get('dest_airport_iata', None)\n dest_airport_icao = self.urlvars.get('dest_airport_icao', None)\n if (dest_airport_iata is not None):\n dest_airport_iata = re.split('[;,]', dest_airport_iata)\n elif (dest_airport_icao is not None):\n dest_airport_icao = re.split('[;,]', dest_airport_icao)\n \n # remove trailing slashes from PATH_INFO\n path = re.sub(r\"\\/+$\", \"\", self.request.path)\n\n # if we get /flight_number/basic, we return only top level data, not\n # a list of full flight details\n basic_only = False\n basic_only = (flight_id is None and re.match(r\"basic\", \n path.split(\"/\")[-1:][0]))\n\n # if flight_number == 'MTX':\n # data = self.getMaintenance()\n # self.resp.text = json.dumps(data).encode('utf-8').decode('unicode_escape')\n # self.resp.cache_control.max_age = int(364.9 * 86400)\n # return self.send()\n\n\n #print(self.urlvars, self.request.path, basic_only)\n\n if not game_id or not num.match(game_id):\n # bad request; missing game_id and/or other vars\n self.resp.status = 400\n elif base_airport_iata is not None or base_airport_icao is not None:\n return self.__getBaseFlightData(game_id, base_airport_iata,\n base_airport_icao, fleet_type_id, dest_airport_iata,\n dest_airport_icao)\n elif (flight_id is not None or flight_number is not None):\n return self.__getFlightData(game_id, flight_id, flight_number,\n basic_only)\n elif game_id is not None:\n return self.__getFlightHeaders(game_id, basic_only)\n else:\n return self.error(400)",
"title": ""
},
{
"docid": "e242de30a63800c3dd7c1ee2f3eae0f8",
"score": "0.4870784",
"text": "def cycle_collector(board_name):\n\n #create a request\n r = requests.get(board_name)\n #r = requests.get('https://a.4cdn.org/tv/catalog.json')\n r = r.json()\n\n #getting a date\n now = dt.now()\n\n #UGLY SUBJECT TO CHANGE\n date = str(now.year)+'-'+str(now.month)+'-'+str(now.day)+'_'+str(now.hour)+'-'+str(now.minute)\n\n #open and save threads into the csv file\n with open('dataset/pol_'+date+'.csv', mode='w') as csv_file:\n #with open('dataset/tv_'+date+'.csv', mode='w') as csv_file:\n\n #create field names\n fieldnames = ['thread_num', 'post_time', 'id', 'country', 'com', 'filename', 'url']\n writer = csv.DictWriter(csv_file, fieldnames=fieldnames)\n writer.writeheader()\n\n #for each thread on the board\n for threads in gen_chan():\n\n #thread\n no = get_threads('no')\n #now\n now = get_threads('now')\n #post time\n time = get_threads('time')\n #my time\n my_time = dt.today()\n #post text\n com = handle_com(get_threads('com'))\n #post name\n name = get_threads('name')\n #tripcode\n trip = get_threads('trip')\n #id\n ids = get_threads('id')\n #capcode?\n capcode = get_threads('capcode')\n #filename\n filename = get_threads('filename')\n #resto\n rest = get_threads('resto')\n #semantic_url\n semantic_url = get_threads('semantic_url')\n #replies\n replies = get_threads('replies')\n #images\n images = get_threads('images')\n #url - need to remake this one probably\n url = find_urls(com)\n #country\n country = get_threads('country_name')\n\n writer.writerow({'thread_num': no,\n 'post_time': time,\n 'id': ids,\n 'country': country,\n 'com': com,\n 'filename': filename,\n 'url': url})\n\n #write all thread replies\n if 'last_replies' in threads:\n for comment in threads['last_replies']:\n\n #comment\n com = handle_com(comment.get('com', 'NaN'))\n #poster id\n ids = comment.get('id', 'NaN')\n #poster country\n country = comment.get('country_name', 'NaN')\n #post time\n time = comment.get('time', 'NaN')\n #filename\n filename_com = comment.get('filename', 'NaN') + comment.get('ext', 'NaN')\n #urls if present\n url = find_urls(com)\n\n writer.writerow({'thread_num': no,\n 'post_time': time,\n 'id': ids,\n 'country': country,\n 'com': com,\n 'filename': filename,\n 'url': url})\n\n print(\"Done saving \", date)",
"title": ""
},
{
"docid": "8f82ed7b5b9581f8cc8e0e0ee9034690",
"score": "0.486862",
"text": "def _get_resource(self, url, data_key=None):\n headers = {\"Accept\": \"application/json\"}\n if self.token:\n headers[\"W-Token\"] = \"%s\" % self.token\n response = WhenIWork_DAO().getURL(url, headers)\n\n if response.status != 200:\n raise DataFailureException(url, response.status, response.data)\n\n return json.loads(response.data)",
"title": ""
},
{
"docid": "fc461ee42f20b296dd38e637c486c782",
"score": "0.4866953",
"text": "def get(self, request, *args, **kwargs):\n\n try:\n match_pk = int(kwargs['match_pk'][:6]) # afkappen voor de veiligheid\n match = (CompetitieMatch\n .objects\n .select_related('vereniging',\n 'locatie')\n .get(pk=match_pk))\n except (ValueError, CompetitieMatch.DoesNotExist):\n raise Http404('Wedstrijd niet gevonden')\n\n try:\n klasse_pk = int(kwargs['klasse_pk'][:6]) # afkappen voor de veiligheid\n team_klasse = (CompetitieTeamKlasse\n .objects\n .get(pk=klasse_pk))\n except (ValueError, CompetitieTeamKlasse.DoesNotExist):\n raise Http404('Klasse niet gevonden')\n\n deelkamps_bk = match.kampioenschap_set.filter(deel=DEEL_BK)\n if len(deelkamps_bk) == 0:\n raise Http404('Geen kampioenschap')\n\n deelkamp_bk = deelkamps_bk[0]\n\n comp = deelkamp_bk.competitie\n # TODO: check fase\n\n if comp.afstand == '18':\n aantal_pijlen = 30\n else:\n aantal_pijlen = 25\n\n lid2voorkeuren = dict() # [lid_nr] = SporterVoorkeuren\n for voorkeuren in SporterVoorkeuren.objects.select_related('sporter').all():\n lid2voorkeuren[voorkeuren.sporter.lid_nr] = voorkeuren\n # for\n\n vastgesteld = timezone.localtime(timezone.now())\n\n klasse_str = team_klasse.beschrijving\n\n boog_typen = team_klasse.team_type.boog_typen.all()\n boog_pks = list(boog_typen.values_list('pk', flat=True))\n\n # bepaal de naam van het terug te geven bestand\n fname = \"bk-programma_teams_\"\n fname += klasse_str.lower().replace(' ', '-')\n fname += '.xlsx'\n\n if comp.afstand == '18':\n excel_name = 'template-excel-bk-indoor-teams.xlsx'\n else:\n excel_name = 'template-excel-bk-25m1pijl-teams.xlsx'\n\n # make een kopie van het RK programma in een tijdelijk bestand\n fpath = os.path.join(settings.INSTALL_PATH, 'CompLaagBond', 'files', excel_name)\n tmp_file = NamedTemporaryFile()\n\n try:\n shutil.copyfile(fpath, tmp_file.name)\n except FileNotFoundError:\n raise Http404('Kan BK programma niet vinden')\n\n # open de kopie, zodat we die aan kunnen passen\n try:\n prg = openpyxl.load_workbook(tmp_file)\n except (OSError, zipfile.BadZipFile, KeyError):\n raise Http404('Kan BK programma niet openen')\n\n # maak wijzigingen in het BK programma\n ws = prg['Deelnemers en Scores']\n\n if \"ERE\" in klasse_str:\n max_teams = 12\n else:\n max_teams = 8\n\n # maximaal 4 teams naar de finale, dus verwijder het blad voor 8 team finale\n if comp.afstand == '18':\n del prg['Finales 8 teams']\n\n # verwijder 4 regels in Uitslag (voor teams 9..12)\n prg['Uitslag'].delete_rows(17, 4)\n\n ws['B2'] = 'BK teams %s, Klasse: %s' % (comp.beschrijving, klasse_str)\n ws['H4'] = match.datum_wanneer.strftime('%Y-%m-%d')\n ws['B4'] = match.vereniging.naam # organisatie\n if match.locatie:\n ws['F4'] = match.locatie.adres # adres van de locatie\n else:\n ws['F4'] = 'Onbekend'\n\n try:\n limiet = KampioenschapTeamKlasseLimiet.objects.get(kampioenschap=deelkamp_bk, team_klasse=team_klasse)\n max_teams = limiet.limiet\n except KampioenschapTeamKlasseLimiet.DoesNotExist:\n pass\n\n teams = (KampioenschapTeam\n .objects\n .filter(kampioenschap=deelkamp_bk,\n team_klasse=team_klasse.pk)\n .exclude(deelname=DEELNAME_NEE)\n .exclude(is_reserve=True)\n .select_related('vereniging')\n .prefetch_related('gekoppelde_leden')\n .order_by('volgorde'))\n\n ver_nrs = list()\n\n volg_nr = 0\n for team in teams:\n row_nr = 9 + volg_nr * 6\n row = str(row_nr)\n\n ver = team.vereniging\n if ver.ver_nr not in ver_nrs:\n ver_nrs.append(ver.ver_nr)\n\n # vereniging\n ws['D' + row] = '[%s] %s' % (ver.ver_nr, ver.naam)\n\n # team naam\n ws['F' + row] = team.team_naam\n\n # team sterkte\n sterkte_str = \"%.1f\" % (team.aanvangsgemiddelde * aantal_pijlen)\n sterkte_str = sterkte_str.replace('.', ',')\n ws['G' + row] = sterkte_str\n\n # vul de 4 sporters in\n aantal = 0\n for deelnemer in (team\n .gekoppelde_leden\n .select_related('sporterboog__sporter')\n .order_by('-gemiddelde')): # hoogste gemiddelde bovenaan\n row_nr += 1\n row = str(row_nr)\n\n sporter = deelnemer.sporterboog.sporter\n\n para_mark = False\n try:\n voorkeuren = lid2voorkeuren[sporter.lid_nr]\n except KeyError: # pragma: no cover\n pass\n else:\n if voorkeuren.para_voorwerpen or voorkeuren.opmerking_para_sporter:\n para_mark = True\n\n # bondsnummer\n ws['E' + row] = sporter.lid_nr\n\n # volledige naam\n naam_str = sporter.volledige_naam()\n if para_mark:\n naam_str += ' **'\n ws['F' + row] = naam_str\n\n # RK gemiddelde\n ws['G' + row] = deelnemer.gemiddelde\n\n aantal += 1\n # for\n\n # bij minder dan 4 sporters de overgebleven regels leegmaken\n while aantal < 4:\n row_nr += 1\n row = str(row_nr)\n ws['E' + row] = '-' # bondsnummer\n ws['F' + row] = 'n.v.t.' # naam\n ws['G' + row] = '' # gemiddelde\n ws['H' + row] = '' # score 1\n ws['I' + row] = '' # score 2\n aantal += 1\n # while\n\n volg_nr += 1\n if volg_nr == max_teams:\n break\n # for\n\n while volg_nr < 12:\n row_nr = 9 + volg_nr * 6\n row = str(row_nr)\n\n # vereniging leeg maken\n ws['D' + row] = 'n.v.t.' # vereniging\n ws['F' + row] = 'n.v.t.' # team naam\n ws['G' + row] = '' # team sterkte\n\n # sporters leegmaken\n aantal = 0\n while aantal < 4:\n row_nr += 1\n row = str(row_nr)\n ws['E' + row] = '-' # bondsnummer\n ws['F' + row] = '-' # naam\n ws['G' + row] = '' # gemiddelde\n ws['H' + row] = '' # score 1\n ws['I' + row] = '' # score 2\n aantal += 1\n # while\n\n volg_nr += 1\n # while\n\n ws['B82'] = 'Deze gegevens zijn opgehaald op %s' % vastgesteld.strftime('%Y-%m-%d %H:%M:%S')\n\n if \"ERE\" not in klasse_str:\n # verwijder teams 9..12 in Deelnemers en Scores\n ws.delete_rows(56, 24)\n\n # alle gerechtigde deelnemers opnemen op een apart tabblad, met gemiddelde en boogtype\n ws = prg['Toegestane deelnemers']\n\n cd_font = ws['C18'].font\n c_align = ws['C18'].alignment\n c_format = ws['C18'].number_format\n\n d_align = ws['D18'].alignment\n d_format = ws['D18'].number_format\n\n efgh_font = ws['E18'].font\n e_align = ws['E18'].alignment\n\n f_align = ws['F18'].alignment\n\n g_align = ws['G18'].alignment\n g_format = ws['G18'].number_format\n\n row_nr = 16\n prev_ver = None\n # alle RK deelnemers mogen schieten in het team\n # (sporter hoeft niet persoonlijk geplaatst te zijn voor het BK)\n for deelnemer in (KampioenschapSporterBoog\n .objects\n .filter(kampioenschap__competitie=comp,\n kampioenschap__deel=DEEL_RK,\n bij_vereniging__ver_nr__in=ver_nrs,\n sporterboog__boogtype__pk__in=boog_pks) # filter op toegestane boogtypen\n .select_related('bij_vereniging',\n 'sporterboog__sporter',\n 'sporterboog__boogtype')\n .order_by('bij_vereniging',\n '-gemiddelde')): # hoogste eerst\n\n row_nr += 1\n row = str(row_nr)\n\n # vereniging\n ver = deelnemer.bij_vereniging\n if ver != prev_ver:\n row_nr += 1 # extra lege regel\n row = str(row_nr)\n ws['C' + row] = ver.regio.regio_nr\n if row_nr != 18:\n ws['C' + row].font = copy(cd_font)\n ws['C' + row].alignment = copy(c_align)\n ws['C' + row].number_format = copy(c_format)\n\n ws['D' + row] = '[%s] %s' % (ver.ver_nr, ver.naam)\n if row_nr != 18:\n ws['D' + row].font = copy(cd_font)\n ws['D' + row].alignment = copy(d_align)\n ws['D' + row].number_format = copy(d_format)\n\n prev_ver = ver\n\n # sporter\n sporter = deelnemer.sporterboog.sporter\n\n para_notities = ''\n try:\n voorkeuren = lid2voorkeuren[sporter.lid_nr]\n except KeyError: # pragma: no cover\n pass\n else:\n if voorkeuren.para_voorwerpen:\n para_notities = 'Sporter laat voorwerpen op de schietlijn staan'\n\n if voorkeuren.opmerking_para_sporter:\n if para_notities != '':\n para_notities += '\\n'\n para_notities += voorkeuren.opmerking_para_sporter\n\n ws['E' + row] = sporter.lid_nr\n\n naam_str = sporter.volledige_naam()\n if para_notities:\n naam_str += ' **'\n ws['F' + row] = naam_str\n\n ws['G' + row] = deelnemer.gemiddelde\n ws['H' + row] = deelnemer.sporterboog.boogtype.beschrijving\n\n if para_notities:\n ws['I' + row] = para_notities\n\n if row_nr != 18:\n ws['E' + row].font = copy(efgh_font)\n ws['F' + row].font = copy(efgh_font)\n ws['G' + row].font = copy(efgh_font)\n ws['H' + row].font = copy(efgh_font)\n ws['I' + row].font = copy(efgh_font)\n\n ws['E' + row].alignment = copy(e_align)\n ws['G' + row].alignment = copy(g_align)\n ws['G' + row].number_format = copy(g_format)\n # for\n\n row_nr += 2\n row = str(row_nr)\n ws['B' + row] = 'Deze gegevens zijn opgehaald op %s' % vastgesteld.strftime('%Y-%m-%d %H:%M:%S')\n ws['B' + row].font = copy(efgh_font)\n ws['B' + row].alignment = copy(f_align)\n\n # geef het aangepaste BK programma aan de client\n response = HttpResponse(content_type=CONTENT_TYPE_XLSX)\n response['Content-Disposition'] = 'attachment; filename=\"%s\"' % fname\n prg.save(response)\n\n return response",
"title": ""
},
{
"docid": "09ddbaa1dce7b694aab410df8a8121b4",
"score": "0.48660666",
"text": "def get_boards(org_id):\n\n boards = api(\"GET\", \"organizations/%s/boards\" % org_id)\n return {b[\"id\"]: b for b in boards}",
"title": ""
},
{
"docid": "4712cb9a65388a24fa8cd564144c4a26",
"score": "0.4856035",
"text": "def get_jira_boards(with_slack: Optional[bool] = True):\n gqlapi = gql.get_api()\n query = Template(JIRA_BOARDS_QUERY).render(with_slack=with_slack)\n return gqlapi.query(query)[\"jira_boards\"]",
"title": ""
},
{
"docid": "2da49b5177eaec30d954afc42716a566",
"score": "0.48378506",
"text": "def list(self, irc, msg, args, channel):\n if not instance._check_capability(irc, msg):\n return\n\n boards = instance._load_boards(channel)\n if boards is None or len(boards) == 0:\n irc.error(_('This channel has no registered boards.'))\n return\n\n for board_slug, board_url in boards.items():\n irc.reply(\"%s: %s\" % (board_slug, board_url))",
"title": ""
},
{
"docid": "21d2209a75d6f3dc3f40b8c71098aaf0",
"score": "0.48304555",
"text": "def fetch_from_url(request, event_slug):\n events = Event.active.filter(slug=event_slug)\n if events.count() > 0:\n return events[0], False\n else:\n return None, not_found(request, event_slug)",
"title": ""
},
{
"docid": "6de7bc617d95fdfd2385bc554d78ed51",
"score": "0.48229253",
"text": "def _get(self, url, params=None):\n r = requests.get(url, params=params)\n if r.status_code != 200:\n return None\n else:\n return r.json()",
"title": ""
},
{
"docid": "ad730fd077910d5488b3fd17655bbaa3",
"score": "0.4822001",
"text": "def get_by_url(cls, url):\n\t\treturn DBSession.query(cls).filter(cls.url==url).first()",
"title": ""
},
{
"docid": "a476d0f9c196c6e162f089a082672e15",
"score": "0.48012018",
"text": "def simulate_loading_scoreboard_page(l):\n simulate_loading_any_page(l)\n l.client.get(SCOREBOARD_PAGE_URL)\n l.client.get(SCOREBOARDS_ENDPOINT)\n l.client.get(TEAM_ENDPOINT)\n l.client.get(GROUPS_ENDPOINT)",
"title": ""
},
{
"docid": "e672ccf61b0ebbcfbd6129e757078556",
"score": "0.4799081",
"text": "def fetch(self):\n try:\n self.__baseuri = 'http://ficoforums.myfico.com'\n self.__total_threads_count = 0\n self.__last_timestamp = datetime(1980, 1, 1)\n self.__task_elements_dict = {\n 'priority':self.task.priority,\n 'level': self.task.level,\n 'last_updated_time':datetime.strftime(datetime.utcnow()\n , \"%Y-%m-%dT%H:%M:%SZ\"),\n 'pickup_date':datetime.strftime(datetime.utcnow(), \\\n \"%Y-%m-%dT%H:%M:%SZ\"),\n 'connector_instance_log_id': self.task.\\\n connector_instance_log_id,\n 'connector_instance_id':self.task.connector_instance_id,\n 'workspace_id':self.task.workspace_id,\n 'client_id':self.task.client_id,\n 'client_name':self.task.client_name,\n 'versioned':False,\n 'category':self.task.instance_data.get('category',''),\n 'task_log_id':self.task.id }\n self.__max_threads_count = int(tg.config.get(path='Connector', \\\n key='ficoforums_max_threads_to_process'))\n self.__setSoupForCurrentUri()\n is_search_type = self.currenturi.startswith\\\n ('http://ficoforums.myfico.com/fico/search')\n is_forum_type = self.currenturi.startswith\\\n ('http://ficoforums.myfico.com/fico/board?board')\n if is_forum_type or is_search_type:\n while True:\n try:\n results = self.__getThreads() if is_search_type else \\\n self.__addThreadUrls()\n if not results:\n log.info(self.log_msg('Reached Maxmum threads\\\n /Reached Last Crawled Page'))\n break\n self.currenturi = self.__baseuri + \\\n re.search('go\\(\\'(.*?)\\'\\)', self.soup.find('a', \\\n text='Next Page').findParent('table')['onclick'])\\\n .group(1)\n self.__setSoupForCurrentUri()\n except Exception, exce:\n log.exception(self.log_msg('Cannot found the next page\\\n in url %s'%self.currenturi))\n break\n if self.linksOut:\n updateSessionInfo('Search', self.session_info_out, \\\n self.__last_timestamp , None, 'ForumThreadsPage', \\\n self.task.instance_data.get('update'))\n return True\n elif self.currenturi.startswith\\\n ('http://ficoforums.myfico.com/fico/board/message'):\n self.__setParentPage()\n question_post = self.soup.find('tr', attrs={'id':\\\n re.compile('M\\d+')})\n self.__addPost(question_post, True)\n self.currenturi = self.currenturi + '&view=by_date_descending'\n self.__setSoupForCurrentUri()\n while True:\n try:\n if not self.__iteratePosts():\n log.info(self.log_msg('Crawled all posts in url %s'\\\n %self.currenturi))\n break\n self.currenturi = self.__baseuri + self.soup.find('a', \n text=re.compile('Next Page')).parent['href']\n self.__setSoupForCurrentUri()\n except Exception, exce:\n log.info(self.log_msg('Crawled all pages, no more page\\\n %s'%self.currenturi))\n break\n return True\n except Exception, exce:\n log.exception(self.log_msg('Exception in fetch'))\n return False",
"title": ""
},
{
"docid": "2306521d61fee164f4c2e3689418d6da",
"score": "0.47776565",
"text": "def _read_board_file(self):\n boards = dict(\n easy=list(),\n medium=list(),\n hard=list()\n )\n with self._path.open('r', encoding=\"utf-8\") as infile:\n for index, line in enumerate(infile, start=1):\n board = json.loads(line)\n level = board[\"board_info\"][\"difficoulty\"]\n\n # select target\n state = board[\"state\"]\n target_id = str(board[\"target\"])\n target_obj = state[\"objs\"][target_id]\n state[\"targets\"][target_id] = target_obj\n\n boards[level].append(board)\n\n # load entire dataset\n if self._n == -1:\n self._n = index \n\n return boards",
"title": ""
},
{
"docid": "290194c5192480f9e5ceabc1ea179913",
"score": "0.47751674",
"text": "def get_first_soup(board):\n payload_url = '/bbs/' + board + '/index.html' # /bbs/Gossiping/index.html\n url = PTT_ROOT + payload_url # https://www.ptt.cc/bbs/Gossiping/index.html\n session = requests.Session()\n\n if board in restricted:\n payload = {'from': payload_url, 'yes': 'yes'}\n # Get the session because you have to click \"YES\" on the welcome page\n # This session instance holds the cookie. So use it to get/post later.\n session.post('https://www.ptt.cc/ask/over18', data=payload)\n res = session.get(url)\n # print(type(res)) # <class 'requests.models.Response'>\n soup = BeautifulSoup(res.text, 'html.parser')\n # print(type(soup)) # <class 'bs4.BeautifulSoup'>\n\n return soup, session\n else:\n res = session.get(url)\n soup = BeautifulSoup(res.text, 'html.parser')\n\n return soup, session",
"title": ""
},
{
"docid": "353f2ad18edd445c52b0c675df7fe056",
"score": "0.47695202",
"text": "def get_sudoku(p_link, s_link):\n req = requests.get(p_link)\n c = req.content\n soup = BeautifulSoup(c, 'html.parser')\n\n grid_txt = soup.find_all('div', {'class':'grid'})[0].text\n puzzle_no = grid_txt[str.find(grid_txt, 'Showing puzzle')+23:str.find(grid_txt, 'Puzzletype')]\n\n rows = soup.find_all('tr', {'class':'grid'})\n puzzle = []\n for row in rows:\n cols = row.find_all('td')\n for col in cols:\n txt = col.text\n if txt != '\\xa0':\n puzzle.append(txt)\n else:\n puzzle.append('0')\n puzzle = ' '.join(puzzle)\n\n\n req_sol = requests.get(s_link.format(puzzle_no))\n c = req_sol.content\n soup = BeautifulSoup(c, 'html.parser')\n rows = soup.find_all('tr', {'class':'grid'})\n solution = []\n for row in rows:\n cols = row.find_all('td')\n for col in cols:\n txt = col.text\n if txt != '\\xa0':\n solution.append(txt)\n else:\n solution.append('0')\n solution = ' '.join(solution)\n\n return puzzle, solution",
"title": ""
},
{
"docid": "171f5771e81073c2f0bb79a8cd70fcac",
"score": "0.47672158",
"text": "def getUrl(self):\n return \"http://kegg.jp/dbget-bin/www_bget?\" + self.uniqueID",
"title": ""
},
{
"docid": "4b773b67393cd88c24bd5a8870c6620c",
"score": "0.4767078",
"text": "def get_data(url):\r\n response = requests.get(url)\r\n if response.status_code == 200:\r\n data = response.json()\r\n return data\r\n else:\r\n print('\\nSomething went wrong when getting the data..')\r\n return None",
"title": ""
},
{
"docid": "fcf5b35a43a47c2afb51140de8c7e13f",
"score": "0.47572643",
"text": "def updateBoard(data):\n global my_board\n my_board = data['board']\n print \"**************************************\"\n print '\\n'.join([str(row) for row in my_board])\n print \"**************************************\"\n print data['message']",
"title": ""
},
{
"docid": "1278aed0e6378291d8a44a897b0baceb",
"score": "0.47387874",
"text": "def _get_game_from_url(self, url):\n print \"Getting game from %s.\" %(url)\n game = GamedayObject()\n game.url = url\n boxscore_xml = self._download_from_url(url, \"boxscore.xml\")\n boxscore_dict = self._make_dict_from_xml(boxscore_xml, \"boxscore\")\n if boxscore_dict:\n game.boxscore = self._make_dict_from_xml(boxscore_xml, \"boxscore\")[0]\n game_xml = self._download_from_url(url, \"game.xml\")\n game_dict = self._make_dict_from_xml(game_xml, \"game\")\n if game_dict:\n game.game = self._make_dict_from_xml(game_xml, \"game\")[0]\n players_xml = self._download_from_url(url, \"players.xml\")\n game.players = self._make_dict_from_xml(players_xml, \"player\")\n game.teams = self._make_dict_from_xml(game_xml, \"team\")\n innings_xml = self._download_from_url(url, \"inning/inning_all.xml\")\n game.innings = BeautifulStoneSoup(innings_xml).findAll(\"inning\")\n game.runners = self._make_dict_from_xml(innings_xml, \"runner\")\n return game",
"title": ""
},
{
"docid": "02077f192457752009532fe28e2a4b1a",
"score": "0.47341198",
"text": "def fetchPage (self):\n self.composeURL()\n self.args['request'] = requests.get(self.args['reqeust_url'])",
"title": ""
},
{
"docid": "c64cec725188c0a500806808e8830bb0",
"score": "0.47305077",
"text": "def do_GET(self):\r\n\r\n # skip over the first / . example: /poll -> poll \r\n cmd = self.path[1:]\r\n \r\n # create a command list . \r\n cmd_list = cmd.split('/',1)\r\n \r\n\r\n s = \"不回傳資料\"\r\n\r\n global my_device \r\n \r\n\r\n ###### 處理Scratch送出的命令\r\n ###### 若需回應Scratch的Poll命令，再把文字存在變數s ##\r\n ##############################################################\r\n if cmd_list[0] == \"lass\" : \r\n my_device = \"http://nrl.iis.sinica.edu.tw/LASS/last.php?device_id=\" + cmd_list[1]\r\n #print(my_device)\r\n if (cmd_list[0] == \"poll\") and ( my_device != \"none\"): \r\n with urllib.request.urlopen(my_device) as response:\r\n s = response.read()\r\n j = json.loads(s.decode('utf-8'))\r\n s = \"device \" + str(j['device_id']) + \"\\r\\n\"\r\n s += \"pm \" + str(j['s_d0']) + \"\\r\\n\"\r\n s += \"temp \" + str(j['s_t0']) + \"\\r\\n\"\r\n s += \"hum \" + str(j['s_h0']) + \"\\r\\n\"\r\n #print (s)\r\n \r\n if cmd_list[0] != \"poll\" :\r\n print(cmd_list[0])\r\n \r\n \r\n \r\n #############################################################\r\n self.send_resp(s)",
"title": ""
},
{
"docid": "3efc9c7a9ddde78d8a60d4f4bbfc5c1b",
"score": "0.47165695",
"text": "def get_data():\n project.db.download_data()",
"title": ""
},
{
"docid": "9e4fb287126242c707cbe17e4a3219ba",
"score": "0.47161657",
"text": "def select_board():\n args = parse_arguments()\n if args.empty:\n # if we give the \"empty\" argument then the initial board is gonna be empty\n return create_empty_board()\n else:\n initial_conig_data = read_initial_config_json()\n\n if args.random:\n return random.choice(initial_conig_data[\"starting_boards\"])\n\n if args.given_board_index is None:\n board_index = initial_conig_data[\"default_board\"]\n else:\n try:\n board_index = int(args.given_board_index)\n except ValueError as e:\n print(\"you must enter an interger as the board index\")\n print(e)\n\n if board_index >= len(initial_conig_data[\"starting_boards\"]):\n raise IndexError(\"the index you selected isn't valid there aren't that many boards in initial_configuration.json\")\n\n return initial_conig_data[\"starting_boards\"][board_index]",
"title": ""
},
{
"docid": "117d1e37bac23d11517e2e04cc755e4c",
"score": "0.47133338",
"text": "def test_mock_only_active_board(\n self, requests_mock, test_mock_api_get, caplog\n ):\n company_network = test_mock_api_get(\n requests_mock, exclude_resigned_board_members=True\n )\n assert (\n company_network.nodes[PUNCHDRUNK_COMPANY_ID][\"name\"]\n == PUNCHDRUNK_COMPANY_NAME\n )\n assert len(company_network) == 2\n assert is_connected(company_network)\n punchdrunk, board_members = bipartite.sets(company_network)\n assert len(board_members) == 1\n officer_0 = company_network.nodes[OFFICER_0_ID]\n assert officer_0[\"data\"][\"appointed_on\"] == \"2016-09-06\"\n assert \"resigned_on\" not in officer_0[\"data\"]\n assert OFFICER_1_ID not in company_network.nodes\n assert caplog.records == []",
"title": ""
},
{
"docid": "f85b8cd790ecececd9b40fd376cdaed6",
"score": "0.47104988",
"text": "def network_get(self, url):\n try:\n return self.networking.get(url) \n except:\n return None",
"title": ""
},
{
"docid": "ac0f9b11bece0fc9fd32c08a465eb928",
"score": "0.47097751",
"text": "def test_api_new_game(self):\n\n with self.client as client:\n response = client.get('/api/new-game')\n json_dict = response.get_json()\n board = json_dict['board']\n\n print(response)\n # write a test for this route\n\n self.assertIn('gameId', json_dict)\n self.assertTrue(isinstance(board, list), True)",
"title": ""
},
{
"docid": "94f894c51bb77627f62bb483388d3972",
"score": "0.47075626",
"text": "def search_db(url):\n return Request.query.filter_by(url=url).first()",
"title": ""
},
{
"docid": "8bdaaedb8132ed893753436a2bd649dc",
"score": "0.4703279",
"text": "def _get_data(self, url):\n response = self._req.get(url, headers=self._headers)\n check_response(response)\n\n return response",
"title": ""
},
{
"docid": "9cab55a1a7f873c2f30ae75e3b896731",
"score": "0.46874455",
"text": "def getTournamentData(URL: str, tournamentBoost: float) -> dict: # TODO get name\n NAME = name(URL)\n\n ID = URL.replace(\"https://www.tabroom.com/index/tourn/index.mhtml?tourn_id=\", \"\")\n RESULTS = \"https://www.tabroom.com/index/tourn/results/index.mhtml?tourn_id=\" + ID\n\n eventID = getDivision(RESULTS)\n\n if not eventID:\n print(Fore.YELLOW + f\"Error scraping {URL}: Division Not Found!\")\n return None\n\n # Getting const URLs\n divisionURL = \"https://www.tabroom.com/index/tourn/results/index.mhtml?tourn_id=\" + ID + \"&event_id=\" + eventID\n prelimURL = \"https://www.tabroom.com/index/tourn/results/ranked_list.mhtml?event_id=\" + eventID + \"&tourn_id=\" + ID\n\n # Getting variable URLs\n resultsURLs = getResultsURLs(divisionURL)\n finalsURL = resultsURLs[0]\n bracketURL = resultsURLs[1]\n prelimSeedsURL = resultsURLs[2]\n\n # Parsing prelims\n prelimData = prelims(prelimURL)\n\n # Parsing each team's entry page\n entryData = {}\n for team in prelimData:\n entryData[team] = entry(prelimData[team][\"entryPage\"])\n\n # Choosing either bracket or final places page (prefer finalsURL over bracket)\n if finalsURL:\n resultData = breaks(finalsURL)\n elif bracketURL:\n resultData = bracket(bracketURL)\n else:\n raise Exception(f\"Error scraping {URL}: No result URL found!\")\n #resultData = manualResultData(entryData, 3) # use appropriate num breaks\n \n # Parsing prelim seeds page\n seedData = {}\n if prelimSeedsURL:\n seedData = prelimSeeds(prelimSeedsURL)\n\n rawData = {\"tournamentName\":NAME, \"tournamentBoost\": tournamentBoost, \"prelimData\": prelimData,\n \"entryData\": entryData, \"resultData\": resultData, \"seedData\": seedData}\n with open(f'data/tournaments/{NAME}.json', 'w') as f:\n json.dump(rawData, f)\n data = condense(rawData)\n with open(f'data/tournaments/{NAME}.json', 'w') as f:\n json.dump(data, f)",
"title": ""
},
{
"docid": "b6b37c2e99a1c45ba4e94d99718f7375",
"score": "0.46822464",
"text": "def test_data_in_datebase(self):\n testboard = Board.objects.get(name=\"TestBoard\")\n testboard1 = Board.objects.get(name=\"TestBoard1\", user=self.user)\n self.assertEqual(testboard.__str__(), \"TestBoard\")\n self.assertEqual(testboard1.__str__(), \"TestBoard1\")",
"title": ""
},
{
"docid": "42b056fc9c35ae165f084ead88c765a8",
"score": "0.46720576",
"text": "def import_board_paths(grid, maxwlen):\n fname = \"{}x{}-{}-board.pkl\".format(grid.nrow, grid.ncol, maxwlen)\n fname = op.join(_get_board_data_dir(), fname)\n\n if op.exists(fname):\n with open(fname, \"rb\") as input:\n board_data = pickle.load(input)\n return board_data\n\n return None",
"title": ""
},
{
"docid": "cab3dbae104da9bf4250348bc507680a",
"score": "0.46689382",
"text": "def _query_api(self, url):\n http = urllib2.PoolManager()\n print(url)\n return http.request(method=\"GET\", url=url).data",
"title": ""
},
{
"docid": "2b2a2a883e57312b07ca97cd989685a1",
"score": "0.46674904",
"text": "async def get(self) -> web.Response:\n lopsklasse = self.request.match_info[\"lopsklasse\"]\n logging.debug(f\"Got request for lopsklasse {lopsklasse}\")\n klasse = await KlasserService().get_klasse_by_lopsklasse(\n self.request.app[\"db\"], lopsklasse\n )\n logging.debug(f\"Got result from db {klasse}\")\n if klasse:\n body = json.dumps(klasse, default=str, ensure_ascii=False)\n return web.Response(status=200, body=body, content_type=\"application/json\")\n raise web.HTTPNotFound",
"title": ""
},
{
"docid": "49a7f16a9fc5f90942a86c28f838a5cf",
"score": "0.46657455",
"text": "def fetchResource(url):\n try:\n return requests.get(url)\n except:\n logging.warn('Unable to fetch contents at %s', url, exc_info=True)\n return None",
"title": ""
},
{
"docid": "f240bc76f4d7377d9ce393ca43344553",
"score": "0.46610698",
"text": "def __get_new_boards(self, boards: List[Board]) -> List[Board]:\n unique_board_ids = {[board.get_id() for board in boards]}\n\n # getting boards from Redis\n # the set in Redis is called something like 'news_agent:boards'\n # if there is no such element, or it is empty, it returns just an empty set, not None\n connection = get_redis_connection()\n redis_board_ids = connection.smembers(f'{self.__redis_name}:boards')\n\n # new boards ids\n new_board_ids = unique_board_ids - redis_board_ids\n\n # update Redis\n for board_url in new_board_ids:\n connection.sadd(f'{self.__redis_name}:boards', board_url)\n\n # filter new boards\n new_boards = [board for board in boards if board.get_id() in new_board_ids]\n\n return new_boards",
"title": ""
},
{
"docid": "86c06c7a5717630d7e9bb0ac65a81cb4",
"score": "0.46579754",
"text": "def get_board_cards(board_id):\n cards = api(\"GET\", \"boards/%s/cards\" % board_id, {\"fields\": \"closed,dateLastActivity,\\\n ,desc,due,idLabels,idBoard,idList,idMembers,\\\n ,name,pos,shortUrl,url,labels\"})\n return {card[\"id\"]: card for card in cards}",
"title": ""
},
{
"docid": "2622d7487db4d2b63d11d3f2683476bc",
"score": "0.46464518",
"text": "def getdata(self, coln, *args, **kwargs):\n try:\n # getting the element url in the gallery\n ans = list()\n gm = self.gallery\n ans = gm.getdata(coln, *args, **kwargs)\n\n # returning answer\n return ans\n\n # exception handling\n except Exception as exp:\n Err.reraise(exp, \"Controller: getdata\")",
"title": ""
},
{
"docid": "8bc46176079e51a8130cf8535b2778a6",
"score": "0.46441722",
"text": "def test_get_compute_board_list(self):\n pass",
"title": ""
},
{
"docid": "91c29b43a82e213a91100e4957c79348",
"score": "0.46440044",
"text": "def do_load(self):\n try:\n self.my_controller.get_from_save()\n except IOError:\n print(\"pokemon not in database\")",
"title": ""
},
{
"docid": "a6b1e8fa389fdb1a447532abc421f9ca",
"score": "0.46414992",
"text": "def get_board(self):\r\n return self.board",
"title": ""
},
{
"docid": "098c3016e5c9bc2164675eb86c27e335",
"score": "0.4637695",
"text": "def get_url(self):\r\n self.confirm_db_thread()\r\n return self.url",
"title": ""
},
{
"docid": "4b44c11de4654bfa4afbb96b6fb20cdb",
"score": "0.46368402",
"text": "def test_schedule_retrieve_schedule_none(self):\n # request\n response = self.client.get(reverse(self.view_name, args=[1]))\n # test response\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)",
"title": ""
},
{
"docid": "18a0708599a6af1cc903beccbd144aca",
"score": "0.46341026",
"text": "def _check_status(self):\n self._boards = [ b for b in self._boards if b.connected ]\n # list(filter(lambda b: b.connected(), self._boards))\n if len(self._boards) == 0 or not self._default_board or not self._default_board.connected:\n self._default_board = None\n return",
"title": ""
},
{
"docid": "f7d83419f9e6ee8dba03db3a2d5599fa",
"score": "0.46337855",
"text": "def get_board_actions(board_id, data=dict()):\n actions = api(\"GET\", \"boards/%s/actions\" % board_id, data)\n return actions",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="729"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">9ba5ec79a9302af8d0123c4c9aad8a67</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Sorts all the fabrics in the system in to physical shelves, taking into account maximum capacity, proximity grouping by pattern, and checking if there is actually quantity before assigning a shelf</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "aca164ffe7dc6941b1a4f576fa3a245d",
"score": "0.74741846",
"text": "def sort_fabrics():\n max_shelf_qty = Decimal('240')\n shelves = Shelf.objects.all().order_by('tower', 'name')\n current_shelf_index = 0\n shelf = shelves[current_shelf_index]\n cell_style = \"\"\"\n border-bottom:1px solid #595959;\n border-right:1px solid #595959;\n padding:1em 0.5em;\n text-align:center;\n font-size:1;\n font-family:Tahoma;\n max-height:5em;\n \"\"\"\n header_cell_style = \"\"\"\n border-right:1px solid #595959;\n border-bottom:1px solid #595959;\n border-top:1px solid #595959;\n padding:1em;\n \"\"\"\n item_cell_style = \"\"\"\n padding:0.75em 0.25em;\n \"\"\"\n \n def exceeds_shelf_capacity(shelf, fabric):\n \"\"\"\n Tests whether adding this fabric to shelf will exceed the shelf's maximum \n capacity. Returns a boolean based on the result\n \"\"\"\n shelf_total = Decimal(shelf.fabrics.all().aggregate(Sum('quantity_th'))['quantity_th__sum'] or 0)\n return True if (shelf_total) + fabric.quantity > max_shelf_qty else False\n \n # Reset the shelving arrangements\n Fabric.objects.all().update(shelf=None)\n \n # Loops through the fabrics, organized by patterns so that \n # similar fabrics by patterns are close to each other\n for fabric in Fabric.objects.filter(item__acknowledgement__time_created__gte=date(2014, 1, 1)).distinct().order_by('pattern', 'color'):\n # Only find a shelf if there is fabric to store\n if fabric.quantity > Decimal('0'):\n if not exceeds_shelf_capacity(shelf, fabric):\n fabric.shelf = shelf\n \n else:\n # Loops through all the previous shelves to look for space\n for past_shelf in shelves[0: current_shelf_index]:\n if not exceeds_shelf_capacity(past_shelf, fabric): \n fabric.shelf = past_shelf\n \n try:\n if fabric.shelf is None: \n current_shelf_index += 1\n \n try:\n shelf = shelves[current_shelf_index]\n except (KeyError, IndexError):\n pass#raise ValueError(\"You've run out of space to store fabrics!\")\n \n fabric.shelf = shelf\n \n except Exception:\n current_shelf_index += 1\n \n try:\n shelf = shelves[current_shelf_index]\n except (KeyError, IndexError):\n pass#raise ValueError(\"You've run out of space to store fabrics!\")\n \n fabric.shelf = shelf\n \n fabric.save()\n\n \n \n #return self.message\n return render_to_string('fabric_email.html', {'towers': Tower.objects.all().order_by('id'),\n 'header_style': header_cell_style,\n 'cell_style': cell_style,\n 'item_cell_style': item_cell_style})",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "b4cb2524fec347b6ea4a5fcff1ed1911",
"score": "0.5964946",
"text": "def PantrySorterEmptyShelf(Shelf: Shelf, stackableFood, unstackableFood):\n print(stackableFood, unstackableFood)\n unstackableFood = sorted(unstackableFood, reverse=True)\n stackableFood = sorted(stackableFood, reverse=True)\n print(\"sorted\\n{}\\n{}\".format(unstackableFood, stackableFood))\n for foodList in [unstackableFood, stackableFood]:\n for food in foodList:\n if(food.height >= Shelf.height):\n #can never be added because doesn't fit on Shelf\n print(\"Could not add {} due to exceeding height of Shelf.\".format(food))\n foodList.remove(food)\n iShelf = 0\n remainingWidth = Shelf.width\n\n while (not Shelf.isFull() and (len(stackableFood) != 0 or len(unstackableFood) != 0)) :\n\n Shelf.createStack()\n print(\"stack created\")\n # here I am trying to get the ith stack on the shelf. but it didnt work for me. It was an object of type list(?)\n curStack = Shelf.stacks[iShelf]\n for food in stackableFood:\n # adds as many stackable food items to stack as possible\n if Shelf.height > curStack.height + food.height:\n # if this is not true, it cant be stacked anyways\n if not curStack.items:\n # just first item in stack\n if food.depth < remainingWidth:\n curStack.addItem(food)\n print(\"adding {}\".format(food))\n else:\n curStack.addItem(food)\n remainingWidth -= food.depth\n for food in curStack.items:#remove items that have been shelved\n stackableFood.remove(food)\n removeThisFromUnstackable = None\n for food in unstackableFood:\n # adds as a non stackable food item to stack if possible\n if curStack.stackable and Shelf.height > curStack.height + food.height:\n # if this is not true, it cant be stacked anyways\n if not curStack.items:\n # just first item in stack\n if food.depth < remainingWidth:\n remainingWidth -= food.depth\n curStack.addItem(food, False)\n unstackableFood.remove(food)\n break\n else:\n curStack.addItem(food, False)\n unstackableFood.remove(food)\n break\n\n iShelf += 1 \n try:\n if(len(stackableFood) != 0 and remainingWidth < stackableFood[-1].depth):\n if(len(unstackableFood) != 0 and remainingWidth < unstackableFood[-1].depth):\n Shelf.setFull(True)\n elif(len(unstackableFood) != 0 and remainingWidth < unstackableFood[-1].depth):\n if(len(unstackableFood) != 0 and remainingWidth < unstackableFood[-1].depth):\n Shelf.setFull(True) \n # checks if not even the smallest items in both lists fit onto shelf. \n except:\n print(\"avoided something\")\n print(\"end\")\n return Shelf",
"title": ""
},
{
"docid": "93df0a2704174c9a7df10972a9e0ebe5",
"score": "0.58005416",
"text": "def RoomyStrategy(I_list,box_list):\n SortedItems = quick_sort(I_list)\n lemon = []\n iso = 0\n for element in range(0, len(SortedItems)):\n w = SortedItems[element].weight\n x = FindMaxCap(box_list)\n if w <= x.max_cap - x.curr_cap:\n x.curr_cap += w\n x.items_list.append(SortedItems[element])\n lemon.append(SortedItems[element])\n iso+=1\n else:\n pass\n print('Results from Greedy Strategy 1')\n if len(SortedItems) == iso:\n print('All items successfully packed into boxes!')\n else:\n print('Unable to pack all items!')\n for box in box_list:\n print('Box',box.id,'of weight capacity',box.max_cap,'contains:')\n for item in box.items_list:\n print(item.name,'of weight',item.weight)\n for item in SortedItems:\n if item not in lemon:\n print(item.name,'of weight',item.weight,'got left behind')\n print('\\n')",
"title": ""
},
{
"docid": "e44ff4a2bbffb9161ff63dd131f1fec0",
"score": "0.5362703",
"text": "def squeeze_accept(partition):\n Write a function that\n - Sort districts by most Democratic heavy and most Republican heavy\n\n - Assign a base value of competitiveness for each district\n - Run chain, accept only if districts satisfy values under or order\n \"\"\"\n\n#--- CONSTRAINTS\n\n\"\"\"",
"title": ""
},
{
"docid": "2d16fae395086186a10cdf187d8f3577",
"score": "0.5348469",
"text": "def organize(inventory, grocery_list, exists=set()):\n\tlst = sorted(inventory, key=lambda x : x.aisle) #sort by aisle - O(N*logN)\n\taisles = [[] for y in lst if not exist_test(y.aisle, exists)] #create unique aisles only - O(N)\n\t[aisles[y.aisle].append(y.grocery) for y in lst if y.grocery in grocery_list] #append groceries - O(N*G) \n\treturn aisles",
"title": ""
},
{
"docid": "ac9a8af31da2ff49e5b670b925ff2e63",
"score": "0.5329648",
"text": "def sort(self): # sort all entries to make room for new ones, determine best and worst\n ns = self.num_stored.value\n ys = np.asarray(self.ys[:ns])\n yi = ys.argsort()\n sortRuns = []\n for i in range(len(yi)):\n y = ys[yi[i]]\n xs = self.get_x(yi[i])\n sortRuns.append((y, xs))\n numStored = min(len(sortRuns),int(0.9*self.capacity)) # keep 90% best \n for i in range(numStored):\n self.replace(i, sortRuns[i][0], sortRuns[i][1])\n self.num_sorted.value = numStored \n self.num_stored.value = numStored \n return numStored",
"title": ""
},
{
"docid": "5c366cf90cc0cb2c55e97dc9ff24fce2",
"score": "0.531941",
"text": "def _sort_compounds(self):\n self.sorted_molecules = sorted(self.values(), key=operator.attrgetter('criterion'))",
"title": ""
},
{
"docid": "99aed67f3c201a850ff6c5faffead4e6",
"score": "0.51986617",
"text": "def OneByOneStrategy(I_list,box_list):\n SortedItems = quick_sort(I_list)\n lemon = []\n for i in box_list:\n for item in range(len(SortedItems)):\n if i.max_cap - i.curr_cap == 0:\n break\n if SortedItems[item].weight <= i.max_cap - i.curr_cap:\n if SortedItems[item] not in lemon:\n lemon.append(SortedItems[item])\n i.items_list.append(SortedItems[item])\n i.curr_cap += SortedItems[item].weight\n else:\n pass\n print('Results from Greedy Strategy 3')\n if len(lemon) != len(SortedItems):\n print('Unable to pack all items')\n else:\n print('All items successfully packed!')\n for s in box_list:\n print('Box',s.id,'of weight capacity',s.max_cap,'contains:')\n for item in s.items_list:\n print(item.name,'of weight',item.weight)\n for item in SortedItems:\n if item not in lemon:\n print(item.name,'of weight',item.weight,'got left behind')\n print('\\n')",
"title": ""
},
{
"docid": "3f413bbd0f133bb43616439fb43d9ab0",
"score": "0.51847434",
"text": "def TightStrategy(I_list,box_list):\n iso = 0\n lemon = []\n SortedItems = quick_sort(I_list)\n for element in range(0, len(SortedItems)):\n w = SortedItems[element].weight\n x = FindTightFit(box_list, w)\n if x == None:\n iso+=1\n pass\n else:\n if w <= x.max_cap - x.curr_cap:\n x.curr_cap += w\n x.items_list.append(SortedItems[element])\n lemon.append(SortedItems[element])\n else:\n pass\n print('Results from Greedy Strategy 2')\n if iso > 0:\n print('Unable to pack all items!')\n else:\n print('All items were successfully packed!')\n for s in box_list:\n print('Box',s.id,'of weight capacity',s.max_cap,'contains:')\n for item in s.items_list:\n print(item.name,'of weight',item.weight)\n for item in SortedItems:\n if item not in lemon:\n print(item.name,'of weight',item.weight,'got left behind')\n print('\\n')",
"title": ""
},
{
"docid": "365826d08919e8ec0084e5faa2954bd6",
"score": "0.5135632",
"text": "def volume_sort(self):\n self.jobs_sorted = sorted(\n self.jobs,\n key=lambda job: (job['height'], job['width'] * job['height']),\n # key=lambda job: job['width'] * job['height'],\n reverse=True)",
"title": ""
},
{
"docid": "b01d3e991457747dc2c2b0e7db969032",
"score": "0.5130625",
"text": "def sort_packages(self, pkgs_to_be_delivered):\n # Sort will cherry pick packages by constraints manually and load first time around, otherwise nearest neighbour\n pkgs_with_dd = []\n pkgs_without_dd = []\n temp_route1 = []\n temp_route2 = []\n # packages are pre-loaded based off Deadline Delivery first, packages are decided in order based on same location\n # prevents unnecessary backtracking to deliver to an address that has already been visited\n pre_load_temp_route1 = [15, 16, 34, 14, 19, 20, 21, 31, 32, 13, 39, 8, 30, 4, 40, 1]\n pre_load_temp_route2 = [25, 26, 6, 7, 29, 5, 37, 38]\n\n # creating lists for packages with and without delivery deadlines\n \"\"\"\n Space Complexity: O(N)\n Time Complexity: O(N)\n \"\"\"\n for pkg in pkgs_to_be_delivered:\n if pkg.get_deadline() != \"EOD\":\n pkgs_with_dd.append(pkg)\n else:\n pkgs_without_dd.append(pkg)\n\n \"\"\"\n ***REQUIREMENT A***\n Main Adjusting part of algorithm is based on if there are priority packages still left to deliver. If all are accounted for\n continue to just decide what is the most efficient path by distance. Greedy Algorithm as we are assuming deadline is the most important\n constraint to decide what to change for.\n\n From lines 51 to 131\n Space Complexity: O(N)\n Time Complexity: O(N^2)\n \"\"\"\n if len(pkgs_with_dd) > 0:\n # preloading trucks 1 and 2 with packages based on constraints and bundled to optimize for location\n # assigns pre-loaded routes manually to temporary routes, route 2 still needs to have more packages before leaving\n # truck 2 has delayed packages and will not leave until 9:05\n for pre_load_1 in pre_load_temp_route1:\n temp_route1.append(self.pkg_hash.get(pre_load_1 - 1))\n\n for pre_load_2 in pre_load_temp_route2:\n temp_route2.append(self.pkg_hash.get(pre_load_2 - 1))\n\n # remove from pkgs with and without dd lists to update\n for temp_pkg in temp_route1:\n if temp_pkg in pkgs_without_dd:\n pkgs_without_dd.remove(temp_pkg)\n else:\n pkgs_with_dd.remove(temp_pkg)\n for temp_pkg2 in temp_route2:\n if temp_pkg2 in pkgs_with_dd:\n pkgs_with_dd.remove(temp_pkg2)\n else:\n pkgs_without_dd.remove(temp_pkg2)\n\n # have to make a temporary list to sort next closest packages by distance to last package already in temp route\n # for truck 2, remove package 9 for now so it will get updated later\n for pkg_without_dd_check in pkgs_without_dd:\n if pkg_without_dd_check.get_pkg_id() == '9':\n pkgs_without_dd.remove(pkg_without_dd_check)\n\n pkgs_without_dd_sort = []\n pkgs_without_dd_sort += pkgs_without_dd\n pkgs_without_dd_sort.insert(0, temp_route2[-1])\n\n \"\"\"\n ***REQUIREMENT A***\n Implementation of a Nearest Neighbour Algorithm by determining which location is closest with a package. Decides\n to make the closest location the next destination to visit. \n \"\"\"\n # selection sort to decide what is the optimal path based on distance for order of packages to be delivered\n for pkg_index in range(len(pkgs_without_dd_sort) - 1):\n # start at index 0 and compare to every index after\n smallest_distance = 99\n smallest_distance_pkg = None\n pkg_index_key = pkgs_without_dd_sort[pkg_index].get_key()\n pkg_index_location = Utils.match_pkg_to_distance(self.distance_values, self.pkg_hash, pkg_index_key)\n\n for pkg_next_index in range(pkg_index + 1, len(pkgs_without_dd_sort)):\n\n # match location to pkg in pkgs_without_dd\n pkg_next_index_key = pkgs_without_dd_sort[pkg_next_index].get_key()\n pkg_next_index_location = Utils.match_pkg_to_distance(self.distance_values, self.pkg_hash,\n pkg_next_index_key)\n # plug distances into get_distance\n get_distance_result = Utils.get_distance(self.distance_values, pkg_index_location,\n pkg_next_index_location)\n # find minimum from distance\n if get_distance_result < smallest_distance:\n smallest_distance = get_distance_result\n smallest_distance_pkg = pkg_next_index\n pkgs_without_dd_sort[pkg_index + 1], pkgs_without_dd_sort[smallest_distance_pkg] = pkgs_without_dd_sort[smallest_distance_pkg], pkgs_without_dd_sort[pkg_index + 1]\n\n # adding sorted by distance packages remaining to temp_route 2 to fill up truck2\n for y in range(1, len(pkgs_without_dd_sort)):\n if len(temp_route2) < 16:\n temp_route2.append(pkgs_without_dd_sort[y])\n\n # update list based off of what packages are remaining for next pickup/delivery\n for pkg_cleanup in temp_route2:\n if pkg_cleanup in pkgs_without_dd:\n pkgs_without_dd.remove(pkg_cleanup)\n\n # remove packages on truck routes accounted for from pkgs_to_be_delivered list\n for pkg_remove_route1 in temp_route1:\n if pkg_remove_route1 in pkgs_to_be_delivered:\n pkgs_to_be_delivered.remove(pkg_remove_route1)\n for pkg_remove_route2 in temp_route2:\n if pkg_remove_route2 in pkgs_to_be_delivered:\n pkgs_to_be_delivered.remove(pkg_remove_route2)\n # return temp_routes\n return temp_route1, temp_route2\n\n # no priority packages left, continue\n\n else:\n \"\"\"\n From lines 133 to \n Space Complexity: O(N)\n Time Complexity: O(N^2)\n \"\"\"\n # update package #9 because by this time the address should be corrected\n pkg_9 = self.pkg_hash.get(8)\n pkg_9.set_address(\"410 S State St\")\n pkg_9.set_city(\"Salt Lake City\")\n pkg_9.set_state(\"UT\")\n pkg_9.set_zip(\"84111\")\n\n # create dummy package for hub separate from hash table to add to list to sort\n hub_dummy_package = Pkg.Package(['99', 'HUB', 'Salt Lake City', 'UT', '84107', 'EOD', '0'])\n\n # have to make a temporary list to sort next closest packages by distance to hub\n # for truck 2\n pkgs_without_dd_sort = [hub_dummy_package]\n pkgs_without_dd_sort += pkgs_without_dd\n\n \"\"\"\n ***REQUIREMENT A***\n Implementation of a Nearest Neighbour Algorithm by determining which location is closest with a package. Decides\n to make the closest location the next destination to visit. \n \"\"\"\n # determine closest route from hub with current list of packages\n for pkg_index in range(len(pkgs_without_dd_sort) - 1):\n # start at index and compare to every index after\n\n smallest_distance = 99\n smallest_distance_pkg = None\n if pkg_index == 0:\n pkg_index_location = 0\n else:\n pkg_index_key = pkgs_without_dd_sort[pkg_index].get_key()\n pkg_index_location = Utils.match_pkg_to_distance(self.distance_values, self.pkg_hash, pkg_index_key)\n\n for pkg_next_index in range(pkg_index + 1, len(pkgs_without_dd_sort)):\n\n # match location to pkg in pkgs_without_dd\n pkg_next_index_key = pkgs_without_dd_sort[pkg_next_index].get_key()\n pkg_next_index_location = Utils.match_pkg_to_distance(self.distance_values, self.pkg_hash,\n pkg_next_index_key)\n # plug distances into get_distance\n get_distance_result = Utils.get_distance(self.distance_values, pkg_index_location,\n pkg_next_index_location)\n # find minimum from distance\n if get_distance_result < smallest_distance:\n smallest_distance = get_distance_result\n smallest_distance_pkg = pkg_next_index\n pkgs_without_dd_sort[pkg_index + 1], pkgs_without_dd_sort[smallest_distance_pkg] = pkgs_without_dd_sort[smallest_distance_pkg], pkgs_without_dd_sort[pkg_index + 1]\n\n # fill up route until length of route is 16\n for y in range(1, len(pkgs_without_dd_sort)):\n if len(temp_route2) < 16:\n temp_route2.append(pkgs_without_dd_sort[y])\n\n # remove from pkgs_without_dd if in route 2, updating packages that are left\n for pkg_cleanup in temp_route2:\n if pkg_cleanup in pkgs_without_dd:\n pkgs_without_dd.remove(pkg_cleanup)\n\n # remove from main package list : pkgs_to_be_delivered, updating packages that are left\n for pkg_remove_route2 in temp_route2:\n if pkg_remove_route2 in pkgs_to_be_delivered:\n pkgs_to_be_delivered.remove(pkg_remove_route2)\n\n # return temp_routes\n return temp_route1, temp_route2",
"title": ""
},
{
"docid": "61112577f080d3e5c37900867ac52051",
"score": "0.51098347",
"text": "def inspect_butterfly(pmax,qmax,nu,zu,ucsize=1,t=-1,M=2.3,D1=0.8,D2=0.5):\n # fine sorting\n eps = []\n phis = []\n x_bars = []\n y_bars = []\n\n # coarse sorting\n # phi_surf = []\n # phi_bulk = []\n # eps_surf = []\n # eps_bulk = []\n\n # fill up\n for q in range(1,qmax+1):\n newsize = q * ucsize\n num_eigs = int(20*q) # sweet spot\n if q == qmax: # do the p=0\n for p in range(pmax+1):\n H_pq=soti_block_slab(size=newsize,p=p,q=q,nu=nu,zu=zu,t=t,M=M,D1=D1,D2=D2)\n eps_pq,waves=ssl.eigsh(H_pq,k=num_eigs,sigma=0,return_eigenvectors=True)\n # fine sorting\n x_bar=expectation_x(waves,newsize)\n y_bar=expectation_y(waves,newsize)\n x_bars.extend(x_bar)\n y_bars.extend(y_bar)\n phis.extend([p/q]*len(eps_pq))\n eps.extend(eps_pq)\n\n # coarse sorting\n # idx,idx0=get_indices(waves)\n # phi_surf.extend([p/q]*len(idx)) # no need to get (q-p)/q\n # phi_bulk.extend([p/q]*len(idx0))\n # eps_surf.extend(eps_pq[idx])\n # eps_bulk.extend(eps_pq[idx0])\n else: # p=pmax=1\n p=1\n H_pq=soti_block_slab(size=newsize,p=p,q=q,nu=nu,zu=zu,t=t,M=M,D1=D1,D2=D2)\n eps_pq,waves=ssl.eigsh(H_pq,k=num_eigs,sigma=0,return_eigenvectors=True)\n # fine sorting\n x_bar=expectation_x(waves,newsize)\n y_bar=expectation_y(waves,newsize)\n x_bars.extend(x_bar)\n y_bars.extend(y_bar)\n phis.extend([p/q]*len(eps_pq))\n eps.extend(eps_pq)\n\n # coarse sorting\n # idx,idx0=get_indices(waves)\n # phi_surf.extend([p/q]*len(idx)) # no need to get (q-p)/q\n # phi_bulk.extend([p/q]*len(idx0))\n # eps_surf.extend(eps_pq[idx])\n # eps_bulk.extend(eps_pq[idx0])\n\n return phis, eps, x_bars, y_bars\n # return phi_surf, eps_surf, phi_bulk, eps_bulk",
"title": ""
},
{
"docid": "658824b016d1f0309e2e281502e95c36",
"score": "0.504075",
"text": "def sortAssemsByRing(self):\n sortKey = lambda a: a.spatialLocator.getRingPos()\n self._children = sorted(self._children, key=sortKey)",
"title": ""
},
{
"docid": "1b741b908c8d576b2d595a8d1ebdc52a",
"score": "0.4948022",
"text": "def disc_sort_img(image_list, img_vessel_list, base_label):\n\n return_vessel_list = []\n return_img_list = []\n for idx, vessel_idx in enumerate(img_vessel_list):\n if vessel_idx['idx'] == base_label:\n return_img_list.append(image_list[idx])\n return_vessel_list.append(vessel_idx)\n img_vessel_list.pop(idx)\n image_list.pop(idx)\n\n disc_exist_image_list = []\n disc_exist_vessel_list = []\n # OM_info 0 is fovea, 1 is disc\n\n\n for idx, vessel_idx in enumerate(img_vessel_list):\n tmp_vessel_idx = vessel_idx.copy()\n if tmp_vessel_idx['OM_info'][1] != None:\n tmp_vessel_idx['OM_info'] = np.sqrt(np.power(np.array(tmp_vessel_idx['OM_info'][1]) - np.array(return_vessel_list[0]['OM_info'][1]),2).sum())\n disc_exist_image_list.append([image_list[idx], tmp_vessel_idx['OM_info']])\n disc_exist_vessel_list.append(tmp_vessel_idx)\n\n\n disc_exist_image_list = sorted(disc_exist_image_list, key= lambda image_info : image_info[1])\n disc_exist_vessel_list = sorted(disc_exist_vessel_list, key=lambda vessel_info : vessel_info['OM_info'])\n\n fovea_exist_image_list = []\n fovea_exist_vessel_list = []\n for idx, vessel_idx in enumerate(img_vessel_list):\n tmp_vessel_idx = vessel_idx.copy()\n if tmp_vessel_idx['OM_info'][1] == None and tmp_vessel_idx['OM_info'][0] != None:\n tmp_vessel_idx['OM_info'] = np.sqrt(\n np.power(np.array(tmp_vessel_idx['OM_info'][0]) - np.array(return_vessel_list[0]['OM_info'][0]), 2).sum())\n fovea_exist_image_list.append([image_list[idx], tmp_vessel_idx['OM_info']])\n fovea_exist_vessel_list.append(tmp_vessel_idx)\n\n fovea_exist_image_list = sorted(fovea_exist_image_list, key=lambda image_info: image_info[1])\n fovea_exist_vessel_list = sorted(fovea_exist_vessel_list, key=lambda vessel_info : vessel_info['OM_info'])\n\n for idx, disc_exist_vessel_list_idx in enumerate(disc_exist_vessel_list):\n return_img_list.append(disc_exist_image_list[idx][0])\n return_vessel_list.append(disc_exist_vessel_list_idx)\n\n for idx, fovea_exist_vessel_list_idx in enumerate(fovea_exist_vessel_list):\n return_img_list.append(fovea_exist_image_list[idx][0])\n return_vessel_list.append(fovea_exist_vessel_list_idx)\n\n return return_img_list, return_vessel_list",
"title": ""
},
{
"docid": "e1d5343196f527948c1b74dc96478488",
"score": "0.49372783",
"text": "def test_list_flavors_detailed_filter_by_min_disk(self):\n response = self.flavors_client.list_flavors_with_detail()\n flavors = response.entity\n\n # Sort the flavors by disk size in ascending order\n flavors.sort(key=lambda k: int(k.disk))\n\n # Remove any flavors from the list that are smaller than the\n # flavor with the second smallest disk size\n filter_criteria = lambda x: int(x.disk) >= int(flavors[1].disk)\n expected_flavors = filter(filter_criteria, flavors)\n\n response = self.flavors_client.list_flavors_with_detail(\n min_disk=flavors[1].disk)\n actual_flavors = response.entity\n actual_flavors.sort(key=lambda k: k.id)\n expected_flavors.sort(key=lambda k: k.id)\n self.assertEqual(actual_flavors, expected_flavors)",
"title": ""
},
{
"docid": "5abd31fc0c8350c022319226434d87d6",
"score": "0.49120173",
"text": "def sort_partitions():\n usb_partitions = usb_partition_table()\n sorted_partitions = sorted(usb_partitions.items(), key=lambda x: x[1])\n return sorted_partitions",
"title": ""
},
{
"docid": "83cc52ae4d1346f6971ea23408013279",
"score": "0.4885537",
"text": "def measureSJF(p):\r\n\treturn measureFCFS(msort(p))",
"title": ""
},
{
"docid": "4860ece0d4740fa5edea4437636f2ee6",
"score": "0.4876817",
"text": "def fit_house_in_diamond(houses_copy, batteries):\n\n # Output huis die overgebleven is\n output_missing_house = houses_copy[0].get_output()\n\n # Sorteer batterijen resterend capaciteit hoog > laag, en selecteer meest_resterende batterij\n batteries.sort(key=lambda battery: battery.get_remaining(), reverse=True)\n copy_batteries = deepcopy(batteries)\n copy_batteries.pop(0)\n battery_with_most_capacity = batteries[0]\n\n # Blijf loopen, en huizen efficient verwisselen, tot het huis in de batterij past\n for index, battery in enumerate(copy_batteries):\n second_most_battery_capacity = batteries[index + 1]\n capacity_battery_second = second_most_battery_capacity.get_remaining()\n\n # Kijk naar de huizen in de eerst batterij en pak hoogste output\n houses_first_battery = battery_with_most_capacity.get_houses()\n house_most_output = max(houses_first_battery, key=lambda house: house.get_output())\n house_first_index = houses_first_battery.index(house_most_output)\n output_house_most = house_most_output.get_output()\n\n # Kijk naar de huizen in de tweede batterij\n houses_second_battery = second_most_battery_capacity.get_houses()\n second_index_house = None\n second_house_output = 100\n least_remaining_battery_2 = 100\n\n # Pak het huis met een output dat ervoor zorgt dat missende huis past\n for index, house in enumerate(houses_second_battery):\n output_house = house.get_output()\n remaining_battery_2 = capacity_battery_second - (output_house_most - output_house)\n\n # Let hierop dat de eerste en tweede batterij de wissel aankunnen en dat er in één wissel genoeg ruimte is voor het missende huis\n if remaining_battery_2 < least_remaining_battery_2 and remaining_battery_2 >= 0:\n second_house_output = output_house\n second_index_house = index\n least_remaining_battery_2 = remaining_battery_2\n\n houses_first_battery.pop(house_first_index)\n second_house = houses_second_battery.pop(second_index_house)\n houses_first_battery.append(second_house)\n houses_second_battery.append(house_most_output)\n\n if output_missing_house < battery_with_most_capacity.get_remaining():\n houses_first_battery.append(houses_copy.pop(0))\n break",
"title": ""
},
{
"docid": "bc0507ddcd620daca5cdba684cad2c18",
"score": "0.48569116",
"text": "def induced_sorting(\n lms, tails, heads, SA, type_suffix, text, n, m, alpha, bucket_sizes, sigma\n):\n for i in range(m - 1, -1, -1): # place LMS suffixes at the end of their buckets\n nfs = tails[text[lms[i]]]\n SA[nfs] = lms[i]\n tails[text[lms[i]]] -= 1\n\n for i in range(n): # place the L-type suffixes at the fronts of their buckets\n if SA[i] > 0 and type_suffix[SA[i] - 1] == L_TYPE:\n nfs = heads[text[SA[i] - 1]]\n SA[nfs] = SA[i] - 1\n heads[text[SA[i] - 1]] += 1\n\n # reset bucket counters\n heads, tails = bucket_intervals(alpha, bucket_sizes, sigma)\n\n for i in range(\n n - 1, -1, -1\n ): # place the S-type suffixes at the ends of their buckets\n if SA[i] > 0 and type_suffix[SA[i] - 1] == S_TYPE:\n nfs = tails[text[SA[i] - 1]]\n SA[nfs] = SA[i] - 1\n tails[text[SA[i] - 1]] -= 1",
"title": ""
},
{
"docid": "e7d6a669604217dc315aa28737f4e139",
"score": "0.48494855",
"text": "def organize(select, strain, equals):\n scores = []\n data = list(strainer(select, strain, equals))\n while len(data) != 0:\n number = lowest_number(data)\n scores.append(number)\n data.remove(number)\n return scores",
"title": ""
},
{
"docid": "6f4f213d06e5ddac4f074ad2dd34e69c",
"score": "0.48369095",
"text": "def _best_fit(self, path):\n matching_volumes = (\n x\n for x in self.allowed_volumes\n if self._fits(x, path)\n )\n return sorted(matching_volumes, key=len, reverse=True)",
"title": ""
},
{
"docid": "aa2d42d04325da39b37d5ceef9dd3cea",
"score": "0.48002526",
"text": "def topo_sort(self):\n\n simulations_steps = [[self.nodes['size']]]\n frontier = set()\n\n # add all parentless nodes to the first simulation step, these are the roots\n for node in self.nodes['rooms']:\n if not node.parents:\n simulations_steps[0].append(node)\n\n # add the children of the roots to the frontier\n for root in simulations_steps[0]:\n root.simulated = True\n frontier.update(root.children)\n\n while frontier:\n step = []\n frontier_copy = frontier.copy()\n for node in frontier_copy:\n # add to step if all parents for a node in the frontier have been simulated\n if not [True for parent in node.parents if not parent.simulated]:\n step.append(node)\n frontier.discard(node)\n node.simulated = True\n if type(node) is not Adjacency:\n frontier.update(node.children)\n simulations_steps.append(step)\n \n return simulations_steps",
"title": ""
},
{
"docid": "e65fd8b4ec479b15a4bcbda83cb6d951",
"score": "0.47989663",
"text": "def ffda(items_list, bin_capacity):\n decreased_list = sorted(items_list,reverse=True) #sorts the items list in a decreasing order\n bins =[]\n for item in decreased_list:\n # foeach item we search if there's an open bin where it can fit\n for bin in bins:\n if bin.total_weight + item <= bin_capacity: #if it fits\n bin.add_item(item) #we add the item in the bin\n break\n else:\n # there is no open bin where the item can fit\n #so we open a new bin and add the item in it\n bin = Bin()\n bin.add_item(item)\n bins.append(bin)\n\n return bins",
"title": ""
},
{
"docid": "843a4c93f5a577b98e8d8453b323d128",
"score": "0.47957584",
"text": "def sort_by_ratings():\n\n print(\"***** Find Businesses by Categories Sorted by Rate *****\")\n while True:\n print()\n category = input(\n 'Please enter a type of business (category) or type \"back\" or \"quit\": ')\n print()\n if category == \"quit\":\n print(\"Goodbye!\")\n sys.exit()\n if category == \"back\":\n return\n\n # create a regex pattern for business name\n pattern = r\".*\" + re.escape(category) + r\".*\"\n regx = re.compile(pattern, re.IGNORECASE)\n\n cursor = business_col.find({\"categories\": regx})\n\n business_objects = cursor.limit(10).sort(\"stars\", -1)\n\n if cursor.count() == 0:\n print(\"No businesses found with given category.\")\n continue\n for business_object in business_objects:\n print(f'Stars: {business_object[\"stars\"]}')\n print_business(business_object)",
"title": ""
},
{
"docid": "20dd4628f2980f75a6192b0dc5f18089",
"score": "0.4792606",
"text": "def sortarai(self, datablock, s, Zdiff):\n\n first_Z, first_I, zptrm_check, ptrm_check, ptrm_tail = [], [], [], [], []\n field, phi, theta = \"\", \"\", \"\"\n starthere = 0\n Treat_I, Treat_Z, Treat_PZ, Treat_PI, Treat_M, Treat_AC = [], [], [], [], [], []\n ISteps, ZSteps, PISteps, PZSteps, MSteps, ACSteps = [], [], [], [], [], []\n GammaChecks = [] # comparison of pTRM direction acquired and lab field\n Mkeys = ['measurement_magn_moment', 'measurement_magn_volume',\n 'measurement_magn_mass', 'measurement_magnitude']\n rec = datablock[0]\n for key in Mkeys:\n if key in list(rec.keys()) and rec[key] != \"\":\n momkey = key\n break\n # first find all the steps\n for k in range(len(datablock)):\n rec = datablock[k]\n if 'treat_mw_step' in list(rec.keys()) and rec['treat_mw_step'] is None: rec['treat_mw_step']=\"\"\n if 'treatment_mw_integral' in list(rec.keys()) and rec['treatment_mw_integral'] is None: rec['treatment_mw_integral']=\"\"\n if 'treatment_mw_power' in list(rec.keys()) and rec['treatment_mw_power'] is None: rec['treatment_mw_power']=\"\"\n if 'treatment_temp' in list(rec.keys()) and rec['treatment_temp'] is None:rec['treatment_temp']=\"\"\n if \"treat_mw_step\" in list(rec.keys()) and rec[\"treat_mw_step\"]!=\"\":\n\n THERMAL = False\n MICROWAVE = True\n temp = float(rec[\"treat_mw_step\"])\n elif \"treatment_mw_integral\" in list(rec.keys()) and rec[\"treatment_mw_integral\"]!=\"\":\n THERMAL = False\n MICROWAVE = True\n if \"measurement_description\" in list(rec.keys()):\n MW_step = rec[\"measurement_description\"].strip(\n '\\n').split(\":\")\n for STEP in MW_step:\n if \"Number\" in STEP:\n temp = float(STEP.split(\"-\")[-1])\n elif \"treatment_mw_power\" in list(rec.keys()) and rec[\"treatment_mw_power\"]!=\"\":\n THERMAL = False\n MICROWAVE = True\n if \"measurement_description\" in list(rec.keys()):\n MW_step = rec[\"measurement_description\"].strip(\n '\\n').split(\":\")\n for STEP in MW_step:\n if \"Number\" in STEP:\n temp = float(STEP.split(\"-\")[-1])\n elif \"treatment_temp\" in list(rec.keys()) and rec[\"treatment_temp\"]!=\"\":\n temp = float(rec[\"treatment_temp\"])\n THERMAL = True\n MICROWAVE = False\n methcodes = []\n tmp = rec[\"magic_method_codes\"].split(\":\")\n for meth in tmp:\n methcodes.append(meth.strip())\n # for thellier-thellier\n if 'LT-T-I' in methcodes and 'LP-PI-TRM' in methcodes and 'LP-TRM' not in methcodes:\n Treat_I.append(temp)\n ISteps.append(k)\n if field == \"\":\n field = float(rec[\"treatment_dc_field\"])\n if phi == \"\":\n phi = float(rec['treatment_dc_field_phi'])\n theta = float(rec['treatment_dc_field_theta'])\n\n # for Microwave\n if 'LT-M-I' in methcodes and 'LP-PI-M' in methcodes:\n Treat_I.append(temp)\n ISteps.append(k)\n if field == \"\":\n field = float(rec[\"treatment_dc_field\"])\n if phi == \"\":\n phi = float(rec['treatment_dc_field_phi'])\n theta = float(rec['treatment_dc_field_theta'])\n\n # stick first zero field stuff into first_Z\n if 'LT-NO' in methcodes:\n Treat_Z.append(temp)\n ZSteps.append(k)\n if \"LT-AF-Z\" in methcodes and 'treatment_ac_field' in list(rec.keys()):\n if rec['treatment_ac_field'] != \"\":\n AFD_after_NRM = True\n # consider AFD before T-T experiment ONLY if it comes before\n # the experiment\n for i in range(len(first_I)):\n # check if there was an infield step before the AFD\n if float(first_I[i][3]) != 0:\n AFD_after_NRM = False\n if AFD_after_NRM:\n AF_field = 0\n if 'treatment_ac_field' in rec:\n try:\n AF_field = float(rec['treatment_ac_field']) * 1000\n except ValueError:\n pass\n\n dec = float(rec[\"measurement_dec\"])\n inc = float(rec[\"measurement_inc\"])\n intensity = float(rec[momkey])\n first_I.append([273. - AF_field, 0., 0., 0., 1])\n first_Z.append(\n [273. - AF_field, dec, inc, intensity, 1]) # NRM step\n if 'LT-T-Z' in methcodes or 'LT-M-Z' in methcodes:\n Treat_Z.append(temp)\n ZSteps.append(k)\n if 'LT-PTRM-Z':\n Treat_PZ.append(temp)\n PZSteps.append(k)\n if 'LT-PTRM-I' in methcodes or 'LT-PMRM-I' in methcodes:\n Treat_PI.append(temp)\n PISteps.append(k)\n if 'LT-PTRM-MD' in methcodes or 'LT-PMRM-MD' in methcodes:\n Treat_M.append(temp)\n MSteps.append(k)\n if 'LT-PTRM-AC' in methcodes or 'LT-PMRM-AC' in methcodes:\n Treat_AC.append(temp)\n ACSteps.append(k)\n if 'LT-NO' in methcodes:\n dec = float(rec[\"measurement_dec\"])\n inc = float(rec[\"measurement_inc\"])\n moment = float(rec[\"measurement_magn_moment\"])\n if 'LP-PI-M' not in methcodes:\n first_I.append([273, 0., 0., 0., 1])\n first_Z.append([273, dec, inc, moment, 1]) # NRM step\n else:\n first_I.append([0, 0., 0., 0., 1])\n first_Z.append([0, dec, inc, moment, 1]) # NRM step\n\n #---------------------\n # find IZ and ZI\n #---------------------\n\n for temp in Treat_I: # look through infield steps and find matching Z step\n if temp in Treat_Z: # found a match\n istep = ISteps[Treat_I.index(temp)]\n irec = datablock[istep]\n methcodes = []\n tmp = irec[\"magic_method_codes\"].split(\":\")\n for meth in tmp:\n methcodes.append(meth.strip())\n # take last record as baseline to subtract\n brec = datablock[istep - 1]\n zstep = ZSteps[Treat_Z.index(temp)]\n zrec = datablock[zstep]\n # sort out first_Z records\n # check if ZI/IZ in in method codes:\n ZI = \"\"\n if \"LP-PI-TRM-IZ\" in methcodes or \"LP-PI-M-IZ\" in methcodes or \"LP-PI-IZ\" in methcodes:\n ZI = 0\n elif \"LP-PI-TRM-ZI\" in methcodes or \"LP-PI-M-ZI\" in methcodes or \"LP-PI-ZI\" in methcodes:\n ZI = 1\n elif \"LP-PI-BT-IZZI\" in methcodes:\n ZI == \"\"\n i_intex, z_intex = 0, 0\n foundit = False\n for i in range(len(datablock)):\n if THERMAL:\n if ('treatment_temp' in list(datablock[i].keys()) and float(temp) == float(datablock[i]['treatment_temp'])):\n foundit = True\n if MICROWAVE:\n if ('treat_mw_step' in list(datablock[i].keys())):\n ThisStep=float(datablock[i]['treat_mw_step'])\n if ThisStep == float(temp):\n foundit = True\n\n elif ('measurement_description' in list(datablock[i].keys())):\n MW_step = datablock[i][\"measurement_description\"].strip(\n '\\n').split(\":\")\n for STEP in MW_step:\n if \"Number\" in STEP:\n ThisStep = float(STEP.split(\"-\")[-1])\n if ThisStep == float(temp):\n foundit = True\n if foundit:\n if \"LT-T-Z\" in datablock[i]['magic_method_codes'].split(\":\") or \"LT-M-Z\" in datablock[i]['magic_method_codes'].split(\":\"):\n z_intex = i\n if \"LT-T-I\" in datablock[i]['magic_method_codes'].split(\":\") or \"LT-M-I\" in datablock[i]['magic_method_codes'].split(\":\"):\n i_intex = i\n foundit = False\n\n if z_intex < i_intex:\n ZI = 1\n else:\n ZI = 0\n dec = float(zrec[\"measurement_dec\"])\n inc = float(zrec[\"measurement_inc\"])\n str = float(zrec[momkey])\n first_Z.append([temp, dec, inc, str, ZI])\n # sort out first_I records\n idec = float(irec[\"measurement_dec\"])\n iinc = float(irec[\"measurement_inc\"])\n istr = float(irec[momkey])\n X = pmag.dir2cart([idec, iinc, istr])\n BL = pmag.dir2cart([dec, inc, str])\n I = []\n for c in range(3):\n I.append((X[c] - BL[c]))\n if I[2] != 0:\n iDir = pmag.cart2dir(I)\n if Zdiff == 0:\n first_I.append([temp, iDir[0], iDir[1], iDir[2], ZI])\n else:\n first_I.append([temp, 0., 0., I[2], ZI])\n# gamma=angle([iDir[0],iDir[1]],[phi,theta])\n else:\n first_I.append([temp, 0., 0., 0., ZI])\n# gamma=0.0\n# put in Gamma check (infield trm versus lab field)\n# if 180.-gamma<gamma:\n# gamma=180.-gamma\n# GammaChecks.append([temp-273.,gamma])\n\n #---------------------\n # find Thellier Thellier protocol\n #---------------------\n if 'LP-PI-II'in methcodes or 'LP-PI-T-II' in methcodes or 'LP-PI-M-II' in methcodes:\n # look through infield steps and find matching Z step\n for i in range(1, len(Treat_I)):\n if Treat_I[i] == Treat_I[i - 1]:\n # ignore, if there are more than\n temp = Treat_I[i]\n irec1 = datablock[ISteps[i - 1]]\n dec1 = float(irec1[\"measurement_dec\"])\n inc1 = float(irec1[\"measurement_inc\"])\n moment1 = float(irec1[\"measurement_magn_moment\"])\n if len(first_I) < 2:\n dec_initial = dec1\n inc_initial = inc1\n cart1 = np.array(pmag.dir2cart([dec1, inc1, moment1]))\n irec2 = datablock[ISteps[i]]\n dec2 = float(irec2[\"measurement_dec\"])\n inc2 = float(irec2[\"measurement_inc\"])\n moment2 = float(irec2[\"measurement_magn_moment\"])\n cart2 = np.array(pmag.dir2cart([dec2, inc2, moment2]))\n\n # check if its in the same treatment\n if Treat_I[i] == Treat_I[i - 2] and dec2 != dec_initial and inc2 != inc_initial:\n continue\n if dec1 != dec2 and inc1 != inc2:\n zerofield = (cart2 + cart1) / 2\n infield = (cart2 - cart1) / 2\n\n DIR_zerofield = pmag.cart2dir(zerofield)\n DIR_infield = pmag.cart2dir(infield)\n\n first_Z.append(\n [temp, DIR_zerofield[0], DIR_zerofield[1], DIR_zerofield[2], 0])\n first_I.append(\n [temp, DIR_infield[0], DIR_infield[1], DIR_infield[2], 0])\n\n #---------------------\n # find pTRM checks\n #---------------------\n\n for i in range(len(Treat_PI)): # look through infield steps and find matching Z step\n\n temp = Treat_PI[i]\n k = PISteps[i]\n rec = datablock[k]\n dec = float(rec[\"measurement_dec\"])\n inc = float(rec[\"measurement_inc\"])\n moment = float(rec[\"measurement_magn_moment\"])\n phi = float(rec[\"treatment_dc_field_phi\"])\n theta = float(rec[\"treatment_dc_field_theta\"])\n M = np.array(pmag.dir2cart([dec, inc, moment]))\n\n foundit = False\n if 'LP-PI-II' not in methcodes:\n # Important: suport several pTRM checks in a row, but\n # does not support pTRM checks after infield step\n for j in range(k, 1, -1):\n if \"LT-M-I\" in datablock[j]['magic_method_codes'] or \"LT-T-I\" in datablock[j]['magic_method_codes']:\n after_zerofield = 0.\n foundit = True\n prev_rec = datablock[j]\n zerofield_index = j\n break\n if float(datablock[j]['treatment_dc_field']) == 0:\n after_zerofield = 1.\n foundit = True\n prev_rec = datablock[j]\n zerofield_index = j\n break\n else: # Thellier-Thellier protocol\n foundit = True\n prev_rec = datablock[k - 1]\n zerofield_index = k - 1\n if foundit:\n prev_dec = float(prev_rec[\"measurement_dec\"])\n prev_inc = float(prev_rec[\"measurement_inc\"])\n prev_moment = float(prev_rec[\"measurement_magn_moment\"])\n prev_phi = float(prev_rec[\"treatment_dc_field_phi\"])\n prev_theta = float(prev_rec[\"treatment_dc_field_theta\"])\n prev_M = np.array(pmag.dir2cart(\n [prev_dec, prev_inc, prev_moment]))\n\n if 'LP-PI-II' not in methcodes:\n diff_cart = M - prev_M\n diff_dir = pmag.cart2dir(diff_cart)\n if after_zerofield == 0:\n ptrm_check.append(\n [temp, diff_dir[0], diff_dir[1], diff_dir[2], zerofield_index, after_zerofield])\n else:\n ptrm_check.append(\n [temp, diff_dir[0], diff_dir[1], diff_dir[2], zerofield_index, after_zerofield])\n else:\n # health check for T-T protocol:\n if theta != prev_theta:\n diff = (M - prev_M) / 2\n diff_dir = pmag.cart2dir(diff)\n ptrm_check.append(\n [temp, diff_dir[0], diff_dir[1], diff_dir[2], zerofield_index, \"\"])\n else:\n print(\n \"-W- WARNING: specimen. pTRM check not in place in Thellier Thellier protocol. step please check\")\n\n #---------------------\n # find Tail checks\n #---------------------\n\n for temp in Treat_M:\n # print temp\n step = MSteps[Treat_M.index(temp)]\n rec = datablock[step]\n dec = float(rec[\"measurement_dec\"])\n inc = float(rec[\"measurement_inc\"])\n moment = float(rec[\"measurement_magn_moment\"])\n foundit = False\n for i in range(1, len(datablock)):\n if 'LT-T-Z' in datablock[i]['magic_method_codes'] or 'LT-M-Z' in datablock[i]['magic_method_codes']:\n if (THERMAL and \"treatment_temp\" in list(datablock[i].keys()) and float(datablock[i][\"treatment_temp\"]) == float(temp))\\\n or (MICROWAVE and \"measurement_description\" in list(datablock[i].keys()) and \"Step Number-%.0f\" % float(temp) in datablock[i][\"measurement_description\"]):\n prev_rec = datablock[i]\n prev_dec = float(prev_rec[\"measurement_dec\"])\n prev_inc = float(prev_rec[\"measurement_inc\"])\n prev_moment = float(\n prev_rec[\"measurement_magn_moment\"])\n foundit = True\n break\n\n if foundit:\n ptrm_tail.append([temp, 0, 0, moment - prev_moment])\n\n #\n # final check\n #\n if len(first_Z) != len(first_I):\n print(len(first_Z), len(first_I))\n print(\" Something wrong with this specimen! Better fix it or delete it \")\n input(\" press return to acknowledge message\")\n\n #---------------------\n # find Additivity (patch by rshaar)\n #---------------------\n\n additivity_check = []\n for i in range(len(Treat_AC)):\n step_0 = ACSteps[i]\n temp = Treat_AC[i]\n dec0 = float(datablock[step_0][\"measurement_dec\"])\n inc0 = float(datablock[step_0][\"measurement_inc\"])\n moment0 = float(datablock[step_0]['measurement_magn_moment'])\n V0 = pmag.dir2cart([dec0, inc0, moment0])\n # find the infield step that comes before the additivity check\n foundit = False\n for j in range(step_0, 1, -1):\n if \"LT-T-I\" in datablock[j]['magic_method_codes']:\n foundit = True\n break\n if foundit:\n dec1 = float(datablock[j][\"measurement_dec\"])\n inc1 = float(datablock[j][\"measurement_inc\"])\n moment1 = float(datablock[j]['measurement_magn_moment'])\n V1 = pmag.dir2cart([dec1, inc1, moment1])\n # print \"additivity check: \",s\n # print j\n # print \"ACC=V1-V0:\"\n # print \"V1=\",[dec1,inc1,moment1],pmag.dir2cart([dec1,inc1,moment1])/float(datablock[0][\"measurement_magn_moment\"])\n # print \"V1=\",pmag.dir2cart([dec1,inc1,moment1])/float(datablock[0][\"measurement_magn_moment\"])\n # print \"V0=\",[dec0,inc0,moment0],pmag.dir2cart([dec0,inc0,moment0])/float(datablock[0][\"measurement_magn_moment\"])\n # print \"NRM=\",float(datablock[0][\"measurement_magn_moment\"])\n # print \"-------\"\n\n I = []\n for c in range(3):\n I.append(V1[c] - V0[c])\n dir1 = pmag.cart2dir(I)\n additivity_check.append([temp, dir1[0], dir1[1], dir1[2]])\n # print\n # \"I\",np.array(I)/float(datablock[0][\"measurement_magn_moment\"]),dir1,\"(dir1\n # unnormalized)\"\n X = np.array(I) / \\\n float(datablock[0][\"measurement_magn_moment\"])\n # print \"I\",np.sqrt(sum(X**2))\n araiblock = (first_Z, first_I, ptrm_check, ptrm_tail,\n zptrm_check, GammaChecks, additivity_check)\n\n return araiblock, field",
"title": ""
},
{
"docid": "2f7ae09916746c7defe316fddcc91bb2",
"score": "0.47860554",
"text": "def test_list_flavors_detailed_filter_by_min_ram(self):\n response = self.flavors_client.list_flavors_with_detail()\n flavors = response.entity\n\n # Sort the flavors by RAM in ascending order\n flavors.sort(key=lambda k: int(k.ram))\n\n # Remove any flavors from the list that are smaller than the\n # flavor with the second smallest RAM size\n filter_criteria = lambda x: int(x.ram) >= int(flavors[1].ram)\n expected_flavors = filter(filter_criteria, flavors)\n\n response = self.flavors_client.list_flavors_with_detail(\n min_ram=flavors[1].ram)\n actual_flavors = response.entity\n actual_flavors.sort(key=lambda k: k.id)\n expected_flavors.sort(key=lambda k: k.id)\n self.assertEqual(actual_flavors, expected_flavors)",
"title": ""
},
{
"docid": "8004069744bff01198dfec821b4c7583",
"score": "0.47788075",
"text": "def packBest(self, autorotate=False):\n\n sheets, extraBlocks = [], []\n score = 0\n\n best = {\n 'score': 0,\n 'area': 10000000000000000000,\n 'count': 10000000000000,\n 'eff': 0\n }\n \n # Sort Functions\n def sortHeight(block):\n return (block.w, block.h, block.image.checksum)\n\n def sortWidth(block):\n return (block.h, block.w, block.image.checksum)\n\n def sortArea(block):\n return (block.w * block.h, block.w, block.h, block.image.checksum)\n\n sorts = [sortHeight, sortWidth, sortArea]\n rotationDiff = [(0, 0), (1.4, 0), (0, 1.4), (1.4, 1.4)] # rotate by 90 degrees if either b / a > value\n\n # Determine minimum size for spritesheet generation\n # by averaging the widths and heights of all images\n # while taking the ones in the sorted middile higher into account\n # then the ones at the outer edges of the spectirum\n\n\n l = len(self.files)\n mw = [(l - abs(i - l / 2)) / l * v for i, v in enumerate(sorted([i.width for i in self.files]))]\n mh = [(l - abs(i - l / 2)) / l * v for i, v in enumerate(sorted([i.height for i in self.files]))]\n\n minWidth = max(128, math.pow(2, math.ceil(math.log(sum(mw) / l, 2))))\n minHeight = max(128, math.pow(2, math.ceil(math.log(sum(mh) / l, 2))))\n\n #baseArea = sum([(l - abs(i - l / 2)) / l * v for i, v in enumerate(sorted([i.width * i.height for i in self.files]))])\n\n\n # try to skip senseless generation of way to small sprites\n baseArea = sum([minWidth * minHeight for i in self.files])\n while baseArea / (minWidth * minHeight) >= 20: # bascially an estimate of the number of sheets needed\n minWidth *= 2\n minHeight *= 2\n\n Console.debug('- Minimal size is %dx%dpx' % (minWidth, minHeight))\n\n sizes = list(itertools.product([w for w in [128, 256, 512, 1024, 2048] if w >= minWidth],\n [h for h in [128, 256, 512, 1024, 2048] if h >= minHeight]))\n\n if autorotate:\n methods = list(itertools.product(sorts, sizes, rotationDiff))\n\n else:\n methods = list(itertools.product(sorts, sizes, [(0, 0)]))\n\n Console.debug('Packing sprite sheet variants...')\n Console.indent()\n\n scores = []\n for sort, size, rotation in methods:\n\n # pack with current settings\n sh, ex, _ = self.pack(size[0], size[1], sort, silent=True, rotate=rotation)\n\n if len(sh):\n score = PackerScore(sh, ex)\n\n # Determine score, highest wins\n scores.append(score)\n\n else:\n Console.debug('No sprite sheets generated, no image fit into the sheet')\n\n Console.outdent()\n scores.sort()\n\n Console.debug('Generated the following sheets:')\n for i in scores:\n Console.debug('- ' + str(i))\n\n sheets, external = scores[0].data()\n \n if external:\n for block in external:\n Console.info('Ignored file %s (%dx%dpx)' % (block.image.relPath, block.w, block.h))\n \n return sheets, len(scores)",
"title": ""
},
{
"docid": "e0f2ca7b8b0e6c8e5e7883c9dc696af5",
"score": "0.47784504",
"text": "def get_sectors_with_max_and_min_stocks():\n mydict_sector = dict()\n\n for item in data:\n if item['sector'] not in 'n/a':\n if item['sector'] in mydict_sector.keys():\n mydict_sector[item['sector']] += 1\n else:\n mydict_sector[item['sector']] = 1\n\n foutput = sorted(mydict_sector.items(), key = lambda x:x[1], reverse=True)\n return (foutput[0][0], foutput[-1][0])",
"title": ""
},
{
"docid": "ccc0dfd8377abfbbe22127bc7f029e69",
"score": "0.47752583",
"text": "def solid_surface_density_RC2014_given_physical_catalog(sssp_per_sys, max_core_mass=10.):\n mult_all = sssp_per_sys['Mtot_all']\n a_all_2p = []\n mult_all_2p = []\n sigma_all_2p = []\n for i in np.arange(len(mult_all))[mult_all > 1]: # only consider multi-planet systems\n a_sys = sssp_per_sys['a_all'][i]\n core_mass_sys = np.copy(sssp_per_sys['mass_all'][i][a_sys > 0])\n core_mass_sys[core_mass_sys > max_core_mass] = max_core_mass\n a_sys = a_sys[a_sys > 0]\n a_all_2p += list(a_sys)\n mult_all_2p += [len(a_sys)]*len(a_sys)\n sigma_all_2p += list(solid_surface_density_system_RC2014(core_mass_sys, a_sys))\n a_all_2p = np.array(a_all_2p)\n mult_all_2p = np.array(mult_all_2p)\n sigma_all_2p = np.array(sigma_all_2p)\n return sigma_all_2p, a_all_2p, mult_all_2p",
"title": ""
},
{
"docid": "06260ebf05c4f60771fbafc0d4cef024",
"score": "0.47746333",
"text": "def parse_system(system, comps):\n\n # Load the structure from the data frame given\n # print(system)\n struct = structures.loc[structures['Name'].isin([system['Structure']])].reset_index()\n internal_slots = struct['Internal Slots'].values\n external_slots = struct['External Slots'].values\n internal_vol = struct.X[0] * struct.Y[0] * struct.Z[0]\n total_vol = 0\n\n # Extract the fuzzy values for the system and place them into a numpy array of features for FAM algorithm\n cust_reqs = create_value_array(system['Size'], system['Size Imp'], system['Mass Imp'], system['Down Sp'],\n system['Up Sp'], system['Alt Req'], system['Att Ctrl'], system['Remote'],\n system['RS Wave'], system['RS Accuracy'])\n\n comp_totals = system.to_dict()\n comp_list = list()\n ext_list = list()\n for heading in comp_totals:\n if \"Comp\" in heading:\n comp_list.append(heading)\n elif \"Ext\" in heading:\n ext_list.append(heading)\n\n parts_sum_matrix = np.zeros((0, 0))\n parts_max_matrix = np.zeros((0, 0))\n metric_matrix = np.zeros((0, 0))\n metric_min_matrix = np.zeros((0, 0))\n metric_max_matrix = np.zeros((0, 0))\n\n # This is horrible, work on making it better once it's proved to get the right outputs\n\n for part in comp_list:\n idx = comps['Name'] == system[part]\n metrics_sums, metrics_mins, metrics_max, sum_vals, max_vals = parse_component(comps.loc[idx])\n\n if parts_sum_matrix.shape == (0, 0):\n parts_sum_matrix = sum_vals\n else:\n parts_sum_matrix = np.concatenate((parts_sum_matrix, sum_vals), 1)\n if parts_max_matrix.shape == (0, 0):\n parts_max_matrix = max_vals\n else:\n parts_max_matrix = np.concatenate((parts_max_matrix, max_vals), 1)\n if metric_matrix.shape == (0, 0):\n metric_matrix = metrics_sums\n else:\n metric_matrix = np.concatenate((metric_matrix, metrics_sums), 1)\n if metric_min_matrix.shape == (0, 0):\n metric_min_matrix = metrics_mins\n else:\n metric_min_matrix = np.concatenate((metric_min_matrix, metrics_mins), 1)\n if metric_max_matrix.shape == (0, 0):\n metric_max_matrix = metrics_max\n else:\n metric_max_matrix = np.concatenate((metric_max_matrix, metrics_max), 1)\n\n for part in ext_list:\n idx = comps['Name'] == system[part]\n metrics_sums, metrics_mins, metrics_max, sum_vals, max_vals = parse_component(comps.loc[idx])\n # print(part)\n cube_size = 1\n if part == \"Ext Sides\":\n metrics_sums *= (4 * cube_size) # * metrics_sums\n\n if parts_sum_matrix.shape == (0, 0):\n parts_sum_matrix = sum_vals\n else:\n parts_sum_matrix = np.concatenate((parts_sum_matrix, sum_vals), 1)\n if parts_max_matrix.shape == (0, 0):\n parts_max_matrix = max_vals\n else:\n parts_max_matrix = np.concatenate((parts_max_matrix, max_vals), 1)\n if metric_matrix.shape == (0, 0):\n metric_matrix = metrics_sums\n else:\n metric_matrix = np.concatenate((metric_matrix, metrics_sums), 1)\n if metric_min_matrix.shape == (0, 0):\n metric_min_matrix = metrics_mins\n else:\n metric_min_matrix = np.concatenate((metric_min_matrix, metrics_mins), 1)\n if metric_max_matrix.shape == (0, 0):\n metric_max_matrix = metrics_max\n else:\n metric_max_matrix = np.concatenate((metric_max_matrix, metrics_max), 1)\n\n\n parts_sum_matrix = parts_sum_matrix.astype(np.float)\n parts_max_matrix = parts_max_matrix.astype(np.float)\n min_parts = parts_max_matrix\n min_parts[min_parts == 0] = None\n # print(min_parts)\n min_parts = min_parts[~np.isnan(min_parts)]\n # print(min_parts)\n # print(parts_max_matrix)\n # print(metric_matrix)\n # print(metric_matrix.sum(axis=1))\n metric_min_matrix[metric_min_matrix == 0] = None\n metric_min_matrix = metric_min_matrix[~np.isnan(metric_min_matrix)]\n\n # print(metric_min_matrix.min())\n # print(metric_max_matrix.max(axis=1))\n # print(parts_sum_matrix.sum(axis=1))\n\n # print(parts_max_matrix.shape)\n # Todo calculate all components in the system and provide system outputs that can be converted into metrics\n metrics = np.concatenate((metric_matrix.sum(axis=1), np.array([metric_min_matrix.min()]),\n metric_max_matrix.max(axis=1)), 0)\n\n # cpu_met = calculate_cpu_metric(metrics[4], metrics[5], metrics[6])\n att_met = calculate_attitude_metric(metrics[8], metrics[0], metric_min_matrix[0], metrics[10])\n down_met = calculate_br_down_metric(metrics[2])\n up_met = calculate_br_up_metric(metrics[3])\n wl_met = calculate_wavelength_metric(metrics[16], metrics[17])\n # print(metrics[4], metrics[5], metrics[6])\n # print(metrics[8], metrics[0], metric_min_matrix[0], metrics[10])\n\n return np.array([[att_met], [down_met], [up_met], [wl_met]]), cust_reqs",
"title": ""
},
{
"docid": "0fc73565326af20d23bffbcb718dc77f",
"score": "0.4757012",
"text": "def sort(self):\n # Base Case\n # If the robot has reached the end of the list and his light is off (no swaps have occurred),\n if self.can_move_right() == False and self.light_is_on() == False:\n return\n\n # Grab the first card\n self.swap_item()\n\n # While the robot is still able to move right,\n while self.can_move_right():\n\n # Move right\n self.move_right()\n\n # Compare the item in his hand to that in front of him\n # If the item in front of him is greater than what he is holding (-1), swap items\n if self.compare_item() == -1:\n # Swap the item\n self.swap_item()\n # Turn his light on to indicate that a swap has occured\n self.set_light_on()\n \n # Once the robot can no longer move right, he is at the end of the list and holding the largest value\n # Swap items\n self.swap_item()\n\n # Now the robot needs to traverse back to index 0, grabbing the smallest value as he goes\n # Follow the same logic as when he moved right with the largest value\n\n # If he hits a empty slot in the list, everything in front of it has been sorted\n # He doesn't need to sort anymore, he is holding the smallest value left to be sorted. \n # Put it in the blank spot and turn to move back in the other direction\n\n while self.compare_item() is not None:\n\n # Move left\n self.move_left()\n\n # Compare the item in his hand to that in front of him\n # If the item in front of him is less than what he is holding (1), swap items\n if self.compare_item() == 1:\n # Swap the item\n self.swap_item()\n # Turn his light on to indicate that a swap has occured\n self.set_light_on()\n \n # Once self.compare_item() is None, that means he is in front of a blank space\n # - everything to the left of the blank space has already been sorted\n # Deposit what he is holding\n self.swap_item()\n\n # Reset the light to the off position\n self.set_light_off()\n\n # Move one spot over to the right\n self.move_right()\n\n # Re-run the process all over again\n self.sort()",
"title": ""
},
{
"docid": "7edb8c486042b8b67353a87af9f526b8",
"score": "0.47552618",
"text": "def getShelves(detections, lines):\r\n\r\n for idx,det in enumerate(detections):\r\n dist_to_shelf = np.zeros(len(lines))\r\n b_points = det['box_points']\r\n b_height = b_points[3] - b_points[1] \r\n for l in range(len(lines)): \r\n dist_to_shelf[l] = lines[l] - b_points[1] ### distance of upper-left corner from lines\r\n if dist_to_shelf[l] < 0:\r\n dist_to_shelf[l] = 100000 ## sth huge \r\n #print(dist_to_shelf) \r\n det['shelf'] = np.argmin(dist_to_shelf)\r\n return detections",
"title": ""
},
{
"docid": "fcfbd263b0d088b5b21be83e70370bc4",
"score": "0.47469926",
"text": "def solid_surface_density_nHill_given_physical_catalog(sssp_per_sys, sssp, max_core_mass=10., n=10.):\n a_all = sssp_per_sys['a_all'][sssp_per_sys['a_all'] > 0]\n core_mass_all = np.copy(sssp_per_sys['mass_all'])\n core_mass_all[core_mass_all > max_core_mass] = max_core_mass\n sigma_all = solid_surface_density_nHill(core_mass_all, sssp_per_sys['a_all'], Mstar=sssp['Mstar_all'][:,None], n=n)[sssp_per_sys['a_all'] > 0]\n return sigma_all, a_all",
"title": ""
},
{
"docid": "e05106d516647af39e93354cf05b2fa8",
"score": "0.47334096",
"text": "def sort_table(table, sats_table):",
"title": ""
},
{
"docid": "736875d2bba745cbd494c7dc85ecf2b6",
"score": "0.4720317",
"text": "def findIslands(self):\n\n # First lets find the shores.\n shoreList = self.findShores()\n\n # Initialize Blank Values.\n N, S, E, W = (None for i in range(4))\n\n # Next, we find all the furthest extremities among all shore lists.\n # In theory, the only extremities that can occur for shorelines that\n # Don't belong to the main pond body are along the map edge.\n for index, shore in enumerate(shoreList):\n extremityHash = shore.findExtremities()\n if index == 0:\n N, S, E, W = ([shore] for i in range(4))\n continue\n if extremityHash['N'][0].x < N[0].findExtremities()['N'][0].x:\n N = [shore]\n elif extremityHash['N'][0].x == N[0].findExtremities()['N'][0].x:\n N.append(shore)\n if extremityHash['S'][0].x > S[0].findExtremities()['S'][0].x:\n S = [shore]\n elif extremityHash['S'][0].x == S[0].findExtremities()['S'][0].x:\n S.append(shore)\n if extremityHash['E'][0].y > E[0].findExtremities()['E'][0].y:\n E = [shore]\n elif extremityHash['E'][0].y == E[0].findExtremities()['E'][0].y:\n E.append(shore)\n if extremityHash['W'][0].y < W[0].findExtremities()['W'][0].y:\n W = [shore]\n elif extremityHash['W'][0].y == W[0].findExtremities()['W'][0].y:\n W.append(shore)\n\n # Now, lets flatten the list of cardinal extremities\n flatList = [val for sublist in [N, S, E, W] for val in sublist]\n counter = Counter(flatList)\n\n # In theory, the main pond shore should have the most extremities\n probablyPond = counter.most_common(1)\n\n # Wow, what a piece of crap. I feel ashamed of the next 6 lines.\n if probablyPond[0][0] < 4:\n raise Exception(\"Largest Pond does not have 4 max points.\"\n \" Something is horribly Wrong.\")\n if len(probablyPond) != 1:\n raise Exception(\"Equal number of extremities in pond?\"\n \" How can that be?\")\n\n probablyPond = probablyPond[0][0]\n\n # Find any map edges and add them to the Plain Blob Object mapEdge.\n self.mapEdge = self.findMapEdge()\n\n # Well, this probably isn't an island, so drop it from the list.\n shoreList.remove(probablyPond)\n\n # Find any map edges for the island, and create Island Objects.\n islands = list()\n for island in shoreList:\n islands.append(Island(island.points,\n self.analyzeData,\n self.elevation))\n return islands",
"title": ""
},
{
"docid": "a52d44383c8b585cab6a8874d35f0430",
"score": "0.47156975",
"text": "def test_list_flavors_filter_by_min_disk(self):\n response = self.flavors_client.list_flavors_with_detail()\n flavors = response.entity\n\n # Sort the flavors by disk size in ascending order\n flavors.sort(key=lambda k: int(k.disk))\n\n # Remove any flavors from the list that are smaller than the\n # flavor with the second smallest disk size\n filter_criteria = lambda x: int(x.disk) >= int(flavors[1].disk)\n expected_flavors = filter(filter_criteria, flavors)\n response = self.flavors_client.list_flavors(min_disk=flavors[1].disk)\n actual_flavors = response.entity\n\n actual_flavor_ids = set([flavor.id for flavor in actual_flavors])\n expected_flavor_ids = set([flavor.id for flavor in expected_flavors])\n self.assertEqual(actual_flavor_ids, expected_flavor_ids)",
"title": ""
},
{
"docid": "1541c841ae6b0596879a09f09992dc78",
"score": "0.4709365",
"text": "def get_ordered_filesystems(vm):\n fss = list(vm.filesystems)\n for disk in vm.disks:\n fss += [part.fs for part in disk.partitions]\n fss.sort(lambda x,y: len(x.mntpnt or '')-len(y.mntpnt or ''))\n return fss",
"title": ""
},
{
"docid": "c2dee815c336bcb2bc9accd99e445d69",
"score": "0.46992055",
"text": "def sort_poscar(by, key=None, reverse=False, poscar_filename=\"POSCAR\", cal_loc=\".\"):\n available_by_list = [\"atomic_species\", \"cart_coords\", \"frac_coords\", \"selective_dynamics_mode\", \"lattice_matrix\"]\n assert by in available_by_list, 'Input argument \"by\" of fuction sort_poscar must be \"atomic_species\", \"cart_coords\", \"frac_coords\", \"selective_dynamics_mode\" or \"lattice_matrix\"'\n poscar_dict = read_poscar(poscar_filename=poscar_filename, cal_loc=cal_loc)\n \n sorted_index_list = [ind_value_pair[0] for ind_value_pair in sorted(enumerate(poscar_dict[by]), key=key, reverse=reverse)]\n if by in [\"atomic_species\", \"cart_coords\", \"frac_coords\", \"selective_dynamics_mode\"]:\n for quantity in [\"atomic_species\", \"cart_coords\", \"frac_coords\", \"selective_dynamics_mode\"]:\n poscar_dict[quantity] = [poscar_dict[quantity][ind] for ind in sorted_index_list]\n elif by == \"lattice_matrix\":\n new_lattice_matrix = [poscar_dict[\"lattice_matrix\"][ind] for ind in sorted_index_list]\n poscar_dict[\"lattice_matrix\"] = new_lattice_matrix\n poscar_dict.update(get_lattice_properties(new_lattice_matrix))\n for quantity in [\"cart_coords\", \"frac_coords\", \"selective_dynamics_mode\"]:\n for atom_ind, atom_quantity in enumerate(poscar_dict[quantity]):\n new_atom_quantity = [atom_quantity[ind] for ind in sorted_index_list]\n poscar_dict[quantity][atom_ind] = new_atom_quantity\n else:\n raise Exception(\"You should not arrive here!\")\n \n return poscar_dict",
"title": ""
},
{
"docid": "2814c16d9c7812f9cd14d2a17d1fedf0",
"score": "0.46974882",
"text": "def solution_firstAvailableElf(toy_file, soln_file, myelves, battle_group_size):\n\n hrs = Hours()\n ref_time = datetime.datetime(2014, 1, 1, 0, 0)\n rating_donot_change_time = 2848\n row_count = 0\n # toys = bintrees.RBTree()\n toys_simple = RBTreeBag()\n elves_for_long_works = list()\n elves_for_long_works_left = len(myelves) - battle_group_size\n\n\n\n\n\n\n with open(toy_file, 'r') as f:\n toysfile = csv.reader(f)\n next(toysfile) # header row\n\n with open(soln_file, 'w') as w:\n wcsv = csv.writer(w)\n wcsv.writerow(['ToyId', 'ElfId', 'StartTime', 'Duration'])\n step = 0\n current_minute = 0\n current_day_minute = hrs.day_start\n day_begin_minute = hrs.day_start\n is_day = False\n\n previouse_toy = None\n\n\n\n for row in toysfile:\n current_toy = Toy(row[0], row[1], row[2])\n\n step = step + 1\n elf_available_time, current_elf = heapq.heappop(myelves)\n print(\"heapq.heappop(myelves)<<<<<<<<<<<\")\n if (elf_available_time>=current_toy.arrival_minute):\n print(\"heapq.heappush(myelves - 1\")\n heapq.heappush(myelves, (current_elf.next_available_time, current_elf))\n else:\n while (elf_available_time<current_toy.arrival_minute):\n day_begin_minute = hrs.get_day_begin_minute(elf_available_time, day_begin_minute)\n elves_for_long_works_left, is_no_avalible_toys = process_elf(current_elf, toys_simple, elf_available_time, hrs, wcsv, ref_time, elves_for_long_works_left, elves_for_long_works, day_begin_minute, rating_donot_change_time, False)\n if (is_no_avalible_toys):\n break\n elf_available_time, current_elf = heapq.heappop(myelves)\n print(\"heapq.heappop(myelves)<<<<<<<<<<<\")\n if elf_available_time>=current_toy.arrival_minute:\n print(\"heapq.heappush(myelves - 2\")\n heapq.heappush(myelves, (current_elf.next_available_time, current_elf))\n if (len(elves_for_long_works)>0):\n elf_available_time, current_elf = heapq.heappop(elves_for_long_works)\n print(\"heapq.heappop(elves_for_long_works)<<<<<<<<<<<\")\n while (elf_available_time<current_toy.arrival_minute):\n if (toys_simple.length()>0):\n try:\n key, toy = toys_simple.max_item()\n if (toy.duration>rating_donot_change_time + 1):\n toys_simple.insert(toy.duration, toy)\n try:\n key, toy = toys_simple.ceiling_item(rating_donot_change_time + 1)\n except KeyError:\n\n key, toy = toys_simple.max_item()\n except ValueError:\n break\n assign_toy_to_elf(elf_available_time, current_elf, toy, hrs, wcsv, ref_time)\n print(\"heapq.heappush(elves_for_long_works - 2\")\n heapq.heappush(elves_for_long_works, (current_elf.next_available_time, current_elf))\n else:\n print(\"heapq.heappush(elves_for_long_works - 3\")\n heapq.heappush(elves_for_long_works, (current_elf.next_available_time, current_elf))\n break\n elf_available_time, current_elf = heapq.heappop(elves_for_long_works)\n print(\"heapq.heappop(elves_for_long_works)<<<<<<<<<<<\")\n\n\n\n\n\n\n\n # toys.insert(current_toy.duration * hrs.minutes_in_year_up + current_toy.arrival_minute, current_toy)\n toys_simple.insert(current_toy.duration, current_toy)\n\n\n\n\n if step>0 and step % 1000== 0:\n print(\"step=\",step)\n\n\n\n\n # if step>1000000:\n # break\n print(\"toys_simple.length()=\",toys_simple.length(),\" len(myelves)=\",len(myelves),\" len(elves_for_long_works)=\",len(elves_for_long_works))\n elf_available_time, current_elf, is_long = get_next_elf(elves_for_long_works, myelves)\n while (toys_simple.length()>0):\n if (is_long):\n try:\n key, toy = toys_simple.max_item()\n if (toy.duration>rating_donot_change_time + 1):\n toys_simple.insert(toy.duration, toy)\n try:\n key, toy = toys_simple.ceiling_item(rating_donot_change_time + 1)\n except KeyError:\n key, toy = toys_simple.max_item()\n except ValueError:\n break\n assign_toy_to_elf(elf_available_time, current_elf, toy, hrs, wcsv, ref_time)\n print(\"heapq.heappush(elves_for_long_works - 4\")\n heapq.heappush(elves_for_long_works, (current_elf.next_available_time, current_elf))\n else:\n day_begin_minute = hrs.get_day_begin_minute(elf_available_time)\n elves_for_long_works = process_elf(current_elf, toys_simple, elf_available_time, hrs, wcsv, ref_time, elves_for_long_works_left, elves_for_long_works, day_begin_minute, rating_donot_change_time, True)\n\n\n elf_available_time, current_elf, is_long = get_next_elf(elves_for_long_works, myelves)\n\n\n print(\"finish\")",
"title": ""
},
{
"docid": "7c72a2d8a13883fe76594e8b79471fae",
"score": "0.4663275",
"text": "def exceeds_shelf_capacity(shelf, fabric):\n shelf_total = Decimal(shelf.fabrics.all().aggregate(Sum('quantity_th'))['quantity_th__sum'] or 0)\n return True if (shelf_total) + fabric.quantity > max_shelf_qty else False",
"title": ""
},
{
"docid": "c2b5a892eecc67f84d96169a0d2bbec5",
"score": "0.46632183",
"text": "def get_wall_products(self, wall_bp):\n products = []\n for child in wall_bp.children:\n props = props_closet.get_object_props(child)\n if props.is_closet or props.is_fixed_shelf_and_rod_product_bp:\n child.mv.comment = wall_bp.mv.name_object\n products.append(child)\n products.sort(key=lambda obj: obj.location.x, reverse=False)\n return products",
"title": ""
},
{
"docid": "9ed61f5fce979097ea13a2c0e6c5cc95",
"score": "0.4647531",
"text": "def greedy(self, iterations):\n # turn houses into list\n random_houses = list(self.houses.values())\n\n iterations = int(iterations)\n\n prices = []\n count = 0\n misses = -iterations\n\n # Do untill we have <iterations> succesfull configurations\n while count < iterations:\n self.disconnect()\n # While one or more batteries are over their capacity or not every\n # house is linked to a battery\n while self.check_linked() is False or self.check_full() is True:\n misses += 1\n\n # shuffle order of houses\n random.shuffle(random_houses)\n\n # remove connections, if any\n self.disconnect()\n\n # for every house find closest battery to connect to provided\n # that this house wont over-cap the battery\n for house in random_houses:\n for i in range(len(self.batteries.values())):\n output = house.output\n curr = self.batteries[list(house.diffs)[i]].filled()\n cap = self.batteries[list(house.diffs)[i]].capacity\n if output + curr <= cap:\n batt = self.batteries[list(house.diffs)[i]]\n house.link = batt\n batt.linked_houses.append(house)\n break\n\n # calculate price\n for battery in self.batteries.values():\n if not battery.linked_houses:\n del battery\n price = self.calculate_cost()\n prices.append(price)\n\n count += 1\n\n if min(prices) < self.lowest:\n self.lowest = min(prices)\n with open(f\"weighted_clusters_WIJK{self.input}.dat\",\n \"wb\") as f:\n pickle.dump([self.houses, self.batteries], f)\n\n # self.plot_houses()\n return min(prices)",
"title": ""
},
{
"docid": "a1006c0051cc4d5237e9aedacc1e91e2",
"score": "0.4641694",
"text": "def CheckScafOrder(NestedListBoi, StrandInfo):\n NoOverlap = True\n \n \n \n CurrentLen = 0\n if StrandInfo == '+':\n for item in NestedListBoi:\n AddItems = item[0] + item[1] \n if AddItems > CurrentLen:\n CurrentLen = AddItems\n else:\n print(\"WE ARE FUCKEDDDDDD\")\n NoOverlap = False\n\n elif StrandInfo == '-':\n #Flip list for negative\n NestedListBoi = NestedListBoi[::-1]\n for item in NestedListBoi:\n AddItems = item[0] + item[1] \n if AddItems > CurrentLen:\n CurrentLen = AddItems\n else:\n print(\"WE ARE FUCKEDDDDDD\")\n break\n sys.exit(2)\n NoOverlap = False\n return NoOverlap",
"title": ""
},
{
"docid": "376fe239450c064aa5c3901b037a8dc2",
"score": "0.46364266",
"text": "def sort():\n greed_factor = [2, 7, 5]\n cookie_size = [3, 9]\n\n greed_factor.sort()\n cookie_size.sort()\n\n i = len(cookie_size) -1\n j = len(greed_factor) -1\n satisfied = 0\n\n while i>=0 and j>=0:\n if cookie_size[i] >= greed_factor[j]:\n satisfied = satisfied + 1\n i = i-1\n j = j-1\n else:\n j = j-1\n\n print(satisfied)",
"title": ""
},
{
"docid": "8b9bfac1bae4f1f4b73fc0855e49e042",
"score": "0.46333387",
"text": "def _sorted_moves_per_poketype(self):\n with pd.HDFStore(settings.store_filepath, mode='r') as store:\n poketypes = store['poketypes']\n # move_categories = store['move_categories']\n # poketype_chart = store['poketype_chart']\n # pokedex = store['pokedex']\n attackdex = store['attackdex']\n # learnsets = store['learnsets']\n\n # compute and set the effective power\n effective_power = attackdex['power'] * attackdex['accuracy'] / 100 \\\n * attackdex['repeat'] / attackdex['turns_used']\n\n attackdex['effective_power'] = effective_power\n\n sorted_moves = {}\n\n for poketype in poketypes['poketype']:\n subdex = attackdex[attackdex['poketype'] == poketype]\n\n subdex = subdex.sort_values(by=['effective_power'], ascending=False)\n\n sorted_moves[poketype] = subdex\n\n return sorted_moves",
"title": ""
},
{
"docid": "d0adc285039eeb16ecbd4c3a814dfa47",
"score": "0.46328312",
"text": "def greedy_initial(self):\r\n sol = [] # [[0;2;5;0;4;6;0],[],...]\r\n sol_veh_type = [] # corresponding vehicle type for the solution\r\n route_way_time = []\r\n\r\n to_vist = [i+1 for i in range(store_num - 1)] # [1,5,8,...]\r\n itr = 0\r\n\r\n while len(to_vist) > 0 and itr < 500:\r\n itr += 1\r\n\r\n if itr <= small_veh_cnt:\r\n vehicle_type0 = 2\r\n elif itr <= small_veh_cnt + medium_veh_cnt:\r\n vehicle_type0 = 3\r\n else:\r\n vehicle_type0 = 5\r\n\r\n sol_veh_type.append(vehicle_type0)\r\n\r\n used_res = [0, 0, 0, 0] # used volume, and travel time of the vehicle, leave time, travel distance\r\n veh_rout = [0]\r\n\r\n # print '\\nA new vehicle will be used.'\r\n way_time = 0 # travel time of coming to the store + wait time at the store + operation time at this store\r\n while True:\r\n curr_cust = veh_rout[-1]\r\n\r\n next_one, way_time = self.time_nn(way_time, curr_cust, to_vist, used_res, len(veh_rout), vehicle_type0)\r\n next_cust, next_start = next_one[0], next_one[1]\r\n # print('next start', next_cust, next_start)\r\n if next_cust == 0: # next visiting customer is depot\r\n # print 'Get back to the depot, and ready for a new round.'\r\n veh_rout.append(next_cust)\r\n break\r\n\r\n else: # next visiting customer is a store\r\n used_res[0] += (num_demd[next_cust][0] * bskt_vol + num_demd[next_cust][1] * trsf_vol + (num_demd[next_cust][2] + \\\r\n num_demd[next_cust][3]) * milk_vol + num_demd[next_cust][4] * paper_bskt)\r\n used_res[2] = (next_start + oprt_t)\r\n used_res[3] += dist_mat[curr_cust, next_cust]\r\n\r\n\r\n veh_rout.append(next_cust)\r\n # print 'Vehicle used resource: ', used_res\r\n to_vist.remove(next_cust)\r\n\r\n sol.append(veh_rout)\r\n route_way_time.append(way_time)\r\n\r\n # print 'Last point 0 earliest leave time: ', int(used_res[-1]) / 60, ':', int(used_res[-1]) % 60\r\n # print 'Route %s is: ' % itr, veh_rout\r\n print('*'*10, 'Iteration:', itr, '*'*10)\r\n\r\n\r\n if len(to_vist) > 0:\r\n print('number of stores remained: ', len(to_vist))\r\n\r\n return sol, sol_veh_type, route_way_time",
"title": ""
},
{
"docid": "01b57713ff8a0807441eaa6278a55675",
"score": "0.46279028",
"text": "def sort(self, quant=None):\n if quant is None: # sort bei weight\n self.__sortlist = [key for key, values in sorted(self.__quantile.items(), key=lambda items: sum((10^quantille * count for quantille, count in enumerate(items[1].values()))))]\n elif isinstance(quant, int):\n self.__sortlist = [key for key, values in sorted(self.__quantile.items(), key=lambda items: items[1][quant])]",
"title": ""
},
{
"docid": "282c77ebea1b6d650d8f2a2eb2199bf4",
"score": "0.4627076",
"text": "def build_feudal_hierarchy(self):\n if not self.enough_lords():\n return\n titles = (Title.count, Title.baron, Title.baronet, Title.chevalier)\n vassals_count = 0\n for title in titles:\n lords = self.get_lords_of_title(title)\n for lord in lords:\n for vassal_title, count in LORDS_VASSALS[lord.title].items():\n available = self.get_potential_vassals_for_lord(lord,\n vassal_title)\n for i in range(count):\n if len(lord.vassals_of_title(vassal_title)) == count:\n continue\n try:\n new_vassal = choice(list(available))\n except IndexError:\n print(title, vassal_title)\n else:\n self.set_feudal_bond(lord, new_vassal)\n vassals_count += 1\n print(f'Added {len(lord.vassals)} vassals')\n print(f'Vassals count: {vassals_count}')",
"title": ""
},
{
"docid": "7f90f9ae9748ae606d8dde2022d15fcc",
"score": "0.4598553",
"text": "def updateFCFS_queue(self, junc):\n for tl_combination in junc.tl_combinations:\n for lane in tl_combination.corresponding_lanes:\n for vehicle in traci.lane.getLastStepVehicleIDs(lane.ID):\n junc.FCFS_queue[vehicle] = tl_combination.ryg_state",
"title": ""
},
{
"docid": "bce783cb0d53fc1b85f886d936c4f3a4",
"score": "0.45952976",
"text": "def boxing_order_reduction(zonotope,desired_order=1):\n assert(type(zonotope) == pp.zonotope),\"TypeError: The first argument need to be from \\\"zonotope\\\" class in pypolycontain package \"\n assert(type(desired_order) == int or type(desired_order) == float) , \"TypeError: The second argument need to be a number greater than 1. \\n \\\n It is the order of the reduced zonotpe which equal to the number of \\\n columns over the space dimension.\"\n assert(desired_order >= 1), \"desired order of the outcome zonotope needs to be greater or equal to 1\"\n \n x = np.array(zonotope.x)\n G = np.array(zonotope.G)\n \n dimension , numberofcolumns = G.shape\n desired_numberofcolumns = round(desired_order * dimension)\n \n if numberofcolumns <= desired_numberofcolumns:\n return zonotope\n \n elif dimension == desired_numberofcolumns:\n G_box = np.diag(np.sum(abs( G ) ,axis=1 ))\n return pp.zonotope( G=G_box , x=x) \n \n else:\n G_reduced , G_untouched = sorting_generator( G , desired_numberofcolumns )\n G_box = np.concatenate( ( np.diag(np.sum(abs( G_reduced ) ,axis=1 )) , G_untouched ), axis=1 )\n return pp.zonotope( G=G_box , x=x)",
"title": ""
},
{
"docid": "6555e421dbaa632ba51eadb8b4faf7ce",
"score": "0.45951638",
"text": "def _greedy_packing(items: List[Item], cap: int,\n func: Callable) -> Tuple[Set[int], int]:\n items.sort(key=func)\n included = set()\n total_val, total_weight = 0, 0\n for item in items:\n if total_weight + item.weight > cap:\n continue\n included.add(item.idx)\n total_val += item.val\n total_weight += item.weight\n return included, total_val\n # Running time complexity: O(nlog n)",
"title": ""
},
{
"docid": "0cdfcb03917e5411ee4ce35fa9b6f922",
"score": "0.45902628",
"text": "def get_ordered_slots(scheduled_slots, vols):\n vol_cnts = {}\n for s_slot in scheduled_slots:\n s_key = \"{}-{}-{}\".format(s_slot.day, s_slot.time_period, s_slot.type)\n vol_cnts[s_key] = 0\n for vol in vols:\n for a_slot in vol.available_slots:\n a_key = \"{}-{}\".format(a_slot.day, a_slot.time_period)\n if a_key == s_key:\n vol_cnts[s_key] += 1\n\n sorted_vol_cnts = sorted(vol_cnts.items(), key=lambda x: x[1])\n #print(\"ordered slots: {}\".format(sorted_vol_cnts))\n return sorted_vol_cnts",
"title": ""
},
{
"docid": "f5f03b8588cf4759528721eba357867c",
"score": "0.45897168",
"text": "def selectBestSchedule(self, remainder):\n # gas boiler? no schedules available!\n if self.getTER1() == 0:\n return -1\n\n\n #load_sched = [[0 for x in range(len(self.schedules[0])-1)] for y in range(self.noOfSchedules)]\n abs_sum = [0 for x in range(self.noOfSchedules)]\n max_min_diff = [0 for x in range(self.noOfSchedules)]\n #remainder_average = [0 for x in range(self.noOfSchedules)]\n #NO_worse_slots = [0 for x in range(self.noOfSchedules)] # saves number of timeslots in which the remainder is worse for each schedule\n\n min_diff = 0\n idx_min_diff = -1\n child_load = [0 for x in range(len(self.schedules[0])-1)]\n\n\n #if self.Children: # if not a leave node: use local knowledge about child loads\n # for c in range(len(self.Children)):\n # for t in range(len(child_load)):\n # child_load[t] += self.EConsumptionChildCurves[c][t]\n\n for s in range(self.noOfSchedules):\n\n current_remainder = [0 for x in range(len(remainder))]\n current_remainder_abs = [0 for x in range(len(remainder))]\n\n for t in range(len(remainder)):\n # add schedule load curve to compensation curve\n current_remainder[t] = remainder[t] + self.EConsumptionScheduleCurves[s][t] #- child_load[t]\n\n # as currently chosen schedule is included in remainder, subtract it (if not in first round)\n if self.chosenScheduleIndex != -1:\n current_remainder[t] -= self.EConsumptionChosenSchedule[t]\n\n current_remainder_abs[t] = abs(current_remainder[t])\n #if current_remainder_abs[t] > remainder[t]:\n # NO_worse_slots[s] += 1\n\n\n # accumulated absolute gradients as measure for similarity of curves\n abs_sum[s] = sum(current_remainder_abs)\n max_min_diff[s] = max(current_remainder)- min(current_remainder)\n #remainder_average[s] = sum(current_remainder_abs)/len(current_remainder_abs)\n\n #print 'abs_grad_sum: {0}'.format(abs_grad_sum[s])\n\n # new minimal abs difference?\n if self.OPTcriterion == 'maxmindiff':\n if idx_min_diff == -1 or min_diff - max_min_diff[s] > 0.001 : # min difference is 0.001 Watt to avoid oscillations\n idx_min_diff = s\n min_diff = max_min_diff[s]\n elif self.OPTcriterion == 'absremainder':\n if idx_min_diff == -1 or min_diff - abs_sum[s] > 0.001 : # min difference is 0.001 Watt to avoid oscillations\n idx_min_diff = s\n min_diff = abs_sum[s]\n\n if (idx_min_diff != self.chosenScheduleIndex):\n self.chosenSchedule = copy.deepcopy(self.schedules[idx_min_diff])\n if self.chosenScheduleIndex != -1:\n self.prevChosenScheduleIndex = self.chosenScheduleIndex # remember previously chosen schedule\n self.chosenScheduleIndex = idx_min_diff\n self.EConsumptionChosenSchedule = copy.deepcopy(self.EConsumptionScheduleCurves[idx_min_diff])\n #print 'ID {0}: new schedule has index {1}'.format(self.CommID, idx_min_diff)\n return 1\n else:\n if self.chosenScheduleIndex != -1:\n self.prevChosenScheduleIndex = self.chosenScheduleIndex\n #print 'ID {0}: new schedule = old schedule with index {1}'.format(self.CommID, self.chosenScheduleIndex)\n return 0",
"title": ""
},
{
"docid": "ed57dddb231678350c91d40120cf60d4",
"score": "0.45834634",
"text": "def brh_cogs2(DB, species, missing_factor=0.0, seed_sp=None, min_score=0):\n def _sort_cogs(cogs1, cogs2):\n seed1, mx1, avg1, ncogs1 = cogs1\n seed2, mx2, avg2, ncogs2 = cogs2\n for i, j in ((mx1, mx2), (avg1, avg2), (ncogs1, ncogs2)):\n v = -1 * cmp(i, j)\n if v != 0:\n break\n return v\n \n log.log(26, \"Searching BRH orthologs\")\n species = set(map(str, species))\n \n min_species = len(species) - round(missing_factor * len(species))\n \n if seed_sp == \"auto\":\n sp_to_test = list(species)\n elif seed_sp == \"largest\":\n cmd = \"\"\"SELECT taxid, size FROM species\"\"\"\n db.seqcursor.execute(cmd)\n sp2size = {}\n for tax, counter in db.seqcursor.fetchall():\n if tax in species: \n sp2size[tax] = counter\n \n sorted_sp = sorted(sp2size.items(), lambda x,y: cmp(x[1],y[1]))\n log.log(24, sorted_sp[:6])\n largest_sp = sorted_sp[-1][0]\n sp_to_test = [largest_sp]\n log.log(28, \"Using %s as search seed. Proteome size=%s genes\" %\\\n (largest_sp, sp2size[largest_sp]))\n else:\n sp_to_test = [str(seed_sp)]\n\n analysis_txt = StringIO()\n if sp_to_test:\n log.log(26, \"Finding best COG selection...\")\n seed2size = get_sorted_seeds(seed_sp, species, sp_to_test, min_species, DB)\n size_analysis = []\n for seedname, content in seed2size.iteritems():\n cog_sizes = [size for seq, size in content]\n mx, avg = _max(cog_sizes), round(_mean(cog_sizes))\n size_analysis.append([seedname, mx, avg, len(content)])\n size_analysis.sort(_sort_cogs) \n #print '\\n'.join(map(str, size_analysis))\n seed = size_analysis[0][0]\n print_as_table(size_analysis[:25], stdout=analysis_txt,\n header=[\"Seed\",\"largest COG\", \"avg COG size\", \"total COGs\"])\n if size_analysis[0][1] < len(species)-1:\n print size_analysis[0][1]\n raise ValueError(\"Current COG selection parameters do not permit to cover all species\")\n \n log.log(28, analysis_txt.getvalue())\n # The following loop tests each possible seed if none is\n # specified.\n log.log(28, \"Computing Clusters of Orthologs groups (COGs)\")\n log.log(28, \"Min number of species per COG: %d\" %min_species)\n cogs_selection = []\n log.log(26,\"Using seed species:%s\", seed)\n species_side1 = ','.join(map(quote, [s for s in species if str(s)>str(seed)]))\n species_side2 = ','.join(map(quote, [s for s in species if str(s)<str(seed)]))\n pairs1 = []\n pairs2 = []\n # Select all ids with matches in the target species, and\n # return the total number of species covered by each of\n # such ids.\n if species_side1 != \"\":\n cmd = \"\"\"SELECT seqid1, taxid1, seqid2, taxid2 from ortho_pair WHERE\n taxid1=\"%s\" AND taxid2 IN (%s) \"\"\" % (seed, species_side1)\n DB.orthocursor.execute(cmd)\n pairs1 = DB.orthocursor.fetchall()\n\n if species_side2 != \"\":\n cmd = \"\"\"SELECT seqid2, taxid2, seqid1, taxid1 from ortho_pair WHERE\n taxid1 IN (%s) AND taxid2 = \"%s\" \"\"\" % (species_side2, seed)\n DB.orthocursor.execute(cmd)\n pairs2 = DB.orthocursor.fetchall()\n \n cog_candidates = defaultdict(set)\n for seq1, sp1, seq2, sp2 in pairs1 + pairs2:\n s1 = (sp1, seq1)\n s2 = (sp2, seq2)\n cog_candidates[(sp1, seq1)].update([s1, s2])\n\n all_cogs = [cand for cand in cog_candidates.values() if\n len(cand) >= min_species]\n\n # CHECK CONSISTENCY\n seqs = set()\n for cand in all_cogs:\n seqs.update([b for a,b in cand if a == seed])\n pre_selected_seqs = set([v[0] for v in seed2size[seed]])\n if len(seqs & pre_selected_seqs) != len(set(seed2size[seed])) or\\\n len(seqs & pre_selected_seqs) != len(seqs): \n print \"old method seqs\", len(seqs), \"new seqs\", len(set(seed2size[seed])), \"Common\", len(seqs & pre_selected_seqs)\n raise ValueError(\"ooops\")\n \n cog_sizes = [len(cog) for cog in all_cogs]\n cog_spsizes = [len(set([e[0] for e in cog])) for cog in all_cogs]\n\n if [1 for i in xrange(len(cog_sizes)) if cog_sizes[i] != cog_spsizes[i]]:\n raise ValueError(\"Inconsistent COG found\")\n \n if cog_sizes: \n cogs_selection.append([seed, all_cogs])\n log.log(26, \"Found %d COGs\" % len(all_cogs))\n \n recoded_cogs = []\n for cog in all_cogs:\n named_cog = map(lambda x: \"%s%s%s\" %(x[0], GLOBALS[\"spname_delimiter\"],x[1]), cog)\n recoded_cogs.append(named_cog)\n\n return recoded_cogs, analysis_txt.getvalue()",
"title": ""
},
{
"docid": "545154e2e94599477e031e02811f3b38",
"score": "0.45807922",
"text": "def rank_chanels():\r\n \r\n all_paths = [['data_bci\\\\row_data\\\\subject1\\\\'], ['data_bci\\\\row_data\\\\subject2\\\\'],['data_bci\\\\row_data\\\\subject3\\\\']]\r\n\r\n train_subjects = ['01']\r\n test_subject = '02'\r\n freq = 512\r\n\r\n cutoff_beggining = 0\r\n columns_to_read = ['Fp1', 'AF3' ,'F7', 'F3', 'FC1', 'FC5', 'T7', 'C3', 'CP1', 'CP5',\r\n 'P7', 'P3', 'Pz', 'PO3', 'O1', 'Oz', 'O2', 'PO4', 'P4', 'P8', 'CP6',\r\n 'CP2', 'C4', 'T8', 'FC6', 'FC2', 'F4', 'F8', 'AF4', 'Fp2', 'Fz', 'Cz','class']\r\n seq_len = 0\r\n cut_step = 0\r\n num_perseg = freq\r\n num_overlap = int(num_perseg/2)\r\n min_freq=8\r\n max_freq=45\r\n k = 3\r\n\r\n First_iter = True\r\n for path in all_paths:\r\n train_full_data, train_full_data_filtered, train_full_anots, test_full_data, test_sliced_full_filtered, test_full_annoations = read_filter(path, train_subjects,test_subject, columns_to_read, cutoff_beggining, seq_len, cut_step)\r\n\r\n psd_signals = eval_psd_not_modulated(train_full_data, num_perseg, num_overlap, freq, min_freq, max_freq) \r\n chanels_acc = iterate_over_chanels(psd_signals, train_full_anots, k)\r\n if First_iter:\r\n accuracy = chanels_acc\r\n First_iter = False\r\n else:\r\n accuracy += chanels_acc\r\n accuracy = accuracy/len(all_paths)\r\n sorted_indexies = np.argsort(accuracy)[::-1]\r\n\r\n\r\n #indexis_above_treshohld = [idx for idx in sorted_indexies if accuracy[idx]> min_accary]\r\n return sorted_indexies",
"title": ""
},
{
"docid": "63ae7150f092a22bcfbf9ac0e2ec8a07",
"score": "0.45757744",
"text": "def diskStacking(disks):\n disks.sort(key=lamda disk:disk[2]) # sort disk by width\n heigths = [disk[2] for disk in disks]\n seq = [None, for _ in rang(len(disks))]\n maxHeightIdx = 0\n for idx1 in range(1, len(disks)):\n current_disk = disks[idx1]\n for idx2 in range(0, idx1):\n if validDisk:\n if heigths[idx1] <= current_disk[2] + heigths[idx2]:\n # in case the new disk is valid and increases the height\n # add it and update the height\n heigths[idx1] = current_disk[2] + heigths[idx2]\n seq[idx1] = idx2\n # if the new tower is higher then the old so far update the max height\n if heigths[idx1] >= heigths[maxHeightIdx]:\n maxHeightIdx = idx1\n return build_sequene(disks, seq, maxHeightIdx)",
"title": ""
},
{
"docid": "6ac87952ecb0ca338fa31457a3ef1cdd",
"score": "0.45652217",
"text": "def order_by_fitness(self):\n self.fauna_list['Herbivore'].sort(key=operator.\n attrgetter('animal_fitness'))\n self.fauna_list['Carnivore'].sort(key=operator.\n attrgetter('animal_fitness'),\n reverse=True)",
"title": ""
},
{
"docid": "e1942f59989530d4945eec8e8315f7d7",
"score": "0.45568666",
"text": "def stars(elmin=0,magmax=100,sort='az',northSouth='all',doBackwards=False,binSize=20.0):\n global stars_\n if northSouth == 'north' : cutAz = 269.0\n elif northSouth == 'south' : cutAz = 0.0\n else : cutAz = 330.0\n localLat = 37.0+16.0/60.0+49.37285/3600.0\n def cmpa(x,y):\n # sorting helper for azimuth (note the breakpoint at cutAz!!!)\n def optaz(a):\n if a<cutAz: return a\n return a-360\n a=optaz(x[1])\n b=optaz(y[1])\n if a<b: return -1\n if a>b: return 1\n return 0\n def cmpz(x,y):\n # sorting helper for reverse azimuth (note the breakpoint at cutAz!!!)\n def optaz(a):\n if a<cutAz: return a\n return a-360\n a=optaz(x[1])\n b=optaz(y[1])\n if a<b: return 1\n if a>b: return -1\n return 0\n def cmpe(x,y):\n # sorting helper for elevation\n if x[2]<y[2]: return -1\n if x[2]>y[2]: return 1\n return 0\n def cmpza(x,y) :\n # sorting helper for zenith angle\n if x[2]<y[2]: return 1\n if x[2]>y[2]: return -1\n return 0\n def cmpm(x,y):\n # sorting helper for optical magnitude\n if x[3]<y[3]: return -1\n if x[3]>y[3]: return 1\n return 0\n # report\n if elmin < -99 and magmax > 99: print \"Warning: Selecting all stars, use elmin= or magmax=\"\n if elmin > -99: print \"Selecting stars above elevation %g deg\" % elmin\n if magmax < 99: print \"Selecting stars brighter than %g mag\" % magmax\n print \"Sorting mode: \",sort\n # sorting mode\n if sort == 'el':\n my_cmp=cmpe\n elif sort == 'mag':\n my_cmp=cmpm\n elif sort == 'za':\n my_cmp=cmpza\n elif sort == 'az':\n my_cmp=cmpa\n elif sort == '-az' :\n my_cmp=cmpz\n else:\n print \"Warning: sorting mode %s not supported, using az\" % sort\n my_cmp=cmpa\n # empty the list again\n stars_=[]\n # Keep the user happy\n print \"Hang on, marching through the ephemeris of %d stars\" % len(ostars_)\n for s in ostars_:\n s1=sazelmag(s)\n if s1[2] < elmin or s1[3] > magmax:\n continue\n #if (((short.getDec(s) < localLat) and northSouth == 'north') or (short.getDec(s) > localLat) and (northSouth == 'south')) :\n dec = (SAC.getRaDec(s))[1]\n if (((dec < localLat) and northSouth == 'north') or \\\n (dec > localLat) and (northSouth == 'south')) :\n continue\n stars_.append(s1)\n stars_.sort(cmpa)\n bins = []\n breakIndex = [0]\n starsPart = []\n starsTemp = []\n starsAz_ = []\n for i in range(len(stars_)) :\n starsAz_.append(stars_[i][1])\n if starsAz_[i] > cutAz : starsAz_[i]=starsAz_[i]-360.0\n startAz = cutAz-360.0 # degrees\n for i in range(int(360/binSize+1)) : bins.append(int(startAz+binSize*i))\n j=0\n for i in range(len(bins)-1) :\n while((starsAz_[j] < bins[i+1]) and (starsAz_[j] >= bins[i]) and (j < len(stars_)-1)) : j=j+1\n breakIndex=breakIndex+[j]\n breakIndex[len(breakIndex)-1]=breakIndex[len(breakIndex)-1]+1\n for i in range(len(bins)-1) :\n if i%2 : my_cmp = cmpe\n else : my_cmp = cmpza\n starsPart = stars_[breakIndex[i]:breakIndex[i+1]]\n# Last bin sort in AZ ONLY!!! Saves you alot of trouble later!\n# if i==(len(bins)-2) :\n# starsTemp=starsTemp+starsPart\n# else :\n starsPart.sort(my_cmp)\n starsTemp=starsTemp+starsPart\n stars_ = starsTemp\n counter = 0\n while counter < len(stars_)-1 :\n az1 = int(stars_[counter][1]*10.0)\n az2 = int(stars_[counter+1][1]*10.0)\n el1 = int(stars_[counter][2]*10.0)\n el2 = int(stars_[counter+1][2]*10.0)\n if ((az1 == az2) and (el1== el2)) : cut(counter+2)\n else : counter=counter+1\n i=0\n if doBackwards : stars_.reverse()\n print \" i name az(deg) el(deg) magn\"\n print \"-------------------------------\"\n for s in stars_:\n i=i+1\n if len(s[0]) == 4 : \n print \"%3d %s %6.1f %6.1f %6.2f\" % (i,s[0],s[1],s[2],s[3])\n else : print \"%3d %s %6.1f %6.1f %6.2f\" % (i,s[0],s[1],s[2],s[3])\n print \"-------------------------------\"",
"title": ""
},
{
"docid": "99bf0f92d89a8eb6a9337691aa052615",
"score": "0.45466036",
"text": "def screen_organic(smiles):\n if smiles is None: return False\n remover = SaltRemover.SaltRemover()\n\n# SMARTS pattern for organic elements\n# H, B, C, N, O, F, P, S, Cl, Br, I\n patt = '[!$([#1,#5,#6,#7,#8,#9,#15,#16,#17,#35,#53])]'\n mpatt = Chem.MolFromSmarts(patt)\n m = Chem.MolFromSmiles(smiles, sanitize = True)\n if m is None: return False\n\n # remove salts\n res = remover.StripMol(m)\n if res is not None and res.GetNumAtoms() < m.GetNumAtoms():\n return False\n\n # take only the largest fragment\n frags = AllChem.GetMolFrags(m, asMols=True)\n if len(frags) > 1:\n return False\n# nums = [(f.GetNumAtoms(), f) for f in frags]\n# nums.sort(reverse=True)\n# m = nums[0][1]\n\n # take only organic molecules\n if not m.HasSubstructMatch(mpatt):\n return True\n else:\n return False",
"title": ""
},
{
"docid": "a38ee77bc44b40a87b6f1d8c22bf35b1",
"score": "0.4545367",
"text": "def set_order_conditions(self, df_lt_spm: pd.DataFrame, df_lt_repair: pd.DataFrame, procurement_mode: int = 1):\n\n # Set procurement types based on any match found\n self.is_spm = any([material.is_spm for material in self.materials])\n self.is_repairable = any([material.is_repairable for material in self.materials])\n self.is_buyable = any([material.is_buyable for material in self.materials])\n self.is_dismountable = any([material.is_dismountable for material in self.materials])\n\n # If no procurement type set as buyable\n self.has_procurement_type = self.is_dismountable or self.is_buyable or self.is_spm or self.is_repairable\n if not self.has_procurement_type:\n self.is_buyable = True\n\n # --------------\n\n # Set unique values (and override if needed)\n\n # If CORE VOZ, set dismountable instead of repairable\n if procurement_mode == 0:\n self.procurement_type = 'Buyable'\n self.leadtime = max([material.leadtime for material in self.materials if material.is_buyable])\n self.leadtime_sd = max([material.leadtime_sd for material in self.materials if material.is_buyable])\n return\n elif procurement_mode == 1:\n if self.domain == 'CORE VOZ' and not self.is_spm and not self.is_buyable\\\n and self.is_dismountable and self.is_repairable:\n self.procurement_type = 'Dismountable'\n self.leadtime = 90\n self.leadtime_sd = 0\n return\n\n if self.is_spm:\n self.procurement_type = 'SPM'\n\n # Override\n if (self.domain, self.brand) in df_lt_spm.index:\n try:\n new_leadtime = df_lt_spm.loc[(self.domain, self.brand)]['leadtime_spm']\n new_leadtime = float(new_leadtime)\n\n self.leadtime_override = True\n self.leadtime = new_leadtime\n self.leadtime_sd = 0\n return\n except:\n self.leadtime = 2\n self.leadtime_sd = 0\n return\n else:\n self.leadtime = 2\n self.leadtime_sd = 0\n return\n\n if self.is_repairable:\n self.procurement_type = 'Repairable'\n\n # Override\n if self.domain in df_lt_repair.index:\n try:\n new_leadtime = df_lt_repair.loc[self.domain]['leadtime_reparable']\n new_leadtime = float(new_leadtime)\n\n self.leadtime_override = True\n self.leadtime = new_leadtime\n self.leadtime_sd = 0\n return\n except:\n self.leadtime = 30\n self.leadtime_sd = 0\n return\n else:\n self.leadtime = 30\n self.leadtime_sd = 0\n return\n\n if self.is_buyable:\n self.procurement_type = 'Buyable'\n try:\n self.leadtime = max([material.leadtime for material in self.materials if material.is_buyable])\n self.leadtime_sd = max([material.leadtime_sd for material in self.materials if material.is_buyable])\n return\n except:\n self.leadtime = 90\n self.leadtime_sd = 0\n\n # Else, return Dismountable\n self.procurement_type = 'Dismountable'\n self.leadtime = 90\n self.leadtime_sd = 0\n return\n else:\n raise Exception(\"procurement_mode not valid.\")",
"title": ""
},
{
"docid": "2b395755e6cd77f18b823d92b61a63a9",
"score": "0.4541812",
"text": "def brute_force_cow_transport(cows,limit=10):\n # TODO: Your code here\n #print(list(cows.items()))\n cows_list=list(cows.items())\n curr_list=[[[0]]]\n for i in range(1,len(cows_list)):\n smaller_fun(curr_list,i,limit,cows_list)\n\n ans =sorted(curr_list,key=lambda x:len(x))\n print(ans)\n ansfinal=[]\n for item in ans:\n trip=[]\n for i in range(len(item)):\n trip.append(cows_list[item[i]][0])\n ansfinal.append(trip)\n return ansfinal",
"title": ""
},
{
"docid": "ff669e8996aa523f7bac4ad7a27d72c9",
"score": "0.45413023",
"text": "def ingredients_pointing_to(self,ingredient):\n\t\ttop_sorted = sorted(self.compatible_ingredients[ingredient],key=lambda x:len(self.compatible_ingredients[x]),reverse=True)\n\t\treturn top_sorted",
"title": ""
},
{
"docid": "97dd3845c6301dddbe737dd0e3925a0a",
"score": "0.45316043",
"text": "def sorted_fruit_quantity(f):\n # skip the header of the file\n move_cursor(f)\n # put all the quantities into a list\n # expected output: [5, 10, 3, 15]\n # read the file line by line\n output = []\n for line in f:\n line_list = line.split() # [\"Apple\",\"5\"]\n output.append(int(line_list[1]))\n # sort the list in descending order\n # expected output: [15, 10, 5, 3]\n output.sort(reverse=True)\n # only select the highest two quantities in the list and return them\n # expected output: [15, 10]\n # slicing\n # Hint: ending pos is the index of the first element that I don't want to include\n # in the final result\n return output[0:2]",
"title": ""
},
{
"docid": "c29046ca5d6fafae5a52c0c691fe2aa5",
"score": "0.45300475",
"text": "def _sort_and_name_containers(self, robot_settings):\n def sort_key(c):\n return not c.is_temporary, c.id\n\n def contains_only_control(container):\n return all(well.artifact.is_control for well in container.occupied)\n\n # We need to ensure that the containers that contain only a control always get index=0\n # NOTE: This is very site-specific so it would be better to solve it with handlers\n all_source_containers = set(transfer.source_location.container for transfer in self._transfers)\n source_containers_only_control = set(container for container in all_source_containers\n if contains_only_control(container))\n\n self.container_to_container_slot = dict()\n for container in source_containers_only_control:\n self.container_to_container_slot[container] = self._container_to_slot(robot_settings, container, 0, True)\n\n source_containers = all_source_containers - source_containers_only_control\n target_containers = set(transfer.target_location.container for transfer in self._transfers)\n assert len(source_containers.intersection(target_containers)) == 0\n\n source_containers = sorted(source_containers, key=sort_key)\n target_containers = sorted(target_containers, key=sort_key)\n\n for ix, container in enumerate(source_containers):\n self.container_to_container_slot[container] = \\\n self._container_to_slot(robot_settings, container, ix, True)\n for ix, container in enumerate(target_containers):\n self.container_to_container_slot[container] = \\\n self._container_to_slot(robot_settings, container, ix, False)",
"title": ""
},
{
"docid": "ab2e173f524d318e9fb6f3a00fba0168",
"score": "0.45277873",
"text": "def standardComposition_Min(self):\n self.rulesList = []\n\n self.rulesList.append(np.fmin(self.rule1,self.below_price))\n self.rulesList.append(np.fmin(self.rule2,self.below_price))\n self.rulesList.append(np.fmin(self.rule3,self.below_price))\n self.rulesList.append(np.fmin(self.rule4,self.standard_price))\n self.rulesList.append(np.fmin(self.rule5,self.standard_price))\n self.rulesList.append(np.fmin(self.rule6,self.standard_price))\n self.rulesList.append(np.fmin(self.rule7,self.above_price))\n self.rulesList.append(np.fmin(self.rule8,self.above_price))\n self.rulesList.append(np.fmin(self.rule9,self.above_price))\n self.rulesList.append(np.fmin(self.rule10,self.high_price))\n self.rulesList.append(np.fmin(self.rule11,self.high_price))",
"title": ""
},
{
"docid": "744f6a56e0a318959b92de9c5da5298a",
"score": "0.45247424",
"text": "def MostUsedBuses(self):\n busKM = lambda bus: bus.getTimesUsedRoute() * self.__routeRepo.getObj(bus.getRouteCode()).getLength()\n buses = self.__busRepo.getAll()\n sortedBuses = sorted(buses,key = busKM,reverse=True)\n return sortedBuses",
"title": ""
},
{
"docid": "7fed020905a811ab672dc8d5f08c994b",
"score": "0.4521463",
"text": "def spreads(lines, sport):\n value = []\n for game in lines:\n combos = it.product(game['away_odds'].items(), game['home_odds'].items())\n try:\n value.extend([f'{sport} {game[\"game\"]} {combo[0][0]}: {combo[0][1][0]} {combo[0][1][1]} and '\n f'{combo[1][0]}: {combo[1][1][0]} {combo[1][1][1]}\\n\\n' for combo in combos\n if combo[0][1][0] + combo[1][1][0] >= 0 and combo[0][1][1] + combo[1][1][1] >= 0])\n except TypeError:\n print(combos)\n\n return value",
"title": ""
},
{
"docid": "87396173dc2525a670ee5694d2196b91",
"score": "0.45202985",
"text": "def ubicar_submarino(): #esta clase de barcos no tiene orientacion\n tamano = Submarinos.tamano #se importa el tamano del barco desde su clase\n cantidad = Submarinos.cantidad #se importa la cantidad de barcos desde su clase\n while cantidad > 0:\n mal_ubicado = \"no\"\n coor_fila = randint(1,numero_filas)\n coor_columna = randint(1,numero_columnas)\n ubicacion = (coor_fila, coor_columna)\n for x in lista_ubicacion_barco:\n if x == ubicacion:\n mal_ubicado = \"si\"\n #validacion para que los barcos no queden contiguos entre otros ya posicionados\n elif (ubicacion[0] == x[0] or (ubicacion[0]+1) == x[0] or (ubicacion[0]-1) == x[0]) and ((ubicacion[1]) == x[1] or (ubicacion[1]+1) == x[1] or (ubicacion[1]- 1) == x[1]): \n mal_ubicado = \"si\"\n if mal_ubicado == \"no\":\n cantidad -= 1 #se resta uno a la cantidad de los barcos porque ya este se posiciono correctamente\n lista_ubicacion_barco.append(ubicacion) #si el barco no es contiguo con ningun otro barco se agrega a la lista de los barcos ya posicionados\n elif mal_ubicado == \"si\":\n cantidad = cantidad #la cantidad de barcos se mantiene igual porque el barco quedo contiguo a otro, se repite el proceso d eubicacion para este barco",
"title": ""
},
{
"docid": "1e735830b8a0056c44faa5097b68a0e4",
"score": "0.45202562",
"text": "def ffa(items_list, bin_capacity):\n bins =[]\n randomised_np_list = np.random.permutation(items_list) # list containing initial items in a random order\n items_list = randomised_np_list.tolist() \n \n for item in items_list:\n # foeach item we search if there's an open bin where it can fit\n for bin in bins:\n if bin.total_weight + item <= bin_capacity: #if it fits\n bin.add_item(item) #we add the item in the bin\n break\n else:\n # there is no open bin where the item can fit\n #so we open a new bin and add the item in it\n bin = Bin()\n bin.add_item(item)\n bins.append(bin)\n\n return bins",
"title": ""
},
{
"docid": "cb344274e40948f29e93231ad74fc24f",
"score": "0.45189503",
"text": "def _bom_explode_cost(self, cr, uid, bom, product, factor, properties=None, level=0, routing_id=False, previous_products=None, master_bom=None, context=None):\n uom_obj = self.pool.get(\"product.uom\")\n routing_obj = self.pool.get('mrp.routing')\n master_bom = master_bom or bom\n\n def _factor(factor, product_efficiency, product_rounding):\n factor = factor / (product_efficiency or 1.0)\n factor = _common.ceiling(factor, product_rounding)\n if factor < product_rounding:\n factor = product_rounding\n return factor\n\n factor = _factor(factor, bom.product_efficiency, bom.product_rounding)\n\n result = []\n result2 = []\n\n routing = (routing_id and routing_obj.browse(cr, uid, routing_id)) or bom.routing_id or False\n if routing:\n for wc_use in routing.workcenter_lines:\n wc = wc_use.workcenter_id\n d, m = divmod(factor, wc_use.workcenter_id.capacity_per_cycle)\n mult = (d + (m and 1.0 or 0.0))\n cycle = mult * wc_use.cycle_nbr\n result2.append({\n 'name': tools.ustr(wc_use.name) + ' - ' + tools.ustr(bom.product_tmpl_id.name_get()[0][1]),\n 'workcenter_id': wc.id,\n 'sequence': level + (wc_use.sequence or 0),\n 'cycle': cycle,\n 'hour': float(wc_use.hour_nbr * mult + ((wc.time_start or 0.0) + (wc.time_stop or 0.0) + cycle * (wc.time_cycle or 0.0)) * (wc.time_efficiency or 1.0)),\n })\n\n for bom_line_id in bom.bom_line_ids:\n if self._skip_bom_line(cr, uid, bom_line_id, product, context=context):\n continue\n if set(map(int, bom_line_id.property_ids or [])) - set(properties or []):\n continue\n\n if previous_products and bom_line_id.product_id.product_tmpl_id.id in previous_products:\n #raise osv.except_osv(_('Invalid Action!'), _('BoM \"%s\" contains a BoM line with a product recursion: \"%s\".') % (master_bom.name,bom_line_id.product_id.name_get()[0][1]))\n raise Warning(('Invalid Action!, BoM \"%s\" contains a BoM line with a product recursion: \"%s\".') % (master_bom.name,bom_line_id.product_id.name_get()[0][1]))\n\n quantity = _factor(bom_line_id.product_qty * factor, bom_line_id.product_efficiency, bom_line_id.product_rounding)\n bom_id = self._bom_find(cr, uid, product_id=bom_line_id.product_id.id, properties=properties, context=context)\n\n #If BoM should not behave like PhantoM, just add the product, otherwise explode further\n if bom_line_id.type != \"phantom\" and (not bom_id or self.browse(cr, uid, bom_id, context=context).type != \"phantom\"):\n result.append({\n 'name': bom_line_id.product_id.name,\n 'product_id': bom_line_id.product_id.id,\n 'product_qty': quantity,\n 'product_uom': bom_line_id.product_uom.id,\n 'product_uos_qty': bom_line_id.product_uos and _factor(bom_line_id.product_uos_qty * factor, bom_line_id.product_efficiency, bom_line_id.product_rounding) or False,\n 'product_uos': bom_line_id.product_uos and bom_line_id.product_uos.id or False,\n 'cost_type_id': bom_line_id.cost_type_id.id or False,\n })\n elif bom_id:\n all_prod = [bom.product_tmpl_id.id] + (previous_products or [])\n bom2 = self.browse(cr, uid, bom_id, context=context)\n # We need to convert to units/UoM of chosen BoM\n factor2 = uom_obj._compute_qty(cr, uid, bom_line_id.product_uom.id, quantity, bom2.product_uom.id)\n quantity2 = factor2 / bom2.product_qty\n res = self._bom_explode(cr, uid, bom2, bom_line_id.product_id, quantity2,\n properties=properties, level=level + 10, previous_products=all_prod, master_bom=master_bom, context=context)\n result = result + res[0]\n result2 = result2 + res[1]\n else:\n #raise osv.except_osv(_('Invalid Action!'), _('BoM \"%s\" contains a phantom BoM line but the product \"%s\" does not have any BoM defined.') % (master_bom.name,bom_line_id.product_id.name_get()[0][1]))\n raise Warning(('Invalid Action!, BoM \"%s\" contains a phantom BoM line but the product \"%s\" does not have any BoM defined.') % (master_bom.name,bom_line_id.product_id.name_get()[0][1]))\n\n return result, result2",
"title": ""
},
{
"docid": "509049e61b1d5c4f3932e337d3adb90d",
"score": "0.451175",
"text": "def solid_surface_density_RC2014_given_observed_catalog(sss_per_sys, max_core_mass=10.):\n mult_obs = sss_per_sys['Mtot_obs']\n mult_obs_2p = []\n a_obs_2p = []\n core_mass_obs_2p = []\n sigma_obs_2p = []\n for i in np.arange(len(mult_obs))[mult_obs > 1]: # only consider multi-planet systems\n a_sys = gen.a_from_P(sss_per_sys['P_obs'][i], sss_per_sys['Mstar_obs'][i])\n core_mass_sys = generate_planet_mass_from_radius_Ning2018_table_above_lognormal_mass_earthlike_rocky_below_vec(sss_per_sys['radii_obs'][i][a_sys > 0])\n core_mass_sys[core_mass_sys > max_core_mass] = max_core_mass\n a_sys = a_sys[a_sys > 0]\n\n mult_obs_2p += [len(a_sys)]*len(a_sys)\n a_obs_2p += list(a_sys)\n core_mass_obs_2p += list(core_mass_sys)\n sigma_obs_2p += list(solid_surface_density_system_RC2014(core_mass_sys, a_sys))\n mult_obs_2p = np.array(mult_obs_2p)\n a_obs_2p = np.array(a_obs_2p)\n core_mass_obs_2p = np.array(core_mass_obs_2p)\n sigma_obs_2p = np.array(sigma_obs_2p)\n return sigma_obs_2p, core_mass_obs_2p, a_obs_2p, mult_obs_2p",
"title": ""
},
{
"docid": "d83b8689e2feed6a1bbea2e9526bc642",
"score": "0.45080826",
"text": "def shape(system):\n newSys=system\n alphabetMap = OrderedDict()\n indx = 0\n\n newAlphabet = list(set(chain(*system)))\n theRestOfAlphabets = list(set(alphabet) - set(newAlphabet))\n\n for char in alphabet:\n if char in theRestOfAlphabets:\n alphabetMap.update({char: indx})\n indx = indx + 1\n elif char in newAlphabet:\n #sublist that contain this char(give all chars the same indx)\n #drop this sublist from the system\n systemItem = shapeHelper.searcher(newSys, char)\n for char in newSys[systemItem]:\n alphabetMap.update({char: indx})\n\n newSys=newSys[0:systemItem]+newSys[systemItem+1:]\n newAlphabet = list(set(chain(*newSys)))\n indx = indx + 1\n '''\n for setOfNewAlphabet in system:\n for char in setOfNewAlphabet:\n alphabetMap.update({char: indx})\n indx = indx + 1\n\n for char in theRestOfAlphabets:\n alphabetMap.update({char: indx})\n indx = indx + 1\n '''\n alphabetMap.update({\" \": 70})\n return alphabetMap",
"title": ""
},
{
"docid": "9b61f2c3eca29620e4984bdcdf8d59fc",
"score": "0.45070568",
"text": "def solid_surface_density_nHill_given_observed_catalog(sss_per_sys, max_core_mass=10., n=10.):\n Mstar_obs = np.repeat(sss_per_sys['Mstar_obs'][:,None], np.shape(sss_per_sys['P_obs'])[1], axis=1)[sss_per_sys['P_obs'] > 0] # flattened array of stellar masses repeated for each planet\n a_obs_per_sys = gen.a_from_P(sss_per_sys['P_obs'], sss_per_sys['Mstar_obs'][:,None])\n a_obs = a_obs_per_sys[sss_per_sys['P_obs'] > 0]\n radii_obs = sss_per_sys['radii_obs'][sss_per_sys['P_obs'] > 0]\n core_mass_obs = generate_planet_mass_from_radius_Ning2018_table_above_lognormal_mass_earthlike_rocky_below_vec(radii_obs)\n core_mass_obs[core_mass_obs > max_core_mass] = max_core_mass\n sigma_obs = solid_surface_density_nHill(core_mass_obs, a_obs, Mstar=Mstar_obs, n=n)\n return sigma_obs, core_mass_obs, a_obs",
"title": ""
},
{
"docid": "b433da7bb86b52182313445cc220e187",
"score": "0.4504941",
"text": "def set_prodmaterials(_craftable):\n partial_resources = []\n partial_craftables = []\n _craftable.resources_list = []\n _craftable.craftables_list = []\n\n # Checking for Production Materials\n for material, quantity in _craftable.craft_materials.items():\n # Checking for Resources or Workbench Need\n if 'Minimum Bench Cost' in material or material in resources.keys():\n _craftable.resources_list.append([material, int(quantity)])\n _craftable.res_totalcost = float(_craftable.res_totalcost + resources[material].unit_price * int(quantity))\n\n # Check for sub-craftables as a need\n elif material not in resources.keys():\n _craftable.craftables_list.append([material, int(quantity)])\n\n return partial_resources, partial_craftables",
"title": ""
},
{
"docid": "968bee5cc968d768a5626423bda884b2",
"score": "0.4498971",
"text": "def FilterScafDict(ScafDict):\n\n def CheckScafOrder(NestedListBoi, StrandInfo):\n \"\"\"The purpose of this nested function is to check if the size of the\n previous scaffold is less than the current. Returns True if this is the\n case, and false if this fails\n\n :arg1: [[0, 82558], [82568, 14200], [96783, 4436], [101349, 11648],\n [113468, 12600], [126901, 6375], [136697, 30162]]\n :returns: Boolean value TRUE of FALSE\n \"\"\"\n NoOverlap = True\n \n \n \n CurrentLen = 0\n if StrandInfo == '+':\n for item in NestedListBoi:\n AddItems = item[0] + item[1] \n if AddItems > CurrentLen:\n CurrentLen = AddItems\n else:\n print(\"WE ARE FUCKEDDDDDD\")\n NoOverlap = False\n\n elif StrandInfo == '-':\n #Flip list for negative\n NestedListBoi = NestedListBoi[::-1]\n for item in NestedListBoi:\n AddItems = item[0] + item[1] \n if AddItems > CurrentLen:\n CurrentLen = AddItems\n else:\n print(\"WE ARE FUCKEDDDDDD\")\n break\n sys.exit(2)\n NoOverlap = False\n return NoOverlap\n\n\n for key, value in ScafDict.items():\n StartPGASeq = int(value[0][0][2])\n EndPGaSeq = int(value[-1][0][2])\n \n TotalScaflen = int(value[0][1][5])\n LastLastScafLentoadd = int(value[-1][1][3])\n NegLastScafToAdd = int(value[0][1][3])\n\n\n TakeAllScafStartsAndLens = []\n\n for thing in value:\n StartAndLen = [int(thing[1][2]), int(thing[1][3])]\n TakeAllScafStartsAndLens.append(StartAndLen)\n \n #Check if there is any overlap with scaf hitting different PGA scaf\n TakeStrand = value[0][1][4]\n Overlap = CheckScafOrder(TakeAllScafStartsAndLens, TakeStrand)\n \n\n #Print List out with correct orientation\n if TakeStrand == '-':\n FinalPGSLoc = (EndPGaSeq)\n NegScafEnd = StartPGASeq + NegLastScafToAdd\n FinalListToPrint = [key,str(EndPGaSeq), str(NegScafEnd), str(TakeStrand)]\n print('\\t'.join(FinalListToPrint))\n\n elif TakeStrand == '+':\n FinalPGSLoc = (EndPGaSeq + LastLastScafLentoadd)\n FinalListToPrint = [key,str(StartPGASeq), str(FinalPGSLoc), str(TakeStrand)]\n print('\\t'.join(FinalListToPrint))\n\n #print(\"FINAL\")\n #print(key)\n #print(CurrentVal)\n #print(FinalItem[2][0:5])\n #input()",
"title": ""
},
{
"docid": "94684c03b91a328e7e5677039451f886",
"score": "0.44976544",
"text": "def resort(self):\n self.items.sort(key=lambda node: node.path_weight, reverse=True)",
"title": ""
},
{
"docid": "813f4af20cc03c91b6de056248aacac7",
"score": "0.4497096",
"text": "def fill_first_stool(self: 'TOAHModel', number_of_cheeses: int):\n self._number_of_cheeses = number_of_cheeses\n first_stool = self.stool_lst[0]\n for cheese in range(1, number_of_cheeses+1):\n first_stool.append(Cheese(cheese))\n first_stool.sort(key = lambda cheese:cheese.size, reverse=True)\n self.end_game_stool = first_stool.copy()",
"title": ""
},
{
"docid": "17288fb84015aff7db00d94e715ea580",
"score": "0.4494513",
"text": "def sort(match, ser_if):\n if match:\n ser_if.write('m')\n else:\n ser_if.write('c')\n return check_response(ser_if)",
"title": ""
},
{
"docid": "d6b483a71cb058d54dda9be54317d02f",
"score": "0.4493517",
"text": "def evaluate_elf(calories):\n if calories > top_three[2]:\n top_three.append(calories)\n elif calories > top_three[1]:\n top_three.popleft()\n top_three.insert(1, calories)\n elif calories > top_three[0]:\n top_three[0] = calories",
"title": ""
},
{
"docid": "8a9f975ae84ede951f0199090835d915",
"score": "0.44925147",
"text": "def load_knapsack(things,knapsack_cap):\r\n# iteration using ratio \r\n my_team_number_or_name = \"lwang\" # always return this variable as the first item\r\n \r\n items_to_pack = [] # use this list for the indices of the items you load into the knapsack\r\n load = 0.0 # use this variable to keep track of how much volume is already loaded into the backpack\r\n value = 0.0 # value in knapsack\r\n \r\n item_list = [[k,v,float(v[1])/v[0]] for k,v in things.items()]\r\n j = lambda x:x[2]\r\n item_list=sorted(item_list,key=j,reverse=True)\r\n \r\n item_keys = [item[0] for item in item_list]\r\n \r\n for i in range(len(item_keys)):\r\n if load <= knapsack_cap:\r\n pack_item = item_keys[i]\r\n load += things[pack_item][0]\r\n if load <= knapsack_cap:\r\n items_to_pack.append(pack_item)\r\n #load += things[pack_item][0]\r\n value += things[pack_item][1]\r\n return my_team_number_or_name, items_to_pack",
"title": ""
},
{
"docid": "45132c35a37ca4e5fea05a0a31f5a79c",
"score": "0.44908905",
"text": "def sorting_by_criteria(self, result):\r\n\t\tresult = sorted(result, key=lambda r: r[0])\r\n\t\tflag = False\r\n\t\tm = result[0][0]\r\n\t\tfor i in range(len(result)):\r\n\t\t\tif (result[i][0] == m): continue\r\n\t\t\tflag = True\r\n\t\t\tbreak\r\n\t\tif not flag: i += 1\r\n\t\tresult = result[:i]\r\n\r\n\t\t\"\"\" in prewin status, compare useful_amount only \"\"\"\r\n\t\tif (result[0][0] == 0):\r\n\t\t\tresult = sorted(result, key=lambda r: r[1], reverse=True)\r\n\t\t\ttest = \"\"\r\n\t\t\tfor r in result:\r\n\t\t\t\ttest += \"[{0}, {1}, {2}, {3}], \".format(r[0], r[1], r[2], r[3])\r\n#\t\t\tprint \"prewin status: {0}\".format(test)\r\n\t\t\tself.current_best_state = [result[0][0], result[0][1], result[0][2]]\r\n\t\t\treturn result[0][3]\r\n\r\n\t\t\"\"\" sort by score (big -> small) \"\"\"\r\n\t\tresult = sorted(result, key=lambda r: r[2], reverse=True)\r\n\t\tflag = False\r\n\t\tm = result[0][2]\r\n\t\tfor i in range(len(result)):\r\n\t\t\tif (result[i][2] == m): continue\r\n\t\t\tflag = True\r\n\t\t\tbreak\r\n\t\tif not flag: i += 1\r\n\t\tresult = result[:i]\r\n\r\n\t\t\"\"\" sort by useful card amount (big -> small) \"\"\"\r\n\t\tresult = sorted(result, key=lambda r: r[1], reverse=True)\r\n\r\n\t\t\"\"\" choose one to discard \"\"\"\r\n\t\tdcard = result[0][3]\r\n\t\tm = result[0][1]\r\n\t\tbest = result[0]\r\n\t\tfor r in result:\r\n\t\t\tif (r[1] != m): break\r\n\t\t\tctype = GameBoard.CardType(r[3])\r\n\t\t\tif (ctype == 4) and (self.word_list.count(r[3]) == 1):\r\n\t\t\t\tdcard = r[3]\r\n\t\t\t\tbest = r\r\n\t\t\tif (ctype == 5) and (self.wind_list.count(r[3]) == 1):\r\n\t\t\t\tdcard = r[3]\r\n\t\t\t\tbest = r\r\n\t\tself.current_best_state = [r[0], r[1], r[2]]\r\n\t\treturn dcard",
"title": ""
},
{
"docid": "ad0227a0aec3c638c3cb001370438372",
"score": "0.4488601",
"text": "def chercherChemin(self):\n\n \n liste=self._circuit.vue(self.x,self.y,self.rayonVision)\n \n listeSuppr=[]\n couche_vehicule= self._circuit.Couche_vehicules\n \n for case in liste :\n #on élimine les cases infranchissbles les cases qui ne sont pas sur le chemin à suivre \n\n if self._circuit.numeroWayPoint(case[0],case[1])==0 or ( self._circuit.numeroWayPoint(self.x,self.y)!=self._circuit.lastWayPoint and self._circuit.numeroWayPoint(case[0],case[1])<= self._circuit.numeroWayPoint(self.x,self.y)) or( self._circuit.numeroWayPoint(case[0],case[1])>= 5*self._circuit.numeroWayPoint(self.x,self.y) and self._circuit.numeroWayPoint(self.x,self.y)!=0) or ( self._circuit.numeroWayPoint(self.x,self.y)==self._circuit.lastWayPoint and self._circuit.numeroWayPoint(case[0],case[1])== self._circuit.numeroWayPoint(self.x,self.y)) or self._circuit.plateau[case[1],case[0],couche_vehicule]!=None:#on élimine les points derrière\n \n listeSuppr.append(case)\n\n \n for case in listeSuppr:\n \n liste.remove(case)\n \n if len(liste)>=1:\n l=liste[0]\n\n for nour in liste :\n \n if distance((self.x,self.y),(l[0],l[1])) > distance((self.x,self.y),(nour[0],nour[1])):\n l=nour\n pasx=0\n pasy=0\n if self.x<l[0] : \n pasx=1\n elif self.x>l[0] :\n pasx=-1\n if self.y<l[1] : \n pasy=1\n elif self.y>l[1] :\n pasy=-1\n debug.dprint(\" id {} {}:({},{}) Waypoint {} Point:({},{}) WayPoint {} vitesse :{} reservoir:{}\".format(self.id,self.typeV,self.x,self.y,self._circuit.numeroWayPoint(self.x,self.y),l[0],l[1],self._circuit.numeroWayPoint(l[0],l[1]),self.vitesse,self.reservoir))\n self.orientation=atan2(pasy,pasx)\n\n self.vitesse=1\n\n debug.dprint(self) \n \n super().deplacer()\n \n\n self.rayonVision=4\n else :# on augemente le rayon de vision au cas ou toutes les cases sont occupées ou non franchissables\n self.rayonVision*=3",
"title": ""
},
{
"docid": "167b770545430b963745b2a64f625629",
"score": "0.44860765",
"text": "def doClassification(self):\n halfIndex=int(len(self.dict)/2)\n i=0\n for k, v in sorted(self.dict.items(), key=lambda item: item[1]):\n if i<halfIndex:\n self.lowVolumeStockList.append(k)\n else:\n self.highVolumeStockList.append(k)\n i=i+1",
"title": ""
},
{
"docid": "ccffa75df4edccb4f94d0bd2709a6d87",
"score": "0.44842133",
"text": "def categorize (self):\n\n fout = defaultdict(list)\n\n # Flat lists of files to collect keyed by platform,category\n collect_files = dict()\n for platform in wanted_files:\n for category, flist in wanted_files[platform].items():\n for f in flist:\n collect_files[(platform,category,f)] = list()\n\n for a in self.artifacts:\n try:\n with zfile.ZFile(a.lpath, 'r') as zf:\n if os.path.splitext(a.lpath)[-1] == '.rpm':\n a.info['plat'] = 'rhel'\n\n platform = a.info['plat']\n if platform not in platforms:\n continue\n\n zfiles = zf.getnames()\n if len(zfiles) == 0:\n print('No files in %s?' % a)\n for category, flist in wanted_files[platform].items():\n for f in flist:\n matches = [(a,x) for x in zfiles if os.path.basename(x) == f]\n if len(matches) > 0:\n collect_files[(platform,category,f)] += matches\n fout[category] += matches\n\n except zfile.tarfile.ReadError as e:\n print('ignoring artifact: %s: %s' % (a.lpath, str(e)))\n\n # Verify that all wanted combinations were matched\n errors = 0\n for missing in [x for x in collect_files if len(collect_files[x]) == 0]:\n errors += 1\n print('ERROR: No matching artifact files for', missing)\n\n if errors > 0:\n raise Exception('Not all wanted files found in artifacts, see above.')\n return fout",
"title": ""
},
{
"docid": "13d471134fb7e6539d43e2b421d5adac",
"score": "0.4482698",
"text": "def test_usearch61_sizeorder(self):\r\n\r\n app = Usearch610DeNovoOtuPicker(\r\n params={'save_intermediate_files': False,\r\n 'output_dir': self.output_dir,\r\n 'remove_usearch_logs': True,\r\n 'sizeorder': True\r\n })\r\n\r\n obs_clusters = app(self.tmp_seqs_usearch_97perc_dups)\r\n\r\n # All seqs should fall into a single cluster\r\n expected_clusters = {'denovo0': ['usearch_ecoli_seq_1bp_change',\r\n 'usearch_ecoli_seq_2bp_change', 'usearch_ecoli_seq',\r\n 'usearch_ecoli_seq_1bp_change_dup1',\r\n 'usearch_ecoli_seq_1bp_change_dup2']}\r\n\r\n self.assertEqual(obs_clusters, expected_clusters)",
"title": ""
},
{
"docid": "a8b0e2cf06c85db6f55ea2d5e76da14f",
"score": "0.44797456",
"text": "def high_shelf(fc, Q, gain, fs=48000):\n # Turn lists into numpy arrays\n fc, Q, gain, fs = numpyfy(fc, Q, gain, fs)\n\n A = 10 ** (gain / 40)\n w0 = 2 * np.pi * fc / fs\n alpha = np.sin(w0) / (2 * Q)\n\n a0 = (A + 1) - (A - 1) * np.cos(w0) + 2 * np.sqrt(A) * alpha\n a1 = -(2 * ((A - 1) - (A + 1) * np.cos(w0))) / a0\n a2 = -((A + 1) - (A - 1) * np.cos(w0) - 2 * np.sqrt(A) * alpha) / a0\n\n b0 = (A * ((A + 1) + (A - 1) * np.cos(w0) + 2 * np.sqrt(A) * alpha)) / a0\n b1 = (-2 * A * ((A - 1) + (A + 1) * np.cos(w0))) / a0\n b2 = (A * ((A + 1) + (A - 1) * np.cos(w0) - 2 * np.sqrt(A) * alpha)) / a0\n\n return 1.0, a1, a2, b0, b1, b2",
"title": ""
},
{
"docid": "e40123693d62a0f1060dc84bb0f056af",
"score": "0.44792217",
"text": "def mass_hstab(\n hstab: asb.Wing,\n design_mass_TOGW: float,\n ultimate_load_factor: float,\n wing_to_hstab_distance: float,\n fuselage_width_at_hstab_intersection: float,\n aircraft_y_radius_of_gyration: float = None,\n use_advanced_composites: bool = False,\n) -> float:\n if aircraft_y_radius_of_gyration is None:\n aircraft_y_radius_of_gyration = 0.3 * wing_to_hstab_distance\n\n area = hstab.area()\n\n ### Determine if the hstab is all-moving or not\n all_moving = True\n for xsec in hstab.xsecs:\n for control_surface in xsec.control_surfaces:\n if (\n (control_surface.trailing_edge and control_surface.hinge_point > 0) or\n (not control_surface.trailing_edge and control_surface.hinge_point < 1)\n ):\n all_moving = False\n break\n\n return (\n 0.0379 *\n (1.143 if all_moving else 1) *\n (1 + fuselage_width_at_hstab_intersection / hstab.span()) ** -0.25 *\n (design_mass_TOGW / u.lbm) ** 0.639 *\n ultimate_load_factor ** 0.10 *\n (area / u.foot ** 2) ** 0.75 *\n (wing_to_hstab_distance / u.foot) ** -1 *\n (aircraft_y_radius_of_gyration / u.foot) ** 0.704 *\n np.cosd(hstab.mean_sweep_angle()) ** -1 *\n hstab.aspect_ratio() ** 0.166 *\n (1 + hstab.control_surface_area() / area) ** 0.1 *\n (advanced_composites[\"tails\"] if use_advanced_composites else 1)\n ) * u.lbm",
"title": ""
},
{
"docid": "9f398801833cdf3ec878764f9322b4a4",
"score": "0.44786233",
"text": "def dp_all(foods, cal_goal, pro_goal, carb_goal, fat_goal):\n costs = init_four_d_array((cal_goal, pro_goal, carb_goal, fat_goal),\n 999999999)\n foods_used = init_four_d_array((cal_goal, pro_goal, carb_goal, fat_goal),\n {})\n\n for i in range(cal_goal):\n for j in range(pro_goal):\n for k in range(carb_goal):\n for l in range(fat_goal):\n for n in range(len(foods)):\n food = foods[n]\n if (int(food['calories']) > i\n or int(food['protein']) > j\n or int(food['carbs']) > k\n or int(food['fat']) > l):\n continue\n if (costs[i - int(food['calories'])]\n [j - int(food['protein'])]\n [k - int(food['carbs'])]\n [l - int(food['fat'])]\n == 999999999):\n prev_cost = 0\n prev_foods_used = {}\n else:\n prev_cost = (macros[i - int(food['calories'])]\n [j - int(food['protein'])]\n [j - int(food['carbs'])]\n [j - int(food['fat'])])\n prev_foods_used = \\\n (foods_used[i - int(food['calories'])]\n [j - int(food['protein'])]\n [k - int(food['carbs'])]\n [l - int(food['fat'])]).copy()\n new_cal = calories(\n foods, prev_foods_used) + food['calories']\n new_pro = protein(\n foods, prev_foods_used) + food['protein']\n new_car = carbs(\n foods, prev_foods_used) + food['protein']\n new_fat = fat(\n foods, prev_foods_used) + food['protein']\n if (costs[i][j] > prev_cost + food['serving_cost']\n and new_cal > i - 20 and new_cal < i + 10\n and new_pro < j + 5 and new_pro < j + 5\n and new_car < j + 5 and new_car < j + 5\n and new_fat < j + 5 and new_fat < j + 5):\n costs[i][j][k][l] = prev_cost + \\\n food['serving_cost']\n try:\n prev_foods_used[n] += 1\n except KeyError:\n prev_foods_used[n] = 1\n foods_used[i][j][k][l] = prev_foods_used\n return foods_used[cal_goal - 1][pro_goal - 1][carb_goal - 1][fat_goal - 1]",
"title": ""
},
{
"docid": "208efd2ec5e4418838d931c62ac3db7b",
"score": "0.44778758",
"text": "def solve_tsp_grouped(list_of_locations, list_of_homes, starting_car_location, adjacency_matrix, params=[]):\n drop_off_dict = {}\n car_path = []\n home_map = {}\n group_score = {}\n center_map = {}\n epsilon = 1.2\n home_indexes = convert_locations_to_indices(list_of_homes, list_of_locations)\n orig_home_indexes = home_indexes[:]\n cluster_map = {}\n\n start = list_of_locations.index(starting_car_location)\n graph, msg = adjacency_matrix_to_graph(adjacency_matrix)\n all_paths = dict(nx.all_pairs_dijkstra(graph))\n\n for v in range(len(list_of_locations)):\n group_score[v], cluster_map[v] = compute_group(v, home_indexes[:], all_paths, epsilon)\n\n sorted_v = sorted([k for k in group_score.keys() if group_score[k] > 0], key = lambda x: group_score[x])\n min_group_score = group_score[sorted_v[0]]\n #print(min_group_score)\n delta = 6\n\n high_centrality_homes = set() #LOW GROUP SCORE VERTICES (CLUSTER)\n used_homes = set()\n newHome = home_indexes[:]\n\n while newHome and group_score[sorted_v[0]] < delta * min_group_score:\n\n v = sorted_v[0]\n high_centrality_homes.add(v)\n usedList = cluster_map[v]\n #print(usedList)\n if v in newHome and v not in usedList:\n usedList.append(v)\n if v in center_map.keys():\n center_map[v].extend(usedList)\n else:\n center_map[v] = usedList\n #if v in home_indexes:\n #center_map[v].append(v)\n for vert in usedList:\n used_homes.add(vert)\n if vert in newHome:\n newHome.remove(vert)\n used_homes.add(v)\n while v in newHome:\n newHome.remove(v)\n for x in range(len(list_of_locations)):\n group_score[x], cluster_map[x] = compute_group(x, newHome[:], all_paths, epsilon)\n sorted_v = sorted([k for k in group_score.keys() if group_score[k] > 0], key = lambda x: group_score[x])\n\n for home in newHome:\n if home not in center_map.keys():\n center_map[home] = [home]\n \"\"\"START TIM\n\n for home in high_centrality_homes:\n center_map[home] = [home]\n dist_dict = all_paths.get(home)[0]\n paths_dict = all_paths.get(home)[1]\n dist_dict = {k:v for (k,v) in dist_dict.items() if k not in used_homes and k in home_indexes} #distance dict of all remaing homes\n\n min_dist = min(dist_dict.values()) #closest home to high centrality home\n dist_dict = {k:v for (k,v) in dist_dict.items() if dist_dict[k] <= min_dist*epsilon}\n\n for cluster_home in dist_dict.keys():\n center_map[home].append(cluster_home)\n home_indexes.remove(cluster_home)\n used_homes.add(cluster_home)\n\n start_in_home = start in home_indexes\n if start in home_indexes:\n home_indexes.remove(start)\n home_indexes.insert(0, start)\n home_count = 0;\n\n for home in home_indexes:\n #print(home, end = \" \")\n home_map[home_count] = home\n home_count += 1\n # Instantiate the data problem.\n #print(len(home_map))\n END TIM \"\"\"\n tspInput = list(high_centrality_homes)\n tspInput.extend(newHome)\n if start in tspInput:\n tspInput.remove(start)\n tspInput.insert(0, start)\n home_map.clear()\n for i in range(len(tspInput)):\n home_map[i] = tspInput[i]\n data = create_data_model(tspInput, 0)\n\n # Create the routing index manager.\n manager = pywrapcp.RoutingIndexManager(len(data['locations']),\n data['num_vehicles'], data['depot'])\n\n #print(manager.NodeToIndex(15))\n # Create Routing Model.\n routing = pywrapcp.RoutingModel(manager)\n\n def distance_callback(from_index, to_index):\n \"\"\"Returns the distance between the two nodes.\"\"\"\n # Convert from routing variable Index to distance matrix NodeIndex.\n #print(home_map[to_index], end = \" \")\n from_index = manager.IndexToNode(from_index)\n to_index = manager.IndexToNode(to_index)\n dist_to = all_paths.get(home_map[from_index])[0][home_map[to_index]]\n #if from_index >= 25 or to_index >= 25:\n # print(\"from\" if from_index >= 25 else \"to\", end = \" \")\n #dist_to = all_paths[from_index][0][to_index]\n return dist_to\n\n transit_callback_index = routing.RegisterTransitCallback(distance_callback)\n\n # Define cost of each arc.\n routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n\n # Setting first solution heuristic.\n \"\"\"\n search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n search_parameters.first_solution_strategy = (\n routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)\n \"\"\"\n\n search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n search_parameters.local_search_metaheuristic = (\n routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)\n search_parameters.time_limit.seconds = 3\n #search_parameters.log_search = True\n\n # Solve the problem.\n assignment = routing.SolveWithParameters(search_parameters)\n\n # if assignment:\n # print_solution(manager, routing, assignment)\n # Print solution on console.\n\n #if start in home_indexes:\n # drop_off_dict[start] = [start]\n\n\n index = routing.Start(0)\n car_path.append(start)\n\n while not routing.IsEnd(index):\n previous_index = manager.IndexToNode(index)\n index = assignment.Value(routing.NextVar(index))\n to_index = manager.IndexToNode(index)\n car_path.append(home_map[to_index])\n #path_to = all_paths.get(home_map[previous_index])[1][home_map[to_index]]\n #drop_off_dict[home_map[to_index]] = [home_map[to_index]]\n #print(to_index, end = ' ')\n #car_path.extend(path_to)\n #route_distance += routing.GetArcCostForVehicle(previous_index, index, 0)\n # for i in car_path:\n # print(i)\n\n #print(car_path)\n drop_off_dict = center_map\n new_path = [start]\n previous_index = start\n\n\n #print(center_map)\n for to_index in car_path[1:]:\n new_path.pop()\n path_to = all_paths.get(previous_index)[1][to_index]\n new_path.extend(path_to)\n previous_index = to_index\n keys = drop_off_dict.keys()\n singleadd = []\n for i in keys:\n if i not in orig_home_indexes:\n vals = drop_off_dict[i]\n for v in vals:\n if v in new_path:\n if v in keys:\n drop_off_dict[v].append(v)\n drop_off_dict[i].remove(v)\n else:\n singleadd.append(v)\n drop_off_dict[i].remove(v)\n for item in singleadd:\n drop_off_dict[item] = [item]\n\n for i in drop_off_dict.keys():\n for k in drop_off_dict.keys():\n if i != k:\n if i in drop_off_dict[k]:\n drop_off_dict[k].remove(i)\n drop_off_dict[i].append(i)\n\n removeIt = set()\n for i in drop_off_dict.keys():\n if not drop_off_dict[i]:\n removeIt.add(i)\n\n for i in removeIt:\n drop_off_dict.pop(i, None)\n\n car_path = new_path\n return car_path, drop_off_dict",
"title": ""
},
{
"docid": "66be243e1fa85881a31d27d53a2f53b3",
"score": "0.44756114",
"text": "def initordering(cls):\n for i in range(len(clslist)):\n stages = cls.getConfigStages()\n for j in range(len(stages)):\n for k in range(len(slotlist)):\n cls.initorderingclsslot(clslist[i], stages[j], slotlist[k])\n # print(ordering)\n cls.log(1, ordering)",
"title": ""
},
{
"docid": "254a686b8aaf3ddc1fd320b8db713919",
"score": "0.44736385",
"text": "def create_packing_problems_with_optimal_solution_values():\n\n problems, problem_names, optimal_values = list(), list(), list()\n\n # the capacity is set to infinite so that it never restricts placements; all items have value 1 so that the objective is to maximize the number of placed items\n max_weight = np.inf\n\n # Circles in circle; Wolfram Alpha query: \"pack 7 circles of radius 3.9 in a circle of radius 13\"; full link: https://www.wolframalpha.com/input/?i=pack+7+circles+of+radius+3.9+in+a+circle+of+radius+13\n container_shape = Circle((13, 13), 13)\n container = Container(max_weight, container_shape)\n item_num = 7\n items = [Item(Circle((0, 0), 3.9), 1., 1.)] * item_num\n problem = Problem(container, items)\n problems.append(problem)\n problem_names.append(\"Circles in circle\")\n optimal_values.append(item_num)\n\n # Triangles in circle; Wolfram Alpha query: \"pack 20 triangles of side 4 in a circle of radius 9.5\"; full link: https://www.wolframalpha.com/input/?i=pack+20+triangles+of+side+4+in+a+circle+of+radius+9.5\n container_shape = Circle((9.5, 9.5), 9.5)\n container = Container(max_weight, container_shape)\n item_num = 20\n items = [Item(shape_functions.create_equilateral_triangle((0, 0), 4), 1., 1.)] * item_num\n problem = Problem(container, items)\n problems.append(problem)\n problem_names.append(\"Triangles in circle\")\n optimal_values.append(item_num)\n\n # Squares in circle; Wolfram Alpha query: \"pack 12 squares of side 3 in a circle of radius 7.8\"; full link: https://www.wolframalpha.com/input/?i=pack+12+squares+of+side+3+in+a+circle+of+radius+7.8\n container_shape = Circle((7.8, 7.8), 7.8)\n container = Container(max_weight, container_shape)\n item_num = 12\n items = [Item(shape_functions.create_square((0, 0), 3), 1., 1.)] * item_num\n problem = Problem(container, items)\n problems.append(problem)\n problem_names.append(\"Squares in circle\")\n optimal_values.append(item_num)\n\n # Circles in triangle; Wolfram Alpha query: \"pack 10 circles of radius 3 in a triangle of side 33\"; full link: https://www.wolframalpha.com/input/?i=pack+10+circles+of+radius+3+in+a+triangle+of+side+33\n container_shape = shape_functions.create_equilateral_triangle((19, 9.5), 33)\n container = Container(max_weight, container_shape)\n item_num = 10\n items = [Item(Circle((0, 0), 3), 1., 1.)] * item_num\n problem = Problem(container, items)\n problems.append(problem)\n problem_names.append(\"Circles in triangle\")\n optimal_values.append(item_num)\n\n # Triangles in triangle; Wolfram Alpha query: \"pack 18 triangles of side 3.5 in a triangle of side 20\"; full link: https://www.wolframalpha.com/input/?i=pack+18+triangles+of+side+3.5+in+a+triangle+of+side+20\n container_shape = shape_functions.create_equilateral_triangle((12, 6), 20)\n container = Container(max_weight, container_shape)\n item_num = 18\n items = [Item(shape_functions.create_equilateral_triangle((0, 0), 3.5), 1., 1.)] * item_num\n problem = Problem(container, items)\n problems.append(problem)\n problem_names.append(\"Triangles in triangle\")\n optimal_values.append(item_num)\n\n # Squares in triangle; Wolfram Alpha query: \"pack 30 squares of side 7.5 in a triangle of side 80\"; full link: https://www.wolframalpha.com/input/?i=pack+24+squares+of+side+7.5+in+a+triangle+of+side+80\n container_shape = shape_functions.create_equilateral_triangle((49, 24.5), 80)\n container = Container(max_weight, container_shape)\n item_num = 30\n items = [Item(shape_functions.create_square((0, 0), 7.5), 1., 1.)] * item_num\n problem = Problem(container, items)\n problems.append(problem)\n problem_names.append(\"Squares in triangle\")\n optimal_values.append(item_num)\n\n # Circles in square; Wolfram Alpha query: \"pack 50 circles of radius 17 in a square of side 300\"; full link https://www.wolframalpha.com/input/?i=pack+50+circles+of+radius+17+in+a+square+of+side+300:\n container_shape = shape_functions.create_square((150, 150), 300)\n container = Container(max_weight, container_shape)\n item_num = 50\n items = [Item(Circle((0, 0), 17), 1., 1.)] * item_num\n problem = Problem(container, items)\n problems.append(problem)\n problem_names.append(\"Circles in square\")\n optimal_values.append(item_num)\n\n # Triangles in square; Wolfram Alpha query: \"pack 15 triangles of side 4 in a square of side 14\"; full link: https://www.wolframalpha.com/input/?i=pack+15+triangles+of+side+4+in+a+square+of+side+14\n container_shape = shape_functions.create_square((7, 7), 14)\n container = Container(max_weight, container_shape)\n item_num = 15\n items = [Item(shape_functions.create_equilateral_triangle((0, 0), 4), 1., 1.)] * item_num\n problem = Problem(container, items)\n problems.append(problem)\n problem_names.append(\"Triangles in square\")\n optimal_values.append(item_num)\n\n # Squares in square; Wolfram Alpha query: \"pack 100 squares of side 4 in a square of side 58\"; full link: https://www.wolframalpha.com/input/?i=pack+100+squares+of+side+4+in+a+square+of+side+58\n container_shape = shape_functions.create_square((22.5, 22.5), 58)\n container = Container(max_weight, container_shape)\n item_num = 100\n items = [Item(shape_functions.create_square((0, 0), 4), 1., 1.)] * item_num\n problem = Problem(container, items)\n problems.append(problem)\n problem_names.append(\"Squares in square\")\n optimal_values.append(item_num)\n\n return problems, problem_names, optimal_values",
"title": ""
},
{
"docid": "5cd37ea0c2401ecc5201a4658dbc4441",
"score": "0.44729322",
"text": "def capacitygroup_group():",
"title": ""
},
{
"docid": "42c702de2faa76682e61f48ecadcba8a",
"score": "0.4472505",
"text": "def _bom_explode(self, cr, uid, bom, product, factor, properties=None, level=0, routing_id=False, previous_products=None, master_bom=None, context=None):\n uom_obj = self.pool.get(\"product.uom\")\n routing_obj = self.pool.get('mrp.routing')\n master_bom = master_bom or bom\n\n\n def _factor(factor, product_efficiency, product_rounding):\n factor = factor / (product_efficiency or 1.0)\n factor = _common.ceiling(factor, product_rounding)\n if factor < product_rounding:\n factor = product_rounding\n return factor\n\n factor = _factor(factor, bom.product_efficiency, bom.product_rounding)\n\n result = []\n result2 = []\n\n routing = (routing_id and routing_obj.browse(cr, uid, routing_id)) or bom.routing_id or False\n if routing:\n for wc_use in routing.workcenter_lines:\n wc = wc_use.workcenter_id\n d, m = divmod(factor, wc_use.workcenter_id.capacity_per_cycle)\n mult = (d + (m and 1.0 or 0.0))\n cycle = mult * wc_use.cycle_nbr\n result2.append({\n 'name': tools.ustr(wc_use.name) + ' - ' + tools.ustr(bom.product_tmpl_id.name_get()[0][1]),\n 'workcenter_id': wc.id,\n 'sequence': level + (wc_use.sequence or 0),\n 'cycle': cycle,\n 'hour': float(wc_use.hour_nbr * mult + ((wc.time_start or 0.0) + (wc.time_stop or 0.0) + cycle * (wc.time_cycle or 0.0)) * (wc.time_efficiency or 1.0)),\n })\n\n for bom_line_id in bom.bom_line_ids:\n if self._skip_bom_line(cr, uid, bom_line_id, product, context=context):\n continue\n if set(map(int, bom_line_id.property_ids or [])) - set(properties or []):\n continue\n\n if previous_products and bom_line_id.product_id.product_tmpl_id.id in previous_products:\n raise osv.except_osv(_('Invalid Action!'), _('BoM \"%s\" contains a BoM line with a product recursion: \"%s\".') % (master_bom.name,bom_line_id.product_id.name_get()[0][1]))\n if context.get('production'):\n production = context.get('production')\n for d1 in bom_line_id.dimensions:\n for d2 in production.dimensions:\n if d1.id == d2.dimension.id:\n factor = factor/d2.quantity\n quantity = _factor(bom_line_id.product_qty * factor, bom_line_id.product_efficiency, bom_line_id.product_rounding)\n bom_id = self._bom_find(cr, uid, product_id=bom_line_id.product_id.id, properties=properties, context=context)\n\n #If BoM should not behave like PhantoM, just add the product, otherwise explode further\n if bom_line_id.type != \"phantom\" and (not bom_id or self.browse(cr, uid, bom_id, context=context).type != \"phantom\"):\n result.append({\n 'name': bom_line_id.product_id.name,\n 'product_id': bom_line_id.product_id.id,\n 'product_qty': quantity,\n 'product_uom': bom_line_id.product_uom.id,\n 'product_uos_qty': bom_line_id.product_uos and _factor(bom_line_id.product_uos_qty * factor, bom_line_id.product_efficiency, bom_line_id.product_rounding) or False,\n 'product_uos': bom_line_id.product_uos and bom_line_id.product_uos.id or False,\n })\n elif bom_id:\n all_prod = [bom.product_tmpl_id.id] + (previous_products or [])\n bom2 = self.browse(cr, uid, bom_id, context=context)\n # We need to convert to units/UoM of chosen BoM\n factor2 = uom_obj._compute_qty(cr, uid, bom_line_id.product_uom.id, quantity, bom2.product_uom.id)\n quantity2 = factor2 / bom2.product_qty\n res = self._bom_explode(cr, uid, bom2, bom_line_id.product_id, quantity2,\n properties=properties, level=level + 10, previous_products=all_prod, master_bom=master_bom, context=context)\n result = result + res[0]\n result2 = result2 + res[1]\n else:\n raise osv.except_osv(_('Invalid Action!'), _('BoM \"%s\" contains a phantom BoM line but the product \"%s\" does not have any BoM defined.') % (master_bom.name,bom_line_id.product_id.name_get()[0][1]))\n\n return result, result2",
"title": ""
},
{
"docid": "89f3fb2ba95f4fd5f7c7d20d5878c77c",
"score": "0.44675395",
"text": "def move_orders_job_shop():\n # First: Move order from order_pool to the respective WIP\n # Second: route products as shown below\n # P1: M1-M2-M3\n # P2: M1-M3-M2\n # P3: M2-M1-M3\n # P4: M2-M3-M1\n # P5: M3-M1-M2\n # P6: M3-M2-M1\n # Third: after production is done, move order to FGI\n\n ##################### Step 1: empty the machines that have finished production in the previous step\n # The routing here doesn't contain the first production step, since the routing to that step\n # takes place in the order release process\n list_of_product_types = [1, 2, 3, 4, 5, 6]\n list_of_destinations = [\n [environment.wip_B, environment.wip_C, environment.finished_goods_inventory],\n [environment.wip_C, environment.wip_B, environment.finished_goods_inventory],\n [environment.wip_A, environment.wip_C, environment.finished_goods_inventory],\n [environment.wip_C, environment.wip_A, environment.finished_goods_inventory],\n [environment.wip_A, environment.wip_B, environment.finished_goods_inventory],\n [environment.wip_B, environment.wip_A, environment.finished_goods_inventory]\n ]\n # Move order from machine to the next wip, if processing_time_remaining of order is 0\n for machine_element in environment.list_of_all_machines:\n if len(machine_element.orders_inside_the_machine) == 1:\n order = machine_element.orders_inside_the_machine[0]\n if order.processing_time_remaining <= 0:\n destination = \\\n list_of_destinations[list_of_product_types.index(order.product_type)][order.current_production_step]\n # print(\"destination \" + str(len(destination)) + \" | machine \" + str(len(machine_element.orders_inside_the_machine)))\n destination.append(machine_element.orders_inside_the_machine.pop(0))\n # print(\"destination \" + str(len(destination)) + \" | machine \" + str(len(machine_element.orders_inside_the_machine)))\n ##### example case product type 1, step 0:\n # von destinations nehme list item 0 (prodtype)\n # von list item 0 nehme list item 0 (prodstep)\n # füge da die order ein\n order.current_production_step += 1\n\n ##################### Step 2: move orders from WIPs into the machines\n # Each origin belongs to one destination.\n # The first item in destinations belongs to the first item in origins and so on.\n # The order movements shown in Step 2 do not depend on the order's product type,\n # instead they depend on the machine scheduling policy.\n # In this version, only a first come, first serve policy is implemented.\n list_of_destinations = environment.list_of_all_machines\n list_of_origins = environment.list_of_all_wip_elements\n wip_names = [\"wip_A\", \"wip_B\", \"wip_C\", \"wip_D\", \"wip_E\", \"wip_F\"]\n\n for machine in list_of_destinations:\n if global_settings.scheduling_policy == \"first_come_first_serve\" and \\\n len(machine.orders_inside_the_machine) == 0 and \\\n len(list_of_origins[list_of_destinations.index(machine)]) > 0:\n\n ############ debugging info ############\n if global_settings.show_movements_from_wip_to_machine == True:\n print(\"Step \" + str(\n global_settings.current_time) + \": Order moved from \" +\n wip_names[list_of_destinations.index(machine)] + \" to \" + str(\n machine.name) + \". Orders in \" +\n wip_names[list_of_destinations.index(machine)] + \": \" + str(\n len(list_of_origins[list_of_destinations.index(machine)])))\n ########################\n machine.orders_inside_the_machine.append(list_of_origins[list_of_destinations.index(machine)].pop(0))\n environment.set_new_random_processing_time(machine) # set a new random processing time for the next order\n machine.orders_inside_the_machine[0].processing_time_remaining = machine.processing_time\n machine.orders_inside_the_machine[0].arrival_times_m1m2m3.append(global_settings.current_time)\n\n ##################### Step 3: move orders from FGI to shipped when order due date is reached\n # Move orders from FGI to shipped_orders once they have reached their due_date\n if global_settings.current_time % global_settings.duration_of_one_period == 0:\n ship_orders()\n return",
"title": ""
},
{
"docid": "53d8ddee2befda0c3c306491ca5ed7e8",
"score": "0.44664317",
"text": "def test_categories_are_sorted(self):\n self.data_sorted(self.test_data['shirts'], self.test_data['pants'])",
"title": ""
},
{
"docid": "8e606ad591737129874447ffd32eabfc",
"score": "0.4464597",
"text": "def divide_to_species(self):\n titles = []\n for i in self.rest:\n titles.append(i.title.split(\" \"))\n for i in range(len(titles)):\n for j in range(i, len(titles)):\n if titles[i][0] == titles[j][0] and titles[i][1] == titles[j][1]:\n if \" \".join(titles[i]) not in [z.title for z in self.species[\" \".join(titles[i][:2])]]:\n self.rest[i].species = \" \".join(titles[i])\n self.species[\" \".join(titles[i][:2])].append(self.rest[i])\n if \" \".join(titles[j]) not in [z.title for z in self.species[\" \".join(titles[j][:2])]]:\n self.rest[j].species = \" \".join(titles[j])\n self.species[\" \".join(titles[j][:2])].append(self.rest[j])\n\n self.name_of_species = list(self.species.keys())\n\n for i in self.species.keys():\n self.count_species[i] = len(self.species[i])",
"title": ""
},
{
"docid": "a9a5a26419562031f4ab15b1eef81ac0",
"score": "0.44626477",
"text": "def test_list_flavors_filter_by_min_ram(self):\n response = self.flavors_client.list_flavors_with_detail()\n flavors = response.entity\n\n # Sort the flavors by RAM in ascending order\n flavors.sort(key=lambda k: int(k.ram))\n\n # Remove any flavors from the list that are smaller than the\n # flavor with the second smallest RAM value\n filter_criteria = lambda x: int(x.ram) >= int(flavors[1].ram)\n expected_flavors = filter(filter_criteria, flavors)\n response = self.flavors_client.list_flavors(min_ram=flavors[1].ram)\n actual_flavors = response.entity\n\n actual_flavor_ids = set([flavor.id for flavor in actual_flavors])\n expected_flavor_ids = set([flavor.id for flavor in expected_flavors])\n self.assertEqual(actual_flavor_ids, expected_flavor_ids)",
"title": ""
},
{
"docid": "0cbe08734903ae91fc76426cd8afacd4",
"score": "0.44626394",
"text": "def algo(segregatedJob):\n global total\n rho = computeRho(segregatedJob)\n r = len(rho);\n\n S = [[0 for x in range(r)] for y in range(r)]\n k = 0\n #implementaion of scheduling algorithm\n while(k<len(S)):\n for j in range(k, len(S)):\n if k == j and j != 0 and segregatedJob[j].noOfChildren < 4:\n S[j][k] = max(segregatedJob[j].value + S[rho[j]][k - 1], S[j - 1][k - 1])\n\n elif j > k and j != 0 and segregatedJob[j].noOfChildren >= 4:\n S[j][k] = S[j - 1][k]\n\n elif k == j and j != 0 and segregatedJob[j].noOfChildren >= 4:\n S[j][k] = max(segregatedJob[j].value + S[rho[j]][rho[k]], S[j - 1][k - 1])\n\n elif j > k and j != 0 and segregatedJob[j].noOfChildren < 4:\n S[j][k] = max(segregatedJob[j].value + S[rho[j]][k], S[j - 1][k])\n else:\n pass\n S[k][j] = S[j][k]\n k += 1\n length = len(S)\n\n #Adding the max pay for every individual field in the matrix\n total += S[length-1][length-1]",
"title": ""
},
{
"docid": "46caef9bb84b7a543d1119ad8700ac74",
"score": "0.44615993",
"text": "def brute_force_cow_transport(cows,limit=10):\r\n # TODO: Your code here\r\n #print(cows)\r\n #trip=[]\r\n #import copy\r\n cowsNames=cows.keys()\r\n #print(cowsNames)\r\n cowNamesList=[]\r\n \r\n #for cowName in cowsNames:\r\n # if cows[cowName] <=limit:\r\n # cowNamesList.append(cowName)\r\n # print(cowNamesList)\r\n\r\n herd = sorted(cows.items(), key=lambda cows:cows[1], reverse=True) \r\n #print(herd)\r\n #limit = 10\r\n #weight = [v for x, v in cows.items()] \r\n #name = [x for x, v in cows.items()]\r\n #print('weight', weight)\r\n #print('name', name)\r\n #for i in weight:\r\n #print (i)\r\n # if sum(trip) <= limit: \r\n # trip.append(i)\r\n # print(trip)\r\n #trips=[]\r\n number_of_trips=len(cows)\r\n results=None\r\n limit=10\r\n #best_trips=len(cows) + 1\r\n for trips in get_partitions(herd): \r\n #print(trips) \r\n #flag = False\r\n #numberOfTrips = 0\r\n weights=[]\r\n for trip in trips:\r\n print(trip)\r\n weight=(sum([v for x, v in cows.items() if x in trip]))\r\n #print('weight',weight) \r\n weights.append(weight)\r\n #print('weights',weights)\r\n #print('max weight',max(weights))\r\n for w in weights:\r\n #print (w)\r\n if w <= limit: #and len(trips) <= number_of_trips:\r\n #print(limit) \r\n #print(len(trips))\r\n #number_of_trips=len(trips)\r\n #print(number_of_trips)\r\n results = trips\r\n #print(trips)\r\n return results \r\n #for cow in one_trip:\r\n #print('cow',cow)\r\n #trip_weight+=cow[1]\r\n #print('trip weight', trip_weight)\r\n #temp_results=[] \r\n #if trip_weight > limit: \r\n #print('name',cow[0])\r\n #flag = False \r\n #break\r\n #if flag and (len(trips) < best_trips):\r\n #best_trips = len(trips)\r\n # print(best_trips)\r\n #for trip in trips:\r\n #temp_results=[]\r\n #print(l)\r\n #for cow in trip:\r\n #temp_results = trips.append(cow[0]) \r\n #print(trips)\r\n #print(temp_results)\r\n #results.append(temp_results)\r\n #return results \r\n #print('trips',trips)\r\n #if len(i) < fewest_trips:\r\n\r\n #trips.append(i[0])\r\n\r\n\r\n # trips = len(i)\r\n # for j in i:\r\n # temp = []\r\n # for cow in i:\r\n # temp.append(i[0])\r\n # print(temp)\r\n #for k in j:\r\n # print(k)\r\n #result=[sum(z) for z in trip[1]]\r\n #print(result)\r\n #print('limit',limit)\r\n #for i in result:\r\n # if i <= limit:\r\n # trip.append(name)\r\n # print(trip)\r\n \r\n #print(alist)\r\n #for p in partition:\r\n # print(p) \r\n #if weight <= limit:\r\n #result = (brute_force_cow_transport(weight, limit))\r\n #print(True)\r\n \r\n \r\n #if j==[] or limit==0:\r\n # result = (0,())\r\n \r\n #elif j[1] > limit:\r\n #explore right branch only\r\n # result = brute_force_cow_transport(cows[1], limit) \r\n # else:\r\n #nextItem = cows\r\n #print(nextItem)\r\n #explore left branch\r",
"title": ""
},
{
"docid": "a399228709d4b776dd409447cebc9d17",
"score": "0.44608897",
"text": "def hatch(S, dist, angle=0., flip_horizontal=False, get_hole_count=False, max_count=1000000, eps=1e-10): \n if not is_compound(S):\n S = [S]\n\n hole_count = [0 for i in range(len(S))]\n solid_count = [0 for i in range(len(S))]\n\n if not S:\n return []\n \n # Rotate shape for oriented hatches \n theta = radians(angle)\n mat = rot_2d(-theta, affine=True)\n S = [affine_transform(mat, P) for P in S]\n\n box = bounding_box(S)\n\n # build edge table\n ET = []\n for i, P in enumerate(S):\n P = np.array(P)\n n = P.shape[0]\n if n <= 2:\n continue\n for j in range(n):\n a, b = P[j], P[(j+1)%n]\n # reorder increasing y\n if a[1] > b[1]:\n a, b = b, a\n # slope\n dx = (b[0] - a[0]) \n dy = (b[1] - a[1])\n if abs(dx) > eps:\n m = dy/dx \n else:\n m = 1e15\n if abs(m) < eps:\n m = None\n ET.append(Edge(a=a, b=b, m=m, i=i))\n\n # sort by increasing y of first point\n ET = sorted(ET, key=lambda e: e.a[1])\n\n # intersection x\n def ex(e, y):\n if e.m is None:\n return None\n return e.a[0] + (y - e.a[1])/e.m\n\n y = box[0][1]\n scanlines = []\n\n AET = [] # active edge table\n\n flip = 0\n c = 0\n while ET or AET:\n if y > box[1][1]:\n break\n if c >= max_count:\n print(\"scanlines: reached max number of iterations\")\n break\n c += 1\n\n # move from ET to AET\n i = 0\n for e in ET:\n if e.a[1] <= y:\n AET.append(e)\n i += 1\n else:\n break\n if i < len(ET):\n ET = ET[i:]\n else:\n ET = []\n \n # remove passed edges\n AET = sorted(AET, key=lambda e: e.b[1])\n AET = [e for e in AET if e.b[1] > y] \n \n xs = [(ex(e, y), e.i) for e in AET]\n #brk()\n xs = [xi for xi in xs if xi[0] is not None]\n # sort Xs (flipped each scanline for more efficent plotting )\n if flip:\n xs = sorted(xs, key=lambda v: -v[0])\n else:\n xs = sorted(xs, key=lambda v: v[0])\n \n if flip_horizontal:\n flip = not flip\n \n even_odd = [0 for i in range(len(S))]\n\n if len(xs) > 1:\n #brk()\n parity = 1\n for (x1,i1), (x2,i2) in zip(xs, xs[1:]): \n a, b = (np.array([x1, y]),\n np.array([x2, y]))\n if parity:\n scanlines += [a, b]\n even_odd[i2] += 1\n else:\n # If se are outside of a shape and we enounter \n # an unvisited contour, it means that this is a separate \n # outer contour, so don't count. Otherwise...\n if even_odd[i2]:\n even_odd[i2] += 1\n pass\n parity = not parity\n\n # increment\n y = y + dist\n\n # unrotate\n if scanlines:\n scanlines = affine_transform(mat.T, scanlines) #np.array(scanlines))\n # make list of hatch segments\n scanlines = [[a, b] for a, b in zip(scanlines[0::2], scanlines[1::2])]\n return scanlines",
"title": ""
},
{
"docid": "aa4644dd963299f70226e91154811fd5",
"score": "0.44599688",
"text": "def sort_values(self):\r\n for loopindex in range(0, self.population_size):\r\n index = self.cost_populations.index(min(self.cost_populations))\r\n \r\n if loopindex < int(self.population_size / 2):\r\n self.best_districts.append(self.district_population[index])\r\n self.best_costs.append(self.cost_populations[index])\r\n else:\r\n self.worst_districts.append(self.district_population[index])\r\n \r\n del self.cost_populations[index]\r\n del self.district_population[index]",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="730"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">6ac45f39cb1314b9c014133ecf90e9db</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Convert the product attribute form a string to an enum and return it.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "f8be53873402eb15ea70d1f29c7c3e7a",
"score": "0.6536794",
"text": "def product_as_enum(self):\n return Fit.field_enums.product_enum(self.manufacturer, self.product)",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "32267688a1610b39258f5bd86ac3ab87",
"score": "0.7099658",
"text": "def convert_attr_type_to_enum(attr_value):\r\n if str(attr_value.type) == 'AttrType.INTS':\r\n return 7\r\n elif str(attr_value.type) == 'AttrType.UNDEFINED':\r\n return 0\r\n elif str(attr_value.type) == 'AttrType.FLOATS':\r\n return 6\r\n elif str(attr_value.type) == 'AttrType.GRAPH':\r\n return 5\r\n elif str(attr_value.type) == 'AttrType.GRAPHS':\r\n return 10\r\n elif str(attr_value.type) == 'AttrType.INT':\r\n return 2\r\n elif str(attr_value.type) == 'AttrType.STRING':\r\n return 3\r\n elif str(attr_value.type) == 'AttrType.TENSOR':\r\n return 4\r\n elif str(attr_value.type) == 'AttrType.TENSORS':\r\n return 9\r\n elif str(attr_value.type) == 'AttrType.SPARSE_TENSOR':\r\n return 11\r\n elif str(attr_value.type) == 'AttrType.SPARSE_TENSORS':\r\n return 12\r\n elif str(attr_value.type) == 'AttrType.FLOAT':\r\n return 1\r\n elif str(attr_value.type) == 'AttrType.STRINGS':\r\n return 8\r\n else:\r\n raise Exception('Invalid type passed in')",
"title": ""
},
{
"docid": "bd9050542a7f25ba029494d0fab920a5",
"score": "0.6452569",
"text": "def get_enum_product_value():\r\n faker = Faker()\r\n enum = faker.words(1, ['Keyboard', 'Laptop', 'Car',\r\n 'Motorcycle', 'Cold Drink', 'Football',\r\n 'Mobile', 'Ice Cream', 'Furniture',\r\n 'Air Conditioner', 'Television', 'Property'], True)\r\n return enum[0]",
"title": ""
},
{
"docid": "cb602ab38385751b5b680053d792ef72",
"score": "0.639998",
"text": "def get_type_of(cls, string):\n if string is None:\n raise ValueError(\"get_enum_value function does not get a proper string value\")\n return cls(string)",
"title": ""
},
{
"docid": "7a6a5d9b8f4f6712513da1dd9dbbda2f",
"score": "0.61347604",
"text": "def FromString(val):\n # first, check if the value supplied is the string literal of the enum (e.g. \"String\")\n\n if isinstance(val, bytes):\n val = val.decode('utf-8')\n\n try:\n return ContractParameterType[val]\n except Exception as e:\n # ignore a KeyError if the val isn't found in the Enum\n pass\n\n # second, check if the value supplied is bytes or hex-encoded (e.g. b'07')\n try:\n if isinstance(val, (bytearray, bytes)):\n int_val = int.from_bytes(val, 'little')\n else:\n int_val = int.from_bytes(binascii.unhexlify(val), 'little')\n except (binascii.Error, TypeError) as e:\n # if it's not hex-encoded, then convert as int (e.g. \"7\" or 7)\n int_val = int(val)\n\n return ContractParameterType(int_val)",
"title": ""
},
{
"docid": "f0b04d2c7992257b9154519c545ac3f4",
"score": "0.5931073",
"text": "def param_to_unit_type_enum(unit_type_str):\r\n if unit_type_str == \"Metric\":\r\n return Process_MultiNet.UnitType.Metric\r\n return Process_MultiNet.UnitType.Imperial",
"title": ""
},
{
"docid": "1b6dc796edef5bd3f2b3bcc1ea3342b0",
"score": "0.58912694",
"text": "def _enum_from_op_string(op_string: str) -> int:\n try:\n return _COMPARISON_OPERATORS[op_string]\n except KeyError:\n choices = \", \".join(sorted(_COMPARISON_OPERATORS.keys()))\n msg = _BAD_OP_STRING.format(op_string, choices)\n raise ValueError(msg)",
"title": ""
},
{
"docid": "5ebbb64e8bef687fb29da638ae0f2ba1",
"score": "0.5877983",
"text": "def parse_attribute(cls, name, attr_string):\n\n attr_string = attr_string.lower().strip()\n\n if(attr_string[:len('numeric')] == 'numeric' or\n attr_string[:len('int')] == 'int' or\n attr_string[:len('real')] == 'real'):\n return cls(name)\n else:\n return None",
"title": ""
},
{
"docid": "8d24f54233f845c7c11bcae35292ca38",
"score": "0.5782823",
"text": "def get_enum_value():\r\n faker = Faker()\r\n enum = faker.words(1, ['small', 'medium', 'large', 'extra large'], True)\r\n return enum[0]",
"title": ""
},
{
"docid": "9201370b026b677745f4208b16adb993",
"score": "0.56698394",
"text": "def type_enum(a: Enum):",
"title": ""
},
{
"docid": "5badcefff930cbaafe68bbe10dcb3ad3",
"score": "0.56123877",
"text": "def GetAttributeValueAsInt(self, string, string_1):\n ...",
"title": ""
},
{
"docid": "4511869fc724f5f2ee3361f93c07524d",
"score": "0.5595326",
"text": "def get_CommandStatusType_str(str):\n if str == \"Pending\": return CommandStatusType.Pending\n if str == \"Approved\": return CommandStatusType.Approved\n if str == \"InProcess\": return CommandStatusType.InProcess\n if str == \"Executed\": return CommandStatusType.Executed\n if str == \"Cancelled\": return CommandStatusType.Cancelled",
"title": ""
},
{
"docid": "dd59e8617eeb7ac46e91cdea80613bb7",
"score": "0.5576698",
"text": "def random_attribute(enum_type):\n return random.choice(list(enum_type))",
"title": ""
},
{
"docid": "f9e42507dfebc7515531ba758edaa47c",
"score": "0.5544172",
"text": "def get(self, category_name):\r\n\r\n try:\r\n enum = getattr(DB.BuiltInCategory, category_name)\r\n except AttributeError:\r\n raise RpwCoerceError(category_name, DB.BuiltInCategory)\r\n return enum",
"title": ""
},
{
"docid": "759d1cf26bd6ae6754d9f6f632aca663",
"score": "0.5519111",
"text": "def parse_attribute(cls, name, attr_string):\n\n attr_string = attr_string.lower().strip()\n\n if attr_string[:len('string')] == 'string':\n return cls(name)\n else:\n return None",
"title": ""
},
{
"docid": "98fe9767eaada15bc7f5c0719eb06f2c",
"score": "0.5471572",
"text": "def enum_decode(s: str) -> Optional[Enum]:\n if \".\" in s:\n name, member = s.split(\".\")\n return getattr(VN_ENUMS[name], member)\n else:\n return None",
"title": ""
},
{
"docid": "5f4c0fdca0156668231f794eace13827",
"score": "0.5462397",
"text": "def parse_attribute(cls, name, attr_string):\n\n attr_string_lower = attr_string.lower().strip()\n\n if attr_string_lower[:len('relational')] == 'relational':\n return cls(name)\n else:\n return None",
"title": ""
},
{
"docid": "7d241c9206ac99e7454cf26ff41c79a3",
"score": "0.5441861",
"text": "def ParseEnum(field, value):\n enum_descriptor = field.enum_type\n try:\n number = int(value, 0)\n except ValueError:\n # Identifier.\n enum_value = enum_descriptor.values_by_name.get(value, None)\n if enum_value is None:\n raise ValueError('Enum type \"%s\" has no value named %s.' %\n (enum_descriptor.full_name, value))\n else:\n # Numeric value.\n if hasattr(field.file, 'syntax'):\n # Attribute is checked for compatibility.\n if field.file.syntax == 'proto3':\n # Proto3 accept numeric unknown enums.\n return number\n enum_value = enum_descriptor.values_by_number.get(number, None)\n if enum_value is None:\n raise ValueError('Enum type \"%s\" has no value with number %d.' %\n (enum_descriptor.full_name, number))\n return enum_value.number",
"title": ""
},
{
"docid": "cc9d1fcc6478b152cf812669aa82f33f",
"score": "0.5419953",
"text": "def enumerated_value_factory(sqla):\n attributes = sqla.query(Attribute).all()\n if not attributes:\n create_multiple_attributes(sqla, 5, 1)\n attributes = sqla.query(Attribute).all()\n value_string = rl_fake().name()\n value_i18n = f'enumerated.{value_string}'[:32]\n\n locale_code = 'en-US'\n if not sqla.query(I18NLocale).get(locale_code):\n sqla.add(I18NLocale(code=locale_code, desc='English US'))\n\n if not sqla.query(I18NKey).get(value_i18n):\n i18n_create(value_i18n, locale_code,\n value_string, description=f\"Enum Value {value_string}\")\n\n enumerated_value = {\n 'attributeId': random.choice(attributes).id,\n 'valueI18n': value_i18n,\n 'active': flip()\n }\n return enumerated_value",
"title": ""
},
{
"docid": "d5c632e3a0a496d8803d1d7466f0b96d",
"score": "0.5376583",
"text": "def from_string(cls, string):\n return cls.NAME_TO_VALUES.get(string.upper(), cls.DEVELOPMENT)",
"title": ""
},
{
"docid": "d063d36ac0c5c6a88e77a160dfab8daf",
"score": "0.5348909",
"text": "def convert_type(type_str):\n return types.get(\" \".join(type_str.lower().split()), \"custom\")",
"title": ""
},
{
"docid": "af69540aa633ca61957aa6e45ece5947",
"score": "0.5312507",
"text": "def _to_enum(value, enum_type, enum_default=None):\n assert enum_default is None or isinstance(enum_default, enum_type)\n\n if not isinstance(value, enum_type):\n if value is None and enum_default is not None:\n value = enum_default\n elif isinstance(value, six.string_types):\n value = enum_type[value.upper()]\n else:\n raise TypeError(\"Not a valid {}: {}\".format(\n enum_type.__name__, value,\n ))\n\n return value",
"title": ""
},
{
"docid": "2ae9977a9378d6acb4e8c58f13b72ad3",
"score": "0.53104126",
"text": "def strlist_to_enum(field: str, strlist: list[str], default_value=...) -> tuple[type[StrEnum], Any]:\n return StrEnum(field, {v: v for v in strlist}), default_value # type: ignore[call-overload]",
"title": ""
},
{
"docid": "ba706687da8ace84307fd2b685ee6802",
"score": "0.52763164",
"text": "def unmarshal(self, value):\n if self.deprecated:\n warnings.warn(\n \"The schema is deprecated\", DeprecationWarning)\n casted = self.cast(value)\n\n if casted is None and not self.required:\n return None\n\n if self.enum and casted not in self.enum:\n raise InvalidValue(\n \"Value of {0} not in enum choices: {1}\".format(\n value, self.enum)\n )\n\n return casted",
"title": ""
},
{
"docid": "92685e431aafd1010ab4dd2ed5d2ba8d",
"score": "0.524886",
"text": "def parse_attribute(cls, name, attr_string):\n if attr_string[0] == '{':\n values = cls._get_nom_val(attr_string)\n return cls(name, values)\n else:\n return None",
"title": ""
},
{
"docid": "67fa97313662e94a8e1fbf24731724e2",
"score": "0.52441096",
"text": "def __new__(cls, *args, **kwargs):\n enum = super(EnumType, cls).__new__(cls, *args, **kwargs)\n attributes = filter(lambda (k, v): k.isupper(), enum.__dict__.iteritems())\n labels = enum.__dict__.get('labels', {})\n enum.values = {}\n for attribute in attributes:\n enum.values[attribute[1]] = enum.Value(attribute[0], attribute[1], labels.get(attribute[1]), enum)\n return enum",
"title": ""
},
{
"docid": "6ec91957443848624c534909a7be3608",
"score": "0.52248377",
"text": "def _str_to_filter_enum(comparator):\n if comparator == 'Equal to':\n return enums.Filters.EQUAL_TO\n elif comparator == 'Not equal to':\n return enums.Filters.NOT_EQUAL_TO\n elif comparator == 'Less than':\n return enums.Filters.LESS_THAN\n elif comparator == 'Less than equal to':\n return enums.Filters.LESS_THAN_EQUAL_TO\n elif comparator == 'Greater than':\n return enums.Filters.GREATER_THAN\n elif comparator == 'Greater than equal to':\n return enums.Filters.GREATER_THAN_EQUAL_TO\n elif comparator == 'In':\n return enums.Filters.IN\n elif comparator == 'Not In':\n return enums.Filters.NOT_IN\n else:\n return None",
"title": ""
},
{
"docid": "53606c0643d92c7b7e0c0d4891f83327",
"score": "0.5224575",
"text": "def _string_to_attribute(self, device_id, class_id, attr_name, str_value):\n try:\n me_map = self._omci_agent.get_device(device_id).me_map\n\n if class_id in me_map:\n entity = me_map[class_id]\n attr_index = entity.attribute_name_to_index_map[attr_name]\n eca = entity.attributes[attr_index]\n field = eca.field\n else:\n # Here for auto-defined MEs (ones not defined in ME Map)\n from pyvoltha.adapters.extensions.omci.omci_cc import UNKNOWN_CLASS_ATTRIBUTE_KEY\n field = StrFixedLenField(UNKNOWN_CLASS_ATTRIBUTE_KEY, None, 24)\n\n if isinstance(field, StrFixedLenField):\n from scapy.base_classes import Packet_metaclass\n default = field.default\n if isinstance(default, Packet_metaclass) and \\\n hasattr(default, 'to_json'):\n value = json.loads(str_value)\n else:\n value = str_value\n\n elif isinstance(field, OmciSerialNumberField):\n value = str_value\n\n elif isinstance(field, MACField):\n value = str_value\n\n elif isinstance(field, IPField):\n value = str_value\n\n elif isinstance(field, (ByteField, ShortField, IntField, LongField)):\n if str_value.lower() in ('true', 'false'):\n str_value = '1' if str_value.lower() == 'true' else '0'\n value = int(str_value)\n\n elif isinstance(field, BitField):\n value = int(str_value)\n\n elif hasattr(field, 'load_json'):\n value = field.load_json(str_value)\n\n elif isinstance(field, FieldListField):\n value = json.loads(str_value)\n\n else:\n self.log.warning('default-conversion', type=type(field),\n class_id=class_id, attribute=attr_name, value=str_value)\n value = None\n\n return value\n\n except Exception as e:\n self.log.exception('attr-to-string', device_id=device_id,\n class_id=class_id, attr=attr_name,\n value=str_value, e=e)\n raise",
"title": ""
},
{
"docid": "5ea609d2a67f0d0c470ff6f7e5c0db93",
"score": "0.51956856",
"text": "def Enum(enum_name: str, elem: typing.Union[str, int], target: lldb.SBTarget) -> lldb.SBTypeEnumMember:\n enum = Type(enum_name, target)\n assert enum.IsValid(), f\"couldn't find enumeration '{enum_name}'\"\n members = enum.GetEnumMembers()\n assert members.IsValid(), f\"'{enum_name} is not an enumeration\"\n val = members[elem]\n assert val is not None and val.IsValid(), f\"couldn't find enumerator '{elem}' in '{enum_name}'\"\n return val",
"title": ""
},
{
"docid": "dc006a16a3b6ed8e2586398cb68d8f0c",
"score": "0.5190694",
"text": "def get_attribute_for_decode(cls, attr_bytes):\n basic_attr_id = struct.unpack('!B', attr_bytes[:ATTRIBUTE_TYPE_SIZE])[0]\n basic_attr_len = struct.unpack('!B', attr_bytes[ATTRIBUTE_TYPE_SIZE:ATTRIBUTE_TYPE_SIZE+ATTRIBUTE_LEN_SIZE])[0]\n if basic_attr_id == VENDOR_SPECIFIC_ATTRIBUTE:\n vsa_bytes = attr_bytes[ATTRIBUTE_TYPE_SIZE + ATTRIBUTE_LEN_SIZE:]\n # Trying to get vendor by vendor code from dictionary\n vsa_vendor_id = struct.unpack(\"!I\", vsa_bytes[:4])[0]\n vsa_vendor = cls.used_dict.get_vendor_by_number(vsa_vendor_id)\n if vsa_vendor is None:\n raise AttributeDecodingError('Vendor for ID {} is not found in dictionary'.format(vsa_vendor_id))\n # Get vsa_type size and vsa_length size from vendor format\n vsa_type_size, vsa_len_size = vsa_vendor.format\n # Get VendorAttrType, VendorAttrLength and VendorAttrData in bytes view\n vsa_attr_bytes = vsa_bytes[4:]\n vsa_attr_type_bytes = vsa_attr_bytes[0:vsa_type_size]\n vsa_attr_len_bytes = vsa_attr_bytes[vsa_type_size:vsa_type_size+vsa_len_size]\n vsa_attr_value_bytes = vsa_attr_bytes[vsa_type_size+vsa_len_size:]\n\n vsa_attr_type = cls.decode_vsa_attr_type(vsa_attr_type_bytes, vsa_type_size)\n vsa_attr_len = cls.decode_vsa_attr_length(vsa_attr_len_bytes, vsa_len_size)\n attribute_definition = cls.used_dict.get_attribute_definition_by_id(vsa_vendor_id, vsa_attr_type)\n return AttributeType(attribute_definition)\n else:\n attribute_definition = cls.used_dict.get_attribute_definition_by_id(0, basic_attr_id)\n return AttributeType(attribute_definition)",
"title": ""
},
{
"docid": "ce5dbc4875418c1484bbe23a8807c9f9",
"score": "0.518345",
"text": "def parse_attribute(cls, name, attr_string):\n\n attr_string_lower = attr_string.lower().strip()\n\n if attr_string_lower[:len('date')] == 'date':\n date_format, datetime_unit = cls._get_date_format(attr_string)\n return cls(name, date_format, datetime_unit)\n else:\n return None",
"title": ""
},
{
"docid": "4c6658088c2305965419a75994310789",
"score": "0.51768285",
"text": "def get_enum_city_value():\r\n faker = Faker()\r\n enum = faker.words(1, ['Lahore', 'Islamabad', 'Gujrat', 'Multan', 'Karachi', 'Faisalabad', 'Sialkot', 'Gujranwala'], True)\r\n return enum[0]",
"title": ""
},
{
"docid": "4bea4eeb49cbb49c0df3669a8a8b10cd",
"score": "0.5171195",
"text": "def decode_vsa_attr_type(type_bytes, type_size):\n if type_size == 1:\n vendor_attr_type = struct.unpack('!B', type_bytes)[0]\n elif type_size == 2:\n vendor_attr_type = struct.unpack('!H', type_bytes)[0]\n elif type_size == 4:\n vendor_attr_type = struct.unpack('!L', type_bytes)[0]\n else:\n raise AttributeDecodingError(\"Unsupported Vendor Type Format\")\n return vendor_attr_type",
"title": ""
},
{
"docid": "ef03a1b0221ed3e100c7b6f59823aeb9",
"score": "0.5159195",
"text": "def field2choices(self, field, **kwargs):\n attributes = {}\n vals = []\n\n if hasattr(field, 'validators'):\n vals = field.validators\n\n comparable = [\n validator.comparable\n for validator in vals\n if hasattr(validator, \"comparable\")\n ]\n if comparable:\n attributes[\"enum\"] = comparable\n else:\n choices = [\n OrderedSet(validator.choices)\n for validator in vals\n if hasattr(validator, \"choices\")\n ]\n if choices:\n attributes[\"enum\"] = list(functools.reduce(operator.and_, choices))\n\n return attributes",
"title": ""
},
{
"docid": "94934de0492f7e3ef834912b08bdbc18",
"score": "0.51579064",
"text": "def enum_string(self) -> Optional[numpy.ndarray]:\n return (\n lookup_enum_string(self.value, self.enum_options)\n if self.has_enum_options\n else None\n )",
"title": ""
},
{
"docid": "8d32c2d947258de909503c0414611bbd",
"score": "0.5154725",
"text": "def _validate_value(self, name, value):\n if value is None:\n if self._required_attr(name):\n raise ValueError('Attribute \"{}\" is required'.format(name))\n return\n\n if not isinstance(value, self.__attributes__[name].get('type')):\n cast_from = self.__attributes__[name].get('cast_from')\n if cast_from and isinstance(value, cast_from):\n value = self.__attributes__[name]['type'](value)\n else:\n raise TypeError(\n 'Attribute \"{}\" must be of type {} not {}'.format(\n name, self.__attributes__[name]['type'].__name__,\n value.__class__.__name__))\n\n if self.__attributes__[name].get('enum') \\\n and value not in self.__attributes__[name]['enum']:\n raise ValueError(\n 'Attribute \"{}\" value {!r} not valid'.format(name, value))\n\n validator = self.__attributes__[name].get('validator')\n if callable(validator):\n if not validator(value, self):\n raise ValueError(\n 'Attribute \"{}\" value {!r} did not validate'.format(\n name, value))\n return value",
"title": ""
},
{
"docid": "0b26347cdd88d9a2de43d2162a054635",
"score": "0.5147201",
"text": "def valueOf(string: str) -> 'CernContextCategory':\n ...",
"title": ""
},
{
"docid": "4a7a0251435f1d83718b4ba25aa17590",
"score": "0.5139957",
"text": "def get_attribute_for_encode(cls, attr_name):\n attribute_definition = cls.used_dict.get_attribute_definition_by_name(attr_name)\n return AttributeType(attribute_definition) if attribute_definition is not None else None",
"title": ""
},
{
"docid": "90975d466d297b5bc52b7033dcc81509",
"score": "0.51390487",
"text": "def currency_type(string):\n if is_available_code(string.upper()): # if is available code return it\n return string.upper()\n else:\n if is_available_symbol(string): # if is in available symbols return symbols code\n return retrieve_symb_code(string)\n else: \n raise argparse.ArgumentTypeError(\"Unknown currency code/symbol\") # unknown symbol/code raise error",
"title": ""
},
{
"docid": "bc413daa6363f0009b25b410b8bdf1df",
"score": "0.51144695",
"text": "def python_value(self, raw: str) -> Optional[enum.Enum]:\n if raw is None: # pragma: no cover\n return None\n value = super().python_value(raw)\n return self.options(value)",
"title": ""
},
{
"docid": "93dae63c20145765d7f9f61d5c44d246",
"score": "0.5110974",
"text": "def get_attribute(self, attr_name):\n if not self.has_attribute(attr_name):\n return None\n node, attr = self.node_attribute_token(attr_name)\n sr = gxapi.str_ref()\n self.gxmeta.get_attrib_string(node, attr, sr)\n try:\n i = int(sr.value)\n return i\n except ValueError:\n try:\n f = float(sr.value)\n return f\n except ValueError:\n if sr.value.startswith('__json__'):\n return json.loads(sr.value[8:])\n return sr.value",
"title": ""
},
{
"docid": "e0fb5c54fabf47ce9fae155bbba7a75c",
"score": "0.5099911",
"text": "def _str_to_summary_operator_enum(summary_operator):\n if summary_operator == 'Sum':\n return enums.SummaryOperators.SUM\n elif summary_operator == 'Mean':\n return enums.SummaryOperators.MEAN\n elif summary_operator == 'Median':\n return enums.SummaryOperators.MEDIAN\n elif summary_operator == 'Count':\n return enums.SummaryOperators.COUNT\n elif summary_operator == 'Maximum':\n return enums.SummaryOperators.MAX\n elif summary_operator == 'Minimum':\n return enums.SummaryOperators.MIN\n elif summary_operator == 'Standard Deviation':\n return enums.SummaryOperators.STD\n elif summary_operator == 'Variance':\n return enums.SummaryOperators.VAR\n elif summary_operator == 'First':\n return enums.SummaryOperators.FIRST\n elif summary_operator == 'Last':\n return enums.SummaryOperators.LAST\n elif summary_operator == 'Count Distinct':\n return enums.SummaryOperators.DISTINCT\n else:\n return None",
"title": ""
},
{
"docid": "195981627f7f1476a5d3fd6066de18ee",
"score": "0.5095735",
"text": "def get(self, parameter_name):\r\n try:\r\n enum = getattr(DB.BuiltInParameter, parameter_name)\r\n except AttributeError:\r\n raise RpwCoerceError(parameter_name, DB.BuiltInParameter)\r\n return enum",
"title": ""
},
{
"docid": "6308c782b5aa47debe8761a689da131d",
"score": "0.50923735",
"text": "def from_string(cls, str):\r\n return getattr(cls, str.upper(), None)",
"title": ""
},
{
"docid": "522f64cf54c120b2be4b1ae9fd16443c",
"score": "0.5085959",
"text": "def get_attr_type(self):\n if self.ui.rb_float.isChecked():\n return _TYPE_FLOAT\n if self.ui.rb_integer.isChecked():\n return _TYPE_INT\n if self.ui.rb_string.isChecked():\n return _TYPE_STR\n if self.ui.rb_boolean.isChecked():\n return _TYPE_BOOL\n if self.ui.rb_matrix.isChecked():\n return _TYPE_MATRIX\n if self.ui.rb_message.isChecked():\n return _TYPE_MESSAGE\n raise Exception(\"No attribute type provided\")",
"title": ""
},
{
"docid": "efa8965ca9e0ac5a2ab61786de68423c",
"score": "0.5074031",
"text": "def _set_enum_attr(self, value: str, attr: str, enum: Enum) -> None:\n try:\n setattr(self, f\"_{attr}\", enum(value))\n except ValueError:\n _LOGGER.error(\"Unknown %s value %s\", attr, value)",
"title": ""
},
{
"docid": "647dcfdd42aead6760ae06b2b537ed7e",
"score": "0.50687146",
"text": "def deserialize(value, **kwargs):\n # Value can be an Enum\n if isinstance(value, Enum):\n return value\n\n # Value can be a string equal the name or value of the Enum\n for name, member in kwargs.get('enum').__members__.items():\n if name == value or member.value == value:\n return member\n\n raise TypeError(\"{} does not match enum type: {}\".format(\n value, kwargs.get('enum')))",
"title": ""
},
{
"docid": "94a16f028ec3912f56ed04ac61c51123",
"score": "0.50511223",
"text": "def decode(self, attr_bytes):\n attr_value = attr_bytes[self.definition.header_size:]\n attribute_coder_class = DATATYPES.get(self.definition.type)\n coder = attribute_coder_class()\n decoded_value = coder.decode(attr_value)\n decoded_value_view = self.definition.values.inv.get((str(self.definition.number), str(decoded_value)), None)\n if decoded_value_view is not None:\n decoded_value = decoded_value_view\n return self.definition.name, decoded_value",
"title": ""
},
{
"docid": "bda89ef66db126f5d4af4a5034d73b85",
"score": "0.50436944",
"text": "def get_enum(self, key, allowed_values):\n def convert(val, context):\n val = str(val)\n if val in allowed_values:\n return val\n else:\n raise ConfigConversionError(\n \"must be one of: \" + \", \".join(allowed_values))\n return self.get(key, convert)",
"title": ""
},
{
"docid": "4ecacf9b828e857b38a42a5c5c8d1236",
"score": "0.50234663",
"text": "def ChoiceToEnumName(choice):\n return choice.replace('-', '_')",
"title": ""
},
{
"docid": "a5c1b6e3bfd0254e2df7cf5a69624701",
"score": "0.4998477",
"text": "def check_enum(enum, name=None, valid=None):\n name = name or 'enum'\n # Try to convert\n res = None\n if isinstance(enum, int):\n if hasattr(enum, 'name') and enum.name.startswith('GL_'):\n res = enum.name[3:].lower()\n elif isinstance(enum, string_types):\n res = enum.lower()\n # Check\n if res is None:\n raise ValueError('Could not determine string represenatation for'\n 'enum %r' % enum)\n elif valid and res not in valid:\n raise ValueError('Value of %s must be one of %r, not %r' % \n (name, valid, enum))\n return res",
"title": ""
},
{
"docid": "0c527ad7d7801064abbdfa4a20ddb7cf",
"score": "0.49859345",
"text": "def ChoiceToEnumName(choice):\n return choice.replace('-', '_').upper()",
"title": ""
},
{
"docid": "4f9eaeed78ca6cf70a05d667e0e8af7a",
"score": "0.4974136",
"text": "def _type_from_enum(i):\n if type(i) != int:\n # print('tree node type wasnt an int, no need to convert?', i)\n return i\n return ['root', 'output', 'con', 'floating_con', 'workspace', 'dockarea'][i]",
"title": ""
},
{
"docid": "250dacbad86ced5d48e0f7b2ad1be522",
"score": "0.4972855",
"text": "def GetEnumForChoice(self, choice_value):\n return self._choice_to_enum.get(choice_value)",
"title": ""
},
{
"docid": "41c2f73b44c644c36f3bfd158bd4dbc2",
"score": "0.49649695",
"text": "def convert_to_enum(value: str | Enum | None, enum: type[Enum]) -> Enum:\n if isinstance(value, enum):\n return value\n else:\n return enum(value)",
"title": ""
},
{
"docid": "cc906153f3b7f19bea039b68064ddc17",
"score": "0.49630758",
"text": "def parse_attribute(cls, name, attr_string):\n return None",
"title": ""
},
{
"docid": "5035c0b518f27915e402ada821f280d8",
"score": "0.494304",
"text": "def attr_type(self, attr):\n\n return maya.cmds.getAttr('{0}.{1}'.format(self.meta_node, attr), type=True)",
"title": ""
},
{
"docid": "3373b14c867140d3c732e984af13ad77",
"score": "0.49363768",
"text": "def getConvertedValue(self, attr_name, str_value, property_type, spc=None):\n value = None\n\n if property_type == property_editor_id.STRING_EDITOR:\n value = str_value\n\n elif property_type == property_editor_id.TEXT_EDITOR:\n value = str_value\n\n elif property_type == property_editor_id.INTEGER_EDITOR:\n try:\n value = int(str_value)\n except:\n log_func.fatal(u'Error casting to a integer <%s>' % str_value)\n value = 0\n\n elif property_type == property_editor_id.FLOAT_EDITOR:\n try:\n value = float(str_value.replace(',', '.'))\n except:\n log_func.fatal(u'Error casting to a real number <%s>' % str_value)\n value = 0.0\n\n elif property_type == property_editor_id.CHOICE_EDITOR:\n value = str_value\n\n elif property_type == property_editor_id.SINGLE_CHOICE_EDITOR:\n value = str_value\n\n elif property_type == property_editor_id.CHECKBOX_EDITOR:\n try:\n value = eval(str_value)\n except:\n log_func.fatal(u'Error casting to a boolean <%s>' % str_value)\n value = False\n\n elif property_type == property_editor_id.MULTICHOICE_EDITOR:\n try:\n # log_func.debug(u'Multichoice property. Value <%s>' % str_value)\n value = str_value\n except:\n log_func.fatal(u'Error casting to a multichoice string list <%s>' % str_value)\n\n elif property_type == property_editor_id.STRINGLIST_EDITOR:\n try:\n value = tuple([eval(item) if item.isdigit() else item for item in str_value.split(', ')])\n except:\n log_func.fatal(u'Error casting to a string list <%s>' % str_value)\n\n elif property_type == property_editor_id.READONLY_EDITOR:\n try:\n value = eval(str_value)\n except:\n value = str_value\n\n elif property_type == property_editor_id.EXTERNAL_EDITOR:\n value = str_value\n\n elif property_type == property_editor_id.CUSTOM_EDITOR:\n value = str_value\n\n elif property_type == property_editor_id.COLOUR_EDITOR:\n try:\n value = eval(str_value)\n except NameError:\n # If the colour is specified by name\n colour_name = str_value.upper()\n # colour = wx.Colour(colour_name)\n # value = (colour.Red(), colour.Green(), colour.Blue())\n value = colour_name\n\n elif property_type == property_editor_id.FONT_EDITOR:\n log_func.debug(u'Get font %s' % str_value)\n value = dict()\n value_list = str_value.split('; ')\n value['type'] = 'Font'\n value['name'] = 'default'\n value['size'] = value_list[0]\n value['face_name'] = value_list[1]\n value['style'] = value_list[2]\n value['weight'] = value_list[3]\n value['underline'] = value_list[4]\n value['family'] = value_list[5]\n\n elif property_type == property_editor_id.POINT_EDITOR:\n try:\n value = eval(str_value)\n except:\n log_func.fatal(u'Point value <%s> convert error' % str_value)\n value = (0, 0)\n\n elif property_type == property_editor_id.SIZE_EDITOR:\n try:\n value = eval(str_value)\n except:\n log_func.fatal(u'Size value <%s> convert error' % str_value)\n value = (0, 0)\n\n elif property_type == property_editor_id.PASSWORD_EDITOR:\n value = hashlib.md5(str_value.encode()).hexdigest()\n\n elif property_type == property_editor_id.IMAGE_EDITOR:\n value = str_value if str_value else None\n\n elif property_type == property_editor_id.SCRIPT_EDITOR:\n value = str_value if str_value else None\n\n elif property_type == property_editor_id.METHOD_EDITOR:\n value = str_value if str_value else None\n\n elif property_type == property_editor_id.EVENT_EDITOR:\n value = str_value if str_value else None\n\n elif property_type == property_editor_id.PASSPORT_EDITOR:\n value = str_value if str_value and str_value != str(None) else None\n\n elif property_type == property_editor_id.ICON_EDITOR:\n lib_icon_path = icon_func.getIconPath()\n value = str_value.replace(lib_icon_path, '')\n if value.endswith(icon_func.ICON_FILENAME_EXT):\n value = value.replace(icon_func.ICON_FILENAME_EXT, '')\n if value.startswith(os.path.sep):\n value = value[1:]\n\n elif property_type == property_editor_id.FLAG_EDITOR:\n choice_dict = spc.get(spc_func.EDIT_ATTR_NAME, dict()).get(attr_name, dict()).get('choices', dict())\n values = [item.strip('\"') for item in str_value.split(' ')]\n value = 0\n for value_name in values:\n value |= choice_dict.get(value_name, 0)\n\n elif property_type == property_editor_id.FILE_EDITOR:\n framework_path = file_func.getFrameworkPath()\n if framework_path in str_value:\n value = str_value.replace(framework_path, '')\n if value.startswith(os.path.sep):\n value = value[1:]\n else:\n value = str_value\n\n elif property_type == property_editor_id.DIR_EDITOR:\n framework_path = file_func.getFrameworkPath()\n if framework_path in str_value:\n value = str_value.replace(framework_path, '')\n if value.startswith(os.path.sep):\n value = value[1:]\n else:\n value = str_value\n\n else:\n log_func.warning(u'Not support property editor. Code [%d]' % property_type)\n\n return value",
"title": ""
},
{
"docid": "841292282da4d207b9e0f3209379903c",
"score": "0.4918691",
"text": "def get_enum_type(data, default=None):\n if not data:\n return None\n\n # transform enum types, otherwise assume list of string choices\n try:\n choices = [x.value for x in data]\n except AttributeError:\n choices = data\n\n # pylint: disable=too-few-public-methods\n class DefaultAction(argparse.Action):\n\n def __call__(self, parser, args, values, option_string=None):\n\n def _get_value(val):\n return next((x for x in self.choices if x.lower() == val.lower()), val)\n\n if isinstance(values, list):\n values = [_get_value(v) for v in values]\n else:\n values = _get_value(values)\n setattr(args, self.dest, values)\n\n def _type(value):\n return next((x for x in choices if x.lower() == value.lower()), value) if value else value\n\n default_value = None\n if default:\n default_value = next((x for x in choices if x.lower() == default.lower()), None)\n if not default_value:\n raise CLIError(\"Command authoring exception: unrecognized default '{}' from choices '{}'\"\n .format(default, choices))\n arg_type = CLIArgumentType(choices=CaseInsensitiveList(choices), action=DefaultAction, default=default_value)\n else:\n arg_type = CLIArgumentType(choices=CaseInsensitiveList(choices), action=DefaultAction)\n return arg_type",
"title": ""
},
{
"docid": "cbda55c1acdc84e3cc8e70496393ce2e",
"score": "0.4916766",
"text": "def resourceTypeFromString(s):\n if s == \"alert\":\n return Alert\n if s == \"dashboard\":\n return Dashboard\n else:\n raise argparse.ArgumentTypeError(\n \"Cannot parse string {0} for resourceType\")",
"title": ""
},
{
"docid": "8786f3515192931f9efd990d27ac4e86",
"score": "0.49153122",
"text": "def safe_enum__new__(cls, value):\n if not isinstance(value, (str, int, bool)):\n return super().__new__(cls, value)\n else:\n vals = {v.value: v for v in cls.__members__.values()}\n return vals.get(value, cls.invalid_api_enum_value)",
"title": ""
},
{
"docid": "9c4f88b9f782c1f6e13d4858022c9a37",
"score": "0.49089772",
"text": "def enumstr(self, key, v):\n if key in self.enums:\n m = self.enums[key]\n return m[v] if v < len(m) else v\n return v",
"title": ""
},
{
"docid": "7f8800598184fceb72a539bf7c5cd018",
"score": "0.49073014",
"text": "def convert_from_xml(self, v):\n if self.type == Attribute.NUMBER:\n return int(v)\n if self.type == Attribute.BOOLEAN:\n return v.lower() in (\"t\", \"true\", \"1\")\n\n return v",
"title": ""
},
{
"docid": "fc9fa551232794a955306365ae8bd52f",
"score": "0.4901498",
"text": "def convert_gender( s ):\n if s == 'Female':\n return 1\n elif s == 'Male':\n return 0\n else:\n return 2",
"title": ""
},
{
"docid": "4c70f82795820cc11658b00dca300a5d",
"score": "0.4894273",
"text": "def get_val(self):\n val = int(self.get_model().get_val())\n return self.enum_i.v2e(val)",
"title": ""
},
{
"docid": "62c7cd947b9e9bc0683c27ae600a0be8",
"score": "0.4893334",
"text": "def lookup_data_type(self, field_type: str, attribute: str) -> str:\n try:\n if field_type == \"dimension\":\n if attribute.startswith((\"ga:dimension\", \"ga:customVarName\", \"ga:customVarValue\", \"ga:segment\")):\n # Custom Google Analytics Dimensions that are not part of self.dimensions_ref. They are always\n # strings\n return \"string\"\n\n elif attribute.startswith(\"ga:dateHourMinute\"):\n return \"integer\"\n\n attr_type = self.dimensions_ref[attribute]\n\n elif field_type == \"metric\":\n # Custom Google Analytics Metrics {ga:goalXXStarts, ga:metricXX, ... }\n # We always treat them as strings as we can not be sure of their data type\n if attribute.startswith(\"ga:goal\") and attribute.endswith(\n (\"Starts\", \"Completions\", \"Value\", \"ConversionRate\", \"Abandons\", \"AbandonRate\")\n ):\n return \"string\"\n elif attribute.startswith(\"ga:searchGoal\") and attribute.endswith(\"ConversionRate\"):\n # Custom Google Analytics Metrics ga:searchGoalXXConversionRate\n return \"string\"\n elif attribute.startswith((\"ga:metric\", \"ga:calcMetric\")):\n return \"string\"\n\n attr_type = self.metrics_ref[attribute]\n else:\n attr_type = None\n self.logger.error(f\"Unsupported GA type: {field_type}\")\n except KeyError:\n attr_type = None\n self.logger.error(f\"Unsupported GA {field_type}: {attribute}\")\n\n return self.map_type.get(attr_type, \"string\")",
"title": ""
},
{
"docid": "93c5ed5ebe090bbdf50f697f21e8d276",
"score": "0.48932838",
"text": "def enum(self):\n return self._enum",
"title": ""
},
{
"docid": "74513ac09f28953161d3b033c510da2b",
"score": "0.4889533",
"text": "def from_str(clazz):\n def convert(str_value: str):\n try:\n return clazz[str_value.upper().replace('-', '_')]\n except KeyError as error:\n raise ValueError() from error\n return convert",
"title": ""
},
{
"docid": "b1c07def686f1766beda053a1ae41492",
"score": "0.48780668",
"text": "def EnumNameToChoice(name):\n return name.replace('_', '-').lower()",
"title": ""
},
{
"docid": "0613d02e5760fb778c6044c3379d08d9",
"score": "0.4876278",
"text": "def parse_term(schedule):\n try:\n (term, _) = schedule.split(u'\\xa0\\xa0', 1)\n except ValueError:\n logging.warning(f\"Unable to parse term from '{schedule}'\")\n return \"undecided\"\n if term not in term_enum_map.keys():\n logging.error(f\"Unknown term '{term}'\")\n return \"\"\n return to_enum(term_enum_map)(term)",
"title": ""
},
{
"docid": "4c23212078764a1263133be49239d11f",
"score": "0.48644596",
"text": "def map_attribute(self, attribute, value):\n\n if attribute == \"occupied_heating_setpoint\":\n # centidegree to degree\n return {MOESBHT_TARGET_TEMP_ATTR: round(value / 100)}\n if attribute == \"system_mode\":\n if value == self.SystemMode.Off:\n return {MOESBHT_ENABLED_ATTR: 0}\n if value == self.SystemMode.Heat:\n return {MOESBHT_ENABLED_ATTR: 1}\n self.error(\"Unsupported value for SystemMode\")\n elif attribute == \"programing_oper_mode\":\n # values are inverted\n if value == self.ProgrammingOperationMode.Simple:\n return {MOESBHT_MANUAL_MODE_ATTR: 0, MOESBHT_SCHEDULE_MODE_ATTR: 1}\n if value == self.ProgrammingOperationMode.Schedule_programming_mode:\n return {MOESBHT_MANUAL_MODE_ATTR: 1, MOESBHT_SCHEDULE_MODE_ATTR: 0}\n self.error(\"Unsupported value for ProgrammingOperationMode\")\n\n return super().map_attribute(attribute, value)",
"title": ""
},
{
"docid": "bb088e4aaa2486e25587e7517aa506fa",
"score": "0.4858773",
"text": "def GetChoiceForEnum(self, enum_value):\n return self._enum_to_choice.get(six.text_type(enum_value))",
"title": ""
},
{
"docid": "fcd006445bdabe430ddd96682c0c4c22",
"score": "0.48584026",
"text": "def _deserialize(self, value, attr, obj, **kwargs):\n bet = value\n\n if bet in BET_TYPES:\n return bet\n\n if bet.isdigit():\n return int(bet)\n\n raise ValidationError(f'The following bet `{bet}` is invalid.')",
"title": ""
},
{
"docid": "2fb73458d0f3297b4fdb958bc46db34b",
"score": "0.48526177",
"text": "def attribute_token(self, attr_name):\n if self.has_attribute(attr_name):\n return self.gxmeta.resolve_umn(_umn(META_TYPE_ATTRIBUTE, attr_name))\n return META_INVALID",
"title": ""
},
{
"docid": "4012f34cacfed854c0a5b61073659732",
"score": "0.4852136",
"text": "def test_enum_parameter_value():\n values = [Option('A', 1), Option('B', 2)]\n para = Select('0000', values, 0)\n assert para.cast(1) == 1\n assert para.cast(2) == 2\n with pytest.raises(err.InvalidArgumentError):\n para.cast('A')\n with pytest.raises(err.InvalidArgumentError):\n para.cast(None)",
"title": ""
},
{
"docid": "d149956ec4fcce0644a0259b5d0bf7f8",
"score": "0.4849247",
"text": "def enum_as_string(self) -> Optional[pulumi.Input[bool]]:\n return pulumi.get(self, \"enum_as_string\")",
"title": ""
},
{
"docid": "450d96f7a0c121c15c9754ea1e6ac7cf",
"score": "0.48416057",
"text": "def parse(name):\n try:\n return NameValueClass.Strings.index(name)\n \n except ValueError:\n return NameValueClass.Default",
"title": ""
},
{
"docid": "9eb76ca748426bdc877d786d0e8cee20",
"score": "0.48398104",
"text": "def type(self, type):\n allowed_values = [\"NFS\", \"RECOVERY_APPLIANCE\", \"OBJECT_STORE\", \"LOCAL\"]\n if not value_allowed_none_or_none_sentinel(type, allowed_values):\n type = 'UNKNOWN_ENUM_VALUE'\n self._type = type",
"title": ""
},
{
"docid": "bccd13749711e08aff71c306efa324cc",
"score": "0.48341244",
"text": "def _valtype_from_dbtype(tup):\n categorical_attributes = ['VARCHAR', 'CHARACTER', 'VARGRAPHIC',\n 'GRAPHIC', 'CLOB']\n numerical_attributes = ['SMALLINT', 'INTEGER', 'BIGINT', 'REAL',\n 'DOUBLE', 'FLOAT', 'DECIMAL', 'NUMERIC']\n if tup[0] in categorical_attributes:\n if factor_threshold is None:\n return \"CATEGORICAL\"\n elif tup[1] <= factor_threshold:\n return \"CATEGORICAL\"\n else:\n return \"STRING\"\n elif tup[0] in numerical_attributes:\n if factor_threshold is None:\n return \"NUMERIC\"\n elif tup[1] > factor_threshold:\n return \"NUMERIC\"\n else:\n return \"CATEGORICAL\"\n else:\n return \"NONE\"",
"title": ""
},
{
"docid": "96dd7f720da552dbf15ad22e3b230c84",
"score": "0.48261338",
"text": "def get_item_type(self, xml_str: bytes):\n root = Etree.fromstring(xml_str)\n for x in root.findall('entry'):\n if x.get('key') == \"type\":\n raw_type = x.text\n if raw_type in SUPPORTED_ELEMENT_TYPES:\n if raw_type == 'ScriptModule':\n return \"Action\" # rename scriptmodule --> action\n return raw_type\n else:\n logger.warning(\"Unsupported element type for item: %s (%s)\" % (self.id, raw_type))\n return \"Unsupported\"",
"title": ""
},
{
"docid": "65afe89cddfd634960731dbf12953e2b",
"score": "0.47945482",
"text": "def ParseTypedefEnumValue(name, typename, filename, loadfile=True):\n\n enums = {}\n\n # Use the ParseTypedefEnum function to perform the first\n # half of this function. This will return all the enumeration\n # values of the typedef.\n\n enums = ParseTypedefEnum(typename, filename, loadfile)\n\n if name not in enums:\n raise ValueError(\"%r: enumeration not found\" % name)\n\n return int(enums[name])",
"title": ""
},
{
"docid": "e061fe042d1c29ea37a197a2cae258fd",
"score": "0.4790122",
"text": "def get_value(self, name, kind=\"auto\", enum_str=True, default=\"error\"):\n if kind==\"auto\":\n if name in AndorSDK3_feature_types:\n kind=AndorSDK3_feature_types[name]\n else:\n raise AndorError(\"can't determine feature kind: {}\".format(name))\n if not lib3.AT_IsImplemented(self.handle,name):\n if default==\"error\":\n raise AndorError(\"feature is not implemented: {}\".format(name))\n else:\n return default\n if not lib3.AT_IsReadable(self.handle,name):\n raise AndorError(\"feature is not readable: {}\".format(name))\n if kind==\"int\":\n return lib3.AT_GetInt(self.handle,name)\n if kind==\"float\":\n return lib3.AT_GetFloat(self.handle,name)\n if kind==\"str\":\n strlen=lib3.AT_GetStringMaxLength(self.handle,name)\n return lib3.AT_GetString(self.handle,name,strlen)\n if kind==\"bool\":\n return bool(lib3.AT_GetBool(self.handle,name))\n if kind==\"enum\":\n val=lib3.AT_GetEnumIndex(self.handle,name)\n if enum_str:\n val=lib3.AT_GetEnumStringByIndex(self.handle,name,val,512)\n return val\n raise AndorError(\"can't read feature '{}' with kind '{}'\".format(name,kind))",
"title": ""
},
{
"docid": "a0900b5f7ad1f11f6d34b3bf941242f4",
"score": "0.4783372",
"text": "def _convert_to_target_value(\n value: Text,\n value_type: constants.ValueType\n) -> Union[int, Text, List[float], List[int], List[Text]]:\n if value_type == constants.ValueType.INT:\n return int(value)\n elif value_type == constants.ValueType.STRING:\n return value\n for string_to_ignore in _STRINGS_TO_IGNORE:\n value = value.replace(string_to_ignore, '')\n values = value.split(_ELEMENT_DELIMITER)\n if value_type == constants.ValueType.LIST_FLOAT:\n return list(map(float, values))\n elif value_type == constants.ValueType.LIST_INT:\n return list(map(int, values))\n elif value_type == constants.ValueType.LIST_STRING:\n return values\n else:\n raise ValueError(f'Enum value {value_type.name} not supported.')",
"title": ""
},
{
"docid": "8fa1748dc3c50fe7ffa424dc0e0e3cdf",
"score": "0.4782957",
"text": "def string_to_type(string):\n if string.lower() in [\"string\", \"str\"]:\n data_type = str\n elif string.lower() in [\"float\", \"number\", \"decimal\"]:\n data_type = float\n elif string.lower() in [\"integer\", \"int\"]:\n data_type = int\n elif string.lower() in [\"boolean\", \"bool\"]:\n data_type = bool\n else:\n raise RuntimeError(\"Invalid input. Enter a type str, int, float, or bool.\")\n \n return data_type",
"title": ""
},
{
"docid": "454efc67a4ef5ed89ec657cd6587c059",
"score": "0.47818497",
"text": "def enum_field_filter(self, queryset, name, value):\n choices = queryset.model._meta.get_field(name).choices\n # create a dictionary string -> integer\n value_map = {v.lower(): k for k, v in choices}\n # get the integer value for the input string\n try:\n value = value_map[value.lower().strip()]\n except KeyError:\n raise ValueError(\"Invalid\" + name + \", choices are: \" +\n ', '.join([ch[1] for ch in choices]))\n return queryset.filter(**{name: value})",
"title": ""
},
{
"docid": "4627e33b55bcdde27ac5dfb6610afbc2",
"score": "0.47595072",
"text": "def type(self, type: \"str\"):\n if isinstance(type, Enum):\n self._attrs[\"type\"] = type.value\n else:\n self._attrs[\"type\"] = type # If you supply a string, we presume you know the service will take it.",
"title": ""
},
{
"docid": "365f3183b3ccb0e2477722aec7c181d9",
"score": "0.4747006",
"text": "def _get_type(self, name):\n return default_parsers.get(name, UnknownAttribute)",
"title": ""
},
{
"docid": "cc5c93ebcc78abe96c082ee2b16d21e2",
"score": "0.4746685",
"text": "def attribute_factory(sqla, name, locale_code='en-US', active=1):\n name_i18n = f'attribute.name'\n add_i18n_code(name, sqla, locale_code, name_i18n)\n attributes = sqla.query(Attribute).all()\n add_attribute_type('radio', sqla, 'en-US')\n add_attribute_type('check', sqla, 'en-US')\n add_attribute_type('dropdown', sqla, 'en-US')\n add_attribute_type('float', sqla, 'en-US')\n add_attribute_type('integer', sqla, 'en-US')\n add_attribute_type('string', sqla, 'en-US')\n add_attribute_type('date', sqla, 'en-US')\n\n attribute = {\n 'nameI18n': name_i18n,\n 'typeI18n': random.choice(Attribute.available_types()),\n 'seq': random.randint(5, 15),\n 'active': active\n }\n return attribute",
"title": ""
},
{
"docid": "fb84f96e4305d9ac81d6844f57013ba1",
"score": "0.47243962",
"text": "def _get_alias_type(attribute_type):\n dtype = attribute_type[\"dtype\"]\n if dtype == \"bool\":\n return \"boolean\"\n if dtype == \"int\":\n return \"long\"\n if dtype == \"float\":\n return \"double\"\n if dtype == \"enum\":\n return \"keyword\"\n if dtype == \"string\":\n return \"text\" if \"long_string\" in attribute_type.get(\"style\", \"\") else \"keyword\"\n if dtype == \"datetime\":\n return \"date\"\n if dtype == \"geopos\":\n return \"geo_point\"",
"title": ""
},
{
"docid": "7d3b37af9f07b9f80babd79144b1de92",
"score": "0.4716627",
"text": "def parse(name):\n\n try:\n return NameValueType.Strings.index(name)\n \n except ValueError:\n return NameValueType.Default",
"title": ""
},
{
"docid": "5db87cc81512e4b1faeb783915bcba4e",
"score": "0.47112793",
"text": "def enum_value(self, key, name):\n if not (values := self.value(key, [TYPE_ENUM, TYPE_BOOL])):\n return None\n\n options = values.options\n for opt_key, value in options.items():\n if value == name:\n return opt_key\n return None",
"title": ""
},
{
"docid": "17e033efa49b54fa39c9703d97cc08c6",
"score": "0.47050643",
"text": "def type(self) -> \"TypeEnum\":\n return TypeEnum.from_value(self._attrs.get(\"type\"))",
"title": ""
},
{
"docid": "f98b39e5c0a09dc1f1e0461327762eea",
"score": "0.46893167",
"text": "def convert(cls, value):\n # convert non-string values\n value = str(value)\n try:\n return cls._registered[value]\n except KeyError:\n raise ValueError(\"Invalid value for {}: {}\".format(cls.__name__, value))",
"title": ""
},
{
"docid": "a8f75db8d6648b3accf921f0ec79cf3e",
"score": "0.4683351",
"text": "def convert_to_int(string: str) -> int:\n return 0 if string == 'N/A' else int(string)",
"title": ""
},
{
"docid": "45dac0b5c0671a86f56e8dcc1a2c874b",
"score": "0.46827734",
"text": "def abbreviatedAxisSpecifier1(self, __stack, __ptr):\n __val = ParsedAxisSpecifier.ParsedAxisSpecifier(\"attribute\")\n return __val",
"title": ""
},
{
"docid": "b9f528606d5abb687a66535575567a44",
"score": "0.46818292",
"text": "def xattr_type(self, xattribute):\n if not xattribute:\n raise ValueError('The xattribute is empty.')\n first_character = xattribute[0]\n\n if first_character == '_':\n return 'SYS_ATTR'\n\n if first_character == '$':\n return 'VIR_ATTR'\n\n return 'USR_ATTR'",
"title": ""
},
{
"docid": "073ec9e48cdc17b276dd27d12c84106d",
"score": "0.4674674",
"text": "def testGetEnum(self):\n oid, a = self.session.get(mib.get('IF-MIB', 'ifType').oid + (1,))[0]\n self.assertEqual(a, 24) # This is software loopback\n b = basictypes.build('IF-MIB', 'ifType', a)\n self.assertEqual(b, \"softwareLoopback\")",
"title": ""
},
{
"docid": "4ae919689c056becc0860e0063d08e75",
"score": "0.4674417",
"text": "def from_string(dlstr):\n\n try:\n name, atype, strmass, charge = dlstr.split()\n\n # Parse mass as float if a decimal point appears in the string\n if \".\" in strmass:\n mass = float(strmass)\n else:\n mass = int(strmass)\n charge = float(charge)\n atomtype = AtomType(name, atype, mass, charge)\n\n except (ValueError, IndexError):\n raise ValueError(\"Failed to parse atom type: {!r}\".format(dlstr))\n\n return atomtype",
"title": ""
},
{
"docid": "09ac30577c4ed3904d83784987bbebb4",
"score": "0.46714723",
"text": "def enum_name(self, key, value):\n if not (values := self.value(key, [TYPE_ENUM, TYPE_BOOL])):\n return None\n\n options = values.options\n if self.value_type(key) == TYPE_BOOL:\n bool_val = options.get(value, 0)\n return BIT_ON if bool_val else BIT_OFF\n return options.get(value, \"\")",
"title": ""
},
{
"docid": "da9a24394545858e246c89d20d114841",
"score": "0.4660853",
"text": "def get_enum_class(enum_type):\n if isinstance(enum_type, list):\n for list_type in list(enum_type):\n if isinstance(list_type, dict) and NAME in list_type:\n return list_type[NAME]\n elif isinstance(enum_type, dict) and NAME in enum_type:\n return enum_type[NAME]\n else:\n raise Exception(\"invalid schema, enum type has no name\")",
"title": ""
},
{
"docid": "992c805f3ce0f66373c746197d32b052",
"score": "0.46606666",
"text": "def UEnum(enum):\n assert isinstance(enum, EnumType)\n\n # find first property of EnumItemType\n attrs = [n for n in enum.__dict__.values() if isinstance(n, EnumItemType)]\n assert len(attrs) > 0\n return Signal(attrs[0])",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="731"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">da0b6b90adf08064d06211309e30161c</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Init. Remove if found. Convert to uppercase. Convert Os to 0s if found.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "20f2ed6679f7ab64cf9345c9245666cb",
"score": "0.0",
"text": "def __init__(self, tag: str):\n if tag.startswith('#'):\n tag = tag[1:]\n tag = tag.replace('O', '0')\n tag = tag.upper()\n self._tag = tag",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "d296894557ee5f0907bdb0319940102c",
"score": "0.530492",
"text": "def swapcase(self):\n return \"\"",
"title": ""
},
{
"docid": "086fdf579ea6e309c3117571e27ef338",
"score": "0.5259351",
"text": "def __init__(self, inString):\r\n # replacing every keyboard symbol with ''\r\n inString = inString.replace('@', '')\r\n inString = inString.replace('~', '')\r\n inString = inString.replace('!', '')\r\n inString = inString.replace('#', '')\r\n inString = inString.replace('$', '')\r\n inString = inString.replace('%', '')\r\n inString = inString.replace('^', '')\r\n inString = inString.replace('&', '')\r\n inString = inString.replace('*', '')\r\n inString = inString.replace('(', '')\r\n inString = inString.replace(')', '')\r\n inString = inString.replace(',', '')\r\n inString = inString.replace('.', '')\r\n inString = inString.replace('<', '')\r\n inString = inString.replace('>', '')\r\n inString = inString.replace('[', '')\r\n inString = inString.replace(']', '')\r\n inString = inString.replace('=', '')\r\n inString = inString.replace(';', '')\r\n inString = inString.replace('}', '')\r\n inString = inString.replace('{', '')\r\n inString = inString.replace('|', '')\r\n inString = inString.replace('\"', '')\r\n inString = inString.replace('^', '')\r\n inString = inString.replace('?', '')\r\n inString = inString.replace('1', '')\r\n inString = inString.replace('2', '')\r\n inString = inString.replace('3', '')\r\n inString = inString.replace('4', '')\r\n inString = inString.replace('5', '')\r\n inString = inString.replace('6', '')\r\n inString = inString.replace('7', '')\r\n inString = inString.replace('8', '')\r\n inString = inString.replace('9', '')\r\n inString = inString.replace('0', '')\r\n inString = inString.upper()\r\n\r\n self.initAAcomp = {\r\n 'A': inString.count('A'), 'G': inString.count('G'),\r\n 'M': inString.count('M'), 'S': inString.count('S'),\r\n 'C': inString.count('C'), 'H': inString.count('H'),\r\n 'N': inString.count('N'), 'T': inString.count('T'),\r\n 'D': inString.count('D'), 'I': inString.count('I'),\r\n 'P': inString.count('P'), 'V': inString.count('V'),\r\n 'E': inString.count('E'), 'K': inString.count('K'),\r\n 'Q': inString.count('Q'), 'W': inString.count('W'),\r\n 'F': inString.count('F'), 'L': inString.count('L'),\r\n 'R': inString.count('R'), 'Y': inString.count('Y')\r\n }\r\n\r\n # multipliers for + charges\r\n self.kCount = self.initAAcomp['K']\r\n self.rCount = self.initAAcomp['R']\r\n self.hCount = self.initAAcomp['H']\r\n\r\n # multipliers for - charges\r\n self.dCount = self.initAAcomp['D']\r\n self.eCount = self.initAAcomp['E']\r\n self.cCount = self.initAAcomp['C']\r\n self.yCount = self.initAAcomp['Y']\r\n\r\n # + charge pka values\r\n kCharge = self.aa2chargePos['K']\r\n rCharge = self.aa2chargePos['R']\r\n hCharge = self.aa2chargePos['H']\r\n\r\n # - charge pka values\r\n dCharge = self.aa2chargeNeg['D']\r\n eCharge = self.aa2chargeNeg['E']\r\n cCharge = self.aa2chargeNeg['C']\r\n yCharge = self.aa2chargeNeg['Y']\r\n\r\n # for use in excluding the charges of aminos from the pI calculation\r\n self.pospKaVal = []\r\n\r\n self.pospKaVal.insert(0, kCharge)\r\n self.pospKaVal.insert(1, rCharge)\r\n self.pospKaVal.insert(2, hCharge)\r\n\r\n # - charge presence loads list with the peptides - charged amino values\r\n self.negpKaVal = []\r\n\r\n self.negpKaVal.insert(0, dCharge)\r\n self.negpKaVal.insert(1, eCharge)\r\n self.negpKaVal.insert(2, cCharge)\r\n self.negpKaVal.insert(3, yCharge)",
"title": ""
},
{
"docid": "fda7d0f60549dc866882d599a70aebe4",
"score": "0.5247609",
"text": "def swapcase(self):\n\t\treturn \"\"",
"title": ""
},
{
"docid": "54fbe47c0d5b8880ee4f818c081f5bd6",
"score": "0.5237193",
"text": "def to_upper(cls, source_string):\n return \"\"",
"title": ""
},
{
"docid": "ce4d1f866e8eb705a99fc82b7203407b",
"score": "0.5174887",
"text": "def _normalize_code(original):\n return original.upper().replace(\" \", \"_\")",
"title": ""
},
{
"docid": "0b4fc26670e32b1923680fd854ee4f75",
"score": "0.51605505",
"text": "def reset(self):\n self.letter = \"\"\n self.leader = \"\"\n self.number = \"\"\n self.punc0 = \"\"\n self.lower = \"\"\n self.punc1 = \"\"\n self.roman = \"\"",
"title": ""
},
{
"docid": "aafb3fa6e68f35b05c309bf60dd92ac6",
"score": "0.50749826",
"text": "def clean(self):\n if self.description is None:\n self.description = ''\n else:\n self.description = self.description.upper()\n self.supply = self.supply.upper()\n self.load = self.load.upper()\n if self.type.upper() == 'DEFAULT':\n self.type = cableVar.default_cableType\n else:\n self.type = self.type.upper()\n\n # print('cable run cleaned')",
"title": ""
},
{
"docid": "90a080a8ae85d4120ab8f1f4a3064ee5",
"score": "0.50249046",
"text": "def swapcase(self): # real signature unknown; restored from __doc__\n return \"\"",
"title": ""
},
{
"docid": "1f1724622682f64b0dd06a7d29985591",
"score": "0.49761254",
"text": "def convert_pybites_chars(text):\n new_text = ''\n for char in text:\n if char.lower() in PYBITES:\n char = char.swapcase()\n new_text += char\n return new_text",
"title": ""
},
{
"docid": "8636f23063e49205387580860ca8f7be",
"score": "0.4974436",
"text": "def __init__(self):\n # Options\n self.lowercase = True\n self.unicode_norm = \"NFKC\"\n self.norm_digits = False\n\n # Compiled regexes\n self.digit_re = regex.compile(r\"\\p{N}\", regex.U)",
"title": ""
},
{
"docid": "72cd1494181d1c301771b993186a9a57",
"score": "0.48862493",
"text": "def upper(self): \r\n return newbytes(super(newbytes, self).upper())",
"title": ""
},
{
"docid": "7eeb0fee93e08b28a089b0a11ce4bec5",
"score": "0.48858422",
"text": "def reverse(system):\n if system is \"L\":\n system = \"R\"\n elif system is \"R\":\n system = \"L\"\n elif system is \"S\":\n system = \"I\"\n elif system is \"I\":\n system = \"S\"\n elif system is \"A\":\n system = \"P\"\n elif system is \"P\":\n system = \"A\"\n return system",
"title": ""
},
{
"docid": "a937977f195f3c79709a6d9905687df0",
"score": "0.48837334",
"text": "def CASE06( self, main ):\n pass",
"title": ""
},
{
"docid": "f8720ce54009e2352de7045b253e8fda",
"score": "0.48652866",
"text": "def normalize(word):\n if word.isdigit(): return Config.NUM\n else: return word.lower()",
"title": ""
},
{
"docid": "43ac278f40661ad4d9709a31912c63d6",
"score": "0.48423252",
"text": "def text_to_upper(cls, text):\n return Text(\"\")",
"title": ""
},
{
"docid": "ed9928cf08bb52443412d5601de39a15",
"score": "0.48254427",
"text": "def upper(self):\n return \"\"",
"title": ""
},
{
"docid": "09821f8e635030df86bf68a351b8f10d",
"score": "0.4823671",
"text": "def upper(self):\n\t\treturn \"\"",
"title": ""
},
{
"docid": "a64698c6f704dec6c81b5293cd6b5ea2",
"score": "0.4812812",
"text": "def str_to_move(self, string):\r\n if not string.strip().isalpha():\r\n return -1\r\n return string.strip().upper()",
"title": ""
},
{
"docid": "35ebc636aea40c47d2f1e870f83dcf82",
"score": "0.4807156",
"text": "def clean(self, plain):\n\n return \"\".join(p for p in plain.lower() if p in self.to_num)",
"title": ""
},
{
"docid": "9f6e3af1a20d313b79f438f0885a6883",
"score": "0.4789348",
"text": "def __to_upper(val):\n try:\n return val.upper()\n except (AttributeError, ValueError, TypeError):\n return val",
"title": ""
},
{
"docid": "c772f3ea6173ef403017d552504deeff",
"score": "0.47712117",
"text": "def zero(self):\n self._issue_command('*ZER\\n')",
"title": ""
},
{
"docid": "8211f49037c02a1e714950254ec03d0c",
"score": "0.47704166",
"text": "def compact(number):\n return clean(number, ' ').upper().strip()",
"title": ""
},
{
"docid": "ea77a33e819d3370ede0a5472f46ec9d",
"score": "0.47678307",
"text": "def string_to_undefined(sequence):\n return \" \".join([str(ord(s)) for s in sequence])",
"title": ""
},
{
"docid": "e610208e1857ab8f9acd573a7731d0d1",
"score": "0.4764921",
"text": "def _clean(s: str) -> str:\n\t\treturn ''.join(c for c in s if ord(c) >= 32)",
"title": ""
},
{
"docid": "66854c36846611e41af5e7001da897e8",
"score": "0.474221",
"text": "def _upper(val):\n return val.upper()",
"title": ""
},
{
"docid": "2f00cb30d9ee5681bb7ac015aef2dea4",
"score": "0.47382876",
"text": "def ucase(inchar: str, lenout: Optional[int] = None) -> str:\n if lenout is None:\n lenout = len(inchar) + 1\n inchar = stypes.string_to_char_p(inchar)\n outchar = stypes.string_to_char_p(\" \" * lenout)\n lenout = ctypes.c_int(lenout)\n libspice.ucase_c(inchar, lenout, outchar)\n return stypes.to_python_string(outchar)",
"title": ""
},
{
"docid": "447ce6f045f7b2b62bedf1b1b045329b",
"score": "0.47250846",
"text": "def normalize_enum_constant(s):\n if s.islower(): return s\n if s.isupper(): return s.lower()\n return \"\".join(ch if ch.islower() else \"_\" + ch.lower() for ch in s).strip(\"_\")",
"title": ""
},
{
"docid": "b2eedef2cab317f8fbf53e7c846799a1",
"score": "0.47238928",
"text": "def sanitize(self):\n required_length = 4\n version_length = len(self.version.split(\".\"))\n if version_length < required_length:\n missing_places = required_length - version_length\n self.version += \".0\" * missing_places\n for key, value in self.__dict__.items():\n setattr(self, key, value.strip())",
"title": ""
},
{
"docid": "d408604596e35c6785ca46cff82c88ce",
"score": "0.47054872",
"text": "def normalize(val):\n \n if val.find('-') != -1:\n val = val.replace('-','_')\n\n return val",
"title": ""
},
{
"docid": "7b6ed8d0147f80f71845dfac638fa2b3",
"score": "0.46978262",
"text": "def construireBord(self, mur):\n\t\tself.case = bin(int(self.case,2)|int(mur,2))",
"title": ""
},
{
"docid": "190d592e6e23185f8d922feb740e9756",
"score": "0.46951878",
"text": "def __call__(self, value):\n if value is not None:\n return value.upper().replace(' ', '').replace('-', '').replace('.', '')\n return value",
"title": ""
},
{
"docid": "190d592e6e23185f8d922feb740e9756",
"score": "0.46951878",
"text": "def __call__(self, value):\n if value is not None:\n return value.upper().replace(' ', '').replace('-', '').replace('.', '')\n return value",
"title": ""
},
{
"docid": "0d7f70cef5470322abc534383c54377f",
"score": "0.46830797",
"text": "def __init__(self, val):\n self.value = val.upper()",
"title": ""
},
{
"docid": "f18021d2650677c04479b0eafd30e1d1",
"score": "0.46736985",
"text": "def __init__(self):\n self.plain_alphabet = list(string.ascii_uppercase)\n self.keyword_alphabet = list(string.ascii_uppercase)",
"title": ""
},
{
"docid": "4915a01b9b6298ef73c412ffc09d9ed0",
"score": "0.46591482",
"text": "def remove_umlaut(string):\r\n u = 'ü'.encode()\r\n U = 'Ü'.encode()\r\n a = 'ä'.encode()\r\n A = 'Ä'.encode()\r\n o = 'ö'.encode()\r\n O = 'Ö'.encode()\r\n ss = 'ß'.encode()\r\n\r\n string = string.encode()\r\n string = string.replace(u, b'ue')\r\n string = string.replace(U, b'Ue')\r\n string = string.replace(a, b'ae')\r\n string = string.replace(A, b'Ae')\r\n string = string.replace(o, b'oe')\r\n string = string.replace(O, b'Oe')\r\n string = string.replace(ss, b'ss')\r\n\r\n string = string.decode('utf-8')\r\n return string",
"title": ""
},
{
"docid": "e755b7c4310b35730c841cec6978bc8a",
"score": "0.46551144",
"text": "def lower(self): \r\n return newbytes(super(newbytes, self).lower())",
"title": ""
},
{
"docid": "bcf231bebe4e9d63d19d2a428b2b33b5",
"score": "0.46436232",
"text": "def test_noCapitalLetters(self):\n for room in union_rooms:\n for room_synonym in room.get_names():\n self.assertTrue(room_synonym.islower(),\n \"{room!s} name synonym {syn!s} has a capital letter\".format(room=room,\n syn=room_synonym))",
"title": ""
},
{
"docid": "11b9e48467701ad5bcf8c9bf65997b22",
"score": "0.46363387",
"text": "def CASE103( self, main ):\n pass",
"title": ""
},
{
"docid": "433a8c9422076dd67c24c4aa54f00c04",
"score": "0.46232405",
"text": "def _toupper_(item):\n\treturn item.upper()",
"title": ""
},
{
"docid": "10b99418bfa4cb53a1465f65e5eef313",
"score": "0.46205834",
"text": "def upper(self): # real signature unknown; restored from __doc__\n return \"\"",
"title": ""
},
{
"docid": "649d5b335e50c1a4b6af44afea33054e",
"score": "0.4618309",
"text": "def __init__(self):\n self.clean_re = re.compile('\\W+') # no debería ser w minuscula?\n self.clean = re.compile('\\w+')",
"title": ""
},
{
"docid": "7816de3e6de259a5f94d1f79384aead8",
"score": "0.46178663",
"text": "def clean_console(self):\n if system() == 'Windows':\n run(['cls'], shell=True)\n return 0\n elif system() == 'Linux' or system() == 'Darwin':\n run(['clear'], shell=True)\n return 0\n else:\n print('Sorry, console cannot be cleared. System platform not recognized.')\n return 1",
"title": ""
},
{
"docid": "96f5fe69327072aff0f1457ba382920b",
"score": "0.4615396",
"text": "def _clean_up_symbol(symbol):\r\n\r\n new_sym = ''.join([' ' if char.isdigit() else char for char in symbol]) # Replace digits\r\n\r\n # Split string and take the first entry, ensure correct formatting\r\n new_sym = str(new_sym.split()[0])\r\n new_sym = new_sym.lower()\r\n new_sym = new_sym.capitalize()\r\n\r\n if len(new_sym) > 2:\r\n logger.pt_unsupported_element(new_sym)\r\n raise ValueError(logger.pt_exiting())\r\n\r\n return new_sym",
"title": ""
},
{
"docid": "5c23cf2a0e2f68247acde359b05fa9d5",
"score": "0.46132278",
"text": "def _convert_enum_value(value: str):\n\n if value:\n value = value.replace(\"-\", \"_\").lower()\n else:\n value = None\n\n return value",
"title": ""
},
{
"docid": "0d38d68e7b482b206bc1fa374a944449",
"score": "0.46092257",
"text": "def workaround_for_windows_auto_bidi(text):\n # todo: should find away to disable windows bidi completely\n\n # convert all unshaped letters to isolated to bypass windows auto-bidi \n text = ''.join([unshaped_to_isolated.get(c, c) for c in text])\n\n # remove arabic TATWEEL letter '\\u0640', it has no isolated form\n text = text.replace('\\u0640', '')\n\n return text",
"title": ""
},
{
"docid": "8eb98b8f25d17286b17bf814b8d307b6",
"score": "0.46088102",
"text": "def CASE203( self, main ):\n pass",
"title": ""
},
{
"docid": "ceb16f8a53c33bfd93e72e37be0d8e37",
"score": "0.4576185",
"text": "def canonical(s):\n # eventually (once Python 2.6 repo eggs are no longer supported), this\n # function should only return s.lower()\n s = s.lower()\n s = s.replace('-', '_')\n if s == 'tables':\n s = 'pytables'\n return s",
"title": ""
},
{
"docid": "a3a46884ed718c5ad2460bbfee1e2f2b",
"score": "0.45702556",
"text": "def clean_text(self, text, **kwargs):\n return text.replace(\" \", \"\").upper()",
"title": ""
},
{
"docid": "8c02ac5acb7213444d7b50db45295d58",
"score": "0.45695296",
"text": "def unumlaut(word):\n if \"\\\\xc3\\\\xb6\" in word:\n return word.replace(\"\\\\xc3\\\\xb6\", \"o\")\n elif \"\\\\xc3\\\\xbc\" in word:\n return word.replace(\"\\\\xc3\\\\xbc\", \"u\")\n elif \"\\\\\" in word:\n return word.replace(\"\\\\\", \"\")\n else:\n # This tool will replace any accented chars with non-accented chars\n return unidecode(word)",
"title": ""
},
{
"docid": "bd67bd3ef17a7b53398800d715dadd5e",
"score": "0.45592356",
"text": "def strB(logical):\n return str(logical).upper()",
"title": ""
},
{
"docid": "8c3885bf408a4b1664e81f97429c0f7e",
"score": "0.45476764",
"text": "def __s2u(self, string): # {{{\n if(Evervim.windows):\n return unicode(string, 'sjis').encode('utf-8')\n else:\n return string",
"title": ""
},
{
"docid": "1cabb6060048c13c584a7cd1021c68bd",
"score": "0.45473102",
"text": "def resetCommunityString(uid):",
"title": ""
},
{
"docid": "2823013852abab8ea924cbfbe8eeb8e3",
"score": "0.4541052",
"text": "def resolve_ncase(*argv):",
"title": ""
},
{
"docid": "171d7ca7db01d2b066ef6a94c9cbbdcd",
"score": "0.4540662",
"text": "def isupper(self):",
"title": ""
},
{
"docid": "0b4fec58c0a282432e8e5a882bb6ebbc",
"score": "0.4531953",
"text": "def normalize_case(self, data):\n\n return data.lower()",
"title": ""
},
{
"docid": "741bf84565b80c5cf72c2da43915ab26",
"score": "0.45309165",
"text": "def noAschii(s):\n\treturn re.sub(r'\\W+', '', s)",
"title": ""
},
{
"docid": "005b43f44794267d5e0863ceb10285e0",
"score": "0.45267978",
"text": "def handle_char_removal(obj):\n\n new_name = obj.name\n\n if REMOVE_START_CHARS:\n if START_CHAR_COUNT != 0 and START_CHAR_COUNT <= len(new_name):\n new_name = new_name[START_CHAR_COUNT:]\n\n if REMOVE_END_CHARS:\n if END_CHAR_COUNT != 0 and END_CHAR_COUNT <= len(new_name):\n new_name = new_name[:-END_CHAR_COUNT]\n\n if REMOVE_START_CHARS or REMOVE_END_CHARS:\n if new_name:\n rename_object(obj, new_name)\n else:\n message = (\"Did not remove characters on [{0}] item. \"\n \"Cannot remove all characters from the original name, \"\n \"please adjust your character removal settings.\").format(obj.name)\n\n modo.dialogs.alert(\"Warning\", message, 'warning')",
"title": ""
},
{
"docid": "76e636565e98ece51d26233cdc1151d7",
"score": "0.4515285",
"text": "def ascii_trim_lowercase(text):\n return unidecode.unidecode(text).strip().lower()",
"title": ""
},
{
"docid": "f289d3b5e18e02c8d160eb842d75fd0f",
"score": "0.4511653",
"text": "def CASE204( self, main ):\n pass",
"title": ""
},
{
"docid": "e14179f5518cdf5d5dcf119eb2b7386d",
"score": "0.450306",
"text": "def _normalize(self, text: str):\n text = unicodedata.normalize(\"NFKC\", text)\n text = re.sub(r\"\\s+\", \" \", text).strip()\n return text",
"title": ""
},
{
"docid": "f7bca3289fcc22a12cc2381f6ab67959",
"score": "0.44986287",
"text": "def capitalize(self):",
"title": ""
},
{
"docid": "2b145e1f728bfe6b209f94716d4c3034",
"score": "0.4493237",
"text": "def remove_zeros(self):\n pass",
"title": ""
},
{
"docid": "c398ebff44b2a37c32e333c214830e69",
"score": "0.44926688",
"text": "def to_upper(self, *args):\n pass",
"title": ""
},
{
"docid": "16916737582a4bcd56f631ce9285652f",
"score": "0.44905332",
"text": "def CASE07( self, main ):\n pass",
"title": ""
},
{
"docid": "26196d4ed56827afa305579ae43c9a0c",
"score": "0.44849905",
"text": "def underscorify(s, to_upper=True):\n if not s:\n return s\n # Remove all non-word characters (everything except numbers and letters)\n s = re.sub(r\"[^\\w\\s]\", '', s)\n\n # Replace all runs of whitespace with a single underscore\n s = re.sub(r\"\\s+\", '_', s)\n\n return s.upper() if to_upper else s.lower()",
"title": ""
},
{
"docid": "446c5e3d5510c1ab086b58f95db20c05",
"score": "0.44821018",
"text": "def casefold(self):\n\t\treturn \"\"",
"title": ""
},
{
"docid": "afa85dd318367e0458082a45afd2fb27",
"score": "0.4478287",
"text": "def set_unicode_normalization(self, value):\n self.unicode_norm = value",
"title": ""
},
{
"docid": "80088cd712c7c622535885f53a5be4f6",
"score": "0.4475316",
"text": "def test_clear_text(self):\n sp = utils.clear_text('São Paulo')\n rand = utils.clear_text('ç~ã`é´â^ô')\n\n self.assertEqual('Sao Paulo', sp)\n self.assertEqual('caeao', rand)",
"title": ""
},
{
"docid": "a7add7f159c66bcba284b9067f02c544",
"score": "0.44739085",
"text": "def _convert(text):\n return int(text) if text.isdigit() else text.lower()",
"title": ""
},
{
"docid": "e86a029c27709c9c0367eefb605a617e",
"score": "0.4469616",
"text": "def a06(self):\n self.cump = \"\"\n self.cump_old = \"\"\n self.led = [0, 0] # [led_num, led_dur]\n self.override = \"\"",
"title": ""
},
{
"docid": "dff80c19fc404b0320df35bea09f870f",
"score": "0.44630483",
"text": "def clean(self):\n self.cmd(135)",
"title": ""
},
{
"docid": "0ac3dbd1558afcfc2b6d866074a53893",
"score": "0.44606298",
"text": "def CASE102( self, main ):\n pass",
"title": ""
},
{
"docid": "3b665888e746d0220e290a70ef91ca60",
"score": "0.44597292",
"text": "def get_os_name(os_id):\n os_id = os_id.lower()\n if os_id.find('0000') != -1: return '(not provided)'\n if os_id.find('ffff') != -1: return '(not provided)'\n if os_id.find('0011') != -1: return 'Schlumberger'\n if os_id.find('0027') != -1: return 'STM027'\n if os_id.find('0230') != -1: return 'G230'\n if os_id.find('1291') != -1: return 'Gemplus/Gemalto TOP'\n if os_id.find('1671') != -1: return 'G&D Sm@rtCafe'\n if os_id.find('1981') != -1: return 'Schlumberger'\n if os_id.find('2041') != -1: return 'Axalto'\n if os_id.find('3231') != -1: return 'Gemplus TOP'\n if os_id.find('4041') != -1: return 'Oberthur OCS'\n if os_id.find('4051') != -1: return 'IBM JCOP2'\n if os_id.find('4070') != -1: return 'JCOP ?'\n if os_id.find('4091') != -1: return 'Trusted Logic jTOP'\n if os_id.find('4700') != -1: return 'NXP JCOP3&4'\n if os_id.find('4791') != -1: return 'NXP JCOP2'\n if os_id.find('4A5A') != -1: return 'JCOP ?'\n if os_id.find('544c') != -1: return 'Trusted Logic jTOP'\n if os_id.find('8211') != -1: return 'Athena SCS OS'\n if os_id.find('8231') != -1: return 'Oberthur OCS'\n if os_id.find('86aa') != -1: return 'JavaCOS'\n if os_id.find('a006') != -1: return 'G&D Sm@rtCafe'\n if os_id.find('d000') != -1: return 'Gemalto OS'\n if os_id.find('d001') != -1: return 'G&D Sm@rtCafe 7'\n if os_id.find('010b') != -1: return 'FT-JCOS'\n if os_id.find('25c3') != -1: return 'FT-JCOS'\n if os_id.find('4654') != -1: return 'FT-JCOS'\n if os_id.find('4090') != -1: return 'Secora ID S'\n return ''",
"title": ""
},
{
"docid": "7cb6c98a5b4d883dc1c897e10a225016",
"score": "0.44543806",
"text": "def test_char_args(self):\n from _rawffi.alt import CDLL, types\n libfoo = CDLL(self.libfoo_name)\n my_toupper = libfoo.getfunc('my_toupper', [types.char],\n types.char)\n assert my_toupper('c') == 'C'",
"title": ""
},
{
"docid": "cabf99a7db05177ac0e3385255c425dc",
"score": "0.44479182",
"text": "def test_zerophone(self):\n ui = UniversalInput(get_mock_input(\"zerophone\"), get_mock_output(), name=ui_name)\n self.assertIsInstance(ui, NumpadCharInput)",
"title": ""
},
{
"docid": "a72a036dc27fb09e504364734afc9093",
"score": "0.44467798",
"text": "def __init__(self):\n self.init_alphabet()\n self.sequence = \"\"",
"title": ""
},
{
"docid": "ea25669b7942f859fe24f87b1f8ede50",
"score": "0.4440763",
"text": "def depunctuate_character(c):\n if c in string.ascii_uppercase:\n return 'UPPER'\n elif c in string.ascii_lowercase:\n return 'LOWER'\n else:\n return c",
"title": ""
},
{
"docid": "06acaf4860025ac9ff8f7907f1458349",
"score": "0.4440174",
"text": "def _convert_name(self, name):\n if re.search('^\\d+$', name):\n if len(name) > 1 and name[0] == '0':\n # Don't treat strings beginning with \"0\" as ints\n return name\n return int(name)\n return name",
"title": ""
},
{
"docid": "dd56dc2da2baa66b39e51f4a445ce071",
"score": "0.44375905",
"text": "def removeUnicode(cad):\n\n global unicode_tweets\n\n ascii_cad = cad.encode('ascii', 'ignore')\n\n if ascii_cad != cad:\n unicode_tweets += 1\n\n return ascii_cad",
"title": ""
},
{
"docid": "7125496a56868dd205362dd74fe3eb2e",
"score": "0.44372186",
"text": "def test_key_cleaner(self):\n\n # check all legal characters\n string = ''\n for i in range(48, 57):\n string += chr(i)\n for i in range(65, 91):\n string += chr(i)\n for i in range(97, 123):\n string += chr(i)\n\n string += \"_-:.@()+,=;$!*'%\"\n\n self.assertEqual(re_client.clean_key(string), string)\n\n # check some illegal characters\n self.assertEqual(re_client.clean_key(\"f|g~i]u/oԊy\"), \"f_g_i_u_o_y\")",
"title": ""
},
{
"docid": "c0b94c607df5c54de41af36c3a79c0cb",
"score": "0.44345003",
"text": "def CASE202( self, main ):\n pass",
"title": ""
},
{
"docid": "b5d8acc3eaa49e78a4f7c2b6f3926c48",
"score": "0.44334388",
"text": "def caseless(my_input):\n if sys.version_info >= (3, 3): # pylint: disable=no-else-return\n return str(my_input).casefold()\n else:\n return str(my_input).lower()",
"title": ""
},
{
"docid": "c61138bc955a7ce01b841364505a83a8",
"score": "0.44329605",
"text": "def uniformiserString(inputStr : str) -> str:\r\n #On sort les accents\r\n inputStr = unicodedata.normalize('NFD', inputStr).encode('ascii', 'ignore').decode('ascii', 'ignore')\r\n #Puis on passe en majuscules\r\n inputStr = inputStr.upper()\r\n #Enfin on retire les espaces\r\n inputStr = inputStr.replace(\" \", \"\")\r\n \r\n return inputStr",
"title": ""
},
{
"docid": "52be845c39d96cc359c4cdf96cf6d74b",
"score": "0.4432401",
"text": "def clean_text(doc):\n if type(doc) == str:\n #lowercasing the text\n text = doc.lower()\n # Removing non ASCII chars\n text = re.sub(r'[^\\x00-\\x7f]',r' ',text)\n return text\n else:\n return \"\"",
"title": ""
},
{
"docid": "7c204b0864abf06945be752fcf69f173",
"score": "0.4432082",
"text": "def airplane_custom_validation(self, data):\n remove_whitespace_make_lowercase(data, '')",
"title": ""
},
{
"docid": "a8498851ca0bc7f8b63e4ac2a826e71c",
"score": "0.44239447",
"text": "def _case_normalizer(self,word, dictionary):\n w = word\n lower = (dictionary.get(w.lower(), 1e12), w.lower())\n upper = (dictionary.get(w.upper(), 1e12), w.upper())\n title = (dictionary.get(w.title(), 1e12), w.title())\n results = [lower, upper, title]\n results.sort()\n index, w = results[0]\n if index != 1e12:\n return w\n return word",
"title": ""
},
{
"docid": "a04889ddccf906a4342ba93a7a30149d",
"score": "0.44236207",
"text": "def test_uppercase_code(self):\n self.assertEqual(\"pronoun\", pos_map.get_pos_name(\"Rg\"))",
"title": ""
},
{
"docid": "c9628109a641ad654919c390b27fb94e",
"score": "0.44206208",
"text": "def strip_chars(value, arg):\n return value.replace(arg, \"\")",
"title": ""
},
{
"docid": "e949d73614b0dcce87627fe68267289c",
"score": "0.44183916",
"text": "def test_uppercase(self):\n\t\tclass Capit(pymajka.Majka):\n\t\t\tdef postprocess(self, token, results):\n\t\t\t\t# We expect that it is used only with \"w\" dictionaries\n\t\t\t\tif token == token.upper():\n\t\t\t\t\tfinal_result = []\n\t\t\t\t\tfor result in results:\n\t\t\t\t\t\tfinal_result.append((result[0].upper(),))\n\t\t\t\t\treturn final_result\n\n\t\t\t\treturn results\n\n\t\tmajka = Capit(self.MAJKA_DICT_W, \"w\")\n\t\tresult = majka.get_tuple(u\"ŽIRAFY\")\n\t\tself.assertEquals(u\"ŽIRAFY\", result[0][0])",
"title": ""
},
{
"docid": "a15a5bb577886164216e368d43c6e5e5",
"score": "0.4410809",
"text": "def capitalize(self):\n\t\treturn \"\"",
"title": ""
},
{
"docid": "165bdf29ffbbe4cedea1e327dce373d1",
"score": "0.44098088",
"text": "def uppercase_words_text(document):\r\n \r\n if len(document) == 0:\r\n return 0\r\n \r\n v_is_upper = np.vectorize(str.isupper)\r\n \r\n mask = v_is_upper(document)\r\n \r\n ratio = np.mean(mask == True)\r\n \r\n return ratio",
"title": ""
},
{
"docid": "43926bc6bdee32d7a4c296850a9fe544",
"score": "0.4407817",
"text": "def remover_vocales():\r\n palabra = str(input(\"Ingrese una palabra para retirarle las vocales: \"))\r\n letras = []\r\n for caracter in palabra:\r\n if caracter.lower() not in 'aeiou':\r\n letras.append(caracter)\r\n return ''.join(letras)",
"title": ""
},
{
"docid": "1f08e9d4bf071fcacd3031c940686278",
"score": "0.44068468",
"text": "def convu(s: str) -> str:\n\tif s.startswith(('\\\\u', '\\\\U')):\n\t\tif len(s) in (6, 10) and all(c in hexdigits for c in s[2:]):\n\t\t\treturn chr(int(s[2:], 16))\n\telif s.startswith(('u', 'U')):\n\t\tif len(s) in (5, 9) and all(c in hexdigits for c in s[1:]):\n\t\t\treturn chr(int(s[1:], 16))\n\treturn None",
"title": ""
},
{
"docid": "c700225ed61424e25e79dd4f2bf3a40f",
"score": "0.44060662",
"text": "def clean_val(self, val):\n char_list = list(str(val))\n for character in list(str(val)):\n if not character.isdigit():\n char_list.remove(character)\n val = int(\"\".join(char_list))\n return val",
"title": ""
},
{
"docid": "33ddfc29b1dea6a27daa0afb2643d013",
"score": "0.44029212",
"text": "def lower(self):\n return \"\"",
"title": ""
},
{
"docid": "b48cc58af7c2963a6942f191e497b2da",
"score": "0.44003078",
"text": "def clean(self, input):\n output = input\n if self.digits:\n for s in output:\n if s.isdigit():\n return None\n if self.case:\n output = output.lower()\n if self.stop:\n punctuation = [str(x) for x in string.punctuation]\n stops = set(nltk.corpus.stopwords.words('english') + punctuation)\n if output in stops:\n return None\n if self.stem:\n stemmer = nltk.PorterStemmer()\n output = stemmer.stem(output)\n return str(output)",
"title": ""
},
{
"docid": "811d82183b36c717ecaf682a90bf4c73",
"score": "0.43994907",
"text": "def _normalize_rtl_override(text: str) -> str:\n if not text:\n return text\n if text[0] != RTL_OVERRIDE or text[-1] != RTL_OVERRIDE:\n return text\n return text[-2:0:-1]",
"title": ""
},
{
"docid": "31d2600ed5b0a8df7f6980c9da948779",
"score": "0.4398276",
"text": "def test_upper(self):\n\n # Run the command `python ./echo.py -u hello` in a separate process,\n # then collect its output.\n args_u = [\"hello\", \"-u\"]\n namespace_u = self.parser.parse_args(args_u)\n\n args_upper = [\"hello\", \"--upper\"]\n namespace_upper = self.parser.parse_args(args_upper)\n\n process = subprocess.Popen(\n [\"python\", \"./echo.py\", \"-u\", \"hello\"],\n stdout=subprocess.PIPE)\n stdout, _ = process.communicate()\n stdout_u = stdout.decode(\"utf-8\")\n\n process = subprocess.Popen(\n [\"python\", \"./echo.py\", \"--upper\", \"hello\"],\n stdout=subprocess.PIPE)\n stdout, _ = process.communicate()\n stdout_upper = stdout.decode(\"utf-8\")\n\n usage = open(\"./USAGEUPPER\", \"r\").read()\n\n self.assertTrue(namespace_u.upper)\n self.assertTrue(namespace_upper.upper)\n self.assertEquals(stdout_u, usage)\n self.assertEquals(stdout_upper, usage)",
"title": ""
},
{
"docid": "d549178f0ec07eba79e3ff85134e63a9",
"score": "0.43916267",
"text": "def get_invalid_file_system_chars(cls):\n return \"\"",
"title": ""
},
{
"docid": "0fc53294af613c853c117f1f21f53f61",
"score": "0.43894312",
"text": "def __init__(self):\r\n self.word = ''\r\n self.guess_letter = ['_', '_', '_', '_', '_']",
"title": ""
},
{
"docid": "83b31811e218fd0329db2cca5fae20e5",
"score": "0.43890703",
"text": "def test_clear_term_base(self):\n pass",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="732"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">9dc543c1b6023a5fcc5e4833ba4c797f</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Returns the amount of damage this weapon will do</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "589d861fab5d94fe94d7d1d2b235c62f",
"score": "0.0",
"text": "def get_amount(self):\n return 5",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "d071e053f13c61fa0c111ee136d462df",
"score": "0.8673491",
"text": "def damage(self, weapon):\n damage = self.ability_scores.strength.modifier\n for die in range(weapon.multiplier):\n damage += Dice.roll(weapon.damage_die)\n return damage",
"title": ""
},
{
"docid": "3e456f204f427ce40927030176f99a72",
"score": "0.8248572",
"text": "def damage(self) -> float:\n return round(0.05 + self.experience / 100, 2)",
"title": ""
},
{
"docid": "5e447fe5aa47f3a09895a474337ca6ce",
"score": "0.8187313",
"text": "def damage(self) -> int:\n return self._damage",
"title": ""
},
{
"docid": "81e3990c4b8b91adf924f4be7690936c",
"score": "0.8089979",
"text": "def get_damage(self):\n percentages = self.db.percentages \n if self.db.equipment['weapon'] is not None:\n weapon = self.db.equipment['weapon']\n damage = weapon.damage.split('d')\n damage[0] = int(damage[0])\n damage[1] = int(damage[1])\n if damage[0] == 1:\n damage_roll = random.randrange(damage[0], damage[1])\n else:\n damage_roll = random.randrange(damage[0],(damage[1] *2))\n else:\n damage_roll = random.randrange(1,4)\n\n if percentages['melee_damage_bonus'] != 0.0:\n self.msg(\"{mDEBUG: Damage is: %s{n\" % damage_roll)\n increase_in_damage = int(damage_roll * percentages['melee_damage_bonus']) + 1\n damage_roll = damage_roll + increase_in_damage\n self.msg(\"{mDEBUG: Damage after increase: %s{n\" % damage_roll)\n \n return damage_roll",
"title": ""
},
{
"docid": "ced15ea795be64fef2b1c3ec02c932fa",
"score": "0.8059766",
"text": "def get_damage(self) -> int:\r\n return round(self.level**2)*rarities[self.rarity][0]",
"title": ""
},
{
"docid": "005398b2cf79deb60536679b18bf93da",
"score": "0.78863233",
"text": "def get_damage(self) -> int:\r\n base_damage = self.level**3\r\n if choice([0,1]):\r\n if choice([0,1]):\r\n base_damage += round(base_damage/3)\r\n return base_damage\r\n base_damage -= round(base_damage/2)\r\n return base_damage\r\n return base_damage",
"title": ""
},
{
"docid": "f965ad750993c0abfeca679192e8f015",
"score": "0.788484",
"text": "def get_max_damage(self):\n\t\t_ret = self.get_base_damage()\n\t\t\n\t\tif self.weapon: _ret+=self.weapon['damage']\n\t\t\n\t\treturn _ret",
"title": ""
},
{
"docid": "4d35a5f1fa9fa8f018eb002244262bfa",
"score": "0.7842579",
"text": "def get_base_damage(self):\n\t\t_ret = 0\n\t\t\n\t\t_ret += self.atk\n\t\t\n\t\treturn _ret",
"title": ""
},
{
"docid": "b149de1025eca9c6963c6fc16ff4e28e",
"score": "0.780979",
"text": "def do_damage(self) -> float:\n res_damage = 0\n for unit in self.__units:\n res_damage += unit.do_damage()\n return res_damage",
"title": ""
},
{
"docid": "169f8b93aaf802ca2fec469beddc05df",
"score": "0.7781609",
"text": "def get_bullet_damage(self):\n return self.__damage",
"title": ""
},
{
"docid": "25940ede822cc6b15fac5a5200481e81",
"score": "0.7676825",
"text": "def get_damage_dealt(attacker: Army, enemy: Army):\n base_damage = attacker.get_effective_power()\n assert base_damage > 0\n if attacker.attack_type in enemy.immunities:\n base_damage = 0\n if attacker.attack_type in enemy.weaknesses:\n base_damage *= 2\n return base_damage",
"title": ""
},
{
"docid": "fb281d9795603a5293021c6eada1a6bc",
"score": "0.75128436",
"text": "def damage_reduce(self, damage):\n # TODO: agi = Dodge?\n return int(damage)",
"title": ""
},
{
"docid": "af2f2a38e91e5740b3c8a01a8141ec60",
"score": "0.74176574",
"text": "def get_student_flee_attack_damage(self): # returns random value, did not doctest\n student_flee_attack = Die(4)\n student_flee_attack.roll_die()\n self.student_flee_damage = student_flee_attack.get_value()\n return self.student_flee_damage",
"title": ""
},
{
"docid": "b84bf4b936f5698ea28008cbc52c7350",
"score": "0.7397336",
"text": "def calculate_damage(self, weapon, attack_modifier, critical: bool=False):\n if not critical:\n damage = weapon.damage() + attack_modifier\n else:\n damage = weapon.damage() + weapon.damage() + attack_modifier\n\n self.hp = self.hp - damage",
"title": ""
},
{
"docid": "92628508190b0f9bde55f75d612b8a6e",
"score": "0.7390189",
"text": "def take_damage(self, dmg):\n self.hp -= dmg\n if self.hp < 0:\n self.hp = 0\n return self.hp",
"title": ""
},
{
"docid": "1d8c9342297d92974ce2c445b7701505",
"score": "0.7363288",
"text": "def most_powerful_weapon(self):\n max_damage = 0\n # check the damage of each weapon in the inventory\n for weapon in self.weapon:\n if weapon.count:\n max_damage += weapon.damage\n if weapon.name == 'Bullet':\n weapon.count = weapon.count - 1\n return max_damage",
"title": ""
},
{
"docid": "ab821f3ef1ce88da07d4d52ec9fc3b13",
"score": "0.7304967",
"text": "def make_damage(self) -> float:\n return reduce(lambda a, b: a + b.make_damage(), self.units, 0)",
"title": ""
},
{
"docid": "bac38e8ceb1774db1adbe2908d483003",
"score": "0.7293573",
"text": "def dmg(self):\n return self.real_dmg + self.temp_dmg",
"title": ""
},
{
"docid": "bda0a59d730853ebf21100ad64c60d2b",
"score": "0.72414356",
"text": "def damage_calc(self):\n base = self.lvl * 5\n rand = self.lvl * 2\n return random.randrange(base, base + rand)",
"title": ""
},
{
"docid": "fca468993132117c9e3be768987e350b",
"score": "0.723001",
"text": "def total_attack_modifier(self):\n base = self.attack_modifier\n base -= self.fatigue_atk_penalty()\n return base",
"title": ""
},
{
"docid": "de06a695fb4a239e57584bcd4ad28a7a",
"score": "0.7198457",
"text": "def get_student_attack_damage(self): # returns random value, did not doctest\n student_attack = Die(6)\n student_attack.roll_die()\n self.student_damage = student_attack.get_value()\n return self.student_damage",
"title": ""
},
{
"docid": "248a8d6450f738cebb80284a5b471bb8",
"score": "0.71944094",
"text": "def get_damage_percentage(self, damage=None):\n if damage is None:\n damage = self.dmg\n return int(100 * (float(damage) / float(self.max_hp)))",
"title": ""
},
{
"docid": "d249f6c775dc4f46c9418bcda3feeb8c",
"score": "0.7151508",
"text": "def get_dmg(self):\r\n return self.dmg",
"title": ""
},
{
"docid": "d249f6c775dc4f46c9418bcda3feeb8c",
"score": "0.7151508",
"text": "def get_dmg(self):\r\n return self.dmg",
"title": ""
},
{
"docid": "9c9fbceb78af685afc758724eca77fe3",
"score": "0.7062058",
"text": "def attack(self):\n\n print(self._name, \"attacked\")\n return self._damage",
"title": ""
},
{
"docid": "e9559e2b10a7cbe822247c67d85233f7",
"score": "0.6973515",
"text": "def total_defense_modifier(self):\n base = self.defense_modifier\n base -= self.fatigue_def_penalty()\n # it gets increasingly hard to defend the more times you're attacked per round\n overwhelm_penalty = self.times_attacked * 10\n if overwhelm_penalty > 40:\n overwhelm_penalty = 40\n base -= overwhelm_penalty\n return base",
"title": ""
},
{
"docid": "4a8523bebfa1d032ec2ab2e9a07ababf",
"score": "0.6933605",
"text": "def critical_damage(self):\n if self.owner and self.equipment:\n equipment = self.equipment.critical_damage_bonus\n else:\n equipment = 0\n\n return self.attributes.base_critical_damage + equipment",
"title": ""
},
{
"docid": "538ae461b887133b90cbcb6facd0f01e",
"score": "0.6926257",
"text": "def take_hit(self, damage):\n self.health -= damage\n return self.health",
"title": ""
},
{
"docid": "9e9fd426a48679b9daccf24f2c6d9f20",
"score": "0.68569976",
"text": "def calculate_dmg(a_hit: bool) -> int:\n if a_hit:\n return roll_die(1, 6)\n else:\n return 0",
"title": ""
},
{
"docid": "ecce128be1bc12402a45f9fa8f0f4117",
"score": "0.6856287",
"text": "def attack(self):\n # TODO: Use integer division to find half of the max_damage value\n # then return a random integer between half of max_damage and max_damage\n return random.randint(self.max_damage/2, self.max_damage)",
"title": ""
},
{
"docid": "db3cf512e7aabcb8b23e4afc8c2808d6",
"score": "0.68544537",
"text": "def get_hero_damage_output(self, hero_attack, ls_flag, taunt_flag):\r\n if ls_flag and not taunt_flag:\r\n damage = hero_attack * 2\r\n elif ls_flag or not taunt_flag:\r\n damage = hero_attack\r\n else:\r\n damage = 0\r\n return damage",
"title": ""
},
{
"docid": "fa68fc348e543b979ed4be1dff2d6953",
"score": "0.6839928",
"text": "def get_effective_power(self):\n return self.units * self.attack",
"title": ""
},
{
"docid": "792e8e2bbb2aace2300d140a911deb72",
"score": "0.6827507",
"text": "def get_damage(self, dmg: float) -> None:\n dmg = dmg / len(self.units)\n for i in self.units:\n i.get_damage(dmg)",
"title": ""
},
{
"docid": "3143db5bce69853a03c9556731669cbf",
"score": "0.68268263",
"text": "def calculate_damage(power):\n if power == 0:\n return 0\n elif power > 10:\n return 100\n else:\n damage = power * 3 + random.randint(-power, power)\n if damage < 0:\n damage = 0\n return damage",
"title": ""
},
{
"docid": "6849d811c2ca00a3a68b0100baa9e150",
"score": "0.67973495",
"text": "def take_damage(self, damage: int):\n total_health = self.hit_points * self.units\n total_health -= damage\n self.units = total_health // self.hit_points\n if total_health % self.hit_points != 0:\n self.units += 1",
"title": ""
},
{
"docid": "d159bddcff50e0479feb2f50e4a16dce",
"score": "0.6795098",
"text": "def calcDamage(self, user, target):\n return self.coreDamage(user, target)*self.applyRand()",
"title": ""
},
{
"docid": "3b9b88507b77bf84400320459fc05900",
"score": "0.6752531",
"text": "def attack_damage(g1, g2):\n dmg = g1.effective_power()\n if g1.attack_type in g2.immunities:\n dmg = 0\n if g1.attack_type in g2.weaknesses:\n dmg *= 2\n return dmg",
"title": ""
},
{
"docid": "ab533b1e3ce4140a9bf91c82660e6212",
"score": "0.6731463",
"text": "def injury(self, damage):\n self.health = self.get_health() - damage",
"title": ""
},
{
"docid": "1f7d3c6c4278bc7cbaa1a1c5b3b31529",
"score": "0.67275316",
"text": "def coreDamage(self, user, target):\n atkStat, defStat = self.getAtkAndDefType()\n \n attack = self.getStatWithMod(atkStat, user)\n defense = self.getStatWithMod(defStat, target)\n level = user.getLevel()\n \n power = self.getPower(user, target)\n \n return ((((2*level/5 + 2)*attack*power/defense)/50) + 2)",
"title": ""
},
{
"docid": "7f4a55db41f974b5eed2d8aa96004609",
"score": "0.6701011",
"text": "def calculate_dmg(a_hit, char_class):\n if a_hit:\n return calculate_hp(char_class)\n else:\n return 0",
"title": ""
},
{
"docid": "90658c5e2da1016cc287545523243e62",
"score": "0.6672593",
"text": "def player_attack(self):\n if self.player_effects[\"stunned\"]:\n return\n weapon = self.player.equipment[\"right_hand\"]\n offhand = self.player.equipment[\"left_hand\"]\n\n # If no weapon, skip to unarmed attack\n if weapon is False:\n self.unarmed_attack()\n return\n\n extra_damage = {\n \"fire\": 0,\n \"occult\": 0,\n \"frost\": 0,\n \"arcane\": 0,\n \"nature\": 0,\n }\n\n # Add weapon damage\n if weapon.attack > 0:\n weapon_damage = random.randint(1, weapon.attack)\n else:\n weapon_damage = 0\n\n # Add in gear damage\n gear_damage = 0\n for k, value in self.player.equipment.items():\n if value:\n gear_damage += value.attack\n weapon_damage += gear_damage\n \n # Add in strength modifier\n strength_modifier = random.randint(int(0.75 * self.player.stats[\"Strength\"]), self.player.stats[\"Strength\"])\n self.update_log([\"player\", \"You attack with {}\".format(weapon.readable_name)])\n\n # MODIFIERS and DAMAGE CALC\n\n # Add strength modifier for melee hits\n damage = weapon_damage + strength_modifier\n\n # Weapon modifier t.ex Rat mace, Moonlight sword intelligence scaling\n weapon_unique_modifier = weapon.modifier(self.player, self.opponent)\n if weapon_unique_modifier:\n damage = damage * weapon_unique_modifier\n\n if offhand:\n offhand_effect = offhand.effect(self.player, self.opponent)\n if offhand_effect:\n if offhand_effect[\"combat_text\"] is not False:\n for item in offhand_effect[\"combat_text\"]:\n self.update_log([\"player\", item])\n\n\n\n #Add status_effect ex. temporary stuff like Molten Strike\n for item in self.player.status_effects:\n if item.damage_type == \"enhance_melee_hit\":\n result = item.execute(self.opponent)\n if result[\"combat_text\"] is not False:\n self.update_log([\"player_effect\", result[\"combat_text\"]])\n if result[\"done\"] is True:\n self.player.status_effects.remove(item)\n if result[\"damage\"] is not False:\n if result[\"type\"] == \"physical\":\n damage += result[\"damage\"]\n else:\n extra_damage[result[\"type\"]] += result[\"damage\"]\n\n # Add limb damage\n if self.opponent.has_limbs:\n # Limb modifier, t.ex 2x damage against head\n limb, modifier = self.limb_damage_modifier()\n damage = int(damage * modifier)\n damage_text = \"It hits {} in the {}, dealing {} ({})\".format(self.opponent.readable_name, limb, damage, weapon.damage_type)\n for k,v in extra_damage.items():\n if v > 0:\n damage_text += f\" + {v} ({k})\"\n damage += v\n self.update_log([\"player\", damage_text + \" damage.\"])\n\n # Find the limb and deal damage to it and the result of that\n for opp_limb in self.opponent.limbs:\n if opp_limb.name == limb:\n opp_limb.health -= damage\n \n # If the limb dies or get chopped off\n limb_result = opp_limb.check_limb_weapon(weapon)\n for item in limb_result[\"combat_text\"]:\n self.update_log([\"opponent_effect\", item])\n else:\n\n # Update combat log with attack message and damage\n self.update_log([\"player\", \"it hits {} for {} ({}) damage.\".format(self.opponent.readable_name, damage, weapon.damage_type)])\n\n # Remove damage from opponent health pool\n self.opponent.health -= damage\n\n # EFFECTS ex. Bleed, burn, chill, stun\n effect = weapon.effect(self.player, self.opponent)\n if effect:\n if effect[\"combat_text\"] is not False:\n for item in effect[\"combat_text\"]:\n self.update_log([\"player\", item])",
"title": ""
},
{
"docid": "d4b6f6065f6415ad66ff68a5daca539b",
"score": "0.6666282",
"text": "def attack(self):\n return randint((self.max_damage // 2), self.max_damage)",
"title": ""
},
{
"docid": "6abe91e6ded36fa07b250e25d2d462d9",
"score": "0.66434735",
"text": "def attack(attributes):\n global attack\n attack = attributes[\"strength\"] + random.randrange(attributes[\"weaponAtt\"][0],attributes[\"weaponAtt\"][1]+1) #uses lowest and highest weapon dmg as range\n print(\"Attack value: \" +str(attack))\n return attack",
"title": ""
},
{
"docid": "f4f303635b1ff2b3ad7f392f477bfac4",
"score": "0.66393673",
"text": "def getAverageDamage(self):\r\n totalAvDam = 0\r\n self.numOfCards = len(self.deck)\r\n for i in Deck.deckOrder:\r\n totalAvDam += i[3]\r\n totalAvDam = round((totalAvDam/self.numOfCards), 1)\r\n \r\n return totalAvDam",
"title": ""
},
{
"docid": "b6865e135edd036ff34c5579403c137d",
"score": "0.6628567",
"text": "def get_defense() -> int:\n return defense",
"title": ""
},
{
"docid": "7eaf8205343f1af27bf25bc3d8c3cbae",
"score": "0.66266763",
"text": "def calc_gold(self):\n i = self.lvl\n value = 10 * math.pow(i, 2)\n return int(value)",
"title": ""
},
{
"docid": "7ad2890d81df15b983d6c2cb0bb4a597",
"score": "0.657309",
"text": "def take_damage(self, amount):\n results = []\n self.hps -= amount\n if self.hps <= 0:\n # destroy the weapon\n results.append({'message': 'A {} was destroyed!'.format(self.name)})\n else:\n results.append({'message': 'A {} took {} damage!'.format(self.name, amount)})\n return results",
"title": ""
},
{
"docid": "93c4038e09f71b28e925878f26c2f95f",
"score": "0.6563106",
"text": "def attack(self):\n # always use the best weapon in the inventory\n damage = self.most_powerful_weapon()\n # define the enemy's poition in the room\n position = map.room_at(self.x, self.y, self)\n enemy = position.enemy\n # declare which weapon is used and change the value of the enemy's hp\n print(\"You use {} against {}!\".format('guns', enemy.name))\n enemy.hp -= damage\n # print out if the enemy is alive and how many hps remain\n if not enemy.is_alive():\n print(\"You killed a {}\".format(enemy.name))\n else:\n print(\"{} HP is {}.\".format(enemy.name, enemy.hp))",
"title": ""
},
{
"docid": "c9680fba5345244403ec116c5c2b67b6",
"score": "0.65539294",
"text": "def dodge(self):\n if self.owner and self.equipment:\n equipment = self.equipment.dodge_bonus\n else:\n equipment = 0\n\n return self.attributes.base_dodge + equipment",
"title": ""
},
{
"docid": "555627a77380d048d29fa873857731d9",
"score": "0.65445703",
"text": "def attack(self):\n half = self.max_damage // 2\n full = self.max_damage\n return randint(half, full)",
"title": ""
},
{
"docid": "53f72bd1f7a944f043badb6650e69d61",
"score": "0.65303725",
"text": "def damage_dealt_to(self, other):\n if self.damage_type in other.immunities:\n return 0\n multiplier = 2 if self.damage_type in other.weaknesses else 1\n return self.effective_power() * multiplier",
"title": ""
},
{
"docid": "bc8ed45e8b1174d02ce6de7a4e51eb29",
"score": "0.65283924",
"text": "def hurt(self, damage):\n self.health = max(self.health - damage, 0)\n if self.health <= 0:\n print(self.name + ' is dead!')\n else: print(self.name + ' has ' + str(self.health) + ' health left.')",
"title": ""
},
{
"docid": "2cc8ca6a18becf73d12edcf7e2b134d0",
"score": "0.6515798",
"text": "def heal(self):\n heal_points = random.randint(1, 20)\n print(f'You take additional {heal_points} heal points!')\n self._hp += heal_points\n print(f'Current hp: {self._hp}')\n\n return self._hp",
"title": ""
},
{
"docid": "b7432edd9fccde2605f5bcea423faf3a",
"score": "0.65070564",
"text": "def take_damage(self, damage):\n self.hp -= damage \n print(self.name,\"took\",damage,\"points of damage\")\n if self.hp <= 0:\n self.hp = 0\n self.status = \"Dead\"\n print(self.name,\"died!\")",
"title": ""
},
{
"docid": "b352eb249fc427b6d2e8f2267268c053",
"score": "0.6485543",
"text": "def healing(self):\n if 0 < self.health <= 600 :\n self.heath += 30\n Magician.mana -= 50\n return self.heath \n \n elif Magician.mana < 50 :\n print(\"votre mana est trop basse pour utiliser le sort de soin\")\n\n else :\n print(\"vous vous soigniez mais votre health est au maximum \")\n Magician.mana -=50 \n self.health == self.health\n return self.health",
"title": ""
},
{
"docid": "1b503a725bf16304d1870d0a29f03627",
"score": "0.6469328",
"text": "def desired_water_amt(self):\n healthy_amt = super().desired_water_amt()\n return (1 - self.current_time / self.duration) * healthy_amt",
"title": ""
},
{
"docid": "9af5b402285242c8e4f865bcf57885e8",
"score": "0.64456195",
"text": "def get_hp(self) -> int:\n return self.hp",
"title": ""
},
{
"docid": "9fbf17d072e077a16bdc957246cfcf98",
"score": "0.6430824",
"text": "def take_damage(self, damage: int):\n self.hp -= damage\n # game_message(f\"{self.name_instance}'s health is {self.hp}/{self.MAX_HP}\", COLOR_RED)\n\n if self.hp <= 0:\n if self.death_function is not None:\n self.death_function(self.owner)",
"title": ""
},
{
"docid": "99f96476ddfddb99579a4f264a6e7947",
"score": "0.64118296",
"text": "def damage(self, amount):\n self.hp -= amount\n self.hp = max(self.hp, 0)",
"title": ""
},
{
"docid": "5e2a1d994f556f2854b0b9cba03b1306",
"score": "0.6402883",
"text": "def get_spell_damage_output(self, base_damage, targets, spell_damage,\r\n multi_factor, spell_number):\r\n damage = ((base_damage + spell_damage) * targets *\r\n multi_factor * spell_number)\r\n return damage",
"title": ""
},
{
"docid": "b4f41c9d0bc213086ed6daed0ff70f54",
"score": "0.64022154",
"text": "def generate_damage(self):\n # Set variable for low range point\n low = self.dmg - 15\n # Set variable for high range point\n high = self.dmg + 15\n # Calculate and return final damage (int)\n return randrange(low, high)",
"title": ""
},
{
"docid": "0e9d68c83694ba05e00e9ed86ba24467",
"score": "0.63916135",
"text": "def get_minion_damage_output(self, board_attack, taunt_flag):\r\n if not taunt_flag:\r\n damage = board_attack\r\n else:\r\n damage = 0\r\n return damage",
"title": ""
},
{
"docid": "4ced9d2981310c83c1763c86eb78ff20",
"score": "0.63906455",
"text": "def reward_battle(self):\n \n if self.reward_sparse:\n return 0\n \n remaining_hp = sum([unit.health for _, unit in self.agents.items()])\n damage_done = sum([unit.health_max-unit.health for _, unit in self.enemies.items()])\n \n reward = remaining_hp+damage_done\n \n return reward",
"title": ""
},
{
"docid": "0e99ff43a8ac61ec65aefb3923d14b2c",
"score": "0.63821965",
"text": "def damage(self, amount):\n self.health -= amount\n\n if self.health <= 0:\n if self.lives > 0:\n self.lives -= 1\n self.health = self.max_health\n else:\n self.game.game_over()",
"title": ""
},
{
"docid": "ddcc410c66479911fd21b1f0102471d5",
"score": "0.63752717",
"text": "def affect_real_dmg(self):\n return self.combat.ndb.affect_real_dmg",
"title": ""
},
{
"docid": "2812624cadc320a90d76e2fe92c9c229",
"score": "0.63752574",
"text": "def hit(self, damage):\n # Check for a dodge\n if np.random.random() > 0.5:\n return \"{} dodged the attack!\".format(self.name)\n \n # Otherwise, hit the Speeder with damage\n self.shield -= damage\n \n # If the shields are depleted, take hull damage at 50% shot strength \n if self.shield < 0:\n self.hull += self.shield / 2.\n self.shield = 0\n \n # Return a string detailing the result of the damage\n result_str = \"{} was hit for {} damage. \".format(self.name, damage)\n if self.isDestroyed():\n self.hull = 0\n result_str += \"{} was destroyed!\".format(self.name)\n else:\n result = (self.shield, self.hull)\n result_str += \"It now has {} shields and {} hull.\".format(*result)\n return result_str",
"title": ""
},
{
"docid": "59db7bfbab49240584c6f2c2bbd91312",
"score": "0.6354254",
"text": "def hit(self, damage):\n self.shield -= damage\n \n # If the shields are depleted, take hull damage at 50% shot strength \n if self.shield < 0:\n self.hull += self.shield / 2.\n self.shield = 0\n \n # Return a string detailing the result of the damage.\n result_str = \"{} was hit for {} damage. \".format(self.name, damage)\n if self.isDestroyed():\n self.hull = 0\n result_str += \"{} was destroyed!\".format(self.name)\n else:\n result = (self.shield, self.hull)\n result_str += \"It now has {} shields and {} hull.\".format(*result)\n return result_str",
"title": ""
},
{
"docid": "64a1a21bf9e0859ff73362aeeea61f43",
"score": "0.6343325",
"text": "def get_player_hand_strength(self):\n return self.hand_strength",
"title": ""
},
{
"docid": "12e1eba3f8d9eda2f3fdc597e00c0f28",
"score": "0.63409054",
"text": "async def damage(self, ctx, amount : int, reason=\"\"):\n await perform_async(deal_damage, ctx, get_team(ctx.channel), amount, reason)",
"title": ""
},
{
"docid": "b69f74d1c34076ee7ee45e335f3c7cb2",
"score": "0.63264596",
"text": "def fire(self) -> int:\n return self._attributes.get(\"fireDefense\", 0)",
"title": ""
},
{
"docid": "1906cd3fbcefcd596ba56cd6501c9fc5",
"score": "0.6314839",
"text": "def heal_damage(self, amt):\n self.current_hp += amt\n if self.current_hp > self.max_hp:\n self.current_hp = self.max_hp\n print(\"You are fully healed.\")\n self.show_health()",
"title": ""
},
{
"docid": "6b990401af89e7d81ad5827123ab4153",
"score": "0.63131917",
"text": "def attack(self, opponent_ac):\n skill_multiplier = 1\n str_mult = (self.strength-10) / 2\n dex_mult = (self.dexterity-10) /2\n hits = True\n damage = str_mult + skill_multiplier * random.randint(1,4)",
"title": ""
},
{
"docid": "0fb7ae43e8a62c49193d825dfbbe7b69",
"score": "0.6281204",
"text": "def take_damage(self, amt):\n self.current_hp -= amt\n if self.current_hp < 0:\n self.current_hp = 0\n print(\"You have died.\")\n self.show_health()",
"title": ""
},
{
"docid": "1e8a9651e8e8bae6e68e7fc7d94dfd79",
"score": "0.62806916",
"text": "def generate_damage(self):\n return random.randrange(self.atkl, self.atkh)",
"title": ""
},
{
"docid": "55eaa097733574343d36b05cf711378c",
"score": "0.6257789",
"text": "def damage_calc(atker: gs.Pokemon,\n defder: gs.Pokemon,\n move: gs.Move) -> (float, bool):\n\n if move.category == \"Physical\":\n atk = atker.atk\n defense = defder.defense\n else:\n atk = atker.spa\n defense = defder.spd\n\n Type = 1 # TODO\n percent_dmg = min(defder.hp_percent, 0.9*(((2*atker.level)/5+2)*move.power*atk/defense/50+2)*Type/defder.maxhp)\n does_kill = percent_dmg == defder.hp_percent\n print(\"!@\" + str(percent_dmg))\n return percent_dmg, does_kill",
"title": ""
},
{
"docid": "c7ca70b83b4f236d70c8fdd72c7e8f1c",
"score": "0.6237703",
"text": "def trapDamage(self, charObj, traplevel):\n damage = random.randint(traplevel, traplevel * 10)\n return max(0, damage)",
"title": ""
},
{
"docid": "31aa6ebc211f5e00d5d8eab7f749c243",
"score": "0.6227419",
"text": "def total_monster_power(self):\n total_power = 0\n for elem in self.active_monsters:\n total_power += elem.power\n return total_power",
"title": ""
},
{
"docid": "0eadb12308a10716feffbdac075e9bdf",
"score": "0.6198009",
"text": "def getDodgeBonus(self):\n return 0",
"title": ""
},
{
"docid": "315b4b4bfd01bb2ac149227679a513ad",
"score": "0.61923003",
"text": "def monster_fight():\r\n global health,deffence\r\n attack = random_monster.attck\r\n get_percentage = deffence / 100\r\n get_final_percentage = attack * get_percentage\r\n get_final_damage = int(attack - get_final_percentage)\r\n print (str(random_monster.m_name) + \" attacked \" + str(name) + \" for \" + str(get_final_damage) + \" Damage!\")\r\n print()\r\n health = health - get_final_damage\r\n get_stats()",
"title": ""
},
{
"docid": "33c6b126792ede05054ec8f04c102270",
"score": "0.6177589",
"text": "def damage(self, user, target):\n messages = []\n \n # Get damage\n damage = self.calcDamage(user, target)\n \n # Get modifiers\n mod = self.getEffectiveness(messages, target)\n mod = mod*self.getStab(user)\n mod = mod*self.getCrit(messages, user, target)\n \n return self.normalize(damage*mod, target), messages",
"title": ""
},
{
"docid": "f51ff6785c5fc9db818fe808bd56c315",
"score": "0.6173881",
"text": "def player_fight():\r\n global attck\r\n deffence = random_monster.deff\r\n get_percentage = deffence / 100\r\n get_final_percentage = attck * get_percentage\r\n get_final_damage = int(attck - get_final_percentage)\r\n print()\r\n print (str(name) + \" attacked \" + str(random_monster.m_name) + \" for \" + str(get_final_damage) + \" Damage!\")\r\n print()\r\n random_monster.health = random_monster.health - get_final_damage\r\n get_m_stats()",
"title": ""
},
{
"docid": "9b26685ad59f18f9d77574ea3a37fad5",
"score": "0.617027",
"text": "def take_damage(self, modifier=0):\n self.cooldown = self.defensive_cd + modifier",
"title": ""
},
{
"docid": "2c7f50514c4a6fd03309726699d2a84a",
"score": "0.61642444",
"text": "def totalweight(loot):\n total_amount = sum([item.weight*item.amount for item in loot])\n print('Total weight of items: %s\\n' % total_amount)\n return total_amount",
"title": ""
},
{
"docid": "6e973c01ec255350cc2c90e75782bc54",
"score": "0.6153011",
"text": "def get_attack() -> int:\n return attack",
"title": ""
},
{
"docid": "aab98687c3b979015220fbe9d853dd79",
"score": "0.6146218",
"text": "def get_type_damage(weapon: dict, avg: float) -> dict:\n type_damage = {}\n for damage_type in weapon[\"dps_spread\"].items():\n type_damage[damage_type[0]] = damage_type[1] * avg\n return type_damage",
"title": ""
},
{
"docid": "3a0d837dc13ef29025f0b000120ed60c",
"score": "0.61432546",
"text": "def take_damage(self, amount):\n\n self.hp -= amount",
"title": ""
},
{
"docid": "375976b764e3916cdd7d963e9972b2e6",
"score": "0.614059",
"text": "def heavy_attack(self, enemy):\n damage = random.randint(0, 50)\n print(f\"Pokemon {self.name} used {self.moves[1]}.\")\n if damage < 10:\n print(\"The attack missed!!!\")\n else:\n print(f\"it dealt {damage} damage.\")\n enemy.current_health -= damage",
"title": ""
},
{
"docid": "f9e3e3eb905db9870d00d36fb1a6d3cb",
"score": "0.61276364",
"text": "def shooting_percentage(self) -> float:\n return self.team.shooting_percentage",
"title": ""
},
{
"docid": "0c3ce96d91e60fcd9e248f31523c041d",
"score": "0.6122913",
"text": "def current_damage(self, value):\r\n self.__current_damage = value",
"title": ""
},
{
"docid": "4e42352f8b48126c5264fd52237404b3",
"score": "0.6113827",
"text": "def get_weapon(self):\n return self._cur_weapon",
"title": ""
},
{
"docid": "50f836a6a287b7defd22ac111a11a9b9",
"score": "0.61075455",
"text": "def water(self) -> int:\n return self._attributes.get(\"waterDefense\", 0)",
"title": ""
},
{
"docid": "b99a6c09234a9c39b6035bbe91736cb1",
"score": "0.6085646",
"text": "def get_excitation_power(self):\n # TODO: Confirm units on this are watts\n resp = self.ls.msg(f\"RDGPWR? {self.channel_num}\").strip()\n return float(resp)",
"title": ""
},
{
"docid": "a7cb530174890bddb66b1688bdcad7bc",
"score": "0.60811186",
"text": "def modDmg(self, atk: Attack) -> Attack:\n self.rageUpkeep(5) \n return atk",
"title": ""
},
{
"docid": "83ebefbab2b912b939bc09262de1f328",
"score": "0.6078251",
"text": "def get_atk(self):\n return self.attack",
"title": ""
},
{
"docid": "62385ab5ce9c2871987ebcc219678bdc",
"score": "0.6048214",
"text": "def normalize(self, damage, target):\n if damage == 0:\n return 0\n elif damage < 1:\n return 1\n elif damage > target.getCurrHP():\n return target.getCurrHP()\n return int(damage)",
"title": ""
},
{
"docid": "9c25b85f445eae08be0c0cdff6814f0d",
"score": "0.6044728",
"text": "def mana(self):\n return self.manap",
"title": ""
},
{
"docid": "5e394718d013178e7b5fa4118b010f47",
"score": "0.60426575",
"text": "def take_damage(self, amount):\n self.health -= amount\n if self.health <= 0.0:\n if self.debug > 0:\n print 'Entity Dies!'\n self.alive = False",
"title": ""
},
{
"docid": "461d6df689caca3968d00c85cadf9197",
"score": "0.6041431",
"text": "def health(self) -> float:\n self.estimate_health()\n return self.__health",
"title": ""
},
{
"docid": "cfa38b01b681ebc360c9cfaf1330ef15",
"score": "0.6027059",
"text": "def damage(self, d):\n if self.shielded:\n print(self.name, \"shielded!\")\n print(\"50% of damage was blocked!\\n\")\n self.hp -= d*0.5\n return True\n else:\n self.hp -= d\n if self.hp <= 0:\n return False\n else:\n return True",
"title": ""
},
{
"docid": "685a90562f160821f5dcdbde9f3cf822",
"score": "0.60185456",
"text": "def liabilities_total(self):\n return self._liabilities_total",
"title": ""
},
{
"docid": "2612a778502a7dbc6e3db8f41f3a913e",
"score": "0.59915847",
"text": "def take_damage(self, damage):\n\n if self.blocking: \n self.hp -= int(damage / 3)\n self.blocking = False \n else:\n self.hp -= damage",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="733"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">d31396fa7e71a367f792e2fd3dee2c06</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">A convenience method to create a Worker that uses a shared thread pool for performing operations for GUI clients in a background thread</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "f0bc37e7f8d034b9e1461a4fbe8fc835",
"score": "0.75226104",
"text": "def createGuiWorker() -> ghidra.util.worker.Worker:\n ...",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "d033a6b043ea48866266aacd0e9d067c",
"score": "0.71658456",
"text": "def create_worker(self):",
"title": ""
},
{
"docid": "b26e67465cf3a9098c42cf2a2533bda0",
"score": "0.6285358",
"text": "def worker_pool(self, *, max_workers=None):\n return concurrent.futures.ThreadPoolExecutor(max_workers=max_workers)",
"title": ""
},
{
"docid": "dae6d7733098bcb6cb6511470cc66e0d",
"score": "0.62639695",
"text": "def get_worker_pool() -> ProcessPoolExecutor:\n global _WORKER_POOL\n if not _WORKER_POOL:\n _WORKER_POOL = ProcessPoolExecutor()\n return _WORKER_POOL",
"title": ""
},
{
"docid": "3a914730077682f4312dc9d5ade83481",
"score": "0.6148672",
"text": "def __init__(self):\n\n self.thread_pool = ThreadPool()",
"title": ""
},
{
"docid": "58ee8b6098a8ea301bc8111e80f81668",
"score": "0.6063198",
"text": "def worker(\n ui, costperarg, func, staticargs, args, hasretval=False, threadsafe=True\n):\n enabled = ui.configbool(b'worker', b'enabled')\n if enabled and _platformworker is _posixworker and not ismainthread():\n # The POSIX worker has to install a handler for SIGCHLD.\n # Python up to 3.9 only allows this in the main thread.\n enabled = False\n\n if enabled and worthwhile(ui, costperarg, len(args), threadsafe=threadsafe):\n return _platformworker(ui, func, staticargs, args, hasretval)\n return func(*staticargs + (args,))",
"title": ""
},
{
"docid": "818ecc78c6bfed7f25178cb5a21b0882",
"score": "0.6047329",
"text": "def _create_thread(self):\n # Creates a thread with target function _worker\n thread = threading.Thread(target=self._worker)\n\n # thread dies when main thread (only non-daemon thread) exits.\n thread.daemon = True\n\n # Activates the thread\n thread.start()",
"title": ""
},
{
"docid": "1759425a811c3602845f7cc78a8d22d2",
"score": "0.60266256",
"text": "def __init__(self):\n super(MainWindow, self).__init__()\n self.setupUi(self)\n self.threadpool = QThreadPool()\n print(\"Multithreading with maximum %d threads\" % self.threadpool.maxThreadCount())\n self.testi()",
"title": ""
},
{
"docid": "16b43c0f7ffd6cd88cfd374a19f7ceae",
"score": "0.598993",
"text": "def AsQThread(func):\n @wraps(func)\n def AsyncFunc(*args, **kwargs):\n runnable = Runnable(func = func, args = args, kwargs = kwargs)\n global pool\n pool.start(runnable)\n \n return AsyncFunc",
"title": ""
},
{
"docid": "c37cab95a49f7d761bb8a080d7df0436",
"score": "0.59571266",
"text": "def topo_controller_thread(self):\n\n worker = Worker(self.topo_controller) # Any other args, kwargs are passed to the run function\n #worker.signals.progress.connect(self.progress_bar)\n #worker.signals.finished.connect(self.bl_spectrum)\n\n # Execute\n self.threadpool.start(worker)",
"title": ""
},
{
"docid": "bb8b59114043d025b4c570f770c9ae5b",
"score": "0.59400713",
"text": "def worker_run(self):\n\n threads = [\n (self.thread(target=self.run_socket_server), True),\n (self.thread(target=self.run_heartbeat), True),\n (self.thread(target=self.run_interactions), True),\n ]\n\n if self.args.run_ui:\n # low import to ensure nothing flask is loading needlessly.\n from directord import ui # noqa\n\n ui_obj = ui.UI(\n args=self.args, jobs=self.return_jobs, nodes=self.workers\n )\n threads.append((self.thread(target=ui_obj.start_app), True))\n\n self.run_threads(threads=threads)",
"title": ""
},
{
"docid": "e2ad1a696ec49e2446c585e67e3e69e3",
"score": "0.5910882",
"text": "def dcm_controller_manually_thread(self):\n\n worker = Worker(self.dcm_controller_manually) # Any other args, kwargs are passed to the run function\n #worker.signals.progress.connect(self.progress_bar)\n #worker.signals.finished.connect(self.bl_spectrum)\n\n # Execute\n self.threadpool.start(worker)",
"title": ""
},
{
"docid": "2f189a475fa3935775044b4fe117b93d",
"score": "0.579713",
"text": "def on_update(self): \r\n worker = xUpdater()\r\n self.threadpool.start(worker)",
"title": ""
},
{
"docid": "641afaff1baf872355ef928ac1dda548",
"score": "0.5791766",
"text": "def worker(_, worker_args, app=Injected):\n app.run_worker(worker_args)",
"title": ""
},
{
"docid": "15d45e2ec7322a20c73c95609535882c",
"score": "0.57839704",
"text": "def start_worker(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "2e49221563f4d5969d7ab2cb344797df",
"score": "0.57769495",
"text": "def _make_scheduler(max_workers: int):\n return ThreadScheduler(\n executor=ThreadPoolExecutor(\n max_workers=max_workers, thread_name_prefix=\"callback-handler\"\n )\n )",
"title": ""
},
{
"docid": "5ab8fcc9d17b13b0208d409eb7549e49",
"score": "0.5741514",
"text": "def __init__(self, name, maxWorkItems = 0):\r\n self.works = ConcurrentBag()\r\n \r\n self.is_loop_enabled = True\r\n self.join_flag = False\r\n \r\n # acquire the work item to show the thread that it does not have the currentWorkItem.\r\n \r\n self._thread = Thread(target=worker, args = (self,), name=name)\r\n self._thread.start()",
"title": ""
},
{
"docid": "ba7e51e904435a91f8fc0062b3a8e7b0",
"score": "0.5730037",
"text": "def start_worker(self):\n pass",
"title": ""
},
{
"docid": "25c7095a82456d63eb77e595562dd19a",
"score": "0.5719902",
"text": "def worker_task_builder(worker_f, backend_address):\n\n def worker_task(worker_id):\n # setup service\n socket = zmq.Context().socket(zmq.REQ)\n socket.identity = u\"Worker-{}\".format(worker_id).encode(\"ascii\")\n socket.connect(backend_address)\n\n # signal to the broker that we are ready\n socket.send(b'READY')\n\n # start working (pun intended)\n while True:\n address, _, msg = socket.recv_multipart()\n reply = {'error': 'none'}\n\n try:\n data = json.loads(msg)\n reply = worker_f(data=data)\n except Exception as e:\n logging.exception(e)\n reply = {'error': str(e)}\n\n reply = json.dumps(reply)\n socket.send_multipart([address, b\"\", reply])\n\n return worker_task",
"title": ""
},
{
"docid": "c83494ca34ae246aa5f8da1f85491b0f",
"score": "0.5681695",
"text": "def create_executor(self, max_workers: Optional[int] = None, thread_name_prefix: Optional[str] = None) -> ThreadPoolExecutor:\n return ThreadPoolExecutor(max_workers=max_workers, thread_name_prefix=thread_name_prefix)",
"title": ""
},
{
"docid": "f714723e7b9b584bcde3de6e86ce18ed",
"score": "0.5677397",
"text": "def _worker(self):\n print(\"Worker thread started\")\n client = self._socket\n\n poll = zmq.Poller()\n poll.register(client, zmq.POLLIN)\n\n while self._do_work.is_set():\n self._process_queue()\n self._receive_response(poll)\n\n self._disconnect()",
"title": ""
},
{
"docid": "85ba7c3e9fce1a2286d52295b92f845f",
"score": "0.56665635",
"text": "def __init__(self, *args, **kwargs):\n self.concurrency = kwargs.pop(\"concurrency\", 1)\n self.disconnect_wait = kwargs.pop(\"disconnect_wait\", 15)\n self.log = kwargs.pop(\"log\", logging.getLogger(\"faktory.worker\"))\n\n self._queues = kwargs.pop(\n \"queues\",\n [\n \"default\",\n ],\n )\n self._executor_class = kwargs.pop(\n \"executor\",\n ThreadPoolExecutor\n if kwargs.pop(\"use_threads\", False)\n else ProcessPoolExecutor,\n )\n self._last_heartbeat = None\n self._tasks = dict()\n self._pending = list()\n self._disconnect_after = None\n self._executor = None\n\n signal.signal(signal.SIGTERM, self.handle_sigterm)\n\n if \"labels\" not in kwargs:\n kwargs[\"labels\"] = [\"python\"]\n self.labels = kwargs[\"labels\"]\n\n if \"worker_id\" not in kwargs:\n kwargs[\"worker_id\"] = self.get_worker_id()\n self.worker_id = kwargs[\"worker_id\"]\n\n self.faktory = Connection(*args, **kwargs)\n # self.faktory.debug = True",
"title": ""
},
{
"docid": "051503ff99f423573e7cab74047e9d47",
"score": "0.56571656",
"text": "def background_worker(func):\n t1 = threading.Thread(target=func)\n t1.start()",
"title": ""
},
{
"docid": "a1343bb2011525258c5ac8770adc74dd",
"score": "0.56304073",
"text": "async def run_in_worker(func: typing.Callable[..., T],\n *args: typing.Any,\n **kwargs: typing.Any) -> T:\n loop = asyncio.get_event_loop()\n f = functools.partial(func, **kwargs)\n worker_pool = get_worker_pool()\n return await loop.run_in_executor(worker_pool, f, *args)",
"title": ""
},
{
"docid": "e0c691c0e762ec0e3284a3bc590a67f4",
"score": "0.5590424",
"text": "def worker(_, argv):\n run_worker(argv)",
"title": ""
},
{
"docid": "71e8aca048cf8aa14be512196dfa3598",
"score": "0.55398715",
"text": "def _new_worker(self):\n\n # create a worker instance\n w = Worker(self)\n\n # append new worker to list\n self.all_workers.append(w)\n\n # return new worker\n return w",
"title": ""
},
{
"docid": "a6b72bd4411aca3eb9cfc727cb19e3a4",
"score": "0.5498215",
"text": "def _worker_main(self):\n\t\tlog.debug(\"Starting worker in %s thread pool\", self.name)\n\t\tif self.profile:\n\t\t\tprofiler = cProfile.Profile()\n\t\t\tprofiler.enable()\n\t\ttry:\n\t\t\twhile self.running:\n\t\t\t\ttarget = None\n\t\t\t\t# With the lock held wait for a non-empty queue and get an item from it\n\t\t\t\twith self.lock:\n\t\t\t\t\t#log.debug(\"Checking queue contents\")\n\t\t\t\t\twhile self.queue.empty():\n\t\t\t\t\t\tif self.running:\n\t\t\t\t\t\t\t#log.debug(\"Wait for queue to become full\")\n\t\t\t\t\t\t\tself.condition.wait()\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\treturn\n\t\t\t\t\tif not self.running: return\n\t\t\t\t\tself.workerCount = self.workerCount + 1 # increment the number of running workers\n\t\t\t\t\t(priority, target) = self.queue.get_nowait()\n\t\t\t\t\tself.inprogress.add(target)\n\n\t\t\t\t# Without the lock, run the function\n\t\t\t\tlog.debug(\"Worker running target %s with priority %s\", target, priority)\n\t\t\t\tfailed = False\n\t\t\t\terrs = []\n\t\t\t\tkeepgoing = False\n\t\t\t\tenqueue = []\n\t\t\t\ttry:\n\t\t\t\t\tself.utilisation.incr()\n\t\t\t\t\ttry: \n\t\t\t\t\t\t(enqueue, errs, keepgoing) = self.fn(target)\n\t\t\t\t\texcept Exception as e: \n\t\t\t\t\t\tlog.exception('Serious problem in thread pool worker: ') # log it but mustn't throw and miss the code below\n\t\t\t\t\t\terrs.append('Serious problem in thread pool worker: %r'%e)\n\t\t\t\t\t\tfailed = True\n\t\t\t\tfinally:\n\t\t\t\t\tself.utilisation.decr()\n\n\t\t\t\t# Take the lock again to update the errors, pending items in the queue and decrement the number of running workers\n\t\t\t\twith self.lock:\n\t\t\t\t\tlog.debug(\"Updating errors and queue contents\")\n\t\t\t\t\tself._errors.extend(errs)\n\t\t\t\t\tif not failed:\n\t\t\t\t\t\tfor i in enqueue:\n\t\t\t\t\t\t\tself.queue.put_nowait(i)\n\t\t\t\t\tif not keepgoing:\n\t\t\t\t\t\tself.running = False\n\t\t\t\t\tself.workerCount = self.workerCount - 1\n\t\t\t\t\tself.completed = self.completed + 1\n\t\t\t\t\tself.inprogress.remove(target)\n\t\t\t\t\t\n\t\t\t\t\tself.condition.notifyAll()\n\n\t\tfinally:\n\t\t\tif self.profile:\n\t\t\t\tprofiler.disable()\n\t\t\t\tprofiler.create_stats()\n\t\t\t\twith self.lock:\n\t\t\t\t\tself.threadProfiles.append(profiler)\n\t\t\t\t\t\"\"\"\n\t\t\t\t\t# in case we ever need per-thread profile data:\n\t\t\t\t\tdirpath = os.path.join(os.getcwd(), 'profile-xpybuild-%s' % os.getpid())\n\t\t\t\t\tmkdir(dirpath)\n\t\t\t\t\tfile = os.path.join(dirpath, \"%s-thread-%s\" % (self.name, threading.current_thread().name))\n\t\t\t\t\tif os.path.exists(file): # probably won't ever happen\n\t\t\t\t\t\tindex=0\n\t\t\t\t\t\twhile os.path.exists(file+'.%s' % index):\n\t\t\t\t\t\t\tindex = index + 1\n\t\t\t\t\t\tfile+'.%s' % index\n\t\t\t\t\tprofiler.dump_stats(file)\n\t\t\t\t\t\"\"\"\n\n\t\t\twith self.lock:\n\t\t\t\tself.condition.notifyAll()",
"title": ""
},
{
"docid": "f41940dc86b366ecef3d21bb27bba053",
"score": "0.5442329",
"text": "def __init__(self, num_workers, name=\"Pool\"):\n self.queue = multiprocessing.Manager().Queue()\n self.workers = []\n\n for idx in range(num_workers):\n process = PoolWorker(self.queue, name=\"%s-Worker-%d\" % (name, idx))\n process.daemon = True\n try:\n process.start()\n except:\n # If one thread has a problem, undo everything\n self.terminate()\n raise\n else:\n self.workers.append(process)",
"title": ""
},
{
"docid": "f5143ed08904a0789c0e05e315eed50e",
"score": "0.5431996",
"text": "def worker():\n\n if is_shutdown: # if this thread was somehow spawned during shutdown\n return None\n else: # let the games begin\n # Do init\n # ws = websocket.create_connection(Config.api_url.format(token=bearer_token), )\n # spawn a websocket client instance\n url =Config.api_url.format(token=bearer_token)\n # print(\"url = {}\".format(url))\n ws_client = websocket.WebSocketApp(url=url,\n on_close=Api.on_close,\n on_error=Api.on_error,\n on_message=Api.on_recv)\n ws_client.on_open = Api.on_open\n # loop = asyncio.get_event_loop()\n ws_client.run_forever()",
"title": ""
},
{
"docid": "55cc7d9868e556e432aeb38bdd3c29ec",
"score": "0.54220617",
"text": "def get_threadpool_executor_class():\n pass",
"title": ""
},
{
"docid": "00ea31eb35019e47b1cca2a9c9369d19",
"score": "0.5417288",
"text": "def __init__(self, api_handler, workers_pool):\n AsyncApiHandler.__init__(self)\n self.handler = api_handler\n self.workers_pool = workers_pool",
"title": ""
},
{
"docid": "c3124cb5907f05a15c5b65399a38710d",
"score": "0.54093117",
"text": "def create(*args):\n return _coin.SbThread_create(*args)",
"title": ""
},
{
"docid": "fb9fbb5e52fd7730a1c2d04a59546b16",
"score": "0.5373793",
"text": "def shared_pool():\n global _pool\n if _pool is None:\n _pool = ObjectPool()\n\n return _pool",
"title": ""
},
{
"docid": "b3db435c8bedb540c9b7b37a716f8814",
"score": "0.5352462",
"text": "def launch_worker(run_id):\n core_screen._init(run_id)\n worker_run_loop(run_id)",
"title": ""
},
{
"docid": "1fd6c4faf83d9893700fbb38e107c940",
"score": "0.53438383",
"text": "def __init__(self, workers=None):\n\n # Number of workers to use in thread/process pools\n self.workers = workers\n\n self.thread = None\n self.process = None",
"title": ""
},
{
"docid": "ac70070c01f0522a02f810f783f92776",
"score": "0.5329712",
"text": "def bot_worker():\n bot = Bot()\n bot.run()",
"title": ""
},
{
"docid": "233a88881b84f8c57f6f9854a57d6ebf",
"score": "0.53244275",
"text": "def get_task_pool() -> ProcessPoolExecutor:\n global _TASK_POOL\n if not _TASK_POOL:\n _TASK_POOL = ProcessPoolExecutor()\n return _TASK_POOL",
"title": ""
},
{
"docid": "4b2554dcf6af282d21a784db7ef8d435",
"score": "0.53243846",
"text": "def runworker():\n app.run(debug=False)",
"title": ""
},
{
"docid": "d93d624408e3bf31fdbe08323cb364bd",
"score": "0.5322505",
"text": "def __start_workers(self, nworkers: int = DEFAULT_WORKERS):\n # if nworkers is None:\n # nworkers = self.config.nworkers\n\n # self.pool = cf.ProcessPoolExecutor(max_workers=nworkers)\n self.pool = DynamicProcessPool(\n queue=self.queue, max_workers=nworkers, feed_delay=0.05, manager_delay=2.0\n )\n self.pool._start_manager_thread()\n # self.pool.add_event_callback(self.receive_pool_events)\n self.log.info(\"Worker pool started with {} workers\".format(nworkers))",
"title": ""
},
{
"docid": "6787c1a666d854cd6a027691554a63c6",
"score": "0.53217614",
"text": "def get_executor(self):\n pass",
"title": ""
},
{
"docid": "dfe34e4f9d04a28de3e1cd4e1aceafa9",
"score": "0.5305583",
"text": "def create_internal_thread(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "c232a7235ee20a5a424cfedc08171b22",
"score": "0.5304932",
"text": "def start_worker(self):\n self._process_worker = Process(target=worker_loop, args=(self.task_obj, \n self._qin, self._qout, self._qout_sync, self.impatient))\n self._process_worker.start()",
"title": ""
},
{
"docid": "2bb6bffe7e3a0196a0b4c2110351fc07",
"score": "0.5301534",
"text": "def _create_thread_pool(self):\n for _ in range(self.threads):\n self._create_thread()",
"title": ""
},
{
"docid": "d9ab849adeed09f084a248ca67d1255a",
"score": "0.52831304",
"text": "def run_pooling(): # pragma: no cover\n updater = Updater(TELEGRAM_TOKEN, use_context=True)\n\n dp = updater.dispatcher\n dp = setup_dispatcher(dp)\n\n bot_info = telegram.Bot(TELEGRAM_TOKEN).get_me()\n bot_link = f\"https://t.me/\" + bot_info[\"username\"]\n\n print(f\"Pooling of '{bot_link}' started\")\n updater.start_polling()\n updater.idle()",
"title": ""
},
{
"docid": "615fae6c4650a5fa340ca72e7ee5add1",
"score": "0.5275489",
"text": "def useWorker(self):\n if self.worker.isRunning():\n self.lazyInstrumentUpdate(100)\n return\n self.worker.start()",
"title": ""
},
{
"docid": "7b4fee0ab8fa57303e6eb2227826f09b",
"score": "0.52752817",
"text": "def __init__(self, **kwargs):\n\n # set required reference to task manager\n self.task_manager = kwargs['task_manager']\n\n # define the maximum number of worker threads for this module\n # either its set from kwargs or from the default (defined under imports)\n self.max_workers = kwargs['max_workers'] if 'max_workers' in kwargs else DEFAULT_MAX_WORKERS\n\n # define a list to keep all worker instances in (active or inactive)\n self.all_workers = []\n\n # define a list for workers to append them selves for when looking for work\n self.inactive_workers = []",
"title": ""
},
{
"docid": "790cb4689db47ec37d3384e9348ad7ef",
"score": "0.52715945",
"text": "def worker(self) -> Worker:\n if self._worker is None:\n self._worker = Worker.objects.get(data=self.data)\n return self._worker",
"title": ""
},
{
"docid": "06705317b38b3601e91e83f2be184e67",
"score": "0.5270553",
"text": "def run(self):\n # create a pool of workers\n if not self.faktory.is_connected:\n self.faktory.connect(worker_id=self.worker_id)\n\n self.log.debug(\n \"Creating a worker pool with concurrency of {}\".format(self.concurrency)\n )\n\n self._last_heartbeat = datetime.now() + timedelta(\n seconds=self.send_heartbeat_every\n ) # schedule a heartbeat for the future\n\n self.log.info(\"Queues: {}\".format(\", \".join(self.get_queues())))\n self.log.info(\"Labels: {}\".format(\", \".join(self.faktory.labels)))\n\n while True:\n try:\n # tick runs continuously to process events from the faktory connection\n self.tick()\n if not self.faktory.is_connected:\n break\n except KeyboardInterrupt as e:\n # 1st time through: soft close, wait 15 seconds for jobs to finish and send the work results to faktory\n # 2nd time through: force close, don't wait, fail all current jobs and quit as quickly as possible\n if self.is_disconnecting:\n break\n\n self.log.info(\n \"Shutdown: waiting up to 15 seconds for workers to finish current tasks\"\n )\n self.disconnect(wait=self.disconnect_wait)\n except (BrokenProcessPool, BrokenThreadPool):\n self.log.info(\"Shutting down due to pool failure\")\n self.disconnect(force=True, wait=15)\n break\n\n if self.faktory.is_connected:\n self.log.warning(\"Forcing worker processes to shutdown...\")\n self.disconnect(force=True)\n\n self.executor.shutdown(wait=False)\n sys.exit(1)",
"title": ""
},
{
"docid": "40400a3c66a557339ca808d192c45441",
"score": "0.5261",
"text": "def __init__(self, name: unicode, isPersistentThread: bool, useSharedThreadPool: bool, monitor: ghidra.util.task.TaskMonitor):\n ...",
"title": ""
},
{
"docid": "52bb61ace1f53e0330a824cdaec1dff7",
"score": "0.5254446",
"text": "def get_io_pool():\n return _io_pool",
"title": ""
},
{
"docid": "9c7801b8f69f486e04eb1eb6bb6404b8",
"score": "0.5251884",
"text": "def SbThread_create(*args):\n return _coin.SbThread_create(*args)",
"title": ""
},
{
"docid": "ba6cbea44a65d79081a3e233b58a0329",
"score": "0.5229064",
"text": "def create_chat_process_worker(self) -> (type, dict):",
"title": ""
},
{
"docid": "25066bf1ca275e7837a93b7731b64314",
"score": "0.52211887",
"text": "def create_workers(batch):\n threads = []\n start_volume = 1\n for x in range(0, WORKER_COUNT):\n end = start_volume + batch - 1\n print(f\"Created worker for (START: {start_volume}, END: {end}, BATCH: {batch})\")\n threads.append(threading.Thread(target=worker, args=(start_volume, batch, x)))\n start_volume = end + 1\n\n total_downloaded.append(0)\n return threads",
"title": ""
},
{
"docid": "137d7b0051d9b8fdd9e774ded130d2f0",
"score": "0.52203274",
"text": "def invoke(self, job_payload):\n executor_id = job_payload['executor_id']\n job_id = job_payload['job_id']\n total_calls = job_payload['total_calls']\n chunksize = job_payload['chunksize']\n workers = job_payload['workers']\n\n total_workers = min(workers, total_calls // chunksize + (total_calls % chunksize > 0)\n if self.exec_mode in ['create', 'reuse'] else 1)\n\n def start_master_instance(wait=True):\n if not self._is_master_service_ready():\n self.backend.master.create(check_if_exists=True, start=True)\n if wait:\n self._wait_master_service_ready()\n\n def get_workers_on_master():\n workers_on_master = []\n try:\n cmd = (f'curl -X GET http://127.0.0.1:{STANDALONE_SERVICE_PORT}/workers -H \\'Content-Type: application/json\\'')\n workers_on_master = json.loads(self.backend.master.get_ssh_client().run_remote_command(cmd))\n except Exception:\n pass\n\n return workers_on_master\n\n def create_workers():\n current_workers_old = set(self.backend.workers)\n with ThreadPoolExecutor(total_workers+1) as ex:\n ex.submit(start_master_instance, wait=False)\n for vm_n in range(total_workers):\n worker_id = \"{:04d}\".format(vm_n)\n name = 'lithops-worker-{}-{}-{}'.format(executor_id, job_id, worker_id)\n ex.submit(self.backend.create_worker, name)\n current_workers_new = set(self.backend.workers)\n new_workers = current_workers_new - current_workers_old\n logger.debug(\"Total worker VM instances created: {}/{}\"\n .format(len(new_workers), total_workers))\n\n return new_workers\n\n worker_instances = []\n\n if self.exec_mode == 'create':\n workers = create_workers()\n total_workers = len(workers)\n worker_instances = [(inst.name,\n inst.ip_address,\n inst.instance_id,\n inst.ssh_credentials)\n for inst in workers]\n\n elif self.exec_mode == 'reuse':\n workers = get_workers_on_master()\n total_workers = len(workers)\n if total_workers == 0:\n self.backend.workers = []\n workers = create_workers()\n total_workers = len(workers)\n worker_instances = [(inst.name,\n inst.ip_address,\n inst.instance_id,\n inst.ssh_credentials)\n for inst in workers]\n\n if total_workers == 0:\n raise Exception('It was not possible to create any worker')\n\n logger.debug('ExecutorID {} | JobID {} - Going to run {} activations '\n 'in {} workers'.format(executor_id, job_id, total_calls,\n total_workers))\n\n logger.debug(\"Checking if {} is ready\".format(self.backend.master))\n start_master_instance(wait=True)\n\n job_payload['worker_instances'] = worker_instances\n\n if self.is_lithops_worker:\n url = \"http://127.0.0.1:{}/run\".format(STANDALONE_SERVICE_PORT)\n requests.post(url, data=json.dumps(job_payload))\n else:\n cmd = ('curl http://127.0.0.1:{}/run -d {} '\n '-H \\'Content-Type: application/json\\' -X POST'\n .format(STANDALONE_SERVICE_PORT,\n shlex.quote(json.dumps(job_payload))))\n self.backend.master.get_ssh_client().run_remote_command(cmd)\n self.backend.master.del_ssh_client()\n\n logger.debug('Job invoked on {}'.format(self.backend.master))\n\n self.jobs.append(job_payload['job_key'])",
"title": ""
},
{
"docid": "cc13fbbfb1944011b5171c73b629ee42",
"score": "0.5210205",
"text": "def testAddWorker(self):\n config = self.config\n self.tempDir = self.testInit.generateWorkDir(config)\n config.component_(\"TestComponent\")\n config.TestComponent.logLevel = 'INFO'\n config.section_(\"General\")\n config.TestComponent.componentDir = os.path.join( \\\n self.tempDir, \"Components/TestComponent1\")\n config.General.workDir = config.TestComponent.componentDir\n os.makedirs( config.TestComponent.componentDir )\n testComponent = TestComponent(config)\n testComponent.prepareToStart()\n myThread = threading.currentThread()\n myThread.workerThreadManager.addWorker(TestComponentPoller(config),\n 10)\n myThread.workerThreadManager.terminateWorkers()\n query = {'key':\"TestComponent\"}\n workers = self.agent_db.loadView('Agent', 'existWorkers', query)['rows']\n assert ('TestComponentPoller' in workers[0]['value']) == True",
"title": ""
},
{
"docid": "32f3b3fed311256ce65db5f6ba5c116e",
"score": "0.52083045",
"text": "def start(self):\n Thread(target=self.worker, args=()).start()\n return self",
"title": ""
},
{
"docid": "1d1e9dd64f9e37db9b02c44aeb3eb516",
"score": "0.5204524",
"text": "def pool(self, method):\n\n if method == \"thread\":\n if not self.thread:\n self.thread = ThreadPool(self.workers)\n\n return self.thread\n\n if method == \"process\":\n if not self.process:\n # Importing torch.multiprocessing will register torch shared memory serialization for cuda\n self.process = Pool(self.workers, context=torch.multiprocessing.get_context(\"spawn\"))\n\n return self.process\n\n return None",
"title": ""
},
{
"docid": "c8330ecb60e3de042d55d373764f5224",
"score": "0.51920587",
"text": "def __init__(self, config):\n BaseWorkerThread.__init__(self)\n self.config = config",
"title": ""
},
{
"docid": "46c7e6f49e7ac017c10feb42f7198a82",
"score": "0.51898235",
"text": "def init_executor(self):\n if self.n_jobs == 1:\n return\n\n if self.n_jobs == -1:\n self.pool = Pool()\n else:\n self.pool = Pool(self.n_jobs)",
"title": ""
},
{
"docid": "5fc0cb4d3240a1df99fd9e8893f87bcb",
"score": "0.51893634",
"text": "def run(self):\n self._setup()\n self.timer.start(100)\n print \"Starting worker thread\"\n return self.exec_()",
"title": ""
},
{
"docid": "a3104b48eaad65fa236b99d6e41c3e33",
"score": "0.5185756",
"text": "def start_worker(self, worker_arguments=None):\n raise NotImplementedError",
"title": ""
},
{
"docid": "293aa6fdee8351219398533632064e63",
"score": "0.5182574",
"text": "def start(self):\r\n for i in range(self.min):\r\n self._threads.append(WorkerThread(self.server))\r\n for worker in self._threads:\r\n worker.setName(\"CP Server \" + worker.getName())\r\n worker.start()\r\n for worker in self._threads:\r\n while not worker.ready:\r\n time.sleep(.1)",
"title": ""
},
{
"docid": "293aa6fdee8351219398533632064e63",
"score": "0.5182574",
"text": "def start(self):\r\n for i in range(self.min):\r\n self._threads.append(WorkerThread(self.server))\r\n for worker in self._threads:\r\n worker.setName(\"CP Server \" + worker.getName())\r\n worker.start()\r\n for worker in self._threads:\r\n while not worker.ready:\r\n time.sleep(.1)",
"title": ""
},
{
"docid": "d276bdc3b7c5ee13202766e25572ef30",
"score": "0.5181946",
"text": "def add(self, target, args):\n self.proc_pool.append(threading.Thread(target=target, args=args))",
"title": ""
},
{
"docid": "c2da908877e092dc7cb758b0b1e9cd24",
"score": "0.5149293",
"text": "def run_worker(self, args, options):\r\n import tornado.options\r\n from tornado.httpserver import HTTPServer\r\n from tornado.ioloop import IOLoop\r\n from signalqueue.worker.vortex import Application\r\n from signalqueue.worker import backends\r\n import signalqueue\r\n \r\n queue_name = options.get('queue_name')\r\n queues = backends.ConnectionHandler(settings.SQ_QUEUES, signalqueue.SQ_RUNMODES['SQ_ASYNC_MGMT'])\r\n queue = queues[queue_name]\r\n \r\n try:\r\n queue_available = queue.ping()\r\n except:\r\n self.echo(\"\\n--- Can't ping the backend for %s named '%s'\" % (queue.__class__.__name__, queue_name), color=16)\r\n self.echo(\"\\n--- Is the server running?\", color=16)\r\n self.exit(2)\r\n \r\n if not queue_available:\r\n self.echo(\"\\n--- Can't ping the backend for %s named '%s'\" % (queue.__class__.__name__, queue_name), color=16)\r\n self.echo(\"\\n--- Is the server running?\", color=16)\r\n self.exit(2)\r\n \r\n http_server = HTTPServer(Application(queue_name=queue_name,\r\n halt_when_exhausted=options.get('halt_when_exhausted', False),\r\n log_exceptions=options.get('log_exceptions', True),\r\n ))\r\n \r\n http_server.listen(int(options.get('port')), address=options.get('addr'))\r\n \r\n try:\r\n IOLoop.instance().start()\r\n \r\n except KeyboardInterrupt:\r\n self.echo(\"Shutting down signal queue worker ...\", color=31)",
"title": ""
},
{
"docid": "97563a653cd2f1d0fec3b836f749d089",
"score": "0.5145615",
"text": "def executor(self) -> Executor:\n if self._executor is None:\n kwargs = dict(max_workers=self.concurrency)\n if self._executor_class is ThreadPoolExecutor:\n kwargs['thread_name_prefix'] = 'Worker'\n self._executor = self._executor_class(**kwargs)\n return self._executor",
"title": ""
},
{
"docid": "819cdb2dbfe89bd95b05b2c4ed65844c",
"score": "0.5143967",
"text": "def new(db: \"MephistoDB\", worker_name: str) -> \"Worker\":\n raise NotImplementedError()",
"title": ""
},
{
"docid": "fdc3050daa0ce017a11c7233a39ecda7",
"score": "0.51397",
"text": "def _work(self):\n pid = os.getpid()\n with open(constant.PID_WORKER_TMP_FILE, \"w\") as file_handle:\n file_handle.write(str(pid))\n worker = asciipic_worker.Worker(\n queues=self.args.queues,\n name=self.args.name,\n redis_host=self.args.redis_host,\n redis_port=self.args.redis_port,\n redis_database=self.args.redis_database,\n redis_password=self.args.redis_password)\n\n # Start the worker\n worker.work()",
"title": ""
},
{
"docid": "fabcc7f8e523fbdb7e62eb43f3eb6fb3",
"score": "0.5131152",
"text": "def __init__(self, func, *args, **kwargs):\r\n super(Worker, self).__init__()\r\n self.func = func\r\n self.args = args\r\n self.kwargs = kwargs",
"title": ""
},
{
"docid": "ec96c6fa48bc184feb51643635545e0d",
"score": "0.51273954",
"text": "def _submit(self, pool, args, callback):\n if self.config.args.single_threaded:\n callback(self.call_runner(*args))\n else:\n pool.apply_async(self.call_runner, args=args, callback=callback)",
"title": ""
},
{
"docid": "612e8ec1e0693cab7c7a50e30220183c",
"score": "0.5126948",
"text": "def __init__(self, tasks, shutdown_event, hup_event):\n super(Worker, self).__init__()\n self._tasks = tasks\n self._shutdown_event = shutdown_event\n self._hup_event = hup_event\n # Daemon mode set just in case there's a way the main/server thread's\n # shutdown event can fail, which would leave this thread spinning.\n self.daemon = True",
"title": ""
},
{
"docid": "cbda5703fbda417aeddcd988c770e3d3",
"score": "0.5124775",
"text": "def run_in_executor(self, callable, *args):\n loop = self._loop\n future = loop.run_in_executor(None, callable, *args)\n if self.green_pool and self.green_pool.in_green_worker:\n return self.green_pool.wait(future, True)\n else:\n return future",
"title": ""
},
{
"docid": "a493805b5edec662efa2af3cbaa5b58d",
"score": "0.51242554",
"text": "def main():\n try:\n pool = ThreadPool(4)\n for number in range(20):\n pool.add_task(echo, number)\n time.sleep(EXACT4THREADS1)\n #time.sleep(QUEUE_THREADS1)\n #QUEUE_THREADS0 & QUEUE_THREADS1 can prove that daemon thread \"TERMINATES\" with main thread.(self.setDaemon(True) && WITHOUT pool.destroy())\n pool.destroy() \n #1. 只要有pool.detroy()不管是否设置self.setDaemon(True)，都可以保证程序正常退出，因为有destroy()中的dismiss()和join()让程序正常退出 \n #2. 如果没有pool.detroy():\n ##1).不设置self.setDaemon(True)，程序无法退出[Worker.run()里面的while 1会一直执行下去，程序不结束. 即使有exit(0)，程序也不会结束]\n ##2).设置self.setDaemon(True)，程序退出[没有执行完的工作也不继续执行了]\n logging.debug(\"Main Thread Terminates before exit(0).\")\n exit(0)\n logging.debug(\"Main Thread Terminates after exit(0).\")\n except Exception as e:\n logging.error(e)\n else:\n logging.debug(\"OK\")",
"title": ""
},
{
"docid": "fcedf777e0f72b9499d0834220f69e5b",
"score": "0.5111542",
"text": "def create_worker_block(WorkerId=None, Reason=None):\n pass",
"title": ""
},
{
"docid": "6cd12a897d5d5f8761759bad331e957d",
"score": "0.5109314",
"text": "def start_worker(self):\n self._thread_worker = _start_thread(self._start)",
"title": ""
},
{
"docid": "f1448e700fb4e323affbc0f1030d1035",
"score": "0.5107868",
"text": "def build_worker():\n log.info(\"build worker image\")\n packages = [\n \"bash\",\n \"bzip2\",\n \"coreutils\",\n \"coreutils-stat\",\n \"diffutils\",\n \"file\",\n \"gawk\",\n \"gcc\",\n \"getopt\",\n \"git\",\n \"libncurses\",\n \"make\",\n \"patch\",\n \"perl\",\n \"perlbase-attributes\",\n \"perlbase-findbin\",\n \"perlbase-getopt\",\n \"perlbase-thread\",\n \"python-light\",\n \"tar\",\n \"unzip\",\n \"wget\",\n \"xz\",\n \"xzdiff\",\n \"xzgrep\",\n \"xzless\",\n \"xz-utils\",\n \"zlib-dev\",\n ]\n\n packages_hash = get_packages_hash(packages)\n database.insert_packages_hash(packages_hash, packages)\n\n params = {\n \"distro\": \"openwrt\",\n \"version\": config.get(\"openwrt\").get(\"latest\"),\n \"target\": \"x86/64\",\n \"profile\": \"Generic\",\n \"packages_hash\": packages_hash,\n }\n\n params[\"request_hash\"] = get_request_hash(params)\n\n database.insert_dict(\"requests\", params)",
"title": ""
},
{
"docid": "1aecaa04571b38de01b1328fbcd8afb8",
"score": "0.5105317",
"text": "def beta_create_Worker_server(servicer, pool=None, pool_size=None, default_timeout=None, maximum_timeout=None):\n request_deserializers = {\n ('Worker', 'SetDataRange'): Range.FromString,\n ('Worker', 'SetParamsLocation'): ParamsLocations.FromString,\n ('Worker', 'SetParamsRange'): Range.FromString,\n ('Worker', 'StartWork'): StateMessage.FromString,\n }\n response_serializers = {\n ('Worker', 'SetDataRange'): StateMessage.SerializeToString,\n ('Worker', 'SetParamsLocation'): StateMessage.SerializeToString,\n ('Worker', 'SetParamsRange'): StateMessage.SerializeToString,\n ('Worker', 'StartWork'): StateMessage.SerializeToString,\n }\n method_implementations = {\n ('Worker', 'SetDataRange'): face_utilities.unary_unary_inline(servicer.SetDataRange),\n ('Worker', 'SetParamsLocation'): face_utilities.unary_unary_inline(servicer.SetParamsLocation),\n ('Worker', 'SetParamsRange'): face_utilities.unary_unary_inline(servicer.SetParamsRange),\n ('Worker', 'StartWork'): face_utilities.unary_unary_inline(servicer.StartWork),\n }\n server_options = beta_implementations.server_options(request_deserializers=request_deserializers, response_serializers=response_serializers, thread_pool=pool, thread_pool_size=pool_size, default_timeout=default_timeout, maximum_timeout=maximum_timeout)\n return beta_implementations.server(method_implementations, options=server_options)",
"title": ""
},
{
"docid": "9bc21033443ed7cb5f9a4f4951576bc9",
"score": "0.51022667",
"text": "def launch_workers (self):\n nw = self.cf.getint ('worker-threads', 10)\n for i in range (nw):\n wname = \"%s.worker-%i\" % (self.hname, i)\n self.log.info (\"starting %s\", wname)\n w = TailWriter_Worker(\n wname, self.xtx, self.zctx, self.ioloop,\n self.dealer_url, self.router_url, self.wparams)\n w.stat_inc = self.stat_inc # XXX\n self.workers.append (w)\n w.start()",
"title": ""
},
{
"docid": "2fe895d086191f8299a24d79814067e3",
"score": "0.51005745",
"text": "def _create_workers(self):\n for worker_config in self.__config.worker_configs:\n worker = CopyingManagerWorker(self.__config, worker_config)\n self.__workers[worker_config[\"id\"]] = worker",
"title": ""
},
{
"docid": "9388e71eab0aa29a509c6bc5e4d47021",
"score": "0.50888425",
"text": "def start(self):\n pool = mp.Pool()\n [pool.apply_async(func=self.ij_instance, args=(self.q_in, self.q_out,)) for _ in range(self.proc_num)]\n pool.close()",
"title": ""
},
{
"docid": "53d07bf4765c3b85b66427baf4c1248a",
"score": "0.5088512",
"text": "def get_task_queue():",
"title": ""
},
{
"docid": "7f9b05ca194d72db9aee5ebdd926c194",
"score": "0.5080464",
"text": "def start(self, *args, **kwargs):\n args = (self.counter,) + args\n threading.Thread(target=self._work_callback, args=args, kwargs=kwargs).start()",
"title": ""
},
{
"docid": "a19719416738769b4bf64c252e69e1d4",
"score": "0.5077711",
"text": "def create_workers(hash_of_preceding_coin, miner_id):\r\n print(\"Creating workers\")\r\n for i in range(num_workers):\r\n p = Process(\r\n target=f,\r\n args=(event, i,))\r\n p.start()\r\n jobs.append(p)",
"title": ""
},
{
"docid": "81c53a746d2e252ed2c1afb2d46af1be",
"score": "0.50618196",
"text": "def _add_worker_(self):\n\n process = \\\n ComplexProcessWorker(max_threads=self._max_threads,\n init_func=self._init_func)\n\n self._processes.append(process)\n process.start()\n\n return process",
"title": ""
},
{
"docid": "9f662e30d66aa501d367ea07ee6007f7",
"score": "0.5053917",
"text": "def __init__(self, num_workers, eval_function, timeout=None):\n self.num_workers = num_workers\n self.eval_function = eval_function\n self.timeout = timeout\n self.pool = Pool(num_workers)",
"title": ""
},
{
"docid": "53d3014dec2009d83db72e28efe45c43",
"score": "0.5053304",
"text": "def get_cpu_pool():\n return _cpu_pool",
"title": ""
},
{
"docid": "b35df3c83f0d753c6edf4aab4ce77734",
"score": "0.5051563",
"text": "def indicator_thread(self):\r\n\r\n job = multiprocessing.Process(target=self.indicator)\r\n job.start()\r\n return job",
"title": ""
},
{
"docid": "7b9fd3922dac3250d87770e08b26476d",
"score": "0.50511503",
"text": "def __init__(self, max_workers):\r\n self._max_workers = max_workers\r\n self._work_queue = queue.Queue()\r\n self._threads = set()\r\n self._shutdown = False\r\n self._shutdown_lock = threading.Lock()",
"title": ""
},
{
"docid": "4757a21961bcb7d6185143dec58d4ed2",
"score": "0.50405276",
"text": "def run(self):\n logger.info('Service is running (main loop processing)')\n\n while True:\n try:\n socks = dict(self.poller.poll())\n except KeyboardInterrupt:\n break\n\n if self.backend in socks and socks[self.backend] == zmq.POLLIN:\n\n # TODO: collect responses in fragments, issue response to the frontend when complete\n\n worker_addr = self.backend.recv()\n\n # the empty frame\n self.backend.recv()\n\n clientmsg = self.backend.recv()\n data, tt, msgtype = messages.get(clientmsg)\n self.available_workers += 1\n self.workers_list[tt].append(worker_addr)\n\n logger.debug('Received message from worker id: %s - tt: %s - msgtype: %s', worker_addr, tt, msgtype)\n\n if msgtype == messages.MESSAGE_TYPE_ROUTING:\n assert 'address' in data\n client_addr = data['address']\n\n self.backend.recv()\n\n msg = self.backend.recv()\n\n logger.debug('Routing RESPONSE from worker %s to client %s', worker_addr, client_addr)\n\n self.frontend.send_multipart([\n client_addr, b'', msg\n ])\n\n\n logger.debug('Workers available: %d', self.available_workers)\n if self.available_workers > 0:\n\n if self.frontend in socks and socks[self.frontend] == zmq.POLLIN:\n\n client_addr, empty, msg = self.frontend.recv_multipart()\n data, tt, msgtype = messages.get(msg)\n routing_data = messages.create_routing(task = tt, data = {\n 'address': client_addr\n })\n\n assert msgtype == messages.MESSAGE_TYPE_DATA\n assert tt in self.workers_list\n\n self.available_workers -= 1\n logger.debug('Routing task type: %s', tt)\n logger.debug('Workers list keys: %s', str(self.workers_list.keys()))\n\n worker_id = self.workers_list[tt].pop()\n\n logger.debug('Routing REQUEST from client %s to worker %s', client_addr, worker_id)\n\n self.backend.send_multipart([\n worker_id, b'', routing_data, b'', msg\n ])\n\n\n # shutdown workers\n self.shutdown()",
"title": ""
},
{
"docid": "571702ea8f231370f70e8e476d668603",
"score": "0.5040452",
"text": "def run_worker(\n self,\n work: WorkType[ResultType],\n name: str | None = \"\",\n group: str = \"default\",\n description: str = \"\",\n exit_on_error: bool = True,\n start: bool = True,\n exclusive: bool = False,\n thread: bool = False,\n ) -> Worker[ResultType]:\n worker: Worker[ResultType] = self.workers._new_worker(\n work,\n self,\n name=name,\n group=group,\n description=description,\n exit_on_error=exit_on_error,\n start=start,\n exclusive=exclusive,\n thread=thread,\n )\n return worker",
"title": ""
},
{
"docid": "d302c7ed10456cb2a4a778b84d705ed2",
"score": "0.5039407",
"text": "def multi_thread_runner(data_list, worker, extra_arg=None):\n threads = []\n thread_count = 32\n data_size = len(data_list)\n if data_size <= thread_count:\n thread_count = 1\n step_size = data_size/thread_count\n for i in range(0, data_size, step_size):\n data_subset = data_list[i: step_size + i]\n if extra_arg is None:\n t = threading.Thread(target=worker, args=(data_subset,))\n else:\n t = threading.Thread(target=worker, args=(data_subset, extra_arg))\n threads.append(t)\n t.start()\n for thread in threads:\n thread.join()",
"title": ""
},
{
"docid": "a2079d596be7da55224f4d15224af778",
"score": "0.5028708",
"text": "def do_with_threads(self, size, func, *args):\n pool = [threading.Thread(target=func, args=args) for i in range(size)]\n for thread in pool:\n thread.start()\n return pool",
"title": ""
},
{
"docid": "e70ecb4baa5d5275064e544ffc1d29c9",
"score": "0.5028322",
"text": "def __init__(self, queue, client_factory, transfer_monitor, osutil):\n super(GetObjectWorker, self).__init__(client_factory)\n self._queue = queue\n self._client_factory = client_factory\n self._transfer_monitor = transfer_monitor\n self._osutil = osutil",
"title": ""
},
{
"docid": "5db83cf7c0131c357812d8f5025a3576",
"score": "0.5024398",
"text": "def main():\r\n # Prepare context and sockets\r\n url_worker = \"tcp://localhost:5679\"\r\n context = zmq.Context(1)\r\n frontend = context.socket(zmq.ROUTER)\r\n backend = context.socket(zmq.ROUTER)\r\n front_monitor = frontend.get_monitor_socket()\r\n back_monitor = backend.get_monitor_socket()\r\n\r\n frontend.bind(FRONTEND_HOST)\r\n backend.bind(BACKEND_HOST)\r\n # Start background tasks\r\n def start(task, *args):\r\n process = multiprocessing.Process(target=task, args=args)#多进程，每个进程需要自己的context\r\n #process = threading.Thread(target=task,args=args) #多线程，参数中的变量每个线程各自拥有\r\n process.daemon = True\r\n process.start()\r\n for i in range(NBR_WORKERS):\r\n start(worker_task, url_worker,i)\r\n\r\n t = threading.Thread(target=event_monitor, args=(front_monitor,))\r\n t.start()\r\n t2 = threading.Thread(target=event_monitor, args=(back_monitor,))\r\n t2.start()\r\n start(event_monitor,front_monitor)\r\n start(event_monitor,back_monitor)\r\n\r\n # Initialize main loop state\r\n workers = WorkerQueue()\r\n poller = zmq.Poller()\r\n # Only poll for requests from backend until workers are available\r\n poll_workers = zmq.Poller()\r\n poll_workers.register(backend, zmq.POLLIN)\r\n\r\n poll_both = zmq.Poller()\r\n poll_both.register(frontend, zmq.POLLIN)\r\n poll_both.register(backend, zmq.POLLIN)\r\n\r\n while True:\r\n if len(workers.queue) > 0:\r\n poller = poll_both\r\n else:\r\n poller = poll_workers\r\n sockets = dict(poller.poll(HEARTBEAT_INTERVAL * 1000))\r\n print(\"sockets=:\",sockets)\r\n print(\"sockets backend:\",sockets.get(backend))\r\n print(\"sockets frontend:\",sockets.get(frontend))\r\n #print(zmq.POLLIN)\r\n if backend in sockets:\r\n # Handle worker activity on the backend\r\n frames = backend.recv_multipart()\r\n print(\"get from workers:\",frames)\r\n if not frames:\r\n break\r\n address = frames[0]\r\n print(\"length socks:\",len(workers.queue))\r\n print(\"workers queue:\",workers.queue)\r\n #if len(workers.queue) == 0:\r\n #poller.register(frontend, zmq.POLLIN)\r\n workers.ready(Worker(address))\r\n msg = frames[1:]\r\n if len(msg) == 1:\r\n if msg[0] not in (PPP_READY):\r\n print(\"E: Invaild message from worker: %s\" %msg)\r\n else:\r\n frontend.send_multipart(msg)\r\n\r\n if frontend in sockets:\r\n frames = frontend.recv_multipart()\r\n print(\"get from clients\")\r\n if not frames:\r\n break\r\n frames.insert(0,workers.next())\r\n #frames = [workes.next, ''] + frames\r\n backend.send_multipart(frames)\r\n #if len(workers.queue) == 0:\r\n #poller.unregister(frontend)\r\n \r\n #workers.purge()\r\n \r\n # Clean up\r\n backend.close()\r\n frontend.close()\r\n context.term()",
"title": ""
},
{
"docid": "b10d5c3a0f7b2262e572da431c8c54d9",
"score": "0.5023578",
"text": "def __init__(self):\r\n self.pool = []",
"title": ""
},
{
"docid": "4727dc4bfb8c88d9fed20dbbfa26e960",
"score": "0.5023207",
"text": "def runPool(aPool, innerFunc, inputData):\n\n temp = aPool.map_async(innerFunc, inputData)\n temp.wait()\n\n return np.array(temp.get())",
"title": ""
},
{
"docid": "83145592ca71cabf1c3b13faab980654",
"score": "0.50208646",
"text": "def start(self):\n self.threads.clear()\n for _ in range(self.nthreads):\n thread = threading.Thread(target=self._worker)\n self.threads.append(thread)\n thread.start()\n return self",
"title": ""
},
{
"docid": "e5a9acbacfde7e790372cc74265bebb8",
"score": "0.50207436",
"text": "def process(self, func, args=None):\r\n\t\tfunc = self.pool_thread(func)\r\n\t\tt = threading.Thread(target=func, args=args)\r\n\t\tt.start()",
"title": ""
},
{
"docid": "8f342783a22461f4ba88d4f288873d54",
"score": "0.50202155",
"text": "def test_advance_queue_subtask_reuse_waiting_worker(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "935a31056532c02a7c4c973825a6c07a",
"score": "0.50188977",
"text": "def __init__(self, userManager):\n self.userManager = userManager\n self.workingThread = None\n self.progressBar = None\n self.done = True",
"title": ""
},
{
"docid": "cb33387fb83e9a84e3449cc990d062bf",
"score": "0.5018774",
"text": "def websocket_thread(self):\n self.worker_event_loop = asyncio.new_event_loop()\n self.worker_event_loop.run_until_complete(self.websocket_loop())",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="734"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">458659e045cff9aa4dc4d0a41aa4ed9d</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Stop the client. Must be invoked when done with the client for graceful exit. If already stopped, will not do anything. Cannot be cancelled if you try, the client will still fully shut down as much as possible, although CancelledError will still be raised.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "57620fd574d7a038b1ee2c20ccbaddad",
"score": "0.6313185",
"text": "async def stop(self) -> None:\n cancelled_tasks = []\n logger.debug(\"Stopping ProvisioningMQTTClient...\")\n\n if self._process_dps_responses_task:\n logger.debug(\"Cancelling '_process_dps_responses' background task\")\n self._process_dps_responses_task.cancel()\n cancelled_tasks.append(self._process_dps_responses_task)\n self._process_dps_responses_task = None\n\n results = await asyncio.gather(\n *cancelled_tasks, asyncio.shield(self.disconnect()), return_exceptions=True\n )\n for result in results:\n # NOTE: Need to specifically exclude asyncio.CancelledError because it is not a\n # BaseException in Python 3.7\n if isinstance(result, Exception) and not isinstance(result, asyncio.CancelledError):\n raise result",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "4a6bf9e069d2867b325f8891a1068339",
"score": "0.7935486",
"text": "def stop(self):\n self.info(\"Stopping client\")\n with self.__lock:\n self.__isocket.disconnect(self.__raddr)\n self.__isocket.close()\n\n self.__done = True\n\n self.__background.join()\n self.__background = None\n\n self.info(\"Client stopped\")",
"title": ""
},
{
"docid": "c59b9de49a5eb32c7f4e84e72eb37b8a",
"score": "0.69268423",
"text": "def stop(self):\n\n #Stops MQTT client\n self.client.loop_stop()",
"title": ""
},
{
"docid": "902962f05cf16219f4bde261595997ba",
"score": "0.6871644",
"text": "def stop(self):\n return self.__interrupter_helper(self._stop_coro())",
"title": ""
},
{
"docid": "f5fa9fc114cb83c1446d541c26ac63cb",
"score": "0.6749134",
"text": "def stop(self):\n if self._running:\n self._running = False\n self._server.shutdown()",
"title": ""
},
{
"docid": "77e7eeac3dcf4799ebf73a1e8d117952",
"score": "0.6703952",
"text": "def do_stop(self, raw_args):\n self.ctrl_client.stop_full()",
"title": ""
},
{
"docid": "f87f59c5d3efcc35947647d64b7536fe",
"score": "0.6703151",
"text": "def stop(self):\n self._client_alive.clear()",
"title": ""
},
{
"docid": "05f0eadaf918838e7979739ee6a3de7a",
"score": "0.66867584",
"text": "async def stop(self):\n await self._server.stop(1)",
"title": ""
},
{
"docid": "2907eec760e14dbf42f56c5d9d816b22",
"score": "0.6673615",
"text": "def stop_client(self) -> None:\n if self.mqtt_client:\n if self.connected:\n self.mqtt_client.disconnect()\n self.connected = False\n self.mqtt_client.loop_stop()\n logging.debug(\n f\"disconnected MQTT connection to server \"\n f\"{hl(self.config.mqtt['server'] + ':' + str(self.config.mqtt['port']))}\"\n )",
"title": ""
},
{
"docid": "3ea01651ab1cf30633d1f407941fb62a",
"score": "0.66230524",
"text": "def stop(self):\n self._server.stop()",
"title": ""
},
{
"docid": "3ea01651ab1cf30633d1f407941fb62a",
"score": "0.66230524",
"text": "def stop(self):\n self._server.stop()",
"title": ""
},
{
"docid": "96e94987492e7b72b52ae71aabf9b160",
"score": "0.6583312",
"text": "def stop(self):\n\t\tlogging.info(\"Stopping Server\")\n\t\ttry:\n\t\t\tself.ss.shutdown(SHUT_RDWR)\n\t\t\tself.ss.close()\n\t\texcept:\n\t\t\tlogging.exception(\"Server.stop\")\n\n\t\tfor csock in self.clients:\n\t\t\ttry:\n\t\t\t\tself.clients[csock].close() # Client.close!\n\t\t\texcept:\n\t\t\t\t# this should not happen since close is protected...\n\t\t\t\tlogging.exception(\"clients[csock].close\")\n\n\t\t# If we delClient instead, the following would be unnecessary...\n\t\tself.clients.clear()\n\t\tself.id2client.clear()",
"title": ""
},
{
"docid": "0d4d27eaa2cceb906a6a7bba6e7027e9",
"score": "0.6540483",
"text": "def stop(self):\n try:\n self.container.stop()\n except:\n raise",
"title": ""
},
{
"docid": "ea907c28ec2987ad1794a10bc4af428f",
"score": "0.6487896",
"text": "def stopService(self):\n self._stop = True\n self.reInstanceThreads()",
"title": ""
},
{
"docid": "9c36344ab688242b914f32c25d7e20dc",
"score": "0.64717054",
"text": "def stop(self):\n try:\n if self.channel is not None:\n if self.cancel_on_close is True:\n self.channel.cancel()\n self.channel.close()\n if self.connection is not None:\n self.connection.close()\n except:\n pass",
"title": ""
},
{
"docid": "a7920ffd328406db61e82874f78fa21b",
"score": "0.6451544",
"text": "def terminate_connection(self):\n self.__keep_running_client = False\n self.send_message(\"OUT|OK\")",
"title": ""
},
{
"docid": "0f62975607dd57ecc098e48d52c4a7db",
"score": "0.6450876",
"text": "def stop(self):\n self.server.stop()",
"title": ""
},
{
"docid": "9dde451698861ef5bb1395c2b6d7cc2e",
"score": "0.6442813",
"text": "def svc_stop(self):\n # pylint recognizes all the variables except ReportServiceStatus, since\n # pylint is tested only on linux environments now, disabling this error\n self.ReportServiceStatus(win32service.SERVICE_STOP_PENDING) # pylint: disable=E1101\n try:\n self.server.shutdown()\n except Exception:\n self.log(traceback.format_exc(sys.exc_info))\n win32event.SetEvent(self.stop_event)\n self.ReportServiceStatus(win32service.SERVICE_STOPPED) # pylint: disable=E1101",
"title": ""
},
{
"docid": "0c376f44d50f2cf8e49b514ad2613b07",
"score": "0.64425063",
"text": "def stop_serverclient(self):\n tray_icon.setIcon(tray_icon.ICON_DISCONNECTED)\n if self.serverclient is not None:\n self.serverclient.stop()\n self.serverclient.wait()\n self.serverclient.deleteLater()",
"title": ""
},
{
"docid": "9cb68388079bde91bdd3156bc16f0d59",
"score": "0.6431416",
"text": "async def stop(self):\n await self.operations.run_standard_event(self, 'stop')",
"title": ""
},
{
"docid": "36c6c13431038faf8dc3e8e87b1e95bb",
"score": "0.6417287",
"text": "def stop_server(self):\n self._close_client_connections()\n self.server_connection.close()\n self.server_connection = None",
"title": ""
},
{
"docid": "2dddbfbcf13e9b8dad7f59b8a04de88d",
"score": "0.6415029",
"text": "def stop(self):\n logging.info(\"Stopping Server\")\n try:\n self.ss.close()\n except:\n logging.exception(\"Server.stop\")\n\n for csock in self.clients:\n try:\n self.clients[csock].close() # Client.close!\n except:\n # this should not happen since close is protected...\n logging.exception(\"clients[csock].close\")\n\n # If we delClient instead, the following would be unnecessary...\n self.clients.clear()\n self.id2client.clear()",
"title": ""
},
{
"docid": "2dddbfbcf13e9b8dad7f59b8a04de88d",
"score": "0.6415029",
"text": "def stop(self):\n logging.info(\"Stopping Server\")\n try:\n self.ss.close()\n except:\n logging.exception(\"Server.stop\")\n\n for csock in self.clients:\n try:\n self.clients[csock].close() # Client.close!\n except:\n # this should not happen since close is protected...\n logging.exception(\"clients[csock].close\")\n\n # If we delClient instead, the following would be unnecessary...\n self.clients.clear()\n self.id2client.clear()",
"title": ""
},
{
"docid": "a5dc25044ebeedcfc309e00b8dfb6ef8",
"score": "0.6407461",
"text": "def stop(self):\n if not self.__serving:\n raise RuntimeError(\"Server not started yet\")\n self.__serving = False\n self.__stopped = True\n self.join()",
"title": ""
},
{
"docid": "77b0dc53b52f69ad1391c675e0db9b1c",
"score": "0.63965774",
"text": "def stop(self):\n CBTLOG.debug('CBT: Stop requested')\n self._stop = True\n CBTLOG.info('CBT: Stopped')",
"title": ""
},
{
"docid": "e2d5c8d9e22c6ee29bf729e5812524f6",
"score": "0.63800174",
"text": "def stop(self) -> None:\n with self.__lock:\n self.__socket.disconnect(self.__addr)\n self.__socket.close()",
"title": ""
},
{
"docid": "cabb971be424af03c0a5792f48ae2e90",
"score": "0.63661593",
"text": "def stop(self):\n self.service_stop()\n return self.service_disable()",
"title": ""
},
{
"docid": "a737472cbad693843c68e64413217e9a",
"score": "0.6358268",
"text": "def stop(self):\n\n logger.debug(\"Stopping triton server.\")\n\n if self._tritonserver_container is not None:\n logger.debug(f\"Stopping Triton Server id={self._tritonserver_container.id}\")\n self._tritonserver_container.stop()\n self._tritonserver_container.remove(force=True)\n\n self._tritonserver_container = None\n self._docker_client.close()\n\n logger.debug(\"Triton server stopped\")",
"title": ""
},
{
"docid": "e3f746edde9d87d82ed14fbf6915182c",
"score": "0.63537925",
"text": "def shutdown(self):\r\n self.Client.Shutdown()",
"title": ""
},
{
"docid": "6ea1085f50f96aef4bbcfaf8e621d1fb",
"score": "0.6330279",
"text": "def stop(self):\n self.rpc_server.stop()",
"title": ""
},
{
"docid": "993f4fd97bac89b2be83517721074dff",
"score": "0.63156796",
"text": "async def stop(self, ctx):\n logger.debug(\"stop command called\")\n\n ctx.voice_client.stop()\n self.song_queue = []\n await self._wait_to_disconnect(ctx)",
"title": ""
},
{
"docid": "196ec9dfc12087f9c3d00afe5e04ff11",
"score": "0.63025117",
"text": "def stop(self):\n self._running = False\n\n print(\"Closing connection\")",
"title": ""
},
{
"docid": "1eb0067619d22b98c2d463ca3cf39705",
"score": "0.62611854",
"text": "def stop(self):\n\n if self.server_thread is not None:\n self.server_event_loop.add_callback(self.server_event_loop.stop)\n self.server_thread.join()\n self.server_thread = None\n self.server.stop()",
"title": ""
},
{
"docid": "0a0faca5284f25e5848a042d9cdb9578",
"score": "0.6251361",
"text": "def __del__(self):\n logging.debug(\"Terminating client...\")\n self.quit(force=True)",
"title": ""
},
{
"docid": "a665143a3e7352e5b6e0be2c109f35e7",
"score": "0.6236522",
"text": "def stop(self):\n self._terminate = True",
"title": ""
},
{
"docid": "6697ed0c82fde13424d6ca592d55c46f",
"score": "0.6228163",
"text": "def stop(self):\r\n if self._server_thread is None:\r\n return\r\n self.running = False\r\n self._server_thread.join()\r\n self.server_close()",
"title": ""
},
{
"docid": "d64cede58014ada55768a68379e364e6",
"score": "0.62228906",
"text": "def stop(self):\n self.server.socket.close()\n self.server.server_close()\n self.server.shutdown()",
"title": ""
},
{
"docid": "c9fb874e13af0543f95bcf8b0ba690eb",
"score": "0.6215924",
"text": "def stop(self):\n self.loop.call_soon_threadsafe(self.loop.stop)",
"title": ""
},
{
"docid": "4a13223f9585a8a5a8175c3e6bae29df",
"score": "0.6208519",
"text": "def stop(self):\n\n self.sd_notify.stopping()\n self.sd_notify.status(\"Stopping...\")\n\n self.log.info(\"Disconnect all client...\")\n for connection in self.connections:\n connection.close()\n\n self.log.info(\"Closing all the threads...\")\n\n self.watcher.stop()\n for server in self._servers:\n server.stop()\n\n self.watcher.join()\n for server in self._servers:\n server.join()",
"title": ""
},
{
"docid": "24e85390066945a0374fa3b8ea6815df",
"score": "0.6177351",
"text": "def stop(self):\n self._shutdown_event.set()",
"title": ""
},
{
"docid": "8d969ccb8404fcf4a03c57755eb02798",
"score": "0.6173969",
"text": "def SvcStop(self):\n self.ReportServiceStatus(win32service.SERVICE_STOP_PENDING)\n win32event.SetEvent(self.h_waitstop)\n self.stop_requested = True\n self.logger.info('Stop requested')",
"title": ""
},
{
"docid": "de030b0cc9df7d33777856cf1a5dd495",
"score": "0.6169071",
"text": "def stop(self):\n command = Command('stop')\n self.command_channel.send(command)\n command.wait()",
"title": ""
},
{
"docid": "6f5b316a79d78547883551a9b55c12cb",
"score": "0.6163531",
"text": "def stop(self):\n self._http_srv.shutdown()\n self._http_srv.server_close()",
"title": ""
},
{
"docid": "12f1728ebf1e125b77197e34fbad04fb",
"score": "0.61512715",
"text": "def stop(self):\n self._stop.set()",
"title": ""
},
{
"docid": "081f7b3d2b0e8d40dbbd0ec05f9875e7",
"score": "0.6148367",
"text": "def stop(self):\n self._stop = True\n self.log.info(\"stopping\")",
"title": ""
},
{
"docid": "00c03c154d93905323ca9b23d640976b",
"score": "0.61326855",
"text": "async def async_stop(self):\n _LOGGER.debug(\"%s:stop %s\", self.host, self.host)\n self._closing = True\n self._queue_future.cancel()\n self.is_connected = False",
"title": ""
},
{
"docid": "bdec8de2a2a67144ea7b78d132a33d6c",
"score": "0.6131733",
"text": "def stop(self):\n self.shutdown = True",
"title": ""
},
{
"docid": "2b3f62e766ee104221d1ab8fac2e67d0",
"score": "0.6127585",
"text": "def stop(self):\n self.stop_communication()\n self.running = False",
"title": ""
},
{
"docid": "08d51e91781435c9cfad67fee939623f",
"score": "0.61272496",
"text": "def stop(self):\n self.running = False\n client = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)\n client.connect(StateChangeListener.SOCKET_PATH)\n client.close()\n if threading.current_thread() != self.thread:\n self.thread.join()",
"title": ""
},
{
"docid": "0436dae13698b57d7049a59d17a3f70b",
"score": "0.61151594",
"text": "def stop(self):\n assert self.pid is not None\n os.kill(self.pid, signal.SIGINT)\n return self.wait(expect=None)",
"title": ""
},
{
"docid": "86f5abf1fd8cc4b7804e7744b2bcbccf",
"score": "0.61128914",
"text": "def stop():\n server.stop()",
"title": ""
},
{
"docid": "22d4bdbf8a3ca7afcc42a22a958a1f5a",
"score": "0.6096593",
"text": "async def stop(self) -> None:",
"title": ""
},
{
"docid": "0a83c1ecef0a44c569635e21ab39df49",
"score": "0.6095849",
"text": "def stop(self):\n\n _log.info(\"Stopping %s\", self)\n\n assert self._initialized\n assert self._started\n assert not self._stopped\n\n self._stopped = True",
"title": ""
},
{
"docid": "2f1f0786f2c7164615afb6859836cb10",
"score": "0.609432",
"text": "def stop(self):\n \n self.sock.close()\n super().stop()",
"title": ""
},
{
"docid": "46b6c0f62aedad71745fb8323fd61612",
"score": "0.6090076",
"text": "def stop():\n self._mqttc.disconnect()\n self._mqttc.loop_stop()",
"title": ""
},
{
"docid": "288f30a050e702d0669f3a4defa1edc9",
"score": "0.60882217",
"text": "def stop(self):\n if not self._started:\n return\n\n self._started = False\n self._socket = None\n self._read_mtu = None\n self._worker.join()",
"title": ""
},
{
"docid": "171622dfea43f231c5d0ceb3c2969a4f",
"score": "0.6084307",
"text": "def stopService(self):\n # TODO: stopping this service mid-import should really stop the import\n # loop, but this is not implemented because nothing will actually do it\n # except hitting ^C (which also calls reactor.stop(), so that will exit\n # anyway).",
"title": ""
},
{
"docid": "338561bf179ee9932201c65bda3f9c01",
"score": "0.6073953",
"text": "async def stop(self, ctx):\n\n await ctx.voice_client.disconnect()",
"title": ""
},
{
"docid": "338561bf179ee9932201c65bda3f9c01",
"score": "0.6073953",
"text": "async def stop(self, ctx):\n\n await ctx.voice_client.disconnect()",
"title": ""
},
{
"docid": "5348f53007dfff234869bfbbddad722e",
"score": "0.60714227",
"text": "def shut_down(self):\n\n if self.client.isconnected():\n self.client.disconnect()\n if self.sim is not None:\n self.sim.terminate()\n self.sim = None",
"title": ""
},
{
"docid": "0cdb886b21a8a050ef527f3ee4789f83",
"score": "0.60665834",
"text": "def stopService(self):\n pass",
"title": ""
},
{
"docid": "4c92bd435c25df40e99bc1c4c05ca013",
"score": "0.60606587",
"text": "def stop(self):\n data = tcp_sendrecv(self.sock, 'stop')\n return",
"title": ""
},
{
"docid": "a8d1dec0fa0976a2402f77c1ffe91db8",
"score": "0.6038519",
"text": "def stop(self):\n self._connection.close()",
"title": ""
},
{
"docid": "8f28c09bdedc7e5c2360abc888ef9054",
"score": "0.6038084",
"text": "def stop(self):\r\n super(Service, self).stop()",
"title": ""
},
{
"docid": "5ef3bba9c24dee52108cc00d326e0e95",
"score": "0.6030163",
"text": "def stop(self) -> None:\n\n self._loop.stop()",
"title": ""
},
{
"docid": "212a03099e301954d260c0f6b68b7f34",
"score": "0.6024357",
"text": "def stop(self):\n self.logger.debug(\"Stopping resource manager server\")\n self._resource_manager.stop()\n self._reactor.callFromThread(self._reactor.stop)",
"title": ""
},
{
"docid": "92affc10f27c4042b31ac44a68b64710",
"score": "0.6018708",
"text": "def stop(self):\n if self.running:\n self.controller.stop()\n self.httpd.stop()",
"title": ""
},
{
"docid": "94a85247533bffe549ba41c1d845a0c7",
"score": "0.6016916",
"text": "async def stop(self, ctx):\n \n\n await ctx.voice_client.disconnect()",
"title": ""
},
{
"docid": "a8ddea27b8273cd450ab2e2a90521627",
"score": "0.60134727",
"text": "def stop(self):\n if self.logger.isEnabledFor(logging.DEBUG):\n self.logger.debug(\"Plugin '{}': stop method called\".format(self.get_fullname()))\n self.alive = False\n # added to effect better cleanup on stop\n if self.scheduler_get(f'KNX[{self.get_instance_name()}] time'):\n self.scheduler_remove(f'KNX[{self.get_instance_name()}] time')\n self._client.close()",
"title": ""
},
{
"docid": "d2718b7fc5a726eb5551c85b23d8558a",
"score": "0.6012764",
"text": "def stop(self):\n\t\tself._stop.set()",
"title": ""
},
{
"docid": "57b740e75a06a8304d978cf52ca0de77",
"score": "0.6006833",
"text": "def stop(self):\n self._stop = True\n # Resume all threads XXX: TODO:\n for t in self.get_threads():\n t.resume()\n # Kill communications\n self.channel.quit()",
"title": ""
},
{
"docid": "f36beae4131f8830d56af18ff1ea4ca9",
"score": "0.5999425",
"text": "def shutdown(self):\n self.cancel = True\n # Enqueue None to optimistically unblock background threads so\n # they can check for the cancellation flag.\n self.request_queue.put(None)\n\n # Wait for request thread to finish with a timeout in seconds.\n self.request_thread.join(1)\n\n # close the underlying writer.\n self.writer.close()\n logger.info('Shutting down Json rpc client.')",
"title": ""
},
{
"docid": "574d154a4479de16d3d59d31d55fa173",
"score": "0.5995033",
"text": "def stop(self, _arg=None, **kwargs):\n assert _arg is None or not kwargs\n self.__stop_args = kwargs or _arg\n if self.__running:\n self.io_loop.stop()\n self.__running = False\n self.__stopped = True",
"title": ""
},
{
"docid": "64ee4711dede1e3b84e4a9240daeabb2",
"score": "0.5986096",
"text": "async def stop(self, ctx):\n await ctx.voice_client.disconnect()",
"title": ""
},
{
"docid": "d93ddfa2929b2c84e9a2886f01fc6449",
"score": "0.5982277",
"text": "def stop(self):\n self.httpserver.stop()\n logging.debug(\"Stopping CnCServer %s on %s:%d\" % (self.name, self.host, self.port))",
"title": ""
},
{
"docid": "877332bcc8493d4d86d448731ca338e1",
"score": "0.5981737",
"text": "def stop(self):\n self.log.info(\"disconnecting from {0:s}\".format(self.broker))\n return self.__MQTT_Client.disconnect()",
"title": ""
},
{
"docid": "48b1d0c3f452811a765d5b645ec6d067",
"score": "0.5971506",
"text": "def stop(self):\n if not self._started:\n return\n\n # close any open sessions\n logger.debug(\"Disconnecting {} forgotten Obex session(s)...\".format(\n len(self._clients)))\n for dest, _ in dict(self._clients).items():\n try:\n self.disconnect(destination=dest)\n except ConnectionError:\n pass\n self._clients = None\n self._started = False",
"title": ""
},
{
"docid": "9b0b669dd75ee09e0730e495718a871c",
"score": "0.59696525",
"text": "def stop(self):\n self.soc.close()\n self.connected = False",
"title": ""
},
{
"docid": "d2f5e3c741a442c0032b1cabad3390d1",
"score": "0.5967429",
"text": "def stop(self, loop: asyncio.AbstractEventLoop):\n if self.server is not None:\n self.server.close()\n for task in asyncio.Task.all_tasks():\n task.cancel()\n loop.run_until_complete(self.server.wait_closed())\n self.server = None",
"title": ""
},
{
"docid": "dd81c3991e6654fe75bb8e91d57bdec4",
"score": "0.59662014",
"text": "def stop(self):\n\n super().stop()\n\n # Set the interact thread kill switch\n with self._should_stop_lock:\n self._should_stop = True\n\n # Wait for the interact thread to die\n if self._thread_interact is not None:\n self._thread_interact.join()\n self._thread_interact = None",
"title": ""
},
{
"docid": "dd81c3991e6654fe75bb8e91d57bdec4",
"score": "0.59662014",
"text": "def stop(self):\n\n super().stop()\n\n # Set the interact thread kill switch\n with self._should_stop_lock:\n self._should_stop = True\n\n # Wait for the interact thread to die\n if self._thread_interact is not None:\n self._thread_interact.join()\n self._thread_interact = None",
"title": ""
},
{
"docid": "c9b6d5e24a70b021d647a352f4c76e87",
"score": "0.59651864",
"text": "def stop_serve(self) -> None:\r\n\t\tself.__logger.info('Stop signaled')\r\n\t\tself._interrupt_event.set()\r\n\t\tself._server_thread.join()\r\n\t\tself.__logger.info('Stop completed')",
"title": ""
},
{
"docid": "9d6b13a6d051f06f6d039e46d25fc76e",
"score": "0.59595597",
"text": "def stop(self):\r\n self._running = False\r\n if self._self_thread is not None:\r\n self._self_thread.cancel()\r\n self._self_thread = None",
"title": ""
},
{
"docid": "5ab5de2aaf4e00596bad2b5ddbc77eca",
"score": "0.59572554",
"text": "def stop(self):\n self._stop_event.set()",
"title": ""
},
{
"docid": "9b25fec474a66f9c5098e5808879bda5",
"score": "0.59567225",
"text": "def stop(self):\n self.cbot.stop()",
"title": ""
},
{
"docid": "6efcb08de0313c2341853ebaa68eedd2",
"score": "0.59474266",
"text": "def stop(self):\n self.connection.disconnect()",
"title": ""
},
{
"docid": "5c69b5611eac5f981490e87dfbee0db3",
"score": "0.5946602",
"text": "def stop(self) -> None:\n self._event_loop.stop()",
"title": ""
},
{
"docid": "d2ccdc4e1efc27e669ce86a780621021",
"score": "0.59350985",
"text": "def stop(self):\n self._logger.debug('Stop using %s', str(self._vswitch_class))\n self._vswitch.stop()",
"title": ""
},
{
"docid": "acd37a843279b67feb076251e33ed09d",
"score": "0.5912144",
"text": "def stop(self):\n self._closed = True\n if self._arecord:\n self._arecord.kill()",
"title": ""
},
{
"docid": "85491a896bf22c0c60776807dc95d0f5",
"score": "0.5904255",
"text": "def do_stop(self):\n if self.status.is_set(RunnerStatus.stopped) or \\\n self.status.is_set(RunnerStatus.stopping):\n self.logger.info(\"Already stopping or stopped\")\n return\n\n self.status.replace(RunnerStatus.started, RunnerStatus.stopping)\n try:\n self.stop()\n self.status.replace(RunnerStatus.stopping, RunnerStatus.stopped)\n except Exception as e:\n self.logger.exception(\"Failed to stop\")\n self.status.add(RunnerStatus.error, value={\n \"exception\": e\n })",
"title": ""
},
{
"docid": "a47e32a38f95bd200166c9408f885eba",
"score": "0.58872503",
"text": "def stop(self):\r\n # http://stackoverflow.com/questions/323972/is-there-any-way-to-kill-a-thread-in-python\r\n self._stop_event.set()\r\n self.join()",
"title": ""
},
{
"docid": "65da87939fa8653db019061571f42518",
"score": "0.58868784",
"text": "def stop(self):\n # TODO: cleanup things and close the connection\n self.handle_close()",
"title": ""
},
{
"docid": "be6445feaa8527ef68e6b3356f23f712",
"score": "0.588274",
"text": "def stopService(self):\n return self.stopAllTasks()",
"title": ""
},
{
"docid": "72c5a8be05b7a66203c989c6625ac968",
"score": "0.58808076",
"text": "def _stop_clients(self):\n for client in self.client_threads:\n client.stop()\n client.join(1)",
"title": ""
},
{
"docid": "3a5aa400e9575b44472741c1c65d4e64",
"score": "0.58801216",
"text": "def kill(self):\n logging.info(\"shutting down connection\")\n self._clientSocket.shutdown(socket.SHUT_RDWR)\n self._clientSocket.close()\n\n self._serverSocket.shutdown(socket.SHUT_RDWR)\n self._serverSocket.close()",
"title": ""
},
{
"docid": "d003ed00a75c10eeb6fd037ca22b4af8",
"score": "0.5879575",
"text": "def stop(self):\n self.proxy_target.http_client.close()\n self.test_server.stop()",
"title": ""
},
{
"docid": "0a7104cc55adfc2e9a471f7528d3f4e9",
"score": "0.58742785",
"text": "def stop(self):\n self.stopped = True",
"title": ""
},
{
"docid": "461557fec898c1e548d9a48f37ddb27a",
"score": "0.5874041",
"text": "def stop(self):\n self.logger.info('request to stop main loop')\n self.loop = False\n self.join()",
"title": ""
},
{
"docid": "1ffe7053ad35c956494569353ec132ce",
"score": "0.586563",
"text": "def _stop():\n shutdown_function()",
"title": ""
},
{
"docid": "7c6932e1a67f6e41c4aa3efc7b74f4f4",
"score": "0.5864609",
"text": "def stop(self) -> None:\n ServerHandler.stop_connections = True\n if self._httpd is not None:\n self._httpd.shutdown()",
"title": ""
},
{
"docid": "534223683de82aa8015d8eea4305294a",
"score": "0.5864364",
"text": "def stop(self):\n if self._ventilator:\n self._ventilator.stop()\n self._stop_event.set()",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="735"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">b6d7b86e4b7bd65a0ada5fbd5a190a1b</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Initialize internal set which will hold all tokens.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "180d6e6813da247d9c756bbaaac23ba4",
"score": "0.7904349",
"text": "def __init__(self):\n self._tokens = set()",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "0c47979dfc53180adde213d01b1ccd89",
"score": "0.6796376",
"text": "def __init__(self):\n\t\tself.s = set()",
"title": ""
},
{
"docid": "02efeb7887570eb52bfc0a23403e9739",
"score": "0.6761306",
"text": "def __init__(self):\n self.all_words = set()\n self.unique_words = set()\n self.words_by_file = {}",
"title": ""
},
{
"docid": "cf39bbcf6b83f6597ba426fd56e72df7",
"score": "0.6741223",
"text": "def __init__(self, init = None):\n\t\tself.set = []\n\n\t\tif init is not None:\n\t\t\tfor i in init:\n\t\t\t\tself.add(i)",
"title": ""
},
{
"docid": "dcb5dd0fef1bb152f9a2f89698da3d93",
"score": "0.6697913",
"text": "def __init__(self):\n self.words = defaultdict(set)",
"title": ""
},
{
"docid": "93106fa5d2bf1b644d1e3f0d60eea065",
"score": "0.6635313",
"text": "def __init__(self):\n self.d = collections.Counter()\n self.word_list = set()",
"title": ""
},
{
"docid": "819668dfde3fa5b7717d8d2483277e00",
"score": "0.6581799",
"text": "def __init__(self, tokens):\n self.tokens = tokens\n self.nodes_queue = []\n self.word = ()",
"title": ""
},
{
"docid": "a63f9fadbb7f1295429267eda2ad732e",
"score": "0.65623564",
"text": "def __init__(self):\n # key: a thread- or task-unique identifier\n # value: a token itself\n self._tokens = dict()",
"title": ""
},
{
"docid": "b4ce0ab5e4674cd6660597e9b0fbed99",
"score": "0.6502809",
"text": "def __init__(self, tokens):\n ...",
"title": ""
},
{
"docid": "4d1e9c6a66d5b1017ebcf76d0ce3db35",
"score": "0.64913726",
"text": "def __init__(self, tokens):\n self.tokens = tokens\n self.tokens.reverse()\n self.previous_token = None\n self.current_token = self.tokens.pop()\n self.codes = []\n self.symboltable = []",
"title": ""
},
{
"docid": "ff034bf3087db836d4603832eb31c8b8",
"score": "0.6462534",
"text": "def __init__(self, set_items):\n\n self.n = 0 # for iterating over set\n self.set = set()\n self.ordered_set = []\n for item in set_items:\n if item not in self.set:\n self.set.add(item)\n self.ordered_set.append(item)",
"title": ""
},
{
"docid": "1411512056b3843559a8dbfe39db2e29",
"score": "0.64594287",
"text": "def initialize(self) -> None:\n self.visited: Set[Any] = set() #",
"title": ""
},
{
"docid": "905ba8298d8bf6086117361123573c6a",
"score": "0.63811433",
"text": "def __init__(self):\n\n self._words = collections.defaultdict(_zero)\n self._total = 0",
"title": ""
},
{
"docid": "1a0b314ecd0b9aab53e52d705a854f6f",
"score": "0.6361223",
"text": "def __init__(self):\n self.lexer = None\n # Keeps track of the last token returned from self.token()\n self.last_token = None",
"title": ""
},
{
"docid": "2a9a46b28e02ae4f2798fb6095391d07",
"score": "0.63518983",
"text": "def __init__(self):\n self.data = set()",
"title": ""
},
{
"docid": "9df24b154647ba5ca88bcfa6842f8b71",
"score": "0.63004816",
"text": "def __init__(self):\n self.lookup_punctuation = set(punctuation)\n self.lookup_uppercase = set(string.ascii_uppercase)\n self.lookup_lowercase = set(string.ascii_lowercase)\n self.lookup_digits = set(string.digits)\n self.stop_words = set(nltk.corpus.stopwords.words('english'))\n self.tagVector = ['NOUN','PRON','VERB','ADV','ADJ','ADP','CONJ','PRT','DET','NUM','X','.']\n None",
"title": ""
},
{
"docid": "53916093451dafa873303e4d4d7a4c7d",
"score": "0.62245023",
"text": "def __init__(self):\n self._l = []\n self._ts = None\n self.symbols = []",
"title": ""
},
{
"docid": "a8a46c2ffccb446c38d9598e82c57cde",
"score": "0.62191224",
"text": "def __init__(self):\n self.hash_set = {}",
"title": ""
},
{
"docid": "22d1817bdf0b931cf596cdd26deb382a",
"score": "0.62148684",
"text": "def initialize(self, token):\n assert chktype(1, token, Token)\n SUBTOKENS = self.property('SUBTOKENS')\n self._stack = []\n self._remaining_text = token[SUBTOKENS][:]\n self._history = []",
"title": ""
},
{
"docid": "1155c5c08fb41d129b26f9f44428c015",
"score": "0.619755",
"text": "def __init__(self):\n self.words = []\n self.cache = []",
"title": ""
},
{
"docid": "67bea63ea3ec87f3dc166c8f69326b44",
"score": "0.61569196",
"text": "def tokens(self, tokens):\n\n self._tokens = tokens",
"title": ""
},
{
"docid": "b3c02c0d78a64b466543e62b6b0dfd5a",
"score": "0.6143229",
"text": "def __init__(self):\r\n self.d = set()",
"title": ""
},
{
"docid": "7129e79d10728f9a49131504200600ab",
"score": "0.61389506",
"text": "def __init__(self):\n self.top = None\n self.set = set()",
"title": ""
},
{
"docid": "16795c43a18c250ed7dd4e5303f3be74",
"score": "0.6134335",
"text": "def __init__(self):\n self.hashmap = []\n self.numbers = set()",
"title": ""
},
{
"docid": "6a11dbce8aab98a0efe3729b8b41416e",
"score": "0.61234677",
"text": "def __init__(self, corpus=None, \n special_tokens=('<PAD>', '<UNK>', '<S>', '</S>', '<NULL>'), max_tokens=0):\n\n self.counter = OrderedCounter()\n self.t2i = OrderedDict()\n self.i2t = []\n self.special_tokens = [t for t in special_tokens]\n \n if corpus is not None:\n for tokens in corpus:\n self.counter.update(tokens)\n \n if max_tokens > 0:\n self.trim(max_tokens)\n else: \n self.update_dicts()",
"title": ""
},
{
"docid": "e04bd56b451cb399176ed82b42948263",
"score": "0.60863817",
"text": "def __init__(self):\n self.node_set = set()\n self._head = None\n self.depth_left = 0\n self.depth_right = 0",
"title": ""
},
{
"docid": "16ba2a1dcfec877f74b9d5c53ab44425",
"score": "0.60779274",
"text": "def initialize(self, token):\n assert chktype(1, token, Token)\n SUBTOKENS = self.property('SUBTOKENS')\n \n self._rtext = token[SUBTOKENS]\n start = self._grammar.start().symbol()\n self._tree = Tree(start, [])\n self._frontier = [()]\n self._tried_e = {}\n self._tried_m = {}\n self._history = []\n self._parses = []\n if self._trace:\n self._trace_start(self._tree, self._frontier, self._rtext)",
"title": ""
},
{
"docid": "1d54a5dd327b4c5529f57d2089633fdb",
"score": "0.605219",
"text": "def __init__(self, initial_terms=None):\n self._documents = collections.Counter()\n self._terms = {}\n self._freeze = False\n if initial_terms is not None:\n for term in initial_terms:\n self._terms[term] = {}",
"title": ""
},
{
"docid": "5f80c3f42108a94932383c1b6911c65e",
"score": "0.6029715",
"text": "def __init__(self):\n self.dicts = {\n \"pairs\": {},\n \"unigrams\": {},\n \"bigrams\": {},\n \"trigrams\": {},\n \"prefixes\": {},\n \"suffixes\": {},\n \"prev_w_curr_t\": {},\n \"next_w_curr_t\": {},\n \"index_tag\": {},\n \"index_word\": {},\n \"capital_tag\": {},\n }",
"title": ""
},
{
"docid": "53558c6cf7946a39eb9dfeb2373fbe62",
"score": "0.6023535",
"text": "def __init__(self):\n self.collection = list()\n self.v2idxset = defaultdict(set)",
"title": ""
},
{
"docid": "7a30a5c3c49acc7703232c418c1e730c",
"score": "0.5998499",
"text": "def __init__(self, character_strings: List[str] = None) -> None:\n\n if character_strings is None:\n character_strings = [\n string.ascii_lowercase,\n string.ascii_uppercase,\n string.digits,\n string.punctuation,\n ]\n elif not isinstance(character_strings, list) or not all(\n isinstance(s, str) for s in character_strings\n ):\n raise TypeError(\n \"parameter 'character_strings' must be a list of strings\"\n )\n elif not character_strings:\n raise ValueError(\"parameter 'character_strings' cannot be empty\")\n\n self.__character_sets: List[FrozenSet[str]] = list(\n frozenset(character_string)\n for character_string in character_strings\n )",
"title": ""
},
{
"docid": "1b384f412612bade327c014bb8f7e408",
"score": "0.59923375",
"text": "def __init__(self):\n import collections\n self.hashtable = collections.defaultdict(set)\n self.array = []",
"title": ""
},
{
"docid": "d588d07287aaccf9eb5265be3dc550b9",
"score": "0.5984427",
"text": "def __init__(self):\n self.__list = []\n self.__used = defaultdict(list)",
"title": ""
},
{
"docid": "6f4523f06ca8a07d09c4d4cc416330a9",
"score": "0.5973898",
"text": "def __init__(self):\n self.end = False # is there any number that ends here\n self.neg = None\n self.zeros = None\n self.ones = None\n self.freq = 0 # total number of elements in the trie",
"title": ""
},
{
"docid": "74dea6ae8993fb8c304f3b9bb32a94e1",
"score": "0.5968586",
"text": "def __init__(self, docs):\n self.tokens = tokenize(docs)\n self.dictionary = make_id2word(self.tokens)\n self.corpus = make_corpus(self.dictionary, self.tokens)",
"title": ""
},
{
"docid": "015f76d5fb23c71a13409f3d1d6762d2",
"score": "0.5962655",
"text": "def InitializeTokenList(self):\n\n for i in range(15):\n self._playerTokens.append(Token.Token(self._playerColour, [None, None]))",
"title": ""
},
{
"docid": "38b77913a687c81af2369bec88474623",
"score": "0.5961373",
"text": "def __init__(self, data):\n\n\t\tself.data = [set(rec) for rec in data]",
"title": ""
},
{
"docid": "d2a6c0c0001b4d38df1a047b3cf871d5",
"score": "0.5957939",
"text": "def __init__(self):\n self._id = 0\n self.ids_for_tac = set() # type: Set[int]\n self.ids_for_sellers = set() # type: Set[int]\n self.ids_for_buyers = set() # type: Set[int]",
"title": ""
},
{
"docid": "5117a05c6e802aaba29fe6d82a130a20",
"score": "0.593484",
"text": "def initialize(self):\n # self.filter_candidates([])\n pass",
"title": ""
},
{
"docid": "57c6c53fcc58edf2f247856af3b9bc90",
"score": "0.5934548",
"text": "def __init__(self):\n self.vals = []\n self.val_to_idx = defaultdict(set)",
"title": ""
},
{
"docid": "0a1193aa54d6cf7c6197d584650b5c6a",
"score": "0.59281737",
"text": "def __init__(self):\n self._loaded = set()",
"title": ""
},
{
"docid": "764e74213b7c8e8806db910c07621334",
"score": "0.5907752",
"text": "def __init__(self):\n self.vertices_ = set()\n self.adjacency_lists_ = {}\n self.vertex_labels_ = {}\n self.edge_labels_ = {}",
"title": ""
},
{
"docid": "400167a047e4f4e998ae6221ce47b7b3",
"score": "0.5904567",
"text": "def init() -> None:\n # The words list includes too many 1-3 letter words, so we exclude\n # these and give our own short list.\n for word in ['a', 'an', 'the', 'of', 'art', 'gun', 'for', 'new', 'acm',\n 'age', 'air', 'all', 'and', 'war', 'use', 'to', 'tax',\n 'sun', 'tax', 'sky', 'tap', 'sex', 'on', 'or', 'owl',\n 'pop', 'oil', 'men', 'man', 'law', 'its', 'in', 'ibm',\n 'hiv/aids', 'dna', 'at', 'j', 'car', 'bioorganic',\n 'biomolecular']:\n EnglishWordList.wordSet.add(word)\n with open('/usr/share/dict/words') as f:\n for line in f:\n line = line.strip().casefold()\n # if len(line) > 2:\n if line not in ['co'] and len(line) > 3:\n EnglishWordList.wordSet.add(line)\n for word in ['bianco', 'nero']:\n EnglishWordList.wordSet.remove(word)",
"title": ""
},
{
"docid": "dd25585d6f411677de9319e3ecd6fc18",
"score": "0.58923864",
"text": "def __init__(self, raw_text: str):\n self._raw_text: str = raw_text\n tokenized = tokenizer.tokenize(self.raw_text)\n self._tokens: List[Token] = [t for t in tokenized]",
"title": ""
},
{
"docid": "636cd45e7006e87903d4202729dfcaae",
"score": "0.5884261",
"text": "def __init__(self):\n self.unique = set()\n self.dup = set()",
"title": ""
},
{
"docid": "61ba74883afbd435ef1f440354e5f473",
"score": "0.5881688",
"text": "def fillTerms(self,sets,settings):\n self.settings = settings\n return",
"title": ""
},
{
"docid": "62f209f6d95675ef5792505116f3bfc0",
"score": "0.5873621",
"text": "def __init__(self):\n\n self.marked = []\n self.unmarked = []",
"title": ""
},
{
"docid": "48bead75b82c72bd5cd26a4c53b3980b",
"score": "0.5872867",
"text": "def _parse(self):\n self._check_tokenized()\n if self._tokens:\n self._temp_tokens = copy.deepcopy(self._tokens)\n self._ast = self._do_parse()",
"title": ""
},
{
"docid": "d6d15d6b9e8b835559caab130acd3bfb",
"score": "0.5870841",
"text": "def test_sets():\n objs = tokenize(\"#{1 2}\")\n assert objs == [Set([Integer(1), Integer(2)])]\n objs = tokenize(\"(bar #{foo bar baz})\")\n assert objs == [\n Expression([Symbol(\"bar\"), Set([Symbol(\"foo\"), Symbol(\"bar\"), Symbol(\"baz\")])])\n ]\n\n objs = tokenize(\"#{(foo bar) (baz quux)}\")\n assert objs == [\n Set(\n [\n Expression([Symbol(\"foo\"), Symbol(\"bar\")]),\n Expression([Symbol(\"baz\"), Symbol(\"quux\")]),\n ]\n )\n ]\n\n # Duplicate items in a literal set should be okay (and should\n # be preserved).\n objs = tokenize(\"#{1 2 1 1 2 1}\")\n assert objs == [Set([Integer(n) for n in [1, 2, 1, 1, 2, 1]])]\n assert len(objs[0]) == 6\n\n # https://github.com/hylang/hy/issues/1120\n objs = tokenize(\"#{a 1}\")\n assert objs == [Set([Symbol(\"a\"), Integer(1)])]",
"title": ""
},
{
"docid": "070644db84adf73a734dab651c33a0e3",
"score": "0.5869745",
"text": "def __init__(self, chars):\n self.chars = sorted(set(chars))\n self.char_indices = dict((c, i) for i, c in enumerate(self.chars))\n self.indices_char = dict((i, c) for i, c in enumerate(self.chars))",
"title": ""
},
{
"docid": "070644db84adf73a734dab651c33a0e3",
"score": "0.5869745",
"text": "def __init__(self, chars):\n self.chars = sorted(set(chars))\n self.char_indices = dict((c, i) for i, c in enumerate(self.chars))\n self.indices_char = dict((i, c) for i, c in enumerate(self.chars))",
"title": ""
},
{
"docid": "b57f5c1fe1c63445124179838cbdc3b2",
"score": "0.58656317",
"text": "def __init__(self) -> None:\n self.provenance_sets = {path: set() for path in self.DATA_PATHS}\n self.ner_hist_1 = defaultdict(int)\n self.ner_hist_2 = defaultdict(int)\n self.ner_pair_hist = defaultdict(int)",
"title": ""
},
{
"docid": "681f4c625d916b58422c15a050d19d13",
"score": "0.5862585",
"text": "def __init__(self, corpus):\n self._chain = {}\n self._build_chain(corpus)",
"title": ""
},
{
"docid": "9d8b3d65535967ccb9e5b8288212dfdd",
"score": "0.586131",
"text": "def fillTerms(self,sets,settings):\n return",
"title": ""
},
{
"docid": "9d8b3d65535967ccb9e5b8288212dfdd",
"score": "0.586131",
"text": "def fillTerms(self,sets,settings):\n return",
"title": ""
},
{
"docid": "9d8b3d65535967ccb9e5b8288212dfdd",
"score": "0.586131",
"text": "def fillTerms(self,sets,settings):\n return",
"title": ""
},
{
"docid": "30c1abcf02913586e02733e977056d89",
"score": "0.58542114",
"text": "def __init__(self):\n\n self.subtrie = dict()\n self.isWord = False\n self.val = ''",
"title": ""
},
{
"docid": "d8ea75fe725a0d87981e37766624e6e8",
"score": "0.5851353",
"text": "def __init__(self, values=None):\n self.dict = {} # each instance of Set has its own dict property \n # which is what we'll use to track memberships\n if values is not None: \n for value in values:\n self.add(value)",
"title": ""
},
{
"docid": "1b7f1df23e2fee68b18b706d2f723938",
"score": "0.5844354",
"text": "def __init__(self):\n self.len_to_words = collections.defaultdict(set)",
"title": ""
},
{
"docid": "899d21ebb11d7c76ef81f89f39f44943",
"score": "0.5842436",
"text": "def __init__(self) -> None:\n self._contains = []",
"title": ""
},
{
"docid": "8172fbd4bb1aba09463afa775be16ae9",
"score": "0.58378565",
"text": "def init_subtokenizer(self, codes):\n self._codes = codes",
"title": ""
},
{
"docid": "23af8253e7bb62b61db41d5e81894dfe",
"score": "0.58326083",
"text": "def __init__(self):\n self.search_trie = TrieDict()\n self.starts_with_trie = TrieDict()",
"title": ""
},
{
"docid": "a77ac9133542591541549f4288bb10da",
"score": "0.5828446",
"text": "def __init__(self):\n self.d = collections.defaultdict(set)\n self.num = []",
"title": ""
},
{
"docid": "b8b9351dd53a108c735177be11ec1ccc",
"score": "0.5818692",
"text": "def __init__(self):\n self.valList = list()\n self.valToIndices = collections.defaultdict(set)",
"title": ""
},
{
"docid": "6f9575afc0b9175129d47788bc22e15f",
"score": "0.5815725",
"text": "def __init__(self, *scopes):\n self._scopes = set(scopes)",
"title": ""
},
{
"docid": "128d16109fc86c547f40dc3b563810b6",
"score": "0.58041185",
"text": "def initialize(self):\n raise NotImplementedError('method initialize() must be implemented in derived class')\n self.countiter = 0\n self.xcurrent = [xi for xi in self.xstart]",
"title": ""
},
{
"docid": "13db3a0200d953f5e40b3c1f9af99c30",
"score": "0.58025485",
"text": "def __init__( self ):\n self._values = []\n return",
"title": ""
},
{
"docid": "a4dfb5dbcdd4444d2645fb3fca8482b5",
"score": "0.5799393",
"text": "def __init__(self, token_defs):\n \"\"\" \n token_defs = [(\"myToken\",\"a+\"),(\"myOtherToken\",[0-9]{0,3})]\n \"\"\"\n self.next_state=0\n self.prepare(\"\")\n \n # Store hash of token classes\n self.token_classes = {}\n for tdef in token_defs:\n if len(tdef) > 2 and tdef[2]!=None:\n self.token_classes[tdef[0]] = tdef[2]\n \n # Store hash of token callbacks\n self.token_callbacks = {}\n for tdef in token_defs:\n if len(tdef) > 3 and tdef[3]!=None:\n self.token_callbacks[tdef[0]] = tdef[3]\n \n self.dfa = self.make_dfa(token_defs)",
"title": ""
},
{
"docid": "197c286b33f31b50332eda498ed54eb4",
"score": "0.57946247",
"text": "def _initialize_parser_keys(self):\n pass",
"title": ""
},
{
"docid": "d5f5500bd8fb1afb6cb5fef5d82c0d30",
"score": "0.5774757",
"text": "def __init__(self, literals=None,frozen_hash=True):\n self.frozen_hash = frozen_hash\n if literals:\n self.literals = frozenset(literals) if frozen_hash else literals\n else:\n self.literals = set()",
"title": ""
},
{
"docid": "a4fe9b20b216a99ffd8113e973499777",
"score": "0.5773651",
"text": "def __init__(self):\n self.trie={}",
"title": ""
},
{
"docid": "9dffefc901d8760c3e4c8b86422cba63",
"score": "0.57720643",
"text": "def __init__(self):\n self.word_dict = defaultdict(list)",
"title": ""
},
{
"docid": "5985e08b38922a676c78c7a3a3a3a4a6",
"score": "0.57671046",
"text": "def __init__(self):\n self.lineString = \"\"\n self.targetTokensPositions = []\n self.rightBoundary = 0\n self.leftBoundary = 0\n self.string = \"\"\n self.filename = \"\"\n self.lineNumber = -1",
"title": ""
},
{
"docid": "b46065c537c1605e0dceb6d0770f287d",
"score": "0.57656604",
"text": "def __post_init__(self):\n # Normalize case-sensitivity for addresses\n self.addresses = set(map(str.lower, self.addresses))\n # Make sure that all the collections are sets\n self.names = set(self.names)\n self.manufacturers = set(self.manufacturers)",
"title": ""
},
{
"docid": "1dec3f2d209833c8ae643cf57f434371",
"score": "0.5763473",
"text": "def __init__(self, corpus):\n self.unigrams = {}\n self.bigrams = {}\n self.train(corpus)",
"title": ""
},
{
"docid": "b986c46c1f20e70fddb2d330594fc87a",
"score": "0.57616776",
"text": "def __init__(self):\n from collections import defaultdict\n self.dataList = []\n self.dataDict = defaultdict(set)",
"title": ""
},
{
"docid": "4180e9c0c545e24cbcb28e68c822c13e",
"score": "0.5757872",
"text": "def __init__(self):\n\t\tprint \"** Initialize..\"\n\t\tfor i, row in enumerate(self.data):\n\t\t\t# Ignores header\n\t\t\tif(i == 0):\n\t\t\t\tpass\n\t\t\telse:\n\t\t\t# TEMP, for testing only\n\t\t\t\tbegin = 1\n\t\t\t\tend = 2000\n\t\t\t\tif (i >= begin and i <= end):\n\t\t\t\t\t# TEMP, Only add if class is known! for testing only\n\t\t\t\t\tif (self.class_dict.get(row[5].upper()) is 0):\n\t\t\t\t\t\tsubstituted = re.sub(r'\\.\\.+\\s', r' ', row[3])\n\t\t\t\t\t\tsubstituted = re.sub(r'\\sen\\svanavond', r'. vanavond', substituted)\n\t\t\t\t\t\tsubstituted = self.remove_stopwords(substituted)\n\n\t\t\t\t\t\tself.tweets.append(substituted)\n\n\t\tself.dictionary_pos = defaultdict(list)",
"title": ""
},
{
"docid": "81163ad266fc776b4c9f37e56f99f12a",
"score": "0.57576174",
"text": "def __init__(self):\n self.lexicon = lexicon.SynsetLexicon()",
"title": ""
},
{
"docid": "917e654d83d6fe9c529723641be778dd",
"score": "0.5755792",
"text": "def __init__(self):\n import random\n self.set = set()",
"title": ""
},
{
"docid": "194138691bb26b6a451938687e8c35d2",
"score": "0.5753141",
"text": "def tokenize(self) -> None:\n self.tokens = list()\n for word in self.file_words:\n self.current_word = word\n self.tokens.append(self.read_token())\n self.length = len(self.tokens)\n self.current_index = -1\n # self.write_xml_file()",
"title": ""
},
{
"docid": "40e0ad664b2b0fadc6659ab42e42ca60",
"score": "0.5740566",
"text": "def __init__(self):\n\n self._config = dict()\n self._edids = set()",
"title": ""
},
{
"docid": "d7fd251113fc3bde37556800bbf5febc",
"score": "0.5730701",
"text": "def __init__(self):\r\n self._corp = Corpus()\r\n self._approx_docs = {}\r\n self._doc_ids = {}\r\n self._one_vs_one_classifier = None",
"title": ""
},
{
"docid": "316bb3259c2a201553866960245113c1",
"score": "0.5729036",
"text": "def A(self, tokens):\n model = len(tokens)\n assert model != 0\n thesetoks = self.my_A[model]\n if tokens not in thesetoks.keys():\n myset = {}\n else:\n myset = thesetoks[tokens]\n return myset",
"title": ""
},
{
"docid": "d42d576ae3016042961ef18410f2842d",
"score": "0.57287353",
"text": "def __init__(self):\n self.keywords = {}",
"title": ""
},
{
"docid": "87af30910774d3281fc74f8f39d1c17c",
"score": "0.5719589",
"text": "def __init__(self, pattern):\n self.pattern = pattern\n self.result = set()\n super(ASTMatcher, self).__init__()",
"title": ""
},
{
"docid": "7a919269e9e368b00d183fb25bfe43e6",
"score": "0.571895",
"text": "def __init__(self):\n self._values = {\n }\n self._initialize()",
"title": ""
},
{
"docid": "afe64954ca28c24660c66f62d5733895",
"score": "0.5716615",
"text": "def __init__(self, feature_list):\n\n self.features = set(feature_list)",
"title": ""
},
{
"docid": "625de9fc80d7f703d703afa8d393c6e1",
"score": "0.5715006",
"text": "def start(self):\n # build the Big Regex to match all tokens at once\n self.token_pat = _re.compile('(' + ')|('.join(lm.re for lm in self.match_list) + ')')\n \n # build index to _Line_match map\n self.match_map = []\n for lm in self.match_list:\n self.match_map.append(lm)\n # leave empty slots for subgroups contained within lm.re\n self.match_map += [None] * lm.group_count",
"title": ""
},
{
"docid": "8ef7ec65bee3def512683af555104115",
"score": "0.5714867",
"text": "def __init__(self, words):\n # Insert all words into trie\n self.root = TrieNode()\n for word in words:\n self.insert(word)",
"title": ""
},
{
"docid": "569b70cd2dd029071c28ed0560f2aefd",
"score": "0.5699198",
"text": "def __init__(self):\n self.key_ls = []\n self.val_ls = []\n self.timestamp_ls = []",
"title": ""
},
{
"docid": "fedc49813bf9ef552bec03a151b0c691",
"score": "0.5695563",
"text": "def __init__(self):\n self.tweets=collections.defaultdict(collections.deque)\n self.followlist=collections.defaultdict(set)\n self.timestamp=2**31",
"title": ""
},
{
"docid": "cc6f0c027e61b1e52b8551f916e1d555",
"score": "0.5693656",
"text": "def __init__(self):\n self.nums = set()\n self.dup_nums = set()",
"title": ""
},
{
"docid": "0bf37c06fd83cbe74be389a711f95667",
"score": "0.5692104",
"text": "def __init__(self) -> None:\n # Note that using a dictionary for children (as in this implementation)\n # would not by default lexicographically sort the children, which is\n # required by the lexicographic sorting mentioned in the next section\n # (Sorting).\n self.children: Dict[str, Trie] = {} # mapping from character to Node\n self.end_of_word: bool = False",
"title": ""
},
{
"docid": "8db1996324bfd26a7d335a974325b316",
"score": "0.5690681",
"text": "def load_tokens(self):\n try:\n with open(self.token_store) as f:\n contents = json.load(f)\n self.tokens = {k: TokenSet(**v) for k, v in contents.items()}\n except FileNotFoundError:\n pass\n except JSONDecodeError:\n raise EnvironmentError(\n \"Token cache for Timer CLI is corrupted; please run a `session revoke`\"\n \" and try again\"\n )",
"title": ""
},
{
"docid": "0e71ba34c527f7f90354558ea23f0a03",
"score": "0.56824005",
"text": "def __init__(self):\n self.s = []",
"title": ""
},
{
"docid": "7d3bf961336f3464bd89539a1c0580c9",
"score": "0.56708175",
"text": "def __init__(self):\n\n self.attr_extractors = []\n self.results = []",
"title": ""
},
{
"docid": "2b8e85d9f7ed60e1af8baad5c1e04acd",
"score": "0.56655985",
"text": "def __init__(self):\n self.t={}",
"title": ""
},
{
"docid": "30fb47c06e194b1079514443610a7f05",
"score": "0.56622297",
"text": "def __init__(self, text: str):\n self.lexer = Lexer(text)\n self.tokens = self.lexer.tokenize()\n self.position = 0\n self.errors = self.lexer.errors\n self.identifiers = set()\n self.binary_operator_precedences = {\n TokenKind.PLUS: 1,\n TokenKind.MINUS: 1,\n TokenKind.STAR: 2,\n TokenKind.SLASH: 2,\n TokenKind.CARET: 4\n }\n\n self.unary_operator_precedences = {\n TokenKind.PLUS: 4,\n TokenKind.MINUS: 4,\n }",
"title": ""
},
{
"docid": "1d45c9a06a5bd114dc8e96cfcb9e7032",
"score": "0.5660554",
"text": "def _set_A(self):\n modelslist = []\n n = self.n\n\n for i in range(n):\n modeldict = dict()\n model = self.models[i]\n tok_lst = [x for x in list(model.counts.keys()) if len(x) == i + 1]\n # Initiate all the sets for the tokens\n for token in tok_lst:\n modeldict[token[:i]] = set()\n # Create the sets relationed to the prev_tokens correspondly\n for token in tok_lst:\n # The token that is related to prev_tokens\n tok = token[i:]\n # The prev_tokens related to token\n prev_tok = token[:i]\n\n modeldict[prev_tok].add(tok[0])\n # Add the correspondly dictionary of sets\n modelslist.append(modeldict)\n\n self.my_A = modelslist",
"title": ""
},
{
"docid": "0bd5f3573f10ac3d55742586ace025e1",
"score": "0.56598276",
"text": "def __init__(self):\n self.temp_lexicon = \"temp_lexicon\"\n self.fetch_lexicon()\n\n self.sentence_splitter = SentenceSplitter()\n self.segment = Segmentor()\n self.segment.load_with_lexicon(CWS_MODEL, self.temp_lexicon)\n self.pos = Postagger()\n self.pos.load_with_lexicon(POS_MODEL, self.temp_lexicon)\n self.tree_parser = Parser()\n self.tree_parser.load(PARSER_MODEL)\n\n self.rules = IterDocument(\"data/rule\")",
"title": ""
},
{
"docid": "d305fe9bdde5978018681e8e1223aa55",
"score": "0.5659512",
"text": "def __init__(self):\n self._entries = {}",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="736"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">01c89c0f326c0ef8a2645d1ed77f5ee1</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Take a list of blocks and place them into the world in one go.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "c78b2f5eb4f94285754458a48d9118a6",
"score": "0.52899325",
"text": "def sendBlocks(blockList, x=0, y=0, z=0, retries=5, flags='0100011'):\n body = str.join(\"\\n\", ['~{} ~{} ~{} {}'.format(*bp) for bp in blockList])\n try:\n response = setBlock(x, y, z, body, flags)\n return response\n except ConnectionError as e:\n print(\"Request failed: {} Retrying ({} left)\".format(e, retries))\n if retries > 0:\n return sendBlocks(x, y, z, retries - 1)\n return False",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "f3b2707d26453c19feb0ecb5eb8cd48a",
"score": "0.6527537",
"text": "def update_body_blocks(self) -> None:\n for i in range(0, len(self.body_blocks) - 1):\n self.body_blocks[i].setpos(self.body_blocks[i + 1].pos())",
"title": ""
},
{
"docid": "9909accc7b21b06034cd93d579e3bd71",
"score": "0.65235186",
"text": "def placeblock(self):\n\t\tindex = WINDOWSIZE[1] * self.pos[2] + self.pos[3]\n\t\tnewitem = None\n\t\tfor i in range(len(self.player.inv)-1, -1, -1):\n\t\t\tif self.player.inv[i] < 256:\n\t\t\t\tnewitem = self.player.inv.pop(i)\n\t\t\t\tbreak\n\t\tif newitem and self.dig():\n\t\t\tself.world[self.pos[:2]] = self.world[self.pos[:2]][:index] + bytes([newitem]) + self.world[self.pos[:2]][index+1:]",
"title": ""
},
{
"docid": "c6cd9811087c3836a9eedeed1b930f5b",
"score": "0.6478129",
"text": "def blocks(self, blocks):\n\n self._blocks = blocks",
"title": ""
},
{
"docid": "264e74819972f1c2b41b4bc3736ec270",
"score": "0.6431747",
"text": "def use_blocks(self):\n\n self.block_list = pygame.sprite.Group()\n self.all_sprites_list = pygame.sprite.Group()\n\n # The for loop is used to control how many blocks the game initializes for the player to \n # collect\n for i in range(10):\n\n # a single block is created \n self.block = self.Block(self.black, 20, 15)\n\n # Set a random location for the block\n self.block.rect.x = random.randrange(self.screen_width)\n self.block.rect.y = random.randrange(self.screen_height)\n\n # Add the block to the list of objects\n self.block_list.add(self.block)\n self.all_sprites_list.add(self.block)\n\n # Create a white player block\n self.player = self.Block(self.white, 20, 15)\n self.all_sprites_list.add(self.player)\n\n # Used to manage how fast the screen updates\n self.clock = pygame.time.Clock()\n\n self.score = 0",
"title": ""
},
{
"docid": "6d8c4b041570bbcc6a7735b7b65a1141",
"score": "0.6399331",
"text": "def handle_blocks(self):\n self.add_blocks_to_chain()\n self.make_blocks()",
"title": ""
},
{
"docid": "4916c38286cc91d9726163e3b6e49735",
"score": "0.6230576",
"text": "def fill_tower(self, number_of_blocks):\r\n\r\n\t\t#Fill a tower to start the game\r\n\t\tblock_visual_list = [] \t#temporary list of the positions to be later passed on to an array\r\n\t\tfor block_size in range(1, self.total_block_number + 1):\r\n\t\t\tblock = Block(block_size, [block_size - 1, self.pos])\r\n\t\t\tself.block_object_list[block_size - 1] = block\r\n\t\t\tblock_visual_list.append(block.size)\r\n\t\t\tself.blocks_in_tower += 1\r\n\r\n\t\tself.block_visual_array[:, 0] = block_visual_list\r\n\t\tself.update_block_on_top_flag()",
"title": ""
},
{
"docid": "98fce64804a16bdb8408a69aefc6d52e",
"score": "0.62234235",
"text": "def build_from_sequence(blocks, positions, orientations):\n # print(\"Building these block indices:\", blocks)\n # print(\"At these positions:\", positions)\n response = client.spawnBlocks(Blocks(blocks=[Block(position=Point(x=int(positions[i, 0]), y=int(\n positions[i, 1]), z=int(positions[i, 2])), type=blocks[i], orientation=orientations[i]) for i in range(len(blocks))]))\n # print(response)",
"title": ""
},
{
"docid": "2da3e6f6db8fbfd5e83db5f2de5289cc",
"score": "0.61867416",
"text": "def set_block(self, coords, block):\n\n x, y, z = coords\n index = triplet_to_index(coords)\n\n if self.blocks[index] != block:\n self.blocks[index] = block\n\n # Regenerate heightmap at this coordinate. Honestly not sure\n # whether or not this is cheaper than the set of conditional\n # statements required to update it in relative terms instead of\n # absolute terms. Revisit this later, maybe?\n for y in range(127, -1, -1):\n if self.blocks[index]:\n break\n self.heightmap[x * 16 + z] = y\n\n self.dirty = True\n self.damaged.add(coords)",
"title": ""
},
{
"docid": "b6c09169db8a08a8e57ba7415ff11536",
"score": "0.6130388",
"text": "def _plus_blocks(self):\n\n # Define two crossing wall portions\n self._blocks.append(Block(self.game, (self.size[0]/2)-30,\n (self.size[0]/2)+30,\n math.floor(self.size[1]/3),\n math.ceil(2*self.size[1]/3)))\n self._blocks.append(Block(self.game, math.floor(self.size[0]/3),\n math.ceil(2*self.size[0]/3),\n (self.size[1]/2)-30, (self.size[1]/2)+30))",
"title": ""
},
{
"docid": "0e30e03411ab6fd72aaaa3ea91d2e6b9",
"score": "0.6128654",
"text": "def get_blocks_to_file(self, fout, blocklist):\n xl = [x for x in blocklist]\n xl = np.array(xl)\n \n #open file\n #convert lines to start from end\n for x in xl:\n if self.loadlist[x]:\n data = self.convert_data_to_lines(x)\n else:\n data = self.get_block(x)\n for line in data:\n fout.write(line)",
"title": ""
},
{
"docid": "b108306fd1f54053e40d0d0de5d0c52e",
"score": "0.60810363",
"text": "def setBlocks(self, x1, y1, z1, x2, y2, z2, blockType, blockData = 0):\n #order x, y, z's\n if x1 > x2: x1, x2 = x2, x1\n if y1 > y2: y1, y2 = y2, y1\n if z1 > z2: z1, z2 = z2, z1\n\n #create the cuboid\n for x in range(x1, x2 + 1):\n for y in range(y1, y2 + 1):\n for z in range(z1, z2 + 1):\n self._setBlock(x, y, z, blockType, blockData)\n\n #if the shape is visible (re)draw it\n if self.visible:\n self.draw()",
"title": ""
},
{
"docid": "50097d4677b2cf821c2ee68357d18661",
"score": "0.6057812",
"text": "def _place_available_objects(self):\n\n def create_block(width, height, shape):\n \"\"\"Returns a block with the specified properties.\"\"\"\n block = block_utils.Block(\n width=width, height=height, angle=0., shape=shape,\n x=0, y=0) # x and y will be set later.\n return block\n\n observation_blocks = [\n create_block(\n self.small_width, self.height, unity_constants.BOX_SHAPE),\n create_block(\n 2*self.small_width, self.height, unity_constants.BOX_SHAPE),\n create_block(\n self.small_width, 2*self.height, unity_constants.BOX_SHAPE),\n create_block(\n self.medium_width, self.height*2/3, unity_constants.BOX_SHAPE),\n create_block(\n self.large_width, self.height/10*3, unity_constants.BOX_SHAPE),\n create_block(\n -self.medium_width, self.height, unity_constants.RAMP_SHAPE),\n create_block(\n self.medium_width, self.height, unity_constants.RAMP_SHAPE),\n ]\n\n # Calculate margin of blocks.\n block_abs_widths = [np.abs(block.width) for block in observation_blocks]\n empty_width = self.scene_width - sum(block_abs_widths)\n\n if empty_width <= 0:\n raise ValueError(\"Not enough space between available objects.\")\n\n horizontal_margin = empty_width / (len(observation_blocks) - 1)\n\n # Update the position of the blocks using the margin.\n observation_block_with_positions = []\n current_x = 0\n display_y_pos = -2 * (self.margin + self.height)\n for block in observation_blocks:\n abs_width = np.abs(block.width)\n display_x_pos = current_x + abs_width / 2\n observation_block_with_positions.append(\n block._replace(x=display_x_pos, y=display_y_pos))\n current_x += abs_width + horizontal_margin\n\n assert current_x - horizontal_margin <= self.scene_width\n return observation_block_with_positions",
"title": ""
},
{
"docid": "c4cee15f3fdeec519acc77d1827ae89b",
"score": "0.5983258",
"text": "def place_block(self):\n pass",
"title": ""
},
{
"docid": "aa164235b78a345d049a6eefe4716b65",
"score": "0.59711",
"text": "def J_block():\n block1 = sprites.Block((242.5, 37.5), orange)\n block4 = sprites.Block((172.5, 72.5), orange)\n block3 = sprites.Block((207.5, 72.5), orange)\n block2 = sprites.Block((242.5, 72.5), orange)\n return block1, block2, block3, block4",
"title": ""
},
{
"docid": "fbdd1ea42d7b18bbd990173665b18e33",
"score": "0.59455645",
"text": "def T_block():\n block1 = sprites.Block((207.5, 37.5), blue)\n block2 = sprites.Block((172.5, 72.5), blue)\n block3 = sprites.Block((242.5, 72.5), blue)\n block4 = sprites.Block((207.5, 72.5), blue)\n return block1, block2, block3, block4",
"title": ""
},
{
"docid": "c749d5acbfe08ac9d5d4094d917b1628",
"score": "0.5943176",
"text": "def _doorway_blocks(self):\n\n # Define two wall portions\n self._blocks.append(Block(self.game, (self.size[0]/2)-30,\n (self.size[0]/2)+30, -40,\n (self.size[1]/2)-60))\n self._blocks.append(Block(self.game, (self.size[0]/2)-30,\n (self.size[0]/2)+30, (self.size[1]/2)+60,\n self.size[1]+40))",
"title": ""
},
{
"docid": "a8b2381ab3c31ca4c9e5a91cdf22b5cb",
"score": "0.5919157",
"text": "def O_block():\n block1 = sprites.Block((207.5, 37.5), red)\n block2 = sprites.Block((207.5, 72.5), red)\n block3 = sprites.Block((242.5, 37.5), red)\n block4 = sprites.Block((242.5, 72.5), red)\n return block1, block2, block3, block4",
"title": ""
},
{
"docid": "125a16d07458801fa526c2ba363e6fd5",
"score": "0.5913495",
"text": "def L_block():\n block1 = sprites.Block((172.5, 37.5), dark_blue)\n block2 = sprites.Block((172.5, 72.5), dark_blue)\n block3 = sprites.Block((207.5, 72.5), dark_blue)\n block4 = sprites.Block((242.5, 72.5), dark_blue)\n return block1, block2, block3, block4",
"title": ""
},
{
"docid": "55fe9eef1df4bc899aee42f69165c554",
"score": "0.5904851",
"text": "def build_zone_3D(blocks, offset, restricted_flag, orientations, oriented):\n positions = []\n blocks_index = []\n ALLOWED_BLOCKS = allowed_blocks(restricted_flag) # Here want air\n orientations_ = [] # LIst with orientations\n for x in range(blocks.shape[0]):\n for y in range(blocks.shape[1]): # this is height in minecraft\n for z in range(blocks.shape[2]):\n index = int(blocks[x, y, z])\n if not index == -1: # AS INDEX - 1 means air block\n try:\n blocks_index.append(ALLOWED_BLOCKS[index])\n except:\n print(\n \"Following index out of bound of allowed blocks.\", index)\n # Update position\n position = bounded([x+offset[0], y+offset[1], z+offset[2]])\n positions.append(position)\n if oriented:\n orientations_.append(int(orientations[x, y, z]))\n\n #print(\"Building these block indices:\", blocks_index)\n zone = [offset[0], offset[1], offset[2], offset[0]+blocks.shape[0],\n offset[1]+blocks.shape[1], offset[2]+blocks.shape[2]]\n if oriented:\n response = client.spawnBlocks(Blocks(blocks=[Block(position=Point(x=int(positions[i][0]), y=int(positions[i][1]), z=int(\n positions[i][2])), type=blocks_index[i], orientation=int(orientations_[i])) for i in range(len(blocks_index))]))\n else:\n response = client.spawnBlocks(Blocks(blocks=[Block(position=Point(x=int(positions[i][0]), y=int(positions[i][1]), z=int(\n positions[i][2])), type=blocks_index[i], orientation=NORTH) for i in range(len(blocks_index))]))",
"title": ""
},
{
"docid": "aad267e72ab7ec236de4e86737e62e5f",
"score": "0.5904082",
"text": "def _random_blocks(self):\n \n # Initialize a random block height and width\n h = random.randrange(10, 151)\n w = random.randrange(10, 151)\n \n # Decide whether to include a central block\n if random.random() < 0.5:\n \n # Add central block\n self._blocks.append(Block(self.game, (self.size[0]/2)-w,\n (self.size[0]/2)+w, (self.size[1]/2)-h,\n (self.size[1]/2)+h))\n \n # Determine number of additional blocks on sides\n num = random.randrange(1, 4)\n \n # Generate side blocks\n iter = 0 # iteration counter\n while iter < num:\n \n # Generate random dimensions and centers\n h = random.randrange(10, 121)\n w = random.randrange(10, 121)\n cx = random.randrange(self.size[0]+1)\n cy = random.randrange(self.size[1]+1)\n \n # Generate tentative blocks\n self._blocks.append(Block(self.game, cx-w, cx+w, cy-h, cy+h))\n self._blocks.append(Block(self.game, self.size[0]-cx-w,\n self.size[0]-cx+w, self.size[1]-cy-h,\n self.size[1]-cy+h))\n \n # Test whether the starting coordinates are free\n if (self.blocked(self.get_p1_coords()) or\n self.blocked(self.get_p2_coords())):\n \n # If not, delete the tentative blocks and retry\n del self._blocks[-1]\n del self._blocks[-1]\n continue\n \n else:\n \n # Otherwise increment the counter\n iter += 1",
"title": ""
},
{
"docid": "7e3f9bb42025f88ce03a4ac4bd487572",
"score": "0.58796775",
"text": "def _setBlock(self, x, y, z, blockType, blockData):\n #does the block already exist?\n for shapeBlock in self.shapeBlocks:\n if shapeBlock.originalPos.x == x and shapeBlock.originalPos.y == y and shapeBlock.originalPos.z == z:\n #it does exist, update it\n shapeBlock.blockType = blockType\n shapeBlock.blockData = blockData\n break\n else:\n #it doesn't append it\n newShapeBlock = ShapeBlock(x, y, z, blockType, blockData)\n self._recalcBlock(newShapeBlock)\n self.shapeBlocks.append(newShapeBlock)",
"title": ""
},
{
"docid": "745382986c179a09ed95cdb2e3d74296",
"score": "0.58715045",
"text": "def I_block():\n block1 = sprites.Block((207.5, 37.5), purple)\n block2 = sprites.Block((207.5, 72.5), purple)\n block3 = sprites.Block((207.5, 107.5), purple)\n block4 = sprites.Block((207.5, 142.5), purple)\n return block1, block2, block3, block4",
"title": ""
},
{
"docid": "e1cc862b49839e075fd4e70f3946344e",
"score": "0.58593357",
"text": "def S_block():\n block1 = sprites.Block((242.5, 37.5), yellow)\n block2 = sprites.Block((207.5, 37.5), yellow)\n block3 = sprites.Block((207.5, 72.5), yellow)\n block4 = sprites.Block((172.5, 72.5), yellow)\n return block1, block2, block3, block4",
"title": ""
},
{
"docid": "d0353501e3259dc8e3a588c266230b3b",
"score": "0.5855901",
"text": "def build_zone_2D(blocks, offset, restricted_flag, orientations, oriented):\n positions = []\n blocks_index = []\n ALLOWED_BLOCKS = allowed_blocks(restricted_flag) # Here want air\n orientations_ = [] # LIst with orientations\n for x in range(blocks.shape[0]):\n for y in range(blocks.shape[1]): # this is height in minecraft\n index = int(blocks[x, y])\n if not index == -1: # AS INDEX - 1 means air block\n try:\n blocks_index.append(ALLOWED_BLOCKS[index])\n except:\n print(\"Following index out of bound of allowed blocks.\", index)\n # Update position\n position = bounded([x+offset[0], y+offset[1], offset[2]])\n positions.append(position)\n if oriented:\n orientations_.append(int(orientations[x, y]))\n\n # print(\"Building these block indices:\", blocks_index)\n if oriented:\n response = client.spawnBlocks(Blocks(blocks=[Block(position=Point(x=int(positions[i][0]), y=int(positions[i][1]), z=int(\n positions[i][2])), type=blocks_index[i], orientation=int(orientations_[i])) for i in range(len(blocks_index))]))\n else:\n response = client.spawnBlocks(Blocks(blocks=[Block(position=Point(x=int(positions[i][0]), y=int(positions[i][1]), z=int(\n positions[i][2])), type=blocks_index[i], orientation=NORTH) for i in range(len(blocks_index))]))\n\n # print(response)",
"title": ""
},
{
"docid": "2699fc2cdc9057c1d05749ce529b4141",
"score": "0.58151484",
"text": "def _fake_blocks(self, id_list_intersect, id_list_host, replacement=True):\n intersect_count = id_list_intersect.count()\n self.target_block_index = random.SystemRandom().randint(0, self.block_num - 1)\n for i in range(self.block_num):\n if i == self.target_block_index:\n id_block = id_list_intersect.join(id_list_host, lambda x, y: y)\n else:\n id_block = self.take_exact_sample(data_inst=id_list_host, exact_num=intersect_count)\n if not replacement:\n id_list_host = id_list_host.subtractByKey(id_block)\n # id_block = self._decrypt_id_list(id_block)\n id_block = id_block.map(lambda k, v: (v, -1))\n self._sync_natural_indexation(id_block, time=i)",
"title": ""
},
{
"docid": "074f6bdbc347310b166a91ab0f7be42e",
"score": "0.57908916",
"text": "def fill(self, x1, y1, z1, x2, y2, z2, replaceBlock):\n from lib.toolbox import loop3d\n textured = False\n if type(replaceBlock) != str:\n textured = True\n\n for x, y, z in loop3d(x1, y1, z1, x2, y2, z2):\n if textured:\n self.setBlock(x, y, z, choice(replaceBlock))\n else:\n self.setBlock(x, y, z, replaceBlock)",
"title": ""
},
{
"docid": "256c4700b78b15262712e3bbd7b777d7",
"score": "0.5771765",
"text": "def clean_positions(positions):\n for i in range(positions.shape[0]):\n response = client.spawnBlocks(Blocks(blocks=[Block(position=Point(x=int(positions[i, 0]), y=int(\n positions[i, 1]), z=int(positions[i, 2])), type=AIR, orientation=NORTH)]))\n # print(response)",
"title": ""
},
{
"docid": "64cf61395f2fa93ab029806c35caf3a9",
"score": "0.57613724",
"text": "def Z_block():\n block1 = sprites.Block((172.5, 37.5), green)\n block2 = sprites.Block((207.5, 37.5), green)\n block3 = sprites.Block((207.5, 72.5), green)\n block4 = sprites.Block((242.5, 72.5), green)\n return block1, block2, block3, block4",
"title": ""
},
{
"docid": "8d32c111e5ce844077c7b9fda75c3832",
"score": "0.5731618",
"text": "def reload_blocks(self) -> None:\n files = listdir(self.proj_dir)\n self.block_list = [f for f in files if 'block_' in f]\n if len(self.block_list) > len(self.structure):\n raise ValueError(\n 'More block files than indicies in `structure.pickle`',\n )",
"title": ""
},
{
"docid": "b97732a7b6bd5930d48f4d3720346270",
"score": "0.5721267",
"text": "def main():\n \n cam = Camera()\n \n movement = [0, 0, 0]\n position = [0, 0, 0]\n display = (800, 600)\n \n blocks = {}\n \n #Load saved position and blocks if exist.\n try:\n blocks = pickle.load(open(\"blocks\", \"rb\"))\n position = pickle.load(open(\"position\", \"rb\"))\n except(FileNotFoundError):\n pass\n \n \n def add_block(block_type):\n buffer = glGetDoublev(GL_MODELVIEW_MATRIX)\n m = buffer.flatten()\n x = -int(position[0] + 0.3 + 3*m[2])\n y = -int(position[1] + 0.25+ 3*m[6])\n z = -int(position[2] + 1 + 3*m[10])\n index = coords_to_index(x, y, z)\n blocks[index] = block_type(x, y, z)\n print(\"Block added at: \", str(x),\", \", str(y), \", \", str(z))\n \n def remove_block():\n buffer = glGetDoublev(GL_MODELVIEW_MATRIX)\n m = buffer.flatten()\n x = -int(position[0] + 0.3 + 3*m[2])\n y = -int(position[1] + 0.25+ 3*m[6])\n z = -int(position[2] + 1 + 3*m[10])\n index = coords_to_index(x, y, z)\n if index in blocks:\n del blocks[index]\n \n #Initialize pygame \n pygame.init()\n \n initialize_opengl()\n \n #Stick mouse to the window\n pygame.mouse.set_visible(False)\n pygame.event.set_grab(True)\n \n #Move to saved position\n glTranslate(position[0], position[1], position[2])\n \n #Index of chosen block\n chosen_index = 0\n \n #List of available blocks\n block_list = [Dirt, Sand, Grass, Stone]\n \n while True:\n \n #Clear scene\n glClear(GL_COLOR_BUFFER_BIT|GL_DEPTH_BUFFER_BIT)\n \n #Set matrix mode\n glMatrixMode(GL_MODELVIEW)\n \n \n chosen_block = block_list[chosen_index%len(block_list)]\n \n buffer = glGetDoublev(GL_MODELVIEW_MATRIX)\n m = buffer.flatten()\n \n #Display coords info and chosen block type\n pygame.display.set_caption(\"MinePy: x: {:.3f}, y: {:.3f}, z: {:.3f} | Chosen block: {}\".format(position[0], position[1], position[2], chosen_block.__name__))\n \n #Mouse and keyboard controls\n for event in pygame.event.get():\n \n #FPS Camera\n if event.type == pygame.MOUSEMOTION:\n \n rel = pygame.mouse.get_rel()\n \n #Motion of mouse on screen\n motionX = rel[0] \n motionY = rel[1]\n \n #Move to (0, 0, 0), rotate, and move back to position.\n glTranslatef(-position[0], -position[1], -position[2])\n cam.rotate(90.0*(motionY)/display[1], 90.0*(motionX)/display[0])\n glTranslatef(position[0], position[1], position[2])\n \n \n \n if event.type == pygame.QUIT:\n pygame.quit()\n quit()\n \n #Keyboard controls (moving and exit)\n if event.type == pygame.KEYDOWN:\n \n if event.key == pygame.K_a:\n movement[0] = 1\n if event.key == pygame.K_d:\n movement[0] = -1\n \n if event.key == pygame.K_w:\n movement[2] = 1\n if event.key == pygame.K_s:\n movement[2] = -1 \n \n if event.key == pygame.K_SPACE:\n movement[1] = -1\n if event.key == pygame.K_LSHIFT:\n movement[1] = 1\n \n if event.key == pygame.K_ESCAPE:\n pygame.mouse.set_visible(True)\n pygame.event.set_grab(False)\n pickle.dump(blocks, open(\"blocks\", \"wb\"))\n pickle.dump(position, open(\"position\", \"wb\"))\n \n #Mouse controls - block operations\n if event.type == pygame.MOUSEBUTTONDOWN:\n if(event.button == 1):\n add_block(block_type = chosen_block)\n if(event.button == 3):\n remove_block()\n if(event.button == 4):\n chosen_index += 1\n chosen_block = block_list[chosen_index%len(block_list)]\n if(event.button == 5):\n chosen_index -= 1\n chosen_block = block_list[chosen_index%len(block_list)]\n \n \n #Stop movement on keyup\n if event.type == pygame.KEYUP:\n if event.key == pygame.K_a or event.key == pygame.K_d:\n movement[0] = 0\n if event.key == pygame.K_s or event.key == pygame.K_w:\n movement[2] = 0\n if event.key == pygame.K_SPACE or event.key == pygame.K_LSHIFT:\n movement[1] = 0\n \n # \"Move the character\"\n glTranslate(movement[2]*0.1*m[2], movement[2]*0.1*m[6], movement[2]*0.1*m[10]) #forward\n glTranslate(movement[0]*0.1*m[0], movement[0]*0.1*m[4], movement[0]*0.1*m[8]) #strafe\n glTranslatef(0, movement[1]*0.1, 0) #up and down\n \n #Update position vector\n position[0] += movement[2]*0.1*m[2]+movement[0]*0.1*m[0]\n position[1] += movement[2]*0.1*m[6]+movement[0]*0.1*m[4]+movement[1]*0.1\n position[2] += movement[2]*0.1*m[10]+movement[0]*0.1*m[8]\n \n #Draw edges for focuesed block in space\n x_foc = -int(position[0] + 0.3 + 3*m[2])\n y_foc = -int(position[1] + 0.25 + 3*m[6])\n z_foc = -int(position[2] + 1 + 3*m[10])\n \n focused = Air(x_foc, y_foc, z_foc)\n \n glBegin(GL_LINES)\n focused.drawEdges()\n glEnd()\n \n #Draw every cube\n for cube in blocks.values():\n glBegin(GL_QUADS)\n cube.drawSurfaces()\n glEnd()\n \n \n pygame.display.flip()\n pygame.time.wait(10)",
"title": ""
},
{
"docid": "867379fc18899c9cdf1bfd6b0ad5559b",
"score": "0.56573915",
"text": "def AddBlock(self, block):\r\n self.blocks.insert(self.blockInsertionIndex, block)\r\n self.blockInsertionIndex += 1\r\n block.engine = self",
"title": ""
},
{
"docid": "b3889c9ca4d26d8f3e4852de695a3b0c",
"score": "0.5645164",
"text": "def load_simple_world(world):\n block_weights = [\n (100, 'dirt'),\n (30, 'stone'),\n ]\n\n cells = {}\n\n ground = []\n\n width, height = world.get_grid_size()\n\n for x in range(width):\n for y in range(height):\n if x < 22:\n if y <= 8:\n continue\n else:\n if x + y < 30:\n continue\n\n ground.append((x, y))\n\n weights, blocks = zip(*block_weights)\n kinds = random.choices(blocks, weights=weights, k=len(ground))\n\n for cell, block_id in zip(ground, kinds):\n cells[cell] = create_block(block_id)\n\n trunks = [(3, 8), (3, 7), (3, 6), (3, 5)]\n\n for trunk in trunks:\n cells[trunk] = create_block('wood')\n\n leaves = [(4, 3), (3, 3), (2, 3), (4, 2), (3, 2), (2, 2), (4, 4), (3, 4), (2, 4)]\n\n for leaf in leaves:\n cells[leaf] = create_block('leaf')\n\n for cell, block in cells.items():\n # cell -> box\n i, j = cell\n\n world.add_block_to_grid(block, i, j)\n\n world.add_block_to_grid(create_block(\"mayhem\", 0), 14, 8)\n\n world.add_mob(Bird(\"friendly_bird\", (12, 12)), 400, 100)\n\n world.add_mob(Sheep(\"sheep\", (35, 15)), 400, 300)",
"title": ""
},
{
"docid": "252e426d88e0b233236a50a8a7a87178",
"score": "0.5642141",
"text": "def place(self, block = Block(0,0), location = Vec3(0,0,0)):\n args = [int(self.robotId)]\n if not location.lengthSqr() == 0:\n args.append(True)\n args.append(location.x)\n args.append(location.y)\n args.append(location.z)\n else:\n args.append(False)\n if not block == Block(0,0):\n args.append(True)\n args.append(block.id)\n args.append(block.data)\n else:\n args.append(False)\n self.mc.conn.sendReceive_flat(\"robot.place\", floorFlatten(args))\n self.delay(self.delayTime)",
"title": ""
},
{
"docid": "6c066d4d9b985a67d539fe8e46ec8f05",
"score": "0.5635437",
"text": "def spawn(self, entities: list) -> None:\n for entity, x, y in entities:\n if entity['size'][0] + x > self.cells_x or \\\n entity['size'][1] + y > self.cells_y:\n print(\"Cannot spawn a {}, too large\".format(\n entity['name']))\n else:\n for y2 in range(entity['size'][1]):\n for x2 in range(entity['size'][0]):\n self.map[y + y2][x + x2] = entity['body'][y2][x2]",
"title": ""
},
{
"docid": "e44b2a27e0033f4c60ce2252d8b892c2",
"score": "0.563051",
"text": "def _split_available_obstacles_placed_balls(self, blocks):\n\n (available, targets, obstacles, placed\n ) = self._split_available_obstacles_placed(blocks)\n\n # We know that the balls are always last, since they are instantitated in\n # the scene last.\n num_balls = len(self._initial_scene.balls)\n balls = placed[-num_balls:]\n placed = placed[:-num_balls]\n return available, targets, obstacles, placed, balls",
"title": ""
},
{
"docid": "795e0a002e2d076e6c91a44ee72634fd",
"score": "0.5621724",
"text": "def updateVisibleBlocks(playerPos, visibleBlocks):\n \n for i in range(-1, 2):\n for j in range(-1, 2):\n visiblePos = [playerPos[0]+i, playerPos[1]+j]\n if not visiblePos in visibleBlocks:\n visibleBlocks.append(visiblePos)",
"title": ""
},
{
"docid": "9dddcbaaec485154aed3e58cdbc2e5c1",
"score": "0.5615271",
"text": "def _single_block(self):\n\n # Define column block\n self._blocks.append(Block(self.game, (self.size[0]/2)-80,\n (self.size[0]/2)+80, (self.size[1]/2)-80,\n (self.size[1]/2)+80))",
"title": ""
},
{
"docid": "d71cc1496daee29f39cdeaf9f0e63588",
"score": "0.56082",
"text": "def _transferStationaryBlocks(self, assembly1, assembly2):\n for index in self.cs[\"stationaryBlocks\"]:\n # this block swap is designed to ensure that all blocks have the\n # correct parents and children structure at the end of the swaps.\n tempBlock1 = assembly1[index]\n assembly1.remove(tempBlock1)\n\n tempBlock2 = assembly2[index]\n assembly2.remove(tempBlock2)\n\n assembly1.insert(index, tempBlock2)\n assembly2.insert(index, tempBlock1)",
"title": ""
},
{
"docid": "ac6cbe8894f4716028857b9027e1d5ef",
"score": "0.5585762",
"text": "def build_square_tower(x_origin, y_origin, z_origin,\n width, length, height,\n block_type):\n try:\n for y in range(y_origin, y_origin + height + 1):\n for x in range(x_origin - (width // 2),\n x_origin + (width // 2) + 1 + (width % 2)):\n for z in range(z_origin - (length // 2),\n z_origin + (length // 2) + 1 + (width % 2)):\n mc.setBlock(x, y, z, block_type)\n except Exception as build_square_tower_Error:\n print(\"build_square_tower_Error:\", build_square_tower_Error)",
"title": ""
},
{
"docid": "9b59c0d6cff7b3472c7c0405e0d2f42f",
"score": "0.55810785",
"text": "def blocks(self):\n for _y in range(self.block_size):\n for _x in range(self.block_size):\n block = []\n x = _x * self.block_size\n y = _y * self.block_size * self.size\n start = x + y\n for r in range(self.block_size):\n block += self.grid[start:start + self.block_size]\n start += self.size\n yield block",
"title": ""
},
{
"docid": "5ba23ac4d6b5390c5a4f8baca61176d3",
"score": "0.5572072",
"text": "def add_block(self, block):\r\n\r\n\t\tglobal move_counter\r\n\r\n\t\t#Check if the size of the block is within the range of the game.\r\n\t\tassert block.size <= self.total_block_number, \"Size is out of bounds of this game instance.\"\r\n\r\n\t\t#Check if the tower is empty. If so, add the block on the bottom.\r\n\t\tif self.check_if_empty() == True:\r\n\t\t\tself.block_object_list[-1] = block\r\n\t\t\tself.block_visual_array[-1] = block.size\r\n\t\t\tblock.position[0] = self.block_object_list.index(block)\r\n\t\t\tblock.position[1] = self.pos\r\n\t\t\tself.blocks_in_tower += 1\r\n\t\t\tself.update_block_on_top_flag()\r\n\t\t\tmove_counter += 1\r\n\t\t\treturn True\r\n\r\n\t\t#Find the top of the tower and add the block if its size is smaller than the top block\r\n\t\t#of the tower. Returns False if the size is larger.\r\n\t\tfor i in range(self.total_block_number):\r\n\t\t\tif type(self.block_object_list[i]) == int:\r\n\t\t\t\tcontinue\r\n\t\t\telse:\r\n\t\t\t\tif block.size < self.block_object_list[i].size:\r\n\t\t\t\t\tself.block_object_list[i-1] = block\r\n\t\t\t\t\tself.block_visual_array[i-1, 0] = block.size\r\n\t\t\t\t\tblock.position[0] = self.block_object_list.index(block)\r\n\t\t\t\t\tself.blocks_in_tower += 1\r\n\t\t\t\t\tself.update_block_on_top_flag()\r\n\t\t\t\t\tmove_counter += 1\r\n\t\t\t\t\treturn True\r\n\t\t\t\telse:\r\n\t\t\t\t\t# print(\"That is not a valid move.\")\r\n\t\t\t\t\treturn False",
"title": ""
},
{
"docid": "d6bc950da12b0268671d6942453721c4",
"score": "0.5565695",
"text": "def fit(self, blocks):\n\t\tif self.root is None:\n\t\t\tself.root = Box(blocks[0].size)\n\t\tfor block in blocks:\n\t\t\tbox = self.root.find(block)\n\t\t\tif not box:\n\t\t\t\tbox = self._grow(block).find(block)\n\t\t\tbox.split(block)\n\t\tself.root._postprocess()",
"title": ""
},
{
"docid": "2e918336dabc627ec8c0c9deaa17bff0",
"score": "0.5564114",
"text": "def _create_vars_from_blocklist(\n self, program, block_list, add_trainer_suffix=False\n ):\n\n # varname->[(block_id, current_block_size)]\n block_map = collections.OrderedDict()\n\n var_mapping = collections.OrderedDict()\n for block_str in block_list:\n varname, offset, size = block_str.split(\":\")\n if varname not in block_map:\n block_map[varname] = []\n block_map[varname].append((int(offset), int(size)))\n\n for varname, split in block_map.items():\n orig_var = program.global_block().var(varname)\n if len(split) == 1:\n if self.sync_mode and add_trainer_suffix:\n new_var_name = \"%s.trainer_%d\" % (\n orig_var.name,\n self.trainer_id,\n )\n program.global_block()._rename_var(varname, new_var_name)\n var_mapping[varname] = [\n program.global_block().var(new_var_name)\n ]\n else:\n var_mapping[varname] = [\n program.global_block().var(orig_var.name)\n ]\n continue\n var_mapping[varname] = []\n orig_shape = orig_var.shape\n orig_dim1_flatten = 1\n if len(orig_shape) >= 2:\n orig_dim1_flatten = reduce(\n lambda x, y: x * y, orig_shape[1:], 1\n )\n\n for i, block in enumerate(split):\n size = block[1]\n rows = size // orig_dim1_flatten\n splited_shape = [rows]\n if len(orig_shape) >= 2:\n splited_shape.extend(orig_shape[1:])\n new_var_name = \"\"\n if self.sync_mode and add_trainer_suffix:\n new_var_name = \"%s.block%d.trainer_%d\" % (\n varname,\n i,\n self.trainer_id,\n )\n else:\n new_var_name = \"%s.block%d\" % (varname, i)\n var = program.global_block().create_var(\n name=new_var_name,\n persistable=False,\n dtype=orig_var.dtype,\n type=orig_var.type,\n shape=splited_shape,\n ) # flattend split var\n var_mapping[varname].append(var)\n program.global_block()._sync_with_cpp()\n return var_mapping",
"title": ""
},
{
"docid": "13417eb2a375072343908f0ce5f9bb91",
"score": "0.55590665",
"text": "def process_block(self, block, block_index):\n if not self.coords_only:\n self.write_block(block, block_index)\n for im in self._imagers:\n im.update_from_block(self.vis_header(), block)",
"title": ""
},
{
"docid": "847e98b3de6bd91e8cafc34df13d8edb",
"score": "0.55506",
"text": "def attempt_update_blocks(self, blocks, new_coords):\n self.remove_blocks(blocks)\n if self.has_collision(new_coords):\n self.add_blocks(blocks)\n return False\n\n # update co-ordinates\n for block, (r, c) in zip(blocks, new_coords):\n self.grid[r][c] = block\n block.r, block.c = r, c\n return True",
"title": ""
},
{
"docid": "d72b46de411354f7bb945829b7eaf54a",
"score": "0.55374455",
"text": "def make_blocks(self):\n new_block = self.wallet.make_block()\n if self.is_validator and new_block:\n self.peer.udp_send(new_block)\n self.ui.transaction_pool_tree.clear()\n\n qtc.QTimer.singleShot(5000, self.make_blocks)",
"title": ""
},
{
"docid": "df7b48345aa8af94b3f33b0cb31b0cde",
"score": "0.5534619",
"text": "def merge(self, chain):\n self.blocks += chain.blocks\n self.place()\n return [self]",
"title": ""
},
{
"docid": "2f764ad0452582cb8557c1041da658f2",
"score": "0.5528519",
"text": "def placeBlockBatched(self, x, y, z, blockStr, limit=50):\n x, y, z = self.local2global(x, y, z)\n\n self.buffer.append((x, y, z, blockStr))\n if len(self.buffer) >= limit:\n return self.sendBlocks()\n else:\n return None",
"title": ""
},
{
"docid": "6728e59d9715e71588388c3856a034db",
"score": "0.5521412",
"text": "def process_blocks(cls):\n block_list = []\n metastring1 = ''\n\n for metastring2 in cls.rawspecmodel_metastring_list:\n processed_already = False\n\n if int(metastring2[0]) == 6 and int(metastring2[metastring2.find('%') + 1]) != 0:\n pos_of_next_field = metastring1.find('#', metastring1.find('#' + metastring2[:metastring2.find('%')]) + 1)\n metastring1 = metastring1[:pos_of_next_field] + metastring2[metastring2.find('%'):] + metastring1[pos_of_next_field:]\n elif int(metastring2[0]) == 1 and cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])]['keyword'] == 'package':\n if int(metastring2[metastring2.find('%') + 1]) == 0:\n metastring1 += '#' + metastring2\n else:\n pos_of_next_field = metastring1.find('#', metastring1.find('#' + metastring2[:metastring2.find('%')]) + 1)\n metastring1 = metastring1[:pos_of_next_field] + metastring2[metastring2.find('%'):] + metastring1[pos_of_next_field:]\n elif int(metastring2[0]) == 3 and int(metastring2[metastring2.find('%') + 1]) != 0:\n pos_of_next_field = metastring1.find('#', metastring1.find('#' + metastring2[:metastring2.find('%')]) + 1)\n metastring1 = metastring1[:pos_of_next_field] + metastring2[metastring2.find('%'):] + metastring1[pos_of_next_field:]\n\n pos = cls.get_outer_block_pos(block_list, cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])])\n cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])]['content'] = [block_list[pos+1]]\n block_list = block_list[:pos] + block_list[pos + 2:]\n else:\n metastring1 += '#' + metastring2\n\n if 'package' in cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])]:\n del cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])]['package']\n\n if int(metastring2[0]) == 1 and cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])]['keyword'] == 'package':\n if int(metastring2[metastring2.find('%') + 1]) == 4:\n pos = cls.get_outer_block_pos(block_list, cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])])\n cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])]['content'] = deepcopy(block_list[pos+1:])\n block_list = block_list[:pos]\n\n elif int(metastring2[0]) == 1 and cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])]['keyword'] == 'files':\n merged_content = ''\n\n if isinstance(cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])]['content'], list):\n last_field_id = len(cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])]['content']) - 1\n else:\n last_field_id = 1 \n file_records = re.findall(r'%4[^%]*', metastring2)\n first_record = True\n original_files_line_id = 0\n files_line_id = 0\n processed_already = False\n subtract = 0\n\n for single_file in file_records:\n files_line_id = int(re.match(r'\\d+', single_file[2:]).group())\n original_files_line_id = files_line_id\n\n if first_record and files_line_id != 0:\n subtract = files_line_id - 1\n processed_already = True\n files_line_id -= subtract\n\n if first_record:\n first_record = False\n\n if len(cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])]['content']) - 1 >= files_line_id:\n merged_content += cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])]['content'][files_line_id]\n\n if files_line_id != last_field_id:\n merged_content += single_file[len(str(original_files_line_id)) + 2:]\n else:\n metastring1 += single_file[len(str(original_files_line_id)) + 2:]\n\n if files_line_id == 0:\n metastring1 = metastring1.replace(single_file, '%4')\n elif processed_already and files_line_id == last_field_id:\n metastring1 = metastring1.replace(re.search(r'\\s*#\\d+' + single_file, metastring1).group(), '')\n else:\n metastring1 = metastring1.replace(single_file, '')\n\n if not processed_already:\n del cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])]['content'][0:files_line_id]\n cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])]['content'][0] = merged_content\n else:\n del cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])]['content'][1:files_line_id + 1]\n cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])]['content'][0] += merged_content\n\n if len(cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])]['content']) == 1:\n cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])]['content'] = cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])]['content'][0]\n\n elif int(metastring2[0]) == 5 and 'files' in cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])]:\n\n processed_already = True\n\n pos = cls.get_files_block_pos(block_list, cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])])\n if pos != -1:\n comment_metastring = re.findall(r'\\s*#5' + metastring2[1:metastring2.find('%')] + r'[^#]*', metastring1)[0]\n pre_comment_whitespace = re.findall(r'[^#]*', comment_metastring)[0]\n post_comment_whitespace = re.search(r'\\s*$', comment_metastring).group()\n\n # due to some unicode assignment failures\n tmp = []\n if isinstance(block_list[pos]['content'], list):\n tmp.append(block_list[pos]['content'][0] + pre_comment_whitespace + cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])]['content'] + post_comment_whitespace)\n tmp += block_list[pos]['content'][1:]\n else:\n tmp.append(block_list[pos]['content'] + pre_comment_whitespace + cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])]['content'])\n\n del block_list[pos]['content']\n block_list[pos]['content'] = tmp\n if len(block_list[pos]['content']) == 1:\n block_list[pos]['content'] = block_list[pos]['content'][0]\n metastring1 = metastring1.replace(comment_metastring, post_comment_whitespace)\n else:\n metastring1 = metastring1.replace(comment_metastring, '')\n\n elif int(metastring2[0]) == 6:\n if int(metastring2[metastring2.find('%') + 1]) == 3 or (int(metastring2[metastring2.find('%') + 1]) == 5 and 'content' not in cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])]):\n pos = cls.get_outer_block_pos(block_list, cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])])\n cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])]['content'] = block_list[pos+1:]\n block_list = block_list[:pos]\n\n elif int(metastring2[metastring2.find('%') + 1]) == 5:\n pos = cls.get_outer_block_pos(block_list, cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])])\n cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])]['else_body'] = block_list[pos+1:]\n block_list = block_list[:pos]\n\n if not processed_already:\n block_list.append(cls.list_of_blocks[int(metastring2[0])][int(metastring2[1:metastring2.find('%')])])\n\n return (block_list, metastring1)",
"title": ""
},
{
"docid": "8a4871f1fdbaccbc5f84e01e6491658a",
"score": "0.55078",
"text": "def extendBlocks(cdsId, geneId,blockListToextend, geneExon, cdsExon, intronList, geneLen, cdsLen,\n cdsSeq, geneSeq):\n\n\n\n\n blockListextend=[]\n for bloc in blockListToextend:\n\n\n [cdsBegin, cdsEnd, genBegin, geneEnd, position] = bloc\n if position == 'endExon':\n blockListextend.append(extendLeftToRight( bloc, cdsId, geneId, cdsSeq, geneSeq))\n elif position == 'beginExon':\n blockListextend.append(extendRightToLeft( bloc, cdsId, geneId, cdsSeq, geneSeq))\n\telse:\n\t\tblockListextend.append(extendLeftToRight_Middle( bloc, cdsId, geneId, cdsSeq, geneSeq))\n\t\t\n return blockListextend",
"title": ""
},
{
"docid": "901dab0d12de14ad1d5fdc682899179e",
"score": "0.5476975",
"text": "def generate_strips_3op_blocksworld_problem(nblocks):\n lang = generate_strips_3op_bw_language(nblocks=nblocks)\n problem = create_fstrips_problem(lang, domain_name=BASE_DOMAIN_NAME, problem_name=f'random-{nblocks}-blocks')\n\n clear, on, ontable = lang.get('clear', 'on', 'ontable')\n\n # Generate init pattern\n clearplaces, locations = generate_random_bw_pattern(lang)\n for x, y in locations:\n if y == 'table':\n problem.init.add(ontable, lang.get(x))\n else:\n problem.init.add(on, lang.get(x), lang.get(y))\n for x in clearplaces:\n if x != 'table':\n problem.init.add(clear, lang.get(x))\n\n # Generate goal pattern\n _, locations = generate_random_bw_pattern(lang)\n conjuncts = []\n for x, y in locations:\n if y == 'table':\n conjuncts.append(ontable(lang.get(x)))\n else:\n conjuncts.append(on(lang.get(x), lang.get(y)))\n problem.goal = land(*conjuncts, flat=True)\n\n b = lang.variable('b', 'object')\n f = lang.variable('from', 'object')\n t = lang.variable('to', 'object')\n\n problem.action('move-block-to-block', [b, f, t],\n precondition=land(clear(b), clear(t), on(b, f), flat=True),\n effects=[\n fs.AddEffect(on(b, t)),\n fs.DelEffect(on(b, f)),\n fs.AddEffect(clear(f)),\n fs.DelEffect(clear(t)),\n ])\n\n problem.action('move-block-to-table', [b, f],\n precondition=land(clear(b), on(b, f), flat=True),\n effects=[\n fs.AddEffect(ontable(b)),\n fs.DelEffect(on(b, f)),\n fs.AddEffect(clear(f)),\n ])\n\n problem.action('move-table-to-block', [b, t],\n precondition=land(clear(b), clear(t), ontable(b), flat=True),\n effects=[\n fs.AddEffect(on(b, t)),\n fs.DelEffect(ontable(b)),\n fs.DelEffect(clear(t)),\n ])\n\n return problem",
"title": ""
},
{
"docid": "3252b710201c3bdccc5060f31b23aebb",
"score": "0.5449252",
"text": "def _corner_blocks(self):\n\n # Define four small columns\n self._blocks.append(Block(self.game, 200, 260, 200, 260))\n self._blocks.append(Block(self.game, self.size[0]-260,\n self.size[0]-200, 200, 260))\n self._blocks.append(Block(self.game, 200, 260, self.size[1]-260,\n self.size[1]-200))\n self._blocks.append(Block(self.game, self.size[0]-260,\n self.size[0]-200, self.size[1]-260,\n self.size[1]-200))",
"title": ""
},
{
"docid": "9537d957fe9de2fff48908f1f91ce010",
"score": "0.5448576",
"text": "def update(self, snake_head, body_blocks):\n\t\tif self.index_in_list == 0:\n\t\t\tself.rect.x = snake_head.rect.x\n\t\t\tself.rect.bottom = snake_head.rect.bottom\n\t\t\n\t\telse: \n\t\t\tself.rect.x = body_blocks[self.index_in_list - 1].rect.x\n\t\t\tself.rect.bottom = body_blocks[self.index_in_list - 1].rect.bottom\n\t\t\tbody_blocks[self.index_in_list - 1].update(snake_head, body_blocks)",
"title": ""
},
{
"docid": "a313c497c73e98c9f3aac9589e280b97",
"score": "0.5444522",
"text": "def addBlock(self, block, tile = None):\n\t\tif tile != None:\n\t\t\ttile.block = block\n\t\tself.addEntity(block)",
"title": ""
},
{
"docid": "85a3adeae6f74cf8b61ad367a6597dc2",
"score": "0.5442213",
"text": "def __init__(self, blocks):\n self.block_classes = self.normalize_block_classes(blocks)",
"title": ""
},
{
"docid": "78f3b57c6603f2fb827fbf4ba5335524",
"score": "0.5440645",
"text": "def set_children(block: Block, colours: List[Optional[Tuple[int, int, int]]]) \\\r\n -> None:\r\n size = block._child_size()\r\n positions = block._children_positions()\r\n level = block.level + 1\r\n depth = block.max_depth\r\n\r\n block.children = [] # Potentially discard children\r\n for i in range(4):\r\n b = Block(positions[i], size, colours[i], level, depth)\r\n block.children.append(b)",
"title": ""
},
{
"docid": "5233816890a13897a5a3833414e768a8",
"score": "0.54095596",
"text": "def build_round_tower(x_origin, y_origin, z_origin,\n diameter, height, block_type):\n try:\n r = diameter // 2\n for y in range(y_origin, y_origin + height + 1):\n for x, z in circle_coordinates(x_origin, z_origin, r):\n mc.setBlock(x, y, z, block_type)\n except Exception as build_round_tower_Error:\n print(\"build_round_tower_Error:\", build_round_tower_Error)",
"title": ""
},
{
"docid": "7a3d6528f12d20ba07f131dd4750017a",
"score": "0.5406578",
"text": "def simple_blocks_world():\n\n return PlanningProblem(initial='On(A, B) & Clear(A) & OnTable(B) & OnTable(C) & Clear(C)',\n goals='On(B, A) & On(C, B)',\n actions=[Action('ToTable(x, y)',\n precond='On(x, y) & Clear(x)',\n effect='~On(x, y) & Clear(y) & OnTable(x)'),\n Action('FromTable(y, x)',\n precond='OnTable(y) & Clear(y) & Clear(x)',\n effect='~OnTable(y) & ~Clear(x) & On(y, x)')])",
"title": ""
},
{
"docid": "8090d6c2670f8e80336604afe1e03378",
"score": "0.53945655",
"text": "def draw_block_list(ax, blocks):\n v = np.array(\n [\n [0, 0, 0],\n [1, 0, 0],\n [1, 1, 0],\n [0, 1, 0],\n [0, 0, 1],\n [1, 0, 1],\n [1, 1, 1],\n [0, 1, 1],\n ],\n dtype=\"float\",\n )\n f = np.array(\n [\n [0, 1, 5, 4],\n [1, 2, 6, 5],\n [2, 3, 7, 6],\n [3, 0, 4, 7],\n [0, 1, 2, 3],\n [4, 5, 6, 7],\n ]\n )\n clr = blocks[:, 6:] / 255\n n = blocks.shape[0]\n d = blocks[:, 3:6] - blocks[:, :3]\n vl = np.zeros((8 * n, 3))\n fl = np.zeros((6 * n, 4), dtype=\"int64\")\n fcl = np.zeros((6 * n, 3))\n for k in range(n):\n vl[k * 8 : (k + 1) * 8, :] = v * d[k] + blocks[k, :3]\n fl[k * 6 : (k + 1) * 6, :] = f + k * 8\n fcl[k * 6 : (k + 1) * 6, :] = clr[k, :]\n\n if type(ax) is Poly3DCollection:\n ax.set_verts(vl[fl])\n else:\n pc = Poly3DCollection(vl[fl], alpha=0.25, linewidths=1, edgecolors=\"k\")\n pc.set_facecolor(fcl)\n h = ax.add_collection3d(pc)\n return h",
"title": ""
},
{
"docid": "c6976e51c86b705c3d8b0f22086dbc08",
"score": "0.5390532",
"text": "def populate_blockchain(blockchain, block_count):\n for i in range(len(blockchain), len(blockchain) + block_count):\n # genesis block\n previous_block_hash = '0'*32\n\n if i > 0:\n previous_block_hash = block_hash(blockchain[i-1])\n\n new_block = {\n 'metadata': {\n 'block_number': i,\n 'create_date': time.time()\n },\n 'data': 'random data for block %d' % (i,),\n 'previous_block_hash': previous_block_hash\n }\n blockchain.append(new_block)",
"title": ""
},
{
"docid": "7b69733a34f89298b7134ec157e36262",
"score": "0.5383096",
"text": "def __init__(self, genesis_block_data=None):\r\n self.blocks = [] # initialize the list of blocks\r\n self.mine_block(genesis_block_data) # add the genesis block\r",
"title": ""
},
{
"docid": "1ee53e9609c6e357cae8af74e0e16e32",
"score": "0.53772235",
"text": "def dis_multiblock(self, offset, blocks=None):\n log_asmblock.info(\"dis bloc all\")\n job_done = set()\n if blocks is None:\n blocks = AsmCFG()\n todo = [offset]\n\n bloc_cpt = 0\n while len(todo):\n bloc_cpt += 1\n if self.blocs_wd is not None and bloc_cpt > self.blocs_wd:\n log_asmblock.debug(\"blocks watchdog reached at %X\", int(offset))\n break\n\n target_offset = int(todo.pop(0))\n if (target_offset is None or\n target_offset in job_done):\n continue\n cur_block, nexts = self._dis_block(target_offset, job_done)\n todo += nexts\n blocks.add_node(cur_block)\n\n blocks.apply_splitting(self.symbol_pool,\n dis_block_callback=self.dis_block_callback,\n mn=self.arch, attrib=self.attrib,\n pool_bin=self.bin_stream)\n return blocks",
"title": ""
},
{
"docid": "3f735a6fe0ac21577bddd5685ead1029",
"score": "0.5361986",
"text": "def place(self):\n self._set_pinned_block_idx()\n self.max_size = 0\n for block in self.blocks:\n self.max_size += block.max_size + block.alignment - 1\n\n # Check if chain has one block pinned\n if not self.pinned:\n return\n\n offset_base = self.blocks[self.pinned_block_idx].label.offset\n assert(offset_base % self.blocks[self.pinned_block_idx].alignment == 0)\n\n self.offset_min = offset_base\n for block in self.blocks[:self.pinned_block_idx - 1:-1]:\n self.offset_min -= block.max_size + \\\n (block.alignment - block.max_size) % block.alignment\n\n self.offset_max = offset_base\n for block in self.blocks[self.pinned_block_idx:]:\n self.offset_max += block.max_size + \\\n (block.alignment - block.max_size) % block.alignment",
"title": ""
},
{
"docid": "0b83a9e9b67dd5dd928bb78cc4e5dbac",
"score": "0.5360611",
"text": "def handle_collected_blocks(self, collected_blocks_lists_list):\n if collected_blocks_lists_list:\n valid_collected_blocks_lists_list = []\n for collected_blocks_lists in collected_blocks_lists_list:\n for collected_block in collected_blocks_lists:\n if collected_block.is_valid(self.wallet.blockchain):\n valid_collected_blocks_lists_list.append(collected_blocks_lists)\n\n valid_collected_blocks_lists_list_tuples = []\n for valid_collected_blocks_lists in valid_collected_blocks_lists_list:\n valid_collected_blocks_list_tuples = []\n for valid_collected_block in valid_collected_blocks_lists:\n valid_collected_blocks_list_tuples.append((valid_collected_block.block_number, valid_collected_block.hash_block))\n valid_collected_blocks_lists_list_tuples.append(valid_collected_blocks_list_tuples)\n\n correct_valid_collected_blocks_list_tuples = most_frequent(valid_collected_blocks_lists_list_tuples)\n index_of_correct_valid_collected_blocks_list = valid_collected_blocks_lists_list_tuples.index(correct_valid_collected_blocks_list_tuples)\n correct_valid_collected_blocks_list = valid_collected_blocks_lists_list[index_of_correct_valid_collected_blocks_list]\n\n for correct_valid_collected_block in correct_valid_collected_blocks_list:\n self.wallet.add_proposed_block(correct_valid_collected_block)\n if not self.wallet.add_a_block_to_chain():\n self.request_missing_blocks()\n\n with open(f\"storage{SLASH_SIGN}blockchain.json\", \"w\") as blockchain_file:\n pass",
"title": ""
},
{
"docid": "b0a2894e335d82f1d283058502aae2f7",
"score": "0.5348675",
"text": "def _moveShapeBlock(self, shapeBlock, x, y, z):\n shapeBlock.actualPos.x = shapeBlock.relativePos.x + x\n shapeBlock.actualPos.y = shapeBlock.relativePos.y + y\n shapeBlock.actualPos.z = shapeBlock.relativePos.z + z",
"title": ""
},
{
"docid": "80e0fa187e6fcaf0aa002db97931bfbd",
"score": "0.534808",
"text": "def makeBlockList(self, onlyClosedBlocks = False, sites=None, \n providedOnlyBlocks=None):\n reader = DBSReader( getLocalDBSURL())\n dbsBlocks = reader.listFileBlocks(self.inputDataset(), onlyClosedBlocks)\n \n if self.persistData.blocks != []:\n remover = lambda x : x not in self.persistData.blocks\n newBlocks = filter(remover, dbsBlocks)\n \n else:\n newBlocks = dbsBlocks\n\n # //\n # // Skipping blocks without site info\n #//\n msg = \"Filtering blocks according to Site information...\"\n logging.info(msg)\n blocksAtSites = []\n for block in newBlocks:\n locations = reader.listFileBlockLocation(block)\n if not locations:\n msg = \"\\nSkipping block: \"\n msg += \"No site info available for block %s \" % block\n logging.info(msg)\n elif sites is not None:\n locationInSites = False\n for location in locations:\n if location in sites:\n locationInSites = True\n break\n if locationInSites:\n blocksAtSites.append(block)\n else:\n msg = \"\\nSkipping block: \"\n msg += \"Block %s has no replicas in %s\" % (block,\n \", \".join(sites))\n logging.info(msg)\n else:\n blocksAtSites.append(block)\n newBlocks = blocksAtSites\n \n if len(newBlocks) == 0:\n msg = \"No New Blocks found for dataset\\n\"\n raise RuntimeError, msg\n \n # //\n # // Check presence of provided Blocks in newBlocks\n #//\n blocksToProcess = []\n if providedOnlyBlocks is not None :\n providedOnlyBlocksList = providedOnlyBlocks.split(',')\n msg = \"OnlyBlocks setting provided. Processing it...\"\n logging.info(msg)\n msg = \"OnlyBlocks list contains %s Blocks.\" % (\n len(providedOnlyBlocksList))\n logging.info(msg)\n blockCount = 1\n for block in providedOnlyBlocksList :\n if block.strip() in newBlocks :\n blocksToProcess.append(block.strip())\n msg = \"Block %s: Adding Block %s\" % (\n blockCount, block)\n msg += \" to the Whitelist\"\n logging.info(msg)\n else:\n msg = \"Block %s: Skipping Block %s \" % (\n blockCount, block)\n msg += \"It's no New or it has been processed\"\n msg += \" already.\"\n logging.info(msg)\n blockCount += 1\n else : \n blocksToProcess = newBlocks\n msg = \"OnlyBlocks setting not provided. Processing\"\n msg += \" all New Blocks for Dataset\\n\"\n logging.info(msg)\n\n if len(blocksToProcess) == 0 :\n msg = \"OnlyBlocks list does not match any New Blocks\"\n msg += \" found for Dataset\\n\"\n raise RuntimeError, msg\n\n blockList = str(blocksToProcess)\n blockList = blockList.replace(\"[\", \"\")\n blockList = blockList.replace(\"]\", \"\")\n blockList = blockList.replace(\"\\'\", \"\")\n blockList = blockList.replace(\"\\\"\", \"\")\n self.workflow.parameters['OnlyBlocks'] = blockList\n self.persistData.blocks.extend(blocksToProcess)\n return",
"title": ""
},
{
"docid": "72ae63d9c4b7876c5280ee00055fd1e2",
"score": "0.5339692",
"text": "def test_set_block_heightmap_underneath(self):\n\n self.c.populated = True\n\n self.c.set_block((0, 20, 0), 1)\n self.assertEqual(self.c.heightmap[0], 20)\n\n self.c.set_block((0, 10, 0), 1)\n self.assertEqual(self.c.heightmap[0], 20)",
"title": ""
},
{
"docid": "455a80e1fd0141c2618b103bd6c373a7",
"score": "0.5339439",
"text": "def construct(self):\r\n row=MAZELEFT #the row that the tiles will be placed\r\n col=MAZETOP #the column that the tiles will be placed\r\n self.walls= pygame.sprite.Group() #a group of walls\r\n self.floors= pygame.sprite.Group() #a group of floors\r\n\r\n #construct the maze\r\n for x in range(len(self.mazelist)):\r\n for y in range(len(self.mazelist[0])):\r\n \r\n if self.mazelist[x][y]=='#':\r\n self.walls.add(tile(get_image('block 2.png'),row,col))\r\n else:\r\n self.floors.add(tile(get_image('floor.png'),row,col))\r\n row+=BLOCKWIDTH\r\n col+=BLOCKHEIGHT\r\n row=MAZELEFT",
"title": ""
},
{
"docid": "cd03ee3a5291b232d3c67f701c7560bc",
"score": "0.5336396",
"text": "def replace(self, x1, y1, z1, x2, y2, z2, searchBlock, replaceBlock):\n from lib.toolbox import loop3d\n textured = False\n if type(replaceBlock) != str:\n textured = True\n\n for x, y, z in loop3d(x1, y1, z1, x2, y2, z2):\n if self.getBlock(x, y, z) == searchBlock:\n if textured:\n self.setBlock(x, y, z, choice(replaceBlock))\n else:\n self.setBlock(x, y, z, replaceBlock)",
"title": ""
},
{
"docid": "b34b60216be69870cb65839434e4bb22",
"score": "0.53357625",
"text": "def add_blocks_to_specfile(cls):\n cls.specmodel.HeaderTags = cls.list_of_blocks[0]\n cls.specmodel.SectionTags = cls.list_of_blocks[1]\n cls.specmodel.MacroDefinitions = cls.list_of_blocks[2]\n cls.specmodel.MacroConditions = cls.list_of_blocks[3]\n cls.specmodel.MacroUndefinitions = cls.list_of_blocks[4]\n cls.specmodel.Comments = cls.list_of_blocks[5]\n cls.specmodel.Conditions = cls.list_of_blocks[6]",
"title": ""
},
{
"docid": "7bdf19fdb00d086df4743fa94346cb89",
"score": "0.53285646",
"text": "def divide_blocks(\n blocks: List[int],\n world_size: int) -> Dict[int, List[int]]:\n if len(blocks) < world_size:\n raise Exception(\"do not have enough blocks to divide\")\n results = {}\n tmp_queue = {}\n for i in range(world_size):\n results[i] = []\n tmp_queue[i] = 0\n indexes = range(len(blocks))\n blocks_with_indexes = dict(zip(indexes, blocks))\n blocks_with_indexes = dict(sorted(blocks_with_indexes.items(),\n key=lambda item: item[1],\n reverse=True))\n for i, block in blocks_with_indexes.items():\n rank = sorted(tmp_queue, key=lambda x: tmp_queue[x])[0]\n results[rank].append(i)\n tmp_queue[rank] = tmp_queue[rank] + block\n\n for i, indexes in results.items():\n results[i] = sorted(indexes)\n return results",
"title": ""
},
{
"docid": "e6b01d5ef054d70921549ee129f10f18",
"score": "0.532244",
"text": "def set_block(self, block: Block, x: int, y: int, z: int):\n if not self.inside(x, y, z):\n raise OutOfBoundsCoordinates('X Y and Z must be in range of 0-15')\n index = y * 256 + z * 16 + x\n self.blocks[index] = block",
"title": ""
},
{
"docid": "ac0d6a951bd018fa0725c314f0504333",
"score": "0.53091866",
"text": "def split(self):\n # Prepare a site list in case we need it\n siteWhitelist = self.initialTask.siteWhitelist()\n siteBlacklist = self.initialTask.siteBlacklist()\n self.sites = makeLocationsList(siteWhitelist, siteBlacklist)\n\n for block in self.validBlocks(self.initialTask):\n parentList = {}\n parentFlag = False\n if self.initialTask.parentProcessingFlag():\n parentFlag = True\n parentList[block[\"Name\"]] = block['Sites']\n\n self.newQueueElement(Inputs={block['Name']: block['Sites']},\n ParentFlag=parentFlag,\n ParentData=parentList,\n NumberOfLumis=block[self.lumiType],\n NumberOfFiles=block['NumberOfFiles'],\n NumberOfEvents=block['NumberOfEvents'],\n Jobs=ceil(float(block[self.args['SliceType']]) /\n float(self.args['SliceSize'])),\n ACDC=block['ACDC'],\n NoInputUpdate=self.initialTask.getTrustSitelists().get('trustlists'),\n NoPileupUpdate=self.initialTask.getTrustSitelists().get('trustPUlists')\n )",
"title": ""
},
{
"docid": "78f9646a4a44b69dbfe1762d95915bf9",
"score": "0.53084534",
"text": "def generate_random_block(self):\n # create random 10 x 10 block with each cell in shape of (1, (255,255,255))\n block1 = [[(randrange(2), (random.choice([0, 255]),\n random.choice([0, 255]),\n random.choice([0, 255])))\n for _ in range(self.rows // 2)]\n for _ in range(self.rows // 2)]\n # reverse the above list (horizontal mirror)\n block2 = [list(reversed(x)) for x in block1]\n # vertical mirror of block1\n block3 = [x for x in reversed(block1)]\n # vertical mirror of block2\n block4 = [x for x in reversed(block2)]\n # join all 4 lists into 1 list\n block = [x + y for x, y in list(zip(block1, block2))\n ] + [x + y for x, y in list(zip(block3, block4))]\n return block",
"title": ""
},
{
"docid": "0e96e3f953999d2efa7779252bac6926",
"score": "0.52998286",
"text": "def handle_blocks(self, db, p):\n\n\t\t# Fill code to get file name and blocks from packet\n\t\tprint \"\\t- Retrieving file name and blocklist from packet...\"\n\t\tfname = p.getFileName()\n\t\tblocklist = p.getDataBlocks()\n\n\t\t# Fill code to add blocks to file inode\n\t\tif db.AddBlockToInode(fname, blocklist):\n\t\t\tprint \"\\t- Succesfully added blocks to MDS!\"\n\t\telse:\n\t\t\tprint \"\\t- An error ocurred when trying to add blocks to MDS...\"",
"title": ""
},
{
"docid": "b80eece6062c9c70db5ea47e65561ca5",
"score": "0.5298544",
"text": "def update(self):\n self.center_x = self.block.center_x\n blocks = arcade.check_for_collision_with_list(\n self, self.block.game.blocks\n )\n for block in blocks:\n if block != self.block:\n self.remove_from_sprite_lists()\n return",
"title": ""
},
{
"docid": "ea28e1816eff4d553067f4cb35fc8df0",
"score": "0.52937776",
"text": "def place_stones(surface):\n for i in range(len(game_map)):\n for j in range(len(game_map[i])):\n if game_map[i][j] == Constants.STONE:\n surface.blit(Constants.Assets.STONE_IMG, (Constants.BLOCK_SIZE * i, Constants.BLOCK_SIZE * j))",
"title": ""
},
{
"docid": "aa53f47cb18df88ac5a706e5642df929",
"score": "0.52897704",
"text": "def estimate_coordinates(self):\n #first, find the abs block and make sure there is only one\n abs_inds = self.find_abs_blocks()\n assert len(abs_inds) > 0, \"did not find any absolute blocks in self.bd_parent.blocklist\"\n assert len(abs_inds) == 1, \"found more than one absolute blocks in self.bd_parent.blocklist\"\n\n #I want to be able to undo this if I need to\n backup_list = copy.copy(self.bd_parent.blocklist)\n\n\n abs_block = self.bd_parent.blocklist.pop(abs_inds[0])\n sorted_blocks = [abs_block]\n\n relative_list = [block.params['relative_block'] for block in self.bd_parent.blocklist]\n\n #now,how to do the sortin?\n #\n # - each block can only have one relative block, so it shouldn't be too bad\n\n # for each item in sorted_blocks, search for any block that is\n # relative to it and add that block to the list\n\n i = 0\n\n while i < len(sorted_blocks):\n curname = sorted_blocks[i].name\n try:\n next_index = relative_list.index(curname)\n relative_list.pop(next_index)\n curblock = self.bd_parent.blocklist.pop(next_index)\n sorted_blocks.append(curblock)\n except ValueError:\n i += 1\n\n\n if len(self.bd_parent.blocklist) > 0:\n #sorting failed\n self.bd_parent.blocklist = backup_list\n print('sorting failed')\n return\n else:\n #blocks are correctly sorted\n self.bd_parent.blocklist = sorted_blocks\n #!#!#: sort the blocks in the list box here\n #self.sort_list_box()\n\n\n for block in self.bd_parent.blocklist:\n #block.set_params_as_attrs()#<--- this should be done before calling this method\n if block.params['position_type'] == 'absolute':\n coords_str = block.params['abs_coordinates']\n coords_str_list = coords_str.split(',')\n coords_str_list = [item.strip() for item in coords_str_list]\n coords = [float(item) for item in coords_str_list]\n assert len(coords) == 2, \"Problem with abs coords: %s\" % coords_str\n block.coordinates = np.array(coords)\n else:\n rel_name = block.params['relative_block']\n rel_block = self.find_block(rel_name)\n rel_distance = float(block.params['relative_distance'])\n direction = block.params['relative_direction']\n dir_dict = {'right of':np.array([1.0,0]), \\\n 'left of':np.array([-1.0,0]), \\\n 'above of':np.array([0.0,1.0]), \\\n 'below of':np.array([0.0,-1.0]), \\\n }\n shift = rel_distance*dir_dict[direction]\n if hasattr(block, 'xshift') and block.xshift:\n shift += np.array([block.xshift,0])\n if hasattr(block, 'yshift') and block.yshift:\n shift += np.array([0,block.yshift])\n\n block.coordinates = rel_block.coordinates + shift\n\n print('block name: %s' % block.name)\n print(' coordinates: ' + str(block.coordinates))",
"title": ""
},
{
"docid": "009864ccdb4daa5ab5eea6510f464a3d",
"score": "0.5277327",
"text": "def fill(self, fill_block):\n\n for block in self.blocks:\n block.id = fill_block.id\n block.data = fill_block.data",
"title": ""
},
{
"docid": "6051d08d0d004e02c35c84208db5aea4",
"score": "0.5240025",
"text": "def loadBlockers(self):\n loadAll = base.config.GetBool('golf-all-blockers',0)\n self.createLocatorDict()\n self.blockerNums = self.holeInfo['blockers']\n\n for locatorNum in self.locDict:\n if locatorNum in self.blockerNums or loadAll:\n locator = self.locDict[locatorNum]\n locatorParent = locator.getParent()\n locator.getChildren().wrtReparentTo(locatorParent)\n else:\n self.locDict[locatorNum].removeNode()\n\n self.hardSurfaceNodePath.flattenStrong()",
"title": ""
},
{
"docid": "76a26414aeaed69df02a7535f44ef2dc",
"score": "0.5239784",
"text": "def initialize_list_of_blocks(cls):\n number_of_blocktypes = len([a for a in dir(BlockTypes) if not a.startswith('__')])\n for _ in range(number_of_blocktypes):\n cls.list_of_blocks.append([])",
"title": ""
},
{
"docid": "ebea86e9765182b58ad7cff9bbb23566",
"score": "0.52254945",
"text": "def update(self, dt):\n for i in range(self.pos, min(self.pos + self.length, len(self.blockmap))):\n for block in self.blockmap[i]:\n block.update(dt)\n\n # once the leftmost block goes out of frame, shift the frame over one\n if (self.blockmap[self.pos][0].right() <= 0):\n self.pos += 1",
"title": ""
},
{
"docid": "4f0017cb6b59b48e10ebeda4889f96eb",
"score": "0.52136207",
"text": "def announce_new_block(block):\n for peer in peers:\n url = \"{}add_block\".format(peer)\n headers = {'Content-Type': \"application/json\"}\n requests.post(url,\n data=json.dumps(block.__dict__, sort_keys=True),\n headers=headers)",
"title": ""
},
{
"docid": "31c6733faed6c8084cba98d51cf5925d",
"score": "0.5209911",
"text": "def xy2block(x, y):\n return [((x - 1) // 3) * 3 + 1,((y - 1) // 3) * 3 + 1]",
"title": ""
},
{
"docid": "158525f3e699a191d42ca5cc565295f4",
"score": "0.5205631",
"text": "def place(self, row_list: List[Row], start_row: int = 0):\n # Prologue. Split vertical elements into left and right columns\n vertical_left = []\n vertical_right = []\n right = True\n\n for decoder in self.decoders:\n target = vertical_right if right else vertical_left\n target.append(decoder)\n right = not right\n\n final_rows = []\n\n # Act 1. Place Left Vertical Elements\n current_row = start_row\n for decoder in vertical_left:\n current_row = decoder.place(row_list, start_row)\n\n final_rows.append(current_row)\n\n # Act 2. Place Horizontal Elements\n current_row = start_row\n for word in self.words:\n current_row = word.place(row_list, current_row)\n\n Row.fill_rows(row_list, start_row, current_row)\n\n place_clkbuf_alone = False\n last_column = [*self.webufs]\n if len(last_column) == 8:\n place_clkbuf_alone = True\n else:\n last_column.append(self.clkbuf)\n\n while len(last_column) < 8:\n last_column.append(None)\n\n for i in range(8):\n r = row_list[start_row + i]\n if last_column[i] is not None:\n r.place(last_column[i])\n\n if place_clkbuf_alone:\n row_list[start_row].place(self.clkbuf)\n\n Row.fill_rows(row_list, start_row, current_row)\n\n final_rows.append(current_row)\n\n # Act 3. Place Right Vertical Elements\n current_row = start_row\n for decoder in vertical_right:\n current_row = decoder.place(row_list, start_row)\n\n #Row.fill_rows(row_list, start_row, current_row)\n final_rows.append(current_row)\n\n # Epilogue\n max_row = max(*final_rows)\n # Row.fill_rows(row_list, start_row, max_row)\n return max_row",
"title": ""
},
{
"docid": "f52a68e2ee87e8a289faf33fca1410c6",
"score": "0.5200605",
"text": "def block_update(self,directions):\n \"\"\"for pure cellular automata action make sure to not set any blocks but only return new state for this block (use schedule to do stuff that effects other blocks)\"\"\"",
"title": ""
},
{
"docid": "ec03e67ad6dbc9f9b8303195f7a46156",
"score": "0.51959306",
"text": "def placeBlock(self, x, y, z, blockStr):\n x, y, z = self.local2global(x, y, z)\n result = di.setBlock(x, y, z, blockStr)\n if not result.isnumeric():\n print(f\"{TCOLORS['orange']}Warning: Server returned error \"\n f\"upon placing block:\\n\\t{TCOLORS['CLR']}{result}\")\n return result",
"title": ""
},
{
"docid": "b908a7deae2379f8dfc82ef25660ecb8",
"score": "0.518497",
"text": "def _scale_vertical_positions(self, blocks):\n return [block._replace(y=(block.y+0.5)*self.height)\n for block in blocks]",
"title": ""
},
{
"docid": "68c44b36f2e1197125c99cd677a31855",
"score": "0.5180645",
"text": "def draw(self, screen, fade_pct):\n for i in range(self.pos, min(self.pos + self.length, len(self.blockmap))):\n for block in self.blockmap[i]:\n block.draw(screen, fade_pct)",
"title": ""
},
{
"docid": "f977bfe1484a2929d518ebb3160c0f3c",
"score": "0.51754385",
"text": "def load_chain(self, chain):\n for block in chain:\n block = Block.init_from_json(block)\n self.add_block(block)",
"title": ""
},
{
"docid": "dd96fa24efd9ab012ecf855356e894f3",
"score": "0.5171934",
"text": "def run(self, machine_client):\n for b in self.blocks:\n self.tool, self.x, self.y, self.z = b.run(machine_client, self.tool, self.x, self.y, self.z)",
"title": ""
},
{
"docid": "70945e4c09450861f3c715e9317118a5",
"score": "0.51715",
"text": "def place_block(self, placeLocation, placeShape = Shape(1, 1)):\n \n # place the block\n try:\n self._board.put(placeLocation, placeShape)\n except Exception as ex:\n # the preconditions were not fulfilled\n raise ex\n \n # clear rows and columns that have been filled\n self._clear_full()\n \n return self",
"title": ""
},
{
"docid": "202f7699b46db7676321e64665e7a639",
"score": "0.51604927",
"text": "def _write_blocks(blocks: Sequence[Block], file: Path, tag: str = None):\n\n tag = '' if tag is None else tag\n print(f'{tag} saving {len(blocks)}')\n blocks = [b.convert_to_row() for b in blocks]\n with file.open('w+', newline='') as out:\n csv.writer(out, delimiter=',').writerows(blocks)\n return len(blocks)",
"title": ""
},
{
"docid": "cfbf964773b7ebea6123f2796ccd80fc",
"score": "0.5159401",
"text": "def add_block(self,bk):\n self.blocks.append(bk)",
"title": ""
},
{
"docid": "757723aa7b53adcc57a1dc94b94c70e5",
"score": "0.5157452",
"text": "def _update_block_array(self, data):\n data.pop(0)\n for new_block in data:\n # Test if the object has been seen before using its id.\n block_in_list = next((item for item in self.attention_table if item[\"id\"] == new_block[\"id\"]), None)\n if block_in_list:\n # This block is already in the list, update the list\n for key, value in new_block.items():\n block_in_list[key] = value\n else:\n # This block was not in the list, add it\n new_block[\"lh\"] = 0\n new_block[\"at\"] = 0\n new_block[\"include\"] = True\n self.attention_table.append(new_block)\n # Iterate through the blocks and comform the HSV colors to string values.\n for block in self.attention_table:\n if isinstance(block[\"c\"], int):\n block[\"c\"] = self._hsv_to_string(block[\"c\"])\n #self._print_blocks()",
"title": ""
},
{
"docid": "7e420cb718b154f2573519870bc73924",
"score": "0.5155958",
"text": "def add_blocks_to_chain(self):\n if self.wallet.add_a_block_to_chain():\n # checking if validator now:\n if self.wallet.public_key.export_key(format=PUBLIC_KEY_FORMAT) in self.wallet.blockchain.get_validators():\n self.is_validator = True\n else:\n self.is_validator = False\n\n # clearing trees:\n self.ui.transaction_pool_tree.clear()\n self.ui.proposed_blocks_tree.clear()\n\n # update_particle blockchain file and blockchain tree:\n self.update_blockchain_file()\n with open(f\"storage{SLASH_SIGN}blockchain.json\", \"r\") as blockchain_file:\n self.put_json_chain_on_tree(blockchain_file)\n\n qtc.QTimer.singleShot(10000, self.add_blocks_to_chain)",
"title": ""
},
{
"docid": "bc5419c60e9f785509d89499dc4766c8",
"score": "0.5151933",
"text": "def nex_blocks(session):\n def gen_blocks(note_tuple):\n # index, note\n return Block(tile_positions[note_tuple[1][0], note_tuple[0]], note_tuple[1][0], note_tuple[1][1])\n return list(map(gen_blocks, enumerate(islice(session, 4))))",
"title": ""
},
{
"docid": "d6b43bf81b16c27002db0d0aaa99a9a9",
"score": "0.51518893",
"text": "def _grab_blocks(self):\n r = BlockRecordRenderer()\n mistune.markdown(self.source, renderer=r)\n \n blocks = {}\n for block in r.blocks:\n name, content = self._parse_raw_block(block)\n blocks[name] = content \n self.blocks = blocks",
"title": ""
},
{
"docid": "4e70436570df06053fc618a0fe321405",
"score": "0.5142677",
"text": "def setup_world(self):\n\t\tself.world = []\n\t\tfor y in range(10):\n\t\t\tcharRow = []\n\t\t\tfor x in range(10):\n\t\t\t\tcharRow.insert(x,'#')\n\t\t\tself.world.insert(y,charRow)",
"title": ""
},
{
"docid": "b28dc83044d2f8d258ad0fa6c4cd9403",
"score": "0.5140945",
"text": "def buildHighlightBlocklist(self):\n places = self.placelist()\n placesPath = os.path.join(self.dpath, 'geoblocks.places')\n highlightBlocklistPath = os.path.join(self.dpath, 'highlight.ubidz')\n self.logf('%s[%r] -> %s', placesPath, places, highlightBlocklistPath)\n if self.options.dryrun:\n return\n filterPlacesToUbidList(placesPath, places, highlightBlocklistPath)",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="737"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">7b201d064d8a0dad0a690c43f7b08b79</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Return a randomly shuffled copy of a list</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "e9b8bf65db11c18fca07b2f27e6b7946",
"score": "0.80562687",
"text": "def shuffle(input_list):\n output_list = list(input_list)\n random.shuffle(output_list)\n return output_list",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "6ec0efb610d74977bfdf18b035d06be7",
"score": "0.8064399",
"text": "def sample_list(l):\n outl = l[:]\n random.shuffle(outl)\n return outl[0]",
"title": ""
},
{
"docid": "3b6a6728b7215bc758b15f145b70a21b",
"score": "0.795556",
"text": "def partial_shuffle_orig(ls, randomness):\n copy = ls[:]\n for i in range(len(copy)):\n if random.random() < randomness:\n copy[i] = random.choice(ls)\n return copy",
"title": ""
},
{
"docid": "d37460a638706d50b462de9c28b75f17",
"score": "0.7944599",
"text": "def shuffle(self):\n retArr = self.baseList.copy()\n for i in range(len(retArr)):\n newPos = random.randint(0, i)\n retArr[i], retArr[newPos] = retArr[newPos], retArr[i]\n\n return retArr",
"title": ""
},
{
"docid": "ef3343c88fad3c046fb85af18e768539",
"score": "0.7787292",
"text": "def shuffle(self):\n randomized_copy_list = list(self)\n shuffle(randomized_copy_list)\n return self.__class__(''.join(randomized_copy_list),Info=self.Info)",
"title": ""
},
{
"docid": "73cbb1611196bbe4415e8958895927ae",
"score": "0.76628226",
"text": "def shuffler_in_place(lst):\n\n counter = 0\n while counter < len(lst):\n\n new_index = random_num(counter,len(lst))\n shift = lst[counter] \n lst[counter] = lst[new_index]\n lst[new_index] = shift\n counter += 1\n return lst",
"title": ""
},
{
"docid": "75f7d216c9e996d3345df35432d89a28",
"score": "0.7647371",
"text": "def shuffle(self):\n shuffle(self) # shuffle list from random module",
"title": ""
},
{
"docid": "10abb816dff29d937a09e5798345fe88",
"score": "0.76311815",
"text": "def partial_shuffle(ls, randomness, reverse=True):\n copy = ls[:] if reverse and random.getrandbits(1) else ls[::-1]\n for i in range(len(copy)):\n if random.random() < randomness:\n j = random.choice(range(i, len(copy)))\n if i != j:\n tmp = copy[i]\n copy[i] = copy[j]\n copy[j] = tmp\n return copy",
"title": ""
},
{
"docid": "b49f0fbce7cf2c30a2f4b3c20222748f",
"score": "0.7622539",
"text": "def partial_shuffle_sample(ls, randomness):\n num_shuffle = int(math.ceil(randomness * len(ls)))\n #print(num_shuffle)\n indicies = random.sample(range(len(ls)), num_shuffle)\n #print(indicies)\n indicies_shuffled = indicies[:]\n random.shuffle(indicies_shuffled)\n #print(indicies_shuffled)\n copy = ls[:]\n for i in range(len(indicies)):\n copy[indicies_shuffled[i]] = ls[indicies[i]]\n return copy",
"title": ""
},
{
"docid": "c1975befb700bc2b6a6245da700461f7",
"score": "0.7477695",
"text": "def full_shuffle(ls, randomness=True):\n if not randomness: return ls[:] # for compatiblitly with other shuffles\n return random.sample(ls, k=len(ls))",
"title": ""
},
{
"docid": "eb2ac7fa9744aaa92d7109a93b4ae84d",
"score": "0.74376494",
"text": "def shuffled(seq):\n seq = list(seq)\n random.shuffle(seq)\n return seq",
"title": ""
},
{
"docid": "95092a99b9cc277701938b159ec836ae",
"score": "0.74336004",
"text": "def shuffle(self):\n\n\n current = self.head\n newList = SingleList()\n listLen = 0\n\n while current.next is not None:\n listLen += 1\n current = current.next\n\n current = SingleList()\n current.head = copy.deepcopy(self.head)\n\n while current.head is not None:\n randIndex = random.randint(0, listLen)\n\n newList.addNode(copy.deepcopy(current.get(randIndex)))\n current.remove(randIndex)\n\n listLen -= 1\n self.head = newList.head",
"title": ""
},
{
"docid": "b73ae22a2229c001324d51a5053828ee",
"score": "0.73684794",
"text": "def shuf(self, index_list):\n random.shuffle(index_list)\n return index_list",
"title": ""
},
{
"docid": "a2db43abc665eea68735d54139bd5481",
"score": "0.73663586",
"text": "def shuffle(items):\n n = len(items)\n while n > 1:\n k = randrange(n) # 0..n-1\n n = n - 1\n items[k], items[n] = items[n], items[k]\n return items",
"title": ""
},
{
"docid": "d3a42546f8f472e43510bc4ec4c0a93a",
"score": "0.7360941",
"text": "def shuffle(): \r\n return",
"title": ""
},
{
"docid": "e09147ec6c78a002d29556b2d3740371",
"score": "0.72903806",
"text": "def shuffle(self):\r\n size = self.n - 1\r\n s = list(self.arr)\r\n while size != -1:\r\n idx = random.randint(0,size)\r\n s.append(s.pop(idx))\r\n size -= 1\r\n return s",
"title": ""
},
{
"docid": "4a9f705109f0ef71542e9ad4c5fb03e0",
"score": "0.7209054",
"text": "def shuffle(L, seed):\n\n L = list(L).copy()\n for i in range(len(L)):\n hash_input = bytearray(str(seed)+\",\"+str(i),'utf-8')\n hash_value = sha256(hash_input)\n j = hash_value % (i+1) # random modulo (i+1)\n L[i], L[j] = L[j], L[i] # swap\n return L",
"title": ""
},
{
"docid": "ba8fb2b56be410d3b92feec72e15ca44",
"score": "0.718217",
"text": "def shuffle(self, list_to_shuffle):\n\t\treturn list_to_shuffle[0]",
"title": ""
},
{
"docid": "a6f1b33b926a739a1ed6c088c6252664",
"score": "0.7146848",
"text": "def shuffle(self):",
"title": ""
},
{
"docid": "f1b6b277a6c0ab03e85215d5ee7a60c3",
"score": "0.7124117",
"text": "def my_random_permutation(data: List) -> List:\n\n items = data.copy()\n permutation = []\n\n for i in range(len(data)):\n choice = random.randint(0, len(items) - 1)\n permutation.append(items[choice])\n items.pop(choice)\n \n return permutation",
"title": ""
},
{
"docid": "da3b610e0838cb40a107ff6cd1948de4",
"score": "0.71192724",
"text": "def shuffle(self):\n new_nums = []\n for i in range(len(self.nums)):\n new_nums.append(self.nums[i])\n for i in range(len(self.nums)):\n rand = randint(0, len(new_nums)-1)\n new_nums[i], new_nums[rand] = new_nums[rand], new_nums[i]\n return new_nums",
"title": ""
},
{
"docid": "d19f0a067d8eb2f65f8547b0a2b887a8",
"score": "0.7104075",
"text": "def _random_shuffle(*lsts):\n permutation = np.random.permutation(len(lsts[0]))\n shuffled = []\n for lst in lsts:\n shuffled.append((np.array(lst)[permutation]).tolist())\n return tuple(shuffled)",
"title": ""
},
{
"docid": "d03414a248af4cac0db90120ddc1a64b",
"score": "0.7080016",
"text": "def shuffled(iterable):\r\n items = list(iterable)\r\n random.shuffle(items)\r\n return items",
"title": ""
},
{
"docid": "5daace4020614da69e2db2be336ee393",
"score": "0.70366865",
"text": "def random_permutation(data):\n d = data[:]\n random.shuffle(d)\n return d",
"title": ""
},
{
"docid": "f9f3a9f0a88e85613e9943ec5b0703df",
"score": "0.69920164",
"text": "def sort_random(self, netlist):\n\n\t\tshuffle(netlist)\n\n\t\treturn netlist",
"title": ""
},
{
"docid": "83f720ee725fedef50d2e78f84f444a1",
"score": "0.697935",
"text": "def shuffled(array):\n array_copy = copy.copy(array)\n random.shuffle(array_copy)\n return array_copy",
"title": ""
},
{
"docid": "980ea3a1deac07a19dbe108fece88162",
"score": "0.69748795",
"text": "def shuffle_random_list(self):\n self.randomized_list = deque()\n \n for item in self.original_list:\n self.randomized_list.append(item)\n \n random.shuffle(self.randomized_list)\n\n \"\"\"If the next 2 elements to be popped are the same as the 2 previously popped elements, then reshuffle random list.\"\"\"\n while(self.randomized_list[len(self.randomized_list) - 1] == self.last_item or \n self.randomized_list[len(self.randomized_list) - 1] == self.penultimate_item or\n self.randomized_list[len(self.randomized_list) - 2] == self.last_item or \n self.randomized_list[len(self.randomized_list) - 2] == self.penultimate_item):\n self.shuffle_random_list()\n\n self.last_item = self.randomized_list[0]\n self.penultimate_item = self.randomized_list[1]",
"title": ""
},
{
"docid": "61b621093eae47f62a018c5600cadcbf",
"score": "0.6971611",
"text": "def random_sample(data_list, sample_size):\n data = list(data_list)\n newlist = []\n counter = 0 \n while counter < sample_size:\n num = random.choice(data)\n newlist.append(num)\n data.pop(data.index(num))\n counter = counter + 1\n return newlist",
"title": ""
},
{
"docid": "962ab95099bfbd647fd83fda0c5c52d2",
"score": "0.6962563",
"text": "def _rad_shuffle_ ( self ) :\n result = self.emptyClone ( dsID () )\n \n indices = [ i for i in range( len ( self ) ) ] \n random.shuffle ( indices )\n\n while indices :\n i = indices.pop()\n result.add ( self[i] )\n \n return result",
"title": ""
},
{
"docid": "85fee38371df4a0098028ae311062340",
"score": "0.69569623",
"text": "def shuffle(self):\r\n shuffle(self.cardList)#randomize the order of cards\r",
"title": ""
},
{
"docid": "013e890c772dc8e8f024407a04e5ec51",
"score": "0.693422",
"text": "def shuffle(self) -> List[int]:\n n = len(self.nums)\n ans = self.nums[:]\n for i in range(len(self.nums)):\n rand = randint(i, n)\n ans[i], ans[rand] = ans[rand], ans[i]\n return ans\n # sorted(self.ori_nums, key=lambda x: random.random())",
"title": ""
},
{
"docid": "909733ad8e75bdd81591ff17baf7d82d",
"score": "0.69330114",
"text": "def shuffled(iterable):\n items = list(iterable)\n random.shuffle(items)\n return items",
"title": ""
},
{
"docid": "a60560d73fd02a73842d36ad8b0e4077",
"score": "0.6920275",
"text": "def random_sample(lst, num, msg_if_cut=\"\"):\n\n try:\n # Remove redundancies.\n lst = list(set(lst))\n except:\n # Because someitems lst element may be unhashable.\n pass\n\n # Shuffle the list.\n random.shuffle(lst)\n if num < len(lst):\n # Keep the top ones.\n lst = lst[:num]\n if msg_if_cut != \"\":\n log(msg_if_cut)\n return lst",
"title": ""
},
{
"docid": "f7861bddfee433631060bed0994d5789",
"score": "0.68900055",
"text": "def shuffle(self) -> List[int]:\n res = []\n while self.curvec:\n idx = randint(0, len(self.curvec) - 1)\n res.append(self.curvec.pop(idx))\n self.curvec = res\n return self.curvec",
"title": ""
},
{
"docid": "d22a15df24326afa911e2ff770961eb5",
"score": "0.6885702",
"text": "def shuffled(values):\n return apply_perm(values, random_permutation(len(values)))",
"title": ""
},
{
"docid": "fa685f96c1710ad52dd42c2af2a8a0c2",
"score": "0.6869177",
"text": "def shuffle(self, x):\n indices = np.arange(len(x))\n np.random.shuffle(indices)\n x = x[indices]\n return x",
"title": ""
},
{
"docid": "cbf59bb54aa6e058509fd5c7aa76068c",
"score": "0.6866491",
"text": "def scramble(b):\n assert type(b) == list, repr(b)+' is not a list'\n\n # Start from the beginning\n i = 0\n while i < len(b):\n size = len(b)-i\n pos = int(random.random()*size)\n _swap(b,i,i+pos)\n i = i+1",
"title": ""
},
{
"docid": "63012089a5d69d267fec6985f1945182",
"score": "0.68551743",
"text": "def shuffle(s):\n return random.sample(s, len(s))",
"title": ""
},
{
"docid": "c30d601b5ed37e115ecdeecc6ac5f7ba",
"score": "0.68506515",
"text": "def shuffle(self):\n\n for i in range(len(self.array)):\n newIdx = random.randrange(i, len(self.array))\n self.array[i], self.array[newIdx] = self.array[newIdx], self.array[i]\n\n return self.array\n\n # time O(n)\n # space O(1)",
"title": ""
},
{
"docid": "ce95c0b4a288fc259d44bc63b1d75409",
"score": "0.68498605",
"text": "def packet_shuffle(list, packet_size):\n if packet_size >= 3 and packet_size <= len(list):\n lists = list_split(list, packet_size)\n shuffled_lists = []\n for sublist in lists:\n shuffled_lists.append(randshuffle(sublist))\n\n big_list = []\n for shuffled_list in shuffled_lists:\n big_list.extend(shuffled_list)\n return big_list",
"title": ""
},
{
"docid": "b86eae22613f368668a2242035b073a4",
"score": "0.6843578",
"text": "def getRandomOrdering():\n candidates_copy = list(CANDIDATES)\n random.shuffle(candidates_copy)\n return candidates_copy",
"title": ""
},
{
"docid": "467c38802e2b3c9e132898237210bfe4",
"score": "0.6812863",
"text": "def shuffle(self):\n \n for i in range(len(self.nums)):\n ind = random.randint(i, len(self.ori)-1)\n temp = self.nums[i]\n self.nums[i] = self.nums[ind]\n self.nums[ind] = temp\n \n return self.nums",
"title": ""
},
{
"docid": "edc40c907764e1278cd366495296e1e8",
"score": "0.67953336",
"text": "def shuffle(self):\n nums = self.nums\n for i in range(len(nums) - 1, -1, -1):\n j = self.rand.randint(0, i)\n nums[i], nums[j] = nums[j], nums[i]\n return nums",
"title": ""
},
{
"docid": "99578723e9aa68315193468d1877a68c",
"score": "0.677274",
"text": "def shuffle(self):\n pass",
"title": ""
},
{
"docid": "1f3f33f63a2b4a609591c59d0754cd81",
"score": "0.67662",
"text": "def random_list_value(list):\r\n return random.choice(list)",
"title": ""
},
{
"docid": "e583f0afbd8c97362ed92da4bc48b9b8",
"score": "0.675568",
"text": "def shuffle(self) -> List[int]:\n for i in range(len(self.arr1)):\n ind = random.randrange(i, len(self.arr1))\n self.arr1[i], self.arr1[ind] = self.arr1[ind], self.arr1[i]\n return self.arr1",
"title": ""
},
{
"docid": "f84c93c88c8af394a7ef19ea84cc7e2f",
"score": "0.6752759",
"text": "def scramble_list( self, the_list ) :\n new_order = []\n i = 0\n while i < len( the_list ) :\n this_index = self.wichmann.next( 1, 32 ) % len( the_list )\n if this_index not in new_order :\n new_order.append( this_index )\n i += 1\n\n out_list = []\n for i in range( len( the_list ) ) :\n out_list.append( the_list[ new_order[ i ] ] )\n\n return out_list",
"title": ""
},
{
"docid": "9f211e0ae50d76da9bb5fb6df184e8a0",
"score": "0.6732564",
"text": "def shuffle(self):\n shuffleNums = list(self.workingNums)\n arrSize = len(shuffleNums)\n for i in xrange(arrSize):\n anchor = random.randint(i, arrSize - 1)\n shuffleNums[i], shuffleNums[anchor] = shuffleNums[anchor], shuffleNums[i]\n return shuffleNums",
"title": ""
},
{
"docid": "13ce957ca705e597869f51d214ad767b",
"score": "0.67192936",
"text": "def __init__(self, original_list):\n self.original_list = original_list\n self.last_item = None\n self.penultimate_item = None\n \n self.shuffle_random_list()\n\n self.last_item = self.randomized_list[0]\n self.penultimate_item = self.randomized_list[1]",
"title": ""
},
{
"docid": "551dafdd41fd79157a392f4c81bd35e2",
"score": "0.67157155",
"text": "def shuffle(values):\n perm = random_permutation(len(values))\n v = copy(values)\n for (i, pi) in enumerate(perm):\n values[i] = v[pi]",
"title": ""
},
{
"docid": "f421d5af155670d3179231721ce570ba",
"score": "0.6704604",
"text": "def shuffle(lst1, lst2):\n combined = list(zip(lst1, lst2))\n np.random.shuffle(combined)\n (shuffled_lst1, shuffled_lst2) = zip(*combined)\n return [list(shuffled_lst1), list(shuffled_lst2)]",
"title": ""
},
{
"docid": "279528457bc516fbfdef5be7556a1ba3",
"score": "0.66677755",
"text": "def shuffle(self):\n random.shuffle(self)",
"title": ""
},
{
"docid": "c5fc225631cbfe0a6f7b3966e3472ae2",
"score": "0.66634274",
"text": "def randomRearrange(yourList):\n \n import random\n\n\n for i in yourList:\n oldPos = yourList.index(i) #get the original position of the element in the list\n newPos = random.randint(0,(len(yourList)-1)) #get the new random position that the element will be moved to\n \n yourList[oldPos], yourList[newPos] = yourList[newPos], yourList[oldPos] #swapping the elements position \n\n return(yourList)",
"title": ""
},
{
"docid": "fe902b8b5c5ef6f2fb86e13624c41c65",
"score": "0.6642214",
"text": "def shuffle(self):\n if len(self.shuffle_arr) == 0:\n return []\n for ele in range(len(self.shuffle_arr)):\n idx = random.randint(0, len(nums) - 1)\n temp = self.shuffle_arr[ele]\n self.shuffle_arr[ele] = self.shuffle_arr[idx]\n self.shuffle_arr[idx] = temp\n return self.shuffle_arr",
"title": ""
},
{
"docid": "6f1db8fecd2ee2dd38819892461629f6",
"score": "0.6631214",
"text": "def shuffle(data):\n data_c = data.copy()\n np.random.shuffle(data_c)\n return data_c",
"title": ""
},
{
"docid": "bdb812633d0c0a9c5a9d25a861d80235",
"score": "0.66234285",
"text": "def shuffle_line(a_list, percent):\n n = int(math.floor(percent * len(a_list)))\n if n < 2 and len(a_list) >= 2:\n n = 2\n idx = range(len(a_list))\n random.shuffle(idx)\n idx = idx[:n]\n mapping = dict((idx[i], idx[i - 1]) for i in range(n))\n return [a_list[mapping.get(x, x)] for x in range(len(a_list))]",
"title": ""
},
{
"docid": "3f824a3032f7d0cb425c07d02aac7d93",
"score": "0.66158575",
"text": "def shuffle(self) -> List[int]:\n try:\n self._current = next(self._perm)\n except:\n self._perm = permutations(self._orig)\n return self.shuffle()\n return self._current",
"title": ""
},
{
"docid": "8ff50128b25fe4c9f3bb02a71581cf23",
"score": "0.6591386",
"text": "def sample_list(list, fraction):\n return random.sample(list, int(len(list) * fraction))",
"title": ""
},
{
"docid": "35e392f9b2ed50bf26194aaa7cf1364e",
"score": "0.65794927",
"text": "def shuffle_and_extend(itemlist, new_nominated):\r\n shuffle(itemlist)\r\n new_nominated.extend(itemlist)",
"title": ""
},
{
"docid": "02fe891c361c8963d71270df1950daa3",
"score": "0.65787566",
"text": "def random_sampling(alist, num):\n sampling = []\n len_list = len(alist)\n assert len_list, 'len_list is zero!'\n indices = np.arange(len_list)\n np.random.shuffle(indices)\n\n for i in range(num):\n item = alist[indices[i % len_list]]\n sampling.append(item)\n return sampling",
"title": ""
},
{
"docid": "e9a72d547a08b877f71f7f7094ac7def",
"score": "0.65742636",
"text": "def shuffle(self):\n self.cur_nums = sorted(self.cur_nums, key=lambda _: random.random())\n return self.cur_nums",
"title": ""
},
{
"docid": "9ee9d2191f3e2725007371bbcd49155c",
"score": "0.65671694",
"text": "def shuffle(self) -> List[int]:\n shuffle_nums = self.nums[:]\n random.shuffle(shuffle_nums)\n return shuffle_nums",
"title": ""
},
{
"docid": "a70cf8afb6e04c1330ed4fd97c0d8e16",
"score": "0.65542835",
"text": "def shuffle_aligned_list(data):\n num = data[0].shape[0]\n p = np.random.permutation(num)\n return [d[p] for d in data]",
"title": ""
},
{
"docid": "78f9582bff0942a07f5cb6244cb3abbc",
"score": "0.6553356",
"text": "def shuffle_inner_lists(lists):\n for l in lists:\n random.shuffle(l)",
"title": ""
},
{
"docid": "b8b43de6034e69ce6bde2a98acb3ab8b",
"score": "0.65508467",
"text": "def shuffle(self) -> List[int]:\n shuffle_nums = self.nums[:]\n n = len(self.nums)\n \n for i in range(n):\n rand = random.randrange(i, n)\n shuffle_nums[i], shuffle_nums[rand] = shuffle_nums[rand], shuffle_nums[i]\n \n return shuffle_nums",
"title": ""
},
{
"docid": "c8c032f75780fe4694ecc5096cdce506",
"score": "0.65488297",
"text": "def shuffle(deck):\n\n shuffled_deck = random.sample(deck, len(deck))\n\n return shuffled_deck",
"title": ""
},
{
"docid": "3cbfee4b07eb3b19ff0b5ff1357cee61",
"score": "0.65483594",
"text": "def getRandom(self):\n import random\n return random.choice(self.list)",
"title": ""
},
{
"docid": "c4e19f446f8b2ef42dcb83d4ef40bff6",
"score": "0.6539115",
"text": "def zipShuffleReturn(listname):\r\n listToReturn = map(list, zip(*listname))\r\n shuffle(listToReturn[0])\r\n listToReturn = map(list, zip(*listToReturn))\r\n return listToReturn",
"title": ""
},
{
"docid": "95880bc62538efd6d90cdc22bd70e599",
"score": "0.65379816",
"text": "def shuffle(wallpapers):\n\n return random.shuffle(wallpapers)\n\n \"\"\"END OF SHUFFLE\"\"\"",
"title": ""
},
{
"docid": "4d6c079215b33e3ae4da453c4de9d95b",
"score": "0.65235853",
"text": "def shuffle(self):\n\t\tfor i in range(len(self.deck)):\n\t\t\to = random.randint(i)\n\t\t\tself.deck[i], self.deck[o] = self.deck[o], self.deck[i]",
"title": ""
},
{
"docid": "68b992b12070d0ee4e886e5c1d9d811e",
"score": "0.6506014",
"text": "def shuffle_data(self,data):\n n_sample = data[0].shape[0]\n index = np.random.permutation(n_sample)\n return [d[index] for d in data]",
"title": ""
},
{
"docid": "24251d64300c03e33b0ab6f510408517",
"score": "0.64981204",
"text": "def shuffle(self):\n random.shuffle(self.cards)",
"title": ""
},
{
"docid": "24251d64300c03e33b0ab6f510408517",
"score": "0.64981204",
"text": "def shuffle(self):\n random.shuffle(self.cards)",
"title": ""
},
{
"docid": "70b023c7054031b630bb327940f34003",
"score": "0.64831775",
"text": "def scramble_mutation(arr):\n if arr is None or len(arr) < 2:\n return ValueError()\n \n n = len(arr)\n start_point = randint(0, n//2)\n end_point = randint(n//2, n-1)\n\n shuffled_subset = sample(arr[start_point:end_point], end_point-start_point)\n\n for i in range(start_point, end_point):\n arr[i] = shuffled_subset[i-start_point]",
"title": ""
},
{
"docid": "f27888e34e0df0fdf621dd4f62bfc198",
"score": "0.6474268",
"text": "def shuffle(self) -> List[int]:\n \n for i in range(self.nums_len - 1, -1 ,-1):\n \n pos = random.randint(0,i)\n self.nums[i],self.nums[pos] = self.nums[pos],self.nums[i]\n self.pos_list[i],self.pos_list[pos] = self.pos_list[pos],self.pos_list[i]\n \n \n return self.nums",
"title": ""
},
{
"docid": "e811290c828cd561d5ad420d83e1a401",
"score": "0.64742476",
"text": "def shuffle(arr):\n\tn = len(arr)\n\tfor j in range(n-1):\n\t\tk = random.randint(j,n-1)\n\t\tarr[j], arr[k] = arr[k], arr[j]\n\treturn arr",
"title": ""
},
{
"docid": "56ee78388b185f2074ff08825acbc955",
"score": "0.64731663",
"text": "def shuffle(self):\n\n random.shuffle(self.cards)\n\n #n = self._size()\n #cards = self.cards\n #for i,card in enumerate(cards):\n # pos = randrange(i,n)\n # cards[i] = cards[pos]\n # cards[pos] = card",
"title": ""
},
{
"docid": "d58413de97c4cec74c6106e1ba7b51a4",
"score": "0.64626485",
"text": "def prod_discrete_random_mutation(x, list_of_list_of_items):\n ret = [copy(elem) for elem in x]\n change_idx = np.random.choice(len(x))\n change_list = copy(list_of_list_of_items[change_idx])\n change_list.remove(x[change_idx])\n change_val = np.random.choice(change_list)\n ret[change_idx] = change_val\n return ret",
"title": ""
},
{
"docid": "f855e047ce9972f16b90afbb9977cc7c",
"score": "0.6446528",
"text": "def elitist_shuffle(items, inequality):\n weights = np.power(\n np.linspace(1, 0, num=len(items), endpoint=False),\n inequality\n )\n weights = weights / np.linalg.norm(weights, ord=1)\n return np.random.choice(items, size=len(items), replace=False, p=weights)",
"title": ""
},
{
"docid": "06476671ead77f2be394c982692d69d0",
"score": "0.6434033",
"text": "def shuffle(self) -> List[int]:\n random.shuffle(self.nums)\n return self.nums",
"title": ""
},
{
"docid": "8460bad54d8827e57ddeb720eb12b17e",
"score": "0.6431755",
"text": "def shuffle( self ):\r\n random.shuffle(self.__deck)",
"title": ""
},
{
"docid": "680ce7c94d2dd69225fb3b2fa4c5145c",
"score": "0.641145",
"text": "def random_sort(l):\n\tnl = []\n\tlength = len(l)\n\tfor i in range(length):\n\t\tnl.append(l.pop(random.randrange(len(l))))\n\treturn nl",
"title": ""
},
{
"docid": "6d20b00ad4eb66070f5364788751ce01",
"score": "0.6387936",
"text": "def randomize_carefully(elems, n_repeat=2):\n s = set(elems)\n res = []\n for n in range(n_repeat):\n if res:\n # Avoid the last placed element\n lst = list(s.difference({res[-1]}))\n # Shuffle\n np.random.shuffle(lst)\n lst.append(res[-1])\n # Shuffle once more to avoid obvious repeating patterns in the last position\n lst[1:] = np.random.choice(lst[1:], size=len(lst)-1, replace=False)\n else:\n lst = elems[:]\n np.random.shuffle(lst)\n res.extend(lst)\n return res",
"title": ""
},
{
"docid": "d5d99f9b8574e0ae728562540805216b",
"score": "0.638032",
"text": "def cap_list(ls, n=100, split=.8, oversample=True):\n pivot = int(len(ls) * split)\n np.random.shuffle(ls)\n\n if not oversample or n < pivot:\n return ls[:min(pivot, n)]\n\n return np.random.choice(ls[:min(pivot, n)], size=n)",
"title": ""
},
{
"docid": "caaa6b514a59438691cf198d4df91c74",
"score": "0.63755274",
"text": "def shuffleCards(self):\n random.shuffle(self.cards)",
"title": ""
},
{
"docid": "6a66160faaf9b52c33950d0cebc7606a",
"score": "0.6370366",
"text": "def shuffle_tetriminos(self) -> list:\n random.shuffle(self.tetrimino_list)",
"title": ""
},
{
"docid": "43a8b19599fd5a4538a9c0821e6131b3",
"score": "0.6367211",
"text": "def shuffle(self):\n from random import random\n\n if not self.nums:\n return None\n for j in xrange(1, len(self.nums)):\n i = int(random() * (j + 1))\n self.swap(i, j)\n return self.nums",
"title": ""
},
{
"docid": "08a07dbedcddc29c03c54b7c90f06d6a",
"score": "0.6364749",
"text": "def test_merge_sort_shuffled_list_gets_sorted():\n expected = [num for num in range(20)]\n unsorted = expected[:]\n shuffle(unsorted)\n print(unsorted)\n now_sorted = merge_sort(unsorted)\n print(now_sorted)\n assert expected == now_sorted",
"title": ""
},
{
"docid": "0812709855d062bac6a262b5593e539f",
"score": "0.6347731",
"text": "def shuffle(self):\n random.shuffle(self.cards)\n self.current = 0",
"title": ""
},
{
"docid": "d00a2e5adbf381308acdfb3e951c0e5b",
"score": "0.63428646",
"text": "def shuffle(self):\n shuffle(self._array)",
"title": ""
},
{
"docid": "d75620e5c6aaab7e81d01f963580a8c3",
"score": "0.6341844",
"text": "def shuffle_together(*lists):\r\n tmp = list(zip(*lists))\r\n random.shuffle(tmp)\r\n return tuple(zip(*tmp))",
"title": ""
},
{
"docid": "8a18d6a0923dec82817c88308a9dd592",
"score": "0.63409805",
"text": "def unison_shuffled_copies(a, b):\n assert len(a) == len(b)\n p = np.random.permutation(len(a))\n return a[p], b[p]",
"title": ""
},
{
"docid": "46fc9efd60b8bcbbc7da48836199e5a5",
"score": "0.63259244",
"text": "def shuffle(self, x, random=None):\n\n if random is None:\n randbelow = self._randbelow\n for i in tqdm(reversed(range(1, len(x))), desc=\"Shuffle\", total=len(x)):\n # pick an element in x[:i+1] with which to exchange x[i]\n j = randbelow(i + 1)\n x[i], x[j] = x[j], x[i]\n else:\n _int = int\n for i in tqdm(reversed(range(1, len(x))), desc=\"Shuffle\", total=len(x)):\n # pick an element in x[:i+1] with which to exchange x[i]\n j = _int(random() * (i + 1))\n x[i], x[j] = x[j], x[i]",
"title": ""
},
{
"docid": "3d2fc2315223b522f0555dabb074526a",
"score": "0.6324415",
"text": "def shuffle(self):\n lenOfDeck = len(self._deck)\n newDeck = []\n for _ in range(lenOfDeck):\n randomCard = self._deck[random.randrange(len(self._deck))]\n newDeck.append(randomCard)\n self._deck.remove(randomCard)\n self._deck = newDeck",
"title": ""
},
{
"docid": "b8142a637e898619a5977acef8febb9d",
"score": "0.6323407",
"text": "def shuffle(self) -> List[int]:\n import random\n \n length = len(self._nums)\n for i in range(length):\n randi = random.randint(0, length - 1)\n self._nums[i], self._nums[randi] = self._nums[randi], self._nums[i]\n \n return self._nums",
"title": ""
},
{
"docid": "a0908cf7ee3caf763baed5aa95d55582",
"score": "0.6316534",
"text": "def shuffle(self):\n random.shuffle(self.playing_cards)",
"title": ""
},
{
"docid": "92cbf1b1aaaa719f3690588ea81c7e3b",
"score": "0.6305725",
"text": "def select_random(_list):\n selector = random.randint(0, len(_list) - 1)\n return _list[selector]",
"title": ""
},
{
"docid": "55ec2a0dceef8f3f4bdc7cd11b7fdacc",
"score": "0.62977785",
"text": "def random_permutation(size):\r\n return random.sample(range(size), size)",
"title": ""
},
{
"docid": "7fe121a73d205b9171b2beed2f8fede1",
"score": "0.6297261",
"text": "def array_shuffle(elems: List[np.ndarray], random: Union[int, RandomState] = None) -> List[np.ndarray]:\n random = optional_random(random)\n elems = list(elems)\n if random is None:\n random = np.random\n indices = np.arange(len(elems))\n np.random.shuffle(indices)\n return [elems[i] for i in indices]",
"title": ""
},
{
"docid": "4fdb257aa8f9f2ab06da0345000ca83c",
"score": "0.62894213",
"text": "def copyRandomList(self, head):\n\n # Insert each node's copy right after it, already copy .label\n node = head\n while node:\n copy = RandomListNode(node.label)\n copy.next = node.next\n node.next = copy\n node = copy.next\n\n # Set each copy's .random\n node = head\n while node:\n node.next.random = node.random and node.random.next\n node = node.next.next\n\n # Separate the copied list from the original, (re)setting every .next\n node = head\n copy = head_copy = head and head.next\n while node:\n # ? 未看懂 node.next = node 含义? 死循环？\n node.next = node = copy.next\n copy.next = copy = node and node.next\n\n return head_copy",
"title": ""
},
{
"docid": "2f8de81f93f6eb3116c839e9b57c32e9",
"score": "0.6280093",
"text": "def shuffle_sort(values):\n values = copy(values)\n while not is_sorted(values):\n print(values)\n shuffle(values)\n return values # modified in place but also returned",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="738"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">e0ddcae60b69a4318a11a246c1454df9</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Applies 2D Gaussian Blur.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "224cc2963a2ab7b5c1f2b34bf9aa12ba",
"score": "0.0",
"text": "def __init__(self, in_channels, out_channels, ksize=5):\n\n super(GaussianConv2d, self).__init__()\n weight = (np.arange(ksize, dtype=np.float32) - ksize // 2) ** 2\n weight = np.sqrt(weight[None, :] + weight[:, None])\n weight = np.reshape(weight, (1, 1, ksize, ksize)) / weight.sum()\n self.weight = Parameter(\n torch.Tensor(weight).expand(out_channels, -1, -1, -1))\n self._in_channels = in_channels\n self._out_channels = out_channels",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "687ae079bfddc7119b5409b0a5bdd133",
"score": "0.7160371",
"text": "def _gaussian_blur(self, img, kernel_size):\r\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)",
"title": ""
},
{
"docid": "50efc7ccae0b729fdb6b07bd4e9a7ca3",
"score": "0.70999116",
"text": "def blur(image, sigma=1):\n img = np.copy(image)\n img[0,:,:] = ndimage.filters.gaussian_filter(img[0,:,:], sigma)\n img[1,:,:] = ndimage.filters.gaussian_filter(img[1,:,:], sigma)\n img[2,:,:] = ndimage.filters.gaussian_filter(img[2,:,:], sigma)\n return img",
"title": ""
},
{
"docid": "3874a53b956dd50487680f901dc58b32",
"score": "0.7063035",
"text": "def gaussian_blur(self,img, kernel_size):\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)",
"title": ""
},
{
"docid": "cdcea70bdbdce6b2e588ffbcedf10da0",
"score": "0.69970536",
"text": "def gaussian_blur(self, img, kernel_size):\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)",
"title": ""
},
{
"docid": "8f63a8baf9ce99657f5b3cd6f0083814",
"score": "0.6991185",
"text": "def blur_gauss(img: np.array, sigma: float) -> np.array:\n ######################################################\n # Write your own code here\n\n # calculate kernel size\n k_size = 2 * round(3 * sigma) + 1\n # k_size = 39\n\n # create the gaussian kernel according to the slides\n g_kernel = np.zeros((k_size, k_size))\n for x in range(k_size):\n for y in range(k_size):\n g_kernel[x][y] = 1 / (2 * np.pi * sigma ** 2) \\\n * np.exp(-((x - (k_size - 1) / 2) ** 2 + (y - (k_size - 1) / 2) ** 2) / (2 * sigma ** 2))\n\n # sum of the kernel must be 1\n g_kernel /= g_kernel.sum()\n\n # use transparent border type so that it doesn't affect the image\n img_blur = cv2.filter2D(img, -1, g_kernel, cv2.BORDER_TRANSPARENT)\n\n # helper_functions.plot_row_intensities(img_blur, 100, \"1.2_30\")\n\n ######################################################\n return img_blur",
"title": ""
},
{
"docid": "6e952893dbdb5403562cb2462337f2a7",
"score": "0.6977875",
"text": "def gaussian_blur(img, kernel_size):\r\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)",
"title": ""
},
{
"docid": "6e952893dbdb5403562cb2462337f2a7",
"score": "0.6977875",
"text": "def gaussian_blur(img, kernel_size):\r\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)",
"title": ""
},
{
"docid": "a52ea9ff49af941c953f38e28b1da230",
"score": "0.6934352",
"text": "def gaussian_blur(img, kernel_size):\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)",
"title": ""
},
{
"docid": "a52ea9ff49af941c953f38e28b1da230",
"score": "0.6934352",
"text": "def gaussian_blur(img, kernel_size):\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)",
"title": ""
},
{
"docid": "a52ea9ff49af941c953f38e28b1da230",
"score": "0.6934352",
"text": "def gaussian_blur(img, kernel_size):\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)",
"title": ""
},
{
"docid": "a52ea9ff49af941c953f38e28b1da230",
"score": "0.6934352",
"text": "def gaussian_blur(img, kernel_size):\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)",
"title": ""
},
{
"docid": "a52ea9ff49af941c953f38e28b1da230",
"score": "0.6934352",
"text": "def gaussian_blur(img, kernel_size):\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)",
"title": ""
},
{
"docid": "a52ea9ff49af941c953f38e28b1da230",
"score": "0.6934352",
"text": "def gaussian_blur(img, kernel_size):\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)",
"title": ""
},
{
"docid": "a52ea9ff49af941c953f38e28b1da230",
"score": "0.6934352",
"text": "def gaussian_blur(img, kernel_size):\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)",
"title": ""
},
{
"docid": "a52ea9ff49af941c953f38e28b1da230",
"score": "0.6934352",
"text": "def gaussian_blur(img, kernel_size):\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)",
"title": ""
},
{
"docid": "a52ea9ff49af941c953f38e28b1da230",
"score": "0.6934352",
"text": "def gaussian_blur(img, kernel_size):\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)",
"title": ""
},
{
"docid": "a52ea9ff49af941c953f38e28b1da230",
"score": "0.6934352",
"text": "def gaussian_blur(img, kernel_size):\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)",
"title": ""
},
{
"docid": "a52ea9ff49af941c953f38e28b1da230",
"score": "0.6934352",
"text": "def gaussian_blur(img, kernel_size):\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)",
"title": ""
},
{
"docid": "a52ea9ff49af941c953f38e28b1da230",
"score": "0.6934352",
"text": "def gaussian_blur(img, kernel_size):\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)",
"title": ""
},
{
"docid": "a52ea9ff49af941c953f38e28b1da230",
"score": "0.6934352",
"text": "def gaussian_blur(img, kernel_size):\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)",
"title": ""
},
{
"docid": "a52ea9ff49af941c953f38e28b1da230",
"score": "0.6934352",
"text": "def gaussian_blur(img, kernel_size):\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)",
"title": ""
},
{
"docid": "a52ea9ff49af941c953f38e28b1da230",
"score": "0.6934352",
"text": "def gaussian_blur(img, kernel_size):\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)",
"title": ""
},
{
"docid": "a52ea9ff49af941c953f38e28b1da230",
"score": "0.6934352",
"text": "def gaussian_blur(img, kernel_size):\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)",
"title": ""
},
{
"docid": "a52ea9ff49af941c953f38e28b1da230",
"score": "0.6934352",
"text": "def gaussian_blur(img, kernel_size):\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)",
"title": ""
},
{
"docid": "763e5de5843bb4781d948090f0e86efb",
"score": "0.69251686",
"text": "def gaussian_blur(img, kernel_size):\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 20)",
"title": ""
},
{
"docid": "d5d310b710349a8f63b0e7f21c2a3ee8",
"score": "0.6885021",
"text": "def gaussian_blur(img, kernel_size):\n return cv.GaussianBlur(img, (kernel_size, kernel_size), 0)",
"title": ""
},
{
"docid": "42d8d9d1960bd1f9fdd39b01bc61ed5c",
"score": "0.6691535",
"text": "def gaussianBlur(self, window = '', sigmaX=0 , sigmaY=0 ,grayscale=False):\r\n try:\r\n import cv2\r\n ver = cv2.__version__\r\n new_version = False\r\n #For OpenCV versions till 2.4.0, cv2.__versions__ are of the form \"$Rev: 4557 $\"\r\n if not ver.startswith('$Rev:'):\r\n if int(ver.replace('.','0'))>=20300 :\r\n new_version = True\r\n except :\r\n new_version = False\r\n pass\r\n\r\n if is_tuple(window):\r\n win_x, win_y = window\r\n if ( win_x>=0 and win_y>=0 and win_x%2==1 and win_y%2==1 ) :\r\n pass\r\n else :\r\n logger.warning(\"The aperture (win_x,win_y) must be odd number and greater than 0.\")\r\n return None\r\n\r\n elif (is_number(window)):\r\n window = (window, window)\r\n\r\n else:\r\n window = (3,3) #set the default aperture window size (3x3)\r\n\r\n if (not new_version):\r\n grayscale_ = grayscale\r\n return self.smooth(algorithm_name='blur', aperture=window, grayscale=grayscale_)\r\n else:\r\n image_gauss = cv2.GaussianBlur(self.getNumpyCv2(), window, sigmaX, sigmaY=sigmaY)\r\n\r\n if grayscale:\r\n return Image(image_gauss, colorSpace=ColorSpace.GRAY, cv2image=True)\r\n else:\r\n return Image(image_gauss, colorSpace=self._colorSpace, cv2image=True)",
"title": ""
},
{
"docid": "3f3a7ae3dd0d572390dbf29d28779e5e",
"score": "0.66440105",
"text": "def gaussian_blur(gray, kernel_size):\r\n return cv2.GaussianBlur(gray, (kernel_size, kernel_size), 0)",
"title": ""
},
{
"docid": "869079d20767d8302e251d4ddc61313c",
"score": "0.66159683",
"text": "def lsv_blur(self, sigma):\n suppMat = find_support(sigma)\n maxSupp = suppMat.max()\n maxs = (maxSupp - 1) / 2\n imgt1 = self.append_zeros(maxs,maxs)\n imgt2 = Img(imgt1.row, imgt1.col)\n for i in arange(len(suppMat)):\n for j in arange(len(suppMat)):\n kern = create_gauss_kernel(suppMat[i][j], sigma[i][j])\n s = (suppMat[i][j] - 1) /2\n imgt2.pix[i+maxs-s:i+maxs+s+1][:,j+maxs-s:j+maxs+s+1] =\\\n imgt2.pix[i+maxs-s:i+maxs+s+1][:,j+maxs-s:j+maxs+s+1] +\\\n imgt1.pix[i+maxs][j+maxs] * kern\n #imgt1.pix[i+maxs-s:i+maxs+s+1][:,j+maxs-s:j+maxs+s+1] * kern\n imgt2.pix = imgt2.pix[maxs:maxs+self.row][:,maxs:maxs+self.col]\n imgt2.pix = floor(imgt2.pix)# / imgt2.pix.max() * 255\n imgt2.row = self.row\n imgt2.col = self.col\n return (suppMat, imgt2)",
"title": ""
},
{
"docid": "4426776ec648458b9c6be9a815329ad7",
"score": "0.65866125",
"text": "def get_blurred_image(image):\n return ndimage.gaussian_filter(image, sigma=1)",
"title": ""
},
{
"docid": "c39f4bf1656d3e9c2b34306b5d938073",
"score": "0.6578956",
"text": "def guassian_blur(img, kernel_size=3):\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)",
"title": ""
},
{
"docid": "e86558e540b0e2dec2e2ac8afaa6ebf4",
"score": "0.6552372",
"text": "def apply_blur(image, size=7, sig=0.788):\n size = (size, size)\n image = cv2.GaussianBlur(image, size, sig, sig)\n\n return image",
"title": ""
},
{
"docid": "b62fffc8420085f5b9544f079794b2b8",
"score": "0.6534208",
"text": "def gaussian_blur(self, image, blur_amount=33):\n return cv2.GaussianBlur(image, (blur_amount,blur_amount),0)",
"title": ""
},
{
"docid": "6aa0ce05443773a6528e71f82008a8dc",
"score": "0.6470156",
"text": "def gaussian_blur(img: np.ndarray, kernel_size: int) -> np.ndarray:\n if not is_valid_kernel_size(kernel_size):\n raise ValueError(\n \"kernel_size must either be 0 or a positive, odd integer\")\n\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)",
"title": ""
},
{
"docid": "00e2f95ce1fe35be31f1a105069fb365",
"score": "0.6462802",
"text": "def gaussian_filter(img):\n\n filtered_img = cv.GaussianBlur(img, (3, 3), 1)\n\n return filtered_img",
"title": ""
},
{
"docid": "2a9bc68538d4da1ac62803ddf0b5e5d3",
"score": "0.6439576",
"text": "def gaussian_filter(self,sigma=1):\n\n\t\tsize = int(self.kernel_size) // 2\n\t\tx, y = np.mgrid[-size:size+1, -size:size+1]\n\t\tnormal = 1 / (2.0 * np.pi * sigma**2)\n\t\tkernel = np.exp(-((x**2 + y**2) / (2.0*sigma**2))) * normal\n\n\t\treturn cv2.filter2D(self.img, -1, kernel)",
"title": ""
},
{
"docid": "5522e1500a78ff1629c55c5b35cafb7a",
"score": "0.6435283",
"text": "def blur(self, img, kernel_size, height_factor=1):\n \n # make a gaussian kernel filter by calculating the cartesian\n # product of two gaussian signals\n # also check for n elements and throw idk error before\n # screwing up memory\n # note, that resize want's a np array of type float\n\n gauss_1D = signal.gaussian(kernel_size, std=kernel_size/5)\n height = int(max(1, np.ceil(kernel_size*height_factor)))\n a = gauss_1D[None,:]\n b = cv2.resize(gauss_1D[:,None], (1, height))\n kernel = a * b # cartesian product\n\n kernel /= kernel.sum()\n # apply kernel\n img_blur = cv2.filter2D(img, -1, kernel)\n\n # the blur will also remove the contrast, renormalize\n # img_blur -= img_blur.min()\n # img_blur = img_blur / img_blur.max()\n \n return img_blur",
"title": ""
},
{
"docid": "3df7e53f22f1571e5fc3a88ab1e94821",
"score": "0.64133877",
"text": "def gaussian_blur_im(im, var=(1,1,1), p2c=False):\n \n from image_tools.attrs_info import get_im_info\n \n if p2c:\n print('\\n Image info for im prior to Gaussian blurring:')\n \n pixID, pixIdtypeAsStr, uniqueValsPostTx, f2sInds = get_im_info(im, p2c)\n \n # Ensure im is a 32-bit float (pixID = 8).\n if pixID != 8: \n if p2c:\n print(f'\\n im has PixelID = {pixID} ({pixIdtypeAsStr})). '\\\n 'It will be converted to Float32.')\n \n # Convert image to Float32:\n im = change_im_dtype(im=im, newPixType='Float32')\n \n if p2c:\n print('\\n Image info for im after converting to Float32:')\n \n pixID, pixIdtypeAsStr, uniqueVals, f2sInds = get_im_info(im, p2c)\n \n imFilt = sitk.DiscreteGaussianImageFilter()\n #imFilt.SetMaximumKernelWidth(sigma)\n #print(f' imFilt.GetMaximumKernelWidth() = {imFilt.GetMaximumKernelWidth()}')\n imFilt.SetVariance(var)\n \n blurredIm = imFilt.Execute(im)\n \n if p2c:\n print('\\nApplying Gaussian blur...')\n print(f' imFilt.GetMaximumError() = {imFilt.GetMaximumError()}')\n print(f' imFilt.GetMaximumKernelWidth() = {imFilt.GetMaximumKernelWidth()}')\n print(f' imFilt.GetUseImageSpacing() = {imFilt.GetUseImageSpacing()}')\n print(f' imFilt.GetVariance() = {imFilt.GetVariance()}')\n print(f'\\n im.GetPixelID() = {im.GetPixelID()}')\n print(f' im.GetPixelIDValue() = {im.GetPixelIDValue()}')\n print(f' im.GetPixelIDTypeAsString() = {im.GetPixelIDTypeAsString()}')\n print(f' blurredIm.GetPixelID() = {blurredIm.GetPixelID()}')\n print(f' blurredIm.GetPixelIDValue() = {blurredIm.GetPixelIDValue()}')\n print(f' blurredIm.GetPixelIDTypeAsString() = {blurredIm.GetPixelIDTypeAsString()}')\n \n return blurredIm",
"title": ""
},
{
"docid": "7e4603c04f779aeea2a706351ccb983e",
"score": "0.64028186",
"text": "def MatlabStyleGauss2D(size,sigma):\n u = cv2.getGaussianKernel(size, sigma)\n kernel = np.outer(u, u)\n return kernel",
"title": ""
},
{
"docid": "3f277109ea4fc45324db83157472b24a",
"score": "0.63838875",
"text": "def blur_spatial(im, kernel_size):\n if kernel_size == 1:\n return im\n kernel_mat = get_gaussian_kernel(kernel_size)\n return sc.convolve2d(im, kernel_mat, mode='same', boundary='symm')",
"title": ""
},
{
"docid": "478679877f6ab6c09a935b231d08ce94",
"score": "0.63614833",
"text": "def blur_spatial(im, kernel_size):\n gaus_matrix = get_2D_gaussian(kernel_size)\n return convolve2d(im, gaus_matrix, mode=\"same\")",
"title": ""
},
{
"docid": "679ee7007e3dc252457d3c1747b7e8ce",
"score": "0.63495034",
"text": "def gaussian_smoothing(im, sigma):\r\n ksize = (int(4 * sigma + 1), int(4 * sigma + 1))\r\n return cv2.GaussianBlur(im.astype(np.float32), ksize, sigma)",
"title": ""
},
{
"docid": "26ef1c615b9b495ff5051c7415cc601b",
"score": "0.6343005",
"text": "def apply_smoothing(image, kernel_size=3):\n return cv2.GaussianBlur(image, (kernel_size, kernel_size), 0)",
"title": ""
},
{
"docid": "d26873192bc2ca87dd757dd33d4c8e98",
"score": "0.6322719",
"text": "def applyGaussianBlur(self, radius):\n return self.app.applyGaussianBlur(radius)",
"title": ""
},
{
"docid": "05260e4387750a504b9b610b8cf0ed9c",
"score": "0.6314294",
"text": "def blur_spatial(im, kernel_size):\n # assuming kernel_size is odd\n gaussian_kernel = create_gaussian_kernel(kernel_size)\n conv_im = scipy.signal.convolve2d(im, gaussian_kernel, mode='same')\n return conv_im",
"title": ""
},
{
"docid": "2a130189c75c89f942bbc398a3f49431",
"score": "0.62645555",
"text": "def gaussian_filter(self,kernel_size= 3 ,sigma=1):\n\n\t\tsize = int(kernel_size) // 2\n\t\tx, y = np.mgrid[-size:size+1, -size:size+1]\n\t\tnormal = 1 / (2.0 * np.pi * sigma**2)\n\t\tkernel = np.exp(-((x**2 + y**2) / (2.0*sigma**2))) * normal\n\n\t\treturn cv2.filter2D(self.img, -1, kernel)",
"title": ""
},
{
"docid": "094249fcf49aa0a298ba2233a28a6f7b",
"score": "0.6249322",
"text": "def blur_image(im, n, ny=None) :\n g = gauss_kern(n, sizey=ny)\n improc = signal.convolve(im,g, mode='valid')\n return(improc)",
"title": ""
},
{
"docid": "ee02b0d522a397a826e3d3ce4a610583",
"score": "0.623757",
"text": "def gaussian_blur(in_array, gt, size):\n # expand in_array to fit edge of kernel; constant value is zero\n padded_array = np.pad(in_array, size, 'constant')\n # build kernel\n x, y = np.mgrid[-size:size + 1, -size:size + 1]\n g = np.exp(-(x**2 / float(size) + y**2 / float(size)))\n g = (g / g.sum()).astype(in_array.dtype)\n # do the Gaussian blur\n ar = fftconvolve(padded_array, g, mode='full')\n # convolved increased size of array ('full' option); update geotransform\n gt2 = Affine(\n gt.a, gt.b, gt.xoff - (2 * size * gt.a),\n gt.d, gt.e, gt.yoff - (2 * size * gt.e))\n return ar, gt2",
"title": ""
},
{
"docid": "e16a5f94a680f245e932a878095b012f",
"score": "0.6236621",
"text": "def boxBlur(image):",
"title": ""
},
{
"docid": "2bd6ba1a92fd41d5ab8abd36a2c647bb",
"score": "0.6222622",
"text": "def smoothen_b (img) :\n\n # Step 1\n img[img < 0] = 0\n # Step 2\n img[img >= 0] -= np.min(img[img >= 0])\n # Step 3\n vmin = max(0.1, np.median(img)/reduc)\n vmax = np.max(img)\n if vmin >= vmax :\n batchlog.info(\"{} --> Appreciable intensity not found while smoothing\".format(self.objid))\n return Galaxy.emptyImage()\n # Step 4\n imgNorm = colors.LogNorm(vmin, vmax, True).__call__(img)\n # Step 5\n imgNorm.data[imgNorm.mask] = 0\n\n ######################## Step 7 ################## Step 6 #####################\n return (lambda d:cv2.GaussianBlur(np.floor(255*(lambda x,mn,mx : (x-mn)/(mx-mn))\n (d, np.min(d), np.max(d))\n ).astype(np.uint8), (sgx, sgy),0\n ))(imgNorm.data)",
"title": ""
},
{
"docid": "df67b17e6792bcbbc3e0f631973e907f",
"score": "0.6206984",
"text": "def blur_image(im, n, ny=None) :\n g = gauss_kern(n, sizey=ny)\n improc = signal.convolve(im, g, mode='valid')\n return(improc)",
"title": ""
},
{
"docid": "8ce6f7fe16c331bdab01b6c1e38ce894",
"score": "0.6172756",
"text": "def blur_spatial(im, kernel_size):\n # if(kernel_size == 1):\n # return im\n\n gaus_ker = d1_gaus(kernel_size)\n\n # kernel = signal.convolve2d(partial_kernel.reshape(1, kernel_size), \\\n # partial_kernel.reshape(1, kernel_size).T, \\\n # mode=\"full\").astype(np.float64)\n\n # kernel = kernel / (np.sum(kernel))\n # im = signal.convolve2d(im, kernel, mode='same', boundary='wrap')\n\n im = ndimage.filters.convolve(im, gaus_ker)\n im = ndimage.filters.convolve(im, gaus_ker.reshape(kernel_size, 1))\n\n return np.float32(im)",
"title": ""
},
{
"docid": "2dacb195702a14c013feddc0361ff92e",
"score": "0.61514354",
"text": "def gaussian(self,height, mu_x, mu_y, sd_x, sd_y):\n sd_x = float(sd_x)\n sd_y = float(sd_y)\n return lambda x, y: height * np.exp(-((x - mu_x) ** 2 / (sd_x ** 2) + (y - mu_y) ** 2 / (sd_y ** 2)) / 2)",
"title": ""
},
{
"docid": "c1b96ef9a72c45c55e00dbf17a688f09",
"score": "0.6140024",
"text": "def recursive_gaussian_blur_im(im, sigma, direction, p2c=False):\n \n from image_tools.attrs_info import get_im_info\n \n if p2c:\n print('\\n Image info for im prior to Gaussian blurring:')\n \n pixID, pixIdtypeAsStr, uniqueValsPostTx, f2sInds = get_im_info(im, p2c)\n \n # Ensure im is a 32-bit float (pixID = 8).\n if pixID != 8: \n if p2c:\n print(f'\\n im has PixelID = {pixID} ({pixIdtypeAsStr})). '\\\n 'It will be converted to Float32.')\n \n # Convert im to Float32:\n im = change_im_dtype(im=im, newPixType='Float32')\n \n if p2c:\n print('\\n Image info for image after converting to Float32:')\n \n pixID, pixIdtypeAsStr, uniqueVals, f2sInds = get_im_info(im, p2c)\n \n imFilt = sitk.RecursiveGaussianImageFilter()\n imFilt.SetSigma(sigma)\n imFilt.SetDirection(direction)\n \n blurredIm = imFilt.Execute(im)\n \n if p2c:\n print('\\nApplying Gaussian blur...')\n print(f' imFilt.GetDirection() = {imFilt.GetDirection()}')\n print(f' imFilt.GetNormalizeAcrossScale() = {imFilt.GetNormalizeAcrossScale()}')\n print(f' imFilt.GetOrder() = {imFilt.GetOrder()}')\n print(f' imFilt.GetSigma() = {imFilt.GetSigma()}')\n print(f'\\n im.GetPixelID() = {im.GetPixelID()}')\n print(f' im.GetPixelIDValue() = {im.GetPixelIDValue()}')\n print(f' im.GetPixelIDTypeAsString() = {im.GetPixelIDTypeAsString()}')\n print(f' blurredIm.GetPixelID() = {blurredIm.GetPixelID()}')\n print(f' blurredIm.GetPixelIDValue() = {blurredIm.GetPixelIDValue()}')\n print(f' blurredIm.GetPixelIDTypeAsString() = {blurredIm.GetPixelIDTypeAsString()}')\n \n return blurredIm",
"title": ""
},
{
"docid": "fae4633655353f4dd4c5b4c70a73bab3",
"score": "0.6124708",
"text": "def compute_ssim(\n img0,\n img1,\n max_val,\n filter_size=11,\n filter_sigma=1.5,\n k1=0.01,\n k2=0.03,\n return_map=False,\n):\n device = img0.device\n ori_shape = img0.size()\n width, height, num_channels = ori_shape[-3:]\n img0 = img0.view(-1, width, height, num_channels).permute(0, 3, 1, 2)\n img1 = img1.view(-1, width, height, num_channels).permute(0, 3, 1, 2)\n batch_size = img0.shape[0]\n\n # Construct a 1D Gaussian blur filter.\n hw = filter_size // 2\n shift = (2 * hw - filter_size + 1) / 2\n f_i = ((torch.arange(filter_size, device=device) - hw + shift) / filter_sigma) ** 2\n filt = torch.exp(-0.5 * f_i)\n filt /= torch.sum(filt)\n\n # Blur in x and y (faster than the 2D convolution).\n # z is a tensor of size [B, H, W, C]\n filt_fn1 = lambda z: F.conv2d(\n z, filt.view(1, 1, -1, 1).repeat(num_channels, 1, 1, 1),\n padding=[hw, 0], groups=num_channels)\n filt_fn2 = lambda z: F.conv2d(\n z, filt.view(1, 1, 1, -1).repeat(num_channels, 1, 1, 1),\n padding=[0, hw], groups=num_channels)\n\n # Vmap the blurs to the tensor size, and then compose them.\n filt_fn = lambda z: filt_fn1(filt_fn2(z))\n mu0 = filt_fn(img0)\n mu1 = filt_fn(img1)\n mu00 = mu0 * mu0\n mu11 = mu1 * mu1\n mu01 = mu0 * mu1\n sigma00 = filt_fn(img0 ** 2) - mu00\n sigma11 = filt_fn(img1 ** 2) - mu11\n sigma01 = filt_fn(img0 * img1) - mu01\n\n # Clip the variances and covariances to valid values.\n # Variance must be non-negative:\n sigma00 = torch.clamp(sigma00, min=0.0)\n sigma11 = torch.clamp(sigma11, min=0.0)\n sigma01 = torch.sign(sigma01) * torch.min(\n torch.sqrt(sigma00 * sigma11), torch.abs(sigma01)\n )\n\n c1 = (k1 * max_val) ** 2\n c2 = (k2 * max_val) ** 2\n numer = (2 * mu01 + c1) * (2 * sigma01 + c2)\n denom = (mu00 + mu11 + c1) * (sigma00 + sigma11 + c2)\n ssim_map = numer / denom\n ssim = torch.mean(ssim_map.reshape([-1, num_channels*width*height]), dim=-1)\n return ssim_map if return_map else ssim",
"title": ""
},
{
"docid": "0e79fddda48d8b9a1eba850e37380c80",
"score": "0.6111214",
"text": "def gaussian_blur(image, kernel_size, sigma, padding='SAME'):\n radius = tf1.to_int32(kernel_size / 2)\n kernel_size = radius * 2 + 1\n x = tf1.to_float(tf1.range(-radius, radius + 1))\n blur_filter = tf1.exp(\n -tf1.pow(x, 2.0) / (2.0 * tf1.pow(tf1.to_float(sigma), 2.0)))\n blur_filter /= tf1.reduce_sum(blur_filter)\n # One vertical and one horizontal filter.\n blur_v = tf1.reshape(blur_filter, [kernel_size, 1, 1, 1])\n blur_h = tf1.reshape(blur_filter, [1, kernel_size, 1, 1])\n num_channels = tf1.shape(image)[-1]\n blur_h = tf1.tile(blur_h, [1, 1, num_channels, 1])\n blur_v = tf1.tile(blur_v, [1, 1, num_channels, 1])\n expand_batch_dim = image.shape.ndims == 3\n if expand_batch_dim:\n # Tensorflow requires batched input to convolutions, which we can fake with\n # an extra dimension.\n image = tf1.expand_dims(image, axis=0)\n blurred = tf1.nn.depthwise_conv2d(\n image, blur_h, strides=[1, 1, 1, 1], padding=padding)\n blurred = tf1.nn.depthwise_conv2d(\n blurred, blur_v, strides=[1, 1, 1, 1], padding=padding)\n if expand_batch_dim:\n blurred = tf1.squeeze(blurred, axis=0)\n return blurred",
"title": ""
},
{
"docid": "b907f45e481837ab6500be1ff842819f",
"score": "0.6093872",
"text": "def blur_image(im, n, ny=None):\n\n import scipy.signal\n from scipy.signal import convolve\n from . import gauss_kern\n\n g = gauss_kern(n, sizey=ny)\n improc = scipy.signal.convolve(im,g, mode='same')\n\n return improc",
"title": ""
},
{
"docid": "0d726799744e03ec4b9b137eb2085951",
"score": "0.60925066",
"text": "def blur(im, filter_1D):\n im = scipy.ndimage.filters.convolve(im, filter_1D)\n\n return scipy.ndimage.filters.convolve(im, filter_1D.T)",
"title": ""
},
{
"docid": "b5318c97be1406e423f6a0b5ec7f3d28",
"score": "0.60569996",
"text": "def _gaussian(self, x, sigma, height, mu):\n return height * np.exp(-(x - mu)**2 / (2.0 * sigma**2))",
"title": ""
},
{
"docid": "c5633e6468101455a8b75c6440cde9ad",
"score": "0.6054455",
"text": "def filter(self, image):\n image.data = gaussian_filter(image.data, self.scale)",
"title": ""
},
{
"docid": "bed7a3d79f8812700744c6caad3e1e18",
"score": "0.6016059",
"text": "def gaussian_blur(image, strength):\n res = image.copy()\n gauss = iaa.Sequential([iaa.GaussianBlur(sigma=strength)])\n images_to_modified = image[0:2]\n res[0:2] = convert_back_to_uint(gauss(images=images_to_modified))\n return res",
"title": ""
},
{
"docid": "10e3fd2310a07264b3b4ec81cfca8c9d",
"score": "0.60098004",
"text": "def gaussian_blurr(im, sigma, trim=4.0):\n kernel = gaussian_mask(sigma, trim)\n filtered = convolve(im, kernel)\n return filtered",
"title": ""
},
{
"docid": "8e4c1d59fba9c28f59e403597b51cccc",
"score": "0.59449387",
"text": "def fitGaussian_b (info) :\n\n grays, counts = info\n ming = 1 if not np.min(grays) else 0\n x, y = grays[ming:], counts[ming:]\n gaussian = lambda z, gp, sg : gp*np.exp(-np.square(z/(2*np.pi*sg)))\n\n # try block to fit the gaussian\n try :\n gaussPeak, sigma = curve_fit(gaussian, y.flatten(), x.flatten(), p0=[np.max(x), np.max(y)/20])[0]\n except :\n batchlog.warning(\"Fault in curve_fit\")\n return ()\n\n return gaussPeak, sigma, np.floor(gaussPeak/noiseSig), np.floor(gaussPeak/hwhmSig)",
"title": ""
},
{
"docid": "8c74ed325781c752fb4b58cc1d77ebae",
"score": "0.59277135",
"text": "def gaussian_kernel(x1, x2):\n return np.exp( -np.linalg.norm(x1 - x2) ** 2 / two_sigma_sq )",
"title": ""
},
{
"docid": "1b4f6a7e61dbd03bc1affc44df2cac43",
"score": "0.5887492",
"text": "def rbf(x1, x2, sigma=1):\n return np.exp(-(np.linalg.norm(x1 - x2) / (2 * sigma**2)))",
"title": ""
},
{
"docid": "57edfb106b98e3d4ec16dd0a4493368c",
"score": "0.5851424",
"text": "def two_stage_blur(input_img):\n \n temp_img = np.zeros(input_img.shape)\n \n output_img = np.zeros(input_img.shape)\n \n #first stage blur\n for r in range(input_img.shape[0]-8):\n for c in range(input_img.shape[1]):\n \n temp_img[r, c] = (input_img[r, c] + input_img[r + 1, c] + input_img[r + 2, c]) / 3.0\n \n #second stage blur\n for r in range(input_img.shape[0]-8):\n for c in range(input_img.shape[1]-2):\n \n output_img[r, c] = (temp_img[r, c] + temp_img[r, c + 1] + temp_img[r, c + 2]) / 3.0\n \n return output_img",
"title": ""
},
{
"docid": "beb3f326d1006833889a70cba602320b",
"score": "0.58402985",
"text": "def gaussian2D(radius, sigma=1, dtype=torch.float32, device='cpu'):\n x = torch.arange(\n -radius, radius + 1, dtype=dtype, device=device).view(1, -1)\n y = torch.arange(\n -radius, radius + 1, dtype=dtype, device=device).view(-1, 1)\n\n h = (-(x * x + y * y) / (2 * sigma * sigma)).exp()\n\n h[h < torch.finfo(h.dtype).eps * h.max()] = 0\n return h",
"title": ""
},
{
"docid": "6bf4eed31abbe3398f04ba63bdf35ec6",
"score": "0.583199",
"text": "def blur_mapping(level, src_img):\n if level == 1:\n radius = 1.5\n elif level == 2:\n radius = 3\n elif level == 3:\n radius = 6\n else:\n radius = 10\n return src_img.filter(ImageFilter.GaussianBlur(radius=radius))",
"title": ""
},
{
"docid": "f45adcb35a2c215760b48823e518a9e9",
"score": "0.58319867",
"text": "def _createFilters(self):\n \n # Create the debiasing filter\n sigmad = 5 * self.minScale\n ksized = int(sigmad*3) #kernel half size\n self.Gdebias = cv2.getGaussianKernel(2*ksized+1, sigmad)\n \n # Set sigma and kernel size for the second and first order derivatives\n sigma2 = float(self.minScale)\n sigma1 = self.minScale*1.7754\n ksize2 = int(sigma2*3) + 1\n ksize1 = int(sigma1*3) + 1\n \n # Set steerable filter basis orientations\n theta1 = 0\n theta2 = np.pi/3\n theta3 = 2*np.pi/3\n \n # Create a meshgrid for second order derivatives\n X, Y = np.meshgrid(list(range(-ksize2,ksize2+1)), list(range(-ksize2,ksize2+1)))\n u1 = X*np.cos(theta1) - Y*np.sin(theta1)\n u2 = X*np.cos(theta2) - Y*np.sin(theta2)\n u3 = X*np.cos(theta3) - Y*np.sin(theta3)\n \n # Create an isotropic Gaussian.\n # All second derivatives are defined in terms of G0\n self.G01d = cv2.getGaussianKernel(2*ksize2+1, sigma2)\n G0 = self.G01d * self.G01d.T\n \n # Compute second partial derivatives of Gaussian\n self.G20 = (((u1**2)/(sigma2**4)) - (1/(sigma2**2))) * G0\n self.G260 = (((u2**2)/(sigma2**4)) - (1/(sigma2**2))) * G0\n self.G2120 = (((u3**2)/(sigma2**4)) - (1/(sigma2**2))) * G0\n \n # Create a separable basis filter for first partial derivative of Gaussian\n x_1 = np.linspace(-ksize1, ksize1, 2*ksize1+1)\n x_1 = np.reshape(x_1, (1, -1))\n self.G0_a = cv2.getGaussianKernel(2*ksize1+1, sigma1)\n self.G1 = -((1/sigma1)**2) * x_1 * self.G0_a.T\n \n # Set the completion flag\n self.isCreated = True",
"title": ""
},
{
"docid": "b8fbb58b830670a5d7ba81b745b596e2",
"score": "0.5820318",
"text": "def consistentBlur(image, ksize, gaussian_or_box='gaussian', save_image=False):\n\theight, width = image.shape[:2]\n\n\tif gaussian_or_box == 'gaussian':\n\t\tblur_img = cv2.GaussianBlur(image,(ksize,ksize),0)\n\t\tblur_img = cv2.GaussianBlur(blur_img,(ksize,ksize),0)\n\telif gaussian_or_box == 'box':\n\t\tblur_img = cv2.blur(image,(ksize,ksize))\n\t\tblur_img = cv2.blur(blur_img,(ksize,ksize))\n\n\tif save_image:\n\t\tsaveImage(blur_img, 'consistentBlur.png')\n\n\treturn blur_img",
"title": ""
},
{
"docid": "61e158238aee27874dccce2ca468b5a8",
"score": "0.58194345",
"text": "def blur(img_array, blur_factor):\n for i in range(3):\n img_array[:, :, i] = ndimage.gaussian_filter(img_array[:, :, i], blur_factor)\n return np.uint8(img_array)",
"title": ""
},
{
"docid": "55c854803b3ed63f1f55693aed51d507",
"score": "0.5799596",
"text": "def gaussian_kernel(kernel, std):\n Gaussian_Kernel_1 = signal.gaussian(kernel, std=std).reshape(kernel, 1)\n Gaussian_Kernel_2 = np.outer(Gaussian_Kernel_1, Gaussian_Kernel_1)\n return Gaussian_Kernel_2",
"title": ""
},
{
"docid": "254994753e78e2028081f427df2bbd79",
"score": "0.577339",
"text": "def gaussianFilter(imp, sigmaX=30, sigmaY=30, sigmaZ=1):\n # Store image calibration\n cal = imp.getCalibration()\n\n # Duplicate input ImagePlus\n gaussian = imp.duplicate()\n\n # Perform the gaussian filter with input radius.\n GaussianBlur3D.blur(gaussian, sigmaX, sigmaY, sigmaZ)\n\n # Subtract gaussian filter and return output ImagePlus.\n impout = ImageCalculator().run(\"Subtract create 32-bit stack\", \n imp, gaussian)\n impout.setCalibration(cal)\n return impout",
"title": ""
},
{
"docid": "592729f4d7b81de1933b5b35d0a1a18f",
"score": "0.5770924",
"text": "def convGauss(carteSym,sigma):\n conv = gaussian_filter(carteSym,sigma)\n return conv",
"title": ""
},
{
"docid": "7dbe5b917c962fa4c874708b7740e419",
"score": "0.57637805",
"text": "def test_gaussian_fwhm_after_convolution(self):\n\t\tnx,ny = 31,61\n\t\tfwhmi = 7\n\t\tfwhmk = 5\n\t\tfactor = 5\n\t\tx,y = testimage.xy_data(nx,ny)\n\t\tm = guassian_convolved_with_gaussian(nx,ny,amplitude=1.0,x0=None,y0=None,\n\t\t\tfwhmi=fwhmi,fwhmk=fwhmk,factor=factor)\n\n\t\tx,y = testimage.xy_data(nx,ny)\n\n\t\t# fit Gaussian2D to the convolved model\n\t\tg_init = models.Gaussian2D(amplitude=m.max(), \n\t\t\tx_mean=(ny-1)/2, y_mean=(nx-1)/2,\n\t\t\tx_stddev=fwhmi, y_stddev=fwhmi)\n\t\tfit_g = fitting.LevMarLSQFitter()\n\t\tg = fit_g(g_init, x, y, m)\n\n\n\t\t# calculate the the theoretical standard deviation for convolution \n\t\t# of two Gaussians in 1D.\n\t\tstddevi,stddevk = fwhmi/2.35,fwhmk/2.35\n\t\tstddevt = np.sqrt(stddevi**2 + stddevk**2)\n\n\t\t# make sure that it is within 10%\n\t\trel_dev = (g.x_stddev.value - stddevt)/stddevt\n\n\t\treturn self.assertTrue(np.abs(rel_dev) < 0.1)",
"title": ""
},
{
"docid": "6e28f18360c540eb1b33121888f34558",
"score": "0.57559496",
"text": "def _blur_image(image):\n image = cv2.medianBlur(image, 5)\n image = cv2.GaussianBlur(image, (3, 3), 0)\n return image",
"title": ""
},
{
"docid": "6e28f18360c540eb1b33121888f34558",
"score": "0.57559496",
"text": "def _blur_image(image):\n image = cv2.medianBlur(image, 5)\n image = cv2.GaussianBlur(image, (3, 3), 0)\n return image",
"title": ""
},
{
"docid": "196e373a19fdd3977f5f943ab33475ca",
"score": "0.5752366",
"text": "def ssmooth(xsize, ysize, zsize, sigma, inputdata):\n return ndimage.gaussian_filter(inputdata, [sigma / xsize, sigma / ysize, sigma / zsize])",
"title": ""
},
{
"docid": "3949a5e309d68264d2536ab90a193401",
"score": "0.5744818",
"text": "def blur_image(X):\n from fast_layers import conv_forward_fast\n w_blur = np.zeros((3, 3, 3, 3))\n b_blur = np.zeros(3)\n blur_param = {'stride': 1, 'pad': 1}\n for i in range(3):\n w_blur[i, i] = np.asarray([[1, 2, 1], [2, 188, 2], [1, 2, 1]],\n dtype=np.float32)\n w_blur /= 200.0\n return conv_forward_fast(X, w_blur, b_blur, blur_param)[0]",
"title": ""
},
{
"docid": "847bb71891e74f8d6431b5e7922cf146",
"score": "0.5734575",
"text": "def __gaussian_k(x0, y0, sigma, width, height):\n x = np.arange(0, width, 1, float)\n y = np.arange(0, height, 1, float)[:, np.newaxis]\n return np.exp(-((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2))",
"title": ""
},
{
"docid": "92ef9095205f2c18309eb6bc6759557e",
"score": "0.5719853",
"text": "def thick_gaussian(x, x0, dx, Tex, tau):\n return Tex * (1. - np.exp(-gaussian(x, x0, dx, tau)))",
"title": ""
},
{
"docid": "327676ea2ee65017de510c97640288e3",
"score": "0.57122046",
"text": "def gaussian_kernel(x1, x2, sigma=0.1):\n\tmod_diff = np.sum((x1 - x2)**2)\n\treturn np.exp(-(mod_diff)/(2*sigma**2))",
"title": ""
},
{
"docid": "4891b880d9e0cb595247114282420cce",
"score": "0.57007456",
"text": "def gaussian_blur(surface, std, strength):\n\n matrix = surfarray.array3d(surface)\n width = matrix.shape[1] // 2\n\n def in_gkernel():\n # generate one dimensional kernel\n drange = lambda x: range(-x, 1 + x)\n return [np.exp(-std * abs(i)) for i in drange(width)]\n\n kern_l = in_gkernel()\n kernel = np.array(kern_l) / np.sum(kern_l)\n\n in_pad = lambda x: np.pad(x, width, mode=\"edge\")\n in_cnv = lambda x: np.convolve(in_pad(x), kernel, mode=\"valid\")\n\n for i in range(matrix.shape[1]):\n matrix[i, :, 0] = in_cnv(matrix[i, :, 0])\n matrix[i, :, 1] = in_cnv(matrix[i, :, 1])\n matrix[i, :, 2] = in_cnv(matrix[i, :, 2])\n\n for j in range(matrix.shape[0]):\n matrix[:, j, 0] = in_cnv(matrix[:, j, 0])\n matrix[:, j, 1] = in_cnv(matrix[:, j, 1])\n matrix[:, j, 2] = in_cnv(matrix[:, j, 2])\n\n return surfarray.make_surface(matrix)",
"title": ""
},
{
"docid": "6e471efd611da334cbbfd20f1ea87466",
"score": "0.5689957",
"text": "def gaussian_filter(image, kernel_size=5, kernel_sigma=1.0):\n return convolve(image, gaussian_kernel(kernel_size, kernel_sigma))",
"title": ""
},
{
"docid": "1e9cd75559fbdd5ae5c795a1a0828d4a",
"score": "0.5687282",
"text": "def gaussian2D(radius, sigma=1):\n m, n = radius\n y, x = np.ogrid[-m:m + 1, -n:n + 1]\n gauss = np.exp(-(x * x + y * y) / (2 * sigma * sigma))\n gauss[gauss < np.finfo(gauss.dtype).eps * gauss.max()] = 0\n return gauss",
"title": ""
},
{
"docid": "188cd16d036cc3207bec48a2f6ce7198",
"score": "0.5686423",
"text": "def rbf_kernel(x, y, sigma=3):\n return np.exp(-np.linalg.norm(x - y) ** 2 / (2 * (sigma ** 2)))",
"title": ""
},
{
"docid": "d626bff77095a4d40d85a268b57a74d7",
"score": "0.5682585",
"text": "def resize_rescale_image_gaussian_blur(img, new_dims, new_scale, interp_order=1, blur_strength=4, current_scale=None, no_clip=False):\n img = skimage.img_as_float( img )\n img = resize_image( img, new_dims, interp_order )\n img = rescale_image( img, new_scale, current_scale=current_scale, no_clip=True )\n blurred = gaussian_filter(img, sigma=blur_strength)\n if not no_clip:\n min_val, max_val = new_scale\n np.clip(blurred, min_val, max_val, out=blurred)\n return blurred",
"title": ""
},
{
"docid": "336e8fabe476303c72aea88a2ea0b265",
"score": "0.56799006",
"text": "def blur_filter(img):\n k_size = 11\n kernel = np.ones((k_size, k_size))/k_size\n return convolution(img, kernel)",
"title": ""
},
{
"docid": "b98c1393384d7192c4264218d421006d",
"score": "0.5677683",
"text": "def matlab_style_gauss2D(w, sigma):\n shape = (2*w+1,2*w+1)\n m,n = [(ss-1.)/2. for ss in shape]\n y,x = np.ogrid[-m:m+1,-n:n+1]\n h = np.exp( -(x*x + y*y) / (2.*sigma*sigma) )\n h[ h < np.finfo(h.dtype).eps*h.max() ] = 0\n sumh = h.sum()\n if sumh != 0:\n h /= sumh\n return h",
"title": ""
},
{
"docid": "9f2d227f5935ca0a03bf0e5d00aecc79",
"score": "0.567405",
"text": "def twoD_Gaussian(xy, amplitude, sigma_x, sigma_y, xo, yo, h):\n x = xy[0]\n y = xy[1]\n xo = float(xo)\n yo = float(yo)\n theta = 0\n a = (np.cos(theta) ** 2) / (2 * sigma_x ** 2) + (np.sin(theta) ** 2) / (\n 2 * sigma_y ** 2\n )\n b = -(np.sin(2 * theta)) / (4 * sigma_x ** 2) + (np.sin(2 * theta)) / (\n 4 * sigma_y ** 2\n )\n c = (np.sin(theta) ** 2) / (2 * sigma_x ** 2) + (np.cos(theta) ** 2) / (\n 2 * sigma_y ** 2\n )\n g = (\n amplitude\n * np.exp(\n -(a * ((x - xo) ** 2) + 2 * b * (x - xo) * (y - yo) + c * ((y - yo) ** 2))\n )\n + h\n )\n return g.flatten()",
"title": ""
},
{
"docid": "0ee202378da485a45250bd448c876871",
"score": "0.56715274",
"text": "def add_gaussnoise(img, noise_sigma):\n from skimage.util import random_noise\n if noise_sigma==0:\n return img \n else: \n return random_noise(img, mode='gaussian',var=noise_sigma**2,clip=False)",
"title": ""
},
{
"docid": "da265cc8d1a10d2388891a3c1c2744e8",
"score": "0.5654089",
"text": "def gaussian_filter(input, win):\n N, C, H, W = input.shape\n out = F.conv2d(input, win, stride=1, padding=0, groups=C)\n out = F.conv2d(out, win.transpose(2, 3), stride=1, padding=0, groups=C)\n return out",
"title": ""
},
{
"docid": "4eca47e4572f041186c833fa250a97ed",
"score": "0.56378114",
"text": "def add_gaussian(img, min_stddev=0, max_stddev=50):\n\n stddev = 7\n noise = np.random.randn(*img.shape) * stddev\n\n noised_img = img + noise\n noised_img = np.clip(noised_img, a_min=0, a_max=255).astype(np.uint8)\n\n return noised_img",
"title": ""
},
{
"docid": "27877e3126d0c7b43cda5460c977bbc2",
"score": "0.5631132",
"text": "def fit_double_gaussian(self, cat, spec, axis, weights, uncert=True, param=None, process='fit'):\n\t\tif process == 'fit':\n\t\t\tkwargs = {'max_nfev': int(1e6)}\n\t\t\tif cat.linetype == 'emission':\n\t\t\t\thigh = 2*np.nanmax(spec)\n\t\t\t\tlow = 0.\n\t\t\t\tamp = np.nanmax(spec)\n\t\t\telse:\n\t\t\t\tlow = 2*np.nanmin(spec)\n\t\t\t\tamp = np.nanmin(spec)\n\t\t\t\thigh = 0.\n\t\t\tp0 = [0.7*amp, cat.mean+cat.restdv.value, cat.restdv.value, 0.7*amp, cat.mean-cat.restdv.value, cat.restdv.value]\n\t\t\tpopt, pcov = opt.curve_fit( uf.doubleGaussian, axis, spec, p0=p0, sigma=weights, absolute_sigma=uncert, bounds=( [low, cat.mean-cat.maxgalw.value/10., cat.restdv.value/2., low, cat.mean-cat.maxgalw.value/5., cat.restdv.value/2. ], [high, cat.mean+cat.maxgalw.value/5., cat.maxgalw.value, high, cat.mean+cat.maxgalw.value/10., cat.maxgalw.value] ), **kwargs )\n\t\t\tdgauss = uf.doubleGaussian(popt[0], popt[1], popt[2], popt[3], popt[4], popt[5])\n\n\t\t\tfitparam = astfit.Column( name='Double Gaussian', format='D', array=popt )\n\t\t\tfitparamuncert = astfit.Column( name='Double Gaussian Uncert', format='D', array=np.sqrt(np.diag(pcov)) )\n\t\t\tintflux = np.sum(dgauss(axis))\n\t\t\tfitrms = [self.calcFitRMS(spec, dgauss(axis), weights),self.calChiSquare(data=spec, model=dgauss(axis), sigma=weights, null=None)]\n\t\t\tw50 = self.calcW50( dgauss(axis), axis )\n\t\t\treturn fitparam, fitparamuncert, abs(intflux), fitrms, w50\n\t\telif process == 'plot':\n\t\t\tdgauss = uf.DoubleGaussian(param[0], param[1], param[2], param[3], param[4], param[5])\n\t\t\treturn dgauss(axis)",
"title": ""
},
{
"docid": "adbfe488e6891d7da18631ba936b42ab",
"score": "0.56297237",
"text": "def __create_gaussian_kernel(self):\n kernel_width = self._road_length * 2 + self._road_width\n g1 = sg.gaussian(kernel_width, std=self._road_width / 2)\n\n r1 = np.tile(g1, (kernel_width, 1))\n r2 = np.transpose(r1)\n\n kernel = np.maximum(r1, r2)\n return kernel",
"title": ""
},
{
"docid": "30d0983503f3f32ad06bdcdbbaac12bb",
"score": "0.5629356",
"text": "def test_gauss_normalization(self):\n\t\ts = (11,15)\n\t\tx0,y0 = 7,8\n\t\tfwhm = 2\n\t\tx,y = testimage.xy_data(s[0],s[1])\n\n\t\tm = SymmetricGaussian2D(x_0=x0,y_0=y0,fwhm=fwhm)\n\t\tm.oversample_factor(20)\n\t\tz1 = m(x,y).sum()\n\n\t\treturn self.assertAlmostEqual(z1,1.,places=1)",
"title": ""
},
{
"docid": "18f310387943a90bac01865a6115d41c",
"score": "0.5628191",
"text": "def localGaussian(self, radius, scale=0.95, down=True):\n java_object = gateway.jvm.boofcv.factory.filter.binary.FactoryThresholdBinary.\\\n localGaussian(int(radius),float(scale),down,self.boof_image_type)\n return InputToBinary(java_object)",
"title": ""
},
{
"docid": "94796eb0d963491130cd00ad87244722",
"score": "0.5625056",
"text": "def gaussian(x, y, x0, y0, sigma, A):\r\n return A * np.exp( -0.5*( (x-x0)**2 + (y-y0)**2 )/(sigma**2) )",
"title": ""
},
{
"docid": "767c3f775f0cf6bf8c5555738e2bc81f",
"score": "0.5625024",
"text": "def test_oversampling_gauss_flux_conservation2D(self):\n\t\ts = (11,15)\n\t\tx0,y0 = 7,8\n\t\tfwhm = 2\n\t\tx,y = testimage.xy_data(s[0],s[1])\n\n\t\tm = SymmetricGaussian2D(x_0=x0,y_0=y0,fwhm=fwhm)\n\t\tm.oversample_factor(3)\n\t\tz1 = m(x,y).sum()\n\n\t\tm.oversample_factor(21)\n\t\tz2 = m(x,y).sum()\n\n\t\treturn self.assertAlmostEqual(z1,z2,places=2)",
"title": ""
},
{
"docid": "6a095fd2028480a55b88682005ad6b0b",
"score": "0.5621017",
"text": "def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):\n\n v = np.dot(\n np.array([[np.cos(theta), -np.sin(theta)],\n [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))\n V = np.array([[v[0], v[1]], [v[1], -v[0]]])\n D = np.array([[l1, 0], [0, l2]])\n Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))\n k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)\n return k",
"title": ""
},
{
"docid": "4c67d1ed2c0cd37668c6131f9cd2a904",
"score": "0.5619995",
"text": "def matlab_style_gauss2D(self, shape=(3,3),sigma=0.5):\n m,n = [(ss-1.)/2. for ss in shape]\n y,x = np.ogrid[-m:m+1,-n:n+1]\n h = np.exp( -(x*x + y*y) / (2.*sigma*sigma) )\n h[ h < np.finfo(h.dtype).eps*h.max() ] = 0\n sumh = h.sum()\n if sumh != 0:\n h /= sumh\n return h",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="739"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">5986bd6a59c7c3d96fa913666a852520</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Get the tools name by locating the line starting with 'STATS SUMMARY' which contains the tool name in the format e.g 'STATS SUMMARY blr.cli.tool_name. Return tool name</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "9b1cfc2336ee1482c2814c3c7d9e4afd",
"score": "0.79888105",
"text": "def get_tool_name(file):\n for line in file:\n if line.startswith(\"STATS SUMMARY\"):\n return line.strip().split(\".\")[-1]\n\n return None",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "9d45da86d3750ac2f3c9160162bb916c",
"score": "0.6053547",
"text": "def GetInfo(self, tool):\n return (INFO % tool.replace('\\\\', '/'))",
"title": ""
},
{
"docid": "924224d8f4de85f9f0c86155e8c450d4",
"score": "0.5898121",
"text": "def extract_tool_details(self, node):\n if node.get('class') == 'CommandLineTool':\n # The id of a tool is a long URI string prefixed with the workflow's URL at time of loading\n # For readability, remove that prefix so that\n # 'file:///tmp/folder1/ba4wt23/workflow-dir/tools/Tool.cwl' can become 'tools/Tool.cwl'\n tool_name = remove_prefix(node.get('id'), self.prefix)\n # Avoid duplicating tools included multiple times in the same workflow\n if self.tool_exists(tool_name):\n return\n tool_info = {}\n reqs = node.get('requirements')\n if reqs:\n self.extract_requirements(reqs, tool_info)\n hints = node.get('hints')\n if hints:\n self.extract_requirements(hints, tool_info)\n if tool_info: # only add if we have data\n self.details.append({'tool_name': tool_name, 'tool_info': tool_info})",
"title": ""
},
{
"docid": "38399718e75df30d0d34cf014d1f3763",
"score": "0.5833653",
"text": "def getTool(self):\n return self.design.get(\"tool\")",
"title": ""
},
{
"docid": "9f512f28778fa0294bc92ecdb5cc15ba",
"score": "0.5814984",
"text": "def get_tool(name):\n tool = name.lower()\n if tool not in __tools__:\n raise ValueError(\"Tool {0} not found!\\n\".format(name))\n\n t = __tools__[tool]()\n\n if not t.is_installed():\n sys.stderr.write(\"Tool {0} not installed!\\n\".format(tool))\n\n if not t.is_configured():\n sys.stderr.write(\"Tool {0} not configured!\\n\".format(tool))\n\n return t",
"title": ""
},
{
"docid": "d194242d84080fc40c2167ccbc3032b0",
"score": "0.5754821",
"text": "def describe_tools_profile(name=None, exact_match=True):\n return generic_list_builder('tools_profile', name, exact_match)",
"title": ""
},
{
"docid": "cb63d5b12cbb750ba8a159200fa22d7e",
"score": "0.57035595",
"text": "def find_tool_by_name(self, name):\n return None\n # return self.tools.find_one({'name':name})",
"title": ""
},
{
"docid": "33f6f25508508ad7d5fc610340a516d2",
"score": "0.5653368",
"text": "def utility_name(self):\n return self.current_data['outputs']['utility_name']",
"title": ""
},
{
"docid": "c9dc02ca986603b632ab249046f30f90",
"score": "0.55887926",
"text": "def tool_info(tcp_position: Position, name: str = \"unnamed_tool\") -> ToolInfo:\n return ToolInfo(name=name, tcp=tcp_position)",
"title": ""
},
{
"docid": "a043c2a75487bef46ce37fc696aabc50",
"score": "0.5544188",
"text": "def test_get_suite_name():\n nodeid1 = \"ons_tests/test_synapsert.py::test_demo_synapsert2\"\n assert get_suite_name(nodeid1) == \"ons_tests.test_synapsert\"",
"title": ""
},
{
"docid": "6116835e00c4bae878850635445de5e7",
"score": "0.5521929",
"text": "def getLabel(line):\n cols = line.strip().split(\"\\t\")\n if \"deepTools_group\" in cols:\n return cols.index(\"deepTools_group\")\n return None",
"title": ""
},
{
"docid": "dcfc3fcd8b1d77e3c865664e7335eca9",
"score": "0.5413568",
"text": "def get_summary(self):\n summary = self.contestant_desc\n match = re.search(\n r'^(.*?\\s[a-z)]{3,}\\.\\s.*?\\s[a-z)]{3,}\\.\\s)',\n summary,\n re.DOTALL\n )\n if match:\n return match.group(1)\n return summary",
"title": ""
},
{
"docid": "a215b1818fcc590ed2606ee518291af9",
"score": "0.5412005",
"text": "def get_tool_context(self, tool_alias):\n tools_dict = self.get_tools()\n data = tools_dict.get(tool_alias)\n if data:\n return data[\"context_name\"]\n return None",
"title": ""
},
{
"docid": "29a1cb43674849c3ebcdf9337b857a11",
"score": "0.54063356",
"text": "def main():\n # tool description",
"title": ""
},
{
"docid": "cba154beb2cc66557af37b1175008359",
"score": "0.5384703",
"text": "def locate_tool(name, verbose=True):\n m = get_tool(name)\n tool_bin = which(m.cmd)\n if tool_bin:\n if verbose:\n print(\"Found {} in {}\".format(m.name, tool_bin))\n return tool_bin\n else:\n print(\"Couldn't find {}\".format(m.name))",
"title": ""
},
{
"docid": "9e4953247160a35802306f3f7f3c3eed",
"score": "0.52807206",
"text": "def get_llvm_tool(self, tool):\n return self.env_version(tool)",
"title": ""
},
{
"docid": "ad51b136ae43238b859575f84086cca6",
"score": "0.52214026",
"text": "def _stat_name(target):\n tokens = target.split(' ')\n assert tokens[0] == instance['cluster']\n return tokens[1].lower()",
"title": ""
},
{
"docid": "3a85cc8d818283bd3c78c61cc07599f3",
"score": "0.5210636",
"text": "def get_help(cls) -> str:\n line = cls.get_description().strip().partition(\"\\n\")[0]\n # Good luck debugging this! Slightly reformat short description\n # (toplevel --help)\n if line and line[0].isupper():\n first_word, sp, rem = line.partition(\" \")\n line = f\"{first_word.lower()}{sp}{rem}\"\n if line and line[-1] == \".\":\n line = line[:-1]\n return line",
"title": ""
},
{
"docid": "fe7a1b1d1b8f2e5c75b5c0361672741f",
"score": "0.520893",
"text": "def get_util_name(soup):\n if re.search(r'<title>EWG\\s+Tap\\s+Water\\s+Database\\s+\\|\\s+|[:,a-zA-Z\\s\\-]+</title>', str(soup)) == None:\n return None\n m_obj = re.search(r'\\|\\s+[:,a-zA-Z\\s\\-]+', str(soup))\n util_name = m_obj.group(0)\n util_name = util_name.replace('|', '')\n util_name = util_name.strip()\n return util_name",
"title": ""
},
{
"docid": "7e0125b4c3c0641002f4b3d74235e28b",
"score": "0.5199245",
"text": "def get_summary(self):\r\n return stat_desc[self.stat_index]",
"title": ""
},
{
"docid": "2d6007e43f0142061e2e4d9cc06407e4",
"score": "0.5162146",
"text": "def usage(cls): # pragma: no cover\n names = []\n docs = []\n for name, (stage, _) in cls.pipeline_stages.items():\n # find the first non-empty doc line, if there is one.\n try:\n doc_lines = [s.strip() for s in stage.__doc__.split(\"\\n\")]\n doc_lines = [d for d in doc_lines if d]\n doc = doc_lines[0]\n except (AttributeError, IndexError):\n doc = \"\"\n # cut off any very long lines\n if len(doc) > 100:\n doc = doc[:100] + \" ...\"\n # print the text\n names.append(name)\n docs.append(doc)\n\n # Make it look like a nice table by finding the maximum\n # length of the names, so that all the docs line up\n n = max(len(name) for name in names) + 1\n stage_texts = [f\"- {name:{n}} - {d}\" for name, d in zip(names, docs)]\n stage_text = \"\\n\".join(stage_texts)\n\n try:\n module = cls.get_module().split(\".\")[0]\n except: # pylint: disable=bare-except\n module = \"<module_name>\"\n sys.stderr.write(\n f\"\"\"\nUsage: python -m {module} <stage_name> <stage_arguments>\n\nIf no stage_arguments are given then usage information\nfor the chosen stage will be given.\n\nI currently know about these stages:\n\n{stage_text}\n\"\"\"\n )",
"title": ""
},
{
"docid": "4c5ab3ecdf26df05e62390d8af776cc8",
"score": "0.51510125",
"text": "def tool_id(self) -> str:\n ...",
"title": ""
},
{
"docid": "5193a8f6ab77bd83024577f8d95f9e34",
"score": "0.51319045",
"text": "def runsummary(run, debug):\n pat = '<runNumber>%s</runNumber>' % run\n key, cert = get_key_cert()\n url = 'https://cmswbm.web.cern.ch/cmswbm/cmsdb/servlet/RunSummary?'\n url += 'RUN=%s&DB=cms_omds_lb&FORMAT=XML' % run\n data = get_data(url, key, cert, debug)\n for line in data.read().split('\\n'):\n if line == pat:\n return pat",
"title": ""
},
{
"docid": "add4f57c673e5c6422d1487ca150a818",
"score": "0.5094626",
"text": "def show_xtool_info(xtools):\n prefix_len = max([len(item['target']) for item in xtools.values()])\n gcc_len = max([len(item['gcc']['version']) for item in xtools.values()])\n abi_len = max([len(item['gcc']['abi']) for item in xtools.values()])\n vendor_len = max([len(item['vendor']) for item in xtools.values()])\n\n cell_format = \"%%s%%-3s%%-%ds%%-%ds%%-%ds%%-%ds\" % (\n prefix_len + 1,\n gcc_len + 1,\n abi_len + 1,\n vendor_len + 1)\n\n print cell_format % (\" \", \"\", 'Target', 'gcc', \"abi\", \"vendor\")\n idx = 1\n for item in sorted(xtools.itervalues(), key=lambda x: x['gcc']['version']):\n if item['current']:\n cur_mark = \"*\"\n else:\n cur_mark = \" \"\n\n print cell_format % (cur_mark, idx,\n item['target'],\n item['gcc']['version'],\n item['gcc']['abi'],\n item['vendor'])\n idx += 1",
"title": ""
},
{
"docid": "a7204a60b92706c4f0465a3333341d3e",
"score": "0.5084564",
"text": "def get_switch_name(module):\n cli = pn_cli(module)\n cli += ' switch-setup-show format switch-name '\n return run_cli(module, cli).split()[1]",
"title": ""
},
{
"docid": "e0a1797cf33447191bbdff98e8f02d78",
"score": "0.5083102",
"text": "def cli(ctx, index=\"\", tool_ids=\"\", resolver_type=\"\", include_containers=False, container_type=\"\", index_by=\"requirements\"):\n return ctx.gi.tool_dependencies.summarize_toolbox(index=index, tool_ids=tool_ids, resolver_type=resolver_type, include_containers=include_containers, container_type=container_type, index_by=index_by)",
"title": ""
},
{
"docid": "98b1cbfdf52b2e7be35d08400fcf28c0",
"score": "0.5043068",
"text": "def get_utility_command_output_filename(name, selector=None):\n\n if name == 'MemoryMonitor':\n filename = get_memory_monitor_summary_filename(selector=selector)\n else:\n filename = \"\"\n\n return filename",
"title": ""
},
{
"docid": "3134021c5e9041867e4e3a1ae00eab33",
"score": "0.50269765",
"text": "def print_tools(self, buf=sys.stdout, verbose=False, context_name=None):\n def _get_row(entry):\n context_name_ = entry[\"context_name\"]\n tool_alias = entry[\"tool_alias\"]\n tool_name = entry[\"tool_name\"]\n properties = []\n col = None\n\n variant = entry[\"variant\"]\n if isinstance(variant, set):\n properties.append(\"(in conflict)\")\n col = critical\n if verbose:\n package = \", \".join(x.qualified_package_name for x in variant)\n else:\n v = next(iter(variant))\n package = \"%s (+%d more)\" % (v.qualified_package_name,\n len(variant) - 1)\n else:\n package = variant.qualified_package_name\n\n if tool_name == tool_alias:\n tool_name = \"-\"\n else:\n properties.append(\"(aliased)\")\n if col is None:\n col = alias_col\n\n msg = \" \".join(properties)\n row = [tool_alias, tool_name, package, context_name_, msg]\n return row, col\n\n if context_name:\n self._context(context_name) # check context exists\n context_names = [context_name]\n else:\n context_names = sorted(self.contexts.keys())\n\n rows = [[\"TOOL\", \"ALIASING\", \"PACKAGE\", \"CONTEXT\", \"\"],\n [\"----\", \"--------\", \"-------\", \"-------\", \"\"]]\n colors = [None, None]\n\n entries_dict = defaultdict(list)\n for d in self.get_tools().values():\n entries_dict[d[\"context_name\"]].append(d)\n\n if verbose:\n # add hidden entries\n for d in self.hidden_tools:\n d_ = d.copy()\n d_[\"hidden\"] = True\n entries_dict[d[\"context_name\"]].append(d_)\n\n # add conflicting tools\n for docs in self.tool_conflicts.values():\n for d in docs:\n d_ = d.copy()\n d_[\"conflicting\"] = True\n entries_dict[d[\"context_name\"]].append(d_)\n\n for i, context_name in enumerate(context_names):\n entries = entries_dict.get(context_name, [])\n if entries:\n if i:\n rows.append(('', '', '', '', ''))\n colors.append(None)\n\n entries = sorted(entries, key=lambda x: x[\"tool_alias\"].lower())\n for entry in entries:\n row, col = _get_row(entry)\n if \"hidden\" in entry:\n row[-1] = \"(hidden)\"\n rows.append(row)\n colors.append(warning)\n elif \"conflicting\" in entry:\n row[-1] = \"(not visible)\"\n rows.append(row)\n colors.append(warning)\n else:\n rows.append(row)\n colors.append(col)\n\n _pr = Printer(buf)\n\n if rows:\n for col, line in zip(colors, columnise(rows)):\n _pr(line, col)\n else:\n _pr(\"No tools available.\")",
"title": ""
},
{
"docid": "bd517991eae5e6341a89322c0bee8a91",
"score": "0.49987844",
"text": "def print_info(self, buf=sys.stdout, verbose=False):\n _pr = Printer(buf)\n\n if not self.contexts:\n _pr(\"Suite is empty.\")\n return\n\n context_names = sorted(self.contexts.keys())\n _pr(\"Suite contains %d contexts:\" % len(context_names))\n\n if not verbose:\n _pr(' '.join(context_names))\n return\n\n tools = self.get_tools().values()\n context_tools = defaultdict(set)\n context_variants = defaultdict(set)\n for entry in tools:\n context_name = entry[\"context_name\"]\n context_tools[context_name].add(entry[\"tool_name\"])\n context_variants[context_name].add(str(entry[\"variant\"]))\n\n _pr()\n rows = [[\"NAME\", \"VISIBLE TOOLS\", \"PATH\"],\n [\"----\", \"-------------\", \"----\"]]\n\n for context_name in context_names:\n context_path = self._context_path(context_name) or '-'\n ntools = len(context_tools.get(context_name, []))\n if ntools:\n nvariants = len(context_variants[context_name])\n short_desc = \"%d tools from %d packages\" % (ntools, nvariants)\n else:\n short_desc = \"no tools\"\n rows.append((context_name, short_desc, context_path))\n\n _pr(\"\\n\".join(columnise(rows)))",
"title": ""
},
{
"docid": "729c96a8a1a1362f9a659c1c980b3014",
"score": "0.49983412",
"text": "def driver_summary(self):\n buf = ctypes.create_string_buffer(256)\n ver = ctypes.c_uint32()\n self._call(GxAo.GxAoGetDriverSummary, buf, sizeof(buf), byref(ver))\n ver = ver.value\n\n return (buf.value, ver >> 16, ver & 0xffff)",
"title": ""
},
{
"docid": "462b662decb22c7e8f5850b56bf0d7a6",
"score": "0.4958056",
"text": "def get_lower_param_desc(self):\n sub_param_desc = \"\"\n for inp in self.tool_inp_desc[\"inputs\"]:\n tool_inp = ToolInput(inp, self.wf_param_values, self.wf_steps, self.level + 1)\n sub_param_desc += tool_inp.get_formatted_desc()\n return sub_param_desc",
"title": ""
},
{
"docid": "1b91458ec5d9eebc0f0d27ebd3a82e65",
"score": "0.49571204",
"text": "def usage(self):\n return '%(prog)s'",
"title": ""
},
{
"docid": "480156002d04772874d1d286fbfbc1c2",
"score": "0.49407947",
"text": "def tool(t: str) -> str:\n if not Configuration().no_color:\n return colors.color(t, fg='cyan')\n return t",
"title": ""
},
{
"docid": "c3b521d78b2252723590007cc6b29623",
"score": "0.49283633",
"text": "def get_tools(self):\n\t\treturn self.tools",
"title": ""
},
{
"docid": "661fa83e352e796c7e6645e3c8f2064a",
"score": "0.49079785",
"text": "def tool():\n\treturn render_template(\"tool.html\", config=Config())",
"title": ""
},
{
"docid": "9124e3cd059201098d557f6ad16258bb",
"score": "0.49061358",
"text": "def get_usage(self):\n return \"This is a usage statement.\"",
"title": ""
},
{
"docid": "e0799aba84e4f553c71456f69bfa4573",
"score": "0.49059838",
"text": "def read_stat_tests(lines):\n stat_res_line_id = ut.find_line(\" passed\", lines, 7)\n stat_line = lines[stat_res_line_id]\n stat_line = ut.string_cleaner(stat_line)\n stat_line = stat_line.split(\" \")[1:]\n return stat_line",
"title": ""
},
{
"docid": "5b86c25741690e7ab95fd9244075c66b",
"score": "0.48878306",
"text": "def getTool(self) -> ghidra.framework.plugintool.PluginTool:\n ...",
"title": ""
},
{
"docid": "2a988f68972ac279479966eae931678b",
"score": "0.48871577",
"text": "def sea_runtool(request):\n return request.config.getoption(\"sea_runtool\")",
"title": ""
},
{
"docid": "2a6ab1617813174066c4bb2b48cf9150",
"score": "0.48633263",
"text": "def get_most_recently_used_tool_async( self, trans ):\n\n # Get most recently used tool.\n query = trans.sa_session.query( self.app.model.Job.tool_id ).join( self.app.model.History ) \\\n .filter( self.app.model.History.user == trans.user ) \\\n .order_by( self.app.model.Job.create_time.desc() ).limit(1)\n tool_id = query[0][0] # Get first element in first row of query.\n tool = self.get_toolbox().get_tool( tool_id )\n\n # Return tool info.\n tool_info = {\"id\": tool.id,\n \"link\": url_for( controller='tool_runner', tool_id=tool.id ),\n \"target\": tool.target,\n \"name\": tool.name, # TODO: translate this using _()\n \"minsizehint\": tool.uihints.get( 'minwidth', -1 ),\n \"description\": tool.description}\n return tool_info",
"title": ""
},
{
"docid": "8e2113f8db40e1169ac1c5b40a7a1b37",
"score": "0.4859388",
"text": "def get_usage(hosts=[]):\n ret = []\n for k in sorted(hosts.keys()):\n short_name = hosts[k].get('short_name')\n ret.append( \" %-20s # %s%s\" % (\n k, \n '[SN: %s] ' % short_name if short_name else '',\n hosts[k].get(\"summary\", \"\")\n ))\n hosts_str = \"\\n\".join(ret)\n\n usage = '''\nUsage:\n\n %s (hostname or short name)\n \n You can type a full ip address like 192.168.11.3 or a part of it\n such as `11.3` to ssh into it.\n\n If short_name is configured, you can use that name too.\n \n - modify %s to add your hosts.\n - available hosts:\n\n%s\n ''' % (sys.argv[0], config_path, hosts_str if hosts else \"no available hosts\")\n return usage",
"title": ""
},
{
"docid": "d3fe86b868be94068ebd000daddd04fe",
"score": "0.48576623",
"text": "def tool_help( self, trans, id ):\n toolbox = self.get_toolbox()\n tool = toolbox.get_tool( id )\n yield \"<html><body>\"\n if not tool:\n # TODO: arent tool ids strings now?\n yield \"Unknown tool id '%d'\" % id\n elif tool.help:\n yield tool.help\n else:\n yield \"No additional help available for tool '%s'\" % tool.name\n yield \"</body></html>\"",
"title": ""
},
{
"docid": "047ac866e3b0148248a9edb2fa5f36be",
"score": "0.4854792",
"text": "def infoStr(self):\n headStr = '%s-%s-%s-%s' % (self.osStr(), self.pyStr(), \n self.aoStr(), self.external.getCvsStat())\n return headStr",
"title": ""
},
{
"docid": "32b37bd867561ff26a4a6b16f5ea9e26",
"score": "0.48547545",
"text": "def usage(self):\n if self._usage is None:\n sio = StringIO()\n self.parser.print_usage(file=sio)\n usage = self._ws_re.sub(' ', sio.getvalue()).strip()[7:]\n doc = self._ws_re.sub(' ', getattr(self, \"__doc__\") or \"\").strip()\n if not doc:\n self._usage = usage\n else:\n self._usage = \"%s - %s\" % (usage, doc)\n return self._usage",
"title": ""
},
{
"docid": "4c874429f75aac2449fa0df0848ade80",
"score": "0.48517656",
"text": "def get_input_tool_name(step_id, steps):\n inp_provenance = \"\"\n inp_prov_id = str(step_id)\n if inp_prov_id in steps:\n name = steps[inp_prov_id][\"name\"]\n if \"Input dataset\" in name:\n inp_provenance = f\"({name})\"\n else:\n inp_provenance = f\"(output of **{name}** {{% icon tool %}})\"\n return inp_provenance",
"title": ""
},
{
"docid": "963eb628d18587ff767cdac1e939bcc3",
"score": "0.4843957",
"text": "def get_llvm_tool(tool):\n return CFG.get_llvm_tool(tool)",
"title": ""
},
{
"docid": "f6d4ad3d6c9929b7739149df49c56b1d",
"score": "0.4843766",
"text": "def tag_for_tool( tool ):\n # Biostar can now handle tags with spaces, do we want to generate tags differently now?\n return slugify( tool.name, delim='-' )",
"title": ""
},
{
"docid": "99d50316178c60af81e57cc71b015af1",
"score": "0.48278475",
"text": "def usage(self):\n return '\\n'.join(sorted(self.usageLines))",
"title": ""
},
{
"docid": "af1e1ef1958712f1d4bd6dc5e6828ca9",
"score": "0.479732",
"text": "def __getitem__(self, suite):\n return self.summary[suite]",
"title": ""
},
{
"docid": "c52765f07765df3b6a50e03efd3a0a52",
"score": "0.47928897",
"text": "def parse_test_desc(line):\n if not (('(' in line) and (')' in line)):\n raise ValueError(f'Not a snow.h macro call: {line}')\n _, _, name = line.partition('(')\n name, _, _ = name.partition(')')\n if name.startswith('\"') and name.endswith('\"'):\n name = name[1:-1]\n return name",
"title": ""
},
{
"docid": "701201b63d704ffaebf004c6967ee2c6",
"score": "0.47845766",
"text": "def testUsageInfo(self):\n r = envoy.run('python fahrplan.py')\n self.assertIn('usage: fahrplan.py', r.std_err)",
"title": ""
},
{
"docid": "1548076d0e4a2597aca8bb6944999a8c",
"score": "0.47811598",
"text": "def tool_name(self) -> str:\n return \"BlackbirdPassthrough\"",
"title": ""
},
{
"docid": "cbb85e77a83e2277e0fb83de17ba0c53",
"score": "0.47792345",
"text": "def _summary(self):\n\n if self.profile:\n row = \"%s [flops: %s, elapsed: %.2f]\"\n summary = \" >>\\n \".join(row % (\"\".join(filter(lambda c: not c.isdigit(),\n k[1:])),\n str(self.ops.get(k, \"?\")), v)\n for k, v in self.timings.items())\n elapsed = sum(self.timings.values())\n dse(\"%s\\n [Total elapsed: %.2f s]\" % (summary, elapsed))",
"title": ""
},
{
"docid": "a50b61baaf1cabf5961a98e9d01995f6",
"score": "0.47714382",
"text": "def find_tool_by_id(self, tool_id):\n return None\n # return self.tools.find_one({'_id':tool_id})",
"title": ""
},
{
"docid": "f19ad024e9f3d9114b5c66ca3ab84a0b",
"score": "0.4736285",
"text": "def parse_bamtools_stats(bamtools_stats):\n with open(bamtools_stats) as open_file:\n for line in open_file:\n sp_line = line.strip().split()\n if line.startswith('Total reads:'):\n total_reads = int(sp_line[2])\n elif line.startswith('Mapped reads:'):\n mapped_reads = int(sp_line[2])\n elif line.startswith('Duplicates:'):\n duplicate_reads = int(sp_line[1])\n elif line.startswith(\"'Proper-pairs':\"):\n proper_pairs = int(sp_line[1])\n return total_reads, mapped_reads, duplicate_reads, proper_pairs",
"title": ""
},
{
"docid": "846bba30752e62f210c9a4191f6d1cf7",
"score": "0.4728701",
"text": "def GetStageHelp(type_name):\n stage_class = GetStageClass(type_name)\n if hasattr(stage_class, 'GetHelp'):\n return stage_class.GetHelp()\n if stage_class.__doc__:\n return stage_class.__doc__\n if sys.modules[stage_class.__module__].__doc__:\n return sys.modules[stage_class.__module__].__doc__\n return ''",
"title": ""
},
{
"docid": "132834eb13b6c31ca79c8543ee478b75",
"score": "0.47252154",
"text": "def print_tools(title,tool_list,library_name):\n print(title,*prepend(tool_list,library_name),sep=\"\\n\")\n print(\"\\n\")",
"title": ""
},
{
"docid": "b07bde3fc2477166520a0df1503719b1",
"score": "0.47247365",
"text": "def show_info():\n\n echo.bold(' - flowtool information dump -')\n echo.bold()\n echo.bold(colors.cyan('python executable:'), (sys.executable))\n echo.bold(colors.cyan('python version:'), str(sys.version_info))\n echo.bold()\n\n echo.bold(colors.cyan('flowtool_packages:'))\n for e in sorted(get_extensions(), key=attrgetter('project_name')):\n echo.white(' -', colors.cyan(e.project_name), '(%s)' % e.version)\n\n echo.bold()\n echo.bold(colors.cyan('installed commands:'))\n for c in get_commands():\n echo.white(' -', colors.green(c.name), '(from %s)' % c.dist.project_name)",
"title": ""
},
{
"docid": "6be8b7f72dd3298b4d031e3f6d9ecc42",
"score": "0.4724104",
"text": "def monitoring_tool(self):\n return self._monitoring_tool",
"title": ""
},
{
"docid": "fa3b47430f95a608349c5ce70d761d60",
"score": "0.47139633",
"text": "def is_tool(name):\n return find_executable(name) is not None",
"title": ""
},
{
"docid": "e18e120dfa3a910e393503cea091f41e",
"score": "0.4710905",
"text": "def test_help(tool_cls):\r\n tool = tool_cls()\r\n \r\n try:\r\n tool.run(\"--help\")\r\n\r\n except SystemExit as e:\r\n if not hasattr(sys.stdout, \"getvalue\"):\r\n raise Exception('stdout not captured in test.')\r\n output = sys.stdout.getvalue().strip()\r\n assert output.startswith('usage:')",
"title": ""
},
{
"docid": "1a12919bfc1424960f8b959e7434cdee",
"score": "0.4707729",
"text": "def getReadGroupString(samtools, inBamVar):\n\n return ('$(' + samtools + ' view -H $' + inBamVar + ' 2>> $logFile | '\n 'grep \"@RG\"| sed \"s:\\\\t:\\\\\\\\t:g\"| sed \"s:\\\\t:\\\\\\\\t:g\"\\n')",
"title": ""
},
{
"docid": "b93ffb5f67b4a904b746830e0e5468d1",
"score": "0.47070873",
"text": "def _get_telemetry_tag(check_bundle):\n return _get_tag_string(check_bundle[\"type\"], \"telemetry\")",
"title": ""
},
{
"docid": "7defe246cb7e21ad9dcdf5535bdd70ee",
"score": "0.47068003",
"text": "def test_tcname_basic(report):\n\n report.nodeid = \"ons_tests/test_synapsert.py::test_demo_synapsert1\"\n report.keywords = None\n report.keywords = {\"test_demo_synapsert1\": 1, \"tests\": 1, \"ons_tests/test_synapsert.py\": 1}\n assert get_tcname(report) == \"test_demo_synapsert1\"",
"title": ""
},
{
"docid": "a1172d1e9bf54c9fc32c779e8b8ce946",
"score": "0.4697443",
"text": "def diagnostics():\n versions = \"\"\n for package, version in get_status_as_odict().items():\n versions += \"{0}: {1}\\n\".format(package, version)\n return versions.strip()",
"title": ""
},
{
"docid": "0523814bea83221936bea31eda81b969",
"score": "0.46973848",
"text": "def get_stats(stats):\n data = (stats['FF'], stats['Ch'], stats['S'], stats['IE'])\n text = \", [FF:{0[0]}, CH:{0[1]}, SF:{0[2]}, IE:{0[3]}]\".format(data)\n\n return text",
"title": ""
},
{
"docid": "f6e687dfc0b97310266b76dc09d52c48",
"score": "0.46906045",
"text": "def displayName(self):\n return self.tr(\"Finding Optimal Radius script\")",
"title": ""
},
{
"docid": "f4cc96718c85c60f9e0b17100ece0be8",
"score": "0.46824923",
"text": "def _get_stat(self):\n def dev_filter(x):\n # get first word and remove trailing interface number\n x = x.strip().split(\" \")[0][:-1]\n\n if x in self.interfaces_blacklist:\n return False\n\n if self.all_interfaces:\n return True\n\n if x in self.interfaces:\n return True\n\n return False\n\n # read devfile, skip two header files\n x = filter(dev_filter, open(self.devfile).readlines()[2:])\n\n try:\n # split info into words, filter empty ones\n return [list(filter(lambda x: x, _x.split(\" \"))) for _x in x]\n\n except StopIteration:\n return None",
"title": ""
},
{
"docid": "c0720686849148319efb0fa69bfb9205",
"score": "0.46820185",
"text": "def get_summary(nameurl, nbtaburl):\n file = open(\"output/summaryPython.txt\", \"w\")\n file.write(\"Summary of the extracted tables\" + os.linesep)\n for i in range(len(nbtaburl)):\n file.write(nameurl[i] + \", \" + str(nbtaburl[i]) + os.linesep)\n file.close()",
"title": ""
},
{
"docid": "9f9e045e10e0281f5907a0df512d1e76",
"score": "0.46801883",
"text": "def name_trial(trial):\n assist = args.assist.capitalize() + 'Assist' if args.assist != 'none' else 'NoAssist'\n mcts = f'vsMCTS{args.mcts}' if args.mcts > 0 else ''\n debug = '-debug' if args.debug else ''\n return f'{num_learners}x{trial.trainable_name}{mcts}-{assist}{debug}'",
"title": ""
},
{
"docid": "a9242c28e8ceda1a6329cd9a50728899",
"score": "0.46787298",
"text": "def get_stata_short_title(title: str) -> str:\n\t# Only record part of title up to Stata version\n\tpattern = r'((?:\\d*\\s*-\\s*)?(Stata\\/(IC|SE|MP))\\s\\d*(\\.\\d*)?)'\n\tmatched = re.match(pattern, title)\n\tif matched is not None:\n\t\treturn matched.groups(0)[0]\n\telse:\n\t\traise Error('Invalid title for the Stata window')",
"title": ""
},
{
"docid": "5506ef5846d3f26f34d2deb1c14e6a62",
"score": "0.46689385",
"text": "def is_tool(name):\n\n # from whichcraft import which\n from shutil import which\n\n return which(name) is not None",
"title": ""
},
{
"docid": "129b4db14977f1babac1dce4efd507bc",
"score": "0.46582034",
"text": "def get_toolnlp_base(self):\n if self._pipeline[\"tools\"][\"stanza\"][\"processors\"]:\n return \"stanza\", self._pipeline[\"tools\"][\"stanza\"][\"processors\"]\n elif self._pipeline[\"tools\"][\"stanfordcorenlp\"][\"processors\"]:\n return \"stanfordcorenlp\", self._pipeline[\"tools\"][\"stanfordcorenlp\"][\"processors\"]\n else:\n print(\"This tool nlp is not defined as a base tool, try: [stanza|stanfordecorenlp]\")\n sys.exit(0)",
"title": ""
},
{
"docid": "871c51144960a7422ea8c83ab7013b6d",
"score": "0.46559164",
"text": "def fighter_name_line_long_gym(line):\n pos1 = line.find(NAME_LINE_START)\n pos2 = line.find(NAME_LINE_END)\n fname = line[pos1 + len(NAME_LINE_START): pos2]\n\n return fname",
"title": ""
},
{
"docid": "be8acd245f87c8e9eccaf8b14adef2c2",
"score": "0.4655777",
"text": "def _gather_terraform_info():\n import subprocess\n\n try:\n process = subprocess.run([\"terraform\", \"version\", \"-json\"], capture_output=True, text=True, check=True)\n info_dict = json.loads(process.stdout)\n return info_dict.get(\"terraform_version\", \"Not available\")\n except Exception:\n return \"Not available\"",
"title": ""
},
{
"docid": "fcfb011e213d84e69ddde3eba1769daf",
"score": "0.4651216",
"text": "def getPublicTool(self, name=None):\n return self.__getOne( self._publicTools, name, \"PublicTools\")",
"title": ""
},
{
"docid": "fb89464eadc99d41f8f6ae1c93554eeb",
"score": "0.4647319",
"text": "def get_tool_filepath(self, tool_alias):\n tools_dict = self.get_tools()\n if tool_alias in tools_dict:\n if self.tools_path is None:\n return None\n else:\n return os.path.join(self.tools_path, tool_alias)\n else:\n return None",
"title": ""
},
{
"docid": "a0b5be8154f1b8b5e8c331feb5ea02e3",
"score": "0.46450967",
"text": "def _ExtractSourceName(self, gcno_summary, file_name):\n src_file_path = None\n for key in gcno_summary.functions:\n src_file_path = gcno_summary.functions[key].src_file_name\n src_parts = src_file_path.rsplit(\".\", 1)\n src_file_name = src_parts[0]\n src_extension = src_parts[1] if len(src_parts) > 1 else None\n if src_extension not in [\"c\", \"cpp\", \"cc\"]:\n logging.warn(\"Found unsupported file type: %s\", src_file_path)\n continue\n if src_file_name.endswith(file_name):\n logging.info(\"Coverage source file: %s\", src_file_path)\n break\n return src_file_path",
"title": ""
},
{
"docid": "998d9b8ad77de3f770ad2e2d6dbeb71e",
"score": "0.46346086",
"text": "def __get_info(self):\n slo_full_name = self.__get_slo_full_name()\n step_name = self.error_budget_policy_step_name\n return f\"{slo_full_name :<32} | {step_name :<8}\"",
"title": ""
},
{
"docid": "6aedce844dcce0c4ef186cdbd400af2a",
"score": "0.46297896",
"text": "def statsStringShort(self) -> str:\n additiveStats = { \"Max secondaries\": self.maxSecondaries, \"Max primaries\": self.maxPrimaries,\n \"Max turrets\": self.maxTurrets, \"Max modules\": self.maxModules, \"Cargo\": self.cargo,\n \"Armour\": self.armour, \"Handling\": self.handling}\n\n multiplierStats = { \"Max secondaries\": self.maxSecondariesMultiplier, \"Max primaries\": self.maxPrimariesMultiplier,\n \"Max turrets\": self.maxTurretsMultiplier, \"Max modules\": self.maxModulesMultiplier,\n \"Cargo\": self.cargoMultiplier, \"Armour\": self.armourMultiplier,\n \"Handling\": self.handlingMultiplier}\n\n statsStr = \"*\"\n additiveStrs = (statName + \": \" + lib.stringTyping.formatAdditive(additiveStats[statName])\n for statName in additiveStats if additiveStats[statName] != 0)\n multiplierStrs = (statName + \": \" + lib.stringTyping.formatMultiplier(multiplierStats[statName])\n for statName in additiveStats if multiplierStats[statName] != 1)\n statsStr = \", \".join(tuple(additiveStrs) + tuple(multiplierStrs))\n\n return statsStr if len(statsStr) > 1 else \"*No effect*\"",
"title": ""
},
{
"docid": "9bd0d79ea4814d389cfaafbe553e7fb5",
"score": "0.46184602",
"text": "def statistic(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"statistic\")",
"title": ""
},
{
"docid": "e5962d794b70bbea2788abae3ebae9dd",
"score": "0.46162924",
"text": "def test_usage():\n for flag in ['-h', '--help']:\n ret_val, out = getstatusoutput(f'{SOURCE_PATH} {flag}')\n assert re.match('usage', out, re.IGNORECASE)\n assert ret_val == 0",
"title": ""
},
{
"docid": "0fa2054ab5fb2be5e8538788cd5993c9",
"score": "0.46098056",
"text": "def __get_tb_summary_title(type_of_model):\n if type_of_model == \"BatchNorm\":\n return \"With-Batch_Normalization-\"\n elif type_of_model == \"NoBatchNorm\":\n return \"Without-Batch_Normalization-\"\n elif type_of_model == \"Dropout\":\n return \"WithDropout_BN-\"",
"title": ""
},
{
"docid": "e08f36904d4fc5155f1961cf48f9a937",
"score": "0.46072674",
"text": "def get_host_tool_path(tool):\n\n return os.path.join(SDK_TOOLS_DIR, tool)",
"title": ""
},
{
"docid": "c92c226ea98c5c35e005dfdf719270aa",
"score": "0.4606301",
"text": "def stats_output(stats):\n stats = \"Length: \" + str(stats[0]) + \"\\n\" + \\\n \"Heterozygotic sites: \" + str(stats[1]) + \"\\n\" + \\\n \"Phased sites: \" + str(stats[2]) + \"\\n\" + \\\n \"Not phased sites: \" + str(stats[3]) + \"\\n\" + \\\n \"Two alternatives: \" + str(stats[4]) + \"\\n\" + \\\n \"Low quality sites: \" + str(stats[5]) + \"\\n\" + \\\n \"Lowercases: \" + str(stats[6]) + \"\\n\" + \\\n \"Deletions: \" + str(stats[7]) + \"\\n\\n\\n\"\n return stats",
"title": ""
},
{
"docid": "adbbf90f6586a3876f3f24f44bfe9977",
"score": "0.46054733",
"text": "def bot_serverinfo( self, mess, args):\n version = open('/proc/version').read().strip()\n loadavg = open('/proc/loadavg').read().strip()\n\n return '%s\\n\\n%s' % ( version, loadavg, )",
"title": ""
},
{
"docid": "b439d14715d4ae0b5047d81011c8a1b6",
"score": "0.46054035",
"text": "def GetStatus(self):\n msg = []\n msg.append('Load average: %s' % self._GetLoadString())\n\n # Get column legend from 'top' and throw away summary header and legend\n top_output = self.dut.CheckOutput(\n ['top', '-b', '-c', '-n', '1', '-w', '512']).splitlines()\n column_ids = top_output[self.HEADER_LINES].split()\n pid_column = column_ids.index('PID')\n cpu_column = column_ids.index('%CPU')\n command_column = column_ids.index('COMMAND')\n top_output = top_output[self.HEADER_LINES + 1:]\n\n # Find up to NUM_TOP_PROCESSES processes with CPU usage >= CPU_THRESHOLD\n for process in top_output[0:self.NUM_TOP_PROCESSES]:\n attr = process.split(None, command_column)\n if float(attr[cpu_column]) < self.CPU_THRESHOLD:\n break\n command = attr[command_column][0:self.COMMAND_LENGTH]\n msg.append('Process %s using %s%% CPU: %s' %\n (attr[pid_column], attr[cpu_column], command))\n\n return '; '.join(msg)",
"title": ""
},
{
"docid": "260b27c363f728bb144650cca60ba8af",
"score": "0.45890865",
"text": "def summary(self):\n\tcut = 100\n\tif len(self.desc) > cut:\n\t\treturn self.desc[:cut]+\" ...\"\n\telse:\n\t\treturn self.desc[:cut]",
"title": ""
},
{
"docid": "c27174d2da953c976df06ab1d0f860fa",
"score": "0.45850247",
"text": "def tools(self, point):\n return self.valid_tools[self.region(point)]",
"title": ""
},
{
"docid": "859926bff2e51f4e5cfec107186dd64e",
"score": "0.45774147",
"text": "def get_strainname():\n parser=argparse.ArgumentParser()\n parser.add_argument('--strainname', type=str, default='31')\n arg=parser.parse_args()\n return arg.strainname",
"title": ""
},
{
"docid": "1c835a7c65c81bdff852607d942aead0",
"score": "0.45549962",
"text": "def get_test_case_name(self):\n name = self.visible_element_get_text(RunTestPageLocators.TC_TITLE)\n return name",
"title": ""
},
{
"docid": "10fa8d6b76c898dc63d22be79c9da77a",
"score": "0.45505133",
"text": "def sultan_get_monit_summary(self, msg, hostname=None):\n username = self.config['username']\n if not hostname:\n return 'Sultan will only respond, if you provide a hostname'\n \n with Sultan.load(hostname=hostname,user=username) as s:\n rtn = s.sudo('monit summary').run()\n \n return \"/code {}\".format(\"\\n\".join(rtn.stdout))",
"title": ""
},
{
"docid": "cae2074e0929796e9a5fdeff9c14fe1d",
"score": "0.4549214",
"text": "def getProgramsName(self):\n\t\tprogramStr = \"\"\n\t\tfor program in self._programs:\n\t\t\tprogramStr += program.getName() + \" \"\n\t\treturn programStr",
"title": ""
},
{
"docid": "136e3b6a2b350b62af6690016de4ada3",
"score": "0.45478302",
"text": "def parse_locust_stats(env):\n stats = env.runner.stats\n statistics = {\n \"requests\": {},\n \"failures\": {},\n \"num_requests\": stats.num_requests,\n \"num_requests_fail\": stats.num_failures,\n }\n\n for name, value in stats.entries.items():\n locust_task_name = \"{0}_{1}\".format(name[1], name[0])\n statistics[\"requests\"][locust_task_name] = {\n \"num_requests\": value.num_requests,\n \"min_response_time\": value.min_response_time,\n \"median_response_time\": value.median_response_time,\n \"avg_response_time\": value.avg_response_time,\n \"max_response_time\": value.max_response_time,\n \"total_rps\": value.total_rps,\n }\n\n for id, error in env.runner.errors.items():\n error_dict = error.to_dict()\n locust_task_name = \"{0}_{1}\".format(\n error_dict[\"method\"], error_dict[\"name\"]\n )\n statistics[\"failures\"][locust_task_name] = error_dict\n\n return statistics",
"title": ""
},
{
"docid": "796db8794f0b1830994a9c4238087632",
"score": "0.45450106",
"text": "def details_text():\r\n text = \"SSIS Deployment Checker\\n©2016, Paul Lucas\\nVersion {}\"\r\n version = changelog.main()[-1]\r\n\r\n return text.format(version[0])",
"title": ""
},
{
"docid": "3c7a0dc0cdb25a86751dd36ab605e034",
"score": "0.45432496",
"text": "def get_descriptive(self):\n long_name = f\"{self.year} {self.make} {self.model} {self.price}\" \\\n f\"{self.engine} {self.fuel_type} {self.vin} {self.options} \"\n return long_name.title()",
"title": ""
},
{
"docid": "84bb273bb2269ebb448523cda4b4cf76",
"score": "0.4542082",
"text": "def statistic(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"statistic\")",
"title": ""
},
{
"docid": "a7929dc44227578080ea9dc1ad1b5e54",
"score": "0.4528613",
"text": "def getExternalNames(self, externalTool: unicode) -> List[unicode]:\n ...",
"title": ""
},
{
"docid": "121304e9ca8f6316f1527a0128bfb20c",
"score": "0.45264125",
"text": "def get_cli(self, param: 'str') -> str:\n\n assert isinstance(param,\n str), f'Argument must be a string and not {type(param)}. Try get_some() or get_all() method instead'\n metadata_dict = self._utility()\n\n param = param.upper()\n\n if param in metadata_dict:\n\n print(metadata_dict.get(param))\n\n else:\n\n print(self._incomplete_str_support(param, metadata_dict))",
"title": ""
},
{
"docid": "58775708a0ef501f724448fab39c809b",
"score": "0.4518934",
"text": "def get_usage(self):\n pad = max(map(len, self._commands.iterkeys())) + 2\n format = ' %%- %ds%%s' % pad\n\n rv = []\n\n if self.usage:\n rv.append(self.usage)\n\n for name, command in self._commands.iteritems():\n usage = name\n description = command.description or ''\n usage = format % (name, description)\n rv.append(usage)\n\n return \"\\n\".join(rv)",
"title": ""
},
{
"docid": "db3d2f57c106399117ce4ad84fdefee7",
"score": "0.45150036",
"text": "def test_usage():\n for flag in ['-h', '--help']:\n cmd = f'{SOURCE_PATH} {flag}'\n ret_val, out = getstatusoutput(cmd)\n assert ret_val == 0\n assert re.match('usage', out, re.IGNORECASE)",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="740"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">696d3c1b102fbc3a1cbd5a1e81a7febb</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Get a list of tracks and save them to the database Return the number of track saved</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "ed30a712872bcfd2eb98e0d4efd63b99",
"score": "0.53171325",
"text": "def write_track_to_datastore(track, user, location): \n logging.info(\"Saving track \\\"%s\\\" by \\\"%s\\\" (id: %s, created at: %s) to datastore ...\" % \\\n (track['title'], user.username, track['id'], track['created_at'])) \n created_at = datetime.datetime.strptime(track['created_at'], \"%Y/%m/%d %H:%M:%S +0000\")\n try:\n release_date = datetime.date(year=int(track['release_year'] or 1900), month=int(track['release_month'] or 1), day=int(track['release_day'] or 1))\n except ValueError:\n release_date = datetime.date(year=1900, month = 1, day = 1)\n if track['genre']:\n genre = track['genre'].strip().lower()\n else:\n genre = ''\n \n new_track = models.Track( \\\n track_id = int(track['id']), \n permalink = track['permalink'], \n permalink_url = track['permalink_url'], \n title = track['title'],\n \\\n stream_url = track['stream_url'],\n waveform_url = track['waveform_url'],\n artwork_url = track['artwork_url'],\n purchase_url = track['purchase_url'],\n \\\n created_at = created_at,\n downloadable = track['downloadable'], \n original_format = track['original_format'],\n release_date = release_date,\n release = track['release'],\n isrc = track['isrc'],\n label_name = track['label_name'],\n label_id = track['label_id'],\n license = track['license'],\n genre = genre,\n bpm = track['bpm'],\n key_signature = track['key_signature'],\n duration = track['duration'],\n description = track['description'],\n \\\n user = user.key(),\n location = location.key()) \n new_track.put()\n logging.info(\"Track saved to datastore.\")",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "7113dbafa0725fb0b398d2bfaf371758",
"score": "0.65143496",
"text": "def getTrackCount(self):",
"title": ""
},
{
"docid": "c22c60d02e82c1160fd75ea7d88939ed",
"score": "0.6346815",
"text": "def task_7_song_counter():\n list = Song.objects.all()\n return len(list)",
"title": ""
},
{
"docid": "d81c6b4fce4f7af8828989e084a4a5ce",
"score": "0.633222",
"text": "def track_to_artistid_dict(sp, tracks):\n track_artist_dict = {}\n for i in range(0, len(tracks), 50):\n track_ids = tracks[i : i + 50]\n track_infos = sp.tracks(track_ids)[\"tracks\"]\n assert len(track_ids) == len(track_infos)\n for track_id, info in zip(track_ids, track_infos):\n if not info:\n continue # song not found in Spotify\n track_artist_dict[track_id] = info[\"artists\"][0][\"id\"]\n if len(track_artist_dict) % 100 == 0:\n print(\"Stored {} artist IDs\".format(len(track_artist_dict)))\n print(list(track_artist_dict.items())[0])\n return track_artist_dict",
"title": ""
},
{
"docid": "64dc3e6d1989bfc5022c3692b1c75eb6",
"score": "0.6273711",
"text": "def tracks(self):\r\n\t\treturn None",
"title": ""
},
{
"docid": "b0549f5fe6e385de5a770844b6a20d21",
"score": "0.613569",
"text": "def returnTracks():\n return song().return_tracks",
"title": ""
},
{
"docid": "c53d0deec7ac5ca19ae2a2500f7d2300",
"score": "0.6110667",
"text": "def get_tracks(self, get_size=False):\n # TODO Converge implementation by creating a Track class?\n # It could get the size only on demand per-track\n # Retrieve and remember the filesize of each track:\n if get_size and self.library.true_file_size:\n for t in self.__tracks:\n if not 'bytes' in t:\n r = urllib2.Request(self.get_track_stream(t)[0])\n r.get_method = lambda: 'HEAD'\n u = urllib2.urlopen(r)\n t['bytes'] = int(u.headers['Content-Length']) + ID3V1_TRAILER_SIZE\n return self.__tracks",
"title": ""
},
{
"docid": "be9f60a55bb0b1911ea7055685199b5f",
"score": "0.60817415",
"text": "def get_dum_tracks():\n\n # Load dummy user data from JSON file\n with open(\"data/tracks_OU.json\") as f:\n dummyuser_tracks = json.loads(f.read())\n\n # Create dummy users, store them in list so we can use them\n dum_tracks_in_db = []\n\n for user in dummyuser_tracks:\n for user_id, detail_array in user.items():\n user_id = int(user_id)\n for item in detail_array:\n sp_track_id, track_name, artist_name, artist_id = (\n item[\"sp_track_id\"],\n item[\"track_name\"],\n item[\"artist_name\"],\n item[\"artist_id\"],\n )\n db_track = crud.add_track(\n user_id, track_name, sp_track_id, artist_name, artist_id\n )\n dum_tracks_in_db.append(db_track)\n model.db.session.commit()\n\n return dum_tracks_in_db",
"title": ""
},
{
"docid": "59a065f12b2dce7fbbf46b3cf903557e",
"score": "0.6065309",
"text": "def get_tracks(self, get_size=False):\n # Re-sort by track number\n if not self.__sorted:\n self.__tracks.sort(key=lambda t: t.get('track'))\n # Retrieve and remember the filesize of each track\n if get_size and self.library.true_file_size:\n for t in self.__tracks:\n if not 'bytes' in t:\n r = urllib2.Request(self.get_track_stream(t))\n r.get_method = lambda: 'HEAD'\n u = urllib2.urlopen(r)\n t['bytes'] = int(u.headers['Content-Length']) + ID3V1_TRAILER_SIZE\n return self.__tracks",
"title": ""
},
{
"docid": "d88d57715ae27fb7ea20f6a00a8449fd",
"score": "0.60168767",
"text": "def saved_tracks(max_results=None, track_only=False):\n limit = get_limit(max_results, 50)\n for item in iterate_results(\n 'current_user_saved_tracks',\n max_results=max_results,\n limit=limit):\n if track_only:\n yield item['track']\n else:\n yield item",
"title": ""
},
{
"docid": "4fcfe2eee677a096aa771f9e6a30aa53",
"score": "0.6004392",
"text": "def get_num_tracks(self, track_type):\n return 0",
"title": ""
},
{
"docid": "1ecd053097e7ac24dd37bf2413d28b51",
"score": "0.5992482",
"text": "def numTracks(self):\r\n\t\treturn self.trackGroup.numTracks",
"title": ""
},
{
"docid": "cebd6ba6974c99d59445cb023c75abb6",
"score": "0.591721",
"text": "def h_tracks(self, _source, _ref, tracks):\n \n ## Should occur but hey, better safe than sorry ;-)\n if tracks is None:\n return\n\n track=tracks[0]\n\n if track is None:\n self.pub(\"warning\", \"Received null track object\")\n return\n\n if track.get(\"track_mbid\") is None:\n track[\"track_mbid\"]=\"\"\n\n if track.get(\"artist_mbid\") is None:\n track[\"artist_mbid\"]=\"\"\n\n\n ## Update the cache regardless of the 'track' object we get:\n ## if the entry wasn't found on Musicbrainz, we'll have at least\n ## a trace of the attempt (i.e. 'updated' field) and thus we can\n ## rate limit the retries. \n try: \n new=self._updateOrInsert(track)\n except Exception,e:\n self.pub(\"log\", \"error\", \"* Exception whilst accessing database: %s\" % e)\n #print track\n return\n \n if new:\n artist_name=track[\"artist_name\"]\n track_name= track[\"track_name\"]\n track_mbid= track[\"track_mbid\"]\n self.pub(\"log\", \"New: artist(%s) track(%s) mbid(%s)\" % (artist_name, track_name, track_mbid))\n \n ## Insert/Update a record based on the answer provided\n ## by Musicbrainz: this way, we have more ways to \"hit\"\n ## a potential track target in the cache\n mb_artist_name=track.get(\"mb_artist_name\", None)\n mb_track_name=track.get(\"mb_track_name\", None)\n \n if mb_artist_name is not None:\n if mb_track_name is not None:\n details=(0,0,0, ## filled out anyhow by update/insert method\n mb_track_name, track[\"track_mbid\"],\n mb_artist_name, track[\"artist_mbid\"]\n )\n \n mb_track=makeTrackDict(details)\n try:\n self._updateOrInsert(mb_track)\n except Exception,e:\n self.pub(\"log\", \"error\", \"Exception whilst accessing database: %s\" % e)",
"title": ""
},
{
"docid": "db070cb429efc0f603ff18bdcf622e69",
"score": "0.58947957",
"text": "def get_tracks():\n conn = db_connect.connect()\n query = conn.execute('select trackid, name, composer, unitprice from tracks;')\n result = {'data': [dict(zip(tuple (query.keys()) ,i)) for i in query.cursor]}\n return jsonify(result)",
"title": ""
},
{
"docid": "a6d14b47e2ec7c9ab8f7e69ddcc88bff",
"score": "0.5808395",
"text": "def getTracks():\n return getSong().visible_tracks",
"title": ""
},
{
"docid": "3cae7c2e5fed0a7ad719d95f5dbbc58c",
"score": "0.5796001",
"text": "def test_saved_tracks_multi(self):\n # find if tracks are saved initially\n xs = [self.tracks[0], self.tracks[1]]\n xs_names = [self.track_names[0], self.track_names[1]]\n init_status = list(self.api.are_tracks_saved(xs))\n try:\n # delete all currently saved tracks\n for i, status in enumerate(init_status):\n if status:\n self.api.saved_tracks_remove([xs[i]])\n # save tracks\n self.api.saved_tracks_add(xs)\n # check save status\n sts = list(self.api.are_tracks_saved(xs))\n self.assertListEqual(sts, [True, True])\n # check general endpoint now that at least 2 are saved\n track_gen = self.api.saved_tracks()\n for i in range(2):\n track = next(track_gen)['track']\n self.assertEqual(track['type'], 'track')\n # remove one\n self.api.saved_tracks_remove([xs[0]])\n # check save status\n sts = list(self.api.are_tracks_saved(xs))\n self.assertListEqual(sts, [False, True])\n # reset initial status\n for i, i_st in enumerate(init_status):\n if i_st != sts[i]:\n if i_st == True:\n self.api.saved_tracks_add([xs[i]])\n else:\n self.api.saved_tracks_remove([xs[i]])\n final_status = list(self.api.are_tracks_saved(xs))\n self.assertListEqual(init_status, final_status)\n except:\n raise Exception(\n \"Potentially invalid account state - saved status \" +\n \"for tracks: %s\" % (\n ', '.join(\n [\n '%s (%s)' % (xs[i], xs_names[i])\n for i in range(len(xs))\n ]\n )\n )\n )",
"title": ""
},
{
"docid": "ec359d6dd709e449bc98896441794800",
"score": "0.5789012",
"text": "def all_tracks():\n doc = TrackDocument()\n return doc.all_resources()",
"title": ""
},
{
"docid": "8ab1e3d71b6095670c804966d0442349",
"score": "0.57848865",
"text": "def test_added_track_update(self):\n added_filename = 'new_one.mp3'\n added_track = {'artist': 'Third Artist', 'title': 'Blobs'}\n create_mock_tracks({added_filename: added_track})\n update_db(TRACK_DIR)\n found_track = Track.query.filter_by(\n artist=added_track['artist'],\n title=added_track['title']\n )\n assert found_track is not None\n assert filenames_unique(Track.query.all()) # no duplicates",
"title": ""
},
{
"docid": "3fa2e81e9bcb34753f17ecc880a0f198",
"score": "0.57650495",
"text": "def getTracksPageCount(self):\n\t\t\n\t\tif self._track_pages:\n\t\t\treturn self._track_pages\n\t\t\n\t\tself._get_tracks_info()\n\t\t\n\t\treturn self._track_pages",
"title": ""
},
{
"docid": "e24998682b3413687533e88c6be5eb0f",
"score": "0.5760378",
"text": "def tracks(self):\n for item in self.items:\n yield int(item[1].text)",
"title": ""
},
{
"docid": "b6dcf7e1d6cc32d941ab56fe0a660bc7",
"score": "0.5737525",
"text": "def getTracks(self):",
"title": ""
},
{
"docid": "65b1be7429c5572c8a8a86c892b7adf5",
"score": "0.57178885",
"text": "def __len__(self):\n return len(self._tracks)",
"title": ""
},
{
"docid": "2639c7d8d527b37fc4eff00638067891",
"score": "0.57108665",
"text": "def audio_get_track_count(self):\n e=VLCException()\n return libvlc_audio_get_track_count(self, e)",
"title": ""
},
{
"docid": "100aefe1fd8272e2f6f8fdea5597046b",
"score": "0.57074857",
"text": "def save_multiple_user_histories(uid_list, id_playlist):\n base_playlist = get_playback(id_playlist)\n for uid in uid_list:\n history_user = get_playback_history(uid, base_playlist)\n # Connects and write into DataBase\n db = connection_database()\n collection = db['playback_history']\n collection.insert_many(history_user)\n print(\"A Sample of Playback-History was added to DataBase\")",
"title": ""
},
{
"docid": "b690fc40c00a9b91d1f7eac5ad990150",
"score": "0.5657861",
"text": "def getRowCount(self):\n try:\n self.c.execute(\"\"\"SELECT Count(*) FROM tracks\"\"\") \n count=self.c.fetchone()[0]\n except: count=0\n \n return count",
"title": ""
},
{
"docid": "cb3d3b9c4a59bc2cadb6bed174d511e1",
"score": "0.56423044",
"text": "def get_tracks(album):\n #initiate a list to put the data for each album\n\t\talbum_tracks=[]\n\n #search the spotify API for tracks of each album\n\t\tresults=sp.album_tracks(album)\n\n #list of all the album tracks\n\t\titems=results['items']#contains a list of all the album tracks\n \n #iterate acrross album tracks\n\t\tfor it in items:\n \t#save each attribute in a variable\n\t\t\ttrack_id=it['id']\n\t\t\tdisc_number=it['disc_number']\n\t\t\tduration_ms=it['duration_ms']\n\t\t\texplicit=it['explicit']\n\t\t\tis_local=it['is_local']\n\t\t\ttrack_name=it['name']\n\t\t\tpreview_url=it['preview_url']\n\t\t\ttrack_number=it['track_number']\n\t\t\ttrack_type=it['type']\n\t\t\talbum_id=album\n\n #consolidate the variables in a list\n\t\t\ttrack=[track_id,album_id,disc_number,duration_ms,explicit,is_local,track_name,preview_url,track_number,track_type]\n\n #append the each track to a lsit containing all the album tracks\n\t\t\talbum_tracks.append(track)\n\n #return in list of lists the album tracks\n\t\treturn album_tracks",
"title": ""
},
{
"docid": "5cfa1033090692d3cc5704b10f45cc52",
"score": "0.5631614",
"text": "def to_file(cls, tracks: List['Track'], file_path: str):\n with open(file_path, 'w+', encoding='utf8') as file:\n for track in tracks:\n file.write(track.to_string() + '\\n')",
"title": ""
},
{
"docid": "49cb734a6f4b618c664df8bbe5cad474",
"score": "0.5569487",
"text": "def create_mock_tracks(tracks, src_track=\"test/sinewave.mp3\"):\n for track in tracks.keys():\n filename = TRACK_DIR + track\n shutil.copyfile(src_track, filename)\n song_tag = MP3(filename)\n for k, v in tracks[track].items():\n song_tag[k] = str(v)\n song_tag.save()",
"title": ""
},
{
"docid": "bf092ce2c70e61bfac494aaa5627a4ee",
"score": "0.55594593",
"text": "def save_data(data):\n\n conn = sqlite3.connect('db/music.db')\n c = conn.cursor()\n for d_set in data:\n c.execute(\"\"\"INSERT INTO music\n (year, rank,\n song_name, artist)\n values (?,?,?,?)\"\"\",\n (d_set['year'], d_set['num'], d_set['name'], d_set['artist']))\n conn.commit()\n conn.close()",
"title": ""
},
{
"docid": "06d1c82aaacecfa3302c2e77d8fb74ba",
"score": "0.555714",
"text": "def loadTracks(self, tracks, mark_ids, sample_ids, lab_ids, tissue_ids=None, devstage_ids=None):\n\t\tids = {}\n\n\t\t\n\t\tcursor = self.cnx.cursor()\n\n\t\tadd_track_tiss = (\"INSERT INTO track \"\n\t\t\t \"(tissue_id, mark_id, sample_id, lab_id)\"\n\t\t\t \"VALUES (%s, %s, %s, %s)\"\n\t\t\t \"ON DUPLICATE KEY UPDATE id= LAST_INSERT_ID(id)\")\n\t\tadd_track_devstage = (\"INSERT INTO track \"\n\t\t\t \"(mark_id, sample_id, lab_id, devstage_id)\"\n\t\t\t \"VALUES (%s, %s, %s, %s)\"\n\t\t\t \"ON DUPLICATE KEY UPDATE id= LAST_INSERT_ID(id)\")\n\n\t\tfor track_name in tracks:\n\t\t\ttrack_id=None\n\t\t\t\n\t\t\ttrack = tracks[track_name]\n\t\t\tmark_id = mark_ids[track.mark_id]\n\t\t\tsample_id = sample_ids[track.sample_id]\n\t\t\tlab_id = lab_ids[track.lab_id]\n\t\t\t\n\t\t\t\n\n\t\t\ttissue_id = \"NULL\"\n\t\t\tif (track.tissue_id != None):\n\t\t\t\ttissue_id = tissue_ids[track.tissue_id]\n\t\t\t\ttrack_tuple = (tissue_id, mark_id, sample_id, lab_id)\n\t\t\t\tcursor.execute(add_track_tiss, track_tuple)\n\t\t\t\ttrack_id = cursor.lastrowid\n\n\t\t\tdevstage_id = \"NULL\"\n\t\t\tif (track.devstage_id != None):\n\t\t\t\tdevstage_id = devstage_ids[track.dev_stage_id]\n\t\t\t\ttrack_tuple = (mark_id, sample_id, lab_id, devstage_id)\n\t\t\t\tcursor.execute(add_track_devstage, track_tuple)\n\t\t\t\ttrack_id = cursor.lastrowid\n\t\t\t\n\n\t\t\ttrack.track_id = track_id\n\n\t\t\tids[track_name] = track_id\n\n\t\tself.cnx.commit()\n\t\tcursor.close()\n\n\t\treturn ids",
"title": ""
},
{
"docid": "9184c2b6866b7771c8e1ff9de2d13ce3",
"score": "0.5529213",
"text": "def pickle_track(self):\n import pickle\n pickle.dump(self, open(self.module_path + '/../data/track/' + self.name + '.tracks', 'wb'))",
"title": ""
},
{
"docid": "ee8a0d27d5c957759e4819b5a83e331d",
"score": "0.55270225",
"text": "def test_update_mp3_with_maxtracks(self):\n self.add_mp3(filename='song.mp3', artist='Artist',\n album='Album', title='Title', tracknum=1)\n self.update_mp3('song.mp3', tracknum=2, maxtracks=10)",
"title": ""
},
{
"docid": "2378598557cce592268a0b5462db7d5d",
"score": "0.55008054",
"text": "def save_track_file(self, file_name):\r\n SlTrace.lg(\"save_track_file %s\" % file_name)\r\n with open(file_name, \"w\") as fout:\r\n print(\"# %s\" % file_name, file=fout)\r\n today = date.today()\r\n d2 = today.strftime(\"%B %d, %Y\")\r\n print(\"# On: %s\\n\" % d2, file=fout)\r\n print(\"from homcoord import *\", file=fout)\r\n print(\"from road_straight import RoadStraight\", file=fout)\r\n print(\"from road_turn import RoadTurn\", file=fout)\r\n print(\"from car_simple import CarSimple\", file=fout)\r\n print(\"from block_commands import *\", file=fout)\r\n print(\"\", file=fout)\r\n road_track = self.get_road_track()\r\n for road in road_track.roads.values():\r\n road.out_road_cmd(file=fout)\r\n for car in road_track.cars.values():\r\n car.out_car_cmd(file=fout)\r\n # restore from list TBD : save select list ???\r\n # save groups ??? TBD\r\n \r\n return True",
"title": ""
},
{
"docid": "5a61ea1114dc9e6777f408095981eeeb",
"score": "0.54922265",
"text": "def updateTrackList(self):\n self.tracks = dictFromList(self.mainGUI.data['track_number'].to_list()) # Convert a column of track numbers into a dictionary\n self.trackSelector.setItems(self.tracks) # Set the track list in the GUI",
"title": ""
},
{
"docid": "f5ae45b6c9782676e2176152c1331b41",
"score": "0.5489709",
"text": "def save_sample_playback_history(uid, id_playlist):\n\n # Create a user playback history\n base_playlist = get_playback(id_playlist)\n history_user = get_playback_history(uid, base_playlist)\n\n # Connects and write into DataBase\n db = connection_database()\n collection = db['playback_history']\n collection.insert_many(history_user)\n\n print(\"A Sample of Playback-History was added to DataBase\")",
"title": ""
},
{
"docid": "3e675aed7c67abf490194a42bf7847a8",
"score": "0.5481354",
"text": "def getTracks(self, limit = None, page = None):\n\t\t\n\t\tparams = self._getParams()\n\t\tif limit: params['limit'] = unicode(limit)\n\t\tif page: params['page'] = unicode(page)\n\t\t\n\t\tdoc = _Request(self, 'library.getTracks', self.api_key, params).execute()\n\t\t\n\t\tif not doc:\n\t\t\treturn None\n\t\t\n\t\ttracks = doc.getElementsByTagName('track')\n\t\tlist = []\n\t\t\n\t\tfor track in tracks:\n\t\t\t\n\t\t\ttitle = self._extract(track, 'name')\n\t\t\tartist = self._extract(track, 'name', 1)\n\t\t\t\n\t\t\tplaycount = self._extract(track, 'playcount')\n\t\t\ttagcount = self._extract(track, 'tagcount')\n\t\t\t\n\t\t\tt = Track(artist, title, *self.auth_data)\n\t\t\tlist.append(t)\n\t\t\t\n\t\t\tself._tracks_playcounts[t._hash()] = playcount\n\t\t\tself._tracks_tagcounts[t._hash()] = tagcount\n\t\t\n\t\treturn list",
"title": ""
},
{
"docid": "73f2c2d86ead651578e8aef85602c4ae",
"score": "0.54749143",
"text": "def getRowCountWithTrackMbid(self):\n try:\n self.c.execute(\"\"\"SELECT Count(*) FROM tracks WHERE track_mbid<>'' \"\"\") \n count=self.c.fetchone()[0]\n except: count=0\n \n return count",
"title": ""
},
{
"docid": "49583775808c3e64490737120ccbebe1",
"score": "0.5461034",
"text": "def track_count(self):\n if \"trackCount\" in self._prop_dict:\n return self._prop_dict[\"trackCount\"]\n else:\n return None",
"title": ""
},
{
"docid": "9c926d212b4b5a6af2660ae9fc7b69d4",
"score": "0.545227",
"text": "def addTracks(self, playlistId: str, tracks: list, position: int = None) -> dict:\n \n # define param and header args for request\n url = BASE_URL + \"/playlists/\" + playlistId + \"/tracks\"\n headers = {\n \"Authorization\": \"Bearer \" + self.client.getCurrentToken(),\n \"Content-Type\": \"application/json\"\n }\n params = {\"uris\": tracks}\n if position:\n params[\"position\"] = position\n\n # send request\n response = self.client._sendHTTPRequest(\"POST\", url, params, headers)\n return response",
"title": ""
},
{
"docid": "8965e89410800f804d573ab4be65af6e",
"score": "0.5433848",
"text": "def save(self, playlist):\n raise NotImplementedError",
"title": ""
},
{
"docid": "2efe54ef9d9aef03db31175e841b5a99",
"score": "0.54249835",
"text": "def store_calls(database_filename,html_str):\r\n\r\n saved = 0\r\n unsaved = 0\r\n\r\n conn = sqlite3.connect(database_filename)\r\n c = conn.cursor()\r\n Call.init_db(c)\r\n for call in get_calls(html_str):\r\n if call.save(c) == True:\r\n saved += 1\r\n else:\r\n unsaved += 1\r\n conn.commit()\r\n return saved,unsaved",
"title": ""
},
{
"docid": "56434ab5938b950202075c239a5ec4dd",
"score": "0.54229814",
"text": "def make_song_id_list(data):\n\n song_ids = []\n failed_tracks = []\n for track in data:\n nid = GooglePlay.get_song_id(track[\"Title\"], track[\"Artist\"])\n if nid == \"0\":\n failed_tracks.append(track)\n pass\n else:\n song_ids.append(nid)\n track[\"TrackID\"] = nid\n print('finished getting song_ids')\n return song_ids",
"title": ""
},
{
"docid": "509a9e39826fdf7e7b880be53902ca85",
"score": "0.54191816",
"text": "def test_albums(self):\n album_mock = {\n '01 - Bicycle Day.mp3': {\n 'artist': 'Static Bass',\n 'album': 'Bicycle Day',\n 'tracknumber': '1/2',\n 'title': 'Bicycle Day'\n },\n '02 - No Phuture.mp3': {\n 'artist': 'Static Bass',\n 'album': 'Bicycle Day',\n 'tracknumber': '2/2',\n 'title': 'No Phuture'\n }\n }\n create_mock_tracks(album_mock)\n update_db(TRACK_DIR)\n albums = Album.query.filter_by(\n title='Bicycle Day',\n artist='Static Bass'\n ).all()\n assert len(albums) == 1\n tracks = (Track.query.filter_by(album_id=albums[0].id)\n .order_by(Track.track_num)\n .all())\n assert len(tracks) == 2\n assert tracks[0].artist == 'Static Bass'\n assert tracks[0].track_num == 1\n assert tracks[0].title == 'Bicycle Day'\n assert tracks[1].artist == 'Static Bass'\n assert tracks[1].track_num == 2\n assert tracks[1].title == 'No Phuture'",
"title": ""
},
{
"docid": "67ad35bc8eceb6b09e9ad875e03b9291",
"score": "0.5411009",
"text": "def test_count(self):\n models.storage.close()\n models.storage = models.engine.file_storage.FileStorage()\n models.storage.reload()\n objects = self.populate()\n count = 13\n self.assertEqual(6, len(objects))\n for obj in objects:\n obj.delete()\n models.storage.save()\n count -= 1\n self.assertEqual(models.storage.count(), count)",
"title": ""
},
{
"docid": "007bf3d9fb51f7b7444f004b49b9bd97",
"score": "0.5409719",
"text": "def get_track_info(self, spotify_connection: spotipy.Spotify):\n # Delete any existing track info before adding them all back in.\n self.tracks = list()\n\n track_list = get_all_paged_items(\n spotify_connection=spotify_connection,\n first_page=self.playlist_json[\"tracks\"],\n )\n\n for track in track_list:\n self.tracks.append(SpotifyPlaylistTrack(playlist_track_json=track))",
"title": ""
},
{
"docid": "5a1c7b7b7a815f1a4547636dec60d9e2",
"score": "0.53938854",
"text": "def create_tracks(self):\n if not self.working:\n\n fin = len(self.track_manager)\n \"\"\" May be good looking to start a timer and do something meanwhile.. \"\"\"\n self.working = True\n absents = []\n all_num = []\n\n for i in range(0, fin):\n all_num.append(i)\n volume, instrument = self.track_manager[i].get_data()\n active = True\n if volume == 0 or instrument == '':\n absents.append(i)\n active = False\n self.backend.assign_instrument_to_track(i, instrument, volume/100.0, active)\n\n if len(absents) == len(self.track_manager):\n print('Compilando vacio')\n return\n\n for a in self.track_manager: # This may be useful in future\n a.setStyleSheet(\n 'QWidget { border-style: solid; background-color: rgbrgb(81, 76, 149); border-radius: 5px;}')\n\n self.available_to_play = list(set(all_num).difference(set(absents)))\n self.progress_bar.setMaximum(len(self.available_to_play))\n self.progress_bar.setValue(0)\n self.progress_bar.show()\n\n self.backend.start()",
"title": ""
},
{
"docid": "a357848bef38c7c9236dee8db643d2b3",
"score": "0.5371731",
"text": "def getTracks(self):\n\t\t\n\t\turi = u'lastfm://playlist/%s' %unicode(self.getPlaylistID())\n\t\t\n\t\treturn Playlist(uri, *self.auth_data).fetch()",
"title": ""
},
{
"docid": "2af691abfe3e0386dff07a10112c2e40",
"score": "0.53681475",
"text": "def make_song_id_list(data):\n song_ids = []\n failed_tracks = []\n for track in data:\n nid = SpotMeths.get_song_id(track[\"Title\"], track[\"Artist\"])\n if nid == \"0\":\n failed_tracks.append(track)\n else:\n song_ids.append(nid)\n track[\"TrackID\"] = nid\n print('Finished getting song_ids\\n')\n print(\"Couldn't find id for the following tracks:\\n\")\n for track in failed_tracks:\n print('{0!s} - {1!s}'.format(track[\"Title\"], track[\"Artist\"]))\n return song_ids",
"title": ""
},
{
"docid": "9134e23b6e940c377fc13d5b8ba57f13",
"score": "0.53631",
"text": "def video_get_track_count(self):\n e=VLCException()\n return libvlc_video_get_track_count(self, e)",
"title": ""
},
{
"docid": "fc49b179d532979c1270ac1aa0d4a6d4",
"score": "0.5352887",
"text": "def getTrack(num):\n return getSong().visible_tracks[num]",
"title": ""
},
{
"docid": "8fd84feead34202f6608494726845ae5",
"score": "0.53414994",
"text": "def get_music_features(tracks, sp):\n track_features = []\n\n track_list = []\n for i in range(len(tracks)):\n track_list.append(tracks[i]['id'])\n \n num_tracks = len(tracks)\n for i in range(0, num_tracks, 100):\n if i + 100 > num_tracks:\n features = track_list[i:num_tracks]\n features = sp.audio_features(features)\n for j in range(len(features)):\n track_features.append({'id': features[j]['id'], 'energy': features[j]['energy'], 'valence': features[j]['valence']})\n else:\n features = track_list[i:i+100]\n features = sp.audio_features(features)\n for j in range(len(features)):\n track_features.append({'id': features[j]['id'], 'energy': features[j]['energy'], 'valence': features[j]['valence']})\n \n return track_features",
"title": ""
},
{
"docid": "1706247933875fe9e4361349d052a054",
"score": "0.5332056",
"text": "def save(self, store):\n all_items = {}\n for k, item in enumerate(self.queue):\n all_items.update({\"item\" + str(k + 1): item[\"filename\"]})\n store.put(\"Playlist\",\n current=self.current,\n items=all_items)",
"title": ""
},
{
"docid": "27fa66d85d8fc7d8e62a3db7b811dc14",
"score": "0.5317487",
"text": "def _updateOrInsert(self, track):\n new=False\n now=time.time() \n\n artist_name=unicode(track[\"artist_name\"])\n track_name=unicode(track[\"track_name\"])\n\n self.c.execute(\"\"\"UPDATE tracks SET \n track_mbid=?, artist_mbid=?,\n updated=? WHERE artist_name=? AND track_name=?\"\"\", \n (track[\"track_mbid\"], track[\"artist_mbid\"],\n now,\n artist_name, track_name,\n ))\n \n \n if self.c.rowcount != 1:\n self.c.execute(\"\"\"INSERT INTO tracks (created, updated, \n track_name, track_mbid,\n artist_name, artist_mbid\n ) VALUES (?, ?, ?, ?, ?, ?)\"\"\", \n (now, 0, track_name, track[\"track_mbid\"],\n artist_name, track[\"artist_mbid\"]) )\n new=True\n \n self.conn.commit()\n return new",
"title": ""
},
{
"docid": "3c3012f4d4c2290a8a0bf95e40210180",
"score": "0.53046215",
"text": "def visibleTracks():\n return song().visible_tracks",
"title": ""
},
{
"docid": "fa569cc1294c9f09b2460cf74805b2d3",
"score": "0.529719",
"text": "def tracks_from_year(sp, year, num_tracks):\n tracks = sp.search(q='year:' + str(year), type='track', offset=0, limit=50)\n print(\"Number of tracks in {}: {}\".format(year, tracks['tracks']['total']))\n info = [(item[\"id\"], item[\"name\"], item[\"popularity\"]) for item in tracks[\"tracks\"][\"items\"]]\n while tracks[\"tracks\"][\"next\"] and len(info) < num_tracks:\n tracks = sp.next(tracks[\"tracks\"])\n info.extend([(item[\"id\"], item[\"name\"], item[\"popularity\"]) for item in tracks[\"tracks\"][\"items\"]])\n if len(info) % 1000 == 0:\n print(\"Retrieved {} songs\".format(len(info)))\n return info[:num_tracks]",
"title": ""
},
{
"docid": "c12eef82c1ac74ee1caf5539d3673e23",
"score": "0.5296097",
"text": "def _save_queue(self):\n if self.queue is not None:\n # Maximum batch is 486, anything larger will still only\n # return 486\n batch_size = 400\n total = 0\n num_return = batch_size\n\n # Need to get all the tracks in batches, but Only get the next\n # batch if all the items requested were in the last batch\n while num_return == batch_size:\n queue_items = self.device.get_queue(total, batch_size)\n # Check how many entries were returned\n num_return = len(queue_items)\n # Make sure the queue is not empty\n if num_return > 0:\n self.queue.append(queue_items)\n # Update the total that have been processed\n total = total + num_return",
"title": ""
},
{
"docid": "e298dcae13edb812643ab9ca806b7067",
"score": "0.5296038",
"text": "def Get(self):\n return _pcbnew.TRACK_List_Get(self)",
"title": ""
},
{
"docid": "dda53782d3fd396b998c8ce6c1882bcb",
"score": "0.5283854",
"text": "def pickle_track(self):\n import pickle\n pickle.dump(self, open(self.module_path + '/../data/track/' + self.track_name + '.track', 'wb'))",
"title": ""
},
{
"docid": "9e88390d08fd8e3e8299dedab3c38a08",
"score": "0.52796054",
"text": "def save():\n records.save()",
"title": ""
},
{
"docid": "1d21913cd74fdcd559d772fa387286b4",
"score": "0.5270283",
"text": "def __bil_list_of_tracks(self, result):\n \n if isinstance(result, X2RV):\n if not self.__pa._check_result(result):\n return\n tracks = result.value()\n else:\n tracks = result\n \n for minfo in tracks:\n\n self.__reply.ids.append(minfo['id'])\n self.__reply.names.append(self.__get_item_name(minfo))\n \n self.__reply.send()",
"title": ""
},
{
"docid": "6fce7d854381fd544156572b1559fee2",
"score": "0.5266289",
"text": "def save_data():\n geosjon_truck_data = fetch_data(settings.SFGOV_TRUCKS_API_URL)\n if geosjon_truck_data:\n mongo_client = pymongo.MongoClient(host=settings.MONGODB_HOST)\n db = mongo_client.trucks\n db.trucks.delete_many({})\n db.trucks.create_index([(\"geometries.coordinates\", pymongo.GEOSPHERE)])\n result = db.trucks.insert_many(geosjon_truck_data)\n print \"%s truck objects has been synced\" % len(result.inserted_ids) # TODO: add a real logger\n else:\n print \"No data was fetched, potentially the SFGOVAPI is down or moved.\" # TODO: add a real logger",
"title": ""
},
{
"docid": "44ceee9a98cb5ca51abb3073305fdaf9",
"score": "0.5258436",
"text": "def get_tracks(self, track_name = '', authors = list(), tags = list(), id_track = ''):\n if id_track != '':\n try:\n self.cursor.execute(\"select id_track, track_name, durration, track_price, path, track_status, cover_path \\\n from track where id_track = %s\",\n (id_track)\n )\n except (MySQLError):\n Ex_Handler.call('Data base error')\n else:\n return set(self.cursor.fetchall())\n temp = list()\n rvalue = list()\n track_name = '%' + track_name + '%'\n if authors != []:\n try:\n self.cursor.execute(\"select id_track, track_name, durration, track_price, path, track_status, cover_path \\\n from track where track_name like %s\",\n ('%' + track_name + '%'))\n except (MySQLError):\n Ex_Handler.call('Data base error')\n return\n t = self.cursor.fetchall()\n for o in t:\n for i in authors:\n if not self.cursor.execute(\"select id_track from authors_to_tracks \\\n join authors on authors.id_author = authors_to_tracks.id_author \\\n where id_track like %s and author.author_name like %s\",\n (o[0], '%' + i + '%')\n ):\n break\n else:\n temp.append(o)\n else:\n try:\n self.cursor.execute(\"select id_track, track_name, durration, track_price, path, track_status, cover_path \\\n from track where track_name like %s\",\n ('%' + track_name + '%')\n )\n except (MySQLError):\n Ex_Handler.call('Data base error')\n else:\n temp = self.cursor.fetchall()\n if tags != []:\n for i in temp:\n for j in tags:\n if self.cursor.execute(\"select id_track from tags \\\n join tags_to_tracks on tags.id_tag = tags_to_tracks.id_tag \\\n where id_track = %s and tag_name = %s\",\n (i[0], j)\n ):\n break\n else: rvalue.append(i)\n else: rvalue = temp\n return set(rvalue)",
"title": ""
},
{
"docid": "5abca3325ea30e65d88a7df40a0b4e81",
"score": "0.52479124",
"text": "def createTrack(self):",
"title": ""
},
{
"docid": "4338de3b23b46d7664ca851f3779191e",
"score": "0.5245912",
"text": "def insert_rows_tracks(**kwargs):\n\n\t#Get the spotify credentials for airflow\n\tclient_id = Variable.get(\"spotify_client_id\", deserialize_json=True)['client_id_sp']\n\tlog.info(\"Got client_id\")\n\tclient_secret = Variable.get(\"spotify_client_secret\", deserialize_json=True)['key']\n\tlog.info(\"Got client secret\")\n\n #Initiate the credentials with the function provided by spotify and create the connection\n\tclient_credentials_manager = SpotifyClientCredentials(client_id, client_secret)\n\tsp = spotipy.Spotify(client_credentials_manager=client_credentials_manager)\n\tlog.info(\"Established connection to sp\")\n\n #initiate a connection to postgress, create a connection and initiate a cursor\n\tpg_hook = PostgresHook(postgres_conn_id=kwargs['postgres_conn_id'], schema=kwargs['db_name'])\n\tconn = pg_hook.get_conn()\n\tcursor = conn.cursor()\n\tlog.info(\"Got connection to postgres\")\n\n # select album from albums table\n\tq = \"SELECT album_id FROM spotify.albums;\"\n\tcursor.execute(q)\n\tconn.commit()\n\talbum_ids = cursor.fetchall()\n\tlog.info(\"Got album_ids\")\n\n # get tracks\n\tdef get_tracks(album):\n\t\t\"\"\"\n\t\ta function that extracts from spotify all tracks of a given album\n\n\t\tinput: album id\n\t\toutput: list of containing lists with data of each song\n\t\t\"\"\"\n #initiate a list to put the data for each album\n\t\talbum_tracks=[]\n\n #search the spotify API for tracks of each album\n\t\tresults=sp.album_tracks(album)\n\n #list of all the album tracks\n\t\titems=results['items']#contains a list of all the album tracks\n \n #iterate acrross album tracks\n\t\tfor it in items:\n \t#save each attribute in a variable\n\t\t\ttrack_id=it['id']\n\t\t\tdisc_number=it['disc_number']\n\t\t\tduration_ms=it['duration_ms']\n\t\t\texplicit=it['explicit']\n\t\t\tis_local=it['is_local']\n\t\t\ttrack_name=it['name']\n\t\t\tpreview_url=it['preview_url']\n\t\t\ttrack_number=it['track_number']\n\t\t\ttrack_type=it['type']\n\t\t\talbum_id=album\n\n #consolidate the variables in a list\n\t\t\ttrack=[track_id,album_id,disc_number,duration_ms,explicit,is_local,track_name,preview_url,track_number,track_type]\n\n #append the each track to a lsit containing all the album tracks\n\t\t\talbum_tracks.append(track)\n\n #return in list of lists the album tracks\n\t\treturn album_tracks\n\n\n #turn the album ids we got from the database to a list of that can be \n #iterated with mapping every element\n\talbum_ids = map(list, album_ids)\n\n #this query will insert into the table the arguments in every list by order\n\ts = \"\"\"INSERT INTO spotify.tracks(track_id,album_id,disc_number,duration_ms,explicit,is_local,track_name,preview_url,track_number,track_type) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)\"\"\"\n\n\tlog.info(\"Getting album tracks\")\n\tlog.info(\"Getting album tracks and inserting to table\")\n #for each album id\n\tfor album in album_ids:\n \t#create an empty list\n\t\ttracks_list=[]\n\n #get all the tracks for each album\n #for each album we have the id and a coma (that is returned by sql)\n \t#select just the artist id (1st element), not the comma\n\t\talbum_tracks=get_tracks(album[0])\n\n #add the tracks in the list above\n\t\ttracks_list=tracks_list+album_tracks\n\n #Insert them now to the table\n #we don't want many tracks at once in RAM\n\t\tcursor.executemany(s, list(tracks_list))\n\t\tconn.commit()\n\n #close the connection\n\tconn.close()",
"title": ""
},
{
"docid": "b9c3513fa874117df531c1d093e1705e",
"score": "0.5234347",
"text": "def get_tracks(self, *args, **kwargs):\n args = tuple([\"tracks\"] + list(args))\n return self.get_music_library_information(*args, **kwargs)",
"title": ""
},
{
"docid": "1b212eea92f48440ff750b8fc9453416",
"score": "0.5221742",
"text": "def save(self):\n new_chapters = 0\n database = Database()\n check_query = \"\"\"SELECT id, all_pages FROM chapter WHERE manga_id=%s AND number=%s\"\"\"\n insert_query = \"\"\"INSERT INTO chapter VALUES (NULL, %s, %s, 0, %s)\"\"\"\n update_query = \"\"\"UPDATE chapter SET all_pages=1 WHERE id=%s\"\"\"\n for url in self.urls:\n chapter_id = None\n chapter_number = url.split(\"/\")[-1]\n result = database.execute(check_query, [self.manga_id, chapter_number])\n\n if result is ():\n database.execute(insert_query, [chapter_number, url, self.manga_id])\n chapter_id = database.last_inserted_id()\n new_chapters += 1\n\n else:\n chapter_id = result[0][0]\n self.all_pages = True if result[0][1] == 1 else False\n\n if not self.all_pages:\n chapter_pages = Pages(self.manga_id, chapter_id, url)\n chapter_pages.save()\n database.execute(update_query, [chapter_id])\n\n self.log.info(\"Found %s new chapter(s)\" % new_chapters)",
"title": ""
},
{
"docid": "7154d21a56ab70fafe7957e9f2fdf9b3",
"score": "0.52166134",
"text": "async def read_track(page: int = 0, per_page: int = 10):\n\n app.db_connection.row_factory = aiosqlite.Row\n data = app.db_connection.execute(f'''\n SELECT * FROM tracks WHERE TrackId >= {page*per_page}''').fetchmany(per_page)\n return data\n\n \"\"\"\n cursor = await app.db_connection.execute(f'''\n SELECT tracks.TrackId as TrackId, tracks.name as Name, tracks.albumid as AlbumId, tracks.MediaTypeId as MediaTypeId,\n tracks.GenreId as GenreId, tracks.Composer as Composer, tracks.Milliseconds as Milliseconds, tracks.Bytes as Bytes, \n tracks.UnitPrice as UnitPrice FROM tracks WHERE TrackId >= {page*per_page}''')\n data = await cursor.fetchmany(per_page)\n return data\n \"\"\"",
"title": ""
},
{
"docid": "a9d6292a85218b54d21ef06b4f9bea6e",
"score": "0.5213016",
"text": "def track_list_all(request):\n tracks = DBSession.query(Track).\\\n options(\\\n joinedload(Track.tags),\\\n joinedload('tags.parent')\\\n )\n return action_ok(data={'list':[track.to_dict(include_fields='tags') for track in tracks]})",
"title": ""
},
{
"docid": "f908bcdaf19ac7434c47c53b35f0ce5e",
"score": "0.52005565",
"text": "def h_mb_tracks(self, source, ref, list_dic):\n if list_dic is None:\n return\n \n for track_details in list_dic:\n artist_name=track_details[\"artist_name\"]\n track_name=track_details[\"track_name\"]\n track_mbid=track_details[\"track_mbid\"]\n self._updateMbid(artist_name, track_name, track_mbid)",
"title": ""
},
{
"docid": "525e22035d4863e5ee7577d23792bd9b",
"score": "0.51827174",
"text": "def recommendation(track_list):\n # Get track data\n recommended = []\n for i in range(5):\n track = spotify.track('spotify:track:' + track_list[i]['id'])\n track_url = 'https://open.spotify.com/track/' + track_list[i]['id']\n title = track['name']\n artist = track['album']['artists'][0]['name']\n album_cover = track['album']['images'][1]['url']\n recommended.append({'url': track_url, 'artist': artist,\n 'title': title, 'album_cover': album_cover})\n pass\n return recommended",
"title": ""
},
{
"docid": "ce42a88f8adc340aa2baba1984309689",
"score": "0.51821643",
"text": "def save_urls_in_db(song_info, db):\n c = db.cursor()\n diff = get_difference(song_info, db)\n if diff:\n c.executemany('INSERT INTO songs VALUES (?, ?, ?)', diff)\n db.commit()\n return\n else:\n c.executemany('INSERT INTO songs VALUES (?, ?, ?)', song_info)\n db.commit()",
"title": ""
},
{
"docid": "890504eceb28e897976c7d422f50a6f6",
"score": "0.51776206",
"text": "def add_track(self, track_name, durration, track_price, path, track_status, cover_path):\n if track_name == '' or durration == '' or track_price == '' or path == '' or track_status == '':\n return 0\n else:\n try:\n self.connection.begin()\n self.cursor.execute(\"INSERT INTO track (track_name, durration, track_price, path, track_status, cover_path)\\\n VALUES (%s, %s, %s, %s, %s)\",\n (track_name, durration, track_price, path, track_status, cover_path)\n )\n except (MySQLError):\n Ex_Handler.call('Data base error')\n else:\n self.connection.commit()\n return 1",
"title": ""
},
{
"docid": "32291daa67a16d1a5733202da3d816cd",
"score": "0.5171312",
"text": "def save(Corn):\n make_data(Corn)\n items = list(Corn.all.execute())\n assert len(items) == 3",
"title": ""
},
{
"docid": "327d4f6c4a93fdf1c2214b00a2984a81",
"score": "0.5149815",
"text": "def getTrackPlaycount(self, track_object):\n\t\t\n\t\tkey = track_object._hash()\n\t\tif key in self._tracks_playcounts.keys():\n\t\t\treturn self._tracks_playcounts[key]\n\t\t\n\t\tfor i in range(1, self.getTracksPageCount() +1):\n\t\t\tstack = self.getTracks(page = i)\n\t\t\t\n\t\t\tfor track in stack:\n\t\t\t\tif track._hash() == track_object._hash():\n\t\t\t\t\treturn self._tracks_playcounts[track._hash()]",
"title": ""
},
{
"docid": "a49090502e7b528cac7e5bc80c3611a6",
"score": "0.513614",
"text": "def getItemInfoFromTrackList(self):\n\n print(\"Accessing track list...\")\n #a element cover this thing, so we have to split click into 2-step\n wait = WebDriverWait(self.browser, 10).until(\n EC.visibility_of_element_located((By.XPATH, \"//a[@title='追蹤清單']\")))\n element = self.browser.find_element_by_xpath(\"//a[@title='追蹤清單']\") \n self.browser.execute_script(\"arguments[0].click();\", element)\n print(\"Track list accessed\")\n\n while True:\n try:\n self.itemTag = input(\"輸入商品編號(追蹤清單裡面會顯示): \").replace(\" \", \"\")\n #self.itemTag = \"4836742\"#for testing\n \n #ensure the item exists\n self.browser.find_element_by_xpath(\"//a[@name='{}' and @target='_blank']\".format(self.itemTag))\n\n break\n except Exception as e:\n print('找不到對應的 \"商品編號\" 請重新輸入....')",
"title": ""
},
{
"docid": "5566af17e3af504b0f66ead3445f2d1a",
"score": "0.51122665",
"text": "def tracks_from_albums(sp, year, num_albums):\n album_ids = album_ids_from_year(sp, year, num_albums)\n print(\"Pulled {} albums\".format(num_albums))\n info = []\n for i, album_id in enumerate(album_ids):\n try:\n sleep(0.1)\n album = sp.album(album_id)\n track_ids = [item[\"id\"] for item in album[\"tracks\"][\"items\"]]\n for tid in track_ids:\n sleep(0.1)\n track = sp.track(tid)\n info.append((track[\"id\"], track[\"name\"], track[\"popularity\"]))\n except:\n print(\"Caught Spotipy error - breaking out of API calls\")\n break\n if len(info) % 100 == 0:\n print(\"Retrieved {} songs\".format(len(info)))\n if i % 50 == 0:\n print(\"Retrived songs from {} albums\".format(i))\n print(\"{} songs retrieved from {} albums\".format(len(info), len(album_ids)))\n return info",
"title": ""
},
{
"docid": "1500324781b2008a5671d68a2017b154",
"score": "0.5108589",
"text": "def find_duplicates(song_data):\n\n #obtain song name & length from ITunes dump & save song & count to dictionary\n playlst = plistlib.readPlist(song_data)\n tracks = playlst['Tracks']\n track_names = {}\n for track_id, track in tracks.items():\n try:\n name = track['Name']\n duration = track['Total Time']\n if name in track_names:\n if duration//1000 == track_names[name][0]//1000:\n count = track_names[name][1]\n track_names[name] = (duration, count+1)\n else:\n track_names[name] = (duration, 1)\n except:\n pass\n return track_names",
"title": ""
},
{
"docid": "5ef39acf3213885e4685f81882f66dbb",
"score": "0.5105684",
"text": "def posttrack(self, \n artist_name,\n song_title,\n length,\n date_played, \n album=u'',\n mbid=u''):\n \n self.addtrack(artist_name=artist_name,\n song_title=song_title,\n length=length,\n date_played=date_played,\n album=album,\n mbid=mbid)\n self.post()",
"title": ""
},
{
"docid": "41136214bba328e8a5ef0e5b4d990c35",
"score": "0.51051724",
"text": "def upload_to_db(self, events):\n updated = 0\n\n for event in filter(None, events):\n existing_similar_event = self.get_similar_event(event)\n\n if existing_similar_event is not None:\n self.update_in_db(existing_similar_event, event)\n updated += 1\n else:\n self.add_to_db(event)\n\n self.session.commit()\n return updated",
"title": ""
},
{
"docid": "5f2ff9a646e6ef3a2583a43704378e2b",
"score": "0.5103887",
"text": "def add_to_database(self):\n try:\n score_list = self.get_scores()\n except Exception:\n os.remove(\"%s%s.sqlite\" % (settings.BASE_DIR + settings.ACCESSORY_DIR, self.pid))\n return False\n\n statement = \"CREATE TABLE IF NOT EXISTS {}(\" \\\n \"SID INT, \" \\\n \"BAS_Score INT DEFAULT -1, \" \\\n \"ADV_Score INT DEFAULT -1, \" \\\n \"EXT_Score INT DEFAULT -1, \" \\\n \"BAS_FC INT DEFAULT 0, \" \\\n \"ADV_FC INT DEFAULT 0, \" \\\n \"EXT_FC INT DEFAULT 0, \" \\\n \"Play_Count INT DEFAULT 0, \" \\\n \"PRIMARY KEY(SID))\".format(self.uid)\n self.cursor.execute(statement)\n\n for song in score_list:\n try:\n statement = \"INSERT INTO {} VALUES (%d, %d, %d, %d, %d, %d, %d, %d)\".format(self.uid)\n self.cursor.execute(statement % (song.id, song.bas, song.adv, song.ext,\n song.fc_bas, song.fc_adv, song.fc_ext, song.pc))\n except Exception:\n statement = \"UPDATE {} SET \" \\\n \"BAS_Score = %d, \" \\\n \"ADV_Score = %d, \" \\\n \"EXT_Score = %d, \" \\\n \"BAS_FC = %d, \" \\\n \"ADV_FC = %d, \" \\\n \"EXT_FC = %d, \" \\\n \"Play_Count = %d \" \\\n \"WHERE SID = %d\".format(self.uid)\n self.cursor.execute(statement % (song.bas, song.adv, song.ext, song.fc_bas,\n song.fc_adv, song.fc_ext, song.pc, song.id))\n self.conn.commit()\n os.remove(\"%s%s.sqlite\" % (settings.BASE_DIR + settings.ACCESSORY_DIR, self.pid))\n\n return True",
"title": ""
},
{
"docid": "0c6f4666b8a8c3d4d45842414c79411e",
"score": "0.51008695",
"text": "def track_number(self) -> int:\n\n return self._raw_track_meta[\"track_number\"]",
"title": ""
},
{
"docid": "059eefe559d4efb08a51f64528fdb3e3",
"score": "0.509889",
"text": "def writeTracks(self, tracks, track_type):\n for i, track in enumerate(tracks):\n if i%3==0:\n self.string += [\"{id:<6}\".format(id=track_type)]\n if track_type == 'BTRK':\n self.string += [\" {eta:>5} {etaFine:>5} {phi:>5} {qual:>5} {sel:>5} {wheelSide:>5} {wheelNum:>5} {sect1:>5} {sect2:>5} {sect3:>5} {sect4:>5} {empty:>5}\".format(eta=track[0], etaFine=track[11], phi=track[1], qual=track[2], sel=track[3], wheelSide=track[4], wheelNum=track[5], sect1=track[6], sect2=track[7], sect3=track[8], sect4=track[9], empty=track[10])]\n else:\n self.string += [\" {eta:>5} {etaFine:>5} {phi:>5} {qual:>5} {empty:>5}\".format(eta=track[0], etaFine=track[4], phi=track[1], qual=track[2], empty=track[3])]\n if (i+1)%3 == 0:\n self.string += [\"\\n\"]",
"title": ""
},
{
"docid": "bf999d878de7a69047dedce82088fe78",
"score": "0.50965345",
"text": "def test_update_media_tracks(self):\n # This method utilises the PUT request method and will make changes to the Canvas instance. This needs consideration.\n pass",
"title": ""
},
{
"docid": "465e97cbf0bb0d25002eb855d5ef660d",
"score": "0.5073725",
"text": "def fetch_tracks(sp, item_type, item_id):\n songs_list = []\n offset = 0\n songs_fetched = 0\n\n if item_type == \"playlist\":\n with Progress() as progress:\n songs_task = progress.add_task(description=\"Fetching songs from playlist..\")\n while True:\n items = sp.playlist_items(\n playlist_id=item_id,\n fields=\"items.track.name,items.track.artists(name, uri),\"\n \"items.track.album(name, release_date, total_tracks, images),\"\n \"items.track.track_number,total, next,offset,\"\n \"items.track.id\",\n additional_types=[\"track\"],\n offset=offset,\n )\n total_songs = items.get(\"total\")\n track_info_task = progress.add_task(\n description=\"Fetching track info\", total=len(items[\"items\"])\n )\n for item in items[\"items\"]:\n track_info = item.get(\"track\")\n # If the user has a podcast in their playlist, there will be no track\n # Without this conditional, the program will fail later on when the metadata is fetched\n if track_info is None:\n offset += 1\n continue\n track_album_info = track_info.get(\"album\")\n track_num = track_info.get(\"track_number\")\n spotify_id = track_info.get(\"id\")\n track_name = track_info.get(\"name\")\n track_artist = \", \".join(\n [artist[\"name\"] for artist in track_info.get(\"artists\")]\n )\n if track_album_info:\n track_album = track_album_info.get(\"name\")\n track_year = (\n track_album_info.get(\"release_date\")[:4]\n if track_album_info.get(\"release_date\")\n else \"\"\n )\n album_total = track_album_info.get(\"total_tracks\")\n if len(item[\"track\"][\"album\"][\"images\"]) > 0:\n cover = item[\"track\"][\"album\"][\"images\"][0][\"url\"]\n else:\n cover = None\n\n artists = track_info.get(\"artists\")\n main_artist_id = (\n artists[0].get(\"uri\", None) if len(artists) > 0 else None\n )\n genres = (\n sp.artist(artist_id=main_artist_id).get(\"genres\", [])\n if main_artist_id\n else []\n )\n if len(genres) > 0:\n genre = genres[0]\n else:\n genre = \"\"\n songs_list.append(\n {\n \"name\": track_name,\n \"artist\": track_artist,\n \"album\": track_album,\n \"year\": track_year,\n \"num_tracks\": album_total,\n \"num\": track_num,\n \"playlist_num\": offset + 1,\n \"cover\": cover,\n \"genre\": genre,\n \"spotify_id\": spotify_id,\n \"track_url\": None,\n }\n )\n offset += 1\n songs_fetched += 1\n progress.update(\n task_id=track_info_task,\n description=f\"Fetching track info for \\n{track_name}\",\n advance=1,\n )\n\n progress.update(\n task_id=songs_task,\n description=f\"Fetched {songs_fetched} of {total_songs} songs from the playlist\",\n advance=100,\n total=total_songs,\n )\n if total_songs == offset:\n break\n\n elif item_type == \"album\":\n with Progress() as progress:\n album_songs_task = progress.add_task(\n description=\"Fetching songs from the album..\"\n )\n while True:\n album_info = sp.album(album_id=item_id)\n items = sp.album_tracks(album_id=item_id, offset=offset)\n total_songs = items.get(\"total\")\n track_album = album_info.get(\"name\")\n track_year = (\n album_info.get(\"release_date\")[:4]\n if album_info.get(\"release_date\")\n else \"\"\n )\n album_total = album_info.get(\"total_tracks\")\n if len(album_info[\"images\"]) > 0:\n cover = album_info[\"images\"][0][\"url\"]\n else:\n cover = None\n if (\n len(sp.artist(artist_id=album_info[\"artists\"][0][\"uri\"])[\"genres\"])\n > 0\n ):\n genre = sp.artist(artist_id=album_info[\"artists\"][0][\"uri\"])[\n \"genres\"\n ][0]\n else:\n genre = \"\"\n for item in items[\"items\"]:\n track_name = item.get(\"name\")\n track_artist = \", \".join(\n [artist[\"name\"] for artist in item[\"artists\"]]\n )\n track_num = item[\"track_number\"]\n spotify_id = item.get(\"id\")\n songs_list.append(\n {\n \"name\": track_name,\n \"artist\": track_artist,\n \"album\": track_album,\n \"year\": track_year,\n \"num_tracks\": album_total,\n \"num\": track_num,\n \"track_url\": None,\n \"playlist_num\": offset + 1,\n \"cover\": cover,\n \"genre\": genre,\n \"spotify_id\": spotify_id,\n }\n )\n offset += 1\n\n progress.update(\n task_id=album_songs_task,\n description=f\"Fetched {offset} of {album_total} songs from the album {track_album}\",\n advance=offset,\n total=album_total,\n )\n if album_total == offset:\n break\n\n elif item_type == \"track\":\n items = sp.track(track_id=item_id)\n track_name = items.get(\"name\")\n album_info = items.get(\"album\")\n track_artist = \", \".join([artist[\"name\"] for artist in items[\"artists\"]])\n if album_info:\n track_album = album_info.get(\"name\")\n track_year = (\n album_info.get(\"release_date\")[:4]\n if album_info.get(\"release_date\")\n else \"\"\n )\n album_total = album_info.get(\"total_tracks\")\n track_num = items[\"track_number\"]\n spotify_id = items[\"id\"]\n if len(items[\"album\"][\"images\"]) > 0:\n cover = items[\"album\"][\"images\"][0][\"url\"]\n else:\n cover = None\n if len(sp.artist(artist_id=items[\"artists\"][0][\"uri\"])[\"genres\"]) > 0:\n genre = sp.artist(artist_id=items[\"artists\"][0][\"uri\"])[\"genres\"][0]\n else:\n genre = \"\"\n songs_list.append(\n {\n \"name\": track_name,\n \"artist\": track_artist,\n \"album\": track_album,\n \"year\": track_year,\n \"num_tracks\": album_total,\n \"num\": track_num,\n \"playlist_num\": offset + 1,\n \"cover\": cover,\n \"genre\": genre,\n \"track_url\": None,\n \"spotify_id\": spotify_id,\n }\n )\n\n return songs_list",
"title": ""
},
{
"docid": "fd9e48f28cc51b9e5f2ae95a26711750",
"score": "0.5060168",
"text": "def count_urls_stored(self):\n sql_count_total = \"SELECT COUNT(*) FROM urls;\"\n self._create_connection()\n cursor = self._execute_sql(sql_count_total)\n return list(cursor.fetchone())[0]",
"title": ""
},
{
"docid": "8213c02c52b7139b12ee295d2de6dc11",
"score": "0.5037414",
"text": "def insert_track(self, track):\n\n sql = (\n \"INSERT INTO `track` (`name`, `artist`, `album`, `path`, `modified`) \"\n \"VALUES (%s, %s, %s, %s, %s)\"\n )\n\n try:\n connection = self.get_connection()\n with connection.cursor() as cursor:\n cursor.execute(\n sql,\n (\n track['track'],\n track['artist'],\n track['album'],\n track['path'],\n utils.get_cur_datetime()\n )\n )\n connection.commit()\n finally:\n connection.close()",
"title": ""
},
{
"docid": "26ed76e85edfac8b4c9424686d9b8a46",
"score": "0.50348693",
"text": "def getTrack(num):\r\n tracks = getSong().tracks\r\n \r\n if num < len(tracks):\r\n return tracks[num]\r\n else:\r\n return None",
"title": ""
},
{
"docid": "9fe1049ff055e31c5dd766c8789d9c79",
"score": "0.5027604",
"text": "def load_songs_to_database():\n df = pandas.read_csv(\"songRecommender_project/not_empty_songs_relative_path.txt\", sep=';', header=None, index_col=False, names=['artist', 'title', 'lyrics', 'link', 'path'])\n if df.shape[0] == 16594:\n for i, row in df.iterrows():\n song = Song(song_name=row['title'], artist=row['artist'], text=row['lyrics'], link=row['link'],\n link_on_disc=row['path'])\n song.save()\n print('song', i, 'saved')\n else:\n print(\"This datagframe has the wrong number of songs.\")",
"title": ""
},
{
"docid": "e757393e7ef3dfea9675869ba78b5608",
"score": "0.50266665",
"text": "def _save_results(self):\n saver = PhotosetSaver()\n for photoset in self.results:\n saver.save_photoset(photoset)\n self.results_count += len(self.results)",
"title": ""
},
{
"docid": "8b1cb26c0bde5e99a80528fca7ef2e6e",
"score": "0.50248444",
"text": "def findExistingTracks(self, session =None):\n if not session:\n session = self.mySession\n model = self.__class__\n query = session.query(model)\n for enum in Reflection.getReflectionList(TrackPropEnum):\n if enum.unique:\n query = query.filter(\n getattr(model, enum.name) == getattr(self, enum.name))\n return query.all()",
"title": ""
},
{
"docid": "91bc53c8760dc023b7d9026a20b7290c",
"score": "0.50220907",
"text": "def save_all_players(cls):\n players_db = cls.__db\n players_db.truncate()\n serialized_players = [cls.player.serialize_player() for cls.player\n in cls.PLAYERS]\n players_db.insert_multiple(serialized_players)",
"title": ""
},
{
"docid": "3957a1e7cf63ce1271a5b27d16d5d94b",
"score": "0.5020876",
"text": "def get_recent_tracks(sp):\n data = {\"artists\": [], \"albums\": [], \"uri\": []}\n albums = {}\n\n artists_id = []\n\n recent_tracks = sp.current_user_recently_played(limit=50)\n count = 0\n for item in recent_tracks[\"items\"]:\n if (\n item[\"track\"] is not None\n ): # Has to check because apparently it can be None???\n item = item[\"track\"]\n\n # Track uri to be used for audio features\n data[\"uri\"].append(item[\"uri\"])\n\n # Only get album name if it's not a 'single'\n if item[\"album\"][\"album_type\"] != \"SINGLE\":\n album = item[\"album\"][\"name\"]\n if album in albums:\n albums[album][\"times_appear\"] += 1\n if album not in albums and count < 10:\n albums[album] = __parse_album(item)\n count+=1\n else:\n album = \"single\"\n\n # Artist id\n artists_id.append(item[\"artists\"][0][\"id\"])\n\n artists_id = list(set(artists_id))\n\n artists = sp.artists(artists_id)\n\n for item in artists[\"artists\"]:\n try:\n artist = {\n \"name\": item[\"name\"],\n \"images\": item[\"images\"][0][\"url\"],\n \"url\": item[\"external_urls\"][\"spotify\"],\n }\n\n if artist not in data[\"artists\"]:\n data[\"artists\"].append(artist)\n\n except IndexError:\n continue\n\n data[\"artists\"] = data[\"artists\"][:10]\n data[\"albums\"] = albums\n\n return data",
"title": ""
},
{
"docid": "58b3548bbcefd7bba66f6eae2ac4b34d",
"score": "0.5008976",
"text": "def post(self, request):\n serializer = SongsSerializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n serializer.save()\n return Response(serializer.validated_data, status=status.HTTP_201_CREATED)",
"title": ""
},
{
"docid": "88568837df15056499c90cbe0cee10f1",
"score": "0.50048643",
"text": "def test_count(self):\n file_count = 10\n\n filename = os.path.join(self.directory, 'location.db')\n with closing(sqlite3.connect(filename)) as connection:\n with closing(connection.cursor()) as cursor:\n cursor.execute(\n 'CREATE TABLE location (filename TEXT)')\n\n for index in range(file_count):\n cursor.execute(\n 'INSERT INTO location VALUES (\"{}.jpg\")'.format(index))\n connection.commit()\n\n with LocationDB() as location_db:\n result = location_db.count()\n self.assertEqual(result, file_count)",
"title": ""
},
{
"docid": "096ce1e36d6840c0fdd2731fb33d2ba0",
"score": "0.5004317",
"text": "def on_track_list_changed(self):\n pass",
"title": ""
},
{
"docid": "41744539cfe947247e56f8a2dcfa02f9",
"score": "0.49937183",
"text": "def track_view(request):\n id = request.matchdict['id']\n track = DBSession.query(Track).\\\n options(\\\n joinedload(Track.tags),\\\n joinedload(Track.attachments),\\\n joinedload('tags.parent'),\\\n joinedload('lyrics')\n )\n track = track.get(id).to_dict('full')\n \n queue = DBSession.query(QueueItem).\\\n filter(QueueItem.track_id==track['id']).\\\n filter(QueueItem.time_added>datetime.datetime.now()-datetime.timedelta(hours=12)).\\\n order_by(QueueItem.id)\n #filter(QueueItem.status=='pending').\\\n queue = [queue_item.to_dict('full', exclude_fields='track_id,session_owner') for queue_item in queue]\n \n track['queued'] = queue\n \n return action_ok(data={\n 'track' : track\n })",
"title": ""
},
{
"docid": "97864fbb420faa7fb7a7525b184dd2eb",
"score": "0.49935135",
"text": "def _save_results(self):\n saver = PhotoSaver()\n for photo in self.results:\n saver.save_photo(photo)\n self.results_count += len(self.results)",
"title": ""
},
{
"docid": "c92e76e95f148be25d8a10b992703bdc",
"score": "0.49844274",
"text": "def track_list(self):\n for track in self.parsed.find(\"./TRACK_LIST\").iter():\n if track.tag == 'TRACK':\n yield IXMLTrack(channel_index=track.xpath('string(CHANNEL_INDEX/text())'),\n interleave_index=track.xpath('string(INTERLEAVE_INDEX/text())'),\n name=track.xpath('string(NAME/text())'),\n function=track.xpath('string(FUNCTION/text())'))",
"title": ""
},
{
"docid": "abeef4c0fa413459032ce718ebf8860d",
"score": "0.4983233",
"text": "def save_new_playlist(uid=None):\n\n # Creates a new playlist\n if uid:\n playlist = create_playlist_user(uid)\n else:\n playlist = create_playlist_general()\n\n # Connects and write into DataBase\n db = connection_database()\n collection = db['spot_playlists']\n collection.insert_one(playlist)\n\n print(\"A new Playlist was added to DataBase\")",
"title": ""
},
{
"docid": "187828ec2370db9ff026692378f40007",
"score": "0.49807703",
"text": "def GetLength(self):\n return _pcbnew.TRACK_List_GetLength(self)",
"title": ""
},
{
"docid": "dd10130de44b0cd77e27fe1d1e4df38c",
"score": "0.49782342",
"text": "def tracking(cont_path_list, out_path):\n tracker = Tracker(10, 0, 0)\n count = 0\n contours_list = np.load(cont_path_list)\n for contours in contours_list:\n centers = []\n x_list = []\n y_list = []\n\n for contour in contours:\n # Find center of mass of contours\n x_list = [vertex[0] for vertex in contour]\n y_list = [vertex[1] for vertex in contour]\n n_vertex = len(contour)\n x = sum(x_list) / n_vertex\n y = sum(y_list) / n_vertex\n centers.append([x, y])\n\n if (len(centers) > 0):\n tracker.update(centers)\n # print(count)\n count += 1\n\n to_save = np.zeros((len(tracker.track_history), 2), dtype=object)\n for i in range(len(tracker.track_history)):\n history = []\n for j in range(len(tracker.track_history[i].center_history)):\n coords = []\n # x and y are flipped becuase of graphics geometry.\n coords.append(\n int(tracker.track_history[i].center_history[j][0][0]))\n coords.append(\n int(tracker.track_history[i].center_history[j][0][1]))\n history.append(coords)\n to_save[i][0] = tracker.track_history[i].frame_count\n to_save[i][1] = history\n np.save(out_path, to_save)",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="741"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">33b66aeb908a685ae028f257f6cdab5c</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">returns y_predicted.shape[2] binary clf curves calculated axis[1]wise this is a numpy implementation</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "1a2d8e903a57cfb31df4f796ac19f1ca",
"score": "0.7147785",
"text": "def _last_axis_binary_clf_curve(y_true, y_predicted):\n assert y_true.shape == y_predicted.shape\n axis = -1\n sort_idx = list(np.ogrid[[slice(x) for x in y_predicted.shape]])\n sort_idx[axis] = y_predicted.argsort(axis=axis).astype('int8')\n reverse = [slice(None)] * y_predicted.ndim\n reverse[axis] = slice(None, None, -1)\n sorted_y_predicted = y_predicted[sort_idx][reverse]\n sorted_y_true = y_true[sort_idx][reverse]\n\n\n tps = sorted_y_true.cumsum(axis=axis)\n count = (np.ones(y_predicted.shape) * np.arange(y_predicted.shape[-1]))\n fps = 1 + count - tps\n threshold_values = sorted_y_predicted\n\n return fps, tps, threshold_values",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "e6770e82198d0d0915e0a55298116c41",
"score": "0.7294076",
"text": "def predicty(self, x):\n \"\"\" checked: yis0 + yis1 = 1, even with posteriorLabelGivenHypo \"\"\"\n yis0 = np.zeros(1)\n yis1 = np.zeros(1)\n for iconfig in range(self.ntotPerm):\n y = self.label[3].gety(x, iconfig) #hard-wired\n if y == 0:\n yis0 += self.postLabel[iconfig]\n elif y == 1:\n yis1 += self.postLabel[iconfig]\n #print(\"yis0 + yis1 = %s\" %(yis0 + yis1))\n return yis0, yis1",
"title": ""
},
{
"docid": "b4e7633151511c29cdb5506e8afb6ee8",
"score": "0.6965175",
"text": "def cal_predicted_y(self):\n self.predicted_y = (\n self.x * self.parameters[-1][1]) + self.parameters[-1][0]",
"title": ""
},
{
"docid": "d4da1f070b6adf254de93a38d4df2dc5",
"score": "0.6922446",
"text": "def predict(self):\n assert self.Yhat is not None, \\\n \"Compute Yhat before calling predict, but don't compute too often!\"\n\n classes = np.argmax(self.Yhat, axis=1)\n return classes",
"title": ""
},
{
"docid": "21d57ff8e94657a06f2d4cb2a7424b90",
"score": "0.661294",
"text": "def predict2(self, X, y):\n\n m = X.shape[1]\n p = np.zeros((1, m), dtype=np.int)\n\n # Forward propagation\n a3 = self.forward_propagation(X, self.weights)\n\n # convert probas to 0/1 predictions\n for i in range(0, a3.shape[1]):\n if a3[0, i] > 0.5:\n p[0, i] = 1\n else:\n p[0, i] = 0\n\n # print results\n\n # print (\"predictions: \" + str(p[0,:]))\n # print (\"true labels: \" + str(y[0,:]))\n print(\"Accuracy: \" + str(np.mean((p[0, :] == y[0, :]))))\n\n return p",
"title": ""
},
{
"docid": "5cc47baef78dc7a3ac229e0d87c6b80c",
"score": "0.6573917",
"text": "def _binary_clf_curves(y_true, y_predicted):\n if not (y_true.ndim == y_predicted.ndim):\n raise ValueError('Dimension mismatch, ({}, {})'.format(y_true.ndim, y_predicted.ndim))\n if not isinstance(y_true, T.TensorVariable) or not isinstance(y_predicted, T.TensorVariable):\n raise TypeError('This only works for symbolic variables.')\n\n if y_true.ndim == 1:\n clf_curve_fn = _vector_clf_curve\n elif y_true.ndim == 2:\n clf_curve_fn = _matrix_clf_curve\n elif y_true.ndim == 3: \n clf_curve_fn = _tensor3_clf_curve\n elif y_true.ndim == 4:\n clf_curve_fn = _tensor4_clf_curve\n else:\n raise NotImplementedError('Not implemented for ndim {}'.format(y_true.ndim))\n\n fps, tps, thresholds = clf_curve_fn(y_true, y_predicted)\n return fps, tps, thresholds",
"title": ""
},
{
"docid": "bd5a6611ebf9734f5d1fffddd88a77b3",
"score": "0.6518169",
"text": "def predict(self, X):\n y_pred = np.zeros(X.shape[0])\n ###########################################################################\n # TODO: #\n # Implement this method. Store the predicted labels in y_pred. #\n ###########################################################################\n z = X.dot(self.w)\n h = self.sigmoid(z)\n for i in range(X.shape[0]):\n if h[i] > 0.5:\n y_pred[i] = 1\n else:\n y_pred[i] = 0\n \n if self.mw is not None:\n lables_num = self.mw.shape[1]\n h = self.sigmoid(X.dot(self.mw))\n y_pred = np.argmax(h,axis=1)\n \n ###########################################################################\n # END OF YOUR CODE #\n ###########################################################################\n return y_pred",
"title": ""
},
{
"docid": "b303162301a81ed8181e766d21c2dffa",
"score": "0.65142304",
"text": "def classify(self,features):\n probs = self.P(self.labels[0],features)\n y_hat = self.labels[0]\n\n for i in range (1,len(self.labels)):\n if self.P(self.labels[i],features)> probs:\n probs = self.P(self.labels[i],features)\n y_hat = self.labels[i]\n return y_hat",
"title": ""
},
{
"docid": "9c5f413514a677f569ec1f18451134fa",
"score": "0.64584905",
"text": "def classify(self):\n assert self.X_train.shape[0] == len(self.labels_train)\n # fit the classifier\n self.clf.fit(self.X_train, self.labels_train, sample_weight=self.weights)\n\n y_train = self.clf.predict_proba(self.X_train)[:, 1]\n y_test = self.clf.predict_proba(self.X_test)[:, 1]\n y_scraped = self.clf.predict_proba(self.X_scraped)[:, 1]\n\n return y_train, y_test, y_scraped",
"title": ""
},
{
"docid": "1e44fbd352d7f4af6e7cc27008fc4800",
"score": "0.6441187",
"text": "def predict(self,X):\n y = np.zeros((len(X),self.__class_count))\n for i in range(len(X)):\n max_score_index = 0\n max_score = 0\n for j in range (self.__class_count):\n score = self.__calculate_softmax(j,X[i])\n if score > max_score:\n max_score = score\n\n max_score_index = j\n\n y[i][max_score_index] = 1\n\n return y",
"title": ""
},
{
"docid": "f69194f30324ec227213d1b18f7b48b6",
"score": "0.6435099",
"text": "def calculate_predicted_y(x, w, y):\n # create a list storing the the predicted value and the true value\n true_vs_pred = []\n # create an all 1 vector\n all_one = []\n for i in range(len(x[0])):\n all_one.append(1)\n\n x_temp = x[:]\n x_temp.append(all_one)\n x_temp = np.array(x_temp)\n # print(np.shape(x_temp))\n\n temp = []\n for i in range(len(x_temp[0])):\n for j in range(len(x_temp)):\n temp.append(x_temp[j][i])\n\n # compute the predicted value using the weights\n y_predicted = np.dot(temp, w)\n temp = []\n if y_predicted > 0:\n y_predicted = 1\n else:\n y_predicted = 0\n # the first one is the true mean, second one is the predicted\n true_vs_pred.append([y[i], y_predicted])\n\n return true_vs_pred",
"title": ""
},
{
"docid": "bdf75a86d021f696f8384e55dfe2a79e",
"score": "0.6401065",
"text": "def predict(self, X):\n y_pred = np.zeros(X.shape[1])\n ###########################################################################\n # TODO: #\n # Implement this method. Store the predicted labels in y_pred. #\n ###########################################################################\n\n y_pred = np.zeros(X.shape[1]).T\n y_pred = self.W.T.dot(X.T)\n y_pred = np.argmax(y_pred, axis=0)\n ###########################################################################\n # END OF YOUR CODE #\n ###########################################################################\n return y_pred",
"title": ""
},
{
"docid": "6e5e24b85d668bc04bf664e5fc40d4ed",
"score": "0.6375939",
"text": "def predict(self, X):\n\n # return the numpy array y which contains the predicted values\n proba = self.predict_proba(X)\n# print(proba.shape)\n y_pred = np.argmax(proba, axis = 1)\n return y_pred",
"title": ""
},
{
"docid": "151a0356a5030b57854928ee14243997",
"score": "0.6354161",
"text": "def constructing_predicted_matrix():",
"title": ""
},
{
"docid": "c536dcad625ef8d16600d81947c14e39",
"score": "0.6332887",
"text": "def decision_function(self, X: Union[csr_matrix, np.ndarray]) -> np.ndarray:\n if not hasattr(self, 'clf_store_'):\n raise NotFittedError\n\n y_scores = list()\n\n if self.verbose:\n label_iterator = tqdm(range(self.clf_store_.shape[0]))\n print('Predicting label scores')\n else:\n label_iterator = range(self.clf_store_.shape[0])\n\n for clf_index in label_iterator:\n label_scores = self.clf_store_[clf_index].decision_function(X)\n y_scores.append(label_scores)\n\n return np.array(y_scores).T",
"title": ""
},
{
"docid": "603e780193fc2a99cff442183dc53311",
"score": "0.631133",
"text": "def _pred_shape(self, X):\n return X.shape",
"title": ""
},
{
"docid": "607901fa843118621269ebf36df29b0e",
"score": "0.63055897",
"text": "def predict(self, x, ):\n yPred = np.zeros(x.shape[0])\n ###########################################################################\n # TODO: 10 points #\n # - Store the predict output in yPred #\n ###########################################################################\n result0 = np.dot(x, self.params['w1']) + self.params['b1']\n result1 = np.maximum(result0 * 0.03, result0)\n result2 = np.dot(result1, self.params['w2']) + self.params['b2']\n scores = np.maximum(result2 * 0.03, result2)\n yPred = np.argmax(scores,axis=1)\n pass\n\n ###########################################################################\n # END OF YOUR CODE #\n ###########################################################################\n return yPred",
"title": ""
},
{
"docid": "6dcd65d6700da3edd3029fcffaf21385",
"score": "0.6298992",
"text": "def get_y_vectors(self, y_true, y_pred, w):\n y_true = np.array(np.squeeze(y_true[w != 0]))\n y_pred = np.array(np.squeeze(y_pred[w != 0]))\n\n if len(y_true.shape) == 0:\n n_samples = 1\n else:\n n_samples = y_true.shape[0]\n # If there are no nonzero samples, metric is ill-defined.\n if not y_true.size:\n return np.nan, np.nan\n\n y_true = np.reshape(y_true, (n_samples,))\n if self.mode == \"classification\":\n n_classes = y_pred.shape[-1]\n # TODO(rbharath): This has been a major source of bugs. Is there a more\n # robust characterization of which metrics require class-probs and which\n # don't?\n if \"roc_auc_score\" in self.name or \"prc_auc_score\" in self.name:\n y_true = to_one_hot(y_true).astype(int)\n y_pred = np.reshape(y_pred, (n_samples, n_classes))\n else:\n y_true = y_true.astype(int)\n # Reshape to handle 1-d edge cases\n y_pred = np.reshape(y_pred, (n_samples, n_classes))\n y_pred = from_one_hot(y_pred)\n else:\n y_pred = np.reshape(y_pred, (n_samples,))\n\n if self.threshold is not None:\n y_pred = np.greater(y_pred, self.threshold) * 1\n y_true = np.greater(y_true, self.threshold) * 1\n n_classes = 2\n y_pred = to_one_hot(y_pred).astype(int)\n y_true = to_one_hot(y_true).astype(int)\n\n return y_true, y_pred",
"title": ""
},
{
"docid": "84bf4d2544ab5f8485485f35b420dbfb",
"score": "0.6285286",
"text": "def predict(self, X):",
"title": ""
},
{
"docid": "84bf4d2544ab5f8485485f35b420dbfb",
"score": "0.6285286",
"text": "def predict(self, X):",
"title": ""
},
{
"docid": "84bf4d2544ab5f8485485f35b420dbfb",
"score": "0.6285286",
"text": "def predict(self, X):",
"title": ""
},
{
"docid": "5619c7d9a3cbd234b94681f8f0c2091a",
"score": "0.6284215",
"text": "def get_acc(y_pred: np.ndarray, y: np.ndarray) -> float:\n return np.sum(y_pred == y) / y.shape[0]",
"title": ""
},
{
"docid": "6c058eee493bfe8f8a79e5e6b4e472af",
"score": "0.6274305",
"text": "def predict(self, Xs):",
"title": ""
},
{
"docid": "b1d4f2f1e429ecefcae1592fbd327c3f",
"score": "0.62705",
"text": "def predict_proba(self, X):\n # Check is fit had been called\n check_is_fitted(self, \"model_\")\n\n # Input validation\n X = check_array(X)\n if self.fit_intercept:\n X = sm.add_constant(X, has_constant=\"add\")\n\n # print(X.shape)\n decision_2d = np.empty(shape=(X.shape[0], 2))\n\n sklearn_models = self.model_.predict(X)\n decision_2d[:, 1] = sklearn_models\n decision_2d[:, 0] = 1 - sklearn_models\n\n return decision_2d",
"title": ""
},
{
"docid": "6bc27559c20f38bc36b76597085bcb12",
"score": "0.62697554",
"text": "def predict(self, X):\n y_hat, _ = self.forward(X.T)\n if self.n_neurons[-1] == 1: # 2-class problem\n return (y_hat > .5).squeeze().astype('int')\n else: # C > 2 class problem\n return np.argmax(y_hat, axis=0)",
"title": ""
},
{
"docid": "8f4ac394a2470d1efaf85d3c85443965",
"score": "0.6261199",
"text": "def predict(self, features):\n\n\n\n return 1.0",
"title": ""
},
{
"docid": "766a87f59dc881a569e271869ed0f11a",
"score": "0.62555385",
"text": "def predict_proba(self, X):",
"title": ""
},
{
"docid": "963392a2ba26c766ef2e61d51903cf12",
"score": "0.6233869",
"text": "def calculate(self, y_true, y_pred):",
"title": ""
},
{
"docid": "b65d99b1f1203958b8beb8c6d59c6f16",
"score": "0.62148976",
"text": "def _expected_Y(self):\n return np.dot(self.PY,self.A)",
"title": ""
},
{
"docid": "19a5a3d57f6813a82dd51d7f266ca67a",
"score": "0.6187502",
"text": "def predict(self, X):\r\n linear_model = np.dot(X, self.weights) + self.bias\r\n y_predicted = self._sigmoid(linear_model)\r\n y_predicted_cls = [1 if i > 0.5 else 0 for i in y_predicted]\r\n return np.array(y_predicted_cls)",
"title": ""
},
{
"docid": "fd25dae506d6d224d728f786493cb42f",
"score": "0.6182327",
"text": "def predict(self, X, y=None):\n\n # Return labels with highest probability.\n return np.exp(self._gbm.predict(xgb.DMatrix(X))) - 1",
"title": ""
},
{
"docid": "abdb6bb0b789f039401e89b2f3d0d390",
"score": "0.617636",
"text": "def predict_labels(self):\n # Turn the test data frame into an appropriate\n # matrix / vector\n X = self.test_df.copy()\n del X['y']\n\n # Predict the label of the test examples\n self.ypred = self.clf.predict(X)",
"title": ""
},
{
"docid": "c260329d3e17d1d79e165b6352f9f41c",
"score": "0.6173415",
"text": "def predictClasses(p):\n\n return np.argmax(p, axis=0)",
"title": ""
},
{
"docid": "130938c57329464d3b5e69b88451fc9b",
"score": "0.61684465",
"text": "def predict(self,X):\n\n y_pred = np.zeros(X.shape[0])\n\n ###########################################################################\n # Compute the predicted outputs for X #\n # TODO: 2 lines of code expected #\n ###########################################################################\n XX = np.vstack([np.ones((X.shape[0],)),X.T]).T\n for i in range(0, XX.shape[0]):\n y_pred[i] = np.argmax(utils.sigmoid(np.dot(XX[i], self.theta.T)))\n ###########################################################################\n # END OF YOUR CODE #\n ###########################################################################\n return y_pred",
"title": ""
},
{
"docid": "75ca9feb6b0c918c2673a1f6a9187c6b",
"score": "0.6144583",
"text": "def class_proba(self, X, y, c):\n \n numerator = 1 + np.sum( np.equal( y, c) , axis=0)\n denominator = X.shape[0] + self.n_labels\n return numerator/denominator",
"title": ""
},
{
"docid": "07a7faa7deaf1de7e2923c41bf9c12af",
"score": "0.61368316",
"text": "def predict(self, X):\n y_pred = []\n for i, x in enumerate(X):\n y_pred.append([])\n for j, _ in enumerate(self.classes):\n y_pred[i].append(1)\n for k, _ in enumerate(self.parameters):\n y_pred[i][j] *= self._conditionalProba(j, k, x)\n y_pred[i][j] /= self.class_proba[j]\n y_pred[i] = self.classes[np.argmax(y_pred[i])]\n return y_pred",
"title": ""
},
{
"docid": "f48d2db6dd3ac4805e3327c13add2974",
"score": "0.6131202",
"text": "def supervised_predict(self, x):\n y_hat = []\n max_indices = np.argmax(self.get_posterior(x), axis=1)\n for i in range(len(x)):\n y_hat.append(self.cluster_label_map[max_indices[i]])\n return np.array(y_hat)",
"title": ""
},
{
"docid": "a9403798d3048fe640acea031526003b",
"score": "0.6119534",
"text": "def predict_proba(self, X, y=None):\n if not hasattr(X, 'shape'):\n raise Exception('X must be a numpy object')\n\n predicted = []\n for i in range(len(self.estimators)):\n predicted.append(self.estimators[i].predict(X) * -1)\n\n predicted = np.array(predicted).T\n\n scores = []\n for i in range(predicted.shape[0]):\n tiled_predictions = np.tile(predicted[i], (self.error_code.shape[0], 1))\n\n class_score = np.sum(np.abs(self.error_code - tiled_predictions), axis=1)\n class_score = EcocEstimator.softmax(class_score)\n\n scores.append(class_score)\n\n return np.array(scores)",
"title": ""
},
{
"docid": "22020d4cd8866875c5e727770c03ed7d",
"score": "0.61134666",
"text": "def get_y(self, data):\n length = int(data.rows)\n preds = ctypes.POINTER(ctypes.c_float)()\n _call_and_throw_if_error(_LIB.AGetY(self.handle,\n data.handle,\n ctypes.byref(preds)))\n\n if not isinstance(preds, ctypes.POINTER(ctypes.c_float)):\n raise RuntimeError('expected float pointer')\n\n if self.labels_count == 1:\n res = np.copy(np.ctypeslib.as_array(preds, shape=(length,)))\n else:\n res = np.copy(np.ctypeslib.as_array(\n preds, shape=(length, self.labels_count)))\n\n _call_and_throw_if_error(_LIB.ADeleteArray(preds))\n\n return res",
"title": ""
},
{
"docid": "09b8959550502aafec4b84d0436765c9",
"score": "0.61035615",
"text": "def predict_numpy(self, X):\n distances = spatial.distance.cdist(X[:,0:2], self.iris[:,0:2]) # calculates the distances between every point in X and every point in the iris data. Returns a matrix shape (len(X) rows * len(iris.data) columns)\n closest = np.argsort(distances, axis=1).transpose()[:self.k].transpose() # creates a sorted matrix of the indexes of the smallest k distances. Returns a matrix shape (len(X) rows * k colummns)\n targets = np.take(self.target_name, closest) # creates a matrix of target_names associated with the indexes from the previous matrix. Returns a matrix shape (len(X) rows * k colummns)\n return stats.mode(targets, axis=1).mode.flatten() # creates an array of the modes which represents the predicted label for each query point. Returns a matrix shape (1 row * len(X) columns)",
"title": ""
},
{
"docid": "54bfdc48e4be2b4fe612d98cdf48ec6a",
"score": "0.6096192",
"text": "def predict(self, X_test):\n y_predicted = []\n indicies = self.kneighbors(X_test)[1]\n for i in range(len(X_test)):\n classes = [self.y_train[x] for x in indicies[i]]\n values,value_counts = myutils.get_frequencies(classes)\n max_value = max(value_counts)\n max_value_index = value_counts.index(max_value)\n y_predicted.append(values[max_value_index])\n return y_predicted",
"title": ""
},
{
"docid": "b91654cb47b66635ab0a6b019a8f2117",
"score": "0.6094885",
"text": "def predict( self, X ):\n\t\tpredictions = np.zeros( ( X.shape[0], 1 ) ).astype(self.output_type)\n\n\t\tprobability_of_class_one = self.predict_proba(X)\n\t\tpredictions[ probability_of_class_one >= 0.5 ] = self.labels[1]\n\t\tpredictions[ probability_of_class_one < 0.5 ] = self.labels[0]\n\n\t\treturn predictions",
"title": ""
},
{
"docid": "df8f07aedf815c0bc7e298d70ee4d4d7",
"score": "0.6092934",
"text": "def predict(self, X):\r\n\t\ty_scores = self._decision_scores(X)\r\n\r\n\t\treturn self.label_binarizer_.inverse_transform(y_scores)",
"title": ""
},
{
"docid": "bdfbd47e7d42fec95a7aea376784dbf5",
"score": "0.60903496",
"text": "def predict(self, x):\r\n if(x.shape[1]!=self.input_size):\r\n raise Exception('Input array size does not fit')\r\n \r\n y = np.zeros((x.shape[0], self.output_size))\r\n for i in range(x.shape[0]):\r\n \r\n net1 = x[i]@self.w1.T + self.b1\r\n o1 = 1/(1+np.exp(-net1))\r\n \r\n net2 = o1@self.w2.T + self.b2\r\n o2 = 1/(1+np.exp(-net2))\r\n \r\n y[i] = o2\r\n \r\n return y",
"title": ""
},
{
"docid": "81745097f5ff5c65aaee965ef94b6cd3",
"score": "0.607558",
"text": "def predict(self, X):\r\n\t\ty_pred = self._decision_scores(X)\r\n\r\n\t\tif self.n_outputs_ == 1:\r\n\t\t\treturn y_pred.ravel()\r\n\t\telse:\r\n\t\t\treturn y_pred",
"title": ""
},
{
"docid": "2f60e74cc4f0fbc2fce4f4387bdfcc29",
"score": "0.6068877",
"text": "def predict(self, X):\n\n check_is_fitted(self, self.fitted_)\n X, _ = check_arrays_X_y(X, None)\n\n # labels\n scores = self.decision_function(X)\n probs = self.scaler.transform(scores)\n labels = np.ones(len(X), dtype=int) * -1\n labels[probs > self.scaler.get_threshold()] = 1\n return labels",
"title": ""
},
{
"docid": "c01fe38580fa41619154826c2f6a42fa",
"score": "0.6061635",
"text": "def predict(self, X):\n X_t = torch.from_numpy(X).float()\n y_class_f = np.zeros((1,), dtype=int)\n y_angle_f = np.zeros((1,))\n \"\"\"\n h1 = self.relu(torch.matmul(X_t, self.w1_t) + self.b1_t) + torch.cos(torch.matmul(X_t, self.w1_t) + self.b1_t)\n h2 = self.relu(torch.matmul(h1, self.w2_t) + self.b2_t) + torch.cos(torch.matmul(h1, self.w2_t) + self.b2_t)\n h3 = self.relu(torch.matmul(h2, self.w3_t) + self.b3_t) + torch.cos(torch.matmul(h2, self.w3_t) + self.b3_t)\n h4 = self.relu(torch.matmul(h2, self.w4_t) + self.b4_t) + torch.cos(torch.matmul(h2, self.w4_t) + self.b4_t)\n #y_class_pred = F.cross_entropy(torch.matmul(h3, self.w5_t) + self.b5_t, y_class_t, reduction='none')\n y_class_pred = F.softmax(torch.matmul(h3, self.w5_t) + self.b5_t, dim=1)\n print(y_class_pred.shape)\n y_class_pred = y_class_pred.argmax(1)\n print(y_class_pred.shape)\n\n y_angle_pred = torch.matmul(h4, self.w6_t) + self.b6_t\n y_angle_pred = y_angle_pred.reshape((y_angle_pred.shape[0], ))\n \"\"\"\n\n #h1 = F.relu(self.fc1(X_t))\n #h2 = F.relu(self.fc2(h1))\n #h3_p = F.relu(self.fc3(h2))\n #h3_a = F.relu(self.fc4(h2))\n #y_class_pred = F.softmax(self.fc5(h3_p), dim=1)\n\n for i in range(0, X_t.shape[0], 100):\n X_t = X_t[i:i+100-1][:]\n X_t.resize_((100,1, 28,28))\n out = self.layer1(X_t)\n out = self.layer2(out)\n out = out.reshape(out.size(0), -1)\n out = self.drop_out(out)\n out = self.fc1(out)\n y_class_pred = self.fc2(out)\n y_class_pred = y_class_pred.argmax(1)\n #y_angle_pred = self.fc6(h3_a)\n y_angle_pred = self.fc3(out)\n y_angle_pred = y_angle_pred.reshape((y_angle_pred.shape[0], ))\n\n y_class_pred = y_class_pred.data.numpy()\n y_angle_pred = y_angle_pred.data.numpy()\n\n y_class_f = np.append(y_class_f, y_class_pred)\n y_angle_f = np.append(y_angle_f, y_angle_pred)\n\n print(y_class_f[1:].shape)\n print(y_angle_f[1:].shape)\n\n return [y_class_f[1:], y_angle_f[1:]]",
"title": ""
},
{
"docid": "4354d5f14d64cd3e38be5a220efd040b",
"score": "0.60615706",
"text": "def predict(x,w):\n y_p = y_pred(x,w)\n return y_p.T",
"title": ""
},
{
"docid": "7cab1971bf550301cd997cce9419ccb6",
"score": "0.6052667",
"text": "def predict(self, X, y=None):\n\n if not hasattr(X, 'shape'):\n raise Exception('X must be a numpy object')\n\n predicted = []\n for i in range(len(self.estimators)):\n predicted.append(self.estimators[i].predict(X) * -1)\n\n predicted = np.array(predicted).T\n\n scores = []\n for i in range(predicted.shape[0]):\n tiled_predictions = np.tile(predicted[i], (self.error_code.shape[0], 1))\n\n class_score = np.sum(np.abs(self.error_code - tiled_predictions), axis=1)\n class_score = np.argmax(class_score)\n\n scores.append(class_score)\n\n return self._map_values(np.array(scores), self.id2class)",
"title": ""
},
{
"docid": "a1eeccc99b6d8e0068e98e7b9fcbab5f",
"score": "0.60329187",
"text": "def predict(model, X):\n h = np.dot(X, model['W1']) + model['b1']\n h[h < 0] = 0\n y_hat = np.argmax(np.dot(h, model['W2']) + model['b2'], axis=1)\n return y_hat",
"title": ""
},
{
"docid": "8389fcb33fd698b33772b70049f9f3c1",
"score": "0.60316706",
"text": "def predict(self, x):",
"title": ""
},
{
"docid": "0b1874d897f38af6b9dd8cc7736bdcfa",
"score": "0.6022124",
"text": "def predict(self, X):\n\n for label in self.classifiers:\n self.predictions[label] = self.classifiers[label].predict(X)\n\n labels = []\n for i in range(X.shape[0]):\n labels.append([\n l for l in self.classifiers if self.predictions[l][i] == 1\n ])\n\n # labels = []\n #\n # for x in X:\n # x_labels = []\n #\n # for label in self.classifiers:\n #\n # p = self.classifiers[label].predict(x)\n # if p == 1:\n # x_labels.append(label)\n #\n # labels.append(numpy.array(x_labels))\n\n return numpy.array(labels)",
"title": ""
},
{
"docid": "2bad0a4523b5ddde69050c5f4824633d",
"score": "0.60205835",
"text": "def predicted_lab(self,X_test):\r\n label = []\r\n for i in range(0,len(X_test)):\r\n label.append(self.comp_label(X_test.iloc[i]))\r\n return label",
"title": ""
},
{
"docid": "64dc0fd62b97e28c0834cf26a7043e68",
"score": "0.6020575",
"text": "def predictY(self, X):\n \"\"\" to get predictY given hypo, run posteriorLabelGivenHypo then predictY \"\"\"\n self.probYis0 = np.zeros(self.nx)\n self.probYis1 = np.zeros(self.nx)\n for x in X:\n yis0, yis1 = model.predicty(self, x)\n self.probYis0[x] = yis0\n self.probYis1[x] = yis1",
"title": ""
},
{
"docid": "94283b7153ec07ee52f0cee8b7875aa3",
"score": "0.60127985",
"text": "def predict(self, X):\n check_is_fitted(self, ['X_', 'y_'])\n X = check_array(X)\n if self.loss == 'log':\n pp = self.predict_proba(X)\n y_pred = np.argmax(pp, axis=1)\n else:\n scores = self.decision_function(X)\n if len(scores.shape) > 1:\n y_pred = scores.argmax(axis=1)\n else:\n y_pred = (scores > 0).astype(int)\n return self.classes_[y_pred]",
"title": ""
},
{
"docid": "d97b47f7e7ca1adefee482353d531bfe",
"score": "0.6010366",
"text": "def predict(self, X):\n ...",
"title": ""
},
{
"docid": "d97b47f7e7ca1adefee482353d531bfe",
"score": "0.6010366",
"text": "def predict(self, X):\n ...",
"title": ""
},
{
"docid": "d97b47f7e7ca1adefee482353d531bfe",
"score": "0.6010366",
"text": "def predict(self, X):\n ...",
"title": ""
},
{
"docid": "33745a5cdb6a2f29868335bc60514416",
"score": "0.6009253",
"text": "def _fit(self, X, y):\n pos_y, neg_y = np.where(y == 1)[0], np.where(y == -1)[0]\n pos_X, neg_X = X[pos_y], X[neg_y]\n\n pos_probs, neg_probs = [], []\n max_feat_vec_len = 0\n\n for i, _ in enumerate(self.schema.feature_names):\n feat_vec_len = max(pos_X[:, i]) + 1\n m = self.m\n p = 1 / (feat_vec_len - 1)\n max_feat_vec_len = max(feat_vec_len, max_feat_vec_len)\n pos_probs.append((np.bincount(pos_X[:, i]) + (m * p)) /\n (len(pos_y) + m))\n neg_probs.append((np.bincount(neg_X[:, i]) + (m * p)) /\n (len(neg_y) + m))\n\n # Normalize the arrays so we can safely put them in a 2darray\n self.pos_probs = np.array(\n [np.concatenate([p, np.zeros(max_feat_vec_len - len(p))])\n for p in pos_probs]\n )\n self.neg_probs = np.array(\n [np.concatenate([p, np.zeros(max_feat_vec_len - len(p))])\n for p in neg_probs]\n )\n\n self.y_prob = len(pos_y) / len(y)",
"title": ""
},
{
"docid": "42203da1be68cfca67b030b8da999f29",
"score": "0.6007016",
"text": "def supervised_predict(self, x):\n global label_dict\n\n p = dict(zip(label_dict.values(),label_dict.keys()))\n z_ik = self.get_posterior(x)\n cluster = np.argmax(z_ik, axis=1)\n y_hat = list()\n for element in cluster:\n y_hat.append(p[self.cluster_label_map[element]])\n\n\n return np.array(y_hat)",
"title": ""
},
{
"docid": "41701e474d9b4b1a1f02fb4bc2211e53",
"score": "0.60057515",
"text": "def predict(self, X):\n \n num_test = X.shape[0]\n \n \n # Your Code here\n return self.hypothesis(X,self.theta)",
"title": ""
},
{
"docid": "35e4e108bde54126819ebd4293401d84",
"score": "0.60022473",
"text": "def predict(self):\n self.yhat = self.clf.predict(self.X_scaled)\n self.CalcErrorMetric()",
"title": ""
},
{
"docid": "7bb60fd05e566306046a8a71ca0e6cf7",
"score": "0.5998014",
"text": "def predict_proba(self, test_data):",
"title": ""
},
{
"docid": "c28be9568de7a17f62291e59b711c660",
"score": "0.59969443",
"text": "def predict(self, frame):\n model = pickle.load(open(self.model_path, 'rb'))\n\n process = np.max(extract_features(frame)).reshape((1, 1))\n\n predicted = model.predict(process)\n predict_data = model.predict_proba(process)\n\n labels = ['FAKE', 'REAL']\n\n print(\"Prediction: \", labels[predicted[0]])\n print(\"Confidence: \", predict_data)\n return labels[predicted[0]]",
"title": ""
},
{
"docid": "0818d042a4828225b6de505f3acbc3cc",
"score": "0.59878623",
"text": "def predict_labels(self, X_test, k=None):\n if k == None:\n k = self.k\n\n num_test = X_test.shape[0]\n y_pred = numpy.zeros(num_test)\n\n for i in range(num_test):\n closest_y = self.y_train[numpy.argsort(self.dist_mt[i])[0:k]]\n count = Counter(closest_y)\n # print(count.most_common(1))\n y_pred[i] = count.most_common (1)[0][0]\n\n return y_pred",
"title": ""
},
{
"docid": "c0181e9dc6a433d336615a50079d2cf9",
"score": "0.5982086",
"text": "def class_label_arr(y):\n c1_label = one_hot(y)[:, :1]\n c2_label = one_hot(y)[:, 1:2]\n c3_label = one_hot(y)[:, -1:]\n return c1_label, c2_label, c3_label",
"title": ""
},
{
"docid": "f6315ee35af400c513df214aa2fe0610",
"score": "0.5981862",
"text": "def predict(self, X):\n N = X.shape[0]\n y_pred_proba_highest = np.zeros((N,)) # contains highest probabilities for each sample so far\n y_pred = np.zeros((N,), dtype=np.int8) # doesn't allow more than 127 classes\n\n for k, classifier in enumerate(self.classifiers):\n predict_proba_k = classifier.predict_proba(X)[:, 1] # probability of 1 for this class\n _update_predictions(y_pred, y_pred_proba_highest, predict_proba_k, k)\n\n return y_pred",
"title": ""
},
{
"docid": "34aaa1c25d1166c82296a426607af34a",
"score": "0.59748733",
"text": "def covariance_matrix(predicted, y):\n true_positive = 0\n false_positive = 0\n false_negative = 0\n true_negative = 0\n size_y = y.shape[0]\n for i in range(size_y):\n if(predicted[0][i] == 1 and y[i] == 1):\n true_positive += 1\n elif(predicted[0][i] == 1 and y[i] == 0):\n false_positive += 1\n elif(predicted[0][i] == 0 and y[i] == 1):\n false_negative += 1\n elif(predicted[0][i] == 0 and y[i] == 0):\n true_negative += 1\n \n # calculates the accuracy\n accuracy = (true_positive + true_negative)/(true_positive + true_negative\n + false_negative + false_positive)\n # calculates the precision\n precision = (true_positive)/(true_positive + false_positive)\n #calculates the recall\n recall = (true_positive)/(true_positive + false_negative)\n # calculates the specificity\n specificity = (true_negative)/(true_negative + false_positive)\n # calculates the f1_score\n f1_score = (2*precision*recall)/(precision+recall)\n print_confusion_matrix(true_positive,false_positive,false_negative,\n true_negative, accuracy, precision, recall, specificity, f1_score)",
"title": ""
},
{
"docid": "fdde0f15ca65629fae9545114631dbef",
"score": "0.59742576",
"text": "def predict(self, X):\r\n pass",
"title": ""
},
{
"docid": "4b64bd3ed2029c02e4b68b1a3267c4c0",
"score": "0.5965891",
"text": "def predict(self, X):\n Xk = self._get_kernel(X,self.X_fit_)\n scores = self.decision_function(Xk)\n if len(scores.shape) == 1:\n indices = (scores > 0).astype(np.int)\n else:\n indices = scores.argmax(axis=1)\n return self.classes_[indices]",
"title": ""
},
{
"docid": "101808e58f7cc596fa2d7b425c01dde7",
"score": "0.5964672",
"text": "def predict(self, X,y=None):\n oldn = X.count()\n minusOnes = self.sc.parallelize(np.array([-1] * oldn))\n y = (minusOnes + self.labeledy).zipWithIndex().map(lambda (a,b): (b,a))\n lpoints = self.labeledy.count()\n X = (X.map(lambda (i,v): v) + self.labeledX).zipWithIndex().map(lambda (a,b): (b,a))\n newn = X.count()\n XforPCA = X.map(lambda (ind,vec): (int(ind),mlvec.dense(vec))).toDF(schema = ('idd','features'))\n rotatedData = self.PCA.transform(XforPCA)\n rotatedData=rotatedData.select([\"idd\", \"pcaFeatures\"]).rdd.map(lambda r: (r.idd,r.pcaFeatures.toArray()))\n dictData = self.makeDF(rotatedData, self.dimensions)\n bc_EdgeMeans, bc_newg, kb = self.broadcaster()\n dataBounds = self.sc.broadcast(self.getdataboundaries(dictData, self.k))\n useful_data = dictData.select(['idd']+[str(i+1) for i in self.jj])\n jj = self.sc.broadcast(self.jj)\n useful_dataRDD = useful_data.rdd.map(lambda rw: (int(rw['idd']), [rw[str(i+1)] for i in jj.value]))\n approxValues = IndexedRowMatrix(useful_dataRDD.map(lambda (ind,vec) : IndexedRow(ind, transformer(vec, dataBounds, bc_EdgeMeans, bc_newg, kb)) ))\n sumofSquares = approxValues.rows.flatMap(lambda irow: [(i,x) for i,x in enumerate(irow.vector.toArray()**2)]).reduceByKey(lambda a,b: a+b).map(lambda (a,b): (a,b)).collect()\n sos = self.sc.broadcast(np.array(sorted(sumofSquares, key = lambda x: x[0]))[:,1])\n norm_approxValues = IndexedRowMatrix(approxValues.rows.map(lambda irow: IndexedRow(irow.index,np.divide(np.array(irow.vector, dtype = 'float32'), np.array(sos.value, dtype = 'float32')))))\n self.tinterpolated = norm_approxValues\n self.talpha = self.getAlpha(norm_approxValues, y, newn, self.newsig)\n efunctions = self.solver(norm_approxValues, self.talpha)\n efunctions = efunctions.map(lambda (ind,val):(ind,mlvec.dense([val]))).toDF(schema = (\"idd\",\"features\"))\n kmeans = kmml(featuresCol=\"features\",predictionCol=\"prediction\", k= self.numClasses, initMode=\"k-means||\",initSteps=10, tol=1e-4, maxIter=2000, seed=0)\n kmodel = kmeans.fit(efunctions)\n labeled=kmodel.transform(efunctions)\n predicted = self.relabel(labeled, kmodel)\n return predicted.filter(lambda (i,v): i < (newn - lpoints))",
"title": ""
},
{
"docid": "d037f01b5bd549d8dfd550d91571ba10",
"score": "0.5963657",
"text": "def accuracy(self, xs, ys):\n correctlyPredicted = 0\n for index, row in enumerate(xs):\n y_hat, z_2, h, z_1 = self.predict(row)\n indexOfMax = np.argmax(y_hat)\n if ys[index][indexOfMax]:\n correctlyPredicted += 1\n \n # for each row in xs make predict\n # take only y_hat\n # y_hat get index of biggest\n # ys should have one on same index\n # if yes counter++\n # return counter / len xs\n return correctlyPredicted / len(xs)",
"title": ""
},
{
"docid": "118aa7271c032a6c97d2c5acfebdd4dd",
"score": "0.59546566",
"text": "def predict(self, x):\n labels = np.zeros((x.shape[0]), dtype=int)\n ##################################\n # YOUR CODE GOES HERE #\n ##################################\n\n comp_distance = np.zeros((x.shape[0], self.k))\n for i in range(self.k):\n distance = 0\n distance = x - self.centers[i]\n distance = np.square(distance)\n distance = np.sum(distance, axis = 1)\n comp_distance[:,i] = distance\n\n labels = np.argmin(comp_distance, axis=1)\n \n return labels",
"title": ""
},
{
"docid": "958c56925a54660be699dff0017450c2",
"score": "0.5937588",
"text": "def predict(self, data):",
"title": ""
},
{
"docid": "32f9b84fccea603b5cbb4c787e2ca676",
"score": "0.5936847",
"text": "def predict(self, x): \n\n pred_proba = self.predict_proba(x)[:, 1]\n return self._label_binarizer.inverse_transform(pred_proba)",
"title": ""
},
{
"docid": "737fbc12cbdb4ef53c2f8dc1e02bdfce",
"score": "0.59338164",
"text": "def predict(self, x):\n x = validate_samples(x, n_dim=1)\n y = np.empty(len(x))\n\n for i, x0 in enumerate(x):\n d = self.kernel((x0 - self.x) / self.bandwidth)\n y[i] = d.dot(self.y) / d.sum()\n return y",
"title": ""
},
{
"docid": "b47c8aacb08e2a3e29e7ce4f56653604",
"score": "0.5932985",
"text": "def predict(self, X):\n y_pred = [self._viterbi(seq) for seq in X]\n return y_pred",
"title": ""
},
{
"docid": "f91bb7e50cc5241cf53e2efa9934ef94",
"score": "0.5932868",
"text": "def predict_proba(self, X):\n ...",
"title": ""
},
{
"docid": "f91bb7e50cc5241cf53e2efa9934ef94",
"score": "0.5932868",
"text": "def predict_proba(self, X):\n ...",
"title": ""
},
{
"docid": "24ab4e2a7e4e9f7f8736b3e1f0e416bb",
"score": "0.59271705",
"text": "def predict(self, X: List[int]):\n mat = np.hstack([v.transform(X).sum(axis=1) for v in self.vectorizers])\n self.last_scores = mat # for later use, just in case\n return mat.argmax(axis=1).A1",
"title": ""
},
{
"docid": "28d09719924fd1b91c3839ff5d3bc3cd",
"score": "0.5917681",
"text": "def _predict_y(self, x = None, terminal_node = None):\n # Grab coefficients from terminal node\n coef = self.summary[terminal_node].coef.reshape(-1, 1)\n\n # Calculate predicted probability and threshold to get class label\n if self.fit_intercept:\n p = self._logit(exog = np.insert(x, 0, 1).reshape(1, -1), coef = coef)\n else:\n p = self._logit(exog = x.reshape(1, -1), coef = coef)\n\n if p < .5:\n return 0\n else:\n return 1",
"title": ""
},
{
"docid": "6e835da3fef52391f21cb98391820df5",
"score": "0.5911059",
"text": "def predict(self, X):\n\t\t\n\t\t# Check is fit had been called\n\t\tcheck_is_fitted(self, ['classifier_'])\n\n\t\t# Input validation\n\t\tX = check_array(X)\n\n\t\tpredicted_y = svorex.predict(X.tolist(), self.classifier_)\n\t\t\n\t\treturn predicted_y",
"title": ""
},
{
"docid": "ab8e343fe4f8826be4f05f8f9a0a57e2",
"score": "0.5906067",
"text": "def predict(self, X):\n class_label = np.zeros(X.shape[0])\n #############################################################################\n # TODO: Return the best class label. #\n #############################################################################\n\n if self.bias and X.ndim > 1: # augment for generalization\n X = augment(X)\n class_label = np.argmax((X.dot(self.W)).T, axis=0)\n\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n return class_label",
"title": ""
},
{
"docid": "8e91acf63f501442ce271e9851d70717",
"score": "0.5904482",
"text": "def get_label_y_counts(y):\n \n return np.unique(y,return_counts=True)",
"title": ""
},
{
"docid": "1fd110e27981e2592b2df6ea8288cfe6",
"score": "0.59033126",
"text": "def predict(self, X):\n return (self.clf.predict(X) + 1) / 2",
"title": ""
},
{
"docid": "0f61daa4aafbe08a25811312d0f6080f",
"score": "0.5902834",
"text": "def predict(self):\n\n\t\t# Implement this method in the inherited class to predict the class-labels of unknown data.\n\t\traise NotImplementedError",
"title": ""
},
{
"docid": "0378089d1088f324073ef14f9095a3a5",
"score": "0.58971524",
"text": "def predict(self,x):\n y_pred = []\n progress = \"---\" #shows a progress bar. Only for asthetics\n for i in range(x.shape[0]):\n #Displaying the progress bar\n if i % (x.shape[0]//20) == 0:\n print(progress,end = \"\\r\")\n \n data = x[i]\n #Forward Pass\n for layer in self.layers:\n #Set the input data for each layer, call the corresponding layer's compute() \n #method and pass the data to it. Passing data is redundant.\n layer.data = data\n data = layer.compute()\n \n #Recording the class with highest probability\n y_pred.append(np.argmax(data,axis = 1)[0]) \n\n self.ypred = y_pred\n return y_pred",
"title": ""
},
{
"docid": "0849007ad3a315cb11ee9dc487c935f9",
"score": "0.5893658",
"text": "def predict(self, X):\n assert(self.w is not None)\n assert(self.w.shape[0] == X.shape[1])\n return 2 * (np.matmul(X, self.w) > 0) - 1",
"title": ""
},
{
"docid": "08dd5cd9fe6fcb770a7728ea9444d529",
"score": "0.58916765",
"text": "def predict(self,X):\n\t\tif self.__voting=='hard':\n\t\t\tclasses = np.empty( (len(X),0), dtype=np.int )\n\t\t\tfor i in range(self.__n_estimators):\n\t\t\t\tpredictions = self.__estimators[i][1].predict(X)\n\t\t\t\tfor _ in range( self.__weights[i] ):\n\t\t\t\t\tclasses = np.c_[ classes, predictions]\n\t\t\treturn np.apply_along_axis(lambda x: np.bincount(x).argmax(), axis=1, arr=classes)\n\t\telse:\n\t\t\tprobs = np.zeros( (len(X),self.__n_classes) )\n\t\t\tfor i in range(self.__n_estimators):\n\t\t\t\tprobabilities = self.__estimators[i][1].predict_proba(X)*self.__weights[i]\n\t\t\t\tprobs += probabilities\n\t\t\treturn np.argmax( probs, axis=1 )",
"title": ""
},
{
"docid": "ec66f2f24b0e45e8517f5b1831ac12fb",
"score": "0.58871996",
"text": "def predict(self, x):\n\n if not self.classifier:\n raise Exception(\"You have to train the model before use it\")\n if None in x:\n raise ValueError(\"Dataset contains None value\")\n\n y = np.empty(len(x), dtype=np.int)\n for index, point in enumerate(x):\n for ball in self.classifier:\n if point in ball:\n y[index] = ball.label\n break\n\n return y",
"title": ""
},
{
"docid": "815d5c16c89b12da3ec58c86da620dbc",
"score": "0.58868325",
"text": "def predict(self, xarr):\n outarr = []\n for x in xarr:\n out = self.feedforward(x)\n outarr.append(np.argmax(out))\n return outarr",
"title": ""
},
{
"docid": "ba9f832ef68264bfc09ed334bd8f7a87",
"score": "0.58735985",
"text": "def predict(self, x):\n #On retourne le argmax des scores calculés par les différents classifiers\n #Puis on renvoie le signe correspondant au résultat de la fonction score du classifieur\n #Ayant eu le meilleur résultat\n return np.argmax(self.score(x))",
"title": ""
},
{
"docid": "7d3f261b4fa1a5283ad08d081b931567",
"score": "0.58717763",
"text": "def predict(self, x):\n # predict_list = [self.dot_product(x, i) for i in range(self.num_of_labels)]\n # label_index = predict_list.index(max(predict_list))\n # return self.reverse_label_mapping[label_index]\n\n predict_mat = x.dot(self.weight.transpose()) # type: np.matrix\n\n max_perdition = predict_mat.argmax(1) # hopefully max index per row\n\n return max_perdition",
"title": ""
},
{
"docid": "622bdede24f4081533a9b283fdb46668",
"score": "0.58715564",
"text": "def predict(self, X, y=None, threshold=0.5):\n if self.output == 'binary':\n y_pred_list = [estimator.predict_proba(X)[:, 1] for estimator in self._estimator_list]\n y_pred_proba = np.stack(y_pred_list).mean(axis=0)\n return np.where(y_pred_proba > threshold, 1, 0)\n elif self.output == 'regression':\n y_pred_list = [estimator.predict(X) for estimator in self._estimator_list]\n return np.stack(y_pred_list).mean(axis=0)",
"title": ""
},
{
"docid": "8c5a31f3ca6c097edafb3e7368ba7a2c",
"score": "0.5869973",
"text": "def Generalised_dice_coef_multilabel2(y_true, y_pred, numLabels=2):\n dice=0\n for index in range(numLabels):\n dice -= dice_coef(y_true[:,:,:,:,index], y_pred[:,:,:,:,index])\n return numLabels + dice",
"title": ""
},
{
"docid": "e18cbe4e3f81f66e75624ad0795574e4",
"score": "0.5867833",
"text": "def accuracy(y_hat, y):\n # if the minibatch has more than one data point\n if len(y_hat.shape) > 1 and y_hat.shape[1] > 1:\n # pick the largest probability as the predicted category\n y_hat = y_hat.argmax(axis=1)\n # comparison after converting to the same datatype\n cmp = (y_hat.type(y.dtype) == y)\n # sum to get the number of correct predictions\n return float(cmp.type(y.dtype).sum())",
"title": ""
},
{
"docid": "bd84683e8ee2c0673c08eba50bd47aad",
"score": "0.5866891",
"text": "def score(self, X, y):\n ### YOUR CODE HERE\n n_samples, n_features = X.shape\n preds = self.predict(X)\n score = np.sum(y==preds)/n_samples*100\n return score\n ### END YOUR CODE",
"title": ""
},
{
"docid": "6b560592df39e5ca77bff27713003b40",
"score": "0.5860534",
"text": "def predict(self, X):\n X = self._check_data_type_X(X)\n return X.apply(self._generate_y_hat, axis=1)",
"title": ""
},
{
"docid": "5d56e03537e8e4748500cf178509dc90",
"score": "0.5859186",
"text": "def eval(self, X, y):\n accuracy = []\n\n predictions = self.model.predict(X)\n\n for i in range(len(predictions)):\n if np.argmax(y[i]) == np.argmax(predictions[i]):\n accuracy.append(1)\n else:\n accuracy.append(0)\n\n\n return sum(accuracy) / float(len(accuracy))",
"title": ""
},
{
"docid": "3fb1f27f28e7e2e23961b9c047f6211a",
"score": "0.5858296",
"text": "def get_adversary(self, y_true, n_classes):\n c = self.get_center()\n s = self.get_errors()\n g = (tf.cast(tf.one_hot(y_true, depth=n_classes), tf.float32) * 2 - 1)\n return c - s * g",
"title": ""
},
{
"docid": "8e2f631e9546e75701a120eb6c3d3a1b",
"score": "0.58577865",
"text": "def __multiclass_acc(self, y_pred, y_true):\n return np.sum(np.round(y_pred) == np.round(y_true)) / float(len(y_true))",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="742"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">101d81614ab0a5a889c0c5c8f30c8a93</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Method to get all connection types</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "1c2bedd0979e2655c426ccbacf5c59b7",
"score": "0.75797945",
"text": "def connectionTypes(request: HttpRequest) -> Response:\n if request.method == \"GET\":\n res = Connections.getConnectionTypes()\n return Response(res.json())",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "98b5f976a955658a2f18d8853d873c9f",
"score": "0.70869887",
"text": "def network_connections(self, type: str = 'inet') -> List[dict]:\n schema = ConnectionSchema()\n return schema.dump(\n schema.load(psutil.net_connections(kind=type), many=True), # type: ignore\n many=True,\n )",
"title": ""
},
{
"docid": "f090d016c9911e8241cf6917ecbf820d",
"score": "0.6695734",
"text": "def get_all_connections(self):\n global REDIS_CONNECTION\n return [REDIS_CONNECTION, ]",
"title": ""
},
{
"docid": "7402bc40296e4b75f99952f7b9aafb90",
"score": "0.65959716",
"text": "def cen_connections(self):\n return self.__get_objects_by_type('GKCenConnection')",
"title": ""
},
{
"docid": "d084f5c65d94076654409116da6a1c2a",
"score": "0.6578578",
"text": "def get_all_connections(self):\n return self.connections.values()",
"title": ""
},
{
"docid": "01acc26d71de27407991bb476ba2d826",
"score": "0.65110725",
"text": "def connections(self) -> List[Dict[str, Any]]:\n url = self.http_connection.url + '/connections'\n response = requests.get(url, auth=self.http_connection.auth,\n headers=self._prepare_request_headers(),\n timeout=self.http_connection.timeout)\n response.raise_for_status()\n return response.json()",
"title": ""
},
{
"docid": "2292e310da70f1d6dbdd422b0165403c",
"score": "0.64856666",
"text": "def connection_type(self):\n return self._connection_type",
"title": ""
},
{
"docid": "3db16cc8ef8dd39c28f120081b4c9364",
"score": "0.6476795",
"text": "def getConnections(self):\n s = QSettings() \n s.beginGroup(\"PostgreSQL/connections\")\n currentConnections = s.childGroups()\n s.endGroup()\n return currentConnections",
"title": ""
},
{
"docid": "d51b87e3ef98225c44a82dd78ae65639",
"score": "0.6476435",
"text": "def get_connections(self):\n return self._all_connections",
"title": ""
},
{
"docid": "920a48a0d5ed75c60cb9d3575e7c15d8",
"score": "0.6413483",
"text": "def get_by_type(self,conn_type):\n ret = []\n for c in self.db:\n if c[\"type\"] == conn_type:\n ret.append(c)\n return ret",
"title": ""
},
{
"docid": "b692257453e9c6a4dfd0e13220b750c5",
"score": "0.63768655",
"text": "def get_connections(self) -> List[Connection]:\n return self.connections",
"title": ""
},
{
"docid": "b692257453e9c6a4dfd0e13220b750c5",
"score": "0.63768655",
"text": "def get_connections(self) -> List[Connection]:\n return self.connections",
"title": ""
},
{
"docid": "4917c3d1a1088195f2cd0994ce68e9d7",
"score": "0.62738985",
"text": "def get_name_of_all_connections(self) -> List[str]:\n return [item['name'] for item in self.connections()]",
"title": ""
},
{
"docid": "ba00d9c60544ad4dff4a14d9baef398a",
"score": "0.62604326",
"text": "def get(self):\n r = requests.get(f\"http://{os.environ['BUILDER_HOST']}:{os.environ['BUILDER_PORT']}/api/connections\")\n connections = r.json()\n\n return connections",
"title": ""
},
{
"docid": "b5d109cdc748c36e8c98689582f816ef",
"score": "0.6245524",
"text": "def RetrieveAllConnection(self, session):\n return True, CController._connectModel.RetrieveAll()",
"title": ""
},
{
"docid": "b990eb9cc1b5dd35ea34e229ed2a8511",
"score": "0.61917186",
"text": "def connections(self):\n pass",
"title": ""
},
{
"docid": "51651d50f5a9019ae7393a87e38f4f08",
"score": "0.6168508",
"text": "def bot_conn_types(self) -> Sequence[MOSWireType]:\n if self.flip:\n return MOSWireType.DS_GATE, MOSWireType.DS, MOSWireType.DS_MATCH\n else:\n return MOSWireType.G, MOSWireType.G_MATCH",
"title": ""
},
{
"docid": "414e4e983f4648158df59f9b8668a583",
"score": "0.61651456",
"text": "def get_connectors(self, type_=\"source\"):\n\n if type_ not in valid_connect_types:\n raise ValueError\n try :\n r = requests.get(f\"http://{self._manager_ip}/connectors\", headers=self._headers)\n r.raise_for_status()\n except ConnectionError:\n raise NoConnectServerAvailable\n except Exception as e:\n raise e\n response = []\n for connector in r.json() :\n info = self.get_connector_info(connector)\n if type_ == \"all\" or info[\"type\"] == type_:\n response.append(info)\n return response",
"title": ""
},
{
"docid": "e3e3c6096ceead8d8f73a228fa0619b5",
"score": "0.6156305",
"text": "def getConnections(self):\n return self.connectedTo.keys()",
"title": ""
},
{
"docid": "708b5d2544c150e440bdaa1c764c06dd",
"score": "0.6151528",
"text": "def get_port_connections(self) -> List[Tuple]:",
"title": ""
},
{
"docid": "de14450dda14b532095be41c572e271c",
"score": "0.61175525",
"text": "def connections():\n from django.db import connections\n return connections",
"title": ""
},
{
"docid": "d01aa652363166e20252e6a819816d9d",
"score": "0.6062543",
"text": "def get_connections(self):\r\n ret = self.__wd.get_connections()\r\n if(True == ret[0]):\r\n return ret[2]\r\n else:\r\n return None",
"title": ""
},
{
"docid": "f5676ab70143863b5ec56d2c96d7afb3",
"score": "0.5992369",
"text": "def top_conn_types(self) -> Sequence[MOSWireType]:\n if self.flip:\n return MOSWireType.G, MOSWireType.G_MATCH\n else:\n return MOSWireType.DS_GATE, MOSWireType.DS, MOSWireType.DS_MATCH",
"title": ""
},
{
"docid": "e65d345b2d74a98f7f04d18eb67a0acb",
"score": "0.59882253",
"text": "def open_connections(self):\r\n return [(host, len(li)) for (host, li) in self._cm.get_all().items()]",
"title": ""
},
{
"docid": "29b4df62510b197c81cbaa2c4d1619f3",
"score": "0.59485996",
"text": "def connections( self ):\n return self.findItems(XNodeConnection)",
"title": ""
},
{
"docid": "53f78077c6207162741cad98d910f620",
"score": "0.5947987",
"text": "def get_connections(self):\n body = {\"workspaceId\": AIRBYTE_WORKSPACE_ID}\n url = f\"{self.base_url}/api/v1/web_backend/connections/list\"\n response = requests.post(url, json=body)\n try:\n response_json = response.json()['connections'] if 'connections' in response.json() else {}\n except Exception as e:\n logging.exception(f\"Error getting connections for subscription_id: {self.subscription_id}\\nError {e}\")\n response_json = {}\n return response_json",
"title": ""
},
{
"docid": "4d932ee1e0a659240727e2b519294b54",
"score": "0.59273356",
"text": "def itemTypeName():\n return \"connection\"",
"title": ""
},
{
"docid": "c40eefe3c6ec016c25767cf9d427085d",
"score": "0.5922144",
"text": "def connectivity_type(self) -> str:\n return pulumi.get(self, \"connectivity_type\")",
"title": ""
},
{
"docid": "984f25f60948aaa71d28e65866f3a8c7",
"score": "0.5901656",
"text": "def connections(self):\n names = _ffi.gc(_lib.jack_port_get_connections(self._ptr),\n _lib.jack_free)\n return self._client._port_list_from_pointers(names)",
"title": ""
},
{
"docid": "e701f38d5f0c7d4b9322bb6c31803921",
"score": "0.5874965",
"text": "def getSessionTypes(self):\r\n sessionTypes = self.httpGet(self.kGetSupportedApplicationTypes)\r\n return [session.type for session in sessionTypes]",
"title": ""
},
{
"docid": "08e9408b3508ede68f1053cd0e79c645",
"score": "0.58634585",
"text": "def listConnections(*args, **kwargs):\n \n pass",
"title": ""
},
{
"docid": "eabbd56dfde303cba7ffcb2eb825842a",
"score": "0.58516747",
"text": "def get_server_types(self):\n return self._metadata.get_server_types()",
"title": ""
},
{
"docid": "98a94d33a6571d6249590460322c879c",
"score": "0.57710344",
"text": "def listConnections(*args, **kwargs):\n\n pass",
"title": ""
},
{
"docid": "db0f9b2504f63b4f2c49b527d274bcbf",
"score": "0.5761008",
"text": "def list_backend_types(self) -> None:\n backends = self.get_backend_types()\n for backend in backends:\n print(f'Backend type: {backend[\"name\"]}, number of qubits: {backend[\"number_of_qubits\"]}')",
"title": ""
},
{
"docid": "affed3c261942c467cd2d9b7b9e274a8",
"score": "0.57494324",
"text": "def get_protocols(self):\n protocols_count = self.__count(\"protocolName\")\n return list(protocols_count.keys())",
"title": ""
},
{
"docid": "d0433bf3b0752e562d5101f191abf609",
"score": "0.5748978",
"text": "def get_all_connection(self):\n queues = [self.device_present, self.connect, self.disconnect]\n result = {}\n\n for q in queues:\n lst = []\n while not q.empty():\n lst.append(handle_event(q.get()))\n result[q._name] = lst\n\n return result",
"title": ""
},
{
"docid": "d7e1f90ea17af2e317f628c203e4bc3d",
"score": "0.5743992",
"text": "def _connection_keys(self):\n return (\"host\", \"username\", \"port\", \"database\", \"schema\")",
"title": ""
},
{
"docid": "7664c182d2fbb43593bc41ca9aa539c1",
"score": "0.5742529",
"text": "def clientList(self):\n cl = []\n for k in self.connections:\n cl.append(self.connections[k].asDict())\n\n return cl",
"title": ""
},
{
"docid": "a7fa4a35e05feae2ca221bd3f3ee506c",
"score": "0.5696383",
"text": "def get_active_connections(self):\n query = \"\"\"WITH act_base as (\n SELECT unnest(ARRAY['idle','active','idle in transaction']) as \"state\" )\n SELECT ab.state, count(sa.state)\n FROM act_base ab LEFT OUTER JOIN pg_stat_activity sa ON ab.state = sa.state\n GROUP BY ab.state\n UNION ALL\n SELECT 'total', count(1) FROM pg_stat_activity\n UNION ALL\n SELECT 'max', setting::bigint FROM pg_settings WHERE name='max_connections'\"\"\"\n cursor = self.conn.cursor()\n cursor.execute(query)\n columns = [desc[0] for desc in cursor.description]\n result = []\n for res in cursor:\n result.append(dict(zip(columns, res)))\n cursor.close()\n return result",
"title": ""
},
{
"docid": "e44322dbe63b0c9056d1b73e6d9d64ca",
"score": "0.5674726",
"text": "def get_backend_types(self) -> List[Dict[str, Any]]:\n ret: List[Dict[str, Any]] = self._action(['backendtypes', 'list'])\n return ret",
"title": ""
},
{
"docid": "34fa2f11010cb0178a82b9b6a2c73a7c",
"score": "0.56540453",
"text": "def get_all_connections(self, port):\n port = self._get_port_ptr(port)\n names = _ffi.gc(_lib.jack_port_get_all_connections(self._ptr, port),\n _lib.jack_free)\n return self._port_list_from_pointers(names)",
"title": ""
},
{
"docid": "b3ab60cfe37c0bdd931d31fd61499293",
"score": "0.5648636",
"text": "def connection_types(id1, id2, long_stmts=set()):\n\n ctypes = relation_types(direct_relation(id1=id1, id2=id2,\n long_stmts=long_stmts))\n if has_common_parent(id1=id1, id2=id2):\n ctypes += ['parent']\n return ctypes",
"title": ""
},
{
"docid": "aef573d912831d7fddf157f73ae4dece",
"score": "0.5635971",
"text": "def list_connections(ProviderTypeFilter=None, MaxResults=None, NextToken=None):\n pass",
"title": ""
},
{
"docid": "bc6445937279b8c4ade7e4fc54dee2fd",
"score": "0.56279695",
"text": "def connection_monitor_type(self) -> str:\n return pulumi.get(self, \"connection_monitor_type\")",
"title": ""
},
{
"docid": "c441652faa1e15acef7983e4eed89673",
"score": "0.562291",
"text": "def connectivity_type(self) -> Optional[str]:\n return pulumi.get(self, \"connectivity_type\")",
"title": ""
},
{
"docid": "7942b1c6508c996996b45de0e0214a0d",
"score": "0.5599811",
"text": "def types(self):\n return self._types",
"title": ""
},
{
"docid": "ca98930f58cdf5a14c6ec9386b30106f",
"score": "0.5583045",
"text": "def list_connections(self, mount_point=DEFAULT_MOUNT_POINT):\n\n api_path = utils.format_url(\"/v1/{mount_point}/config\", mount_point=mount_point)\n return self._adapter.list(\n url=api_path,\n )",
"title": ""
},
{
"docid": "4057e00b7ba4f874614f81e603fe4fa2",
"score": "0.55746377",
"text": "def sync_connections(self):\n return self.get('sync_connections')",
"title": ""
},
{
"docid": "91318995589d35bc35fc25e235df6991",
"score": "0.5572416",
"text": "def unicode_connections(self):\n return exclusions.open()",
"title": ""
},
{
"docid": "ad33a78e246b5d962992e24f02070798",
"score": "0.55682063",
"text": "def retrieve_connectors(self):\n return self.start().uri('/api/connector') \\\n .get() \\\n .go()",
"title": ""
},
{
"docid": "06238cb93e0b6d10ef5bba40cd754076",
"score": "0.55634475",
"text": "def transports(self):\n return self.__transports.keys()",
"title": ""
},
{
"docid": "dcaed7d5806760e2b0a0eb9b478bd6c8",
"score": "0.55597126",
"text": "def _get_networks():\n\n database = Database()\n networks = database.query(\"SELECT Identifier FROM Networks;\")\n database.close()\n\n return [identifier[0] for identifier in networks]",
"title": ""
},
{
"docid": "db289671feb2599aa090b568f13dfdea",
"score": "0.55591625",
"text": "def all_ne_type(self, ctx=None) -> List[str]:\n if ctx is None:\n ctx = self.__make_db_ctx()\n return self.jmnedict.all_ne_type(ctx=ctx)",
"title": ""
},
{
"docid": "2698631c9e65e0004c7235b655af1edc",
"score": "0.5557278",
"text": "def get_all_channels(self):\n return self.all()",
"title": ""
},
{
"docid": "241ffafd7f8a622116e8c6668f612208",
"score": "0.5550913",
"text": "def interfaces(self) -> List[str]:\n return self._driver.interfaces()",
"title": ""
},
{
"docid": "bcfe32ebbbca0c555c0775bdd361f491",
"score": "0.55480015",
"text": "def get_interface_types():\n return get_choices('luma.core.interface.serial') + get_choices('luma.core.interface.parallel')",
"title": ""
},
{
"docid": "53a9045411fdebe4db65e35f7c1dffd4",
"score": "0.55471814",
"text": "def get_all_types(cls):\n return cls.query.order_by(\"order\", \"name\").all()",
"title": ""
},
{
"docid": "1e2ae603934d36a5d5628adb90cb8bd7",
"score": "0.55419225",
"text": "def GetConnectorType(self):\n return self._connector_type",
"title": ""
},
{
"docid": "91dc07427907628ef9510edcbba324b3",
"score": "0.55306494",
"text": "def collect_conn_info(self):\n self.conn_info = {}\n\n for eq in self:\n eq.collect_conn_info(self.conn_info)\n\n ## print_structs(self.conn_info)\n ## pause()\n\n return self.conn_info",
"title": ""
},
{
"docid": "884ec75f264235113c68994f71edbbbd",
"score": "0.5529466",
"text": "def get_all(self):\n result = []\n for rc in pecan.request.dbapi.get_resource_classes(None):\n result.append(ResourceClass.convert(rc, pecan.request.host_url))\n\n return result",
"title": ""
},
{
"docid": "0b1ce65e30ea8b6347fca73068c34e67",
"score": "0.5524769",
"text": "def get_list_cla_type(self):\n self.do_command('/type')\n return",
"title": ""
},
{
"docid": "93b020b5d30c6a60caa3085615307cfa",
"score": "0.5524572",
"text": "def list_types (self) :\n return self._plugins.keys ()",
"title": ""
},
{
"docid": "d71a6701b03790b5d54064cd88b767ee",
"score": "0.5519128",
"text": "def items(self):\n return (self.config_type(i) for i in self.items_query.run(self.conn))",
"title": ""
},
{
"docid": "2cb77bbeec6bd7b779505717fbaab96f",
"score": "0.5505832",
"text": "def list_networks():\n return __sets.keys()",
"title": ""
},
{
"docid": "074eac29b49b67f53726220748948264",
"score": "0.55036914",
"text": "def get_schemes(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "ea7b16f022d8331058dbd3c87a169c1b",
"score": "0.54986036",
"text": "def get_object_types(self):\n return # osid.type.Type",
"title": ""
},
{
"docid": "7826d1212e7fb30c9e68e75c320ba5ec",
"score": "0.54924065",
"text": "def get_currency_types(self):\n return # osid.type.Type",
"title": ""
},
{
"docid": "31c2662c8c835806e03031d0b1dbc686",
"score": "0.54919046",
"text": "def getalldbs():\n with closing(connect()) as conn:\n with conn.cursor() as curs:\n curs.execute('''select datname from pg_database where datallowconn order by datname''')\n row = curs.fetchall()\n return row",
"title": ""
},
{
"docid": "0c312e78251634d304a0df8170511635",
"score": "0.5489278",
"text": "def icpw_types(cls):\n\n types = set()\n\n for name, metric in iter_metric_objects_from_type(cls):\n types.add(metric.type)\n\n for name, command in iter_command_objects_from_type(cls):\n types.add(command.type)\n\n return types",
"title": ""
},
{
"docid": "475779856757a0a6b61fbbfcc4d8e65d",
"score": "0.5487744",
"text": "def protocol_names(self):\n return self.m_valid_protocols",
"title": ""
},
{
"docid": "8668f80ab054d9944cf88f7560e2fa83",
"score": "0.5487085",
"text": "def connections(self) -> Tuple[Optional[BLEConnection], ...]:\n self._clean_connection_cache()\n connections = self._adapter.connections\n wrapped_connections = [None] * len(connections)\n for i, connection in enumerate(connections):\n if connection not in self._connection_cache:\n self._connection_cache[connection] = BLEConnection(connection)\n wrapped_connections[i] = self._connection_cache[connection]\n\n return tuple(wrapped_connections)",
"title": ""
},
{
"docid": "097d636fc728cfe1e1e85832e6e61b10",
"score": "0.54695934",
"text": "def get_types():\n query = f\"SELECT `{sensor_DAO.type_col_name}` FROM {sensor_DAO.type_table_name}\"\n\n # Get connection\n factory = connection_manager()\n connection = factory.connection\n cursor = connection.cursor()\n\n try:\n cursor.execute(query)\n result = cursor.fetchall()\n\n types = []\n if result != None:\n for r in result:\n types.append(r[sensor_DAO.type_col_name])\n return types\n except:\n raise\n finally:\n factory.close_all(cursor=cursor, connection=connection)",
"title": ""
},
{
"docid": "8109c19a82c3ded829021da2f8a0a77f",
"score": "0.54662293",
"text": "def get_capable_databases():\r\n for database in connections:\r\n if router.allow_syncdb(database, Migration):\r\n yield database",
"title": ""
},
{
"docid": "25e3d7adb20bb8dd00593ec986bc1292",
"score": "0.54580456",
"text": "def list_raw_types(conn):\n cursor_type = conn.cursor()\n sql_command = \"SELECT type FROM storm_stint;\"\n cursor_type.execute(sql_command)\n raw_types = []\n for raw_type in cursor_type:\n if raw_type[0] not in raw_types:\n raw_types.append(raw_type[0])\n return raw_types",
"title": ""
},
{
"docid": "ad5cdc85a159b1b65b0f718f41fca9fc",
"score": "0.54567814",
"text": "def interfaces(self):\n return tools.netifaces()",
"title": ""
},
{
"docid": "02ebf5d304e4f38ea78d131e40a05f51",
"score": "0.5451039",
"text": "def clients(self) -> Dict[str, 'WSRPCBase']:\n return self.get_clients()",
"title": ""
},
{
"docid": "3df1ab87484a760387840a4045d94dc0",
"score": "0.5448496",
"text": "def protocols(self):\n return self._request('/shodan/protocols', {})",
"title": ""
},
{
"docid": "37779853c63f5dd0ab3cbea57ee550e0",
"score": "0.54419786",
"text": "def local_pluggables_types():\n _discover_on_demand()\n return list(_REGISTERED_PLUGGABLES.keys())",
"title": ""
},
{
"docid": "ab86df770501c2583145bfc4ed3d3024",
"score": "0.5439445",
"text": "def get_device_list(self):\n return self.device_types",
"title": ""
},
{
"docid": "f9b42d7438189d54d9759bf8b6147fbd",
"score": "0.5436029",
"text": "def getClientTransports(self): # XXX why is this client-specific ???\n\n return self.transports",
"title": ""
},
{
"docid": "b4aab77efb12018c8f5a916d7a35a148",
"score": "0.5425751",
"text": "def get_environment_types(self):",
"title": ""
},
{
"docid": "86aaad7b9db29e813abb96e016d01314",
"score": "0.5413234",
"text": "def get_all_info():\n c = ConfigParser.ConfigParser()\n c.readfp(open('network.cfg'))\n hostname = c.get('wifi','hostname')\n auth = c.get('wifi','user'), c.get('wifi','pass')\n t = connect(hostname, *auth)\n result = []\n for mac_addr in get_clients(t):\n try:\n info = get_client_info(t, mac_addr)\n assert info[0] != 'Unknown'\n result.append((mac_addr,) + info)\n except: pass\n t.close()\n return result",
"title": ""
},
{
"docid": "709d8059e8de14c0ca402b014aa45bb7",
"score": "0.54099023",
"text": "def _logconnections(*args):\n list([p.logstatus() for p in DatabaseRESTApi._ALL_POOLS])",
"title": ""
},
{
"docid": "346e49a5d34611bac4ba28d3048b8495",
"score": "0.5395074",
"text": "def netapi32_NetConnectionEnum(jitter):\n ret_ad, args = jitter.func_args_stdcall([\"servername\", \"qualifier\", \"level\", \"bufptr\", \"prefmaxlen\", \"entriesread\", \"totalentries\", \"resume_handle\"])\n raise RuntimeError('API not implemented')\n jitter.func_ret_stdcall(ret_ad, ret_value)",
"title": ""
},
{
"docid": "dc3b5cb72386f8be851660867c029c51",
"score": "0.53864723",
"text": "def _get_service_endpoints(organization, project, endpoint_type=None):\n client = get_service_endpoint_client(organization)\n all_connections = client.get_service_endpoints(project)\n if endpoint_type is None:\n return all_connections\n filtered_connection = []\n for connection in all_connections:\n if connection.type.lower() == endpoint_type.lower():\n filtered_connection.append(connection)\n return filtered_connection",
"title": ""
},
{
"docid": "4248652338a6464f4eeae9642cc9f545",
"score": "0.5379829",
"text": "def get_all(self) -> Dict[str, Channel]:\n return self._data",
"title": ""
},
{
"docid": "58b01e8a8374c13ac3675435bee4def7",
"score": "0.53731626",
"text": "def get_confs():\n\n\n get_nginx_confs()\n get_apache_confs()",
"title": ""
},
{
"docid": "df76a04d64e7ed9af009f5f086e4f194",
"score": "0.5369523",
"text": "def getInterfaces(self):\n\t\treturn self.interfaces",
"title": ""
},
{
"docid": "e42341b50e89e17d8db65abd3c48f4e1",
"score": "0.5367139",
"text": "def _list_all(_, __) -> t.Type[ConnectionResource]:\n\n class _ListAll(ConnectionResource):\n def get(self) -> dict:\n res = all_ports()\n\n return {\n \"message\": \"OK\",\n \"ports\": [[port, desc, tech] for port, desc, tech in res]\n }\n \n return _ListAll",
"title": ""
},
{
"docid": "d77035655f45421129a22eb725136485",
"score": "0.5359777",
"text": "def get_base_types(self, node):\r\n return self._send({'name': 'getBaseTypes', 'args': [node]})",
"title": ""
},
{
"docid": "3764ea553892a46771470821ed51a257",
"score": "0.5359771",
"text": "def get_connections(self): ##TODO TOPRZEROBIC !!!!!!!!!!\n neurons = list()\n neurons.extend(self.get_cnn_neurons())\n neurons.extend(self.get_hidden_neurons())\n neurons.extend(self.get_output_layer())\n # neurons.append(self.output_neuron)\n connections = list()\n [connections.extend(neuron.input_connections) for neuron in neurons]\n return connections",
"title": ""
},
{
"docid": "1d205beaa366b89d785c1314e9c0ace3",
"score": "0.53590655",
"text": "def available_protocols(self):\n return [\"irods://\"]",
"title": ""
},
{
"docid": "af55730ecc20c3c20995cee83bdba9c9",
"score": "0.5353662",
"text": "def _do_get_storage_connectors(featurestore_metadata):\n return list(map(lambda sc: (sc.name, sc.type), featurestore_metadata.storage_connectors.values()))",
"title": ""
},
{
"docid": "48cd2fa2361aaeabf1b967bf9e126b71",
"score": "0.5351842",
"text": "def protocols(self) -> pulumi.Output[Sequence[str]]:\n return pulumi.get(self, \"protocols\")",
"title": ""
},
{
"docid": "44f5413d0607591d6fa0593b54988bc1",
"score": "0.5350637",
"text": "def Connectivity(self):\n return self.Conn_file_format",
"title": ""
},
{
"docid": "410395bd43f5873fdfac18f42b39e01e",
"score": "0.5343289",
"text": "def selectedConnections( self ):\n output = []\n for item in self.selectedItems():\n if ( isinstance(item, XNodeConnection) ):\n output.append(item)\n return output",
"title": ""
},
{
"docid": "b50694e5dd39152927d70b8582ad1c48",
"score": "0.53411955",
"text": "def monitored_protocols(self):\n return self._monitored_protocols",
"title": ""
},
{
"docid": "23869987d4b76cfd76a606315fef19d9",
"score": "0.53315663",
"text": "def get_channels(self):\n return self._channel.keys()",
"title": ""
},
{
"docid": "a7710372d3722e523ef2cacf194bd309",
"score": "0.5330439",
"text": "def network_interfaces(self):\r\n return self._network_interfaces",
"title": ""
},
{
"docid": "5556ead705e791d72ddae52517b5adaf",
"score": "0.5329094",
"text": "def get_version_types(self):\n return # osid.type.Type",
"title": ""
},
{
"docid": "ff878450ca42c02de3637e2679cc2eb4",
"score": "0.53211415",
"text": "def db_type(self, connection):\r\n return None",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="743"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">3ed796d7fb0d790e934c120b07f97043</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Will produce the isolation source linear combination predictions.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "36765d0d6933adb56f64042231c3107b",
"score": "0.0",
"text": "def seqenv(self):\n return Seqenv(self, self.p.seqenv_dir)",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "98a17fef7ec9649ac5bc58e7b786d499",
"score": "0.5872101",
"text": "def predict(self, cfg):",
"title": ""
},
{
"docid": "3d2569a6dfe82ae752eb4dc1b5e5c312",
"score": "0.57712275",
"text": "def linear_nonlinear_combining_model():\r\n# combining_type = ['arima_ffnn', 'arima_cnn', 'arima_lstm', 'arima_svr']\r\n combining_type = ['arima_lstm']\r\n for cb in combining_type:\r\n linoncb.combine_linear_nonlinear_model(cb)",
"title": ""
},
{
"docid": "71a1cff4b4174415a7b7aa01f3ab02e7",
"score": "0.561639",
"text": "def get_one_year_linear_imputed(self):\n return self.one_year_linear_imputed_train, self.one_year_linear_imputed_validate, self.one_year_linear_imputed_test",
"title": ""
},
{
"docid": "766154837f27d1cf74df5a65a5af925c",
"score": "0.55435723",
"text": "def _make_predictions(self):\n test_data = dc.clean_data(test)\n predictors = [pred for pred in self.predictors if pred in list(\n test_data.columns.values)]\n features = [i for i in predictors]\n model_coefficients = pd.DataFrame(columns=features)\n model = self._build_model(self.design_matrix, predictors)\n model_coefficients.loc[0] = model.feature_importances_\n test_data['SalePrice'] = model.predict(test_data[predictors])\n test_data['SalePrice'] = (test_data['SalePrice'] * self.stdev)\\\n + self.mean\n return test_data, model_coefficients",
"title": ""
},
{
"docid": "acff8026562daf3c4671619cae6fd459",
"score": "0.55257535",
"text": "def staged_predict_proba(self, X):\n ...",
"title": ""
},
{
"docid": "151a0356a5030b57854928ee14243997",
"score": "0.5504131",
"text": "def constructing_predicted_matrix():",
"title": ""
},
{
"docid": "e4fef7d67b77baa184cce02b888b2271",
"score": "0.5445783",
"text": "def predict_verbruik_lr(df_input, predict_type=\"mid\", power: int = 2, model=\"lasso\"):\n # Check of het meegegeven predict_type bekend is\n valid_types = [\"low\", \"mid\", \"high\"]\n if not predict_type in valid_types:\n raise ValueError(\"predict_type not correctly specified (low, mid, high\")\n\n # Creëer het lineaire regressie model\n if model == \"lasso\":\n regressor = linear_model.Lasso()\n else:\n regressor = linear_model.LinearRegression()\n print(f\"Using a {model} model\", end=\" \")\n if power:\n if power == 1:\n print(f\"with 1 power term\")\n else:\n print(f\"with {power} power terms\")\n else:\n print(\"with no additional feature\")\n print(\"\", flush=True)\n\n # Creëer een nieuw dataframe wat we zullen vullen met de voorspelling\n df_output = pd.DataFrame(\n columns=[\n \"SJV_TOTAAL\",\n \"E1A_TOTAAL\",\n \"E1B_TOTAAL\",\n \"E1C_TOTAAL\",\n \"AANSLUITINGEN_AANTAL\",\n \"LEVERINGSRICHTING_PERC\",\n \"PC4\",\n \"JAAR\",\n ]\n )\n\n # Creëer een lijst van alle pc4's\n list_of_pc4 = df_input[\"PC4\"].unique()\n\n # Voorspel voor de jaren 2021-2023\n # voeg feature toe\n X_pred = np.array([2021, 2022, 2023])\n X_pred = vp_add_feature_lr(X_pred, power)\n\n # Bandbreedte voor de voorspellingen\n low_decr = 0.99\n high_incr = 1.01\n\n # Bandbreedte bepaling\n multiplier = 1\n if predict_type == \"low\":\n multiplier = low_decr\n if predict_type == \"high\":\n multiplier = high_incr\n\n # Loop door alle pc4's en voorspel 2021-2023\n for pc4 in tqdm(list_of_pc4):\n df_pc4 = df_input[df_input.PC4 == pc4]\n\n # Skip deze pc4 als er minder dan 9 jaren in zitten\n if len(df_pc4) < 9:\n continue\n\n # Creëer de features\n X = df_pc4.JAAR.values\n X = vp_add_feature_lr(X, power)\n\n # Train het model en maak een forecast voor SJV_TOTAAL\n y = np.array(df_pc4.SJV_TOTAAL.values.reshape(-1, 1))\n regressor.fit(X, y)\n forecast_totaal = regressor.predict(X_pred)\n\n # Train het model en maak een forecast voor E1A_TOTAAL\n y = np.array(df_pc4.E1A_TOTAAL.values.reshape(-1, 1))\n regressor.fit(X, y)\n forecast_e1a = regressor.predict(X_pred)\n\n # Train het model en maak een forecast voor E1B_TOTAAL\n y = np.array(df_pc4.E1B_TOTAAL.values.reshape(-1, 1))\n regressor.fit(X, y)\n forecast_e1b = regressor.predict(X_pred)\n\n # Train het model en maak een forecast voor E1C_TOTAAL\n y = np.array(df_pc4.E1C_TOTAAL.values.reshape(-1, 1))\n regressor.fit(X, y)\n forecast_e1c = regressor.predict(X_pred)\n\n # Train het model en maak een forecast voor AANSLUITINGEN_AANTAL\n y = np.array(df_pc4.AANSLUITINGEN_AANTAL.values.reshape(-1, 1))\n regressor.fit(X, y)\n forecast_aantal = regressor.predict(X_pred)\n\n # Train het model en maak een forecast voor LEVERINGSRICHTING_PERC\n y = np.array(df_pc4.LEVERINGSRICHTING_PERC.values.reshape(-1, 1))\n regressor.fit(X, y)\n forecast_perc = regressor.predict(X_pred)\n\n # Voeg de voorspellingen toe aan het output dataframe\n for index, jaar in enumerate(X_pred):\n m = pow(multiplier, index + 1)\n df_output = df_output.append(\n {\n \"SJV_TOTAAL\": forecast_totaal[index] * m,\n \"E1A_TOTAAL\": forecast_e1a[index] * m,\n \"E1B_TOTAAL\": forecast_e1b[index] * m,\n \"E1C_TOTAAL\": forecast_e1c[index] * m,\n \"AANSLUITINGEN_AANTAL\": forecast_aantal[index] * m,\n \"LEVERINGSRICHTING_PERC\": forecast_perc[index] * m,\n \"PC4\": pc4,\n \"JAAR\": jaar[0],\n },\n ignore_index=True,\n )\n df_output.JAAR = df_output.JAAR.astype(\"int\")\n df_output.PC4 = df_output.PC4.astype(\"int\")\n\n return df_output",
"title": ""
},
{
"docid": "b97108ebd613405426c61000ae2bfcd6",
"score": "0.5418297",
"text": "def _linear_response_matrix(\n self,\n kwargs_lens,\n kwargs_source,\n kwargs_lens_light,\n kwargs_ps,\n kwargs_extinction=None,\n kwargs_special=None,\n unconvolved=False,\n ):\n x_grid, y_grid = self.ImageNumerics.coordinates_evaluate\n source_light_response, n_source = self.source_mapping.image_flux_split(\n x_grid, y_grid, kwargs_lens, kwargs_source\n )\n extinction = self._extinction.extinction(\n x_grid,\n y_grid,\n kwargs_extinction=kwargs_extinction,\n kwargs_special=kwargs_special,\n )\n lens_light_response, n_lens_light = self.LensLightModel.functions_split(\n x_grid, y_grid, kwargs_lens_light\n )\n\n ra_pos, dec_pos, amp, n_points = self.point_source_linear_response_set(\n kwargs_ps, kwargs_lens, kwargs_special, with_amp=False\n )\n num_param = n_points + n_lens_light + n_source\n\n num_response = self.num_data_evaluate\n A = np.zeros((num_param, num_response))\n n = 0\n # response of lensed source profile\n for i in range(0, n_source):\n image = source_light_response[i]\n\n # multiply with primary beam before convolution\n if self._pb is not None:\n image *= self._pb_1d\n\n image *= extinction\n image = self.ImageNumerics.re_size_convolve(image, unconvolved=unconvolved)\n A[n, :] = np.nan_to_num(self.image2array_masked(image), copy=False)\n n += 1\n # response of deflector light profile (or any other un-lensed extended components)\n for i in range(0, n_lens_light):\n image = lens_light_response[i]\n\n # multiply with primary beam before convolution\n if self._pb is not None:\n image *= self._pb_1d\n\n image = self.ImageNumerics.re_size_convolve(image, unconvolved=unconvolved)\n A[n, :] = np.nan_to_num(self.image2array_masked(image), copy=False)\n n += 1\n # response of point sources\n for i in range(0, n_points):\n # raise warnings when primary beam is attempted to be applied for point sources\n if self._pb is not None:\n raise Warning(\"Antenna primary beam does not apply to point sources!\")\n\n image = self.ImageNumerics.point_source_rendering(\n ra_pos[i], dec_pos[i], amp[i]\n )\n A[n, :] = np.nan_to_num(self.image2array_masked(image), copy=False)\n n += 1\n return A * self._flux_scaling",
"title": ""
},
{
"docid": "8b6309cfee585c249e437d037c7410b6",
"score": "0.54177034",
"text": "def staged_predict(self, X):\n ...",
"title": ""
},
{
"docid": "8b6309cfee585c249e437d037c7410b6",
"score": "0.54177034",
"text": "def staged_predict(self, X):\n ...",
"title": ""
},
{
"docid": "1372eea98d02fa4d4a9630ec78fd3d1a",
"score": "0.54013187",
"text": "def predict(self):\n self.pred_target = self.target\n self.pred_ab_index = \"b\" if self.ab_index == \"a\" else \"a\"\n self.pred_X = torch.load(self.path_dataset + \"X\" + \"_\" + self.pred_ab_index + \"_\" + \"train\")\n self.pred_X = torch.zeros_like(self.pred_X)\n self.pred_targets = torch.load(self.path_dataset + self.pred_target + \"_\" + self.pred_ab_index + \"_\" + \"train\")\n self.pred_X, self.pred_targets = Variable(self.pred_X).to(self.device), Variable(self.pred_targets).to(self.device)\n # self.load_pred_data()\n self.model.eval()\n with torch.no_grad():\n self.pred_mean, _, _ = self.model(self.pred_targets, self.pred_X)\n self.save_latent_var_img()",
"title": ""
},
{
"docid": "4e400156ee080b7a8ba48fc3ee88e12c",
"score": "0.5397657",
"text": "def build_dataset():\n generator = generate_all_equations(max_count=N_EXAMPLES)\n\n n_test = round(SPLIT * N_EXAMPLES)\n n_val = round(SPLIT * N_EXAMPLES)\n n_train = N_EXAMPLES - n_test - n_val\n\n order = -1 if REVERSE else 1\n\n x_test = np.zeros((n_test, MAX_EQUATION_LENGTH, N_FEATURES), dtype=np.float32)\n y_test = np.zeros((n_test, MAX_RESULT_LENGTH, N_FEATURES), dtype=np.float32)\n\n for i, equation in enumerate(itertools.islice(generator, n_test)):\n result = to_padded_string(\n eval(equation),\n padding=MAX_RESULT_LENGTH,\n decimals=DECIMALS,\n )\n\n for t, char in enumerate(equation[::order]):\n x_test[i, t, encoder.char_to_one_hot_index(char)] = 1\n\n for t, char in enumerate(result[::order]):\n y_test[i, t, encoder.char_to_one_hot_index(char)] = 1\n\n x_val = np.zeros((n_test, MAX_EQUATION_LENGTH, N_FEATURES), dtype=np.float32)\n y_val = np.zeros((n_test, MAX_RESULT_LENGTH, N_FEATURES), dtype=np.float32)\n\n for i, equation in enumerate(itertools.islice(generator, n_val)):\n # print(equation)\n result = to_padded_string(\n eval(equation),\n padding=MAX_RESULT_LENGTH,\n decimals=DECIMALS,\n )\n\n for t, char in enumerate(equation[::order]):\n x_val[i, t, encoder.char_to_one_hot_index(char)] = 1\n\n for t, char in enumerate(result[::order]):\n y_val[i, t, encoder.char_to_one_hot_index(char)] = 1\n\n x_train = np.zeros((n_train, MAX_EQUATION_LENGTH, N_FEATURES), dtype=np.bool)\n y_train = np.zeros((n_train, MAX_RESULT_LENGTH, N_FEATURES), dtype=np.bool)\n\n for i, equation in enumerate(generator):\n result = to_padded_string(\n eval(equation),\n padding=MAX_RESULT_LENGTH,\n decimals=DECIMALS,\n )\n\n for t, char in enumerate(equation[::order]):\n x_train[i, t, encoder.char_to_one_hot_index(char)] = 1\n\n for t, char in enumerate(result[::order]):\n y_train[i, t, encoder.char_to_one_hot_index(char)] = 1\n\n return x_test, y_test, x_val, y_val, x_train, y_train",
"title": ""
},
{
"docid": "428c365eaf2082dfb648fc41c09f7e87",
"score": "0.53895676",
"text": "def test_existing_regression_coef(self):\r\n from natcap.invest.recreation import recmodel_client\r\n\r\n # Initialize a TaskGraph\r\n taskgraph_db_dir = os.path.join(\r\n self.workspace_dir, '_taskgraph_working_dir')\r\n n_workers = -1 # single process mode.\r\n task_graph = taskgraph.TaskGraph(taskgraph_db_dir, n_workers)\r\n\r\n response_vector_path = os.path.join(\r\n self.workspace_dir, 'no_grid_vector_path.shp')\r\n response_polygons_lookup_path = os.path.join(\r\n self.workspace_dir, 'response_polygons_lookup.pickle')\r\n recmodel_client._copy_aoi_no_grid(\r\n os.path.join(SAMPLE_DATA, 'andros_aoi.shp'), response_vector_path)\r\n\r\n predictor_table_path = os.path.join(SAMPLE_DATA, 'predictors.csv')\r\n\r\n # make outputs to be overwritten\r\n predictor_dict = utils.read_csv_to_dataframe(\r\n predictor_table_path,\r\n recmodel_client.MODEL_SPEC['args']['predictor_table_path']\r\n ).to_dict(orient='index')\r\n predictor_list = predictor_dict.keys()\r\n tmp_working_dir = tempfile.mkdtemp(dir=self.workspace_dir)\r\n empty_json_list = [\r\n os.path.join(tmp_working_dir, x + '.json') for x in predictor_list]\r\n out_coefficient_vector_path = os.path.join(\r\n self.workspace_dir, 'out_coefficient_vector.shp')\r\n _make_empty_files(\r\n [out_coefficient_vector_path] + empty_json_list)\r\n\r\n prepare_response_polygons_task = task_graph.add_task(\r\n func=recmodel_client._prepare_response_polygons_lookup,\r\n args=(response_vector_path,\r\n response_polygons_lookup_path),\r\n target_path_list=[response_polygons_lookup_path],\r\n task_name='prepare response polygons for geoprocessing')\r\n # build again to test against overwriting output\r\n recmodel_client._schedule_predictor_data_processing(\r\n response_vector_path, response_polygons_lookup_path,\r\n prepare_response_polygons_task, predictor_table_path,\r\n out_coefficient_vector_path, tmp_working_dir, task_graph)\r\n\r\n expected_coeff_vector_path = os.path.join(\r\n REGRESSION_DATA, 'test_regression_coefficients.shp')\r\n\r\n utils._assert_vectors_equal(\r\n expected_coeff_vector_path, out_coefficient_vector_path, 1e-6)",
"title": ""
},
{
"docid": "62c3f7da9d108af0489c6556dcd02775",
"score": "0.53834975",
"text": "def predict(self) -> None:\n self.base_model.step()\n if self.filtering:\n for i in range(self.ensemble_size):\n self.models[i].step()\n if self.run_vanilla:\n for i in range(self.vanilla_ensemble_size):\n self.vanilla_models[i].step()",
"title": ""
},
{
"docid": "1cbc00f860548a83c005612bdad17bbe",
"score": "0.5381371",
"text": "def predictions(self):\n pass",
"title": ""
},
{
"docid": "c41b2bd920fe881a33b89cc6113c6406",
"score": "0.5366855",
"text": "def pred_on_rel(X, option, filename):\n if option == \"l\":\n\n X, _, _, _ = process_data(X)\n models = load_rel()\n\n first = models[0].predict(X)\n pred = pd.DataFrame(first)\n #print(pred)\n X = pd.DataFrame(X)\n joined_df = pd.concat([X,pred], axis=1)\n\n sec = models[1].predict(joined_df)\n pred_two = pd.DataFrame(sec)\n joined_df_two = pd.concat([X, pred_two], axis=1)\n\n size = len(joined_df_two.columns) -1\n X_df = joined_df_two.iloc[:, 0:size]\n\n third = models[2].predict(X_df)\n pred_class_labels = third\n probabilities = models[2].predict_proba(X_df)\n return pred_class_labels, probabilities\n\n elif option == \"a\":\n models = load_rel(filename)\n\n first = models[0].predict(X)\n pred = pd.DataFrame(first)\n #print(pred)\n X = pd.DataFrame(X)\n joined_df = pd.concat([X,pred], axis=1)\n\n sec = models[1].predict(joined_df)\n pred_two = pd.DataFrame(sec)\n joined_df_two = pd.concat([X, pred_two], axis=1)\n\n size = len(joined_df_two.columns) -1\n X_df = joined_df_two.iloc[:, 0:size]\n\n third = models[2].predict(X_df)\n pred_class_labels = third\n probabilities = models[2].predict_proba(X_df)\n return pred_class_labels, probabilities",
"title": ""
},
{
"docid": "0442f0c9e11739f482f8273aa0ce5a58",
"score": "0.53628445",
"text": "def fit_model(features_and_targets: Iterator[Feature_Target_Pair], innings: int) -> pd.DataFrame:\n assert innings in [1, 2]\n\n def fit_model_for_over(pair: Tuple[int, List[Feature_Target_Pair]]) -> Parameter:\n \"\"\"Helper function that fits the model for data from a single over.\n Note that this method uses variables from the outer scope.\n\n :param pair: A tuple - first element is the over and the second element is the data\n :return: Parameter fit for that over\n \"\"\"\n\n def feature_vector(feature_target_pair: Feature_Target_Pair) -> np.array:\n \"\"\"Converts the feature into a numpy array.\n\n :param feature_target_pair:\n :return: numpy array\n \"\"\"\n feature, _ = feature_target_pair\n log_run_rate = np.log(feature.RunRate + EPSILON_RUNS)\n log_wicket = np.log((MAX_WICKETS - feature.Wickets) + EPSILON_WICKETS)\n return np.array([log_run_rate, log_wicket])\n\n def target_vector(feature_target_pair: Feature_Target_Pair) -> np.array:\n \"\"\"Converts the target into a numpy array.\n\n :param feature_target_pair:\n :return: numpy array\n \"\"\"\n _, target = feature_target_pair\n return np.array(np.log(target.Target)) if innings == 1 else np.array(target.Target)\n\n def fit_first_innings(x_matrix: np.array, y: np.array, over: int) -> Parameter:\n \"\"\"Fits the linear regression model to predict the end of the innings run rate.\n\n :param x_matrix: Matrix of regressors\n :param y: Vector of observed values\n :param over: Over for which we are predicting\n :return: Parameters of that over\n \"\"\"\n lr = LinearRegression()\n lr.fit(x_matrix, y)\n predictions = lr.predict(x_matrix)\n errors = predictions - y\n return Parameter(over, lr.intercept_, lr.coef_[0], lr.coef_[1], np.mean(errors), np.std(errors))\n\n def fit_second_innings(x_matrix: np.array, y: np.array, over: int) -> Parameter:\n \"\"\"Fits the logistic regression model to predict the winner at the end of second innings.\n\n :param x_matrix: Matrix of regressors\n :param y: ector of observed values\n :param over: Over for which we are predicting\n :return: Parameters of that over\n \"\"\"\n lr = LogisticRegression(solver='lbfgs')\n lr.fit(x_matrix, y)\n return Parameter(over, lr.intercept_[0], lr.coef_[0][0], lr.coef_[0][1], 0, 0)\n\n over_num, feature_target_pair_list = pair\n features = np.array([feature_vector(pair) for pair in feature_target_pair_list])\n targets = np.array([target_vector(pair) for pair in feature_target_pair_list])\n return fit_first_innings(features, targets, over_num) if innings == 1 else \\\n fit_second_innings(features, targets, over_num)\n\n def group_by_overs() -> Iterator[Tuple[int, List[Feature_Target_Pair]]]:\n return groupby(lambda x: x[0].Over, features_and_targets).items()\n\n parameters = [fit_model_for_over(pair) for pair in group_by_overs()]\n return pd.DataFrame(parameters, columns=['overs', 'intercept',\n 'log_rr_param', 'log_wickets_param', 'mu', 'error_std'])",
"title": ""
},
{
"docid": "f94246d8b45421454daf5e26268e4340",
"score": "0.5360082",
"text": "def linear_classifier(self) -> List:\n sgd = sklearn.linear_model.SGDClassifier()\n sgd.fit(self.input_values, self.target_values)\n return sgd.predict(self.input_values)",
"title": ""
},
{
"docid": "7d5955f47ae1f1bf654bf220e8c1adbe",
"score": "0.53479165",
"text": "def linear_model(data, a, b, c, d):\n\n return a + b * data[0] + c * data[1] + d * data[2]",
"title": ""
},
{
"docid": "04a47deaca45a0d7db0efd6214ad9946",
"score": "0.53361124",
"text": "def predicting(self):\n self.y_pred = self.model.predict(self.X_test)",
"title": ""
},
{
"docid": "8dab0acb7cff64d5788a0e5baba3fc85",
"score": "0.53311914",
"text": "def generate_model(self):\n\n activation = self.config['arch']['activation']\n dropout = self.config['arch']['drop']\n dropoutf = self.config['arch']['dropf']\n dropouta = self.config['arch']['dropa']\n full_layers = self.config['arch']['full']\n fulladd_layers = self.config['arch']['fulladd']\n\n # Extra added from training function\n idimensions = self.config['idimensions']\n odimension = self.config['odimensions']\n\n # Dependent variable input\n data_input = Input(shape=(idimensions[0]))\n\n mlp_layers = Dense(full_layers[0])(data_input)\n mlp_layers = generate_activation(activation)(mlp_layers)\n mlp_layers = Dropout(rate=dropout)(mlp_layers)\n for units in full_layers[1:]:\n mlp_layers = Dense(units=units)(mlp_layers)\n mlp_layers = generate_activation(activation)(mlp_layers)\n mlp_layers = Dropout(rate=dropout)(mlp_layers)\n\n dataadd_input = Input(shape=(idimensions[1]))\n\n mlpadd_layers = Dense(full_layers[0])(dataadd_input)\n mlpadd_layers = generate_activation(activation)(mlpadd_layers)\n mlpadd_layers = Dropout(rate=dropout)(mlpadd_layers)\n for units in fulladd_layers[1:]:\n mlpadd_layers = Dense(units=units)(mlpadd_layers)\n mlpadd_layers = generate_activation(activation)(mlpadd_layers)\n mlpadd_layers = Dropout(rate=dropouta)(mlpadd_layers)\n\n fusion = self.config['arch']['funits']\n mlp_layers = concatenate([mlp_layers, mlpadd_layers])\n\n mlp_layers = Dense(units=fusion)(mlp_layers)\n mlp_layers = generate_activation(activation)(mlp_layers)\n mlp_layers = Dropout(rate=dropoutf)(mlp_layers)\n\n output = Dense(odimension, activation='linear')(mlp_layers)\n self.model = Model(inputs=[data_input, dataadd_input], outputs=output)",
"title": ""
},
{
"docid": "c0ad73cfec467bdeba6d45ef80b76a2b",
"score": "0.53162116",
"text": "def v1_model() -> Sequential:\n X, Y = data_reshape.read_v2_file()\n # test_x = X[-5:]\n # test_y = Y[-5:]\n model = data_analyze.multi_layer_transformation(X[:-5], Y[:-5])\n # pred = model.predict_classes(X, batch_size=1)\n # lm = model.evaluate(X, Y)\n return model",
"title": ""
},
{
"docid": "766a87f59dc881a569e271869ed0f11a",
"score": "0.52982664",
"text": "def predict_proba(self, X):",
"title": ""
},
{
"docid": "744dce8f66198cf15b4d381297ed9b6e",
"score": "0.5297546",
"text": "def seperate_model():\n X_train, Y_train, X_test, Y_test, index, testIndex = get_data_split()\n Y_train = Y_train[:, 0].reshape(-1, 1)\n Y_test = Y_test[:, 0].reshape(-1, 1)\n J = 0\n for i in range(0, len(testIndex)):\n if i == 0:\n test_X = X_test[:int(testIndex[i])]\n test_Y = Y_test[:int(testIndex[i])]\n train_X = X_train[:int(index[i])]\n train_Y = Y_train[:int(index[i])]\n else:\n test_X = X_test[int(testIndex[i - 1]):int(testIndex[i])]\n test_Y = Y_test[int(testIndex[i - 1]):int(testIndex[i])]\n train_X = X_train[int(index[i - 1]):int(index[i])]\n train_Y = Y_train[int(index[i - 1]):int(index[i])]\n linreg = LinearRegression()\n linreg.fit(train_X, train_Y)\n # clf = RandomForestRegressor(max_depth=2, random_state=0)\n # clf.fit(train_X,train_Y)\n predict1 = linreg.predict(test_X)\n J += np.linalg.norm((predict1 - test_Y), ord=1)\n print(\"single state error:\",i, np.linalg.norm((predict1 - test_Y), ord=1)/test_Y.shape[0])\n print(J / Y_test.shape[0])",
"title": ""
},
{
"docid": "91bb2e4bb52025a665b738727cd31cab",
"score": "0.5292867",
"text": "def predict(self, m_x, s_x):\n t = 0\n cost = Decimal(0)\n print(\"Starting internal simulation\")\n while t < self.horizon:\n m_x, s_x = self.propagate(m_x, s_x)\n for i in range(m_x.shape[1]):\n print(m_x[0, i])\n m_x[0, i] = Decimal(m_x[0, i])\n for j in range(m_x.shape[1]):\n s_x[i, j] = Decimal(s_x[i, j])\n # print(\"m_x\")\n # print(m_x)\n # print(\"s_x\")\n # print(s_x)\n # print(self.cost.compute_cost(m_x, s_x)[0][0])\n cost += Decimal(self.cost.compute_cost(m_x, s_x)[0][0][0])\n print(\"Cost at timestep %i is %f\" % (t, cost))\n t += 1\n return m_x, s_x, cost",
"title": ""
},
{
"docid": "c7ffacb5f9b50117456efe6d7e92ce46",
"score": "0.5291713",
"text": "def prediction_maker(file_name, housing_train, housing_test, test_ID):\r\n \r\n ENet = make_pipeline(RobustScaler(), ElasticNet(alpha = .0005,l1_ratio = .9,random_state = 52918))\r\n lasso = make_pipeline(RobustScaler(), Lasso(alpha = .00022, random_state = 42))\r\n RF = RandomForestRegressor(n_estimators = 50, max_depth = 30)\r\n train = housing_train.drop(['SalePrice'], axis = 1).values\r\n train_y = housing_train.SalePrice.values\r\n test = housing_test.values\r\n \r\n ENet.fit(train, train_y)\r\n lasso.fit(train, train_y)\r\n RF.fit(train, train_y)\r\n predictions = [ENet.predict(test), lasso.predict(test), RF.predict(test)]\r\n \r\n predictions = np.expm1(predictions) \r\n \r\n \r\n \r\n sub = pd.DataFrame()\r\n sub['ID'] = test_ID\r\n sub['SalePrice'] = np.mean(predictions, axis = 0) \r\n sub.to_csv(file_name, index = False)",
"title": ""
},
{
"docid": "ee868b123ef67a79929ed91026824315",
"score": "0.52832747",
"text": "def predict(input=''):\n\n mlb = load(\"./diagnoses_enc.pkl\")\n clf = load('./LogReg_pipeline.joblib')\n\n ans = clf.predict_proba([input])\n ans = ans[0]\n idx = (-ans).argsort()[:5]\n ans = np.zeros(4121)\n ans[idx] = 1\n ans = ans.astype(int)\n ans = np.reshape(ans, (1, 4121))\n ans = mlb.inverse_transform(ans)[0]\n\n print(f\"diagnoses:{ans}\")\n\n return ans",
"title": ""
},
{
"docid": "db46c54eef48ac7435e03dbbe95c6193",
"score": "0.5268678",
"text": "def predict(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "f91bb7e50cc5241cf53e2efa9934ef94",
"score": "0.52636904",
"text": "def predict_proba(self, X):\n ...",
"title": ""
},
{
"docid": "6238daf852c5be0f4059ca2d4b543ec2",
"score": "0.52599084",
"text": "def predict(self, generator_fn, consensus=True):\n prediction = np.array([])\n target = np.array([])\n for input in generator_fn():\n pred_new = self.sess.run(self.predict_output_op,\n feed_dict={self.predict_input_op: np.reshape(input[0], [1, -1])})\n prediction = np.concatenate((prediction, pred_new))\n target = np.concatenate((target, np.array([input[1]])))\n if consensus:\n pred_consensus, target_consensus = self._combine_predictions(prediction, target)\n return pred_consensus, target_consensus\n else:\n return prediction, target",
"title": ""
},
{
"docid": "7a80ff36337ecc388cb138b4700640cf",
"score": "0.5252624",
"text": "def _calculate_linear(self, previous_layer_output):\n\n linear_layer = None\n previous_layer_output = np.hstack(\n (np.ones((previous_layer_output.shape[0], 1)), previous_layer_output)\n )\n linear_solution = np.linalg.lstsq(previous_layer_output, self.y, rcond=None)\n linear_layer = linear_solution[0]\n y_pred = np.dot(previous_layer_output, linear_layer)\n return y_pred, linear_layer",
"title": ""
},
{
"docid": "1b1f35c672a1bbb28acf619dbf7db6d6",
"score": "0.5245477",
"text": "def predict_and_sample(inference_model, x_initializer = x_initializer, a_initializer = a_initializer, \n c_initializer = c_initializer):\n \n ### START CODE HERE ###\n # Step 1: Use your inference model to predict an output sequence given x_initializer, a_initializer and c_initializer.\n pred = inference_model.predict([x_initializer, a_initializer, c_initializer])\n # Step 2: Convert \"pred\" into an np.array() of indices with the maximum probabilities\n indices = np.argmax(pred, axis=2)\n # Step 3: Convert indices to one-hot vectors, the shape of the results should be (Ty, n_values)\n results = to_categorical(indices)\n ### END CODE HERE ###\n \n return results, indices",
"title": ""
},
{
"docid": "126ab06e1081d2cd6c00764f078d66ca",
"score": "0.52404386",
"text": "def load_prediction(self):\n for name, output in zip(['Low_1', 'High_1'], [self.low_output, self.high_output]):\n for i, j in enumerate(output):\n transfer_dict = self.stock_dict_list[i+1]\n self.stock_dict_list[i+1][f'Predict_{name}'] = j\n self.stock_dict_list[i+1][f'Round_Predict_{name}'] = round(j, 2)\n if (i+2) < len(self.stock_dict_list):\n self.stock_dict_list[i+1][f'Residual_{name}'] = transfer_dict[name]-j\n pass",
"title": ""
},
{
"docid": "1f57c787df59aa464d678ffedcba1104",
"score": "0.52396035",
"text": "def rin():\r\n # load the data first\r\n df = load_data()\r\n\r\n # now split the data into predictotrs and target variables\r\n X,y = create_target_and_predictors(data=df)\r\n\r\n #finally, train the machine learning model\r",
"title": ""
},
{
"docid": "f6dae0e086beeb2f0860810b29eb7e35",
"score": "0.5229349",
"text": "def predicting(self, value):\n return linear_regression(self.info, value)",
"title": ""
},
{
"docid": "7023dd3c48f0c70b12da3cbc5ba01a46",
"score": "0.52287614",
"text": "def predict(self, x):\n return self.inner_learner.predict(x)",
"title": ""
},
{
"docid": "a8fe27cc930c922132a9728c919d1e12",
"score": "0.52224374",
"text": "def MSE_DTR(train_data,lag,t_ahead,s_i):\n sample_x = np.transpose(train_data[:,:-lag])\n \n for i in range(1, lag):\n sample_x = np.hstack([sample_x, np.transpose(train_data[:,i:-(lag-i)])])\n \n sample_x = sample_x[:-t_ahead,:]\n \n# num_stream = 1\n slding_predict_t = 730\n landmark_win_ini_size = 367\n# for s_i in range(num_stream):\n sample_y_si = np.transpose(train_data[s_i,t_ahead+lag-1:])\n reg_si = DecisionTreeRegressor(max_depth = 10, random_state = 0)\n pre_y = []\n act_y = []\n for landmark_win in range(slding_predict_t):\n train_x = sample_x[:landmark_win_ini_size+landmark_win,:]\n train_y = sample_y_si[:landmark_win_ini_size+landmark_win]\n reg_si.fit(train_x,train_y)\n y_hat = reg_si.predict(sample_x[landmark_win_ini_size+landmark_win:landmark_win_ini_size+landmark_win+1,:])\n pre_y.append(y_hat)\n act_y.append(sample_y_si[landmark_win_ini_size+landmark_win:landmark_win_ini_size+landmark_win+1])\n\n# plt.plot(pre_y,label='prediction s'+str(s_i))\n# plt.plot(act_y,label='actual')\n# plt.legend()\n# plt.show()\n MSE = 0\n for i in range (0,len(pre_y)-1):\n MSE = MSE + (pre_y[i]-act_y[i])**2\n return MSE,pre_y",
"title": ""
},
{
"docid": "84bf4d2544ab5f8485485f35b420dbfb",
"score": "0.5219832",
"text": "def predict(self, X):",
"title": ""
},
{
"docid": "a4bedba66f414e13fe37ebef6c6e2e5d",
"score": "0.5219302",
"text": "def model_training_response_extinction():\n sample = pnl.TransferMechanism(\n default_variable=np.zeros(60),\n name=pnl.SAMPLE\n )\n\n action_selection = pnl.TransferMechanism(\n default_variable=np.zeros(60),\n function=pnl.Linear(slope=1.0, intercept=1.0),\n name='Action Selection'\n )\n\n stimulus_onset = 42\n reward_delivery = 54\n\n samples = np.zeros(60)\n samples[stimulus_onset:] = 1\n samples = np.tile(samples, (150, 1))\n\n targets = np.zeros(60)\n targets[reward_delivery] = 1\n targets = np.tile(targets, (150, 1))\n\n # stop delivering reward after trial 70\n for i in range(71, 150):\n targets[i][reward_delivery] = 0\n\n pnl.MappingProjection(\n sender=sample,\n receiver=action_selection,\n matrix=np.zeros((60, 60))\n )\n\n learning_projection = pnl.LearningProjection(\n learning_function=pnl.TDLearning(learning_rate=0.3)\n )\n\n p = pnl.Process(\n default_variable=np.zeros(60),\n pathway=[sample, action_selection],\n learning=learning_projection,\n size=60,\n target=np.zeros(60)\n )\n\n trial = 0\n\n def print_header():\n nonlocal trial\n print(\"\\n\\n*** EPISODE: {}\".format(trial))\n\n input_list = {\n sample: samples\n }\n\n target_list = {\n action_selection: targets\n }\n\n s = pnl.System(processes=[p])\n\n delta_vals = np.zeros((150, 60))\n trial = 0\n\n def store_delta_vals():\n nonlocal trial\n delta_vals[trial] = s.mechanisms[2].value\n trial += 1\n\n s.run(\n num_trials=150,\n inputs=input_list,\n targets=target_list,\n learning=True,\n call_before_trial=print_header,\n call_after_trial=store_delta_vals\n )\n with plt.style.context('seaborn'):\n fig = plt.figure()\n ax = fig.add_subplot(111, projection='3d')\n x_vals, y_vals = np.meshgrid(np.arange(150), np.arange(40, 60, step=1))\n ax.plot_surface(x_vals, y_vals, delta_vals[:, 40:60].transpose())\n ax.invert_yaxis()\n ax.set_xlabel(\"Trial\")\n ax.set_ylabel(\"Timestep\")\n ax.set_zlabel(\"∂\")\n ax.set_title(\"Montague et. al. (1996) -- Figure 5C\")\n plt.show()",
"title": ""
},
{
"docid": "2eedf0d46c93208f0612b69fb4dcddf8",
"score": "0.51963943",
"text": "def predict(self, X):\r\n x = X\r\n for i in range(len(self.abc)):\r\n a = tf.matmul(x,self.abc[i][\"weights\"]) + self.abc[i][\"B\"]\r\n if self.abc[i][\"transfer_function\"] == \"Linear\" or self.abc[i][\"transfer_function\"] == \"linear\":\r\n a = a\r\n elif self.abc[i][\"transfer_function\"] == \"Relu\" or self.abc[i][\"transfer_function\"] == \"relu\":\r\n a = tf.nn.relu(a, name='ReLU')\r\n else:\r\n a = tf.nn.sigmoid(a, name='sigmoid')\r\n x = a\r\n return x",
"title": ""
},
{
"docid": "7b0cc26f9342d2b868f501a1c119eb67",
"score": "0.5193686",
"text": "def predict(self, inputs):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "2c6256e2b59cf8fe8573d1aaa3e55036",
"score": "0.5193671",
"text": "def _predict(self, X):",
"title": ""
},
{
"docid": "f70fef6255e8098ce61f5d20949bb2f6",
"score": "0.51891285",
"text": "def generate_model(self):\n\n activation = self.config['arch']['activation']\n dropout = self.config['arch']['drop']\n full_layers = self.config['arch']['full']\n\n # Extra added from training function\n idimensions = self.config['idimensions']\n odimension = self.config['odimensions']\n\n\n # Dependent variable input\n data_input = Input(shape=(idimensions[0]))\n future_input = Input(shape=(idimensions[1]))\n\n to_mlp = concatenate([data_input,future_input])\n\n mlp_layers = Dense(full_layers[0])(to_mlp)\n mlp_layers = generate_activation(activation)(mlp_layers)\n mlp_layers = Dropout(rate=dropout)(mlp_layers)\n for units in full_layers[1:]:\n mlp_layers = Dense(units=units)(mlp_layers)\n mlp_layers = generate_activation(activation)(mlp_layers)\n mlp_layers = Dropout(rate=dropout)(mlp_layers)\n\n output = Dense(odimension, activation='linear')(mlp_layers)\n self.model = Model(inputs=[data_input, future_input], outputs=output)",
"title": ""
},
{
"docid": "3020c6f46ee8bbb95a27d0e626276a32",
"score": "0.51870185",
"text": "def predict(self, y, state):\n if state is None:\n state = [None] * self.nlayers\n\n # Path through RNN\n xs_lower = None\n for l in range(self.nlayers):\n y, state[l] = self.rnn[l](y, hx=state[l])\n y = self.dropout[l](y)\n\n # Residual connection\n if self.residual and l > 0:\n y += xs_lower\n xs_lower = y\n\n logits_step = self.output(y)\n\n return logits_step, y, state",
"title": ""
},
{
"docid": "90f2aa7c84820e39f8c2c96ce1af5369",
"score": "0.5186516",
"text": "def get_two_year_linear_imputed(self):\n return self.two_year_linear_imputed_train, self.two_year_linear_imputed_validate, self.two_year_linear_imputed_test",
"title": ""
},
{
"docid": "e8d538a7aae859c61df6c488284efd39",
"score": "0.51783866",
"text": "def model_predict(trn_data, features, target, tst_data):\n \n # fit the linear regression model\n model = sm.OLS(endog=trn_data[target], exog=sm.add_constant(trn_data[features])).fit()\n \n # show the summary\n display(model.summary())\n \n # calculate train and test predictions\n trn_preds = model.predict(sm.add_constant(trn_data[features]))\n tst_preds = model.predict(sm.add_constant(tst_data[features]))\n \n return trn_preds, tst_preds",
"title": ""
},
{
"docid": "778282e03cdce878450b91cf978e889e",
"score": "0.517729",
"text": "def predict(self, df): \n # print(\"***********\")\n # print(\"df\", df.iloc[0,:].T)\n # print(\"***********\")\n #print(\"colunas\", df.columns)\n df = self.artifacts.model_a.transform(df)\n print(df)\n return self.artifacts.ml.predict(df)",
"title": ""
},
{
"docid": "1a79bb47ead5559694160aeb8984512b",
"score": "0.5176969",
"text": "def predict(self, x):\n return x # Note: Identity function is necessary because our lvq loss function works on the input (not on the final classification) ",
"title": ""
},
{
"docid": "6798896a6fe9f5d743a0b2d218eb45b3",
"score": "0.5173089",
"text": "def predict(self):\n self.y_pred = predict(self.model, self.x_init)",
"title": ""
},
{
"docid": "d2dfbb5d577f36f79d722cb8fad39eba",
"score": "0.51569813",
"text": "def predict(self, Xtest):\n Ytest = np.zeros(len(Xtest))\n testSet = self.createDataset(Xtest, Ytest)\n testLoader = torch.utils.data.DataLoader(testSet, batch_size=self.params[\"bSize\"], shuffle=False, num_workers=0)\n with torch.no_grad():\n for data in valLoader:\n inputs, labels = data\n outputs = self.net(inputs)\n outputs[outputs>0.5] = 1\n outputs[outputs<=0.5] = 0\n return outputs",
"title": ""
},
{
"docid": "d97b47f7e7ca1adefee482353d531bfe",
"score": "0.51469886",
"text": "def predict(self, X):\n ...",
"title": ""
},
{
"docid": "d97b47f7e7ca1adefee482353d531bfe",
"score": "0.51469886",
"text": "def predict(self, X):\n ...",
"title": ""
},
{
"docid": "c5ff73a4c577787984e1ddb05757c77f",
"score": "0.5145766",
"text": "def predict(self, peptides):\n average_scores = self.predict_scores(peptides)\n return regression_target_to_ic50(average_scores, max_ic50=self.max_ic50)",
"title": ""
},
{
"docid": "e417b16e472866325df8860617c65852",
"score": "0.514269",
"text": "def apply_linear_regression(model, xs):\n return np.dot(np.array([1.0] + xs), model)",
"title": ""
},
{
"docid": "4702b2b00818a2f1b83ceb814bc113fb",
"score": "0.51418054",
"text": "def test_predict_batch(self):\n\n df = mp.predict_batch(target_region_list=[\"ERCO\"],\n year=2039,\n data_dir=pkg_resources.resource_filename(\"tell\", \"tests/data\"))\n\n pd.testing.assert_frame_equal(TestMlpPredict.COMP_PREDICT_DF, df)",
"title": ""
},
{
"docid": "e34d212a3f52ea284cc51e141f47ee55",
"score": "0.5131806",
"text": "def main():\n model = tf.keras.Sequential([keras.layers.Dense(units=1, input_shape=[1])])\n model.compile(optimizer='sgd', loss='mean_squared_error')\n\n xs = np.array([-1.0, 0.0, 1.0, 2.0, 3.0, 4.0], dtype=float)\n ys = np.array([-2.0, 1.0, 4.0, 7.0, 10.0, 13.0], dtype=float)\n\n model.fit(xs, ys, epochs=1000)\n prediction = model.predict([2813.18])\n\n print(prediction)",
"title": ""
},
{
"docid": "6438584d51268de797303e451eec1668",
"score": "0.51248765",
"text": "def load_linear_example1():\n X=np.array([[1,4],[1,8],[1,13],[1,17]])\n Y=np.array([7, 10, 11, 14])\n return X,Y",
"title": ""
},
{
"docid": "e43a734dd56e1c4555f7bc409f82944f",
"score": "0.512244",
"text": "def predict_and_sample(inference_model, x_initializer = x_initializer, a_initializer = a_initializer, \n c_initializer = c_initializer):\n \n # Step 1: Use your inference model to predict an output sequence given x_initializer, a_initializer and c_initializer.\n pred = inference_model.predict([x_initializer, a_initializer, c_initializer])\n # Step 2: Convert \"pred\" into an np.array() of indices with the maximum probabilities\n indices = np.argmax(pred, axis=-1)\n # Step 3: Convert indices to one-hot vectors, the shape of the results should be (1, )\n results = to_categorical(indices, num_classes=78)\n \n return results, indices",
"title": ""
},
{
"docid": "e7032f7a668918bace049e1a4fe9ab91",
"score": "0.5121757",
"text": "def predict_with_blending_lgr(blend_train, blend_test,y):\n \n print \"Start blending...\"\n n_class = len(np.unique(y))\n class_index = range(n_class)\n y_submission = np.zeros((len(blend_test), n_class))\n \n for index in class_index:\n print \"Applying logistics regression on class %d \" % (index)\n y_class = y==index\n X_class = blend_train[:,:,index]\n \n lgr = linear_model.LogisticRegression()\n lgr.fit(X_class, y_class)\n y_submission[:,index] = lgr.predict_proba(blend_test[:,:,index])[:,1]\n\t\ty_submission = normalize_prediction(y_submission)\n\t\t\n return y_submission",
"title": ""
},
{
"docid": "49582a6e12bc2454cc2245c1030756b6",
"score": "0.51181185",
"text": "def generate(self):\n self.crossover()\n self.mutate()\n self.fit()\n self.selection()",
"title": ""
},
{
"docid": "e009e464d9f855509afdb0b50861094e",
"score": "0.51140475",
"text": "def get_three_year_linear_imputed(self):\n return self.three_year_linear_imputed_train, self.three_year_linear_imputed_validate, self.three_year_linear_imputed_test",
"title": ""
},
{
"docid": "7e20072cb08ebdd5cc9acb1f79a1b182",
"score": "0.51138306",
"text": "def predict(self, model, query):",
"title": ""
},
{
"docid": "7eaffd9d6f27ff88d7f8852532cdb583",
"score": "0.5104908",
"text": "def predict(self, input_data):\n # Set up working dataframe\n empty_cols=list()\n empty_cols.append(\"Acol\")\n empty_cols.append(\"Bcol\")\n empty_cols.append(\"Cc\")\n pdf = pd.DataFrame(index=input_data.index, columns=empty_cols)\n pdf = pdf.merge(input_data, left_index=True, right_index=True)\n # Set up shortcut variables \n wtP = pdf[self.wtP_feature]\n wtNi = pdf[self.wtNi_feature]\n wtMn = pdf[self.wtMn_feature]\n wtCu = pdf[self.wtCu_feature]\n tempC = pdf[self.temp_C_feature]\n product = pdf[self.product_id_feature]\n fluence_n_cm2 = pdf[self.fluence_n_cm2_feature]\n # Convert fluence\n pdf[\"fluence_n_m2\"] = fluence_n_cm2 * 100.0 * 100.0\n # Get product indices\n plates_idx = pdf[product == \"P\"].index\n forgings_idx = pdf[product == \"F\"].index\n srm_plates_idx = pdf[product == \"SRM\"].index #standard ref. matl plates\n welds_idx = pdf[product == \"W\"].index\n # Set Acol according to product type\n pdf.loc[forgings_idx, 'Acol'] = 1.011\n pdf.loc[plates_idx, 'Acol'] = 1.080\n pdf.loc[srm_plates_idx, 'Acol'] = 1.080\n pdf.loc[welds_idx, 'Acol'] = 0.919\n # Set Bcol according to product type\n pdf.loc[forgings_idx, 'Bcol'] = 0.738\n pdf.loc[plates_idx, 'Bcol'] = 0.819\n pdf.loc[srm_plates_idx, 'Bcol'] = 0.819\n pdf.loc[welds_idx, 'Bcol'] = 0.968\n # Get TTS1\n pdf['TTS1_front'] = pdf.Acol * (5./9.) * 1.8943e-12 * np.power(pdf.fluence_n_m2, 0.5695)\n pdf['TTS1_temp'] = np.power( ((1.8 * tempC + 32.)/550.) , -5.47)\n pdf['TTS1_P'] = np.power( (0.09 + (wtP/0.012)) , 0.216)\n pdf['TTS1_Ni'] = np.power( (1.66 + (np.power(wtNi, 8.54)/0.63)) , 0.39)\n pdf['TTS1_Mn'] = np.power( (wtMn/1.36), 0.3)\n pdf['TTS1'] = pdf.TTS1_front * pdf.TTS1_temp * pdf.TTS1_P * pdf.TTS1_Ni * pdf.TTS1_Mn\n # Get TTS2\n pdf['TTS2_front'] = (5./9.)\n pdf['TTS2_Cu'] = np.maximum(np.minimum(wtCu, 0.28) - 0.053, 0)\n pdf['M_flux'] = np.maximum(np.minimum(113.87 * (np.log(pdf.fluence_n_m2) - np.log(4.5e20)), 612.6), 0)\n pdf['M_temp'] = np.power( ((1.8*tempC + 32.0)/550.) , -5.45)\n pdf['M_P'] = np.power( (0.1 + (wtP/0.012)), -0.098)\n pdf['M_Ni'] = np.power( (0.168 + (np.power(wtNi, 0.58)/0.63)), 0.73)\n pdf['Mcol'] = pdf.Bcol * pdf.M_flux * pdf.M_temp * pdf.M_P * pdf.M_Ni\n pdf['TTS2'] = pdf.TTS2_front * pdf.TTS2_Cu * pdf.Mcol\n # Get TTS\n pdf['TTS'] = pdf.TTS1 + pdf.TTS2\n # Get Cc according to product type\n c_welds = [0.55, 1.2e-3, -1.33e-6, 0.0]\n c_plates = [0.45, 1.945e-3, -5.496e-6, 8.473e-9]\n # Cc default to plates\n pdf.Cc = c_plates[0] + c_plates[1]*pdf.TTS + c_plates[2]*np.power(pdf.TTS, 2.0) + c_plates[3]*np.power(pdf.TTS, 3.0)\n # Cc for welds\n pdf.loc[welds_idx, 'Cc'] = c_welds[0] + c_welds[1]*pdf.TTS + c_welds[2]*np.power(pdf.TTS, 2.0) + c_welds[3]*np.power(pdf.TTS, 3.0)\n # Get hardening (delta_sigma_y_MPa)\n pdf['delta_sigma_y_MPa'] = pdf.TTS / pdf.Cc\n pdf.to_csv(os.path.join(os.getcwd(),\"E900.csv\"))\n return pdf['delta_sigma_y_MPa']",
"title": ""
},
{
"docid": "f1a10f84f8245c4dd50082e03eb3318b",
"score": "0.51010007",
"text": "def Training(tuples):\r\n data = SupervisedDataSet(1, 1)\r\n for input, expected in tuples:\r\n data.addSample([input], [expected])\r\n return data",
"title": ""
},
{
"docid": "53e3fbd989d0cbed954a2734c7def3e9",
"score": "0.50960875",
"text": "def _beam_predict(self, src_seq):\n\t\t\"\"\" encoder side \"\"\"\n\t\tsrc_idx = src_seq\n\n\t\treturn (self.__predict(src_idx))",
"title": ""
},
{
"docid": "3a81e2dfbd1fcbb56fcefbe881e6c81d",
"score": "0.50936675",
"text": "def train_and_predict(self):\n\n # train the classifier\n self.clf.fit(self.train_data, self.target_data)\n\n # predict from unkown\n predicted_probas = self.clf.predict_proba(self.unknown_data)\n\n return predicted_probas",
"title": ""
},
{
"docid": "935b04baade93279fe9087e6b5ff1aaa",
"score": "0.5093314",
"text": "def _predict(self):\r\n raise NotImplementedError",
"title": ""
},
{
"docid": "e5a8f8f17bb00b75736d9ce3a0fd83cf",
"score": "0.5092527",
"text": "def get_predictions(self, sentences):\n \"\"\"\n Makes prediction on sentences\n :param sentences: the sentences\n :return: a dataframe a dataframe with sentences and predictions\n \"\"\"\n self.tag2idx = get_existing_tag2idx(self.model_folder)\n tag2name = {self.tag2idx[key]: key for key in self.tag2idx.keys()}\n\n model = XLNetForSequenceClassification.from_pretrained(\n self.model_folder, num_labels=len(tag2name)\n )\n model.to(self.device)\n model.eval()\n\n logger.info(\"Setting input embedding\")\n\n input, masks, segs = generate_dataloader_input(sentences)\n dataloader = get_dataloader(input, masks, segs, BATCH_NUM)\n\n nb_eval_steps, nb_eval_examples = 0, 0\n\n y_predict = []\n logger.info(\"Running evaluation...\")\n\n for step, batch in enumerate(dataloader):\n if nb_eval_steps % 100 == 0:\n logger.info(f\"Step {nb_eval_steps}\")\n\n batch = tuple(t.to(self.device) for t in batch)\n b_input_ids, b_input_mask, b_segs = batch\n\n with torch.no_grad():\n outputs = model(\n input_ids=b_input_ids,\n token_type_ids=b_segs,\n input_mask=b_input_mask,\n )\n logits = outputs[0]\n\n # Get text classification predict result\n logits = logits.detach().cpu().numpy()\n\n for predict in np.argmax(logits, axis=1):\n y_predict.append(predict)\n\n nb_eval_steps += 1\n\n\n final_df = pd.DataFrame(\n {\n \"sentences\": sentences,\n \"label\": [tag2name[pred] for pred in y_predict],\n \"y_pred\": y_predict\n }\n )\n\n return final_df",
"title": ""
},
{
"docid": "4f8528e1c52cfc2eb0c9fcf2cc3198c9",
"score": "0.5086949",
"text": "def predictions(self):\n\n self.train_preds = self.tf_model.predict(self.data.X_train)\n self.test_preds = self.tf_model.predict(self.data.X_test)",
"title": ""
},
{
"docid": "98c0a9fe48ed1f5139cd7f9470ee6777",
"score": "0.50824565",
"text": "def predict(self):\n n = 0#np.random.normal(scale=self._sigma)\n self._x += (self._v + n)*self._dt/self._e_len\n self.split = 0\n\n if self._x >= 1.:\n dest_list = list(self._roadmap.graph[self._e[1]].keys())\n # no U-turns\n dest_list.remove(self._e[0])\n self.split = len(dest_list)\n self._e = (self._e[1], random.choice(dest_list))\n self._e_len = self._roadmap.graph[self._e[0]][self._e[1]]\n self._x -= 1\n\n return self.state",
"title": ""
},
{
"docid": "20f62fe00cd0c760389401af10849586",
"score": "0.50722253",
"text": "def train(self, xs):\n x1, x2, x1_pairs, x2_pairs = xs\n\n feed = {self.x1: x1, self.x2: x2, self.x1p: x1_pairs, self.x2p: x2_pairs}\n outputs = [self.summary, self.step, self.loss, self.lx1, self.lx2, self.lx12, self.tx1, self.tx2, self.lx1p, self.lx2p]\n\n summary, _, loss, lx1, lx2, lx12, tx1, tx2, lx1p, lx2p = self.sess.run(outputs, feed_dict=feed)\n\n if self.objective == 'joint':\n bound = lx12\n terms = {'lower_bound_on_log_p_x_y': bound, 'loss': loss,\n 'lx1': lx1, 'lx2': lx2, 'lx12': lx12, 'tx1': tx1, 'tx2': tx2}\n else: # translate\n bound_t1 = tx1 + lx2p\n bound_t2 = tx2 + lx1p\n terms = {'lower_bound_on_log_p_x_y_t1': bound_t1, 'lower_bound_on_log_p_x_y_t2': bound_t2, 'loss': loss,\n 'lx1': lx1, 'lx2': lx2, 'lx12': lx12, 'tx1': tx1, 'tx2': tx2}\n\n self._track(terms, prefix='train_')\n self.tr_writer.add_summary(summary, self.n_steps)\n\n self.n_steps = self.n_steps + 1",
"title": ""
},
{
"docid": "f6400427fd93b043e1589ab96a495556",
"score": "0.5072122",
"text": "def predict_model():\n\n fitted_model = joblib.load('fitted_model/mmp_phase1_D2.clf')\n x_predict = pd.read_csv('data/X_train_v2.csv', index_col='msisdn').replace('\\N', np.nan)\n x_predict = x_predict.convert_objects(convert_numeric=True)\n x_predict = x_predict.query('segment in (1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0)')\n df = fitted_model.predict(x_predict)\n fitted_model.predict_proba(x_predict.copy()).to_csv('tmp/x_predict.csv', header=True)\n print(df.shape)",
"title": ""
},
{
"docid": "f73a13c67a6b92449c48f0494ac377ab",
"score": "0.50682765",
"text": "def predict(self, x):\n pass",
"title": ""
},
{
"docid": "85cf5d9fb5c5213fccb92d58eb791791",
"score": "0.50677717",
"text": "def predict(cls, data):\n my_model = cls.get_model()\n print(\"start ti call model\")\n summary = my_model(data)\n return summary",
"title": ""
},
{
"docid": "1f6b2a4e39f5b0c17770d3ea4baf77dd",
"score": "0.50665087",
"text": "def predict_proba(self, x):\n rule_evaluations_list = []\n for rule in self.rules:\n rule_evaluations_list.append(\n getattr(x[rule[\"feature_name\"]], rule[\"operator\"])(rule[\"threshold\"])\n )\n rule_evaluations_dataframe = pd.concat(rule_evaluations_list, axis=1)\n scores = getattr(rule_evaluations_dataframe, self.rule_combination_method)(\n axis=1\n )\n scores = list(scores.astype(int))\n\n # format it like sklearn output and return\n return np.array([scores, scores]).transpose()",
"title": ""
},
{
"docid": "fa2332bc3c0ce5e9bc07f1147d8874a0",
"score": "0.50650847",
"text": "def _predict_core(self):\n pass",
"title": ""
},
{
"docid": "881bd71c17eb031994b9547ced22148a",
"score": "0.505961",
"text": "def loadPrediction(self):\n self.prediction = [0,1,2,1,0,2,0,1,2,0,0,2,1,0,0,2,0,1,2,0,2,1,0,0,2,1,0,0,2,0,1,0]",
"title": ""
},
{
"docid": "49354e836949925d402223c7e4862150",
"score": "0.50552696",
"text": "def predict(self):\n return self.act_type.predict(self.A)",
"title": ""
},
{
"docid": "b28a98cee84f565b45ff6e59e09d8b9c",
"score": "0.5053132",
"text": "def linear_regression(self):\n \n cid_list = sorted(self.cid2pmap_dict.keys())\n cid2species = {}\n for cid in cid_list:\n comp = self.kegg.cid2compound(cid)\n cid2species[cid] = (comp.get_nH(), comp.get_charge())\n \n N = len(self.train_rowids)\n y = zeros((N, 1))\n X = zeros((N, len(cid_list)))\n for r in range(N):\n row_data = self.data[self.train_rowids[r]]\n dG0_r = row_data.dG0_r\n for (cid, coeff) in row_data.sparse.iteritems():\n c = cid_list.index(cid)\n X[r, c] = coeff\n (nH, z) = cid2species[cid]\n dG0_r -= coeff * (nH*R*row_data.T*log(10)*row_data.pH - 2.91482*(z**2 - nH)*sqrt(row_data.I) / (1 + 1.6*sqrt(row_data.I)))\n y[r, 0] = dG0_r\n\n inv_corr_mat = pinv(dot(X.T, X))\n dG0_f_vec = dot(dot(inv_corr_mat, X.T), y)\n \n # add the formation energies to the CID dictionary\n for c in range(len(cid_list)):\n cid = cid_list[c]\n (nH, z) = cid2species[cid]\n cid2species[cid] = (dG0_f_vec[c], nH, z)\n return cid2species",
"title": ""
},
{
"docid": "edb3d9e84ff98c1cac57bb86d2501ffa",
"score": "0.5048065",
"text": "def mlp_to_str(conf, head_str, **kw):\n # model = Sequential()\n # Dense(64) is a fully-connected layer with 64 hidden units.\n # in the first layer, you must specify the expected input data shape:\n # here, 20-dimensional vectors.\n result_sds = kw.pop('result_sds', None)\n project_id = kw.pop('project_id', None)\n f = conf['fit']\n e = conf['evaluate']\n if result_sds is None:\n raise RuntimeError('no result_sds created')\n\n str_model = 'from keras.models import Sequential\\n'\n str_model += 'from keras.callbacks import LambdaCallback\\n'\n str_model += 'from server3.lib.models.keras_callbacks import ' \\\n 'MongoModelCheckpoint\\n'\n str_model += 'from server3.service import logger_service\\n'\n str_model += 'from server3.service import job_service\\n'\n str_model += 'from server3.business import staging_data_set_business\\n'\n str_model += 'from keras.layers import Dense, Dropout\\n'\n str_model += 'from keras.optimizers import SGD\\n'\n str_model += head_str\n str_model += \"result_sds = staging_data_set_business.get_by_id('%s')\\n\" % \\\n result_sds['id']\n str_model += \"project_id = '%s'\\n\" % project_id\n mlp_main_str = inspect.getsource(mlp_main)\n mlp_main_str = mlp_main_str.replace(\"**f['args']\",\n generate_args_str(f['args']))\n mlp_main_str = mlp_main_str.replace(\"**e['args']\",\n generate_args_str(e['args']))\n str_model += mlp_main_str\n str_model += 'print(mlp_main(result_sds, project_id, x_train, y_train, ' \\\n 'x_val, y_val, x_test, y_test))\\n'\n print(str_model)\n return str_model",
"title": ""
},
{
"docid": "289be39f2e2b05aca3a4e13e8e7ba3a9",
"score": "0.5047663",
"text": "def predict(self, test_path, pred_path):\n raise NotImplementedError",
"title": ""
},
{
"docid": "e82f868cfaf9522644c235504ac48f2e",
"score": "0.50462365",
"text": "def RegressionModelComparaison(Df, IndependentVariable, ColToDrop=None):\n\n Models = {}\n\n # We eliminate the \"Combination\" column as there are one line per combination name - this can't be a factor\n Df = Df.drop(ColToDrop, axis=1)\n\n # We apply the following preprocessing tasks:\n # - Split the dataframe into x and y dataframes\n # - Encode Categorical Variables\n # - Normalize results to avoid scale issues\n # - Split x and y into train and test subsets\n # - Add a constant\n # - Sort Index\n Df_X_Train, Df_X_Test, Df_y_Train, Df_y_Test = FT.MachineLearning.Preprocessing.GlobalPreprocessing(\n df=Df,\n Split_x_y=True, IndependentColumnName=[IndependentVariable],\n EncodeCategoricalVariable=True,\n Normalize=True,\n Split_Train_Test=True, TestSetSize=0.2, Randomize=False,\n AddConst=True,\n SortIndex=False)\n\n # All In Simple Regression results in sample\n Train_SimpleLReg_AllIn_ResultsInSample_Series, Train_SimpleLReg_AllIn_ResultsInSample_OneValueIndicators = FT.SeriesAnalysis.RegressionAnalysis.RegressionAnalysis(\n df=pd.DataFrame(Df_X_Train.iloc[:, 1:]).merge(Df_y_Train, left_index=True, right_index=True),\n Explanatory=Df_X_Train.columns[1:],\n Independent=Df_y_Train.columns,\n Indicators=[\"OLS\"])\n\n Models[\"SimpleReg AllIn\"] = [Train_SimpleLReg_AllIn_ResultsInSample_Series, Train_SimpleLReg_AllIn_ResultsInSample_OneValueIndicators]\n\n print(\"All In Simple Regression Stats\")\n print(Train_SimpleLReg_AllIn_ResultsInSample_OneValueIndicators)\n # Prediction in sample VS Observed Graph\n # Train_SimpleLReg_AllIn_ResultsInSample_Series.plot(x=\"Speed Mean Reversion\", y=[IndependentVariable, \"OLS Fitted Values\"], style=\"o\")\n\n #Prediction Out of Sample\n model = sm.OLS(Df_y_Train, Df_X_Train)\n model = model.fit()\n SimpleLReg_AllIn_PredOutSample_y = FT.MachineLearning.Metrics.Predict_WithCorrectIndex(model, Df_X_Test)\n\n # The threshold for elimination based on pvalues\n SL = 0.05\n\n # Simple Linear Regression Backward Elimination\n # We get the dataframe ONLY with the variables relevant (regarding the elimination step)\n Df_X_Train_SimpleLReg_BackwardElimination = FT.MachineLearning.LinearRegression.BackwardElimination(Df_X_Train,\n Df_y_Train, SL)\n\n # Get the OLS regression results for in sample\n Train_SimpleLReg_BackwardElimination_ResultsInSample_Series, Train_SimpleLReg_BackwardElimination_ResultsInSample_OneValueIndicators = FT.SeriesAnalysis.RegressionAnalysis.RegressionAnalysis(\n df=Df_X_Train_SimpleLReg_BackwardElimination.merge(Df_y_Train, left_index=True, right_index=True),\n Explanatory=Df_X_Train_SimpleLReg_BackwardElimination.columns,\n Independent=Df_y_Train.columns,\n Indicators=[\"OLS\"])\n\n Models[\"SimpleReg BackwardElimination\"] = [Train_SimpleLReg_BackwardElimination_ResultsInSample_Series, Train_SimpleLReg_BackwardElimination_ResultsInSample_OneValueIndicators]\n print(\"Simple Reg Backward Elimination\")\n print(Train_SimpleLReg_BackwardElimination_ResultsInSample_OneValueIndicators)\n # Prediction in sample VS Observed Graph\n # Train_SimpleLReg_BackwardElimination_ResultsInSample_Series.plot(x=\"Speed Mean Reversion\", y=[IndependentVariable, \"OLS Fitted Values\"], style=\"o\")\n\n #Prediction Out of Sample\n #We first need to reduce Df_X_Test to the variable contained in Df_X_Train_SimpleLReg_BackwardElimination\n Df_X_Test_SimpleLReg_BackwardElimination = Df_X_Test[Df_X_Train_SimpleLReg_BackwardElimination.columns]\n\n model = sm.OLS(Df_y_Train, Df_X_Train_SimpleLReg_BackwardElimination)\n model = model.fit()\n SimpleLReg_BackwardElimination_PredOutSample_y = FT.MachineLearning.Metrics.Predict_WithCorrectIndex(model, Df_X_Test_SimpleLReg_BackwardElimination)\n\n\n\n # Polynomial Linear Regression Backward Elimination\n # Convert to a polunomial form\n degrees = [1, 2]\n Df_X_Train_PolyLReg = FT.MachineLearning.LinearRegression.ConvertToPolynomial(pd.DataFrame(Df_X_Train.iloc[:, 1:]),\n degrees)\n\n # We get the dataframe ONLY with the variables relevant (regarding the elimination step)\n Df_X_Train_PolyLReg_BackwardElimination = FT.MachineLearning.LinearRegression.BackwardElimination(\n Df_X_Train_PolyLReg, Df_y_Train, SL)\n\n # Get the OLS regression results\n Train_PolyLReg_BackwardElimination_ResultsInSample_Series, Train_PolyLReg_BackwardElimination_ResultsInSample_OneValueIndicators = FT.SeriesAnalysis.RegressionAnalysis.RegressionAnalysis(\n df=Df_X_Train_PolyLReg_BackwardElimination.merge(Df_y_Train, left_index=True, right_index=True),\n Explanatory=Df_X_Train_PolyLReg_BackwardElimination.columns,\n Independent=Df_y_Train.columns,\n Indicators=[\"OLS\"])\n\n Models[\"PolyReg BackwardElimination\"] = [Train_PolyLReg_BackwardElimination_ResultsInSample_Series, Train_PolyLReg_BackwardElimination_ResultsInSample_OneValueIndicators]\n print(\"Poly Reg Backward Elimination Stats\")\n print(Train_PolyLReg_BackwardElimination_ResultsInSample_OneValueIndicators)\n # Prediction in sample VS Observed Graph\n # Train_PolyLReg_BackwardElimination_ResultsInSample_Series.plot(x=\"Speed Mean Reversion\", y=[IndependentVariable, \"OLS Fitted Values\"], style=\"o\")\n\n # Prediction Out of Sample\n # We first need to reduce Df_X_Test to the variable contained in Df_X_Train_SimpleLReg_BackwardElimination\n Df_X_Test_PolyLReg = FT.MachineLearning.LinearRegression.ConvertToPolynomial(pd.DataFrame(Df_X_Test.iloc[:, 1:]),degrees)\n Df_X_Test_PolyLReg_BackwardElimination = Df_X_Test_PolyLReg[Df_X_Train_PolyLReg_BackwardElimination.columns]\n\n model = sm.OLS(Df_y_Train, Df_X_Train_PolyLReg_BackwardElimination)\n model = model.fit()\n PolyLReg_BackwardElimination_PredOutSample_y = FT.MachineLearning.Metrics.Predict_WithCorrectIndex(model, Df_X_Test_PolyLReg_BackwardElimination)\n\n # Support Vector Regression\n SVRModel, SVRResults = FT.MachineLearning.NonLinearRegression.SVR_Regression(Df_X_Train, Df_y_Train)\n\n Models[\"SVR\"] = [SVRModel, SVRResults]\n print(\"SVR Stats\")\n print(FT.MachineLearning.Metrics.ModelEvaluation(Df_y_Test, SVRModel.predict(Df_X_Test),\n Indicators=[\"Explained Variance Score\", \"Max Error\",\n \"Mean Squared Error\", \"R² Score\"]))\n #Prediction in sample\n SVR_PredInSample_y = FT.MachineLearning.Metrics.Predict_WithCorrectIndex(SVRModel, Df_X_Train)\n # Prediction in sample VS Observed Graph\n #SVRComp = pd.DataFrame(SVR_PredInSample_y).merge(Df_y_Train, how=\"inner\", left_index=True, right_index=True)\n #SVRComp.plot(style=\"o\")\n # Prediction out sample\n SVR_PredOutSample_y = FT.MachineLearning.Metrics.Predict_WithCorrectIndex(SVRModel, Df_X_Test)\n\n # Decision Tree Regression\n DecisionTreeModel, DecisionTreeResults = FT.MachineLearning.NonLinearRegression.DecisionTree_Regression(Df_X_Train,\n Df_y_Train)\n\n Models[\"DecisionTree\"] = [DecisionTreeModel, DecisionTreeResults]\n print(\"Decision Tree Stats\")\n print(FT.MachineLearning.Metrics.ModelEvaluation(Df_y_Test, DecisionTreeModel.predict(Df_X_Test),\n Indicators=[\"Explained Variance Score\", \"Max Error\",\n \"Mean Squared Error\", \"R² Score\"]))\n\n\n # Prediction in sample\n DecisionTree_PredInSample_y = FT.MachineLearning.Metrics.Predict_WithCorrectIndex(DecisionTreeModel, Df_X_Train)\n # Prediction in sample VS Observed\n #DecisionTreeComp = pd.DataFrame(DecisionTree_PredInSample_y).merge(Df_y_Train, how=\"inner\", left_index=True, right_index=True)\n #DecisionTreeComp.plot(style=\"o\")\n # Prediction out sample\n DecisionTree_PredOutSample_y = FT.MachineLearning.Metrics.Predict_WithCorrectIndex(DecisionTreeModel, Df_X_Test)\n\n # Random Forests\n RandomForestModel, RandomForestResults = FT.MachineLearning.NonLinearRegression.RandomForest_Regression(Df_X_Train,\n Df_y_Train,\n NumberOfTrees=1000)\n Models[\"RandomForests\"] = [RandomForestModel, RandomForestResults, RandomForestModel.feature_importances_]\n print(\"Random Forests Stats\")\n print(FT.MachineLearning.Metrics.ModelEvaluation(Df_y_Test, RandomForestModel.predict(Df_X_Test),\n Indicators=[\"Explained Variance Score\", \"Max Error\",\n \"Mean Squared Error\", \"R² Score\"]))\n print('Features Importances')\n print(RandomForestModel.feature_importances_)\n\n # Prediction in sample\n RandomForests_PredInSample_y = FT.MachineLearning.Metrics.Predict_WithCorrectIndex(RandomForestModel, Df_X_Train)\n # Prediction in sample VS Observed\n #RandomForestsComp = pd.DataFrame(RandomForests_PredInSample_y).merge(Df_y_Train, how=\"inner\", left_index=True, right_index=True)\n #RandomForestsComp.plot(style=\"o\")\n # Prediction out sample\n RandomForests_PredOutSample_y = FT.MachineLearning.Metrics.Predict_WithCorrectIndex(RandomForestModel, Df_X_Test)\n\n #Create a dataframe to compare the observed values with all predicted in sample values\n AllModelComparaison_Prediction_InSample = Train_SimpleLReg_AllIn_ResultsInSample_Series[[IndependentVariable, \"OLS Fitted Values\"]]\n AllModelComparaison_Prediction_InSample = AllModelComparaison_Prediction_InSample.rename(columns={\"OLS Fitted Values\":\"SimpleReg AllIn\"})\n AllModelComparaison_Prediction_InSample = AllModelComparaison_Prediction_InSample.merge(Train_SimpleLReg_BackwardElimination_ResultsInSample_Series[\"OLS Fitted Values\"], how=\"inner\", left_index=True, right_index=True)\n AllModelComparaison_Prediction_InSample = AllModelComparaison_Prediction_InSample.rename(columns={\"OLS Fitted Values\":\"SimpleReg BE\"})\n AllModelComparaison_Prediction_InSample = AllModelComparaison_Prediction_InSample.merge(Train_PolyLReg_BackwardElimination_ResultsInSample_Series[\"OLS Fitted Values\"], how=\"inner\", left_index=True, right_index=True)\n AllModelComparaison_Prediction_InSample = AllModelComparaison_Prediction_InSample.rename(columns={\"OLS Fitted Values\":\"PolyReg BE\"})\n AllModelComparaison_Prediction_InSample = AllModelComparaison_Prediction_InSample.merge(pd.DataFrame(SVR_PredInSample_y), how=\"inner\", left_index=True, right_index=True)\n AllModelComparaison_Prediction_InSample = AllModelComparaison_Prediction_InSample.rename(columns={0:\"SVR\"})\n AllModelComparaison_Prediction_InSample = AllModelComparaison_Prediction_InSample.merge(pd.DataFrame(DecisionTree_PredInSample_y), how=\"inner\", left_index=True, right_index=True)\n AllModelComparaison_Prediction_InSample = AllModelComparaison_Prediction_InSample.rename(columns={0:\"Decision Tree\"})\n AllModelComparaison_Prediction_InSample = AllModelComparaison_Prediction_InSample.merge(pd.DataFrame(RandomForests_PredInSample_y), how=\"inner\", left_index=True, right_index=True)\n AllModelComparaison_Prediction_InSample = AllModelComparaison_Prediction_InSample.rename(columns={0:\"Random Forests\"})\n\n # Graph of all predicted values and the observed values\n # AllModelComparaison_Prediction_InSample[\"x\"] = AllModelComparaison_Prediction_InSample.index\n # AllModelComparaison_Prediction_InSample.plot(x=\"x\", y=[IndependentVariable, \"SimpleReg AllIn\", \"SimpleReg BE\", \"PolyReg BE\", \"Random Forests\"], style=\".\")\n\n #Create a dataframe to compare the in sample residual series for each model\n AllModelComparaison_Residual_InSample = AllModelComparaison_Prediction_InSample.sub(AllModelComparaison_Prediction_InSample[IndependentVariable], axis=0)\n AllModelComparaison_Residual_InSample = AllModelComparaison_Residual_InSample.drop(IndependentVariable, axis=1)\n # Graph of the residual for each model\n # AllModelComparaison_Residual_InSample.reset_index().plot(x=\"index\", y=[\"SimpleReg AllIn\", \"SimpleReg BE\", \"PolyReg BE\", \"Random Forests\"], style=\".\")\n\n\n\n #Create a dataframe to compare the observed values with all predicted out sample values\n AllModelComparaison_Prediction_OutSample = Df_y_Test\n AllModelComparaison_Prediction_OutSample = AllModelComparaison_Prediction_OutSample.merge(SimpleLReg_AllIn_PredOutSample_y, how=\"inner\", left_index=True, right_index=True)\n AllModelComparaison_Prediction_OutSample = AllModelComparaison_Prediction_OutSample.rename(columns={0:\"SimpleReg AllIn\"})\n AllModelComparaison_Prediction_OutSample = AllModelComparaison_Prediction_OutSample.merge(SimpleLReg_BackwardElimination_PredOutSample_y, how=\"inner\", left_index=True, right_index=True)\n AllModelComparaison_Prediction_OutSample = AllModelComparaison_Prediction_OutSample.rename(columns={0:\"SimpleReg BE\"})\n AllModelComparaison_Prediction_OutSample = AllModelComparaison_Prediction_OutSample.merge(PolyLReg_BackwardElimination_PredOutSample_y, how=\"inner\", left_index=True, right_index=True)\n AllModelComparaison_Prediction_OutSample = AllModelComparaison_Prediction_OutSample.rename(columns={0:\"PolyReg BE\"})\n AllModelComparaison_Prediction_OutSample = AllModelComparaison_Prediction_OutSample.merge(pd.DataFrame(SVR_PredOutSample_y), how=\"inner\", left_index=True, right_index=True)\n AllModelComparaison_Prediction_OutSample = AllModelComparaison_Prediction_OutSample.rename(columns={0:\"SVR\"})\n AllModelComparaison_Prediction_OutSample = AllModelComparaison_Prediction_OutSample.merge(pd.DataFrame(DecisionTree_PredOutSample_y), how=\"inner\", left_index=True, right_index=True)\n AllModelComparaison_Prediction_OutSample = AllModelComparaison_Prediction_OutSample.rename(columns={0:\"Decision Tree\"})\n AllModelComparaison_Prediction_OutSample = AllModelComparaison_Prediction_OutSample.merge(pd.DataFrame(RandomForests_PredOutSample_y), how=\"inner\", left_index=True, right_index=True)\n AllModelComparaison_Prediction_OutSample = AllModelComparaison_Prediction_OutSample.rename(columns={0:\"Random Forests\"})\n\n # Graph of all predicted values and the observed values\n # AllModelComparaison_Prediction_OutSample[\"x\"] = AllModelComparaison_Prediction_OutSample.index\n # AllModelComparaison_Prediction_OutSample.plot(x=\"x\", y=[IndependentVariable, \"SimpleReg AllIn\", \"SimpleReg BE\", \"PolyReg BE\", \"Random Forests\"], style=\".\")\n\n #Create a dataframe to compare the out sample residual series for each model\n AllModelComparaison_Residual_OutSample = AllModelComparaison_Prediction_OutSample.sub(AllModelComparaison_Prediction_OutSample[IndependentVariable], axis=0)\n AllModelComparaison_Residual_OutSample = AllModelComparaison_Residual_OutSample.drop(IndependentVariable, axis=1)\n # Graph of the residual for each model\n # AllModelComparaison_Residual_OutSample.reset_index().plot(x=\"index\", y=[\"SimpleReg AllIn\", \"SimpleReg BE\", \"PolyReg BE\", \"Random Forests\"], style=\".\")\n\n return Models, \\\n AllModelComparaison_Prediction_InSample, AllModelComparaison_Residual_InSample, \\\n AllModelComparaison_Prediction_OutSample, AllModelComparaison_Residual_OutSample",
"title": ""
},
{
"docid": "66cba9af327fce72400e191665a5eee5",
"score": "0.50435066",
"text": "def predict_segment_lines(predictor):\n preds = predictor()\n pass",
"title": ""
},
{
"docid": "54ba17516bd25028041ea436ef082759",
"score": "0.5029889",
"text": "def train_predict(model_list) :\n\tP = np.zeros((ytest.shape[0], len(model_list)))\n\tP = pd.DataFrame(P)\n\n\tprint(\"Fitting models\")\n\tcols = list()\n\tfor i, (name, m) in enumerate(model_list.items()):\n\t\tprint(\"%s...\" % name, end=\" \", flush=False)\n\t\tm.fit(xtrain, ytrain)\n\t\tP.iloc[:, i] = m.predict_proba(xtest)[:, 1]\n\t\tcols.append(name)\n\t\tprint(\"done\")\n\n\tP.columns = cols\n\tprint(\"Done. \\n\")\n\treturn P",
"title": ""
},
{
"docid": "521149dbc7c057aca05ac1752e6aba96",
"score": "0.5027814",
"text": "def predict_log_proba(self, X):\n ...",
"title": ""
},
{
"docid": "e9d9b3e86f323edf7e2f41a62a25a1b1",
"score": "0.5027703",
"text": "def predict(self, reg, test_base_pred, test_aux_pred):\n X = []\n for i in range(len(test_base_pred)):\n X.append(test_base_pred[i] + test_aux_pred[i])\n\n hierarchical_pred = reg.predict(X)\n return hierarchical_pred",
"title": ""
},
{
"docid": "deb242218c56d94383f0b4e2d99f8c26",
"score": "0.5023876",
"text": "def evaluate_models_on_training(x, y, models, display_graphs):\n\n r_values = []\n for coeffs in models:\n y_preds = []\n isLinear = True\n #yields: y_predictions and isLinear\n for i in x:\n y_temp = np.polyval(coeffs, i)\n y_preds.append(y_temp)\n if len(coeffs) > 2:\n isLinear = isLinear and False\n # #highest degree to lowest\n # #e.g. 2 3 5 2\n # #is.. 3 2 1 0\n # #so.. len() - index - 1\n # for j in range(len(coeffs)):\n # y_temp += coeffs[j]*(x[i]**(len(coeffs) - j - 1)) \n # if (len(coeffs) - j - 1) > 1 and coeffs[j] > 0:\n # isLinear = isLinear and False\n # y_preds.append(y_temp)\n #each model has an r_value associated with it\n y_preds = np.array(y_preds)\n r_score = round(r2_score(y, y_preds), 4)\n r_values.append(r_score)\n #graphing\n if display_graphs:\n plt.plot(x, y, 'bo', label = 'Measured')\n plt.plot(x, y_preds, 'r-', label = 'Best First Curve')\n plt.xlabel('Year')\n plt.ylabel('Temperature')\n if isLinear:\n # b = linear_regression(x, y)[1]\n # y_preds += b\n se = round(standard_error_over_slope(x, y, y_preds, coeffs), 4)\n plt.title(\"Degree of regression model: \" + str(len(coeffs)-1) + \"\\nR-squared of model evaluated on the given data points: \" + str(r_score) + \"\\nStandard Error Over Slope: \" + str(se))\n else:\n plt.title(\"Degree of regression model: \" + str(len(coeffs)-1) + \"\\nR-squared of model evaluated on the given data points: \" + str(r_score))\n plt.show()\n return r_values",
"title": ""
},
{
"docid": "f688f77e78dfa14e22edd7b05ed26a55",
"score": "0.50176287",
"text": "def predict_proba(self, id1_feats, id2_feats):\n\t\treturn np.ones(len(id1_feats)) * .5",
"title": ""
},
{
"docid": "5f9204a780e284a6a2d65ece0017950a",
"score": "0.5013738",
"text": "def predict1(self, input_p ):\n #first layer pass\n n1 = np.dot(self.getHyperParam()['weights']['1'], input_p)\n a1 = self.ReLu(n1)\n #second layer pass\n n2 = np.dot(a1, self.getHyperParam()['weights']['2'])\n a2 = self.sigmoid(n2)\n\n return n1, a2",
"title": ""
},
{
"docid": "7e45b91ff70bdc885eceb5af75771873",
"score": "0.5008427",
"text": "def predict_graph(self, step_n, name=None, excluded_cols=None):\n if self.name is not None:\n name = self.name\n else:\n name = str() if name is None else name\n df = self.predict_df(step_n=step_n)\n if excluded_cols is not None:\n df = df.drop(excluded_cols, axis=1)\n r0 = self.param_dict[\"R0\"]\n title = f\"Prediction in {name} with {self.model.NAME} model: R0 = {r0}\"\n line_plot(df, title, v= datetime.today(), h=self.total_population)",
"title": ""
},
{
"docid": "db4a0af63778ac331a6e47c92c88e122",
"score": "0.5007888",
"text": "def prepare():\n def create_sequence_1(start_value):\n x = np.array(range(start_value, start_value+SEQUENCE_LENGTH))\n noise = np.random.normal(0, NOISE_RANGE, SEQUENCE_LENGTH)\n y = np.sin(np.pi * x / OSCILIATION) + (x / TREND + noise)\n return y\n\n def create_sequence_2(start_value):\n x = np.array(range(start_value, start_value+SEQUENCE_LENGTH))\n noise = np.random.normal(0, NOISE_RANGE, SEQUENCE_LENGTH)\n y = -x + noise\n return y\n\n def create_sequence_3(start_value):\n x = np.array(range(start_value, start_value+SEQUENCE_LENGTH))\n y = []\n\n for x_i in x:\n y_i = 0\n if x_i % 2 == 0:\n y_i = x_i * 2\n else:\n y_i = - x_i * 2\n y += [y_i]\n\n return y\n\n def create_sequence_4(unused_variable):\n return np.random.uniform(-100,100,SEQUENCE_LENGTH)\n\n\n if not os.path.exists(LOCAL_DIR):\n os.makedirs(LOCAL_DIR)\n\n struct = ['creator', 'label']\n sequences = [\n [create_sequence_1, TARGET_LABELS[0]],\n [create_sequence_2, TARGET_LABELS[1]],\n [create_sequence_3, TARGET_LABELS[2]],\n [create_sequence_4, TARGET_LABELS[3]]\n ]\n sequences = [dict(zip(struct, sequence)) for sequence in sequences]\n\n patterns = 3\n start = int(-1*TRAIN_DATA_SIZE/(2*patterns))\n end = int(TRAIN_DATA_SIZE/(2*patterns))\n\n train_file_name = os.path.join(LOCAL_DIR, \"train.csv\")\n test_file_name = os.path.join(LOCAL_DIR, \"test.csv\")\n train_file = open(train_file_name, \"w\")\n test_file = open(test_file_name, \"w\")\n\n for sequence in sequences:\n for line_index in range(start, end):\n seq = sequence['creator'](line_index)\n csv_line = \",\".join(map(str, seq))\n csv_line += \",{}\\n\".format(sequence['label'])\n\n if 0 == line_index % 6:\n test_file.write(csv_line)\n else:\n train_file.write(csv_line)\n\n train_file.close()\n test_file.close()",
"title": ""
},
{
"docid": "ec46ce182920bfcbd60293b7a914d60c",
"score": "0.5006211",
"text": "def linear_combinations(self):\n for i in range(2*self.n):\n print(la.solve(self.x_matrix,self.y_matrix[:,i]))",
"title": ""
},
{
"docid": "da124dacc639f5fc3d459f5024f5f98b",
"score": "0.5003695",
"text": "def predict(cls, prediction_input: DataFrame):\n prediction_data = task.Dataset(df=prediction_input)\n print(\"Prediction Data: \")\n print(prediction_data.head())\n return cls.model.predict(prediction_data)",
"title": ""
},
{
"docid": "427e99350fd68eb8afa32c32be95fd86",
"score": "0.50003296",
"text": "def predict(self,X): \n \n #Calcula el grado de pertenencia. Por cada clase con todos los objetos.\n \n i ={'i': 0} #Indice por la clase que toca\n def di(w):\n j = i['i']\n #p1 => primera parte de la ecuacion -1/2 * ln(det(E_ag))\n p1 = self.__lndetcov(self.covs_l_g[j])/-2\n \n #P3 => calcula el log(priori)\n #p3 = np.log(self.priors[i])\n p3 = self.priors[j]\n p13 = np.sum([p1,p3])\n \n #g => (X-mu_i)\n g = X-self.means[j]\n \n #p2 calcula (X-mu_i)^T * E_ag^-1 * (X-mu_i)\n p2 = g.dot(np.linalg.inv(self.covs_l_g[j]))\n p2 = (p2*g).sum(1) #Esta ultima parte se hace asi ya que dot no es capaz de aplicarlo adecuadamente \n p2 = p2/-2\n i['i'] += 1\n \n #Se suma todo\n \n return p1+p2+p3\n \n \n belonging = np.apply_along_axis(di,0,self.labels)\n \n return belonging",
"title": ""
},
{
"docid": "12ddce695417acfc28bf089e9d5a738d",
"score": "0.49963963",
"text": "def predict(self, x_test):\n if Constant.LIMIT_MEMORY:\n pass\n test_loader = text_dataloader(x_test)\n model = self.cnn.best_model\n model.eval()\n\n outputs = []\n with torch.no_grad():\n for index, inputs in enumerate(test_loader):\n outputs.append(model(inputs).numpy())\n output = reduce(lambda x, y: np.concatenate((x, y)), outputs)\n return self.inverse_transform_y(output)",
"title": ""
},
{
"docid": "f045d3eac6902164b3dda9501f0c1796",
"score": "0.49963602",
"text": "def generate_prediction(model, test_feats):\r\n\tprint('predicting for', model)\r\n\tmodel = jl.load(model)\r\n\tpredictions = model.predict(test_feats)\r\n\treturn predictions",
"title": ""
},
{
"docid": "a07b6626e7ce0c8350c1764e5cceee84",
"score": "0.49930802",
"text": "def train_predict(model_list, xtrain, ytrain, xtest, ytest):\n P = np.zeros((ytest.shape[0], len(model_list)))\n P = pd.DataFrame(P)\n\n print(\"Fitting models.\")\n cols = list()\n for i, (name, m) in enumerate(models.items()):\n print(\"%s...\" % name, end=\" \", flush=False)\n m.fit(xtrain, ytrain)\n P.iloc[:, i] = m.predict_proba(xtest)[:, 1]\n cols.append(name)\n print(\"done\")\n\n P.columns = cols\n print(\"Done.\\n\")\n return P",
"title": ""
},
{
"docid": "783bf22061710c5326bffee929ef96f4",
"score": "0.4992512",
"text": "def map(model, x_test, y_test, transcripts):\n start = datetime.now()\n y_pred = model.predict(x_test)\n y_pred = np.where(y_pred<0.5, 0, 1)\n print (\"Time taken to predict all data: \", datetime.now()-start)\n start = datetime.now() \n N = len(transcripts)\n precision = {}\n count = {}\n for i in range(N):\n if transcripts[i] not in precision.keys():\n precision[transcripts[i]] = 1\n count[transcripts[i]] = 0\n else:\n precision[transcripts[i]] += 1\n\n for i in range(N):\n pred = y_pred[i]\n acc = np.sum(abs(y_test-pred), axis=1)\n tmp = np.argmin(acc)\n if transcripts[tmp] == transcripts[i]:\n count[transcripts[tmp]] += 1\n\n mean_avg_prec = [0, 0]\n for i in range(N):\n if precision[transcripts[i]] <= 1:\n continue\n mean_avg_prec[0] += count[transcripts[i]]*1.0/precision[transcripts[i]] \n mean_avg_prec[1] += 1\n\n print (\"Time taken to calculate l2 dist: \", datetime.now() - start)\n print (\"The Mean Average Precision = \", mean_avg_prec[0]*1./mean_avg_prec[1])\n print (\"Total test cases = \", N)",
"title": ""
},
{
"docid": "a73b48e8fa302453f551f7e225844673",
"score": "0.4989169",
"text": "def extract_prediction(self, all_states):\n pass",
"title": ""
},
{
"docid": "ca5c9ce94f6f0a6d34886700bfc6c554",
"score": "0.49871138",
"text": "def predict_proba(self,x_test): # Meta probability output\r\n y_pred = self._predict_meta(x_test)\r\n if self.save_blend_sets is not None:\r\n self._save_array(y_pred,'blend_pred')\r\n return(y_pred)",
"title": ""
},
{
"docid": "240e18d49e12576c78ac8805de3bf6b1",
"score": "0.49851882",
"text": "def test_linear(self):\n q = np.linspace(0,1,1000)\n xy = np.array([[2,3]]).T * q\n print(xy)\n graph = tf.Graph()\n with graph.as_default():\n stream = atu.make_datastream(\n dataset,batch_size=1000,buffer_size=1000)\n tr_x = stream #tf.transpose(stream)\n #au = Autoencoder(2,1,tr_x)\n au = PolyAutoencoder(2,1,tr_x, 1,1)\n init=tf.global_variables_initializer()",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="744"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">b15d38c3c411fae60885cd3a5ea2f768</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Here we know the architecture num_nodes = num_inter + 2, so we add another 1</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "6f301c58d3a55bc85294f05fb5ae76fc",
"score": "0.0",
"text": "def generate_arch(self, n, num_nodes, num_ops=8):\n # def _get_arch():\n # arch = []\n # for i in range(2, num_nodes):\n # p1 = np.random.randint(0, i)\n # op1 = np.random.randint(0, num_ops)\n # p2 = np.random.randint(0, i)\n # op2 = np.random.randint(0 ,num_ops)\n # arch.extend([p1, op1, p2, op2])\n # return arch\n # archs = [_get_arch() + _get_arch() for i in range(n)] #[[[conv],[reduc]]]\n num_nodes = num_nodes or self.num_nodes\n archs = []\n ids = set()\n for _ in range(n):\n while True:\n mid, model_spec = self.dataset.random_topology()\n if mid not in ids:\n break\n archs.append(self.parse_model_spec_to_arch(model_spec))\n ids.add(mid)\n return archs",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "b090a3bd6217391aa1e78b910d578ed3",
"score": "0.5962054",
"text": "def number_of_edges(self):",
"title": ""
},
{
"docid": "7bbfecdfee7c69775885f676dc237ff0",
"score": "0.5906666",
"text": "def add_node(self, node_for_adding, **attr):\n\n self.num_states += 1\n\n return super(nx.MultiDiGraph, self).add_node(node_for_adding, **attr)",
"title": ""
},
{
"docid": "9f3e4b6c6e9bbc05a60f8ab82446eebb",
"score": "0.5870382",
"text": "def mutate_add_node(self, config):\r\n if config.max_node_num - config.num_inputs - config.num_outputs > len(self.nodes):\r\n super().mutate_add_node(config)",
"title": ""
},
{
"docid": "e3da6fbea8acf77d52cc54ba10e2ea0b",
"score": "0.58630127",
"text": "def _topology_computation(self, nx: int):\n for cell in range(self.num_cells):\n line = cell // nx\n rem = cell % nx\n self.cells[cell] = [\n line * (nx + 1) + rem,\n line * (nx + 1) + rem + 1,\n (line + 1) * (nx + 1) + rem,\n (line + 1) * (nx + 1) + rem + 1,\n ]",
"title": ""
},
{
"docid": "1f896ec6a1b74e9e36aea0835ed4c929",
"score": "0.58618486",
"text": "def count_nodes(self) -> int:\n return 1",
"title": ""
},
{
"docid": "159e79ea5f5233c87b94980f0b20fced",
"score": "0.57114625",
"text": "def num_nodes(self) -> int:\n raise NotImplementedError",
"title": ""
},
{
"docid": "6765850c639cba7038ca2f35ab4fc298",
"score": "0.5702105",
"text": "def p2(binary_in): # return labels_out\n label_count = 1\n num_rows = len(binary_in)\n num_cols = len(binary_in[0])\n s = (num_rows, num_cols)\n labels_out = np.zeros(s)\n equivalence_relationships = UnionFind()\n for i in range(num_rows):\n for j in range(num_cols):\n curr = binary_in[i][j]\n nw = binary_in[i - 1][j - 1]\n n = binary_in[i - 1][j]\n w = binary_in[i][j - 1]\n\n if curr == 0:\n labels_out[i][j] = 0\n elif nw == 0 and n == 0 and w == 0:\n label_count += 1\n labels_out[i][j] = label_count\n equivalence_relationships[label_count] # create new set\n elif labels_out[i - 1][j - 1] != 0:\n labels_out[i][j] = labels_out[i - 1][j - 1]\n elif nw == 0 and n == 0 and labels_out[i][j - 1] != 0:\n labels_out[i][j] = labels_out[i][j - 1]\n elif nw == 0 and w == 0 and labels_out[i - 1][j] != 0:\n labels_out[i][j] = labels_out[i - 1][j]\n elif nw == 0 and labels_out[i][j - 1] != 0 and labels_out[i - 1][j] != 0:\n labels_out[i][j] = labels_out[i - 1][j]\n set_a = equivalence_relationships[labels_out[i][j - 1]]\n set_b = equivalence_relationships[labels_out[i - 1][j]]\n if set_a != set_b:\n equivalence_relationships.union(equivalence_relationships[labels_out[i][j - 1]],\\\n equivalence_relationships[labels_out[i - 1][j]])\n new_label_dict = {}\n # print(equivalence_relationships)\n start_label = 70\n count = 1\n offset = 30\n for i in range(num_rows):\n for j in range(num_cols):\n label = 0\n curr = labels_out[i][j]\n curr_set = equivalence_relationships[curr]\n if curr == 0:\n labels_out[i][j] = 0\n else:\n if not new_label_dict.has_key(curr_set):\n count += 1\n new_label_dict[curr_set] = start_label + count * offset\n # new_label_dict[curr_set] = count\n labels_out[i][j] = new_label_dict[curr_set]\n return labels_out",
"title": ""
},
{
"docid": "fbd0dff8f3f346dbce52889ef0898554",
"score": "0.5679378",
"text": "def n_nodes(self):\n return self.nx * self.ny",
"title": ""
},
{
"docid": "d92224d203d6ecec43e1d5aa6ed8ecc9",
"score": "0.5650376",
"text": "def get_final_architecture(self):\n graph = self.graph.clone().unparse()\n graph.prepare_discretization()\n normalization_exponent=self.normalization_exponent\n def update_l2_weights(edge):\n \"\"\"\n For operations like SepConv etc that contain suboperations like Conv2d() etc. the square of \n l2 norm of the weights is stored in the corresponding weights shared attribute.\n Suboperations like ReLU are ignored as they have no weights of their own.\n For operations (not suboperations) like Identity() etc. that do not have weights,\n the weights attached to them are used.\n \"\"\" \n if edge.data.has(\"alpha\"):\n weight=0.0\n group_dim=torch.zeros(1)\n for i in range(len(edge.data.op.primitives)):\n try:\n for j in range(len(edge.data.op.primitives[i].op)):\n try: \n group_dim += torch.numel(edge.data.op.primitives[i].op[j].weight)\n weight+= (torch.norm(edge.data.op.primitives[i].op[j].weight,2)**2).item()\n except (AttributeError, TypeError) as e:\n try:\n for k in range(len(edge.data.op.primitives[i].op[j].op)): \n group_dim += torch.numel(edge.data.op.primitives[i].op[j].op[k].weight)\n weight+= (torch.norm(edge.data.op.primitives[i].op[j].op[k].weight,2)**2).item()\n except AttributeError:\n continue \n edge.data.weights[i]+=weight\n edge.data.dimension[i]+=group_dim.item() \n weight=0.0\n group_dim=torch.zeros(1)\n except AttributeError: \n size=torch.tensor(torch.numel(edge.data.op.primitives[i].weight)) \n edge.data.weights[i]+=(edge.data.op.primitives[i].weight.item())**2\n edge.data.dimension[i]+=size\n \n def normalize_weights(edge):\n if edge.data.has(\"alpha\"):\n for i in range(len(edge.data.op.primitives)):\n edge.data.weights[i]=torch.sqrt(edge.data.weights[i])/torch.pow(edge.data.dimension[i], normalization_exponent).item()\n\n \n def prune_weights(edge):\n \"\"\"\n Operations whose l2 norm of the weights across all cells of the same\n type (normal or reduced) is less than the threshold are pruned away.\n To achieve this, the alpha flag for the corresponding operation is\n turned off (replaced with zero).\n \"\"\"\n if edge.data.has(\"alpha\"): \n for i in range(len(edge.data.weights)):\n if torch.sqrt(edge.data.weights[i]) < self.threshold:\n edge.data.alpha[i]=0 \n \n def reinitialize_l2_weights(edge):\n if edge.data.has(\"alpha\"): \n for i in range(len(edge.data.weights)):\n edge.data.weights[i]=0\n edge.data.dimension[i]=0\n\n def discretize_ops(edge):\n if edge.data.has(\"alpha\"):\n primitives = edge.data.op.get_embedded_ops()\n alphas = edge.data.alpha.detach().cpu()\n \"\"\"\n The next 2 lines of code is just to make sure only 1 operation is chosen per edge\n so that the resulting architecture is comparable to other optimizers and \n queryable from the benchmark.\n \"\"\"\n weights= edge.data.weights.detach().cpu()\n alphas = torch.nn.Parameter(torch.zeros(size=[len(alphas)], requires_grad=False), requires_grad=False)\n alphas[torch.argmax(weights)]=1\n \"\"\"\n Only the operations whose alpha are non-zero are retained,\n others are pruned away. If on an edge, more than 1 operations \n are to be retained, then the operation of the edge is set to a MixedOp\n of these operations.\n \"\"\"\n positions = alphas.nonzero() \n if len(positions)>1:\n operations=[]\n for pos in positions:\n operations.append(primitives[pos])\n edge.data.set(\"op\", GSparseMixedOp(operations))\n else: \n edge.data.set(\"op\", primitives[positions.item()])\n\n # Detailed description of the operations are provided in the functions.\n graph.update_edges(update_l2_weights, scope=self.scope, private_edge_data=True) \n graph.update_edges(normalize_weights, scope=self.scope, private_edge_data=True)\n #graph.update_edges(prune_weights, scope=self.scope, private_edge_data=True)\n \n graph.update_edges(discretize_ops, scope=self.scope, private_edge_data=True)\n graph.update_edges(reinitialize_l2_weights, scope=self.scope, private_edge_data=False)\n graph.prepare_evaluation()\n graph.parse()\n #graph.QUERYABLE=False\n graph = graph.to(self.device)\n return graph",
"title": ""
},
{
"docid": "283a62de7b7f5e3679ed1ce5bb5623f8",
"score": "0.5643571",
"text": "def __init__(self, num_nodes):\n self._num_nodes = num_nodes \n self._node_numbers = [node for node in range(num_nodes) for dummy_idx in range(num_nodes)]",
"title": ""
},
{
"docid": "0e7f49f27115b645631671908b6ffa37",
"score": "0.5634099",
"text": "def __init__(self,num_steps):\r\n self._num_nodes = num_steps*(num_steps+1)/2\r\n self._nodes = np.zeros(self._num_nodes)",
"title": ""
},
{
"docid": "fa4332faf7662110945082a03812396b",
"score": "0.5623917",
"text": "def computeConnectedComponentLabeling(pixel_array, image_width, image_height):\n\n # def dfs(x, y, old, new, val, label):\n # if (x<0) or (y<0) or (x>=image_height) or (y>=image_width) or (old[x][y]==0) or (new[x][y] != 0):\n # return 0\n # count = 1\n # old[x][y] = 0\n # new[x][y] = label\n \n # count += dfs(x-1, y, old, new, val, label)\n # count += dfs(x+1, y, old, new, val, label)\n # count += dfs(x, y-1, old, new, val, label)\n # count += dfs(x, y+1, old, new, val, label)\n # return count\n \n # label = 1\n # d = {}\n # old = pixel_array\n # new = [[0 for _ in range(image_width)] for _ in range(image_height)]\n # for x in range(image_height):\n # for y in range(image_width):\n # if old[x][y] != 0:\n # d[label] = dfs(x, y, old, new, old[x][y], label)\n # label += 1\n \n # return new, d\n \n label = 1\n d = {}\n old = pixel_array\n new = ImageProcessor.createInitializedGreyscalePixelArray(image_width, image_height)\n visited = ImageProcessor.createInitializedGreyscalePixelArray(image_width, image_height)\n \n for x in range(image_height):\n for y in range(image_width):\n \n if old[x][y] != 0 and visited[x][y] == 0: # Find a pixel yet not visited\n count = 0\n q = Queue()\n q.enqueue((x,y)) # Add to the queue\n \n while not q.isEmpty():\n (a, b) = q.dequeue()\n new[a][b] = label\n visited[a][b] = 1\n count += 1\n \n # Visit neighbouring pixels that haven't visited\n if (b-1 >= 0) and (old[a][b-1] != 0) and (visited[a][b-1] == 0):\n q.enqueue((a,b-1))\n visited[a][b-1] = 1\n if (b+1 < image_width) and (old[a][b+1] != 0) and (visited[a][b+1] == 0):\n q.enqueue((a,b+1))\n visited[a][b+1] = 1\n if (a-1 >= 0) and (old[a-1][b] != 0) and (visited[a-1][b] == 0):\n q.enqueue((a-1,b))\n visited[a-1][b] = 1\n if (a+1 < image_height) and (old[a+1][b] != 0) and (visited[a+1][b] == 0):\n q.enqueue((a+1,b))\n visited[a+1][b] = 1\n \n # Finished counting the current component region, set the dictionary\n d[label] = count\n label += 1\n\n return new, d",
"title": ""
},
{
"docid": "6b6f14f14c2b3ef8fbc7e3a2507e20ec",
"score": "0.56174594",
"text": "def add_node_g(self, neti: int, node: Node) -> int:\r\n pass",
"title": ""
},
{
"docid": "0b0728bbef83d7954ac2cdd12db645e4",
"score": "0.5614157",
"text": "def add_nodes(self, nodes: int = 1):\n for _ in range(nodes):\n self._evolve()\n pos = self._collapse_walkers()\n\n self.wave_functions = np.pad(self.wave_functions, ((0,0),(0,1)))\n\n self.data += [1,1]*self.walkers\n self.row += [self.nodes]*self.walkers + pos\n self.col += pos + [self.nodes]*self.walkers\n self.adjacency_matrix_ = None\n \n # reset lazy properties \n self.diameter_ = None\n self.degree_distribution_ = None\n self.leaf_fraction_ = None\n self.clustering_coefficient_ = None",
"title": ""
},
{
"docid": "3e9fe33378433b21d08f0179a11e0572",
"score": "0.55988777",
"text": "def __init__(self, num_nodes):\r\n self._num_nodes = num_nodes\r\n self._node_numbers = [node for node in range(num_nodes) for dummy_idx in range(num_nodes)]",
"title": ""
},
{
"docid": "aacc16e62d6fa652704df8b4ed9116bc",
"score": "0.5598683",
"text": "def add_nodes(self, how_many):",
"title": ""
},
{
"docid": "2375aa2f458d12dd5e992d009841fe92",
"score": "0.55907315",
"text": "def nx_graph(self):\n self.nx_labels = ...",
"title": ""
},
{
"docid": "c7770c455e840c33d7ca1756b31cc657",
"score": "0.5541816",
"text": "def generate_nodes(self):\n \n start_time = time.time()\n print('Computing nodes.. ', end = '')\n \n # n-D grid of node ID\n self.node_id_from_index = np.zeros( self.x_grid_dim , dtype = int ) # grid of node ID\n \n # 1-D List of nodes\n self.state_from_node_id = np.zeros(( self.nodes_n , self.sys.n ), dtype = float ) # Number of nodes x state dimensions\n self.index_from_node_id = np.zeros(( self.nodes_n , self.sys.n ), dtype = int ) # Number of nodes x state dimensions\n \n # For all state nodes\n node_id = 0\n \n if self.sys.n == 2 :\n \n for i in range(self.x_grid_dim[0]):\n for j in range(self.x_grid_dim[1]):\n \n # State\n x = np.array([ self.x_level[0][i] , self.x_level[1][j] ])\n \n # State and grid index based on node id\n self.state_from_node_id[ node_id , : ] = x\n self.index_from_node_id[ node_id , : ] = np.array([i,j])\n \n # Node # based on index ij\n self.node_id_from_index[i,j] = node_id\n \n # Increment node number\n node_id = node_id + 1\n \n \n elif self.sys.n == 3:\n \n for i in range(self.x_grid_dim[0]):\n for j in range(self.x_grid_dim[1]):\n for k in range(self.x_grid_dim[2]):\n \n # State\n x = np.array([ self.x_level[0][i] , self.x_level[1][j] , self.x_level[2][k] ])\n \n # State and grid index based on node #\n self.state_from_node_id[ node_id , : ] = x\n self.index_from_node_id[ node_id , : ] = np.array([i,j,k])\n \n # Node # based on index ijk\n self.node_id_from_index[i,j,k] = node_id\n \n # Increment node number\n node_id = node_id + 1\n \n \n \n elif self.sys.n == 4:\n \n for i in range(self.x_grid_dim[0]):\n for j in range(self.x_grid_dim[1]):\n for k in range(self.x_grid_dim[2]):\n for l in range(self.x_grid_dim[3]):\n \n # State\n x = np.array([ self.x_level[0][i] , self.x_level[1][j] , self.x_level[2][k] , self.x_level[3][l]])\n \n # State and grid index based on node #\n self.state_from_node_id[ node_id , : ] = x\n self.index_from_node_id[ node_id , : ] = np.array([i,j,k,l])\n \n # Node # based on index ijkl\n self.node_id_from_index[i,j,k,l] = node_id\n \n # Increment node number\n node_id = node_id + 1\n \n else:\n \n raise NotImplementedError\n \n # Print update\n computation_time = time.time() - start_time\n print('completed in %4.2f sec'%computation_time)",
"title": ""
},
{
"docid": "13af5280b2504eeeee4a9df9ee919603",
"score": "0.55391264",
"text": "def M(self) -> int:\n return self.num_nodes",
"title": ""
},
{
"docid": "9274013c23db8cfcb738b068a7a3a7cc",
"score": "0.54769623",
"text": "def __init__(self, num_nodes):\n self._num_nodes = num_nodes\n self._node_numbers = [node for node in range(num_nodes) for dummy_idx in range(num_nodes)]",
"title": ""
},
{
"docid": "9274013c23db8cfcb738b068a7a3a7cc",
"score": "0.54769623",
"text": "def __init__(self, num_nodes):\n self._num_nodes = num_nodes\n self._node_numbers = [node for node in range(num_nodes) for dummy_idx in range(num_nodes)]",
"title": ""
},
{
"docid": "96f6cc53d0c3fdfd2740d7862d22f556",
"score": "0.5466392",
"text": "def update_dependencies():\n\n ###\n ### Putting together network structure\n ###\n\n # Turn excitatory-inhibitory settings on or off\n if par['architecture'] == 'BIO':\n par['EI'] = True if par['exc_inh_prop'] < 1 else False\n elif par['architecture'] == 'LSTM':\n print('Using LSTM networks; setting to EI to False')\n par['EI'] = False\n par['exc_inh_prop'] = 1.\n par['synapse_config'] = None\n par['spike_cost'] = 0.\n\n # Generate EI matrix\n par['num_exc_units'] = int(np.round(par['n_hidden']*par['exc_inh_prop']))\n par['num_inh_units'] = par['n_hidden'] - par['num_exc_units']\n par['EI_list'] = np.ones(par['n_hidden'], dtype=np.float32)\n if par['EI']:\n n = par['n_hidden']//par['num_inh_units']\n par['ind_inh'] = np.arange(n-1,par['n_hidden'],n)\n par['EI_list'][par['ind_inh']] = -1.\n par['EI_matrix'] = np.diag(par['EI_list'])\n\n # Number of output neurons\n par['n_output'] = par['num_motion_dirs'] + 1\n par['n_pol'] = par['num_motion_dirs'] + 1\n\n # Number of input neurons\n par['n_input'] = par['num_motion_tuned'] + par['num_fix_tuned'] + par['num_rule_tuned']\n\n # General network shape\n par['shape'] = (par['n_input'], par['n_hidden'], par['n_output'])\n\n # Specify time step in seconds and neuron time constant\n par['dt_sec'] = par['dt']/1000\n par['alpha_neuron'] = np.float32(par['dt'])/par['membrane_time_constant']\n\n # Generate noise deviations\n par['noise_rnn'] = np.sqrt(2*par['alpha_neuron'])*par['noise_rnn_sd']\n par['noise_in'] = np.sqrt(2/par['alpha_neuron'])*par['noise_in_sd']\n\n # Set trial step length\n par['num_time_steps'] = par['multistim_trial_length']//par['dt']\n\n # Set up gating vectors for hidden layer\n gen_gating()\n\n ###\n ### Setting up weights, biases, masks, etc.\n ###\n\n # Specify initial RNN state\n par['h_init'] = 0.1*np.ones((par['batch_size'], par['n_hidden']), dtype=np.float32)\n\n # Initialize weights\n c = 0.05\n\n par['W_in_init'] = c*np.float32(np.random.gamma(shape=0.25, scale=1.0, size = [par['n_input'], par['n_hidden']]))\n par['W_out_init'] = np.float32(np.random.uniform(-c, c, size = [par['n_hidden'], par['n_output']]))\n\n if par['EI']:\n par['W_rnn_init'] = c*np.float32(np.random.gamma(shape=0.25, scale=1.0, size = [par['n_hidden'], par['n_hidden']]))\n par['W_rnn_mask'] = np.ones((par['n_hidden'], par['n_hidden']), dtype=np.float32) - np.eye(par['n_hidden'])\n par['W_rnn_init'] *= par['W_rnn_mask']\n else:\n par['W_rnn_init'] = np.float32(np.random.uniform(-c, c, size = [par['n_hidden'], par['n_hidden']]))\n par['W_rnn_mask'] = np.ones((par['n_hidden'], par['n_hidden']), dtype=np.float32)\n\n # Initialize biases\n par['b_rnn_init'] = np.zeros((1,par['n_hidden']), dtype = np.float32)\n par['b_out_init'] = np.zeros((1,par['n_output']), dtype = np.float32)\n\n # Specify masks\n par['W_out_mask'] = np.ones((par['n_hidden'], par['n_output']), dtype=np.float32)\n par['W_in_mask'] = np.ones((par['n_input'], par['n_hidden']), dtype=np.float32)\n if par['EI']:\n par['W_out_init'][par['ind_inh'], :] = 0\n par['W_out_mask'][par['ind_inh'], :] = 0\n\n # Initialize RL-specific weights\n par['W_pol_out_init'] = np.float32(np.random.uniform(-c, c, size = [par['n_hidden'], par['n_pol']]))\n par['b_pol_out_init'] = np.zeros((1,par['n_pol']), dtype = np.float32)\n\n par['W_val_out_init'] = np.float32(np.random.uniform(-c, c, size = [par['n_hidden'], par['n_val']]))\n par['b_val_out_init'] = np.zeros((1,par['n_val']), dtype = np.float32)\n\n ###\n ### Setting up LSTM weights and biases, if required\n ###\n\n if par['architecture'] == 'LSTM':\n c = 0.05\n par['Wf_init'] = np.float32(np.random.uniform(-c, c, size = [par['n_input'], par['n_hidden']]))\n par['Wi_init'] = np.float32(np.random.uniform(-c, c, size = [par['n_input'], par['n_hidden']]))\n par['Wo_init'] = np.float32(np.random.uniform(-c, c, size = [par['n_input'], par['n_hidden']]))\n par['Wc_init'] = np.float32(np.random.uniform(-c, c, size = [par['n_input'], par['n_hidden']]))\n\n par['Uf_init'] = np.float32(np.random.uniform(-c, c, size = [par['n_hidden'], par['n_hidden']]))\n par['Ui_init'] = np.float32(np.random.uniform(-c, c, size = [par['n_hidden'], par['n_hidden']]))\n par['Uo_init'] = np.float32(np.random.uniform(-c, c, size = [par['n_hidden'], par['n_hidden']]))\n par['Uc_init'] = np.float32(np.random.uniform(-c, c, size = [par['n_hidden'], par['n_hidden']]))\n\n\n par['bf_init'] = np.zeros((1, par['n_hidden']), dtype = np.float32)\n par['bi_init'] = np.zeros((1, par['n_hidden']), dtype = np.float32)\n par['bo_init'] = np.zeros((1, par['n_hidden']), dtype = np.float32)\n par['bc_init'] = np.zeros((1, par['n_hidden']), dtype = np.float32)\n\n ###\n ### Setting up synaptic plasticity parameters\n ###\n\n \"\"\"\n 0 = static\n 1 = facilitating\n 2 = depressing\n \"\"\"\n\n par['synapse_type'] = np.zeros(par['n_hidden'], dtype=np.int8)\n\n # only facilitating synapses\n if par['synapse_config'] == 'stf':\n par['synapse_type'] = np.ones(par['n_hidden'], dtype=np.int8)\n\n # only depressing synapses\n elif par['synapse_config'] == 'std':\n par['synapse_type'] = 2*np.ones(par['n_hidden'], dtype=np.int8)\n\n # even numbers facilitating, odd numbers depressing\n elif par['synapse_config'] == 'std_stf':\n par['synapse_type'] = 2*np.ones(par['n_hidden'], dtype=np.int8)\n ind = range(1,par['n_hidden'],2)\n #par['synapse_type'][par['ind_inh']] = 1\n par['synapse_type'][ind] = 1\n\n par['alpha_stf'] = np.ones((par['n_hidden'], 1), dtype=np.float32)\n par['alpha_std'] = np.ones((par['n_hidden'], 1), dtype=np.float32)\n par['U'] = np.ones((par['n_hidden'], 1), dtype=np.float32)\n\n # initial synaptic values\n par['syn_x_init'] = np.zeros((par['n_hidden'], par['batch_size']), dtype=np.float32)\n par['syn_u_init'] = np.zeros((par['n_hidden'], par['batch_size']), dtype=np.float32)\n\n for i in range(par['n_hidden']):\n if par['synapse_type'][i] == 1:\n par['alpha_stf'][i,0] = par['dt']/par['tau_slow']\n par['alpha_std'][i,0] = par['dt']/par['tau_fast']\n par['U'][i,0] = 0.15\n par['syn_x_init'][i,:] = 1\n par['syn_u_init'][i,:] = par['U'][i,0]\n\n elif par['synapse_type'][i] == 2:\n par['alpha_stf'][i,0] = par['dt']/par['tau_fast']\n par['alpha_std'][i,0] = par['dt']/par['tau_slow']\n par['U'][i,0] = 0.45\n par['syn_x_init'][i,:] = 1\n par['syn_u_init'][i,:] = par['U'][i,0]\n\n par['alpha_stf'] = np.transpose(par['alpha_stf'])\n par['alpha_std'] = np.transpose(par['alpha_std'])\n par['U'] = np.transpose(par['U'])\n par['syn_x_init'] = np.transpose(par['syn_x_init'])\n par['syn_u_init'] = np.transpose(par['syn_u_init'])",
"title": ""
},
{
"docid": "de6a460ff5b7c6c410e4ce4ce594e3f3",
"score": "0.5444504",
"text": "def totalNumberOfNodes(height):\n return 2 ** (height + 1) - 1",
"title": ""
},
{
"docid": "986229284f29937245634dbcf50a8e18",
"score": "0.5414974",
"text": "def karate_rule(base, new):\n # If the base graph has multiple components, first attempt to unify them into a single component\n if nx.components.number_connected_components(base)>1:\n comps=nx.connected_components(base)\n # Select two random components and form bridge with new structure\n rand_comps=random_integers(low=0,high=len(comps)-1,size=2)\n # Select random node from each component\n rand0=comps[rand_comps[0]][random_integers(low=0,high=len(comps[rand_comps[0]])-1)]\n rand1=comps[rand_comps[1]][random_integers(low=0,high=len(comps[rand_comps[1]])-1)]\n while rand0==rand1:\n rand1=comps[rand_comps[1]][random_integers(low=0,high=len(comps[rand_comps[1]])-1)]\n outer_bound=[rand0,rand1]\n outer_bound.extend(range(base.number_of_nodes(),base.number_of_nodes()+((new.number_of_nodes())-1)))\n mapping=dict(zip(new.nodes(),outer_bound))\n new=nx.relabel_nodes(new,mapping)\n else:\n # Use Eigenvector centrality as pref attachment attribute\n cent=nx.eigenvector_centrality_numpy(base).items()\n # Normalize values to sum to 1.0\n norm_const=sum([(b) for (a,b) in cent])\n pref_prob=[(b/norm_const) for (a,b) in cent]\n # Step through probability mass to find a node to attach to. Same method used in \n # gmm.algorithms.draw_structure to select a probability weighted motif from the set.\n draw=uniform()\n node_index=0\n mass_sum=pref_prob[node_index]\n while draw>mass_sum:\n node_index+=1\n mass_sum+=pref_prob[node_index]\n rand0=cent[node_index][0] # Return the appropriate node ID\n outer_bound=[rand0]\n outer_bound.extend(range(base.number_of_nodes(),base.number_of_nodes()+((new.number_of_nodes())-1)))\n mapping=dict(zip(new.nodes(),outer_bound))\n new=nx.relabel_nodes(new,mapping)\n return nx.compose(base,new)",
"title": ""
},
{
"docid": "d173f459aeeaa44664f00c402d946079",
"score": "0.54054993",
"text": "def merge_nodes_old(nodes_real, nodes_unreal, radius, image_bin):\n local_messages = [\"merge_nodes\"]\n local_images = []\n next = nodes_real + nodes_unreal\n couples, singles = separate_indices(next, radius)\n new_index = - len(nodes_unreal) - 1\n image_display = get_binary_image(image_bin.copy(), 0, 255)\n '''\n for n in next:\n cv2.circle(image_display, (int(n.location[0]), int(n.location[1])), 5, 255)\n cv2.putText(image_display, str(n.index), (int(n.location[0] + 2),\\\n int(n.location[1]) + 2), cv2.FONT_HERSHEY_SIMPLEX, 0.5, 255,\\\n 1, cv2.LINE_AA, False)\n local_images.append(image_display.copy())\n '''\n while len(couples) > 0:\n prev = next\n next = []\n for pair in couples:\n new_node, new_index = merge_2_nodes(prev[pair[0]], prev[pair[1]], new_index)\n # new_node, new_index = merge_n_nodes([prev[pair[0]], prev[pair[1]]], new_index)\n next.append(new_node) \n for i in singles:\n next.append(prev[i])\n '''\n image_display = get_binary_image(image_bin.copy(), 0, 255)\n for n in next:\n cv2.circle(image_display, (int(n.location[0]), int(n.location[1])), 5, 255)\n cv2.putText(image_display, str(n.index), (int(n.location[0] + 2),\\\n int(n.location[1]) + 2), cv2.FONT_HERSHEY_SIMPLEX, 0.5, 255,\\\n 1, cv2.LINE_AA, False)\n local_images.append(image_display.copy())\n '''\n couples, singles = separate_indices(next, radius)\n '''\n for image in local_images:\n cv2.imshow(\"image_show\", image)\n cv2.waitKey()\n '''\n local_messages.append(END_OF_FUNCTION)\n return next",
"title": ""
},
{
"docid": "5350dbe22f2de87ea2d4561099310d7d",
"score": "0.53968716",
"text": "def complex_network_mapping(graph):\n vect = [] \n \n n = nx.number_of_nodes(graph)\n e = nx.number_of_edges(graph)\n print n,e\n \n adj = np.array(nx.adjacency_matrix(graph))\n adj_bin = np.where(adj>0, 1., 0.)\n adj_conn = 1 - adj\n \n #Node Betweenness binary\n bt_bin = betweenness_bin(adj_bin)\n bt_bin = bt_bin/((n-1)*(n-2))\n avg_btb = np.mean(bt_bin)\n vect.append(avg_btb)\n\n #Node betweenness weighted\n bt_wei = betweenness_wei(adj_conn)\n bt_wei = bt_wei/((n-1)*(n-2))\n avg_btw = np.mean(bt_wei)\n vect.append(avg_btw)\n \n #Edge betweenness binary\n ebt_bin,_ = edge_betweenness_bin(adj_bin)\n ebt_bin = ebt_bin/((n-1)*(n-2))\n avg_ebtb = np.mean(ebt_bin)\n vect.append(avg_ebtb)\n \n #Edge betweenness weighted\n ebt_wei,_ = edge_betweenness_wei(adj_conn)\n ebt_wei = ebt_wei/((n-1)*(n-2))\n avg_ebtw = np.mean(ebt_wei)\n vect.append(avg_ebtw)\n \n #Eigen vector centrality binary \n evc_bin = eigenvector_centrality_und(adj_bin)\n avg_evcb = np.mean(evc_bin)\n vect.append(avg_evcb)\n \n #Eigen vector centrality weighted \n evc_bin = eigenvector_centrality_und(adj)\n avg_evcb = np.mean(evc_bin)\n vect.append(avg_evcb)\n \n #Erange\n era_bin,_,_,_ = erange(adj_bin)\n avg_era = np.mean(era_bin)\n vect.append(avg_era)\n \n #Flow coefficient \n _,flow_bin,_ = flow_coef_bd(adj_bin)\n avg_flow = np.mean(flow_bin)\n vect.append(avg_flow)\n \n #Kcoreness centrality\n kcor_bin,_ = kcoreness_centrality_bu(adj_bin)\n avg_kcor = np.mean(kcor_bin)\n vect.append(avg_kcor)\n \n #Page rank centrality\n pgr_wei = pagerank_centrality(adj,d=0.85)\n avg_pgr = np.mean(pgr_wei)\n vect.append(avg_pgr)\n \n #Subgraph centrality\n# sgc_bin = subgraph_centrality(adj_bin)\n# avg_sgc = np.mean(sgc_bin)\n# vect.append(avg_sgc)\n \n return vect",
"title": ""
},
{
"docid": "9da57bc74135205c044fb25cab5fbc1e",
"score": "0.53836095",
"text": "def nearest_neighbor_conv_kernel(input, layer_conf, nout, name):\n\n wnames = ['Lself', 'LleftL', 'LdownL', 'LrightL', 'LupL', 'Sself', 'SleftS', 'SdownS', 'SrightS', 'SupS', 'LrightS0', 'LrightS1', 'LrightS2', 'LrightS3', 'LupS0', 'LupS1', 'LupS2', 'LupS3', 'SleftL', 'SdownL']\n\n adder = AddWeight(name, wnames, nout, input.shape[2])\n\n layer = 0\n adder.in_layer_offset = 0\n adder.out_layer_offset = 0\n for itype in range(4):\n base = bases[itype]\n\n nlargecol = base * 2\n nleft = nlargecol * base\n nlarge = nleft + base * base\n nsmallcol = base * 4\n nsmall = nsmallcol * nsmallcol\n ntotal = nlarge + nsmall\n\n while layer - sum(layer_conf[:itype]) < layer_conf[itype]:\n for i in range(nlarge):\n adder.out_index = i\n adder.add(i, 'Lself')\n \n if i < nlargecol:\n pass\n elif i < nleft + base:\n adder.add(i - nlargecol, 'LleftL')\n else:\n adder.add(i - base, 'LleftL')\n \n if (i < nleft and i % nlargecol != 0) or (i >= nleft and i % base != 0):\n adder.add(i - 1, 'LdownL')\n \n if i < nleft - base:\n adder.add(i + nlargecol, 'LrightL')\n elif i >= nleft and i < nlarge - base:\n adder.add(i + base, 'LrightL')\n \n if (i < nleft and i % nlargecol != (nlargecol - 1)) or (i >= nleft and i % base != (base - 1)):\n adder.add(i + 1, 'LupL')\n \n for i in range(nleft - base, nleft):\n adder.out_index = i\n offset = nlarge + (i - (nleft - base)) * 4\n \n adder.add(offset, 'LrightS0')\n adder.add(offset + 1, 'LrightS1')\n adder.add(offset + 2, 'LrightS2')\n adder.add(offset + 3, 'LrightS3')\n \n for i in range(nleft + base, nlarge, base):\n adder.out_index = i\n offset = nlarge + (i - (nleft + base)) * 16\n \n adder.add(offset, 'LupS0')\n adder.add(offset + base * 4, 'LupS1')\n adder.add(offset + base * 8, 'LupS2')\n adder.add(offset + base * 12, 'LupS3')\n \n for i in range(nlarge, ntotal):\n adder.out_index = i\n adder.add(i, 'Sself')\n \n if (i - nlarge) % nsmallcol != 0:\n adder.add(i - 1, 'SdownS')\n \n if i >= nlarge + nsmallcol:\n adder.add(i - nsmallcol, 'SleftS')\n \n if (i - nlarge) % nsmallcol != (nsmallcol - 1):\n adder.add(i + 1, 'SupS')\n \n if i < ntotal - nsmallcol:\n adder.add(i + nsmallcol, 'SrightS')\n \n for i in range(nlarge, nlarge + nsmallcol):\n adder.out_index = i\n adder.add((i - nlarge) // 4 + nleft - base, 'SleftL')\n \n for i in range(nlarge, ntotal, nsmallcol):\n adder.out_index = i\n adder.add(((i - nlarge) // (4 * nsmallcol)) * base + nleft + base, 'SdownL')\n\n adder.in_layer_offset += layer_sizes[itype]\n adder.out_layer_offset += layer_sizes[itype]\n layer += 1\n\n return adder.generate()",
"title": ""
},
{
"docid": "a826f0031590359907476068f2dd33a9",
"score": "0.53818583",
"text": "def build_inn(self):\n \n nodes = [Ff.InputNode(self.in_ch, self.img_size[0], self.img_size[1],\n name='inp')]\n \n\n \n conditions = [Ff.ConditionNode(4*self.in_ch, # 4\n int(1/2*self.img_size[0]),\n int(1/2*self.img_size[1]), \n name='cond_1'),\n Ff.ConditionNode(8*self.in_ch, # 16\n int(1/4*self.img_size[0]),\n int(1/4*self.img_size[1]), \n name='cond_2'), \n Ff.ConditionNode(16*self.in_ch, # 32\n int(1/8*self.img_size[0]),\n int(1/8*self.img_size[1]),\n name='cond_3')]\n \n split_nodes = []\n \n # 1 x 128 x 128 -> 4 x 64 x 64 (1/2)\n _add_downsample(nodes, 'invertible', in_ch=self.in_ch, coupling=self.coupling)\n\n # Condition level 0\n _add_conditioned_section(nodes, depth=6, in_ch=4*self.in_ch, \n cond_level=0, conditions=conditions, coupling=self.coupling)\n\n # 4 x 64 x 64 -> 16 x 32 x 32 (1/4)\n _add_downsample(nodes, 'invertible', in_ch=4*self.in_ch, coupling=self.coupling)\n\n # Condition level 1\n _add_conditioned_section(nodes, depth=6, in_ch=16*self.in_ch, \n cond_level=1, conditions=conditions, coupling=self.coupling)\n\n # 16 x 32 x 32 -> 64 x 16 x 16 (1/8)\n _add_downsample(nodes, 'invertible', in_ch=16*self.in_ch, coupling=self.coupling)\n \n # Split: each 32 x 16 x 16\n nodes.append(Ff.Node(nodes[-1], Fm.Split1D,\n {'split_size_or_sections':[32*self.in_ch,32*self.in_ch],\n 'dim':0}, name=\"split_1\"))\n split_nodes.append(Ff.Node(nodes[-1].out1, Fm.Flatten, {},\n name='flatten_split_1'))\n\n # Condition level 2\n _add_conditioned_section(nodes, depth=6, in_ch=32*self.in_ch, \n cond_level=2, conditions=conditions, coupling=self.coupling)\n \n # 32 x 16 x 16 -> 128 x 8 x 8 (1/16)\n _add_downsample(nodes, 'invertible', in_ch=32*self.in_ch, coupling=self.coupling)\n\n nodes.append(Ff.Node(nodes[-1].out0, Fm.Flatten, {}, name='flatten')) \n\n\n nodes.append(Ff.Node([s.out0 for s in split_nodes] + [nodes[-1].out0],\n Fm.Concat1d, {'dim':0}, name='concat_splits'))\n \n nodes.append(Ff.OutputNode(nodes[-1], name='out'))\n \n return Ff.ReversibleGraphNet(nodes + conditions + split_nodes,\n verbose=False)",
"title": ""
},
{
"docid": "0baf0e1f7cd68a6957ce1702f4dc0d52",
"score": "0.5377782",
"text": "def setup_layers(self):\n\t\t# self.nodes is the list of actors\n\t\tself.nodes = range(self.node_features.shape[0])\n\t\t# self.layers are the integration of layers(input_features, out_Features) -- dimensions\n\t\tself.layers = self.args.layers\n\t\t# self.num_layers are the number of layers\n\t\tself.num_layers = len(self.layers)\n\t\t# we should write the first layer of networks\n\t\tself.positive_firstlayer_aggregator = signedconvolutioninit(in_features = 2*self.node_features.size(1),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tout_features = self.layers[0]).to(self.device)\n\t\tself.negative_firstlayer_aggregator = signedconvolutioninit(in_features = 2*self.node_features.size(1),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tout_features = self.layers[0]).to(self.device)\n\t\t# construct collections of aggregators for balanced links and negative links.\n\t\tself.positive_aggregators = []\n\t\tself.negative_aggregators = []\n\t\tfor i in range(1, self.num_layers):\n\t\t\tself.positive_aggregators.append(signedconvolutiondeep(in_features = 3*self.layers[i-1],\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t out_features = self.layers[i]).to(self.device))\n\t\t\tself.negative_aggregators.append(signedconvolutiondeep(in_features = 3*self.layers[i-1],\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t out_features = self.layers[i]).to(self.device))\n\t\tself.positive_aggregators = ListModule(*self.positive_aggregators)\n\t\tself.negative_aggregators = ListModule(*self.negative_aggregators)\n\t\t# we have that the regression weight matrix is 4 * layers[-1], [zi,zj]--(zi=[hi,hj]))\n\t\tself.final_regression_weight = Parameter(torch.FloatTensor(4*self.layers[-1], 2))\n\t\tinit.xavier_uniform_(self.final_regression_weight)",
"title": ""
},
{
"docid": "75fd66112185c22d66840c226a0a151c",
"score": "0.53662384",
"text": "def som2Main(input, epoch, closeMethod,lr,pivot,sigma,problem,labels):\r\n n = input.shape[0]*pivot\r\n print(n)\r\n if problem == 1 or problem == 3:\r\n net=genNet(n,input.shape[1],problem)\r\n if problem == 2:\r\n n=15\r\n net = genNet(n,n,problem)\r\n activationMap = np.zeros((n,n))\r\n classMap = np.zeros((n,n,2))\r\n\r\n for i in range(epoch):\r\n if i % 1000 ==0:\r\n print(\"epoch: \" + str(i))\r\n r = np.random.randint(0,input.shape[0])\r\n selected=input[r]\r\n if problem == 2:\r\n label = labels[r]\r\n if label[0] == 1:\r\n label = 1\r\n elif label[1] == 1:\r\n label = 2\r\n elif label[2] == 1:\r\n label = 3\r\n if closeMethod=='manhattan':\r\n eucl=euclidean(selected,net,problem)\r\n if problem == 1 or problem ==3:\r\n minVal=min(eucl)\r\n\r\n minNdx = eucl.argmin()\r\n if problem == 2:\r\n minNdx = np.where(eucl == np.min(eucl))\r\n y = minNdx[0][0]\r\n x = minNdx[1][0]\r\n activationMap[y,x]+=1\r\n\r\n classMap[y,x,0] += int(label)\r\n classMap[y,x,1] += 1\r\n\r\n if problem == 1 or problem == 3:\r\n localGuass=localize(minNdx,n//sigma,net.shape[0],problem,net)\r\n net += lr * localGuass[:, np.newaxis] * (selected - net)\r\n if problem == 2:\r\n localGuass = localize((x,y), n // sigma, net.shape[0],problem,net)\r\n newGuass = np.dstack([localGuass,localGuass])\r\n for i in range(11):\r\n newGuass = np.dstack([newGuass,localGuass])\r\n a = lr*newGuass\r\n b = (selected - net)\r\n c = a*b\r\n net += c\r\n\r\n lr = lr * 0.9999\r\n n = n * 0.999\r\n if problem == 1 or problem == 3:\r\n return net\r\n if problem == 2:\r\n return activationMap,classMap",
"title": ""
},
{
"docid": "563cb86ae0e0d74edf27be7326c95d09",
"score": "0.5343948",
"text": "def branch_classification(thres):\n\n skeleton = skeletonize(thres)\n skel = Skeleton(skeleton, source_image=thres)\n summary = summarize(skel)\n\n is_main = np.zeros(summary.shape[0])\n us = summary['node-id-src']\n vs = summary['node-id-dst']\n ws = summary['branch-distance']\n\n edge2idx = {\n (u, v): i\n for i, (u, v) in enumerate(zip(us, vs))\n }\n\n edge2idx.update({\n (v, u): i\n for i, (u, v) in enumerate(zip(us, vs)) \n })\n\n g = nx.Graph()\n\n g.add_weighted_edges_from(\n zip(us, vs, ws)\n )\n\n for conn in nx.connected_components(g):\n curr_val = 0\n curr_pair = None\n h = g.subgraph(conn)\n p = dict(nx.all_pairs_dijkstra_path_length(h))\n for src in p:\n for dst in p[src]:\n val = p[src][dst]\n if (val is not None\n and np.isfinite(val)\n and val > curr_val):\n curr_val = val\n curr_pair = (src, dst)\n for i, j in tz.sliding_window(\n 2,\n nx.shortest_path(\n h, source=curr_pair[0], target=curr_pair[1], weight='weight'\n )\n ):\n is_main[edge2idx[(i, j)]] = 1\n\n summary['main'] = is_main\n\n # Branch Length Fraction\n\n total_length = np.sum(skeleton)\n trunk_length = 0\n \n \n for i in range(summary.shape[0]):\n if summary['main'][i]:\n trunk_length += summary['branch-distance'][i]\n \n\n branch_length = total_length - trunk_length\n BLF = branch_length/total_length\n\n \n\n return skel, is_main, BLF",
"title": ""
},
{
"docid": "96f50d65c96e3a0451a02645ab91f4d8",
"score": "0.53372717",
"text": "def get_node_increments_for_dense_hidden_dense_hidden_layers(self):\n\n # PROTIP: IF YOU WANT THE NODE INCREMENTATION TO BE SPACED DIFFERENTLY\n # THEN YOU'LL NEED TO CHANGE THE WAY THAT IT'S CALCULATED - HAVE FUN!\n # when set to True number of nodes are decreased for subsequent layers\n if self.negative_node_incrementation:\n # subtract this amount from previous layer's nodes in order to increment towards smaller numbers\n self.nodes_increment = (self.last_dense_hidden_layer_nodes - self.first_dense_hidden_layer_nodes) / (\n self.n_dense_hidden_layers - 1)\n\n # when set to False number of nodes are increased for subsequent layers\n else:\n # add this amount from previous layer's nodes in order to increment towards larger numbers\n self.nodes_increment = (self.first_dense_hidden_layer_nodes - self.last_dense_hidden_layer_nodes) / (\n self.n_dense_hidden_layers - 1)",
"title": ""
},
{
"docid": "412dca671bb068ba6a18d25c1728df80",
"score": "0.53312093",
"text": "def _increment_neighbors(self, cell: Cell):\n for cell in self.surrounding_cells(cell):\n cell.mine_count += 1",
"title": ""
},
{
"docid": "0caa04e7b4bb251740af8f50b1ae3cb1",
"score": "0.53218925",
"text": "def nx1(self) -> int:\n return self._header.nx1",
"title": ""
},
{
"docid": "b8263c862164029d7c8968c96a5ecfe1",
"score": "0.53211343",
"text": "def add_cluster_pins(G):\n\n p = -1\n for i in range(0, cluster_inputs):\n p += 1\n ble_cnt = i / (cluster_inputs / N)\n ble_i_cnt = i % (cluster_inputs / N)\n u = \"ble_%d_cb_out_%d\" % (ble_cnt, ble_i_cnt)\n G.add_node(u, node_type = \"cb_out\", p = p)\n\n for n in range(0, N):\n for o in range(0, O):\n p += 1\n u = \"ble_%d_o_%d\" % (n, o)\n G.add_node(u, node_type = \"clb_out\", p = p)\n p += 1\n G.add_node(\"ble_clk\", node_type = \"clb_clk\", p = p)",
"title": ""
},
{
"docid": "917bf37d9b90c71867b03e233de02d94",
"score": "0.5314869",
"text": "def _set_number_of_nodes(self):\n\n node_set = set()\n for a, b in self.connectivity:\n node_set = node_set.union({a, b})\n self.n_nodes = len(node_set)\n\n return self",
"title": ""
},
{
"docid": "c6b5f39b1b1108ad79a9cbf727331b1c",
"score": "0.5306329",
"text": "def simulation_nodes_number(simulation_number: int, parallel_simulations_number: int) -> int:\n\n sim_nodes_number = self.pool_workers_number // parallel_simulations_number\n if simulation_number < self.pool_workers_number - parallel_simulations_number * sim_nodes_number:\n sim_nodes_number += 1\n return sim_nodes_number",
"title": ""
},
{
"docid": "b848da77b0dbb13fe804e16783e5b086",
"score": "0.5299529",
"text": "def addNodes(self,nodes):\n for node in nodes:\n self.addNode(node)",
"title": ""
},
{
"docid": "6fbf2fef8651aefdc6d8be46e1644432",
"score": "0.52952486",
"text": "def _add_node(self, n):\n self._nodes.add(n)\n self._internPairs=None #for optimization",
"title": ""
},
{
"docid": "8f047b325de18590aaf161975088817b",
"score": "0.52917415",
"text": "def neighbors(self, node):",
"title": ""
},
{
"docid": "171905252b1dec960a31bf10acc50fa8",
"score": "0.5287725",
"text": "def initialize_nodes(self) -> None:\n\n # input nodes\n for index in range(self.inputs):\n self.nodes.append(Node(index))\n self.next_node += 1\n self.nodes[index].layer = 0\n\n # output nodes\n for index in range(self.outputs):\n self.nodes.append(Node(index + self.inputs))\n self.next_node += 1\n self.nodes[index + self.inputs].layer = 1\n\n self.nodes.append(Node(self.next_node))\n self.bias_node = self.next_node\n self.next_node += 1\n self.nodes[self.bias_node].layer = 0",
"title": ""
},
{
"docid": "f3cb8678e3141194e480c7e23aa5f093",
"score": "0.5284644",
"text": "def Nodes2D(N):\n\n from math import sqrt, cos, sin, pi\n\n alpopt = ( 0.0000, 0.0000, 0.0000, 1.4152, 0.1001, 0.2751, 0.9800, 1.0999, \\\n 1.2832, 1.3648, 1.4773, 1.4959, 1.5743, 1.5770, 1.6223, 1.6258)\n\n # set optimized parameter, alpha, depending on order\n if (N < 16):\n alpha = alpopt[N]\n else:\n alpha = 5.0 / 3.0\n\n # total number of nodes\n Np=(N+1)*(N+2)/2\n\n # create equidistributed nodes on equilateral triangle\n L1 = np.zeros(Np)\n L3 = np.zeros(Np)\n sk = 2\n for k in range(N):\n for m in range(N+1-k):\n if(m!=N or k!=0):\n L1[sk] = float(k)/N\n L3[sk] = float(m)/N\n sk = sk+1\n\n L1[2] = 1.0\n L3[1] = 1.0\n L2 = 1.0 - L1 - L3\n\n x = -L2 + L3\n y = (-L2 - L3 + 2*L1) / sqrt(3.0)\n\n # compute blending function at each node for each edge\n blend1 = 4*L2*L3\n blend2 = 4*L1*L3\n blend3 = 4*L1*L2\n\n # amount of warp for each node, for each edge\n warpf1 = Warpfactor(N, L3-L2)\n warpf2 = Warpfactor(N, L1-L3)\n warpf3 = Warpfactor(N, L2-L1)\n\n # combine blend & warp\n warp1 = blend1 * warpf1 * (1 + (alpha*L1)**2)\n warp2 = blend2 * warpf2 * (1 + (alpha*L2)**2)\n warp3 = blend3 * warpf3 * (1 + (alpha*L3)**2)\n\n # accumulate deformations associated with each edge\n x = x + 1.0*warp1 + cos(2.0*pi/3.0)*warp2 + cos(4.0*pi/3.0)*warp3\n y = y + 0.0*warp1 + sin(2.0*pi/3.0)*warp2 + sin(4.0*pi/3.0)*warp3\n\n return x,y",
"title": ""
},
{
"docid": "508dd3a0c63d71d03da97345195f5ed8",
"score": "0.5279095",
"text": "def connect(self):\n for linea in self._lines.values():\n nodeLabel=linea._label[0]\n linea._successive.update({nodeLabel:self._nodes[nodeLabel]})\n self._nodes[nodeLabel]._successive.update({linea._label:linea})\n for nodo in self._nodes.values():\n nodo._switching_matrix={}\n #length=len(nodo._connected_nodes)\n new_dict={}\n for nodo2 in self._nodes.keys():#nodo._connected_nodes:\n for nodo3 in self._nodes.keys():#nodo._connected_nodes:\n newVect=[]\n if nodo2==nodo3 or (nodo._label not in self._nodes[nodo2]._connected_nodes) or (nodo3 not in self._nodes[nodo._label]._connected_nodes):\n [newVect.append(0) for i in range(NUMBER_OF_CHANNELS)]\n else:\n [newVect.append(1) for i in range(NUMBER_OF_CHANNELS)]\n new_dict.update({nodo3:copy.copy(newVect)})\n nodo._switching_matrix.update({nodo2:copy.copy(new_dict)})",
"title": ""
},
{
"docid": "47f875bb02e09cd1e2caadd11c89d0e1",
"score": "0.52786905",
"text": "def incrementalDim(self):\n\t\tiofun.writeMsg(message.createStdMsg(\n\t\t\tInsteonAddress(self.getAddress()), 0x0F, 0x16, 0x00, -1))",
"title": ""
},
{
"docid": "e4577d53f175ca5154033e1df4d131f7",
"score": "0.5247388",
"text": "def indegree(self, nodes):\n pass",
"title": ""
},
{
"docid": "9dad0864fac9720635b2ace503414483",
"score": "0.52465034",
"text": "def get_node_increments_for_lstm_hidden_dense_hidden_layers(self):\n\n # PROTIP: IF YOU WANT THE NODE INCREMENTATION TO BE SPACED DIFFERENTLY\n # THEN YOU'LL NEED TO CHANGE THE WAY THAT IT'S CALCULATED - HAVE FUN!\n # when set to True number of nodes are decreased for subsequent layers\n if self.negative_node_incrementation:\n # subtract this amount from previous layer's nodes in order to increment towards smaller numbers\n self.nodes_increment = (self.last_lstm_layer_notes - self.first_lstm_layer_nodes) / (\n self.n_lstm_hidden_layers - 1)\n\n # when set to False number of nodes are increased for subsequent layers\n else:\n # add this amount from previous layer's nodes in order to increment towards larger numbers\n self.nodes_increment = (self.last_lstm_layer_notes - self.first_lstm_layer_nodes) / (\n self.n_lstm_hidden_layers - 1)",
"title": ""
},
{
"docid": "c2f492fc52989869a5ac0697b60fac05",
"score": "0.524243",
"text": "def attach_limb(self):\r\n (node_list, distance) = self.sub_tree() # initilize\r\n self.node.update(node_list)\r\n\r\n node_1, node_2 = node_list # 1st edge\r\n self.pair[node_1] = [node_2]\r\n self.pair[node_2] = [node_1]\r\n self.edge[(node_1, node_2)] = distance\r\n self.edge[(node_2, node_1)] = self.edge[(node_1, node_2)]\r\n\r\n for n in range(self.num_node - 2):\r\n node_in = self.num_node - 2 - n # exist node inserted\r\n node_new = self.num_node + n # new node inserted\r\n\r\n i, j = self.limb_pair[node_in]\r\n path = self.search(i, j) # i -> j\r\n distance_bald_i = self.limb_group[(node_in, i)]\r\n distance_bald_j = self.limb_group[(node_in, j)]\r\n\r\n distance_sum = 0\r\n for k in range(len(path)):\r\n node_pre = path[k]\r\n node_pass = path[k + 1]\r\n\r\n distance_sum += self.edge[(node_pre, node_pass)]\r\n if distance_bald_i < distance_sum:\r\n self.pair[node_new] = [node_pre]\r\n self.pair[node_new] += [node_pass]\r\n self.pair[node_pre] += [node_new]\r\n self.pair[node_pre].remove(node_pass)\r\n self.pair[node_pass] += [node_new]\r\n self.pair[node_pass].remove(node_pre)\r\n self.edge[(node_pass, node_new)] = distance_sum - distance_bald_i\r\n self.edge[(node_new, node_pass)] = self.edge[(node_pass, node_new)]\r\n self.edge[(node_pre, node_new)] = self.edge[(node_pre, node_pass)] - self.edge[\r\n (node_new, node_pass)]\r\n self.edge[(node_new, node_pre)] = self.edge[(node_pre, node_new)]\r\n del self.edge[(node_pre, node_pass)]\r\n del self.edge[(node_pass, node_pre)]\r\n break\r\n\r\n self.pair[node_in] = [node_new]\r\n self.pair[node_new] += [node_in]\r\n self.edge[(node_in, node_new)] = self.limb_group[(node_in, node_in)]\r\n self.edge[(node_new, node_in)] = self.edge[(node_in, node_new)]\r\n self.node.update([node_in, node_new])",
"title": ""
},
{
"docid": "c4b8396fe0f0db6bfa62c67fae2c8c5e",
"score": "0.52424115",
"text": "def instance8_1000():\n \n #create a star of 4 stars\n starList = []\n \n for _ in range(0,6):\n starList.append(nx.heawood_graph())\n \n T = nx.Graph()\n for star in starList:\n T = nx.disjoint_union(T,star)\n \n T.add_node(84)\n T.add_edges_from([(84,0),(84,14),(84,28),(84,42),(84,56),(84,70)])\n \n #add 10 more nodes with random edges\n T.add_nodes_from(range(85,100))\n for i in range(85,100):\n x = int(random()*5371)%90\n T.add_edge(i,x)\n \n #count the number of leaves\n leaves = list(T.degree(T.nodes()).values()).count(1)\n \n #randomize the label of nodes\n n = range(100)\n new = range(100)\n\n r.shuffle(new)\n\n T = nx.relabel_nodes(T,dict(zip(n,new)))\n \n G = nx.Graph()\n G.add_nodes_from(T.nodes())\n G.add_edges_from(T.edges())\n\n # add random edges\n for i in range(1000):\n x = int(random()*15897)%100\n y = int(random()*17691)%100\n G.add_edge(G.nodes()[x],G.nodes()[y])\n\n for e in G.edges():\n if e[0] == e[1]:\n G.remove_edge(e[0],e[1])\n\n G = G.to_undirected() \n #T = mlst.one_edge_swap(G) \n \n T = nx.Graph()\n return (G,T)",
"title": ""
},
{
"docid": "fa0f89a3308fdb6cc1a4f72f8c63ab63",
"score": "0.5238793",
"text": "def autoCreate(self):\n try:\n for x in self.__names:\n self.add(x)\n except TypeError:\n for x in range(self.nodeNumber()):\n self.add(x)\n row_count = 0 #x-coordinate\n floor_count = 0 #z-coordinate\n node_count = 0\n for node in self:\n column_count = node_count % self.x #x-coordinate\n row_count = floor(node_count/self.x) % self.y\n floor_count = floor(node_count/(self.x*self.y))\n self.add(node)\n #above adds coordinate as feature\n if column_count % self.x != self.x -1: #checks if at x-max\n self.addEdge(node,self.nodes[node_count+1])\n if column_count % self.x != 0: #checks if at x-min\n self.addEdge(node,self.nodes[node_count-1])\n if row_count % self.y != self.y-1: #checks if at y-max\n self.addEdge(node,self.nodes[node_count+self.x])\n if row_count % self.y != 0: #checks if at y-min\n self.addEdge(node,self.nodes[node_count-self.x])\n if floor_count != self.z-1: #checks if at z-max\n self.addEdge(node,self.nodes[node_count+self.floorArea()])\n if floor_count != 0: #checks if at z-min\n self.addEdge(node,self.nodes[node_count-self.floorArea()])\n node_count += 1",
"title": ""
},
{
"docid": "e87136d934b4181efae04f3c46a8c57b",
"score": "0.5237627",
"text": "def part_1_solver(instruction_list):\r\n current_index = 0\r\n counter = 0\r\n\r\n while True:\r\n try:\r\n #jump forward and increment step\r\n next_step = current_index + instruction_list[current_index]\r\n instruction_list[current_index] += 1\r\n current_index = next_step\r\n counter += 1\r\n except IndexError:\r\n return counter",
"title": ""
},
{
"docid": "9b38ff265f0b3a8f59807ebce50b557e",
"score": "0.5230585",
"text": "def assign_weight_count_all_0_after_2(cell, in_dim, out_dim):\n param_dict = {}\n param_dict['wgx'] = np.zeros((in_dim, out_dim))\n param_dict['wgh'] = np.zeros((out_dim, out_dim))\n param_dict['wgh'] = np.asarray([[00., 0.],[100., 0.]])\n param_dict['bg'] = [[0. , 100.]]\n\n\n #param_dict['wix'] = [[[100.] if i == 0 else [-100.] for i in range(10)] , [[100.] if i == 2 else [-100.] for i in range(10)]]\n param_dict['wix'] = np.asarray([100. if (i == 0 or i == 5) else -100. for i in range(20)])\n param_dict['wix'] = np.reshape(param_dict['wix'], (10,2))\n #print(param_dict['wix'])\n #print(param_dict['wix'].shape)\n param_dict['wih'] = np.zeros((out_dim, out_dim))\n param_dict['bi'] = np.zeros((1, out_dim))\n\n param_dict['wfx'] = np.zeros((in_dim, out_dim))\n param_dict['wfh'] = np.zeros((out_dim, out_dim))\n param_dict['bf'] = 100*np.ones((1, out_dim))\n\n param_dict['wox'] = np.zeros((in_dim, out_dim))\n param_dict['woh'] = np.zeros((out_dim, out_dim))\n param_dict['bo'] = 100*np.ones((1, out_dim))\n\n for key in param_dict:\n cell.set_config_by_name(key, param_dict[key])",
"title": ""
},
{
"docid": "7e5c8b29d45308b5bb56f45cd8806480",
"score": "0.522425",
"text": "def cost(self, node1, node2):\n return 1",
"title": ""
},
{
"docid": "925806fa39013c21e671c6c66b4e19f5",
"score": "0.5224217",
"text": "def equalizeNodes():\n\tL1num = getLabels(\"L1\")\n\tL2num = getLabels(\"L2\")\n\tprint \"\\n---\",L1num,\"\\t---\\t\",L2num,\"\\n\"\n\n\tif L1num < L2num:\n\t\tkeepNumof(L1num,\"L2\")\n\telse:#if L2num > L1num:\n\t\tkeepNumof(L2num,\"L1\")",
"title": ""
},
{
"docid": "378931efea12b8e1bd775467c44b5518",
"score": "0.52207595",
"text": "def __init__(self, nueron_size,component_size , transfer_target):\n # Type: (int, ndarray) -> None\n self.W = np.zeros((nueron_size,component_size))\n self.B = np.zeros((nueron_size, 1))\n self.epochs = 0\n self.iterator = 0\n self.lr = 1\n self.e = np.zeros((nueron_size, 1))+1\n self.transfer_target = transfer_target\n self.labels = ['W', 'P', 'O', 'B']",
"title": ""
},
{
"docid": "146e39d75ac430845d12d7ab5cc63032",
"score": "0.5220617",
"text": "def get_n_output_node(self):\n return 1",
"title": ""
},
{
"docid": "7254b17c086344bbba8283ddd9a0c248",
"score": "0.5217543",
"text": "def neighbors(self):",
"title": ""
},
{
"docid": "3787b933cd1809c3c0f98b9877d7f09e",
"score": "0.5214378",
"text": "def graph(self):",
"title": ""
},
{
"docid": "56502176335adc7e3ecbfafe0b05c582",
"score": "0.52124995",
"text": "def __init__(self, id, node_type=NodeType.HIDDEN, activation=F.relu, layer_type=nn.Conv2d,\n conv_window_size=7, conv_stride=1, max_pool_size=2):\n\n super(ModulenNEATNode, self).__init__(id, node_type)\n\n batch_norm_chance = 0.65 # chance that a new node will start with batch norm\n use_batch_norm = random.random() < batch_norm_chance\n\n dropout_chance = 0.2 # chance that a new node will start with drop out\n use_dropout = random.random() < dropout_chance\n\n max_pool_chance = 0.3 # chance that a new node will start with drop out\n use_max_pool = random.random() < max_pool_chance\n\n self.activation = Mutagen(F.relu, F.leaky_relu, torch.sigmoid, F.relu6,\n discreet_value=activation, name=\"activation function\",\n mutation_chance=0.15) # TODO try add in Selu, Elu\n\n conv_out_features = 25 + random.randint(0, 25)\n linear_out_features = 100 + random.randint(0, 100)\n\n linear_submutagens = \\\n {\n \"regularisation\": Mutagen(None, nn.BatchNorm1d,\n discreet_value=nn.BatchNorm1d if use_batch_norm else None,\n mutation_chance=0.15),\n\n \"dropout\": Mutagen(None, nn.Dropout, discreet_value=nn.Dropout if use_dropout else None, sub_mutagens=\n {\n nn.Dropout: {\n \"dropout_factor\": Mutagen(value_type=ValueType.CONTINUOUS, current_value=0.15, start_range=0,\n end_range=0.75)}\n }, mutation_chance=0.08),\n\n \"out_features\": Mutagen(value_type=ValueType.WHOLE_NUMBERS, current_value=linear_out_features,\n start_range=10,\n end_range=1024, name=\"num out features\", mutation_chance=0.22,\n distance_weighting=Props.LAYER_SIZE_COEFFICIENT if Config. allow_attribute_distance else 0)\n }\n\n conv_submutagens = {\n \"conv_window_size\": Mutagen(3, 5, 7, discreet_value=conv_window_size, mutation_chance=0.13),\n\n \"conv_stride\": Mutagen(value_type=ValueType.WHOLE_NUMBERS, current_value=conv_stride, start_range=1,\n end_range=5),\n\n \"reduction\": Mutagen(None, nn.MaxPool2d, discreet_value=nn.MaxPool2d if use_max_pool else None,\n sub_mutagens=\n {\n nn.MaxPool2d: {\"pool_size\": Mutagen(\n value_type=ValueType.WHOLE_NUMBERS, current_value=max_pool_size, start_range=2,\n end_range=5)}\n }, mutation_chance=0.15),\n\n \"regularisation\": Mutagen(None, nn.BatchNorm2d, discreet_value=nn.BatchNorm2d if use_batch_norm else None,\n mutation_chance=0.15),\n\n \"dropout\": Mutagen(None, nn.Dropout2d, discreet_value=nn.Dropout2d if use_dropout else None, sub_mutagens=\n {\n nn.Dropout2d: {\n \"dropout_factor\": Mutagen(value_type=ValueType.CONTINUOUS, current_value=0.1,\n start_range=0, end_range=0.75)}\n }, mutation_chance=0.08),\n\n \"out_features\": Mutagen(value_type=ValueType.WHOLE_NUMBERS, current_value=conv_out_features, start_range=1,\n end_range=100, name=\"num out features\", mutation_chance=0.22,\n distance_weighting=Props.LAYER_SIZE_COEFFICIENT if Config.allow_attribute_distance else 0)\n }\n\n if use_linears and not use_convs:\n self.layer_type = Mutagen(nn.Linear, discreet_value=nn.Linear,\n distance_weighting=Props.LAYER_TYPE_COEFFICIENT if Config.allow_attribute_distance else 0,\n sub_mutagens={nn.Linear: linear_submutagens}\n )\n if use_convs and not use_linears:\n self.layer_type = Mutagen(nn.Conv2d, discreet_value=nn.Conv2d,\n distance_weighting=Props.LAYER_TYPE_COEFFICIENT if Config.allow_attribute_distance else 0,\n sub_mutagens={nn.Conv2d: conv_submutagens})\n if use_convs and use_linears:\n self.layer_type = Mutagen(nn.Conv2d, nn.Linear, discreet_value=layer_type,\n distance_weighting=Props.LAYER_TYPE_COEFFICIENT if Config.allow_attribute_distance else 0,\n sub_mutagens={\n nn.Conv2d: conv_submutagens,\n nn.Linear: linear_submutagens\n }, name=\"deep layer type\", mutation_chance=0.08)",
"title": ""
},
{
"docid": "015eaf1f7d32bf4e3aa3416cb4768ab4",
"score": "0.5206471",
"text": "def __init__(self,n):\n self.root = list(range(n))\n self.rank = [0]*n\n self.num = n # the number of clusters",
"title": ""
},
{
"docid": "3b7983232d6855639c200187a937ac2c",
"score": "0.5199914",
"text": "def count_neigbors(self):\n for i in range(self.xsize):\n for j in range(self.ysize):\n self.neigh_counts[i, j] = sum(v for _, _, v in self.neighbors(i, j))",
"title": ""
},
{
"docid": "12c47318d768e6c97844ed7c53ebcb9c",
"score": "0.51994795",
"text": "def node_flow(self):\n\n for i in range(1, self.num_node+1):\n b1,b2,c1,c2 = 0.0, 0.0, 0.0, 0.0\n for ele in self.list_elements:\n if ele.type_ == 'gene':\n ii = ele.i\n kk = ele.j\n if i == ii and kk == 0: # Balance nodes\n b1 = self.P[ii]\n b2 = self.Q[ii]\n for ele2 in self.list_elements:\n if ele2.type_ == 'load' and i == ele2.i:\n c1 = ele2.p1\n c2 = ele2.p2\n b1 += c1\n b2 += c2\n break\n if i == ii and kk == -1: # PV nodes\n b2 = self.Q[ii]\n for ele2 in self.list_elements:\n if ele2.type_ == 'load' and i == ele2.i:\n c1 = ele2.p1\n c2 = ele2.p2\n b2 += c2\n\n break \n for ele in self.list_elements:\n if ele.type_ == 'load' and i == ele.i:\n c1 = ele.p1\n c2 = ele.p2\n break\n self.df_node.loc[i] = [self.Um[i], self.Ua[i]*180.0/pi, b1, b2, c1, c2]",
"title": ""
},
{
"docid": "a46ddfbe71b361aa305dfb9f2e670064",
"score": "0.5186735",
"text": "def __init__(self, in_out_dim, mid_dim, hidden, mask_config):\n super(AdditiveCoupling, self).__init__()\n # TODO fill in\n\n self.mask_config = mask_config\n # Divide into even and odd\n assert (mask_config in [0, 1]), \"[AdditiveCoupling] mask_config type must be `0` or `1`!\"\n if (mask_config == 1):\n self.first_func = get_odd # Stay as is\n self.second_func = get_even\n self.in_dim = int(in_out_dim / 2)\n self.out_dim = int(in_out_dim / 2) + (in_out_dim % 2 > 0) #out dim for v - Coupling func\n else:\n self.first_func = get_even # Stay as is\n self.second_func = get_odd\n self.in_dim = int(in_out_dim / 2) + (in_out_dim % 2 > 0)\n self.out_dim = int(in_out_dim / 2)\n\n\n # Coupling_func with 5 hidden layers\n Coupling_funcc = []\n Coupling_funcc.append(nn.Linear(self.in_dim, mid_dim))\n Coupling_funcc.append(nn.ReLU())\n for _ in range(hidden - 1):\n Coupling_funcc.append(nn.Linear(mid_dim, mid_dim))\n Coupling_funcc.append(nn.ReLU())\n Coupling_funcc.append(nn.Linear(mid_dim, self.out_dim))\n self.Coupling_func = nn.Sequential(*Coupling_funcc) # unpack the list",
"title": ""
},
{
"docid": "bda4b77815458f3ebb6e10799ba6e0d2",
"score": "0.5184901",
"text": "def update_adjacent(self, mask):\r\n for idx in zip(mask[0], mask[1]): # For each cell\r\n for adj_idx in self.graph[idx]: # Find all neighbours\r\n self.arr[adj_idx] += 1",
"title": ""
},
{
"docid": "9c4de8c6d998604ddd37d9282dd90323",
"score": "0.51830834",
"text": "def onnx_nodes(self) -> OnnxNodes:\n mul_name = f'{self.name}_mul'\n nodes = OnnxNodes(make_node(\"Mul\", self.inputs[0:2], [mul_name], mul_name))\n\n add_inputs = [mul_name, self.inputs[2]]\n nodes.add(make_node(\"Add\", add_inputs, self.outputs, self.name))\n return nodes",
"title": ""
},
{
"docid": "419115a488e2f4f6ccf7a77e24a53849",
"score": "0.51824087",
"text": "def discover_nodes(self):",
"title": ""
},
{
"docid": "7274a2d03caddc9f932cd5e47e92b9e5",
"score": "0.5179589",
"text": "def assign_weight_count_task_2(cell, in_dim, out_dim):\n param_dict = {}\n param_dict['wgx'] = [[100., 0.] if i == 0 else [0., 100.] if i == 2 else [0., 0.] for i in range(10)]\n param_dict['wgh'] = np.zeros((out_dim, out_dim))\n param_dict['bg'] = np.zeros((1, out_dim))\n\n param_dict['wix'] = [[-100., 100.] if i == 2 else [-100., -100.] for i in range(10)]\n param_dict['wih'] = [[0., 200.], [200., 200.]]\n param_dict['bi'] = np.zeros((1, out_dim))\n\n #param_dict['wfx'] = [[100., -100.] if i == 2 else [100., 100.] for i in range(10)]\n param_dict['wfx'] = np.zeros((in_dim, out_dim))\n param_dict['wfh'] = np.zeros((out_dim, out_dim))\n param_dict['bf'] = 100. * np.ones((1, out_dim))\n #param_dict['bf'] = np.zeros((1, out_dim))\n\n param_dict['wox'] = np.zeros((in_dim, out_dim))\n param_dict['woh'] = np.zeros((out_dim, out_dim))\n param_dict['bo'] = 100. * np.ones((1, out_dim))\n\n for key in param_dict:\n cell.set_config_by_name(key, param_dict[key])",
"title": ""
},
{
"docid": "2cb870f2f2fe9be693dd601874df9632",
"score": "0.51752937",
"text": "def addNodesFrom(self, nodes):\n for v in nodes:\n self.V.append(v)\n self.neighbours[v] = []\n self.visited[v] = 0",
"title": ""
},
{
"docid": "1e763be8533bfbe35e83135cbe710a92",
"score": "0.5168673",
"text": "def test_get_number_of_padding_with_graphs_nodes(self):\n _, graphs_tuple = _get_list_and_batched_graph()\n expected = 2\n with self.subTest('nojit'):\n self.assertAllClose(\n utils.get_number_of_padding_with_graphs_nodes(graphs_tuple),\n expected,\n check_dtypes=True)\n with self.subTest('jit'):\n self.assertAllClose(\n jax.jit(utils.get_number_of_padding_with_graphs_nodes)(graphs_tuple),\n expected,\n check_dtypes=True)",
"title": ""
},
{
"docid": "e992f0ff5775c8844acb4cf7b4284b5e",
"score": "0.5161093",
"text": "def build_graph( e1,e2,ct,i1,i2 ) :\n\n\tG1 = nx.Graph()\n\tG2 = nx.Graph()\n\t#print 'queue is :',e1,e2\n\tc1 = []\n\tc1.extend( e1 )\n\n\tc2 = []\n\tc2.extend( e2 )\n\n\tcurrent_queue = [ [ e1[0],e2[0] ], [ e1[1],e2[1] ] ] #current queue , this is a list of list\n\t#TODO\n\n\tcurrent_edge = [ [ e1[0],e1[1] ], [ e2[0],e2[1] ] ]\n\n\tcurrent_pair = current_queue.pop( 0 ) #This is a list\n\n\tuse_index = True\n\n\twhile use_index : #True should be a condition where no more compatible edges could be found\n\t\t'''Find an edge in second minutiae table which is similar to the current edge\n\t\t\tTo find an edge similar to this edge find the entry in the compatibility_table\n\t\t\t\twhich has both the nodes '''\n\t\t\n\n\t\t#We have the current pair use these to add edges\n\t\t#print 'while use_index'\n\t\tG1.add_edge( current_edge[0][0], current_edge[0][1] )\n\t\tG2.add_edge( current_edge[1][0], current_edge[1][1] )\n\n\t\t\n\n\t\ttry :\n\t\t\tavlbl = set( i1[ current_pair[0] ] ) & set( i2[ current_pair[0] ] )\n\t\t\t#print 'avlbl finding'\n\t\texcept KeyError :\n\t\t\t#print 'KeyError'\n\t\t\tbreak\n\n\n\t\tavlbl = list( avlbl )\n\t\t#print 'avlbl is : ',avlbl\n\n\t\tuse_index = use_entry( avlbl,ct ) #which edge to proceed\n\n\t\t#We have the index from compatibility_table from where similar edges to use\n\n\t\tif use_index : #something is still left in compatibility table to use\n\t\t\tcurrent_queue += [ [ ct[ use_index ][0], ct[ use_index ][2] ], [ ct[ use_index ][1], ct[ use_index ][3] ] ]\n\t\t\tcurrent_edge = [ [ ct[ use_index ][0],ct[ use_index ][1] ], [ ct[ use_index ][2],ct[ use_index ][3] ] ]\n\n\t\t\t#all used. Now set it to None\n\t\t\tct[ use_index ] = None\n\n\t\t\n\t\n\t\tcurrent_pair = current_queue.pop( 0 )\n\t\t\n\t\t#print \"G1 currently is :\", G1.nodes()\n\t\t#print 'use_index is : ',use_index\n\t\t#print 'hello'\n\n\treturn G1,G2",
"title": ""
},
{
"docid": "60a937c2a2a8c5f6035a5416553c9e8c",
"score": "0.5161048",
"text": "def calculate_incoming(array,day,node):\n num_nodes = array.shape[1]\n \n total_outgoing = 0\n for n in range(num_nodes):\n total_outgoing = total_outgoing + array[day][n][node]\n return total_outgoing",
"title": ""
},
{
"docid": "e18fceaaaa582e6acdd0a163dd1e7f4d",
"score": "0.5157698",
"text": "def forward(self):\n inputs = self.inbound_nodes[0].value\n weights = self.inbound_nodes[1].value\n bias = self.inbound_nodes[2].value\n #self.value = bias\n for x, w in zip(inputs, weights):\n self.value += np.dot(x, w) + bias",
"title": ""
},
{
"docid": "d3ad03008ae935f184709f0c96ecd136",
"score": "0.51540613",
"text": "def __init__(self, intra_b, intra_k, inter_adj, inter_b, device='cpu'):\n super().__init__()\n\n self.N = intra_k.shape[0] # number of nodes\n self.intra_b = Parameter(intra_b.to(device)) # b: infection probability within the city\n self.intra_k = Parameter(intra_k.to(device)) # k: healing probability within the city\n self.inter_adj = inter_adj.to(device) # adjacency matrix among all the cities in the models \n self.inter_b = Parameter(inter_b) # inter_b: infection coupling among different cities \n self.device = device # what device to use, \"cpu\" as default ",
"title": ""
},
{
"docid": "35eb6fe4f4ee5abb2785f2dd7510938c",
"score": "0.51528394",
"text": "def _compute_transitions(self):\n\n self.row_sum_l = self.subgraph_ll.weight_matrix.sum(axis=1) + \\\n self.subgraph_lu.weight_matrix.sum(axis=1)\n\n self.row_sum_u = self.subgraph_ul.weight_matrix.sum(axis=1) + \\\n self.subgraph_uu.weight_matrix.sum(axis=1)\n\n # Avoid division by zero\n actual_l = self.row_sum_l[:self.n_labeled]\n actual_l[actual_l < self.eps] = 1.\n # print('Min value l: ', actual_l.min())\n actual_u = self.row_sum_u[:self.n_unlabeled]\n actual_u[actual_u < self.eps] = 1.\n # print('Min value u: ', actual_u.min())\n\n row_sum_l_inv = 1 / np.asarray(actual_l, dtype=self.dtype)\n row_sum_l_inv[row_sum_l_inv == np.inf] = 1\n\n row_sum_u_inv = 1 / np.asarray(actual_u, dtype=self.dtype)\n row_sum_u_inv[row_sum_u_inv == np.inf] = 1\n\n # Temporary divisors (diagonal pre-multiplier matrices)\n diag_l = spdiags(row_sum_l_inv.ravel(), 0, *self.subgraph_ll.shape)\n diag_u = spdiags(row_sum_u_inv.ravel(), 0, *self.subgraph_uu.shape)\n\n self.subgraph_ll.update_transitions(diag_l)\n self.subgraph_lu.update_transitions(diag_l)\n self.subgraph_ul.update_transitions(diag_u)\n self.subgraph_uu.update_transitions(diag_u)",
"title": ""
},
{
"docid": "71350b50cf3df13c4e757ad8846ae8a4",
"score": "0.5147021",
"text": "def test_add_node(graph_1):\n for num in range(1, 7):\n graph_1.add_node(num)\n assert graph_1.nodes() == [1, 2, 3, 4, 5, 6]",
"title": ""
},
{
"docid": "ff27d4a7665c835a170fe3293a2e0638",
"score": "0.51469237",
"text": "def n_nodes(self):\n return self.adj.shape[0]",
"title": ""
},
{
"docid": "01399ab1aeddd9a037cfdbce6e193178",
"score": "0.5145569",
"text": "def producing_network_graph(input_file):\r\n\r\n print(\"Reading the input file:\",input_file)\r\n ppi_data=pd.read_csv(input_file)\r\n \r\n out_node=ppi_data['out'].unique().tolist()\r\n in_node=ppi_data['in'].unique().tolist()\r\n \r\n # creating the total nodes\r\n total_nodes= (out_node)+ (in_node)\r\n total_nodes=list(set(total_nodes))\r\n total_nodes=sorted(total_nodes)\r\n \r\n # assigning node identifier\r\n node_list={(i+1): total_nodes[i] for i in range(len(total_nodes))}\r\n \r\n # writing the output node file \r\n with open('node_list.tsv','w') as f:\r\n for node in node_list:\r\n f.write(\"{}\\t{}\\n\".format(node, node_list[node]))\r\n \r\n \r\n edge_list=[]\r\n node_list_inv={j:i for i,j in node_list.items()}\r\n for i in range(len(ppi_data['out'])):\r\n edge_list.append((node_list_inv[ ppi_data['out'][i]],node_list_inv[ ppi_data['in'][i]] ,ppi_data['interaction_type'][i]))\r\n \r\n \r\n # writing the output edge file \r\n edge_list_no_metadata=[(i[0],i[1]) for i in edge_list]\r\n with open('edge_list_no_metadata.tsv','w') as f: # writing with no metadata\r\n for edge in edge_list_no_metadata:\r\n f.write(\"{}\\t{}\\n\".format( *edge ))\r\n \r\n \r\n def drawing_graph_from_edge_list(edge_file_path:str)->nx.Graph:\r\n \"\"\"function to produce a network graph\r\n input: file path/file name containing the edge information\r\n output: graph\"\"\"\r\n graph=nx.read_edgelist(edge_file_path,delimiter='\\t')\r\n return graph\r\n \r\n def graph_node_labels(node_file_path:str)->dict:\r\n \"\"\"funtion to generate the dict version of the node list\r\n arg: node list file path\r\n return : dictionary version of it\"\"\"\r\n f = open(node_file_path,'r') \r\n interactions= f.readlines()\r\n nodes_dict= {}\r\n for pair in interactions:\r\n identifier,node=pair.strip().split('\\t')\r\n nodes_dict[identifier]=node\r\n return nodes_dict\r\n \r\n \r\n def generate_graph( edge_list ,node_list ,output_file):\r\n \"\"\"function to generate pdf graph network file\r\n Arg 1- path of edge list file\r\n Arg 2- path of node list file\r\n Arg 3- path of output graph file\r\n Return - pdf version of the network graph \"\"\"\r\n graph=drawing_graph_from_edge_list(edge_list)\r\n graph_pos=nx.spring_layout(graph)\r\n nx.draw_networkx_nodes(graph, graph_pos,node_color='red')#,alpha=0.5)\r\n nx.draw_networkx_edges(graph,graph_pos)\r\n node_labels= graph_node_labels(node_list)\r\n nx.draw(graph, labels=node_labels,with_labels=True,font_size=20)\r\n #nx.draw_networkx_labels(node_list, pos = nx.spring_layout(node_list), font_size = 20, with_labels = True);\r\n plt.savefig(output_file)\r\n plt.title(\"Protein Protein Interactions \",fontsize=100)\r\n plt.show() \r\n \r\n print(\"Generating the output graph file:\",output_file)\r\n generate_graph( 'edge_list_no_metadata.tsv','node_list.tsv',output_file)",
"title": ""
},
{
"docid": "86e03b0b4f5b547427711cfb609cf943",
"score": "0.51427543",
"text": "def do_add_nodes(self):\n node_ids = self.inputs.get('nodes')\n errors = []\n nodes = []\n for nid in node_ids:\n node = no.Node.get(self.context, nid)\n if not node:\n errors.append('Node %s is not found.' % nid)\n continue\n\n if node.cluster_id:\n errors.append('Node %(n)s is already owned by cluster %(c)s.'\n '' % {'n': nid, 'c': node.cluster_id})\n continue\n\n if node.status != consts.NS_ACTIVE:\n errors.append('Node %s is not in ACTIVE status.' % nid)\n continue\n\n nodes.append(node)\n\n if len(errors) > 0:\n return self.RES_ERROR, '\\n'.join(errors)\n\n reason = 'Completed adding nodes.'\n # check the size constraint\n current = no.Node.count_by_cluster(self.context, self.target)\n desired = current + len(node_ids)\n res = scaleutils.check_size_params(self.entity, desired, None,\n None, True)\n if res:\n return self.RES_ERROR, res\n\n child = []\n for node in nodes:\n nid = node.id\n kwargs = {\n 'name': 'node_join_%s' % nid[:8],\n 'cluster_id': self.entity.id,\n 'cause': consts.CAUSE_DERIVED,\n 'inputs': {'cluster_id': self.target},\n }\n action_id = base.Action.create(self.context, nid, consts.NODE_JOIN,\n **kwargs)\n child.append(action_id)\n\n if child:\n dobj.Dependency.create(self.context, [c for c in child], self.id)\n for cid in child:\n ao.Action.update(self.context, cid,\n {'status': base.Action.READY})\n dispatcher.start_action()\n\n # Wait for dependent action if any\n result, new_reason = self._wait_for_dependents()\n if result != self.RES_OK:\n reason = new_reason\n else:\n self.entity.eval_status(self.context, consts.CLUSTER_ADD_NODES,\n desired_capacity=desired)\n self.outputs['nodes_added'] = node_ids\n creation = self.data.get('creation', {})\n creation['nodes'] = node_ids\n self.data['creation'] = creation\n for node in nodes:\n obj = node_mod.Node.load(self.context, db_node=node)\n self.entity.add_node(obj)\n\n return result, reason",
"title": ""
},
{
"docid": "06b9a497a487b1f7567fc5c58c0cf7ba",
"score": "0.5136953",
"text": "def generate_neural_network(self) -> None:\n self.connect_nodes()\n for layer in range(self.layers):\n for node in self.nodes:\n if node.layer == layer:\n self.network.append(node)",
"title": ""
},
{
"docid": "a6219a44f0a420ab80b320d52cf0bd8a",
"score": "0.513645",
"text": "def convert_nasnet_action(graph: nx.MultiDiGraph, matches: dict):\n input = matches['input']\n output = matches['output']\n\n pad_op = matches['pad_op']\n pad_const = matches['pad_const']\n pad_out = matches['pad_out']\n\n sslice = matches['sslice']\n begin = matches['begin']\n end = matches['end']\n stride = matches['stride']\n sslice_out = matches['sslice_out']\n\n avg_pool = matches['avg_pool']\n\n if not np.array_equal(pad_const.value, np.array([[0, 0], [0, 1], [0, 1], [0, 0]])):\n log.error(\" Pad values doesn't match!\")\n return\n\n if not np.array_equal(begin.value, np.array([0, 1, 1, 0])):\n log.error(\"StridedSlice has wron begin\")\n return\n\n if sslice.end_mask != 15 or sslice.begin_mask != 9:\n log.error(\"StridedSlice has wrong masks\")\n return\n\n # Cut Smth-x->Pad->StrudedSlice-x->AvgPool\n graph.remove_edge(input.id, pad_op.id)\n graph.remove_edge(sslice.id, sslice_out.id)\n\n # Pad -> Conv\n conv_node = unique_id(graph, pad_op.name + '/Conv_')\n conv_weights_node = unique_id(graph, pad_op.name + '/ConvW_')\n conv_weights = np.ones((1, 1, input.shape[3], 1))\n conv_output = unique_id(graph, pad_op.name + '/ConvOut_')\n output_shape = np.array([input.shape[0], input.shape[1] + 1, input.shape[2] + 1, input.shape[3]])\n\n graph.add_node(conv_node,\n **add_attrs_props(dict(kind='op', precision=\"FP32\", type='Convolution', name=conv_node, op='Conv2D',\n stride=np.array([1, 1, 1, 1]), dilation=np.array([1, 1, 1, 1]),\n group=input.shape[3], bias_addable=True, bias_term=False, spatial_dims=np.array([1, 2]),\n pad=np.array([[0, 0], [0, 0], [0, 0], [0, 0]]), output_shape=output_shape,\n channel_dims=np.array([3]))))\n\n graph.add_node(conv_weights_node, **add_attrs_props(\n dict(kind='data', precision=\"FP32\", name=conv_weights_node, value=np.array(conv_weights),\n shape=np.array(conv_weights.shape),\n data_type=input.data_type, infer=None,\n spatial_dims=np.array([0, 1]),\n input_channel_dim=np.array([2]),\n output_channel_dim=np.array([3]),\n dims_number=np.array([4]), can_be_bias=True)))\n graph.add_node(conv_output, **add_attrs_props(\n dict(kind='data', precision=\"FP32\", name=conv_output, value=None, shape=output_shape,\n data_type=input.data_type)))\n\n # StridedSlice -> Crop\n Crop = Op.get_op_class_by_name('Crop')\n crop = Crop(graph, dict(name=sslice.name + '/Crop_', axis=np.array([1, 2]),\n dim=np.array([output_shape[1] - 1, output_shape[2] - 1]), offset=np.array([1, 1])))\n crop.create_node_with_data([Node(graph, conv_output)], data_nodes=sslice_out)\n # graph.add_node(crop_node, **add_attrs_props(dict(kind='op', precision=\"FP32\", type='Crop', name=crop_node,\n # op='Crop', axis=[1,2], dim=[output_shape[1]-1, output_shape[2]-1], offset=[1,1])))\n\n # Connect : Conv->Crop->AvgPool\n graph.add_edges_from([\n (input.id, conv_node, {'in': 0}),\n (conv_weights_node, conv_node, {'in': 1, 'bin': 'weights'}),\n (conv_node, conv_output, {'out': 0}),\n ])\n update_ie_fields(graph.node[conv_node])",
"title": ""
},
{
"docid": "1145ce80f51a568c4bb12c98f7e530d6",
"score": "0.51362413",
"text": "def compute_nodes_label(ins_sol_set):\n nodes_label = torch.zeros(size=(len(ins_sol_set['loc']), 1))\n # input('enter')\n # print(nodes_label)\n # input('enter')\n # only one(first) of k sols are used to create labels\n nodes_label[ins_sol_set['sol_set'][0]] = 1.0\n # print(nodes_label)\n # input('enter')\n nodes_label_numpy = nodes_label.numpy()\n # print(nodes_label_numpy)\n # input( 'enter' )\n # print(list( itertools.chain( *nodes_label_numpy ) ))\n # input( 'enter' )\n\n return list(itertools.chain(*nodes_label_numpy))",
"title": ""
},
{
"docid": "18699fd2b431ade6488030c3bc425f6f",
"score": "0.513607",
"text": "def addDynamic(self, node_num, edge_num):\n nodes = list(range(self.V[-1] + 1, self.V[-1] + 1 + node_num))\n for v in nodes:\n self.V.append(v)\n self.neighbours[v] = []\n for v in self.V:\n self.visited[v] = 0\n self._createEdges(len(self.E) + edge_num)\n self.updateComponents()",
"title": ""
},
{
"docid": "bb31d8a898a4e4626b3dda919326a025",
"score": "0.51349753",
"text": "def number_of_nodes(numberofsimulations, numberofexperiments):\n numberofthingspernode = 5000.0 # This is an educated guess\n totalnumberofthings = numberofsimulations * numberofexperiments\n return int(math.ceil(totalnumberofthings / numberofthingspernode))",
"title": ""
},
{
"docid": "68a34ea78c0f98329bc9c88428de71ba",
"score": "0.5133703",
"text": "def change_topology(topology: Topology):\n for ta_node in TAState.get_ta_nodes(topology):\n for block in ta_node.memory_blocks:\n if block is not None and block.tensor is not None:\n block.tensor.add_(1)",
"title": ""
},
{
"docid": "bb5c8eb344f8141218031f165c48b9ef",
"score": "0.512801",
"text": "def next_it(self):\n self.n_l = np.zeros((15, 15))\n for i in range(self.l.shape[0]):\n for j in range(self.l.shape[1]):\n self.alive(i,j)\n '''\n if self.l[i][j] == 1:\n self.calc_alive(i, j)\n else:\n self.calc_dead(i, j)\n '''\n self.l=self.n_l",
"title": ""
},
{
"docid": "efc199177885c0b1fd47aff1f49bb7d6",
"score": "0.5126739",
"text": "def build_graph2( index,ct,i1,i2 ) :\n\n\tG1 = nx.Graph()\n\tG2 = nx.Graph()\n\n\tcurrent = index #current index for the compatibility_table ct\n\n\tqueue = [[ ct[ index ][0],ct[ index ][2] ], [ ct[ index ][1],ct[ index ][3] ] ] #this contains the nodes\n\n\t##print '#build_graph2()'\n\n\twhile current is not None :\n\t\t#do your stuff\n\n\t\t#add edge and continue\n\t\t##print 'current in while is :',current\n\t\t##print 'compatibility_table is :' , ct\n\t\tG1.add_edge( ct[ current ][0], ct[ current ][1] )\n\t\tG2.add_edge( ct[ current ][2], ct[ current ][3] )\n\n\t\t\n\t\t#print 'Going to remove : ', current\n\t\tremove_value_from_index( current,i1,i2 )\n\t\t##print 'Removed'\n\t\t#after adding edge make that entry None\n\t\tct[ current ] = None\n\t\tcurrent = None\n\n\t\t#print 'queue is : ',queue\n\n\n\n\t\t#find next current\n\t\tpop_queue = False\n\t\twhile queue :\n\t\t\t##print 'while queue is :', queue\n\t\t\tcm = queue.pop( 0 ) #cm : corresponding minutiae\n\n\t\t\t#print 'i1 is :',i1\n\t\t\t#print 'i2 is :',i2\n\n\t\t\tintersection = set( i1[ cm[0] ] ) & set( i2[ cm[1] ] )\n\t\t\tintersection = list( intersection )\n\t\t\t##print 'intersection is :',intersection\n\t\t\tif intersection : #this means we found the next corresponding edge\n\t\t\t\tcurrent = intersection.pop( 0 )\n\t\t\t\tif ct[ current ] :\n\t\t\t\t\tqueue += [ [ ct[ current ][0],ct[ current ][2] ], [ ct[ current ][1], ct[ current ][3] ] ]\n\t\t\t\t##print 'current after popping is : ', current\n\t\t\t\twhile not ct[ current ] :\n\t\t\t\t\tif intersection :\n\t\t\t\t\t\tcurrent = intersection.pop( 0 )\n\t\t\t\t\telse : #if no intersection found try for the next pair in the queue\n\t\t\t\t\t\tpop_queue = True\n\t\t\t\t\t\tbreak\n\t\t\t\t\tif ct[ current ] :\n\t\t\t\t\t\tqueue += [ [ ct[ current ][0],ct[ current ][2] ], [ ct[ current ][1], ct[ current ][3] ] ]\n\t\t\t\tif pop_queue :\n\t\t\t\t\tcontinue\n\t\t\t\tbreak #we got current index to work with\n\t\t\telse :\n\t\t\t\tcurrent = None\n\t\t\t\tcontinue\n\n\t\t\t##print 'current is : ',current\n\t\t#work with current in the next loop it will work :P\n\n\n\treturn G1,G2",
"title": ""
},
{
"docid": "35f9e69a8a6c96123dc7cf19e39d2f09",
"score": "0.51257604",
"text": "def cell_edges1d(self):",
"title": ""
},
{
"docid": "2f459d5ec06ea7998abcca64dda4b277",
"score": "0.5125582",
"text": "def num_nodes(self):\n return self.adj_matrix.shape[0]",
"title": ""
},
{
"docid": "d2259f4b0cbf218a40ebe863f8bc4641",
"score": "0.51141685",
"text": "def n_components(self):\n return 1",
"title": ""
},
{
"docid": "d2259f4b0cbf218a40ebe863f8bc4641",
"score": "0.51141685",
"text": "def n_components(self):\n return 1",
"title": ""
},
{
"docid": "d2259f4b0cbf218a40ebe863f8bc4641",
"score": "0.51141685",
"text": "def n_components(self):\n return 1",
"title": ""
},
{
"docid": "d2259f4b0cbf218a40ebe863f8bc4641",
"score": "0.51141685",
"text": "def n_components(self):\n return 1",
"title": ""
},
{
"docid": "15ec7769894f09ce8df1db16e3f687c5",
"score": "0.5107576",
"text": "def _createPair(self, nodes):\n self.neighbours.get(nodes[0]).append(nodes[1])\n self.neighbours.get(nodes[1]).append(nodes[0])\n self.E.append((nodes[0], nodes[1]))\n self.connected_nodes.extend(nodes)\n self.source +=2",
"title": ""
},
{
"docid": "583740207870cad386002ec52644bf98",
"score": "0.5105273",
"text": "def connectFeedforward(num_nodes,node_types, arch_list=None,connActive=None,connWeights= None,use_NormDistWeigts = False):\n \n for i in xrange(num_nodes):\n \n #skip for inactive nodes\n if arch_list[i] != constants.ACTIVE:\n continue;\n \n for j in xrange(num_nodes):\n \n #skip for inactive nodes\n if arch_list[j] != constants.ACTIVE:\n continue;\n \n \n #only connect to other nodes - not to self\n if(i != j):\n #input to hidden layer\n if((node_types[i] == 1 and arch_list[i] == constants.ACTIVE ) and\n (node_types[j] == 2 and arch_list[j] == constants.ACTIVE)):\n \n \n #make connection active\n connActive[i][j] = constants.ACTIVE;\n \n #generate random weight\n w = randomWithinParamRange(use_NormDistWeigts);\n connWeights[i][j] = w;\n\n \n #hidden to output layer\n if((node_types[i] == 2 and arch_list[i] == constants.ACTIVE ) and\n (node_types[j] == 3 and arch_list[j] == constants.ACTIVE)):\n \n #make connection active\n connActive[i][j] = constants.ACTIVE;\n \n #generate random weight\n w = randomWithinParamRange(use_NormDistWeigts);\n connWeights[i][j] = w;",
"title": ""
},
{
"docid": "fa4caf118b74255578e6932cb0abe3d0",
"score": "0.5103513",
"text": "def test_intermediate_iteration_2(self):\n remaining_nodes = [4, 8, 9, 15, 16, 18, 20, 21, 22, 26, 29, 30, 32, 36]\n condensed_matrix = CondensedMatrix([2.2360679774997898, 2.2360679774997898, 3.6055512754639891, 31.144823004794873, 4.2426406871192848, 33.241540277189323, 4.0, 28.442925306655784, 6.4031242374328485, 34.525353003264136, 6.7082039324993694, 6.0827625302982193, 32.756678708318397, 29.832867780352597, 23.086792761230392, 17.464249196572979, 35.902646142032481, 29.732137494637012, 33.837848631377263, 31.016124838541646, 20.124611797498108, 25.0, 21.095023109728988, 23.086792761230392, 22.360679774997898, 29.832867780352597, 24.186773244895647, 25.709920264364882, 28.792360097775937, 32.649655434629018, 32.140317359976393, 36.055512754639892, 34.713109915419565, 35.227829907617071, 36.013886210738214, 36.013886210738214, 3.1622776601683795, 5.0990195135927845, 29.068883707497267, 2.2360679774997898, 31.144823004794873, 3.6055512754639891, 26.305892875931811, 7.2111025509279782, 32.388269481403292, 5.0990195135927845, 5.8309518948453007, 30.594117081556711, 27.658633371878661, 20.880613017821101, 15.231546211727817, 33.734255586865999, 27.513632984395208, 31.622776601683793, 28.792360097775937, 18.384776310850235, 23.021728866442675, 20.0, 21.587033144922902, 21.095023109728988, 27.802877548915689, 23.021728866442675, 24.331050121192877, 27.202941017470888, 30.870698080866262, 31.016124838541646, 34.481879299133332, 33.376638536557273, 34.058772731852805, 35.014282800023196, 35.128336140500593, 2.0, 32.015621187164243, 4.1231056256176606, 34.058772731852805, 2.2360679774997898, 29.154759474226502, 4.2426406871192848, 35.227829907617071, 5.6568542494923806, 4.0, 33.376638536557273, 30.413812651491099, 23.53720459187964, 17.720045146669349, 36.496575181789318, 30.083217912982647, 34.205262752974143, 31.32091952673165, 18.867962264113206, 24.083189157584592, 19.235384061671343, 21.540659228538015, 20.615528128088304, 29.0, 22.360679774997898, 24.041630560342615, 27.313000567495326, 31.384709652950431, 30.265491900843113, 34.539832078341085, 32.984845004941285, 33.376638536557273, 34.058772731852805, 34.0, 34.014702703389901, 6.0827625302982193, 36.055512754639892, 3.6055512754639891, 31.144823004794873, 3.1622776601683795, 37.215588131856791, 7.2111025509279782, 4.4721359549995796, 35.355339059327378, 32.388269481403292, 25.495097567963924, 19.646882704388499, 38.470768123342687, 32.015621187164243, 36.138621999185304, 33.241540277189323, 20.0, 25.45584412271571, 19.646882704388499, 22.360679774997898, 21.189620100417091, 30.413812651491099, 22.803508501982758, 24.698178070456937, 28.178005607210743, 32.449961479175904, 30.594117081556711, 35.341194094144583, 33.526109228480422, 33.734255586865999, 34.23448553724738, 34.058772731852805, 28.0, 2.2360679774997898, 31.016124838541646, 3.6055512754639891, 35.057096285916209, 4.2426406871192848, 28.160255680657446, 32.140317359976393, 4.1231056256176606, 4.4721359549995796, 9.8488578017961039, 15.524174696260024, 6.4031242374328485, 7.6157731058639087, 8.0622577482985491, 8.2462112512353212, 26.627053911388696, 23.345235059857504, 34.132096331752024, 30.610455730027933, 33.015148038438355, 23.323807579381203, 35.0, 33.301651610693426, 31.89043743820395, 30.528675044947494, 40.311288741492746, 36.359317925395686, 39.204591567825318, 41.868842830916641, 44.598206241955516, 45.967379738244816, 30.016662039607269, 3.1622776601683795, 25.079872407968907, 7.2801098892805181, 31.144823004794873, 3.0, 5.0, 29.274562336608895, 26.305892875931811, 19.416487838947599, 13.601470508735444, 32.388269481403292, 25.96150997149434, 30.083217912982647, 27.202941017470888, 16.15549442140351, 20.808652046684813, 18.027756377319946, 19.416487838947599, 19.026297590440446, 25.612496949731394, 21.0, 22.203603311174518, 25.0, 28.635642126552707, 29.0, 32.280024783137947, 31.256999216175569, 32.015621187164243, 33.060550509633082, 33.241540277189323, 33.0, 5.0990195135927845, 37.013511046643494, 2.2360679774997898, 30.066592756745816, 34.058772731852805, 3.1622776601683795, 5.0, 11.401754250991379, 17.262676501632068, 4.4721359549995796, 7.810249675906654, 7.0710678118654755, 8.0622577482985491, 27.784887978899608, 24.083189157584592, 35.355339059327378, 31.622776601683793, 34.132096331752024, 23.600847442411894, 36.055512754639892, 34.205262752974143, 32.526911934581186, 30.805843601498726, 41.036569057366385, 36.61966684720111, 39.698866482558415, 42.449970553582247, 45.254833995939045, 46.690470119715009, 28.0178514522438, 4.1231056256176606, 34.058772731852805, 3.6055512754639891, 2.2360679774997898, 32.140317359976393, 29.154759474226502, 22.203603311174518, 16.278820596099706, 35.227829907617071, 28.635642126552707, 32.756678708318397, 29.832867780352597, 16.643316977093239, 21.931712199461309, 17.11724276862369, 19.313207915827967, 18.439088914585774, 26.870057685088806, 20.223748416156685, 21.840329667841555, 25.079872407968907, 29.154759474226502, 28.160255680657446, 32.310988842807021, 30.805843601498726, 31.256999216175569, 32.015621187164243, 32.015621187164243, 32.0, 6.0827625302982193, 25.019992006393608, 29.017236257093817, 4.4721359549995796, 2.2360679774997898, 6.324555320336759, 12.165525060596439, 7.6157731058639087, 5.0, 7.2111025509279782, 6.0827625302982193, 23.021728866442675, 19.849433241279208, 30.528675044947494, 27.018512172212592, 29.410882339705484, 20.124611797498108, 31.400636936215164, 29.732137494637012, 28.42534080710379, 27.294688127912362, 36.796738985948195, 33.120990323358392, 35.805027579936315, 38.418745424597091, 41.109609582188931, 42.449970553582247, 38.013155617496423, 7.0710678118654755, 3.1622776601683795, 36.055512754639892, 33.060550509633082, 26.076809620810597, 20.09975124224178, 39.11521443121589, 32.388269481403292, 36.496575181789318, 33.541019662496844, 18.384776310850235, 24.207436873820409, 17.088007490635061, 20.248456731316587, 18.788294228055936, 29.206163733020468, 20.248456731316587, 22.360679774997898, 26.076809620810597, 30.610455730027933, 27.892651361962706, 33.120990323358392, 31.016124838541646, 31.048349392520048, 31.400636936215164, 31.144823004794873, 31.0, 35.0, 2.2360679774997898, 5.0990195135927845, 12.041594578792296, 18.027756377319946, 2.2360679774997898, 7.2111025509279782, 5.3851648071345037, 7.0710678118654755, 27.730849247724095, 23.600847442411894, 35.341194094144583, 31.384709652950431, 34.0, 22.671568097509269, 35.846896657869841, 33.837848631377263, 31.89043743820395, 29.832867780352597, 40.459856648287818, 35.608987629529715, 38.897300677553446, 41.725292090050132, 44.598206241955516, 46.097722286464439, 4.0, 29.017236257093817, 26.019223662515376, 19.026297590440446, 13.038404810405298, 32.062439083762797, 25.317977802344327, 29.427877939124322, 26.476404589747453, 13.416407864998739, 18.439088914585774, 15.033296378372908, 16.492422502470642, 16.031219541881399, 23.345235059857504, 18.0, 19.235384061671343, 22.135943621178654, 25.942243542145693, 26.0, 29.410882339705484, 28.284271247461902, 29.017236257093817, 30.066592756745816, 30.265491900843113, 33.015148038438355, 30.016662039607269, 23.021728866442675, 17.029386365926403, 36.055512754639892, 29.274562336608895, 33.376638536557273, 30.413812651491099, 15.620499351813308, 21.2602916254693, 15.297058540778355, 17.888543819998318, 16.763054614240211, 26.248809496813376, 18.439088914585774, 20.248456731316587, 23.706539182259394, 28.0178514522438, 26.305892875931811, 30.870698080866262, 29.120439557122072, 29.427877939124322, 30.066592756745816, 30.0, 3.0, 10.0, 16.0, 3.1622776601683795, 5.0, 4.0, 5.0, 25.495097567963924, 21.400934559032695, 33.105890714493697, 29.154759474226502, 31.76476034853718, 20.615528128088304, 33.61547262794322, 31.622776601683793, 29.732137494637012, 27.802877548915689, 38.288379438153292, 33.600595232822883, 36.796738985948195, 39.597979746446661, 42.449970553582247, 43.931765272977593, 7.0, 13.0, 6.0827625302982193, 3.1622776601683795, 5.0, 4.0, 22.825424421026653, 19.104973174542799, 30.413812651491099, 26.627053911388696, 29.154759474226502, 18.867962264113206, 31.064449134018133, 29.206163733020468, 27.586228448267445, 26.076809620810597, 36.069377593742864, 31.906112267087632, 34.828149534535996, 37.536648758246919, 40.311288741492746, 41.725292090050132, 6.0, 13.038404810405298, 6.7082039324993694, 10.770329614269007, 8.0622577482985491, 17.029386365926403, 14.7648230602334, 24.413111231467404, 21.213203435596427, 23.430749027719962, 16.278820596099706, 25.495097567963924, 24.083189157584592, 23.323807579381203, 23.086792761230392, 31.400636936215164, 28.792360097775937, 30.886890422961002, 33.286633954186478, 35.805027579936315, 37.013511046643494, 19.026297590440446, 12.369316876852981, 16.492422502470642, 13.601470508735444, 13.038404810405298, 13.038404810405298, 19.798989873223331, 17.4928556845359, 19.209372712298546, 16.278820596099706, 21.400934559032695, 20.591260281974002, 20.880613017821101, 22.022715545545239, 28.178005607210743, 27.294688127912362, 28.460498941515414, 30.463092423455635, 32.649655434629018, 33.61547262794322, 7.2801098892805181, 4.2426406871192848, 6.7082039324993694, 27.856776554368238, 23.323807579381203, 35.468295701936398, 31.304951684997057, 34.014702703389901, 21.931712199461309, 35.777087639996637, 33.61547262794322, 31.400636936215164, 29.0, 40.0, 34.713109915419565, 38.209946349085598, 41.109609582188931, 44.045431091090478, 45.607017003965517, 4.1231056256176606, 1.4142135623730951, 20.615528128088304, 16.401219466856727, 28.231188426986208, 24.186773244895647, 26.832815729997478, 15.811388300841896, 28.653097563788805, 26.627053911388696, 24.758836806279895, 23.021728866442675, 33.301651610693426, 28.844410203711913, 31.89043743820395, 34.655446902326915, 37.483329627982627, 38.948684188300895, 3.0, 24.041630560342615, 19.235384061671343, 31.622776601683793, 27.313000567495326, 30.083217912982647, 17.691806012954132, 31.780497164141408, 29.529646120466801, 27.202941017470888, 24.758836806279895, 35.805027579936315, 30.479501308256342, 33.97057550292606, 36.878177829171548, 39.824615503479755, 41.400483088968905, 21.189620100417091, 16.643316977093239, 28.792360097775937, 24.596747752497688, 27.313000567495326, 15.620499351813308, 29.068883707497267, 26.92582403567252, 24.839484696748443, 22.803508501982758, 33.421549934136806, 28.600699292150182, 31.827660925679098, 34.655446902326915, 37.536648758246919, 39.05124837953327, 6.324555320336759, 7.6157731058639087, 4.4721359549995796, 6.4031242374328485, 11.180339887498949, 8.4852813742385695, 7.6157731058639087, 9.0553851381374173, 12.529964086141668, 15.231546211727817, 16.278820596099706, 16.124515496597098, 17.720045146669349, 19.697715603592208, 20.591260281974002, 13.038404810405298, 8.2462112512353212, 11.180339887498949, 5.0, 12.649110640673518, 10.295630140987001, 8.6023252670426267, 9.0, 16.970562748477139, 14.317821063276353, 16.124515496597098, 18.601075237738275, 21.2602916254693, 22.627416997969522, 5.0990195135927845, 2.2360679774997898, 17.11724276862369, 3.1622776601683795, 5.6568542494923806, 10.0, 15.264337522473747, 11.045361017187261, 16.401219466856727, 13.928388277184119, 14.142135623730951, 15.033296378372908, 15.297058540778355, 3.0, 12.041594578792296, 4.4721359549995796, 3.1622776601683795, 5.8309518948453007, 10.63014581273465, 10.770329614269007, 13.0, 12.0, 13.341664064126334, 15.231546211727817, 16.124515496597098, 15.033296378372908, 2.2360679774997898, 3.6055512754639891, 7.810249675906654, 13.038404810405298, 10.04987562112089, 14.422205101855956, 12.369316876852981, 13.0, 14.317821063276353, 14.866068747318506, 16.031219541881399, 13.152946437965905, 9.8488578017961039, 7.2111025509279782, 18.357559750685819, 13.038404810405298, 16.278820596099706, 19.209372712298546, 22.203603311174518, 23.853720883753127, 3.1622776601683795, 7.6157731058639087, 13.0, 8.0, 13.45362404707371, 10.770329614269007, 11.045361017187261, 12.165525060596439, 12.649110640673518, 4.4721359549995796, 9.8488578017961039, 7.6157731058639087, 10.816653826391969, 9.0553851381374173, 10.198039027185569, 12.083045973594572, 13.038404810405298, 5.3851648071345037, 8.6023252670426267, 7.2801098892805181, 7.6157731058639087, 10.0, 12.727922061357855, 14.212670403551895, 12.369316876852981, 5.8309518948453007, 9.4339811320566032, 12.529964086141668, 15.652475842498529, 17.464249196572979, 9.2195444572928871, 4.4721359549995796, 3.1622776601683795, 4.4721359549995796, 5.6568542494923806, 5.0, 8.0622577482985491, 11.180339887498949, 13.152946437965905, 3.1622776601683795, 6.324555320336759, 8.2462112512353212, 3.1622776601683795, 5.0990195135927845, 2.0])\n gromos_algorithm = GromosAlgorithm(condensed_matrix)\n cluster = gromos_algorithm._GromosAlgorithm__do_one_iteration(remaining_nodes,4.0)\n expected_cluster = Cluster(4,[4,8])\n self.assertEqual(cluster, expected_cluster)",
"title": ""
},
{
"docid": "7128572b138cebce688b35bcefe6ffee",
"score": "0.51026094",
"text": "def test_propagation_node_adds(self):\n p = NXGraph()\n primitives.add_nodes_from(\n p, [\"B\"]\n )\n\n l = NXGraph()\n primitives.add_nodes_from(\n l, [\"B\"]\n )\n\n r = NXGraph()\n primitives.add_nodes_from(\n r, [\"B\", \"B_res_1\", \"X\", \"Y\"]\n )\n primitives.add_edge(r, \"B_res_1\", \"B\")\n\n rule = Rule(p, l, r)\n\n instance = {\"B\": \"B\"}\n\n rhs_typing = {\n \"mm\": {\"B_res_1\": \"residue\"},\n \"mmm\": {\"X\": \"component\"}, \"colors\": {\"Y\": \"red\"}\n }\n try:\n self.hierarchy.rewrite(\n \"n1\", rule, instance,\n rhs_typing=rhs_typing, strict=True)\n raise ValueError(\"Error was not caught!\")\n except RewritingError:\n pass\n\n new_hierarchy = NXHierarchy.copy(self.hierarchy)\n\n new_hierarchy.rewrite(\n \"n1\", rule, instance, rhs_typing=rhs_typing)\n\n # test propagation of node adds\n assert(\"B_res_1\" in new_hierarchy.get_graph(\"n1\").nodes())\n assert(\"B_res_1\" in new_hierarchy.get_graph(\"ag\").nodes())\n assert(new_hierarchy.get_typing(\"n1\", \"ag\")[\"B_res_1\"] == \"B_res_1\")\n assert(new_hierarchy.get_typing(\"ag\", \"mm\")[\"B_res_1\"] == \"residue\")\n assert((\"B_res_1\", \"B\") in new_hierarchy.get_graph(\"n1\").edges())\n assert((\"B_res_1\", \"B\") in new_hierarchy.get_graph(\"ag\").edges())\n\n assert(\"X\" in new_hierarchy.get_graph(\"n1\").nodes())\n assert(\"X\" in new_hierarchy.get_graph(\"ag\").nodes())\n assert(\"X\" in new_hierarchy.get_graph(\"mm\").nodes())\n assert(\"X\" in new_hierarchy.get_graph(\"colors\").nodes())\n assert(new_hierarchy.get_typing(\"n1\", \"ag\")[\"X\"] == \"X\")\n assert(new_hierarchy.get_typing(\"ag\", \"mm\")[\"X\"] == \"X\")\n assert(new_hierarchy.get_typing(\"mm\", \"mmm\")[\"X\"] == \"component\")\n assert(new_hierarchy.get_typing(\"mm\", \"colors\")[\"X\"] == \"X\")\n\n assert(\"Y\" in new_hierarchy.get_graph(\"n1\").nodes())\n assert(\"Y\" in new_hierarchy.get_graph(\"ag\").nodes())\n assert(\"Y\" in new_hierarchy.get_graph(\"mm\").nodes())\n assert(\"Y\" in new_hierarchy.get_graph(\"mm\").nodes())\n assert(new_hierarchy.get_typing(\"n1\", \"ag\")[\"Y\"] == \"Y\")\n assert(new_hierarchy.get_typing(\"ag\", \"mm\")[\"Y\"] == \"Y\")\n assert(new_hierarchy.get_typing(\"mm\", \"mmm\")[\"Y\"] == \"Y\")\n assert(new_hierarchy.get_typing(\"mm\", \"colors\")[\"Y\"] == \"red\")",
"title": ""
},
{
"docid": "ff6bd53cc1c92da6eec00ccbfd5a3759",
"score": "0.51002103",
"text": "def num_nodes(self) -> int:\n return self._num_nodes",
"title": ""
},
{
"docid": "9c76c784a49f4b277cfe9380ad32b21e",
"score": "0.5086227",
"text": "def update(self):\r\n\r\n # create the node list\r\n for node in self.output_nodes_probs:\r\n node_dic = {\"node\": node, \"value\": self.output_nodes_probs[node]}\r\n self.output_nodes_trained.append(node_dic)\r\n\r\n # sorte the list\r\n self.output_nodes_trained = sorted(self.output_nodes_trained, key= lambda k : k[\"value\"])\r\n\r\n # update the value of each node\r\n total = 0\r\n for dic in self.output_nodes_trained:\r\n total += dic[\"value\"] \r\n dic[\"value\"] = total / self.passage",
"title": ""
},
{
"docid": "6f21be07eb943f3342dda7e288246d6d",
"score": "0.5085842",
"text": "def merge_graphs(res_list, num_nodes): # -> DGLHeteroGraph:\n ...",
"title": ""
},
{
"docid": "f74c6ada382edb49ca8f0ef25067fe23",
"score": "0.50840366",
"text": "def plusOne(self, head: ListNode) -> ListNode:\n\n node_map = {}\n\n temp = head\n\n index = 0\n prev = None\n while temp:\n node_map[index] = temp\n prev = temp\n temp = temp.next\n index+=1\n\n last_val = prev.val\n len_list = index\n\n if last_val<9:\n node_map[len_list-1].val+=1\n return head\n else:\n node_map[len_list-1].val = 0\n carry = 1\n index = len_list-2\n while carry and index>=0:\n\n if node_map[index].val==9:\n node_map[index].val = 0\n carry = 1\n index-=1\n else:\n node_map[index].val+=1\n carry = 0\n index-=1\n\n if carry:\n newNode = ListNode(1)\n newNode.next = head\n head = newNode\n return head\n else:\n return head",
"title": ""
},
{
"docid": "11c29437566d4086eb08d4cc66177db0",
"score": "0.5083451",
"text": "def nplotprep(x_dom, y_dom, z2, layers, weights, layer_sizes):\n X, Y = np.meshgrid(x_dom, y_dom)\n z2 = np.zeros((x_dom.size, y_dom.size)).astype(np.float64)\n\n # Forward pass\n # For each point (x,y) in the meshgrid domain, the trained Protein Network\n # returns an output that corresponds with f(x,y)\n rows = np.size(X, 0)\n columns = np.size(X, 1)\n for row in range(rows):\n for column in range(columns):\n #\n layers[0] = X[row, column]\n layers[1] = Y[row, column]\n current_layer_size = layer_sizes[0]\n nodes_before_current_layer = 0\n for layer in range(1, NLAYERS):\n previous_layer_size = current_layer_size\n current_layer_size = layer_sizes[layer]\n nodes_before_previous_layer = nodes_before_current_layer\n nodes_before_current_layer += previous_layer_size\n for node in range(current_layer_size):\n\n # Connectivity between nodes from adjacent layers\n u1 = 2*node\n while u1 >= previous_layer_size:\n u1 -= previous_layer_size\n u2 = u1 + 1\n while u2 >= previous_layer_size:\n u2 -= previous_layer_size\n\n# u1 = node\n# u2 = node + 1\n# while (u2 >= previous_layer_size):\n# u2 -= previous_layer_size\n\n\n x1 = bound(layers[nodes_before_previous_layer + u1])\n x2 = bound(layers[nodes_before_previous_layer + u2])\n\n layers[nodes_before_current_layer + node] = \\\n weights[WPERNODE*(nodes_before_current_layer + node)] \\\n + weights[WPERNODE*(nodes_before_current_layer + node) + 1] * x1 \\\n + weights[WPERNODE*(nodes_before_current_layer + node) + 2] * x2 \\\n + weights[WPERNODE*(nodes_before_current_layer + node) + 3] * x1 * x2\n\n\n z2[row, column] = layers[nodes_before_current_layer]\n\n fig = plt.figure(2)\n ax = fig.gca(projection = '3d')\n ax.scatter(X, Y, z2, c = 'b')\n plt.show()",
"title": ""
},
{
"docid": "9833b91a1c6cdf9524cfafaeea1cfe2f",
"score": "0.5082563",
"text": "def assign_weight_count_all_0_after_2_del_3(cell, in_dim, out_dim):\n param_dict = {}\n param_dict['wgx'] = np.zeros((in_dim, out_dim))\n param_dict['wgh'] = np.zeros((out_dim, out_dim))\n param_dict['wgh'] = np.asarray([[00., 0.],[100., 0.]])\n param_dict['bg'] = [[0. , 100.]]\n\n\n #param_dict['wix'] = [[[100.] if i == 0 else [-100.] for i in range(10)] , [[100.] if i == 2 else [-100.] for i in range(10)]]\n param_dict['wix'] = np.asarray([100. if (i == 0 or i == 5) else -100. for i in range(20)])\n param_dict['wix'] = np.reshape(param_dict['wix'], (10,2))\n #print(param_dict['wix'])\n #print(param_dict['wix'].shape)\n param_dict['wih'] = np.zeros((out_dim, out_dim))\n param_dict['bi'] = np.zeros((1, out_dim))\n\n param_dict['wfx'] = np.asarray([-100. if (i == 6 or i == 7) else 100. for i in range(20)])\n param_dict['wfx'] = np.reshape(param_dict['wfx'], (10, 2))\n param_dict['wfh'] = np.zeros((out_dim, out_dim))\n param_dict['bf'] = np.zeros((1, out_dim))\n\n param_dict['wox'] = np.zeros((in_dim, out_dim))\n param_dict['woh'] = np.zeros((out_dim, out_dim))\n param_dict['bo'] = 100*np.ones((1, out_dim))\n\n for key in param_dict:\n cell.set_config_by_name(key, param_dict[key])",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="745"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">ec8f1251875f46906e98641a9876f069</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Test all known migration cases.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "46833f24f414b7e1599600f9531df70b",
"score": "0.0",
"text": "def test_compiled_string_is_expected():\n builder = DjangoTagMigrationBuilder()\n file_migration = builder.build_migration(DJANGO_TEMPLATE)\n compiled = file_migration.compile()\n assert compiled == TRANSIFEX_TEMPLATE\n\n # Make sure the migration is idempotent\n file_migration = builder.build_migration(compiled)\n assert file_migration.compile() == TRANSIFEX_TEMPLATE",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "c12c7465e867794386171e686d0362be",
"score": "0.7462017",
"text": "def run_migration_checks():\n check_model_state()\n check_migration_state()",
"title": ""
},
{
"docid": "0336de207b599a5978811d3f4062925c",
"score": "0.7103489",
"text": "def migration():",
"title": ""
},
{
"docid": "074db9d0c3aac3bdbcbd204a6c6d4a95",
"score": "0.67845577",
"text": "def _run_unit_test_migrations(engine):\n with temporary_sys_path('rdr_service'): # the revision files need to find modules (like model) in rdr_service\n migrations_directory = os.path.join(os.getcwd(), \"rdr_service\", \"alembic\")\n migrations_api = ScriptDirectory(migrations_directory)\n\n # walk_revisions returns revisions in order of newest to oldest,\n # reversing to start with the first and work up to the latest\n for revision in reversed(list(migrations_api.walk_revisions())):\n with warnings.catch_warnings(): # Ignore warnings from 'DROP IF EXISTS' sql statements\n warnings.simplefilter(\"ignore\")\n\n if hasattr(revision.module, 'unittest_schemas'):\n for operation in revision.module.unittest_schemas():\n engine.execute(operation)",
"title": ""
},
{
"docid": "36728ccc494e3768f90313aa412fd26d",
"score": "0.6619223",
"text": "def _check_and_apply_migrations(self) -> None:\n from hathor.transaction.storage.exceptions import OutOfOrderMigrationError, PartialMigrationError\n db_is_empty = self.is_empty()\n self.log.debug('step through all migrations', count=len(self._migrations))\n migrations_to_run = []\n # XXX: this is used to ensure migrations don't advance out of order\n previous_migration_state = MigrationState.COMPLETED\n for migration in self._migrations:\n migration_name = migration.get_db_name()\n self.log.debug('step migration', migration=migration_name)\n\n # short-cut to avoid running migrations on empty database\n if migration.skip_empty_db() and db_is_empty:\n self.log.debug('migration is new, but does not need to run on an empty database',\n migration=migration_name)\n self.set_migration_state(migration_name, MigrationState.COMPLETED)\n continue\n\n # get the migration state to decide whether to run, skip or error\n migration_state = self.get_migration_state(migration_name)\n\n if migration_state > previous_migration_state:\n raise OutOfOrderMigrationError(f'{migration_name} ran after a migration that wasn\\'t advanced')\n previous_migration_state = migration_state\n\n should_run_migration: bool\n if migration_state is MigrationState.NOT_STARTED:\n self.log.debug('migration is new, will run', migration=migration_name)\n should_run_migration = True\n elif migration_state is MigrationState.STARTED:\n self.log.warn('this migration was started before, but it is not marked as COMPLETED or ERROR, '\n 'it will run again but might fail', migration=migration_name)\n should_run_migration = True\n elif migration_state is MigrationState.COMPLETED:\n self.log.debug('migration is already complete', migration=migration_name)\n should_run_migration = False\n elif migration_state is MigrationState.ERROR:\n self.log.error('this migration was run before but resulted in an error, the database will need to be '\n 'either manually fixed or discarded', migration=migration_name)\n raise PartialMigrationError(f'Migration error state previously: {migration_name}')\n else:\n raise ValueError(f'Unexcepted migration state: {migration_state!r}')\n\n # run if needed, updating the state along the way\n if should_run_migration:\n migrations_to_run.append(migration)\n self.log.debug('stepped through all migrations')\n if migrations_to_run:\n self.log.info('there are migrations that need to be applied')\n migrations_to_run_count = len(migrations_to_run)\n for i, migration in enumerate(migrations_to_run):\n migration_name = migration.get_db_name()\n self.log.info(f'running migration {i+1} out of {migrations_to_run_count}', migration=migration_name)\n self.set_migration_state(migration_name, MigrationState.STARTED)\n try:\n migration.run(self)\n # XXX: we catch \"any\" exception because just we want to mark the state as \"ERROR\"\n except Exception as exc:\n self.set_migration_state(migration_name, MigrationState.ERROR)\n raise PartialMigrationError(f'Migration error state: {migration_name}') from exc\n else:\n self.set_migration_state(migration_name, MigrationState.COMPLETED)\n if migrations_to_run:\n self.log.info('all migrations have been applied')",
"title": ""
},
{
"docid": "375327eedfe5d1482fadf15b5d8df227",
"score": "0.6544895",
"text": "def setUp(self):\n super(MigrationTestCase, self).setUp()\n\n self.executor = MigrationExecutor(connection)\n self.executor.migrate(self.migrate_from)",
"title": ""
},
{
"docid": "effdad4063936bd8895e1723e52e83db",
"score": "0.6538441",
"text": "def migrate(self):\n\tpass",
"title": ""
},
{
"docid": "6d8e3f7c5dde57e5632b854e919b79e4",
"score": "0.6448131",
"text": "def migrate():\n if apply_migrations():\n click.echo(OK)\n else:\n sys.exit(1)",
"title": ""
},
{
"docid": "1d6bb952eb1ee914f45c74c9abbea80f",
"score": "0.6445694",
"text": "def test_vm_migration(self):\n testflow.step(\n \"Migrate VM %s on host %s\", conf.VM_NAME[0], conf.HOSTS[2]\n )\n assert not ll_vms.migrateVm(\n positive=True, vm=conf.VM_NAME[0], host=conf.HOSTS[2]\n )",
"title": ""
},
{
"docid": "67c1a14dead70d7edcb5ab95c4baf29e",
"score": "0.64454705",
"text": "def test_vm_migration(self):\n self.check_vm_host_after_migration(positive=False)",
"title": ""
},
{
"docid": "67c1a14dead70d7edcb5ab95c4baf29e",
"score": "0.64454705",
"text": "def test_vm_migration(self):\n self.check_vm_host_after_migration(positive=False)",
"title": ""
},
{
"docid": "1143ebe9793de697cf267876c09b5c7b",
"score": "0.6443427",
"text": "def test_vm_migration(self):\n self.check_vm_host_after_migration(positive=True)",
"title": ""
},
{
"docid": "1143ebe9793de697cf267876c09b5c7b",
"score": "0.6443427",
"text": "def test_vm_migration(self):\n self.check_vm_host_after_migration(positive=True)",
"title": ""
},
{
"docid": "74f18e84593f4443ad188fa1760f23b3",
"score": "0.6423938",
"text": "def test_startMigration(self):\n source = MockContentStore()\n destination = MockContentStore(store=self.store)\n result = self.manager.migrate(source, destination)\n self.assertEquals(result.ran, 1)\n self.assertEquals(source.migrationDestination, destination)\n self.assertEquals(IMigration(self.store), result)",
"title": ""
},
{
"docid": "9f1f8db39c553390078364fdfb9464d5",
"score": "0.6409702",
"text": "def setUpBeforeMigration(self, apps):\n pass",
"title": ""
},
{
"docid": "b4fac48d4b228a42ed71f553414e6f04",
"score": "0.6407803",
"text": "def test_vm_migration(self):\n testflow.step(\n \"Migrate VM %s on host %s\", conf.VM_NAME[0], conf.HOSTS[1]\n )\n assert not ll_vms.migrateVm(\n positive=True, vm=conf.VM_NAME[0], host=conf.HOSTS[1]\n )",
"title": ""
},
{
"docid": "8e75bc433b6fa8f46401588f60fc4221",
"score": "0.640438",
"text": "def test_vm_migration(self):\n testflow.step(\n \"Migrate VM %s on host %s\", conf.VM_NAME[0], conf.HOSTS[2]\n )\n assert ll_vms.migrateVm(\n positive=True, vm=conf.VM_NAME[0], host=conf.HOSTS[2]\n )",
"title": ""
},
{
"docid": "38f4966417154c4bf07f5d9e6f4e4f18",
"score": "0.6400735",
"text": "def migrations(request):\n marker = request.node.get_marker('migrations')\n migrations = [] if marker is None else marker.args\n\n # we don't need to test all the variations of migration comments here, only\n # in the parser. If this requirement changes in the future, the statements\n # here should just use the up_stmt and down_stmt fixtures.\n up_cmd = '%s %s' % (TEST_COMMENTS[0], UP_CMD)\n down_cmd = '%s %s' % (TEST_COMMENTS[0], DOWN_CMD)\n\n migrations_dir = py.path.local.mkdtemp()\n\n for name, (up, down) in migrations:\n migration = '\\n'.join([up_cmd, up, down_cmd, down])\n migrations_dir.join(name).write(migration)\n\n yield migrations_dir\n\n migrations_dir.remove()",
"title": ""
},
{
"docid": "5300e962e50ed046c20bf55a256c91e3",
"score": "0.6385144",
"text": "def test_serviceRunsMigrations(self):\n m1 = TestMigration(store=self.store)\n m2 = TestMigration(store=self.store)\n self.store.powerUp(m1)\n self.store.powerUp(m2)\n self.assertEquals(m1.ran, 0)\n self.assertEquals(m2.ran, 0)\n self.manager.startService()\n self.assertEquals(m1.ran, 1)\n self.assertEquals(m2.ran, 1)",
"title": ""
},
{
"docid": "d0a19873af5caed4f9734d01bf10e8fd",
"score": "0.63626474",
"text": "def test_vm_migration(self):\n testflow.step(\n \"Migrate VM %s on host %s\", conf.VM_NAME[0], conf.HOSTS[1]\n )\n assert ll_vms.migrateVm(\n positive=True, vm=conf.VM_NAME[0], host=conf.HOSTS[1]\n )",
"title": ""
},
{
"docid": "aca76d387186b6ba63d4c515c2d279bc",
"score": "0.630026",
"text": "def run_migration(env, upgrade_type):\n pass",
"title": ""
},
{
"docid": "723663cd0f622d9d338ce0480b58a42e",
"score": "0.6276595",
"text": "def process_all_migrations(self):\n available_migrations = self.get_available_migrations()\n candidates = []\n for migration in available_migrations:\n candidates += self.get_migrations_to_run(migration)\n # Remove duplicates after all the migrations have been processed\n # to lower time complexity.\n migrations_to_run = list()\n unique_candidates = set()\n for candidate in candidates:\n if candidate.get(\"name\") not in unique_candidates:\n migrations_to_run.append(candidate)\n unique_candidates.add(candidate.get(\"name\"))\n self.run_migrations(migrations_to_run)",
"title": ""
},
{
"docid": "501cd410426a1e85dc5cd23ebbb24f56",
"score": "0.6259719",
"text": "def post_migrations(self):",
"title": ""
},
{
"docid": "643ed25775275d326178564f3a39dff1",
"score": "0.62148243",
"text": "async def migrate(self):\n # Controlla se ci sono tabelle nel db\n async with self.db.acquire() as conn:\n async with conn.cursor() as cur:\n await cur.execute(\"\"\"SELECT COUNT(DISTINCT table_name) as c\n FROM information_schema.columns\n WHERE table_schema = %s\"\"\", (conn.db,))\n db_empty = (await cur.fetchone())[\"c\"] <= 0\n\n # Se ci sono tabelle, prova a leggere `db_version`\n if not db_empty:\n await cur.execute(\"SELECT db_version FROM db_version LIMIT 1\")\n db_version_in_db = await cur.fetchone()\n db_version = 0 if db_version_in_db is None else db_version_in_db[\"db_version\"]\n else:\n db_version = 0\n\n # Prendi la lista di file sql e py da eseguire\n new_migrations = [x for x in self.migrations if x.id > db_version]\n\n # Controlla se ci sono migration da eseguire\n if not new_migrations:\n self.logger.info(\"No new migrations. The database is already up to date!\")\n return\n\n # Esegui migrations\n self.logger.info(\"Current db version: @{}\".format(db_version))\n db_version += 1\n current_migration = self.get_migration(db_version)\n while current_migration is not None:\n self.logger.info(\"Executing {}\".format(current_migration.file_name))\n\n if current_migration.type == \"sql\":\n # Leggi ed esegui file sql\n with open(\n os.path.join(os.path.dirname(__file__), \"migrations/{}\".format(current_migration.file_name)), \"r\"\n ) as f:\n data = f.read()\n async with self.db.acquire() as conn:\n async with conn.cursor() as cur:\n await cur.execute(data)\n await conn.commit()\n elif current_migration.type == \"py\":\n # Importa modulo py\n module = importlib.import_module(\"migrator.migrations.{}\".format(current_migration.file_name[:-3]))\n migr = getattr(module, \"do\")\n await migr()\n\n # Migration eseguita, aggiorna `db_version`\n self.logger.info(\"Migration {} executed with no errors\".format(current_migration.file_name))\n await self.save_db_version(db_version)\n\n # Vai alla prossima migration\n db_version += 1\n current_migration = self.get_migration(db_version)\n self.logger.info(\"All migrations executed correctly\")",
"title": ""
},
{
"docid": "351977b6b92b4c7cd18d02781876719a",
"score": "0.6207705",
"text": "def check_missing_migrations():\n from django.db.migrations.autodetector import MigrationAutodetector\n from django.db.migrations.loader import MigrationLoader\n from django.db.migrations.questioner import (\n NonInteractiveMigrationQuestioner as Questioner,\n )\n from django.db.migrations.state import ProjectState\n\n loader = MigrationLoader(None, ignore_no_migrations=True)\n conflicts = loader.detect_conflicts()\n if conflicts:\n raise Exception(\n \"Migration conflicts detected. Please fix your migrations.\"\n )\n questioner = Questioner(dry_run=True, specified_apps=None)\n autodetector = MigrationAutodetector(\n loader.project_state(),\n ProjectState.from_apps(apps),\n questioner,\n )\n changes = autodetector.changes(\n graph=loader.graph,\n trim_to_apps=None,\n convert_apps=None,\n migration_name=None,\n )\n if changes:\n raise Exception(\n \"Migration changes detected. \"\n \"Please update or add to the migration file as appropriate\"\n )\n print(\"Migration-checker detected no problems.\")",
"title": ""
},
{
"docid": "1f048a29f9fbdc62abf6407f0460091f",
"score": "0.6202128",
"text": "def run_migrations(self, migrations):\n for migration in migrations:\n name = migration[\"name\"]\n migration[\"script\"] = self.get_sql_script(name)\n\n if self.dry_run:\n for migration in migrations:\n print(f'---------------- {migration[\"name\"]} ----------------')\n print(migration[\"script\"])\n return\n\n if not self.accept_all and not self.prompt_for_migrations(migrations):\n return\n\n applied_migrations = []\n with self.target_db.begin() as conn:\n for migration in migrations:\n name = migration[\"name\"]\n script = migration[\"script\"]\n if self.apply_migrations:\n print(f\"Applying {name}\")\n conn.execute(script)\n applied_migrations.append(name)\n if self.register:\n self.register_migrations(applied_migrations)",
"title": ""
},
{
"docid": "7a6045660715a792c0ce1e7b7c2a8cb3",
"score": "0.6199002",
"text": "def test_migrating_old_and_new_with_post_migrate_function(self):\n yield self.mk_simple_models_old(1)\n yield self.mk_simple_models_new(1, start=1)\n yield self.mk_simple_models_old(1, start=2)\n model_migrator = self.make_migrator(\n post_migrate_function=fqpn(post_migrate_function))\n loads, stores = self.recorded_loads_and_stores(model_migrator)\n\n yield model_migrator.run()\n self.assertEqual(model_migrator.output, [\n \"Migrating ...\",\n \"Done, 3 objects migrated.\",\n ])\n self.assertEqual(sorted(loads), [u\"key-%d\" % i for i in range(3)])\n self.assertEqual(sorted(stores), [u\"key-%d\" % i for i in range(3)])\n for i in range(3):\n obj = yield self.model.load(u\"key-%d\" % i)\n self.assertEqual(obj.a, u\"value-%d-modified\" % i)",
"title": ""
},
{
"docid": "955ec7b2058c52ab03599bc74f7d6434",
"score": "0.61968714",
"text": "def db_upgrade():\n generate_migration_file()\n dbu_query = anosql.from_path(MIGRATION_FILE, 'psycopg2')\n\n for time_step in [_.strip('.sql') for _ in migration_files()]:\n decide = MySQLScheme.fetch_one(REVISION_EXISTS,\n **{\"args\": {'revision': time_step}})\n if not decide:\n MySQLScheme.commit(getattr(dbu_query, f\"upgrade_{time_step}\").sql)\n LOGGER.info(f\"successful migration: {time_step}\")\n else:\n LOGGER.info(f'migration already exists: {time_step}')",
"title": ""
},
{
"docid": "fd6ef83f22034f79ee97cd5e3d15861c",
"score": "0.6191808",
"text": "def test_migrating_with_post_migrate_function(self):\n yield self.mk_simple_models_old(3)\n model_migrator = self.make_migrator(\n post_migrate_function=fqpn(post_migrate_function))\n loads, stores = self.recorded_loads_and_stores(model_migrator)\n\n yield model_migrator.run()\n self.assertEqual(model_migrator.output, [\n \"Migrating ...\",\n \"Done, 3 objects migrated.\",\n ])\n self.assertEqual(sorted(loads), [u\"key-%d\" % i for i in range(3)])\n self.assertEqual(sorted(stores), [u\"key-%d\" % i for i in range(3)])\n for i in range(3):\n obj = yield self.model.load(u\"key-%d\" % i)\n self.assertEqual(obj.a, u\"value-%d-modified\" % i)",
"title": ""
},
{
"docid": "49d5ae4370384d3021bafaffc051cad3",
"score": "0.6172413",
"text": "async def _migrate(db, configs, migration_name):\n for test_config in configs:\n try:\n await TestConfig(**test_config).save_to_db(db)\n except Exception as exc:\n log.error(f\"Migration {migration_name} has failed\")\n else:\n db.Migrations.insert_one({migration_name: True})",
"title": ""
},
{
"docid": "263a42042556a23bf6103ab27fcb8faa",
"score": "0.61551446",
"text": "def test_migration(self):\n def _mkObject(content):\n return self.contentStore._storeObject(\n content=content,\n contentType=u'application/octet-stream')\n\n obj1 = _mkObject(u'object1')\n obj2 = _mkObject(u'object2')\n\n dest = self.mockStore\n migration = self.contentStore.migrateTo(dest)\n d = migration.run()\n\n # Already running, so a new run should not be started\n self.assertIdentical(migration.run(), None)\n\n # This is created after the migration, so should not be migrated\n _mkObject(u'object2')\n\n def _verify(ign):\n self.assertEquals(\n dest.events,\n [('storeObject', dest, self.successResultOf(obj1.getContent()),\n obj1.contentType, obj1.metadata, obj1.created, obj1.objectId),\n ('storeObject', dest, self.successResultOf(obj2.getContent()),\n obj2.contentType, obj2.metadata, obj2.created,\n obj2.objectId)])\n return d.addCallback(_verify)",
"title": ""
},
{
"docid": "b1d26ce1d745bdbfd18917bd3f1a9d2b",
"score": "0.6149439",
"text": "def test12(self):\n ###get a dataset to migrate from global dbs\n dest_datasets = set((dataset['dataset'] for dataset in self.api.listDatasets()))\n ###only dataset after last DBS2->3 because of the parentage issue in DBS 2 min_cdate=1368162000 =10May2013\n src_datasets = set((dataset['dataset'] for dataset in self.cmsweb_api.listDatasets(min_cdate=1368162000)))\n dataset_to_migrate = choice(list(src_datasets.difference(dest_datasets)))\n\n ###submit migration request\n toMigrate = {'migration_url': self.source_url,\n 'migration_input': dataset_to_migrate}\n migration_request = self.migration_api.submitMigration(toMigrate)\n self.assertTrue('migration_request_id' in migration_request['migration_details'])\n migration_request_id = migration_request['migration_details']['migration_request_id']\n print(\"____toMigrate___\")\n print(toMigrate)\n print(\"----------migration_request -----------\")\n print(migration_request)\n ###check migration status for max. 300s (should be enough time to migrate the dataset)\n with Timeout(300):\n while True:\n request_status = self.migration_api.statusMigration(migration_rqst_id=migration_request_id)\n if request_status[0]['migration_status'] == 2:\n break\n\n ###validate dataset migration\n def check(input, output):\n non_comparable_keys = ('block_id', 'dataset_id', 'last_modification_date',\n 'parent_file_id', 'primary_ds_id')\n if isinstance(input, dict):\n for key, value in input.items():\n if key in non_comparable_keys:\n continue ###do not compare id's\n if key in ('processing_era',): ###do compare create_by, creation_date for re-used entries\n for key2remove in ('create_by', 'creation_date',):\n try:\n del input[key][key2remove]\n del output[key][key2remove]\n except KeyError:\n pass\n self.assertTrue(key in output)\n check(value, output[key])\n elif isinstance(input, list):\n for element_in, element_out in zip(sorted(remove_non_comparable_keys(input, non_comparable_keys)),\n sorted(remove_non_comparable_keys(output, non_comparable_keys))):\n check(element_in, element_out)\n else:\n self.assertEqual(str(input), str(output))\n\n for block_name in (block['block_name'] for block in self.cmsweb_api.listBlocks(dataset=dataset_to_migrate)):\n block_dump_src = self.cmsweb_api.blockDump(block_name=block_name)\n block_dump_dest = self.api.blockDump(block_name=block_name)\n check(block_dump_src, block_dump_dest)\n\n ###try to delete successfully executed migration request\n toDelete = {'migration_rqst_id': migration_request_id}\n self.assertRaises(HTTPError, self.migration_api.removeMigration, toDelete)",
"title": ""
},
{
"docid": "2b6d68fdd434cca9853824ae8edef888",
"score": "0.61327255",
"text": "def check_migration(self, migration: str) -> bool:\n pass",
"title": ""
},
{
"docid": "b1f3a562cf1b1858f339e894a3e615e6",
"score": "0.60994345",
"text": "def test_normalUpgrade(self):\n\n self.setUpInitialStates()\n\n config.DocumentRoot = self.olddocroot\n config.DataRoot = self.newdataroot\n\n # Check pre-conditions\n self.assertTrue(os.path.exists(os.path.join(config.DocumentRoot, \"principals\")))\n self.assertTrue(os.path.isdir(os.path.join(config.DocumentRoot, \"principals\")))\n self.assertTrue(os.path.exists(os.path.join(config.DocumentRoot, \"principals\", OLDPROXYFILE)))\n self.assertFalse(os.path.exists(os.path.join(config.DataRoot, NEWPROXYFILE)))\n\n (yield self.doUpgrade(config))\n\n # Check post-conditions\n self.assertFalse(os.path.exists(os.path.join(config.DocumentRoot, \"principals\",)))\n self.assertTrue(os.path.exists(os.path.join(config.DataRoot, NEWPROXYFILE)))",
"title": ""
},
{
"docid": "a44a03878316fcf7fa9b0674d82bb62f",
"score": "0.6072146",
"text": "def update_migrations_run(self, migration: str):\n pass",
"title": ""
},
{
"docid": "55beb3d6b1260935df999b428d285ef2",
"score": "0.6054114",
"text": "def _run_migrations(self, current_migration_version: int):\n logger.debug(\"Checking for necessary database migrations...\")\n\n while current_migration_version < latest_migration_version:\n next_migration_version = current_migration_version + 1\n logger.info(\n f\"Migrating the database from v{current_migration_version} to v{next_migration_version}...\",\n )\n\n migration = importlib.import_module(f\".migrations.{str(next_migration_version).rjust(3, '0')}\", \"middleman\")\n # noinspection PyUnresolvedReferences\n migration.migrate(self)\n\n # Update the stored migration version\n self._execute(\"UPDATE migration_version SET version = ?\", (next_migration_version,))\n\n logger.info(f\"Database migrated to v{next_migration_version}\")\n current_migration_version += 1",
"title": ""
},
{
"docid": "978b5a29960f60c50d4923e992165843",
"score": "0.6022861",
"text": "def test_migration(self) -> None:\n before = \"\"\"\n from qgreenland.models.config.layer_group import LayerGroupSettings\n\n settings = LayerGroupSettings(\n order=[\n \":foo\",\n \":bar\",\n \"Baz\",\n ],\n )\n \"\"\"\n after = \"\"\"\n from qgreenland.models.config.layer_group import LayerGroupIdentifier, LayerIdentifier, LayerGroupSettings\n\n settings = LayerGroupSettings(\n order=[\n LayerIdentifier(\"foo\"),\n LayerIdentifier(\"bar\"),\n LayerGroupIdentifier(\"Baz\"),\n ],\n )\n \"\"\"\n\n self.assertCodemod(\n before,\n after,\n context_override=CodemodContext(filename=MOCK_FILEPATH),\n )",
"title": ""
},
{
"docid": "6d7fb22b7f3f87dddcf0664d741d2a7e",
"score": "0.601145",
"text": "def test_migrating_old_and_new_with_new_only_post_migrate_function(self):\n yield self.mk_simple_models_old(1)\n yield self.mk_simple_models_new(1, start=1)\n yield self.mk_simple_models_old(1, start=2)\n model_migrator = self.make_migrator(\n post_migrate_function=fqpn(post_migrate_function_new_only))\n loads, stores = self.recorded_loads_and_stores(model_migrator)\n\n yield model_migrator.run()\n self.assertEqual(model_migrator.output, [\n \"Migrating ...\",\n \"Done, 3 objects migrated.\",\n ])\n self.assertEqual(sorted(loads), [u\"key-0\", u\"key-1\", u\"key-2\"])\n self.assertEqual(sorted(stores), [u\"key-0\", u\"key-2\"])\n\n obj_0 = yield self.model.load(u\"key-0\")\n self.assertEqual(obj_0.a, u\"value-0-modified\")\n obj_1 = yield self.model.load(u\"key-1\")\n self.assertEqual(obj_1.a, u\"value-1\")\n obj_2 = yield self.model.load(u\"key-2\")\n self.assertEqual(obj_2.a, u\"value-2-modified\")",
"title": ""
},
{
"docid": "4c6441c81e9555f4727204facf08306c",
"score": "0.60112655",
"text": "def test_migrating_old_and_new_keys(self):\n yield self.mk_simple_models_old(1)\n yield self.mk_simple_models_new(1, start=1)\n yield self.mk_simple_models_old(1, start=2)\n model_migrator = self.make_migrator(self.default_args)\n loads, stores = self.recorded_loads_and_stores(model_migrator)\n\n yield model_migrator.run()\n self.assertEqual(model_migrator.output, [\n \"Migrating ...\",\n \"Done, 3 objects migrated.\",\n ])\n self.assertEqual(sorted(loads), [u\"key-0\", u\"key-1\", u\"key-2\"])\n self.assertEqual(sorted(stores), [u\"key-0\", u\"key-2\"])",
"title": ""
},
{
"docid": "e4baf32b2107abbe11bc3c432cc51394",
"score": "0.5988913",
"text": "def setUpClass(cls):\n super(OVSOVNMigrationTest, cls).setUpClass()",
"title": ""
},
{
"docid": "5c8546f23e3a831e03097ec54328cc4b",
"score": "0.59685075",
"text": "def run_all_tests():\n remove_dbs()\n run_training_tests()\n run_custom_training_tests()\n run_training_save_tests()\n run_validation_tests()\n run_feature_extraction_tests()",
"title": ""
},
{
"docid": "62909f5d0ef6f29e2c279e3f1053540e",
"score": "0.5964635",
"text": "def setup_before_migration(self, apps):",
"title": ""
},
{
"docid": "5cdb1a98062cd5b73d74f458d012ff4e",
"score": "0.59640235",
"text": "def test_Migration_columns(self):\n migration = self.DBSession.query(Migration).filter_by().first()\n if self.engine.dialect.name == 'sqlite': # pragma: no cover\n # pysqlite driver always convert the strings collumns to unicode\n self.assertIsInstance(migration.version_num, unicode)\n else: # pragma: no cover\n self.assertIsInstance(migration.version_num, str)",
"title": ""
},
{
"docid": "4d4d8a264094dca1c3ee35fa487a4fef",
"score": "0.5958875",
"text": "def test_change_provisioned_throughput_usual_case():",
"title": ""
},
{
"docid": "5c34421487540c40646f8a2a35728f7d",
"score": "0.5951084",
"text": "def migrate(cls)->None:\n pass",
"title": ""
},
{
"docid": "98beba5657a4b777badb0fc12b98872b",
"score": "0.5950841",
"text": "def run_necessary_migrations(sql_migrations: List[str], english_migrations: List[str]):\n\n\n con = sqlite3.connect(DB_NAME)\n cur = con.cursor()\n\n cur.execute('''\n SELECT name FROM sqlite_master WHERE type='table' AND name = '__plainapi_migrations';\n ''')\n rows = cur.fetchall()\n existing_migrations: List[Any] = []\n if len(rows) == 0:\n # create the table\n cur.execute('''\n CREATE TABLE __plainapi_migrations (\n id INTEGER PRIMARY KEY AUTOINCREMENT, \n sql_query VARCHAR(500) NOT NULL, \n english_query VARCHAR(500) NOT NULL\n );\n ''')\n else:\n cur.execute('''\n SELECT sql_query, english_query FROM __plainapi_migrations ORDER BY id ASC;\n ''')\n for sql_query, english_query in cur.fetchall():\n existing_migrations.append({'sql': sql_query, 'english': english_query})\n\n # ensure the existing migrations are correct\n for a, b in zip(existing_migrations, english_migrations):\n if a['english'] != b:\n raise ValueError(f'Invalid previously applied migration (it has been changed):\\n \"{a[\"english\"]}\" -> \"{b}\"')\n\n if len(sql_migrations) != len(english_migrations):\n raise ValueError('Internal: There are more SQL migrations than original English migrations')\n\n if len(existing_migrations) < len(sql_migrations):\n print('Running migrations...')\n for idx, (sql, english) in enumerate(zip(sql_migrations, english_migrations)):\n if idx < len(existing_migrations):\n pass\n else:\n print(f' ...{english}')\n cur.execute(sql)\n cur.execute('''\n INSERT INTO __plainapi_migrations (sql_query, english_query) VALUES (?, ?);\n ''', (sql, english,))\n print('All up to date.')\n else:\n print('No migrations to run.')\n\n con.commit()",
"title": ""
},
{
"docid": "4b1ed8a71b779cd90f5ac0eb049b934d",
"score": "0.59297144",
"text": "def test_changeVersions(self):\n self._testVersionChanging(8, 2, 3)",
"title": ""
},
{
"docid": "77a20f7ecee78fbf5f4f260686eb390a",
"score": "0.58919775",
"text": "def run_migration(self):\n step = \"Migrating Database\"\n try:\n self.slacker.send_thread_reply(step)\n self.kuber.run_migration(tag=self.tag, source=config.APP_MIGRATOR_SOURCE)\n self.migration_completed = True\n except Exception as e:\n self.raise_step_error(step=step, error=e)",
"title": ""
},
{
"docid": "6d993a60df405335813d1f9efa050486",
"score": "0.5889825",
"text": "def run_tests(self):\n # Trigger a config change which triggers a deferred hook.\n self.run_charm_change_hook_test('configure_ovs')\n\n # Trigger a package change which requires a restart\n self.run_package_change_test(\n 'openvswitch-switch',\n 'openvswitch-switch')",
"title": ""
},
{
"docid": "6d993a60df405335813d1f9efa050486",
"score": "0.5889825",
"text": "def run_tests(self):\n # Trigger a config change which triggers a deferred hook.\n self.run_charm_change_hook_test('configure_ovs')\n\n # Trigger a package change which requires a restart\n self.run_package_change_test(\n 'openvswitch-switch',\n 'openvswitch-switch')",
"title": ""
},
{
"docid": "d06483311b22d2404a9ee38174ee8292",
"score": "0.58458394",
"text": "def test():\r\n import unittest \r\n tests = unittest.TestLoader().discover('tests_sql') \r\n unittest.TextTestRunner(verbosity=2).run(tests)",
"title": ""
},
{
"docid": "a70c19004cd3d38e7f75cde5f226c573",
"score": "0.5833573",
"text": "def run(origin, destination, originusername, destinationusername, originpassword, destinationpassword, testconnection):\n if testconnection:\n pass\n else: \n click.secho('Starting migration: %s:%s ==> %s:%s' % (origin, originusername, destination, destinationusername), blink=True, bold=True, fg='green')",
"title": ""
},
{
"docid": "8d5e8e5e8b051114f149d3ecee17c2b0",
"score": "0.58183414",
"text": "def migrate_fake():\n run('source /home/indabom/web/bin/activate && /home/indabom/web/site/manage.py migrate --fake')",
"title": ""
},
{
"docid": "3ce41a80a248cb52ca9c8ebce2e7085b",
"score": "0.5811082",
"text": "def test_migrations_and_tasks(tmp_path: Path) -> None:\n # Convert demo_migrations in a git repository with 2 versions\n src, dst = tmp_path / \"src\", tmp_path / \"dst\"\n copytree(SRC, src)\n with local.cwd(src):\n git(\"init\")\n git(\"config\", \"user.name\", \"Copier Test\")\n git(\"config\", \"user.email\", \"test@copier\")\n git(\"add\", \".\")\n git(\"commit\", \"-m1\")\n git(\"tag\", \"v1.0.0\")\n git(\"commit\", \"--allow-empty\", \"-m2\")\n git(\"tag\", \"v2.0\")\n # Copy it in v1\n run_copy(src_path=str(src), dst_path=dst, vcs_ref=\"v1.0.0\", unsafe=True)\n # Check copy was OK\n assert (dst / \"created-with-tasks.txt\").read_text() == \"task 1\\ntask 2\\n\"\n assert not (dst / \"delete-in-tasks.txt\").exists()\n assert (dst / \"delete-in-migration-v2.txt\").is_file()\n assert not (dst / \"migrations.py\").exists()\n assert not (dst / \"tasks.py\").exists()\n assert not list(dst.glob(\"*-before.txt\"))\n assert not list(dst.glob(\"*-after.txt\"))\n answers = yaml.safe_load((dst / \".copier-answers.yml\").read_text())\n assert answers == {\"_commit\": \"v1.0.0\", \"_src_path\": str(src)}\n # Save changes in downstream repo\n with local.cwd(dst):\n git(\"add\", \".\")\n git(\"config\", \"user.name\", \"Copier Test\")\n git(\"config\", \"user.email\", \"test@copier\")\n git(\"commit\", \"-m1\")\n # Update it to v2\n run_update(dst_path=dst, defaults=True, overwrite=True, unsafe=True)\n # Check update was OK\n assert (dst / \"created-with-tasks.txt\").read_text() == \"task 1\\ntask 2\\n\" * 2\n assert not (dst / \"delete-in-tasks.txt\").exists()\n assert not (dst / \"delete-in-migration-v2.txt\").exists()\n assert not (dst / \"migrations.py\").exists()\n assert not (dst / \"tasks.py\").exists()\n assert (dst / \"v1.0.0-v2-v2.0-before.json\").is_file()\n assert (dst / \"v1.0.0-v2-v2.0-after.json\").is_file()\n assert (dst / \"PEP440-1.0.0-2-2.0-before.json\").is_file()\n assert (dst / \"PEP440-1.0.0-2-2.0-after.json\").is_file()\n answers = yaml.safe_load((dst / \".copier-answers.yml\").read_text())\n assert answers == {\"_commit\": \"v2.0\", \"_src_path\": str(src)}",
"title": ""
},
{
"docid": "02ad65cd70401dcda8baed88315882b0",
"score": "0.58068687",
"text": "def migrate(ctx):\n connecter = ScalingoInterface(ctx.obj)\n connecter.manage_py(\"migrate\")",
"title": ""
},
{
"docid": "ec94086725d9d09b3cec9322d2af2f09",
"score": "0.58064413",
"text": "def test_generate_all_testing(self):\n pass",
"title": ""
},
{
"docid": "057b0068683e088db60d1ef9dffd07f9",
"score": "0.58058727",
"text": "def conditional_neutron_migration():\n if CompareOpenStackReleases(os_release('neutron-server')) <= 'icehouse':\n log('Not running neutron database migration as migrations are handled '\n 'by the neutron-server process.')\n return\n\n if not is_elected_leader(CLUSTER_RES):\n log('Not running neutron database migration, not leader')\n return\n\n allowed_units = relation_get('allowed_units')\n if not (allowed_units and local_unit() in allowed_units.split()):\n log('Not running neutron database migration, either no '\n 'allowed_units or this unit is not present')\n return\n\n migrate_neutron_database()",
"title": ""
},
{
"docid": "bd9a4ba96898f004bf6e18e0c07e1fd3",
"score": "0.5805322",
"text": "def test_content_migrator(self):\n output = migrateContents(self.portal, \"Document\", \"News Item\")\n self.assertEqual(output.get('counter', 0), 10)\n self.assertEqual(output.get('error', []), [])\n self.assertTrue(self.portal.portal_catalog(portal_type=\"Document\").actual_result_count == 0)\n self.assertTrue(self.portal.portal_catalog(portal_type=\"News Item\").actual_result_count == 15)",
"title": ""
},
{
"docid": "97cfc967a31602048464c2f2fd5d451e",
"score": "0.57953894",
"text": "def _load_migrations():\n\n upgrade_script_rex = re.compile(\n r'^upgrade_(0|[1-9][0-9]*)_to_([1-9][0-9]*)\\.py$')\n migrations = {}\n\n # Currently, we only load migrations for a '__core__' schema, and only from\n # the migrations directory. One idea if we need to eventually support\n # migrations for the per-testsuite tables is to add subdirectories keyed on\n # the testsuite.\n for schema_name in ('__core__',):\n schema_migrations_path = os.path.join(os.path.dirname(__file__),\n 'migrations')\n schema_migrations = {}\n for item in os.listdir(schema_migrations_path):\n # Ignore certain known non-scripts.\n if item in ('README.txt', '__init__.py', 'new_suite.py',\n 'util.py') or item.endswith('.pyc'):\n continue\n\n # Ignore non-matching files.\n m = upgrade_script_rex.match(item)\n if m is None:\n logger.warning(\n \"ignoring item %r in schema migration directory: %r\",\n item, schema_migrations_path)\n continue\n\n # Check the version numbers for validity.\n version, next_version = map(int, m.groups())\n if next_version != version + 1:\n logger.error(\n \"invalid script name %r in schema migration directory: %r\",\n item, schema_migrations_path)\n continue\n\n schema_migrations[version] = os.path.join(\n schema_migrations_path, item)\n\n # Ignore directories with no migrations.\n if not schema_migrations:\n logger.warning(\"ignoring empty migrations directory: %r\",\n schema_migrations_path)\n continue\n\n # Check the provided versions for sanity.\n current_version = max(schema_migrations) + 1\n for i in range(current_version):\n if i not in schema_migrations:\n logger.error(\"schema %r is missing migration for version: %r\",\n schema_name, i)\n\n # Store the current version as another item in the per-schema migration\n # dictionary.\n schema_migrations['current_version'] = current_version\n\n # Store the schema migrations.\n migrations[schema_name] = schema_migrations\n\n return migrations",
"title": ""
},
{
"docid": "3ae397bb4bd68a7ea7a2ebdaed95a839",
"score": "0.5780383",
"text": "def test_all():\n test_get_to()\n test_error_type()\n test_exchange()\n print(\"All tests passed.\")",
"title": ""
},
{
"docid": "d859acb1e66ce437f3751f04efecf475",
"score": "0.5767438",
"text": "def test_upgrade_with_auto_upgrade_latest_engine_enabled():",
"title": ""
},
{
"docid": "4f32db0d5a6f2dedaeedfadb7aba8090",
"score": "0.57607394",
"text": "def setUp(self):\n db.create_all()",
"title": ""
},
{
"docid": "09a287708dcd3fe5bc8105fe14111f3c",
"score": "0.575223",
"text": "def setUp(self):\n db.drop_all() # clean up the last tests\n db.create_all() # make our sqlalchemy tables",
"title": ""
},
{
"docid": "bfdb8d7535cf61da4b13290ee458f3d9",
"score": "0.5747715",
"text": "def tearDownClass(cls):\n management.call_command(\"migrate\")",
"title": ""
},
{
"docid": "5f753ef01c34107787dd60b7fc86dbfc",
"score": "0.5744146",
"text": "def update_db_version():\n print(\"Checking Database states...\")\n os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"ADSM.settings\")\n try:\n call_command('migrate', database='scenario_db', interactive=False, fake_initial=True)\n call_command('migrate', database='default', interactive=False, fake_initial=True)\n except:\n print(\"Error: Migration failed.\")\n print('Done migrating databases.')",
"title": ""
},
{
"docid": "2bfca31d79bf156c5bdccb18c88606ee",
"score": "0.57307404",
"text": "def run_migrations_online():\n db_host = context.get_x_argument(as_dictionary=True).get('DB_HOST')\n db_port = context.get_x_argument(as_dictionary=True).get('DB_PORT')\n db_user = context.get_x_argument(as_dictionary=True).get('DB_USER')\n db_password = context.get_x_argument(as_dictionary=True).get('DB_PASSWORD')\n db_name = context.get_x_argument(as_dictionary=True).get('DB_NAME')\n\n try_to_create_database(db_host, db_port, db_user, db_password, db_name)\n\n connectable = get_connectable(db_host, db_port, db_user, db_password, db_name)\n with connectable.connect() as connection:\n context.configure(\n connection=connection,\n target_metadata=target_metadata,\n compare_type=True,\n render_item=render_item\n )\n with context.begin_transaction():\n context.run_migrations()",
"title": ""
},
{
"docid": "ac5dcf752592ca6f4fb5eb03297079c2",
"score": "0.572811",
"text": "def migrate(migrator, database, fake=False, **kwargs):\n\n tables = database.get_tables()\n\n if 'tea_vendors' not in tables:\n @migrator.create_model\n class TeaVendor(pw.Model):\n description = pw.CharField(max_length=255)\n link = pw.CharField(max_length=255)\n logo = pw.CharField(max_length=255, null=True)\n name = pw.CharField(max_length=255)\n twitter = pw.CharField(max_length=255, null=True)\n slug = pw.CharField(max_length=255, unique=True)\n order = pw.IntegerField(default=0)\n\n class Meta:\n db_table = \"tea_vendors\"\n\n if 'tea_teas' not in tables:\n @migrator.create_model\n class Tea(pw.Model):\n deleted = pw.DateTimeField(null=True)\n description = pw.CharField(max_length=255, null=True)\n illustration = pw.CharField(max_length=255)\n ingredients = pw.TextField(null=True)\n link = pw.CharField(max_length=255)\n long_description = pw.TextField(null=True)\n name = pw.CharField(max_length=255)\n price = pw.FloatField(null=True)\n price_unit = pw.CharField(max_length=255, null=True)\n slug = pw.CharField(max_length=255)\n tips_raw = pw.CharField(max_length=255, null=True)\n tips_duration = pw.IntegerField(null=True)\n tips_mass = pw.IntegerField(null=True)\n tips_temperature = pw.IntegerField(null=True)\n tips_volume = pw.IntegerField(null=True)\n tips_extra = pw.CharField(max_length=255, null=True)\n tips_max_brews = pw.IntegerField(default=1)\n updated = pw.DateTimeField(default=dt.datetime.now)\n vendor = pw.ForeignKeyField(db_column='vendor', rel_model=migrator.orm['tea_vendors'], to_field='id')\n vendor_internal_id = pw.CharField(db_column='vendor_id', max_length=255, null=True)\n\n class Meta:\n db_table = \"tea_teas\"\n\n if 'tea_lists' not in tables:\n @migrator.create_model\n class TeaList(pw.Model):\n name = pw.CharField(max_length=255)\n created_at = pw.DateTimeField(default=dt.datetime.now)\n share_key = pw.CharField(max_length=255, null=True, unique=True)\n cookie_key = pw.CharField(max_length=255, unique=True)\n creator_ip = pw.CharField(max_length=255)\n share_key_valid_until = pw.DateTimeField(null=True)\n\n class Meta:\n db_table = \"tea_lists\"\n\n if 'tea_lists_items' not in tables:\n @migrator.create_model\n class TeaListItem(pw.Model):\n is_empty = pw.IntegerField()\n tea_list = pw.ForeignKeyField(db_column='list_id', rel_model=migrator.orm['tea_lists'], to_field='id')\n tea = pw.ForeignKeyField(db_column='tea_id', rel_model=migrator.orm['tea_teas'], to_field='id')\n\n class Meta:\n db_table = \"tea_lists_items\"\n\n if 'tea_types' not in tables:\n @migrator.create_model\n class TeaType(pw.Model):\n name = pw.CharField(max_length=255, unique=True)\n slug = pw.CharField(max_length=255, unique=True)\n is_origin = pw.BooleanField()\n order = pw.IntegerField(null=True)\n\n class Meta:\n db_table = \"tea_types\"\n\n if 'tea_teas_types' not in tables:\n @migrator.create_model\n class TypeOfATea(pw.Model):\n tea = pw.ForeignKeyField(db_column='tea_id', rel_model=migrator.orm['tea_teas'], to_field='id')\n tea_type = pw.ForeignKeyField(db_column='type_id', rel_model=migrator.orm['tea_types'], to_field='id')\n\n class Meta:\n db_table = \"tea_teas_types\"\n\n primary_key = pw.CompositeKey('tea', 'tea_type')",
"title": ""
},
{
"docid": "8e6869c1020f673e1553f14d779f7f8b",
"score": "0.571857",
"text": "def test_oldtestcases(self):\n\t\treturn oldtests()",
"title": ""
},
{
"docid": "d491dcf9a648cbb09bf5569816163c0a",
"score": "0.57073176",
"text": "def upgrade_test_mixed(self):\n self.upgrade_scenario(mixed_version=True)",
"title": ""
},
{
"docid": "eda53526073ea8547c7e0e7a9b386777",
"score": "0.5687111",
"text": "def test_defaultChangeVersionsVersionChanger(self):\n versionChanger = ChangeVersionsScript()\n self.assertEquals(versionChanger.changeAllProjectVersions,\n changeAllProjectVersions)",
"title": ""
},
{
"docid": "57ffb993426d2df7778e605fcd0fd051",
"score": "0.5686888",
"text": "def test_ovs_ovn_migration(self):\n # The setUp method of this test class will perform the migration steps.\n # The tests.yaml is programmed to do further validation after the\n # migration.\n\n # Reset the n-gw and n-ovs instance-mtu configuration option so it does\n # not influence how further tests are executed.\n reset_config_keys = ['instance-mtu']\n for app in ('neutron-gateway', 'neutron-openvswitch'):\n try:\n zaza.model.reset_application_config(app, reset_config_keys)\n logging.info('Reset configuration to default on \"{}\" for \"{}\"'\n .format(app, reset_config_keys))\n except KeyError:\n pass\n zaza.model.wait_for_agent_status()\n zaza.model.block_until_all_units_idle()\n zaza.model.wait_for_application_states(\n states=self.target_deploy_status)",
"title": ""
},
{
"docid": "5cefce87b80a8a8ced636340e27a3361",
"score": "0.5686778",
"text": "def do_migrate(self, arg): \n arg = str(arg).split(' ')\n arg = [i for i in arg if i != '']\n\n if len(arg) >= 2:\n if arg[0] == 'server':\n if len(arg) == 3:\n migrateServer(arg[0], arg[1], arg[2], arg[3], 'None')\n elif len(arg) == 2:\n migrateServer(arg[0], 'None', 'None', arg[1] , 'None')\n else:\n print \" Please enter \\'help migrate\\' for valid commands!\" \n \n elif arg[0] == 'client':\n if '@' in arg[1]:\n if len(arg) == 5:\n migrateServer(arg[0], arg[1], arg[2], arg[3], arg[4])\n elif len(arg) == 4:\n migrateServer(arg[0], arg[1], 'None', arg[2], arg[3]) \n else:\n print \" Please enter \\'help migrate\\' for valid commands!\" \n else:\n print \" Please enter \\'help migrate\\' for valid commands!\" \n else:\n print \" Please enter \\'help migrate\\' for valid commands!\"\n else:\n print \" Please enter \\'help migrate\\' for valid commands!\"",
"title": ""
},
{
"docid": "3a0ffd7efba2815e2a4942ada3c8d447",
"score": "0.5684293",
"text": "def test_valid_cases(monkeypatch) -> None: # noqa: TYP001\n for case in [\n 'snake case',\n 'camel case',\n 'pascal case',\n 'kebab case',\n None,\n 'snake_case',\n 'camelCase',\n 'PascalCase',\n 'kebab-case',\n ]:\n monkeypatch.setattr(django_settings, 'SWAGGER_TESTER', {'CASE': case})\n SwaggerTesterSettings()",
"title": ""
},
{
"docid": "bd01a7f0c5633ae96a30d6e7858a1892",
"score": "0.5653344",
"text": "def test_add_or_update_case(self):\n pass",
"title": ""
},
{
"docid": "243cc53af6f56ad3f891a9bec0d568bd",
"score": "0.5651258",
"text": "def test_success(self):\n # cleanup and prepare python script\n tmp_sql_table.metadata.drop_all(self.engine, checkfirst=True)\n script_path = self.tmp_py()\n pyscript = PythonScript.create(script_path)\n\n # populate python script\n contents = open(script_path, 'r').read()\n contents = contents.replace(\"pass\", \"tmp_sql_table.create(migrate_engine)\")\n contents = 'from migrate.tests.fixture.models import tmp_sql_table\\n' + contents\n f = open(script_path, 'w')\n f.write(contents)\n f.close()\n\n # write SQL script from python script preview\n pyscript = PythonScript(script_path)\n src = self.tmp()\n f = open(src, 'w')\n f.write(pyscript.preview_sql(self.url, 1))\n f.close()\n\n # run the change\n sqls = SqlScript(src)\n sqls.run(self.engine)\n tmp_sql_table.metadata.drop_all(self.engine, checkfirst=True)",
"title": ""
},
{
"docid": "cf2efc0ce525dae90ec3a36d68363371",
"score": "0.5646555",
"text": "def migrate(cr, version):\n pass",
"title": ""
},
{
"docid": "7a29fb75d51af04074c84820aaabab59",
"score": "0.56424177",
"text": "def test_walk_versions(self):\n for key, engine in self.engines.items():\n config = self._get_alembic_config(self.test_databases[key])\n self._walk_versions(config, engine, self.snake_walk)",
"title": ""
},
{
"docid": "895b418c349518b67e4da9d4622b6a02",
"score": "0.56417865",
"text": "def test_upgrade_apply_all_fine(setup, platform, skuba):\n\n setup_kubernetes_version(skuba)\n\n # node upgrade apply\n outs = {}\n for (r, n) in [(\"master\", 0), (\"worker\", 0)]:\n node = \"my-{}-{}\".format(r, n)\n outs[node] = skuba.node_upgrade(\"apply\", r, n)\n\n master = outs[\"my-master-0\"]\n assert master.find(\n \"Node my-master-0 is up to date\"\n ) != -1\n\n worker = outs[\"my-worker-0\"]\n assert worker.find(\n \"Node my-worker-0 is up to date\"\n ) != -1",
"title": ""
},
{
"docid": "46d93357bb24563e1ec232ebd868b508",
"score": "0.5634939",
"text": "async def migrate_tables(self) -> int:\n for table in self._migration_queue:\n current_level = await self._get_migration_level(table['name'])\n assert current_level is not None\n await self._run_migrations(table['name'], current_level)\n return len(self._migration_queue)",
"title": ""
},
{
"docid": "93d634925ca4cd42ab67364dab3dcb40",
"score": "0.5629616",
"text": "def tests():",
"title": ""
},
{
"docid": "89929534a4962c10a690a537d8396063",
"score": "0.56292623",
"text": "def unitary_test():",
"title": ""
},
{
"docid": "903ee6752a51637613106d0f1530dac2",
"score": "0.56201303",
"text": "def doMigration(self):\n self.sqlStore.setMigrating(True)\n self.log.warn(\"Beginning filesystem -> database upgrade.\")\n\n for homeType, eachFunc in [\n (\"calendar\", self.fileStore.withEachCalendarHomeDo),\n (\"addressbook\", self.fileStore.withEachAddressbookHomeDo),\n ]:\n yield eachFunc(\n lambda txn, home: self._upgradeAction(\n txn, home, homeType\n )\n )\n\n # Set attachment directory ownership. FIXME: is this still necessary\n # since attachments started living outside the database directory\n # created by initdb? default permissions might be correct now.\n sqlAttachmentsPath = self.sqlStore.attachmentsPath\n if (\n sqlAttachmentsPath and sqlAttachmentsPath.exists() and\n (self.uid or self.gid)\n ):\n uid = self.uid or -1\n gid = self.gid or -1\n for fp in sqlAttachmentsPath.walk():\n os.chown(fp.path, uid, gid)\n\n yield self.doDataUpgradeSteps()\n\n self.sqlStore.setMigrating(False)\n\n self.log.warn(\n \"Filesystem upgrade complete, launching database service.\"\n )",
"title": ""
},
{
"docid": "689f8a7367111a95635e95c411b5138a",
"score": "0.5619898",
"text": "def test_does_not_move(self):\n Herbivore.set_parameters({\"mu\": 0})\n nt.assert_false(self.herb.check_migrate())",
"title": ""
},
{
"docid": "ee9e6cf7927fc486ce0c375f14948a26",
"score": "0.5619554",
"text": "def main(arguments):\n migration = Migration(arguments)\n return migration.run()",
"title": ""
},
{
"docid": "01aac560e67980857bed3aa0e1924815",
"score": "0.5615095",
"text": "def run_migrations_online():\n\n # this callback is used to prevent an auto-migration from being generated\n # when there are no changes to the schema\n # reference: http://alembic.zzzcomputing.com/en/latest/cookbook.html\n def process_revision_directives(context, revision, directives):\n if getattr(config.cmd_opts, \"autogenerate\", False):\n script = directives[0]\n if script.upgrade_ops.is_empty():\n directives[:] = []\n LOGGER.info(\"No changes in schema detected.\")\n\n # TODO: Enable postgres version 7/23/2019 # configuration = config.get_section(config.config_ini_section)\n # TODO: Enable postgres version 7/23/2019 # configuration['sqlalchemy.url'] = get_url()\n connectable = engine_from_config(\n config.get_section(config.config_ini_section),\n prefix=\"sqlalchemy.\",\n poolclass=pool.NullPool,\n )\n\n with connectable.connect() as connection:\n context.configure(\n connection=connection,\n target_metadata=target_metadata,\n process_revision_directives=process_revision_directives,\n )\n\n try:\n with context.begin_transaction():\n context.run_migrations()\n finally:\n connection.close()",
"title": ""
},
{
"docid": "988cc369a124289933e5db0674f1e374",
"score": "0.56147635",
"text": "def test_unique(self):\n leading_digits = re.compile(r'^\\d+')\n seen_numbers = set()\n path = self._migrations_path()\n for filename in listdir(path):\n match = leading_digits.match(filename)\n if match:\n number = match.group()\n if number in seen_numbers:\n self.fail('There is more than one migration #%s in %s.' %\n (number, path))\n seen_numbers.add(number)",
"title": ""
},
{
"docid": "065f3e8d487f6e88ad4bb79187d00949",
"score": "0.5614552",
"text": "def setUp(self):\n self.conn = seed.connect_to_db(\"testing\")\n self.cur = self.conn.cursor()\n\n seed.cur = self.conn.cursor()\n seed.conn = self.conn\n\n self.tables = [\n {\n \"name\": \"people\", \n \"schema\": [(\"firstname\", \"10\", \"VARCHAR\"), (\"lastname\", \"10\", \"VARCHAR\"), (\"age\", \"3\", \"INTEGER\"), (\"active\", \"1\", \"BOOLEAN\")]\n },\n {\n \"name\": \"animals\",\n \"schema\": [(\"animal_id\", \"7\", \"INTEGER\"), (\"name\", \"10\", \"VARCHAR\"), (\"species\", \"20\", \"VARCHAR\")]\n },\n {\n \"name\":\"testformat1\",\n \"schema\": [(\"name\", \"10\", \"VARCHAR\"), (\"valid\", \"1\", \"BOOLEAN\"), (\"count\", \"3\", \"INTEGER\")]\n }\n ]\n for table in self.tables:\n seed.create_table(table[\"name\"], table[\"schema\"])",
"title": ""
},
{
"docid": "db86b02c9969cd249c4d8d9498e77ac5",
"score": "0.56128937",
"text": "def model_post_migrate(*args, **kwargs):\n global IN_MIGRATIONS\n IN_MIGRATIONS = False",
"title": ""
},
{
"docid": "6f64423eb1a6e16fe6421196ba362a81",
"score": "0.5607878",
"text": "def run_tests():\n \n test_constructor_positive()\n test_constructor_negative()\n test_game_move_positive()\n test_game_move_negative()\n test_game_move_edge()\n print(\"Congratulations ! You passed all the game test cases.\")",
"title": ""
},
{
"docid": "2342db4e655972ae0f0530ceb557c45b",
"score": "0.55961794",
"text": "def _load_migrations(self):\n self.migrations.clear()\n files = os.listdir(os.path.join(os.path.dirname(__file__), \"migrations\"))\n for file in files:\n matches = re.search(\"(?:m)(\\d+)(?:_(.+))?\\.(sql|py)\", file, re.IGNORECASE)\n if matches is None:\n continue\n self.migrations.append(Migration(*matches.groups()))",
"title": ""
},
{
"docid": "de07e9903fe78b1617599e0315e87496",
"score": "0.55944693",
"text": "def setUp(self):\n # ensure there is no data in the test database when the test starts\n db.session.commit()\n db.drop_all()\n db.create_all()\n usRoles = [\"Guest\",\"Couple\",\"2nd line\",\"Wedding party\"]\n\n for i in usRoles:\n roleAdd = User_roles(role = i)\n db.session.add(roleAdd)\n db.session.commit()\n\n # create test admin user\n admin = User(first_name=\"admin\", last_name=\"admin\",permission=\"Couple\", email=\"admin@admin.com\", password=\"admin2016\")\n\n # create test non-admin user\n employee = User(first_name=\"test\", last_name=\"user\",permission = \"Guest\", email=\"test@user.com\", password=\"test2016\")\n\n # save users to database\n db.session.add(admin)\n db.session.add(employee)\n db.session.commit()",
"title": ""
},
{
"docid": "3eed1e8964b5ccbe3a26df59c59286ad",
"score": "0.5592027",
"text": "def test_attemptMigrationSucceeds(self):\n obj, migration, pendingMigration = self._mkMigrationJunk()\n def _cb(ign):\n # .store is set to None on deletion\n self.assertIdentical(pendingMigration.store, None)\n return pendingMigration.attemptMigration().addCallback(_cb)",
"title": ""
},
{
"docid": "b6148fa7700707d637d2880df63de0d1",
"score": "0.5586622",
"text": "def test_migration_task_rollback(self):\n server, source_host, target_host = self._create_server()\n self._disable_target_host(target_host)\n self._stub_delete_server_during_scheduling(server)\n\n # Now start the cold migration which will fail due to NoValidHost.\n self.api.post_server_action(server['id'], {'migrate': None},\n check_response_status=[202])\n # We cannot monitor the migration from the API since it is deleted\n # when the instance is deleted so just wait for the failed instance\n # action event after the task rollback happens.\n # Note that we get InstanceNotFound rather than NoValidHost because\n # the NoValidHost handler in ComputeTaskManager._cold_migrate calls\n # _set_vm_state_and_notify which raises InstanceNotFound and masks\n # the NoValidHost error.\n self._assert_resize_migrate_action_fail(\n server, instance_actions.MIGRATE, 'InstanceNotFound')\n self._assert_no_allocations(server)",
"title": ""
},
{
"docid": "5d529a90c9b10e731f3f7638850a2102",
"score": "0.55764735",
"text": "def test13(self):\n ###get a block to migrate from global dbs\n dest_datasets = set((dataset['dataset'] for dataset in self.api.listDatasets()))\n ###only dataset after last DBS2->3 because of the parentage issue in DBS 2 min_cdate=1368162000 =10May2013\n src_datasets = set((dataset['dataset'] for dataset in self.cmsweb_api.listDatasets(min_cdate=1368162000)))\n dataset_to_migrate = choice(list(src_datasets.difference(dest_datasets)))\n block_to_migrate = choice([block['block_name']\n for block in self.cmsweb_api.listBlocks(dataset=dataset_to_migrate)])\n\n ###submit migration request\n toMigrate = {'migration_url': self.source_url,\n 'migration_input': block_to_migrate}\n migration_request = self.migration_api.submitMigration(toMigrate)\n self.assertTrue('migration_request_id' in migration_request['migration_details'])\n migration_request_id = migration_request['migration_details']['migration_request_id']\n print(\"____toMigrate___\")\n print(toMigrate)\n print(\"----------migration_request -----------\")\n print(migration_request) \n\n ###check migration status for max. 300s (should be enough time to migrate the dataset)\n with Timeout(300):\n while True:\n request_status = self.migration_api.statusMigration(migration_rqst_id=migration_request_id)\n if request_status[0]['migration_status'] == 2:\n break\n\n ###validate block migration\n def check(input, output):\n non_comparable_keys = ('block_id', 'dataset_id', 'last_modification_date',\n 'parent_file_id', 'primary_ds_id')\n if isinstance(input, dict):\n for key, value in input.items():\n if key in non_comparable_keys:\n continue ###do not compare id's\n if key in ('processing_era',): ###do compare create_by, creation_date for re-used entries\n for key2remove in ('create_by', 'creation_date',):\n try:\n del input[key][key2remove]\n del output[key][key2remove]\n except KeyError:\n pass\n self.assertTrue(key in output)\n check(value, output[key])\n elif isinstance(input, list):\n for element_in, element_out in zip(sorted(remove_non_comparable_keys(input, non_comparable_keys)),\n sorted(remove_non_comparable_keys(output, non_comparable_keys))):\n check(element_in, element_out)\n else:\n self.assertEqual(str(input), str(output))\n\n block_dump_src = self.cmsweb_api.blockDump(block_name=block_to_migrate)\n block_dump_dest = self.api.blockDump(block_name=block_to_migrate)\n check(block_dump_src, block_dump_dest)\n\n ###try to delete successfully executed migration request\n toDelete = {'migration_rqst_id': migration_request_id}\n self.assertRaises(HTTPError, self.migration_api.removeMigration, toDelete)",
"title": ""
},
{
"docid": "ec350929e658d2757488db8070f1379d",
"score": "0.5564516",
"text": "def test_all_asserts():\n \n test_remove_punctuation()\n test_prepare_text()\n test_string_concatenator()\n test_list_to_string()\n test_end_chat()\n test_check_link()\n test_check_region()\n test_check_area()\n test_check_city()\n test_check_industry()\n test_check_back()\n test_check_alumni_region()\n test_check_alumni_area()\n test_check_alumni_city()\n test_check_alumni_industry()",
"title": ""
},
{
"docid": "5e1cc8d7be626d73d20e603c3de3784f",
"score": "0.5564069",
"text": "def testCheck(self):\n change = ChangeState(self.config, \"changestate_t\")\n\n # Run through all good state transitions and assert that they work\n for state in self.transitions:\n for dest in self.transitions[state]:\n change.check(dest, state)\n dummystates = ['dummy1', 'dummy2', 'dummy3', 'dummy4']\n\n # Then run through some bad state transistions and assertRaises(AssertionError)\n for state in self.transitions:\n for dest in dummystates:\n self.assertRaises(AssertionError, change.check, dest, state)\n return",
"title": ""
},
{
"docid": "4d688a87b535dc777ce4fd022467937c",
"score": "0.5560362",
"text": "def test_all():\n test_prepare_text()\n test_end_chat()\n test_choose_author()\n test_choose_book()",
"title": ""
},
{
"docid": "22a8cc77c9d49c6012dc60fc7eb431f5",
"score": "0.5550197",
"text": "def _migration_supported(self):\n if self.compute_cnt > 1:\n return True\n return False",
"title": ""
},
{
"docid": "5723ff1ab2b9fc9ea0fe4501d6afe055",
"score": "0.5533033",
"text": "async def check_migration(hass: core.HomeAssistant, entry: ConfigEntry) -> None:\n host = entry.data[CONF_HOST]\n\n # migrate CONF_USERNAME --> CONF_API_KEY\n if CONF_USERNAME in entry.data:\n LOGGER.info(\"Migrate %s to %s in schema\", CONF_USERNAME, CONF_API_KEY)\n data = dict(entry.data)\n data[CONF_API_KEY] = data.pop(CONF_USERNAME)\n hass.config_entries.async_update_entry(entry, data=data)\n\n conf_api_version = entry.data.get(CONF_API_VERSION, 1)\n if conf_api_version == 1:\n # a bridge might have upgraded firmware since last run so\n # we discover its capabilities at every startup\n websession = aiohttp_client.async_get_clientsession(hass)\n if await is_v2_bridge(host, websession):\n supported_api_version = 2\n else:\n supported_api_version = 1\n LOGGER.debug(\n \"Configured api version is %s and supported api version %s for bridge %s\",\n conf_api_version,\n supported_api_version,\n host,\n )\n\n # the call to `is_v2_bridge` returns (silently) False even on connection error\n # so if a migration is needed it will be done on next startup\n\n if conf_api_version == 1 and supported_api_version == 2:\n # run entity/device schema migration for v2\n await handle_v2_migration(hass, entry)\n\n # store api version in entry data\n if (\n CONF_API_VERSION not in entry.data\n or conf_api_version != supported_api_version\n ):\n data = dict(entry.data)\n data[CONF_API_VERSION] = supported_api_version\n hass.config_entries.async_update_entry(entry, data=data)",
"title": ""
},
{
"docid": "179e1fb3c1ce4fe978463bb8382a0fe0",
"score": "0.5524832",
"text": "def test_4_4_1_1(self):\n pass",
"title": ""
},
{
"docid": "775e7d13e5596b9ad5dac2af1603ca51",
"score": "0.55247986",
"text": "def test_wait_for_upgrade(self):\n self.run_test_suites(self.wait_for_upgrade_test_suite_list)",
"title": ""
},
{
"docid": "111335eed7d3a82c192ed5bf4cc1d7f8",
"score": "0.5522305",
"text": "def test_migrateTo(self):\n objs = [self._mkObject() for _ in xrange(5)]\n\n dest = ContentStore(store=self.store, hash=u'sha256')\n migration = self.contentStore.migrateTo(dest)\n self.assertIdentical(migration.source, self.contentStore)\n self.assertIdentical(migration.destination, dest)\n self.assertEquals(migration.start, 0)\n self.assertEquals(migration.end, objs[-1].storeID)\n self.assertEquals(migration.current, -1)",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="746"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">ce1c96a4d80733f9dded1747970509c6</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Uses the frequency as the fit parameters</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "4e7b8a5e8df2789f646c09fbe8cd8f6e",
"score": "0.0",
"text": "def get_fit_old(self):\n def fit_PSD_target_function(f, f_QP, f_RO):\n tail = True\n # tail = False\n if tail:\n return (4 * f_RO ** 2 * f_QP) / ((2 * f_QP) ** 2 + (2 * np.pi * f) ** 2) + (1 - f_RO ** 2) / self.fs\n else:\n return (4 * f_RO ** 2 * f_QP) / \\\n ((2 * f_QP) ** 2 + (2 * np.pi * f) ** 2)\n\n psd, f = self.psd_avg, self.f_data\n psd = psd[~np.isnan(f)]\n f = f[~np.isnan(f)]\n f = f[~np.isnan(psd)]\n psd = psd[~np.isnan(psd)]\n\n initial_guess = [0.3, 0.5, 0.7, 0.9]\n covariance = float('inf')\n for ig in initial_guess:\n sigma = f**1\n params_curr, params_covariance_curr = curve_fit(\n fit_PSD_target_function, f, psd,\n bounds=[(100, 0), (10000, 1.0)], p0=[100, ig], method='trf',\n sigma=sigma)\n if params_covariance_curr[0][0] < covariance:\n self.params = params_curr\n params_covariance = params_covariance_curr\n covariance = params_covariance_curr[0][0]\n f_QP = self.params[0]\n f_RO = self.params[1]\n return fit_PSD_target_function(f, f_QP, f_RO), f",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "4a53186e3fdf2d627b95ae8f897e6ed3",
"score": "0.70693344",
"text": "def set_frequency(self, frequency):",
"title": ""
},
{
"docid": "a7ad01f2056a664fe2cdc21a5693024b",
"score": "0.66535306",
"text": "def fit(self):",
"title": ""
},
{
"docid": "a7ad01f2056a664fe2cdc21a5693024b",
"score": "0.66535306",
"text": "def fit(self):",
"title": ""
},
{
"docid": "e7dc9e6898c23a5ae69bd3c5b08c1c7b",
"score": "0.6634344",
"text": "def fit(self, **kwargs):\n pass",
"title": ""
},
{
"docid": "6c4736a5c16408f6b53296fdf6b607f0",
"score": "0.6577753",
"text": "def fit_motion_freq(self, fit_params=None):\n \n self.motion_popt = [ [ [] for i in range(3) ] for Rswitch in range(2) ]\n self.motion_pcov = [ [ [] for i in range(3) ] for Rswitch in range(2) ]\n if fit_params==None:\n fit_params=[ [5e-3,each,pi/4,0] for each in self.p.trap_omega ]\n \n for j in range(2):\n for i in range(3):\n self.motion_popt[j][i],self.motion_pcov[j][i] = curve_fit(fit_sine,self.p.time_steps,self.simul_r[j][i],p0=fit_params[i])\n \n # Slosh frequencies [Hz]\n self.motion_slosh = array(self.motion_popt)[:,:,1]/2/pi\n \n self.fitted_motion = True",
"title": ""
},
{
"docid": "993b48f8f6ac8e744afe95234e6653f8",
"score": "0.6442806",
"text": "def fit(self, X, y, sample_weight=...):\n ...",
"title": ""
},
{
"docid": "a28eb702be89993f07754e239341576a",
"score": "0.6438098",
"text": "def fittedFreqs(self, prcntVol):\n nPoints = self._coeff.shape[0]\n nModes = self._coeff.shape[1]\n fittedFreqs = numpy.zeros(shape = (nPoints, nModes) )\n for i in range( nPoints ):\n for j in range( nModes ):\n p = self._coeff[i,j]\n fittedFreqs[i,j] = self.fitter.func(p, prcntVol)\n return (fittedFreqs+1.0)*self._freqs0",
"title": ""
},
{
"docid": "43dfcd275bf05702145dc3172c02e80a",
"score": "0.6416718",
"text": "def fit(self):\n pass",
"title": ""
},
{
"docid": "b70f07079938a2eb8f4645bd90c92834",
"score": "0.64026135",
"text": "def fit(self, data):\n pass",
"title": ""
},
{
"docid": "629e7f329d8eb32b4a99dc0ab01440ee",
"score": "0.64009356",
"text": "def _fit_freqs(self, freqs):\n vocab = defaultdict(int)\n for word, freq in freqs.items():\n for token, count in self.tokenizer.tokenize(word).items():\n vocab[token] += count * freq\n return vocab",
"title": ""
},
{
"docid": "4c229c23e5956ee913bf24ac2269592e",
"score": "0.63994116",
"text": "def freqInterpolation (\r\n y:ArrayLike[DType[T]] ,\r\n /, \r\n buffer:Optional[Tuple[float]] = None , \r\n kind: str ='freq' \r\n )-> ArrayLike[DType[T]]:\r\n kind =str (kind).lower().strip() \r\n if kind.find('peri')>=0 :\r\n kind ='periods'\r\n y = 1./ np.array (y) if kind =='periods' else np.array (y)\r\n \r\n buffer = Processing.controlFrequencyBuffer(y, buffer ) \r\n ix_s, ix_end = np.argwhere (np.isin(y, buffer)) \r\n \r\n y = y[slice ( int(ix_s), int(ix_end) +1)]\r\n # put frequency in logspace and return\r\n # the same order like the input value\r\n y = np.log10 (y)\r\n if y[0] < y[-1]: \r\n f = np.logspace(y.min() ,y.max() , len(y))\r\n else : \r\n f = np.logspace(y.min(),y.max() , len(y))[::-1]\r\n \r\n return f",
"title": ""
},
{
"docid": "1afe0d87128294a5f28406468b1fb47b",
"score": "0.6399086",
"text": "def get_frequency(self):",
"title": ""
},
{
"docid": "585ad0ee4ed9ac9ead1f653b5baf6b8a",
"score": "0.636081",
"text": "def set_prediction_parameters(self, freq, prediction_length):\n self.freq = freq\n self.prediction_length = prediction_length",
"title": ""
},
{
"docid": "90025bd2bec0808dbe60f4199a7b384c",
"score": "0.63561976",
"text": "def set_freq_watt(self, params=None):\n pass",
"title": ""
},
{
"docid": "de6d9e09c5e615f6ca95d07944b84189",
"score": "0.63075787",
"text": "def fit(self, X, y=...):\n ...",
"title": ""
},
{
"docid": "de6d9e09c5e615f6ca95d07944b84189",
"score": "0.63075787",
"text": "def fit(self, X, y=...):\n ...",
"title": ""
},
{
"docid": "de6d9e09c5e615f6ca95d07944b84189",
"score": "0.63075787",
"text": "def fit(self, X, y=...):\n ...",
"title": ""
},
{
"docid": "6f6920d50d49b2ec7cda5eca027ff462",
"score": "0.62432426",
"text": "def test_frequency(self):\r\n\r\n # Input parameters\r\n ampl = 2.\r\n w = 1.\r\n phi = 0.5 * np.pi\r\n nin = 100\r\n nout = 1000\r\n p = 0.7 # Fraction of points to select\r\n\r\n # Randomly select a fraction of an array with timesteps\r\n np.random.seed(2353425)\r\n r = np.random.rand(nin)\r\n t = np.linspace(0.01*np.pi, 10.*np.pi, nin)[r >= p]\r\n\r\n # Plot a sine wave for the selected times\r\n x = ampl * np.sin(w*t + phi)\r\n\r\n # Define the array of frequencies for which to compute the periodogram\r\n f = np.linspace(0.01, 10., nout)\r\n\r\n # Calculate Lomb-Scargle periodogram\r\n P = lombscargle(t, x, f)\r\n\r\n # Check if difference between found frequency maximum and input\r\n # frequency is less than accuracy\r\n delta = f[1] - f[0]\r\n assert_(w - f[np.argmax(P)] < (delta/2.))",
"title": ""
},
{
"docid": "ed762fb0504af8b1effcdfc6cd322540",
"score": "0.623223",
"text": "def set_frequency(self, *args):\n if self.osc: self.osc.frequency = float(self.input_freq.get())",
"title": ""
},
{
"docid": "12ab465cfd6b1e87c50cc33ec391f4eb",
"score": "0.62285626",
"text": "def fit(self, X, y = None):\n self.maps ={}\n for col in self.freq_cols:\n self.maps[col] = {}\n uniques = list(X[col].unique())\n frequencies = list(X.groupby(col).size()/ len(X))\n self.maps[col] = dict(zip(uniques, [round(x,3) for x in frequencies])) \n return self",
"title": ""
},
{
"docid": "00dacc0ca67adbc55c220c11784e339f",
"score": "0.6216395",
"text": "def frequency(self, frequency):\n\n self._frequency = frequency",
"title": ""
},
{
"docid": "00dacc0ca67adbc55c220c11784e339f",
"score": "0.6216395",
"text": "def frequency(self, frequency):\n\n self._frequency = frequency",
"title": ""
},
{
"docid": "fe4b22dfa5fdd2cedf170e922b5fcbc6",
"score": "0.621576",
"text": "def _fit(self, **kwargs):\n pass",
"title": ""
},
{
"docid": "79d4091f89d615d3e52fc4993bdb3ba8",
"score": "0.62122935",
"text": "def fit(self, input_data):\n pass",
"title": ""
},
{
"docid": "79d4091f89d615d3e52fc4993bdb3ba8",
"score": "0.62122935",
"text": "def fit(self, input_data):\n pass",
"title": ""
},
{
"docid": "79d4091f89d615d3e52fc4993bdb3ba8",
"score": "0.62122935",
"text": "def fit(self, input_data):\n pass",
"title": ""
},
{
"docid": "79d4091f89d615d3e52fc4993bdb3ba8",
"score": "0.62122935",
"text": "def fit(self, input_data):\n pass",
"title": ""
},
{
"docid": "536b05a22b320c6f0fee4b10988e5ad9",
"score": "0.6210124",
"text": "def fit(self):\n self.Distribution.fit(self)",
"title": ""
},
{
"docid": "e29a551f861b4b973a18236b661bb0f5",
"score": "0.61554",
"text": "def fit(self, measure, ds):\r\n pass",
"title": ""
},
{
"docid": "3711370fa24a1ccd0d7021543a2846c0",
"score": "0.6151473",
"text": "def fit_known_frequencies(signal: numpy.ndarray, times: numpy.ndarray,\n frequencies: numpy.ndarray) -> numpy.ndarray:\n generation_matrix = numpy.array([\n [numpy.exp(1j * time * freq) for freq in frequencies] for time in times\n ])\n amplitudes = scipy.linalg.lstsq(generation_matrix, signal)[0]\n return amplitudes",
"title": ""
},
{
"docid": "e5ec2a1871e15535316aa8a0c0e5925d",
"score": "0.61337924",
"text": "def set_freq(self, freq):\n raise NotImplementedError(\"You're trying to use an abstract method to get frequency.\")",
"title": ""
},
{
"docid": "21a54020fa34ac89bd4b5357324cea2b",
"score": "0.6133402",
"text": "def _setFrequency(self, list):\n assert len(list) == 4\n m, e, i, pad = list\n if e == 0:\n self.frequency = m\n else:\n self.frequency = m #float(m)*10**e",
"title": ""
},
{
"docid": "02d0a250612879e335b1d0ec8fa36077",
"score": "0.6132619",
"text": "def fit(self, x):\n raise \"fit not implemented\"",
"title": ""
},
{
"docid": "df68588f52667f9b12e16b88aecc22a5",
"score": "0.61257637",
"text": "def fit(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "70c22a862ff4e0e333704694241116e4",
"score": "0.6124066",
"text": "def fit(self, X, y):\n ...",
"title": ""
},
{
"docid": "70c22a862ff4e0e333704694241116e4",
"score": "0.6124066",
"text": "def fit(self, X, y):\n ...",
"title": ""
},
{
"docid": "19b4f41c799e54f7dd4b4391c55d974c",
"score": "0.60832065",
"text": "def fit(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "38bbb9d923cffc030e14cfe60ea9d6a7",
"score": "0.6068936",
"text": "def fit(self, x, y=None, **kwargs):",
"title": ""
},
{
"docid": "a32b77eea81ba15cb4850e839ce237cf",
"score": "0.60424274",
"text": "def step_freq_fit(i, f, o=5):\n p = np.polyfit(i, f, o)\n fpoly = np.polyval(p, i)\n return fpoly",
"title": ""
},
{
"docid": "97ae46ea3169d3142311a697ad19ddd7",
"score": "0.6013297",
"text": "def fit(self, X, y):",
"title": ""
},
{
"docid": "6911abcdc691ae0c251043c1d98605c4",
"score": "0.59977216",
"text": "def set_frequency(self, frequency):\n # check the input is a numeric\n if type(frequency) != float and type(frequency) != int \\\n and np.float64 != np.dtype(frequency) and np.int64 != np.dtype(frequency):\n raise TypeError('Frequency value is not a float or int')\n # check the output is a positive number\n elif frequency < 0:\n raise ValueError('Frequency value is < 0')\n\n self.Frequency = frequency\n f_str = str(frequency).replace('.', ',')\n if len(f_str) == 6:\n # Adding the truncated 0\n f_str = f_str + '0'\n freq_str = ''.join((\"FREQ:RF \" + f_str))\n # Write the frequency value in MHz\n ret, check = itechbl12hi_common.telnet_query(self.tn, self.timeout, freq_str)\n return check",
"title": ""
},
{
"docid": "e2e0e8eabd8841c111d5217af0d424bf",
"score": "0.59943247",
"text": "def fit(self, X, Y):\n ...",
"title": ""
},
{
"docid": "e2e0e8eabd8841c111d5217af0d424bf",
"score": "0.59943247",
"text": "def fit(self, X, Y):\n ...",
"title": ""
},
{
"docid": "25c4e9974af5c36e5940c07cfa1b1ca8",
"score": "0.5986724",
"text": "def set_frequency(self, freq):\n\t\treturn self.power.set_frequency(freq)",
"title": ""
},
{
"docid": "2abed3cfe6bd43974d24b500d129fc2a",
"score": "0.5957654",
"text": "def partial_fit(self, X, y, classes=None, sample_weight=None):",
"title": ""
},
{
"docid": "a9ed9ea079c434fabaabc7f0c834bef9",
"score": "0.5957176",
"text": "def check_frequency(freq, res, aniso, epermH, epermV, mpermH, mpermV, verb):\n global _min_freq\n\n # Check if the user provided a model for etaH/etaV/zetaH/zetaV\n if isinstance(res, dict):\n res = res['res']\n\n # Check frequency\n freq = _check_var(freq, float, 1, 'freq')\n\n # As soon as at least one freq >0, we assume frequencies. Only if ALL are\n # below 0 we assume Laplace and take the negative of it.\n if np.any(freq > 0):\n laplace = False\n text_min = \"Frequencies\"\n text_verb = \" frequency\"\n else:\n laplace = True\n freq = -freq\n text_min = \"Laplace val\"\n text_verb = \" s-value \"\n\n # Minimum frequency to avoid division by zero at freq = 0 Hz.\n # => min_freq can be set with utils.set_min\n freq = _check_min(freq, _min_freq, text_min, \"Hz\", verb)\n if verb > 2:\n _prnt_min_max_val(freq, text_verb+\" [Hz] : \", verb)\n\n # Define Laplace parameter sval.\n if laplace:\n sval = freq\n else:\n sval = 2j*np.pi*freq\n\n # Calculate eta and zeta (horizontal and vertical)\n c = 299792458 # Speed of light m/s\n mu_0 = 4e-7*np.pi # Magn. permeability of free space [H/m]\n epsilon_0 = 1./(mu_0*c*c) # Elec. permittivity of free space [F/m]\n\n etaH = 1/res + np.outer(sval, epermH*epsilon_0)\n etaV = 1/(res*aniso*aniso) + np.outer(sval, epermV*epsilon_0)\n zetaH = np.outer(sval, mpermH*mu_0)\n zetaV = np.outer(sval, mpermV*mu_0)\n\n return freq, etaH, etaV, zetaH, zetaV",
"title": ""
},
{
"docid": "49232a413344f11a90b58b051537186e",
"score": "0.5953769",
"text": "def fit(self, data_set, max_time):\n pass",
"title": ""
},
{
"docid": "a2d48dcf7871bc49c1eb6a863fab69b8",
"score": "0.59497267",
"text": "def fit(self):\n self.is_fitted = True",
"title": ""
},
{
"docid": "0f632a1e1de595ef8b4aa8195648735c",
"score": "0.59486824",
"text": "def fit(self, df):\n pass",
"title": ""
},
{
"docid": "cabedd080eccea9dd3b34e53b912d93a",
"score": "0.5932209",
"text": "def sound_freq(self, hz=0, ds=0):\n pass",
"title": ""
},
{
"docid": "77a1ba2a1ec2127335285944645f829d",
"score": "0.5926736",
"text": "def reference_frequency(self):\n return float(self.send_and_receive('FREQ?'))",
"title": ""
},
{
"docid": "3510deb4f77b1daf38c013b3892cff33",
"score": "0.5919594",
"text": "def fit(self):\n\n self.is_fitted = True",
"title": ""
},
{
"docid": "0ec8d6b6d9a3e199c8cacc7e35dd6f46",
"score": "0.5906986",
"text": "def tuneScanFreq(p, nu, scanFreq, scope = 0.002, nsamp = 100, plot = False):\n df = nu[2] - nu[1]\n freqs = (np.arange(nsamp, dtype = 'float')/nsamp-0.5)*scope + scanFreq\n pow = np.zeros(nsamp)\n for i in range(nsamp):\n index = np.where(np.mod(nu,freqs[i]) < df)[0]\n pow[i] = np.mean(np.log(p[index]))\n if plot: plt.plot(pow), plt.show()\n mf = freqs[pow == pow.max()]\n if np.ndim(mf) > 0: return mf[0]\n else: return mf",
"title": ""
},
{
"docid": "5ba89416401ee82f70682cd7eb0de06e",
"score": "0.59057826",
"text": "def fit(self, samples):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "d2c0d904d20a5cb1c7988e4ce124552f",
"score": "0.5902111",
"text": "def set_frequency(self):\r\n\t\t\"\"\"For Frequency Prescalar-0\"\"\"\r\n\t\tbus.write_byte_data(PCA9530_2C_1_DEFAULT_ADDRESS, PCA9530_2C_1_REG_PSC0, PCA9530_2C_1_PSC0_USERDEFINED)\r\n\t\t\r\n\t\t\"\"\"For Frequency Prescalar-1\"\"\"\r\n\t\tbus.write_byte_data(PCA9530_2C_1_DEFAULT_ADDRESS, PCA9530_2C_1_REG_PSC1, PCA9530_2C_1_PSC1_USERDEFINED)",
"title": ""
},
{
"docid": "57470306e56055f5355044d30affe3d5",
"score": "0.59012645",
"text": "def set_frequency(self, frequency): #pragma: no cover\n\n return self._request('F %s' % frequency)",
"title": ""
},
{
"docid": "4aab1fc20cf0d6b3d12b72afced99357",
"score": "0.58938265",
"text": "def fit(self, x_train, y_train, **parameter):\n pass",
"title": ""
},
{
"docid": "8d81495d344ebd6a58a63d91ac9bf27d",
"score": "0.5889814",
"text": "def fit(self, X, y):\n pass",
"title": ""
},
{
"docid": "8d81495d344ebd6a58a63d91ac9bf27d",
"score": "0.5889814",
"text": "def fit(self, X, y):\n pass",
"title": ""
},
{
"docid": "8d81495d344ebd6a58a63d91ac9bf27d",
"score": "0.5889814",
"text": "def fit(self, X, y):\n pass",
"title": ""
},
{
"docid": "5c49115b4385eefe19b7c844da849b08",
"score": "0.58877987",
"text": "def fit(self, train_data):\n\n pass",
"title": ""
},
{
"docid": "d8d86a5e8e0f20f9987d3c90427d0789",
"score": "0.58859885",
"text": "def fit(self):\n self.initialize()\n self.iterate()",
"title": ""
},
{
"docid": "be9b234d56c358d146907287c0f42612",
"score": "0.58762693",
"text": "def fit_peaks_manual(self):\n #set peaklist to perform fit on\n self.ph.peaklist = self.get_peaks_forfit()\n #start the wavelength calibration\n #self.ph.calibrate_wavelength(peakguess = 0)\n self.ph.calibrate_wavelength_bounds(peakguess=0)",
"title": ""
},
{
"docid": "a06d6e20bdf5e3b208376e911252d927",
"score": "0.5871672",
"text": "def fit(self):\n\n if self.verb >= 1:\n print('Fitting data to ' + ('QDA' if self.quadratic else 'LDA') + ' model.')\n start = time.time()\n self.Classifier = Classifier(self.TrainingData)\n self.Classifier.fit() #want to pass in whether quadratic or linear\n\n if self.verb >= 2:\n print('Fit LDA model execution time: ' + str(time.time() - start) + 's')\n\n print('')",
"title": ""
},
{
"docid": "7336201ac12a4ee15c0ad7e43a21aa33",
"score": "0.5869757",
"text": "def SetFrequency(self, freq):\n self.freq = freq\n self.pwm.ChangeFrequency(self.freq)",
"title": ""
},
{
"docid": "5a88b4e76788e20ed4f49b324dce0008",
"score": "0.586235",
"text": "def frequency(self, frequency):\n if not frequency is None and self.mhat.pootlespwmfrequ != frequency:\n self.mhat._pwm.setPWMFreq(frequency)\n self.mhat.pootlespwmfrequ=frequency\n return self.mhat.pootlespwmfrequ",
"title": ""
},
{
"docid": "ee185c63467aeb2fa59f941270582080",
"score": "0.5858956",
"text": "def Frequency(self):\n return float(self.query('FREQ?'))",
"title": ""
},
{
"docid": "fe3cb82d575d814f7a644188da4a2b3b",
"score": "0.5855187",
"text": "def fit(self, x, y):",
"title": ""
},
{
"docid": "d3c4fc596092a3de9e7e1e7ebfa853ac",
"score": "0.5850758",
"text": "def vocabulary_to_frequency(self, mincount=0):\n self.fit_vocabulary(mincount=mincount)",
"title": ""
},
{
"docid": "8c78383580f47d6e4c9f72210dda05ad",
"score": "0.5845188",
"text": "def _fit_params(self, times, values, alpha=None, method='regression'):\n guess = self.get_freq(times, values, interp_exp=3)\n self.f0 = self.refine_frequency(times, values, guess, verbose=False)\n\n method_dict = {\n 'regression': (LinearRegression, dict()),\n 'ridge': (Ridge, dict(alpha=alpha)),\n 'lasso': (Lasso, dict(alpha=alpha)),\n 'lassocv': (LassoCV, dict())\n }\n if method not in method_dict.keys():\n raise ValueError(f'Method must be one of {list(method_dict.keys())}')\n\n basis = self._get_bases(times)\n\n model_class, kwargs = method_dict[method]\n model = model_class(**kwargs)\n\n model.fit(basis, values)\n self.sines = model.coef_[:self.num_freqs]\n self.cosines = model.coef_[self.num_freqs:]\n return model.coef_",
"title": ""
},
{
"docid": "2654fac642707496cc8c6cce2df86b62",
"score": "0.5844481",
"text": "def fit(self):\n try:\n # Create Dictionary\n self.id2word = corpora.Dictionary(self.tokens)\n # Term Document Frequency\n self.corpus = \\\n [self.id2word.doc2bow(text) for text in self.tokens]\n except:\n raise Exception('tokens not compatible')",
"title": ""
},
{
"docid": "2654fac642707496cc8c6cce2df86b62",
"score": "0.5844481",
"text": "def fit(self):\n try:\n # Create Dictionary\n self.id2word = corpora.Dictionary(self.tokens)\n # Term Document Frequency\n self.corpus = \\\n [self.id2word.doc2bow(text) for text in self.tokens]\n except:\n raise Exception('tokens not compatible')",
"title": ""
},
{
"docid": "375fe2faf73e1ca1c5ab1702ddab0721",
"score": "0.5839151",
"text": "def setupFrequencies(self):\n\n # This is going to give us nice frequencies at 1, 2, 4Hz\n steps = np.arange(NUMBER_OF_FREQUENCIES) * OCTAVE_PERIOD_STEP\n numbers = MINIMUM_PERIOD * 2 ** steps\n\n return np.reciprocal(numbers)",
"title": ""
},
{
"docid": "884e2ec8912a1402262e8c6e11e41112",
"score": "0.58287674",
"text": "def fit(self, X, y):\n count0 = 0\n count1 = 0\n for i in y:\n if(i == 0): count0 += 1\n else: count1 += 1\n if(count1 > count0):\n self.mode = 1\n else: self.mode = 0\n return self",
"title": ""
},
{
"docid": "0b3187e7de5652ca967ec7564c7f6d2f",
"score": "0.58244485",
"text": "def _fit_resample(self, X, y):\n pass",
"title": ""
},
{
"docid": "10c3242307677fc25fe46f0a3727ea7f",
"score": "0.5820852",
"text": "def _pre_fit(self):\n pass",
"title": ""
},
{
"docid": "8af7fae9fa1de31bb3dc4eac40907349",
"score": "0.5803258",
"text": "def fit_peaks_automatic(self):\n #set peaklist to perform fit on\n self.ph.peaklist = self.get_peaks_forfit()\n #start the wavelength calibration\n #self.ph.calibrate_wavelength(peakguess = 1)\n self.ph.calibrate_wavelength_bounds(peakguess=1)",
"title": ""
},
{
"docid": "306465589d6a08049691aaab0eb1d14c",
"score": "0.57979983",
"text": "def fit(self, x_train=None, y_train=None, time_limit=None):",
"title": ""
},
{
"docid": "44e5681a27cfa8949d14887aeab3d696",
"score": "0.57979184",
"text": "def fit(self,train):\n\t\tself.train = train",
"title": ""
},
{
"docid": "9dea89128abb2cb4ffdab956a0b1aeaa",
"score": "0.5793625",
"text": "def frequency(self, signal):\n pi = tf.constant(np.pi, dtype=tf.float64)\n phi = self.params[\"phi\"].get_value()\n omega_0 = self.params[\"omega_0\"].get_value()\n # phi_0 = self.params[\"phi_0\"].get_value()\n if \"d\" in self.params:\n d = self.params[\"d\"].get_value()\n max_freq = omega_0\n min_freq = omega_0 * tf.sqrt(\n tf.sqrt(tf.cos(pi * 0.5) ** 2 + d ** 2 * tf.sin(pi * 0.5) ** 2)\n )\n else:\n max_freq = omega_0\n min_freq = tf.constant(0.0, dtype=tf.float64)\n self.freq = 2 * (signal - phi) * (min_freq - max_freq)\n return self.freq",
"title": ""
},
{
"docid": "9da50a9e9aa4158946d8829fa2a90076",
"score": "0.5790219",
"text": "def amp_to_freq(self, amp):\n return np.polyval(self.poly_fit, amp)",
"title": ""
},
{
"docid": "402fd3903827b9c40c8927f26a82b9ca",
"score": "0.57857466",
"text": "def fit(self, x0, us_init, *args, **kwargs):\n raise NotImplementedError",
"title": ""
},
{
"docid": "5beb1d6f834c8394a291ac2d65fc56d1",
"score": "0.5779505",
"text": "def _create_fitting(self, **kwargs):\n pass",
"title": ""
},
{
"docid": "87066cff3cf4750f9bc21bfd27fb01f2",
"score": "0.57531416",
"text": "def frequencies(self):\n return fftpack.fftfreq(self.window_size, 1.0 / self.framerate)",
"title": ""
},
{
"docid": "7ef497af9bdb74e2d70a9c7573f3d26e",
"score": "0.5743224",
"text": "def fit(self, x, sampling_rate):\n\n\n\n x = np.array(x).flatten()\n\n sample_stdev = np.std(x) #standard deviation\n\n upper_percentile = 1 - (self.alpha / 2) #upper alpha/2 percentile\n z_alpha = stats.norm.ppf(upper_percentile)\n\n n_zero = np.square(((z_alpha / 2) * sample_stdev) / self.E) #lower bound for the windows\n\n n_one = self.minimum_transient_length_s * sampling_rate #upper bound for the windows\n\n self.n_one_ = n_one\n self.n_zero_ = n_zero\n\n try:\n self.n_mean_ = int(np.floor(np.mean([n_one,n_zero])))\n except:\n pdb.set_trace()\n self.is_fitted = True\n\n return self",
"title": ""
},
{
"docid": "61894662ea5f9ad77cdd5b46184ec60e",
"score": "0.5739293",
"text": "def frequency(self):\n return 1.4 / ((self.r1 + (2 * self.r2)) * self.c1)",
"title": ""
},
{
"docid": "f267f6d63df4b265fe35ffc8bdebbf9c",
"score": "0.5737804",
"text": "def _fit(self) -> None:\n pass",
"title": ""
},
{
"docid": "c4a6eb60905dbd6e4caa9776c0e5917b",
"score": "0.5735967",
"text": "def fit(self, X, y=None):\n super().fit(X,y)\n \n self.imputer_dict_ = {}\n \n for var in self.variables:\n self.imputer_dict_[var] = X[var].mode()[0]\n \n self.input_shape_ = X.shape \n \n return self",
"title": ""
},
{
"docid": "8778f3159b95d64e2c6c47039559ddd6",
"score": "0.5721276",
"text": "def fit(self, data):\n raise NotImplementedError('method fit() is not implemented in ' +\n str(self))",
"title": ""
},
{
"docid": "044f00bf9f3d38d95feb4f17941d8b68",
"score": "0.5712383",
"text": "def update_fit(self):\n self._do_fit()",
"title": ""
},
{
"docid": "7136c72b8e60ed6aa7993b2c42c579ee",
"score": "0.57089895",
"text": "def fit(self, documents):\n features = {}\n for document in documents:\n document = list(document['raw'])\n for n in self.n_list:\n features.update(self.freq_dict([''.join(item) for item in self.find_ngrams(document, n)]))\n self.features = [i for i,j in sorted(features.items(), reverse=True, key=operator.itemgetter(1)) if not bool(set(i.split(\"_\")) & set(self.blackfeats)) and not (i == '') and j >= self.mt]",
"title": ""
},
{
"docid": "652dc5958ec55c547f41e9ff1c9b84a5",
"score": "0.57074016",
"text": "def frequency(self, idx):\n\t\treturn self.power.frequency(idx)",
"title": ""
},
{
"docid": "7e11a6a2f9ae853dd81fee49f2ba40bc",
"score": "0.57034475",
"text": "def fit(self, X, **kwargs):\n return self",
"title": ""
},
{
"docid": "a2ed3afe38cf6a0532b2b9512cccb70e",
"score": "0.57016164",
"text": "def __init__(self, prcntVolume, freqs, *fitDirective):\n self.fitter = VoluFit(*fitDirective)\n# Fit.__init__(self, *fitDirective)\n self._prcntVolume = prcntVolume\n self._freqs = freqs\n self._fit()",
"title": ""
},
{
"docid": "78fe76564002bbd6aa5ed68faf35c0ff",
"score": "0.569435",
"text": "def set_freq(self, freq):\n\n freq = int(round(freq))\n\n # Check for valid range\n if freq < self.FREQ_RANGE[0] or freq > self.FREQ_RANGE[1]:\n self.log.warn(\n f'Warning, frequency outside acceptable range {self.FREQ_RANGE}. '\n f'Output frequency was not updated.'\n )\n # Set freq\n else:\n self.device.write(f'FREQ {freq}')\n self.log.info(f'Set MW freq to {freq}')",
"title": ""
},
{
"docid": "983046ec5de2dae3d07b223febea758c",
"score": "0.5692494",
"text": "def fit(self, *args):\n return self",
"title": ""
},
{
"docid": "3285d83f9b797e8113f4420c373e253a",
"score": "0.5690291",
"text": "def get_frequency(self):\n return float(self.fgen.query(\"{}FREQuency?\".format(self.source)))",
"title": ""
},
{
"docid": "1eb86a881076f24c468f205e4e7e0fd0",
"score": "0.5690121",
"text": "def spike_train_isi(length=100, frequency=10):\n\n isi = [nr.exponential(1/frequency)]\n while sum(isi) < 100:\n isi.append(nr.exponential(1/frequency))\n\n spikes = [sum(isi[:i]) for i in range(1,len(isi))]\n\n return spikes",
"title": ""
},
{
"docid": "fe696f31d6b773c52456266fe3b4b072",
"score": "0.56699646",
"text": "def get_freq(self):\n self.values_ = []\n self.counts_ = []\n self.freqs_ = []\n self.levels_ = []\n for col in range(self.X_.shape[1]):\n values, counts = np.unique(self.X_[:, col], return_counts=True)\n freqs = counts / sum(counts)\n levels = int(len(values))\n self.values_ = np.append(self.values_, values)\n self.counts_ = np.append(self.counts_, counts)\n self.freqs_ = np.append(self.freqs_, freqs)\n self.levels_ = np.append(self.levels_, levels)\n self.levels_ = self.levels_.astype(int)\n self.counts_ = self.counts_.astype(int)\n return self",
"title": ""
},
{
"docid": "cc2420fd97c3f2fca3f9289941fb9bfd",
"score": "0.5665725",
"text": "def _compute_frequency_resolution(nsteps, sampling_period):\n fs = 1 / sampling_period\n return fs / (2 * nsteps - 1)",
"title": ""
},
{
"docid": "f3bcadf4e329e22c43e60231ee9534bc",
"score": "0.5664254",
"text": "def samplefreq(self):\n return 1. / self.sampletime",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="747"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">28e8d58e031b6e9984ce908b755a5e1d</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Update parameter dictionary, while also performing some basic checks.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "ca91c3445747af00598d0000a0615a04",
"score": "0.0",
"text": "def update(self, values, check_conflict=False, check_already_exists=True, path=\"\"):\n # check if values is not a dictionary\n if not isinstance(values, dict):\n values = values._dict_items\n # check parameter values\n self.check_parameter_values(values)\n # update\n for name, value in values.items():\n # check for conflicts\n if (\n check_conflict is True\n and name in self.keys()\n and not (self[name] == float(value) or self[name] == value)\n ):\n raise ValueError(\n \"parameter '{}' already defined with value '{}'\".format(\n name, self[name]\n )\n )\n # check parameter already exists (for updating parameters)\n if check_already_exists is True:\n try:\n self._dict_items[name]\n except KeyError as err:\n raise KeyError(\n \"Cannot update parameter '{}' as it does not \".format(name)\n + \"have a default value. ({}). If you are \".format(err.args[0])\n + \"sure you want to update this parameter, use \"\n + \"param.update({{name: value}}, check_already_exists=False)\"\n )\n # if no conflicts, update\n if isinstance(value, str):\n if (\n value.startswith(\"[function]\")\n or value.startswith(\"[current data]\")\n or value.startswith(\"[data]\")\n or value.startswith(\"[2D data]\")\n ):\n raise ValueError(\n \"Specifying parameters via [function], [current data], [data] \"\n \"or [2D data] is no longer supported. For functions, pass in a \"\n \"python function object. For data, pass in a python function \"\n \"that returns a pybamm Interpolant object. \"\n \"See https://tinyurl.com/merv43ss for an example with both.\"\n )\n\n elif value == \"[input]\":\n self._dict_items[name] = pybamm.InputParameter(name)\n # Anything else should be a converted to a float\n else:\n self._dict_items[name] = float(value)\n elif isinstance(value, tuple) and isinstance(value[1], np.ndarray):\n # If data is provided as a 2-column array (1D data),\n # convert to two arrays for compatibility with 2D data\n # see #1805\n func_name, data = value\n data = ([data[:, 0]], data[:, 1])\n self._dict_items[name] = (func_name, data)\n else:\n self._dict_items[name] = value\n # reset processed symbols\n self._processed_symbols = {}",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "b6f27b5b1708baf25527a92069a0bb4d",
"score": "0.7634509",
"text": "def update_parameters(self):",
"title": ""
},
{
"docid": "52a41537d757e88af18f861bdb7b2c79",
"score": "0.75093085",
"text": "def update_params(self) -> None:\n raise NotImplementedError",
"title": ""
},
{
"docid": "85714608af3a638ff73450acf28d0d8d",
"score": "0.74865335",
"text": "def updateParameters(self):\n return",
"title": ""
},
{
"docid": "85714608af3a638ff73450acf28d0d8d",
"score": "0.74865335",
"text": "def updateParameters(self):\n return",
"title": ""
},
{
"docid": "cbd5b279c5b7c5dacfa7f3023b1e9d8b",
"score": "0.74534905",
"text": "def updateParameters(self):\n return",
"title": ""
},
{
"docid": "67ced2576edc87551504d28e2c8e1048",
"score": "0.7417093",
"text": "def _update_params(self, *args, **kwargs):\n log.trace(\"in _update_params\")\n error = None\n logging = self._is_logging()\n\n try:\n if logging:\n # Switch to command mode,\n self._stop_logging()\n\n # UPDATE CODE HERE\n # Get old param dict config.\n old_config = self._param_dict.get_config()\n\n kwargs['expected_prompt'] = Prompt.COMMAND\n\n cmds = dir(Parameter)\n results = \"\"\n for attr in sorted(cmds):\n if attr not in ['dict', 'has', 'list', 'ALL']:\n if not attr.startswith(\"_\"):\n key = getattr(Parameter, attr)\n result = self._do_cmd_resp(InstrumentCmds.GET, key, **kwargs)\n results += result + NEWLINE\n\n new_config = self._param_dict.get_config()\n\n if not dict_equal(new_config, old_config):\n self._driver_event(DriverAsyncEvent.CONFIG_CHANGE)\n\n # Catch all error so we can put ourself back into\n # streaming. Then rethrow the error\n except Exception as e:\n log.error(\"EXCEPTION WAS \" + str(e))\n error = e\n\n finally:\n # Switch back to streaming\n if logging:\n my_state = self._protocol_fsm.get_current_state()\n log.trace(\"current_state = %s calling start_logging\", my_state)\n self._start_logging()\n\n if(error):\n raise error\n\n return results",
"title": ""
},
{
"docid": "82d92cb150ae0f2f22efa83a4026a684",
"score": "0.73772645",
"text": "def update_parameters(self, params):\n if not isinstance(params, dict):\n try:\n params = params.get_parameters()\n except:\n raise TypeError(\"Wrong data type for update_parameters.\")\n\n for key, value in params.items():\n self.__setitem__(key, value)\n # Compute some values on top of the given input parameters\n self.compute_parameters()",
"title": ""
},
{
"docid": "38b2f7d496774f0acfa797bb93396f94",
"score": "0.73131156",
"text": "def update_params(self, params):\n pass",
"title": ""
},
{
"docid": "42c51a19ad9ddea3ea1d639d19fbc244",
"score": "0.7173092",
"text": "def params_update(self):\n if self.rho_update is not None:\n self.rho = self.rho_update(self.rho)\n if self.sigma_update is not None:\n self.sigma = self.sigma_update(self.sigma)\n if self.tau_update is not None:\n self.tau = self.tau_update(self.tau)\n if self.extra_factor_update is not None:\n self.extra_factor = self.extra_factor_update(self.extra_factor)",
"title": ""
},
{
"docid": "0c98107d68cda54205d44eacd9c0c8a7",
"score": "0.7128356",
"text": "def update_params(old_params, new_params, check=False):\n updated_params = dict(old_params)\n if new_params: # allow for new_params to be None\n for key, val in new_params.items():\n if key not in old_params and check:\n raise ValueError('\\'' + key\n + '\\' is not a valid parameter key, '\n + 'consider one of \\n'\n + str(list(old_params.keys())))\n if val is not None:\n updated_params[key] = val\n return updated_params",
"title": ""
},
{
"docid": "8fc9613fcba7ac6684f79a625a09f0f3",
"score": "0.70711124",
"text": "def updateParameters(self, parameters):\r\n return",
"title": ""
},
{
"docid": "8fc9613fcba7ac6684f79a625a09f0f3",
"score": "0.70711124",
"text": "def updateParameters(self, parameters):\r\n return",
"title": ""
},
{
"docid": "8fc9613fcba7ac6684f79a625a09f0f3",
"score": "0.70711124",
"text": "def updateParameters(self, parameters):\r\n return",
"title": ""
},
{
"docid": "8fc9613fcba7ac6684f79a625a09f0f3",
"score": "0.70711124",
"text": "def updateParameters(self, parameters):\r\n return",
"title": ""
},
{
"docid": "8fc9613fcba7ac6684f79a625a09f0f3",
"score": "0.70711124",
"text": "def updateParameters(self, parameters):\r\n return",
"title": ""
},
{
"docid": "7240a117e03639552649117a37dc3cb0",
"score": "0.7032692",
"text": "def _set_params(self, *args, **kwargs):\n log.trace(\"in _set_params\")\n # Retrieve required parameter.\n # Raise if no parameter provided, or not a dict.\n result = None\n startup = False\n try:\n params = args[0]\n except IndexError:\n raise InstrumentParameterException('Set command requires a parameter dict.')\n\n try:\n startup = args[1]\n except IndexError:\n pass\n\n log.trace(\"_set_params calling _verify_not_readonly ARGS = \" + repr(args))\n self._verify_not_readonly(*args, **kwargs)\n\n for (key, val) in params.iteritems():\n result = self._do_cmd_resp(InstrumentCmds.SET, key, val, **kwargs)\n log.trace(\"_set_params calling _update_params\")\n self._update_params()\n return result",
"title": ""
},
{
"docid": "faa5e2e39ba5b75b6a1ebce704dee902",
"score": "0.7028393",
"text": "def update(self, param_dict):\n temp_ps=[]\n for i in range(len(self._param_names)):\n if self._param_names[i] in param_dict.keys():\n temp_ps.append(param_dict[self._param_names[i]])\n else:\n temp_ps.append(self._parameters[i])\n self._parameters[:]=temp_ps",
"title": ""
},
{
"docid": "091083ab83d915bb4cad6e0ed6b9add7",
"score": "0.701084",
"text": "def _update_params(self, *args, **kwargs):\n log.debug(\"Updating parameter dict\")\n\n old_config = self._param_dict.get_config()\n\n # Not sure what this was for. \n #self.get_config()\n\n self._go_to_root_menu()\n #self._navigate_and_execute(Event.SHOW_CONFIG, dest_submenu=SubMenues.SHOW_CONFIG_MENU, timeout=5)\n if len(Command.SHOW_CONFIG_CMD) > 2:\n expected_response = Command.SHOW_CONFIG_CMD[2]\n else:\n expected_response = None\n self._navigate_and_execute(Command.SHOW_CONFIG_CMD, expected_response = expected_response, \n dest_submenu=SubMenues.SHOW_CONFIG_MENU, timeout=5)\n # DHE Trying to get DEPLOYMENT_MODE\n print \"--->>> DHE Trying to get DEPLOYMENT_MODE\"\n self._go_to_root_menu()\n self._navigate_and_execute(Command.DEPLOYMENT_MODE_NO, dest_submenu=SubMenues.OPERATIONAL_MODE_MENU, \n expected_prompt=Prompt.SETUP_DEPLOY_MENU, timeout=5)\n self._go_to_root_menu()\n\n new_config = self._param_dict.get_config() \n if (new_config != old_config) and (None not in old_config.values()):\n print \"--------> DHE: publishing CONFIG_CHANGE event\"\n self._driver_event(DriverAsyncEvent.CONFIG_CHANGE)",
"title": ""
},
{
"docid": "85abe1f28009e4fd3da7ec8f1d54b4f6",
"score": "0.69958735",
"text": "def update_params(self):\n for key in self.params.iterkeys():\n val = getattr(self, key)\n self.params[key] = val",
"title": ""
},
{
"docid": "103192510777fb8c4ac127f646fb6b94",
"score": "0.69400287",
"text": "def updateParameters(self, parameters):\r\n\t\treturn",
"title": ""
},
{
"docid": "69abeb21c066a3769219aa5eb0ae2456",
"score": "0.6917276",
"text": "def _update_params(self):\n if self.data.get('title'):\n self.params['title'] = self.data.get('title')\n if self.data.get('pageid'):\n self.params['pageid'] = self.data.get('pageid')\n if self.data.get('wikibase'):\n self.params['wikibase'] = self.data.get('wikibase')",
"title": ""
},
{
"docid": "906d8e49cd19951835f6bf22f0cee8f9",
"score": "0.69142705",
"text": "def updateParam(self, paramName, updateDict):\n modelParam = self.getComponent(paramName)\n if not isinstance(modelParam, pyomo.base.param.IndexedParam):\n raise IOError(\n '`{}` not a Parameter in the optimization model. Sets and decision variables '\n 'cannot be updated by the user'.format(paramName)\n )\n elif not isinstance(updateDict, dict):\n raise TypeError('`updateDict` must be a dictionary')\n else:\n print(\n 'WARNING: we currently do not check that the updated value is the '\n 'correct data type for this Optimization Parameter, this is your '\n 'responsibility to check!'\n )\n modelParam.store_values(updateDict)",
"title": ""
},
{
"docid": "2b91ffb320ec713a47f9f0e42df93db3",
"score": "0.69048053",
"text": "def _update_params(**params):\n\n env.update(params)\n _build_parameters()",
"title": ""
},
{
"docid": "1c41f1ca10b8d4d97f2a847d71c4cd29",
"score": "0.6878575",
"text": "def _update_param(self):\n # Update relaxation parameter.\n if not isinstance(self._rho_update, type(None)):\n self._rho = self._rho_update(self._rho)\n\n # Update proximal dual parameter.\n if not isinstance(self._sigma_update, type(None)):\n self._sigma = self._sigma_update(self._sigma)\n\n # Update proximal primal parameter.\n if not isinstance(self._tau_update, type(None)):\n self._tau = self._tau_update(self._tau)",
"title": ""
},
{
"docid": "e98d842f39cf40978ec334fa9acb6aef",
"score": "0.68500113",
"text": "def set_parameters(self, params):\n if not isinstance(params, dict):\n try:\n params = params.get_parameters()\n except:\n raise TypeError(\"Wrong data type for set_parameters.\")\n\n assert type(params) == dict\n\n self._params = deepcopy(params)\n # Compute some values on top of the given input parameters\n self.compute_parameters()",
"title": ""
},
{
"docid": "e27c6a89c0eec4e8536610346ebdd362",
"score": "0.68439245",
"text": "def update(self, *args, **kwargs) -> None:\n if isinstance(self.params, dict):\n self.params.update(*args, **kwargs)",
"title": ""
},
{
"docid": "976de09189bfa5940ea5844de655e87b",
"score": "0.6825981",
"text": "def _checkParams(self):\n\n params = self._params()\n config = self._config()\n\n for key, value in self.getParams().items():\n # If we already have it, then we don't need to do anything\n if key in params:\n continue\n # If we don't have it, but it's required, then fail\n if value.req:\n raise KeyError(\n f\"Missing required param {key} for {type(self).__name__.lower()}\"\n )\n # If it's a reference by default, fill that in\n if value.config_ref is not None:\n tmp = getattr(config, value.config_ref[0])\n params[key] = (\n tmp[value.config_ref[1:]] if len(value.config_ref) > 1 else tmp\n )\n # Otherwise, put in the default value (if it exists)\n elif value.default is not None:\n params[key] = value.default",
"title": ""
},
{
"docid": "10befec5f657a160651ff38ffbd6a72a",
"score": "0.6820287",
"text": "def update(self):\n\t\tself._initParams()",
"title": ""
},
{
"docid": "c0d0f51adb70737af3b517db654f44b7",
"score": "0.6799115",
"text": "def _set_params(self):\n if not self.query_handler:\n return\n for (param, val_tuple) in self.params_dict.iteritems():\n (p_type, _, p_set, p_err_str) = val_tuple\n try:\n p_set(p_type(self.query_handler.get_param(param)))\n except ValueError, e:\n QtWidgets.QMessageBox.warning(self, 'Wrong parameter type',\n '%s: %s!' % (str(e), p_err_str))\n except KeyError, e:\n # No such param in JSON, pass\n pass",
"title": ""
},
{
"docid": "0390c4a44259fd621a5c27ada71b9893",
"score": "0.6770032",
"text": "def set_params(self, param_dict):\n # NOT IMPLEMENTED YET\n pass\n return None",
"title": ""
},
{
"docid": "06e0e19533117762b40fc20dcd495643",
"score": "0.6756491",
"text": "def update_parameters(self, member, pre_processor):\r\n values = self.read_params(member, member, pre_processor)\r\n for key in values:\r\n parameter = self.get_parameter(key)\r\n parameter.set_value(member, values[key])",
"title": ""
},
{
"docid": "ea3993240b360c210f746702325613fb",
"score": "0.67495",
"text": "def update_constraints(self):\n self.updated = dict([(name, False) for name in self.params])\n for name in self.params:\n self.__update_paramval(name)",
"title": ""
},
{
"docid": "9b4e0d0697dc9145367ce50ca3eb6df3",
"score": "0.670826",
"text": "def update_parameters(self):\n\n for param_name, form_input in self.controls.items():\n param = self.params[param_name]\n value = form_input.value()\n if value != param['value']:\n self.params[param_name]['value'] = value",
"title": ""
},
{
"docid": "69df0fa96ec0ce8264ac1919fa27233e",
"score": "0.66962266",
"text": "def updateParams(self, paramUpdates):\n for paramName, newValue in paramUpdates:\n self.params[paramName] = newValue",
"title": ""
},
{
"docid": "afc6f5f7c326692adde5e8d6e7aa1ddc",
"score": "0.6690656",
"text": "def _update_params(self, params: dict):\n for name, value in params.items():\n # WARN: this might be potentially risky\n inner_name = '_{}'.format(name)\n assert inner_name in self.__dict__\n assert isinstance(value, type(self.__dict__[inner_name]))\n setattr(self, inner_name, value)",
"title": ""
},
{
"docid": "eadafa6d769a0c39f1eb1e2d915034df",
"score": "0.66886455",
"text": "def _set_params(self, *args, **kwargs):\n try:\n params = args[0]\n except IndexError:\n raise InstrumentParameterException('Set command requires a parameter dict.')\n\n self._verify_not_readonly(*args, **kwargs)\n\n for (key, val) in params.iteritems():\n log.debug(\"KEY = %s VALUE = %s\", key, val)\n\n if(key in ConfirmedParameter.list()):\n # We add a write delay here because this command has to be sent\n # twice, the write delay allows it to process the first command\n # before it receives the beginning of the second.\n response = self._do_cmd_resp(Command.SET, key, val, write_delay=0.2)\n else:\n response = self._do_cmd_resp(Command.SET, key, val, **kwargs)\n\n log.debug(\"set complete, update params\")\n self._update_params()",
"title": ""
},
{
"docid": "67db64b5edbea422c58f1b9a81e2cba8",
"score": "0.6685017",
"text": "def updateParameters(self, parameters):\n return",
"title": ""
},
{
"docid": "67db64b5edbea422c58f1b9a81e2cba8",
"score": "0.6685017",
"text": "def updateParameters(self, parameters):\n return",
"title": ""
},
{
"docid": "67db64b5edbea422c58f1b9a81e2cba8",
"score": "0.6685017",
"text": "def updateParameters(self, parameters):\n return",
"title": ""
},
{
"docid": "3f60a2c7e8ee402f23bb3c546beb80e6",
"score": "0.66049063",
"text": "def set_params(self, params): \n pass",
"title": ""
},
{
"docid": "de3864a78228325d57c71fa0786b0d74",
"score": "0.6600923",
"text": "def updateParams(self, updateDict):\n for paramName, paramDict in updateDict.items():\n self.updateParam(paramName, paramDict)",
"title": ""
},
{
"docid": "d50166e5d50a7a7bd5064ffad3ae203f",
"score": "0.6582693",
"text": "def update_parameters(updates):\n for (key, val) in updates.items():\n par[key] = val\n print('Updating:', key, '-->', val)\n update_dependencies()",
"title": ""
},
{
"docid": "bc4ea99fdf77f79589f8ebc56bb8a71e",
"score": "0.6510116",
"text": "def set_params(self, **params):",
"title": ""
},
{
"docid": "036835a7e87d1f2ffdd90f6207665db1",
"score": "0.65091187",
"text": "def set_parameters(param_key_values_dict):\n return",
"title": ""
},
{
"docid": "c69d0caab6f60c534fedfc8eca657dbd",
"score": "0.6506085",
"text": "def _apply_params(self):\n log.trace(\"IN _apply_params\")\n config = self.get_startup_config()\n # Pass true to _set_params so we know these are startup values\n self._set_params(config, True)",
"title": ""
},
{
"docid": "0eb673ad191ab44f21d891e13f063143",
"score": "0.65053",
"text": "def check_params(self):",
"title": ""
},
{
"docid": "5b61dd4d7d8fc2f38a32da1393b2f84c",
"score": "0.65041625",
"text": "def _update_param(self, item):\n self._update_source(item)\n self._update_value(item)",
"title": ""
},
{
"docid": "4c533f2a695f6889b50b0ba10ca000f4",
"score": "0.6499983",
"text": "def updateParameters(self, parameters):\n\n return",
"title": ""
},
{
"docid": "b36f8f37dd35280c198e4d289ed5f187",
"score": "0.64948297",
"text": "def set_parameters(self, **params):\n for k, v in params.items():\n if k in self.parameters:\n setattr(self, k, v)\n else:\n log.warning(\"[{}] unknown parameter provided {} = {}\".format(self.name, k, v))",
"title": ""
},
{
"docid": "bf7185ede9ac3644d9d27c877ba85c10",
"score": "0.6488238",
"text": "def updateParameters(self):\n\n if self.params[0].value and not self.params[0].hasBeenValidated:\n try:\n # Create a describe object for 'Input Table'\n desc = arcpy.Describe(self.params[0].value)\n\n # Check if 'Input Table' contains aliases, and if so enable Add Field Aliases to CSV Table (optional)' parameter\n aliases = [field.aliasName for field in desc.fields if field.aliasName != field.name]\n if aliases:\n self.params[3].enabled = True\n else:\n self.params[3].enabled = False\n\n except:\n pass\n return",
"title": ""
},
{
"docid": "08d2923ccf53c7bb4633aee3f2fc4767",
"score": "0.6473144",
"text": "def SetParameters(self, params):",
"title": ""
},
{
"docid": "08d2923ccf53c7bb4633aee3f2fc4767",
"score": "0.6473144",
"text": "def SetParameters(self, params):",
"title": ""
},
{
"docid": "08d2923ccf53c7bb4633aee3f2fc4767",
"score": "0.6473144",
"text": "def SetParameters(self, params):",
"title": ""
},
{
"docid": "08d2923ccf53c7bb4633aee3f2fc4767",
"score": "0.6473144",
"text": "def SetParameters(self, params):",
"title": ""
},
{
"docid": "08d2923ccf53c7bb4633aee3f2fc4767",
"score": "0.6473144",
"text": "def SetParameters(self, params):",
"title": ""
},
{
"docid": "08d2923ccf53c7bb4633aee3f2fc4767",
"score": "0.6473144",
"text": "def SetParameters(self, params):",
"title": ""
},
{
"docid": "08d2923ccf53c7bb4633aee3f2fc4767",
"score": "0.6473144",
"text": "def SetParameters(self, params):",
"title": ""
},
{
"docid": "fd180f3a4ae21184f3de5ba0758db30c",
"score": "0.6466713",
"text": "def test_param_validator_success(params):\n params[\"test\"] = \"good\"",
"title": ""
},
{
"docid": "ae96730a95ca74275b9029228a364f2f",
"score": "0.64457095",
"text": "def __set_params(self, params_in):\n self.__params = params_in\n return 0",
"title": ""
},
{
"docid": "173658df7aaed5003e1a40589773d1b6",
"score": "0.64284045",
"text": "def update_parameters(self, new_parameter):\n\t\tA = [self.parameters]\n\t\ti = 0\n\t\twhile str(type(A[-1])) == \"<type 'dict'>\":\n\t\t\tA.append(A[-1][self.config['Variable'][i]])\n\t\t\ti += 1\n\t\tA[-1][self.config['Variable'][i]] = new_parameter",
"title": ""
},
{
"docid": "395117e6869672fb17f86102d52c3f87",
"score": "0.64219606",
"text": "def set_params(self, params: dict) -> None:\n \n pass",
"title": ""
},
{
"docid": "3ffc7ac6a1a042090a9c7c7f93e07f41",
"score": "0.6414967",
"text": "def set_params(self, params):\n from scipy.linalg import norm\n self.k.set_params(params)\n self._update() # params have changed so recalculate\n # if norm( params - self.k.get_parameters() ) > 1e-12:\n # # print 'Params changed:', params, self.k.get_parameters()\n # self.k.set_parameters( params )\n # self._update() # they have so recalculate",
"title": ""
},
{
"docid": "1ade8072490a0c708e6c902ae171e461",
"score": "0.6397278",
"text": "def ptp_parameter_update(self, ptp_parameter_id, values):",
"title": ""
},
{
"docid": "55219c7639edf85ea9dc11921747662d",
"score": "0.63713527",
"text": "def fix_params(self, params):\n self._fixed_params.update(params)",
"title": ""
},
{
"docid": "b318190683c53e190f682fbdcb64dcad",
"score": "0.6360462",
"text": "def param_update(self, params):\n self.params = params\n # Update each parameter by name\n [setattr(self, self.param_l[i], params[i]) for i in range(self.dim)]\n for i in self.sub_pops: # Update parameters of each sub-population:\n i.param_update(params)\n self._unsolved = True",
"title": ""
},
{
"docid": "f563f10d1979981cdbc11d90c9b760bf",
"score": "0.63600165",
"text": "def updateParameters(self, parameters):\r\n return parameters",
"title": ""
},
{
"docid": "5d7497ad17815c2ac0b88dcf5e9f0040",
"score": "0.63377905",
"text": "def _internal_update_parameters(self, **kwargs: Any) -> None:\n # We handle None as not set.\n kwargs = {k: v for k, v in kwargs.items() if v is not None}\n # check type of replaced variables\n generics = AutoExecutor._typed_parameters()\n for name, expected_type in generics.items():\n if expected_type == float:\n expected_type = (int, float) # type: ignore\n if name in kwargs:\n assert isinstance(kwargs[name], expected_type), (\n f'Parameter \"{name}\" expected type {expected_type} ' f'(but value: \"{kwargs[name]}\")'\n )\n\n _convert_deprecated_args(kwargs, self._deprecated_args)\n specific = [x.split(\"_\", 1) for x in kwargs if x not in generics]\n\n invalid = []\n executors = plugins.get_executors()\n for ex_arg in specific:\n if len(ex_arg) != 2:\n invalid.append(f\"Parameter '{ex_arg[0]}' need to be prefixed by an executor name.\")\n continue\n ex, arg = ex_arg\n\n if ex not in executors:\n invalid.append(f\"Unknown executor '{ex}' in parameter '{ex}_{arg}'.\")\n continue\n\n valid = executors[ex]._valid_parameters()\n if arg not in valid and arg not in generics:\n invalid.append(\n f\"Unknown argument '{arg}' for executor '{ex}' in parameter '{ex}_{arg}'.\"\n + \" Valid arguments: \"\n + \", \".join(valid)\n )\n continue\n if invalid:\n invalid.extend(\n [\n f\"Known executors: {', '.join(executors.keys())}\",\n f\"As a reminder, shared/generic (non-prefixed) parameters are: {generics}.\",\n ]\n )\n raise NameError(\"\\n\".join(invalid))\n\n # add cluster specific generic overrides\n kwargs.update(\n **{\n arg: kwargs.pop(f\"{ex}_{arg}\")\n for ex, arg in specific\n if ex == self.cluster and arg in generics\n }\n )\n parameters = self._executor._convert_parameters({k: kwargs[k] for k in kwargs if k in generics})\n # update parameters in the core executor\n for (ex, arg) in specific:\n # update cluster specific non-generic arguments\n if arg not in generics and ex == self.cluster:\n parameters[arg] = kwargs[f\"{ex}_{arg}\"]\n\n self._executor._internal_update_parameters(**parameters)",
"title": ""
},
{
"docid": "43aba87e85e85e10f8eed2463efe7cca",
"score": "0.63097864",
"text": "def set_params(self):\r\n pass",
"title": ""
},
{
"docid": "5cb19367c7306990d319688bd0065c52",
"score": "0.63081914",
"text": "def update_params(argv: list, prm: dict):\n\n\tfor a in argv[1:]:\n\t\ttoks = a.split('=',1)\n\t\tif len(toks)<2: continue\n\t\tk,v = toks[:2]\n\t\tif k not in prm: continue\n\t\tprm[k] = v",
"title": ""
},
{
"docid": "a5885a407528e680a37e111e6f0853dd",
"score": "0.6304848",
"text": "def paramChanger(self, params, newValues):\n\n for i, param in enumerate(params):\n for block in [self.controlBlock, self.systemBlock, self.electronsBlock, self.others]:\n for key, value in block.items():\n if (key == param):\n block[key] = newValues[i]\n self.inputParametersMaker()",
"title": ""
},
{
"docid": "d38a5f6997ad4bc89be13bc22f815ce6",
"score": "0.63021547",
"text": "def update_parameter(self, param_name, bead_names, param_value):\n\n if (\n param_name in [\"ai\", \"bi\"]\n and self._test_critical[self.beads.index(bead_names[0])]\n ):\n raise ValueError(\n \"Bead, {}, initialized with critical properties, not ai and bi\".format(\n bead_names[0]\n )\n )\n super().update_parameter(param_name, bead_names, param_value)",
"title": ""
},
{
"docid": "ffcd89be55fa642091f138a43f0a9d70",
"score": "0.63001055",
"text": "def update_params(self, kwargs):\n if kwargs is not None:\n for k, v in kwargs.iteritems():\n setattr(self, k, v)",
"title": ""
},
{
"docid": "16c16e592737126fe032c51e2fe45344",
"score": "0.6281303",
"text": "def update_param_dict():\n operation_params[\"length_side\"] = operation_params[\"high_cv_voltage\"] - operation_params[\"low_cv_voltage\"]\n operation_params[\"data_pts\"] = 2*(operation_params[\"length_side\"]+1)",
"title": ""
},
{
"docid": "afa07979c99687ac67eeb5760fb0c2ea",
"score": "0.6279899",
"text": "def setParams(self, **params):\n self.__params.update(params)\n self.paramStateChanged()",
"title": ""
},
{
"docid": "e192b1cb3792700a7ff05dbc7c61fad5",
"score": "0.6279802",
"text": "def update_param(self,name,value) :\n found=False\n\n if self.contain_cmb :\n if name=='r_prim' :\n self.r_prim=value\n found=True\n if name=='A_lens' :\n self.A_lens=value\n found=True\n if self.contain_sync :\n if name=='A_sync' :\n self.A_sync=value\n found=True\n if name=='alpha_sync' :\n self.alpha_sync=value\n found=True\n if name=='beta_sync' :\n self.beta_sync=value\n found=True\n if name=='nu0_sync' :\n self.nu0_sync=value\n found=True\n if name=='xi_sync' :\n self.xi_sync=value\n found=True\n\n if self.contain_dust :\n if name=='A_dust' :\n self.A_dust=value\n found=True\n if name=='alpha_dust' :\n self.alpha_dust=value\n found=True\n if name=='beta_dust' :\n self.beta_dust=value\n found=True\n if name=='temp_dust' :\n self.temp_dust=value\n found=True\n if name=='nu0_dust' :\n self.nu0_dust=value\n found=True\n if name=='xi_dust' :\n self.xi_dust=value\n found=True\n \n if self.contain_dust_sync_corr :\n if name=='r_dust_sync' :\n self.r_dust_sync=value\n found=True\n \n if self.contain_CO1 :\n if name=='A_CO1' :\n self.A_CO1=value\n found=True\n if name=='alpha_CO1' :\n self.alpha_CO1=value\n found=True\n \n if self.contain_CO2 :\n if name=='A_CO2' :\n self.A_CO2=value\n found=True\n if name=='alpha_CO2' :\n self.alpha_CO2=value\n found=True\n\n if not found :\n ValueError(\"Parameter \"+name+\" not found\")",
"title": ""
},
{
"docid": "fffd658b3f326acc0d4ba9936a00668b",
"score": "0.6276293",
"text": "def _verify_param_integrity(self):\n pass",
"title": ""
},
{
"docid": "5cad37991d393eeca46c0beb234d1246",
"score": "0.62717557",
"text": "def _update_all_parameters(m, fixed_baseline, fixed_permit_price, capacity_continuous, capacity_discrete):\r\n\r\n _update_fixed_baseline(m, fixed_baseline)\r\n _update_fixed_permit_price(m, fixed_permit_price)\r\n _update_candidate_unit_capacity_continuous(m, capacity_continuous)\r\n _update_candidate_unit_capacity_discrete(m, capacity_discrete)",
"title": ""
},
{
"docid": "30b50e7ee750d51f4b62190f5dfcd806",
"score": "0.6263435",
"text": "def update_params(self, beta=None, U=None, D=None, P=None):\n if beta is not None:\n self.beta = beta\n if U is not None:\n self.U = U\n if D is not None:\n self.D = D\n if P is not None:\n self.P = P\n else:\n self.P = self.compute_P(\n X=self.X, m=self.m, group_sizes=self.group_sizes, offset=self.offset\n )",
"title": ""
},
{
"docid": "21e560aa4b2956698d14532db7f68c8f",
"score": "0.62615955",
"text": "def _set_params(self, args, kwargs):\n self._set_args(args)\n self._set_kwargs(kwargs)\n\n self._invalidate()",
"title": ""
},
{
"docid": "8231fe62a87e48b91a9e1b555d6227cd",
"score": "0.6257427",
"text": "def _check_params(self, attrs):\n\n _attrs = '_' + attrs\n\n setattr(self._params, _attrs, [None] * len(self._params._names))\n try:\n for i, param in enumerate(self._params._names):\n for posterior in self._subset[1:]:\n if (getattr(self._subset[0], attrs)[param] \\\n != getattr(posterior, attrs)[param]):\n raise ValueError('Inconsistent %s for parameter'\n ' %s between posteriors %s and %s.' %\n (attrs, param,\n self._subset[0].ID, posterior.ID))\n getattr(self._params, _attrs)[i] = \\\n getattr(self._subset[0], attrs)[param]\n except (AttributeError, KeyError):\n print('Parameter %s not specified correctly.' % attrs)\n raise",
"title": ""
},
{
"docid": "a0624f0e2ad887cc1707fc109ce634e4",
"score": "0.6248526",
"text": "def update_params(self):\n self.sess.run(self.update_net_params)",
"title": ""
},
{
"docid": "7a27f48e41470fba4aa55ab85e687e01",
"score": "0.6220421",
"text": "def check_fields(self):\n self.params = {\n key: self.params[key]\n for key in self.params if Constants.ADDITIONAL_DATA_PREFIX not in key\n }\n super().check_fields()",
"title": ""
},
{
"docid": "fdb0ded16fb191824ef87ec93e8bb3f9",
"score": "0.6216237",
"text": "def __update_paramval(self, name):\n # Has this param already been updated?\n # if this is called as an expression dependency,\n # it may have been!\n if self.updated[name]:\n return\n par = self.params[name]\n if getattr(par, 'expr', None) is not None:\n if getattr(par, 'ast', None) is None:\n par.ast = self.asteval.parse(par.expr)\n if par.deps is not None:\n for dep in par.deps:\n self.__update_paramval(dep)\n par.value = self.asteval.run(par.ast)\n out = check_ast_errors(self.asteval.error)\n if out is not None:\n self.asteval.raise_exception(None)\n self.asteval.symtable[name] = par.value\n self.updated[name] = True",
"title": ""
},
{
"docid": "3328ad1d60517d3424632045e2f571c9",
"score": "0.61789674",
"text": "def param_update(self, params):\n self.params = params\n # Update each parameter by name\n [setattr(self, self.param_l[i], params[i]) for i in range(self.dim)]",
"title": ""
},
{
"docid": "f299403d4a6b48b4ec3fa62ddeed9117",
"score": "0.6162596",
"text": "def update_param(args: argparse.Namespace, config: edict, params: List[str]) -> edict:\n args_dict = vars(args)\n for param in params:\n if args_dict[param] is not None:\n config[param] = args_dict[param]\n return config",
"title": ""
},
{
"docid": "2e60faab9844d74affc223442ecfd36d",
"score": "0.61622083",
"text": "def service_parameter_update(self, uuid, values):",
"title": ""
},
{
"docid": "e0a8a3139790a800d7fbca90ced3212c",
"score": "0.61564416",
"text": "def _UpdateFromRequestParameters(self, anomaly_config_entity):\n # This overrides the method in the superclass.\n anomaly_config_entity.config = self._GetAndValidateConfigContents()",
"title": ""
},
{
"docid": "3264c7672028098534a2a82b10eccf8a",
"score": "0.61546147",
"text": "def _set_params(self, args, kwargs):\n raise NotImplementedError(\"set_params\")",
"title": ""
},
{
"docid": "67f54ef94af4cd574bb155fd71428a13",
"score": "0.61444074",
"text": "def _set_params(self, params):\n result = self._ia_client.set_param(params)\n log.info(\"set result = %s\" % str(result))\n\n if result is None:\n # TODO check why self._ia_client.set_param returns None\n return\n\n assert isinstance(result, dict)\n\n # check all requested params are in the result\n for (p, v) in params.items():\n self.assertTrue(p in result)\n\n return result",
"title": ""
},
{
"docid": "da132c2298e6cae2bfbb14ffea1f2f35",
"score": "0.61424744",
"text": "def on_parameter_update(cls, context: Context, pose_bone: PoseBone, params, param_name: str):",
"title": ""
},
{
"docid": "de78e6c7490e6272f2ae2506bcec8d6d",
"score": "0.61411977",
"text": "def set(self, **param_dict):\n self.update(param_dict)",
"title": ""
},
{
"docid": "0f0598553779b16a2fc6a6932f9b5c35",
"score": "0.6134246",
"text": "def _set_params_spec(self, spec_params, global_params):\n for item_key, item_params in spec_params.items():\n if isinstance(item_params, _params):\n self[item_key]._set_params(item_params.args, dict(global_params, **item_params.kwargs))\n elif isinstance(item_params, dict):\n self[item_key]._set_params_spec(item_params, global_params)\n else:\n raise ValueError(\"Unsupported parameter specification `%s`\" % type(item_params))",
"title": ""
},
{
"docid": "d6a6fa49bf0722583e033865db897966",
"score": "0.6125908",
"text": "def modifyParametersFunction(self): # real signature unknown; restored from __doc__\n pass",
"title": ""
},
{
"docid": "ea6e3b2ad42ee401d7435525dabd3006",
"score": "0.612126",
"text": "def update(self, data: Dict[str, Any]) -> None:\n for param_type, value in data.items():\n self[param_type] = value",
"title": ""
},
{
"docid": "fc71991ae7b13b9a02bcf8ca3dfdfd31",
"score": "0.6120745",
"text": "def setParams(self, paramDict):\n for name, val in paramDict.items():\n if name == 'name':\n self.name = val\n elif name == 'driver':\n self._driver = val\n elif name == 'tax':\n self._taxable = val\n elif name == 'inflation':\n self._inflation = val\n elif name == 'mult_target':\n self._multTarget = val\n elif name == 'multiply':\n self._multiplier = val\n elif name == 'alpha':\n self._alpha = np.atleast_1d(val)\n elif name == 'reference':\n self._reference = val\n elif name == 'X':\n self._scale = val\n elif name == 'depreciate':\n self._depreciate = val\n self.checkInitialization()",
"title": ""
},
{
"docid": "1a25fd9c98a47ca2209d118fbeffae27",
"score": "0.6117094",
"text": "def set_params(self,**kwargs):\n for k,v in kwargs.items():\n self.params[k] = v",
"title": ""
},
{
"docid": "70d311f5d29686ea71eb12b18b7bb23f",
"score": "0.6115395",
"text": "def testParameterUpdates(self):\n\t\tad = self._createTestActuatorData()\n\t\t\n\t\tself.assertEquals(ad.getName(), self.DEFAULT_NAME)\n\t\tself.assertEquals(ad.getCommand(), ActuatorData.COMMAND_ON)\n\t\tself.assertEquals(ad.getStateData(), self.DEFAULT_STATE_DATA)\n\t\tself.assertEquals(ad.getValue(), self.DEFAULT_VALUE)",
"title": ""
},
{
"docid": "8b1f29d08acae4c468f9df60840d74ee",
"score": "0.61132616",
"text": "def update_parameters(self, member, pre_processor):\r\n values = self.read_preprocessor(member, pre_processor)\r\n for key in values:\r\n parameter = self.get_parameter(key)\r\n parameter.set_value(member, values[key])",
"title": ""
},
{
"docid": "94a70df51a827fbfd24ce757ea6ce9ca",
"score": "0.6109652",
"text": "def __set_params(self, params):\n if params is None or isinstance(params, Parameters):\n self.params = params\n elif isinstance(params, (list, tuple)):\n _params = Parameters()\n for _par in params:\n if not isinstance(_par, Parameter):\n raise MinimizerException(self.err_nonparam)\n else:\n _params[_par.name] = _par\n self.params = _params\n else:\n raise MinimizerException(self.err_nonparam)",
"title": ""
},
{
"docid": "886877757153b5238bb0f72122ca2dee",
"score": "0.6103366",
"text": "def test_changing_parameters(self):\n \n # test with bad keys\n self.assertRaises(KeyError, self.hi.set_carnivore_parameters, {'w_mon' : 20})\n self.assertRaises(KeyError, self.hi.set_herbivore_parameters, {'w_minimum' : 20})\n self.assertRaises(KeyError, self.hi.set_jungle_parameters, {'lotoffood' : 20})\n self.assertRaises(KeyError, self.hi.set_savannah_parameters, {'maxfood' : 20})\n \n # test for negative parameter setting\n for param in self.hi._default_params_c:\n self.assertRaises(ValueError, self.hi.set_carnivore_parameters, {param : -20})\n \n # test for negative parameter setting\n for param in self.hi._default_params_h:\n self.assertRaises(ValueError, self.hi.set_herbivore_parameters, {param : -20})\n \n # test for negative parameter setting\n for param in self.hi._default_params_j:\n self.assertRaises(ValueError, self.hi.set_jungle_parameters, {param : -20})\n \n # test for negative parameter setting\n for param in self.hi._default_params_s:\n self.assertRaises(ValueError, self.hi.set_savannah_parameters, {param : -20})\n \n # test if parameter is stored in dict\n carni = slog.ani.Carnivore(12.5, 3) \n for param in self.hi._default_params_c:\n self.hi.set_carnivore_parameters({param : 9})\n self.assertEqual(carni.params[param], 9)\n herbi = slog.ani.Herbivore(12.5, 3) \n for param in self.hi._default_params_h:\n self.hi.set_herbivore_parameters({param : 9})\n self.assertEqual(herbi.params[param], 9)\n jungle = slog.lnd.Jungle() \n for param in self.hi._default_params_j:\n self.hi.set_jungle_parameters({param : 9})\n self.assertEqual(jungle.params[param], 9)\n savannah = slog.lnd.Savannah() \n for param in self.hi._default_params_s:\n self.hi.set_savannah_parameters({param : 9})\n self.assertEqual(savannah.params[param], 9)\n \n self.assertRaises(ValueError, self.hi.set_graph_ylim, -300)\n self.hi.set_graph_ylim(1337)\n self.assertEqual(1337, self.hi._graphics._ylim)\n \n self.assertRaises(ValueError, self.hi.set_herbivore_luminance_scale, vmin=1000, vmax=0)\n self.hi.set_herbivore_luminance_scale(vmin=0, vmax=1000)\n self.assertEqual(0, self.hi._graphics._min_colormap_h)\n self.assertEqual(1000, self.hi._graphics._max_colormap_h)\n \n self.assertRaises(ValueError, self.hi.set_carnivore_luminance_scale, vmin=1000, vmax=0)\n self.hi.set_carnivore_luminance_scale(vmin=0, vmax=1000)\n self.assertEqual(0, self.hi._graphics._min_colormap_c)\n self.assertEqual(1000, self.hi._graphics._max_colormap_c)\n \n self.assertRaises(ValueError, self.hi.set_plot_update_interval, 0)\n self.assertRaises(ValueError, self.hi.set_plot_update_interval, -10)\n self.assertRaises(ValueError, self.hi.set_plot_update_interval, 0.5)\n self.hi.set_plot_update_interval(20)\n self.assertEqual(20, self.hi._graphics._update_interval)\n self.assertEqual(11, self.hi._graphics.update_interval(11))",
"title": ""
},
{
"docid": "f0f09a460960cdbc3a0917615f520af8",
"score": "0.6087682",
"text": "def set_params(**params):\n # will be useful for storing model information\n raise NotImplementedError",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="748"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">7dcb2c2e1cd2aabf754f610640b6b72d</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Return the node corresponding to line n of external file given by path.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "e096dfb90f0f6450e621e8b673c3c206",
"score": "0.7063621",
"text": "def find_line(path: str, n: int) -> tuple[Position, int]:\n if path == root_path:\n p, offset = c.gotoCommands.find_file_line(n, root)\n else:\n # Find an @<file> node with the given path.\n for p in c.all_positions():\n if p.isAnyAtFileNode():\n norm_path = os.path.normpath(c.fullPath(p))\n if path == norm_path:\n p, offset = c.gotoCommands.find_file_line(n, p)\n break\n return (p, offset) if p else (root, n)",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "9782e0ee913fb0e1297522a755a2c9c4",
"score": "0.61165345",
"text": "def getLineNumber(lnr, path):\n global realmap\n lnr = int(lnr)\n\n if not path in realmap:\n realmap[path] = getDecoded(path)\n\n if realmap.get(path) == \"not found\":\n return str(lnr)\n\n lastLnr = 0\n for m in realmap[path]:\n orig, new = m.split(\":\")\n if lnr < int(new):\n return lastLnr\n else:\n lastLnr = orig",
"title": ""
},
{
"docid": "0ec88ccbf1ad516a2b7513b7b3454899",
"score": "0.6069023",
"text": "def find_in_source_code(line_number, file_name):\n for node in renpy.game.script.all_stmts:\n head, tail = os.path.split(node.filename)\n node_file_name = tail or os.path.basename(head)\n if node.linenumber == line_number and node_file_name == file_name:\n return node\n return None",
"title": ""
},
{
"docid": "c22161002613feba2cd0c355e50d7640",
"score": "0.6031446",
"text": "def nodeFromPath(self, path):\r\n \r\n return self.nodeFromIndex(self.indexFromPath(path))",
"title": ""
},
{
"docid": "330ba10adc4cd1a02fd1a8dc4785ffcc",
"score": "0.60309386",
"text": "def _get_line(path, line_no):\n\n with open(path, 'r') as f:\n for _ in range(line_no - 1):\n l = f.readline()\n if not l:\n raise InputError(\"Line number beyond end-of-file\")\n line = f.readline()\n if not line:\n raise InputError(\"Line number beyond end-of-file\")\n line = line.strip()\n return line",
"title": ""
},
{
"docid": "78e9c1b66fb5baa1a5e969b1c431cf6a",
"score": "0.6016329",
"text": "def getline(file: str, line_number: int) -> str:\r\n counter = 0\r\n with open(file, 'r') as file:\r\n for line in file:\r\n counter += 1\r\n if counter == line_number:\r\n return line\r\n # If we are here, then the file has a number of lines inferior\r\n # to line_number\r\n return \"\"",
"title": ""
},
{
"docid": "df43d51a81aa673bb7a423228821e869",
"score": "0.59929156",
"text": "def get_node(self, path):\n raise NotImplementedError",
"title": ""
},
{
"docid": "377297ad6533ca831d8c59b38ba89709",
"score": "0.5963919",
"text": "def rline1(path):\n with open(path) as f:\n for line in f:\n return line",
"title": ""
},
{
"docid": "4c642ba7be81cc0c8f2e283ba00a1cff",
"score": "0.5928625",
"text": "def get_path_line_number(self, path, with_attrs=False):\n pass",
"title": ""
},
{
"docid": "0d9e75a23cd92e73934abae2cd69c306",
"score": "0.5761765",
"text": "def line(self, nr: int):\n return self.lines[nr]",
"title": ""
},
{
"docid": "9c9c4cc4e145a4988000075597d21f2f",
"score": "0.5742823",
"text": "def get_line_from_module_code(file_name, line_number):\n normalized_file_name = ModuleLineNumbering.__normalize_path_name(file_name)\n normalized_file_name = ModuleLineNumbering.__get_original_source_code_file(normalized_file_name)\n\n linenumbers = ModuleLineNumbering.get_line_numbered_module_code(normalized_file_name)\n if linenumbers is not None:\n try:\n return linenumbers[line_number]\n except Exception as ex:\n return None\n else:\n try:\n ModuleLineNumbering.__populate_cache_with_source_code(normalized_file_name)\n linenumbers = ModuleLineNumbering.get_line_numbered_module_code(normalized_file_name)\n if linenumbers is not None:\n try:\n return linenumbers[line_number]\n except Exception as ex:\n return None\n except Exception as ex:\n return None\n return None",
"title": ""
},
{
"docid": "bd14705d986119f64cc0024e3b6db571",
"score": "0.57373327",
"text": "def _get_src_path_line_nodes(xml_document, src_path):\n files = [file_tree\n for file_tree in xml_document.findall(\".//file\")\n if GitPathTool.relative_path(file_tree.get('path')) == src_path\n or []]\n if not files:\n return None\n lines = [file_tree.findall('./line[@type=\"stmt\"]')\n for file_tree in files]\n return [elem for elem in itertools.chain(*lines)]",
"title": ""
},
{
"docid": "a504b86ab2f8385118fb9f725088a8fe",
"score": "0.57207257",
"text": "def createNodeFromExternalFile(self, fn: str) -> None:\n c = self\n s, e = g.readFileIntoString(fn)\n if s is None:\n return\n head, ext = g.os_path_splitext(fn)\n if ext.startswith('.'):\n ext = ext[1:]\n language = g.app.extension_dict.get(ext)\n if language:\n prefix = f\"@color\\n@language {language}\\n\\n\"\n else:\n prefix = '@killcolor\\n\\n'\n # pylint: disable=no-member\n # Defined in commanderOutlineCommands.py\n p2 = c.insertHeadline(op_name='Open File', as_child=False)\n p2.h = f\"@edit {fn}\"\n p2.b = prefix + s\n w = c.frame.body.wrapper\n if w:\n w.setInsertPoint(0)\n c.redraw()\n c.recolor()",
"title": ""
},
{
"docid": "cc332f77c4bf2ddef20dc63fe76b1fb9",
"score": "0.5615968",
"text": "def goToLineNumber(self, n: int) -> None:\n c = self\n c.gotoCommands.find_file_line(n)",
"title": ""
},
{
"docid": "844a55af790e11183ab8bb980919a0cc",
"score": "0.5515584",
"text": "def get_node_at_path(tree, path):\n assert isinstance(path, list) or isinstance(path, int)\n if isinstance(path, int):\n path = [path]\n node = tree\n for d in path:\n try:\n node = node[d]\n except IndexError:\n raise(IndexError, 'Attempted to index subtree {0} with path {1}'\\\n .format(tree.get('id'), path))\n return node",
"title": ""
},
{
"docid": "470619ee30042ea95bfbddfc0dff2ef5",
"score": "0.549308",
"text": "def server_goto_file_row(self, filename, linenum):\n return self.process_lfun(\"server-goto-file-row\",\n argument=filename + \" \" + str(linenum))",
"title": ""
},
{
"docid": "1f4b69cee8a4cb9186e87d3a0750d85e",
"score": "0.54902714",
"text": "def line_in_file(path, line_no=None, regex=None):\n\n line = _get_line(path, line_no)\n if regex:\n m = re.search(regex, line)\n if m:\n version = m.groups()[0]\n else:\n raise InputError({'message': \"Pattern not found\",\n 'regex': regex,\n 'line': line})\n else:\n version = line\n return version",
"title": ""
},
{
"docid": "ac89a10e07e5d74ac42097fcb73c3d3b",
"score": "0.5425996",
"text": "def first_n_lines(file_name, n):\r\n return",
"title": ""
},
{
"docid": "cdd21c411331e1f87a49010dc358de43",
"score": "0.54237103",
"text": "def GetNodeByContentPath( self, path ):\n cur = self._conn.cursor()\n cur.execute( \"SELECT nid FROM nodes WHERE contentPath = ? ORDER BY nid ASC LIMIT 1\", (path,) )\n try:\n return Node(cur.fetchone()[0], self)\n except TypeError:\n raise KeyError(name, \"not a content path in the DB\")",
"title": ""
},
{
"docid": "ea227d39e524f2fae94a544617e79593",
"score": "0.53909",
"text": "def get_node(self, where, name=None):\n try:\n node = self.h5file.get_node(where, name=name)\n except tb.NoSuchNodeError:\n p = where\n if name: p = join(where, name)\n self.out(p, prefix='InvalidPath', error=True)\n return None\n return node",
"title": ""
},
{
"docid": "901e7556410fdc91ea91de12b4a9ab92",
"score": "0.53108364",
"text": "def nodeinfo_from_file(s):\n cmd = \"grep %s %s\" % (s, ARGS.map)\n p = subprocess.Popen(shlex.split(cmd), stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n stdout, stderr = p.communicate()\n nid, nic, cname, gemini, x, y, z = stdout.split()\n\n return nid, int(x), int(y), int(z)",
"title": ""
},
{
"docid": "2e8a0e674ed8fc88d3e46f12803ad69d",
"score": "0.53005785",
"text": "def _get_node(self, index):\n try:\n n = np.ravel_multi_index(index, self._size)\n except ValueError:\n raise IndexError('invalid index: {}'.format(index))\n return LatticeNode(index, self._table[n])",
"title": ""
},
{
"docid": "aba5b3c5e7a5af6350f32ab0d9b128a9",
"score": "0.5299837",
"text": "def reader():\n nonlocal index\n if index > len(lines):\n raise IndexError\n line = lines[index]\n index += 1\n return line",
"title": ""
},
{
"docid": "2b1b664168a7ae69ea8ca6f240c71d94",
"score": "0.5286065",
"text": "def get_line(self, line):\n return self.lines[line]",
"title": ""
},
{
"docid": "06c058f3230855267ff29f03bbb10530",
"score": "0.52758044",
"text": "def line(N):\n return nx.path_graph(N)",
"title": ""
},
{
"docid": "fa7f3b956e2f519e98159614a3f300a9",
"score": "0.5230063",
"text": "def getLineNumber(startIndex, fileText):\n return fileText[:startIndex].count(\"\\n\") - 1",
"title": ""
},
{
"docid": "fabb01a02d7c27e3b76f70105e32039a",
"score": "0.52285105",
"text": "def find_tp_ln(fileName):\n\n with open(fileName, 'r') as file:\n for num, line in enumerate(file, 1):\n if 'TEST PROCEDURE' in line:\n return num\n\n return -1 # if here no TEST PROCEDURE found",
"title": ""
},
{
"docid": "a82d3631fad847af1068dc72d3e6b63f",
"score": "0.5202598",
"text": "def path_in_line(self, text):\n pattern = re.compile(r'([\\w\\-\\.\\/\\\\]+)(\", line )(\\d+)')\n path = ''\n for fp, _, lineno in re.findall(pattern, text):\n return ':'.join([fp, lineno])\n return None",
"title": ""
},
{
"docid": "d4335e9ee01f122319d2ee276a7f955e",
"score": "0.5190245",
"text": "def line(self, num):\n self.__checkRange(num)\n return self.__lines[num]",
"title": ""
},
{
"docid": "3c5e30f45e0668704f715394aa040468",
"score": "0.51752114",
"text": "def getLineNumber(line):\n return int(line.split(\":\")[0].split(\"Line \")[1]) - 1",
"title": ""
},
{
"docid": "1b890c2b25c1e2e9bc0684b58d4c3a4e",
"score": "0.515463",
"text": "def getNodeByIndex(self, i):\n return self.nodeList[i]",
"title": ""
},
{
"docid": "c06ee80501a9df86bc6aeeb7bfcf1618",
"score": "0.51439697",
"text": "def get_line_numbered_module_code(file_name):\n normalized_file_name = ModuleLineNumbering.__normalize_path_name(file_name)\n normalized_file_name = ModuleLineNumbering.__get_original_source_code_file(normalized_file_name)\n\n try:\n for file_ in ModuleLineNumbering.file_numbered_code_cache.keys():\n if file_ in normalized_file_name:\n return ModuleLineNumbering.file_numbered_code_cache[file_]\n except Exception as ex:\n return None",
"title": ""
},
{
"docid": "050ef0835d74aa7984ebfc933cc2019e",
"score": "0.5128585",
"text": "def inode(path):\n return os.stat(path)[stat.ST_INO]",
"title": ""
},
{
"docid": "746515d01a2e9de868f30cf150cc58b3",
"score": "0.51260287",
"text": "def scan_linepos(path):\n linepos = []\n offset = 0\n with gzopen(path) as inf: \n # WARNING: CPython 2.7 file.tell() is not accurate on file.next()\n for line in inf:\n linepos.append(offset)\n offset += len(line)\n return linepos",
"title": ""
},
{
"docid": "f7cf1407053992388cd07a856faa6d7d",
"score": "0.5120429",
"text": "def read_ind_files(path):\n fin = open(path, 'r')\n links = []\n for line in fin:\n links.append(data_path + line.strip())\n return links",
"title": ""
},
{
"docid": "2716b9607cb6ccddf76e34c324546964",
"score": "0.51101166",
"text": "def get_line_number_by_title(file_name, title):\n with open (file_name, \"r\") as f:\n for number_line, line in enumerate(f, 1):\n game = line.split(\"\\t\")\n if title == game[0]:\n return number_line\n raise ValueError ()",
"title": ""
},
{
"docid": "910944cfbda0a2df650aceb8de49a8b9",
"score": "0.51061594",
"text": "def next_line(the_file):\n line = the_file.readline()\n line = line.replace(\"/\", \"\\n\")\n return line",
"title": ""
},
{
"docid": "e90d1905391c5cb66cd7a1dc7171ad50",
"score": "0.5077419",
"text": "def get_stackid_from_file(fname, esn):\r\n # fname must exist, guaranteed by caller\r\n fname = os.path.basename(fname)\r\n with open(fname, 'rb') as item:\r\n for line in item:\r\n token = line.strip('[\\r\\n]')\r\n token = token.split()\r\n if token[0] == esn:\r\n return token[2]\r\n return None",
"title": ""
},
{
"docid": "cd636d02c28eccc5b764ac13c05ed3b8",
"score": "0.5072264",
"text": "def map_line_from_source_file(source_filename, source_line_num, target_filename):\n assert(source_line_num > 0)\n map = fline_map(target_filename)\n\n for i, (target_line_num, found_source_filename, found_source_line_num) in enumerate(map):\n if found_source_filename != source_filename: continue\n if found_source_line_num > source_line_num: continue\n result = target_line_num + (source_line_num - found_source_line_num)\n if i + 1 == len(map) or map[i + 1][0] > result:\n return result + 1\n raise RuntimeError(\"line not found\")",
"title": ""
},
{
"docid": "6bbb409909509b14df00c67297d07dad",
"score": "0.5070241",
"text": "def get_node_with_index(self, index):\n return self._find_node(self.root, index)",
"title": ""
},
{
"docid": "a9475d1a4e45b4363ec2828a5946a36f",
"score": "0.50671464",
"text": "def get_file(self, repository, path, revision, base_commit_id=None,\r\n *args, **kwargs):\r\n try:\r\n return self._api_get_node(repository, path, revision,\r\n base_commit_id, contents=True)\r\n except (HTTPError, URLError):\r\n raise FileNotFoundError(path, revision)",
"title": ""
},
{
"docid": "6140d686334b96444d6847c49b61a9e7",
"score": "0.5066484",
"text": "def findHeaderLine(self, file):\n\n n = 0\n with file.open(encoding='utf-8') as f:\n for line in f:\n if line.startswith('#'):\n break\n else:\n n += 1\n\n return n + 2",
"title": ""
},
{
"docid": "08cbabe1033be69351b33a93b0cc5e67",
"score": "0.50641704",
"text": "def lookup(self, node_index: int):\n return self.nodes.lookup(node_index)",
"title": ""
},
{
"docid": "f67aecec050dd7e62da2fee2e55b4ed6",
"score": "0.5042142",
"text": "def fname_to_node(fname):\r\n return lxml.parse(fname)",
"title": ""
},
{
"docid": "4d9faae8cfdb9d83c6d7ae7ba67f2da2",
"score": "0.50376856",
"text": "def map_line_to_source_file(target_filename, target_line_num):\n assert(target_line_num > 0)\n map = fline_map(target_filename)\n index = bisect.bisect_left(map, (target_line_num, '', 0))\n base = map[index - 1]\n return base[1], base[2] + (target_line_num - base[0] - 1)",
"title": ""
},
{
"docid": "c92809a4f71319007f3c70c750b5e088",
"score": "0.5036139",
"text": "def readlink(self, path: PurePath) -> PurePath:",
"title": ""
},
{
"docid": "016b22e7bacc008d33ae3110cd591a31",
"score": "0.5035871",
"text": "def readNode(nodeName,h5FileName):\n with openFile(h5FileName,'r') as h5File:\n content= h5File.getNode(h5File.root,nodeName)[:]\n return content",
"title": ""
},
{
"docid": "1293bd04c0bfc2c493effaa4614f0726",
"score": "0.50348693",
"text": "def get_node(self, num):\n if num not in self.node_dict:\n raise Exception(f\"The number {num} does not appear in this tree\")\n return self.node_dict[num]",
"title": ""
},
{
"docid": "dbe2f16200c07b08e1a208ce128612b1",
"score": "0.503037",
"text": "def head(file, n):\n import os\n dirname = os.path.dirname(__file__)\n filename = file\n\n with open(os.path.join(dirname, filename), \"r\") as f:\n for i in range(n):\n print(f.readlines(), end=\"\")",
"title": ""
},
{
"docid": "69f9b3af2ff32dc7cfbb67bdaf6f4c88",
"score": "0.5019111",
"text": "def get(self, i):\n while i >= self.next_addr and self.pos < len(self.lnotab):\n self.lineno += six.indexbytes(self.lnotab, self.pos + 1)\n self.pos += 2\n if self.pos < len(self.lnotab):\n self.next_addr += six.indexbytes(self.lnotab, self.pos)\n return self.lineno",
"title": ""
},
{
"docid": "312a60fce9e7ba14019e8ca17ea13edc",
"score": "0.50031775",
"text": "def get_corresponding_lineno(self, lineno):\r\n for template_line, code_line in reversed(self.debug_info):\r\n if code_line <= lineno:\r\n return template_line\r\n return 1",
"title": ""
},
{
"docid": "47fe9b56809b26cd188a83763806dcab",
"score": "0.5002536",
"text": "def nbr_lines(fname):\n with open(fname) as f:\n for i, l in enumerate(f):\n pass\n return i + 1",
"title": ""
},
{
"docid": "077251af7b8988eeb0b64c11bf7d08e5",
"score": "0.5002051",
"text": "def _node(repo, n):\n rn = None\n if len(n) == 2 * repo.nodeconstants.nodelen:\n try:\n rn = repo.changelog.rev(bin(n))\n except error.WdirUnsupported:\n rn = wdirrev\n except (LookupError, TypeError):\n rn = None\n else:\n try:\n pm = scmutil.resolvehexnodeidprefix(repo, n)\n if pm is not None:\n rn = repo.changelog.rev(pm)\n except LookupError:\n pass\n except error.WdirUnsupported:\n rn = wdirrev\n return rn",
"title": ""
},
{
"docid": "4742b792e682701a66ce62d96811e7e8",
"score": "0.50019145",
"text": "def by_path(self, path: str) -> Prv_or_PubKeyNode:\n path = Bip32Path.parse(s=path)\n return self.master.derive_path(index_list=path.to_list())",
"title": ""
},
{
"docid": "a17a37689cba525e117bdfdb39ce32d0",
"score": "0.5001601",
"text": "def find_node(self, node, path):\n for hash_value in path:\n if isinstance(node, LeafStatisticNode):\n break\n for stats in node.get_child_keys():\n if hash(stats) == hash_value:\n node = node.get_child_node(stats)\n break\n else:\n break\n return node",
"title": ""
},
{
"docid": "462fc519a50dcddfead691e347161841",
"score": "0.5001029",
"text": "def getLine(self,index):\n return self._lines[index]",
"title": ""
},
{
"docid": "ed86ee47991441dea8524809604506e4",
"score": "0.49994844",
"text": "def find_line(self,fi,reg):\n id = reg\n regexp=re.compile(id)\n j = 1\n with open(fi) as f:\n for i,line in enumerate(f):\n if (regexp.search(line)==None):\n pass\n else:\n j = j+i\n break\n return(j)",
"title": ""
},
{
"docid": "e990fd41875c15f91a8d02829bc8b31e",
"score": "0.49959666",
"text": "def get_line_number_by_title(file_name, title):\n if type(title) != str:\n raise TypeError(\"Invalid title\")\n content = open_file(file_name)\n title_index = 0\n titles = [game[title_index] for game in content]\n try:\n line = titles.index(title) + 1\n except ValueError as err:\n raise err\n return line",
"title": ""
},
{
"docid": "a7b1e27f0ea247855b9c30690fd0e096",
"score": "0.4995241",
"text": "def __find_external_index(self, node_id: model.NodeID) -> int:\n return self.__discovered_nodes.index(node_id)",
"title": ""
},
{
"docid": "28b0b21780e7c7210fa6d5971ee0250a",
"score": "0.49937567",
"text": "def readnode(nodepath):\n import numpy as np\n import pandas as pd\n \n N = pd.read_excel(nodepath)\n \n return np.array(N.iloc[:, ])",
"title": ""
},
{
"docid": "83c7e4841e489e7b1c541e4d4e5fb608",
"score": "0.49909297",
"text": "def open_file(self, path):\n path = os.path.normpath(path)\n path = utils.relative_path(None, path)\n path = nodes.reprunicode(path)\n self.state.document.settings.record_dependencies.add(path)\n return open(path, 'r')",
"title": ""
},
{
"docid": "e56aee06d1aed446d4113b19ad9fe977",
"score": "0.4985464",
"text": "def find_require_ln(fileName):\n\n with open(fileName, 'r') as file:\n for num, line in enumerate(file, 1):\n if 'REQUIREMENTS' in line.upper():\n return num\n return -1 # if here no requirements found",
"title": ""
},
{
"docid": "dd6a901c174e58083e62b5d85560b301",
"score": "0.49673557",
"text": "def getLineIndex(self,line):\n return self._lines.index(line)",
"title": ""
},
{
"docid": "1ba441bde951127b70e0d05027cbf201",
"score": "0.49664113",
"text": "def get_node(self, code):\n for n in self.nodes:\n if n.code == code:\n return n\n return None",
"title": ""
},
{
"docid": "ba1102b8b452136d29d7866a99b67fc0",
"score": "0.49363962",
"text": "def get_file(self, path, revision=HEAD):\r\n raise NotImplementedError",
"title": ""
},
{
"docid": "e0ec13f374202720d1a53e130ce8fe25",
"score": "0.493187",
"text": "def _get_node(self, modtree, path):\n node = None\n if path is not None:\n subtree = self._get_subtree(treelist, path)\n if subtree is not None:\n node = subtree[0]\n return node",
"title": ""
},
{
"docid": "80241f762d57b56c41acff1b21e1eb9e",
"score": "0.49289462",
"text": "def get_rinex_file_version(file_path: pathlib.PosixPath) -> str:\n with files.open(file_path, mode=\"rt\") as infile:\n try:\n version = infile.readline().split()[0]\n except IndexError:\n log.fatal(f\"Could not find Rinex version in file {file_path}\")\n\n return version",
"title": ""
},
{
"docid": "a14cfc3e5e4468923688bd870736e819",
"score": "0.49174505",
"text": "def get_vertex(self, n):\n # check to ensure the given 'n' index is in\n # bounds of the self.vertices array\n # and then return the LL object at that index.\n return (self.vertices[n-1] if n-1 < self.numberOfVertices and n > 0\n else\n \"Vertex index out of bounds.\" +\n \"Please enter a vertex id between 1 and \"\n + str(self.numberOfVertices) + \".\")",
"title": ""
},
{
"docid": "146ba42350e4aa15f15d704b9d7d39ec",
"score": "0.49165922",
"text": "def get_by_path(d, path):\n current_node = d\n for p in path.split('.'):\n try:\n current_node = current_node[p]\n except KeyError:\n raise KeyError(\n 'Path \"{}\" could not be resolved. Key \"{}\" missing. Available '\n 'keys are: [{}]'.format(\n path, p, \", \".join(sorted(current_node.keys()))))\n return current_node",
"title": ""
},
{
"docid": "b3c70f653e4530668050d3cce515a4ce",
"score": "0.49156624",
"text": "def getVertex(self, n):\n if n in self.vertList:\n return self.vertList[n]\n else:\n return None",
"title": ""
},
{
"docid": "942cde3c194ead19857c3cb20a2493c4",
"score": "0.49101934",
"text": "def read_file_by_line(self,path):\n \n try:\n with open(path) as f:\n self.content = f.readlines()\n except FileNotFoundError:\n raise Exception(\"The file is not present at the specified location.\")",
"title": ""
},
{
"docid": "f39993f6365be50ae5afc055d0157f0f",
"score": "0.49086985",
"text": "def find(self, index):\n # Lazy build file index.\n if self._file_index is None:\n self._file_index = self._build_index()\n\n return bisect.bisect_left(self._file_index, index)",
"title": ""
},
{
"docid": "f2a385c32d9774804bb2a4a78142b654",
"score": "0.49016133",
"text": "def _get_line(self, path):\n\n match = re.search(\"^.*?{}\".format(\".*?\".join(path)), self.raw_data, re.S | re.M)\n return match.group().count(\"\\n\")",
"title": ""
},
{
"docid": "0dc02b7bfdf4f3d42606bc951c773e2e",
"score": "0.48999608",
"text": "def _element_at_path(self, yaml_path):\n path_parts = yaml_path.split(\"\\n\")\n parent_parts, key_part = path_parts[:-1], path_parts[-1]\n parent_item = self.origin_yaml\n for path_part in parent_parts:\n if path_part.isnumeric():\n path_part = int(path_part)\n parent_item = parent_item[path_part]\n return parent_item, key_part",
"title": ""
},
{
"docid": "5b6ce5a35243f01e324a14460e46894d",
"score": "0.48917183",
"text": "def read(self, n):\n\n # open file\n f = open(n, 'r')\n\n # readin lines\n w = []\n for i in f:\n self.append(i[:-1])\n\n # close flie\n f.close()\n\n return None",
"title": ""
},
{
"docid": "300c983b435423a0841bff94a08e8d50",
"score": "0.4887097",
"text": "def find_path(path_file, key):\r\n\r\n file = open(path_file, \"r\")\r\n text = file.read().split(\"\\n\")\r\n for j in range(len(text)):\r\n # We assume that in the file : \"Key : value\"\r\n line = text[j].split(\" : \")\r\n if line[0] == key:\r\n return line[1]\r\n\r\n return (\"not found\")",
"title": ""
},
{
"docid": "8c04c5d38bc6cbab66b898dfdc07aa84",
"score": "0.4874285",
"text": "def tree_open_file(self, index):\n self.dicomFilePath = self.model.filePath(index)\n self.filePath.emit(str(self.dicomFilePath))",
"title": ""
},
{
"docid": "d630babb7fcb958ba60dc665fdd1db6a",
"score": "0.4869776",
"text": "def getNth(self, n):\n current = self.head\n while current is not None and n > 1:\n current = current.next\n n -= 1\n\n if current is not None:\n print current.val\n else:\n print \"No such node exist\"",
"title": ""
},
{
"docid": "05d089d990e2a4f37ce05d42007cacab",
"score": "0.48630774",
"text": "def from_file(cls, path, common_prefix=None):\n\n with open(path, \"r\") as f:\n lines = [l.strip() for l in f]\n if common_prefix is not None:\n lines = [pathlib.Path(l).relative_to(common_prefix) for l in lines]\n return Indices([str(l) for l in lines if l])",
"title": ""
},
{
"docid": "313ad45e7410fbe87e5a7bfffea08156",
"score": "0.48586702",
"text": "def _find(self, filename):\n\n # if filename is a string instead of a list, split it on slashes to\n # convert to a list:\n if isinstance(filename, basestring):\n filename = filename.split('/')\n # walk across storage tree, following given path:\n node = self.root\n for name in filename:\n for kid in node.kids:\n if kid.name.lower() == name.lower():\n break\n else:\n raise IOError(\"file not found\")\n node = kid\n return node.sid",
"title": ""
},
{
"docid": "0594dfcc4d2712112de6f263b4886c03",
"score": "0.4852323",
"text": "def GetIndexedIPAddressFromLine(line, index):\n addresses = re.findall(r\"(?:[0-9]{1,3}\\.){3}[0-9]{1,3}\", line)\n if len(addresses) >= index : \n return addresses[index-1]\n else: \n return \"\"",
"title": ""
},
{
"docid": "e3baea8492857d1c87c16df9c881257d",
"score": "0.48512",
"text": "def get_line(self):\n iLine = self.line\n if iLine == 0:\n id = self.id\n # The start function depends on there being a root or rootcond\n start_function = None\n if self.root != None:\n start_function = self.root.function\n elif self.rootcond != None:\n start_function = self.rootcond.function\n elif self.rootfeat != None:\n start_function = self.rootfeat.function\n # Double check: do we have a start_function?\n if start_function != None:\n lFunc = start_function.get_functions()\n for idx in range(len(lFunc)):\n if lFunc[idx].id == id:\n iLine = idx+1\n break\n self.line = iLine\n self.save()\n return iLine",
"title": ""
},
{
"docid": "f98f18cf56cb74c4f3f520c6233eea8a",
"score": "0.48505974",
"text": "def node(self, node_i):\n \n return self._nodes[node_i]",
"title": ""
},
{
"docid": "9c7a3d5d97b9e800651ee2b0f8dd83ea",
"score": "0.48432204",
"text": "def line_number(self):\n line_no_attr = self.get_attribute('line')\n line_no = line_no_attr if line_no_attr else self.get_attribute('line_number')\n if not line_no:\n return 0\n return int(line_no)",
"title": ""
},
{
"docid": "198295ac10b369b260294a422b2cf2b1",
"score": "0.48410526",
"text": "def trace_back(file_name, text):\n with open(file_name) as file_object:\n file_object.seek(0, 2) # seek to the end\n position = file_object.tell()\n for line, position in readline_backwards(file_object, position):\n if text in line:\n return line",
"title": ""
},
{
"docid": "64f950d01d578d6e69a6cafbfbe33e36",
"score": "0.48406345",
"text": "def nextLine(theFile):\n line = theFile.readline()\n line = line.replace(\"/\", \"\\n\")\n return line",
"title": ""
},
{
"docid": "50b081e4bfe02de43fdd0c42b3ff3ab1",
"score": "0.48383582",
"text": "def line_number(self) -> Optional[int]:\n ...",
"title": ""
},
{
"docid": "f9bfe5fbe53dbc627f6f3e239ad63a97",
"score": "0.4836156",
"text": "def load_cfg(path: str):\n with open(path, 'r') as f:\n config_node = CN.load_cfg(f)\n\n return config_node",
"title": ""
},
{
"docid": "881316a19aa6c45553c2cf16ee0a7860",
"score": "0.48346576",
"text": "def line_num_of( self, pos = None):\n\n if pos == None:\n pos = self.cur_pos()\n lines = self.line_range_external(0, pos)\n return lines[1]",
"title": ""
},
{
"docid": "99f720a757ace339b3743f6c19247a08",
"score": "0.48268893",
"text": "def get_vertex(self, n):\n\n return self.vertList[n] if self.vertList[n] else None",
"title": ""
},
{
"docid": "b448a9938992919cd9ee2a8bf3383fb1",
"score": "0.482553",
"text": "def __getitem__(self, key):\r\n if not self._is_loaded:\r\n err = \"Error: tried to read a line on an unloaded file.\"\r\n raise IOError(err)\r\n else:\r\n return self._lines[key]",
"title": ""
},
{
"docid": "9b79cd018660cd033d0d6ac13d03971b",
"score": "0.48237562",
"text": "def get_line_number_by_title(filename, title):\n games = read_data_from_file(filename)\n line_number = [(index + 1)\n for index, game in enumerate(games) if title in game]\n if len(line_number) != 0:\n return line_number[0]\n else:\n raise ValueError(\"Could not find {} in {}\".format(title, filename))",
"title": ""
},
{
"docid": "c17fa5199a97d9b7a6f344ca23c6e82c",
"score": "0.4820551",
"text": "def get_label_from_path(path):",
"title": ""
},
{
"docid": "21fc95aec6370f0cf47a7036956fbd5c",
"score": "0.48183408",
"text": "def get_line(self, line_num):\r\n the_index = find_nth(self._screen, \"\\n\", line_num)\r\n if the_index == -1:\r\n return None\r\n\r\n end_index = find_nth(self._screen, \"\\n\", line_num)\r\n the_line = self._screen[the_index:end_index]\r\n\r\n return the_index, the_line",
"title": ""
},
{
"docid": "a0de990974eaeea36eb14bbb40c708c8",
"score": "0.48173222",
"text": "def random_line(file_path):\n try:\n with open(file_path, 'r') as f:\n contents = yaml.load(f.read())\n item_no = random.randint(0, (len(contents) - 1))\n line = contents[item_no]\n except IOError as e:\n line = '\\n %s' % e\n\n return line",
"title": ""
},
{
"docid": "40891d086e8f26c3ef70542ee32b612d",
"score": "0.48153022",
"text": "def get_by_path(self, path):\n path = path.strip('/')\n try:\n if '/' in path:\n prop, key = path.split('/', 1)\n return getattr(self.node(), prop)[key]\n else:\n return getattr(self.node(), path)\n except AttributeError:\n raise KeyError(path)",
"title": ""
},
{
"docid": "4c7038aa44a72bf1810001eda98caed1",
"score": "0.48139554",
"text": "def line(self, index_from=1):\n return self._location.line() + index_from",
"title": ""
},
{
"docid": "dd7e2f0ee441b572182b1c0051ded322",
"score": "0.4805007",
"text": "def new_sample(path, i):\n with open(path,'r') as file:\n str = file.readlines()[i]\n return modify(str)",
"title": ""
},
{
"docid": "48e07a993dcdc2e99c03be460a16feeb",
"score": "0.47987244",
"text": "def _file_line(self, tb):\n\t\tprefix = \"file://\"\n\t\tprefix = \"\"\n\n\t\tf = tb.tb_frame\n\t\tif '__unittest' in f.f_globals:\n\t\t\t# this is the magical flag that prevents unittest internal\n\t\t\t# code from junking up the stacktrace\n\t\t\treturn None\n\n\t\tfilename = f.f_code.co_filename\n\t\tlineno = tb.tb_lineno\n\t\tlinecache.checkcache(filename)\n\t\tfunction_name = f.f_code.co_name\n\n\t\tline_contents = linecache.getline(filename, lineno, f.f_globals).strip()\n\n\t\treturn \" %s line %s in %s\\n %s\" % (\n\t\t\ttermstyle.blue(prefix, self._relative_path(filename)),\n\t\t\tlineno,\n\t\t\ttermstyle.cyan(function_name),\n\t\t\tline_contents)",
"title": ""
},
{
"docid": "e47d2ece6ae309479f44a4a6f7796f3f",
"score": "0.4792136",
"text": "def _get_next_file(idx, file):\n base, ext = os.path.splitext(os.path.basename(file))\n return os.path.join(os.path.dirname(file), str(idx).zfill(len(base)) + ext)",
"title": ""
},
{
"docid": "ca17939b0364723a6cde67a78b3745a3",
"score": "0.4776096",
"text": "def open(self, filename, line=None, **kwargs): #@ReservedAssignment",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="749"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">ecb3abf80a6c753d2a9bb905fc601082</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">It is an array of string values. If the operator is In or NotIn, the values array must be nonempty. If the operator is Exists or DoesNotExist, the values array must be empty.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "83bedbf18fa8b31ed46d6fdafb101161",
"score": "0.46206558",
"text": "def values(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"values\")",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "2144af1d3298794a7da56c26a4c8fea0",
"score": "0.6495714",
"text": "def contains_operator(metadata, values, logical_operator=\" or \"):\n if isinstance(values, (list, tuple)):\n filters = [f\"contains({metadata},'{item}')\" for item in values]\n filters_joined = logical_operator.join(filters)\n return f\"({filters_joined})\"\n return f\"contains({metadata},'{values}')\"",
"title": ""
},
{
"docid": "c9ad6f31cc612b954b04eb097069f020",
"score": "0.64219224",
"text": "def eval_operator(self, op: str, value: ValueType) -> Set[str]:\n result = set()\n index_dct = self.index_dct\n index_type = self.dtype\n index_arr = None\n if index_type is not None and index_type and not pa.types.is_date(index_type):\n index_type = index_type.to_pandas_dtype()\n try:\n index_arr = np.fromiter(index_dct.keys(), dtype=index_type)\n except ValueError:\n pass\n if index_arr is None:\n index_arr = np.array(list(index_dct.keys()))\n\n index = filter_array_like(\n index_arr, op, value, strict_date_types=True, column_name=self.column\n )\n allowed_values = index_arr[index]\n # Need to determine allowed values to include predicates like `in`\n for value in allowed_values:\n result.update(set(self.index_dct[value]))\n return result",
"title": ""
},
{
"docid": "127f1785befc5c3ad76c1869dedc10e4",
"score": "0.6257768",
"text": "def equal_operator(metadata, values):\n if isinstance(values, (list, tuple)):\n filters = [f\"{metadata} eq {quote_if_str(item)}\" for item in values]\n return f\"({' or '.join(filters)})\"\n return f\"{metadata} eq {quote_if_str(values)}\"",
"title": ""
},
{
"docid": "a4708b78cab08389f7443a07f5fe2fc0",
"score": "0.582816",
"text": "def expecting_collection(cls) -> Collection[\"ConditionOperator\"]:\n return (cls.IN, cls.NOTIN)",
"title": ""
},
{
"docid": "f31d124ce887f6d2dee0ff573c9f19de",
"score": "0.56368834",
"text": "def isin(self, values):\n return self._with_expr(exprs.In, [lit(v).expr for v in values])",
"title": ""
},
{
"docid": "965d106a1adcbdd58f2b3587efc9629c",
"score": "0.55415547",
"text": "def label_value_queries(self) -> List[ValueQuery]:\n return []",
"title": ""
},
{
"docid": "f60e1c980e51866d12494830536dabb4",
"score": "0.5540659",
"text": "def _in(self, rhs):\n return self._create_query( lambda value : value in rhs)",
"title": ""
},
{
"docid": "13d038f802317cbbe17c0b7ad5d1be78",
"score": "0.5490533",
"text": "def prop_in(self, key, values):\n entity = self.entity()\n col = entity._props\n if is_list_prop(entity, key):\n # applicable only to text arrays\n # see https://www.postgresql.org/docs/9.4/functions-json.html\n # has_any is `?|` under the hood and that requires the right operand to be a text array\n return self.filter(col[key].has_any(array(values)))\n assert isinstance(key, str) and isinstance(values, list)\n return self.filter(col[key].astext.in_([str(v) for v in values]))",
"title": ""
},
{
"docid": "5f0366ff2aaf043eef1309bdf82ba4ca",
"score": "0.54187435",
"text": "def in_(self, *others):\n if len(others) == 1 and isinstance(others[0], (list, tuple)):\n others = others[0]\n\n return Expression(self, operators.IN, others)",
"title": ""
},
{
"docid": "812886786c1b34910c944c664affac7a",
"score": "0.5385475",
"text": "def isin(self, values: Value | Sequence[Value]) -> ir.BooleanValue:\n from ibis.expr.types import ArrayValue\n\n if isinstance(values, ArrayValue):\n return ops.ArrayContains(values, self).to_expr()\n elif isinstance(values, Column):\n return ops.InColumn(self, values).to_expr()\n else:\n return ops.InValues(self, values).to_expr()",
"title": ""
},
{
"docid": "0136756ff767b2718b874c8923f51099",
"score": "0.53735423",
"text": "def get_by_values(\n self,\n values: t.List[str],\n field: str,\n not_flag: bool = False,\n pre: str = \"\",\n post: str = \"\",\n field_manual: bool = False,\n **kwargs,\n ) -> GEN_TYPE: # pragma: no cover\n field = self.fields.get_field_name(value=field, field_manual=field_manual)\n\n match = listify(values)\n match = [f\"'{x.strip()}'\" for x in match]\n match = \", \".join(match)\n\n inner = f\"{field} in [{match}]\"\n\n kwargs[\"query\"] = self._build_query(\n inner=inner,\n pre=pre,\n post=post,\n not_flag=not_flag,\n )\n\n return self.get(**kwargs)",
"title": ""
},
{
"docid": "ffa5df5d7b836374510d25b7886f992a",
"score": "0.5339626",
"text": "def _search_status(self, operator, value):\n if isinstance(value, str):\n value = [value]\n value = [v for v in value if v in ['in_need_of_action', 'with_overdue_invoices', 'no_action_needed']]\n if operator not in ('in', '=') or not value:\n return []\n followup_data = self._query_followup_level(all_partners=True)\n return [('id', 'in', [d['partner_id'] for d in followup_data.values() if d['followup_status'] in value])]",
"title": ""
},
{
"docid": "aa9ef7e8d6c8bb38adcffc5d57717d08",
"score": "0.5307814",
"text": "def assign_operators(self):\n for i, op in enumerate(self.operators):\n if op in Condition.OPERATORS:\n self.operators[i] = Condition.OPERATORS[op]\n continue\n try:\n n_args = len(inspect.getargspec(op)[0])\n return n_args == 2\n except:\n print(\"Condition has invalid operator(s). Operators must \" \\\n \"accept two args. Hint: to define your own, use lamdbas\")\n raise",
"title": ""
},
{
"docid": "24cf5b152841658baf66d7d102f6d597",
"score": "0.53033066",
"text": "def get_operators(self):\r\n raise NotImplementedError",
"title": ""
},
{
"docid": "4db4603275113f93482166e4d9d17ebb",
"score": "0.52539986",
"text": "def get_operators(self):\r\n return self.SELECTION_OPERATORS + self.VALID_OPERATORS",
"title": ""
},
{
"docid": "4db4603275113f93482166e4d9d17ebb",
"score": "0.52539986",
"text": "def get_operators(self):\r\n return self.SELECTION_OPERATORS + self.VALID_OPERATORS",
"title": ""
},
{
"docid": "b85086c6749abb73d0b28a4bd74e77ab",
"score": "0.523619",
"text": "def operator(self, operatorType, value):\n if value is None:\n if operatorType == Query.Op.Is:\n return 'is none'\n else:\n return 'is not none'\n \n return super(ForeignKeyPlugin, self).operator(operatorType, value)",
"title": ""
},
{
"docid": "32857fccb67d48eae284c37f6a40ff82",
"score": "0.519266",
"text": "def parse(self, data):\n val = data.get(self.fname, missing)\n if not isinstance(val, dict):\n return (self.operators['$eq'], self.field.deserialize(val)),\n\n return tuple(\n (\n self.operators[op],\n (self.field.deserialize(val)) if op not in self.list_ops else [\n self.field.deserialize(v) for v in val])\n for (op, val) in val.items() if op in self.operators\n )",
"title": ""
},
{
"docid": "a94b2938d40ff26c271095b744818f6a",
"score": "0.51688784",
"text": "def values(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['RuleRuleConditionQueryStringConfigValueArgs']]]]:\n return pulumi.get(self, \"values\")",
"title": ""
},
{
"docid": "ba8fdde85e0ece60c6503f93464f55dc",
"score": "0.5147826",
"text": "def test_is_operator_allowed(self):\n pass",
"title": ""
},
{
"docid": "d9fc9893243fc601df2ec4a77177f571",
"score": "0.5137876",
"text": "def evaluate(self, values):\n return self.value in values",
"title": ""
},
{
"docid": "d0183cdf2e68dbfbac7bd5b26716b2e5",
"score": "0.5132198",
"text": "def __create_clause(self, col_names, col_subs):\n clause = []\n for ind, name in enumerate(col_names):\n if col_subs[ind].find(\",\") != -1:\n clause.append(\"`%s` IN (%s)\" % (name, col_subs[ind]))\n else:\n clause.append(\"`%s` = %s\" % (name, col_subs[ind]))\n return clause",
"title": ""
},
{
"docid": "028cc1fd13ffc94683a4fad61270bd3e",
"score": "0.51180995",
"text": "def get_operator_list(self):\n return self.operator_collection_list",
"title": ""
},
{
"docid": "fbcd5887be7ce3f36890fc77bb1e3c1d",
"score": "0.51083535",
"text": "def eq_or_in(val, options):\n return val in options if isinstance(options, tuple) else val == options",
"title": ""
},
{
"docid": "e8c2202a3d926c94168315f9963052ed",
"score": "0.51053",
"text": "def operator_circuit(self, operator):\n if isinstance(operator, np.ndarray):\n return [{'name': 'unitary', 'qubits': [0], 'params': [operator]}]\n\n if isinstance(operator, list) and isinstance(operator[0],\n np.ndarray):\n if len(operator) == 1:\n return [{'name': 'unitary', 'qubits': [0], 'params': operator}]\n else:\n return [{'name': 'kraus', 'qubits': [0], 'params': operator}]\n\n return operator",
"title": ""
},
{
"docid": "4a9e1e9d0e3714a101b16ff1058a2737",
"score": "0.51026875",
"text": "def generate_values(self, left_col, right_col=None):\n if self.operation == Operation.NOT:\n self.values += [not val for val in left_col.values[1:]]\n elif self.operation == Operation.AND:\n self.values += [left_col.values[i] and right_col.values[i] for i in range(1, len(left_col.values))]\n elif self.operation == Operation.OR:\n self.values += [left_col.values[i] or right_col.values[i] for i in range(1, len(left_col.values))]\n else:\n #Implies\n self.values += [not left_col.values[i] or right_col.values[i] for i in range(1, len(left_col.values))]",
"title": ""
},
{
"docid": "a1bafdc4ddcc6facc72757c48e94aa2b",
"score": "0.5086541",
"text": "def not_in(self, *others):\n op = \"{} {}\".format(operators.NOT, operators.IN)\n\n if len(others) == 1 and isinstance(others[0], (list, tuple)):\n others = others[0]\n\n return Expression(self, op, others)",
"title": ""
},
{
"docid": "b508bf89cb1acdec61dc945f356de77f",
"score": "0.50768894",
"text": "def filter_array_like(\n array_like,\n op: str,\n value,\n mask=None,\n out=None,\n strict_date_types: bool = False,\n column_name: Optional[str] = None,\n):\n if mask is None:\n mask = np.ones(len(array_like), dtype=bool)\n\n if out is None:\n out = np.zeros(len(array_like), dtype=bool)\n\n # In the case of an empty list, don't bother with evaluating types, etc.\n if is_list_like(value) and len(value) == 0:\n false_arr = np.zeros(len(array_like), dtype=bool)\n np.logical_and(false_arr, mask, out=out)\n return out\n\n require_ordered = \"<\" in op or \">\" in op\n array_like, value = _ensure_type_stability(\n array_like, value, strict_date_types, require_ordered, column_name\n )\n\n with np.errstate(invalid=\"ignore\"):\n if op == \"==\":\n if pd.isnull(value):\n np.logical_and(pd.isnull(array_like), mask, out=out)\n else:\n np.logical_and(array_like == value, mask, out=out)\n elif op == \"!=\":\n if pd.isnull(value):\n np.logical_and(~pd.isnull(array_like), mask, out=out)\n else:\n np.logical_and(array_like != value, mask, out=out)\n elif op == \"<=\":\n np.logical_and(array_like <= value, mask, out=out)\n elif op == \">=\":\n np.logical_and(array_like >= value, mask, out=out)\n elif op == \"<\":\n np.logical_and(array_like < value, mask, out=out)\n elif op == \">\":\n np.logical_and(array_like > value, mask, out=out)\n elif op == \"in\":\n value = np.asarray(value)\n nullmask = pd.isnull(value)\n if value.dtype.kind in (\"U\", \"S\", \"O\"):\n # See GH358\n\n # If the values include duplicates, this would blow up with the\n # join below, rendering the mask useless\n unique_vals = np.unique(value[~nullmask])\n value_ser = pd.Series(unique_vals, name=\"value\")\n arr_ser = pd.Series(array_like, name=\"array\").to_frame()\n matching_idx = (\n ~arr_ser.merge(\n value_ser, left_on=\"array\", right_on=\"value\", how=\"left\"\n )\n .value.isna()\n .values\n )\n else:\n matching_idx = (\n np.isin(array_like, value)\n if len(value) > 0\n else np.zeros(len(array_like), dtype=bool)\n )\n\n if any(nullmask):\n matching_idx |= pd.isnull(array_like)\n\n np.logical_and(\n matching_idx, mask, out=out,\n )\n else:\n raise NotImplementedError(\"op not supported\")\n\n return out",
"title": ""
},
{
"docid": "d1dceefc527b3ba8dc2d2546a58fa727",
"score": "0.5062532",
"text": "def _value_query(self, key: str, value: str):\n if not isinstance(key, str):\n try:\n key = str(key)\n except Exception as excep:\n print(excep)\n raise ValueError('key argument must be a string or',\n 'coercable to string!')\n if not isinstance(value, str):\n try:\n value = str(value)\n except Exception as excep:\n print(excep)\n raise ValueError('value argument must be a string or',\n 'coercable to string!')\n self._query.extend([key, value])",
"title": ""
},
{
"docid": "24e9838db7261e56c84b7d8bb18a0ca8",
"score": "0.50578976",
"text": "def parse_query_args(self, _model, **query):\n model = _model\n parsed = []\n for lhs, rhs in query.iteritems():\n if self.query_separator in lhs:\n lhs, op = lhs.rsplit(self.query_separator, 1)\n else:\n op = 'eq'\n\n if lhs in model._meta.columns:\n lhs = model._meta.columns[lhs].name\n\n try:\n field = model._meta.get_field_by_name(lhs)\n except AttributeError:\n field = model._meta.get_related_field_by_name(lhs)\n if field is None:\n raise\n\n if isinstance(rhs, R):\n expr, params = rhs.sql_where()\n lookup_value = [field.db_value(o) for o in params]\n\n combined_expr = self.operations[op] % expr\n operation = combined_expr % tuple(self.interpolation for p in params)\n elif isinstance(rhs, F):\n lookup_value = rhs\n operation = self.operations[op] # leave as \"%s\"\n else:\n if op == 'in':\n if isinstance(rhs, SelectQuery):\n lookup_value = rhs\n operation = 'IN (%s)'\n else:\n if not rhs:\n raise EmptyResultException\n lookup_value = [field.db_value(o) for o in rhs]\n operation = self.operations[op] % \\\n (','.join([self.interpolation for v in lookup_value]))\n elif op == 'is':\n if rhs is not None:\n raise ValueError('__is lookups only accept None')\n operation = 'IS NULL'\n lookup_value = []\n elif op == 'isnull':\n operation = 'IS NULL' if rhs else 'IS NOT NULL'\n lookup_value = []\n elif isinstance(rhs, (list, tuple)):\n # currently this only happens on 'between' lookups, but leave\n # it general to lists and tuples\n lookup_value = [field.db_value(o) for o in rhs]\n operation = self.operations[op] % \\\n tuple(self.interpolation for v in lookup_value)\n else:\n lookup_value = field.db_value(rhs)\n operation = self.operations[op] % self.interpolation\n\n parsed.append(\n (field.db_column, (operation, self.lookup_cast(op, lookup_value)))\n )\n\n return parsed",
"title": ""
},
{
"docid": "d4c54e681d216fc476b938c21724b244",
"score": "0.5045081",
"text": "def __init__(__self__, *,\n name: str,\n operator: str,\n values: Sequence[str]):\n pulumi.set(__self__, \"name\", name)\n pulumi.set(__self__, \"operator\", operator)\n pulumi.set(__self__, \"values\", values)",
"title": ""
},
{
"docid": "7b7da66c09e16ed4b01d2e62e8bc95b4",
"score": "0.5044846",
"text": "def get_operators(self):\r\n return self.SELECTION_OPERATORS",
"title": ""
},
{
"docid": "986b126644da37212f20895b6eb47228",
"score": "0.50279593",
"text": "def any_of(values):\n return list(values)",
"title": ""
},
{
"docid": "b690185aa56bdc8df4db4d62858983d4",
"score": "0.49895206",
"text": "def list_validation_operators(self):\n\n validation_operators = []\n for (\n name,\n value,\n ) in self.variables.validation_operators.items():\n value[\"name\"] = name\n validation_operators.append(value)\n return validation_operators",
"title": ""
},
{
"docid": "ba8da0dd87ff5f836ec6b77c296a4c28",
"score": "0.49857548",
"text": "def gen_filter(self, op='eq', **kwargs):\n q_filter = []\n for kwarg in kwargs:\n q_filter.append({'field': kwarg, 'op': op, 'value': kwargs[kwarg]})\n return q_filter",
"title": ""
},
{
"docid": "1f6f21a6a8a15cd57a933ca41b20d254",
"score": "0.49715912",
"text": "def _is_list(self):\n return isinstance(self.value['operand'], list)",
"title": ""
},
{
"docid": "93404e8ce502185085176be314891727",
"score": "0.49682137",
"text": "def get_by_values(\n self,\n values: List[str],\n field: str,\n not_flag: bool = False,\n pre: str = \"\",\n post: str = \"\",\n field_manual: bool = False,\n **kwargs,\n ) -> Union[Generator[dict, None, None], List[dict]]:\n field = self.fields.get_field_name(value=field, field_manual=field_manual)\n\n match = listify(values)\n match = [f\"'{x.strip()}'\" for x in match]\n match = \", \".join(match)\n\n inner = f\"{field} in [{match}]\"\n\n kwargs[\"query\"] = self._build_query(\n inner=inner,\n pre=pre,\n post=post,\n not_flag=not_flag,\n )\n\n return self.get(**kwargs)",
"title": ""
},
{
"docid": "0ab6a82f0a5c079957961a0182725562",
"score": "0.49647245",
"text": "def guess_operator(self, value) -> Tuple[Union[str, None], str]:\n if value.startswith('<='):\n return '$lte', value[2:]\n\n if value.startswith('>='):\n return '$gte', value[2:]\n\n if value.startswith('<'):\n return '$lt', value[1:]\n\n if value.startswith('>'):\n return '$gt', value[1:]\n\n if value.startswith('!'):\n return '$ne', value[1: ]\n\n if value.startswith('[]'):\n return '$in', value[2: ]\n\n if value.startswith('[!]'):\n return '$nin', value[3: ]\n\n return None, value",
"title": ""
},
{
"docid": "5ec149af93f16daa590103641e7b652f",
"score": "0.49641567",
"text": "def _generate_expressions(cls, op, value, attr, target_type, full_data_key,\n gettext):\n _ = gettext\n try:\n if op == \"$lt\":\n expression = attr < cls.convert_to_alchemy_type(\n value, target_type)\n elif op == \"$lte\":\n expression = attr <= cls.convert_to_alchemy_type(\n value, target_type)\n elif op == \"$eq\":\n expression = attr == cls.convert_to_alchemy_type(\n value, target_type)\n elif op == \"$ne\":\n expression = attr != cls.convert_to_alchemy_type(\n value, target_type)\n elif op == \"$gte\":\n expression = attr >= cls.convert_to_alchemy_type(\n value, target_type)\n elif op == \"$gt\":\n expression = attr > cls.convert_to_alchemy_type(\n value, target_type)\n elif op == \"$like\":\n expression = attr.like(\n \"%\" + str(value) + \"%\")\n elif op == \"$in\" or op == \"$nin\":\n if not isinstance(value, list):\n raise MqlFieldError(\n data_key=full_data_key,\n op=op,\n filters=value,\n message=_(\"$in and $nin values must \"\n \"be a list.\"),\n code=\"invalid_in_comp\"\n )\n converted_list = []\n for value in value:\n converted_list.append(\n cls.convert_to_alchemy_type(\n value, target_type))\n expression = attr.in_(converted_list)\n if op == \"$nin\":\n expression = sqlalchemy.not_(expression)\n elif op == \"$mod\":\n if target_type not in cls.int_types:\n raise MqlFieldError(\n data_key=full_data_key,\n op=op,\n filters=value,\n message=_(\"$mod may only be used on integer fields.\"),\n code=\"invalid_op\"\n )\n if (isinstance(value, list) and\n len(value) == 2):\n try:\n divider = int(value[0])\n if int(value[0]) != value[0]:\n raise TypeError(\n \"Decimal provided \"\n \"instead of int.\")\n result = int(value[1])\n if int(value[1]) != value[1]:\n raise TypeError(\n \"Decimal provided \"\n \"instead of int.\")\n except (TypeError, ValueError):\n raise MqlFieldError(\n data_key=full_data_key,\n op=op,\n filters=value,\n message=_(\n \"Non int $mod value supplied\"),\n code=\"invalid_mod_values\"\n )\n expression = (\n attr.op(\"%\")(divider) == result)\n else:\n raise MqlFieldError(\n data_key=full_data_key,\n filters=value,\n op=op,\n message=_(\"$mod value must be list of \"\n \"two integers.\"),\n code=\"invalid_mod_values\"\n )\n elif op == \"$exists\":\n exists = cls.convert_to_alchemy_type(value, target_type)\n if isinstance(attr.property, RelationshipProperty):\n if not attr.property.uselist:\n expression = attr.has() if exists else ~attr.has()\n else:\n expression = attr.any() if exists else ~attr.any()\n else:\n expression = ~attr.is_(None) if exists else attr.is_(None)\n else:\n raise MqlFieldError(\n data_key=full_data_key,\n filters=value,\n op=op,\n message=_(\"Invalid operator.\"),\n code=\"invalid_op\"\n )\n except (TypeError, ValueError):\n raise MqlFieldError(\n data_key=full_data_key,\n filters=value,\n op=op,\n message=_(\"Unable to convert provided data to the proper \"\n \"type for this field.\"),\n code=\"data_conversion_error\"\n )\n return expression",
"title": ""
},
{
"docid": "9c86da0adb2664adeb5f732c9a538ae7",
"score": "0.49563447",
"text": "def testWhereClause(self):\n operators = {'!=': 'NOT_EQ', '^=': 'NOT_EQ', '<>': 'NOT_EQ', '=': 'EQ', '<': 'LT', '<=': 'LE', \\\n '>': 'GT', '>=': 'GE'}\n for operator in operators:\n value1 = Identifier(id='value1')\n expected = RelationalOperation(op1=value1, operator=operators[operator], op2=10)\n where = \"WHERE value1 %s 10;\" % operator\n\n result = plsql.parse('where_clause', where)\n self.assertTrue(result, \"Where clause not parsed\")\n self.assertEquals(1, len(result))\n self.assertEquals(expected, result[0])",
"title": ""
},
{
"docid": "8eb3dea03cc3e84938a45a785af9964a",
"score": "0.49481457",
"text": "def operatorNames(self):\n return [\"L01\", \"L10\",\"L11\",\"L02\",\"L20\",\n \"R01\", \"R10\",\"R11\",\"R02\",\"R20\"]",
"title": ""
},
{
"docid": "04227b40cdf30f8338b30455ff1714d4",
"score": "0.49441698",
"text": "def expressions(self):\n return self.args.get(\"expressions\") or []",
"title": ""
},
{
"docid": "abdb82a244b32108906d5ffc8e4656fe",
"score": "0.49337098",
"text": "def _contains(self, rhs):\n return self._create_query( lambda value : rhs in value)",
"title": ""
},
{
"docid": "b17f3b0203659fa8ff9085b56ce7ad3b",
"score": "0.49305573",
"text": "def _is_exists(self):\n return self._has_empty_value() and self.value.get('operator') == '!='",
"title": ""
},
{
"docid": "000970bea882d050ea101e79e78b4d02",
"score": "0.49224913",
"text": "def evaluate(self, values):\n return not self.expression.evaluate(values)",
"title": ""
},
{
"docid": "7add3d3c395f99501de715049dcc1d6b",
"score": "0.4914691",
"text": "def collect_value(self) -> str:\n parser = FilterInputParser()\n\n result = []\n for field, user_input in self.query_fields.items():\n parsed_value = parser.parse(field, user_input.get_user_input)\n if not parsed_value:\n # If the parsed value results in an empty value, skip field\n continue\n\n if isinstance(parsed_value, (list, tuple, set)):\n result.extend(\n [\n f\"{self.FIELD_MAP.get(field, field)}{self.OPERATOR_MAP[field]}{value}\"\n for value in parsed_value\n ]\n )\n elif isinstance(parsed_value, str):\n result.append(\n f\"{self.FIELD_MAP.get(field, field)}{self.OPERATOR_MAP[field]}{parsed_value}\"\n )\n else:\n raise ParserError(\n field=field,\n value=user_input.get_user_input,\n msg=\"Parsed value was neither a list, tuple, set nor str and it wasn't empty \"\n \"or None.\",\n extras=(\"parsed_value\", parsed_value),\n )\n\n result = \" AND \".join(result)\n return result.replace(\"'\", '\"') # OPTIMADE Filter grammar only supports \" not '",
"title": ""
},
{
"docid": "597927adc40776976134afbf66262b85",
"score": "0.4913268",
"text": "def logicals(self, op):\n argc = self.__instruction.argc()\n if argc != 0:\n self.print_error(\"Error, wrong arguments on \" + op + \" instruction!\\n\", 32)\n \n if self.__dataStack.size() < 2 and op != \"NOTS\":\n self.print_error(\"Error, \" + op + \" not enough vars on stack!\\n\", 56)\n elif self.__dataStack.size() < 1:\n self.print_error(\"Error, \" + op + \" not enough vars on stack!\\n\", 56)\n \n src2 = self.__dataStack.pop()\n if op != \"NOTS\":\n src1 = self.__dataStack.pop()\n\n if type(src2) is not bool:\n self.print_error(\"Error, \" + op + \" not bool types!\\n\", 53)\n if op != \"NOTS\":\n if type(src1) is not bool:\n self.print_error(\"Error, \" + op + \" not bool types!\\n\", 53)\n \n if op == \"ANDS\":\n self.__dataStack.push(src1 and src2)\n elif op == \"ORS\":\n self.__dataStack.push(src1 or src2)\n elif op == \"NOTS\":\n self.__dataStack.push(not src2)",
"title": ""
},
{
"docid": "46ea88cde53726cdcd5c672da0e1082e",
"score": "0.49022728",
"text": "def op(value, comparable):\n return all(pydash.juxtapose(*comparable)(value))",
"title": ""
},
{
"docid": "465d13f044b2d68bc21fd699adcef895",
"score": "0.48966202",
"text": "def post_expression(tokens):\n return apply_rule(\n [\n (NullCheck, core_expression, [grammar_literal('IS', 'NULL'),\n grammar_literal('IS', 'NOT', 'NULL')]),\n (InCheck, core_expression, ['IN', grammar_literal('NOT', 'IN')],\n '(', separated_sequence(expression, ','), ')'),\n core_expression,\n ],\n tokens)",
"title": ""
},
{
"docid": "66663e012080b6e4c8fdaf0a7e92a9f7",
"score": "0.4886468",
"text": "def array_values(self) -> Sequence['outputs.QueryParameterValueResponse']:\n return pulumi.get(self, \"array_values\")",
"title": ""
},
{
"docid": "e59eadeef00c7ceba898de2ea77d5adc",
"score": "0.4881967",
"text": "def get_prep_value(self, value):\n if value is not None:\n if not isinstance(value, (list, tuple)):\n raise ValueError(\"value {} is not list or tuple\".format(value))\n value = [self.base_field.get_prep_value(v) for v in value]\n return super().get_prep_value(value)",
"title": ""
},
{
"docid": "0dd584dc3ec5b3f31c140d9121525f73",
"score": "0.4881622",
"text": "def visit_str_oper_expr_rhs(self, node, vc): # noqa\n oper, _, tok = vc\n return [oper[0], tok]",
"title": ""
},
{
"docid": "63d3520d2a4d582f9f0ff4da36219388",
"score": "0.48764914",
"text": "def or_field_filters(field_name, field_values):\n return [\n {\n \"terms\": {\n field_name: field_values[0].split(\",\"),\n },\n },\n ]",
"title": ""
},
{
"docid": "47f5a3b128b0705e9a835b49c6c0c6da",
"score": "0.48761252",
"text": "def query_list_of_single_values(\n self, query, arguments, sql_parameters=[], use_execute_values=False\n ):",
"title": ""
},
{
"docid": "21009ea69bb11a6d78feae53250aaa6e",
"score": "0.4858778",
"text": "def _parse_boolean_operators(self, nest):\n op_lookup = {\n \"and\": BinaryOp.AND,\n \"or\": BinaryOp.OR,\n \"not\": UnaryOp.NOT\n }\n for i, term in enumerate(nest):\n if isinstance(term, list):\n self._parse_boolean_operators(term)\n else:\n nest[i] = op_lookup.get(term.lower(), term)",
"title": ""
},
{
"docid": "d853d01a6a9f75fa56ebdc4308532df7",
"score": "0.48401082",
"text": "def _parse_operands(self, operator, operands):\n expected_operands = self.instruction_schema[operator]\n\n if expected_operands == \"no_operands\":\n if len(operands) > 0:\n raise ParserError(operands[0], \"Unexpected operand\")\n else:\n for i in range(len(operands)):\n self._is_type_valid(operands[i], expected_operands[i])",
"title": ""
},
{
"docid": "4b2684f62897da167ac23499761cea7a",
"score": "0.48341474",
"text": "def test_add_2q_6(self):\n result = self.sapi.add_2q('OR', ['item1', 'item2,item3'])\n self.assertTrue( 'OR item1:\"item2\" AND item1:\"item3\"' == result )",
"title": ""
},
{
"docid": "83dc077fa0453b1eed3c2fc0af5b25d6",
"score": "0.48222494",
"text": "def test_list_to_filter_values__empty(self):\n assert HuntingQueryBuilder.get_filter_values([]) is None",
"title": ""
},
{
"docid": "7671cd692edf48c99ef18785ccb156a8",
"score": "0.48118573",
"text": "def read_operator(self):\n pass",
"title": ""
},
{
"docid": "e54df9aee234acdb98d2b9cefbab77fc",
"score": "0.48104453",
"text": "def security_operators(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"security_operators\")",
"title": ""
},
{
"docid": "4c6b6243d894d6d5659dbb97caf6b529",
"score": "0.48103592",
"text": "def in_array(allowed: Union[Tuple[T], List[T]]) -> ParamValidator[T]:\n\n class InArray(ParamValidator[T]):\n def validate(self, value: T) -> bool:\n return value is not None and value in allowed\n\n return InArray()",
"title": ""
},
{
"docid": "e1f1bc91838de5e6445b9590dec69c8f",
"score": "0.47939005",
"text": "def __init__(__self__, *,\n key: str,\n operator: str,\n values: Optional[Sequence[str]] = None):\n pulumi.set(__self__, \"key\", key)\n pulumi.set(__self__, \"operator\", operator)\n if values is not None:\n pulumi.set(__self__, \"values\", values)",
"title": ""
},
{
"docid": "e1f1bc91838de5e6445b9590dec69c8f",
"score": "0.47939005",
"text": "def __init__(__self__, *,\n key: str,\n operator: str,\n values: Optional[Sequence[str]] = None):\n pulumi.set(__self__, \"key\", key)\n pulumi.set(__self__, \"operator\", operator)\n if values is not None:\n pulumi.set(__self__, \"values\", values)",
"title": ""
},
{
"docid": "8add7872853f16321a90bbb012b438e8",
"score": "0.4780734",
"text": "def get_args_as_to_query(self):\n if self.args:\n return [\n v for v in list(self.args.values())\n if v[\"value\"] is not None and\n v[\"type\"] not in ('list',) and\n v.get(\"ignore_alchemy\", False) is False\n ]\n\n return None",
"title": ""
},
{
"docid": "f126c33b927a83c4628aa63ca57949e5",
"score": "0.4780707",
"text": "def op(value, comparable):\n return any(pydash.juxtapose(*comparable)(value))",
"title": ""
},
{
"docid": "af6b234a93bff1dc2a538f996d9698d2",
"score": "0.4778332",
"text": "def make_query(self, args):\n query = []\n for attribute, value in args.items():\n attribute = attribute.rstrip('_')\n if not isinstance(value, list):\n value = [value]\n\n # Make tuples of escaped values and a flag whether they are postive\n values = [(self.escaped_term(v.lstrip('!')), v[0] != '!') for v in value]\n # Handle positive search first\n positive = [v[0] for v in values if v[1]]\n if positive:\n query.append('(%s:(%s))' % (attribute, ' '.join(v for v in positive)))\n # If there is something positive, don't need to query for negative\n continue\n\n # Negative search\n negative = [v[0] for v in values if not v[1]]\n if negative:\n query.append('(%s:(* %s))' % (attribute, ' '.join('-%s' % (v) for v in negative)))\n\n return 'AND'.join(query)",
"title": ""
},
{
"docid": "9cc484443b90dc7e4a91b82f0b08f1b2",
"score": "0.47706065",
"text": "def operator(self):\n col = self.pos\n operators = [\"||\", \"&&\", \">>\", \"<<\", \"!=\", \">=\", \"<=\", \"==\", \"##\"] + \\\n [\"-\", \"+\", \"!\", \"*\", \"/\", \"|\", \"&\", \"^\", \"<\", \">\", \"?\", \":\", \"~\", \"#\", \"=\", \"%\"]\n try:\n index = self.match_any(operators)\n\n op = Operator(self.line, col, self.prev_white, operators[index])\n return op\n except TokenError:\n self.pos = col\n raise TokenError(\"Invalid operator.\")",
"title": ""
},
{
"docid": "ef61d346f04bdc7b6304750333c31c47",
"score": "0.47641507",
"text": "def create_sql_filter(self, data_list):\n return RegistrationData.data.op('#>>')('{}').in_(data_list)",
"title": ""
},
{
"docid": "d7c850a40d101b35fde5d8fc245f6c09",
"score": "0.47619247",
"text": "def test_list_to_filter_values__list(self):\n list_input = ['a', 'b', 'c']\n assert HuntingQueryBuilder.get_filter_values(list_input) == '(\"a\",\"b\",\"c\")'\n assert HuntingQueryBuilder.get_filter_values(list_input[:1]) == '(\"a\")'",
"title": ""
},
{
"docid": "6e57d764ede297e4521f8ef62806a464",
"score": "0.47616807",
"text": "def operatorNames(self):\n return [\"sendOneCannAcross\", \"sendOneCannBack\",\n \"sendOneMissAcross\", \"sendOneMissBack\",\n \"sendTwoCannAcross\", \"sendTwoCannBack\",\n \"sendTwoMissAcross\", \"sendTwoMissBack\",\n \"sendOneOfEachAcross\", \"sendOneOfEachBack\"]",
"title": ""
},
{
"docid": "92b1e401c095278d2e7a038332ab1976",
"score": "0.47471112",
"text": "def _is_in_placeholders(op, func_arg_placeholders):\n return op.values() and any(x.name in func_arg_placeholders\n for x in op.values())",
"title": ""
},
{
"docid": "960a8299acae842bb4e7d0a20609bca3",
"score": "0.473784",
"text": "def get_matching_conditions(self, s, p, o):\n\t\tcond, values = [], []\n\t\tif s: \n\t\t\tcond.append('`subject`=%s')\n\t\t\tvalues.append(s)\n\t\tif p: \n\t\t\tcond.append('`predicate`=%s')\n\t\t\tvalues.append(p)\n\t\tif o: \n\t\t\tcond.append('`object`=%s')\n\t\t\tvalues.append(o)\n\t\t\n\t\tif not cond:\n\t\t\traise Exception, 'Some filter is necessary'\n\t\t\n\t\treturn cond, values",
"title": ""
},
{
"docid": "a4b37fcd6f5348cd4d49458c7eea916c",
"score": "0.47350058",
"text": "def values_filter(self, results, values):\n results = results.filter(pk__in=values)\n return results",
"title": ""
},
{
"docid": "760fc6f66fe6e5fb75c5d47bdcf0673d",
"score": "0.47198278",
"text": "def prepare_query_value(self, op, value):\n return value",
"title": ""
},
{
"docid": "9365226b0b0c62f81f1e70e562f9a9cb",
"score": "0.47195035",
"text": "def test_unique_operators():\n nreg = NamesRegistry(None)\n nreg.add(\"air__temperature\")\n nreg.add(\"water__temperature\")\n nreg.add(\"air__log_of_temperature\")\n nreg.add(\"air__mean_of_log_of_temperature\")\n\n operators = nreg.operators\n\n assert len(operators) == 2\n assert \"log\" in operators\n assert \"mean\" in operators",
"title": ""
},
{
"docid": "7d34065cb6086dd3642e2702a39f2186",
"score": "0.47066712",
"text": "def check_column_values(self, values):\n raise Exception('this function should be oveloaded')",
"title": ""
},
{
"docid": "e9c73bb045f5d9f982bf4c24a43aa449",
"score": "0.4696973",
"text": "def __WHERE__(self,k,v):\n self.__mutate__(k)\n value_string = ', '.join([str(x) + ' = ' + str(y) for x,y in v])\n self.__mutate__( value_string )",
"title": ""
},
{
"docid": "50d663f0bf7e2810661b7a30e5991d29",
"score": "0.46936324",
"text": "def getCriteriaItems( self ):\n if self.value is None:\n return ()\n elif self.direction is None:\n return ( ( self.Field(), self.value ), )\n else:\n return ( ( self.Field(), {'query': self.value,\n 'range': self.direction} ), )",
"title": ""
},
{
"docid": "e27e2e78fc7c316fe764aa8b9ab4accc",
"score": "0.46802863",
"text": "def _is_action_value_list(action:Action):\n if action.nargs in {'*', '+'} or isinstance(action.nargs, int):\n return True\n return False",
"title": ""
},
{
"docid": "820ba20438ee9e12f6e375fc0337086d",
"score": "0.46750006",
"text": "def test_n_setitem_list(self) -> None:\n resto_criteria = RestoCriteria('dotcloud')\n resto_criteria['platform'] = ['SPOT 5', 'SPOT 6']\n # Verify that 2 criteria are created and not a single one\n self.assertFalse('platform' in resto_criteria)\n self.assertEqual(resto_criteria['platform[0]'], 'SPOT 5')\n self.assertEqual(resto_criteria['platform[1]'], 'SPOT 6')\n del resto_criteria['platform[0]']\n del resto_criteria['platform[1]']\n # Verify when only one is given, it s created as a standard criterion\n resto_criteria['platform'] = 'SPOT 5'\n self.assertEqual(resto_criteria['platform'], 'SPOT 5')",
"title": ""
},
{
"docid": "f14650fbbc57b89a684efef90ad23bd5",
"score": "0.46667925",
"text": "def __call__(self, values) -> typing.List[str]:\n if not values:\n return list()\n try:\n return [self.item_type(val)\n for val in values.split(self.separator)]\n except Exception:\n raise argparse.ArgumentTypeError(\n 'The value {} can not be parsed'.format(values))",
"title": ""
},
{
"docid": "a1d68917060ed798c1b2d18f632b155e",
"score": "0.46665993",
"text": "def get_operators(self):\r\n return self.NUMBER_OPERATORS",
"title": ""
},
{
"docid": "a1d68917060ed798c1b2d18f632b155e",
"score": "0.46665993",
"text": "def get_operators(self):\r\n return self.NUMBER_OPERATORS",
"title": ""
},
{
"docid": "a1d68917060ed798c1b2d18f632b155e",
"score": "0.46665993",
"text": "def get_operators(self):\r\n return self.NUMBER_OPERATORS",
"title": ""
},
{
"docid": "a1d68917060ed798c1b2d18f632b155e",
"score": "0.46665993",
"text": "def get_operators(self):\r\n return self.NUMBER_OPERATORS",
"title": ""
},
{
"docid": "a1d68917060ed798c1b2d18f632b155e",
"score": "0.46665993",
"text": "def get_operators(self):\r\n return self.NUMBER_OPERATORS",
"title": ""
},
{
"docid": "a1d68917060ed798c1b2d18f632b155e",
"score": "0.46665993",
"text": "def get_operators(self):\r\n return self.NUMBER_OPERATORS",
"title": ""
},
{
"docid": "0c2781335b6aaad18935199ca524592f",
"score": "0.46637106",
"text": "def get(self, operator=\"and\", **kwargs):\n pass",
"title": ""
},
{
"docid": "0ff3be009997062daa332f996bcc222a",
"score": "0.46478903",
"text": "def __call__(self, q_values: np.ndarray) -> np.array:\n raise NotImplementedError",
"title": ""
},
{
"docid": "b5f96d6943c32b2f600311be95e61b30",
"score": "0.4638482",
"text": "def get_value ( self, object ):\r\n if object.and_or == 'or':\r\n return 'or'\r\n return ''",
"title": ""
},
{
"docid": "fd218beaab5166126a332efa9cedd65c",
"score": "0.46364158",
"text": "def _test_operator(self, operator, inputs, properties=None):\n properties = properties if properties is not None else {}\n m = Model()\n variables = [Variable() for _ in inputs]\n m.r = operator(*variables, **properties)\n vs = [v for v in m.r.factor.inputs]\n variables = {v[1].uuid: inputs[i] for i,v in enumerate(vs)}\n evaluation = m.r.factor.eval(mx.nd, variables=variables)\n return evaluation",
"title": ""
},
{
"docid": "31b5260dd34a347988b791e23f43d798",
"score": "0.46344018",
"text": "def allPropertyBasedOperators():\n return { '~', '&', '|', '^', '->'}",
"title": ""
},
{
"docid": "a7b67d31a4e5f3ed2dd1d62ac5d55224",
"score": "0.463035",
"text": "def support_op_types(cls):\n return list(cls._operators.keys())",
"title": ""
},
{
"docid": "677b8af351f12ddd3f08d54396de9e7f",
"score": "0.4624197",
"text": "def __init__(self):\r\n self.operator = ['+', '-', '*', '/']",
"title": ""
},
{
"docid": "8f928cfe743c1c14a1b1b4309d24d672",
"score": "0.46200907",
"text": "def is_valid_operator(user_input):\n if has_spaces(user_input):\n operators = ['+', '-', '*', '/', 'square', 'cube', 'pow', 'mod']\n user_input_list = user_input.split(' ')\n if user_input_list[0] in operators:\n return True\n else:\n return False\n else:\n return False",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="750"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">4a1939a5eb8a098543846af3f2c5decb</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">method which returns the status (1 for win of 1, 2 for win of 2, 0 for full board and a tie) we decided to keep the numbers as numbers and not as constant because we because we have noticed that the constants create more confusion in this case after a conversation with lab support</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "454fd2f109148f7d7a8a5fd2f612be93",
"score": "0.0",
"text": "def get_winner(self):\r\n\r\n\r\n player = self.get_current_player()\r\n\r\n\r\n for i in range(self.ROWS):\r\n\r\n for j in range(self.COLOUMNS - 3):\r\n\r\n if self.board[i][j] == player and\\\r\n self.board[i][j + 1] == player and\\\r\n self.board[i][j + 2] == player and\\\r\n self.board[i][j + 3] == player:\r\n\r\n self.legal_move = 0\r\n \r\n self.game = True\r\n\r\n for k in range(4):\r\n self.win_index.append((i, j+k))\r\n \r\n \r\n return self.get_current_player()\r\n\r\n\r\n for i in range(self.ROWS - 3):\r\n\r\n for j in range(self.COLOUMNS):\r\n\r\n if self.board[i][j] == player and\\\r\n self.board[i + 1][j] == player and\\\r\n self.board[i + 2][j] == player and \\\r\n self.board[i + 3][j] == player:\r\n\r\n self.legal_move = 0\r\n \r\n self.game = True\r\n\r\n for k in range(4):\r\n self.win_index.append((i+k, j))\r\n\r\n\r\n return self.get_current_player()\r\n\r\n for i in range(self.ROWS - 3):\r\n\r\n for j in range(self.COLOUMNS - 3):\r\n\r\n if self.board[i][j] == player and\\\r\n self.board[i + 1][j + 1] == player and\\\r\n self.board[i + 2][j + 2] == player and\\\r\n self.board[i + 3][j + 3] == player:\r\n\r\n \r\n self.legal_move = 0\r\n \r\n self.game = True\r\n\r\n for k in range(4):\r\n self.win_index.append((i+k, j+k))\r\n\r\n\r\n return self.get_current_player()\r\n\r\n\r\n for i in range(3, self.ROWS):\r\n\r\n for j in range(self.COLOUMNS - 3):\r\n\r\n\r\n if self.board[i][j] == player and\\\r\n self.board[i - 1][j + 1] == player and\\\r\n self.board[i - 2][j + 2] == player and\\\r\n self.board[i - 3][j + 3] == player:\r\n\r\n \r\n self.legal_move = 0\r\n \r\n self.game = True\r\n\r\n for k in range(4):\r\n self.win_index.append((i-k, j+k))\r\n\r\n\r\n return self.get_current_player()\r\n\r\n if self.counter == (self.ROWS * self.COLOUMNS):\r\n\r\n self.legal_move = 0\r\n \r\n self.game = True\r\n\r\n return 0",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "518cc98d4e81758d6d7a4d60382824b4",
"score": "0.7457999",
"text": "def check_status(mat, stat):\n if stat == GAME_OVER:\n return GAME_OVER\n for r in range(len(mat)):\n if COAL_CELL in mat[r]:\n return PLAY\n return WIN",
"title": ""
},
{
"docid": "843351cc53b77a5dff3ccd8f759af097",
"score": "0.7102674",
"text": "def _checkWinStatus(self):\n p1 = self.playerTurn * 4\n p2 = self.playerTurn + \".{7}\" + self.playerTurn + \".{7}\" + self.playerTurn + \".{7}\" + self.playerTurn\n p3 = self.playerTurn + \".{6}\" + self.playerTurn + \".{6}\" + self.playerTurn + \".{6}\" + self.playerTurn\n p4 = self.playerTurn + \".{8}\" + self.playerTurn + \".{8}\" + self.playerTurn + \".{8}\" + self.playerTurn\n # searching the grid for the four patterns\n c1 = re.search(p1, self.grid)\n c2 = re.search(p2, self.grid)\n c3 = re.search(p3, self.grid)\n c4 = re.search(p4, self.grid)\n if c1 or c2 or c3 or c4: # if any pattern is found thn we haev a winner\n self.winner = self.playerTurn\n print(self.playerTurn, \"Has won the game\")\n self.gameOn = False",
"title": ""
},
{
"docid": "f49d21c59cdbbe8b90529feb5f79c68f",
"score": "0.70529395",
"text": "def utility(board):\n #get the winner\n who_win = winner(board)\n if who_win == X:\n return 1\n elif who_win == O:\n return -1\n else:\n return 0",
"title": ""
},
{
"docid": "bd7c4ab228f109a57dfdb71bd3573eaf",
"score": "0.70056206",
"text": "def get_result(self):\n if any([in_matrix(self.board, goal) for goal in self.playerWinningLines]):\n return \"player won\"\n elif any([in_matrix(self.board, goal) for goal in self.computerWinningLines]):\n return \"computer won\"\n elif len(self.gridAvailable) == 0:\n return \"tie\"\n else:\n return \"no one won\"",
"title": ""
},
{
"docid": "f41d4308a2928da32223bd0da72d18b1",
"score": "0.69247556",
"text": "def board_status(self):\n\n # check if any values of the score are +/- k\n if self.k in self.scores:\n return 1\n if -self.k in self.scores:\n return -1\n\n # check if board is full\n if self.positions_occcupied == self.m * self.n:\n return 0\n\n # otherwise keep playing\n return None",
"title": ""
},
{
"docid": "e603ec4e95c73bdd4c04b431b8ef3458",
"score": "0.6900803",
"text": "def utility(board):\n\n # Get the winner #\n Winner = winner(board)\n\n # return the Winner #\n if (Winner is X):\n return 1\n elif (Winner is O):\n return -1\n else:\n return 0",
"title": ""
},
{
"docid": "d7ebf5d80f735ed514fb1362961f6013",
"score": "0.6887441",
"text": "def utility(board):\n WinnerFound = winner(board)\n if WinnerFound == \"X\":\n ResultIs=1\n elif WinnerFound == \"O\":\n ResultIs=-1\n else:\n ResultIs=0\n\n return(ResultIs)",
"title": ""
},
{
"docid": "f74492abe1ebe2b0f41da362b4ec4580",
"score": "0.687776",
"text": "def utility(board):\n winer = winner(board)\n if winer == X:\n return 1\n elif winer == O:\n return -1\n else:\n return 0",
"title": ""
},
{
"docid": "296653808fe9d41cdc94eb4dca8d94a0",
"score": "0.68615526",
"text": "def utility(board):\n the_winner = winner(board)\n\n if the_winner == X:\n return 1\n elif the_winner == O:\n return -1\n else:\n return 0",
"title": ""
},
{
"docid": "b4005acfa7e4264243017279d43618a0",
"score": "0.6857024",
"text": "def check_winner(self) -> int:\n # check in someone have win in rows\n\n # check in someone have win in columns\n\n # check in someone have win in Diagonals\n # return BALL\n # return CROSS\n\n # Check for a draw\n # empty_positions = self.get_empty_positions()\n # return DRAW\n return NO_WINNER",
"title": ""
},
{
"docid": "6cb1a0af72048386774058594f0224b8",
"score": "0.6822549",
"text": "def utility(board):\n winnerOfRound = winner(board)\n if winnerOfRound == X:\n return 1\n elif winnerOfRound == O:\n return -1\n else:\n return 0",
"title": ""
},
{
"docid": "64e1db9ef945a568d5cdfb79d32267cf",
"score": "0.6814433",
"text": "def utility(board):\n\n gw = winner(board)\n if gw == X:\n return 1\n\n if gw == O:\n return -1\n\n return 0",
"title": ""
},
{
"docid": "84f421ead4f5d05711b5ae2b5920b0ba",
"score": "0.681171",
"text": "def utility(board): \n utility_winner = winner(board)\n if utility_winner == X:\n return 1\n elif utility_winner == O:\n return -1\n else:\n return 0",
"title": ""
},
{
"docid": "dbadf853a28ba0d02e87d92f367b010a",
"score": "0.679367",
"text": "def check_win(self):\n if(((self.board[0] == self.board[1] and self.board[1] == self.board[2]) or\n (self.board[0] == self.board[3] and self.board[3] == self.board[6])) and\n (self.board[0] == -1 or self.board[0] == 1)):\n return -self.board[0]\n\n elif(((self.board[6] == self.board[7] and self.board[7] == self.board[8]) or\n (self.board[2] == self.board[5] and self.board[5] == self.board[8])) and\n (self.board[8] == -1 or self.board[8] == 1)):\n return -self.board[8]\n\n elif(((self.board[3] == self.board[4] and self.board[4] == self.board[5]) or\n (self.board[1] == self.board[4] and self.board[4] == self.board[7]) or\n (self.board[0] == self.board[4] and self.board[4] == self.board[8]) or\n (self.board[2] == self.board[4] and self.board[4] == self.board[6])) and\n (self.board[4] == -1 or self.board[4] == 1)):\n return -self.board[4]\n\n elif 0 not in self.board:\n return 0\n\n else:\n return None",
"title": ""
},
{
"docid": "4e198423ee03d766f6638bf1f27faeba",
"score": "0.6789293",
"text": "def utility(board): # returns utility values\n if winner(board) == X:\n return 1\n elif winner(board) == O:\n return -1\n elif winner(board) == None:\n return 0",
"title": ""
},
{
"docid": "e97f88bfb808323fb524682d5e680045",
"score": "0.6753416",
"text": "def get_score(self):\n # Scores are in the format:\n # (x_wincount, o_wincount)\n # x_wins = (1, 0, 0)\n # o_wins = (0, 1, 0)\n # draw = (0, 0, 1)\n x_wins = p1_indicator\n o_wins = p2_indicator\n x_arr = [x_wins, x_wins, x_wins]\n o_arr = [o_wins, o_wins, o_wins]\n draw = 0\n\n # Check rows\n\n for i in range(3):\n if equal_arrays(self.board[i], x_arr):\n return x_wins\n elif equal_arrays(self.board[i], o_arr):\n return o_wins\n\n # Check columns\n transposed_board = list(np.transpose(np.array(self.board)))\n\n for i in range(3):\n if equal_arrays(transposed_board[i], x_arr):\n return x_wins\n elif equal_arrays(transposed_board[i], o_arr):\n return o_wins\n\n # Check diagonals\n if self.board[0][0] == self.board[1][1] and self.board[2][2] == self.board[1][1] and (\n self.board[1][1] in [x_wins, o_wins]):\n return self.board[1][1]\n\n if self.board[0][2] == self.board[1][1] and self.board[2][0] == self.board[1][1] and (\n self.board[1][1] in [x_wins, o_wins]):\n return self.board[1][1]\n return draw",
"title": ""
},
{
"docid": "c6c16e7c808785f6f26cfff0ab3fee40",
"score": "0.6708446",
"text": "def utility(board):\n if winner(board) == X:\n return 1\n elif winner(board) == O:\n return -1\n else:\n return 0",
"title": ""
},
{
"docid": "c6c16e7c808785f6f26cfff0ab3fee40",
"score": "0.6708446",
"text": "def utility(board):\n if winner(board) == X:\n return 1\n elif winner(board) == O:\n return -1\n else:\n return 0",
"title": ""
},
{
"docid": "c6c16e7c808785f6f26cfff0ab3fee40",
"score": "0.6708446",
"text": "def utility(board):\n if winner(board) == X:\n return 1\n elif winner(board) == O:\n return -1\n else:\n return 0",
"title": ""
},
{
"docid": "e7669a67b8b2d5f457734a90f9d745d4",
"score": "0.66993046",
"text": "def utility(board):\n \n if winner(board) == X:\n return 1\n if winner(board) == O:\n return -1\n return 0",
"title": ""
},
{
"docid": "c684f96b5ea0a917ceab00255cd627c6",
"score": "0.66984206",
"text": "def utility(board):\n jogador = winner(board)\n if jogador == X:\n return 1\n elif jogador == O:\n return -1\n else:\n return 0",
"title": ""
},
{
"docid": "fa1dbd96269b9e26856fcf8faf263d64",
"score": "0.66896933",
"text": "def utility(board):\n w = winner(board)\n return 1 if w == X else -1 if w == O else 0",
"title": ""
},
{
"docid": "9aad6c305a3c993d15abc2a715b71938",
"score": "0.6665486",
"text": "def utility(board):\n if winner(board) == X : return 1\n if winner(board) == O : return -1\n return 0",
"title": ""
},
{
"docid": "431aa843b24bb4bd30bbdf9294c7b209",
"score": "0.6665406",
"text": "def utility(board):\n # Determine winner, or lack thereof\n winning_player = winner(board)\n if winning_player == X:\n return 1\n elif winning_player == O:\n return -1\n elif winning_player == None:\n return 0",
"title": ""
},
{
"docid": "41c1c507d4e0beaadbb47caf1fb46123",
"score": "0.66619074",
"text": "def utility(board):\n\n if winner(board) == X:\n value = 1\n elif winner(board) == O:\n value = -1\n else:\n value = 0\n\n return value",
"title": ""
},
{
"docid": "e8b12530d99ebae52cc6cabee1fa17c5",
"score": "0.6661667",
"text": "def utility(board):\n if winner(board) is X:\n return 1\n elif winner(board) is O:\n return -1\n else:\n return 0",
"title": ""
},
{
"docid": "7cd50e3187c134f48801548fe6a372b3",
"score": "0.6661436",
"text": "def getResult(selfMove, enemyMove):\n opposing = {\"r\": \"p\", \"p\":\"s\", \"s\":\"r\"}\n if(selfMove == opposing[enemyMove]): #win\n return 0\n elif(enemyMove == opposing[selfMove]): #loss\n return 1\n else: #draw\n return 2",
"title": ""
},
{
"docid": "81d0903a9a8d142119803ecd2fa4bd18",
"score": "0.66573054",
"text": "def utility(board):\n # Checking for who the winner is\n temp = winner(board)\n # Returning score based on who won\n if temp == O:\n return -1\n\n elif temp == X:\n return 1\n\n else:\n return 0",
"title": ""
},
{
"docid": "91a20526bc31252d818b6a59b14b9ab6",
"score": "0.66561204",
"text": "def _checkWinnerStatus(self, feedback):\n if feedback == WINNER_FEEDBACK:\n return PLAYER_WON\n else:\n print(\"Feedback: {}\".format(feedback))",
"title": ""
},
{
"docid": "137df79b92ff9bcab0c0d5cb14697a22",
"score": "0.6644779",
"text": "def utility(board):\n if(not terminal(board)):\n raise NotImplementedError\n\n victor = winner(board)\n if(victor == X):\n return 1\n elif(victor == O):\n return -1\n else:\n return 0",
"title": ""
},
{
"docid": "9138ad40fb425279f8fc63f88fc20e0a",
"score": "0.66389114",
"text": "def check_game_status(self):\n val = sum(x.count('X') for x in self.player1.opponent_map)\n if val == 18:\n self.game = False\n self.win = \"Player1\"\n else:\n val = sum(x.count('X') for x in self.player2.opponent_map)\n if val == 18:\n self.game = False\n self.win = \"Player2\"",
"title": ""
},
{
"docid": "716931825dcf2824a222fdbefe9dc265",
"score": "0.66309136",
"text": "def utility(board):\n whowin=winner(board)\n #print('winner is' ,whowin)\n \n if whowin=='X':\n return 1\n elif whowin=='O':\n return -1\n elif whowin==None:\n return 0\n else:\n return 'Game Not over'\n raise NotImplementedError",
"title": ""
},
{
"docid": "5a82d507ab235c0a67c801a0f86c9383",
"score": "0.6607411",
"text": "def who_won(self) -> int:\n for mask in WIN_MASKS:\n if (np.array_equal(self.state[mask], [CROSS, CROSS, CROSS])):\n return CROSS\n elif (np.array_equal(self.state[mask], [CIRCLE, CIRCLE, CIRCLE])):\n return CIRCLE\n\n if self.n_empty_squares == 0:\n return DRAW\n else:\n return EMPTY",
"title": ""
},
{
"docid": "c7c635936fe008745a1be8b8456b34e0",
"score": "0.6595381",
"text": "def utility(board):\n \n if winner(board)==X:\n return 1\n elif winner(board)==O:\n return -1\n else:\n return 0",
"title": ""
},
{
"docid": "d351807479a1d0a6c5972986df41cf03",
"score": "0.65949553",
"text": "def winning_state(self):\n # YOU FILL THIS IN\n # find the consecutive moves of black and red\n rows, cols = len(self.grid), len(self.grid[0])\n if (rows, cols) not in Game.VALID_STATES:\n valid_states = []\n for row, col in product(reversed(range(rows)), reversed(range(cols))):\n if col + 3 < cols: # Horizontal\n valid_states.append(((row, col), (row, col + 1), (row, col + 2), (row, col + 3)))\n if row + 3 < rows: # Vertical\n valid_states.append(((row, col), (row + 1, col), (row + 2, col), (row + 3, col)))\n if col + 3 < cols and row + 3 < rows: # Diagonal\n valid_states.append(((row, col), (row + 1, col + 1), (row + 2, col + 2), (row + 3, col + 3)))\n if col - 3 > -1 and row + 3 < rows: # Diagonal\n valid_states.append(((row, col), (row + 1, col - 1), (row + 2, col - 2), (row + 3, col - 3)))\n Game.VALID_STATES[(rows, cols)] = valid_states\n\n # Check who actually won after finding consecutive moves\n rows, cols, moves_made = len(self.grid), len(self.grid[0]), 0\n for (row0, col0), (row1, col1), (row2, col2), (row3, col3) in Game.VALID_STATES[(rows, cols)]:\n if self.grid[row0][col0] == '-':\n continue\n if self.grid[row0][col0] == self.grid[row1][col1] == self.grid[row2][col2] == self.grid[row3][col3]:\n if self.grid[row0][col0] == 'R':\n return float('inf')\n elif self.grid[row0][col0] == 'B':\n return float('-inf')\n\n # to add up and check number of moves made to check for tie\n moves_made = sum(\n v != \"-\"\n for row in self.grid\n for v in row\n )\n return 0 if moves_made == cols * rows else None",
"title": ""
},
{
"docid": "c05b921f4e9083fef4d76dde5a97a53a",
"score": "0.6583288",
"text": "def utility(board):\n return 1 if winner(board) == \"X\" else -1 if winner(board) == \"O\" else 0",
"title": ""
},
{
"docid": "8d777b4d30449d07a41a12f0b65569b4",
"score": "0.656286",
"text": "def evaluate(board):\n if winning(AI, board):\n return 1\n elif winning(PLAYER, board):\n return -1\n return 0",
"title": ""
},
{
"docid": "17f65a2109a912ece26786eaad12b4ca",
"score": "0.6562569",
"text": "def get_curr_state(mat):\r\n for i in range(4):\r\n for j in range(4):\r\n if mat[i][j] == 2048:\r\n return 'won'\r\n \"\"\" checking if an empty space is available in the matrix \"\"\"\r\n for i in range(0,4):\r\n for j in range(0,4):\r\n if mat[i][j] == 0:\r\n return 'GAME NOT OVER'\r\n \"\"\"checking if any row or col can be merged to create an \r\n empty space for the first 3 row and column\"\"\"\r\n\r\n for i in range(3):\r\n for j in range(3):\r\n if mat[i][j] == mat[i][j + 1] or mat[i][j] == mat[i + 1][j]:\r\n return 'GAME NOT OVER'\r\n\r\n \"\"\" checking the any column can be merged in last row \"\"\"\r\n for j in range(3):\r\n if mat[3][j] == mat[3][j+1]:\r\n return 'GAME NOT OVER'\r\n \"\"\" checking the any row can be merged in last column \"\"\"\r\n for i in range(3):\r\n if mat[i][3] == mat[i+1][3]:\r\n return 'GAME NOT OVER'\r\n\r\n \"\"\" if all above cases fails then you have lost in the game \"\"\"\r\n return 'LOST'",
"title": ""
},
{
"docid": "abceac2f497491ca13da8d768c225976",
"score": "0.6554989",
"text": "def utility(board):\n if winner(board) == X:\n return 1\n elif winner(board) == O:\n return -1\n else: \n return 0\n # raise NotImplementedError",
"title": ""
},
{
"docid": "6ee0648ff56e8b32b26e0c9a5288e03e",
"score": "0.6553552",
"text": "def terminal_state(board):\n if game_completed(board, player_num=1):\n return 1\n elif game_completed(board, player_num=2):\n return -1\n else:\n return None",
"title": ""
},
{
"docid": "99a292e03e662ef6de4a508e875ccf73",
"score": "0.65333915",
"text": "def get_winner(self):\n if self.count_pieces() == 2:\n return \"draw\"\n\n if len(self.get_legal_moves(\"white\")) == 0:\n if self.is_check(\"white\"):\n return \"black\"\n else:\n return \"draw\"\n elif len(self.get_legal_moves(\"black\")) == 0:\n if self.is_check(\"black\"):\n return \"white\"\n else:\n return \"draw\"\n else:\n return \"draw\" # shouldn't really get here",
"title": ""
},
{
"docid": "333d0a7ddc78937fc4b011757ec41c6a",
"score": "0.65260106",
"text": "def utility(board):\n if winner(board) is X: \n return 1\n elif winner(board) is O: \n return -1 \n elif winner(board) is None: \n return 0",
"title": ""
},
{
"docid": "c595eda3234093139a044bcff216f568",
"score": "0.65206283",
"text": "def checkResults(self):\r\n # This code is a big more difficult to understand but\r\n # the principle is simple, \r\n #\r\n # Rock = 2\r\n # Paper = 1\r\n # Scissors = 0\r\n #\r\n # Note smaller number always beats the larger number\r\n # therefore, if we subtract one from one class and \r\n # the numberas are the same, the class we subtracted\r\n # from would have lost. The modulus exists such that\r\n # we can 'wrap' the scissors class back to 2 -> (0-1)%3=2\r\n # \r\n # Now we only need to check for two other states, draw\r\n # and the other player winning. Since it is easy to check\r\n # for a draw (classes are same) we should do this, then\r\n # all remaining states will be from the third class which\r\n # is CPU winning (as I checked for player winning first).\r\n #\r\n # Scores are updated on class variables\r\n #\r\n if (self.uic.cpuThrow-1) % 3 == self.uic.playerThrow:\r\n c.debugPrint(\"Player Wins!\", 0)\r\n self.gameState=\"win\"\r\n self.playerScore += 1\r\n elif self.uic.cpuThrow == self.uic.playerThrow:\r\n c.debugPrint(\"Draw\", 0)\r\n self.gameState=\"draw\"\r\n else:\r\n c.debugPrint(\"Cpu Wins!\", 0)\r\n self.gameState=\"loss\"\r\n self.cpuScore += 1",
"title": ""
},
{
"docid": "20263bb5e118f83023c8fcd998acb83a",
"score": "0.65160966",
"text": "def get_winner(self):\r\n return self.__board.check_win()",
"title": ""
},
{
"docid": "50c8a25c1d49c00cea1bafc2c26c810f",
"score": "0.6514099",
"text": "def utility(board):\n if(terminal(board)):\n if(winner(board) is X):\n return 1\n elif(winner(board) is O):\n return -1\n else:\n return 0",
"title": ""
},
{
"docid": "b3af0f394b979e25d4786878fdd01ecd",
"score": "0.6499798",
"text": "def utility(board):\n n = winner(board)\n if(n == EMPTY):\n return 0\n elif(n == X):\n return 1\n return -1",
"title": ""
},
{
"docid": "d73dda31fba78b12af85101c213abea0",
"score": "0.648349",
"text": "def check_win_conditions(self,board):\n if board[0][0] == board[1][1] == board[2][2] and board[0][0] == self.ai_player: # \\ diagonal\n return 10\n elif board[0][2] == board[1][1] == board[2][0] and board[0][2] == self.ai_player: # / diagonal\n return 10\n elif board[0][0] == board[1][1] == board[2][2] and board[0][0] == self.player: # \\ diagonal\n return -10\n elif board[0][2] == board[1][1] == board[2][0] and board[0][2] == self.player: # / diagonal\n return -10 \n k=0\n while k < 3:\n if board[0][k] == board[1][k] == board[2][k] and board[0][k] == self.ai_player: #collumn\n return 10\n elif board[k][0] == board[k][1] == board[k][2]and board[k][0] == self.ai_player: # row \n return 10\n elif board[0][k] == board[1][k] == board[2][k] and board[0][k] == self.player: #collumn\n return -10\n elif board[k][0] == board[k][1] == board[k][2]and board[k][0] == self.player: # row \n return -10\n k+=1\n return 0",
"title": ""
},
{
"docid": "418c662de8ca3eb390ce87b00acbc42f",
"score": "0.64819",
"text": "def won_state(board):\n return winning(PLAYER, board) or winning(AI, board)",
"title": ""
},
{
"docid": "63a26b61fcebcc49ea1cd100f5405160",
"score": "0.6475418",
"text": "def utility(board):\n\n ### Note: Only called if terminal(board) returns true ###\n\n # If winner(board) returns X then X won\n if winner(board) == X:\n\n return 1\n\n # If winner(board) returns O then O won\n elif winner(board) == O:\n\n return -1\n\n # Else return 0, meaning there was a tie\n else:\n\n return 0",
"title": ""
},
{
"docid": "8751e706815422deb675e4526471d8a8",
"score": "0.6462528",
"text": "def utility(board):\n if(winner(board) == \"X\"):\n return 1\n if(winner(board) == \"O\"):\n return -1\n return 0\n # raise NotImplementedError",
"title": ""
},
{
"docid": "c6e9a5a4758a6681dd2471b0ab668919",
"score": "0.6452374",
"text": "def utility(board):\n if terminal(board):\n if winner(board) == 'X': return 1\n elif winner(board) == 'O': return -1\n else: return 0",
"title": ""
},
{
"docid": "52d66fb6b7a608abf0dc35684ba7be78",
"score": "0.6448701",
"text": "def DetectVictory(self):\n\t\t#Check Lines ?\n\t\tfor c in range(self.numCol-3):\n\t\t\tfor l in range(self.numLines):\n\t\t\t\tif self.board[c][l] == self.board[c+1][l] \\\n\t\t\t\t== self.board[c+2][l] == self.board[c+3][l] != 0:\n\t\t\t\t\tself.winnerID = self.board[c][l]\n\t\t\t\t\tprint(\"win is line\")\n\t\t\t\t\treturn True\n \n\t\t#Check Columns ?\n\t\tfor c in range(self.numCol):\n\t\t\tfor l in range(self.numLines-3):\n\t\t\t\tif self.board[c][l] == self.board[c][l+1] \\\n\t\t\t\t== self.board[c][l+2] == self.board[c][l+3] != 0:\n\t\t\t\t\tself.winnerID = self.board[c][l]\n\t\t\t\t\tprint(\"win is column\")\n\t\t\t\t\treturn True\n\n\t\t#Check Diag : Right (bottom to top)\n\t\tfor c in range(self.numCol-3):\n\t\t\tfor l in range(self.numLines-3):\n\t\t\t\tif self.board[c][l] == self.board[c+1][l+1] \\\n\t\t\t\t== self.board[c+2][l+2] == self.board[c+3][l+3] != 0:\n\t\t\t\t\tself.winnerID = self.board[c][l]\n\t\t\t\t\tprint(\"win is diag1\")\n\t\t\t\t\treturn True\n\n\t\t#Check Diag : Left (bottom to top)\n\t\tfor c in range(self.numCol-3):\n\t\t\tfor l in range(3, self.numLines):\n\t\t\t\tif self.board[c][l] == self.board[c+1][l-1] \\\n\t\t\t\t== self.board[c+2][l-2] == self.board[c+3][l-3] != 0:\n\t\t\t\t\tself.winnerID = self.board[c][l]\n\t\t\t\t\tprint(\"win is diag2\")\n\t\t\t\t\treturn True\n\t\treturn False",
"title": ""
},
{
"docid": "bc13b88bec7e347591f43858a3e6f4c9",
"score": "0.6437052",
"text": "def winner(board):",
"title": ""
},
{
"docid": "2f69a4e3eda55bacd66860105bb0a8b8",
"score": "0.6435688",
"text": "def win(self):\n s = \"\"\n white_num = self.board.white_num\n black_num = self.board.black_num\n if (white_num == black_num):\n s += \"Tie!\" + '\\n'\n else:\n if (black_num > white_num):\n # print(\"The winner is Black Tile with\", black_num, \"tiles\")\n s += \"Black win!\" + '\\n'\n else:\n # print(\"The winner is White Tile with\", white_num, \"tiles\")\n s += \"White win!\" + '\\n'\n s += \"Black \" + str(black_num) + \" VS. \" + \"White \" + str(white_num)\n return s",
"title": ""
},
{
"docid": "579cfa548f58b2d5e441ac5bb41c2f52",
"score": "0.6424775",
"text": "def get_winner(self):\r\n\t\tcounter_cells = 0\r\n\t\tdict_details = {'row': [0, 1], 'col': [1, 0], 'diagonal-A:': [1, 1], 'diagonal-B': [1, -1]}\r\n\t\tfor row in range(len(self.board)):\r\n\t\t\tfor col in range(len(self.board[0])):\r\n\t\t\t\tif self.get_player_at(row, col) == self.get_current_player():\r\n\t\t\t\t\tcounter_cells += 1\r\n\t\t\t\t\tsituation = self.__check_the_situation(self.get_player_at(row, col)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t , row, col, dict_details)\r\n\t\t\t\t\tif situation[0]:\r\n\t\t\t\t\t\treturn self.get_current_player(), situation[1]\r\n\t\tif counter_cells == self.SUM_OF_ALL_BOARD_CELLS:\r\n\t\t\treturn self.DRAW,\r\n\t\treturn self.NO_WINNER,",
"title": ""
},
{
"docid": "bc81ef4c04d071fa56f849b49602e912",
"score": "0.6413802",
"text": "def player_wins(game_state):\n if connectfour.winner(game_state) != 0:\n if connectfour.winner(game_state) == 1:\n print(\"Red Player Wins\")\n elif connectfour.winner(game_state) == 2:\n print(\"Yellow Player Wins\")",
"title": ""
},
{
"docid": "91ae495121c871a36220e16999fee76c",
"score": "0.6391967",
"text": "def _sk_get_my_status(self):\n return skypekit.enumof(Conversation.MY_STATUS, self._sk_property(\"ZG\\227\\007]\\022\", 919, True))",
"title": ""
},
{
"docid": "4fedacefb73f3168b8f02cb9b3889661",
"score": "0.63787746",
"text": "def who_will_win(game):\n # TODO: also take in account islands that are beging captured (i.e enemy about to capture our only island and we have less score)\n who_wins = [0, 0]\n turns_left = game.get_max_turns() - game.get_turn()\n last_points = game.get_last_turn_points() # [0] = ours [1] = enemy\n if last_points[0] == 0 and last_points[1] == 0: # no one is getting points\n if game.get_my_score() == game.get_enemy_score():\n who_wins[0] = 2\n else:\n who_wins[0] = int(game.get_enemy_score() > game.get_my_score())\n who_wins[1] = turns_left\n elif last_points[0] == 0: # just the enemy is getting points\n turns_to_win = math.ceil((game.get_max_points() - game.get_enemy_score()) / float(last_points[1]))\n if turns_to_win > turns_left:\n if game.get_my_score() == game.get_enemy_score() + last_points[1] * turns_left:\n who_wins[0] = 2\n else:\n who_wins[0] = int(game.get_my_score() < game.get_enemy_score() + last_points[1] * turns_left)\n who_wins[1] = turns_left\n else:\n who_wins[0] = 1\n who_wins[1] = turns_to_win\n elif last_points[1] == 0: # just we are getting points\n turns_to_win = math.ceil((game.get_max_points() - game.get_my_score()) / float(last_points[0]))\n if turns_to_win > turns_left:\n if game.get_enemy_score() == game.get_my_score() + last_points[0] * turns_left:\n who_wins[0] = 2\n else:\n who_wins[0] = int(game.get_enemy_score() > game.get_my_score() + last_points[0] * turns_left)\n who_wins[1] = turns_left\n else:\n who_wins[0] = 0\n who_wins[1] = turns_to_win\n else: # both getting points\n my_turns_to_win = math.ceil((game.get_max_points() - game.get_my_score()) / float(last_points[0]))\n enemy_turns_to_win = math.ceil((game.get_max_points() - game.get_enemy_score()) / float(last_points[1]))\n if my_turns_to_win == enemy_turns_to_win:\n who_wins[0] = 2\n who_wins[1] = my_turns_to_win\n else:\n who_wins[0] = int(my_turns_to_win > enemy_turns_to_win)\n who_wins[1] = max(my_turns_to_win, enemy_turns_to_win)\n return who_wins",
"title": ""
},
{
"docid": "bab924593b4aa987c27e4877cb3711cf",
"score": "0.6374774",
"text": "def utility(board):\n player_turn = player(board)\n if player_turn == X:\n return 1\n elif player_turn == O:\n return -1\n return 0",
"title": ""
},
{
"docid": "f9c453e3538337e1bc8ec34250aeda12",
"score": "0.63247246",
"text": "def set_status_text(self):\r\n if self.game.winning_pattern():\r\n self.status_label = self.get_winner() \\\r\n + \" has won this round!\"\r\n else:\r\n self.status_label = self.name_players[self.game.player_turn] \\\r\n + \", it's your turn!\"\r\n return self.status_label",
"title": ""
},
{
"docid": "544a39a641523ff903a2adb3ea2a7d80",
"score": "0.6315199",
"text": "def status(self):\n\n result = None\n\n coordinates = self.state['coordinates']\n current_turn_mark = self.state['last_mark']\n current_turn_mark_coordinates = {coordinate\n for coordinate, mark in coordinates.iteritems()\n if mark == current_turn_mark}\n\n for combination in self.WINNING_COMBINATIONS:\n # Check whether winning combination presents in current turn mark coordiates\n if combination <= current_turn_mark_coordinates:\n result = {\n 'winning_combination': list(combination),\n 'winner': current_turn_mark\n }\n break\n else:\n # Check whether a game result is draw (no result and all coordinates are filled out)\n if all(coordinates.values()):\n result = {'draw': True}\n\n # Finish a game if there is a result\n if result:\n self.state.update({\n 'winner': result.get('winner'),\n 'winning_combination': result.get('winning_combination'),\n 'draw': result.get('draw'),\n 'status': self.STATUS_CHOICES['finish'],\n })\n yield self.save()",
"title": ""
},
{
"docid": "f44819f9f6c8fb41612b5abb6ecbb116",
"score": "0.62968916",
"text": "def get_status(self):\n if self.boats:\n if self.boomed:\n return \"boomed boat\"\n else:\n return \"unboomed boat\"\n else:\n if self.boomed:\n return \"boomed nothing\"\n else:\n return \"nothing\"",
"title": ""
},
{
"docid": "00af172732c5e0447f67716c4c3fad46",
"score": "0.6296576",
"text": "def get_status(self):\n status = self.auth.get_authenticated(self._ENDPOINT_STATUS)\n acfail = status['data']['acfail']\n battery = status['data']['battery']\n tamper = status['data']['tamper']\n jam = status['data']['jam']\n if acfail == battery == tamper == jam == \"main.normal\":\n return \"ok\"\n return \"error\"",
"title": ""
},
{
"docid": "721c691ac1ca9319332f877c2043bdc5",
"score": "0.6270379",
"text": "def get_winner(self):\n w_count = len(self.board_items[W])\n b_count = len(self.board_items[B])\n if w_count == b_count: return DRAW\n return W if w_count > b_count else B",
"title": ""
},
{
"docid": "773af971ca11cef51e75907931a0838e",
"score": "0.62424016",
"text": "def check_win(self):\n # Check against win matrix if game is won or lost\n for player in [1,2]:\n for line in self.win_matrix:\n chain_len = 0\n for idx in line:\n if self.board_matrix[idx] == player:\n chain_len += 1\n if chain_len == 3:\n self.winner = player\n return\n \n # If empty sapce present then game is not over, otherwise its a draw\n for sqr in self.board_matrix:\n if sqr == 0:\n self.winner = None\n return\n self.winner = 0",
"title": ""
},
{
"docid": "a1efb0d04b9ff8fcd13427657f639826",
"score": "0.6241585",
"text": "def judge(self):\n c_cross = self._is_winner(CROSS)\n c_circle = self._is_winner(CIRCLE)\n if c_cross > 1 or c_circle > 1:\n situation = Summary.INVALID\n elif c_cross == 1:\n situation = Summary.CROSS_WINS\n self._finished = True\n elif c_circle == 1:\n situation = Summary.CIRCLE_WINS\n self._finished = True\n elif self.is_full():\n situation = Summary.DRAW\n self._finished = True\n elif c_cross == 0 and c_circle == 0:\n situation = Summary.NOT_FINISHED\n \n return situation",
"title": ""
},
{
"docid": "e6c22041d678b3d45d6789beea53f9a5",
"score": "0.6224818",
"text": "def get_status(self):\n \n if self.end_weight < (self.target_weight - 20) or\\\n self.end_weight > (self.target_weight + 20):\n self.set_status(1)\n return self.status",
"title": ""
},
{
"docid": "27da25168f71ad40954914d4ba7ae265",
"score": "0.6221721",
"text": "def win():\n\timport tile \n\t#the important thing to note here is that we could have not assigned any value to tiles ie 1 or 2 but that would have created an \t\t error in which the combination of 0 0 0 would have resulted in a win situtaion which is absolutely absurd\n\n\tif (tile.tile1==1 and tile.tile1==tile.tile2 and tile.tile2==tile.tile3 and tile.tile3==tile.tile1) or (tile.tile4==1 and tile.tile4==tile.tile5 and tile.tile5==tile.tile6 and tile.tile6==tile.tile4) or (tile.tile7==1 and tile.tile7==tile.tile8 and tile.tile8==tile.tile9 and tile.tile9==tile.tile7) or (tile.tile1==1 and tile.tile1==tile.tile4 and tile.tile4==tile.tile7 and tile.tile7==tile.tile1) or (tile.tile2==1 and tile.tile2==tile.tile5 and tile.tile5==tile.tile8 and tile.tile8==tile.tile2) or(tile.tile3==1 and tile.tile3==tile.tile6 and tile.tile6==tile.tile9 and tile.tile9==tile.tile3) or (tile.tile1==1 and tile.tile1==tile.tile5 and tile.tile5==tile.tile9 and tile.tile9==tile.tile1) or (tile.tile3==1 and tile.tile3==tile.tile5 and tile.tile5==tile.tile7 and tile.tile7==tile.tile3):\n\t\ttile.p1win+=1\n\t\t\t\t\n\t\treturn True\n\t\t\n\telif (tile.tile1==2 and tile.tile1==tile.tile2 and tile.tile2==tile.tile3 and tile.tile3==tile.tile1) or (tile.tile4==2 and tile.tile4==tile.tile5 and tile.tile5==tile.tile6 and tile.tile6==tile.tile4) or (tile.tile7==2 and tile.tile7==tile.tile8 and tile.tile8==tile.tile9 and tile.tile9==tile.tile7) or (tile.tile1==2 and tile.tile1==tile.tile4 and tile.tile4==tile.tile7 and tile.tile7==tile.tile1) or (tile.tile2==2 and tile.tile2==tile.tile5 and tile.tile5==tile.tile8 and tile.tile8==tile.tile2) or (tile.tile3==2 and tile.tile3==tile.tile6 and tile.tile6==tile.tile9 and tile.tile9==tile.tile3) or (tile.tile1==2 and tile.tile1==tile.tile5 and tile.tile5==tile.tile9 and tile.tile9==tile.tile1) or (tile.tile3==2 and tile.tile3==tile.tile5 and tile.tile5==tile.tile7 and tile.tile7==tile.tile3):\n\t\ttile.p2win+=1\t\t#so much obviuosness for variable name !!!!\n\t\n\t\treturn True\n\t\t\n\telse:\n\t\treturn False",
"title": ""
},
{
"docid": "4d2fc805dbcbc65f97ca6c2753d762a3",
"score": "0.62195694",
"text": "def get_outcome(self):\n if self.players[0].is_winner:\n print(f\"Congrats, {self.players[0].player_name}!!! You win!!\")\n elif self.players[1].is_winner:\n print(f\"Congrats, {self.players[1].player_name}!!! You win!!\")\n else:\n print(f\"Oh sorry, {self.players[0].player_name} and \" \\\n + f\"{self.players[1].player_name}. It's a CAT game!!\")",
"title": ""
},
{
"docid": "842bff108aadd447c01432eca98e2a09",
"score": "0.6216537",
"text": "def check_win(self):\n for top_y in range(3):\n for top_x in range(4):\n to_check = get_n_by_n(self.board,top_x,top_y,4)\n row_check = [0]*4\n # Left to right and right to left diagonal check\n diag_check = [0]*2\n # Check columns\n for y, col in enumerate(to_check):\n # Calculate scores of rows\n for x, space in enumerate(col):\n row_check[x] += space\n if x == y:\n diag_check[0] += space\n if x+y == 3:\n diag_check[1] += space\n\n if sum(col) == 4:\n return self.players[0]\n elif sum(col) == -4:\n return self.players[1]\n\n # Check row_check scores\n for row in row_check:\n if row == 4:\n return self.players[0]\n elif row == -4:\n return self.players[1]\n\n for diag in diag_check:\n if diag == 4:\n return self.players[0]\n elif diag == -4:\n return self.players[1]\n return False",
"title": ""
},
{
"docid": "210d6730452a57fc0e30531b71a86391",
"score": "0.62160546",
"text": "def winner(self):\n if self.game.winner() == self.game.PLAYERS[\"white\"]:\n return \"white\"\n elif self.game.winner() == self.game.PLAYERS[\"black\"]:\n return \"black\"\n else:\n return \"none\"",
"title": ""
},
{
"docid": "8736d502b4df63db552f93c404828676",
"score": "0.6212942",
"text": "def winner(board):\n if terminal(board)==True:\n winner=final(board)\n return winner[1]\n else:\n return 'Game not over'\n \n \n \n raise NotImplementedError",
"title": ""
},
{
"docid": "adaba82d4a5f70f8251661c02b715539",
"score": "0.6203008",
"text": "def check_winner(board: np.ndarray) -> tuple:\r\n white_count: int = 0\r\n black_count: int = 0\r\n for i in range(8):\r\n for j in range(8):\r\n if board[i, j] == WHITE:\r\n white_count += 1\r\n elif board[i, j] == BLACK:\r\n black_count += 1\r\n elif make_move(board, i, j, WHITE, False):\r\n return 0, white_count, black_count\r\n elif make_move(board, i, j, BLACK, False):\r\n return 0, white_count, black_count\r\n\r\n if white_count > black_count:\r\n return WHITE, white_count, black_count\r\n elif black_count > white_count:\r\n return BLACK, white_count, black_count\r\n else:\r\n return 2, white_count, black_count",
"title": ""
},
{
"docid": "84b648944fa92204968b1945f4b083ff",
"score": "0.6196616",
"text": "def check_victory(self):\n\n ##TO DO: FIGURE OUT HOW TO WORK THIS NUMPY STUFF\n ##TO DO: CHANGE VICTORY TO AN ALTERATION OF THE MODEL FIELD.\n check_board = np.zeros((3,3)) ##Update this to size if you change it\n for i in range(3):\n for j in range(3):\n check_board[i,j] = self.board[0+i*3 + j]\n for i in range(3):\n if ((np.all(check_board[i,:] == check_board[i,0])) \n and (check_board[i,0] != 0)):\n self.winner = str(self.player)\n if ((np.all(check_board[:, i] == check_board[0,i])) \n and (check_board[0,i] != 0)):\n self.winner = str(self.player)\n ##Also check diagonal\n if (((check_board[0,0] == check_board[1,1] == check_board[2,2])\n or (check_board[2,0] == check_board[1,1] == check_board[0,2]))\n and check_board[1,1] != 0):\n self.winner = str(self.player)\n else:\n if self.total_moves >= 9:\n self.winner = \"D\"",
"title": ""
},
{
"docid": "07a9a1acc84b76e5cf7127a0b29f88a7",
"score": "0.61939394",
"text": "def check_win(self):\n\n if self.three_in_a_row(self.player):\n self.terminal = True\n self.score = 1 if self.player == CONST.WHITE else -1",
"title": ""
},
{
"docid": "2ade27bea1358813477424ba8ffff217",
"score": "0.6175499",
"text": "def get_state(self):\n # convert board to an integer in base 3 \n state = 0\n my_sym = 'x' if self.xturn else 'o'\n for i in range(3):\n for j in range(3):\n state *= 3\n if self.board[i][j] == my_sym:\n state += 2\n elif self.board[i][j] == ' ':\n state += 1\n return state",
"title": ""
},
{
"docid": "5fdb72e66736880572c8308429dab36b",
"score": "0.61749756",
"text": "def _get_reward(self):\n if self.status == FOOBAR:\n return 1\n elif self.status == ABC:\n return self.somestate ** 2\n else:\n return 0",
"title": ""
},
{
"docid": "3e205043f8cd5d9d6d0139d488654c20",
"score": "0.6168434",
"text": "def check_win(self):\n\n # If turn number larger than TurnMax, the game will be a draw.\n if self.n_turns > self.TurnMax:\n self.game_result = self.ResultDraw\n return\n\n # Check play state of CURRENT heroes (when replacing a hero, will check the new hero, so game will not end).\n self.game_result = {\n (True, True): None,\n (True, False): self.ResultWin0,\n (False, True): self.ResultWin1,\n (False, False): self.ResultDraw,\n }[(self.players[0].hero.play_state, self.players[1].hero.play_state)]",
"title": ""
},
{
"docid": "a0da24a798126c6168b04ff4c99c5b87",
"score": "0.61637706",
"text": "def get_wins(self):\n return self._white_wins + self._black_wins",
"title": ""
},
{
"docid": "88b19da88a4861f57ba5585bdf3ea01d",
"score": "0.6161645",
"text": "def status():\n return \"ALIVE\"",
"title": ""
},
{
"docid": "a784c81241a2622cc5fe58e86f5f19d8",
"score": "0.6160086",
"text": "def evaluate_board(self, player):\n\n number_of_wins = 0\n played_spaces = self.collect_positions(player)\n\n for i in range (0, 8):\n if self.compare_lists(played_spaces, self.winning_boards[i]) == True:\n number_of_wins += 1\n\n return number_of_wins",
"title": ""
},
{
"docid": "b6539794c6b09870f7623c58eb9ceb5a",
"score": "0.6149866",
"text": "def get_winner(self):\n for player in [1,2]: # check row\n for x in range(0,3):\n if self.GameBoard[x][0] == player and self.GameBoard[x][1] == player and self.GameBoard[x][2] == player:\n return player\n for y in range(0,3): # check column\n if self.GameBoard[0][y] == player and self.GameBoard[1][y] == player and self.GameBoard[2][y] == player:\n return player\n if self.GameBoard[0][0] == player and self.GameBoard[1][1] == player and self.GameBoard[2][2] == player:\n return player\n if self.GameBoard[2][0] == player and self.GameBoard[1][1] == player and self.GameBoard[0][2] == player:\n return player\n return 0",
"title": ""
},
{
"docid": "dbb0562fcf5cc24618d92638ffbc05f4",
"score": "0.6149748",
"text": "def utility(board):\n # If X has won the game, the utility is 1. If O has won the game, the utility is -1. If the game has ended in a tie, the utility is 0.\n # You may assume utility will only be called on a board if terminal(board) is True.\n if winner(board) == X:\n return 1\n elif winner(board) == O:\n return -1\n else:\n return 0\n #raise NotImplementedError",
"title": ""
},
{
"docid": "47d2b2d166933e7f5788561c6aaf82fe",
"score": "0.6146644",
"text": "def get_status(self) -> LPStatus:",
"title": ""
},
{
"docid": "b732b7a26c28fd808b5a26a385e49fd6",
"score": "0.6146026",
"text": "def _get_status(self):\n self.l_info(\"_get_status\",\"%s:%s\" % (self.host,self.port))\n # Get the led_mode since that is the simplest return status\n rc = self.camera.machine_name\n self.parent.logger.info(\"_get_status: {0}\".format(rc))\n if rc == 0:\n connected = 0\n self.l_error(\"_get_status\",\" Failed to get_status: {0}\".format(rc))\n else:\n connected = 1\n if connected != self.connected:\n self.connected = connected\n self.set_driver('GV4', self.connected, uom=2, report=True)\n return self.connected",
"title": ""
},
{
"docid": "e8746b0a2e694021ada58c637ccd6f34",
"score": "0.61398333",
"text": "def check_win( self ):\n\n\t\tfor x in range( FIELD_WIDTH ):\n\t\t\tfor y in range( FIELD_HEIGHT ):\n\t\t\t\t# We only care about players, not blank fields\n\t\t\t\tif self.board[x][y] == STONE_BLANK:\n\t\t\t\t\tcontinue\n\n\t\t\t\t# Check: UP\n\t\t\t\tblank, ai, human = self._count_stones_up( x, y )\n\t\t\t\tif ai == CONNECT: return STONE_AI\n\t\t\t\telif human == CONNECT: return STONE_HUMAN\n\n\t\t\t\t# Check: RIGHT\n\t\t\t\tblank, ai, human = self._count_stones_right( x, y )\n\t\t\t\tif ai == CONNECT: return STONE_AI\n\t\t\t\telif human == CONNECT: return STONE_HUMAN\n\n\t\t\t\t# Check: DIAGONAL RIGHT UP\n\t\t\t\tblank, ai, human = self._count_stones_rightup( x, y )\n\t\t\t\tif ai == CONNECT: return STONE_AI\n\t\t\t\telif human == CONNECT: return STONE_HUMAN\n\n\t\t\t\t# Check: DIAGONAL RIGHT DOWN\n\t\t\t\tblank, ai, human = self._count_stones_rightdown( x, y )\n\t\t\t\tif ai == CONNECT: return STONE_AI\n\t\t\t\telif human == CONNECT: return STONE_HUMAN\n\n\t\treturn STONE_BLANK",
"title": ""
},
{
"docid": "18a5a94ff114e2c7f6d2b48c14213c65",
"score": "0.6136457",
"text": "def determine_status(score):\n if score < 0 or score > 100:\n return \"Invalid score\"\n elif score >= 90:\n return \"Excellent\"\n elif score >= 50:\n return \"Passable\"\n else:\n return \"Bad\"",
"title": ""
},
{
"docid": "b7454fcefdd3ff07736ec4a1c1a262e5",
"score": "0.6132169",
"text": "def get_status(self):\n\t\tif Enemy.defeated == 1:\n\t\t\tprint(str(Enemy.defeated) + \" enemy defeated, \", end=\"\")\n\t\telse:\n\t\t\tprint(str(Enemy.defeated) + \" enemies defeated, \", end=\"\")\n \n\t\tif Enemy.remaining == 1:\n\t\t\tprint(str(Enemy.remaining) + \" enemy remains.\") # total enemies in game\n\t\telse:\n\t\t\tprint(str(Enemy.remaining) + \" enemies remain.\")",
"title": ""
},
{
"docid": "87633c603ab4b0900fdfd16beb11c316",
"score": "0.6129059",
"text": "def _get_result(self) -> str:\n color = self._board.winner()\n\n if color is None:\n text = \"Draw\"\n elif color == chess.WHITE: # pyre-ignore[16]\n text = \"White win\"\n else:\n text = \"Black win\"\n\n return text",
"title": ""
},
{
"docid": "6d258b1d9059b9f74028c76430267373",
"score": "0.6127233",
"text": "def checkWin(self):\n if self.checkHor() != 0:\n return self.checkHor()\n elif self.checkVert() != 0:\n return self.checkVert()\n elif self.checkSlant() != 0:\n return self.checkSlant()\n else:\n return 0",
"title": ""
},
{
"docid": "0a7315506e54a4e272767b20b0e79960",
"score": "0.61258143",
"text": "def winner(self, board):\n\n light = dark = 0\n for line in board:\n light += line.count(Disk.LIGHT)\n dark += line.count(Disk.DARK)\n print(f\"Light disks: {light} Dark disks: {dark}\")\n if (light > dark):\n return Disk.LIGHT\n elif (dark > light):\n return Disk.DARK\n else:\n return \"draw\"",
"title": ""
},
{
"docid": "0be453434fddcc509440b54e13eb94ec",
"score": "0.6124577",
"text": "def print_status (self, status):\n # === ELSE BEGIN HORROR ===\n # check error flags\n print (\"Driver Status: \")#, bin(status))\n for bit_addr in range(7,15):\n print(\" Flag \", self.STATUS_DICT[bit_addr][0], \": \", end==\"\")\n # we shift a 1 to the bit address, then shift the result down again\n if ((status & 1<<bit_addr)>>bit_addr)==self.STATUS_DICT[bit_addr][1]:\n # the result should either be a 1 or 0. Which is 'ok' depends.\n print(\"ok\")\n else:\n print(\"Alert!\")\n \n # check SCK_MOD\n if status & (1<<15):\n print(\" Step-clock mode is on.\")\n else:\n print(\" Step-clock mode is off.\")\n \n # check MOT_STATUS\n if status & (1<<6):\n if status & (1<<5):\n print(\" Motor is at constant speed.\")\n else:\n print(\" Motor is decelerating.\")\n else:\n if status & (1<<5):\n print(\" Motor is accelerating.\")\n else:\n print(\" Motor is stopped.\")\n # check DIR\n if status & (1<<4):\n print(\" Motor direction is set to forward.\")\n else:\n print(\" Motor direction is set to reverse.\")\n # check BUSY\n if not (status & (1<<1)):\n print(\" Motor is busy with a movement command.\")\n else:\n print(\" Motor is ready to recieve movement commands.\")\n # check HiZ\n if status & 1:\n print(\" Bridges are in high-impedance mode (disabled).\")\n else:\n print(\" Bridges are in low-impedance mode (active).\")\n \n # check SW_EVEN flag\n if status & (1<<3):\n print(\" External switch has been clicked since last check.\")\n else:\n print(\" External switch has no activity to report.\")\n # check SW_F\n if status & (1<<2):\n print(\" External switch is closed (grounded).\")\n else:\n print(\" External switch is open.\")",
"title": ""
},
{
"docid": "5459f2bb9acfcaf26bf4ae34f477af80",
"score": "0.61233085",
"text": "def check_turn(self):\n turn = self.board.state.sum()\n if turn in (0,-1):\n return 1\n if turn == 1:\n return -1\n return 0",
"title": ""
},
{
"docid": "310c260b54a66de50c9d095d4624f95a",
"score": "0.6113416",
"text": "def check_for_winners(self):\n # self.opponent.print_info()\n # self.player.print_info()\n player_margin = self.player.get_margin()\n opponent_margin = self.opponent.get_margin()\n player_win_condition_1 = opponent_margin < 0 and player_margin >= 0\n player_win_condition_2 = opponent_margin >=0 and player_margin >= 0 and player_margin < opponent_margin\n draw_condition_1 = opponent_margin < 0 and player_margin < 0\n draw_condition_2 = opponent_margin >= 0 and player_margin >= 0 and player_margin == opponent_margin\n opponent_win_condition_1 = player_margin < 0 and opponent_margin >= 0\n opponent_win_condition_2 = opponent_margin >=0 and player_margin >= 0 and player_margin > opponent_margin\n if (player_win_condition_1 or player_win_condition_2):\n # print(f'the winner is the {self.player.name}!')\n return 1\n elif(draw_condition_1 or draw_condition_2):\n # print('the game ends in a draw!')\n return 0\n elif(opponent_win_condition_1 or opponent_win_condition_2):\n # print(f'the winner is the {self.opponent.name}!')\n return -1\n else:\n # print('an error has accurred! exiting...')\n exit()",
"title": ""
},
{
"docid": "927fa86867870e64bc5d6dd24b681eee",
"score": "0.6107147",
"text": "def _check_for_winner(self):\n if set(self.board.board[P1_PITS]) == set([0]):\n self.board.board = self.board.gather_remaining(self.player2.number)\n if self.print_game_status:\n print (\"Player 1 finished! %s: %d to %s: %d turns: %d\" % (self.player1_name, self.board.board[P1_STORE][0], self.player2.name, self.board.board[P2_STORE][0], self.num_turns))\n self.finished_first = 1\n if self.board.board[P1_STORE][0] > self.board.board[P2_STORE][0]:\n self.winner = 1\n self.winner_num_pieces = self.board.board[P1_STORE][0]\n elif self.board.board[P2_STORE][0] > self.board.board[P1_STORE][0]:\n self.winner = 2\n self.winner_num_pieces = self.board.board[P2_STORE][0]\n else:\n self.winner = 0\n self.winner_num_pieces = self.board.board[P2_STORE][0]\n self.write_game_stats()\n return True\n elif set(self.board.board[P2_PITS]) == set([0]):\n self.board.board = self.board.gather_remaining(self.player1.number)\n if self.print_game_status:\n print (\"Player 2 finished! %s: %d to %s: %d turns: %d\" % (self.player1_name, self.board.board[P1_STORE][0], self.player2.name, self.board.board[P2_STORE][0], self.num_turns))\n self.finished_first = 2\n if self.board.board[P1_STORE][0] > self.board.board[P2_STORE][0]:\n self.winner = 1\n self.winner_num_pieces = self.board.board[P1_STORE][0]\n elif self.board.board[P2_STORE][0] > self.board.board[P1_STORE][0]:\n self.winner = 2\n self.winner_num_pieces = self.board.board[P2_STORE][0]\n else:\n self.winner = 0\n self.winner_num_pieces = self.board.board[P2_STORE][0]\n self.write_game_stats() \n return True\n else:\n return False",
"title": ""
},
{
"docid": "40e768a4272c55ed4e1949441346972b",
"score": "0.6105037",
"text": "def check_win(self):\n\t\tself.won = False\n\t\trow_win = self.check_row_win()\n\t\tcol_win = self.check_col_win()\n\t\tgroup_win = self.check_group_win()\n\t\tprint(f\"Row win: {row_win}, Col win: {col_win}, Group win: {group_win}\")\n\t\tif row_win and col_win and group_win:\n\t\t\tself.won = True",
"title": ""
},
{
"docid": "1f7f3dfe938876b26333673bc9c16057",
"score": "0.60950834",
"text": "def print_result(player): # Misi\n if player == 0:\n print_color(\"\\nThe game is a tie!\", Color.YELLOW)\n elif player == 1:\n print_color(\"\\nX has won the game!\", Color.GREEN)\n elif player == 2:\n print_color(\"\\nO has won the game!\", Color.GREEN)",
"title": ""
},
{
"docid": "eacb137ec5baca41188f20e1db9a2c83",
"score": "0.608931",
"text": "def best_out_of(win_state, player):\n global total_turns\n if win_state == 1 and player[\"wins\"] < 2: # checa se o Jogador 1 ainda esta no jogo\n print (\"%s venceu o jogo!\") % (player[\"name\"])\n play_again()\n elif win_state == 0 and total_turns == 6:\n print (\"esse jogo foi um empate\")\n play_again()\n elif win_state != 0 and total_turns == 6:\n play_again()\n elif player[\"wins\"] >= 2: # cehca quem ganhou a melhor de 3\n print (\"%s ganhou na melhor de 3\") % (player[\"name\"])\n play_again()\n elif player[\"lose\"] >= 2:\n print (\"%s perdeu na melhor de 3\") % (player[\"name\"])\n play_again()\n else:\n play_again()",
"title": ""
},
{
"docid": "d855269509a8b353ef57cdd46c966a6d",
"score": "0.6085443",
"text": "def AlmostWonCount(board,x,y):\n\n toReturn = 0\n sb = board[x][y]\n #Define threelists and keep sending.\n #Verticals\n tl = [sb[0][0],sb[1][0],sb[2][0]]\n if TwoOfThree(tl,turn):\n toReturn += 1\n if TwoOfThree(tl,other(turn)):\n toReturn -= 1\n tl = [sb[0][1],sb[1][1],sb[2][1]]\n if TwoOfThree(tl,turn):\n toReturn += 1\n if TwoOfThree(tl,other(turn)):\n toReturn -= 1\n tl = [sb[0][2],sb[1][2],sb[2][2]]\n if TwoOfThree(tl,turn):\n toReturn += 1\n if TwoOfThree(tl,other(turn)):\n toReturn -= 1\n\n #Horizontals.\n tl = [sb[0][0],sb[0][1],sb[0][2]]\n if TwoOfThree(tl,turn):\n toReturn += 1\n if TwoOfThree(tl,other(turn)):\n toReturn -= 1\n tl = [sb[1][0],sb[1][1],sb[1][2]]\n if TwoOfThree(tl,turn):\n toReturn += 1\n if TwoOfThree(tl,other(turn)):\n toReturn -= 1\n tl = [sb[2][0],sb[2][1],sb[2][2]]\n if TwoOfThree(tl,turn):\n toReturn += 1\n if TwoOfThree(tl,other(turn)):\n toReturn -= 1\n #Diagonals.\n tl = [sb[0][0],sb[1][1],sb[2][2]]\n if TwoOfThree(tl,turn):\n toReturn += 1\n if TwoOfThree(tl,other(turn)):\n toReturn -= 1\n tl = [sb[0][2],sb[1][1],sb[2][0]]\n if TwoOfThree(tl,turn):\n toReturn += 1\n if TwoOfThree(tl,other(turn)):\n toReturn -= 1\n\n # print toReturn\n return toReturn",
"title": ""
},
{
"docid": "53ccefc4f4dabb715bf2074c25dd0fc1",
"score": "0.60789365",
"text": "def get_winner(self)->str:\n lost_state = [None for x in range(12)]\n if lost_state == self.white_pawns: return 'black'\n elif lost_state == self.black_pawns: return 'white'\n else: return ''",
"title": ""
},
{
"docid": "e303d66c811cfb2d212283cb33601bdd",
"score": "0.6076272",
"text": "def score(self, board):\r\n if board.hasWon(self.num):\r\n return 100.0\r\n elif board.hasWon(self.opp):\r\n return 0.0\r\n else:\r\n return 50.0",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="751"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">44d2eda6e096b216d06513fa51569107</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Saidas do bairro de sabados, domingos e feriados</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "379dc0eb871f2c7d74aefb80aa0e57e1",
"score": "0.0",
"text": "def SaidasBairroSabadosDomingoseFeriados6823(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z52'] + \"\"\"\nEsse onibus nao possui horario nesses dias, deseja consultar as empresas?\n-> /empresas\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "618777769afbe97dc7f9f8b42bdbeaab",
"score": "0.68243456",
"text": "def SaidasBairroSabadosDomingoseFeriados178(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z5'] + \"\"\"\nSabados:\n|06:00| A D\n|06:45| \n|07:30| D\n|08:30| A D\n|09:30|\n|10:30| A D\n|11:00| SC D\n|12:45| D\n|15:20| A D\n|17:10| D\n|19:00| SC D\n-\nDomingos e feriados:\n|10:50| SC D\n|15:00| D\n|17:10| D\n|19:00| SC D\n-\nA: Via Aeroclube\nSC: Via Shopping Continente\nD: Adaptado p/ portadores de necessidades especiais\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "e0678970ebe28043eb5121c0d8200750",
"score": "0.6799082",
"text": "def SaidasBairroSabadosDomingoseFeriados175(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z4'] + \"\"\"\nSabados:\n|06:00| SA\n|06:30| SA\n|07:00| SA\n|07:30| SA\n|08:00| SA\n|08:35| SA\n|09:10| SA\n|10:00| SA\n|11:00| SA\n|12:00| SA\n|13:00| SA\n|14:10| SA\n|15:20| SA\n|16:30| SA\n|17:30| SA\n|18:10| SA\n|19:30| SA\n|21:00| SA\n|23:15| SA\n-\nDomingos e feriados:\n|06:00| SA\n|07:10| SA\n|09:00| SA\n|11:00| SA\n|12:20| SA\n|13:40| SA\n|15:00| SA\n|16:30| SA\n|18:10| SA\n|19:30| SA\n|21:00| SA\n|23:15| SA\n-\nSA: Via Rua Santo Andre\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "233131c966fbabab2f656f46e9cbb482",
"score": "0.67766464",
"text": "def SaidasBairroSabadosDomingoseFeriados177(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z7'] + \"\"\"\nSabados:\n|05:10|\n|06:00| D LV\n|06:40| A SC\n|07:30| D SC\n|08:30| D\n|09:30| D SC LV\n|11:30| D\n|13:10| A D SC\n|14:05| D\n|15:50| D SC LV\n|17:20| A D SC\n|18:10| D\n|19:10| A D\n|20:40| A D\n|21:50| D SC LV\n-\nDomingos e feriados:\n|05:10| D\n|06:00| A D LV\n|06:30| D\n|07:30| D\n|08:35| D\n|09:45| D\n|11:30| D LV\n|12:55| A D\n|14:20| D\n|16:00| A D SC LV\n|17:20| A D SC\n|18:10| D\n|19:20| A D\n|20:30| D SC\n|21:50| D SC LV\n-\nLV: Via Lagoa Vermelha\nA: Via Aeroclube\nSC: Via Shopping Continente\nD: Adaptado p/ portadores de necessidades especiais\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "0b3f476591969c5045bca41679746227",
"score": "0.6605124",
"text": "def SaidasBairroSabadosDomingoseFeriados554(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z8'] + \"\"\"\nSabados:\n|08:30| SC D\n|10:20| SC D\n|12:20| SC D\n|14:10| SC D\n|16:50| SC D\n|18:20| SC D\n|20:00| SC D\n-\nDomingos e feriados:\n|11:30| SC D\n|13:00| SC D\n|15:30| SC D\n|16:50| SC D\n|18:20| SC D\n|20:00| SC D\n-\nSC: Via Shopping Continente\nD: Adaptado p/ portadores de necessidades especiais\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "4d53f6a9615587aedc1b6307d75ba37d",
"score": "0.64794093",
"text": "def SaidasBairroSabadosDomingoseFeriadosSN(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z2'] + \"\"\"\nSabados:\n|07:05|D\n|08:20|D\n|08:40|D\n|10:05|D\n|10:30|D\n|11:15|D\n|12:25|D\n|13:40|D\n|14:20|D\n|16:20|D\n|17:00|D\n|17:55|D\n|18:25|D\n|19:15|D\n|20:10|D\n|22:20|D\n-\nDomingos e feriados:\n|07:00|D\n|08:00|D\n|11:05|D\n|11:40|D\n|13:10|D\n|15:40|D\n|16:35|D\n|16:55|D\n|17:50|D\n|18:25|D\n|19:15|D\n|20:10|D\n|21:00|D\n|22:20|D\n-\nD: Adaptado p/ portadores de necessidades especiais\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "b04dfeba5674fa28cc47325863214524",
"score": "0.63862026",
"text": "def SaidasCentroSabadosDomingoseFeriados177(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w7'] + \"\"\"\nSabados:\n|06:15| D\n|08:00| D\n|09:30| A D LV\n|11:00| A D\n|12:15| D LV\n|12:55| D\n|14:00| D\n|15:08| D\n|15:50| D\n|17:14| A D\n|18:40| D LV\n|19:40| A D LV\n|21:30| A D SC\n|22:30| D SC\n|00:00| A D\n-\nDomingos e feriados:\n|06:15| D\n|07:10| D SC\n|08:00| D\n|09:45| A D SC LV\n|11:00| D\n|12:30| D\n|14:00| D LV\n|15:00| A D\n|15:50| D SC\n|17:14| A D\n|18:30| D LV\n|19:50| A D LV\n|21:40| A D SC\n|22:30| D\n|00:00| 0A D\n-\nLV: Via Lagoa Vermelha\nA: Via Aeroclube\nSC: Via Shopping Continente\nD: Adaptado p/ portadores de necessidades especiais\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "d340e8dff182d812065b744f477641df",
"score": "0.63838416",
"text": "def SaidasBairroSabadosDomingoseFeriados0201(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z25'] + \"\"\"\nOBS:. Nesses dias a linhas vai até o ponto final do Arruda\n\nSábado:\n|05:25| \n|05:55|\n|06:20| \n|06:50| \n|07:12| \n|07:30|\n|08:00| \n|08:40| \n|09:10| \n|09:50|\n|10:25| \n|11:05| \n|11:25|\n|12:10| \n\nDomingos e Feriados:\n06:05 - Linha A\n07:05 - Linha A\n08:00 - Linha A\n09:00 - Linha A\n10:00 - Linha A\n11:00 - Linha A\n12:00 - Linha A\n13:00 - Linha A\n14:00 - Linha A\n15:00 - Linha A\n16:00 - Linha A\n17:00 - Linha A\n18:00 - Linha A\n19:00 - Linha A\n20:00 - Linha A\n21:00 - Linha A\n22:00 - Linha A\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "bffc5f6863cf3c30bf23d70643976bba",
"score": "0.6352495",
"text": "def SaidasBairroSabadosDomingoseFeriados12400(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z42'] + \"\"\"\nSabados:\n06:10\n06:45\n07:10\n07:30\n07:50\n08:10\n08:35\n09:00\n09:30\n10:00\n10:30\n11:00\n11:30\n12:00\n12:30\n13:00\n13:30\n14:00\n14:30\n15:10\n15:50\n16:25\n17:10\n17:50\n18:30\n19:10\n20:00\n20:45\n21:40\n22:30\nDomingos e Feirados:\n06:10\n07:20\n08:30\n09:26\n10:30\n11:34\n12:38\n13:42\n14:35\n15:25\n16:15\n17:05\n17:55\n18:45\n19:35\n20:25\n21:15\n22:05\n22:45\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "070c127b014a83891bb83cc83ad81ed0",
"score": "0.6324893",
"text": "def SaidasCentroSabadosDomingoseFeriados178(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w5'] + \"\"\"\nSabados:\n|06:50| D\n|07:30| SC D\n|08:30| SC\n|09:00| D\n|10:15| SC D\n|11:30| D\n|14:30| A SC D\n|16:24| D\n|18:00| D\n|20:50| D\n-\nDomingos e feriados:\n|09:00| D\n|16:30| D SC\n|18:00| D\n|20:50| D\n-\nA: Via Aeroclube\nSC: Via Shopping Continente\nD: Adaptado p/ portadores de necessidades especiais\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "c46456e8772f0221c60c2944aa635883",
"score": "0.63232404",
"text": "def SaidasBairroSabadosDomingoseFeriados670(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z56'] + \"\"\"\nSabados:\n|05:20|\n|06:20|\n|10:10|\n|12:10|\n|15:20|\n|16:40|\n|17:30|\n|19:50|\n|20:40|\nDomingos e Feriados:\n|05:30|\n|09:20|\n|13:00|\n|15:10|\n|18:10|\nVia:BR282, Rodovias Municipais\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "a91b2a25362f96204bf7e428f0a6e072",
"score": "0.63125163",
"text": "def SaidasCentroSabadosDomingoseFeriados317(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w12'] + \"\"\"\nSabados:\n|06:00|\n|06:20|\n|06:40|\n|07:00|\n|07:20|\n|07:40|\n|08:00|\n|08:2O|\n|08:40|\n|09:00|\n|09:20|\n|09:40|\n|10:00|\n|10:20|\n|10:40|\n|11:00|\n|11:20|\n|11:40|\n|12:00|\n|12:15|\n|12:30|\n|12:45|\n|13:00|\n|13:15|\n|13:30|\n|13:45|\n|14:00|\n|14:20|\n|14:40|\n|15:00|\n|15:20|\n|15:40|\n|16:05|\n|16:30|\n|16:55|\n|17:20|\n|17:45|\n|18:1O|\n|18:35|\n|19:00|\n|19:25|\n|19:50|\n|20:15|\n|20:40|\n|21:05|\n|21:30|\n|22:00|\n|22:40|\n|23:20| R\n|00:00| R\nDomingos:\n|06:30|\n|07:00|\n|07:30|\n|08:00|\n|08:30|\n|09:00|\n|09:30|\n|10:00|\n|10:30|\n|11:00|\n|11:30|\n|12:00|\n|12:30|\n|13:00|\n|13:30|\n|14:00|\n|14:30|\n|15:00|\n|15:30|\n|16:00|\n|16:30|\n|17:00|\n|17:30|\n|18:00|\n|18:30|\n|19:00|\n|19:30|\n|20:00|\n|20:30|\n|21:00|\n|21:30|\n|22:00|\n|22:30|\n|23:00|\n|23:30| R\n|00:00| R\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "591811128f0a260fbb677efc18ce570b",
"score": "0.62702",
"text": "def SaidasCentroSabadosDomingoseFeriados175(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w4'] + \"\"\"\nSabados:\n|06:30| SA\n|07:00| SA\n|07:30| SA\n|08:10| SA\n|08:40| SA\n|09:20| SA\n|10:10| SA\n|11:00| SA\n|11:50| SA\n|12:40| SA\n|13:30| SA\n|14:40| SA\n|16:00| SA\n|17:00| SA\n|18:00| SA\n|19:00| SA\n|20:20| SA\n|22:00| SA\n|23:45| SA\n-\nDomingos e feriados:\n|06:30| SA\n|08:20| SA\n|10:00| SA\n|11:30| SA\n|13:10| SA\n|14:20| SA\n|15:35| SA\n|17:10| SA\n|19:00| SA\n|20:20| SA\n|22:00| SA\n|23:45| SA\n-\nSA: Via Rua Santo Andre\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "f874cb45c570c87dd255c3effc2c5479",
"score": "0.62684125",
"text": "def SaidasBairroSabadosDomingoseFeriados0020(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z24'] + \"\"\"\nLinha A: Até o ponto final do Arruda\n\nSábaos:\n05:25 - Linha A\n05:55 - Linha A\n06:20 - Linha A\n06:50 - Linha A\n07:12 - Linha A\n07:30 - Linha A\n08:00 - Linha A\n08:40 - Linha A\n09:10 - Linha A\n09:50 - Linha A\n10:25 - Linha A\n11:05 - Linha A\n11:25 - Linha A\n12:10 - Linha A\n\nDomingos e Feriados:\n06:05 - Linha A\n07:05 - Linha A\n08:00 - Linha A\n09:00 - Linha A\n10:00 - Linha A\n11:00 - Linha A\n12:00 - Linha A\n13:00 - Linha A\n14:00 - Linha A\n15:00 - Linha A\n16:00 - Linha A\n17:00 - Linha A\n18:00 - Linha A\n19:00 - Linha A\n20:00 - Linha A\n21:00 - Linha A\n22:00 - Linha A\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "0c0d5baf0bfed41c6f30bd9dbb3a84ad",
"score": "0.62490004",
"text": "def SaidasBarreirosSabadosDomingoseFeriados0130(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z32'] + \"\"\"\nSabados:\n|06:30|\n|07:00|\n|07:30|\n|08:10|\n|08:40|\n|09:10|\n|09:50|\n|10:20|\n|11:20|\n|12:00|\n|14:00|\n|17:00|\n|19:30|\nDomingos:\n|07:30|\n|09:30|\n|12:20|\n|14:30|\n|16:10|\n|17:50|\n|20:40|\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "5cd1ed997204f4a7a2e71a95679f075f",
"score": "0.62213856",
"text": "def SaidasBairroSabadosDomingoseFeriados763(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z22'] + \"\"\"\nLinha E: Extensão\nLinha L:Lisboa\nLinha M: Via Loteamento Melo\nLinha R: Via Rodeio\nLinha Z: Via Zenaide\n\nSábado:\n06:00 - Linha R\n06:30 - Linha Z\n07:55 - Linhas Z e R\n10:05 - Linhas Z e R\n11:40 - Linha R\n13:25 - Linhas Z e R\n14:15 - Linha R\n16:05 - Linhas Z e R\n18:05 - Linhas Z e R\n20:05 - Linhas Z e R\n.\nDomingos e feriados:\n06:20 - Linhas Z, L e R\n08:10 - Linhas Z e R\n10:25 - Linhas Z e R\n12:25 - Linhas Z e R\n14:25 - Linhas Z e R\n16:25 - Linhas Z e R\n18:25 - Linhas Z e R\n20:25 - Linhas Z e R\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "fea0fb7a811935077c4f28614ad45632",
"score": "0.6186648",
"text": "def SaidasBairroSegundaaSexta177(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['x7'] + \"\"\"\n|05:00| D\n|05:30| LV\n|06:00| D\n|06:00| D\n|06:20| D LV SC\n|06:30| D\n|07:40| D LV SC\n|08:30| D SC\n|09:30| A LV\n|10:30| A D\n|11:30| A D SC LV\n|12:25| A LV\n|13:00| D\n|13:50| A D LV\n|15:00| D\n|16:00| A SC LV\n|17:05|\n|18:02| D LV A\n|19:10| A D SC\n|20:25| SC D\n|21:05|\n|22:10| D SC\n-\nLV: Via Lagoa Vermelha\nA: Via Aeroclube\nSC: Via Shopping Continente\nD: Adaptado p/ portadores de necessidades especiais\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "0a0b028f923da9a817e2c8316b16e875",
"score": "0.61677164",
"text": "def SaidasBairroSabadosDomingoseFeriados6270(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z58'] + \"\"\"\nSabados:\n|06:40|\n|09:00|\nVia Sul do Rio / Rodovias Municipais\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "dd5d174b2a7ae8972a3e0ea95f90d9c6",
"score": "0.61432946",
"text": "def SaidasBairroSabadosDomingoseFeriados203(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z68'] + \"\"\"\nSabados:\n 06:15 1 Saída da IFSC\n 07:00 1\n 07:40 1 Saída da IFSC\n 08:40 1\n 09:40 1\n 10:30 1\n 11:45 1\n 15:00 1\n 15:55 1\n 17:10 1\n 18:40 1\n 19:30 1\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "2ca071a3f2019394cf04f00fa929f4a4",
"score": "0.61356026",
"text": "def SaidasBairroSabadosDomingoseFeriados64201(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z41'] + \"\"\"\nSabados:\n06:30 Saida Sorocaba dentro - BEPAO - Viaduto Janaina\nDomingos e Feriados:\n18:00 Timbe - Sorocaba - Canto Bepao - Viaduto Janaina\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "d3a4ab743a9fbd0d82f659fa126ddefc",
"score": "0.61323607",
"text": "def SaidasBairroSabadosDomingoseFeriados202(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z67'] + \"\"\"\nSabados:\n|06:15| Saída da IFSC\n|07:00|\n|07:40| Saída da IFSC\n|08:40|\n|09:40|\n|10:30|\n|11:45|\n|15:00|\n|15:55|\n|17:10|\n|18:40|\n|19:30|\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "2051855b8cda37fc8bc90e5a2b8ef27c",
"score": "0.6111035",
"text": "def SaidasBairroSabadosDomingoseFeriados6250(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z63'] + \"\"\"\nSabados:\n|09:00|Via BR282 Rodovias Municipais\n|12:20|Via BR282 Rodovias Municipais\n\nDomingos e Feriados:\n|09:45|Via BR282 Rodovias Municipais\n|16:50|Via BR282 Rodovias Municipais\n|19:40|Via BR282 Rodovias Municipais\n\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "2d98f2fcba9ecfcd270b21e03ef8e646",
"score": "0.6105373",
"text": "def SaidasCentroSabadosDomingoseFeriados554(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w8'] + \"\"\"\nSabados:\n|09:15| SC D\n|11:20| SC D\n|12:05| A SC\n|13:15| SC D\n|16:05| SC D\n|17:40| SC D\n|19:20| SC D\n|20:35| SC D\n-\nDomingos:\n|12:00| SC D\n|14:30| SC D\n|16:10| D\n|17:40| SC D\n|19:20| SC D\n|20:35| SC D\n-\nA: Via Aeroclube\nSC: Via Shopping Continente\nD: Adaptado p/ portadores de necessidades especiais\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "b8743c8dfdaf967a022f2ae9c25ade5f",
"score": "0.6100046",
"text": "def SaidasBairroSabadosDomingoseFeriados6271(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z57'] + \"\"\"\nSabados:\n|07:10|\nVia Sertão, Reta dos Pilões e Rod. Municipais\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "da6a525bb0acc2bc39f3e603b63785f7",
"score": "0.60802734",
"text": "def SaidasBairroSabadosDomingoseFeriados680(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z51'] + \"\"\"\nSabados:\n08:15\n11:10\n13:30\n13:45\n18:00\n21:20\nDomingos e Feriados:\n07:50\n11:25\n14:00\n19:00\n21:30\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "9c11ef4ced6791e35ec4a8a277ef89a8",
"score": "0.6040789",
"text": "def SaidasForquilhasSabadosDomingoseFeriados0120(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z30'] + \"\"\"\nSabados:\n|06:10|\n|07:00|\n|08:35|\n|09:00|\n|10:10|\n|11:00|\n|12:20|\n|13:00|\n|14:30|\n|15:50|\n|16:50|\n|17:50|\n|19:00|\n|20:10|\n|22:10|\n|23:00|\nDomingos:\n|07:00|\n|09:20|\n|11:10|\n|13:10|\n|13:30|\n|15:20|\n|16:20|\n|17:20|\n|18:20|\n|20:00|\n|21:20|\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "a72f304232e81b875fd025cf39b2b475",
"score": "0.6033945",
"text": "def SaidasBairroSabadosDomingoseFeriados10000(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z36'] + \"\"\"\nSabados:\n\n06:00\n06:30\n06:55\n07:10\n07:40\n08:05\n08:30\n09:05\n09:30\n10:15\n11:10\n12:00\n12:35\n13:00\n13:40\n14:20\n15:35\n16:50\n18:05\n19:20\n20:40\n23:15\n\nDom. e feriados:\n\n06:00\n07:30\n09:00\n10:30\n11:45\n12:55\n14:05\n15:25\n16:45\n17:35\n18:35\n19:35\n20:35\n21:50\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "00036f6c15ca7191acdd00603c8127c4",
"score": "0.6028464",
"text": "def SaidasKobrasolSabadosDomingoseFeriados0120(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w30'] + \"\"\"\nSabados:\n|07:30|\n|08:10|\n|09:10|\n|10:00|\n|11:20|\n|12:00|\n|13:35|\n|14:50|\n|15:50|\n|16:50|\n|18:00|\n|19:00|\n|21:10|\n|22:00|\nDomingos:\n|08:30|\n|10:20|\n|12:10|\n|12:40|\n|14:20|\n|15:20|\n|16:20|\n|17:20|\n|19:00|\n|20:30|\n|22:10|\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "5d7bb1e830a6694a5a141d01c7cf3531",
"score": "0.6026887",
"text": "def SaidasBairroSabadosDomingoseFeriados7633(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z17'] + \"\"\"\nSabados:\n06:00\n06:30\n07:00\n07:30\n08:00\n08:30\n09:00\n09:30\n10:00\n10:30\n11:00\n11:30\n12:00\n12:30\n13:00\n13:30\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "56defec2cde6ca049b29e04e70dd2722",
"score": "0.5997084",
"text": "def SaidasCentroSabadosDomingoseFeriados0201(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w25'] + \"\"\"\nLinha A: Até o ponto final do Arruda\n\nSábados\n\n06:20 - Linha A\n06:40 - Linha A\n07:10 - Linha A\n07:45 - Linha A\n08:15 - Linha A\n08:55 - Linha A\n09:35 - Linha A\n10:05 - Linha A\n10:25 - Linha A\n11:05 - Linha A\n11:55 - Linha A\n12:35 - Linha A\n13:35 - Linha A\n14:05 - Linha A\n14:35 - Linha A\n15:35 - Linha A\n16:10 - Linha A\n16:40 - Linha A\n17:20 - Linha A\n18:10 - Linha A\n18:40 - Linha A\n19:40 - Linha A\n20:15 - Linha A\n21:40 - Linha A\n22:10 - Linha A\n23:20 - Linha A\n\nDomingos e Feriados\n\n07:15 - Linha A\n08:15 - Linha A\n09:15 - Linha A\n10:15 - Linha A\n11:15 - Linha A\n12:15 - Linha A\n13:15 - Linha A\n14:15 - Linha A\n15:15 - Linha A\n16:15 - Linha A\n17:15 - Linha A\n18:15 - Linha A\n19:15 - Linha A\n20:15 - Linha A\n21:15 - Linha A\n22:15 - Linha A\n23:30 - Linha A\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "588cb2b5235b4139680e95f0171c913a",
"score": "0.59964365",
"text": "def SaidasCentroSabadosDomingoseFeriadosSN(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w2'] + \"\"\"\nSabados:\n|07:30|D\n|08:30|\n|09:15|D\n|10:15|D\n|11:20|D\n|12:05|\n|13:15|D\n|14:30|D\n|16:05|D\n|17:40|D\n|19:20|D\n|20:35|D\n|21:30|D\n|22:30|D\n-\nDomingos e feriados:\n|07:10|D\n|09:45|D\n|12:00|D\n|14:30|D\n|15:50|D\n|06:30|D\n|17:40|D\n|19:20|D\n|20:35|D\n|21:40|D\n-\nD: Adaptado p/ portadores de necessidades especiais\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "e89612cefe28b630efc3e02ce3dfddc1",
"score": "0.59959143",
"text": "def SaidasBairroSegundaaSexta175(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['x4'] + \"\"\"\n|05:35| SA\n|06:00| SA\n|06:20| SA\n|06:35| SA EXP BR\n|06:50| SA\n|07:03| SA\n|07:25| SA\n|07:50| SA\n|08:10| SA\n|08:30| SA\n|09:10| SA\n|10:00| SA\n|10:55| SA\n|13:00| SA\n|14:10| SA\n|15:00| SA\n|16:00| SA\n|16:40| SA\n|17:15| SA\n|17:40| SA\n|18:10| SA\n|18:30| SA\n|19:20| SA\n|20:05| SA\n|21:00| SA\n|22:00| SA\n|23:00| SA\n-\nSA: Via Rua Santo Andre\nEXP: Via Expressa\nBR: Via BR 101\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "8bc0bbe19a9d7c7c50d037d159060fc7",
"score": "0.59959",
"text": "def SaidasBairroSabadosDomingoseFeriados90900(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z35'] + \"\"\"\nSabado\n\n06:15\n08:00\n09:10\n10:45\n12:25\n13:35\n15:10\n16:00\n17:05\n18:45\n21:40\n\nDomingo\n\n06:20\n08:00\n10:00\n12:30\n14:00\n16:20\n18:10\n19:55\"\"\"\n except:\n yield \"tente novamente\"",
"title": ""
},
{
"docid": "fe188e7bab8b13d9d1d8f9cfa4ab4963",
"score": "0.59846497",
"text": "def SaidasBairroSabadosDomingoseFeriados64300(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z39'] + \"\"\"\nSabados:\n11:00 Tijucas - Timbe- Sorocaba Dentro - Viaduto Janaina\"\"\"\n\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "a696c884f292744977777ac968c7efbc",
"score": "0.5978987",
"text": "def SaidasBairroSabadosDomingoseFeriados7631(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z23'] + \"\"\"\nSábado:\n06:00\n06:30\n07:00\n07:15\n07:30\n07:45\n08:25\n09:15\n10:50\n11:55\n12:45\n13:30\n14:25\n15:25\n16:25\n17:20\n18:15\n19:20\n20:20\n20:55\n22:20\n\nDomingos e Feriados:\n07:00\n08:30\n09:30\n10:30\n11:30\n12:30\n13:30\n14:30\n15:30\n16:30\n17:30\n18:30\n19:30\n20:30\n21:30\n22:30\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "d790429968120369704552255fb6dda9",
"score": "0.5973675",
"text": "def SaidasBairroSabadosDomingoseFeriados7632(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z16'] + \"\"\"\nSabados:\n05:50\n07:10\n08:50*\n12:15*\n14:15*\n17:10*\n19:00*\n.\nDomingos e feriados:\n09:15*\n11:15*\n13:15*\n15:15*\n17:15*\n19:15*\"\"\" \n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "761dcfebb60555fc7cda20638d9bf94c",
"score": "0.59656006",
"text": "def SaidasCentroSabadosDomingoseFeriados328(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w14'] + \"\"\"\nSabados:\n|06:30|\n|07:15|\n|08:00|\n|08:45|\n|09:30|\n|10:15|\n|11:00|\n|11:45|\n|12:15|\n|13:15|\n|14:00|\n|14:45|\n|15:30|\n|16:15|\n|17:00|\n|17:45|\n|18:30|\n|19:15|\n|20:00|\n|20:45|\n|21:30|\n|22:15| R\n|23:15| R\n.\nDomingos e feriados:\n|06:40|\n|07:40|\n|08:40|\n|09:40|\n|10:40|\n|11:40|\n|12:40|\n|13:40|\n|14:40|\n|15:40|\n|16:40|\n|17:40|\n|18:40|\n|19:40|\n|20:40|\n|21:40|\n|22:40| R\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "f06345e0264cc8586d38c7ef109b0bcb",
"score": "0.5936404",
"text": "def SaidasCentroSabadosDomingoseFeriados12400(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w42'] + \"\"\"\nSabados:\n06:45\n07:20\n07:45\n08:05\n08:30\n09:00\n09:30\n10:00\n10:30\n11:00\n11:30\n12:00\n12:30\n13:00\n13:30\n14:00\n14:40\n15:20\n16:00\n16:40\n17:20\n18:00\n18:40\n19:30\n20:20\n21:10\n22:00\n23:00\n\nDomingos e Feriados:\n06:55\n08:05\n09:01\n10:05\n11:09\n12:13\n13:17\n14:10\n15:00\n15:50\n16:40\n17:30\n18:20\n19:10\n20:00\n20:50\n21:40\n22:20\n23:00\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "5e0e0148a07b89abd12a19d769fd7512",
"score": "0.5935903",
"text": "def SaidasBairroSegundaaSexta554(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['x8'] + \"\"\"\n|05:45| A\n|06:35| A\n|07:05| D\n|07:40| D SC\n|08:10|\n|08:30| A D\n|09:00| SC\n|10:30| SC D\n|12:30|\n|13:15| SC D\n|13:45| SC D\n|14:50|\n|15:45| SC D\n|16:05| SC\n|17:00| SC D\n|17:15| SC D\n|17:40| SC D\n|18:55| PE SC\n|21:15| SC D\n|22:10| SC D\n-\nPE: Periodo Escolar\nA: Via Aeroclube\nSC: Via Shopping Continente\nD: Adaptado p/ portadores de necessidades especiais\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "38946417e6f732d2d4da6eb4608f7150",
"score": "0.59258515",
"text": "def SaidasBairroSabadosDomingoseFeriados44800(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z37'] + \"\"\"\nSabados:\n05:00 Velha - P Andrade - Canudos\n06:30 Velha - P Andrade - Canudos\n15:00 P Andrade - Canudos\nDomingos e Feriados:\n06:30\n16:30\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "28887e4d54e1bdda75d9dafd44b4b339",
"score": "0.59213066",
"text": "def SaidasBairroSegundaaSexta178(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['x5'] + \"\"\"\n|05:00| A\n|05:55| \n|06:25|\n|07:05|\n|07:30|\n|09:00|\n|09:30|\n|10:30|\n|11:20| D\n|11:45|\n|12:30|\n|12:50| EXP SC\n|13:45|\n|14:55| SC\n|16:00| D\n|17:00| A\n|18:00| EXP\n|19:00| D\n|20:15|\n-\nEXP: Via Expressa\nA: Via Aeroclube\nSC: Via Shopping Continente\nD: Adaptado p/ portadores de necessidades especiais\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "6a28bc30875fe1fb4206d02366dd0efb",
"score": "0.59175533",
"text": "def horario_Forquilhinha_Via_Rodeio_e_Palmares(self, msg, args):\n yield \"\"\"Qual saida?\n/SaidasBairroSegundaaSexta0392\n/SaidasCentroSegundaaSexta0392\n/SaidasBairroSabadosDomingoseFeriados0392\n/SaidasCentroSabadosDomingoseFeriados0392\"\"\"\n msg.ctx['x19'] = 'Saidas do bairro de segunda a sexta:'\n msg.ctx['y19'] = 'Saidas do centro de segunda a sexta:'\n msg.ctx['z19'] = 'Saidas do bairro de sabados, domingos e feriados:'\n msg.ctx['w19'] = 'Saidas do centro de sabados, domingos e feriados:'",
"title": ""
},
{
"docid": "9b088eee7ccd1c4c5fab5040e56ae2b3",
"score": "0.590639",
"text": "def get_oas():",
"title": ""
},
{
"docid": "6235fa8cd8e57c928fd30fd253e27528",
"score": "0.5899556",
"text": "def SaidasCentroSabadosDomingoseFeriados670(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w56'] + \"\"\"\nSabados:\n|07:20|\n|08:00|\n|10:20|\n|10:50|\n|12:30|\n|17:40|\n|18:20|\n|19:00|\n|19:40|\n|21:00|\n|22:40|\n\nDomingos e Feriados:\n|08:00|\n|10:00|\n|11:00|\n|18:20|\n|19:20|\n|21:00|\n|22:50|\nVia:BR282, Rodovias Municipais\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "47e7cf4c3cf3b4b11307ae57c28581b2",
"score": "0.5890499",
"text": "def SaidasCentroSabadosDomingoseFeriados6270(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w58'] + \"\"\"\nSabados:\n|15:00|\nVia Sul do Rio / Rodovias Municipais\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "563ecce7111072c0c91833009cec1312",
"score": "0.5884286",
"text": "def SaidasCentroSabadosDomingoseFeriados117(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w11'] + \"\"\"\nSabados:\n|06:00|\n|06:30|\n|07:00|\n|07:20|\n|07:40|\n|08:00|\n|08:30|\n|09:00|\n|09:30|\n|10:00|\n|10:30|\n|11:00|\n|11:30|\n|12:00|\n|12:30|\n|13:00|\n|13:30|\n|14:00|\n|14:30|\n|15:00|\n|15:30|\n|16:00|\n|16:30|\n|17:00|\n|17:30|\n|18:00|\n|18:30|\n|19:00|\n|19:30|\n|20:00|\n|20:30|\n|21:00|\n|21:30|\n|22:00|\n|22:30|\nDomingos:\n|06:00|\n|07:00|\n|08:00|\n|09:00|\n|10:00|\n|11:00|\n|12:00|\n|13:00|\n|14:00|\n|15:00|\n|16:00|\n|17:00|\n|18:00|\n|19:00|\n|20:00|\n|21:00|\n|22:00|\n|23:10|\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "70168efa990940371fa42b559aacc778",
"score": "0.5877585",
"text": "def SaidasBiguacuSabadosDomingoseFeriados64201(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w41'] + \"\"\"\nSabados:\n12:40 Viaduto Janaina _ Bepao - Sorocaba Dentro\nDomingos e Feriados:\n17:10 Viaduto Janaina - Canto BEPAO - Sorocaba - Timbe\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "3306daafea633adb68675f2304a88f3a",
"score": "0.58772033",
"text": "def SaidasBairroSabadosDomingoseFeriados0039(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z18'] + \"\"\"\nSábado: \n05:00\n06:00\n06:40\n07:50\n08:20\n09:50\n11:30\n12:30\n13:15\n15:00\n16:55\n18:55\n20:55\n23:25\n.\nDomingos e Feriados:\n05:40\n06:30\n08:15\n09:55\n11:55\n14:55\n17:55\n19:55\n21:50\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "5f319574e629da679986f0ca81f33089",
"score": "0.5853111",
"text": "def contar_envido(self):\r\n self.dic_palos = {\"Oro\":[], \"Basto\":[], \"Copa\":[], \"Espada\":[]}\r\n suma = 0\r\n for palo,valor in self.jugadorH:\r\n for palo_dic in self.dic_palos:\r\n if palo == palo_dic:\r\n if valor >= 10:\r\n self.dic_palos[palo_dic].append(0)\r\n else:\r\n self.dic_palos[palo_dic].append(valor)\r\n \r\n for palo,valor in self.dic_palos.items():\r\n if len(valor) >= 2:\r\n suma += 20\r\n for i in valor:\r\n suma += i\r\n self.envido = palo,suma\r\n elif len(valor) == 1:\r\n for i in valor:\r\n if i > suma:\r\n suma = i\r\n self.envido = palo,suma\r\n \r\n\r\n\r\n \r\n return self.envido",
"title": ""
},
{
"docid": "3435a1f20f8ab2879a8a647acf57ea7f",
"score": "0.5845252",
"text": "def SaidasCentroSabadosDomingoseFeriados6250(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w63'] + \"\"\"\nSabados:\n|06:40|Via BR282 Rodovias Municipais\n|09:50|Via BR282 Rodovias Municipais\n|17:00|Via BR282 Rodovias Municipais\n\nDomingos e Feriados:\n|07:00|Via BR282 Rodovias Municipais\n|13:20|Via BR282 Rodovias Municipais\n|15:40|Via BR282 Rodovias Municipais\n\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "3a269ce40b93d485c578923fe24dc02d",
"score": "0.5839805",
"text": "def SaidasBairroSabadosDomingoseFeriados6240(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z49'] + \"\"\"\nSabados:\n|05:35|\n|10:20|\n|15:40|\n\nDomingos e Feriados:\n|06:25|\n|15:50|\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "f90a563b15885021832f9b1e4d4ed7cd",
"score": "0.58257276",
"text": "def SaidasCentroSabadosDomingoseFeriados0020(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w24'] + \"\"\"\nLinha A: Até o ponto final do Arruda\n\nSábado:\n06:20 - Linha A\n06:40 - Linha A\n07:10 - Linha A\n07:45 - Linha A\n08:15 - Linha A\n08:55 - Linha A\n09:35 - Linha A\n10:05 - Linha A\n10:25 - Linha A\n11:05 - Linha A\n11:55 - Linha A\n12:35 - Linha A\n13:35 - Linha A\n14:05 - Linha A\n14:35 - Linha A\n15:35 - Linha A\n16:10 - Linha A\n16:40 - Linha A\n17:20 - Linha A\n18:10 - Linha A\n18:40 - Linha A\n19:40 - Linha A\n20:15 - Linha A\n21:40 - Linha A\n22:10 - Linha A\n23:20 - Linha A\n\nDomingos e Feriados:\n07:15 - Linha A\n08:15 - Linha A\n09:15 - Linha A\n10:15 - Linha A\n11:15 - Linha A\n12:15 - Linha A\n13:15 - Linha A\n14:15 - Linha A\n15:15 - Linha A\n16:15 - Linha A\n17:15 - Linha A\n18:15 - Linha A\n19:15 - Linha A\n20:15 - Linha A\n21:15 - Linha A\n22:15 - Linha A\n23:30 - Linha A\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "d0f118707481ba11c40032a7780b5a13",
"score": "0.5807509",
"text": "def SaidasCentroSabadosDomingoseFeriados6271(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w57'] + \"\"\"\nSabados:\n|13:40|\nVia Sertão, Reta dos Pilões e Rod. Municipais\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "6b662faad5affa6c960c71bd6ec89a95",
"score": "0.57962924",
"text": "def SaidasBairroSabadosDomingoseFeriados681(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z48'] + \"\"\"\nSabados:\n|05:50|\nDomingos e Feriados:\n|05:50|\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "d4c8b0728aac4ae663bf3a1ea7101e99",
"score": "0.5795246",
"text": "def SaidasCentroSabadosDomingoseFeriados202(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w67'] + \"\"\"\nSabados:\n|06:40|\n|07:25|\n|08:20|\n|09:15|\n|10:05|\n|11:20|\n|12:40| I.Comelli\n|13:40|\n|15:30|\n|16:35|\n|18:15|\n|19:05|\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "f3bf8aaceab3ad30cffb2d8a959c7676",
"score": "0.5793715",
"text": "def SaidasBairroSegundaaSexta202(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['x67'] + \"\"\"\n|06:00| Saída da IFSC\n|06:30|\n|07:00| Saída da IFSC\n|07:20|\n|07:50|\n|08:30|\n|09:00|\n|09:40|\n|10:20|\n|10:53|\n|11:25|\n|11:55|\n|12:25|\n|12:54|\n|13:14| Saída da IFSC\n|13:45| Via Expressa\n|14:15|\n|14:45|\n|15:25|\n|15:55|\n|16:12|\n|16:30|\n|16:55|\n|17:25|\n|18:05|\n|18:30|\n|19:25|\n|20:00|\n|20:25|\n|20:53|\n|21:15| \n|22:10|\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "2a03dc3a26ff627e867cf48e4c5f7871",
"score": "0.57920295",
"text": "def SaidasBairroSabadosDomingoseFeriados830(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z62'] + \"\"\"\nSabados:\n|07:10|\n|13:40|\n|18:00|\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "70d214ea903a5f29bae0c712405c0368",
"score": "0.5789222",
"text": "def SaidasCentroSabadosDomingoseFeriados763(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w22'] + \"\"\"\nLinha E: Extensão\nLinha L:Lisboa\nLinha M: Via Loteamento Melo\nLinha R: Via Rodeio\nLinha Z: Via Zenaide\n\nSábado:\n07:05 - Linhas R e Z\n09:10 - Linhas R e Z\n10:45 - Linha R\n12:25 - Linhas R e Z\n13:15 - Linha R\n15:05 - Linhas R e Z\n17:05 - Linhas R e Z\n19:05 - Linhas R e Z\n22:05 - Linhas R e Z\n\nDomingos e Feriados:\n07:20 - Linhas R e Z\n09:30 - Linhas R e Z\n11:30 - Linhas R e Z\n13:30 - Linhas R e Z\n15:30 - Linhas R e Z\n17:30 - Linhas R e Z\n19:30 - Linhas R e Z\n22:30 - Linhas R e Z\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "afd2b16f33b06f8cbc30785c555c7ea1",
"score": "0.57891124",
"text": "def SaidasBairroSabadosDomingoseFeriados0142(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z27'] + \"\"\"\nSabados: \n|07:20|\n|08:30|\n|10:00|\n|12:45|\n\nDomingos e Feriados:\n|07:20|\n|08:30|\n|10:00|\n|12:45|\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "53ef21133bb10c2040c65a41ca031f0e",
"score": "0.5788446",
"text": "def todasProteinas(self):",
"title": ""
},
{
"docid": "285b9a14bdf2b83b1a137f2af8367240",
"score": "0.57880265",
"text": "def SaidasBairroSegundaaSexta763(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['x22'] + \"\"\"\nLinha E: Extensão\nLinha L:Lisboa\nLinha M: Via Loteamento Melo\nLinha R: Via Rodeio\nLinha Z: Via Zenaide\n\n05:15 - Linhas Z e R\n05:40\n06:00 - Linhas E, Z e R\n06:15 - Expresso\n06:25\n06:50 - Linha Z\n07:05 - Linha Z\n07:20 - Linha M\n08:00 - Linha Z\n09:00\n09:25 - Linha M\n10:00 - Linha Z\n10:55\n11:50\n12:35\n13:05 - Linha E\n14:15 - Linha M\n15:20\n16:20\n17:15 - Linhas E e Z\n18:20 - Expresso\n19:35 - Linha E\n20:20 - Linhas Z e R\n21:10 - Linhas E e Z\n\"\"\" \n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "fad4dd026c4664e83655c78402f1da99",
"score": "0.57829803",
"text": "def SaidasBairroSabadosDomingoseFeriados0110(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z29'] + \"\"\"\nSabados:\n|07:00|\nDomingos: Nao opera\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "86798bdeda80046908a0314c763d3c57",
"score": "0.57730055",
"text": "def geraSom(self):",
"title": ""
},
{
"docid": "e68ea52750268e0e955c926a1de32fe7",
"score": "0.5766043",
"text": "def SaidasBairroSabadosDomingoseFeriados317(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z12'] + \"\"\"\nSabados:\n|05:30|\n|05:55|\nDomingos:\n|05:55|\n|06:25|\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "b684b7f4db4454d86d62893bddcef598",
"score": "0.57596666",
"text": "def SaidasBairroSabadosDomingoseFeriados328(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z14'] + \"\"\"\nSabados:\n|06:00|\n|06:30|\n.\nDomingos e feriados:\n|06:05|\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "dfacd7b7c7e74e969d028eb5242d3690",
"score": "0.5755149",
"text": "def SaidasBairroSabadosDomingoseFeriados117(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z11'] + \"\"\"\nSabado:\n|05:30|\n|06:00|\nDomingos:\n|05:30|\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "e4cbe3941f0c05a89b60c0f208f07319",
"score": "0.5749495",
"text": "def SaidasBarreirosSabadosDomingoseFeriados0140(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z34'] + \"\"\"\nEsse onibus nao possui horario nesses dias, deseja consultar as empresas?\n-> /empresas\"\"\"\n\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "c939be3de7e20cbac9e608e142b0caac",
"score": "0.57462054",
"text": "def SaidasCentroSabadosDomingoseFeriados7632(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w16'] + \"\"\"\nSabados:\n08:05\n11:15\n13:20\n16:15\n18:05\n20:05\n.\nDomingos e feriados:\n08:30\n10:30\n12:30\n14:30\n16:30\n18:30\"\"\" \n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "831b804cc9a71042bc27c2915516ccf7",
"score": "0.5737742",
"text": "def SaidasCentroSabadosDomingoseFeriados7633(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w17'] + \"\"\"\nSabados:\n09:00\n09:30\n10:00\n10:30\n11:00\n11:30\n12:00\n12:30\n13:00\n13:30\n14:00\n14:30\n15:00\n15:30\n16:00\n16:30\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "0d71d9f9cc94a73835dc29dd62ad22c2",
"score": "0.5735835",
"text": "def SaidasBairroSegundaaSexta44801(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['x38'] + \"\"\"\n06:05 P Andrade - Guiomar\n06:20 R Velha - Granja\n06:35 ENCR - RVE - Morro -PAND\n09:00 R Velha - P Andrade\n11:50 Sai COL Rodao p/ Biguacu\n12:30 Canudos - P Andrade - Morro\n12:31\n13:00 R Velha - P Andrade\n16:45 Sai Inicio R Velha - Laranjeiras - Morro - P Andrade\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "d023455fd50dd405df32126a09b133a9",
"score": "0.5727141",
"text": "def SaidasBairroSabadosDomingoseFeriados44303(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z43'] + \"\"\"\nSabados:\n05:50\n06:40\n13:15\n17:45\n\nDomingos e Feriados:\n05:50\n07:00\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "06846d3d431634b4d0660645e4baabf3",
"score": "0.5723721",
"text": "def SaidasBairroSegundaaSexta203(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['x68'] + \"\"\"\n 06:00 1 Saída da IFSC\n 06:30 1\n 07:00 1 Saída da IFSC\n 07:20 1\n 07:50 1\n 08:30 1\n 09:00 1\n 09:40 1\n 10:20 1\n 10:53 1\n 11:25 1\n 11:55 1\n 12:25 1\n 12:54 1\n 13:14 1 Saída da IFSC\n 13:45 2 Via Expressa\n 14:15 1\n 14:45 1\n 15:25 1\n 15:55 1\n 16:12 1\n 16:30 1\n 16:55 1\n 17:25 1\n 18:05 1\n 18:30 1\n 19:25 1\n 20:00 1\n 20:25 1\n 20:53 1\n 21:15 1\n 22:10 1\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "47f5849159e45b56f4261ae626f91212",
"score": "0.571854",
"text": "def SaidasBairroSegundaaSexta0141(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['x20'] + \"\"\"\n06:05 \n06:20 \n06:30 \n06:40 \n06:50 \n07:00 \n07:10 \n07:31 \n07:53 \n08:15 \n08:35 \n08:55\n09:15\n09:35\n09:55\n10:15\n10:37\n10:57\n11:17\n11:37\n11:57\n12:17\n12:32\n12:49\n13:02\n13:17\n13:32\n13:47\n14:03\n14:18\n14:33\n14:48\n15:03\n15:19\n15:34\n15:49\n16:04\n16:19\n16:34\n16:49\n17:04\n17:19\n17:34\n17:49\n18:04\n18:20\n18:35\n18:50\n19:05\n19:19\n19:34\n19:45\n20:00\n20:15\n20:28\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "9e80a1c834770678e4a16a6e253193c8",
"score": "0.57152236",
"text": "def horario_Ceniro_Via_Jardim_das_Palmeiras(self, msg, args):\n yield \"\"\"Qual Saída?\n/SaidasBairroSegundaaSexta131\n/SaidasCentroSegundaaSexta131\n/SaidasBairroSabadosDomingoseFeriados131\n/SaidasCentroSabadosDomingoseFeriados131\"\"\"\n msg.ctx['x15'] = 'Saidas do bairro de segunda a sexta:'\n msg.ctx['y15'] = 'Saidas do centro de segunda a sexta:'\n msg.ctx['z15'] = 'Saidas do bairro de sabados, domingos e feriados:'\n msg.ctx['w15'] = 'Saidas do centro de sabados, domingos e feriados:'",
"title": ""
},
{
"docid": "b5e7ffdde3ea9d774369df651e959b7e",
"score": "0.57126874",
"text": "def SaidasCentroSabadosDomingoseFeriados0039(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w18'] + \"\"\"\nSábado:s\n05:50\n06:50\n07:30\n09:00\n10:30\n11:30\n12:10\n14:00\n16:00\n18:00\n20:00\n22:30\n00:30\n.\nDomingos e Feriados:\n07:30\n09:10\n11:00\n14:00\n17:00\n19:05\n21:00\n22:50\n00:30\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "cb24e6c695ddc5934e32b3c8c30b9b33",
"score": "0.5710361",
"text": "def SaidasBairroSabadosDomingoseFeriados44801(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z38'] + \"\"\"\nSabados:\n08:50 Smarcos - P Andrade - Viaduto Janaina\n13:00 Smarcos - P Andrade - Viaduto Janaina\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "26b1109173e98960692f2e1cfba07abf",
"score": "0.56972647",
"text": "def SaidasBairroSegundaaSexta7631(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['x23'] + \"\"\"\nLinha BR: Via BR101\nLinha LA: Via Los Angeles\n\n05:00\n05:30\n05:45\n06:00\n06:12\n06:25\n06:37\n06:50 - Linhas BR\n07:02\n07:15\n07:30 - Linhas BR\n07:45\n08:05\n08:30\n08:55\n10:20\n11:15\n12:05\n12:25\n12:50\n13:25\n13:45 - Linhas BR\n14:30\n15:25\n15:35\n16:20\n16:55\n17:15 - Linhas BR\n17:35\n18:10\n19:10\n19:35\n20:05\n20:55\n21:45\n22:25\n22:55\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "9b8d76f626fd0920fe78df209369edce",
"score": "0.5695091",
"text": "def SaidasCentroSabadosDomingoseFeriados203(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w68'] + \"\"\"\nSabados:\n 06:40 1\n 07:25 1\n 08:20 1\n 09:15 1\n 10:05 1\n 11:20 1\n 12:40 1 I.Comelli\n 13:40 1\n 15:30 1\n 16:35 1\n 18:15 1\n 19:05 1\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "07ae2e1b0ec82f90b17dd22b5087bf3c",
"score": "0.5665807",
"text": "def SaidasBiguacuSabadosDomingoseFeriados64300(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w39'] + \"\"\"\nSabados:\n07:30 Saida Furacao- Sorocaba Dentro - Timbe - Tijucas\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "781970ca6ce93c93801dd7329e562f84",
"score": "0.56586707",
"text": "def dameBanners(self):\r\n utilidad= ColeccionUtils(self.context)\r\n result=[]\r\n dameE=utilidad.dameExhibicionesR()\r\n if dameE:\r\n for exhi in dameE:\r\n sinBaner=dameE[0].baner==None\r\n obj={'url':exhi.absolute_url(),'titulo':exhi.title,\"vacio\":sinBaner }\r\n result.append(obj)\r\n\r\n return result",
"title": ""
},
{
"docid": "6ed6ebe48d26be9819568b090a30a0a9",
"score": "0.5642443",
"text": "def SaidasBairroSabadosDomingoseFeriados143(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z64'] + \"\"\"\nSabados:\n|07:00|\n|17:00|\n|19:00| \n\nDomingos e Feriados:\n|17:00| \n|19:15| \n \n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "5a47336b929f1c8c3467717cc844795e",
"score": "0.56419116",
"text": "def faz_busca_com_informacao(self):\n abertos = [No(posicao=self.posicao_atual, saida=self.posicao_saida)]\n fechados = []\n\n while len(abertos) > 0:\n for index, no_em_aberto in enumerate(abertos):\n abertos.pop(index) # retirado estado mais a esquerda\n # print(no_em_aberto)\n\n if no_em_aberto == No(posicao=self.posicao_saida):\n # return abertos\n fechados.append(no_em_aberto)\n # return fechados[-1].pega_caminho()\n return no_em_aberto.pega_caminho()\n else:\n # busca vizinhos/filhos retorna posicao da matriz deles\n _filhos_do_no = self.__pega_vizinhos(\n index_linha=no_em_aberto.posicao.x,\n index_coluna=no_em_aberto.posicao.y,\n tipo_lista=False\n )\n for posicao_filho in _filhos_do_no:\n no_filho = No(\n posicao=posicao_filho,\n pai=no_em_aberto,\n saida=self.posicao_saida\n )\n\n if no_filho not in abertos and no_filho not in fechados:\n abertos.append(no_filho)\n continue\n\n if no_filho in abertos:\n _index = abertos.index(no_filho)\n _no = abertos[_index]\n if no_filho.v_caminho <= _no.v_caminho:\n abertos[_index] = no_filho\n continue\n\n if no_filho in fechados:\n _index = fechados.index(no_filho)\n _no = fechados[_index]\n if no_filho.v_heuristico <= _no.v_heuristico:\n fechados.pop(_index)\n abertos.append(no_filho)\n continue\n\n fechados.append(no_em_aberto)\n abertos.sort()",
"title": ""
},
{
"docid": "9eb6bf739b3a1573262edbd4153ca57b",
"score": "0.56355023",
"text": "def SaidasBairroSabadosDomingoseFeriados660(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z55'] + \"\"\"\nSabados:\n|06:20|\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "1410b3cbab6a0ed3589ec293cfa9a132",
"score": "0.5630198",
"text": "def SaidasCentroSabadosDomingoseFeriados44800(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w37'] + \"\"\"\nSabados:\n10:30 Velha - P Andrade - Canudos\n13:00 Velha - P Andrade - Canudos\n17:00 P Andrade -Canudos\nDomingos e Feirados:\n08:00 R velha\n18:00\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "3660abcccc3439a1f43aa5955b49fabd",
"score": "0.56274354",
"text": "def SaidasBairroSegundaaSexta12400(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['x42'] + \"\"\"\n06:10\n06:35\n06:55\n07:20\n07:40\n08:05\n08:30\n09:00\n09:30\n10:05\n10:40\n11:20\n11:55\n12:30\n13:00\n13:30\n14:00 Onibus Articulado\n14:30\n15:00\n15:35 Onibus Articulado\n16:10\n16:45\n17:10\n17:30\n17:55\n18:05\n18:35\n18:59\n19:15\n19:35\n20:05\n20:50\n21:25\n22:00\n22:30\n22:55\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "4196fa47633049b5303e42e0de355b89",
"score": "0.56213415",
"text": "def SaidasBairroSegundaaSextaSN(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['x2'] + \"\"\"\n|07:00|D\n|07:40|D\n|08:15|\n|08:55|D\n|09:10|\n|10:40|D\n|12:05|D\n|13:10|EXP\n|13:25|D\n|13:55|D\n|16:15|\n|16:35|\n|17:10|\n|17:25|D\n|17:50|D\n|18:10|\n|19:05|\n|19:40|D\n|20:55|D\n|21:25|\n|22:08|\n|22:29|\n|22:40|D\n-\nEXP: Via Expressa\nD: Adaptado p/ portadores de necessidades especiais\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "99740e0f0028c3a2df7d80bd753864b4",
"score": "0.56185055",
"text": "def SaidasEstivaSabadosDomingoseFeriados10900(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z44'] + \"\"\"\nSabados:\n06:45\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "713f65f3b54689aa6e5f9398680a996d",
"score": "0.56150544",
"text": "def gerar_saida(areas_descr, gmedia, fmais, fmenos,\n ais, sis):\n output = []\n out = output.append\n\n # QUESTÃO 1\n for func in fmais:\n out(f'global_max|{func[\"nome\"]} {func[\"sobrenome\"]}|{func[\"salario\"]:.2f}')\n for func in fmenos:\n out(f'global_min|{func[\"nome\"]} {func[\"sobrenome\"]}|{func[\"salario\"]:.2f}')\n\n out(f'global_avg|{gmedia:.2f}')\n\n for area, (_, _, asoma, aqtde, afuncsmais, afuncsmenos) in ais.items():\n area_descr = areas_descr[area]\n for func in afuncsmais:\n out(f'area_max|{area_descr}|{func[\"nome\"]} {func[\"sobrenome\"]}|{func[\"salario\"]:.2f}')\n for func in afuncsmenos:\n out(f'area_min|{area_descr}|{func[\"nome\"]} {func[\"sobrenome\"]}|{func[\"salario\"]:.2f}')\n\n out(f'area_avg|{area_descr}|{(asoma / aqtde):.2f}')\n\n # QUESTÃO 3\n max_area_qtde = max(a[3] for a in ais.values())\n min_area_qtde = min(a[3] for a in ais.values())\n\n for area, info in ais.items():\n if info[3] == max_area_qtde:\n out(f'most_employees|{areas_descr[area]}|{max_area_qtde}')\n if info[3] == min_area_qtde:\n out(f'least_employees|{areas_descr[area]}|{min_area_qtde}')\n\n # QUESTÃO 4\n for info in sis.values():\n if info[1] > 1:\n for func in info[2]:\n sob = func['sobrenome']\n out(f'last_name_max|{sob}|{func[\"nome\"]} {sob}|{func[\"salario\"]:.2f}')\n\n print(\"\\n\".join(output))",
"title": ""
},
{
"docid": "6f83b30e0048393ae9aeceb7210635c9",
"score": "0.56089896",
"text": "def SaidasCentroSabadosDomingoseFeriados10000(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w36'] + \"\"\"\nSábados\n\n06:35\n07:05\n07:50\n08:30\n08:55\n09:30\n10:30\n11:20\n12:00\n12:15\n12:30\n13:00\n13:45\n15:00\n16:20\n17:35\n18:50\n20:10\n21:30\n22:45\n24:00\n\nDom. e feriados\n\n06:45\n08:15\n09:45\n11:10\n12:25\n13:35\n14:50\n16:10\n17:00\n18:00\n19:00\n20:00\n21:15\n22:30\n24:00\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "cd976880b9e777c96e931c0d592868e5",
"score": "0.5599968",
"text": "def SaidasCentroSabadosDomingoseFeriados680(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w51'] + \"\"\"\nSabados:\n|09:10|\n|16:20|\n|20:20|\n|21:50|\nDomingos e Feriados:\n|09:40|\n|12:20|\n|17:00|\n|20:20|\n|22:00|\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "0adcbe4014638f5bbcbc81f6382e9f6e",
"score": "0.5589812",
"text": "def SaidasCentroSabadosDomingoseFeriados7631(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w23'] + \"\"\"\nSábado:\n06:40\n07:50\n08:35\n10:10\n11:05\n12:00\n12:45\n13:40\n14:40\n15:40\n16:35\n17:30\n18:35\n19:35\n20:10\n21:35\n22:45\n00:00\n\nDomingos e Feriados:\n07:00\n08:30\n09:30\n10;30\n11:30\n12:30\n13:30\n14:30\n15:30\n16:30\n17:30\n18:30\n19:30\n20:30\n21:30\n22:30\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "0e0ad886d6d3260821deddccd6c92292",
"score": "0.5588213",
"text": "def SaidasBairroSegundaaSexta6250(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['x63'] + \"\"\"\n|05:15|Via BR282 Rodovias Municipais\n|06:25|Via BR282 Rodovias Municipais\n|08:20|Via BR282 Rodovias Municipais\n|11:15|Via BR282 Rodovias Municipais\n|12:00|Via BR-282 BR-101 e Via Expressa\n|16:20|Via BR282 Rodovias Municipais\n|17:00|Via BR282 Rodovias Municipais\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "97541e0c6d3edf3a07823794c08ea3a4",
"score": "0.55872566",
"text": "def horario_Sao_Pedro_de_Alcantra_Florianopolis(self, msg, args):\n yield \"\"\"Qual saida?\n/SaidasBairroSegundaaSexta177\n/SaidasCentroSegundaaSexta177\n/SaidasBairroSabadosDomingoseFeriados177\n/SaidasCentroSabadosDomingoseFeriados177\"\"\"\n msg.ctx['x7'] = 'Saidas do bairro de segunda a sexta:'\n msg.ctx['y7'] = 'Saidas do centro de segunda a sexta:'\n msg.ctx['z7'] = 'Saidas do bairro de sabados, domingos e feriados:'\n msg.ctx['w7'] = 'Saidas do centro de sabados, domingos e feriados:'",
"title": ""
},
{
"docid": "669bfaf76aa260e58490918e09645395",
"score": "0.5577486",
"text": "def shrani_desko(self):\n p = [self.deska[i][:] for i in range(8)]\n self.zgodovina.append((p, self.na_potezi))",
"title": ""
},
{
"docid": "a3fb5279bd825b07d03f08d33c4d5d7f",
"score": "0.5567635",
"text": "def horario_Flor_de_Napolis_Santo_Andre(self, msg, args):\n yield \"\"\"Qual saida?\n/SaidasBairroSegundaaSexta175\n/SaidasCentroSegundaaSexta175\n/SaidasBairroSabadosDomingoseFeriados175\n/SaidasCentroSabadosDomingoseFeriados175\"\"\"\n msg.ctx['x4'] = 'Saidas do bairro de segunda a sexta:'\n msg.ctx['y4'] = 'Saidas do centro de segunda a sexta:'\n msg.ctx['z4'] = 'Saidas do bairro de sabados, domingos e feriados:'\n msg.ctx['w4'] = 'Saidas do centro de sabados, domingos e feriados:'",
"title": ""
},
{
"docid": "681a1d614282ac7e88b06906a4962386",
"score": "0.556666",
"text": "def SaidasFazendaSabadosDomingoseFeriados0130(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w32'] + \"\"\"\nSabados:\n|06:50|\n|07:20|\n|07:50|\n|08:20|\n|09:00|\n|09:30|\n|10:30|\n|11:10|\n|11:40|\n|12:10|\n|12:50|\n|15:30|\n|18:15|\n|20:30|\nDomingos:\n|06:30|\n|08:30|\n|11:30|\n|13:30|\n|15:20|\n|17:00|\n|19:50|\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "6cdf2c0e112f403d78a7e4ba906cc982",
"score": "0.5562274",
"text": "def SaidasBairroSabadosDomingoseFeriados0900(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z1'] + \"\"\"\nEsse onibus nao possui horario nesses dias, deseja consultar as empresas?\n-> /empresas\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "45a1fa9cb9c93e3567de5287f257d61c",
"score": "0.5558264",
"text": "def SaidasBairroSabadosDomingoseFeriados1412(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z21'] + \"\"\"\nEsse onibus nao possui horario nesses dias, deseja consultar as empresas?\n-> /empresas\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "8e2731313f3a09ac4a2b0c9f749ddb72",
"score": "0.5542071",
"text": "def SaidasForquilhinhasSabadosDomingoseFeriados0105(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['w28'] + \"\"\"\nSabados:\n|06:00|\n|07:50|\n|10:20|\n|12:10|\n|13:50|\nDomingos: Nao opera\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "9c9dedeaeb61b392d47cea02c2790ac8",
"score": "0.5541177",
"text": "def SaidasBairroSegundaaSexta553(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['y3'] + \"\"\"\nHorario de segunda:\n|13:10| Coqueiros Via Sao Pedro de Alcantara\n|17:25| Angelina Via Rancho Queimado ate Hospital Angelina\n-\nHorario de terca a quinta:\n|13:10| Coqueiros Via Sao Pedro\n|17:25| Angelina Via Rancho Queimado ate Hospital Angelina\n-\nHorario de sexta:\n|13:10| Coqueiros Via Sao Pedro\n|16:15| Betania Via Sao Pedro\n|17:10| Coqueiros Via Rancho Queimado\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "eee65b81a090b519fb59282130187c5f",
"score": "0.5538612",
"text": "def SaidasBairroSabadosDomingoseFeriados970(self, msg, args, flow_only=True):\n try:\n yield msg.ctx['z53'] + \"\"\"\nSabados:\n|06:50|\n\"\"\"\n except:\n yield \"Primeiro informe a linha que deseja\"",
"title": ""
},
{
"docid": "3e0e4557f5f23f8d1f38004402633750",
"score": "0.5534191",
"text": "def _biophys_soma(self):\n # set soma biophysics specified in Pyr\n # self.pyr_biophys_soma()\n\n # Insert 'hh2' mechanism\n self.soma.insert('hh2')\n self.soma.gkbar_hh2 = self.p_all['L2Pyr_soma_gkbar_hh2']\n self.soma.gl_hh2 = self.p_all['L2Pyr_soma_gl_hh2']\n self.soma.el_hh2 = self.p_all['L2Pyr_soma_el_hh2']\n self.soma.gnabar_hh2 = self.p_all['L2Pyr_soma_gnabar_hh2']\n\n # Insert 'km' mechanism\n # Units: pS/um^2\n self.soma.insert('km')\n self.soma.gbar_km = self.p_all['L2Pyr_soma_gbar_km']",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="752"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">b8931249e149b6f7e5829d521b73dc28</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Ensure we can create a new account object and it returns status code as 201.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "d448f0b7f5691e066ec1b8497ab7f3ed",
"score": "0.0",
"text": "def test_given_valid_details_for_registration(self):\n\n response = self.client.post(self.register_url, self.valid_registration_data, format='json')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "54b1493f11cffca22ef66264bd13bcfe",
"score": "0.7521507",
"text": "def test_create_account(self):\n\n response = self.client.post(\n '/api/v1/users', data=json.dumps(create_account), content_type='application/json')\n result = json.loads(response.data.decode())\n self.assertEqual(result['message'], 'Account created successfully')\n assert response.status_code == 201",
"title": ""
},
{
"docid": "a510711f7204f2088842bca7816f4e4c",
"score": "0.74455726",
"text": "def test_create_account(self):\n\n response = self.client.post(\n '/api/v2/auth/signup', data=json.dumps(new_account), content_type='application/json',\n headers=self.get_token())\n result = json.loads(response.data.decode())\n self.assertEqual(result['message'], 'Account created successfully')\n assert response.status_code == 201",
"title": ""
},
{
"docid": "508a96691186220dfbfbb9961b99bd45",
"score": "0.7225213",
"text": "def test_api_can_create_a_resource(self):\n self.assertEqual(\n self.response_resource.status_code,\n status.HTTP_201_CREATED\n )",
"title": ""
},
{
"docid": "99125892c4f537684f5f378b13038b96",
"score": "0.7196892",
"text": "def test_create_account_status_using_post(self):\n pass",
"title": ""
},
{
"docid": "29199602ed5f1a741ccb5a5e92c0d65a",
"score": "0.7082991",
"text": "def test_api_can_create_post(self):\n self.assertEqual(self.response.status_code, status.HTTP_201_CREATED)",
"title": ""
},
{
"docid": "fc61fa2fd459f1d91e49867e90d31096",
"score": "0.70673245",
"text": "def test_create_account(self):\n\n res = self.client.get('/create-account')\n self.assertEqual(res.status_code, 200)\n self.assertIn(b'Create an account', res.data)",
"title": ""
},
{
"docid": "da3a3c721ccaac88ab0222f5d5ee68a0",
"score": "0.68624073",
"text": "def test_handle_create_account(self):\n\n create_account_data = {'fname': 'Create',\n 'lname': 'Account',\n 'email': 'create@test.test',\n 'username': 'create',\n 'password': 'test',\n 'phone-number': '44444'}\n \n res = self.client.post('/handle-create-account',\n data=create_account_data,\n follow_redirects=True)\n self.assertEqual(User.query.all()[-1], User.query.filter_by(username='create').first())",
"title": ""
},
{
"docid": "42e8e72121a3992b2b7b7a3616453943",
"score": "0.68244565",
"text": "def test_api_can_create_a_testimony(self):\n self.assertEqual(\n self.response_testimony.status_code,\n status.HTTP_201_CREATED\n )",
"title": ""
},
{
"docid": "3240cc274d3c9741e436c4692127774a",
"score": "0.6805648",
"text": "def test_create_account_using_post(self):\n pass",
"title": ""
},
{
"docid": "d8c9c3bf0c320b8432c6274fcc5d2748",
"score": "0.6783555",
"text": "def test_account_create(res, expected, data):\n\n try:\n account = Account.objects.get(email=data['email'])\n except Account.DoesNotExist:\n raise RuntimeError(f'Failed to create account. {data}')\n\n if res.json()['STATUS'] != expected['STATUS']:\n account.delete()\n raise RuntimeError(f'Account creation did not return expected result {expected}')\n\n return account",
"title": ""
},
{
"docid": "e2a1afb2a8f8cff699493a0681f17d48",
"score": "0.67339176",
"text": "def create_account_post():\n\n response = JSONResponse()\n\n # Check login status and privileges\n if \"username\" not in session:\n session[\"create_account_error\"] = \"You must be logged in\"\n return redirect(\"/cryptic/admin/console\")\n\n if \"is_admin\" in session:\n if session[\"is_admin\"] < accounts.ADMIN:\n session[\"create_account_error\"] = \"Insufficient privileges for account creation\"\n return redirect(\"/cryptic/admin/console\")\n else:\n session[\"create_account_error\"] = \"Insufficient privileges for account creation\"\n return redirect(\"/cryptic/admin/console\")\n\n # Form validation\n if \"username\" not in request.form:\n session[\"create_account_error\"] = \"No username provided for account creation\"\n return redirect(\"/cryptic/admin/console\")\n\n username = request.form[\"username\"]\n\n if len(username) > 255 or len(username) < 3:\n session[\"create_account_error\"] = \"Username must be between 3 and 255 characters\"\n return redirect(\"/cryptic/admin/console\")\n\n response = accounts.create_account(session, username)\n\n if response.success:\n session[\"create_account_success\"] = True\n else:\n session[\"create_account_error\"] = response.message\n\n return redirect(\"/cryptic/admin/console\")",
"title": ""
},
{
"docid": "a1c45b6896ce43ff8748067c16589335",
"score": "0.6688132",
"text": "def account_created():\n \n print(f\"Account successfully create.\\n\")",
"title": ""
},
{
"docid": "615af9fcbeeeccf746320c39a427bf16",
"score": "0.66451985",
"text": "def test_api_can_create_a_program(self):\n self.assertEqual(\n self.response_program.status_code,\n status.HTTP_201_CREATED\n )",
"title": ""
},
{
"docid": "0e8aad846a9b1d4e23918fa36fa6bef9",
"score": "0.6586622",
"text": "def test_api_can_create_a_job(self):\n self.assertEqual(self.response.status_code, status.HTTP_201_CREATED)",
"title": ""
},
{
"docid": "b1d91eef879f874b57387eec24895746",
"score": "0.6576313",
"text": "def create_account(self, **params):\n endpoint = 'v1/accounts'\n return self.request(endpoint, \"POST\", params=params)",
"title": ""
},
{
"docid": "5356f55fd44aac51c7e085072f9e7f5f",
"score": "0.6540757",
"text": "def test_api_can_create_an_experience(self):\n self.assertEqual(\n self.response_experience.status_code,\n status.HTTP_201_CREATED\n )",
"title": ""
},
{
"docid": "5bfc2f8f90dfc701d8f702344d29ef85",
"score": "0.64888877",
"text": "def test_create_exists(self):\r\n payload = {\r\n \"email\": \"test@gmail.com\",\r\n \"password\": \"password\",\r\n }\r\n create_user(**payload)\r\n\r\n res = self.client.post(CREATE_USER_URL, payload)\r\n self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)",
"title": ""
},
{
"docid": "9e6059b1224effe4d9e222fdf8226015",
"score": "0.64857805",
"text": "def test_create_account_type_using_post(self):\n pass",
"title": ""
},
{
"docid": "bd283af75099f388c8f11368ccc6e6d7",
"score": "0.64604056",
"text": "def good_response(self, status_code):\n return status_code == http_client.CREATED",
"title": ""
},
{
"docid": "5d7cedc16f2a1e7fd23fd6fcabd91c07",
"score": "0.64452624",
"text": "def test_api_can_create_a_tag(self):\n self.assertEqual(self.response.status_code, status.HTTP_201_CREATED)",
"title": ""
},
{
"docid": "54c149b3cecc4711da3a0d451ba987fd",
"score": "0.64023066",
"text": "def test_request_can_create_successfully(self):\r\n initial_count = len(request_model.requests)\r\n res = self.client().post('/api/v1/request', data=json.dumps(self.request),\r\n headers={\"content-type\": \"application/json\", \"access-token\": self.token})\r\n final_count = len(request_model.requests)\r\n self.assertEqual(res.status_code, 201)\r\n self.assertEqual(final_count - initial_count, 1)\r\n self.assertIn(\"Request created\",str(res.data))",
"title": ""
},
{
"docid": "dffd2621321401c5d9463e6e2ac35658",
"score": "0.6401167",
"text": "def created_object_successfully(object_type, data=None):\r\n return make_response({\r\n 'Status': 'Success',\r\n 'Message': f'Successfully created {object_type}',\r\n 'Data': data\r\n }), HTTPStatus.CREATED",
"title": ""
},
{
"docid": "46f9e4f9ef644ea2473264c89196225c",
"score": "0.63957983",
"text": "def account_create():\n try:\n code = request.form['code']\n password = request.form['password']\n name = request.form['name']\n\n drink_barcode = query_db('SELECT barcode FROM drinks WHERE barcode=?', [code], one=True)\n if drink_barcode is not None:\n return 'This code is already used for a drink', 400\n\n password_hash = generate_password_hash(password)\n query_db('INSERT INTO accounts (name, password_hash, barcode, saldo) VALUES (?, ?, ?, 0)',\n [name, password_hash, code])\n\n if not all((name, password, code)):\n get_db().rollback()\n raise BadRequestKeyError\n\n get_db().commit()\n app.logger.info('Account \"%s (identifier: \"%s\") created', name, code)\n except BadRequestKeyError:\n exc_str = 'Incomplete request'\n app.logger.warning(exc_str)\n return exc_str, 400\n except sqlite3.IntegrityError as exc:\n exc_str = sql_integrity_error(exc)\n app.logger.error(exc_str)\n return exc_str, 400\n except sqlite3.OperationalError as exc:\n app.logger.error(exc)\n return exc, 400\n\n return 'ok'",
"title": ""
},
{
"docid": "867ef83c715762789557616fd096ceee",
"score": "0.6321597",
"text": "def post(self, data):\n check_policy(request.context, \"account:post\")\n\n conn = pecan.request.db_conn\n try:\n account = db_models.Account(**data.as_dict())\n return conn.create_account(request.context, account)\n except Exception:\n LOG.exception('Fail to create account: %s' % data.as_dict())\n raise exception.AccountCreateFailed(user_id=data.user_id,\n domain_id=data.domain_id)",
"title": ""
},
{
"docid": "385e915176ec00851ed726cc5bad1d7b",
"score": "0.63082546",
"text": "def create(self,request,*args,**kwargs):\n\n # create the user\n trigger(self.context+'.create:request',request,*args,**kwargs)\n\n data = request.data.copy()\n trigger(self.context+'.create:before',data)\n\n\n # check for existing account with given email\n try:\n duplicate = User.objects.get(email=request.data.get('email'))\n if duplicate:\n \n response = Response({\n 'status': 'Fail',\n 'message': 'This email is already in use.'\n }, status=status.HTTP_409_CONFLICT)\n\n trigger(self.context+'.create:fail',response)\n return response\n\n return \n except:\n pass\n\n # set default user status\n data['status'] = 0 if AUTHENTICATION['REQUIRE_ACTIVATION'] else 1\n serializer = self.serializer_class(data=data)\n\n # validate supplied data\n if serializer.is_valid():\n \n # create the user account\n instance = serializer.save()\n trigger(self.context+'.create:success',instance)\n\n # if account requires activation\n if AUTHENTICATION['REQUIRE_ACTIVATION']:\n\n # create and send the activation link\n self.dispatch_activation_email(instance,request)\n\n # remove password data from response\n serializer = self.serializer_class(instance)\n serialized_data = serializer.data\n trigger(self.context+'.create:serialize',serialized_data,instance)\n\n\n response = Response(serialized_data, status=status.HTTP_201_CREATED)\n trigger(self.context+'.create:response',response)\n return response\n \n else:\n # data not valid, return bad request response\n response = Response({\n 'status': 'Fail',\n 'message': 'Account could not be created with received data.'\n }, status=status.HTTP_400_BAD_REQUEST)\n trigger(self.context+'.create:fail',response)\n return response",
"title": ""
},
{
"docid": "c919eeab382364be48755fb0325bb20b",
"score": "0.6307647",
"text": "def test_status_code(self):\n # request\n request_body = {\n 'customer': self.customer.id,\n 'start_date': '2019-01-01',\n 'end_date': '2019-01-31'\n }\n response = self.client.post(reverse(self.view_name), request_body)\n # test response\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)",
"title": ""
},
{
"docid": "4bfd5ea30f15b96dfbdaaef36924efcf",
"score": "0.6293686",
"text": "def test_create_valid_user_success(self):\n\n payload = {\n \"email\": \"test@email.com\",\n \"password\": \"testpass\",\n \"name\": \"testname\",\n }\n\n res = self.client.post(CREATE_USER_URL, payload)\n self.assertEqual(res.status_code, status.HTTP_201_CREATED)",
"title": ""
},
{
"docid": "0f49801aaa4623cc1cabdc10d24e5cad",
"score": "0.62904984",
"text": "def post(self):\n\n return None, 201",
"title": ""
},
{
"docid": "7c9878abc14b7406f788d1873e20d6a5",
"score": "0.62835777",
"text": "def test_registration(self):\n res = self.register()\n result = json.loads(res.data.decode())\n self.assertEqual(result['message'], \"Account created successfully\")\n self.assertEqual(res.status_code, 201)",
"title": ""
},
{
"docid": "deb394a893eddbe3b5c64b97ad0ef156",
"score": "0.62662446",
"text": "def post(self):\n new_user = request.json\n user = AuthService.getUser(new_user[\"username\"])\n if not user:\n result = AuthService.createUser(new_user[\"username\"], new_user[\"password\"])\n return result, 201\n else:\n return {\"message\": \"Unable to create because the account with this username already exists\"}, 405",
"title": ""
},
{
"docid": "63c90cf6da1b1ba123a405d3a2bfbacb",
"score": "0.62624073",
"text": "def test_make_account(self):\n d = baker.make(\"Department\")\n f = MakeNewAccount({\n 'isaac': False,\n 'department':d.pk,\n 'college':d.college.pk,\n 'email':'kfldsj@klfjc.com',\n 'username':'jliver',\n 'password1':'pwpwpwpw',\n 'password2':'pwpwpwpw',\n 'first_name':\"Janey\",\n \"last_name\":\"Liverman\"\n })\n self.assertTrue(f.is_valid())",
"title": ""
},
{
"docid": "0fd339b9d33438a0e4d77dfbe22b24a4",
"score": "0.62520504",
"text": "def create():\n account_number = prompt_for_account_number('Account number', 'Invalid account number', False)\n account_name = prompt_for_account_name('Account name', 'Invalid account name')\n print 'Account creation successful'\n return ['create', account_number, account_name]",
"title": ""
},
{
"docid": "57342609889f38df4c59fd738100fdda",
"score": "0.624332",
"text": "def test_endpoint_creates_user(self):\n new_user = {\n \"username\": \"maina\",\n \"password\": \"password123\"\n }\n response = self.client.post('/api/v1/auth/register', data=new_user)\n # status CREATED\n self.assertEqual(response.status_code, 201)",
"title": ""
},
{
"docid": "d017f30f39d4fa6d9e153dc86751f304",
"score": "0.6190105",
"text": "def test_owner_create_auth_not_data(self):\n auth_headers = {\n 'HTTP_AUTHORIZATION': 'Bearer ' + self.token,\n }\n response = self.client.post(self.url+'owners', **auth_headers)\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)",
"title": ""
},
{
"docid": "abd1e57079207fcc6a2b810776954ad4",
"score": "0.6175377",
"text": "def test_create_account_invalid_JSON(self):\n response = self.client.post('/user-added', data=\"not json\", content_type='application/json')\n self.assertEqual(response.status_code, 500)",
"title": ""
},
{
"docid": "8983b900120ae5c0702b856f6d9c62a7",
"score": "0.6168217",
"text": "def validate_create(self):",
"title": ""
},
{
"docid": "2671d17e9c496b7821dfe099fed7315a",
"score": "0.61559886",
"text": "def assertResponseCreated(self, response):\n self.assertResponseCodeEquals(response, status.HTTP_201_CREATED)",
"title": ""
},
{
"docid": "1e312d21c5fcb838084247a047de0d71",
"score": "0.6153248",
"text": "def test_soa_create_function_catches_validation_errors():\n response = zone.create('not a domain', zone_type='Master')\n\n assert not response.success\n assert response.json()['error'] == 'Validation error.'",
"title": ""
},
{
"docid": "f8a28ed26bdb58f19afa2f8f2e0da672",
"score": "0.61497486",
"text": "def test_create_existing_user(self):\n payload = {\n 'email': 'john7ric@mail.com',\n 'name': 'Test Name',\n 'password': '123456'\n }\n create_user(**payload)\n\n res = self.client.post(CREATE_USER_URL, payload)\n\n self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)",
"title": ""
},
{
"docid": "4568bd35dc2837688c68650a751c4d78",
"score": "0.61482054",
"text": "def test_api_can_create_an_course(self):\n self.assertEqual(\n self.response_course.status_code,\n status.HTTP_201_CREATED\n )",
"title": ""
},
{
"docid": "e5f8c0993710003114f8e541edbe62e1",
"score": "0.6114058",
"text": "def test_api_can_create_data_points(self):\n\n if status.HTTP_201_CREATED == self.response.status_code:\n self.assertEqual(self.response.status_code, status.HTTP_201_CREATED)\n else:\n self.assertEqual(self.response.status_code, status.HTTP_204_NO_CONTENT)",
"title": ""
},
{
"docid": "c9351940430660af7462c6ba26234806",
"score": "0.6103559",
"text": "def test_create_valid_user(self):\n response=client.post(\"/user/\",self.valid_payload)\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)",
"title": ""
},
{
"docid": "6240ec238a7716f9fb3f49c10280efa9",
"score": "0.6097667",
"text": "def test_create_cloud_account(self):\n pass",
"title": ""
},
{
"docid": "87eabe16068bf995760be799845e9fff",
"score": "0.60956323",
"text": "def create_account():\n conn = sqlite3.connect('MoneyTransfer.db')\n c = conn.cursor()\n username = request.authorization['username']\n password = request.authorization['password']\n if not verify_non_duplicate_user(username):\n response = {\n 'error': 'Username is already in use'\n }\n return jsonify(response)\n else:\n\n conn = sqlite3.connect('MoneyTransfer.db')\n c = conn.cursor()\n hashed_password = generate_password_hash(password)\n credentials = (username, hashed_password, None,)\n\n c.execute(\"INSERT INTO users VALUES ('%s', '%s', 0.0, '%s')\" % credentials)\n conn.commit()\n conn.close()\n\n token = generate_access_token(username)\n response = {\n 'token': token[0],\n 'expiry': token[1]\n }\n return jsonify(response)",
"title": ""
},
{
"docid": "f09c1c5116588f62cad8836ed3a131db",
"score": "0.6086541",
"text": "def create_account(self, name: str) -> Tuple[bool, int]:\n\t\tfailure = f\"Account {name} could not be created.\"\n\n\t\tif name == Bank.BANK:\n\t\t\tself._status += failure + f\" {Bank.BANK} is not an allowed account name\"\n\t\t\treturn False, -1\n\t\tif self.find_account(name) > 0:\n\t\t\tself._status += f\"\\n{failure} Another account is already using the name {name}\"\n\t\t\treturn False, -1\n\t\tself._status += \"\\n\"\n\n\t\twhile True:\n\t\t\ttry:\n\t\t\t\taccount = Account(name)\n\t\t\texcept AssertionError as e:\n\t\t\t\tself._status = f\"{e}\"\n\t\t\t\treturn False, -1\n\t\t\tif account.account_number not in self.accounts.keys():\n\t\t\t\tverified = self._add_account(account)\n\t\t\t\tif verified:\n\t\t\t\t\treturn True, account.account_number\n\t\t\t\telse:\n\t\t\t\t\treturn False, -1\n\t\t\telse:\n\t\t\t\tprint(f\"{account.account_number} is in {self.accounts.keys()}\")",
"title": ""
},
{
"docid": "5d1e4ec891d5d2ac2e6c5d3fcd515b74",
"score": "0.60682124",
"text": "def test_register_with_success(self):\n url = '/auth/register'\n credentials = {'first_name': 'emre', 'last_name': 'koc', 'email': 'emreh134150@gmail.com', 'password': '1234qwer', 'username': 'emrekoc'}\n response = APIClient().post(url, credentials)\n self.assertEqual(response.status_code, 201)",
"title": ""
},
{
"docid": "b15ae9289c0c3f4e6dd7640652131c7d",
"score": "0.60630697",
"text": "def return_201_created_resource(resource_url):\n headers = {'Content-Type': CONTENT_TYPE_JSON,\n 'Location': resource_url,\n }\n\n body = {'code': 201,\n 'message': 'Successfully created resource',\n 'description': 'Successfully created resource',\n }\n\n return make_response_object(headers=headers,\n response_code=body['code'],\n body=dump_json(data=body, pretty_print=app_config.PRETTY_PRINT_JSON))",
"title": ""
},
{
"docid": "db3cbbc11434907e3ddd211f40ef67ef",
"score": "0.604901",
"text": "def test_create_organization_failure(self):\n self.client.force_authenticate(user=self.system_admin)\n data = {}\n request = self.client.post(\"/organization/\", data, format='json')\n self.assertEqual(request.status_code, status.HTTP_400_BAD_REQUEST)",
"title": ""
},
{
"docid": "1c946dc0adaf68a292083197fe932b6a",
"score": "0.60462755",
"text": "def test_api_can_create_a_case_study(self):\n self.assertEqual(\n self.response_case_study.status_code,\n status.HTTP_201_CREATED\n )",
"title": ""
},
{
"docid": "a22a3f02f19a9b91371f342b53bf92f9",
"score": "0.6043461",
"text": "def post(self):\n new_admin = request.json\n admin = AuthService.getAdmin(new_admin[\"username\"])\n if not admin:\n result = AuthService.createAdmin(new_admin[\"username\"], new_admin[\"password\"])\n return result, 201\n else:\n return {\"message\": \"Unable to create because the account with this username already exists\"}, 405",
"title": ""
},
{
"docid": "b846ca1a774079108134d6d1cd3f0830",
"score": "0.60405225",
"text": "def test_invalid_create(self):\n\n # TODO\n pass",
"title": ""
},
{
"docid": "146b8744f51e9fdf929a595d4855e7c1",
"score": "0.60393155",
"text": "def create_account(email, name, cred, url):\n try:\n req = url + '/accounts'\n res = requests.post(req, json={\n 'email': email,\n 'name': name,\n })\n render_res(res)\n except CLIError as e:\n render_error(e.asdict())",
"title": ""
},
{
"docid": "c375a6b384aded6095829563bbaef794",
"score": "0.60106546",
"text": "def test_api_can_create_an_education(self):\n self.assertEqual(\n self.response_education.status_code,\n status.HTTP_201_CREATED\n )",
"title": ""
},
{
"docid": "eb6be383f59c3f7838fcd75f98f4946c",
"score": "0.60092276",
"text": "def test_create_existing_user(self):\n payload = {\n \"name\": \"Nuno Geraldes\",\n \"email\": \"nhpgeraldes@gmail.com\",\n \"password\": \"existing123Test!\"\n }\n get_user_model().objects.create_user(**payload)\n\n response = self.client.post(self.USER_API_URL, payload)\n\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)",
"title": ""
},
{
"docid": "0a49866f0dcb56e592042bff0b20a40d",
"score": "0.59954596",
"text": "def create_account(self):\n account_type = DataValidation.check_valid_account_type(\"Would you like a checking or savings account? \")\n # exit create_account if user types '0' without proceeding to first_name prompt\n if not account_type:\n return None\n first_name = DataValidation.check_valid_name(\"What is your first name? \")\n # exit create_account if user types '0' without proceeding to last_name prompt\n if not first_name:\n return None\n last_name = DataValidation.check_valid_name(\"What is your last name? \")\n # exit create_account if user types '0' without proceeding to ssn prompt\n if not last_name:\n return None\n ssn = DataValidation.check_valid_ssn(\"What is your social security number? \")\n # exit create_account if user types '0' without proceeding to initial deposit prompt\n if not ssn:\n return None\n initial_deposit = DataValidation.get_valid_initial_deposit(account_type, \"What is your initial deposit? \")\n # exit create_account if user types '0' without proceeding to instanciate account\n if not initial_deposit:\n return None\n\n # create account and add customer to bank\n account = self.bank.add_account(initial_deposit, account_type)\n customer = Customer(first_name, last_name, ssn, account)\n self.bank.add_customer(customer)",
"title": ""
},
{
"docid": "649e302f6a691f60c65604bff6d1d73e",
"score": "0.59934103",
"text": "def create_account(self, username, **kwargs):\n status, data = self.run_gerrit_command('create_account', username, **kwargs)\n\n return status, data",
"title": ""
},
{
"docid": "d71890b3aa6fb4053b9e4e019004c365",
"score": "0.5991471",
"text": "def post(self):\n\n db = get_db()\n if 'username' not in request.form:\n raise RequestError(422, 'username required')\n elif 'password' not in request.form:\n raise RequestError(422, 'password required')\n else:\n response = jsonify(db.insert_account(request.form['username'],\n request.form['password']))\n\n return response",
"title": ""
},
{
"docid": "a44347f76b903eb8fa92ef980df5158b",
"score": "0.5980715",
"text": "def user_create_account():\r\n if('username' not in request.form or\r\n 'password' not in request.form or\r\n 'password_confirm' not in request.form):\r\n\r\n return jsonify(error=True,\r\n message=\"Invalid submission.\")\r\n\r\n if not request.form['password'].strip():\r\n return jsonify(error=True,\r\n message=\"You must enter a non-empty password!\")\r\n\r\n if request.form['password'] != request.form['password_confirm']:\r\n return jsonify(error=True,\r\n message=\"Passwords do not match!\")\r\n\r\n if not MONGO_CLIENT.is_username_available(register_type=\"user\",\r\n username=request.form['username']):\r\n return jsonify(error=True,\r\n message=\"That username is already registered!\")\r\n\r\n user_id = MONGO_CLIENT.add_user(username=request.form['username'],\r\n password=request.form['password'])\r\n\r\n flask.session['id'] = user_id\r\n flask.session['is_user'] = True\r\n\r\n return jsonify(redirect=url_for('index'))",
"title": ""
},
{
"docid": "278f004005d7d7b0970c26ee33c27cf2",
"score": "0.5976983",
"text": "def test_create_account_creation_form(self):\n account_creation_form = self.create_account_creation_form(\n \n )",
"title": ""
},
{
"docid": "653bdad0614b02659ba386edaccdedc9",
"score": "0.5976581",
"text": "def test_cancel_account_failure(self):\n # create a account to cancel\n test_account = AccountFactory()\n resp = self.app.post('/accounts',\n json=test_account.serialize(),\n content_type='application/json')\n self.assertEqual(resp.status_code, status.HTTP_201_CREATED)\n\n # cancel the account\n new_account = resp.get_json()\n # new_account['status'] = 'Cancelled'\n resp = self.app.put('/accounts/{}/cancel'.format(23),\n json=new_account,\n content_type='application/json')\n self.assertEqual(resp.status_code, status.HTTP_404_NOT_FOUND)",
"title": ""
},
{
"docid": "e76c149c5fd7232ab638fb96d6cf0ef4",
"score": "0.59712636",
"text": "def test_api_can_create_a_gene(self):\n self.assertEqual(self.response.status_code, status.HTTP_201_CREATED)",
"title": ""
},
{
"docid": "d5f27a13c3b9198b115361195147a8fc",
"score": "0.59707093",
"text": "def create_account(**account_details):\n return AccountModel(**account_details).save()",
"title": ""
},
{
"docid": "4b34b051aac13d1c592aa6cc724c2900",
"score": "0.596352",
"text": "def click_create_account(self):\n # from nose.tools import set_trace;set_trace()\n self.wait_for_an_element_to_be_present(CREATE_ACCOUNT).click()\n try:\n self.wait_for_an_element_to_be_present(SUBMIT_ACCOUNT)\n return self.wait_for_an_element_to_be_present('.page-subheading').text\n except:\n pass",
"title": ""
},
{
"docid": "2804c40d4d0416f50a608279ef221a68",
"score": "0.5962734",
"text": "def test_api_can_create_plant(self):\n\n self.assertEqual(self.response.status_code, status.HTTP_201_CREATED)",
"title": ""
},
{
"docid": "175fe1914123e2f984957c1ce0e4085f",
"score": "0.5948452",
"text": "def _create(self, url, body):\n code = HTTP_INTERNAL_SERVER_ERROR\n resource_id = None\n location = None\n resource = None\n error = {\n \"status\": {\n \"code\": code,\n \"message\": \"The resource couldn't be created\"}}\n try:\n response = requests.post(url + self.auth, headers=SEND_JSON,\n data=body)\n\n code = response.status_code\n\n if code == HTTP_CREATED:\n location = response.headers['location']\n resource = json.loads(response.content, 'utf-8')\n resource_id = resource['resource']\n error = None\n elif code in [\n HTTP_BAD_REQUEST,\n HTTP_UNAUTHORIZED,\n HTTP_PAYMENT_REQUIRED,\n HTTP_NOT_FOUND]:\n error = json.loads(response.content, 'utf-8')\n else:\n LOGGER.error(\"Unexpected error (%s)\" % code)\n code = HTTP_INTERNAL_SERVER_ERROR\n\n except ValueError:\n LOGGER.error(\"Malformed response\")\n except requests.ConnectionError:\n LOGGER.error(\"Connection error\")\n except requests.Timeout:\n LOGGER.error(\"Request timed out\")\n except requests.RequestException:\n LOGGER.error(\"Ambiguous exception occurred\")\n\n return {\n 'code': code,\n 'resource': resource_id,\n 'location': location,\n 'object': resource,\n 'error': error}",
"title": ""
},
{
"docid": "beef95457828de05d1c9c8a140799698",
"score": "0.5946733",
"text": "def test_update_account_failure(self):\n # create a account to update\n test_account = AccountFactory()\n resp = self.app.post('/accounts',\n json=test_account.serialize(),\n content_type='application/json')\n self.assertEqual(resp.status_code, status.HTTP_201_CREATED)\n\n # update the account\n new_account = resp.get_json()\n new_account['account_id'] = 2\n resp = self.app.put('/accounts/{}'.format(5),\n json=new_account,\n content_type='application/json')\n self.assertEqual(resp.status_code, status.HTTP_404_NOT_FOUND)",
"title": ""
},
{
"docid": "21b7a63c22c6ffe569eb75ae4af2b506",
"score": "0.5941502",
"text": "def test_user_exists(self):\n\n payload = {\n \"email\": \"test@gmail.com\",\n \"password\": \"Test1234\",\n \"name\": \"Test\"\n }\n create_user(**payload)\n\n response = self.client.post(CREATE_USER_URL, payload)\n\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)",
"title": ""
},
{
"docid": "d09bc5e74935ca1d90d31884a967eb4c",
"score": "0.5941156",
"text": "def test_create_valid_user_successfully(self):\n\n payload = {\n \"email\": \"test@gmail.com\",\n \"password\": \"Test1234\",\n \"name\": \"Test Name\"\n }\n response = self.client.post(CREATE_USER_URL, payload)\n\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n\n # From here we just want to be sure, whether a user is created or not\n user = get_user_model().objects.get(**response.data)\n # API and HTTP post method return response of its values after\n # successful attempt and now we are taking it and assigning it to user\n self.assertTrue(user.check_password(payload['password']))\n self.assertNotIn(\"password\", response.data)",
"title": ""
},
{
"docid": "229f69e2faf2fb95ef74b8f0eedb802c",
"score": "0.59388924",
"text": "def test_create_competition_api(self):\n client = Client()\n\n res = client.post('/api/competition/', {'title': 'Test Title',\n 'description': 'Description',\n 'creator': self.user.pk,\n 'modified_by': self.user.pk,\n })\n # Status 201: Created\n self.assertEqual(int(res.status_code), int(201))\n data = json.loads(res.content)\n\n # Just checking to make sure the data was returned correctly\n self.assertTrue('id' in data and data['id'] >= 1)",
"title": ""
},
{
"docid": "bf682766f974a65507f135e569eb9806",
"score": "0.5925374",
"text": "def test_user_is_created(self):\r\n result = self.client.post(\"api/v1/auth/register\", data=self.test_user)\r\n self.assertEqual(result.status_code, 201)\r\n res = json.loads(result.data)\r\n self.assertDictEqual(\r\n res, {\"status\": \"success\", \"message\": \"User registered successfully\"}, \"User registration failed\")",
"title": ""
},
{
"docid": "0a9e08180d5e3c06b1f7624fb025b815",
"score": "0.59208757",
"text": "def test_api_can_create_a_resource_category(self):\n self.assertEqual(\n self.response_resource_category.status_code,\n status.HTTP_201_CREATED\n )",
"title": ""
},
{
"docid": "cb3d1b24d1bae4ceea07d916f8a5e611",
"score": "0.59125984",
"text": "def test_schedule_create_status_code(self):\n # request\n request_body = {\n 'periodic_task': {\n 'minute': '0',\n 'hour': '2',\n 'day_of_week': '*',\n 'day_of_month': '*',\n 'month_of_year': '*',\n },\n 'customer': self.customer.id,\n 'task_type': 'watchman'\n }\n response = self.client.post(reverse(self.view_name), request_body, format='json')\n # test response\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)",
"title": ""
},
{
"docid": "7b6ddaf2388158cc094830ee9eaae15a",
"score": "0.58993626",
"text": "def register_account_endpoint():\n json = request.get_json()\n if json:\n try:\n account = get_account(json)\n\n try:\n sqldb.session.add(account)\n sqldb.session.commit()\n except IntegrityError:\n sqldb.session.rollback()\n account = update_account(account)\n sqldb.session.commit()\n\n degrees = json.get(\"degrees\")\n if degrees:\n add_schools_and_majors(account, degrees)\n\n courses = json.get(\"courses\")\n if courses:\n add_courses(account, courses)\n\n return jsonify({'account_id': account.id})\n except KeyError as e:\n return jsonify({'error': str(e)}), 400\n else:\n return jsonify({'error': \"JSON not passed\"}), 400",
"title": ""
},
{
"docid": "91d3c6e268013b1daa65064e7e394a60",
"score": "0.5899133",
"text": "def test_signup_status_code(self):\n self.assertEquals(self.response.status_code, 200)",
"title": ""
},
{
"docid": "670adf233c2f9899ecff96405e94f5ef",
"score": "0.5897165",
"text": "def test_create_book(self):\n\n res = self.client().post(\n '/books', json=self.new_book)\n data = json.loads(res.data)\n\n self.assertEqual(res.status_code, 200)\n self.assertEqual(data['success'], True)\n self.assertTrue(data['created_book_id'])\n self.assertTrue(data['total_books'])",
"title": ""
},
{
"docid": "6251d5afd0771ef74d5c0c7afe14e405",
"score": "0.58940375",
"text": "def test_create_successful(self):\n response = self.client().post('/register', data=self.register_data)\n self.assertEqual(response.status_code, 302)\n response = self.client().post('/login', data=self.login_data)\n self.assertEqual(response.status_code, 302)\n response = self.client().post('/create_category', data=self.category_data)\n self.assertEqual(response.status_code, 302)",
"title": ""
},
{
"docid": "7063106fdd571f3241f08bfa4452a1ae",
"score": "0.58922017",
"text": "def post(self):\n parser = reqparse.RequestParser()\n parser.add_argument('first_name', type=str, required=True,\n help='First name must be a valid string')\n parser.add_argument('last_name', type=str, required=True,\n help='Last name must be a valid string')\n parser.add_argument('email', type=str, required=True,\n help='Email must be a valid email')\n parser.add_argument('password', type=str, required=True,\n help='Password must be a valid string')\n data = parser.parse_args()\n if (data['first_name'].strip() == \"\") or (data['last_name'].strip() == \"\") or (data['email'].strip() == '') or (data['password'].strip() == ''):\n return make_response(jsonify({\n 'status': 'failed',\n 'message': 'The fistname or lastname or email or password can not be empty.'\n }), 400)\n if (not data['first_name'].isalpha()) or (not data['last_name'].isalpha()):\n return make_response(jsonify({\n 'status': 'failed',\n 'message': 'Firstname or Lastname is invalid'\n }), 400)\n if not re.match(\"[^@]+@[^@]+\\.[^@]+\", data['email']):\n return make_response(jsonify({\n 'status': 'failed',\n 'message': 'Provided email is not a valid email address.'\n }), 400)\n if len(data['password']) < 4:\n return make_response(jsonify({\n 'status': 'failed',\n 'message': 'Password must be atleast 4 characters in length.'\n }), 400) \n user = User(data['first_name'], data['last_name'], data['email'],data['password'] )\n result = user.get_user_by_email(data['email']) \n if result !=0:\n return make_response(\n jsonify({\n 'status': \"failed\",\n 'message': 'This email is already used',\n }), 400)\n user.add_user()\n return make_response(jsonify({\n 'status': \"success\",\n 'message': 'Account successfully created',\n }), 201)",
"title": ""
},
{
"docid": "24b442fc344b759f5fd0da464c4495cb",
"score": "0.5888209",
"text": "def test_failed_create(self):\n resp = self.client.post('/api/v1/acknowledgement-item/', format='json',\n data=self.item_data)\n #self.assertHttpMethodNotAllowed(resp)",
"title": ""
},
{
"docid": "bfa10cc77aeaf52df8c5e41194f5a9ef",
"score": "0.5883143",
"text": "def test_user_exists(self):\n payload = {\n 'email': 'test2@gmail.com',\n 'password': 'pass987',\n 'name': 'Name Testing'\n }\n create_user(**payload)\n\n res = self.client.post(CREATE_USER_URL, payload)\n self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)",
"title": ""
},
{
"docid": "172dfb8d3b3b2b88afae8667da81c26c",
"score": "0.587631",
"text": "def create_new_account(self) -> bool:\n try:\n # Create a new profile (assumes that none already exists)\n self.usersRef.document(u'{}'.format(self.username)).set({\n \"profileVersion\" : \"1.0.0\",\n \"username\" : self.username,\n \"password\" : self.password\n })\n return True\n except Exception as e:\n logging.info(\"%s\", e)\n return False",
"title": ""
},
{
"docid": "9e1e308bf8147668f571bc6a66ee7a09",
"score": "0.58736867",
"text": "def test_create_account(self):\n eprint(\"test_create_account\")\n url = reverse('api_user_create')\n print(url)\n data = {\"email\": \"me@easywork.me\", \"id\": \"admin\", \"user_type\": \"work\", \"password\": \"123123\", \"first_name\": \"firstname\", \"last_name\": \"lastname\"}\n response = self.client.post(url, data, format='json')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n self.assertEqual(BaseUser.objects.count(), 1)\n self.assertEqual(BaseUser.objects.get().first_name, 'firstname')",
"title": ""
},
{
"docid": "d2e64f402ec2fb085f9f7faf239356ef",
"score": "0.58711344",
"text": "def test_successful_registration(self):\n with self.client:\n response = register_user(\n self, 'Random', 'User', 'random@user.com', 'aaaAAA111')\n data = json.loads(response.data.decode())\n self.assertTrue(data['status'] == 'success')\n self.assertTrue(data['msg'] ==\n \"Account for 'random@user.com' has been created.\")\n self.assertTrue(response.content_type == 'application/json')\n self.assertEqual(response.status_code, 201)\n truncate_tables()",
"title": ""
},
{
"docid": "10a3a3992711b16b0bb7321933a3e68f",
"score": "0.587011",
"text": "def create_transaction(account_id):\r\n\r\n create_transaction_url_part1 = \"https://sandbox.capitalone.co.uk/developer-services-platform-pr/api/data/transactions/accounts/\"\r\n create_transaction_url_part2 = \"/create\"\r\n create_transaction_url = create_transaction_url_part1 + account_id + create_transaction_url_part2\r\n\r\n # Number of transactions to create - default 1\r\n quantity = '{\"quantity\": \"1\"}' # Creates transaction/s on specified account_id\r\n create_transaction_response = requests.post(url=create_transaction_url, headers=capitalone_request_header_dict, data=quantity)\r\n\r\n create_transaction_output = create_transaction_response.json()\r\n #print(create_transaction_output)\r\n created_transaction_id = create_transaction_output['Transactions'][0]['transactionUUID']\r\n #print(created_transaction_id)\r\n\r\n return created_transaction_id",
"title": ""
},
{
"docid": "acec8786c25fd3db77589eb4ce33cb57",
"score": "0.5853402",
"text": "def test_if_user_exists(self):\n\n payload = {\n \"email\": \"test@email.com\",\n \"password\": \"testpass\",\n \"name\": \"testname\",\n }\n\n create_user(**payload)\n res = self.client.post(CREATE_USER_URL, payload)\n\n self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)",
"title": ""
},
{
"docid": "a81850fd00bc3bc50f70e66acb61e905",
"score": "0.5844745",
"text": "def test_create_organization_record_succeeds(self, client, org_data):\n\n response = client.post('/api/v1/organization/', org_data['testorg'])\n assert response.status_code == 201\n assert response.data['message'] == SUCCESS['create_entry'].format(\n org_data['testorg']['name'])",
"title": ""
},
{
"docid": "88e4f5ace51a7b7c7ed81b7812d3e87b",
"score": "0.5844366",
"text": "def test_create_with_data(self):\n data = self._get_default_post_data()\n check = self._get_default_post_data()\n\n response = self._create(data=data)\n # check initial state is correct\n\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n self.assertResponseKeys(response)\n\n self.assertThirdPartyDetailsEqual(response.data, check)",
"title": ""
},
{
"docid": "ac9554873f9d77b830f073cbad2e864b",
"score": "0.5842989",
"text": "def newAccount():\n if request.method == 'POST':\n newAccount = Account(company=request.form['company'],\n address=request.form['address'], city=request.form['city'],\n state=request.form['state'],\n zipcode=request.form['zipcode'],\n tel=request.form['tel'],\n fax=request.form['fax']\n )\n session.add(newAccount)\n session.commit()\n flash(\"New account created!\")\n return redirect(url_for('showAccounts'))\n else:\n return render_template('newaccount.html')",
"title": ""
},
{
"docid": "3dc328468c63fb9930a28617346c3674",
"score": "0.58413434",
"text": "def test_create_user(self):\n username = 'cara'\n email = 'cara@snakes.com'\n with self.client:\n response = self.client.post(\n '/users',\n data = json.dumps(dict(\n username=username,\n email=email\n )),\n content_type='application/json',\n )\n\n data = json.loads(response.data.decode())\n self.assertEqual(response.status_code, 201)\n self.assertIn('cara@snakes.com was added!', data['message'])\n self.assertIn('success', data['status'])\n user = User.query.filter_by(email=email).first()\n self.assertFalse(user is None)",
"title": ""
},
{
"docid": "d0b5e1113a991b975d4838430fcc86f7",
"score": "0.58411705",
"text": "def test_user_exist(self):\n payload = {'email':'demo@a.com','password':'ashikashikashik'}\n create_user(**payload)\n response = self.client.post(CREATE_USER_URL, payload)\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)",
"title": ""
},
{
"docid": "6f7300f9811b81a9b7107df5aab19165",
"score": "0.58411145",
"text": "def create(self, request, *args, **kwargs):\r\n serializer = self.get_serializer(data=request.data)\r\n serializer.is_valid(raise_exception=True)\r\n user = self.perform_create(serializer)\r\n\r\n # Setting variables that are content of an email\r\n current_site = get_current_site(request)\r\n html_template = 'Users/account_activation.html'\r\n subject = 'Account activation.'\r\n\r\n # sending verification link to the provided email address\r\n user.send_mail(current_site, html_template, subject)\r\n # If no exception has been raised, 201 is returned.\r\n\r\n return Response(serializer.initial_data,\r\n status=status.HTTP_201_CREATED,\r\n headers=self.get_success_headers(serializer.initial_data))",
"title": ""
},
{
"docid": "46b0ba9077b204295608b4900dc8c615",
"score": "0.58255994",
"text": "def test_create200(self):\n created_postcard = self.api.create(self.postcard_editable)\n self.psc_ids.append(created_postcard.id)\n self.assertIsNotNone(created_postcard.id)",
"title": ""
},
{
"docid": "7e6d5082504e703b333d5906b0743826",
"score": "0.5824919",
"text": "def test_api_can_create_a_menu_item(self):\n self.assertEqual(\n self.response_menu_item.status_code,\n status.HTTP_201_CREATED\n )",
"title": ""
},
{
"docid": "2993e9ca1c978e17d7e0d027e7f92a77",
"score": "0.5821878",
"text": "def create(self, request: Request, *args, **kwargs) -> Response:\n return super().create(request, *args, **kwargs)",
"title": ""
},
{
"docid": "f9c84552eb1cbbfd7986cbb5c3b35fd1",
"score": "0.5821029",
"text": "def test_insert_account_and_related_permission_using_post(self):\n pass",
"title": ""
},
{
"docid": "a5aff55a0b6c448a0bc657c202a6a2e7",
"score": "0.58189654",
"text": "def create(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n self.perform_create(serializer)\n headers = self.get_success_headers(serializer.data)\n return Response({'id': serializer.data['id']}, status=status.HTTP_201_CREATED, headers=headers)",
"title": ""
},
{
"docid": "2ac7353d239ae1a669460c8a268f7223",
"score": "0.58046",
"text": "def test_create_user_and_user_exists(self):\n create_user(**MOCKED_USER)\n\n res = self.client.post(CREATE_USER_URL, MOCKED_USER)\n\n self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)",
"title": ""
},
{
"docid": "f133c9892732db4f72df703594091c05",
"score": "0.58022493",
"text": "def test_create_user(self, *_):\n user = User(email=\"email@test.com\", password=\"test_password\")\n response = self.client.open(\n '/api/v1/user/create',\n method='POST',\n data=json.dumps(user),\n content_type='application/json')\n self.assert200(response,\n 'Response body is : ' + response.data.decode('utf-8'))",
"title": ""
},
{
"docid": "6e76c752adc3e1628421235e052cf52c",
"score": "0.5802233",
"text": "def test_create_user_with_valid_data_success(self):\n res = self.client().post('/api/v2/auth/signup', data=self.user)\n self.assertEqual(res.status_code, 201)\n res = res.get_json()\n\n self.assertEqual(res['data']['message'],\n 'You have successfuly signed up')",
"title": ""
},
{
"docid": "c6fcf651a49a3cd91859d7b2b734f4de",
"score": "0.5796995",
"text": "def test_request_post(self):\n client = APIClient() # creating client\n request_data = {'client_email': 'test@mail.com',\n 'credential_type': 2,\n 'credential': 'apple.com',\n 'description': 'I like pears more',\n }\n\n url = '/api/forbidden/request/create/'\n request = client.post(url, request_data, format='json')\n self.assertEqual(request.status_code, 201) # expecting status 201 - created",
"title": ""
},
{
"docid": "f3d0ea9d81c2d0aa0a9d08dbcb1c3752",
"score": "0.57964325",
"text": "def post(self):\n request_error = self.__validateRegisterRequest(request)\n if request_error:\n return jsonify(error_message=request_error), 400\n self.__registerUserFromRequest(request)\n return \"Created\", 201",
"title": ""
},
{
"docid": "e3eb9c9c9d7415715e0dbcd0c1a80e77",
"score": "0.5790073",
"text": "def test_create_new_booking(self, client):\n\n data = BookingData().random()\n res = client.create_booking(data)\n assert res.status_code == 200\n booking_info = res.json()\n assert booking_info.get(\"booking\") == data\n assert is_validate(booking_info, post_booking_schema), \"Check booking schema\"",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="753"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">3916e3eb546ea428885852405cc3f11e</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Ensambla el sistema de ecuaciones</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "5ae8f9ab0e1f89c7367e33b976ebc38c",
"score": "0.53271186",
"text": "def ensamblar_sistema(self):\n \n Ngdl = self.Nnodos * self.Ndimensiones\n\n self.K = np.zeros((Ngdl,Ngdl), dtype=np.double)\n self.f = np.zeros((Ngdl), dtype=np.double)\n self.u = np.zeros((Ngdl), dtype=np.double)\n \n for b in self.barras:\n \n ke = b.obtener_rigidez(self)\n fe = b.obtener_vector_de_cargas(self)\n \n ni,nj = b.obtener_conectividad()\n \n d = [[2*ni,2*ni+1],[2*nj,2*nj+1]]\n \n for i in range(self.Ndimensiones):\n p = d[i]\n for j in range(self.Ndimensiones):\n q = d[j]\n \n self.K[p,q] += ke[i,j]\n \n self.f[p] += fe[j]",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "230aeb41b55eff2e4983667e22fba701",
"score": "0.6246159",
"text": "def iniciaconfiguracion():\n\tos.system(\"CLS\")\n\truta = os.getcwd()\n\trutatask = ruta + \"\\\\syncloud.exe\"\n\ttimetask = \"\"\n\tkey = _winreg.CreateKey(_winreg.HKEY_LOCAL_MACHINE, 'SOFTWARE\\\\Olesistemas\\\\syncloud')\n\ttry:\n\t\t_winreg.SetValueEx(key, 'Location', 0, _winreg.REG_SZ, ruta)\n\texcept:\n\t\tprint(\"No se ha podido crear el registro del systema\")\n\truta = monolib.validapath(ruta)\n\tprint(ruta)\n\tif os.path.exists(ruta):\n\t\tprint (\"Carpeta Principal encontrada...\")\n\t\tfor rutafinal in rutas:\n\t\t\tcreatefolder(ruta+rutafinal)\n#Se conecta a la base sqlite del programa\n\t\testado = dataconnect.createbase(ruta + \"configuracion/system.db\")\n\t\tif estado:\n\t\t\tprint(\"Base de sistema creada...\\n\")\n\t\t\tdataconnect.createtables(ruta + \"configuracion/system.db\")\n\t\t\trutabase = ruta + \"configuracion/system.db\"\n#cargando parametros por defecto desde archivo account.txt\n\t\t\tif not os.path.isfile(ruta + \"configuracion/account.txt\"):\n\t\t\t\tlistabd = \"\"\n\t\t\telse:\n\t\t\t\tlistabd = monolib.loadparameters(ruta + \"configuracion/account.txt\")\n\t\t\tfor i in listabd:\n#preconfiguracion en archivo account\n\t\t\t\tfor parametro in listaid:\n\t\t\t\t\tif i[0] == parametro:\n\t\t\t\t\t\tprint (\"Valor aceptado: %s\" % i)\n\t\t\t\t\t\tif not dataconnect.existreg(rutabase, parametro):\n\t\t\t\t\t\t\tdataconnect.createreg(rutabase, parametro, i[1])\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tdataconnect.updatereg(rutabase, parametro, i[1])\n#aqui empieza a pedir datos de configuracion\n\t\t\tprint(\"Preconfiguracion desde archivo terminada\\n\\n\")\n\t\t\tingresomanual = autopy.alert.alert(\"Ingresar paramentros manualmente?\",\"Syncloud\", \"SI\", \"NO\")\n\t\t\tfor ids in listaid:\n\t\t\t\tregvalid = dataconnect.existreg(rutabase, ids)\n\t\t\t\tvalorid = dataconnect.searchreg(rutabase, ids)\n\t\t\t\t_updatevalor = valorid\n\t\t\t\tif ingresomanual:\n\t\t\t\t\tmanualinput(regvalid,ids,valorid,_updatevalor,rutabase)\n\t\t\t\tif ids == \"timebackup\":\n\t\t\t\t\ttimetask = _updatevalor\n\t\telse:\n\t\t\tprint (\"No se ha podido verificar la ruta: %s \\n\" % ruta)\n\ttaskstate = os.system('schtasks /create /sc DAILY /TN \"Syncloud\" /TR \"%s -s\" /ST %s /RU SYSTEM /F' % (rutatask, timetask))\n\tif(taskstate != 0):\n\t\tprint(\"Error al crear la tarea programada, %s\" % taskstate)\n\ttaskstate2 = os.system('schtasks /create /SC ONSTART /TN \"VisualWeb\" /TR \"%s -v\" /RU SYSTEM /F' % (rutatask))\n\tif(taskstate2 != 0):\n\t\tprint(\"Error al crear la tarea programada, %s\" % taskstate2)\n\tprint (\"\\nConfiguracion finalizada en %s \\n\" % ruta)\n\tos.system(\"PAUSE\")",
"title": ""
},
{
"docid": "63b79fef116c20bc09257eb6b59f2162",
"score": "0.5960316",
"text": "def obter_casosdesucesso():",
"title": ""
},
{
"docid": "74a4edb1e32174be7d18573b23b5cb73",
"score": "0.59070057",
"text": "def tag_admin(self):\n self.desregistrar_eventos()\n if self.tiene_papel:\n self.expulsar_boleta()\n self.modulo.salir()",
"title": ""
},
{
"docid": "651df13801d7a34d72e0cb56f44d51ae",
"score": "0.55565184",
"text": "def bootup(self):\n pass",
"title": ""
},
{
"docid": "651df13801d7a34d72e0cb56f44d51ae",
"score": "0.55565184",
"text": "def bootup(self):\n pass",
"title": ""
},
{
"docid": "72f75cbc26028f2eef7cf9e7676a3a46",
"score": "0.5533404",
"text": "def __stergere_all(self):\n if not self.__srv_client.get_all():\n print(\"Nu exista clienti!\")\n else:\n self.__srv_client.sterge_all()\n print(\"Stergere efectuata cu succes!\")",
"title": ""
},
{
"docid": "004e6d1ac47110d4b65fc24526253d2a",
"score": "0.54918224",
"text": "def allegro_service():\r\n\r\n # read from config.txt values for authorization with allegro\r\n config = 'config.txt'\r\n\r\n client_id = files.find_value('client_id', config)\r\n client_basic64 = files.find_value('client_basic64', config)\r\n oauth_url = files.find_value('oauth_url', config)\r\n token_url = files.find_value('token_url', config)\r\n refresh_token_url = files.find_value('refresh_token_url', config)\r\n\r\n last_event = files.find_value('last_event', 'tmp.txt')\r\n\r\n auth = authorization.Auth(client_id, client_basic64, oauth_url, token_url, refresh_token_url)\r\n\r\n while True:\r\n\r\n # sprawdz czy jest internet\r\n if internet_check.check():\r\n\r\n try:\r\n access_token = auth.authorize()\r\n except authorization.AuthException as e:\r\n print(e)\r\n break\r\n\r\n # jezeli autoryzacja sie powiodla\r\n if access_token:\r\n data = Data(access_token)\r\n\r\n if not data.check_last_event(last_event):\r\n if last_event == '':\r\n last_event = data.get_latest_event()\r\n\r\n save_attempts = 0\r\n while save_attempts < 5:\r\n\r\n save_flag = data.save_orders(last_event)\r\n # pobierz najnowsze zamowienia i zapisz do bazy danych\r\n if save_flag != -1:\r\n last_event = data.get_latest_event()\r\n files.save_value('last_event', last_event, 'tmp.txt')\r\n break\r\n\r\n save_attempts += 1\r\n\r\n else:\r\n # TODO: obsłużyć ten wyjątek\r\n print('Houston mamy problem')\r\n\r\n time.sleep(5 * 60)",
"title": ""
},
{
"docid": "24ca14b4e6dcd093c96b9039784266b9",
"score": "0.5452101",
"text": "def manager(self):\n\n # Create the files and directories of the system in the server.\n FileInit.init_files()\n\n # Connect to the data base.\n self.general.data_base.connect(\"System Data\")\n\n self.build_data_base()\n\n # The instance this client is going to use in order to commit ant type of outside communication.\n general.com = Communication(general)\n\n # Start the communication thread. Receive and Send are beginning to operate in the background.\n general.com.start()\n\n while not self.general.shut_down:\n\n # Generate new ids if necessary.\n self.generate_ids()\n\n # Enter all the new entities to the data base.\n self.sign_up_new_entities_to_db()\n\n # Check if a client or executor has disconnected from the server or has a network problem. Disconnect him if\n # he does.\n self.look_for_disconnected_clients()\n self.look_for_disconnected_executors()\n\n # Check if the manager wants to delete something from the data base.\n self.delete_from_db()\n\n # If there are new requests, add them to the requests to handle list.\n self.look_for_new_requests()\n\n # Handle all the requests that are currently waiting for the server for setting them up in the system.\n self.handle_requests()\n\n # Check if new data packages has received and update the system according to them.\n # self.look_for_data_packages()\n\n if self.general.update_data_base_gui:\n\n self.general.window.cp_window.pub_update_records(self.general.data_base.db_to_string())\n\n self.general.update_data_base_gui = False\n\n # The server has to shut down. Stop the main process.\n if not self.general.gui_closed:\n self.general.window.on_quit(wx.EVT_CLOSE)\n\n sys.exit()",
"title": ""
},
{
"docid": "8df0124bd5d2651620db3623b4a2d114",
"score": "0.54052675",
"text": "def consutil():\n\n if os.geteuid() != 0:\n click.echo(\"Root privileges are required for this operation\")\n sys.exit(1)",
"title": ""
},
{
"docid": "e8e935350a0e103062f6ca4feeeffd15",
"score": "0.5386726",
"text": "def config():\n if os.geteuid() != 0:\n exit(\"Root privileges are required for this operation\")",
"title": ""
},
{
"docid": "14eaf62c4672cf686b0d06f0846ca55e",
"score": "0.53619325",
"text": "def consutil():\n\n if os.geteuid() != 0:\n click.echo(\"Root privileges are required for this operation\")\n sys.exit(ERR_CMD)",
"title": ""
},
{
"docid": "6c516f5aa85f98b4bf2b5c4dc8ab6100",
"score": "0.53490853",
"text": "def startup(self):\n pass",
"title": ""
},
{
"docid": "6c516f5aa85f98b4bf2b5c4dc8ab6100",
"score": "0.53490853",
"text": "def startup(self):\n pass",
"title": ""
},
{
"docid": "6c516f5aa85f98b4bf2b5c4dc8ab6100",
"score": "0.53490853",
"text": "def startup(self):\n pass",
"title": ""
},
{
"docid": "41e0625adf48cd80401a180863374ece",
"score": "0.5343954",
"text": "def test_init_system(self):\n d2 = date.datetime(1998, 4, 23)\n self.user_controller.add_system_admin(\"admin\", \"1234\", \"ro\", d2)\n admin = self.user_controller.get_user_by_name('admin')\n self.assertTrue(self.user_controller.confirm_user('admin', '1234'))\n self.assertRaises(AssertionError, self.user_controller.delete_signed_user, admin.user_id)\n self.user_db.delete_user(admin.user_id)",
"title": ""
},
{
"docid": "d7faa7e403535ac6c676f5063895fe91",
"score": "0.5331495",
"text": "def evo_os(self):\n logger.info(\"entering evo_os()\")\n result, stdout = self.ss.run(\"test -e /usr/sbin/evo-pfemand\")\n return result",
"title": ""
},
{
"docid": "3c12f0bec4d75916ee886e6a15dcc754",
"score": "0.5324039",
"text": "def main():\n\n if not exists('configs'):\n system('mkdir configs')\n\n try:\n\n archivo_csv = convertir_excel_csv('docs/Direccionamiento_Sucursales.xlsx')\n\n except FileNotFoundError:\n\n print(f'ERROR! No se encontro la base de datos ni en formato xlsx ni en csv')\n exit(0)\n\n with open(archivo_csv) as d:\n \n for row in d:\n clean_row = row.replace('\\n','')\n line = clean_row.split(',')\n \n if line[0] != 'PAIS':\n \n try:\n ips_sitio = subnetting_sitio(line[4])\n valores = crear_valores_jinja(line,ips_sitio)\n jinja_data = crear_jinja_data('docs/plantilla_config.j2',valores)\n crear_archivo_config(valores,jinja_data)\n\n except:\n print(f'ERROR! Problemas en linea: {line}')\n\n print('Trabajo Finalizado!')",
"title": ""
},
{
"docid": "51606b1eec482c9f06b547719712cc25",
"score": "0.5321659",
"text": "def execution_at_startup():\n\n # create_folder()\n # add_final_processing()\n unlock_all_tasks()\n # add_admin_user()",
"title": ""
},
{
"docid": "47faf2d2a5cde8525a7dc63020c13b73",
"score": "0.5313866",
"text": "def setupServices(self):\n\t\tpass",
"title": ""
},
{
"docid": "c4df0650b91a388603c0d86772f84a13",
"score": "0.53006375",
"text": "def an_entrando_cola(self):\n while 1:\n self.cargar_fondo()\n en = self.acl.entrando_cola()\n self.an_update()\n if en:\n self.acl.agregar_cola()\n return",
"title": ""
},
{
"docid": "c734afc5bfb2353826de941c42d40dbd",
"score": "0.52192634",
"text": "def actualizar(self):\r\n pass",
"title": ""
},
{
"docid": "814c445dc8bd3eac37264040c6b0f4c4",
"score": "0.5202413",
"text": "def reiniciar(self):\n\t\tself.dados_atacante = []\n\t\tself.dados_atacado = []\n\t\tself.ejercitos_atacantes_perdidos = 0\n\t\tself.ejercitos_atacados_perdidos = 0",
"title": ""
},
{
"docid": "74e714b93655bbad9a2e5bfc486fc62b",
"score": "0.5194616",
"text": "def exist_reset():\n start_exist(\"IGNORE_ERROR\")\n import time\n time.sleep(5)\n tasks = bungeni.XmldbTasks()\n tasks.ant_prop_config()\n tasks.ant_demo_reset_config()\n tasks.ant_demo_reset()",
"title": ""
},
{
"docid": "e035ccc80e270d025394983c78c4f5ee",
"score": "0.51758796",
"text": "def test_functionality(self):\n self.browserObject = globalVars.browserObject\n \n #Login as Admin user\n self.verifyCurrentUser(userRole='Administrator', loginAsUser=True)\n self.updateCSServerFirmware(\"Servers\")",
"title": ""
},
{
"docid": "d64e00df245e294409c82ee001e6b214",
"score": "0.51727515",
"text": "def imprimirEmOrdem(self):\r\n # Sub-arvore da Esquerda\r\n if self.esq: \r\n self.esq.imprimirEmOrdem()\r\n\r\n # Raiz\r\n print(self.dado)\r\n\r\n # Sub-arvore da Esquerda\r\n if self.dir: \r\n self.dir.imprimirEmOrdem()",
"title": ""
},
{
"docid": "efc97b40d84c0e37e34c2c9e4f07f647",
"score": "0.5135795",
"text": "def start_up(self):\n pass",
"title": ""
},
{
"docid": "4a4e41550a8424a52f743f530663c431",
"score": "0.5121319",
"text": "def salvar_all_comunidades(self, lista):\n try:\n arq = open(\n self.path_comunidades\n + \"todas_comunidades_\"\n + self.nome_arq_sementes\n + \"_\"\n + self.nome_rede.split(\".\")[0]\n + \".txt\",\n 'w',\n )\n for i in lista:\n arq.write(str(i) + \";\")\n print(\"Todas comunidades foram salvas!!\")\n arq.close()\n except Exception as e:\n os.mkdir(self.path_comunidades)\n self.salvar_all_comunidades(lista)",
"title": ""
},
{
"docid": "bc31890441b7a359250b56fe469a18fd",
"score": "0.51163226",
"text": "def manager():",
"title": ""
},
{
"docid": "b32a8b20021d236c9fb7ddf2d75186a2",
"score": "0.51137453",
"text": "def autoReserva(self): \n self._lockin.write('ARSV') \n print('Ejecutando autoReserva... ', end='')\n while not bool(self._lockin.query('*STB?1')):\n pass\n print('Finalizado')",
"title": ""
},
{
"docid": "e2627994b7a0f3f481ed4acab04faf5b",
"score": "0.5111077",
"text": "def tag_admin(self):\n self.modulo.salir()",
"title": ""
},
{
"docid": "c9686d053319752c3915c6375dd3bc87",
"score": "0.50995564",
"text": "def env_start(self):",
"title": ""
},
{
"docid": "cd6ad79f656c66250dd7a4db75b33b4e",
"score": "0.50967926",
"text": "def start_up():\n\tif(not os.path.exists(\"hosts_to_check\")):\n\t\tprint(\"\"\"\\\\Per me funksionu, mua me duhet nje hosts_to_check follder ku \n\t\t\t\t\tjane nje liste .properties fajllash - per secilin host qe \n\t\t\t\t\tdeshiron me e monitoru nga nje fajll\"\"\")\n\t\texit()\n\t\n\t\"\"\"nxjeri krejt .properties fajllat, edhe per secilin prej tyne:\"\"\"\n\tproperty_files = [\"hosts_to_check\\\\\" + f for f in os.listdir(\"hosts_to_check\") if f.strip().endswith(\".properties\")]\n\tif len(property_files) < 1:\n\t\tprint(\"nuk eshte ofruar konfigurim per asnje host per te bere ping\")\n\t\texit()\n\n\tfor property_file in property_files:\n\t\tprint(\"duke lexuar konfigurim prej: \" + property_file)\n\t\tthread.start_new_thread(get_host_to_ping_configuration(property_file).monitor, ())\n\twhile True:\n\t\tpass",
"title": ""
},
{
"docid": "339ec1808a72cc5a9487518bd44f1c6e",
"score": "0.5093892",
"text": "def initialize():\r\n # imports for individual managers to prevent circular imports\r\n from pulp.server.managers.auth.authentication import AuthenticationManager\r\n from pulp.server.managers.auth.cert.certificate import CertificateManager\r\n from pulp.server.managers.auth.cert.cert_generator import CertGenerationManager\r\n from pulp.server.managers.auth.principal import PrincipalManager\r\n from pulp.server.managers.auth.user.cud import UserManager\r\n from pulp.server.managers.auth.user.query import UserQueryManager\r\n from pulp.server.managers.auth.password import PasswordManager\r\n from pulp.server.managers.auth.permission.cud import PermissionManager\r\n from pulp.server.managers.auth.permission.query import PermissionQueryManager\r\n from pulp.server.managers.auth.role.cud import RoleManager\r\n from pulp.server.managers.auth.role.query import RoleQueryManager\r\n from pulp.server.managers.consumer.cud import ConsumerManager\r\n from pulp.server.managers.consumer.agent import AgentManager\r\n from pulp.server.managers.consumer.applicability import ApplicabilityRegenerationManager\r\n from pulp.server.managers.consumer.bind import BindManager\r\n from pulp.server.managers.consumer.content import ConsumerContentManager\r\n from pulp.server.managers.consumer.group.cud import ConsumerGroupManager\r\n from pulp.server.managers.consumer.group.query import ConsumerGroupQueryManager\r\n from pulp.server.managers.consumer.history import ConsumerHistoryManager\r\n from pulp.server.managers.consumer.profile import ProfileManager\r\n from pulp.server.managers.consumer.query import ConsumerQueryManager\r\n from pulp.server.managers.content.cud import ContentManager\r\n from pulp.server.managers.content.catalog import ContentCatalogManager\r\n from pulp.server.managers.content.orphan import OrphanManager\r\n from pulp.server.managers.content.query import ContentQueryManager\r\n from pulp.server.managers.content.upload import ContentUploadManager\r\n from pulp.server.managers.event.crud import EventListenerManager\r\n from pulp.server.managers.event.fire import EventFireManager\r\n from pulp.server.managers.event.remote import TopicPublishManager\r\n from pulp.server.managers.migration_tracker import MigrationTrackerManager\r\n from pulp.server.managers.plugin import PluginManager\r\n from pulp.server.managers.repo.cud import RepoManager\r\n from pulp.server.managers.repo.dependency import DependencyManager\r\n from pulp.server.managers.repo.distributor import RepoDistributorManager\r\n from pulp.server.managers.repo.group.cud import RepoGroupManager\r\n from pulp.server.managers.repo.group.distributor import RepoGroupDistributorManager\r\n from pulp.server.managers.repo.group.publish import RepoGroupPublishManager\r\n from pulp.server.managers.repo.group.query import RepoGroupQueryManager\r\n from pulp.server.managers.repo.importer import RepoImporterManager\r\n from pulp.server.managers.repo.publish import RepoPublishManager\r\n from pulp.server.managers.repo.query import RepoQueryManager\r\n from pulp.server.managers.repo.sync import RepoSyncManager\r\n from pulp.server.managers.repo.unit_association import RepoUnitAssociationManager\r\n from pulp.server.managers.repo.unit_association_query import RepoUnitAssociationQueryManager\r\n from pulp.server.managers.schedule.repo import RepoPublishScheduleManager, RepoSyncScheduleManager\r\n from pulp.server.managers.schedule.consumer import ConsumerScheduleManager\r\n\r\n # Builtins for a normal running Pulp server (used to reset the state of the\r\n # factory between runs)\r\n builtins = {\r\n TYPE_APPLICABILITY_REGENERATION: ApplicabilityRegenerationManager,\r\n TYPE_AUTHENTICATION : AuthenticationManager,\r\n TYPE_CERTIFICATE : CertificateManager,\r\n TYPE_CERT_GENERATION: CertGenerationManager,\r\n TYPE_CONSUMER: ConsumerManager,\r\n TYPE_CONSUMER_AGENT: AgentManager,\r\n TYPE_CONSUMER_BIND: BindManager,\r\n TYPE_CONSUMER_CONTENT: ConsumerContentManager,\r\n TYPE_CONSUMER_GROUP: ConsumerGroupManager,\r\n TYPE_CONSUMER_GROUP_QUERY: ConsumerGroupQueryManager,\r\n TYPE_CONSUMER_HISTORY: ConsumerHistoryManager,\r\n TYPE_CONSUMER_PROFILE: ProfileManager,\r\n TYPE_CONSUMER_QUERY: ConsumerQueryManager,\r\n TYPE_CONSUMER_SCHEDULE: ConsumerScheduleManager,\r\n TYPE_CONTENT: ContentManager,\r\n TYPE_CONTENT_CATALOG: ContentCatalogManager,\r\n TYPE_CONTENT_ORPHAN: OrphanManager,\r\n TYPE_CONTENT_QUERY: ContentQueryManager,\r\n TYPE_CONTENT_UPLOAD: ContentUploadManager,\r\n TYPE_DEPENDENCY: DependencyManager,\r\n TYPE_EVENT_FIRE: EventFireManager,\r\n TYPE_EVENT_LISTENER: EventListenerManager,\r\n TYPE_MIGRATION_TRACKER: MigrationTrackerManager,\r\n TYPE_PASSWORD: PasswordManager,\r\n TYPE_PERMISSION: PermissionManager,\r\n TYPE_PERMISSION_QUERY: PermissionQueryManager,\r\n TYPE_PLUGIN_MANAGER: PluginManager,\r\n TYPE_PRINCIPAL: PrincipalManager,\r\n TYPE_REPO: RepoManager,\r\n TYPE_REPO_ASSOCIATION: RepoUnitAssociationManager,\r\n TYPE_REPO_ASSOCIATION_QUERY : RepoUnitAssociationQueryManager,\r\n TYPE_REPO_DISTRIBUTOR: RepoDistributorManager,\r\n TYPE_REPO_GROUP: RepoGroupManager,\r\n TYPE_REPO_GROUP_DISTRIBUTOR : RepoGroupDistributorManager,\r\n TYPE_REPO_GROUP_PUBLISH : RepoGroupPublishManager,\r\n TYPE_REPO_GROUP_QUERY : RepoGroupQueryManager,\r\n TYPE_REPO_IMPORTER: RepoImporterManager,\r\n TYPE_REPO_PUBLISH: RepoPublishManager,\r\n TYPE_REPO_PUBLISH_SCHEDULE: RepoPublishScheduleManager,\r\n TYPE_REPO_QUERY: RepoQueryManager,\r\n TYPE_REPO_SYNC: RepoSyncManager,\r\n TYPE_REPO_SYNC_SCHEDULE: RepoSyncScheduleManager,\r\n TYPE_ROLE: RoleManager,\r\n TYPE_ROLE_QUERY: RoleQueryManager,\r\n TYPE_TOPIC_PUBLISH: TopicPublishManager,\r\n TYPE_USER: UserManager,\r\n TYPE_USER_QUERY: UserQueryManager,\r\n }\r\n _CLASSES.update(builtins)",
"title": ""
},
{
"docid": "8f093658e6776fc002cf8bd5d2cad6c4",
"score": "0.50885576",
"text": "def efetuar_saque() -> None:\n if len(contas) > 0: # se tiver contas cadastradas\n numero: int = int(input(\"Informe o número da sua conta: \"))\n conta: Conta = buscar_conta_por_numero(numero)\n\n if conta: # Se a conta existe\n valor: float = float(input(\"Informe o valor do saque: \"))\n conta.sacar(valor) # A lógica do método irá estar aqui.\n else:\n print(f\"Conta com número {numero} não foi localizada.\")\n\n else:\n print(\"\\nAinda não existem contas cadastradas.\")\n sleep(0.5)\n menu()",
"title": ""
},
{
"docid": "9cf68b10fa360286fad0b826e955e31a",
"score": "0.5072695",
"text": "def install_sys_dep(self):\n if platform.system() == \"Linux\":\n self.logger.debug(\"Linux OS found.\")\n self.install_apt()\n if self.param == \"vm\":\n self.linux_autostart()\n elif platform.system() == \"Windows\":\n self.logger.debug(\"Windows OS found.\")\n #self.install_msi()\n if self.param == \"vm\":\n self.windows_autostart()\n else:\n self.logger.error(\"[X] Your Operating System is not supported by hystck.\")\n sys.exit(1)",
"title": ""
},
{
"docid": "b76eef06759dfcfff06d7272813886c3",
"score": "0.5042002",
"text": "def InitEnvironnement(self):\r\n try:\r\n fichier = open(self.ProfilDir+\"config.ini\", \"w\")\r\n fichier.writelines(\"Defaut\")\r\n fichier.close()\r\n except:\r\n os.mkdir(self.ProfilDir)\r\n self.InitEnvironnement()",
"title": ""
},
{
"docid": "568d99bdc20210245364352c0cb98e84",
"score": "0.5031161",
"text": "def runApplication():\n #delete\n cleanDatastore()\n #parsring xml\n xmlDispatcher()",
"title": ""
},
{
"docid": "f75bdd968ba8d9c75190ba7d0b93069e",
"score": "0.5028173",
"text": "def system_initializer(cond,event):\n name = multiprocessing.current_process().name \n with cond:\n cond.wait()\n if not os.path.exists(\"appfiles/authorization.dat\"):\n sys.exit()\n with open(\"appfiles/authorization.dat\",\"rb\") as confidential: \n details = pickle.load(confidential) \n for user,passwd in details.items():\n details = [user,passwd] \n \n print '%s running as %s'%( name, details[0])\n import serverManager as SM\n event.set()\n from serverManager import ServerAccess\n server = ServerAccess()\n server.get_appfiles()\n # SM.runKlass(details)",
"title": ""
},
{
"docid": "e8bfe3f2a9460787be5a7b7ccd3c7ed7",
"score": "0.5024907",
"text": "def __securitySet(self):\n self.logger.log(\"Begin set mod of file...\")\n currentPath = os.path.dirname(os.path.abspath(__file__))\n currentReplaceConfigPath = \"%s/ReplaceConfig.py\" % currentPath\n self.logger.log(\"%s\" % currentReplaceConfigPath)\n currentInitInstancePath = \"%s/InitInstance.py\" % currentPath\n self.logger.log(\"%s\" % currentInitInstancePath)\n if(os.path.exists(currentReplaceConfigPath)):\n os.chmod(currentReplaceConfigPath, 0600)\n if(os.path.exists(currentInitInstancePath)):\n os.chmod(currentInitInstancePath, 0600)\n installReplaceConfigPath = \"%s/bin/script/local/ReplaceConfig.py\" % self.installPath \n self.logger.log(\"%s\" % installReplaceConfigPath)\n installInitInstancePath = \"%s/bin/script/local/InitInstance.py\" % self.installPath\n self.logger.log(\"%s\" % installInitInstancePath)\n if(os.path.exists(installReplaceConfigPath)):\n os.chmod(installReplaceConfigPath, 0600)\n if(os.path.exists(installInitInstancePath)):\n os.chmod(installInitInstancePath, 0600)",
"title": ""
},
{
"docid": "97d873d0f01e0f9b572df39a8845d34d",
"score": "0.5023929",
"text": "def __inicializaMotores(self):\n\t if self.cp.clientID !=-1:\n\t # id de comunicacao do python com o vrep.\n\t for i in range(0, 3):\n\t code, self.f[i].mtrE = vrep.simxGetObjectHandle(self.cp.clientID, \"mtrEsqFri\" + str(i), vrep.simx_opmode_oneshot_wait)\n\t code, self.f[i].mtrD = vrep.simxGetObjectHandle(self.cp.clientID, \"mtrDirFri\" + str(i), vrep.simx_opmode_oneshot_wait)\n\t #code, e[i].mtrE = vrep.simxGetObjectHandle(clientID, (\"mtrDirEn\" + str(i+1)), vrep.simx_opmode_oneshot_wait)\n\t else:\n\t print \"O arquivo da cena do VREP nao esta executando...\"\n\t vrep.simxFinish(self.cp.clientID) # Now close the connection to V-REP:\n\t quit()",
"title": ""
},
{
"docid": "aed1291a3b4204f5f5ab7cf8c807007e",
"score": "0.50128204",
"text": "def test_services(self):\n with Wizard() as w:\n w.name.send_keys(OPENSTACK_CENTOS)\n w.release.select_by_visible_text(OPENSTACK_RELEASE_CENTOS)\n for i in range(3):\n w.next.click()\n w.network_neutron_gre.click()\n w.next.click()\n w.next.click()\n w.install_sahara.click()\n w.install_murano.click()\n w.install_ceilometer.click()\n w.next.click()\n w.create.click()\n w.wait_until_exists()\n\n Tabs().settings.click()\n\n with Settings() as s:\n self.assertTrue(s.install_sahara.\n find_element_by_tag_name('input').is_selected())\n self.assertTrue(s.install_murano.\n find_element_by_tag_name('input').is_selected())\n self.assertTrue(s.install_ceilometer.\n find_element_by_tag_name('input').is_selected())",
"title": ""
},
{
"docid": "ef0e2e1f23f018c2a59d32dedfc430d0",
"score": "0.50126934",
"text": "def resolver_sistema(self):\n # 0 : Aplicar restricciones\n \n Ngdl = self.Nnodos * self.Ndimensiones\n gdl_lib = np.arange(Ngdl)\n gdl_restringidos = []\n\n #Identificar gdl_restringidos y llenar u \n # en valores conocidos.\n #\n # Hint: la funcion numpy.setdiff1d es util\n \n for nodo in self.restricciones:\n\t\t\tfor restriccion in self.restricciones[nodo]:\n\t\t\t\tgdl_local = restriccion[0]\n\t\t\t\tvalor = restriccion[1]\n\t\t\t\t\n\t\t\t\tgdl_global = 2*nodo + gdl_local\n\t\t\t\tgdl_restringidos.append(gdl_global)\n\t\t\t\tself.u[gdl_global] = valor\n\t\t\n\tgdl_libres = np.setdiff1d(gdl_lib,gdl_restringidos)\n\t\t\n # Agregar cargas nodales a vector de cargas\n \n for nodo in self.cargas:\n for carga in self.cargas[nodo]:\n gdl = carga[0]\n valor = carga[1]\n gdl_global = 2*nodo + gdl\n self.f[gdl_global] += valor\n \n # 1 Particionar:\n # K en Kff, Kfc, Kcf y Kcc.\n # f en ff y fc\n # u en uf y uc\n \n Kff=self.K[np.ix_( gdl_libres, gdl_libres)]\n Kfc=self.K[np.ix_( gdl_libres, gdl_restringidos)]\n Kcf=Kfc.T\n Kcc=self.K[np.ix_( gdl_restringidos, gdl_restringidos)]\n \n uf=self.u[gdl_libres]\n uc=self.u[gdl_restringidos]\n \n ff=self.f[gdl_libres]\n fc=self.f[gdl_restringidos]\n \n # Resolver para obtener uf --> Kff uf = ff - Kfc*uc\n \n uf=solve(Kff,ff-Kfc@uc)\n \n # Asignar uf al vector solucion\n \n self.u[gdl_libres] = uf\n\n # Marcar internamente que se tiene solucion\n \n self.tiene_solucion = True",
"title": ""
},
{
"docid": "a33875503c59acb226923874b0617077",
"score": "0.49963573",
"text": "def init_host(self):\n\n ctxt = context.get_admin_context()\n\n LOG.info(_(\"Cleaning up incomplete backup operations\"))",
"title": ""
},
{
"docid": "47e3921edb2806c4416dde6c18befd3f",
"score": "0.49941698",
"text": "def nsxt_setup_system(self):\n self.show_step(1) # Install plugin to Fuel Master node with 5 slaves\n self.env.revert_snapshot('ready_with_5_slaves')\n self.install_nsxt_plugin()\n\n self.show_step(2) # Create new environment with vCenter\n cluster_id = self.fuel_web.create_cluster(\n name=self.__class__.__name__,\n mode=DEPLOYMENT_MODE,\n settings=self.default.cluster_settings,\n configure_ssl=False)\n\n self.show_step(3) # Add nodes\n self.fuel_web.update_nodes(cluster_id,\n {'slave-01': ['controller'],\n 'slave-02': ['compute-vmware'],\n 'slave-03': ['compute'],\n 'slave-04': ['compute']})\n\n self.show_step(4) # Configure interfaces on nodes\n self.reconfigure_cluster_interfaces(cluster_id)\n\n self.show_step(5) # Enable and configure plugin, configure networks\n self.enable_plugin(cluster_id)\n\n # Configure VMware settings. 2 Cluster, 1 Nova Instance on controllers\n # and 1 Nova Instance on compute-vmware\n self.show_step(6)\n target_node2 = self.fuel_web.get_nailgun_node_by_name('slave-02')\n self.fuel_web.vcenter_configure(cluster_id,\n target_node_2=target_node2['hostname'],\n multiclusters=True)\n\n self.show_step(7) # Verify networks\n self.fuel_web.verify_network(cluster_id)\n\n self.show_step(8) # Deploy cluster\n self.fuel_web.deploy_cluster_wait(cluster_id)\n\n self.show_step(9) # Run OSTF\n self.fuel_web.run_ostf(cluster_id)\n\n self.env.make_snapshot(\"nsxt_setup_system\", is_make=True)",
"title": ""
},
{
"docid": "eee7db1dee0fe270452a6880669f26cf",
"score": "0.49834037",
"text": "def write(self):\n if not os.path.isdir(\"%s/etc\" % _sysroot):\n os.mkdir(\"%s/etc\" % _sysroot)\n\n self.fsset.write()\n self.makeMtab()\n self.iscsi.write(_sysroot, self)\n self.fcoe.write(_sysroot)\n self.zfcp.write(_sysroot, self.devicetree.getDevicesByType(\"zfcp\"))\n write_dasd_conf(self.devicetree.dasd, _sysroot)",
"title": ""
},
{
"docid": "eb0b1f03f523df6dc0986bc7d631af5e",
"score": "0.49758214",
"text": "def configure(self):",
"title": ""
},
{
"docid": "79f0d8e027d923fd8476f3a47512c67e",
"score": "0.49596223",
"text": "def prepare_deploy():\n #clean folder\n script = lambda osd_host, osd_path:'ssh %s \"if [ ! -d \\\"%s\\\" ];then mkdir %s;else rm -rf %s/*;fi\"' % (osd_host, osd_path, osd_path, osd_path)\n for hostname in get_osd_nodes():\n osd_paths = get_osd_path(hostname)\n\t#print \"osd_paths:\",osd_paths\n\t#[os.system(script(hostname, osd_path)) for osd_path in osd_paths]\n\tfor osd_path in osd_paths:\n\t #print \"osd_path:\", osd_path, \"hostname:\",hostname\n\t if ':' in osd_path or '/dev/sd' in osd_path:\n\t\tcontinue\n\t os.system(script(hostname,osd_path))\n\t #print \"script:\", script(hostname,osd_path)",
"title": ""
},
{
"docid": "efb025f8bcd5869fa28518e5e8c90e4d",
"score": "0.49580133",
"text": "def set_up(self):\n # pass",
"title": ""
},
{
"docid": "c148ed93e44741c0f4e5be9c5f9bca44",
"score": "0.49579102",
"text": "async def automod2(self, ctx):",
"title": ""
},
{
"docid": "26dc3e187a55781f293f9fc119d4a8b2",
"score": "0.49568146",
"text": "def __init__(self):\r\n self.services = Services()\r\n self.backdoor_port = eventlet_backdoor.initialize_if_enabled()",
"title": ""
},
{
"docid": "9c58b18879a19254ba8761cd3812996a",
"score": "0.49562344",
"text": "def db_make_empty():\n\n __db_load_services_stop()\n tasks = bungeni.BungeniTasks()\n tasks.reset_db()\n __db_load_services_start()",
"title": ""
},
{
"docid": "5c0aed2089c8241a203beb9a86f7bb86",
"score": "0.4952596",
"text": "def run(self):\n\n # Preparations\n self.load_config()\n if platform.system() == \"Linux\": #OS LINUX, FUNKTION FUER WINDOWS & LINUX\n self.checkuser()\n\n # Installs\n self.install_sys_dep()\n self.install_pip_dep()\n if platform.system() == \"Windows\":\n self.install_sources()\n\n # Check if installation is host or vm side\n if self.param == \"host\":\n # Setups\n self.setup_tcpdump()\n self.setup_libivrt()\n self.setup_network_interfaces()\n\n # Reboot to enable virt-manager user privileges\n # Python2.7: raw_input(); Python3.7: input()\n # answer = raw_input('Shell needs to be restarted for the changes to take effect. '\n # 'Do you want to restart now?: [y/n]')\n #if not answer or answer[0].lower() != 'y':\n # print('You did not indicate approval')\n # exit(1)\n #else:\n # os.system(\"reboot\")\n elif self.param == \"vm\":\n self.logger.info(\"[X] Nothing to do inside vm.\")\n else:\n self.logger.error(\"[X] Unknown Parameter {}\".format(self.param))",
"title": ""
},
{
"docid": "5ebbc331508a5b3fc445c9680ab8652a",
"score": "0.49454626",
"text": "def desregistrar_eventos(self):\n if sesion.lector is not None:\n sesion.lector.remover_consultar_lector()\n if sesion.impresora is not None:\n sesion.impresora.remover_insertando_papel()\n self.remover_nuevo_papel()\n if USA_ARMVE:\n if hasattr(self, \"signal_ac\") and self.signal_ac is not None:\n self.signal_ac.remove()\n if hasattr(self, \"signal_batt_discharging\") and \\\n self.signal_batt_discharging is not None:\n self.signal_batt_discharging.remove()\n if hasattr(self, \"signal_batt_plugged\") and \\\n self.signal_batt_plugged is not None:\n self.signal_batt_plugged.remove()\n if hasattr(self, \"signal_batt_unplugged\") and \\\n self.signal_batt_unplugged is not None:\n self.signal_batt_unplugged.remove()\n if hasattr(self, \"signal_pir_detected\") and \\\n self.signal_pir_detected is not None:\n self.signal_pir_detected.remove()\n if hasattr(self, \"signal_pir_not_detected\") and \\\n self.signal_pir_not_detected is not None:\n self.signal_pir_not_detected.remove()\n\n pwr_mgr = sesion.powermanager\n pwr_mgr.uncheck_ac()\n pwr_mgr.uncheck_battery_discharging()\n pwr_mgr.uncheck_battery_plugged()\n pwr_mgr.uncheck_battery_unplugged()\n sesion.pir.uncheck_detected()\n sesion.pir.uncheck_not_detected()",
"title": ""
},
{
"docid": "fcf778309d38bda833c5a3a50c4e4aad",
"score": "0.49451935",
"text": "def ejecutar_accion(opcion):\n os.system('cls' if os.name == 'nt' else 'clear')\n if opcion == '1': # registrar al usuario\n registra_nuevo_usuario()\n return\n if opcion == '2':\n inicia_sesion()\n os.system('cls' if os.name == 'nt' else 'clear')\n return\n print \"Opción invalida, intenta de nuevo\"\n time.sleep(2)\n menu_principal()\n return",
"title": ""
},
{
"docid": "f8be5ef3016d2ceb8bccf4ee51373045",
"score": "0.49441588",
"text": "def run(self):\n self.createBackups()\n if not self.checkUser():\n if not self.checkGroup():\n self.createGroup()\n self.createUser()\n else:\n if self.home is None:\n self.getHomeDir()\n self.addGroupToUser()\n if not self.checkMatchGroup():\n self.disableDefaultSubsystem()\n self.appendMatchGroup()\n time.sleep(1)\n self.createChrootDir()\n self.setDirPerms()\n if not self.checkFstab():\n self.appendFstab()\n if not self.checkPam():\n self.appendUmask()\n self.restartSshd()\n self.mountFstab()",
"title": ""
},
{
"docid": "677370f09c5b46a3614cc92e02baf883",
"score": "0.49396777",
"text": "def main(self):\n # start service loop\n pythoncom.CoInitialize()\n config = \"\"\n if os.path.isfile('config.json'):\n with open('config.json', 'r') as f:\n config = json.load(f)\n self.logger.info('Config existed, loading it')\n if config:\n config_uri = \"agent/configuration\"\n try:\n r = requests.get(\"https://assetmgmt.ashleycollinge.co.uk/{}\".format(config_uri), verify=False)\n config = r.json()['configuration']\n self.logger.info('Config loaded, writing it to file')\n writeconfigtofile(config)\n except requests.exceptions.ConnectionError:\n print(\"Could not connect to service\")\n c = wmi.WMI()\n for bios in c.Win32_BIOS():\n serialnumber = bios.SerialNumber\n data = {\"serialnumber\": serialnumber, \"hostname\": socket.gethostname(), \"domain\": \"synseal.com\", \"operating_system\": \"win7\", \"servicepackversion\": 4}\n headers = {'Content-type': 'application/json'}\n registered = False\n no_config = False\n while not registered:\n time.sleep(2)\n if self.stop_requested:\n break # break out of service loop as stop requested\n try:\n self.logger.info('not registered, trying to register now')\n r = requests.post(\"https://assetmgmt.ashleycollinge.co.uk/agent/register\", data=json.dumps(data), headers=headers, verify=False)\n asset_id = r.json()['asset_id']\n config_uri = r.json()['config_uri']\n self.logger.info('registered successfully')\n registered = True\n except requests.exceptions.ConnectionError:\n print(\"Could not connect to service\")\n print(\"Trying again...\")\n while not no_config:\n time.sleep(2)\n if self.stop_requested:\n break # break out of service loop as stop requested\n try:\n r = requests.get(\"https://assetmgmt.ashleycollinge.co.uk{}\".format(config_uri), verify=False)\n config = r.json()['configuration']\n config['asset_id'] = asset_id\n writeconfigtofile(config)\n no_config = True\n except requests.exceptions.ConnectionError:\n print(\"Could not connect to service\")\n print(\"Trying again...\")\n\n data = generate_data()\n data['asset_id'] = config['asset_id']\n r = requests.post(\"https://assetmgmt.ashleycollinge.co.uk/agent/information_upload\", data=json.dumps(data), headers=headers, verify=False)\n while 1:\n self.logger.info('start heartbeating')\n start_heartbeating(config)\n time.sleep(config['heartbeat_interval'])\n if self.stop_requested:\n break # break out of service loop as stop requested",
"title": ""
},
{
"docid": "34bd0b2ad8a698d9607d70df76fe55c7",
"score": "0.4938108",
"text": "def setup(self):\n \n #Nothing to do for static Virtual Box machines.\n pass",
"title": ""
},
{
"docid": "bd07b810b63642cae057d3d19d0bd5e1",
"score": "0.49325058",
"text": "def registrarLocalmente(self,nombre,email,password):\n try:\n #TODO: despues de que envie la contrasena se deberia borrar\n self.cursor.execute('update instalacion set nombretitular=?, email=?, password=?', (str(nombre),str(email),str(password)))\n self.conexion_db.commit()\n admUser=administradorDeUsuarios.AdministradorDeUsuarios()\n admUser.setPassword('admin', str(password))\n modulo_logger.log(logging.INFO,\"Password seteada correctamente\")\n except sqlite3.OperationalError, msg:\n modulo_logger.log(logging.ERROR,\"No se pudo registrar la instalacion localmente. Tal vez no esta la base de datos instalada.\\nERROR: %s\" % msg)",
"title": ""
},
{
"docid": "b294de4ae870b9dd8ce186cde78013e0",
"score": "0.49277383",
"text": "def execute(self):\n menu_admin = MenuAdministrator()\n menu_admin.display_menu()",
"title": ""
},
{
"docid": "350e046c20f5bd8d97f24d9d68124032",
"score": "0.49246877",
"text": "def setKeystoneController():\n\n with prefix(env_config.admin_openrc):\n\n if 'swift' not in sudo(\"keystone user-list\"):\n msg = \"Create user swift\"\n runCheck(msg, \"keystone user-create --name swift --pass {}\".format(passwd['SWIFT_PASS']))\n\n msg = \"Add the role of admin to user swift\"\n runCheck(msg, \"keystone user-role-add --user swift --tenant service --role admin\")\n else:\n print blue('swift is already a user. Do nothing')\n\n if 'swift' not in sudo(\"keystone service-list\"):\n msg = \"Create service swift\"\n runCheck(msg, 'keystone service-create --name swift --type object-store --description \"OpenStack Object Storage\"')\n else:\n print blue('swift is already a service. Do nothing')\n\n if 'http://controller:8080/' not in sudo(\"keystone endpoint-list\"):\n msg = \"Create endpoint for service swift\"\n command = \"keystone endpoint-create \" +\\\n \"--service-id $(keystone service-list | awk '/ object-store / {print $2}') \" +\\\n \"--publicurl 'http://controller:8080/v1/AUTH_%(tenant_id)s' \" +\\\n \"--internalurl 'http://controller:8080/v1/AUTH_%(tenant_id)s' \" +\\\n \"--adminurl http://controller:8080/ \" +\\\n \"--region regionOne\"\n print 'command : ',command\n runCheck(msg, command)\n else:\n print blue('8080 is already an endpoint. Do nothing')",
"title": ""
},
{
"docid": "a4442ca9220170228ba30fb068a2c6a0",
"score": "0.49217275",
"text": "def exist_load_demodata():\n start_exist(\"IGNORE_ERROR\")\n import time\n time.sleep(5)\n tasks = bungeni.XmldbTasks()\n tasks.setup_exist_demo_data()\n tasks.ant_demo_setup_config()\n tasks.ant_demo_install()",
"title": ""
},
{
"docid": "308c2d6615ddd9018d83d2f8e78bcdde",
"score": "0.49166754",
"text": "def setuptest():\n\n import configparser\n from app import db\n\n # Set Enviromental variables\n cfig = configparser.ConfigParser()\n cfig.read('keys.ini')\n os.environ['SECRET_KEY'] = cfig['KEYS']['secret_key']\n os.environ['MAIL_USERNAME'] = cfig['KEYS']['mail_username']\n os.environ['MAIL_PASSWORD'] = cfig['KEYS']['mail_password']\n os.environ['FLASKY_ADMIN'] = cfig['KEYS']['owner_email']\n if os.environ.get('SECRET_KEY') == \"\" or os.environ.get('MAIL_USERNAME') == \"\" \\\n or os.environ.get('MAIL_PASSWORD') == \"\" or os.environ.get('FLASKY_ADMIN') == \"\":\n raise EnvironmentError\n\n # Setup Database Tables\n db.create_all()",
"title": ""
},
{
"docid": "f885fb48f4ca970bfbe40272cc78cd3c",
"score": "0.49139538",
"text": "def IngresoCliente(self, IngresoCliente=None):",
"title": ""
},
{
"docid": "dfb7c4116e04caa89247cd14efec481f",
"score": "0.49139103",
"text": "def file_save(self):\n try:\n self.ventana.pestannas.guardar_tabla()\n except :\n exc=self.dic.ini_p_war+ str(sys.exc_info()[0])+ str(sys.exc_info()[1])\n logging.warning(exc)\n print(self.dic.ini_p_war, sys.exc_info()[0], sys.exc_info()[1])",
"title": ""
},
{
"docid": "7bf27a5ecda61f561c5211679d43980e",
"score": "0.4910109",
"text": "def initCatalog(estructura):\n return controller.initCatalog(estructura)",
"title": ""
},
{
"docid": "cd5c473296aa1fa901b3533c68465b82",
"score": "0.4908569",
"text": "def procesar(self):\n \n # Obtenemos los valores\n ec = self.txtEcuacion.get()\n y = self.txtPVIY.get()\n x = self.txtPVIX.get()\n \n # verificamos que todos los campos esten llenos\n if len(ec)!=0 and len(x)!=0 and len(y)!=0 :\n # Iniciamos un objeto\n edo = EDO(ec, y, x)\n \n # procesamos la EDO\n expr = edo.process()\n \n # Mostramos los resultados\n lblExpr = tk.Label(self.parent, text=\"Resultados:\",\n anchor=tk.W, justify=tk.LEFT)\n lblExpr.place(x=10, y=70, width=780, height=20)\n lblExpr.config(font=self.font, bg=self.bg)\n \n areaExpr = tk.Text(self.parent)\n areaExpr.place(\n x=10, y=100, width=780, height=160)\n areaExpr.insert(tk.END, expr)\n areaExpr.config(font=self.font, bg=self.bg)\n areaExpr.config(state=\"disabled\")\n \n # Mostramos nuevas opciones\n lblMsg = tk.Label(self.parent, text=\"Resolver por método de Euler modificado:\",\n anchor=tk.W, justify=tk.LEFT)\n lblMsg.place(x=10, y=270, width=780, height=20)\n lblMsg.config(font=self.font, bg=self.bg)\n \n lblInicial = tk.Label(self.parent, text=\"I. Inicial:\",\n anchor=tk.W, justify=tk.LEFT)\n lblInicial.place(x=10, y=300, width=140, height=20)\n lblInicial.config(font=self.font, bg=self.bg)\n \n self.txtInicial = tk.Entry(self.parent)\n self.txtInicial.place(x=160, y=300, width=50, height=20)\n self.txtInicial.focus()\n \n lblFinal = tk.Label(self.parent, text=\"I. Final:\",\n anchor=tk.W, justify=tk.LEFT)\n lblFinal.place(x=220, y=300, width=140, height=20)\n lblFinal.config(font=self.font, bg=self.bg)\n \n self.txtFinal = tk.Entry(self.parent)\n self.txtFinal.place(x=370, y=300, width=50, height=20)\n \n lblN = tk.Label(self.parent, text=\"n:\",\n anchor=tk.W, justify=tk.LEFT)\n lblN.place(x=430, y=300, width=50, height=20)\n lblN.config(font=self.font, bg=self.bg)\n \n self.txtN = tk.Entry(self.parent)\n self.txtN.place(x=490, y=300, width=50, height=20)\n \n btnProcesar = tk.Button(self.parent, text=\"Resolver\",\n command=lambda: self.euler(edo))\n btnProcesar.place(x=10, y=330, width=200, height=20)\n btnProcesar.config(font=self.font)\n else :\n # Mensaje de error\n text = \"Por favor llene todos los campos\"\n # Mostramos el mensaje\n tkMessageBox.showerror(title=\"Error\", message=text)",
"title": ""
},
{
"docid": "8def6bf43a9c8bd5be680bbff7332483",
"score": "0.49036747",
"text": "def dvs_vcenter_systest_setup(self):\n self.env.revert_snapshot(\"ready_with_5_slaves\")\n\n self.show_step(1)\n self.show_step(2)\n plugin.install_dvs_plugin(\n self.env.d_env.get_admin_remote())\n\n self.show_step(3)\n cluster_id = self.fuel_web.create_cluster(\n name=self.__class__.__name__,\n mode=DEPLOYMENT_MODE,\n settings={\n \"net_provider\": 'neutron',\n \"net_segment_type\": NEUTRON_SEGMENT_TYPE,\n 'images_vcenter': True\n }\n )\n plugin.enable_plugin(cluster_id, self.fuel_web)\n\n self.show_step(4)\n self.show_step(5)\n self.show_step(6)\n self.fuel_web.update_nodes(\n cluster_id,\n {'slave-01': ['controller'],\n 'slave-02': ['compute-vmware'],\n 'slave-03': ['compute'],\n 'slave-04': ['compute']\n }\n )\n\n # Configure VMWare vCenter settings\n target_node_2 = self.node_name('slave-02')\n self.fuel_web.vcenter_configure(\n cluster_id,\n target_node_2=target_node_2,\n multiclusters=True,\n vc_glance=True\n )\n\n self.show_step(7)\n self.fuel_web.deploy_cluster_wait(cluster_id)\n\n self.show_step(8)\n self.fuel_web.run_ostf(\n cluster_id=cluster_id, test_sets=['smoke'])\n\n self.show_step(9)\n self.env.make_snapshot(\"dvs_vcenter_systest_setup\", is_make=True)",
"title": ""
},
{
"docid": "65f2365425b7d64d21a14e33f87d7100",
"score": "0.4900592",
"text": "def addSystemClicked(self):\n dlgSystemAdd=DlgSystemAdd()\n if dlgSystemAdd.exec_():\n\n # adding new system to db. To get id of new system, commiting\n system=db.Integra()\n system.name=dlgSystemAdd.newSystem.getName()\n self.session.add(system)\n self.session.commit()\n\n # adding detectors to database\n for detector in dlgSystemAdd.newSystem.getDetectors():\n dbDetector=db.Detector()\n dbDetector.name=detector.getName()\n dbDetector.system=system.id\n self.session.add(dbDetector)\n\n # adding outs to database\n for out in dlgSystemAdd.newSystem.getOuts():\n dbOut=db.Out()\n dbOut.name=out.getName()\n dbOut.system=system.id\n self.session.add(dbOut)\n\n # adding zones to database\n for zone in dlgSystemAdd.newSystem.getZones():\n dbZone=db.Zone()\n dbZone.name=zone.getName()\n dbZone.system=system.id\n self.session.add(dbZone)\n\n # commiting session\n self.session.commit()\n self.loadSystems()",
"title": ""
},
{
"docid": "74a3e6b646a432796b24088fd09d0f3b",
"score": "0.48913717",
"text": "def restart_service(self):\r\n try:\r\n print(\"Starting service...\")\r\n self.service = dl.getService()\r\n self.rootId = dl.getRootId(self.service)\r\n self.items = dl.getAllElements(self.service) #Get index of all files at cloud which were not trashed\r\n print('items got')\r\n self.tree = dl.getTreeOfFolders(self.service,self.items,'root') #Get tree structure for treeWidget\r\n print(\"tree's built.\")\r\n except Exception as ex:\r\n self.error(ex)\r\n sys.exit(1)",
"title": ""
},
{
"docid": "6c5b188fffabffabdab4328ddfc1a8ae",
"score": "0.4887453",
"text": "def startServicesStorage():\n\n msg = 'Enable account services'\n # runCheck(msg, \"systemctl enable openstack-swift-account.service openstack-swift-account-auditor.service openstack-swift-account-reaper.service openstack-swift-account-replicator.service\")\n msg = 'Start account services'\n runCheck(msg, \"systemctl start openstack-swift-account.service openstack-swift-account-auditor.service openstack-swift-account-reaper.service openstack-swift-account-replicator.service\")\n\n msg = 'Enable container services'\n # runCheck(msg, \"systemctl enable openstack-swift-container.service openstack-swift-container-auditor.service openstack-swift-container-replicator.service openstack-swift-container-updater.service\")\n msg = 'Start container services'\n runCheck(msg, \"systemctl start openstack-swift-container.service openstack-swift-container-auditor.service openstack-swift-container-replicator.service openstack-swift-container-updater.service\")\n\n msg = 'Enable object services'\n # runCheck(msg, \"systemctl enable openstack-swift-object.service openstack-swift-object-auditor.service openstack-swift-object-replicator.service openstack-swift-object-updater.service\")\n msg = 'Start object services'\n runCheck(msg, \"systemctl start openstack-swift-object.service openstack-swift-object-auditor.service openstack-swift-object-replicator.service openstack-swift-object-updater.service\")",
"title": ""
},
{
"docid": "378538671312bdcbe0b84ca19ed9425e",
"score": "0.4886233",
"text": "def check_os_services_ready(self):\n self.fuel_web.assert_os_services_ready(\n self.cluster_id,\n should_fail=self.os_service_should_failed)",
"title": ""
},
{
"docid": "0b52e6e9b98baa6bcce0182fe99e5685",
"score": "0.4882655",
"text": "def run_all(self):\n print('make event directory')\n self.make_event_directory()\n print('copy gti with ero_vis')\n self.compute_gti(self._simput)\n print('run sixte with erosim')\n self.run_sixte()\n #cmd = self.cmd_postprocess()\n #os.system(cmd)",
"title": ""
},
{
"docid": "3b46486386ee340ef0e184e46356fb76",
"score": "0.4869708",
"text": "def set_up(self):\n pass",
"title": ""
},
{
"docid": "3b46486386ee340ef0e184e46356fb76",
"score": "0.4869708",
"text": "def set_up(self):\n pass",
"title": ""
},
{
"docid": "3c46eaab7e8eff0c32b6e1f99fe34c93",
"score": "0.4863563",
"text": "def set_up(self): \n pass",
"title": ""
},
{
"docid": "2b5f0e4378d466fcef5b94b9a77b2380",
"score": "0.4859014",
"text": "def preRunSetup(self):\n self.logDesc(\"Pre Run Setup\") \n #Login as Admin\n self.verifyCurrentUser(userRole='Administrator', loginAsUser=True)\n #Create Standard User if doesnt exist\n self.verifyCurrentUser(userRole='Standard', loginAsUser=False)\n #Create Template if not exists\n self.createSampleTemplate(templateName=self.templateName, publishedTemplate=True, volumeName=\"autoVolume\",\n deleteAndCreate=True, userList=[globalVars.standardUser])\n #Get Services\n serviceList = self.getServices(serviceName=self.serviceName)\n if len(serviceList) > 0:\n #Deletes Service\n self.deleteService(self.serviceName)\n #Deploys a Service if does not exist\n self.deployService(self.templateName, self.serviceName, userList=[globalVars.standardUser])",
"title": ""
},
{
"docid": "176ee81e0b9d2af7ba37a69b5fe582fe",
"score": "0.48556423",
"text": "def setup(self):\n msg=\"Machine setup completed sucessfully!\"\n self.ocommon.log_info_message(msg,self.file_name)",
"title": ""
},
{
"docid": "ab7ce343237c53971dc55fdc2feaa3e8",
"score": "0.48539656",
"text": "def init_environment():\n # Load Templates\n \n temps_dss = json.load(file(\"configs/template_definition_DSS.json\"))\n templates.load_templates(temps_dss)\n\n #Add Providers login credentials\n provider_details.load_providers()\n\n #Add monitoring capabilities and Collectos's API\n monitoring_details.load_monitoring_capabilities()\n \n clean_violation_from_db()",
"title": ""
},
{
"docid": "639ae61b2f19f2f6bffcdd3020d5ccde",
"score": "0.4852718",
"text": "def ejecuta_compra(self, pers, vend, tipo, producto, tiempo):\n\n if tipo == 'alumno':\n for alumno in self.alumnos:\n if alumno == pers:\n for vendedor in self.vendedores:\n if vendedor == vend:\n if self.concha:\n prec = producto.precio * 1.25\n else:\n prec = producto.precio\n if vendedor.stock == 0:\n vendedor.sin_stock += 1\n continue\n alumno.saldo -= prec\n producto.cant_vendidos += 1\n vendedor.stock -= 1\n vendedor.vendio = 1\n self.vendidos_dia.append(producto)\n self.prod_originales2[str(producto)] += 1\n\n if producto.enferma(tiempo, self.frio_ext,\n self.calor_ext, self.lluvia_ayer):\n if self.imprimir:\n print('{} lamentablemente se '\n 'enfermó'.format(alumno))\n vendedor.enfermos += 1\n alumno.prioridades.remove(str(vendedor))\n\n else:\n for funcionario in self.funcionarios:\n if funcionario == pers:\n for vendedor in self.vendedores:\n if vendedor == vend:\n if self.concha:\n prec = producto.precio * 1.25\n else:\n prec = producto.precio\n if vendedor.stock == 0:\n vendedor.sin_stock += 1\n continue\n funcionario.saldo -= prec\n vendedor.stock -= 1\n vendedor.vendio = 1\n self.vendidos_dia.append(producto)\n producto.cant_vendidos += 1\n self.prod_originales2[str(producto)] += 1\n\n if producto.enferma(tiempo, self.frio_ext,\n self.calor_ext, self.lluvia_ayer):\n if self.imprimir:\n print('{} se enfermó'.format(funcionario))\n vendedor.enfermos += 1\n funcionario.prioridades.remove(str(vendedor))",
"title": ""
},
{
"docid": "e71fcef607c96e9e0ef7a9e5d6249e8c",
"score": "0.48456448",
"text": "def deployInfra(self):\n\n # Deploys the k8 cluster MySQL, Redis DBs and registry container\n self.createKube()\n self.createMysql()\n self.createRedis()\n self.createRegistry()\n\n # Waits for all the infrastucture to be provisoned \n self.waitForRegistry()\n self.waitForCluster()\n self.waitForRedis()\n self.waitForMysql()\n\n # Connects the Registry and creates the storage space\n self.createSpace()\n self.connectRegistry()",
"title": ""
},
{
"docid": "dd28835af55ce58e6fd93c3a6dd6aa26",
"score": "0.48420617",
"text": "def serviceAll(self):\n self.serviceConnects()\n self.serviceReceivesAllIx()\n self.serviceTxesAllIx()",
"title": ""
},
{
"docid": "5801e6251f034a82f206dd0e2e481a50",
"score": "0.4837849",
"text": "def startup():\n pass",
"title": ""
},
{
"docid": "a5f65c7dc5613a9f360f12ecd8d4fbbe",
"score": "0.48356918",
"text": "def configureEFSProvisioner(self):\n methodName = \"configureEFSProvisioner\"\n self.createConfigFile(configFilePath=self.efsProvPath,\n templateFilePath=self.manifestTemplatePath,\n parameters=self.provisionerParameters,\n keywordMap=ProvisionerTemplateKeywordMap,\n multipleAppearances=['MY_CLUSTER'])\n \n self.createConfigFile(configFilePath=self.efsRBACPath,\n templateFilePath=self.rbacTemplatePath,\n parameters=self.rbacParameters,\n keywordMap=RBACTemplateKeywordMap,\n multipleAppearances=['NAMESPACE'])\n \n\n # Create the EFS Provisioner RBAC\n TR.info(methodName,\"Invoking: oc create -f %s\" % self.efsRBACPath)\n \n retcode = call([\"oc\", \"create\", \"-f\", self.efsRBACPath])\n if (retcode != 0):\n raise Exception(\"Error calling oc. Return code: %s\" % retcode)\n #endIf\n \n # Create the EFS provisioner service account\n TR.info(methodName,\"Invoking: oc create -f %s\" % self.serviceAccountPath)\n\n retcode = call([\"oc\", \"create\", \"-f\", self.serviceAccountPath])\n if (retcode != 0):\n raise Exception(\"Error calling oc. Return code: %s\" % retcode)\n #endIf\n \n # Create the EFS provisioner service account\n TR.info(methodName,\"Invoking: oc adm policy add-scc-to-user \")\n scc_cmd = \"oc adm policy add-scc-to-user hostmount-anyuid system:serviceaccount:default:efs-provisioner\"\n retcode = call(scc_cmd, shell=True)\n if (retcode != 0):\n TR.info(methodName,\"Invoking: oc adm policy add-scc-to-user %s\" %retcode)\n raise Exception(\"Error calling oc. Return code: %s\" % retcode)\n #endIf\n\n TR.info(methodName,\"Invoking: oc adm policy add-cluster-role-to-user \")\n clusterrole_cmd = \"oc adm policy add-cluster-role-to-user efs-provisioner-runner system:serviceaccount:default:efs-provisioner\"\n retcode = call(clusterrole_cmd, shell=True)\n if (retcode != 0):\n raise Exception(\"Error calling oc. Return code: %s\" % retcode)\n #endIf\n\n TR.info(methodName,\"Invoking: oc apply -f %s\" % self.efsProvPath)\n retcode = call([\"oc\", \"apply\", \"-f\", self.efsProvPath])\n if (retcode != 0):\n raise Exception(\"Error calling oc. Return code: %s\" % retcode)\n #endIf",
"title": ""
},
{
"docid": "1157187ba8039996d2c46b5fee326ee4",
"score": "0.48347026",
"text": "def desregistrar_eventos(self):\n if sesion.lector is not None:\n sesion.lector.remover_consultar_lector()\n if sesion.impresora is not None:\n sesion.impresora.remover_insertando_papel()\n self.remover_nuevo_papel()",
"title": ""
},
{
"docid": "0a3b43165381bfb885742df88bf0fbc9",
"score": "0.48285714",
"text": "def Install(self):",
"title": ""
},
{
"docid": "c4d262c4945461a36ca988ebd5163364",
"score": "0.48165834",
"text": "def setup_bungeni_admin():\n\n tasks = bungeni.BungeniTasks()\n tasks.add_admin_user()",
"title": ""
},
{
"docid": "b8007818d9575f269906c74ac366f440",
"score": "0.48120463",
"text": "def test_100_services(self):\n u.log.debug('Checking system services on units...')\n\n manila_svcs = [\n 'apache2', 'manila-worker',\n ]\n\n service_names = {\n self.manila_sentry: manila_svcs,\n }\n\n ret = u.validate_services_by_name(service_names)\n if ret:\n amulet.raise_status(amulet.FAIL, msg=ret)\n\n u.log.debug('OK')",
"title": ""
},
{
"docid": "9b8dc7d35164f3876678888dd09f6aa5",
"score": "0.4801814",
"text": "def finalizeInstallation():\n\n confFile = '/etc/swift/swift.conf'\n localFile = 'swift.conf'\n\n msg = 'Put base config file on node'\n out = put(localFile,confFile)\n if out.succeeded:\n printMessage('good',msg)\n else:\n printMessage('oops',msg)\n\n\n # In the [swift-hash] section, configure the hash path prefix and suffix for your environment\n set_parameter(confFile,'swift-hash','swift_hash_path_prefix',env_config.hashPathPrefix)\n set_parameter(confFile,'swift-hash','swift_hash_path_suffix',env_config.hashPathSuffix)\n\n # In the [storage-policy:0] section, configure the default storage policy\n set_parameter(confFile,'storage-policy:0','name','Policy-0')\n set_parameter(confFile,'storage-policy:0','default','yes')\n\n msg = 'Change ownership of the configuration directory to swift'\n run(\"chown -R swift:swift /etc/swift\")\n\n execute(startServicesController)\n execute(startServicesStorage)",
"title": ""
},
{
"docid": "5875f82e688d00a7747bb95822ea1eb1",
"score": "0.48010626",
"text": "def _configure_services(self):\n keystone_config = {\n 'admin-password': 'openstack',\n 'admin-token': 'ubuntutesting',\n }\n # say we don't need an HSM for these tests\n manila_config = {\n }\n configs = {\n 'keystone': keystone_config,\n 'manila': manila_config,\n }\n super(ManilaBasicDeployment, self)._configure_services(configs)",
"title": ""
},
{
"docid": "8b4628a512b400520246ce0f0467768e",
"score": "0.48002052",
"text": "def remove_enviroment(self):\r\n\r\n for i in range(self.get_number_students()): \r\n if self.exist_user(self.get_students_names()[i]) == True:\r\n self.remove_enviroment_student(\"/home/\"+self.get_students_names()[i]+\"/\")\r\n self.remove_enviroment_student(self.get_system_path()+\"tests_files/\"+self.get_name_test()+\"/\")\r\n else:\r\n print(\"> User \"+self.get_students_names()[i]+\" not exist.\")",
"title": ""
},
{
"docid": "7fa78f2f9b9b499e9cea5eccd4add97d",
"score": "0.4797984",
"text": "def update(self):\n\n self.get_system_information()",
"title": ""
},
{
"docid": "a46d91d1651dabf62bb065769b61db5f",
"score": "0.47967082",
"text": "def __db_load_services_start():\n start_bungeni(\"IGNORE_ERROR\")\n start_serializer(\"IGNORE_ERROR\")\n start_portal(\"IGNORE_ERROR\")\n start_plone(\"IGNORE_ERROR\")",
"title": ""
},
{
"docid": "97da2bdbfac713360337efbe36e12692",
"score": "0.47897902",
"text": "def setUp(self):\n dataserver.Clear()\n keyserver.Clear()",
"title": ""
},
{
"docid": "295443666a5348c960179eb543306cbb",
"score": "0.478822",
"text": "def autonomousInit(self):",
"title": ""
},
{
"docid": "4bc4a8772bfb135ecf114fbf348f658b",
"score": "0.4787656",
"text": "def registrarRemotamente(self):\n id, nombre, email, version, password=self.obtenerDatosRegistrados()\n peticionRemota=peticion.Peticion()\n id_obtenido=peticionRemota.registrarUsuario(nombre, email, password,version)\n if (int(id_obtenido) > 0):\n self.cursor.execute('update instalacion set id =?', (id_obtenido,))\n self.conexion_db.commit()\n modulo_logger.log(logging.INFO,'Se registro correctamente la instalacion')\n else:\n modulo_logger.log(loggin.ERROR,'Hubo un error al tratar de registrarse remotamente')",
"title": ""
},
{
"docid": "7df79e936a4be4ab0f04f425a7dca61b",
"score": "0.47852468",
"text": "def checkRegistradoLocalmente(self):\n try:\n registrado=self.cursor.execute('select count(*) from instalacion where nombretitular <> \"\"').fetchone()[0]\n except sqlite3.OperationalError, msg:\n modulo_logger.log(logging.ERROR,\"No se pudieron verificar los datos de instalacion. Tal vez no esta la base de datos instalada.\\nERROR: %s\" % msg)\n registrado=False\n if registrado >= 1:\n return True\n else:\n return False",
"title": ""
},
{
"docid": "d23bf9140c80450d7c1f3449ee4911cb",
"score": "0.47847328",
"text": "def setupOperatingSystem(self):\n\t\tpass",
"title": ""
},
{
"docid": "d6c825850d81c314f96240f98b085c9d",
"score": "0.47813076",
"text": "def shiva_the_destroyer():\n with settings(warn_only=True):\n run('rm -Rf %(SERVER_PROJECT_PATH)s' % app_config.__dict__)\n run('rm -Rf %(SERVER_VIRTUALENV_PATH)s' % app_config.__dict__)\n sudo('rm -Rf %(SERVER_LOG_PATH)s' % app_config.__dict__)\n\n # Remove any installed services\n stop_service('bot')\n installed_service_path = _get_installed_conf_path(service, remote_path, extension)\n sudo('rm -f %s' % installed_service_path)",
"title": ""
},
{
"docid": "438800fecd838a16d31bef7d0f9d18b5",
"score": "0.4778346",
"text": "def execute(self):\n\n self.log.info('Uname = %s' % \" \".join(os.uname()))\n self.log.info('Host Name = %s' % socket.gethostname())\n self.log.info('Host FQDN = %s' % socket.getfqdn())\n self.log.info('WorkingDir = %s' % self.pp.workingDir) # this could be different than rootPath\n\n fileName = '/etc/redhat-release'\n if os.path.exists(fileName):\n with open(fileName, 'r') as f:\n self.log.info('RedHat Release = %s' % f.read().strip())\n\n fileName = '/etc/lsb-release'\n if os.path.isfile(fileName):\n with open(fileName, 'r') as f:\n self.log.info('Linux release:\\n%s' % f.read().strip())\n\n fileName = '/proc/cpuinfo'\n if os.path.exists(fileName):\n with open(fileName, 'r') as f:\n cpu = f.readlines()\n nCPU = 0\n for line in cpu:\n if line.find('cpu MHz') == 0:\n nCPU += 1\n freq = line.split()[3]\n elif line.find('model name') == 0:\n CPUmodel = line.split(': ')[1].strip()\n self.log.info('CPU (model) = %s' % CPUmodel)\n self.log.info('CPU (MHz) = %s x %s' % (nCPU, freq))\n\n fileName = '/proc/meminfo'\n if os.path.exists(fileName):\n with open(fileName, 'r') as f:\n mem = f.readlines()\n freeMem = 0\n for line in mem:\n if line.find('MemTotal:') == 0:\n totalMem = int(line.split()[1])\n if line.find('MemFree:') == 0:\n freeMem += int(line.split()[1])\n if line.find('Cached:') == 0:\n freeMem += int(line.split()[1])\n self.log.info('Memory (kB) = %s' % totalMem)\n self.log.info('FreeMem. (kB) = %s' % freeMem)\n\n ###########################################################################\n # Disk space check\n\n # fs = os.statvfs( rootPath )\n fs = os.statvfs(self.pp.workingDir)\n # bsize; /* file system block size */\n # frsize; /* fragment size */\n # blocks; /* size of fs in f_frsize units */\n # bfree; /* # free blocks */\n # bavail; /* # free blocks for non-root */\n # files; /* # inodes */\n # ffree; /* # free inodes */\n # favail; /* # free inodes for non-root */\n # flag; /* mount flags */\n # namemax; /* maximum filename length */\n diskSpace = fs[4] * fs[0] / 1024 / 1024\n self.log.info('DiskSpace (MB) = %s' % diskSpace)\n\n if diskSpace < self.pp.minDiskSpace:\n self.log.error('%s MB < %s MB, not enough local disk space available, exiting'\n % (diskSpace, self.pp.minDiskSpace))\n sys.exit(1)",
"title": ""
},
{
"docid": "b30d2772bff88c360e160571dfb431df",
"score": "0.4773079",
"text": "def startMaintenance(self, hosts):\n pass",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="754"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">2541eb9e1e33ce147bdb8184afa7d8db</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Returns a secure filename that is mostly guaranteed to be unique.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "210a598d950e07f6021996eb18ce2257",
"score": "0.7789519",
"text": "def secure_uuid_filename(_obj, data):\n\n _, ext = path.splitext(data.filename)\n uid = uuid1()\n return secure_filename('{}{}'.format(uid, ext))",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "8fcf0c370b38efe3b8877bdd8508d2ad",
"score": "0.79068273",
"text": "def generate_secret_filename_for(filename):\n secret = os.urandom(constants.FILENAME_SECRET_LENGTH).hex()\n return secret + filename",
"title": ""
},
{
"docid": "dc03048562e6273eb0e7720bb1857366",
"score": "0.762202",
"text": "def create_random_filename():\n return str(uuid.uuid4())",
"title": ""
},
{
"docid": "b6034a79674b38a2eec9ad1493d365be",
"score": "0.7383958",
"text": "def __random_filename():\n import random\n import string\n\n chars = string.ascii_uppercase + string.digits\n return \"/tmp/{}\".format(\"\".join(\n random.choice(chars) for index in range(6)\n ))",
"title": ""
},
{
"docid": "2b4df6d13af11676ef8e4539464ec29c",
"score": "0.726259",
"text": "def unique_filename(data):\n file = data\n get_ext = file.filename.split(\".\")[-1]\n new_name = \"%s.%s\" % (uuid.uuid4().hex, get_ext)\n return new_name",
"title": ""
},
{
"docid": "996d2eacf809d3665b9afd0daccdd0b9",
"score": "0.721395",
"text": "def get_safe_filename(self, filename):\n return re.sub(\"@[0-9]+x\", \"\", filename).replace('-', '_')",
"title": ""
},
{
"docid": "a66ba0cee00b8338fe22f45bb400dd97",
"score": "0.71503836",
"text": "def random_filename(self):\n file_name = binascii.b2a_hex(os.urandom(15)).decode(\"utf-8\")\n while os.path.exists(self.path + \"/cache/\" + file_name + \".c\"):\n file_name = binascii.b2a_hex(os.urandom(15)).decode(\"utf-8\")\n return file_name",
"title": ""
},
{
"docid": "7682776852968928479ba6044119843b",
"score": "0.7049223",
"text": "def ensure_unique_filename(destination):\n if os.path.exists(destination):\n filename, extension = os.path.splitext(destination)\n return \"{0}_{1}{2}\".format(filename, uuid.uuid4(), extension)\n else:\n return destination",
"title": ""
},
{
"docid": "aa79c400b3b2bac0562727f46546776c",
"score": "0.6930801",
"text": "def make_safe_filename(string):\r\n safe_char = lambda c: c if c.isalnum() else \"_\"\r\n return \"\".join(safe_char(c) for c in string).rstrip(\"_\")",
"title": ""
},
{
"docid": "d5c46a988ff160c7b60bdc74769aaf42",
"score": "0.6871858",
"text": "def generate_unique_name(filename, now=None):\n path, _obs, instr, filekind, _serial, ext = get_reference_properties(filename)\n\n name = generate_unique_name_core(instr, filekind, ext, now)\n\n return os.path.join(path, name)",
"title": ""
},
{
"docid": "868f17485154e33ef8b2a60bdb85ce7a",
"score": "0.6862246",
"text": "def signed_contract_random_path(instance, filename):\n directory = instance.SIGNED_PDF_DIR\n ext = \"\".join(Path(filename).suffixes)\n name = uuid.uuid4()\n return f\"{directory}{name}{ext}\"",
"title": ""
},
{
"docid": "11af086de5490026a5ad771c612da803",
"score": "0.683521",
"text": "def ensure_valid_filename(s: str, min_length: int = 3) -> str:\n s = str(s).strip().replace(\" \", \"_\")\n s = re.sub(r\"(?u)[^-\\w.]\", \"\", s)\n if not s:\n s = \"_\"\n while len(s) < min_length:\n s = base64ify(s, \"+-\")\n return s",
"title": ""
},
{
"docid": "803773ff2b7d520605514930cc2fcc90",
"score": "0.67080176",
"text": "def generate_filename():\n rand_str = \"\".join(random.choices(string.ascii_uppercase + string.digits, k=8))\n now = datetime.datetime.now().strftime(\"%d-%m-%Y-%H-%M-%S\")\n\n return now + rand_str",
"title": ""
},
{
"docid": "a63ea593d9eb83b17c15f231403bcaf8",
"score": "0.66506535",
"text": "def unique_name(full_name):\n if '.' in full_name:\n l = full_name.split('.')\n name = '.'.join(l[:-1])\n ext = l[-1]\n else:\n name = full_name\n ext = ''\n i = 1\n s = full_name\n while os.path.exists(s):\n s = name + \"_{:05d}.\".format(i) + ext\n i += 1\n if s != full_name:\n print(\"WARNING: {} exists, using {} instead\".format(full_name, s))\n return s",
"title": ""
},
{
"docid": "9e67132f8419f7f1df0caf3fc5f7041e",
"score": "0.6634476",
"text": "def make_filename_safe(filename_string):\n safechars = string.ascii_lowercase + string.ascii_uppercase + string.digits + '_- '\n return ''.join([c for c in filename_string if c in safechars])",
"title": ""
},
{
"docid": "44647cc9ece24a8d0e28ec68164c8216",
"score": "0.65715885",
"text": "def file_safe_name(effect: str, display_name: str) -> str:\n valid_filename_chars = f\"-_. {string.ascii_letters}{string.digits}\"\n\n file_name = FILENAME_STRING.format(effect=effect, author=display_name)\n\n # Replace spaces\n file_name = file_name.replace(\" \", \"_\")\n\n # Normalize unicode characters\n cleaned_filename = unicodedata.normalize(\"NFKD\", file_name).encode(\"ASCII\", \"ignore\").decode()\n\n # Remove invalid filename characters\n cleaned_filename = \"\".join(c for c in cleaned_filename if c in valid_filename_chars)\n return cleaned_filename",
"title": ""
},
{
"docid": "4032c1468b30ff93a78cf6c53bbff0b8",
"score": "0.6565967",
"text": "def name_file_unique(dir_directory, base_name_file, ext_file):\n b = base_name_file\n abs_path = os.path.join(BASE_DIR, dir_directory, b + '.{}'.format(ext_file))\n while os.path.isfile(abs_path):\n b = base_name_file + '_{}'.format(int(random.random()*1000000))\n abs_path = os.path.join(BASE_DIR, dir_directory, b + '.{}'.format(ext_file))\n return b + '.{}'.format(ext_file)",
"title": ""
},
{
"docid": "f844b80637b1699893faf38a7accefd7",
"score": "0.65408486",
"text": "def safefilename(filename):\n for i in \"\\\\/:*?\\\"<>|$\":\n filename=filename.replace(i,\"_\")\n return filename",
"title": ""
},
{
"docid": "c67b4e5e18c538e71cb49533328090d7",
"score": "0.6525263",
"text": "def create_secret_filename(secrets_dir):\n result = os.path.join(secrets_dir, \"pass-key-{:06d}.enc\".format(random.randint(9999, 999999)))\n\n # We need to prevent overwriting existing encrypted passphrases, so we keep recursing\n # until we find an unused filename (Python does not have a do-until, and this is more elegant\n # than alternatives).\n return result if not os.path.exists(result) else create_secret_filename(secrets_dir)",
"title": ""
},
{
"docid": "e31b4c6cd6c0fdef75b03798ab0d10e4",
"score": "0.6509491",
"text": "def random_filename() -> str:\n return random_token()",
"title": ""
},
{
"docid": "ac0cbb8f2fd326196fc65422a17e3bc2",
"score": "0.64972526",
"text": "def generate_name(orig_name, path):\n base = orig_name\n if len(orig_name) == 0:\n base = os.path.basename(path)\n return base64.b32encode(hashlib.sha1(base.encode('utf-8')).digest()\n )[8:16].decode('ascii').lower()",
"title": ""
},
{
"docid": "e9fd18c6f9e9fe3372030d9a83ec1954",
"score": "0.6470064",
"text": "def generate_unique_filename(cls: Any, func_name: str, reserve: bool = True) -> str:\n unique_id = uuid.uuid4()\n extra = \"\"\n count = 1\n\n while True:\n unique_filename = \"<cattrs generated {} {}.{}{}>\".format(\n func_name, cls.__module__, getattr(cls, \"__qualname__\", cls.__name__), extra\n )\n if not reserve:\n return unique_filename\n # To handle concurrency we essentially \"reserve\" our spot in\n # the linecache with a dummy line. The caller can then\n # set this value correctly.\n cache_line = (1, None, (str(unique_id),), unique_filename)\n if linecache.cache.setdefault(unique_filename, cache_line) == cache_line:\n return unique_filename\n\n # Looks like this spot is taken. Try again.\n count += 1\n extra = f\"-{count}\"",
"title": ""
},
{
"docid": "0f9680d6c6a9fa2177445993201d31dc",
"score": "0.6460985",
"text": "def _get_client_secret_filename(self, file_path):\n nix = ['darwin', 'linux{}'.format(x for x in range(100))]\n\n if sys.platform in nix:\n parts = file_path.split('/')\n else:\n # Windows\n parts = []\n size = len(parts)\n return parts[size - 1]",
"title": ""
},
{
"docid": "bfc0a20177779bee5508b4f8340c868d",
"score": "0.6433994",
"text": "def safe_filename(filename, replacement=\"_\"):\n if not isinstance(filename, str):\n raise TypeError(\"filename must be a string\")\n if regex.path.linux.filename.search(filename):\n return filename\n safe_name = \"\"\n for char in filename:\n safe_name += char if regex.path.linux.filename.search(char) else replacement\n return safe_name",
"title": ""
},
{
"docid": "a9122fe2e617d6f3d4080e49dcc981f9",
"score": "0.6415311",
"text": "def get_valid_filename(s):\r\n s = Utils.force_text(s).strip().replace(' ', '_')\r\n return re.sub(r'(?u)[^-\\w.]', '', s)",
"title": ""
},
{
"docid": "b3f8b00269899a6b4ce9c10ae0a17fc9",
"score": "0.64051163",
"text": "def unique_filename(prefix=None, suffix=None, unique_id=None, return_id=False):\n\n print 'DEPRECATED, use DataIO instead'\n\n fn = []\n if prefix:\n fn.extend([prefix, '-'])\n\n if unique_id is None:\n unique_id = str(uuid.uuid4())\n\n fn.append(unique_id)\n\n if suffix:\n fn.extend(['.', suffix.lstrip('.')])\n\n if return_id:\n return [''.join(fn), unique_id]\n else:\n return ''.join(fn)",
"title": ""
},
{
"docid": "36fed8035e9ac91e2eaebab5bc1d8aa5",
"score": "0.640089",
"text": "def generate_temporary_filename(fname):\n if g_temporary_directory:\n return g_temporary_directory + \"/\" + os.path.basename(fname)\n return fname",
"title": ""
},
{
"docid": "50c0c1af8a6b737697d1fe2d6be76a9c",
"score": "0.63959795",
"text": "def generate_filename(self, cassette_name):\n for character in ('/', ':', ' '):\n cassette_name = cassette_name.replace(character, '_')\n\n return cassette_name + self.encoder.file_ext",
"title": ""
},
{
"docid": "04ca4ec51aabd965dcf036ff20ea0f11",
"score": "0.6394405",
"text": "def writable_filename(request):\n fobj, name = tempfile.mkstemp()\n os.close(fobj)\n os.remove(name)\n\n def remove_if_exists():\n if os.path.exists(name):\n os.remove(name)\n\n request.addfinalizer(remove_if_exists)\n return name",
"title": ""
},
{
"docid": "9e2732d6d0f2f8a719fe24fd959ae086",
"score": "0.6381832",
"text": "def generate_unique_filename(name, filetype):\n \n extension = \".\"+filetype\n file_name = \"logs/\"+name+extension\n if os.path.exists(file_name):\n expand = 1\n while True:\n expand += 1\n new_file_name = file_name.split(\".\")[0] + \"_\" + str(expand) + extension\n if not os.path.exists(new_file_name):\n return new_file_name\n return file_name",
"title": ""
},
{
"docid": "f8ab10544f6d0191a64aacd3d6b04e82",
"score": "0.63735455",
"text": "def get_filename(length):\r\n chars = string.ascii_letters\r\n filename = ''.join([random.choice(chars) for _ in range(length)])\r\n return filename",
"title": ""
},
{
"docid": "6eb6f698857fff50c8d3789944299664",
"score": "0.63538665",
"text": "def safe_filename(text, max_length=200):\n #Quickly truncates long filenames.\n truncate = lambda text: text[:max_length].rsplit(' ', 0)[0]\n\n #Tidy up ugly formatted filenames.\n text = text.replace('_', ' ')\n text = text.replace(':', ' -')\n\n #NTFS forbids filenames containing characters in range 0-31 (0x00-0x1F)\n ntfs = [chr(i) for i in range(0, 31)]\n\n #Removing these SHOULD make most filename safe for a wide range\n #of operating systems.\n paranoid = ['\\\"', '\\#', '\\$', '\\%', '\\'', '\\*', '\\,', '\\.', '\\/', '\\:',\n '\\;', '\\<', '\\>', '\\?', '\\\\', '\\^', '\\|', '\\~', '\\\\\\\\']\n\n blacklist = re.compile('|'.join(ntfs + paranoid), re.UNICODE)\n filename = blacklist.sub('', text)\n return truncate(filename)",
"title": ""
},
{
"docid": "de30a4edfcc85d7cd8d100ee32242d0d",
"score": "0.63183385",
"text": "def _generateGUID(slnfile, name):\n m = hashlib.md5()\n # Normalize the slnfile path to a Windows path (\\ separators) so\n # the generated file has a consistent GUID even if we generate\n # it on a non-Windows platform.\n m.update(bytearray(ntpath.normpath(str(slnfile)) + str(name),'utf-8'))\n solution = m.hexdigest().upper()\n # convert most of the signature to GUID form (discard the rest)\n solution = \"{\" + solution[:8] + \"-\" + solution[8:12] + \"-\" + solution[12:16] + \"-\" + solution[16:20] + \"-\" + solution[20:32] + \"}\"\n return solution",
"title": ""
},
{
"docid": "816ca5c50688db7e6ebb4a42cb5e3f19",
"score": "0.63093066",
"text": "def filename(self):\n\n try:\n return self.key_name.split(u'/')[-1]\n except Exception as e:\n logger.warn(e)\n return self.key_name",
"title": ""
},
{
"docid": "2ffc9884ef5f5d1d0f004a91acfca6e1",
"score": "0.63078773",
"text": "def get_file_name():\n return datetime.datetime.now().strftime(\"%Y-%m-%d_%H.%M.%S\")",
"title": ""
},
{
"docid": "f7fdbaacd72642c6648d8a38c9b17387",
"score": "0.62967324",
"text": "def create_filename(value):\n filename = slugify(value, u'_')\n\n # Generate a random filename if the title only contains non-ASCII\n # characters (i.e. slugifying it deletes everything).\n if not filename:\n filename = uuid.uuid4()\n\n return '{}.mp3'.format(filename)",
"title": ""
},
{
"docid": "41d21306c056ae31edb443a707cba0ee",
"score": "0.6270947",
"text": "def get_valid_filename(s):\n s = force_text(s).strip().replace(' ', '_')\n return re.sub(r'(?u)[^-\\w.]', '', s)",
"title": ""
},
{
"docid": "76bad65aba4e708204d48636542b8541",
"score": "0.6264713",
"text": "def get_valid_filename(s):\n s = to_text(s).strip().replace(' ', '_')\n return re.sub(r'(?u)[^-\\w\\(\\)_.]', '', s)",
"title": ""
},
{
"docid": "47284f99fb0b7736b131ec00e2d08478",
"score": "0.6252845",
"text": "def _resource_to_filename(resource: str) -> str:\n resource_bytes = resource.encode(\"utf-8\")\n resource_hash = sha256(resource_bytes)\n filename = resource_hash.hexdigest()\n\n return filename",
"title": ""
},
{
"docid": "40af1581cb6f421376df0f3db9898612",
"score": "0.62501967",
"text": "def create_quicksave_filename(self):\n\t\tname = \"%s/%s\" % (self.quicksave_dir, \\\n\t\t\t\t\t\t\t\t\t\t\t\t self.quicksave_filenamepattern % {'timestamp':time.time()})\n\t\tself.log.debug(\"Savegamemanager: creating quicksave-filename: %s\", name)\n\t\treturn name",
"title": ""
},
{
"docid": "86c9433b2462701d59bb081205741f62",
"score": "0.62452656",
"text": "def safe_filename(self, otype, oid):\n permitted = set(['_', '-', '(', ')'])\n oid = ''.join([c for c in oid if c.isalnum() or c in permitted])\n while oid.find('--') != -1:\n oid = oid.replace('--', '-')\n ext = 'json'\n ts = datetime.now().strftime(\"%Y%m%dT%H%M%S\")\n fname = ''\n is_new = False\n while not is_new:\n oid_len = 255 - len('%s--%s.%s' % (otype, ts, ext))\n fname = '%s-%s-%s.%s' % (otype, oid[:oid_len], ts, ext)\n is_new = True\n if os.path.exists(fname):\n is_new = False\n ts += '-bck'\n return fname",
"title": ""
},
{
"docid": "f85606024c5a9dc803b085c8accf0513",
"score": "0.6233706",
"text": "def _file_name_gen(key):\n parts = key.split(\"-\")\n if len(parts) == 1:\n name = key.lower()\n else:\n name = \"\"\n for part in parts[:-1]:\n name += part.lower()+\"_\"\n name += parts[-1].lower()\n return name",
"title": ""
},
{
"docid": "29a9fffc4d3e6fbaaec58d436b8d66f9",
"score": "0.62259656",
"text": "def get_upload_to_uuid(self, filename):\n basename = os.path.basename(filename)\n ext = os.path.splitext(basename)[1].lower()\n new_name = uuid.uuid4().hex\n return os.path.join(self.upload_to, new_name + ext)",
"title": ""
},
{
"docid": "268253ef917f53ca5ec12fecae27b334",
"score": "0.6212332",
"text": "def random_filename():\n\n return ''.join(random.choices(string.ascii_uppercase + string.digits, k=5))",
"title": ""
},
{
"docid": "f6cac79f8c07dd99e55bd8d9fd9a9de1",
"score": "0.62087566",
"text": "def get_free_filename(file_path):\n num = 1\n tmp_path = file_path\n while path.exists(tmp_path):\n tmp_path = file_path.rsplit('.', 1)[0] +\\\n ' (' + str(num) + ').' + file_path.rsplit('.', 1)[1]\n num += 1\n\n return tmp_path",
"title": ""
},
{
"docid": "fb75098bde8554aeb843ff2dee741a09",
"score": "0.62061495",
"text": "def gen_content_file_id(key):\n safe_key = urlsafe_b64encode(key.encode(\"utf-8\")).decode(\"utf-8\").rstrip(\"=\")\n return \"cf_{}\".format(safe_key)",
"title": ""
},
{
"docid": "176467ccffea76423f2967b6d44e5cf2",
"score": "0.6185822",
"text": "def gen_hash_file_path(url):\n ext = guess_file_ext(url)\n url_hash = str_md5(url)\n\n return '%s/%s/%s%s' % (url_hash[0:2], url_hash[2:4], url_hash, ext)",
"title": ""
},
{
"docid": "067b49438d5a3cf03ab8208789c6887d",
"score": "0.6183744",
"text": "def safe_filename(filename):\n # TODO: Look at a slugify module instead\n pattern = re.compile('[\\W_]+') # find all words\n root, ext = os.path.splitext(os.path.basename(filename))\n return pattern.sub('_', root) if ext is '' else ''.join([pattern.sub('_', root), ext])",
"title": ""
},
{
"docid": "c9416b07aa4bc23a5c6ba7c14b19beef",
"score": "0.6181438",
"text": "def tempFilename(prefix = 'hellanzb-tmp'):\n return prefix + str(randint(10000000, 99999999))",
"title": ""
},
{
"docid": "962233e6e0b7d1d257a3ab0ee4ed8712",
"score": "0.6161074",
"text": "def get_safe_path_name(path_name):\n\n def safe_char(char):\n if char.isalnum():\n return char\n else:\n return \"_\"\n\n return \"\".join(safe_char(char) for char in path_name).rstrip(\"_\")",
"title": ""
},
{
"docid": "79e56ea87bb25901acfa812f22c04558",
"score": "0.615425",
"text": "def create_quicksave_filename(cls):\r\n\t\tprepared_filename = time.strftime(cls.quicksave_filenamepattern)\r\n\t\tname = cls.filename.format(directory=cls.quicksave_dir, name=prepared_filename)\r\n\t\tcls.log.debug(\"Savegamemanager: creating quicksave-filename: %s\", name)\r\n\t\treturn name",
"title": ""
},
{
"docid": "70ea1266ba9982788d82946b9011124f",
"score": "0.6144438",
"text": "def make_safe_for_filename(self, text):\r\n \r\n return re.sub('[^a-z0-9\\-_\\.]+', '', text)",
"title": ""
},
{
"docid": "c5e8f677b5dd1966e98ff795d33501ac",
"score": "0.6141691",
"text": "def file_name_no_extension(self) -> str:",
"title": ""
},
{
"docid": "76cd8c59f842a2c32e5e0d4ec9327c4d",
"score": "0.6121472",
"text": "def get_valid_filename(s):\n s = str(s).strip().replace(' ', '_')\n return re.sub(r'(?u)[^-\\w.]', '', s)",
"title": ""
},
{
"docid": "5ee225cc9701fcbad7d4e44219d60f1c",
"score": "0.611512",
"text": "def get_valid_filename(s, max_length=FILENAME_MAX_LENGTH):\n s = str(s).strip().replace(' -- @', '_')\n s = re.sub(r'(?u)[^-\\w]', '_', s).strip('_')\n return s[:max_length]",
"title": ""
},
{
"docid": "d844309ef372bb401d5e6430ffc1f6a1",
"score": "0.61120844",
"text": "def __encodeCacheFilename(self, url):\n hashedUrl = hashlib.sha256(url).hexdigest()\n filename = self.cachePath + hashedUrl\n return filename",
"title": ""
},
{
"docid": "05156c137cf941af21213bff53b153cd",
"score": "0.6105476",
"text": "def friendly_filename(filename):\n # get everything after the last slash\n filename = filename.split('\\\\')[-1].split('/')[-1]\n # remove extension\n filename, ext = split_extension(filename)\n # convert slashes\\underscores\\bracket\\pound to space\n filename = re.sub('[/\\\\_\\[\\]#]', ' ', filename)\n # convert two or more spaces to just one space\n filename = re.sub('\\s{2,}', ' ', filename)\n return filename.strip()[:200]",
"title": ""
},
{
"docid": "711ccf13fe65eb44233256a02839d40e",
"score": "0.6092397",
"text": "def generate_image_filename(prefix, instance, filename):\n _, ext = os.path.splitext(filename)\n return '{0}/{1}{2}'.format(prefix, uuid.uuid4().hex, ext)",
"title": ""
},
{
"docid": "de6448952c1ac1ccd1bfbb031940f378",
"score": "0.6061458",
"text": "def to_valid_filename(s):\n valid_chars = \"-_.() %s%s\" % (string.ascii_letters, string.digits)\n return ''.join(c for c in s if c in valid_chars)",
"title": ""
},
{
"docid": "d5734642ad3c62dc183f009877143205",
"score": "0.60571265",
"text": "def getFilename(filename):\n base,ext=os.path.splitext(filename)\n f=base+\"_\"+date.today().isoformat()\n if os.path.exists(f+ext):\n template=f+\"-%03d\"\n for n in range(1,100):\n if not os.path.exists(template%n+ext):\n return template%n+ext\n print \"no valid filename found for \",filename\n return \"temp\"+ext\n else:\n return f+ext",
"title": ""
},
{
"docid": "893befe8d93eeaaa7ac7d212e1066aeb",
"score": "0.60562384",
"text": "def sanitize_filename(s, restricted=False, is_id=False):\n def replace_insane(char):\n if restricted and char in ACCENT_CHARS:\n return ACCENT_CHARS[char]\n if char == '?' or ord(char) < 32 or ord(char) == 127:\n return ''\n elif char == '\"':\n return '' if restricted else '\\''\n elif char == ':':\n return '_-' if restricted else ' -'\n elif char in '\\\\/|*<>':\n return '_'\n if restricted and (char in '!&\\'()[]{}$;`^,#' or char.isspace()):\n return '_'\n if restricted and ord(char) > 127:\n return '_'\n return char\n\n # Handle timestamps\n s = re.sub(r'[0-9]+(?::[0-9]+)+',\n lambda m: m.group(0).replace(':', '_'), s)\n result = ''.join(map(replace_insane, s))\n if not is_id:\n while '__' in result:\n result = result.replace('__', '_')\n result = result.strip('_')\n # Common case of \"Foreign band name - English song title\"\n if restricted and result.startswith('-_'):\n result = result[2:]\n if result.startswith('-'):\n result = '_' + result[len('-'):]\n result = result.lstrip('.')\n if not result:\n result = '_'\n return result",
"title": ""
},
{
"docid": "051dad1d2867b56d87ac479e36865e72",
"score": "0.6054417",
"text": "def create_autosave_filename(cls):\r\n\t\tprepared_filename = time.strftime(cls.autosave_filenamepattern)\r\n\t\tname = cls.filename.format(directory=cls.autosave_dir, name=prepared_filename)\r\n\t\tcls.log.debug(\"Savegamemanager: creating autosave-filename: %s\", name)\r\n\t\treturn name",
"title": ""
},
{
"docid": "678ddc215dceca0fb4c7cd2fde475b38",
"score": "0.60533214",
"text": "def create_guid_named_file(file, path=None) -> str:\n\n assert len(findall(\"\\\\.\", file)) == 1, \"file must only contain one .\"\n ending = file.split(\".\")[1]\n new_name = str(uuid4())\n destination_file = f\"{new_name}.{ending}\"\n copyfile(\n src=join(path, file),\n dst=join(path, destination_file)\n )\n return destination_file",
"title": ""
},
{
"docid": "62879db1555a6de481cb5333b233c3b3",
"score": "0.60493577",
"text": "def _get_filename(self) -> str:\n if self._fname is None:\n timestamp = datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n fname = \"%s-%s.log\" % (timestamp, abs(id(self)))\n self._fname = os.path.join(self.file_path, fname)\n return self._fname",
"title": ""
},
{
"docid": "855f9f23a322b2910a0a1afdf37f83c9",
"score": "0.6045559",
"text": "def format_filename(s):\n valid_chars = \"-_.() %s%s\" % (string.ascii_letters, string.digits)\n filename = ''.join(c for c in s if c in valid_chars)\n filename = filename.replace(' ','_') # I don't like spaces in filenames.\n return filename",
"title": ""
},
{
"docid": "855f9f23a322b2910a0a1afdf37f83c9",
"score": "0.6045559",
"text": "def format_filename(s):\n valid_chars = \"-_.() %s%s\" % (string.ascii_letters, string.digits)\n filename = ''.join(c for c in s if c in valid_chars)\n filename = filename.replace(' ','_') # I don't like spaces in filenames.\n return filename",
"title": ""
},
{
"docid": "da1f262b7776175b0012ea5c480fadf0",
"score": "0.60434985",
"text": "def format_filename(s):\n valid_chars = \"-_.() %s%s\" % (string.ascii_letters, string.digits)\n filename = ''.join(c for c in s if c in valid_chars)\n filename = filename.replace(' ', '_') # I don't like spaces in filenames.\n return filename",
"title": ""
},
{
"docid": "da1f262b7776175b0012ea5c480fadf0",
"score": "0.60434985",
"text": "def format_filename(s):\n valid_chars = \"-_.() %s%s\" % (string.ascii_letters, string.digits)\n filename = ''.join(c for c in s if c in valid_chars)\n filename = filename.replace(' ', '_') # I don't like spaces in filenames.\n return filename",
"title": ""
},
{
"docid": "236c33c8e2d8636943cc324a2b3bd0c6",
"score": "0.6037429",
"text": "def fix_bad_file_name(file_name):\n file_name_extension = re.search(r\"(\\..*)$\", file_name).group(1)\n file_name = str(uuid.uuid1().int)\n return \"unknown_name_\" + file_name + file_name_extension",
"title": ""
},
{
"docid": "b4207fea9f7cb65fba0b24ab36c37f9c",
"score": "0.60240215",
"text": "def generateSecureUUID():\n return str(uuid.UUID(bytes=Random.get_random_bytes(16)))",
"title": ""
},
{
"docid": "307826bea0aab5956c99bc51fc4157c7",
"score": "0.60199195",
"text": "def _get_cache_token_filename(self):\n filename = 'auth-%s.info' % self.username\n return os.path.join(self._get_cache_token_path(), filename)",
"title": ""
},
{
"docid": "e19d7111d4475e2f22ba5f466a89779d",
"score": "0.6012433",
"text": "def sanitize_filename(filename, replacement='_', max_length=200):\r\n basepath = os.path.basename(filename).strip()\r\n sane_fname = re.sub(r'[^\\w\\.\\- ]', replacement, basepath)\r\n\r\n while \"..\" in sane_fname:\r\n sane_fname = sane_fname.replace('..', '.')\r\n\r\n while \" \" in sane_fname:\r\n sane_fname = sane_fname.replace(' ', ' ')\r\n\r\n if not len(filename):\r\n sane_fname = 'NONAME'\r\n\r\n # limit filename length\r\n if max_length:\r\n sane_fname = sane_fname[:max_length]\r\n\r\n return sane_fname",
"title": ""
},
{
"docid": "23e38d205546994f4dad4768b18002cd",
"score": "0.60050297",
"text": "def format_filename(s):\n valid_chars = \"-_.() {}{}\".format(string.ascii_letters, string.digits)\n filename = \"\".join(c for c in s if c in valid_chars)\n filename = filename.replace(\" \", \"_\")\n return filename",
"title": ""
},
{
"docid": "af1f0063d29afee696de9fbeace8a2d8",
"score": "0.59933907",
"text": "def encrypted_name(apath):\n\treturn apath + POSTFIX",
"title": ""
},
{
"docid": "2a590bec5900a9ea355131e73203d769",
"score": "0.5990244",
"text": "def _generate_unique_file_path(self, variants_num):\n # type: (int) -> str\n unique_dir = filesystems.FileSystems.join(\n self._vcf_shards_output_dir, self._generate_unique_key())\n if filesystems.FileSystems.exists(unique_dir):\n raise RuntimeError('The shard dir {} already exists.'.format(unique_dir))\n filesystems.FileSystems.mkdirs(unique_dir)\n return filesystems.FileSystems.join(\n unique_dir, vep_runner_util._SHARD_PREFIX + str(variants_num))",
"title": ""
},
{
"docid": "a4133e7bb73fa3375a85f8872bd0931c",
"score": "0.59892887",
"text": "def gen_file_path(url):\n ext = guess_file_ext(url) or ''\n name = str_md5(url)\n\n return '%s/%s/%s%s' % (name[0:2], name[2:4], name, ext)",
"title": ""
},
{
"docid": "40130ae1d1547ca9cecbcb61e373d0f6",
"score": "0.59872687",
"text": "def filename(f_name):\n\tvalid_chars = \"-_.() %s%s[]&\" % (string.ascii_letters, string.digits)\n\treturn ''.join(c for c in f_name if c in valid_chars)",
"title": ""
},
{
"docid": "e02cc3fb695a3a53a15a257e5a2ab907",
"score": "0.5983087",
"text": "def get_png_download_name(user_file_name):\n return f\"{user_file_name}-melodie.png\"",
"title": ""
},
{
"docid": "540161e4de08acb705c180bde12f740c",
"score": "0.5982601",
"text": "def create_autosave_filename(self):\n\t\tname = \"%s/%s\" % (self.autosave_dir, \\\n\t\t\t\t\t\t\t\t\t\t\t\t self.autosave_filenamepattern % {'timestamp':time.time()})\n\t\tself.log.debug(\"Savegamemanager: creating autosave-filename: %s\", name)\n\t\treturn name",
"title": ""
},
{
"docid": "10668bae54168cc8280e515096eccd22",
"score": "0.59721154",
"text": "def construct_filename(self):\n self._file_num = self._file_num + 1\n\n filename = os.path.join(self._image_dir, self._serial_number, '{:03d}.cr2'.format(self._file_num))\n\n return filename",
"title": ""
},
{
"docid": "c05f90ffce6867ba5e9f0d7b19cec615",
"score": "0.59685975",
"text": "def format_filename(s):\n valid_chars = \"-_() %s%s\" % (string.ascii_letters, string.digits)\n filename = ''.join(c for c in s if c in valid_chars)\n filename = filename.replace(' ', '_') # I don't like spaces in filenames.\n return filename",
"title": ""
},
{
"docid": "4291457b671fced0f0a4854a2dee5a60",
"score": "0.59565586",
"text": "def get_cache_key(self, name, filename=None):\r\n hash = sha1(name.encode('utf-8'))\r\n if filename is not None:\r\n filename = '|' + filename\r\n if isinstance(filename, unicode):\r\n filename = filename.encode('utf-8')\r\n hash.update(filename)\r\n return hash.hexdigest()",
"title": ""
},
{
"docid": "21f56df25e5966db0a242ac5451dd400",
"score": "0.5954102",
"text": "def name_timestamp() -> str:\n name = 'file{}'.format(time.strftime(\"%Y%m%d%H%M%S\")) + str(random.randint(10000, 99999))\n return name",
"title": ""
},
{
"docid": "3e0463d69ed92304a62fadcb1c0a750d",
"score": "0.5952909",
"text": "def _filename(directory_path: str, url: str) -> Path:\n return Path(\n directory_path,\n hashlib.sha256(url.encode('utf-8')).hexdigest()\n ).with_suffix(PurePath(parse.urlparse(url).path).suffix)",
"title": ""
},
{
"docid": "6bb9e4d2c38b1df0f028e15fb0cd51fd",
"score": "0.5951941",
"text": "def sanitize_filename(filename, allow_dirs = False):\n if filename == '.' and not allow_dirs:\n return '_'\n trans = filename.maketrans('<>:\"|?*', '_______')\n for i in range(0x00, 0x20):\n trans[i] = ord('_')\n if not allow_dirs:\n trans[ord('/')] = ord('_')\n trans[ord('\\\\')] = ord('_')\n else:\n trans[ord('\\\\')] = ord('/') # We use posix paths\n return filename.translate(trans)",
"title": ""
},
{
"docid": "6c2d0989f91ca73befc2ba6eb3b7a6f6",
"score": "0.5948964",
"text": "def format_filename(name):\n #Taken from: https://gist.github.com/seanh/93666\n valid_chars = \"-_() %s%s\" % (string.ascii_letters, string.digits)\n filename = ''.join(c for c in name if c in valid_chars)\n # Remove spaces in filename\n filename = filename.strip()\n filename = filename.replace(' ','_')\n return filename",
"title": ""
},
{
"docid": "5e797f539bc5d3c465136a6d55e2dde2",
"score": "0.59462094",
"text": "def get_fixed_filename(filename):\n # No solution is provided, but you may want to consider the patterns to look out for and fix\n # E.g. a lowercase letter followed by a capital, like \"tN\" should become \"t_N\"\n # Try doing this on paper first and then see if you can systematise it\n new_name = filename.replace(\" \", \"_\").replace(\".TXT\", \".txt\")\n characters = list(filename)\n if \"_\" not in new_name:\n for index, character in enumerate(characters):\n # TODO: rewrite to avoid repeated \"and\"\n if character.isupper() and characters[index-1].isalpha() and index > 0:\n characters.insert(index, \"_\")\n else:\n for index, character in enumerate(characters):\n if character == \"_\":\n characters[index+1] = characters[index+1].upper()\n filename = \"\".join(characters)\n new_name = filename\n return new_name",
"title": ""
},
{
"docid": "13395fff675d544531f4c13ebdee7325",
"score": "0.5941506",
"text": "def get_filename(self):\n if not self.readable_filename:\n self.save()\n return self.readable_filename",
"title": ""
},
{
"docid": "fe671a6002ccc121247f1fea1d463d1c",
"score": "0.59402466",
"text": "def filename(image_number):\n # For speedup, cache the filename for 10 s.\n global filename_cache\n if not \"filename_cache\" in globals(): filename_cache = {}\n if image_number in filename_cache:\n f,timestamp = filename_cache[image_number]\n if time()-timestamp < 10: return f\n f = __filename__(image_number)\n filename_cache[image_number] = (f,time())\n return f",
"title": ""
},
{
"docid": "fc1a58e4dd3e680f32238576431a035a",
"score": "0.59279996",
"text": "def create_filename(self, savegamename):\n\t\tname = \"%s/%s.%s\" % (self.savegame_dir, savegamename, self.savegame_extension)\n\t\tself.log.debug(\"Savegamemanager: creating save-filename: %s\", name)\n\t\treturn name",
"title": ""
},
{
"docid": "48ca63127fbb8d6fa3fd2b7c94ae833f",
"score": "0.5927376",
"text": "def avatar_path(instance, filename):\n file_name, file_ext = os.path.splitext(filename)\n file_name = hashlib.sha256(str(random.getrandbits(256)).encode('utf-8')).hexdigest()\n filename = ''.join([file_name, file_ext])\n return '/'.join([AVATAR_DIRECTORY, filename])",
"title": ""
},
{
"docid": "4f1c23922337a883d2d8b4dda5caba85",
"score": "0.5920146",
"text": "def generate_file_name(self, url):\n temp = url.split('/')\n date_time = date.today()\n unix = time.mktime(date_time.timetuple())\n if temp[len(temp) - 1]:\n return temp[len(temp) - 1] + \"-\" + str(unix)\n elif temp[len(temp) - 2]:\n return temp[len(temp) - 2] + \"-\" + str(unix)\n else:\n return str(unix)",
"title": ""
},
{
"docid": "2ed82bb554b73dd038e7186832a93f02",
"score": "0.5917247",
"text": "def unique_filename_in(path=None):\n if path == None: path = os.getcwd()\n def random_string():\n return \"\".join([random.choice(string.letters + string.digits) for x in range(20)])\n while True:\n filename = random_string()\n files = [f for f in os.listdir(path) if f.startswith(filename)]\n if files == []: break\n return filename",
"title": ""
},
{
"docid": "59d5d500767d25964ffd6ab36066e6c1",
"score": "0.5910821",
"text": "def _truncated_unique_name(name):\n limit = 64\n uuid_len = 32\n if len(name) <= limit:\n return name\n\n shortened = name[:limit-uuid_len]\n return shortened + uuid.uuid4().hex",
"title": ""
},
{
"docid": "3d614d53de253df1ecd46db41c32609a",
"score": "0.5906665",
"text": "def get_random_name(length=config.get_filename_length(), random=random):\n return \"\".join(random.choice(string.ascii_lowercase + string.digits) for _ in range(length))",
"title": ""
},
{
"docid": "e7eb58eab7ff525ee12568d7deb9cb3d",
"score": "0.59055483",
"text": "def get_legal_filename(filename):\n return sub('[^\\w\\d\\-_\\. \\(\\)\\']', '-', filename.replace('\"', '\\'\\'')) # pylint: disable=anomalous-backslash-in-string",
"title": ""
},
{
"docid": "505c1a6939182780fb550671678f3738",
"score": "0.59023136",
"text": "def _generate_url():\n\treturn str(uuid4()).replace('-', '')[:15]",
"title": ""
},
{
"docid": "b4a0d728892786ae7a8756481093ecd6",
"score": "0.5900737",
"text": "def create_unique_name(filename: str, path: Path) -> Path:\n filename = filename.strip() + \".docx\"\n filepath = path / filename\n if filepath.exists():\n # Count available files with same name\n counter = len(list(path.glob(f\"{filepath.stem}*docx\"))) + 1\n filepath = path / f\"{filepath.stem}_{counter}.docx\"\n return filepath",
"title": ""
},
{
"docid": "36414c6bcd283e7f0194da8dcd510d3e",
"score": "0.5896471",
"text": "def unique_lineage_name(path, filename, mode=0o777):\n try:\n return _safely_attempt_open(\n os.path.join(path, \"%s.conf\" % (filename)), mode=mode)\n except OSError as err:\n if err.errno != errno.EEXIST:\n raise\n return _unique_file(\n path, filename_pat=(lambda count: \"%s-%04d.conf\" % (filename, count)),\n count=1, mode=mode)",
"title": ""
},
{
"docid": "f5a90fcee9161c3bc0c6c80546695e0c",
"score": "0.5894792",
"text": "def make_filename(self) -> str:\n if self.fragment:\n with_fragment_filename = '' # type: str\n with_fragment_filename = self._safe_filename(\n self.fragment\n ) + '.png'\n return with_fragment_filename\n\n if self.query:\n with_query_filename = '' # type: str\n with_query_filename = self._safe_filename(self.query) + '.png'\n return with_query_filename\n\n filename = '' # type: str\n filename = self._safe_filename(self.path.split('/')[-1:][0])\n if filename == '':\n filename = 'index'\n return filename + '.png'",
"title": ""
},
{
"docid": "c54047dbc31c7e430e95419f4e3af384",
"score": "0.5894419",
"text": "def build_filename() -> str:\n application_name = GetWindowText(GetForegroundWindow()).split(' - ')[-1]\n if not application_name or application_name == '':\n # Maybe means the user screenshotted the desktop.\n application_name = 'Desktop'\n dt_format = '%y-%m-%d_%H-%M-%S'\n current_time = datetime.now().strftime(dt_format)\n return '%s %s.png' % (application_name, current_time)",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="755"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">6eb19d0511ab549c0fa61b646922241f</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Called after an object has been moved into this object. Anything inside a character is an item, in their inventory or equipped. It's assumed that coordinate 0 is the character's inventory, and coordinates 1+ are their equipment slots.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "3d7df2a78ba3426f8d90607e1587d03b",
"score": "0.51111734",
"text": "def at_object_receive(self, obj: DefaultObject, source_location: typing.Optional[DefaultObject], move_type=\"move\", **kwargs):\n obj.db.coordinates = 0",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "fee4d1485ac8e479d3529f2925b77351",
"score": "0.61136115",
"text": "def move_being_on_map(self, obj, dx, dy):\n newx = obj.x + dx\n newy = obj.y + dy\n # checks\n self.rules.assert_remove_ok(obj)\n self.rules.assert_unoccupied(obj.place, newx, newy)\n self.rules.assert_passable(obj, obj.place, newx, newy)\n # commit\n obj.place.remove_occupant(obj.x, obj.y)\n obj.place.set_occupant(newx, newy, obj)\n obj.step(newx, newy, dx, dy)\n #obj.loc = (obj.place, newx, newy)\n # hooks\n self.rules.on_put_occupant(obj)",
"title": ""
},
{
"docid": "da0bc82004603cb9026b668b42044ead",
"score": "0.6072634",
"text": "def at_object_receive(self, moved_obj, source_location):\n if isinstance(moved_obj, WildernessExit):\n # Ignore exits looping back to themselves: those are the regular\n # n, ne, ... exits.\n return\n\n itemcoords = self.wilderness.itemcoordinates\n if moved_obj in itemcoords:\n # This object was already in the wilderness. We need to make sure\n # it goes to the correct room it belongs to.\n coordinates = itemcoords[moved_obj]\n # Setting the location to None is important here so that we always\n # get a \"fresh\" room if it was in the wrong place\n moved_obj.location = None\n self.wilderness.move_obj(moved_obj, coordinates)\n else:\n # This object wasn't in the wilderness yet. Let's add it.\n itemcoords[moved_obj] = self.coordinates",
"title": ""
},
{
"docid": "4949f2d824e9469c4b34fcdafc4cb761",
"score": "0.58814025",
"text": "def at_post_object_leave(self, obj):\n # Try removing the object from the coordinates system\n if loc := self.db.itemcoordinates.pop(obj, None):\n # The object was removed successfully\n # Make sure there was a room at that location\n if room := self.db.rooms.get(loc):\n # If so, try to clean up the room\n self._destroy_room(room)",
"title": ""
},
{
"docid": "e5b41c0dc5c1d55cd3196bca67704548",
"score": "0.5861113",
"text": "def clicked(self,character,face,item):\n # open inventory\n character.foreign_inventory = (self.world, self.position)\n character[\"open_inventory\"] = True",
"title": ""
},
{
"docid": "d5194f09f233dc724821d72dd92bd493",
"score": "0.5853281",
"text": "def teleport_being_on_map(self, obj, newx, newy):\n # checks\n self.rules.assert_remove_ok(obj)\n self.rules.assert_unoccupied(obj.place, newx, newy)\n self.rules.assert_passable(obj, obj.place, newx, newy)\n # commit\n obj.place.remove_occupant(obj.x, obj.y)\n obj.place.set_occupant(newx, newy, obj)\n obj.loc = (obj.place, newx, newy)\n # hooks\n self.rules.on_put_occupant(obj)",
"title": ""
},
{
"docid": "18c183c55bbd9844cd3f07b1eed7be3a",
"score": "0.5851155",
"text": "def before_InsertingInto_object_into_own_contents(actor, x, y, ctxt) :\n loc = ctxt.world[Location(y)]\n while not ctxt.world[IsA(loc, \"room\")] :\n if loc == x :\n raise AbortAction(str_with_objs(\"{Bob|cap} will have to remove [the $y] from [the $x] first.\",\n x=x, y=y), actor=actor)\n loc = ctxt.world[Location(loc)]",
"title": ""
},
{
"docid": "4b2bac2635c6b79113672274ac2006c3",
"score": "0.57828784",
"text": "def addToRoomFromInventory(self, item, dropLocation):\n itemOnPosition = self.getRoom().getItemOnPosition(dropLocation)\n if dropLocation == [-1, -1]: \n return False\n if itemOnPosition != None:\n if not itemOnPosition.isStackable():\n return False\n if not self.getRoom().addItemFromInventory(item, dropLocation):\n return False\n self.__inventory.remove(item)\n item.setPlayer(None)\n self.save(\"player\")\n self.newChatMessage(item.getName() + \" depositado en el suelo\", 1)",
"title": ""
},
{
"docid": "7bc05c724b12c276bed09e6433b4f838",
"score": "0.57743955",
"text": "def addToInventoryFromRoom(self, item): \n tile = item.getTile()\n itemList = tile.getItemsFrom(item)\n itemList.reverse()\n for itemToInv in itemList:\n self.addPoints(itemToInv.points, itemToInv.label)\n item_with_inventory.GGItemWithInventory.addToInventory(self, itemToInv)\n self.save(\"player\")",
"title": ""
},
{
"docid": "863a9a91c529cf54096b0e98894254f0",
"score": "0.5764143",
"text": "def move_object(self, obj:Object, new_x:int, new_y:int) -> None:\n try:\n x, y = obj.pos.tolist()\n self.cells[y][x].remove_object()\n obj.old_pos = np.array([x, y])\n obj.pos = np.array([new_x, new_y])\n self.cells[new_y][new_x].add_object(obj)\n \n except RuntimeError:\n print(f'Cannot place object at {x},{y}: cell occupied.')",
"title": ""
},
{
"docid": "1be711a0366cc51d68944017d08d2c88",
"score": "0.5721075",
"text": "def at_character_move_in(self, location):\n self.trigger(EventType.EVENT_TRIGGER_ARRIVE, location.get_element_key(), location)",
"title": ""
},
{
"docid": "69ad756e118592b7b6eafa529f509ca8",
"score": "0.57175624",
"text": "def before_PlacingOn_object_onto_own_contents(actor, x, y, ctxt) :\n loc = ctxt.world[Location(y)]\n while not ctxt.world[IsA(loc, \"room\")] :\n if loc == x :\n raise AbortAction(str_with_objs(\"{Bob|cap} will have to take [the $y] off [the $x] first.\",\n x=x, y=y), actor=actor)\n loc = ctxt.world[Location(loc)]",
"title": ""
},
{
"docid": "1247cbb25768f9ba02563bc7578bae3c",
"score": "0.57103497",
"text": "def equip(self,item,slot_number):\n self.inventory[slot_number] = item",
"title": ""
},
{
"docid": "16b42a7975c2e9f345e39869bfd369bd",
"score": "0.5693969",
"text": "def after_move(self):\n pass",
"title": ""
},
{
"docid": "0832b2488f1555f8aea83b51d5555022",
"score": "0.56860584",
"text": "def move_obj(self, obj, new_coordinates):\n # Update the position of this obj in the wilderness\n self.itemcoordinates[obj] = new_coordinates\n old_room = obj.location\n\n # Remove the obj's location. This is needed so that the object does not\n # appear in its old room should that room be deleted.\n obj.location = None\n\n # By default, we'll assume we won't be making a new room and change this flag if necessary.\n create_room = False\n\n # See if we already have a room for that location\n if room := self.db.rooms.get(new_coordinates):\n # There is. Try to destroy the old_room if it is not needed anymore\n self._destroy_room(old_room)\n else:\n # There is no room yet at new_location\n # Is the old room in a wilderness?\n if hasattr(old_room, \"wilderness\"):\n # Yes. Is it in THIS wilderness?\n if old_room.wilderness == self:\n # Should we preserve rooms with any objects?\n if self.preserve_items:\n # Yes - check if ANY objects besides the exits are in old_room\n if len(\n [\n ob\n for ob in old_room.contents\n if not inherits_from(ob, WildernessExit)\n ]\n ):\n # There is, so we'll create a new room\n room = self._create_room(new_coordinates, obj)\n else:\n # The room is empty, so we'll reuse it\n room = old_room\n else:\n # Only preserve rooms if there are players behind\n if len([ob for ob in old_room.contents if ob.has_account]):\n # There is still a player there; create a new room\n room = self._create_room(new_coordinates, obj)\n else:\n # The room is empty of players, so we'll reuse it\n room = old_room\n\n # It's in a different wilderness\n else:\n # It does, so we make sure to leave the other wilderness properly\n old_room.wilderness.at_post_object_leave(obj)\n # We'll also need to create a new room in this wilderness\n room = self._create_room(new_coordinates, obj)\n\n else:\n # Obj comes from outside the wilderness entirely\n # We need to make a new room\n room = self._create_room(new_coordinates, obj)\n\n # Set `room` to the new coordinates, however it was made\n room.set_active_coordinates(new_coordinates, obj)\n\n # Put obj back, now in the correct room\n obj.location = room\n obj.ndb.wilderness = self",
"title": ""
},
{
"docid": "641385fadc6038153604c8454b054ecf",
"score": "0.5587942",
"text": "def addToRoomFromInventory(self, item):\n dropLocation = GG.utils.getFrontPosition(self.getPosition(), self.__heading, self.getRoom().size)\n if not self.getRoom().getTile(dropLocation).stepOn() or dropLocation == [-1, -1]:\n self.newChatMessage(\"No puedo soltarlo ahí\", 1)\n else: \n item_with_inventory.GGItemWithInventory.addToRoomFromInventory(self, item, dropLocation)\n self.save(\"player\")",
"title": ""
},
{
"docid": "a280cb4b821bbf792011d8206a3b02d8",
"score": "0.55823654",
"text": "def _handle_player_collide_item(self, player: Player, dropped_item: DroppedItem, data,\n arbiter: pymunk.Arbiter):\n\n item = dropped_item.get_item()\n\n if self._hot_bar.add_item(item):\n print(f\"Added 1 {item!r} to the hotbar\")\n elif self._inventory.add_item(item):\n print(f\"Added 1 {item!r} to the inventory\")\n else:\n print(f\"Found 1 {item!r}, but both hotbar & inventory are full\")\n return True\n\n self._world.remove_item(dropped_item)\n return False",
"title": ""
},
{
"docid": "61bc80ed4a79e35920bc97250705e138",
"score": "0.5572973",
"text": "def move_items(self):\n pass",
"title": ""
},
{
"docid": "89fa7bab67ac7c8b5941baca3f4cb672",
"score": "0.551856",
"text": "def addToInventory(self, item, position = None): \n self.__inventory.append(item)\n item.setPlayer(self)\n if not position:\n position = item.getPosition()\n self.triggerEvent('addToInventory', item=item, position = position, itemName = item.getName())\n self.save(\"player\")",
"title": ""
},
{
"docid": "9a3d1db5b4d01b2db75e765590ca7341",
"score": "0.550509",
"text": "def equip(self, item):\r\n\r\n #checks if the item is in the inventory, or if\r\n #the item will equipped no matter what\r\n if self.remove_from_inventory(item):\r\n \r\n #TODO use bitmaps for a slot system\r\n for slot in self._equipped.iterkeys():\r\n \r\n slot_value = int(slot)\r\n\r\n #check what slots the item uses\r\n if slot_value & item.get_slot() != 0:\r\n self.unequip(slot)\r\n\r\n self._equipped[str(item.get_slot())] = item\r\n\r\n item_data = item.get_bonuses()\r\n\r\n #applies any bonuses the item has\r\n if(conf.POWER_DATA in item_data.keys()):\r\n self._power += item_data[conf.POWER_DATA]\r\n if(conf.MAX_HEALTH_DATA in item_data.keys()):\r\n self._max_health += item_data[conf.MAX_HEALTH_DATA]\r\n self._health += item_data[conf.MAX_HEALTH_DATA]\r\n if(conf.MAX_MANA_DATA in item_data.keys()):\r\n self._max_mana += item_data[conf.MAX_MANA_DATA]\r\n self._mana += item_data[conf.MAX_MANA_DATA]\r\n if(conf.SPEED_DATA in item_data.keys()):\r\n self._speed -= item_data[conf.SPEED_DATA]\r\n if(self._cspeed > self._speed):\r\n self._cspeed = self._speed",
"title": ""
},
{
"docid": "c39a3d4361ba45ebde6cff0fa30544ad",
"score": "0.54757935",
"text": "def at_object_leave(self, moved_obj, target_location, move_type=\"move\", **kwargs):\n self.wilderness.at_post_object_leave(moved_obj)",
"title": ""
},
{
"docid": "ae4c42782a925d3d19c60e17690966ec",
"score": "0.5475295",
"text": "def equip(self, item: Item) -> str:\n try:\n\n # Ensure the inventory has an instance of the requested item\n self.items.index(item)\n\n temp = self.gear[item.slot]\n self.gear[item.slot] = item\n self.remove(item)\n if temp is not None:\n self.append(temp)\n return f\"You swapped {temp.name} to {item.name}\"\n else:\n return f\"You equip {item.name}\"\n except KeyError:\n return \"You can't equip that\"\n except ValueError:\n return \"You don't have that item in your inventory\"",
"title": ""
},
{
"docid": "73d346d4e5697ea0bb1131c9a1bbf0ce",
"score": "0.5418484",
"text": "def mouse_up(self, position, collision_list):\n self.current = position\n self.tiles = []\n self.start = None",
"title": ""
},
{
"docid": "2322449af09cd39383b77314ab003d44",
"score": "0.53923213",
"text": "def dropItem(self, args):\n\t\t# shortcuts\n\t\tbackpack = self.current_character.inventory.backpack\n\t\tloot = self.current_loot.inventory.backpack\n\t\t# move the RPG item in the character object\n\t\t# determine the source stack\n\t\tsrc_stack = self.findStack(args.dragDropItem)\n\t\t# determine the destination stack\n\t\tif args.window == self.backpack_grid:\n\t\t\titem_area = args.dragDropItem.getUnclippedOuterRect().get()\n\t\t\tdrop_x = args.window.gridXFromPixel(item_area.left())\n\t\t\tdrop_y = args.window.gridYFromPixel(item_area.top())\n\t\t\tdest_stack = backpack[drop_y][drop_x]\n\t\t\tif not self.current_character.inventory.checkBackpackSpace(drop_x, drop_y,\n\t\t\t\t\t\t\tsrc_stack[0].size_x, src_stack[0].size_y, ignore=src_stack):\n\t\t\t\t# not enough space to move the item\n\t\t\t\treturn\n\t\telif args.window == self.loot_grid:\n\t\t\titem_area = args.dragDropItem.getUnclippedOuterRect().get()\n\t\t\tdrop_x = args.window.gridXFromPixel(item_area.left())\n\t\t\tdrop_y = args.window.gridYFromPixel(item_area.top())\n\t\t\tdest_stack = loot[drop_y][drop_x]\n\t\t\tif not self.current_loot.inventory.checkBackpackSpace(drop_x, drop_y,\n\t\t\t\t\t\t\tsrc_stack[0].size_x, src_stack[0].size_y, ignore=src_stack):\n\t\t\t\t# not enough space to move the item\n\t\t\t\treturn\n\t\telse:\n\t\t\tprint(\"Drag destination unknown!\")\n\t\t\treturn\n\t\tif src_stack is dest_stack:\n\t\t\t# destination is source! nothing to do\n\t\t\treturn\n\t\tif dest_stack:\n\t\t\t# destination not empty! modify the args and call self.swapItems() instead\n\t\t\targs.window = args.window.getChildElementAtIdx(0)\n\t\t\tself.swapItems(args)\n\t\t\treturn\n\t\tif len(src_stack) > 1:\n\t\t\t# moving a stack, ask how many items to move\n\t\t\tself.gui.popup_spinner.askForValue(len(src_stack),\n\t\t\t\t\t\tlambda amount: self.moveItems(src_stack, dest_stack, amount))\n\t\t\treturn\n\t\tself.moveItems(src_stack, dest_stack, 1)\n\t\t# refresh the GUI\n\t\tself.refresh()",
"title": ""
},
{
"docid": "ef85d092689e2a0d94cb2c004081be85",
"score": "0.5345734",
"text": "def add_entity_as_inventory(self, x, y, entity):\n tile = self.tiles[x][y]\n if tile.inventory is None:\n tile.inventory = entity\n entity.owner = map\n entity.x = x\n entity.y = y\n self.entities.append(entity)\n else:\n raise LogicException(\"Entity placed as inventory on a tile with full inventory.\")",
"title": ""
},
{
"docid": "477b787a13f8ddb14666e9cb6dbe8c23",
"score": "0.5339185",
"text": "def move_loc(self):\n if self.infected:\n self.x_curr = self.x_curr\n self.y_curr = self.y_curr\n else:\n if not self.dead:\n self.x_curr, self.A_to_B, self.B_to_A = increment_coord(self.x_curr, self.x_A, self.x_B, self.A_to_B, self.B_to_A)\n self.y_curr, self.A_to_B, self.B_to_A = increment_coord(self.y_curr, self.y_A, self.y_B, self.A_to_B, self.B_to_A)\n else:\n self.x_curr = self.x_curr\n self.y_curr = self.y_curr",
"title": ""
},
{
"docid": "d4ec08b431088fa6bd6c8ca990c2be8b",
"score": "0.53351414",
"text": "def put_item_on_map(self, obj, pla, x, y):\n self.rules.assert_passable(obj, pla, x, y)\n loc = (pla, x, y)\n pla.add_item(x, y, obj)\n obj.loc = loc",
"title": ""
},
{
"docid": "e5cacf09f40960cc183a22406916cafa",
"score": "0.5333279",
"text": "def move_item_from_being_to_map(self, item, being):\n self.rules.assert_remove_from_being_ok(item, being)\n pla, x, y = being.loc\n self.rules.assert_passable(item, pla, x, y)\n being.body.remove(item)\n pla.add_item(x, y, item)\n item.loc = (pla, x, y)",
"title": ""
},
{
"docid": "d3f480e6776aec275812eeccac50c7eb",
"score": "0.53294355",
"text": "def swapItems(self, args):\n\t\t# shortcuts\n\t\tbackpack = self.current_character.inventory.backpack\n\t\tloot = self.current_loot.inventory.backpack\n\t\t# swap the RPG items in the character object\n\t\t# determine the source stack\n\t\tif args.dragDropItem.getParent() == self.backpack_grid:\n\t\t\tsrc_coords = map(int, args.dragDropItem.getName().split(\"-\")[-2:])\n\t\t\tsrc_stack = backpack[src_coords[0]][src_coords[1]]\n\t\telif args.dragDropItem.getParent() == self.loot_grid:\n\t\t\tsrc_coords = map(int, args.dragDropItem.getName().split(\"-\")[-2:])\n\t\t\tsrc_stack = loot[src_coords[0]][src_coords[1]]\n\t\telse:\n\t\t\tprint(\"Drag source unknown!\")\n\t\t\treturn\n\t\t# determine the destination stack\n\t\tif args.window.getParent() == self.backpack_grid:\n\t\t\tdest_coords = map(int, args.window.getName().split(\"-\")[-2:])\n\t\t\tdest_stack = backpack[dest_coords[0]][dest_coords[1]]\n\t\t\tif not self.current_character.inventory.checkBackpackSpace(\n\t\t\t\t\t\t\tdest_coords[1], dest_coords[0],\n\t\t\t\t\t\t\tsrc_stack[0].size_x, src_stack[0].size_y, ignore=dest_stack):\n\t\t\t\t# not enough space to move the item\n\t\t\t\treturn\n\t\telif args.window.getParent() == self.loot_grid:\n\t\t\tdest_coords = map(int, args.window.getName().split(\"-\")[-2:])\n\t\t\tdest_stack = loot[dest_coords[0]][dest_coords[1]]\n\t\t\tif not self.current_loot.inventory.checkBackpackSpace(\n\t\t\t\t\t\t\tdest_coords[1], dest_coords[0],\n\t\t\t\t\t\t\tsrc_stack[0].size_x, src_stack[0].size_y, ignore=dest_stack):\n\t\t\t\t# not enough space to move the item\n\t\t\t\treturn\n\t\telse:\n\t\t\tprint(\"Drag destination unknown!\")\n\t\t\treturn\n\t\t# check if the dest item can be swapped back\n\t\tif args.dragDropItem.getParent() == self.backpack_grid:\n\t\t\tif not self.current_character.inventory.checkBackpackSpace(\n\t\t\t\t\t\t\tsrc_coords[1], src_coords[0],\n\t\t\t\t\t\t\tdest_stack[0].size_x, dest_stack[0].size_y, ignore=src_stack):\n\t\t\t\treturn\n\t\telif args.dragDropItem.getParent() == self.loot_grid:\n\t\t\tif not self.current_loot.inventory.checkBackpackSpace(src_coords[1], src_coords[0],\n\t\t\t\t\t\t\tdest_stack[0].size_x, dest_stack[0].size_y, ignore=src_stack):\n\t\t\t\treturn\n\t\t#if isinstance(src_stack[0], Ammo) and isinstance(dest_stack[0], Weapon):\n\t\tif src_stack[0].ammo_data and dest_stack[0].weapon_data:\n\t\t\tif (src_stack[0].weapon_data.ammo_calibre == dest_stack[0].weapon_data.calibre\n\t\t\t\t\t\t) and (\n\t\t\t\t\t\tlen(dest_stack[0].weapon_data.magazine)\n\t\t\t\t\t\t< dest_stack[0].weapon_data.magazine_size):\n\t\t\t\t# don't swap, load ammo in the gun instead\n\t\t\t\tif (len(src_stack) == 1) or (\n\t\t\t\t\t\t\t(dest_stack[0].weapon_data.magazine_size\n\t\t\t\t\t\t\t- len(dest_stack[0].weapon_data.magazine)) == 1):\n\t\t\t\t\t# only one bullet can be loaded\n\t\t\t\t\tself.loadAmmo(dest_stack[0], src_stack)\n\t\t\t\telse:\n\t\t\t\t\t# multiple bullets can be loaded, ask how many\n\t\t\t\t\tself.gui.popup_spinner.askForValue(\n\t\t\t\t\t\t\tmin(len(src_stack),\n\t\t\t\t\t\t\t\tdest_stack[0].weapon_data.magazine_size\n\t\t\t\t\t\t\t\t- len(dest_stack[0].weapon_data.magazine)),\n\t\t\t\t\t\t\tlambda amount: self.loadAmmo(dest_stack[0], src_stack, amount))\n\t\t\t\treturn\n\t\tif (src_stack[0].name == dest_stack[0].name) and (\n\t\t\t\t\t\t\tdest_stack[0].max_stack > len(dest_stack)):\n\t\t\t# moving on top of the same item type and there's free space,\n\t\t\t# stack instead of swapping\n\t\t\tif (len(src_stack) == 1) or ((dest_stack[0].max_stack - len(dest_stack)) == 1):\n\t\t\t\t# only one item can be moved\n\t\t\t\tself.moveItems(src_stack, dest_stack, 1)\n\t\t\telse:\n\t\t\t\t# multiple items can be moved, ask how many\n\t\t\t\tself.gui.popup_spinner.askForValue(\n\t\t\t\t\t\tmin(len(src_stack), dest_stack[0].max_stack - len(dest_stack)),\n\t\t\t\t\t\tlambda amount: self.moveItems(src_stack, dest_stack, amount))\n\t\t\treturn\n\t\t# all checks passed, let's swap\n\t\tsrc_stack[:], dest_stack[:] = dest_stack[:], src_stack[:]\n\t\t# refresh the GUI\n\t\tself.refresh()",
"title": ""
},
{
"docid": "cdf576a41208bba734f552e408273eb4",
"score": "0.5289039",
"text": "def move_entity(self, entity, x, y, is_player = False):\n old_tile = self.tiles[entity.x][entity.y]\n new_tile = self.tiles[x][y]\n \n old_tile.entity = None\n new_tile.entity = entity\n \n entity.x = x\n entity.y = y\n \n if is_player and new_tile.inventory:\n ui.Screens.msg.add_message(\"You see %s on the ground.\" % new_tile.inventory.indef_name)",
"title": ""
},
{
"docid": "fdea18549b088e13543b7b95f4f6c1b3",
"score": "0.52738434",
"text": "def put_being_on_map(self, obj, pla, x, y):\n # checks\n self.rules.assert_unoccupied(pla, x, y)\n self.rules.assert_passable(obj, pla, x, y)\n # commit\n loc = (pla, x, y)\n pla.set_occupant(x, y, obj)\n obj.loc = loc\n # hooks\n self.rules.on_put_occupant(obj)",
"title": ""
},
{
"docid": "7c4670eb76cbc67ab1f4df209beda751",
"score": "0.5270868",
"text": "def move_character(self, name, position):\n self.atlas[name] = position",
"title": ""
},
{
"docid": "29268f7fac7e47b85b6fa6fd3e52c255",
"score": "0.52675426",
"text": "def after_move(self):\n if (self.get_cell_type() == 'Fire'\n and self.level.sprite_can_enter(self.get_pos_in_dir(self.move_dir))):\n self.to_move = 1\n\n Sprite.after_move(self)",
"title": ""
},
{
"docid": "dc41ecb215552a526305899e8a25d2c7",
"score": "0.52495986",
"text": "def unitBack(self,vehicleObj):\n self._spawningObjs.append(vehicleObj)",
"title": ""
},
{
"docid": "167029c397a5a6e08d51db950b4ae113",
"score": "0.5239758",
"text": "def position_items(self, labyrinth):\n self.coordinates = random.choice(labyrinth.void)\n labyrinth.void.remove(self.coordinates)",
"title": ""
},
{
"docid": "f263493ad5443e45c75c4e7f1371ad70",
"score": "0.5228186",
"text": "def move_character(self, character, loc, moves_left):\n #FIXME: check for already existing characters\n del self.map[character.loc.x][character.loc.y]['character']\n self.free_locations[character.loc] = True\n character.set_location(loc)\n self.map[loc.x][loc.y]['character'] = character\n del self.free_locations[character.loc]\n self.invalidate_paths()\n if self.is_character_hero(character):\n gate = self.has_gate(loc)\n if gate and not moves_left:\n self.transition = self.gates[gate]",
"title": ""
},
{
"docid": "9b7f4f2ea7da31a60d87cb93189d57d9",
"score": "0.5219722",
"text": "def __delitem__(self, name):\n equip = self[name]\n equip.out_equipment()\n super(Equipment, self).__delitem__(name)",
"title": ""
},
{
"docid": "bde781149a240db48c67f96077a301d7",
"score": "0.52155334",
"text": "def _update_unity(self):\n self._unity.set_qpos(self.sim.data.qpos)\n if self._agent_type == \"Cursor\":\n for cursor_i in range(2):\n cursor_name = \"cursor%d\" % cursor_i\n cursor_pos = self._get_pos(cursor_name)\n self._unity.set_geom_pos(cursor_name, cursor_pos)",
"title": ""
},
{
"docid": "156afa4ea69f45a6e1d3df62721135cc",
"score": "0.5207746",
"text": "def move_object_to_inventory(sim_info: SimInfo, game_object: GameObject) -> bool:\n inventory = CommonSimInventoryUtils._get_inventory(sim_info)\n if inventory is None:\n return False\n game_object.update_ownership(sim_info, make_sim_owner=True)\n return inventory.player_try_add_object(game_object)",
"title": ""
},
{
"docid": "e94f4d4af1432cf31c6d5a8440365733",
"score": "0.5203318",
"text": "def equip_item(self, item):\n\n # If the specified item is not in the inventory list, return an error.\n if item not in self.inventory_list:\n print(\"ERROR: Inventory List does not contain item %s!\" % item)\n return\n\n # We're going to re-validate the item first to make sure it's valid.\n if self.validate_item(item) is False:\n return\n\n # Now do one of three things.\n # If the item is a sword:\n if item[\"item type\"] == \"sword\":\n self.active_weapon = item\n print(\"INFO: Made active weapon %s\" % item)\n # If the item is armor:\n elif item[\"item type\"] == \"armor\":\n self.active_armor = item\n print(\"INFO: Made active armor %s\" % item)\n else:\n print(\"ERROR: Item Type is not sword or armor. Cannot Equip!\")\n return",
"title": ""
},
{
"docid": "aeadfc243d51323a023868f0c3861b76",
"score": "0.51875585",
"text": "def place_randomly(self, item: Item, child_items: List[Item] = None) -> None:\n child_items = child_items or []\n\n # Check for every possible location combination in each sub inventory\n for inventory in random.sample(self.inventories, k=len(self.inventories)):\n # Checks each cell in random order\n cells = list(inventory.stash_map.iter_cells())\n for x, y in random.sample(cells, k=len(cells)):\n for orientation in ItemOrientationEnum:\n location = ItemInventoryLocation(x=x, y=y, r=orientation.value)\n if inventory.stash_map.can_place(item, child_items, location):\n inventory.place_item(\n item, child_items=child_items, location=location\n )\n return\n raise NoSpaceError",
"title": ""
},
{
"docid": "8467a7e887c2a6ce01656eb5569e12c5",
"score": "0.51554745",
"text": "def move_character(x, y, character, gridboxes):\n\tnew_box = gridboxes[x][y]\n\tcharacter.move(new_box)",
"title": ""
},
{
"docid": "6dc83a371078561ce51fa461e8d0721b",
"score": "0.51452714",
"text": "def move(self, new_location):\n pass",
"title": ""
},
{
"docid": "d4af27c2d163eb492b7e93c27fea5e57",
"score": "0.5142581",
"text": "def placeCharacter(self,character,row,column):\n self.gameState[row,column]=character",
"title": ""
},
{
"docid": "89d04d7aac58aa8e490488c0918e753e",
"score": "0.5137253",
"text": "def when_entering_container(actor, x, ctxt) :\n ctxt.world.activity.put_in(actor, x)",
"title": ""
},
{
"docid": "1c441c42cad7fbe12a6332787d12663c",
"score": "0.5132971",
"text": "def move_loc_chaos(self):\n if self.dead:\n self.x_curr = self.x_curr\n self.y_curr = self.y_curr\n else:\n self.x_curr, self.A_to_B, self.B_to_A = increment_coord(self.x_curr, self.x_A, self.x_B, self.A_to_B, self.B_to_A)\n self.y_curr, self.A_to_B, self.B_to_A = increment_coord(self.y_curr, self.y_A, self.y_B, self.A_to_B, self.B_to_A)",
"title": ""
},
{
"docid": "8830f00eae41e5795f9e6850222d5cd2",
"score": "0.51216334",
"text": "def when_entering_container(actor, x, ctxt) :\n ctxt.world.activity.put_on(actor, x)",
"title": ""
},
{
"docid": "0a13518ae1914e369fecdb2e0ed71445",
"score": "0.5121186",
"text": "def itemMove(arg):\n global BOX_ON_BUTTON\n if arg in rooms[LOC][\"items\"]: # If the item is in the actual room\n if arg == \"box\":\n BOX_ON_BUTTON = True\n printw(items[arg][\"movable1\"][1])\n rooms[LOC][\"doorOpen\"][\"south\"][0] = True\n return\n elif arg == \"bench\":\n if GUARDS_SLEEP == False:\n printw(items[arg][\"movable1\"][1])\n gameOver()\n else:\n printw(items[arg][\"movable2\"][0])\n rooms[LOC][\"doorOpen\"][\"south\"][0] = True\n return\n else:\n printw(items[arg][\"movable1\"][1])\n return\n else:\n printw(\"There is no such thing.\")\n return",
"title": ""
},
{
"docid": "0be329b3e641a7ce7660fae156ed3a51",
"score": "0.51155955",
"text": "def drop(self, task_id: int) -> None:\n print('human@drop - Drop item in-hand')\n random_empty_cell = self.find_random_empty_cell()\n\n if type(self.holding) == Ingredient:\n holding_ingredient = self.holding\n holding_ingredient.location = random_empty_cell\n self.world_state['ingredients'].append(holding_ingredient)\n # For now, just plate\n elif type(self.holding) == Plate:\n holding_plate = self.holding\n holding_plate.location = random_empty_cell\n self.world_state['plate'].append(holding_plate)\n self.holding = None",
"title": ""
},
{
"docid": "631c4a65ddbf96e0a65c55e07519890c",
"score": "0.51111585",
"text": "def place_object(self, grid_object, new_location):\n\t\tself.grid[new_location[0]][new_location[1]] = grid_object",
"title": ""
},
{
"docid": "95fb654dfc931ea8711a89c751c14c97",
"score": "0.5108249",
"text": "def on_put_occupant(self, obj): \n terrain = obj.place.get_terrain(obj.x, obj.y)\n # XXX: get rid of hasattr\n if hasattr(terrain, 'effect') and terrain.effect is not None:\n terrain.effect(obj)",
"title": ""
},
{
"docid": "dff2b8cdd7f2a0b2b710f5b2b4b21283",
"score": "0.51034707",
"text": "def tryToInventory(self, item): \n if item.isTopItem(): \n if item.capture():\n self.addToInventory(item)\n self.setUnselectedItem()\n item.setEnabled()\n else:\n self.newChatMessage(\"alguien se nos ha adelantado\", 1)\n else: \n self.newChatMessage('No puedo coger eso, hay algo encima', 1)",
"title": ""
},
{
"docid": "1d7b01bbfbfe8bae4ab25148c6cba1ba",
"score": "0.5090639",
"text": "def move(self):",
"title": ""
},
{
"docid": "26b831ee898a8f6e29e13cebff9381cd",
"score": "0.50865924",
"text": "def handleLeftClick(self, object_id, targetPoint):\n \n if object_id == 2:\n print \"to location\", targetPoint\n if self.a:\n print self.worldGui\n self.worldGui.moveAnimal(self.a, targetPoint) \n self.a = [] # clear the list of animals that were selected\n \n else :\n \"\"\" If an animal was selected then keep adding it to the list \n until the user clicks on the terrain\"\"\" \n print \"move animal\", object_id\n self.a.append(object_id)",
"title": ""
},
{
"docid": "54dc5beca95f4972133c41eef7e3ab0e",
"score": "0.5085191",
"text": "def test_move_to__components(self):\n self.ship._move_to(7, -2)\n\n self.assertItemsEqual(self.ship._position, (7, -2,))",
"title": ""
},
{
"docid": "903f4b3e39f155447e791a459131a33d",
"score": "0.5071146",
"text": "def mouseMoveEvent(self, event):\r\n\t\t\r\n\t\tsuper(UnPicker_ViewUI, self).mouseMoveEvent(event)\r\n\t\t\r\n\t\titem = self.items(event.pos())\r\n\t\t\r\n\t\t#if the cursor is over the object\r\n\t\tif item:\r\n\t\t\titem = item[0]\r\n\t\t\t\r\n\t\t\t#if this object ItemUI.UnPicker_TextUI\r\n\t\t\tif isinstance(item, ItemUI.UnPicker_TextUI):\r\n\t\t\t\t\r\n\t\t\t\t#if it's the same object, do nothing\r\n\t\t\t\tif self.enterItem == item:\r\n\t\t\t\t\treturn\r\n\t\t\t\t\r\n\t\t\t\t#if another object was painted, first return it to its original color\r\n\t\t\t\telif self.enterItem:\r\n\t\t\t\t\tself.enterItem.parentItem().leaveEv()\r\n\t\t\t\t\titem.parentItem().enterEv()\r\n\t\t\t\t\tself.enterItem = item\r\n\t\t\t\t\t\r\n\t\t\t\telse:\r\n\t\t\t\t\titem.parentItem().enterEv()\r\n\t\t\t\t\tself.enterItem = item\r\n\t\t\t\r\n\t\t\t#if the cursor is no longer over the object, return it to its original color\r\n\t\t\telif self.enterItem:\r\n\t\t\t\tself.enterItem.parentItem().leaveEv()\r\n\t\t\t\tself.enterItem = None\r\n\t\t\r\n\t\t#if the cursor is no longer over the object, return it to its original color\t\t\r\n\t\telif self.enterItem:\r\n\t\t\tself.enterItem.parentItem().leaveEv()\r\n\t\t\tself.enterItem = None\r\n\t\r\n\t\tself.update()",
"title": ""
},
{
"docid": "c50808bdfe2cbf37051d25ff47f23481",
"score": "0.5068534",
"text": "def unequip(self,slot_number):\n for n in range(1,10):\n if self.backpack[n] == \"x\":\n self.backpack[n] = self.inventory.pop([slot_number])",
"title": ""
},
{
"docid": "c45a52a7e2a208f02a74cf6ac1409c95",
"score": "0.5067709",
"text": "def object_location_callback(self, msg):\n if self.object_location != msg:\n position = msg.pose.position\n rospy.loginfo('New object location: %s', position)\n self.object_location = msg\n self.markers.set_marker_array([[position.x, position.y, position.z]])\n self.markers.publish()",
"title": ""
},
{
"docid": "d380e19726644de9106c1e21eba7b7ef",
"score": "0.5052507",
"text": "def move_objects_to_inventory(sim_info: SimInfo, game_objects: Tuple[GameObject]) -> bool:\n if not game_objects:\n return False\n successfully_moved_all = True\n for game_object in game_objects:\n if not CommonSimInventoryUtils.move_object_to_inventory(sim_info, game_object):\n successfully_moved_all = False\n return successfully_moved_all",
"title": ""
},
{
"docid": "8e554ad04068e136664ff8c73abaf41c",
"score": "0.50460994",
"text": "def __init__(self,map, point, color, char = '/',inventory=[]):\n # create inventory and add items to it\n self.inventory = Inventory()\n self.inventory.add_item(inventory)\n super().__init__(map,point,color=color,char=char)\n # check if inventory is empty\n # if empty, mark as false, otherwise true\n self.is_empty = bool(self.inventory)",
"title": ""
},
{
"docid": "816b9e116c5a7c91f67ea0144cec811c",
"score": "0.500717",
"text": "def add_object(self, obj:Object, x:int, y:int) -> None:\n\n self.cells[y][x].add_object(obj)\n obj.pos = np.array([x, y])",
"title": ""
},
{
"docid": "5adfc1d64086745b05580cd182803638",
"score": "0.5006917",
"text": "def mover(self, _x, _y): # pylint: disable=W0221\n result = super().mover(self, self.obj, _x, _y)\n self.log.info(__name__ + ': ' + 'def ' + self.mover.__name__ + '(): ' + self.mover.__doc__)\n\n self.fleet = None\n self.obj = None\n if result:\n self.light = False\n self.light_cells.clear()\n self.speech.speak(self.phrases['move_true'], True)\n self._ai.next_step()\n else:\n self.speech.speak(self.phrases['move_false'], True)",
"title": ""
},
{
"docid": "96de55f7682572a9c00e143164d357bd",
"score": "0.5003391",
"text": "def move(self, direction):\n if direction == \"up\":\n x, y = self.x, self.y - self.speed\n\n elif direction == \"down\":\n x, y = self.x, self.y + self.speed\n\n elif direction == \"left\":\n x, y = self.x - self.speed, self.y\n\n elif direction == \"right\":\n x, y = self.x + self.speed, self.y\n\n if self.maze.is_valid((x, y)):\n \"\"\" check if it's a path and not a wall\n and allow the hero\n \"\"\"\n self.x, self.y = x, y\n\n if self.maze.has_object((x, y)):\n \"\"\" check if there is an item in the new\n Mac's position\n \"\"\"\n item = self.maze.items[x, y]\n self.inventory.append(item)\n self.maze.remove_item((x, y))\n print(\"Items:\" + str(self.item_taken))\n\n if self.maze.is_arrival((x, y)):\n self.is_finished()",
"title": ""
},
{
"docid": "98111c6e2a4d8645a3ee6d22bc423d51",
"score": "0.50032127",
"text": "def remove_being_from_map(self, obj):\n self.rules.assert_remove_ok(obj)\n obj.place.remove_occupant(obj.x, obj.y)\n obj.loc = (None, None, None)",
"title": ""
},
{
"docid": "06fd25522b9cbe2c9ac33266ec423104",
"score": "0.4991407",
"text": "def swap_item(self):\n self._time += 1\n # Swap the held item with the list item at the robot's position\n self._item, self._list[self._position] = self._list[self._position], self._item",
"title": ""
},
{
"docid": "06fd25522b9cbe2c9ac33266ec423104",
"score": "0.4991407",
"text": "def swap_item(self):\n self._time += 1\n # Swap the held item with the list item at the robot's position\n self._item, self._list[self._position] = self._list[self._position], self._item",
"title": ""
},
{
"docid": "6262b62376549ca5de523f80fa56fd93",
"score": "0.49886554",
"text": "def itemDrop(arg):\n # global INV, LOC\n if arg in INV:\n rooms[LOC][\"items\"].append(arg)\n cc = -1\n for _ in INV:\n cc += 1\n if INV[cc] == arg:\n del INV[cc]\n printw(\"You drop: \" + arg)\n else:\n printw(\"You don't have that in the inventory.\")",
"title": ""
},
{
"docid": "77dd101a79abb6308e1e4d845bc1e60c",
"score": "0.49775395",
"text": "def move(self, loc):\n if loc in self.valid_moves():\n self.location = loc\n self.disease[loc] = 0\n else: raise ValueError",
"title": ""
},
{
"docid": "4ba742769c4c117aca6d5b4338b05de6",
"score": "0.49738094",
"text": "def remove_item_from_map(self, obj):\n self.rules.assert_remove_ok(obj)\n obj.place.remove_item(obj.x, obj.y, obj)\n obj.loc = (None, None, None)",
"title": ""
},
{
"docid": "a723ac9896c5fd3809d79cacce60132f",
"score": "0.4967167",
"text": "def update_position_and_clean(self):\n # Calculate new position\n curr_pos = self.get_robot_position()\n new_pos = curr_pos.get_new_position(self.get_robot_direction(), self.speed)\n # If new position is valid, move there, and clean the tile\n if self.room.is_position_valid(new_pos):\n self.set_robot_position(new_pos)\n self.room.clean_tile_at_position(new_pos, self.capacity)\n # Otherwise, rotate the robot to a random new direction, DON'T move, and DON'T clean\n else:\n self.set_robot_direction(360*random.random())",
"title": ""
},
{
"docid": "5b5b7b6e9bd280190c4547e47c8e2a05",
"score": "0.496682",
"text": "def place_soldier(self, x_pos, y_pos):\n obj = self.get_at_pos(x_pos, y_pos)\n if obj and isinstance(obj, Formation) and obj.army == self.active_army:\n if obj.add_soldier(self.active_soldier):\n self.soldiers[self.active_soldier.my_id] = self.active_soldier\n self.active_soldier = None\n return True\n return False",
"title": ""
},
{
"docid": "5b66b3b0b8712fea20d8f616ceb2ef20",
"score": "0.49546975",
"text": "def setPosition(self, pos, jump=None): \n if self.isTopItem():\n if self.getState() == GG.utils.STATE[3]:\n self.setState(GG.utils.STATE[1])\n if self.getState() == GG.utils.STATE[4]:\n self.setState(GG.utils.STATE[2])\n else:\n if self.getState() == GG.utils.STATE[1]:\n self.setState(GG.utils.STATE[3])\n if self.getState() == GG.utils.STATE[2]:\n self.setState(GG.utils.STATE[4]) \n item_with_inventory.GGItemWithInventory.setPosition(self, pos, jump)",
"title": ""
},
{
"docid": "e97688e18ae0ff3a6d068338321cd826",
"score": "0.49443805",
"text": "def is_equipped(self) -> bool:\r\n\t\treturn self.slot > 0",
"title": ""
},
{
"docid": "174f658a8058e9d55effa4fe7ab4678d",
"score": "0.4939662",
"text": "def place_character(self, character, loc, is_hero=False):\n #FIXME: check for already existing characters\n character.set_location(loc)\n if is_hero:\n self.characters.insert(0, character)\n else:\n self.characters.append(character)\n self.map[loc.x][loc.y]['character'] = character\n del self.free_locations[loc]",
"title": ""
},
{
"docid": "1ac908560a8df7c48cf8208e8356fbe9",
"score": "0.49390796",
"text": "def pick_up_items(self, char: IChar) -> bool:\n found_items = False\n keyboard.send(self._config.char[\"show_items\"], do_press=True, do_release=False)\n time.sleep(1.0) # sleep needed here to give d2r time to display items on screen on keypress\n #Creating a screenshot of the current loot\n if self._config.general[\"loot_screenshots\"]:\n img = self._screen.grab()\n cv2.imwrite(\"./loot_screenshots/info_debug_drop_\" + time.strftime(\"%Y%m%d_%H%M%S\") + \".png\", img)\n Logger.debug(\"Took a screenshot of current loot\")\n start = time.time()\n time_out = False\n picked_up_items = []\n while not time_out:\n if (time.time() - start) > 20:\n time_out = True\n Logger.warning(\"Got stuck during pickit, skipping it this time...\")\n img = self._screen.grab()\n item_list = self._item_finder.search(img)\n if not self._ui_manager.check_free_belt_spots():\n # no free slots in belt, do not pick up health or mana potions\n item_list = [x for x in item_list if \"potion\" not in x.name]\n if len(item_list) == 0:\n break\n else:\n closest_item = item_list[0]\n for item in item_list[1:]:\n if closest_item.dist > item.dist:\n closest_item = item\n x_m, y_m = self._screen.convert_screen_to_monitor(closest_item.center)\n if closest_item.dist < self._config.ui_pos[\"item_dist\"]:\n # no need to stash potions, scrolls, or gold \n if ((\"potion\" not in closest_item.name) and (\"tp_scroll\" != closest_item.name) and (\"misc_gold\" not in closest_item.name)):\n found_items = True\n Logger.info(f\"Picking up {closest_item.name}\")\n mouse.move(x_m, y_m)\n time.sleep(0.1)\n mouse.click(button=\"left\")\n time.sleep(0.5)\n\n if self._ui_manager.is_overburdened():\n Logger.warning(\"Inventory full, skipping pickit!\")\n # TODO: should go back to town and stash stuff then go back to picking up more stuff\n # but sm states are not fine enough for such a routine right now...\n break\n else:\n # send log to discord\n if found_items and self._config.items[closest_item.name] == 2 and closest_item.name not in picked_up_items:\n if self._config.general[\"custom_discord_hook\"] != \"\":\n send_discord_thread = threading.Thread(\n target=send_discord,\n args=(f\"{self._config.general['name']} just found: {closest_item.name}\", self._config.general[\"custom_discord_hook\"])\n )\n send_discord_thread.daemon = True\n send_discord_thread.start()\n picked_up_items.append(closest_item.name)\n else:\n char.move((x_m, y_m))\n time.sleep(0.1)\n keyboard.send(self._config.char[\"show_items\"], do_press=False, do_release=True)\n return found_items",
"title": ""
},
{
"docid": "6ef687189ef53e2b37af3ed383fb96af",
"score": "0.49355462",
"text": "def add_to_inventory(self, item_name):\n # if the item is not already in the hero's inventory.\n if item_name not in self.inventory:\n # adds the name of the collected item to the hero's inventory\n self.inventory.append(item_name)\n # sort the hero's inventory in alphabetical order\n self.inventory.sort()",
"title": ""
},
{
"docid": "f508cd6782c9b029cbf3049d3476de46",
"score": "0.49325073",
"text": "def use_item(self, item):\n if self.is_item_owned(item):\n self.items.remove(item)\n self.pet.apply_item_effects(item)\n if item.get_friend_points() > 0:\n self.got_a_raise()",
"title": ""
},
{
"docid": "b293251bddff1f0090ee74513f77f271",
"score": "0.49280497",
"text": "def try_move(self, x, y):\n new_location = Coordinates(x, y)\n new_square = self.level.get_square(new_location)\n # Check if the coordinate includes an obstacle and if it is inside the level\n if not Square.is_obstacle_square(new_square) and self.level.contains(new_location):\n self.location = new_location\n self.graphics_item.update_position()\n return True",
"title": ""
},
{
"docid": "b5cc933637eb716ffed8e58d2721336d",
"score": "0.4925507",
"text": "def add_inventory(self, current_inventory):\n for item in self.inventory:\n current_inventory.append(item)\n # remove supplies from the tile\n self.inventory = []",
"title": ""
},
{
"docid": "3a7aed0662dd9f5c1add03d8d92d23fb",
"score": "0.49209568",
"text": "def on_end(self):\n if self.status == Status.success:\n self.blackboard.send_chat_message(\"holding %s\" % self.itemstack.name)\n self.inventory_man.close()",
"title": ""
},
{
"docid": "c92f0f7b54100d4c3e61e04e00c23057",
"score": "0.49189264",
"text": "def take(self, obj):\n self._inventory.add(obj)\n obj.actor = self\n return self",
"title": ""
},
{
"docid": "2bc00769897c8cb38ba899c2477b1fad",
"score": "0.49187604",
"text": "def move(self, old_cell, new_cell):\n\n new_cell.population[type(self).__name__].append(self)\n old_cell.population[type(self).__name__].remove(self)",
"title": ""
},
{
"docid": "05a1b395faef4c7c4a0922036d03f145",
"score": "0.49184272",
"text": "def MovePartsToLocation(self,x,y):\n for part in self.GetParts():\n self.SetPartPosition(part, x, y)",
"title": ""
},
{
"docid": "e4d21850e18a50c0d1060d4789d1b084",
"score": "0.49156517",
"text": "def update_position_and_clean(self):\n # do not change -- implement in subclasses\n raise NotImplementedError",
"title": ""
},
{
"docid": "03e515c317d82aa1610d1a96002b37f7",
"score": "0.4898191",
"text": "def place_move(self, x, y, player):\n assert (self.is_free(x, y))\n assert (player == 1 or player == 2)\n self.board[x, y] = player\n self.available.remove((x, y))",
"title": ""
},
{
"docid": "11a63e253ab1e0de70a4175c28deee69",
"score": "0.48887104",
"text": "def _move_door(self,):\n\n pass",
"title": ""
},
{
"docid": "3c3d4ab91aa5022022d3d90fccd2fae8",
"score": "0.4888545",
"text": "def update_pos_and_clean(self):\n\n #calculate new position\n new_x = self.position.get_x() + self.speed*math.cos(math.radians(90-self.direction))\n new_y = self.position.get_y() + self.speed*math.sin(math.radians(90-self.direction))\n new_pos = Position(new_x, new_y)\n\n #if valid, move and clean, else change direction\n if self.room.is_position_in_room(new_pos):\n \tself.position = new_pos\n \tself.room.clean_tile_at_position(new_pos, self.capacity)\n else:\n \t#if not a valid position, don't move and change direction\n \tself.direction = random.random()*360",
"title": ""
},
{
"docid": "6591d9ff701f58aadca51caaea56ab11",
"score": "0.4888393",
"text": "def removeFromInventory(self, item):\n if item in self.__inventory:\n self.__inventory.remove(item)\n self.triggerEvent('removeFromInventory', item=item)\n self.save(\"player\")\n return True\n return False",
"title": ""
},
{
"docid": "e24e203e0a9c58bde07b6c64ccc9f761",
"score": "0.48851776",
"text": "def mouse_move(self, position, collision_list):\n pass",
"title": ""
},
{
"docid": "f50e2cdeaf44cf74bdfbeaffe43c85fb",
"score": "0.4884194",
"text": "def move(self):\n if self.orientation == 90:\n self.landscape.empty_coordinate(self.position_x, self.position_y)\n self.position_x, self.position_y = self.landscape.place_item(self.position_x, self.position_y + 1)\n elif self.orientation == 0:\n self.landscape.empty_coordinate(self.position_x, self.position_y)\n self.position_x, self.position_y = self.landscape.place_item(self.position_x + 1, self.position_y)\n elif self.orientation == 180:\n self.landscape.empty_coordinate(self.position_x, self.position_y)\n self.position_x, self.position_y = self.landscape.place_item(self.position_x - 1, self.position_y)\n elif self.orientation == 270:\n self.landscape.empty_coordinate(self.position_x, self.position_y)\n self.position_x, self.position_y = self.landscape.place_item(self.position_x, self.position_y - 1)",
"title": ""
},
{
"docid": "2d28fcdd0ef08fe055ab97159ea5b003",
"score": "0.488092",
"text": "def can_move_object(self, new_x, new_y, new_z):\r\n if not self.verify_world_bounds(new_x, new_y, new_z):\r\n return False\r\n\r\n for block in self._blocks:\r\n if (new_x, new_y, new_z) == block.location():\r\n return False\r\n if self._drone:\r\n if (new_x, new_y, new_z) == self._drone.location():\r\n return False\r\n return True",
"title": ""
},
{
"docid": "15e4c79d7eb35fb948b9c6a90373fb9e",
"score": "0.4878421",
"text": "def cause_dropped_item_gravity(self):\n for drop in self.drops_list:\n if drop.changey == 0:\n drop.changey = -self.base_y_gravity\n else:\n drop.changey -= self.gravity_acceleration\n\n drop.movey(drop.changey)\n hit_list = pygame.sprite.spritecollide(drop, self.block_list, False)\n for block in hit_list:\n if drop.changey > 0:\n drop.rect.bottom = block.rect.top\n elif drop.changey < 0:\n drop.rect.top = block.rect.bottom\n drop.changey = 0",
"title": ""
},
{
"docid": "785bf2e124f80cb4aed33d9444deb025",
"score": "0.48775762",
"text": "def enter(self, t):\n super(WorldObject, self).enter(t)",
"title": ""
},
{
"docid": "ab022d0164a8232eb0dede2357481ca2",
"score": "0.4875814",
"text": "def actorMoved(self, actor, fromPosition):\n self.removeActorAtPosition(actor, fromPosition)\n self.addActor(actor)",
"title": ""
},
{
"docid": "301a7ed708bf1c8f9fb2c95a00d11a7b",
"score": "0.4869376",
"text": "def drop(self, action):\n item_name = action[1]\n for i in self.items:\n if item_name == i.name:\n i.on_drop()\n self.items.remove(i)\n self.current_room.items.append(i)",
"title": ""
},
{
"docid": "0f65aa2d9a3a0184809171f16e38d25b",
"score": "0.4863074",
"text": "def move_stage_to_xy(self, coordinates):\n raise NotImplementedError",
"title": ""
},
{
"docid": "f2b9e660951b022a6d44cf31226f72ed",
"score": "0.48626196",
"text": "def remove_entity_from_inventory(self, x, y):\n tile = self.tiles[x][y]\n entity = tile.inventory\n \n if entity is None:\n raise LogicException(\"Tried to remove inventory from (%d,%d) but there was nothing there.\" % (x, y))\n\n entity.x = -1\n entity.y = -1\n entity.owner = None\n\n tile.inventory = None\n self.entities.remove(entity)\n return entity",
"title": ""
},
{
"docid": "b4d12725e91a1e8c0ec49c0e2a30dbd8",
"score": "0.4862128",
"text": "def _movePerson(self, person, coords):\n self.persons[person] = coords\n self.personChanged.emit(person)",
"title": ""
},
{
"docid": "a69324bced50e0c5a907068c443759c7",
"score": "0.48474482",
"text": "def __setitem__(self, pos, card):\n self._spots[pos - 1] = Spot(pos, card)",
"title": ""
},
{
"docid": "6c2bb621028350e5c460579dd802dd59",
"score": "0.48448238",
"text": "def update_pos_and_clean(self):\n\n #calculate new position\n new_x = self.position.get_x() + self.speed*math.cos(math.radians(90-self.direction))\n new_y = self.position.get_y() + self.speed*math.sin(math.radians(90-self.direction))\n new_pos = Position(new_x, new_y)\n\n #if valid, move and clean, else change direction\n if not self.room.is_position_in_room(new_pos):\n \tself.direction = random.random()*360\n else:\n \tif self.dropping_dirt():\n \t\t#dirty the tile\n \t\tself.room.clean_tile_at_position(new_pos, -1*random.random())\n \t\tself.direction = random.random()*360\n \telse:\n \t\t#if it's a valid position, move\n \t\tself.position = new_pos\n \t\tself.room.clean_tile_at_position(new_pos, self.capacity)",
"title": ""
},
{
"docid": "e270dccfcf2ff1281f06e88e46a1827e",
"score": "0.48442242",
"text": "def at_prepare_room(self, coordinates, caller, room):\n pass",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="756"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">1e3e6aca4e87ac8d47d5b6e268cb6a89</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Delete singleton for given arguments in a class decorated with SingletonWithRegardsTo</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "85015b99acc71fc329f8ac21ee521fd7",
"score": "0.7398244",
"text": "def delete_singleton_for(x, *args) -> None:\n del x.__it__[args]",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "3287075af136606fc1de66d043c06c40",
"score": "0.6534979",
"text": "def singleton(cls):\n \n # Ex: from class 'Fucker' to singleton name \"_FuckerSingleton\"\n # 'closure' for the class name, needed to recover the global object name:\n singletonName = '_{}Singleton'.format(cls.__name__)\n \n # ??? A singleton should have a constructor with arguments? If called a second time as \n # singleton with argument it should recreate it? Or simply a bare constructor and \n # another '.initialize(*args, **kwargs)?'\n def _getInstance(*args, **kwargs):\n if not hasattr(__main__, singletonName):\n print 'Singleton of {0} named \"{1}\" created in __main__!'.format(cls, singletonName)\n setattr(__main__, singletonName, cls(*args, **kwargs))\n return getattr(__main__, singletonName)\n \n #getInstance.__name__ = cls.__name__\n \n # After applying the decorator, your class 'Fucker = singleton(Fucker)' become a function, \n # '_getInstance(...)'; when you call 'Fucker(...)' in reality it calls '_getInstance(...)'\n # which in turn calls (if it's the first time) the constructor of the original 'Fucker'\n return _getInstance",
"title": ""
},
{
"docid": "55deba30f9fe56d8ab8464f1ab813116",
"score": "0.6474262",
"text": "def singleton_pattern(cls):\n return _singleton(cls.__name__, cls.__bases__, dict(cls.__dict__))",
"title": ""
},
{
"docid": "6ec464a44947602a35186defcd44fee3",
"score": "0.64306486",
"text": "def ForceDtor(cls):\n def getinstance(*a, **b):\n inst = cls(*a, **b)\n atexit.register(inst.__del__)\n return inst\n return getinstance",
"title": ""
},
{
"docid": "63ad2b1c8f7378243b9646e244d4a566",
"score": "0.64222157",
"text": "def singleton(cls):\n\n @functools.wraps(cls)\n def wrapper_singleton(*args, **kwargs):\n if not wrapper_singleton.instance:\n wrapper_singleton.instance = cls(*args, **kwargs)\n return wrapper_singleton.instance\n wrapper_singleton.instance = None\n return wrapper_singleton",
"title": ""
},
{
"docid": "dacb11c9783dfd4af4429c1dd8f92944",
"score": "0.63806546",
"text": "def singleton(cls):\n @functools.wraps(cls)\n def wrapper_singleton(*args, **kwargs):\n if not wrapper_singleton.instance:\n wrapper_singleton.instance = cls(*args, **kwargs)\n return wrapper_singleton.instance\n wrapper_singleton.instance = None\n return wrapper_singleton",
"title": ""
},
{
"docid": "f8053fcba0a0b284a6ecdd7841b1addc",
"score": "0.6373963",
"text": "def SingletonWithRegardsTo(num_args: int):\n\n def inner(cls):\n\n cls.__new_old__ = cls.__new__\n\n @wraps(cls.__new__)\n def singleton_new(cls, *args, **kw):\n it = cls.__dict__.get('__it__')\n if it is None:\n it = cls.__it__ = {}\n\n key = args[:num_args]\n if key in it:\n return it[key]\n\n inst = it[key] = cls.__new_old__(cls)\n inst.__init_old__(*args, **kw)\n return inst\n\n cls.__new__ = singleton_new\n cls.__init_old__ = cls.__init__\n cls.__init__ = wraps(cls.__init__)(\n lambda self, *args, **kwargs: object.__init__(self))\n\n return cls\n\n return inner",
"title": ""
},
{
"docid": "09398b1c5d17f56cec4ef7b835ad59dd",
"score": "0.6348292",
"text": "def singleton(cls):\n @functools.wraps(cls)\n def wrapper(*args, **kwargs):\n if wrapper.instance is None:\n wrapper.instance = cls(*args, **kwargs)\n return wrapper.instance\n wrapper.instance = None\n return wrapper",
"title": ""
},
{
"docid": "5363d9f2c03db613f2760fd1c455725a",
"score": "0.62846494",
"text": "def singleton(cls):\n\n # a dict registering single-instances for each class decorated\n registered = {}\n\n # wrapper replacing calls to <class>()\n def wrapped_class(*args, **kwargs):\n if cls not in registered:\n # if 1st time, create single instance, and register with key <cls>\n registered[cls] = cls(*args, **kwargs)\n return registered[cls]\n # <cls> is now replaced. calls to <cls>() now directs to <wrapped_class>()\n return wrapped_class",
"title": ""
},
{
"docid": "aa082b4a69fec113dbb74b15a0f2a2da",
"score": "0.6247752",
"text": "def singleton(parameters=False):\n\n def decorator(cls):\n \"\"\"Return the singleton class wrapper.\"\"\"\n instances = {}\n\n def wrapper(*args, **kwargs):\n \"\"\"Return the single instance of the cls class, depends on args.\"\"\"\n key = (cls, args, str(kwargs)) if parameters else cls\n if key not in instances:\n instances[key] = cls(*args, **kwargs)\n return instances[key]\n\n return wrapper\n\n return decorator",
"title": ""
},
{
"docid": "3ed0cc0adfaeea6b8835aefa634d47cf",
"score": "0.62344253",
"text": "def singleton(*args, **kwargs):\n\n @wraps(Singleton)\n def class_wrapper(klass):\n\n if not str(klass).startswith(\"<class \"):\n raise TypeError(\"Singleton decorator applicable on class only\")\n return Singleton(klass, *args, **kwargs) # wrapper()\n return class_wrapper",
"title": ""
},
{
"docid": "b8c0ab9acfe8cd1080e53b5abaf94134",
"score": "0.6214873",
"text": "def _forgetClassInstanceReferenceForTesting(cls):\n try:\n delattr(cls,'cInstance')\n except AttributeError:\n # run up the chain of base classes until we find the one that has the instance\n # and then delete it there\n for baseClass in cls.__bases__: \n if issubclass(baseClass, Singleton):\n baseClass._forgetClassInstanceReferenceForTesting()",
"title": ""
},
{
"docid": "b5b8de1794d50de0a86b0b57f9c3c07d",
"score": "0.6135503",
"text": "def __call__(cls, *args, **kwargs):\n if cls not in cls._instances:\n cls._instances[cls] = super(SingletonType, cls).__call__(*args, **kwargs)\n return cls._instances[cls]",
"title": ""
},
{
"docid": "5c96dfac4614a51b2251b2e098f4daf9",
"score": "0.6098443",
"text": "def __call__(cls, *args, **kwargs):\n if cls not in cls._instances:\n cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)\n return cls._instances[cls]",
"title": ""
},
{
"docid": "d8ec3e81fdce0f79ee1eaa29aa669a85",
"score": "0.606348",
"text": "def singleton(cls):\n import inspect\n\n # Create a structure to store instances of any singletons that get\n # created.\n instances = {}\n\n # Make sure that the constructor for this class doesn't take any\n # arguments. Since singletons can only be instantiated once, it doesn't\n # make any sense for the constructor to take arguments. If the class \n # doesn't implement its own constructor, don't do anything. This case is \n # considered specially because it causes a TypeError in python 3.3 but not \n # in python 3.4.\n if cls.__init__ is not object.__init__:\n argspec = inspect.getfullargspec(cls.__init__)\n if len(argspec.args) != 1 or argspec.varargs or argspec.varkw:\n raise TypeError(\"Singleton classes cannot accept arguments to the constructor.\")\n\n\n def get_instance():\n \"\"\" Creates and returns the singleton object. This function is what \n gets returned by this decorator. \"\"\"\n\n # Check to see if an instance of this class has already been\n # instantiated. If it hasn't, create one. The `instances` structure\n # is technically a global variable, so it will be preserved between\n # calls to this function.\n if cls not in instances:\n instances[cls] = cls()\n\n # Return a previously instantiated object of the requested type.\n return instances[cls]\n\n # Return the decorator function.\n return get_instance",
"title": ""
},
{
"docid": "a6ead33fa3aefcf5e54e7c87b06b3e40",
"score": "0.6056265",
"text": "def _singleton(*args, **kwargs):\n cls_name = \"_{0}\".format(cls)\n if cls_name not in instances:\n instances[cls_name] = cls(*args, **kwargs)\n return instances[cls_name]",
"title": ""
},
{
"docid": "1be92933055718c70b79cc8a4590101d",
"score": "0.5958961",
"text": "def singleton(cls):\n instance = [None]\n\n def wrapper(*args, **kwargs):\n if instance[0] is None:\n instance[0] = cls(*args, **kwargs)\n return instance[0]\n return wrapper",
"title": ""
},
{
"docid": "8dc422c96af1f751e73341c0e82efaa9",
"score": "0.59418315",
"text": "def singleton(class_):\n instances = {}\n\n def get_instance(*args, **kwargs):\n if class_ not in instances:\n instances[class_] = class_(*args, **kwargs)\n return instances[class_]\n\n return get_instance",
"title": ""
},
{
"docid": "88223754bbc73c0719817a8176a304cc",
"score": "0.59384763",
"text": "def removeMultiInstance(*args, **kwargs):\n pass",
"title": ""
},
{
"docid": "e70cea4bd86a3057dfc4f855fe715345",
"score": "0.59150696",
"text": "def singleton(cls):\n __instance = {}\n\n def wrapper(*args, **kwargs):\n if cls not in __instance:\n __instance[cls] = cls(*args, **kwargs)\n return __instance[cls]\n return wrapper",
"title": ""
},
{
"docid": "0fee9c0c720f72a1f856ce6ef775d920",
"score": "0.5877864",
"text": "def singleton(cls):\n instances = {}\n def getInstance(name=\"main\"):\n if not name in instances.keys():\n instances[name] = cls()\n return instances[name]\n return getInstance",
"title": ""
},
{
"docid": "b74a2ec71b9c430f0162b47966d70cee",
"score": "0.58627486",
"text": "def singleton(cls):\n return _Singleton(cls)",
"title": ""
},
{
"docid": "81f16942a1f0660de7015d9159143fc9",
"score": "0.58563983",
"text": "def singleton(cls):\n instances = {}\n def getinstance(*args,**kwargs):\n if cls not in instances:\n instances[cls] = cls(*args,**kwargs)\n return instances[cls]\n return getinstance",
"title": ""
},
{
"docid": "21b5573feb29d4ad915075b68f75c770",
"score": "0.5819541",
"text": "def singleton(cls):\n return cls()",
"title": ""
},
{
"docid": "21b5573feb29d4ad915075b68f75c770",
"score": "0.5819541",
"text": "def singleton(cls):\n return cls()",
"title": ""
},
{
"docid": "ddbbbe7bd91ea56c0e0c6c0e375109a4",
"score": "0.58141935",
"text": "def singleton(cls):\n\n instances = {}\n\n def getinstance():\n if cls not in instances:\n instances[cls] = cls()\n return instances[cls]\n return getinstance",
"title": ""
},
{
"docid": "19c50a92a4c72c51702909a0a5f0b175",
"score": "0.57986504",
"text": "def singleton(cls):\n\tinstance = cls()\n\tinstance.__call__ = lambda: instance\n\treturn instance",
"title": ""
},
{
"docid": "7b7ee8a49ef6d5cd8eb36e68358272a8",
"score": "0.57450116",
"text": "def singleton(cls):\n instances = {}\n def _singleton(*args, **kw):\n if cls not in instances:\n instances[cls] = cls(*args, **kw)\n return instances[cls]\n return _singleton",
"title": ""
},
{
"docid": "4cea6956abde712a9f8d82898736b1b9",
"score": "0.57341737",
"text": "def singleton(cls):\n instances = {}\n\n def get_instance():\n if cls not in instances:\n instances[cls] = cls()\n return instances[cls]\n\n return get_instance()",
"title": ""
},
{
"docid": "4bade28c2360e1fc66dedbc814c18222",
"score": "0.5730655",
"text": "def singleton(cls):\n instances = {}\n\n def getinstance():\n if cls not in instances:\n instances[cls] = cls()\n return instances[cls]\n\n return getinstance",
"title": ""
},
{
"docid": "6bb704081e1328aab3ce1b5ae9eb92cd",
"score": "0.5729169",
"text": "def remove_instance(self):",
"title": ""
},
{
"docid": "9f907ca4485825029886aaba35d76c6c",
"score": "0.5727843",
"text": "def singleton(cls):\n instances = {}\n def getinstance():\n if cls not in instances:\n instances[cls] = cls()\n return instances[cls]\n return getinstance",
"title": ""
},
{
"docid": "37c2bca8452f45b65780533dc3d23607",
"score": "0.57161653",
"text": "def __call__(cls, *args, **kwargs):\n\n if cls not in cls._instances:\n # if there is no instance, create one\n cls._instances[cls] = super(\n Singleton, cls).__call__(*args, **kwargs)\n #return with the instance\n return cls._instances[cls]",
"title": ""
},
{
"docid": "218fc62eb1777e87237839ef535a6637",
"score": "0.56842804",
"text": "def singleton(cls):\r\n instances = {}\r\n\r\n def getinstance():\r\n if cls not in instances:\r\n instances[cls] = cls()\r\n return instances[cls]\r\n return getinstance",
"title": ""
},
{
"docid": "9a56a97bdafe2fd0f76a9e8c39706082",
"score": "0.5681544",
"text": "def singleton(cls):\n # -- We only allow the decorating of classes, so we need\n # -- to raise an exception if this rule is broken.\n if not isinstance(object, (type, types.ClassType)):\n raise Exception(\n 'Attempt to wrap a non-class object as a solitary (singleton) class.'\n )\n\n # -- Declare our lookup argument as global\n global _WEAK_WRAPPED_CLASSES\n\n def _wrapper(*args, **kwargs):\n\n # -- TODO: Sould we use a weakref?\n # -- If there is a class instance for this already, and its valid\n # -- the we should use it\n if cls in _WEAK_WRAPPED_CLASSES:\n if _WEAK_WRAPPED_CLASSES[cls]():\n return _WEAK_WRAPPED_CLASSES[cls]()\n\n # -- No class was available, so we instance a new one\n inst = cls(*args, **kwargs)\n\n # -- Now we have an instance we store it to allow the next\n # -- instance attempt to utilise it\n _WEAK_WRAPPED_CLASSES[cls] = weakref.ref(inst)\n\n # -- Return our instance\n return inst\n return _wrapper",
"title": ""
},
{
"docid": "bb4c298cf97e3c62f1e290164253b4d7",
"score": "0.5673866",
"text": "def do_destroy(self, args):\n\n if not args: # if no arguments are passed\n print(\"** class name missing **\")\n return\n\n try: # testing arguments\n cls_name, cls_id = args.split(' ')\n obj_constructor(cls_name)\n except ValueError: # test for second argument\n print(\"** instance id missing **\")\n return\n except NameError: # test if class exist\n print(\"** class doesn't exist **\")\n return\n\n inst = cls_name + '.' + cls_id\n if inst in storage.all(): # check if instance exist\n del storage.all()[inst] # delete obj\n storage.save() # save changes into JSON file\n self.__instances = storage.all() # update inst for autocomplete\n else:\n print(\"** no instance found **\")\n\n return",
"title": ""
},
{
"docid": "2101702df6579cb7528e92e218ce5069",
"score": "0.5667389",
"text": "def do_destroy(self, arg):\n tokens = arg.split()\n if not arg:\n print(\"** class name missing **\")\n elif tokens[0] not in self.my_classes:\n print(\"** class doesn't exist **\")\n elif len(tokens) < 2:\n print(\"** instance id missing **\")\n else:\n key_for_search = \"{}.{}\".format(tokens[0], tokens[1])\n all_objects = storage.all()\n\n if key_for_search in all_objects.keys():\n del all_objects[key_for_search]\n storage.save()\n else:\n print(\"** no instance found **\")",
"title": ""
},
{
"docid": "4c90596d8df96075fd36a9d577420abc",
"score": "0.5666589",
"text": "def testSingleton(self):\n factory_name = \"singleton-factory\"\n name_a = \"singleton.A\"\n name_b = \"singleton.B\"\n\n @decorators.SingletonFactory(factory_name)\n class Singleton(object):\n pass\n\n # Register factory\n self.ipopo.register_factory(self.framework.get_bundle_context(),\n Singleton)\n\n # Instantiate once\n self.ipopo.instantiate(factory_name, name_a, {})\n\n # Assert it is in the registry\n self.assertTrue(self.ipopo.is_registered_instance(name_a),\n \"Instance A is not in the registry\")\n\n # Try instantiate twice\n self.assertRaises(ValueError, self.ipopo.instantiate, factory_name,\n name_b, {})\n self.assertFalse(self.ipopo.is_registered_instance(name_b),\n \"Instance B is in the registry\")\n\n # Kill the instance\n self.ipopo.kill(name_a)\n self.assertFalse(self.ipopo.is_registered_instance(name_a),\n \"Instance A is still in the registry\")\n\n # Re-instantiate with same name and different name\n for name in (name_a, name_b):\n self.ipopo.instantiate(factory_name, name, {})\n self.assertTrue(self.ipopo.is_registered_instance(name),\n \"Instance is not in the registry\")\n\n # Kill the instance\n self.ipopo.kill(name)\n self.assertFalse(self.ipopo.is_registered_instance(name),\n \"Instance is still in the registry\")",
"title": ""
},
{
"docid": "986f5e3d71c6d7624a1957ff7936b49a",
"score": "0.5653508",
"text": "def singleton(cls):\r\n instances = {}\r\n def get_instance():\r\n if cls not in instances:\r\n instances[cls] = cls()\r\n return instances[cls]\r\n return get_instance",
"title": ""
},
{
"docid": "f6ef9fada05020590a29af66f0640441",
"score": "0.56477934",
"text": "def del_class(clsname):\n del Factory._factory[clsname]",
"title": ""
},
{
"docid": "ee945a8be36d008a6e0052ae3cf6ae2e",
"score": "0.55769074",
"text": "def singleton(some_class):\n def on_call(*args, **kwargs):\n if on_call.instance is None:\n on_call.instance = some_class(*args, **kwargs)\n return on_call.instance\n\n on_call.instance = None\n return on_call",
"title": ""
},
{
"docid": "0f2861ee5fd5719cf0f5104ef16ea8c3",
"score": "0.5531488",
"text": "def do_destroy(self, arg):\n\n args_list = shlex.split(arg)\n args_len = len(args_list)\n\n if args_len == 0:\n print(\"** class name missing **\")\n return\n\n # call a class by str\n try:\n get_class = getattr(sys.modules[__name__], args_list[0])\n\n except AttributeError:\n print(\"** class doesn't exist **\")\n return\n\n if args_len < 2:\n print(\"** instance id missing **\")\n return\n\n all_storage = storage.all()\n look_key = args_list[0] + \".\" + args_list[1]\n\n try:\n del(all_storage[look_key])\n storage.save()\n\n except KeyError:\n print(\"** no instance found **\")",
"title": ""
},
{
"docid": "90ec6541d4b175626055558514d555ef",
"score": "0.552742",
"text": "def clean_db(func):\n @wraps(func)\n def wrapper(*args, **kwargs):\n DB(clearSingleton=True)\n func(*args, **kwargs)\n return wrapper",
"title": ""
},
{
"docid": "67c8a7c065b6922af5bc358d8d0b8704",
"score": "0.5525312",
"text": "def singleton(cls):\n factory = SingletonFactory(cls, _threading.RLock())\n return factory()",
"title": ""
},
{
"docid": "4a23677fb225fe73cd4b64f1bea1df3f",
"score": "0.5503421",
"text": "def reg_cleanup(func, *args, **kwargs):\n CLEANUP_CALLS.put((func, args, kwargs))",
"title": ""
},
{
"docid": "9daebc96709c9ecb674eae7b460435c7",
"score": "0.5499259",
"text": "def reg_delete(*args):\n return _ida_registry.reg_delete(*args)",
"title": ""
},
{
"docid": "7ef49a506f27c2b5809f34a598fcc558",
"score": "0.5490523",
"text": "def do_destroy(self, args):\n if not args:\n print(\"** class name missing **\")\n return\n token = args.split()\n objects = storage.all()\n if token[0] not in HBNBCommand.checkclass:\n print(\"** class doesn't exist **\")\n return\n elif len(token) == 1:\n print(\"** instance id missing **\")\n return\n\n elif len(token) == 2:\n obj = token[0] + \".\" + token[1]\n for key in objects.keys():\n if key == obj:\n del objects[key]\n storage.save()\n return\n else:\n print(\"** no instance found **\")\n else:\n print(len(token))\n print(\"** class doesn't exist **\")",
"title": ""
},
{
"docid": "371e6865bf226f6b6dac54d7086ed2bc",
"score": "0.54886407",
"text": "def __del__(self):",
"title": ""
},
{
"docid": "371e6865bf226f6b6dac54d7086ed2bc",
"score": "0.54886407",
"text": "def __del__(self):",
"title": ""
},
{
"docid": "371e6865bf226f6b6dac54d7086ed2bc",
"score": "0.54886407",
"text": "def __del__(self):",
"title": ""
},
{
"docid": "b1ae168e3a363ae38d843f8c93c0886b",
"score": "0.54640687",
"text": "def do_destroy(self, args):\n arg = args.split()\n res = storage.all()\n if len(arg) == 0:\n print(\"** class name missing **\")\n elif arg[0] not in class_selection.keys():\n print(\"** class doesn't exist **\")\n elif len(arg) == 1:\n print(\"** instance id missing **\")\n else:\n key = \"{}.{}\".format(arg[0], arg[1])\n if key in res:\n del res[key]\n storage.save()\n else:\n print(\"** no instance found **\")",
"title": ""
},
{
"docid": "6ee02c909104300412ef4ecf28ddb26d",
"score": "0.54607534",
"text": "def hashed_singleton(klass):\n cls_dict = {'_singletons': weakref.WeakValueDictionary()}\n\n # Mirror original class\n cls_name = klass.__name__\n for attr in functools.WRAPPER_ASSIGNMENTS:\n cls_dict[attr] = getattr(klass, attr)\n\n # Make new method that controls singleton behavior\n def __new__(cls, *args, **kwargs):\n hashable_kwargs = tuple(sorted(kwargs.iteritems()))\n signature = (args, hashable_kwargs)\n\n if signature not in cls._singletons:\n obj = klass(*args, **kwargs)\n cls._singletons[signature] = obj\n else:\n obj = cls._singletons[signature]\n\n return obj\n\n # Add new method to singleton class dict\n cls_dict['__new__'] = __new__\n\n # Build and return new class\n return type(cls_name, (object,), cls_dict)",
"title": ""
},
{
"docid": "627e59eb8d5d57eb44f7c17dfc57da3c",
"score": "0.54407686",
"text": "def __del__(self):\n \n pass",
"title": ""
},
{
"docid": "bdd982d3d0540b2f931137bea8f5b12d",
"score": "0.5432782",
"text": "def do_destroy(self, args):\n line = args.split()\n if len(line) == 0:\n print(\"** class name missing **\")\n elif globals().get(line[0]) is None:\n print(\"** class doesn't exist **\")\n elif len(line) == 1:\n print(\"** instance id missing **\")\n else:\n my_dict = storage.all()\n key = line[0]+\".\"+line[1]\n if key in my_dict:\n del my_dict[key]\n storage.save()\n else:\n print(\"** no instance found **\")",
"title": ""
},
{
"docid": "d94d67ca9543d338f9e40b564ad83c12",
"score": "0.5426962",
"text": "def rem_all_classinst_calls(self, class_instance):\n\t\tfor tup in self.schedule:\n\t\t\tif tup[1].class_instance is class_instance:\n\t\t\t\tself.schedule.remove(tup)",
"title": ""
},
{
"docid": "08c5a84b97a33038d0e4e0abcccf7ab5",
"score": "0.54096353",
"text": "def singleton(cls) -> Type:\n new_function = cls.__new__\n\n def get_instance(_cls, *args, **kwargs):\n if cls._Singleton__instance is None:\n cls.__new__ = new_function\n cls._Singleton__instance = cls(*args, **kwargs)\n cls.__new__ = get_instance\n\n def get_none(*_args, **_kwargs) -> None:\n pass\n\n cls.__init__ = get_none\n cls.__call__ = get_instance\n\n return cls._Singleton__instance\n\n def exists_instance() -> bool:\n \"\"\"Get whether an instance of this singleton class exists.\"\"\"\n return cls._Singleton__instance is not None\n\n cls.__new__ = get_instance\n cls.exists_instance = exists_instance\n\n cls._Singleton__instance = None\n\n return cls",
"title": ""
},
{
"docid": "e4b25e6bab05a92fde075e022a36be86",
"score": "0.5405092",
"text": "def setUp(self):\n @singleton\n class TestClass(object):\n \"\"\"Test singleton class.\"\"\"\n def __init__(self, a, b, c=90):\n self.a, self.b, self.c = a, b, c\n self.test_class = TestClass\n self.assertIsInstance(self.test_class, SingletonMeta)\n self.assertFalse(hasattr(self.test_class, '__instance__'))",
"title": ""
},
{
"docid": "87585c8030cf89b742bd13b6f3524735",
"score": "0.5394462",
"text": "def __del__(self) -> None:\n ...",
"title": ""
},
{
"docid": "8ca3a1b69478d7861a8c61d558d3c6c5",
"score": "0.5377417",
"text": "def unregister_instances(cls, indices):\n for c, index in zip(cls.__mro__, indices):\n try:\n del c.__instance_refs[index]\n except KeyError:\n pass",
"title": ""
},
{
"docid": "57d2a223ffab36aefc1860c4fadb3ab2",
"score": "0.53761595",
"text": "def destroy_instance(cls):\n if hasattr(cls, '_instance'):\n del cls._instance",
"title": ""
},
{
"docid": "15b69365f7bc6da1f44677abad1abb98",
"score": "0.53689986",
"text": "def decorator(cls):\n instances = {}\n\n def wrapper(*args, **kwargs):\n \"\"\"Return the single instance of the cls class, depends on args.\"\"\"\n key = (cls, args, str(kwargs)) if parameters else cls\n if key not in instances:\n instances[key] = cls(*args, **kwargs)\n return instances[key]\n\n return wrapper",
"title": ""
},
{
"docid": "841106734e2336200c7293e6363f2569",
"score": "0.53572255",
"text": "def __call__(cls, *args, **kw):\n if not cls.instance:\n cls.instance = super(Singleton, cls).__call__(*args, **kw)\n return cls.instance",
"title": ""
},
{
"docid": "918ca3a321a4f2be473340445ea33074",
"score": "0.5355742",
"text": "def singleton(f: typing.Callable) -> typing.Callable:\n\n # Mark a produce method as a singleton. This is our custom flag.\n f.__singleton__ = True # type: ignore\n\n return f",
"title": ""
},
{
"docid": "85670b2e2b482a24879135dc2b5e8df4",
"score": "0.53529453",
"text": "def del_instance(self, obj):\n to_remove = set()\n for wrkey, _obj in self.iter_instances():\n if obj is _obj:\n to_remove.add(wrkey)\n for wrkey in to_remove:\n del self[wrkey]",
"title": ""
},
{
"docid": "ff524b47bef7b91b30a0ad47c12e889c",
"score": "0.5348199",
"text": "def __del__(self):\n pass",
"title": ""
},
{
"docid": "b5b532b5ad7b2e63df59129fc2ea2d9e",
"score": "0.53086865",
"text": "def singleton(export):\n def wrapper():\n if wrapper.instance is None:\n wrapper.instance = export()\n\n return wrapper.instance\n wrapper.instance = None\n\n return wrapper",
"title": ""
},
{
"docid": "e029d79bcf0f339a61664c5c2db1d86c",
"score": "0.53071344",
"text": "def unregister(self):\n if self._singleton is None:\n raise RegistryError(\"No object to unregister.\")\n self._singleton = None",
"title": ""
},
{
"docid": "8c50b34b94ebb91185de01d12aab198f",
"score": "0.5297472",
"text": "def test_singleton_instantiation():\n @add_metaclass(Singleton)\n class C(object):\n\n def __init__(self, foo=None):\n if foo:\n self.foo = foo\n\n c1 = C('bar')\n c2 = C('baz')\n\n assert c1.foo == c2.foo",
"title": ""
},
{
"docid": "8755777020559874cb772e58d7df7047",
"score": "0.52805257",
"text": "def teardown_class(cls):",
"title": ""
},
{
"docid": "8755777020559874cb772e58d7df7047",
"score": "0.52805257",
"text": "def teardown_class(cls):",
"title": ""
},
{
"docid": "8755777020559874cb772e58d7df7047",
"score": "0.52805257",
"text": "def teardown_class(cls):",
"title": ""
},
{
"docid": "8755777020559874cb772e58d7df7047",
"score": "0.52805257",
"text": "def teardown_class(cls):",
"title": ""
},
{
"docid": "8755777020559874cb772e58d7df7047",
"score": "0.52805257",
"text": "def teardown_class(cls):",
"title": ""
},
{
"docid": "5b46d38a4a72c59685b0c67eab17c90c",
"score": "0.5273664",
"text": "def singleton(cls):\r\n\r\n class SingleClass(cls):\r\n \"\"\" The real singleton. \"\"\"\r\n _instance = None\r\n __module__ = cls.__module__\r\n __doc__ = cls.__doc__\r\n\r\n def __new__(cls, *args, **kwargs):\r\n if SingleClass._instance is None:\r\n SingleClass._instance = super().__new__(cls)\r\n SingleClass._instance._sealed = False\r\n\r\n return SingleClass._instance\r\n\r\n def __init__(self, *args, **kwargs):\r\n if not getattr(self, '_sealed', False):\r\n super().__init__(*args, **kwargs)\r\n self._sealed = True\r\n\r\n SingleClass.__name__ = cls.__name__\r\n return SingleClass",
"title": ""
},
{
"docid": "5c645e70ad7effefba632620e70cc107",
"score": "0.5271335",
"text": "def test_class_group_singleton():\n assert ThirdSingleton(\"aa\") is FourthSingleton(\"aa\")\n assert ThirdSingleton(\"aa\") is not FourthSingleton(\"bb\")\n assert ThirdSingleton(\"aa\") is not ASingleton(\"aa\")",
"title": ""
},
{
"docid": "87a7261b61df6efa04af43a1ecf74d61",
"score": "0.5267224",
"text": "def clean(inst):\n\n return",
"title": ""
},
{
"docid": "6baf51943a2980bfc3245acd86a2b7c4",
"score": "0.52585304",
"text": "def _destroy_facade_instance():\n global _FACADE\n _FACADE = None",
"title": ""
},
{
"docid": "86b8c620565dd08ecbc974449b168826",
"score": "0.5245904",
"text": "def singleton(the_class):\n instances = {} # Dictionary of singleton objects\n\n def get_instance():\n \"\"\"Function returned at definition of a singleton class\"\"\"\n if the_class not in instances:\n # Create a singleton object and store it\n instances[the_class] = the_class()\n return instances[the_class]\n\n return get_instance",
"title": ""
},
{
"docid": "2778250bf29171469ad6739383e3c0f4",
"score": "0.5240306",
"text": "def __delete__(self, instance):\n # i don't know how to do this\n raise NotImplementedError(f\"class '{type(self).__name__}' must implement '__delete__'\")",
"title": ""
},
{
"docid": "b1a35571d9355461beb5ccf0001aa1ec",
"score": "0.5236131",
"text": "def __del__(self):\n\n def GetAttrHelper(obj, name, default):\n if name in object.__getattribute__(obj, '__dict__'):\n return object.__getattribute__(obj, name)\n else:\n return default\n\n insertAsGlobal = GetAttrHelper(self, '*insertAsGlobal', False)\n if insertAsGlobal:\n\n def RetractGlobalMock(globalReference, mockName, refKey):\n oldReference = GetAttrHelper(self, refKey, None)\n if oldReference is not None:\n globalReference[mockName] = oldReference\n else:\n del globalReference[mockName]\n\n mockName = GetAttrHelper(self, '*name', '')\n if globalReference is not None:\n RetractGlobalMock(globalReference, mockName, '*oldReference')\n if '__oldglobals__' in globalReference:\n RetractGlobalMock(globalReference['__oldglobals__'], mockName, '*oldReference2')\n globalReplacements = GetAttrHelper(self, '*globalReplacements', {})\n for name in globalReplacements:\n globalReference[name] = globalReplacements[name]",
"title": ""
},
{
"docid": "147760a40b69da1565d0634090ff6557",
"score": "0.52099776",
"text": "def __del__(self):\r\n del self",
"title": ""
},
{
"docid": "304def32fcc2b0b2364fed02dab8a1c8",
"score": "0.5201339",
"text": "def Singleton(cls):\n\n cls.__new_old__ = cls.__new__\n\n @wraps(cls.__new__)\n def singleton_new(cls, *args, **kw):\n it = cls.__dict__.get('__it__')\n if it is not None:\n return it\n\n cls.__it__ = it = cls.__new_old__(cls)\n it.__init_old__(*args, **kw)\n return it\n\n cls.__new__ = singleton_new\n cls.__init_old__ = cls.__init__\n cls.__init__ = wraps(cls.__init__)(\n lambda self, *args, **kwargs: object.__init__(self))\n\n return cls",
"title": ""
},
{
"docid": "daedf5bceac09d83475380e19b6484de",
"score": "0.5199091",
"text": "def do_destroy(self, arg):\n l_arg = shlex.split(arg)\n if len(l_arg) == 0:\n print(\"** class name missing **\")\n elif l_arg[0] not in models.dict_class:\n print(\"** class doesn't exist **\")\n elif len(l_arg) == 1:\n print(\"** instance id missing **\")\n else:\n key = \"{}.{}\".format(l_arg[0], l_arg[1])\n temp = models.storage.all() # temp is self.__object\n if key in temp:\n del (temp[key])\n models.storage.save()\n else:\n print(\"** no instance found **\")",
"title": ""
},
{
"docid": "3fbcd1059c0835c345cdb138d62100c4",
"score": "0.519099",
"text": "def __del__(self):\n pass",
"title": ""
},
{
"docid": "3fbcd1059c0835c345cdb138d62100c4",
"score": "0.519099",
"text": "def __del__(self):\n pass",
"title": ""
},
{
"docid": "3fbcd1059c0835c345cdb138d62100c4",
"score": "0.519099",
"text": "def __del__(self):\n pass",
"title": ""
},
{
"docid": "3fbcd1059c0835c345cdb138d62100c4",
"score": "0.519099",
"text": "def __del__(self):\n pass",
"title": ""
},
{
"docid": "2b530a3bc26f5eb1f5e3f46b520bb77e",
"score": "0.5169389",
"text": "def do_destroy(self, arg):\n arg = arg.split()\n if len(arg) == 0:\n print(\"** class name missing **\")\n elif not arg[0] in posi:\n print(\"** class doesn't exist **\")\n elif len(arg) == 1:\n print(\"** instance id missing **\")\n else:\n key = str(arg[0]) + \".\" + str(arg[1])\n if key in content:\n del content[key]\n models.storage.save()\n else:\n print(\"** no instance found **\")",
"title": ""
},
{
"docid": "29999f319ebd7d9535148ce488de3ea7",
"score": "0.51189643",
"text": "def __del__(self):\n self.CleanUp()",
"title": ""
},
{
"docid": "f150f7110ce60f7b31062324e94fc76c",
"score": "0.5111599",
"text": "def __delete__(self, instance):\n print(\"Deleting data..\")\n del instance.__dict__[self.name]",
"title": ""
},
{
"docid": "59e4e83e8f4be2b1e08c38f4076a87fb",
"score": "0.51107436",
"text": "def unregister():\n\n for cls in CLASSES:\n bpy.utils.unregister_class(cls)",
"title": ""
},
{
"docid": "f33f16623eeea0d8cd3e953dc169c303",
"score": "0.5110021",
"text": "def decorator_singleton(session):\r\n instance = None\r\n\r\n def new_constructor(*args, **kwargs):\r\n nonlocal instance\r\n if instance is None:\r\n instance = session(*args, **kwargs)\r\n return instance\r\n return new_constructor",
"title": ""
},
{
"docid": "880cfac5fd6d586baf2fc0664580a8a7",
"score": "0.50961703",
"text": "def get_singleton(cls, annotators=None, **options):\n if annotators is not None:\n annotators = tuple(annotators)\n if annotators not in cls._singletons:\n cls._singletons[annotators] = cls(annotators, **options)\n return cls._singletons[annotators]",
"title": ""
},
{
"docid": "6da143c5a8b095f32076f304374fb4e8",
"score": "0.50831753",
"text": "def unregister(meta, cls):\n del meta._action_classes[cls.__name__]",
"title": ""
},
{
"docid": "d9beb24d7ca5f5ca4e7cbbc0fb631e99",
"score": "0.5082797",
"text": "def do_destroy(self, name):\n\n if not name:\n print(\"** class name missing **\")\n return\n args = name.split()\n if not args[0] in class_names:\n print(\"** class doesn't exist **\")\n return\n elif len(args) < 2:\n print(\"** instance id missing **\")\n return\n else:\n obj = args[0] + '.' + args[1]\n if obj not in storage.all():\n print(\"** no instance found **\")\n return\n del storage.all()[obj]\n storage.save()",
"title": ""
},
{
"docid": "03c4e9e83a8b4ec32929b37961d110c3",
"score": "0.50811636",
"text": "def clearInstance(cls):\n\n Config.__instance = None\n\n return True",
"title": ""
},
{
"docid": "a08829605635ecf6ce0c6ac3bff41de0",
"score": "0.50742733",
"text": "def wrapper(*args, **kwargs):\n key = (cls, args, str(kwargs)) if parameters else cls\n if key not in instances:\n instances[key] = cls(*args, **kwargs)\n return instances[key]",
"title": ""
},
{
"docid": "72eb6d0d1493389b9dea92acdbd7f1f0",
"score": "0.507401",
"text": "def __init__(self, name, bases, dict):\n super(MetaSingleton, self).__init__(name, bases, dict)\n self.instance = None",
"title": ""
},
{
"docid": "c171389c20c33980dbed8f400a7c2431",
"score": "0.5073549",
"text": "def _clean_local(self, arguments):\n self.conan_instance.remove(pattern=arguments.reference, force=True)\n self.conan_instance.remove(pattern=ConanPromote._stable_reference(arguments), force=True)",
"title": ""
},
{
"docid": "91e66030dd3fea6e33c58842bb093838",
"score": "0.5072707",
"text": "def do_destroy(self, args):\n args_list = shlex.split(args)\n \"\"\"args_list is a list of arguments passed to the command\n shlex is a lexical analyser for simple shell-like syntax;\n and shlex.split() splits a string into a list of tokens.\"\"\"\n if len(args_list) == 0:\n print(\"** class name missing **\")\n return\n elif args_list[0] in my_classes:\n \"\"\"if the args_list[0] is in my_classes, then the class exists\"\"\"\n if len(args_list) > 1:\n \"\"\"if the lenght of args_list is greater than 1,\n then the id is passed\"\"\"\n key = args_list[0] + \".\" + args_list[1]\n \"\"\"key = args_list[0] + \".\" + args_list[1]\n key is the key to search in the dictionary\"\"\"\n if key in models.storage.all():\n del models.storage.all()[key]\n \"\"\"del(key) removes the key from the dictionary\"\"\"\n models.storage.save()\n \"\"\"save() saves the changes in the JSON file\"\"\"\n else:\n print(\"** no instance found **\")\n else:\n print(\"** instance id missing **\")\n else:\n print(\"** class doesn't exist **\")",
"title": ""
},
{
"docid": "57d9e201ae080d70bdc430bdae733af9",
"score": "0.50677866",
"text": "def test_deregistering_both_patterns_with_decorator_explicitly(self):\n save_pattern = \"fake_explicit_decorated_pattern\"\n SAVE_METHOD_REGISTRY.registry[save_pattern] = None\n LOAD_METHOD_REGISTRY.registry[save_pattern] = None\n self.assertIn(save_pattern, SAVE_METHOD_REGISTRY.registry)\n self.assertIn(save_pattern, LOAD_METHOD_REGISTRY.registry)\n\n @SavePatternDecorators.deregister_save_pattern(\n save_pattern, save=True, load=True\n )\n class FakeSavePattern(object):\n pass\n\n self.assertNotIn(save_pattern, SAVE_METHOD_REGISTRY.registry)\n self.assertNotIn(save_pattern, LOAD_METHOD_REGISTRY.registry)",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="757"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">793273eae081136d8f3febb8da253e13</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Get the boolean value for a given config parameter.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "93fc056817c3321dd9b9a5b6340c474d",
"score": "0.7363348",
"text": "def getConfigBoolean(category, identifier):\n global config\n return config.getboolean(category, identifier)",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "b2ae98a2a0e1024fbd755982bb0f400a",
"score": "0.79825675",
"text": "def get_bool(self, name, default=False):\n param = self.module.params.get(name, default)\n\n if param in (True, False):\n return param\n elif isinstance(param, str):\n return BOOLEAN_TRUE_MATCHER.search(param) is not None\n else:\n return False",
"title": ""
},
{
"docid": "91c99451e50f8dea5a6eea44cb3c9591",
"score": "0.7887732",
"text": "def getConfigAsBool(key):\n value = getConfig(key)\n return asbool(value) if value else False",
"title": ""
},
{
"docid": "de19b155ff81b03b8f3169e768c1d096",
"score": "0.7603581",
"text": "def get_boolean_parameter(self, parameter_id):\n return vrep.simxGetBooleanParameter(self.client_id,\n parameter_id,\n vrep.simx_opmode_blocking)[0]",
"title": ""
},
{
"docid": "8bccf2c646297f88c591d868326698f2",
"score": "0.76010567",
"text": "def config_bool(key, default=None):\n return CONFIG.y_bool(key, default=default)",
"title": ""
},
{
"docid": "ff7af738f3ac85e4ca4a0fdbdb56d74a",
"score": "0.7508843",
"text": "def get_config_boolean(self, name, default=False):\n try: return self.config.getboolean(self.plugin_name, name)\n except: return default",
"title": ""
},
{
"docid": "72b11541146541b4113049b148ca6416",
"score": "0.7432761",
"text": "def get_bool_environ_parameter(\n name: str,\n default: bool = False,\n) -> bool:\n parameter_value = os.environ.get(name)\n\n if parameter_value:\n parameter_value = bool(strtobool(parameter_value))\n else:\n parameter_value = default\n\n return parameter_value",
"title": ""
},
{
"docid": "feeffdc57120edc29b2e65a1ef89e586",
"score": "0.74022657",
"text": "def svn_config_get_bool(svn_config_t_cfg, svn_boolean_t_valuep, char_section, char_option, svn_boolean_t_default_value): # real signature unknown; restored from __doc__\n pass",
"title": ""
},
{
"docid": "2eda16db42884b2604176305d72cc2cf",
"score": "0.7353983",
"text": "def get_bool(self, name, default=None):\n v = self.get(name, default)\n if isinstance(v, bool):\n return v\n # Parameter included with no value, means invert the default.\n if not v:\n return not default\n vl = v.lower()\n if vl in FALSE_VALUES:\n return False\n if vl in TRUE_VALUES:\n return True\n raise HTTPBadRequest(\n 'Query parameter %r value %r not boolean.\\n' % (name, v))",
"title": ""
},
{
"docid": "17bd8ebf43e843fa2e4203ee5c800320",
"score": "0.73155016",
"text": "def getbool(self, sec, name, default_val=None):\n if self.conf.has_option(sec, name):\n return self.conf.getbool(sec, name)\n\n # config item was not found\n self.check_default(sec, name, default_val)\n return default_val",
"title": ""
},
{
"docid": "74a2fd28f577b52994bfb980afc0d7ba",
"score": "0.7306315",
"text": "def booleanValue(self,attribute):\n\t\treturn bool(self.getParam(attribute))",
"title": ""
},
{
"docid": "4cf02f1b846828240f7be90194cb207b",
"score": "0.7262111",
"text": "def get_config_value_as_bool(self, key: str, default_value: bool = False) -> bool:\n return self._config.get_config_value_as_bool(self._section, key, default_value)",
"title": ""
},
{
"docid": "69d057fb10ddd72481ee5a65738e453f",
"score": "0.72341144",
"text": "def GetBoolean(self, key, default=False):\n value = self.Get(key)\n if value is None:\n return default\n\n if value in (\"True\", \"true\", \"yes\", \"on\", \"1\"):\n return True\n elif value in (\"False\", \"false\", \"no\", \"off\", \"0\"):\n return False\n\n logging.error(\"ParameterOutOfRange: invalid Boolean value \\\"%s\\\"\",\n value)\n return False",
"title": ""
},
{
"docid": "305add13f07ecbcbab1a10ca99422fc7",
"score": "0.71981204",
"text": "def svn_config_get_bool(*args):\n return apply(_core.svn_config_get_bool, args)",
"title": ""
},
{
"docid": "1daec67f46fe82ede8fdddeeaa52803b",
"score": "0.7191722",
"text": "def getbool(self, key, section='DEFAULT'):\n return self.config.getboolean(section, key)",
"title": ""
},
{
"docid": "fe2487f898351bfa184c27b613321020",
"score": "0.7164996",
"text": "def _parse_bool(self, propname):\n self._check_for_missing_propname(propname)\n val = self.raw_config[propname]\n if val not in [True, False]:\n msg = '{0}: {1} value \"{2}\" is not a valid boolean value!'\\\n .format(self.section_name, propname, val)\n _logger.error(msg)\n raise ConfigParseException(msg)\n return val",
"title": ""
},
{
"docid": "8fd4bcb61baac335b948264885210364",
"score": "0.715219",
"text": "def get_bool(self, property):\n value = self.get(property)\n if isinstance(value, bool):\n return value\n return value.lower() == \"true\"",
"title": ""
},
{
"docid": "b770cb4b433b56f1d97d520a712a7b65",
"score": "0.7138706",
"text": "def get_bool(self, name):\n return self.m_bools[name]",
"title": ""
},
{
"docid": "f901089d42d7991325aae8d0de12cb39",
"score": "0.7136783",
"text": "def getboolean(self, option):\n v = self[option]\n val = int(v)\n if val not in (0, 1):\n raise ValueError('Not a boolean: %s' % v)\n return val",
"title": ""
},
{
"docid": "fc69f4830f8267fe7f6459737e600db4",
"score": "0.7118267",
"text": "def get_bool(self, name):\n if not self.has(name): return False\n\n s = self.get(name)\n return convert_to_bool(s)",
"title": ""
},
{
"docid": "ec6de16052c3085a3fec3b669465c3c6",
"score": "0.70615196",
"text": "def get_bool(self, section, option, default=None):\n return self.__get_with_type(self.conf.getboolean, section, option,\n default)",
"title": ""
},
{
"docid": "a8d1c1d5b2c604938e018a95df0cea92",
"score": "0.70395005",
"text": "def bool_value(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"bool_value\")",
"title": ""
},
{
"docid": "ae714f2b591c7167e18a613c33fd7c45",
"score": "0.7038218",
"text": "def get_boolean_value(self):\n return self._get_value('BOOLEAN')",
"title": ""
},
{
"docid": "3bc0ee7a9f56a13bf93555b136d44f73",
"score": "0.7024958",
"text": "def get_boolean(self, key, default_value):\r\n try:\r\n value = self.__getitem__(key)\r\n return value == \"true\" or value == \"yes\" or value == \"1\"\r\n except KeyError:\r\n return default_value",
"title": ""
},
{
"docid": "844c6ac854c6167ec039c48cc2ca2a00",
"score": "0.7018689",
"text": "def read_bool(self, key):\n try:\n my_value = self[key]\n except KeyError:\n raise MissingKeyError(key)\n\n if my_value.upper() in _VALID_TRUE_VALUES:\n return True\n elif my_value.upper() in _VALID_FALSE_VALUES:\n return False\n else:\n raise ParameterValueError(key, my_value, 'boolean')",
"title": ""
},
{
"docid": "0d16a0418f69f57b07e2522f9e2a760c",
"score": "0.6997669",
"text": "def config_read_bool(setting, value=False, section='general'):\n try:\n return config_obj.getboolean(section, setting)\n except (ConfigParser.NoOptionError, ConfigParser.NoSectionError, ValueError):\n return value",
"title": ""
},
{
"docid": "a35822d57d6b198653deb8408507a51f",
"score": "0.69854665",
"text": "def get_bool(self, key, default):\n for section in self.sections():\n try:\n return ConfigParser.getboolean(self, section, key)\n except Exception:\n pass\n\n return default",
"title": ""
},
{
"docid": "1bb165886a962bd8daec634cdc7e7e0e",
"score": "0.6960843",
"text": "def get_bool(self, path):\r\n return self.get_value(path, False)",
"title": ""
},
{
"docid": "f8a37162dee8890dfb7238397a1b9e01",
"score": "0.6955594",
"text": "def _get_boolean_option(option_name, default_value): \n value = _get_option_with_default(option_name, default_value).strip() \n if value in ['True', 'true', 'TRUE']:\n return True\n elif value in ['False', 'false', 'FALSE']:\n return False\n else: return default_value",
"title": ""
},
{
"docid": "b6d99769e0ffe08009f583c3e5100f6e",
"score": "0.6942266",
"text": "def get_bool(cls, params, key):\n try:\n raw_value = params[key]\n except KeyError:\n raise CustomerKeyError(cls._format_missing_hyperparameter_error(key))\n\n if type(raw_value) == bool:\n return raw_value\n\n try:\n return cls.STRING_TO_BOOLEAN_DICT[raw_value.lower()]\n except (KeyError, AttributeError):\n raise CustomerValueError(cls._format_wrong_hyperparameter_type_error(key, raw_value, \"boolean\"))",
"title": ""
},
{
"docid": "7b819d51d4ee0e29b9ef4dec41037e9a",
"score": "0.6923064",
"text": "def get_bool(self, name, default = False, *, raise_if_missing_or_empty = False, warn_if_empty = True):\n return self._handle(\n _get_env(name, default, 'bool', _process_bool_env, raise_if_missing_or_empty, warn_if_empty), default\n )",
"title": ""
},
{
"docid": "6cffd5b9e421a500c52733f74591c909",
"score": "0.6895147",
"text": "def get_bool(self, key: str) -> Optional[bool]:\n return self._get(key, bool)",
"title": ""
},
{
"docid": "c51f6c9d5d999cbd82390c537d659d1d",
"score": "0.68847317",
"text": "def get_boolean(self, key, section=GLOBAL_SECTION, default=None):\n return self._parser.getboolean(section=section, option=key, fallback=default)",
"title": ""
},
{
"docid": "26846f9d8f822e4c8eb8cb4c927f6bf4",
"score": "0.68692225",
"text": "def xml_get_bool(bool_input):\n # Normalize the input to be uppercase\n bool_input = str(bool_input).upper()\n\n if bool_input == 'TRUE' or bool_input == '1':\n return True\n else:\n return False",
"title": ""
},
{
"docid": "451fd5710179d617f94ce5ad2e3b4961",
"score": "0.6868159",
"text": "def svn_config_get_bool(cfg: \"svn_config_t\", section: \"char const *\", option: \"char const *\", default_value: \"svn_boolean_t\") -> \"svn_boolean_t *\":\n return _core.svn_config_get_bool(cfg, section, option, default_value)",
"title": ""
},
{
"docid": "cf52abcb4d13453f3d53b349e31d5ea9",
"score": "0.684436",
"text": "def getBool(x):\n\n if(x):\n return True\n else:\n return False",
"title": ""
},
{
"docid": "b1303b2d006834c5e37b60275ead1954",
"score": "0.6836298",
"text": "def bool(self, name, default=None):\n v = self.get(name)\n if v:\n return v.bool()\n else:\n return default",
"title": ""
},
{
"docid": "afacfa7f04717f061e40b9ffc6742985",
"score": "0.68355346",
"text": "def get_as_boolean(self, key):\n value = self.get(key)\n return BooleanConverter.to_boolean(value)",
"title": ""
},
{
"docid": "40f8c7377761f1ed8e9e57d1b6d5f2a1",
"score": "0.68272823",
"text": "def get_boolean(value):\n\n return value == \"True\" or value == \"true\"",
"title": ""
},
{
"docid": "ebb3bd3ef6e0c4bd8d5d244a3f42d3f4",
"score": "0.6822411",
"text": "def _get_bool_argument(ctx: 'mypy.plugin.ClassDefContext', expr: CallExpr,\n name: str, default: bool) -> bool:\n attr_value = _get_argument(expr, name)\n if attr_value:\n ret = ctx.api.parse_bool(attr_value)\n if ret is None:\n ctx.api.fail('\"{}\" argument must be True or False.'.format(name), expr)\n return default\n return ret\n return default",
"title": ""
},
{
"docid": "ecd1b4c77148b5e20b18b02ce0dc1405",
"score": "0.68188137",
"text": "def getBool(self, value):\n\n\t\tif(value == \"False\"):\n\t\t\treturn False\n\t\telse:\n\t\t\treturn True",
"title": ""
},
{
"docid": "1aa5abe0e195319a42d171c3d89a6105",
"score": "0.6809143",
"text": "def bool(self):\n s = self.value\n if s:\n s = s.lower()\n n = len(s)\n if (s == '1' or\n s == 'on' or\n s == 'true'[:n] or\n s == 'yes'[:n]):\n return True\n if (s == '0' or\n s == 'off'[:n] or\n s == 'false'[:n] or\n s == 'no'[:n]):\n return False\n raise self.ConfigurationError('Boolean value should be one of: 1, 0, '\n 'on, off, true, false, yes, no.')",
"title": ""
},
{
"docid": "d9ff5e895ed000e89535862a00e1d966",
"score": "0.67950916",
"text": "def test_true_bool(self):\n self.assertTrue(self.config.get(key='TRUE'))",
"title": ""
},
{
"docid": "0108e6b3557ac70feaa2db105fab0eb3",
"score": "0.6787652",
"text": "def _get_boolean(value):\n self.verbose('trying to convert value to boolean : %s', value)\n x = str(value).lower()\n if x in ('yes', '1', 'on', 'true'):\n return True\n elif x in ('no', '0', 'off', 'false'):\n return False\n else:\n raise ValueError('%s is not a boolean value' % x)",
"title": ""
},
{
"docid": "c562f18a8ad3009a411e220cfb76f3db",
"score": "0.675882",
"text": "def getBoolean(self):\n if self.value in ('yes', '1', 'on', 'true'):\n return True\n elif self.value in ('no', '0', 'off', 'false'):\n return False\n else:\n raise ValueError('%s is not a boolean value' % self.value)",
"title": ""
},
{
"docid": "91dbf3640a8f7aad20ec29db0619a7cd",
"score": "0.6737989",
"text": "def get_bool(name, default=False): # noqa\n if name not in os.environ:\n return default\n if os.environ[name].lower() in ['true', 'yes', '1']:\n return True\n elif os.environ[name].lower() in ['false', 'no', '0']:\n return False\n else:\n return default",
"title": ""
},
{
"docid": "8fcb9e273f91d0a626d9ad5afd51027c",
"score": "0.6726359",
"text": "def getboolean(self, section, option):\n return self[section].get(option)",
"title": ""
},
{
"docid": "13c523f3156ce93ce4cd36603e1722b3",
"score": "0.6725663",
"text": "def getbool(self, key, default=None):\n try:\n return self[key].lower() in (\"true\", \"yes\", \"y\", \"1\")\n except KeyError:\n return default",
"title": ""
},
{
"docid": "a6740b72f4eb8213cf8fdeaf8c73840b",
"score": "0.67126334",
"text": "def get_bool(flag_name):\n\n for flag in bool_flags:\n if flag_name == flag.flag_name:\n return flag.default\n\n\n return None",
"title": ""
},
{
"docid": "866a11a0691b0c939a0c2b24ddea1e7a",
"score": "0.67020303",
"text": "def is_boolean_parameter(param):\n # detect boolean selects of OpenMS\n if type(param.restrictions) is _Choices:\n return set(param.restrictions.choices) == {\"true\", \"false\"}\n else:\n return param.type is bool",
"title": ""
},
{
"docid": "d6e83b15df7c32dea8361c85c3e58dab",
"score": "0.6690893",
"text": "def boolParam(self, key, params, default=None):\n if key not in params:\n return default\n\n return toBool(params[key])",
"title": ""
},
{
"docid": "849b12d34f4f11192e6d01a0839e8d76",
"score": "0.66893697",
"text": "def DEFINE_boolean(config_name, default_value, docstring):\n # Register a custom function for 'bool' so --config=True works.\n def str2bool(v):\n return v.lower() in ('true', 't', '1')\n _global_parser.add_argument('--' + config_name,\n nargs='?',\n const=True,\n help=docstring,\n default=default_value,\n type=str2bool)\n _global_parser.add_argument('--no' + config_name,\n action='store_false',\n dest=config_name)",
"title": ""
},
{
"docid": "a260ff27e9817a67437ab204157a74b2",
"score": "0.66695017",
"text": "def boolean_value(val):\n return self.dbengine.boolean_value(val)",
"title": ""
},
{
"docid": "62ea25ae18d075f5d2e97610116ab6f8",
"score": "0.6668659",
"text": "def get_bool_representation(value):\n return 'enabled' if value else 'disabled'",
"title": ""
},
{
"docid": "60abc8c3db342491ed383565dc1a5e8e",
"score": "0.66202193",
"text": "def parse_bool_param(field):\n return field.lower() == \"true\" if field else False",
"title": ""
},
{
"docid": "c9a6bdbe63c447b3d3dd6b78970dfdcb",
"score": "0.6605913",
"text": "def getBool(string):\n return _getBool(string)",
"title": ""
},
{
"docid": "d0bc71afab95febe12cb9e1241b65205",
"score": "0.6604521",
"text": "def svn_config_get_server_setting_bool(svn_config_t_cfg, svn_boolean_t_valuep, char_server_group, char_option_name, svn_boolean_t_default_value): # real signature unknown; restored from __doc__\n pass",
"title": ""
},
{
"docid": "81b547d812adc06a41ee2134d67b8a07",
"score": "0.65689784",
"text": "def get_bool(key: str) -> bool:\n return str(os.getenv(key.upper())).upper() == 'TRUE'",
"title": ""
},
{
"docid": "f6bb231b7b8aa4a5a17b34eb73bad3d9",
"score": "0.6565615",
"text": "def GetBoolean(val):\n if isinstance(val, str):\n val = val.upper()\n return (val.startswith(\"ON\") or \n val.startswith(\"T\") or\n val.startswith(\"1\"))\n else:\n return bool(val)",
"title": ""
},
{
"docid": "045e1d2b28e54f2e5843dc3a87dba95a",
"score": "0.65622336",
"text": "def _get_config_val(self, key: str, request) -> Union[bool, str]:\n # have to check the ini file first due to the way bools work on the cli\n val = request.config.getini(key)\n\n if val:\n if type(val) == bool:\n return val\n else:\n return val[0]\n\n val = request.config.getoption(f\"--{key}\")\n if val is not None:\n return val",
"title": ""
},
{
"docid": "4c8fc808262fa8bcacb55195c136030b",
"score": "0.6557897",
"text": "def get_env_bool(variable_name, default=False):\n assert isinstance(default, bool), '\"default\" arg must be bool type'\n\n value = os.environ.get(variable_name, None)\n if value is None:\n return default\n\n try:\n return bool(int(value))\n except ValueError:\n return value.lower() == \"true\"",
"title": ""
},
{
"docid": "dff28a109d524bede53ceca9c0ace73d",
"score": "0.6554719",
"text": "def get_checkbox(self, arg, default = 'off'):\n val = self.get(arg, default)\n if val.lower() == 'on':\n return True\n return False",
"title": ""
},
{
"docid": "e35a0fffff7c17816954c1a2b56fa089",
"score": "0.6548515",
"text": "def get_boolean(\n key, value, is_list=False, is_optional=False, default=None, options=None\n):\n if is_list:\n return _get_typed_list_value(\n key=key,\n value=value,\n target_type=bool,\n type_convert=lambda x: bool(strtobool(x)),\n is_optional=is_optional,\n default=default,\n options=options,\n )\n\n return _get_typed_value(\n key=key,\n value=value,\n target_type=bool,\n type_convert=lambda x: bool(strtobool(x)),\n is_optional=is_optional,\n default=default,\n options=options,\n )",
"title": ""
},
{
"docid": "568d9bdc8b4ec01e37c05e7ef8989c45",
"score": "0.6543215",
"text": "def boolean_setting(settings, key, default=None):\n if key not in settings:\n return default\n setting = settings[key]\n if isinstance(setting, str):\n setting_lower = setting.lower()\n if setting_lower in (\"\", \"false\"):\n return False\n elif setting_lower == \"true\":\n return True\n else:\n return setting\n else: # booleans, None, and odd types (though we might want to consider making other types an error).\n return setting",
"title": ""
},
{
"docid": "8233fd84e11ca1457d30bc445dfa1ece",
"score": "0.6530918",
"text": "def BoolParameter(default, **kwargs):\n return attr.ib(default=default, converter=bool, **kwargs)",
"title": ""
},
{
"docid": "a0ea38e8d70cf576384798d469ae6c76",
"score": "0.6529837",
"text": "def _get_valid_bool(section, option, provided):\n\n if provided is None:\n return False\n\n if not isinstance(provided, bool):\n error = \"Value provided for '{0}: {1}' is not a valid boolean\".format(\n section, option)\n raise ValueError(error)\n\n return provided",
"title": ""
},
{
"docid": "7ade22a9118ac3a8180fc5d1b0f33027",
"score": "0.6527005",
"text": "def getbool(self, key, default=False, section=None):\n value = self.getraw(key, section=section)\n if not value:\n return default\n value = value[0]\n if isinstance(value, (str, unicode)):\n value = value.lower()\n if value in self.true_values:\n return True\n elif value in self.false_values:\n return False\n else:\n raise ValueError(\n \"Yes/No value expected for %s (got %r)\"\n % (key, vaule))\n else:\n return value",
"title": ""
},
{
"docid": "b690840b547799384f51d69dd81dea2b",
"score": "0.65259904",
"text": "def bool_prop(self, attr_name):\n value = getattr(self, attr_name)\n if value is None:\n return False\n return value",
"title": ""
},
{
"docid": "fb7ab95258453d635c5d7932eb63f990",
"score": "0.65240806",
"text": "def boolean(value):\n value = value.lower()\n if value in {'on', 'true'}:\n return True\n if value in {'off', 'false'}:\n return False\n return bool(int(value))",
"title": ""
},
{
"docid": "9e191242697330676193e08c1a20bbee",
"score": "0.6517121",
"text": "def read_bool(self) -> bool:\n return self._unpack('?')",
"title": ""
},
{
"docid": "e8fe8699ac1e6ab2b5452ff56338bff8",
"score": "0.6508047",
"text": "def getBoolean(self, section, option, on_error_value = False):\r\n return_value = on_error_value\r\n\r\n WriteDebug('Retrieving: %s.%s as boolean' % (section, option)) \r\n try: \r\n return_value = self._parser.getboolean(section, option)\r\n except Exception as Ex: \r\n raise Ex\r\n WriteDebug('Failure while trying to get value, using default')\r\n WriteDebug('Value: %s.%s => %s' % (section, option, return_value))\r\n return return_value",
"title": ""
},
{
"docid": "e0a050a2cb12366f7254a9ada2023e33",
"score": "0.6487781",
"text": "def getboolean(self, option, default=None):\n try:\n v = self.get(option, default, acquire_lock=False)\n if v == default or not v:\n return default\n if isinstance(v, basestring):\n lv = v.lower()\n if lv not in self._boolean_states:\n raise ValueError(\"Not a boolean: %s\" % (v))\n return self._boolean_states[lv]\n except NoOptionError:\n return default",
"title": ""
},
{
"docid": "b1f7ea89c0a647bcb2fb1e79ea49f50f",
"score": "0.6477978",
"text": "def is_configuration_parameter(self) -> Optional[bool]:\n return pulumi.get(self, \"is_configuration_parameter\")",
"title": ""
},
{
"docid": "e2c1e8ca1b1ec29cef2f59b30a35173f",
"score": "0.64746106",
"text": "def getboolean(self, section, option, default=None, add_if_not_existing=True):\n def boolmap(b):\n return b.lower() not in ['0','no', 'off', 'false']\n\n if default != None and default == True:\n default = 'true'\n if default != None and default == False:\n default = 'false'\n\n return self.get(section, option, default , add_if_not_existing, dtype=boolmap)",
"title": ""
},
{
"docid": "ee263208e30a0a06da9edd065aab2bc3",
"score": "0.64738625",
"text": "def extract_minihead_bool_param(miniHead, name):\n val = extract_minihead_param(miniHead, name, 'ParamBool')\n\n if val is None:\n return False\n elif val.strip('\" ').lower() == 'true'.lower():\n return True\n else:\n return False",
"title": ""
},
{
"docid": "5ede148212d8305377f36c8f7ffa70da",
"score": "0.64735293",
"text": "def boolean(value):\n if value in [INDEF,0,1]:\n return value\n elif value in [\"\", None]:\n return INDEF\n tval = type(value)\n if tval is _types.StringType:\n v2 = _irafutils.stripQuotes(value.strip())\n if v2 == \"INDEF\":\n return INDEF\n ff = v2.lower()\n if ff == \"no\":\n return 0\n elif ff == \"yes\":\n return 1\n elif tval is _types.FloatType:\n # try converting to integer\n try:\n ival = int(value)\n if (ival == value) and (ival == 0 or ival == 1):\n return ival\n except (ValueError, OverflowError):\n pass\n raise ValueError(\"Illegal boolean value %s\" % `value`)",
"title": ""
},
{
"docid": "6f95b849a050073e7e46b2f54f825340",
"score": "0.6462948",
"text": "def get_as_bool(self, key, default):\n val = self._dct.get(key)\n ret = default\n if val:\n val = val.lower()\n if val == 'true':\n ret = True\n elif val == 'false':\n ret = False\n return ret",
"title": ""
},
{
"docid": "c9f03a0bca3ba1191e33c5e12da13f61",
"score": "0.6458891",
"text": "def attr_val_bool(elem, attr_name):\n attr = elem.find_attribute(attr_name)\n return attr.normalized_value.strip() in ('1', 'true') if attr else None",
"title": ""
},
{
"docid": "09d68284f3135086974f91890b472082",
"score": "0.6458601",
"text": "def ReadBool(self, key, defaultVal=False):",
"title": ""
},
{
"docid": "e19ac0df23755cd62ab7df6b2c869365",
"score": "0.6455734",
"text": "def read_bool_cmdline(key):\n my_value = raw_input(key + ': ')\n if my_value.upper() in _VALID_TRUE_VALUES:\n return True\n elif my_value.upper() in _VALID_FALSE_VALUES:\n return False\n else:\n raise ParameterValueError(key, my_value, 'boolean')",
"title": ""
},
{
"docid": "b349a627536adc0d3271881e604add40",
"score": "0.6454015",
"text": "def getboolean(self, key: str, default: Optional[bool] = None) -> bool:\n\n if not self.cf.has_option(self.main_section, key):\n if default is None:\n raise NoOptionError(key, self.main_section)\n return default\n\n return self.cf.getboolean(self.main_section, key)",
"title": ""
},
{
"docid": "bbd1db0d941949d5208c5c1accabd5c7",
"score": "0.6445457",
"text": "def get_bool(self):\n # pylint: disable=comparison-with-callable\n # This doesnt seem to actually be a callable in this case.\n if self.value == \"yes\":\n return True\n\n return False",
"title": ""
},
{
"docid": "bd5d74586c1959cde9e31074a54ea245",
"score": "0.6443532",
"text": "def bool_env(val):\n return True if os.environ.get(val, False) == \"True\" else False",
"title": ""
},
{
"docid": "80f4f91ec5fe09a4dbb3e9ec972f053a",
"score": "0.64431477",
"text": "def getBoolean(self, key: int) -> int:\n return self.app.getBoolean(key)",
"title": ""
},
{
"docid": "c83d4b024338eef677d44093c1e1a2bf",
"score": "0.6435244",
"text": "def get_bool_from_api_params(key, params, default=False, strict=True):\n param = params.get(key, default)\n try:\n param = strutils.bool_from_string(param,\n strict=strict,\n default=default)\n except ValueError:\n msg = _('Invalid value %(param)s for %(param_string)s. '\n 'Expecting a boolean.') % {'param': param,\n 'param_string': key}\n raise exception.InvalidInput(msg)\n return param",
"title": ""
},
{
"docid": "6115d4020685f084bffdbeb6a923cf37",
"score": "0.64296013",
"text": "def to_bool(val):\r\n if val:\r\n strg = str(val).lower()\r\n if (strg == 'true' or strg == 'y'\r\n or strg == 'yes' or strg == 'enabled'\r\n or strg == '1'):\r\n return True\r\n else:\r\n return False\r\n else:\r\n return False",
"title": ""
},
{
"docid": "69c83fa6703181051c37852462f94741",
"score": "0.6426086",
"text": "def getBooleanOption(self, section, option):\n return self.cp.getboolean(section, option)",
"title": ""
},
{
"docid": "d9c1534ba01b62cf151315fb44fb6bd6",
"score": "0.6417047",
"text": "def getBool(self,section,option,default=True):\n if not self.parser.has_section(section):\n return default\n elif not self.parser.has_option(section,option):\n return default\n else:\n return self.parser.getboolean(section,option)",
"title": ""
},
{
"docid": "c7b382e19f358da803ed4ee5929a1b27",
"score": "0.6406096",
"text": "def config_true_value(value):\n return value is True or \\\n (isinstance(value, basestring) and value.lower() in TRUE_VALUES)",
"title": ""
},
{
"docid": "13f5ef0e98bc84a4f1846bb9ea2b0169",
"score": "0.6392684",
"text": "def _bool(var_name):\n\treturn True if _env(var_name) else False",
"title": ""
},
{
"docid": "de0fe789989e2e8814548e9629742e67",
"score": "0.63906103",
"text": "def get_cgi_parameter_bool(form: cgi.FieldStorage, key: str) -> bool:\n return is_1(get_cgi_parameter_str(form, key))",
"title": ""
},
{
"docid": "308208f05139ac7ca9b35546838c7941",
"score": "0.6387845",
"text": "def bool_argument_value(arg_name, bool_str, strict=True, default=False):\n try:\n val = strutils.bool_from_string(bool_str, strict, default)\n except ValueError as e:\n raise exc.CommandError(_(\"argument %(arg)s: %(err)s.\")\n % {'arg': arg_name, 'err': e})\n return val",
"title": ""
},
{
"docid": "07ae1f6eca617da6dce54a1d98d5e577",
"score": "0.6387245",
"text": "def GetSCPIBoolean(something):\n if GetBoolean(something):\n return ON\n else:\n return OFF",
"title": ""
},
{
"docid": "8adaf140df96d844fe75e98832448075",
"score": "0.638172",
"text": "def toConfig(self, value):\n assert isinstance(value, bool)\n return value",
"title": ""
},
{
"docid": "0ef8055f3aa1d9ad4a7c7086c70390f1",
"score": "0.63816595",
"text": "def svn_config_get_server_setting_bool(cfg: \"svn_config_t\", server_group: \"char const *\", option_name: \"char const *\", default_value: \"svn_boolean_t\") -> \"svn_boolean_t *\":\n return _core.svn_config_get_server_setting_bool(cfg, server_group, option_name, default_value)",
"title": ""
},
{
"docid": "475de323733b5cc02e33894fa7072889",
"score": "0.637687",
"text": "def getbool(self):\n return bool(self.getbits(1))",
"title": ""
},
{
"docid": "f346612393819ae13cb992c33be31b5f",
"score": "0.6372097",
"text": "def get_bool(s):\n if s == \"true\":\n return True\n elif s == \"false\":\n return False\n else:\n return None",
"title": ""
},
{
"docid": "f67e07baf9ea5a19b4c143023107af22",
"score": "0.6371898",
"text": "def test_false_bool(self):\n self.assertFalse(self.config.get(key='FALSE'))",
"title": ""
},
{
"docid": "54caccd604bbbb1b2b5a5612ca90789b",
"score": "0.6370884",
"text": "def _get_boolean_env(env_var_name, default=False):\n def_val = 't' if default else 'f'\n value = os.environ.get(env_var_name, def_val).lower()[:1]\n return (not default and value in ['y', '1', 't']) or (default and value not in ['n', '0', 'f'])",
"title": ""
},
{
"docid": "ad5552ab8d19ace5ea32744a3354dc92",
"score": "0.6360994",
"text": "def _parse_boolean(value, default=False):\n if value is None:\n return default\n try:\n return bool(value)\n except ValueError:\n return default",
"title": ""
},
{
"docid": "581776214a07dfec8e49f170b7d470da",
"score": "0.63590807",
"text": "def GetBool(self, id: int, preset: bool) -> bool:\n ...",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="758"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">c7bc01c8c595a41fbdf5c05e887f3f7a</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Return list of column names.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "a8a1f72d635808bf5123e00e24d47356",
"score": "0.75188255",
"text": "def columns(self):\n if self._default_index:\n return list(self._df.columns)\n return list(self._df.index.names) + list(self._df.columns)",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "b50d29d4eb3280d1ab00bfa8ce62d58b",
"score": "0.87906796",
"text": "def getColumnsNames(self):\n self.do('show columns from ' + self.__nameTable + ';')\n return list([name[0] for name in self.done()])",
"title": ""
},
{
"docid": "715fc82ab8aac2989f607f23723d0699",
"score": "0.87876767",
"text": "def colnames(self):\r\n return [col.name for col in self.ordered_columns]",
"title": ""
},
{
"docid": "66f60bfaebd3b8f61bf9c42749b8fa02",
"score": "0.87549645",
"text": "def colnames(self):\n return [c.name for c in self.column_defs]",
"title": ""
},
{
"docid": "153c15dc08467b97fce796c7a2923053",
"score": "0.8753577",
"text": "def get_column_names(self,):\n return self.column_names",
"title": ""
},
{
"docid": "dcc569a7f346a340ff5c570f757fca54",
"score": "0.86209613",
"text": "def columns(self):\n return [col.name for col in self]",
"title": ""
},
{
"docid": "fe63ad28ecd0ed9c5a359878de4ff80e",
"score": "0.85040426",
"text": "def get_column_names(self):\n out = []\n cols = self.db.execute('PRAGMA table_info({})'.format(self.table_name)).fetchall()\n for col in cols:\n out.append(col[1])\n \n return out",
"title": ""
},
{
"docid": "63978080b5b111ecaa0263d4ce080581",
"score": "0.8419981",
"text": "def column_names(self):\n return self._dataset.column_names",
"title": ""
},
{
"docid": "11645c30cae633f54e83667a80c9d271",
"score": "0.8413862",
"text": "def recoverColumnsNames(cls):\n return cls.columns",
"title": ""
},
{
"docid": "fc18d47dd9d94ef382626eab6a004a14",
"score": "0.84016675",
"text": "def get_col_names(self):\n raise NotImplemented",
"title": ""
},
{
"docid": "3720817efc6d3bcffca68f0f16339a2b",
"score": "0.82188404",
"text": "def get_col_names(self, tablename):\n reader = self.cur.execute(\"SELECT * FROM {}\".format(tablename))\n return [x[0] for x in reader.description]",
"title": ""
},
{
"docid": "39f25b0330f4217b358080af17772a6c",
"score": "0.81447667",
"text": "def field_names(self):\n return self.table.colnames",
"title": ""
},
{
"docid": "381b1a8800bea2e4502dc7e2b112842b",
"score": "0.81419903",
"text": "def colnames(self):\n # Get the columns\n a_list = [col for col, val in self.__dict__.items()\n if not isinstance(val, datetime)]\n\n return a_list",
"title": ""
},
{
"docid": "ed8e2e5ff7ce91eddc98fa90ab8a0756",
"score": "0.8072347",
"text": "def get_column_names(\n self,\n include = [],\n exclude = [],\n include_primarykeys = True\n ):\n lst = self.get_column_objects(include, exclude, include_primarykeys)\n return [c.name for c in lst]",
"title": ""
},
{
"docid": "ad799571dcc85f08697514d9826d62e7",
"score": "0.8049124",
"text": "def columns(self) -> List[str]:\n return list(self._columns)",
"title": ""
},
{
"docid": "b136e1e42bf29c2d28f358d210317363",
"score": "0.8035706",
"text": "def column_names(cls):\n return {col.key for col in cls.__mapper__.columns}",
"title": ""
},
{
"docid": "99e276b6621ac1b37ca9c0555d22bee7",
"score": "0.7980292",
"text": "def get_column_names(cls, *args):\n return super().get_column_names(*args)",
"title": ""
},
{
"docid": "619080931a06ca2769c4ce82a228d5bf",
"score": "0.79226345",
"text": "def columns(kls): \n return [x.name for x in kls.__table__.c]",
"title": ""
},
{
"docid": "6f8b275e118eb2ad64bd43671b6ac606",
"score": "0.7884105",
"text": "def generate_col_names(self):\n df_X = self.generate_X_df()\n col_list = list(df_X.columns)\n \n return col_list",
"title": ""
},
{
"docid": "0a5cef7d244ed9d5b5d760e10f9c710c",
"score": "0.7880126",
"text": "def get_column_names(self):\n return [\n key\n for key in list(self.top_level_group.keys())\n if self.top_level_group[key].attrs[self.CLASS] == self.COLUMN\n ]",
"title": ""
},
{
"docid": "22021fa0376b5f9df601b860835cefc4",
"score": "0.7877357",
"text": "def getColumnNames(hdu):\n return [d.name for d in hdu.get_coldefs()]",
"title": ""
},
{
"docid": "df5366d580bea2076f233bd8d66a49b4",
"score": "0.78658396",
"text": "def get_col_list(self, ignore_cols=None):\r\n col_list = \"\";\r\n i = 0\r\n for col in BUDGETING_TABLE.columns:\r\n try:\r\n if ignore_cols and ignore_cols.index(col.name) > -1: continue\r\n except: pass\r\n\r\n if (i > 0):\r\n col_list += \",\"\r\n col_list += col.name\r\n i += 1\r\n return col_list",
"title": ""
},
{
"docid": "8212d16f619d965ed85b811826972f13",
"score": "0.7865091",
"text": "def getColumnNames(self):\n\n\t\tcurs = self.conn.cursor()\n\t\tcurs.execute(\"\"\"\n\t\t\tselect attname from pg_attribute \n\t\t\twhere attrelid = %(oid)s \n\t\t\tand attnum >= 0\n\t\t\tand attisdropped = false\n\t\t\"\"\", {'oid' : self.oid})\n\t\treturn [x[0] for x in curs.fetchall()]",
"title": ""
},
{
"docid": "f73bde88d3c15ee515e954d875b26712",
"score": "0.78484356",
"text": "def return_column_names(table):\r\n conn = create_connection()\r\n\r\n cur = conn.cursor()\r\n sql = \"SELECT * FROM \" + table\r\n cur.execute(sql)\r\n names = [description[0] for description in cur.description]\r\n close_connection(conn)\r\n return names",
"title": ""
},
{
"docid": "768b192fbdedf17c81f53e67cfdfa47d",
"score": "0.78042644",
"text": "def get_column_names(self) -> Tuple[str]:\n cursor = self.get_cursor(f\"SELECT * FROM {self.table_name} LIMIT 1\")\n return tuple(map(lambda raw_: raw_[0], cursor.description))",
"title": ""
},
{
"docid": "674014bc050e49af51452a03de76914d",
"score": "0.77654684",
"text": "def columns(self):\n\n if self._column_names is None:\n self._column_names = list(self._jkt.columns())\n return self._column_names",
"title": ""
},
{
"docid": "a2bd1164a3816ebf1946a7eb6f0a6095",
"score": "0.767373",
"text": "def columns(self):\n # type: (\"NumpyFrame\") -> List[str]\n return self._columns",
"title": ""
},
{
"docid": "19f3e4797ed54a3bf599450c37f1a93c",
"score": "0.7671427",
"text": "def get_columns():\r\n return str(columns)",
"title": ""
},
{
"docid": "1506bc88f92f02f7eb9b96f070a75e10",
"score": "0.7657122",
"text": "def columns(cls):\n return [a.name for a in attr.fields(cls)]",
"title": ""
},
{
"docid": "9b4ac098b945b6d6c26cf78b90ff32da",
"score": "0.76368296",
"text": "def column_name_list(columns):\n if not columns:\n return ''\n return ', '.join([column.name for column in columns])",
"title": ""
},
{
"docid": "b4f1db214b3c176821aba2fe6aa74d90",
"score": "0.7624341",
"text": "def get_columns(self) -> list[pli.Series]:\n return [pli.wrap_s(s) for s in self._df.get_columns()]",
"title": ""
},
{
"docid": "934d69bc3f5e46f8ebb0e8dc8cf4a2cc",
"score": "0.7592211",
"text": "def _column_names(self):\n columns = {\n 'code': Table.Market.CODE,\n 'price_date': Table.Market.PRICE_DATE,\n 'open_price': Table.Market.OPEN_PRICE,\n 'high_price': Table.Market.HIGH_PRICE,\n 'low_price': Table.Market.LOW_PRICE,\n 'settle_price': Table.Market.SETTLE_PRICE,\n 'volume': Table.Market.VOLUME\n }\n return ', '.join([i[0] for i in sorted(columns.items(), key=itemgetter(1))])",
"title": ""
},
{
"docid": "db8e4516ec2f921e1b12fc922b779e1e",
"score": "0.75712836",
"text": "def get_column_names(self):\n return self.df.columns.levels[0]",
"title": ""
},
{
"docid": "a341aa04dd7b6699b03cbde76ab642c6",
"score": "0.7560528",
"text": "def get_columns():",
"title": ""
},
{
"docid": "86e398f6a1f7fdaa777574f8c87477d6",
"score": "0.75592136",
"text": "def _query_column_names(self, query):\n rs = self.dbdriver.q2rs(query)\n column_names = []\n for r in rs.resultset:\n column_names.append(_s2u(r[0]))\n log.trace(\"_query_column_names: \" + unicode(r))\n return column_names",
"title": ""
},
{
"docid": "279cc26ec68d9cf99c3a505658f92509",
"score": "0.7513393",
"text": "def getColumnsAmendedNames(self):\n columnsNamesList = self.getColumnsNames()\n for i in range(len(self.getColumnsNames())):\n if (self.colNameAmendedForeign(columnsNamesList[i])):\n columnsNamesList[i] = self.colNameAmendedForeign(\n columnsNamesList[i])\n if (self.colNameAmendedAutoIncrement(columnsNamesList[i])):\n columnsNamesList[i] = self.colNameAmendedAutoIncrement(\n columnsNamesList[i])\n\n return list(columnsNamesList)",
"title": ""
},
{
"docid": "b5565d5e994d24c48897289123c7205e",
"score": "0.75129706",
"text": "def columns(cls) -> Set[str]:\n return set(cls.list_columns())",
"title": ""
},
{
"docid": "388e0d4c4c21a030a68fbc41f5497fb9",
"score": "0.74918884",
"text": "def get_table_column_names(self):\n columns = self.table.columns.keys()\n if self.conn.dialect.name == 'mysql' or self.conn.dialect.name == 'sqlite':\n columns.remove('index')\n return columns",
"title": ""
},
{
"docid": "a215db05a8d5b8c1bb1885010995041c",
"score": "0.74848735",
"text": "def _column_list(op: saldag.OpNode):\n\n return \", <BR/>\".join([col.name for col in op.out_rel.columns])",
"title": ""
},
{
"docid": "4ea6f2589835823f83d16c95b5742c48",
"score": "0.74155265",
"text": "def columns(self):\n cursor = self._connection.cursor()\n cursor.execute('PRAGMA table_info(' + self._table + ')')\n return [x[1] for x in cursor.fetchall()]",
"title": ""
},
{
"docid": "e4d8b5b64bb8bb4ef2158a4823848882",
"score": "0.7409278",
"text": "def _query_column_names(self, query):\n rs = self.dbdriver.q2rs(query, timeout=self.timeout)\n column_names = []\n for r in rs.resultset:\n# column_names.append(_s2u(r[0]))\n column_names.append(r[0].decode('utf-8'))\n log.trace(\"_query_column_names: \" + unicode(r))\n return column_names",
"title": ""
},
{
"docid": "7c4b677452d172171be5e8c0ed65108a",
"score": "0.74057597",
"text": "def get_column_names(table_name, database):\r\n assert not isinstance(table_name, str), \"'table_name' parameter is not str\"\r\n assert not isinstance(database, sqlite3.Connection), \"'database' parameter is not a database connection\"\r\n names = database.execute(\"SELECT name FROM (PRAGMA table_info({}));\".format(repr(table_name)))\r\n ret = []\r\n for n in names.fetchall():\r\n ret.append(n)\r\n return ret",
"title": ""
},
{
"docid": "892bd576a1780dcdb1a0f1f330b1174f",
"score": "0.7374964",
"text": "def dataset_headers(dataset):\r\n return list(dataset.columns.values)",
"title": ""
},
{
"docid": "6a7c575a9abbb94685e11e2a4a98aa25",
"score": "0.73656464",
"text": "def headers(self):\n\n t, _ = self.schema_term\n\n if t:\n return [self._name_for_col_term(c, i)\n for i, c in enumerate(t.children, 1) if c.term_is(\"Table.Column\")]\n else:\n return None",
"title": ""
},
{
"docid": "e0d5c1c8c704ce8c4e4a2088cb57879a",
"score": "0.73612297",
"text": "def list_columns(self, table):\n return",
"title": ""
},
{
"docid": "bca87633a2c5592245f70cf958b28498",
"score": "0.7347313",
"text": "def get_columns():\n cur = conn.cursor()\n cur.execute(\"SELECT * FROM missing_persons\")\n names = [description[0] for description in cur.description]\n print(names)\n return names",
"title": ""
},
{
"docid": "4b923c16ec9cd7da4d632e16c87d1ef6",
"score": "0.7343847",
"text": "def get_col_names(cursor, tablename):\n reader=cursor.execute(\"SELECT * FROM {}\".format(tablename))\n return [x[0] for x in reader.description]",
"title": ""
},
{
"docid": "c7a33a4a40a907665d1c6dc65803a2f1",
"score": "0.7335129",
"text": "def columns(self, table):\n if table in _column_name_cache:\n return _column_name_cache[table]\n else:\n inspector = inspect(self.engine)\n columns = inspector.get_columns(table)\n column_names = [c['name'] for c in columns]\n _column_name_cache[table] = column_names\n return column_names",
"title": ""
},
{
"docid": "a7647ef47b5c9d44538f551855a85792",
"score": "0.7303142",
"text": "def columns(self):\n return self._data.columns",
"title": ""
},
{
"docid": "52930a92c7e2809b9e6c18989b9e76a2",
"score": "0.72757584",
"text": "def get_column_names(self, table):\n rows = self('describe %s' % table)\n return tuple(rec[0].lower() for rec in rows)",
"title": ""
},
{
"docid": "bdd251ec4a2917ac6295847e86f23598",
"score": "0.72723573",
"text": "def column_names(self) -> ColumnNameCollection:\n x_colname = self.get_example_avp_for_axis('x').attr.col_name\n y_colname = self.get_example_avp_for_axis('y').attr.col_name\n color_colname = '_color'\n size_colname = '_size'\n\n col_names = ColumnNameCollection(x_colname, y_colname, color_colname, size_colname)\n return col_names",
"title": ""
},
{
"docid": "eeb4aa3fc12e1806d94102e97925127d",
"score": "0.7270576",
"text": "def columns(self) -> List[Column]:\n return self._columns",
"title": ""
},
{
"docid": "09c1ed0002f33903fed51bf4a46a37fd",
"score": "0.7240626",
"text": "def columns(self):\n return self._columns",
"title": ""
},
{
"docid": "09c1ed0002f33903fed51bf4a46a37fd",
"score": "0.7240626",
"text": "def columns(self):\n return self._columns",
"title": ""
},
{
"docid": "09c1ed0002f33903fed51bf4a46a37fd",
"score": "0.7240626",
"text": "def columns(self):\n return self._columns",
"title": ""
},
{
"docid": "09c1ed0002f33903fed51bf4a46a37fd",
"score": "0.7240626",
"text": "def columns(self):\n return self._columns",
"title": ""
},
{
"docid": "09c1ed0002f33903fed51bf4a46a37fd",
"score": "0.7240626",
"text": "def columns(self):\n return self._columns",
"title": ""
},
{
"docid": "450e3e73051270d5cf4f9d75e48fe674",
"score": "0.72351986",
"text": "def columns(self) -> Optional[Sequence[str]]:\n return pulumi.get(self, \"columns\")",
"title": ""
},
{
"docid": "a6b76c4370b918f77a54ac52eb25e27c",
"score": "0.7217807",
"text": "def parameterColumnNames(self):\n return [x.getName() for x in self.parameters]",
"title": ""
},
{
"docid": "e38b61a7cb2dbbe8656b01e23b87b009",
"score": "0.7213525",
"text": "def columns(self):\n return list(self._data)",
"title": ""
},
{
"docid": "a352aec0af67571888e7257cd86b942e",
"score": "0.7187782",
"text": "def columns(self):\n return self.query.columns",
"title": ""
},
{
"docid": "512fc0e8b6043cba949de98fc095f638",
"score": "0.7185209",
"text": "def get_column_names(cls, filename):\n with open(filename, 'r') as file_ref:\n return file_ref.readline().strip().split('|')",
"title": ""
},
{
"docid": "793c42354f3816e8fe35c56414b3ab11",
"score": "0.7167576",
"text": "def features_names(self): \n return self.model[\"col_names\"]",
"title": ""
},
{
"docid": "e6d092e6d7757b7fc7525f55f18ebc17",
"score": "0.71191436",
"text": "def read_column_names(self, table_name, where=None):\n\n db_cur = self.db_conn.cursor(dictionary=True)\n\n sql = \"SHOW COLUMNS FROM %s\" % table_name\n if where:\n sql += \" LIKE '%s'\" % where\n\n db_cur.execute(sql)\n cols = [(dic[\"Field\"], dic[\"Type\"]) for dic in db_cur]\n return cols",
"title": ""
},
{
"docid": "588b28625f8969e18fd4167df826a4c1",
"score": "0.7114303",
"text": "def columns(self, fields=None):\r\n\r\n if fields is None:\r\n return self.statement.columns\r\n\r\n cols = [self.statement.c[str(field)] for field in fields]\r\n\r\n return cols",
"title": ""
},
{
"docid": "092cbf2343b987909af5c40009ece11f",
"score": "0.7089987",
"text": "def return_column_headers(results):\n\t\n\tnames = []\n\theaders = results.get('columnHeaders')\n\t\n\tfor header in headers:\n\t\t# Print Dimension or Metric name.\n\t\tnames.append(header.get('name').lstrip('ga:'))\n\n\treturn names",
"title": ""
},
{
"docid": "56e63e33b38bcdc40b3627deb2bafaa0",
"score": "0.7086658",
"text": "def updatable_column_names(self):\n return tuple(col.name for col in self.updatable_columns)",
"title": ""
},
{
"docid": "5a25df4d2ed6843cbeb93a3ae819bdf0",
"score": "0.70847386",
"text": "def numerical_columns(self):\n return self.get_numerical().columns.to_list()",
"title": ""
},
{
"docid": "802bf2a0b6a412e02e0c59ac002c7c8a",
"score": "0.706824",
"text": "def get_table_column_names(self, table):\n column_items = {}\n try:\n self.connect()\n self.execute( \"SHOW COLUMNS FROM \" + table )\n column_list = [field_c for (field_c, _, _, _, _, _) in self.__cursor.fetchall()]\n idx = 0\n for column in column_list:\n column_items[column] = idx\n idx = idx + 1\n self.close()\n except Exception:\n pass\n return column_items",
"title": ""
},
{
"docid": "3f3d513ea414d8b01e5c8e8b2d8d95e8",
"score": "0.7061389",
"text": "def keys(self):\n return self._columns.keys()",
"title": ""
},
{
"docid": "14e26b453f19221341601e46e414d03e",
"score": "0.7039588",
"text": "def names():\n\n # Use Pandas to perform the sql query\n stmt = db.session.query(Metorite_Landings).statement\n df = pd.read_sql_query(stmt, db.session.bind)\n\n # Return a list of the column names (sample names)\n return jsonify(list(df.columns)[2:])",
"title": ""
},
{
"docid": "2087a97824cdffb23ca026d2b6750386",
"score": "0.70349705",
"text": "def return_cols(path):\n data = pd.read_csv(path)\n return list(data.columns)",
"title": ""
},
{
"docid": "671a2dbfd026a9ae6b07e4d900dd3997",
"score": "0.70264345",
"text": "def col_names():\n return [['region', 'country', 'year', 'age', 'y', 'se'] + ['x%d'%i for i in range(5)] + ['w%d'%i for i in range(5)]]",
"title": ""
},
{
"docid": "32954c9a4f2cd503bffccfd0a7f722df",
"score": "0.70225066",
"text": "def get_columns(table):\n\n columns = []\n\n sql = \"SHOW COLUMNS FROM mydb.{}\".format(table)\n cursor.execute(sql)\n for row in cursor.fetchall():\n if row[0] != \"id\":\n columns.append(row[0])\n\n return \",\".join(columns)",
"title": ""
},
{
"docid": "6b9941d5004666adf3c5d0e50efdc181",
"score": "0.7009326",
"text": "def columnsOf(self, table):\n column_list = [row[1] for row in self.genSchemaOf(table)]\n return column_list",
"title": ""
},
{
"docid": "abbdadcc7e37408ee228108f07720195",
"score": "0.69948614",
"text": "def columns(self, fields=None):\r\n\r\n if fields is None:\r\n return self.table.columns\r\n\r\n cols = [self.table.c[str(field)] for field in fields]\r\n\r\n return cols",
"title": ""
},
{
"docid": "44e008a677743bf4b988fb9ffb326752",
"score": "0.69927174",
"text": "def list_columns(cls, detail: bool = True) -> List[str]:\n cols = []\n for f in fields(cls):\n if detail:\n cols.append(f.name)\n elif not f.metadata.get(\"detail\", False):\n cols.append(f.name)\n\n return cols",
"title": ""
},
{
"docid": "34b2839b9e1798af22f68080b6fc259d",
"score": "0.69633514",
"text": "def columns(self, requested):\n result = self.human_names\n if requested:\n result = [header for header in requested if header in self.human_names]\n\n # remove headers that we don't have a query path for\n result = [header for header in result if header in self.query_paths]\n\n return result",
"title": ""
},
{
"docid": "504727c84295c7c5ab1d6692737ea5a8",
"score": "0.69632965",
"text": "def columns(self):\n yield from self.table.columns",
"title": ""
},
{
"docid": "8e5705263238eedcd0d58264c0e2826f",
"score": "0.6949273",
"text": "def columns(self, table):\n self.cur.execute(\"PRAGMA table_info('\" + table + \"')\")\n return [x[:3] for x in self.cur.fetchall()]",
"title": ""
},
{
"docid": "a32efa15c7e0fd2741e378fb1bf1f5bf",
"score": "0.6941032",
"text": "def columns(self):\n print(\"\\n\".join(list(self.COLUMN_INDEX.keys())))",
"title": ""
},
{
"docid": "2aa542a368d596cd7980a21384ffd872",
"score": "0.69325286",
"text": "def bayesdb_table_column_names(bdb, table):\n bayesdb_table_guarantee_columns(bdb, table)\n sql = '''\n SELECT name FROM bayesdb_column WHERE tabname = ?\n ORDER BY colno ASC\n '''\n # str because column names can't contain Unicode in sqlite3.\n return [str(row[0]) for row in bdb.sql_execute(sql, (table,))]",
"title": ""
},
{
"docid": "147f89e1c5de514d850722ad77f29948",
"score": "0.6914392",
"text": "def getNumberColNames(self):\n\n colNames = []\n for col in range(self.columnCount()):\n headerItem = self.horizontalHeaderItem(col)\n colName = headerItem.text()\n if self._isNonNbrColName(colName): continue\n\n colNames.append(colName)\n\n return colNames if colNames else None",
"title": ""
},
{
"docid": "007b96a89f53cfdbaf7440efdb899684",
"score": "0.6900708",
"text": "def items(self):\n return [\n (c.name, getattr(self, c.name, None))\n for c in self.__table__._columns\n ]",
"title": ""
},
{
"docid": "7b82549cc0ac71ff35d822757050fc38",
"score": "0.6879915",
"text": "def fieldnames(self):\n return [f.name for f in self.fields]",
"title": ""
},
{
"docid": "5de50b75883508e62b627d568f2bda6f",
"score": "0.6876541",
"text": "def base_columns(cls) -> Set[str]:\n return set(cls.list_columns(detail=False))",
"title": ""
},
{
"docid": "b01b5315d41dcd24f11f134676d968b2",
"score": "0.6872736",
"text": "def columns(self):\n return [\n prop.key\n for prop in class_mapper(self.__class__).iterate_properties\n if isinstance(prop, ColumnProperty)\n ]",
"title": ""
},
{
"docid": "a2662c119ece3abbd4cf253851ef295f",
"score": "0.6869801",
"text": "def get_columns(table):\n mydb = pymysql.connect(host, username, password, lahmandb)\n cursor = mydb.cursor()\n statement = \"SHOW columns FROM %s\" % (table)\n logger.debug(statement) # nb, test line\n cursor.execute(statement)\n query_results = cursor.fetchall()\n cursor.close()\n columns = [elem[0] for elem in query_results]\n return columns",
"title": ""
},
{
"docid": "331f0548d84f64f0cc501a85f46227e8",
"score": "0.6851447",
"text": "def get_columns(self, table, db=\"default\"):\n columns = []\n try:\n return self.get(\"ddl/database/%s/table/%s/column\" % (db, table))['columns']\n except Exception as ex:\n error = \"\"\"HCatClient: error on getting a column list: %s\"\"\" % unicode(ex)\n LOG.exception(error)\n raise Exception(error)\n return columns",
"title": ""
},
{
"docid": "8e70b0da32a107c874ec61006e3a726c",
"score": "0.6843214",
"text": "def get_columns(data):\n columns = set()\n\n def _seen(col):\n columns.add(str(col))\n\n map(lambda item: map(_seen, item.keys()), data)\n return list(columns)",
"title": ""
},
{
"docid": "8976fae45924ad8ee315295d403fcc0b",
"score": "0.68428284",
"text": "def categorical_columns(self):\n return self.get_categorical().columns.to_list()",
"title": ""
},
{
"docid": "023fe428bd2e51aa324e3b36a95543c4",
"score": "0.68363476",
"text": "def get_column_names(\n self, include_controller_name=False, include_model_name=False\n ):\n if include_model_name:\n self.column_names.insert(0, \"Model\")\n if include_controller_name:\n self.column_names.insert(0, \"Controller\")\n return self.column_names",
"title": ""
},
{
"docid": "278198a6124e5eb571dbe812b30b4c00",
"score": "0.6832846",
"text": "def get_names():\n return COLORS.keys()",
"title": ""
},
{
"docid": "7999afdb399b6d06ca8b08eb813061bf",
"score": "0.6830823",
"text": "def columns(self) -> Sequence['outputs.StandardSqlFieldResponse']:\n return pulumi.get(self, \"columns\")",
"title": ""
},
{
"docid": "16013ade442e7f5688030835a33a0a9c",
"score": "0.6820248",
"text": "def listTableColumns(self, table_name):\n raise NotImplementedError(\"%s does not implement listTableColumns\" % (self.driver_name))",
"title": ""
},
{
"docid": "f6efed65b87e17645d96636e4dce5ecf",
"score": "0.6802586",
"text": "def show_columns(self, name_of_table):\n b = self.c.execute(f\"SELECT * FROM {name_of_table}\")\n return b.description",
"title": ""
},
{
"docid": "227cdfef8bc870cc18bb88da8a1fcb70",
"score": "0.6794821",
"text": "def columns(self):\n\n columns = [list(df.columns) for df in self.values()]\n if not all([c == columns[0] for c in columns[1:]]):\n print(\"Columns differ between dataframes:\")\n for k, df in self:\n print(k, df.columns)\n\n raise Exception()\n\n if columns:\n return columns[0]\n else:\n return []",
"title": ""
},
{
"docid": "8e6b321b6db46f18abd3ea550afcf116",
"score": "0.67921",
"text": "def columns(self) -> pd.core.indexes.multi.MultiIndex:\n return self.df.columns",
"title": ""
},
{
"docid": "fdb42f60c2e3ad9c4059ea54bba44403",
"score": "0.67911494",
"text": "def bayesdb_generator_column_names(bdb, generator_id):\n sql = '''\n SELECT c.name\n FROM bayesdb_column AS c,\n bayesdb_generator AS g,\n bayesdb_generator_column AS gc\n WHERE g.id = ?\n AND gc.generator_id = g.id\n AND c.tabname = g.tabname\n AND c.colno = gc.colno\n ORDER BY c.colno ASC\n '''\n # str because column names can't contain Unicode in sqlite3.\n return [str(row[0]) for row in bdb.sql_execute(sql, (generator_id,))]",
"title": ""
},
{
"docid": "0fe0639a3cf815d792daf730368f60de",
"score": "0.67880607",
"text": "def columns(self):\n columns = getattr(self.featurizer, \"columns\", None)\n cols_to_keep = getattr(self, \"cols_to_keep\", None)\n if columns is not None and cols_to_keep is not None and len(cols_to_keep) > 0:\n columns = [columns[i] for i in cols_to_keep]\n return columns",
"title": ""
},
{
"docid": "3091a5ceb036fa47bc883eb8aaf14262",
"score": "0.67789644",
"text": "def get_raw_columns():",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="759"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">e83d260d7944ed2df03968de04210f5e</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Selectionne toute les equipes</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "ed0a094b03dc336c2dbcf9cba7d0d249",
"score": "0.0",
"text": "def nom_equipes(id_equipe):\n c=conn.cursor()\n p = (id_equipe, )\n c.execute(\"\"\"SELECT nom FROM TEAM WHERE id = ?\"\"\", p)\n r=c.fetchall()\n return r[0][0]",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "fc04a92527757372ee40f2afea1f1ae0",
"score": "0.5983597",
"text": "def select( self ):",
"title": ""
},
{
"docid": "50cf8f958d662ede8e29dba741222c64",
"score": "0.58359784",
"text": "def GetSelectedScalarComponents(self):\n ...",
"title": ""
},
{
"docid": "b6fd5b2f53d7d9c1a91df2bc1d5c8e6c",
"score": "0.5816225",
"text": "def select_points(self):",
"title": ""
},
{
"docid": "5e097f7bfc1f06e490193e5108c0cbb3",
"score": "0.5782605",
"text": "def commnad(self):",
"title": ""
},
{
"docid": "79e9fd575701a2ba30dce32e8e551898",
"score": "0.57475555",
"text": "def multipleChoiceInput():",
"title": ""
},
{
"docid": "f619aaac6978c6471d966c568a71f1e5",
"score": "0.57119596",
"text": "def cb_allSelected(index, fieldValues):\n inclEnabled = not int(fieldValues[index]) and ael_variables[index][9] != 0\n excludedEnabled = not inclEnabled\n\n includedIndex = index + 1\n excludedIndex = includedIndex + 1\n\n fieldValues = setVarValue(includedIndex, fieldValues, inclEnabled, '')\n fieldValues = setVarValue(excludedIndex, fieldValues, excludedEnabled, '')\n return fieldValues",
"title": ""
},
{
"docid": "02e7e79ff9fd05316af9bfc599f30c8d",
"score": "0.5700119",
"text": "def food_select(self):\n pass",
"title": ""
},
{
"docid": "f943f4313d5760514360b82212679856",
"score": "0.5633466",
"text": "def pick(self):",
"title": ""
},
{
"docid": "3c8eced34d730a65a5ba0cdc543394b4",
"score": "0.56324244",
"text": "def select_object_multiple(self, letter):\n\t\tif(self.on_main_screen):\n\t\t\treturn # TEMP. Currently there is no case for multiple selections on main game screen.\n\t\tif(letter in self.control_map):\n\t\t\tif self.active_quantity:\n\t\t\t\tself.select_list.toggle(letter, int(self.active_quantity))\n\t\t\telse:\n\t\t\t\tself.select_list.toggle(letter)\n\t\t\tself.active_quantity = None",
"title": ""
},
{
"docid": "995024e6c6d605459af479b48524350c",
"score": "0.56283647",
"text": "def Selecionar(self, event):\r\n EstruturasSelecionadas = {}\r\n EstruturasSelecionadas[\"PVs\"] = []\r\n EstruturasSelecionadas[\"TRECHOS\"] = []\r\n \r\n itensSelecionados = [x.GetText() for x in self.tctrl_ArvoreSelecaoElementos.GetSelections()]\r\n if \"Grupos\" in itensSelecionados:\r\n pass\r\n elif any((item == \"PVs\" or item == \"TRECHOS\") for item in itensSelecionados):\r\n #Adiciona todo os Nos\r\n if any(item == \"PVs\" for item in itensSelecionados):\r\n EstruturasSelecionadas[\"PVs\"] = self.parent.Estrututura.Dic_Lista_Pvs.keys() \r\n #Adiciona todas as barras\r\n if any(item == \"TRECHOS\" for item in itensSelecionados):\r\n EstruturasSelecionadas[\"TRECHOS\"] = self.parent.Estrututura.Dic_Lista_Tubulacoes.keys()\r\n EstruturasSelecionadas[\"TRECHOS\"] = map(lambda x:x, EstruturasSelecionadas[\"TRECHOS\"])\r\n elif any((item.split(\" \")[0] ==u\"PV\" or item.split(\" \")[0] ==u\"Barra\") for item in itensSelecionados):\r\n #Adiciona os Nos selecionados\r\n nos = [int(x.split(\" \")[1]) for x in itensSelecionados if x.split(\" \")[0] == u\"PV\"]\r\n #Adiciona as barras selecionadas\r\n barras = [int(x.split(\" \")[1]) for x in itensSelecionados if x.split(\" \")[0] == u\"Barra\"]\r\n barras = map(lambda x:x, barras)\r\n \r\n EstruturasSelecionadas[\"PVs\"] = nos\r\n EstruturasSelecionadas[\"TRECHOS\"] = barras \r\n \r\n #Chama a Funcao SelecionaelementosNaTreeCtrl no \"model\" para fazer a\r\n #selecao do elementos selecionados na TreeCtrl\r\n self.parent.SelecionaelementosNaTreeCtrl(EstruturasSelecionadas)",
"title": ""
},
{
"docid": "c1c72988309a77ece45bf28bfe51c384",
"score": "0.5622479",
"text": "def changeSelection(self):",
"title": ""
},
{
"docid": "60e878d2f2df20ac983c041bcac5ba7c",
"score": "0.5596325",
"text": "def Desenselecionar(self, evt):\r\n \r\n EstruturasSelecionadas = {}\r\n EstruturasSelecionadas[\"PVs\"] = []\r\n EstruturasSelecionadas[\"TRECHOS\"] = []\r\n \r\n itensSelecionados = [x.GetText() for x in self.tctrl_ArvoreSelecaoElementos.GetSelections()]\r\n if \"Grupos\" in itensSelecionados:\r\n pass\r\n elif any((item == \"PVs\" or item == \"TRECHOS\") for item in itensSelecionados):\r\n #Adiciona todo os Nos\r\n if any(item == \"PVs\" for item in itensSelecionados):\r\n EstruturasSelecionadas[\"PVs\"] = self.parent.Estrututura.Dic_Lista_Pvs.keys()\r\n #Adiciona todas as barras\r\n if any(item == \"TRECHOS\" for item in itensSelecionados):\r\n EstruturasSelecionadas[\"TRECHOS\"] = self.parent.Estrututura.Dic_Lista_Tubulacoes.keys()\r\n EstruturasSelecionadas[\"TRECHOS\"] = map(lambda x:x, EstruturasSelecionadas[\"TRECHOS\"])\r\n elif any((item.split(\" \")[0] ==u\"PV\" or item.split(\" \")[0] ==u\"Barra\") for item in itensSelecionados):\r\n #Adiciona os Nos selecionados\r\n nos = [int(x.split(\" \")[1]) for x in itensSelecionados if x.split(\" \")[0] == u\"PV\"]\r\n #Adiciona as barras selecionadas\r\n barras = [int(x.split(\" \")[1]) for x in itensSelecionados if x.split(\" \")[0] == u\"Barra\"]\r\n barras = map(lambda x:x, barras)\r\n \r\n EstruturasSelecionadas[\"PVs\"] = nos\r\n EstruturasSelecionadas[\"TRECHOS\"] = barras\r\n \r\n self.parent.DeselecionarElementosNaTreeCtrl(EstruturasSelecionadas)",
"title": ""
},
{
"docid": "d23ced434e2abfeaf05831bccbaa8013",
"score": "0.5578399",
"text": "def selectTerritoireEntite(self, tuile):\n self.deselect()\n entite = tuile.getEntite()\n if entite[0].canMoove():\n Q = Queue()\n Q.put(tuile)\n i = 0\n closed = list()\n closed.append(tuile)\n while not(Q.empty()) and i < entite[0].pa*(2*(entite[0].pa + 1)):\n n = Q.get()\n territoireVoisin, nbVoisin = self.getVoisinComptage(n)\n i += nbVoisin\n for iVoisin in territoireVoisin:\n if not(iVoisin in closed):\n Q.put(iVoisin)\n self.selectedTuile.append(iVoisin)\n closed.append(iVoisin)\n #i += 1\n else:\n i-=1\n self.selectionType = \"Entite\"\n self.selectTuile(self.selectedTuile, \"case selection entite.gif\")\n self.selectionType = \"Entite\"",
"title": ""
},
{
"docid": "a61f74e4a09b543d5517348998aeccad",
"score": "0.5573074",
"text": "def get_selection(self):",
"title": ""
},
{
"docid": "48492f26c4518b577c22727c87516ddb",
"score": "0.55728143",
"text": "def _update_selections(self):\n self._s1 = self.u.selectAtoms(self.selection1)\n self._s2 = self.u.selectAtoms(self.selection2) \n if self.selection1_type in ('donor', 'both'):\n # selection 1 is donor\n # get all donor atoms within selection 1\n self._s1_donor_atoms = self._s1.selectAtoms(' or '.join([ 'name %s' % i for i in self.donors ]))\n # get all acceptor atoms within the selection 2\n self._s2_acceptor_atoms = self._s2.selectAtoms(' or '.join([ 'name %s' % i for i in self.acceptors ]))\n else:\n self._s1_donor_atoms = []\n \n if self.selection1_type in ('acceptor', 'both'):\n # selection 1 is acceptor\n # get all acceptor atoms within selection 1\n self._s1_acceptor_atoms = self._s1.selectAtoms(' or '.join([ 'name %s' % i for i in self.acceptors ]))\n # get all donor atoms within selection 2\n self._s2_donor_atoms = self._s2.selectAtoms(' or '.join([ 'name %s' % i for i in self.donors ]))\n else:\n self._s1_acceptor_atoms = []",
"title": ""
},
{
"docid": "fa7ae53a565671558c8ddd4eae1cf43b",
"score": "0.54850674",
"text": "def clicValeur(self, bouton, etat):\n #On ne garde que la première ligne de texte pour les boutons de la forme :\n #blah blah blah\\r\\n\n #valeur du bouton\n bouton=bouton.split(\"\\n\")[0].strip()\n \n #Quand on modifie des valeurs int ou float, on utilise des boutons +/-, attrape les infos de boutons\n #Elles sont passées sous la forme plus-nom_du_bouton et moins-nom_du_bouton\n modificateur = None\n if bouton.startswith(\"plus-\"):\n modificateur = +1\n bouton=bouton[5:]\n elif bouton.startswith(\"moins-\"):\n modificateur = -1\n bouton=bouton[6:]\n elif bouton.startswith(\"progr-\"):\n modificateur = 0\n bouton=bouton[6:]\n \n #On recherche dans la liste des boutons de quel bouton il sagit\n for nomSection, contenuSection, dicoSection in self.menu:\n if self.select == dicoSection[\"nom\"].lower().strip():\n for nomElement, contenuElement in contenuSection:\n if contenuElement[\"nom\"].lower().split(\"\\n\")[0].strip()==bouton.lower():\n #On a trouvé le bouton, on extrait le chemin de configuration qui lui correspond\n sect, soussect, clef = contenuElement[\"chemin\"].split(\"/\")\n \n #Les float varient entre valeurMin et valeurMax par pas de 1/20 de l'espace à parcourir\n if contenuElement[\"type\"] == \"float\":\n delta = (float(contenuElement[\"valeurmax\"])-float(contenuElement[\"valeurmin\"]))/100\n if modificateur!=0:\n nvVal = float(contenuElement[\"valeur\"])+delta*modificateur\n else:\n nvVal = float(etat)/100*(float(contenuElement[\"valeurmax\"])-float(contenuElement[\"valeurmin\"]))+float(contenuElement[\"valeurmin\"])\n if nvVal<float(contenuElement[\"valeurmin\"]):\n nvVal = float(contenuElement[\"valeurmin\"])\n if nvVal>float(contenuElement[\"valeurmax\"]):\n nvVal = float(contenuElement[\"valeurmax\"])\n #Met à jour la configuration\n general.configuration.setConfiguration(sect, soussect, clef, nvVal)\n \n #Les int varient entre valeurMin et valeurMax par pas de 1\n elif contenuElement[\"type\"] == \"int\":\n if modificateur!=0:\n nvVal = int(float(contenuElement[\"valeur\"]))+modificateur\n else:\n nvVal = int(float(etat)/100*(float(contenuElement[\"valeurmax\"])-float(contenuElement[\"valeurmin\"]))+float(contenuElement[\"valeurmin\"])+0.5)\n \n if nvVal<int(contenuElement[\"valeurmin\"]):\n nvVal = int(contenuElement[\"valeurmin\"])\n if nvVal>int(contenuElement[\"valeurmax\"]):\n nvVal = int(contenuElement[\"valeurmax\"])\n #Met à jour la configuration\n general.configuration.setConfiguration(sect, soussect, clef, nvVal)\n \n #Pour les listes, à chaque clic on passe à l'élément suivant de façon circulaire\n #Si l'élément courant n'est pas dans la liste (modification manuelle du fichier de config)\n #On commence avec l'élément 0\n elif contenuElement[\"type\"] == \"liste\":\n if contenuElement[\"valeur\"] in contenuElement[\"valeurs\"]:\n idx = contenuElement[\"valeurs\"].index(contenuElement[\"valeur\"])\n else:\n idx=-1 #Element pas dans la liste, on commence à l'indice 0 (-1 +1)\n print contenuElement[\"valeur\"],\"n'est pas dans\",contenuElement[\"valeurs\"]\n idx+=1\n if idx>=len(contenuElement[\"valeurs\"]):\n idx-=len(contenuElement[\"valeurs\"])\n #Met à jour la configuration\n general.configuration.setConfiguration(sect, soussect, clef, contenuElement[\"valeurs\"][idx])\n \n #Pour les booléens, on inverse leur valeur\n elif contenuElement[\"type\"] == \"bool\":\n #Met à jour la configuration\n general.configuration.setConfiguration(sect, soussect, clef, str(not contenuElement[\"valeur\"])[0])\n \n #On sauvegarde les changements\n general.configuration.sauve(os.path.join(\".\",\"configuration\",\"utilisateur.cfg\"))\n #On recharge le menu (pour avoir les nouvelles variables de config à jour)\n self.menu = general.configuration.chargeMenu(self.nomMenu)\n #On reconstruit le menu sans l'animer\n self.changeMenu(self.select)\n return\n print \"Pas trouvé\", bouton.lower()",
"title": ""
},
{
"docid": "8fc80459f18807506ba16b8ad467a40c",
"score": "0.5454228",
"text": "def extraeListaValores(self):\r\n\r\n try:\r\n #hay que descubrir como acceder a la lista de nombres de los\r\n #campos de una tabla para quitar esta lista fija de codigo\r\n listaCampos=[\"gid\", \"id_trabajo\", \"gid_linde\", \"nom_arch\"]\r\n listaValores=self.oUtiles.oUtilidadesQgs.get_attrSeleccionCapa(str(self.layer.name()),listaCampos,True)\r\n self.ok=True\r\n return listaValores\r\n except Exception, e:\r\n self.ok = False\r\n QtGui.QMessageBox.information(self,\"Error\", e.message)",
"title": ""
},
{
"docid": "6401562873070339b003e16c285a925b",
"score": "0.5421128",
"text": "def __selecciones(self, treeselection,\n model, path, is_selected, listore):\n if not self.permitir_select:\n return True\n\n # model y listore son ==\n _iter = model.get_iter(path)\n valor = model.get_value(_iter, 2)\n\n if not is_selected and self.valor_select != valor:\n self.scroll_to_cell(model.get_path(_iter))\n self.valor_select = valor\n self.emit('nueva-seleccion', self.valor_select)\n return True",
"title": ""
},
{
"docid": "9d6aaa9c7409f55636b2cd1b0da8ed3d",
"score": "0.5414072",
"text": "def selection(dic,na,sl,of,*pargs):\n na.get()\n sl.set(float(dic[na.get()][0]))\n of.set(float(dic[na.get()][1]))",
"title": ""
},
{
"docid": "5288d66f772dd723bd3be8950b2147da",
"score": "0.5402591",
"text": "def is_selecting(self):",
"title": ""
},
{
"docid": "a006cf67bd1904301be0abedc66cef07",
"score": "0.5374663",
"text": "def only_choice(self, values):\n\t\tfor unit in self.unit_list:\n\t\t\tfor digit in self.digits:\n\t\t\t\tdplaces = [box for box in unit if digit in values[box]]\n\t\t\t\tif len(dplaces) == 1:\n\t\t\t\t\tvalues[dplaces[0]] = digit\n\t\treturn values",
"title": ""
},
{
"docid": "cb418378cd00e200632006c26cdbd156",
"score": "0.53712076",
"text": "def __selecciones(self, treeselection,\n model, path, is_selected, listore):\n\n if not self.permitir_select:\n return True\n\n # model y listore son ==\n _iter = model.get_iter(path)\n valor = model.get_value(_iter, 2)\n\n if not is_selected and self.valor_select != valor:\n self.scroll_to_cell(model.get_path(_iter))\n self.valor_select = valor\n self.emit('nueva-seleccion', self.valor_select)\n\n return True",
"title": ""
},
{
"docid": "a038ba68d2faef0d173f425e62d86f26",
"score": "0.53655124",
"text": "def select(*ids):",
"title": ""
},
{
"docid": "3d254e1c2d8c1991dc13d5e90b5db5c4",
"score": "0.5363296",
"text": "def update_select_eq_elements(self, action):\r\n # selected (x,y,r) initialization\r\n # print('self.eq_matrix', self.eq_matrix)\r\n # print('action', action)\r\n # print('self.eq_matrix[action,[0,1]]', self.eq_matrix[:,[0,1]])\r\n # print(' np.ones([len(action), 2])) ', np.ones([len(action), 2]))\r\n self.eq_matrix[action,:] = cu.generate_xy_elements(len(action))\r\n self.eq_matrix[action,2] = cu.generate_r_elements(len(action),small_start=True)\r\n\r\n return None",
"title": ""
},
{
"docid": "d5544640075c70091f8259ab3f4e3428",
"score": "0.53577423",
"text": "def _add_all_elements_to_selection(self):\n self._enable_plot_updates = False\n for n_row in range(self.table.rowCount()):\n item = self.table.item(n_row, 0)\n if re.search(\"[A-Z][A-Za-z]*_[KLM]\", item.text()):\n item.setCheckState(Qt.Checked)\n self._enable_plot_updates = True\n self._update_map_selections_auto()",
"title": ""
},
{
"docid": "e5b0334149b3310e0ee31819472ba9c2",
"score": "0.53349435",
"text": "def visible_settings(self):\n self.operand_choice.text = self.operand_choice_text()\n self.operand_objects.text = self.operand_objects_text()\n self.operand_measurement.text = self.operand_measurement_text()\n result = [self.operand_choice]\n result += (\n [self.operand_objects] if self.operand_choice == MC_OBJECT else []\n )\n result += [self.operand_measurement, self.multiplicand, self.exponent]\n return result",
"title": ""
},
{
"docid": "5b80e23f9e144ee605e6e0e4bd0947aa",
"score": "0.5320655",
"text": "def select(self):\n for pchain in self.ProteinList:\n pchain.select()\n for nchain in self.NucleotideChainList:\n nchain.select()\n for lig in self.LigandList:\n lig.select()\n for wat in self.WaterList:\n wat.select()",
"title": ""
},
{
"docid": "290b71683621d910f6b7585cbcbb8bfc",
"score": "0.5319533",
"text": "def test_multiselect_option_without_explicit_value():",
"title": ""
},
{
"docid": "8422ee37d2e1fb0dcf767f2144965eee",
"score": "0.5280031",
"text": "def select_all(self):\n self.master.abs_int.set(1)\n self.master.rel_int.set(1)\n self.master.gauss_int.set(1)\n self.master.bck_sub.set(1)\n self.master.bck_noise.set(1)\n self.master.peak_qual.set(1)",
"title": ""
},
{
"docid": "c7a0eca571510dfc791911e2ef4cebdc",
"score": "0.526407",
"text": "def get_select_fields():",
"title": ""
},
{
"docid": "c7a0eca571510dfc791911e2ef4cebdc",
"score": "0.526407",
"text": "def get_select_fields():",
"title": ""
},
{
"docid": "5b7295504b3fee552dc8060eebc10694",
"score": "0.52561975",
"text": "def selection_set(self, items):\r\n self.selection(\"set\", items)",
"title": ""
},
{
"docid": "4156e13a3d351a482804dd13f74af04c",
"score": "0.5243734",
"text": "def determiner_valeur_cartes(joueur, cartes_jouables, manche, premiere_carte, donnees): # Voir les rappels /!\\\r\n if manche > 5: # Au cas ou on joue plus de 5 manches\r\n manche = 5\r\n valeur_cartes_avec_risque = [] # On cree la liste des valeurs\r\n code_position = \"P\" if not premiere_carte else \"S\" # On determine le code position /!\\ (Var = a if c else b)(Python) <=> (Var = c ? a : b)(C,C#...) /!\\\r\n code_premiere_carte = \"\" if not premiere_carte else premiere_carte.id # On determine le code de la premiere carte, pas de code si on joue en premier\r\n for carte in cartes_jouables: # Pour toutes les cartes passees a la fonction\r\n code_joueur = carte.id + code_position + str(\r\n manche) + \"J\" + code_premiere_carte # On genere le code permettant d'acceder aux points gagnes par le joueur de cette carte\r\n code_autres = carte.id + code_position + str(\r\n manche) + \"A\" + code_premiere_carte # Et celui pour les points gagnes par les autres joueurs\r\n points_joueur = donnees[\r\n code_joueur] * joueur.risque if code_joueur in donnees else 1 # Comme pour la ligne suivante, sauf qu'on ignore une partie de ces points en fonction du pourcentage de risque associe au joueur ([0%..90%])\r\n points_autres = donnees[\r\n code_autres] if code_autres in donnees else 1 # On recupere les points (comme on a supprime les valeur nulle pour gagner de la place, on assigne 1 si la situation n'est pas enregistree dans les donnees)\r\n valeur_cartes_avec_risque.append(\r\n points_autres / points_joueur) # Et on determine la valeur d'une carte qu'on ajoute a la liste\r\n return valeur_cartes_avec_risque # On revoie la liste\r",
"title": ""
},
{
"docid": "e779f7f5c394f52c74cc80a49d029747",
"score": "0.52185494",
"text": "def jouer(self,plateau,fenetre):\n self.presenterPionsStables(plateau,fenetre)\n choix=self.jouerAleatoire(plateau)\n self.choix=choix\n return choix",
"title": ""
},
{
"docid": "b160cac21b0054a13287c08742f4b686",
"score": "0.5211648",
"text": "def handleOtherSelectCheckBox(self, event):\n if event.source.selected:\n otherVulnsToTest[event.source.text] = True\n else:\n otherVulnsToTest[event.source.text] = False",
"title": ""
},
{
"docid": "335caea60764da5a0c700f2330f80c02",
"score": "0.52102137",
"text": "def GetSelectedVectorComponents(self, p_int):\n ...",
"title": ""
},
{
"docid": "cfebbb0ea462a46c200143f8ebd2611b",
"score": "0.51999044",
"text": "def selection2(na,*pargs):\n na.get()",
"title": ""
},
{
"docid": "e54b87e49ed5bbcb1dc2276ee0e7db89",
"score": "0.5191834",
"text": "def ObjectsSelection(self,listeObjects,typeSel=\"new\"): \n# dic={\"add\":c4d.SELECTION_ADD,\"new\":c4d.SELECTION_NEW}\n sc = self.getCurrentScene()\n #Put here the code to add/set an object to the current slection\n #[sc.SetSelection(x,dic[typeSel]) for x in listeObjects]",
"title": ""
},
{
"docid": "cc7460000d1bc02380694a92cb4d347a",
"score": "0.5189311",
"text": "def only_choice(self, values):\n for unit in self.unitlist:\n for digit in '123456789':\n dplaces = [box for box in unit if digit in values[box]]\n if len(dplaces) == 1:\n values[dplaces[0]] = digit\n return values",
"title": ""
},
{
"docid": "94d8b463d7fab1d57953fcbae4909ed6",
"score": "0.51772875",
"text": "def _apply_selection(self, event):\n selected = event.obj is self._buttons[True]\n\n new = OrderedDict([(k, self.options[k]) for k in self._selected[not selected]])\n old = self._lists[selected].options\n other = self._lists[not selected].options\n\n merged = OrderedDict([(k, k) for k in list(old)+list(new)])\n leftovers = OrderedDict([(k, k) for k in other if k not in new])\n self._lists[selected].options = merged if merged else {}\n self._lists[not selected].options = leftovers if leftovers else {}\n self.value = [self.options[o] for o in self._lists[True].options if o != '']\n self._apply_filters()",
"title": ""
},
{
"docid": "5aeb37495654a3e7a21eec01fee1d481",
"score": "0.5145377",
"text": "def _update_selection(self, event):\n selected = event.obj is self._lists[True]\n self._selected[selected] = [v for v in event.new if v != '']",
"title": ""
},
{
"docid": "e28c78079cd2a98bb6d01ae14923a691",
"score": "0.5145168",
"text": "def selectVerts(\n self,\n xyz_test: callable,\n ) -> Edit:\n for v in self.verts:\n if xyz_test(v.co):\n v.select = True\n return self",
"title": ""
},
{
"docid": "8944b796b020de9b657e53f1df0a89fa",
"score": "0.51388437",
"text": "def select(self):\n pass",
"title": ""
},
{
"docid": "8944b796b020de9b657e53f1df0a89fa",
"score": "0.51388437",
"text": "def select(self):\n pass",
"title": ""
},
{
"docid": "29aa28216b37d129a42d7f390f614d9f",
"score": "0.51293373",
"text": "def feature_selection():",
"title": ""
},
{
"docid": "338fa174ac7d0af9089dfce102921bb8",
"score": "0.5118289",
"text": "def only_choice(values):\n for unit in units:\n for value in '123456789':\n choices = [box for box in unit if value in values[box]] # Possible boxes for a value in one unit\n if len(choices) == 1: # One choice means the value has only one place to go\n values = assign_value(values, choices[0], value) # Set the only-choice box to the value\n return values",
"title": ""
},
{
"docid": "8719e0feaddd5986526fcec59b46cfb0",
"score": "0.5115793",
"text": "def Eq_cart_(self):\r\n if self.Vers[2] != 0: # Il componente lungo l'asse z non nullo.\r\n v1_x = 1 # componente x del vettore del piano 1\r\n v1_y = 1 # componente y del vettore piano 1\r\n v1_z = ((self.Vers[0] * v1_x) + (self.Vers[1] * v1_y)) * (\r\n (0 - 1) / self.Vers[2]) # componente z del vettore del piano 1\r\n v1 = np.array([v1_x, v1_y, v1_z])\r\n elif self.Vers[1] != 0:\r\n v1_x = 1 # componente x del vettore del piano 1\r\n v1_z = 1 # componente z del vettore del piano 1\r\n v1_y = ((self.Vers[0] * v1_x) + (self.Vers[2] * v1_z)) * (\r\n (0 - 1) / self.Vers[1]) # componente y del vettore piano 1\r\n v1 = np.array([v1_x, v1_y, v1_z])\r\n else:\r\n v1_y = 1 # componente y del vettore del piano 1\r\n v1_z = 1 # componente z del vettore del piano 1\r\n v1_x = ((self.Vers[1] * v1_y) + (self.Vers[2] * v1_z)) * (\r\n (0 - 1) / self.Vers[0]) # componente x del vettore piano 2\r\n v1 = np.array([v1_x, v1_y, v1_z])\r\n v2 = np.cross(self.Vers, v1) # Vettore per il secondo piano\r\n P1 = self.P0 + v1 # Terso punto per creare il piano 1\r\n P2 = self.P0 + v2 # Terso punto per creare il piano 2\r\n piano_1 = Piano(P1, self.P1, self.P2)\r\n piano_2 = Piano(P2, self.P1, self.P2)\r\n return [piano_1, piano_2]",
"title": ""
},
{
"docid": "d17a1cf0ec80d85f2aca0c292433687a",
"score": "0.511494",
"text": "def search_selection(screen: pygame.Surface) -> None:\r\n\r\n clear_screen(screen)\r\n border(screen)\r\n draw_header(screen, \"Types of Searching Algorithms\")\r\n\r\n # Labels\r\n backtrack = PButton(screen, (155, 230, 350, 50))\r\n backtrack.add_text(\"BackTracking\")\r\n astar = PButton(screen, (155, 330, 350, 50))\r\n astar.add_text(\"A*\")\r\n buttons = [backtrack, astar]\r\n \r\n selected = \"\"\r\n while selected == \"\":\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n pygame.quit()\r\n exit()\r\n\r\n if event.type == pygame.MOUSEBUTTONUP:\r\n # Check which box was clicked\r\n x, y = event.pos\r\n if backtrack.is_cursor_on((x, y), True):\r\n selected = \"Backtrack\"\r\n elif astar.is_cursor_on((x, y), True):\r\n selected = \"A*\"\r\n \r\n for b in buttons:\r\n if b.is_cursor_on(pygame.mouse.get_pos()):\r\n b.hover()\r\n else:\r\n b.draw()\r\n \r\n pygame.display.flip()\r\n\r\n if selected == \"Backtrack\":\r\n solve_sudoku_GUI(screen)\r\n elif selected == \"A*\":\r\n render_astar_vis(screen)",
"title": ""
},
{
"docid": "bbe6497eb5db93695c1ff6415083c692",
"score": "0.51125926",
"text": "def get_selected_items(self):\n\n datas = [i.data(NODEROLE) for i in self.view.get_indices()]\n items = [d for d in datas if d is not None] # filter Nones\n\n return items",
"title": ""
},
{
"docid": "dbb90a79bd91869392b999f5cf6e7b14",
"score": "0.51122075",
"text": "def only_choice(values):\n assert len(values) == 81, \"Input grid must be a string of length 81 (9x9)\"\n for unit in unitlist:\n for digit in '123456789':\n dplaces = [box for box in unit if digit in values[box]]\n if len(dplaces) == 1:\n #values = assign_value(values, values[dplaces[0]], digit)\n box = dplaces[0]\n values = assign_value(values,box,digit)\n #values[dplaces[0]] = digit\n return values",
"title": ""
},
{
"docid": "1a8c591af10c2b4298a91ee4f562e4a4",
"score": "0.511038",
"text": "def editSelected(self):\n pass",
"title": ""
},
{
"docid": "29b12a97b6367cc0c2fa5b39f2dd303d",
"score": "0.5109787",
"text": "def only_choice(values):\n for unit in unitlist:\n for val in '123456789':\n locations = [box for box in unit if val in values[box]]\n if len(locations) is 1:\n assign_value(values, locations[0], val)\n return values",
"title": ""
},
{
"docid": "56c9ac8b60ce139e78ff6d864963519e",
"score": "0.50994885",
"text": "def choix_couleur(historique):\n couleur_, remplissage_, epaisseur_ = choix_couleur_remplissage_epaisseur(historique)\n\n couleur = fonction.my_input(\"Couleur:\", \"str\", couleur_)\n if couleur == \"+\":\n couleur = askcolor()[1]\n\n remplissage = fonction.my_input(\"Remplissage:\", \"str\", remplissage_)\n if remplissage == \"+\":\n remplissage = askcolor()[1]\n\n epaisseur = fonction.my_input(\"Epaisseur\", \"int\", epaisseur_)\n\n return couleur, remplissage, epaisseur",
"title": ""
},
{
"docid": "fefc01ede9cf786dc084a9188aec70e8",
"score": "0.5095328",
"text": "def manual_replacement(self, cursor):\n self.name = input(\"\"\"Entrez le nom du produit que vous souhaitez\nremplacer:\n\"\"\")\n formated_name = \"%\" + self.name + \"%\"\n answer_ing = input(\"\"\"\nSouhaitez vous la liste des ingrédients du produit qui sera le remplaçant?\ny pour confirmer \n\"\"\")\n if answer_ing not in (\"y\", \"Y\"):\n sql = (\"\"\"SELECT Product.product_name, product.url\n FROM Product\n INNER JOIN Store_availability\n ON Product.id = Store_availability.product_id\n INNER JOIN Product_category\n ON Product.id = Product_category.product_id\n WHERE Product.product_name LIKE %s\n AND Store_availability.store_id = %s\n AND Product_category.category_id = %s\n ORDER BY Product.nutrition_grades LIMIT 1\"\"\")\n val = (formated_name, self.store_selected, self.category_selected)\n cursor.execute(sql, val)\n for row in cursor:\n self.product_found = True\n print(\"Nous vous avons sélectionné ceci:\")\n self.selec_prod = row[0]\n print(\"la ou le {0} (plus d'info ici: {1})\".format(\n row[0], row[1]))\n else:\n sql = (\"\"\"SELECT Product.product_name, Product.ingredients,\n product.url FROM Product\n INNER JOIN Store_availability\n ON Product.id = Store_availability.product_id\n INNER JOIN Product_category\n ON Product.id = Product_category.product_id\n WHERE Product.product_name LIKE %s\n AND Store_availability.store_id = %s\n AND Product_category.category_id = %s\n ORDER BY Product.nutrition_grades LIMIT 1\"\"\")\n val = (formated_name, self.store_selected, self.category_selected)\n cursor.execute(sql, val)\n for row in cursor:\n self.product_found = True\n print(\"Nous vous avons sélectionné ceci:\")\n self.selec_prod = row[0]\n print(\"\"\"\nla ou le {0} qui contient:\\n{1}\\n Plus d'info ici: {2}\"\"\".format(\n row[0], row[1], row[2]))",
"title": ""
},
{
"docid": "cd6ea4c517bacc65ae462cf56b1878d0",
"score": "0.5089303",
"text": "def test_subcontrols_can_be_selected_by_value():",
"title": ""
},
{
"docid": "419994c48fde8b57cd41b041eb135d4d",
"score": "0.50729716",
"text": "def only_choice(values):\n dictMain = values\n\n listKeys = list(dictMain.keys())\n\n #print(utils.units['A1'])\n\n for key in listKeys:\n\n # create set of current values\n checkVal = set(dictMain[key])\n\n # list of neighbor boxes\n listBoxes = utils.units[key][2]\n\n # remove current key from neighbor set\n listBoxes.remove(key)\n\n #print(utils.square_units)\n if len(checkVal) != 1:\n\n setBoxes = set()\n\n # create set of neighbor values\n for i in listBoxes:\n tempSet = set(dictMain[i])\n setBoxes.update(tempSet)\n\n # find unique values\n x = checkVal.difference(setBoxes)\n\n # if\n if bool(x) is False:\n y = \"\".join(sorted(checkVal))\n dictMain[key] = y\n else:\n x = \"\".join(sorted(x))\n dictMain[key] = x\n\n\n else:\n y = \"\".join(sorted(checkVal))\n dictMain[key] = y\n\n listBoxes.insert(0, key)\n\n\n #print(utils.unitlist)\n #print(utils.units['A1'])\n\n return dictMain",
"title": ""
},
{
"docid": "d4f405ef038524cb1719d457c39dfc54",
"score": "0.5050391",
"text": "def only_choice(values):\n for unit in unitlist:\n usedIntegers = dict((i, []) for i in '123456789')\n for box in unit:\n integers = list(values[box])\n for integer in integers:\n usedIntegers[integer].append(box)\n for integer in usedIntegers:\n if len(usedIntegers[integer]) == 1:\n box = usedIntegers[integer][0]\n # values[box] = integer\n assign_value(values, box, integer)\n return values",
"title": ""
},
{
"docid": "74651c61ae723415db12dadc5ce7c8a2",
"score": "0.5049462",
"text": "def select_all_feature_selectors_parameters(conn):\n cur = conn.cursor()\n cur.execute(\"SELECT * FROM T_NonEstimatorFeatureSelectorParameter\")\n \n rows = cur.fetchall()\n \n\n for row in rows:\n print(row)\n\n print(\"***** AND *****\")\n \n cur.execute(\"SELECT * FROM T_Estimator a WHERE a.F_Estimator_CanFeatureSelect=1\")\n \n rows = cur.fetchall()\n \n\n for row in rows:\n print(row)",
"title": ""
},
{
"docid": "7a26a1f000ffa0545ddc9a80c990b3ee",
"score": "0.5037096",
"text": "def alphatoall(\n selection=\"polymer\", properties=\"b\", operator=\"byca\", quiet=1, *, _self=cmd\n):\n properties = \"(\" + \",\".join(properties.split()) + \")\"\n space = {\"props\": dict()}\n _self.iterate(\n \"%s (%s)\" % (operator, selection),\n \"props[model,segi,chain,resi] = \" + properties,\n space=space,\n )\n _self.alter(\n selection,\n properties + \" = props.get((model,segi,chain,resi), \" + properties + \")\",\n space=space,\n )\n if not int(quiet):\n print(\" Modified %d residues\" % (len(space[\"props\"])))",
"title": ""
},
{
"docid": "9d8d022130c32e2beab1871639a62c85",
"score": "0.503184",
"text": "def set_value(self, values):\n allitems = [item.text() for item in self.all_items()]\n if allitems != values['all_items']:\n self.clear()\n self.addItems(values['all_items'])\n QtWidgets.QApplication.processEvents()\n for item in self.all_items():\n if item.text() in values['selected']:\n item.setSelected(True)",
"title": ""
},
{
"docid": "1441b84296c7057ba486c33d034f8a21",
"score": "0.5031431",
"text": "def set_search_options():\n\n loc_dict = {\"Canning Town\": \"CT\", \"Poplar\": \"PO\", \"Epsom\": \"EP\", \"Lewisham\": \"LE\", \"Walthamstow\": \"WA\",\n \"Hayes\": \"HA\", \"Stepney Green\": \"SG\"}\n fur_dict = {\"Furnished\": \"Y\", \"Unfurnished\": \"N\"}\n price_dict = {\"£1000-£1499\": [1000, 1499], \"£1500-£1799\": [1500, 1799], \"£1800+\": [1800, 10000]}\n\n location = loc_dict[loc_var.get()] if loc_var.get() != \"Any\" else 0\n floor = int(floor_var.get()) if floor_var.get() != \"Any\" else 0\n bedroom = int(bed_var.get()) if bed_var.get() != \"Any\" else 0\n bathroom = int(bath_var.get()) if bath_var.get() != \"Any\" else 0\n furnished = fur_dict[fur_var.get()] if fur_var.get() != \"Any\" else 0\n price = price_dict[price_var.get()] if price_var.get() != \"Any\" else 0\n available = avail_var.get() if avail_var.get() != \"Any\" else 0\n\n if not any([location] + [floor] + [bedroom] + [bathroom] + [furnished] + [price] + [available]):\n create_labels_from_df(pd.DataFrame(), True)\n else:\n create_search_str(location, floor, bedroom, bathroom, furnished, price, available)",
"title": ""
},
{
"docid": "fb336560a463088d5178bc437b3ec8fc",
"score": "0.5029592",
"text": "def cliquer(self):\r\n rsd = Solution(self.taquin)\r\n rsd.SolutionH()\r\n fenetre2 = Tk()\r\n champ_label = Label(fenetre2, text=\"Resultats des Heuristiques \")\r\n champ_label.pack()\r\n\r\n \"\"\"\r\n Ici a ajouter le tableau avec les solution et le comparatif des heuristiques A FAIRE\r\n print(\"Solutions des heuristiques : \")\r\n print(self.complexiteTemps)\r\n print(self.numeroHeuristique)\r\n min.complexiteTemps\r\n \"\"\"",
"title": ""
},
{
"docid": "6c0501fe3fbece4d8b5b2c4b73bcd35d",
"score": "0.502723",
"text": "def display_values_selection(self):\r\n if self.display_values_label != None:\r\n self.display_values_label.destroy() \r\n self.display_values_label = label_frame(self, _(\"SELECT OR ENTER VALUE(S)\"), x=0, y=2)\r\n fields = [QueryField(self.saved_field_to_filter.table, self.saved_field_to_filter.field_name)]\r\n values_list = Query(self.federation, fields, [], [], limit=100, agregate=True)\r\n values_list.execute()\r\n values_list = values_list.query_result \r\n if self.filter_type[0].get() in [\"=\", \">\", \">=\", \"<\", \"<=\"]:\r\n self.values_listbox = list_box(self.display_values_label, vbar=True, hbar=True, \r\n width=20, height=20, values=values_list)\r\n elif self.filter_type[0].get() in [\"IN\", \"NOT IN\"]:\r\n self.values_listbox = list_box(self.display_values_label, vbar=True, hbar=True, \r\n width=20, height=20, values=values_list, selectmode=\"multiple\")\r\n elif self.filter_type[0].get() in [\"BETWEEN\", \"NOT BETWEEN\"]:\r\n self.value1 = entry(self.display_values_label, \"\", width_entry=15)\r\n label(self.display_values_label, \" AND \", y=1)\r\n self.value2 = entry(self.display_values_label, \"\", width_entry=15, y=2)\r\n elif self.filter_type[0].get() in [\"LIKE\", \"NOT LIKE\"]:\r\n self.like_value = entry(self.display_values_label, _(\"Character string\"), width_entry=15, x=1, y=1)\r\n if self.filter_index != None:\r\n i = 0\r\n for index in range(0, self.values_listbox.size()):\r\n if str(self.values_listbox.get(i)[0]) in self.query_filters[self.filter_index].filter_values:\r\n self.values_listbox.selection_set(i)\r\n i += 1\r\n button(self.display_values_label, _(\"Modify filter\"), self.save_filter, x=1)\r\n button(self.display_values_label, _(\"Remove filter\"), self.delete_filter, x=2)\r\n else:\r\n \"\"\" Validation button \"\"\"\r\n button(self.display_values_label, _(\"Add filter\"), self.save_filter, x=1)",
"title": ""
},
{
"docid": "fd4e634fc49c3201362eb03264f16307",
"score": "0.5024688",
"text": "def main(self):\n s = self.selection_creator()\n return sum([self.mem[x] for x in self.comp_int_select(s)])",
"title": ""
},
{
"docid": "435c131f4d6488ef0f0917d0bf957f74",
"score": "0.502311",
"text": "def __selecciones(self, treeselection, model, path, is_selected, listore):\n\n iter = self.get_model().get_iter(path)\n tamanio = self.get_model().get_value(iter, 0)\n\n if self.tamanio != tamanio:\n self.tamanio = tamanio\n self.scroll_to_cell(path)\n self.emit('nueva-seleccion', self.tamanio)\n\n return True",
"title": ""
},
{
"docid": "9257b2a793bd451bdc17d4951e1db894",
"score": "0.50088906",
"text": "def make_choice(self, liste):\n for i, element in enumerate(liste):\n print(str(i + 1), \"-\", str(element))\n answer = self.validate_answer_int(liste)\n element = liste[answer]\n return answer, element",
"title": ""
},
{
"docid": "0b6f9381c3491f4bc2917ba855a32c4c",
"score": "0.5008554",
"text": "def select_transport(self):\n\n \"\"\"Price\"\"\"\n if self.main_criteria == 1:\n self.available_transports.append(\"rail\")\n self.available_transports.append(\"autolib\")\n self.available_transports.append(\"velib\")\n self.available_transports.append(\"walk\")\n self.available_transports.append(\"uber\")\n if self.main_criteria == 2:\n self.available_transports.append(\"rail\")\n self.available_transports.append(\"velib\")\n self.available_transports.append(\"walk\")\n \"\"\"Load\"\"\"\n if self.main_criteria == 3:\n self.available_transports.append(\"autolib\")\n self.available_transports.append(\"walk\")\n self.available_transports.append(\"uber\")\n \"\"\"Tourisme\"\"\"\n if self.main_criteria == 4:\n self.available_transports.append(\"velib\")\n self.available_transports.append(\"walk\")\n\n \"\"\"Conditions to define a rather bad weather which tends to limit cycling and walking\"\"\"\n condition_bad_weather = self.weather.get_type() == \"Rain\" or self.weather.get_type() == \"Snow\" or \\\n self.weather.get_temperature() < 5 or self.weather.get_rain() > 1 or self.weather.get_wind() > 14\n if condition_bad_weather:\n try:\n self.available_transports.remove(\"velib\")\n except:\n print(\"La possibilité d'utiliser un velib a déjà été supprimée.\")",
"title": ""
},
{
"docid": "933e429d463cd912ae23ceb0dbdcd341",
"score": "0.50014234",
"text": "def __selecciones(self, treeselection, model, path, is_selected, listore):\n\n iter = self.get_model().get_iter(path)\n fuente = self.get_model().get_value(iter, 1)\n\n if self.fuente != fuente:\n self.fuente = fuente\n self.scroll_to_cell(path)\n self.emit('nueva-seleccion', self.fuente)\n\n return True",
"title": ""
},
{
"docid": "6d0c55928d80a3b35cd72bae387190ce",
"score": "0.49978793",
"text": "def ITransfertSelection():\n\n\ttransfertSelection()",
"title": ""
},
{
"docid": "7431e21a77da6c0c88b26e57907aaaae",
"score": "0.4991144",
"text": "def test_CHOICES( self ):\n self.assertEqual( EBAY_SHIPPING_CHOICES[5], ( 5, 'Pick Up ONLY!' ) )",
"title": ""
},
{
"docid": "c9bd8116b7a8dd532e44163ac840ba80",
"score": "0.49911344",
"text": "def set_selections(self, names, selections, item, rep, menu=-1):\n name = names[item]\n cmenu = glutGetMenu()\n if menu != -1:\n glutSetMenu(menu)\n # Select all of the submenu entities.\n if name == VisMenuItem.ALL: \n for item,name in enumerate(names):\n if item < 2:\n continue\n entry_name = name + VisMenu.selected_symbol\n selections[name] = True\n glutChangeToMenuEntry(item+1, entry_name, item)\n # Select none of the submenu entities.\n elif name == VisMenuItem.NONE: \n for item,name in enumerate(names):\n if item < 2:\n continue\n selections[name] = False\n glutChangeToMenuEntry(item+1, name, item)\n # Select an entity from the submenu entities.\n else:\n if not selections[name]:\n entry_name = name + VisMenu.selected_symbol\n selections[name] = True\n else:\n entry_name = name\n selections[name] = False\n glutChangeToMenuEntry(item+1, entry_name, item)\n return selections[name]",
"title": ""
},
{
"docid": "d99e9bf2ae33adef7cdd36b3327236a3",
"score": "0.49844941",
"text": "def select_cube(self, row, column): \n for i in range(self.rows):\n for j in range(self.cols):\n self.cubes[i][j].selected = False\n\n self.cubes[row][column].selected = True\n self.selected = (row, column)",
"title": ""
},
{
"docid": "f77e725d87fb0cc2376b89e2e9038557",
"score": "0.49827293",
"text": "def selectTerritoireMairie(self, tuile):\n self.deselect()\n for territoire in tuile.getBatiment().getTerritoire():\n territoireVoisin = self.getVoisin(territoire)\n for iVoisin in territoireVoisin:\n if iVoisin.getBatiment() == None:\n self.selectedTuile.append(iVoisin)\n self.selectTuile(self.selectedTuile, \"case selection.gif\")\n self.selectionType = \"Batiment\"",
"title": ""
},
{
"docid": "b9d860e13f09e6210e5be6412aa0b1c0",
"score": "0.49826953",
"text": "def onTierSelection(self, pairs):",
"title": ""
},
{
"docid": "227923859dc504b17bf4a6d560508419",
"score": "0.49811974",
"text": "def selection_creator(self): \n selection = {}\n for i in range(self.x_arity):\n a = input('Minimum cards of type {0}? - '.format(i+1))\n b = int(input('Maximum cards of type {0}? - '.format(i+1)))\n selection[i] = (int(a) if a!='' else None, int(b) if b!='' else None)\n return selection",
"title": ""
},
{
"docid": "91f99d08f8c17c9776b09bf4c3ee7bd4",
"score": "0.497148",
"text": "def only_choice(values):\n possibilities = '123456789'\n for unit in unitlist:\n for possibility in possibilities:\n count = 0\n value = ''\n for box in unit:\n if possibility in values[box]:\n count += 1\n if count == 1:\n value = box\n if count == 1:\n values = assign_value(values, value, possibility)\n\n return values",
"title": ""
},
{
"docid": "d2fb35aab7163a61008968b27e8e6e35",
"score": "0.4971454",
"text": "def nullSelection( gaMgr ):\n\treturn [0,0]",
"title": ""
},
{
"docid": "c163992f4355658831889828bb27719a",
"score": "0.49669623",
"text": "def only_choice(values):\n for unit in unitlist:\n for digit in '123456789':\n dplaces = [box for box in unit if digit in values[box]]\n if len(dplaces) == 1: # if this is the only number (length of the cell is 1)\n #this number is the only choice, add it to the cell.\n values[dplaces[0]] = digit\n return values",
"title": ""
},
{
"docid": "c93e3934e843f790ae0d377374340222",
"score": "0.4952419",
"text": "def seleccion(poblacion, modelo, cantidad_seleccion):\n _poblacion = [{'cant': 0, 'index': index, 'item': item} for index, item in enumerate(poblacion.copy())]\n\n for i in range(cantidad_seleccion * cantidad_seleccion):\n \"\"\"entra a la seleccion\"\"\"\n _poblacion_seleccionada = seleccion_por_torneo(poblacion=_poblacion, cantidad_seleccion=cantidad_seleccion)\n\n \"\"\"creamos un arreglo con el individuo actual y los puntos que obtubo\"\"\"\n puntos_poblacion = [(evaluar_individuo(individuo=i['item'], modelo=modelo), i) for i in _poblacion_seleccionada]\n\n \"\"\"para obtener los mejores puntuados ordenamos el arreglo con el valor de los puntos obtenidos\"\"\"\n _mejores_puntuados = sorted(puntos_poblacion, key=lambda individuo: individuo[0], reverse=True)\n\n \"\"\"obtenemos al ganador del toneo\"\"\"\n _mejor = _mejores_puntuados[0]\n\n \"\"\"asignamos un punto al ganador del torneo\"\"\"\n _poblacion[_mejor[1]['index']]['cant'] += 1\n\n \"\"\"ordenar la poblacion por los que ganaron el torneo\"\"\"\n _poblacion = sorted(_poblacion, key=lambda individuo: individuo['cant'], reverse=True)\n\n \"\"\"obtnemos la poblacion ganadora desde la posicion 0 hasta la posicion=cantidad_seleccion\"\"\"\n _poblacion = _poblacion[0:cantidad_seleccion]\n\n \"\"\"reconstruimos la estructura de la poblacion inicial con los individuos ganadores\"\"\"\n _poblacion = [i['item'] for i in _poblacion]\n\n \"\"\"retornamos\"\"\"\n return _poblacion",
"title": ""
},
{
"docid": "2bfc8d55477e6d5027b4626ad7a086dc",
"score": "0.49524093",
"text": "def test_multiple(self):\n els = [\"Si\", \"Mg\", \"Ca\"]\n out = common_oxides(elements=els, output=\"formula\")\n self.assertTrue(len(out) >= len(els))\n for ox in out:\n with self.subTest(ox=ox):\n # All oxides are from elements contained in the list\n self.assertIn(get_cations(ox)[0].__str__(), els)",
"title": ""
},
{
"docid": "3f1df8d46ee9bf67154a3e57b6a8c2ce",
"score": "0.49521062",
"text": "def all_equal(self):",
"title": ""
},
{
"docid": "2e8827b7e399bcf9d03456dc41b36302",
"score": "0.4935945",
"text": "def func_choice(self):\r\n self.selection = {\"rise_dwell\": self.rise_dwell,\r\n \"fall_dwell\": self.fall_dwell}\r\n self.selection[self.choice]()\r\n\r\n return self.theta, self.S.flatten(), self.V.flatten(), self.A.flatten(), self.J.flatten()",
"title": ""
},
{
"docid": "9b47f48e347f2e0b87468b588942d44c",
"score": "0.4924185",
"text": "def _tournament_selection(self):\n pass",
"title": ""
},
{
"docid": "b73e74ca92a6c744169a809b87edb3b7",
"score": "0.4923549",
"text": "def quit_q(total, *args):\n poss_keys = []\n for index, (option, optvar, entbox, entstring) in enumerate(list(objs.values())[:total]):\n if index == 0:\n enttext.set(entstring.get())\n optvar.set(optvar.get())\n datatype_picked.set(optvar.get())\n if optvar is not None:\n o = convert_name_to_query.get(optvar.get(), optvar.get())\n q = entstring.get().strip()\n q = remake_special(q, customs=custom_special_dict, \n case_sensitive=case_sensitive.get(), return_list=True)\n output_dict[o] = q\n # may not work on mac ...\n if title == 'Additional criteria':\n if len(list(objs.values())[:total]) > 0:\n plusbut.config(bg='#F4F4F4')\n else:\n plusbut.config(bg='white')\n else:\n if len(list(objs.values())[:total]) > 0:\n ex_plusbut.config(bg='#F4F4F4')\n else:\n ex_plusbut.config(bg='white')\n more_criteria.withdraw()",
"title": ""
},
{
"docid": "2d17a04d7faae186f3ef510a9d599ae7",
"score": "0.4914698",
"text": "def select(self, x, y):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "475eea81774f28938bf3e1d02132fd67",
"score": "0.49109396",
"text": "def hlt1InputSelections ( ) :\n return _hlt_1_input_selections__",
"title": ""
},
{
"docid": "49ae2c8143d0a76ca2368216d837e8cf",
"score": "0.4909682",
"text": "def _test_sel_index(self, selected, deselected):\n #index_current = self.selectionModel.currentIndex()\n src_model = self._item_model\n index_current = None\n index_deselected = None\n index_parent = None\n curr_qstd_item = None\n if selected.indexes():\n index_current = selected.indexes()[0]\n index_parent = index_current.parent()\n curr_qstd_item = src_model.itemFromIndex(index_current)\n elif deselected.indexes():\n index_deselected = deselected.indexes()[0]\n index_parent = index_deselected.parent()\n curr_qstd_item = src_model.itemFromIndex(index_deselected)\n\n if selected.indexes() > 0:\n rospy.logdebug('sel={} par={} desel={} sel.d={} par.d={}'.format(\n index_current, index_parent, index_deselected,\n index_current.data(Qt.DisplayRole),\n index_parent.data(Qt.DisplayRole),)\n + ' desel.d={} cur.item={}'.format(\n None, # index_deselected.data(Qt.DisplayRole)\n curr_qstd_item))\n elif deselected.indexes():\n rospy.logdebug('sel={} par={} desel={} sel.d={} par.d={}'.format(\n index_current, index_parent, index_deselected,\n None, index_parent.data(Qt.DisplayRole)) +\n ' desel.d={} cur.item={}'.format(\n index_deselected.data(Qt.DisplayRole),\n curr_qstd_item))",
"title": ""
},
{
"docid": "ebfdaff94d54b5c66f9361bffca4e209",
"score": "0.4902034",
"text": "def test_selection():\n\n result = [[\"FirstName\", \"Surname\", \"IQ\", \"GPA\"],\n [\"Zoe\", \"Washburne\", 110, 3.5],\n [\"Inara\", \"Serra\", 158, 4.0]]\n\n assert is_equal(result, selection(STUDENTS, filter_students))",
"title": ""
},
{
"docid": "41820d6b55d7390b357ac71add51f3c8",
"score": "0.48965776",
"text": "def poids_select(self, event):\n\n window = tk.Toplevel()\n window.geometry(\"400x300+30+30\")\n \n buttonspoids_tmp = list(self.buttonp.keys())\n nombre_faces = buttonspoids_tmp.index(event.widget) \n liste_entries = list() # pour obtenir la valeur des enrys\n number_dice = int(self.spinbox[nombre_faces].get())\n \n # LABELS\n for i in range(nombre_faces + 2):\n tk.Label(window, text=\"f\" + str(i+1)).grid(row=0, column=i+1) \n for i in range(1, number_dice+1):\n tk.Label(window, text=\"de\" + str(i)).grid(row=i, column=0)\n liste_entries_tmp = list()\n for j in range(nombre_faces + 2):\n tmp = tk.Entry(window, width=2)\n liste_entries_tmp.append(tmp)\n tmp.grid(row=i, column=j+1, padx=3, pady =3)\n \n liste_entries.append(liste_entries_tmp) \n \n # Buttons \n butok =tk.Button(window, text=\"Ok\")\n butok.bind('<Button-1>', lambda x: self.get_entries(window, event.widget, liste_entries)) \n butok.grid(row=number_dice+1, column=0, pady=5, sticky=\"nsew\") \n tk.Button(window, text=\"Annuler\", command=window.destroy).grid(row=number_dice+1, column=1, padx=3, pady=5,sticky=\"nsew\")",
"title": ""
},
{
"docid": "d49198982b107133bedad90538bc1c85",
"score": "0.48948193",
"text": "def change_all(self, event):\n if self.selVar.get():\n self.selVar.set(True)\n for box in self.frames:\n box[0].set(True)\n else:\n self.selVar.set(False)\n for box in self.frames:\n box[0].set(False)",
"title": ""
},
{
"docid": "2284eec51a75315db7b5720dd801268f",
"score": "0.48886234",
"text": "def only_choice(values):\n # TODO: Implement only choice strategy here\n #unSolved = [k for k in values.keys() if len(values[k]) != 1]\n \n for unit in unitlist:\n for digit in '123456789':\n digitAppearance = [box for box in unit if digit in values[box]]\n if len(digitAppearance) == 1:\n values[digitAppearance[0]] = digit\n \n return values",
"title": ""
},
{
"docid": "ef20038efc03ee8f7989e6ab8a5bb844",
"score": "0.48824254",
"text": "def test_one(self):\n els = [\"Si\"]\n out = common_oxides(elements=els, output=\"formula\")\n self.assertTrue(len(out) >= 1)\n for ox in out:\n with self.subTest(ox=ox):\n # All oxides are from elements contained in the list\n self.assertIn(get_cations(ox)[0].__str__(), els)",
"title": ""
},
{
"docid": "9f5d889552195abe5ba8112e7a0c2540",
"score": "0.4872225",
"text": "def convert_lepton_selections(self):\n # found_particle_cut = False\n if self.good_muon or self.common_selection and \"good_muon\" in self.common_selection:\n self.cut_list.append(self.build_particle_cut(self.good_muon, \"good_muon\", self.muon_operator,\n 'muon_n', self.n_muon))\n # found_particle_cut = True\n if self.good_electron or self.common_selection and \"good_electron\" in self.common_selection:\n self.cut_list.append(self.build_particle_cut(self.good_electron, \"good_electron\", self.electron_operator,\n 'electron_n', self.n_electron))\n # found_particle_cut = True\n if self.fake_muon:\n self.inverted_muon_cut_string = self.convert_cut_list_to_string(self.inverted_muon)\n # if not found_particle_cut and self.n_electron > 0 or self.n_muon > 0:\n # self.cut_list.append(['Sum$({:s}) == {:s}')",
"title": ""
},
{
"docid": "bf886c6e54fb22dae2f7f4765a9f7e43",
"score": "0.4869056",
"text": "def values(self):",
"title": ""
},
{
"docid": "bf886c6e54fb22dae2f7f4765a9f7e43",
"score": "0.4869056",
"text": "def values(self):",
"title": ""
},
{
"docid": "fa326070e43bde0eedd8a2877d5706d3",
"score": "0.4866987",
"text": "def nettoyer_ordres(self):\n volontes = self.equipage.volontes\n uniques = []\n args = []\n for ordre in self.ordres:\n if ordre.volonte and ordre.volonte not in volontes:\n continue\n\n arg = (ordre.cle, ) + ordre.arguments_suplementaires\n if arg not in args:\n args.append(arg)\n uniques.append(ordre)\n\n self.ordres[:] = uniques",
"title": ""
},
{
"docid": "fe8e0086e19c1a32670ddd5172d660d4",
"score": "0.4855413",
"text": "def select_bond_vector(self, i, l):\n self.sel1 = self.u.select_atoms(\"resid %s and name %s\" % (i, l[0]))\n self.sel1_list.append(self.sel1)\n self.sel2 = self.u.select_atoms(\"resid %s and name %s\" % (i, l[1]))\n self.sel2_list.append(self.sel2) \n if self.sel1.n_atoms != 1 or self.sel2.n_atoms != 1:\n return False\n else:\n return True",
"title": ""
},
{
"docid": "031554a1e18fdd81a164dc8d65e30dbe",
"score": "0.4853762",
"text": "def elSelect(a, b, p):\n s = (torch.sign(p)+1)/2.\n return a*s + b*(1-s)",
"title": ""
},
{
"docid": "1c59ad841d415b0bdcc496125ec20d36",
"score": "0.4843056",
"text": "def choose_tasks(self, values):",
"title": ""
},
{
"docid": "c59604fbaf6dad7a1e954fdc8aa64f6a",
"score": "0.4837851",
"text": "def test_select_prize():\n prizes = ['prize_1', 'prize_2', 'prize_3', 'prize_4', 'prize_5']\n assert select_prize() in prizes",
"title": ""
},
{
"docid": "03f9395ee3ce63256e4670ae5de91add",
"score": "0.48333335",
"text": "def update_selection(self):\n\n # clear all boxes\n self.clear_boxes()\n self.draw_figure(self.s)\n\n # update temperature list\n if self.Data[self.s]['T_or_MW']==\"T\":\n self.temperatures=np.array(self.Data[self.s]['t_Arai'])-273.\n else:\n self.temperatures=np.array(self.Data[self.s]['t_Arai'])\n\n self.T_list=[\"%.0f\"%T for T in self.temperatures]\n self.tmin_box.SetItems(self.T_list)\n self.tmax_box.SetItems(self.T_list)\n self.tmin_box.SetValue(\"\")\n self.tmax_box.SetValue(\"\")\n self.Blab_window.SetValue(\"%.0f\"%(float(self.Data[self.s]['pars']['lab_dc_field'])*1e6))\n if \"saved\" in self.Data[self.s]['pars']:\n self.pars=self.Data[self.s]['pars']\n self.update_GUI_with_new_interpretation()\n self.Add_text(self.s)\n self.write_sample_box()",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="760"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">99c0d72e525d482329b2e642eb8c4c8b</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Reset state_identifiers to None</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "e6e6ca6d6b2e6722e34171e9ebc6f862",
"score": "0.88936627",
"text": "def reset_state(self):\n self.state_identifiers = {k:None for k in self._get_state()}",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "3711c9c93b006e8b4cf3574e222340bb",
"score": "0.7362603",
"text": "def reset_state(self):",
"title": ""
},
{
"docid": "38bcc6fe5d3b74fe76d2c3754b12f469",
"score": "0.7149735",
"text": "def reset(self, state):\n pass",
"title": ""
},
{
"docid": "48a521a59c2f9702751e8b8bead29449",
"score": "0.7117872",
"text": "def reset(self):\n self.current_state = None",
"title": ""
},
{
"docid": "6c6da26d506f653c93ffa138a27ee552",
"score": "0.70959073",
"text": "def reset(self):\n self._symbol_list = []\n self._next_id = 0\n self._id_dict = {}",
"title": ""
},
{
"docid": "ed8157d228518f5fb41e761d9552e618",
"score": "0.70635337",
"text": "def reset(self, initial_state: types.Observation = None):",
"title": ""
},
{
"docid": "0e70f58154f7728b372818958e7cb582",
"score": "0.69432807",
"text": "def clear_state(self):\n pass",
"title": ""
},
{
"docid": "9ed9c7eccbd41862d59854ac6518de8a",
"score": "0.68554914",
"text": "def reset(self):\n\n self.state = np.array(self.HOME)\n self.state_history = [self.state]\n self.fk_cache = dict()",
"title": ""
},
{
"docid": "73c3bb89e4f390eb92d7d06a78088bba",
"score": "0.6852005",
"text": "def reset_state(self):\n self.state = False",
"title": ""
},
{
"docid": "0947c3fc0e48f61f249b90051c4d2f29",
"score": "0.68455875",
"text": "def reset_internal_states(self, record=None):\n self._record = None\n self._count = 0\n self._record = record",
"title": ""
},
{
"docid": "7f7c367e736f7b755e96c3a5211e95e5",
"score": "0.6834754",
"text": "def reset_state(self):\n self.pptr = 0\n self.asmptr = self.startptr\n self.unexplored = [] \n self.tracker_stack = []\n self.asmatch = []",
"title": ""
},
{
"docid": "c955fe2b8f5bb693ff6a2ee4baff6397",
"score": "0.68079436",
"text": "def reset_current_state(self):",
"title": ""
},
{
"docid": "fecb611e91de6ae2509a3d37a862b065",
"score": "0.6709218",
"text": "def reset(self, start_state):\n abstract",
"title": ""
},
{
"docid": "29b171e189525807bb9f9f7aa4cfcf53",
"score": "0.6679415",
"text": "def reset_state():\n \n state.pktgen_run_time = None\n state.pktgen_sent_pkt_count = None\n state.tcpdump_dropped_pkt_count = None\n state.tcpdump_recvd_pkt_count = None\n \n state.flow_stat = {}\n state.global_stat = {}\n state.parsed_pkt_count = None",
"title": ""
},
{
"docid": "ca70b5b2e3be08979e50419ac0755fbb",
"score": "0.6674993",
"text": "def reset(self, ids=None):\n\n if ids is None:\n # reset all disciples\n self.states = [0x0 * len(self.states)]\n elif type(ids) == str:\n # reset one disciple\n self.states[self.disciples.index(ids)] = 0x0\n elif type(ids) == list:\n # reset a list of disciples\n for id in ids:\n self.states[self.disciples.index(id)] = 0x0",
"title": ""
},
{
"docid": "eff718b5e616407835ae72a1b3f0c57c",
"score": "0.66684246",
"text": "def Reset(self):\n self._state.clear()\n self._loaded = False",
"title": ""
},
{
"docid": "faceaa6d4ad614e051a5639505ac3d47",
"score": "0.6627035",
"text": "def reset(self):\n self.state = self.mu",
"title": ""
},
{
"docid": "f5ce4f5cc92243ac469f22c0e65c5074",
"score": "0.6596645",
"text": "def Reset(self):\n super(JavaScriptStateTracker, self).Reset()\n\n self.__goog_require_tokens = []\n self.__goog_provide_tokens = []\n self.__provided_namespaces = set()\n self.__used_namespaces = []",
"title": ""
},
{
"docid": "0cf79424017adee21b2f2e2a12351324",
"score": "0.65917826",
"text": "def reset(self):\n self.state = 0xffff",
"title": ""
},
{
"docid": "8a1d96f528b20a330dc26cfedbc38a88",
"score": "0.65626836",
"text": "def clear_global_state():\n global_state.PREVIOUS_EDGES = set()\n global_state.MASTER_DNS_CACHE = {}\n global_state.NS_IP_MAP = defaultdict(str)\n global_state.AUTHORITATIVE_NS_LIST = []\n global_state.QUERY_ERROR_LIST = []",
"title": ""
},
{
"docid": "7dd52caf24f384dfda2531eb8b2d55f9",
"score": "0.6557593",
"text": "def reset(self, dimensions, states, set_state):",
"title": ""
},
{
"docid": "03915e03455b20d917eceda17fa64a5a",
"score": "0.65478784",
"text": "def reset_state(self):\n reset_op = tf.group(*[layer.reset() for layer in self.stateful_layers])\n self.session.run(reset_op)",
"title": ""
},
{
"docid": "5e9a41d7cf81f653fc6253e261f4e4f6",
"score": "0.652053",
"text": "def reset():\n\n global state_file\n state_file = None\n \n jobs.clear()",
"title": ""
},
{
"docid": "a5a0d562f727e38027e221e3cc69306f",
"score": "0.652018",
"text": "def resets_in(*identifiers):",
"title": ""
},
{
"docid": "1ea46e4d39202a57b961caf8e6bc359e",
"score": "0.6507194",
"text": "def reset(self):\n self.state.fill(0)",
"title": ""
},
{
"docid": "a9be65953c1d32e1f48e32da54a7afbf",
"score": "0.65041715",
"text": "def clear_state(self):\n self._state = {}\n self.label_grid = copy.deepcopy(self._stateless_label_grid)\n self.set_state()",
"title": ""
},
{
"docid": "23a5c110127b4227d45ae48d575a292c",
"score": "0.646592",
"text": "def reset(self):\n self.state[\"observations\"] = self.envs.reset()\n self.state[\"masks\"] = np.array([[0]]*self.params[\"num_workers\"], dtype=np.float32)\n\n # The initial hidden_state is not saved in the memory. The only use for it is\n # getting passed to the action_generator.\n # So if there is a size mismatch between this and the next hidden_states, no\n # conflicts/errors would happen.\n self.state[\"hidden_state\"] = {}\n for agent_name in self.agents:\n self.state[\"hidden_state\"][agent_name] = self.agents[agent_name].reset_hidden_state(self.params[\"num_workers\"])\n \n self.state[\"was_reset\"] = True",
"title": ""
},
{
"docid": "6575ce299f987b3add90def9cf46180b",
"score": "0.64574206",
"text": "def reset(self):\n self.env.reset()\n obs_list = self.env.get_obs()\n self.init_state = {\"0\": obs_list[0], \"1\": obs_list[1]}\n return self.init_state",
"title": ""
},
{
"docid": "260909800a50db814af226777bacac7a",
"score": "0.637303",
"text": "def __resetAll(self):\n self.update(None, None)",
"title": ""
},
{
"docid": "1e57ee13859623e79dc9940c0f6b6b75",
"score": "0.6372909",
"text": "def _reset(self):\r\n self.personID = None\r\n self.myName = u''\r\n self.billingPos = None",
"title": ""
},
{
"docid": "ef5875fd46c74682137acbc02dcb1bbe",
"score": "0.6368677",
"text": "def reset(self) -> None:",
"title": ""
},
{
"docid": "b05d72cb63a6c94f06e407da4563cd95",
"score": "0.63640827",
"text": "def reset_states(self):\n self.state_c = (\n torch.zeros(self.num_layers, self.batch_size, self.rnn_hidden,\n device=self.device),\n torch.zeros(self.num_layers, self.batch_size, self.rnn_hidden,\n device=self.device),\n )\n self.state_g = (\n torch.zeros(self.num_layers, self.batch_size, self.rnn_hidden,\n device=self.device),\n torch.zeros(self.num_layers, self.batch_size, self.rnn_hidden,\n device=self.device),\n )\n self.state_e = (\n torch.zeros(self.num_layers, self.batch_size, self.rnn_hidden,\n device=self.device),\n torch.zeros(self.num_layers, self.batch_size, self.rnn_hidden,\n device=self.device),\n )",
"title": ""
},
{
"docid": "4b57ce5123fb1dc2bd45a796194fe633",
"score": "0.63616335",
"text": "def reset(self):\n self._rendering_ids.clear()",
"title": ""
},
{
"docid": "3aa356c079048b9ac69b03920ca239d5",
"score": "0.6357857",
"text": "def reset_state_variables(self) -> None:\n # language=rst\n for layer in self.layers:\n self.layers[layer].reset_state_variables()\n\n for connection in self.connections:\n self.connections[connection].reset_state_variables()\n\n for monitor in self.monitors:\n self.monitors[monitor].reset_state_variables()",
"title": ""
},
{
"docid": "5fb0562c7f9290fbe185177db0d1bb91",
"score": "0.632884",
"text": "def reset(self):\n self.stack = list()\n self.timers = dict()",
"title": ""
},
{
"docid": "80f8e3502f8693fff1da67b23680435c",
"score": "0.63252413",
"text": "def reset(self):\n self.uniquename()",
"title": ""
},
{
"docid": "4c676809e6d590a262853131ea852193",
"score": "0.63192093",
"text": "def reset(self):\n self.state = self.init_state\n print(f'state reset to {self.init_state}')\n return()",
"title": ""
},
{
"docid": "11e650d7381c1fe67adf9096b0b68d49",
"score": "0.6303098",
"text": "def reset_history(self):\r\n self.state_action_history = {}",
"title": ""
},
{
"docid": "6e406d336c750158300ecc813bc8d4cd",
"score": "0.6301931",
"text": "def reset_sequential_state(module):\n module._sequential_state = None",
"title": ""
},
{
"docid": "a07acb076462b835277fd67c60ea75fe",
"score": "0.62938416",
"text": "def reset(self):\n self.state = self.gridworld.get_start_state()",
"title": ""
},
{
"docid": "31145d279744d82be8c6e792041454a0",
"score": "0.62937933",
"text": "def reset(self):\n\n self.ptr, self.start_ptr, self.episode_pointers = 0, 0, [0]",
"title": ""
},
{
"docid": "a62fd7c83094736d3694a0daf30eec4a",
"score": "0.62930375",
"text": "def reset_state(self):\n self.current_iteration = 0",
"title": ""
},
{
"docid": "ea27157be0a3bfc2d39a69fedcb4df96",
"score": "0.62917626",
"text": "def reset(self):\n self._state = self.terminal_state.copy()",
"title": ""
},
{
"docid": "7b7efa1683247edde0dd00eb1362a65f",
"score": "0.6288793",
"text": "def _reset(self):\r\n pass",
"title": ""
},
{
"docid": "8739b336d0fd8f95b8d86dcd4860f5a9",
"score": "0.6267508",
"text": "def reset(self):\n self.state = copy.copy(self.mu)",
"title": ""
},
{
"docid": "8739b336d0fd8f95b8d86dcd4860f5a9",
"score": "0.6267508",
"text": "def reset(self):\n self.state = copy.copy(self.mu)",
"title": ""
},
{
"docid": "8739b336d0fd8f95b8d86dcd4860f5a9",
"score": "0.6267508",
"text": "def reset(self):\n self.state = copy.copy(self.mu)",
"title": ""
},
{
"docid": "8739b336d0fd8f95b8d86dcd4860f5a9",
"score": "0.6267508",
"text": "def reset(self):\n self.state = copy.copy(self.mu)",
"title": ""
},
{
"docid": "8739b336d0fd8f95b8d86dcd4860f5a9",
"score": "0.6267508",
"text": "def reset(self):\n self.state = copy.copy(self.mu)",
"title": ""
},
{
"docid": "8739b336d0fd8f95b8d86dcd4860f5a9",
"score": "0.6267508",
"text": "def reset(self):\n self.state = copy.copy(self.mu)",
"title": ""
},
{
"docid": "8739b336d0fd8f95b8d86dcd4860f5a9",
"score": "0.6267508",
"text": "def reset(self):\n self.state = copy.copy(self.mu)",
"title": ""
},
{
"docid": "8739b336d0fd8f95b8d86dcd4860f5a9",
"score": "0.6267508",
"text": "def reset(self):\n self.state = copy.copy(self.mu)",
"title": ""
},
{
"docid": "8739b336d0fd8f95b8d86dcd4860f5a9",
"score": "0.6267508",
"text": "def reset(self):\n self.state = copy.copy(self.mu)",
"title": ""
},
{
"docid": "8739b336d0fd8f95b8d86dcd4860f5a9",
"score": "0.6267508",
"text": "def reset(self):\n self.state = copy.copy(self.mu)",
"title": ""
},
{
"docid": "8739b336d0fd8f95b8d86dcd4860f5a9",
"score": "0.6267508",
"text": "def reset(self):\n self.state = copy.copy(self.mu)",
"title": ""
},
{
"docid": "8739b336d0fd8f95b8d86dcd4860f5a9",
"score": "0.6267508",
"text": "def reset(self):\n self.state = copy.copy(self.mu)",
"title": ""
},
{
"docid": "8739b336d0fd8f95b8d86dcd4860f5a9",
"score": "0.6267508",
"text": "def reset(self):\n self.state = copy.copy(self.mu)",
"title": ""
},
{
"docid": "8739b336d0fd8f95b8d86dcd4860f5a9",
"score": "0.6267508",
"text": "def reset(self):\n self.state = copy.copy(self.mu)",
"title": ""
},
{
"docid": "8739b336d0fd8f95b8d86dcd4860f5a9",
"score": "0.6267508",
"text": "def reset(self):\n self.state = copy.copy(self.mu)",
"title": ""
},
{
"docid": "8739b336d0fd8f95b8d86dcd4860f5a9",
"score": "0.6267508",
"text": "def reset(self):\n self.state = copy.copy(self.mu)",
"title": ""
},
{
"docid": "8739b336d0fd8f95b8d86dcd4860f5a9",
"score": "0.6267508",
"text": "def reset(self):\n self.state = copy.copy(self.mu)",
"title": ""
},
{
"docid": "f14dd24c61d7ac3e4eeba5e208a41b57",
"score": "0.62623477",
"text": "def reset(self):\n self.assigned = []\n self.aggregated = -1\n self.c_i = 0\n self.exposed = 0",
"title": ""
},
{
"docid": "91d5089a33bdf9a84fca032b5e2e8b49",
"score": "0.6251367",
"text": "def reset(self):\n self._L = None",
"title": ""
},
{
"docid": "91d5089a33bdf9a84fca032b5e2e8b49",
"score": "0.6251367",
"text": "def reset(self):\n self._L = None",
"title": ""
},
{
"docid": "91d5089a33bdf9a84fca032b5e2e8b49",
"score": "0.6251367",
"text": "def reset(self):\n self._L = None",
"title": ""
},
{
"docid": "03aace93c59175651aa1a680487369db",
"score": "0.6243167",
"text": "def _init_state(self):\n self.state = self.INITIAL_STATE[:]",
"title": ""
},
{
"docid": "e66f3badda75c3358cc692df26ff3974",
"score": "0.6237339",
"text": "def reset():\r\n pass",
"title": ""
},
{
"docid": "7bde82da6e358c845cc9faff2b227dcc",
"score": "0.62286896",
"text": "def reset(self):\n self.set_hidden_state(self.obj.algo.reset_hidden_state())",
"title": ""
},
{
"docid": "66b6eec983ce45e5820feb46f8f59d46",
"score": "0.62252647",
"text": "def reset(self):\n\t\tprint \"reset\"\n\t\tself.__selected = 0\n\t\tself.__tmpSelection = None\n\t\tdel self.__temp[:]\n\t\tdel self.__edges[:]",
"title": ""
},
{
"docid": "4167ae73f0546c7d1daf1d4e833f95d2",
"score": "0.621968",
"text": "def reset_state(self, state):\n self.game.set_cell_states(state)",
"title": ""
},
{
"docid": "76a3cc413ce9b3ddb31311e911d5da33",
"score": "0.62177813",
"text": "def clear(self):\n with self._write:\n self._statefile._state = list()",
"title": ""
},
{
"docid": "80f0e42be6d288bf04471b9b19423640",
"score": "0.6213098",
"text": "def reset(self):\n self.values = None\n self.keys = None\n self.mask = None",
"title": ""
},
{
"docid": "13662a152f02a53f25db40d6aa023e70",
"score": "0.6212796",
"text": "def reset(self):\n self.allocations.clear()\n self.deallocations.clear()",
"title": ""
},
{
"docid": "2701373b17914bcc3d561db8f0f6bce2",
"score": "0.6209568",
"text": "def unfreeze(self):\n self.states = dd(int, self.states)",
"title": ""
},
{
"docid": "9ecf9f170c3c476eff6ac8a4131111c1",
"score": "0.62035483",
"text": "def reset(self):\n self.named_value = {}\n self.iter = 0",
"title": ""
},
{
"docid": "bffe0dfa740a2944fdfdf28c4c8bf7ad",
"score": "0.61976796",
"text": "def clear(self):\n self._logger.debug(\"Clearing %s [%s]\", self, len(self._identifiers))\n self.cancel_all_pending_tasks()\n self._identifiers.clear()",
"title": ""
},
{
"docid": "bb9d958d0b077b14b36680ee97154fea",
"score": "0.6192936",
"text": "def reset(self):\n self._plays.clear()\n self._coplays.clear()\n self._actions.clear()\n self._state_distribution.clear()",
"title": ""
},
{
"docid": "6b7b4c2e338eacd0057197b6b1d06010",
"score": "0.6190865",
"text": "def _reset(self):\r\n self.characterID = None\r\n self.myName = u''",
"title": ""
},
{
"docid": "f7f4bbf708b7d5a424c0fc2fdb81d805",
"score": "0.61783993",
"text": "def reset() -> NoReturn:",
"title": ""
},
{
"docid": "287e71d4eba41bd872feb0c4b0299605",
"score": "0.61751556",
"text": "def reset(self):\n\n super().reset()\n self.current = self.start_state",
"title": ""
},
{
"docid": "6f46839173e5dbd0c6d801e5547eadfa",
"score": "0.6175003",
"text": "def restore_states(self):\n _comp_ids = self.components.ids()\n for state_id in self.states.ids():\n if state_id in _comp_ids:\n comp = self.components.get(state_id)\n comp.restore_state(self.states.get(state_id))\n Logger.log(LOG_LEVEL[\"debug\"], f\"Restored State for {state_id}\")\n else:\n self.states.remove(state_id)",
"title": ""
},
{
"docid": "fbcb1269c93993519b97927ffbb53542",
"score": "0.6163761",
"text": "def reset(self):",
"title": ""
},
{
"docid": "fbcb1269c93993519b97927ffbb53542",
"score": "0.6163761",
"text": "def reset(self):",
"title": ""
},
{
"docid": "fbcb1269c93993519b97927ffbb53542",
"score": "0.6163761",
"text": "def reset(self):",
"title": ""
},
{
"docid": "fbcb1269c93993519b97927ffbb53542",
"score": "0.6163761",
"text": "def reset(self):",
"title": ""
},
{
"docid": "fbcb1269c93993519b97927ffbb53542",
"score": "0.6163761",
"text": "def reset(self):",
"title": ""
},
{
"docid": "fbcb1269c93993519b97927ffbb53542",
"score": "0.6163761",
"text": "def reset(self):",
"title": ""
},
{
"docid": "fbcb1269c93993519b97927ffbb53542",
"score": "0.6163761",
"text": "def reset(self):",
"title": ""
},
{
"docid": "fbcb1269c93993519b97927ffbb53542",
"score": "0.6163761",
"text": "def reset(self):",
"title": ""
},
{
"docid": "90cdf2043446c4d28d6f6c4f01928590",
"score": "0.6157422",
"text": "def reset(self):\n #dict id <=> genomic position in bp\n #dict id <=> genomic position in rec distance\n self.coords = dict()\n self.coords['pos'] = Coords()\n self.coords['id'] = IDCoords(0,0)\n self.coords['bp'] = self.coords['pos']\n self.coords['rec'] = Coords()\n\n self.sfs = None\n self._data = None\n\n\n self.polarized = None\n self.alleles = None\n\n #dict hid <=> individual name\n self.individual_names = None\n\n self._default_coordinate_system = 'id'\n self._default_individual_selector = 'hid'",
"title": ""
},
{
"docid": "d57e641a9101202e7c9b9bec9e21af54",
"score": "0.61484605",
"text": "def reset(self):\n self.id = self._id\n self.total_machine_num = self._total_machine_num\n\n self.empty_machine_num = self.total_machine_num",
"title": ""
},
{
"docid": "0cd74eeba791ebec6ae8a60253ece174",
"score": "0.6141512",
"text": "def reset(self, state):\n next_state = state.copy()\n for host_addr in self.address_space:\n host = next_state.get_host(host_addr)\n host.compromised = False\n host.access = AccessLevel.NONE\n host.reachable = self.subnet_public(host_addr[0])\n host.discovered = host.reachable\n return next_state",
"title": ""
},
{
"docid": "96bb39eb3866a0e6161bc3f9254347d8",
"score": "0.6136582",
"text": "def reset(self): # real signature unknown; restored from __doc__\n pass",
"title": ""
},
{
"docid": "338061138cd25041b9a38c03ad0d0d0c",
"score": "0.6134436",
"text": "def reset(cls):\n cls._options = None\n cls._scoped_instances = {}",
"title": ""
},
{
"docid": "d1c118d8e4af72d2e0607e069673bfe0",
"score": "0.6128948",
"text": "def clear_and_reset(self):",
"title": ""
},
{
"docid": "12cd5f24be98ead78eef03efd03f17f4",
"score": "0.61286783",
"text": "def clear(self):\n with self._write:\n self._statefile._state = dict()",
"title": ""
},
{
"docid": "909e9b0e5226b77fb416a959c6420a61",
"score": "0.61227727",
"text": "def reset(self) -> None:\n self.in_compact_method = False\n self.in_setup = False\n self.autoname_cursor = dict()",
"title": ""
},
{
"docid": "383d1f8d63b837f2f98f6589a72c45fd",
"score": "0.611954",
"text": "def reset(self):\n pass",
"title": ""
},
{
"docid": "383d1f8d63b837f2f98f6589a72c45fd",
"score": "0.611954",
"text": "def reset(self):\n pass",
"title": ""
},
{
"docid": "383d1f8d63b837f2f98f6589a72c45fd",
"score": "0.611954",
"text": "def reset(self):\n pass",
"title": ""
},
{
"docid": "383d1f8d63b837f2f98f6589a72c45fd",
"score": "0.611954",
"text": "def reset(self):\n pass",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="761"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">588065c092536f7288acf9dbfbdc684a</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Start the FTP client</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "c87cc3a7d39919e3dc281d1fb5e9d120",
"score": "0.7409453",
"text": "def start(self):\n while True:\n try:\n userInput = raw_input('ftp>')\n except KeyboardInterrupt:\n self.commandSock.close()\n break\n\n if userInput != '':\n tokens = userInput.split()\n command = tokens[0].lower()\n\n if command == 'get' and len(tokens) == 2:\n self.retrieve_file(userInput, tokens[1])\n elif command == 'put' and len(tokens) == 2:\n if not isfile(tokens[1]):\n print \"'%s' is not a valid file\" % tokens[1]\n continue\n self.send_file(userInput, tokens[1])\n elif command == 'ls' and len(tokens) == 1:\n self.list_server_files(userInput)\n elif command == 'lls' and len(tokens) == 1:\n subprocess.call(['ls', '-1'])\n elif command == 'quit':\n self.quit(userInput)\n break\n else:\n if not command == 'help':\n print 'Invalid input: %s' % userInput\n print HELP_STRING",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "40a5e6d4c8c6e9800aed333d8fbbeba8",
"score": "0.7501968",
"text": "def startFtp(self, host, port):\n self.ftpfactory = factory = FileClientFactory(self) # setting up the factory\n factory.protocol = FileClientProtocol\n factory.deferred = defer.Deferred()\n factory.deferred.addCallback(self.registerFtp) # Called to register ftp refrence\n reactor.connectTCP(host, port, factory)",
"title": ""
},
{
"docid": "129e1440311e649112fbea091789c79c",
"score": "0.69710284",
"text": "def start_sever():\n listen_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n listen_sock.bind((HOST, PORT))\n listen_sock.listen(5)\n logger.write_to_log_file('Started server on ' + HOST + ':' + str(PORT) + \"\\n\")\n\n #Concurrently accept connection\n while True:\n connection, address = listen_sock.accept()\n f = FTPServer(connection, address, HOST, PORT, FILE_NAME)\n f.start()\n logger.write_to_log_file('Connected on ' + address[0] + ':' + str(address[1]) + \"\\n\")",
"title": ""
},
{
"docid": "4528edc2314069db856372e44d217a51",
"score": "0.67025363",
"text": "def connect(self):\n try:\n # connect to ftp server\n self.ftp = FTP(self.url)\n self.ftp.login(self.user, self.password)\n if debug == True:\n logger.debug(\"Open connection %s\" % self.url)\n except EOFError:\n logger.error('Error in connection')",
"title": ""
},
{
"docid": "a7013b6e8656e864d3415837a0dbbe27",
"score": "0.658001",
"text": "def login(self):\n self.ftp = ftplib.FTP()\n self.ftp.connect(str(self.url))\n self.ftp.login()",
"title": ""
},
{
"docid": "a119c9e388db7f42af1788d8b9d265c7",
"score": "0.65221715",
"text": "def initialize_bg(self):\r\n util.Msg('Starting FTP server...')\r\n self.server_thread = Thread(target=self.initialize)\r\n self.server_thread.start()\r\n return True",
"title": ""
},
{
"docid": "d3119b264d077775a0423f9d1535cbc7",
"score": "0.62836576",
"text": "def start(self, timeout=0.001):\n if self.__serving:\n raise RuntimeError(\"Server already started\")\n if self.__stopped:\n # ensure the server can be started again\n ThreadFTP.__init__(self, self.server.socket.getsockname(),\n self.handler)\n self.__timeout = timeout\n threading.Thread.start(self)\n self.__flag.wait()",
"title": ""
},
{
"docid": "53e07f5e748c3a570e23ef1bbb3e084b",
"score": "0.61653376",
"text": "def start():\n\n host = serverhost\n port = serverport\n\n protocol = SenzcProtocol(host, port)\n reactor.listenUDP(0, protocol)\n reactor.run()",
"title": ""
},
{
"docid": "27f8dc1a9045f5c1fe55f5ef8d075b3a",
"score": "0.6157366",
"text": "def __init__(\n self, username=\"anonymous\", password=\"twisted@twistedmatrix.com\", passive=1\n ):\n FTPClientBasic.__init__(self)\n self.queueLogin(username, password)\n\n self.passive = passive",
"title": ""
},
{
"docid": "73ec869533e41c50b77dab4a7cfa5b5e",
"score": "0.6092297",
"text": "def connect(self):\n if not self.connected:\n self.logger.debug(f\"Connecting to ftp://{DMA_AIS_FTP_HOST}\")\n self.ftp = ftplib.FTP(DMA_AIS_FTP_HOST)\n self.logger.debug(\"Logging in as anonymous\")\n self.ftp.login()\n self.connected = True",
"title": ""
},
{
"docid": "80c44155315a4f2f2984f8a4bbd6e03d",
"score": "0.60853463",
"text": "def handle(self):\n # set timeout\n self.request.settimeout(10)\n wlog(\"[FTP] {} has connected\".format(self.client_address[0]))\n self.request.sendall(\"220 xxe-ftp-server\\n\")\n try:\n while True:\n self.data = self.request.recv(4096).strip()\n wlog(\"[FTP] Received:\\n{}\".format(self.data))\n if \"LIST\" in self.data:\n self.request.sendall(\"drwxrwxrwx 1 owner group 1 Feb 21 04:37 rsl\\n\")\n self.request.sendall(\"150 Opening BINARY mode data connection for /bin/ls\\n\")\n self.request.sendall(\"226 Transfer complete.\\n\")\n elif \"USER\" in self.data:\n self.request.sendall(\"331 password please - version check\\n\")\n elif \"PORT\" in self.data:\n wlog(\"[FTP] ! PORT received\")\n wlog(\"[FTP] > 200 PORT command ok\")\n self.request.sendall(\"200 PORT command ok\\n\")\n elif \"SYST\" in self.data:\n self.request.sendall(\"215 RSL\\n\")\n else:\n wlog(\"[FTP] > 230 more data please!\")\n self.request.sendall(\"230 more data please!\\n\")\n except Exception, e:\n if \"timed out\" in e:\n wlog(\"[FTP] Client timed out\")\n else:\n wlog(\"[FTP] Client error: {}\".format(e))\n wlog(\"[FTP] Connection closed with {}\".format(self.client_address[0]))",
"title": ""
},
{
"docid": "3644bdf0c01a67eb9b92dacd5f018d51",
"score": "0.60358423",
"text": "def start(self):\n self._client = self.client(\n host=self.host,\n port=self.port\n )",
"title": ""
},
{
"docid": "fae71777bd196294dab008e50f3e93b7",
"score": "0.59935635",
"text": "def _login(self):\n\n with contextlib.suppress(error_perm):\n ftp = FTP(self._ftpsite, timeout=600)\n ftp.login(user=self._user, passwd=self._password)\n ftp.voidcmd('NOOP')\n ftp.set_debuglevel(self._debug_lvl)\n\n return ftp",
"title": ""
},
{
"docid": "3c298f500a41c166794c49f624b67b59",
"score": "0.59661764",
"text": "def start( self ):\n pathCheck( self.command )\n cout = '/tmp/' + self.name + '.log'\n if self.cdir is not None:\n self.cmd( 'cd ' + self.cdir )\n self.cmd( self.command + ' ' + self.cargs % self.port +\n ' 1>' + cout + ' 2>' + cout + ' &' )\n self.execed = False",
"title": ""
},
{
"docid": "bcd648ad8b77297409636b6418abfcae",
"score": "0.5943617",
"text": "def connect(self, host='', port=0):\n ret = FTP.connect(self, host, port)\n self.clear_sock = self.sock\n self.clear_file = self.file",
"title": ""
},
{
"docid": "99e1b93cb27a72f3b8e4fcc469fcd122",
"score": "0.58787334",
"text": "def start(self):\n self.server.start()",
"title": ""
},
{
"docid": "99e1b93cb27a72f3b8e4fcc469fcd122",
"score": "0.58787334",
"text": "def start(self):\n self.server.start()",
"title": ""
},
{
"docid": "99e1b93cb27a72f3b8e4fcc469fcd122",
"score": "0.58787334",
"text": "def start(self):\n self.server.start()",
"title": ""
},
{
"docid": "cec5f0c5dd485570a75d78def6153990",
"score": "0.58765006",
"text": "def start(self):\n self.background()\n svc(('-u', self.path.strpath))",
"title": ""
},
{
"docid": "657eb21c08bf8f28a6de6cba6d96cd3a",
"score": "0.5872686",
"text": "def start(self):\n print('Start client: '+self._name)\n self._client.loop_start()",
"title": ""
},
{
"docid": "40cc97e39ff7073bfd77e7270138c428",
"score": "0.58316195",
"text": "def dtmftp(user, passwd, ftp_path, main_dir):\n # as it is parallel required ....\n ftp = FTP(\"ftp.ceda.ac.uk\", \"\", \"\")\n ftp.login(user=user, passwd=passwd)\n # navigate to the dir\n ftp.cwd(ftp_path)\n # I hate ESRI\n esri_types = ['dblbnd.adf', 'hdr.adf', 'prj.adf',\n 'sta.adf', 'w001001.adf', 'w001001x.adf']\n # outdir in which the dinosaur format goes \n dirname = os.path.join(main_dir, ftp_path.split(sep=\"/\")[6])\n if not os.path.isdir(dirname):\n os.mkdir(dirname) \n # loop through the files and write to disk\n for e in esri_types:\n localfile = os.path.join(dirname, e)\n with open(localfile, \"wb\") as lf:\n ftp.retrbinary('RETR ' + e, lf.write, 1024)\n # why is this not quitting.....\n ftp.quit()\n return dirname",
"title": ""
},
{
"docid": "5bc59b19a6015b75d5473eeecc21037d",
"score": "0.58190763",
"text": "def __startFtpResume__(self, restart=None):\r\n if restart:\r\n f = open(self.localFileName , \"wb\")\r\n else:\r\n f = open(self.localFileName , \"ab\")\r\n ftper = ftplib.FTP(timeout=60)\r\n parseObj = urlparse.urlparse(self.url)\r\n baseUrl= parseObj.hostname\r\n urlPort = parseObj.port\r\n bPath = os.path.basename(parseObj.path)\r\n gPath = parseObj.path.replace(bPath, \"\")\r\n unEncgPath = urllib.unquote(gPath)\r\n fileName = urllib.unquote(os.path.basename(self.url))\r\n ftper.connect(baseUrl, urlPort)\r\n ftper.login(self.auth[0], self.auth[1])\r\n if len(gPath) > 1:\r\n ftper.cwd(unEncgPath)\r\n ftper.sendcmd(\"TYPE I\")\r\n ftper.sendcmd(\"REST \" + str(self.getLocalFileSize()))\r\n downCmd = \"RETR \"+ fileName\r\n ftper.retrbinary(downCmd, f.write)",
"title": ""
},
{
"docid": "13c6cd1750bd466fd363cb5f010ff8fd",
"score": "0.5781872",
"text": "def FtpProgram():\n TimeStamp = time.strftime(\"%m%d%Y_%H%M%S\")\n #Create main folder on ftp server\n main_folder = ('Backup_' + TimeStamp)\n ftp.login(user=user, passwd = passwd)\n ftp.mkd(main_folder)\n ftp.cwd(main_folder)\n #for loop to iterate thru folder dict to copy local files to remote ftp server folders\n for path, folder in folders_dict.items():\n ftp.mkd(folder)\n ftp.cwd(folder)\n copyFiles(ftp, path)\n ftp.cwd(\"..\")\n ftp.quit()",
"title": ""
},
{
"docid": "eaa735645f0cf63a9e3a925ffe0171ff",
"score": "0.57809424",
"text": "def start(self):\n\n # Create command list and run subprocess\n cmd = [self._server_path]\n cmd += self._server_config.to_cli_string().replace('=', ' ').split()\n\n self._tritonserver_process = Popen(cmd,\n stdout=PIPE,\n stderr=STDOUT,\n universal_newlines=True)",
"title": ""
},
{
"docid": "edf4f7338a27659399299101ae2bd6bd",
"score": "0.57795304",
"text": "def start_server(self):\n # server that can handle multiple requests, defining our threadpool\n file_server = grpc.server(futures.ThreadPoolExecutor(max_workers=100), options=(('grpc.max_message_length', 50 * 1024 * 1024,),('grpc.max_receive_message_length', 50 * 1024 * 1024)))\n\n # adding the services that this server can serve\n fileservice_pb2_grpc.add_FileServiceServicer_to_server(FileServiceImplementation(self.port), file_server)\n\n # bind the server to the described port\n file_server.add_insecure_port('[::]:{}'.format(self.port))\n\n # start the server\n file_server.start()\n\n print(f'File Server running on port {self.port}...')\n\n try:\n # Keep the program running unless keyboard interruption\n while True:\n time.sleep(60 * 60 * 60)\n except KeyboardInterrupt:\n file_server.stop(0)\n print('File Server Stopped ...')",
"title": ""
},
{
"docid": "a27983a188d11d77794807be0c764678",
"score": "0.5766372",
"text": "def start(self):\n\n if self._server_path:\n # Create command list and run subprocess\n cmd = [self._server_path]\n cmd += self._server_config.to_cli_string().replace(\"=\", \" \").split()\n\n self._tritonserver_process = Popen(\n cmd, start_new_session=True, stdout=PIPE, stderr=STDOUT, universal_newlines=True\n )\n LOGGER.debug(\"Triton Server started.\")",
"title": ""
},
{
"docid": "655cc0ed3f9e1f41b882ac283246ffc0",
"score": "0.5761689",
"text": "def start(self, *args, **kwargs):\r\n self._type = 'telnet'\r\n self._cmd = 'va_telnet'\r\n self._timeout = 180\r\n self._userid = self._user.get('name')\r\n self._passwd = self._user.get('password')\r\n self._rcmd = ['pwd']\r\n\r\n if 'timeout' in kwargs:\r\n self._timeout = kwargs.get('timeout')\r\n if 'rcmd' in kwargs:\r\n self._rcmd.append(kwargs.get('rcmd'))\r\n if 'dest_ip' in kwargs:\r\n self._dest_ip = kwargs.get('dest_ip')\r\n\r\n try:\r\n self._build_telnet_cmd()\r\n except ValueError as e:\r\n raise e\r\n\r\n client = self._conf_client\r\n pid, outfile = client.exec_background(self._cmd, redirect=True, \r\n search_expr=self._cmd.split(' ')[0], search_full=True)\r\n logger.info('Telnet pid: {}'.format(pid))\r\n logger.info('Outfile: {}'.format(outfile))\r\n self._outfile = outfile\r\n if not pid or outfile is None:\r\n raise ValueError('Telnet traffic not started')\r\n\r\n times = 1\r\n sleeptime = 0.2\r\n self._stats = self.get_stats()\r\n while (self._stats == 'failed' or self._stats is None) and times <= 5:\r\n logger.debug('Sleeping {} seconds to start traffic'.format(sleeptime))\r\n time.sleep(sleeptime)\r\n self._stats = self.get_stats()\r\n times += 1\r\n\r\n if self._stats == 'failed' or self._stats is None:\r\n raise ValueError('Telnet traffic not started')\r\n \r\n logger.info('Telnet traffic started')",
"title": ""
},
{
"docid": "9c6e5107406f9e5128bab9d0c6f58530",
"score": "0.575314",
"text": "def __init__(self, host=''):\n\n self.secureConnection = secureSocket \n self.control_state = 0\n self.data_state = 0\n FTP.__init__(self, host)",
"title": ""
},
{
"docid": "64087fceb571bcf3153dd0e0dde8e3b2",
"score": "0.5748028",
"text": "def __init__(self, user_name, password, url, use_cred_manager=True, show_authentication=True):\n self.url = url\n self.user_name = user_name\n self.password = password\n self.ftp = ftplib.FTP(self.url)\n self.authenticated = False\n self.use_cred_mgr = use_cred_manager\n self.show_authentication = show_authentication\n self._service_name = \"FTPHelper\"",
"title": ""
},
{
"docid": "d743fa91e23803352498f7c94b48412c",
"score": "0.57450664",
"text": "def _start(self):\n self._process = subprocess.Popen(\n [self._path_chimera, \"--start\", \"RESTServer\"],\n stdout = subprocess.PIPE,\n close_fds = True\n )\n #reading port nr on localhost\n self._port_nr = self._process.stdout.readline().strip().split()[-1]\n #creating url to communicate with chimera\n self._url = \\\n \"http://localhost:{port_number}/run\".format(\n **{\"port_number\": self._port_nr})",
"title": ""
},
{
"docid": "e2f112b525120b61aff0295c8b2f6967",
"score": "0.57136786",
"text": "def start(self):\n self.log(f\"STARTING CLIENT (username: {self.username})\", level = 'info')\n try:\n self.conn = Client(address=self.addr, authkey=self.authkey)\n except Exception as e:\n print(f\"[ERROR] {e}\")\n else:\n self.receiver = Process(target=self._receive_loop,\n name=f\"{self.username} receiver\")\n self.sender = Process(target=self._send_loop,\n name=f\"{self.username} sender\")\n if self.connect():\n self.ui.start()\n self.sender.start()\n self.receiver.start()\n self.sender.join()\n self.receiver.join()\n else:\n self.stop()",
"title": ""
},
{
"docid": "d78cfdf93f73f77bb7f78542d1739d5b",
"score": "0.5698807",
"text": "def _do_sftp(self, data:list=[]) -> None:\n if not self.hop.transport:\n gkf.tombstone(red('Transport layer is not open. You must create it first.'))\n return\n\n gkf.tombstone(blue('creating sftp client.'))\n start_time = time.time()\n OK = self.hop.open_sftp()\n stop_time = time.time()\n\n if OK: gkf.tombstone(blue('success'))\n else: gkf.tombstone(red('failure '+self.hop.error_msg()))\n\n gkf.tombstone(blue('elapsed time: {}'.format(elapsed_time(start_time, stop_time))))",
"title": ""
},
{
"docid": "e99327b9b213b454cdea5c3bbc6e6a02",
"score": "0.5652263",
"text": "def ftp_STOR(self, path):\n if self.dtpInstance is None:\n raise BadCmdSequenceError(\"PORT or PASV required before STOR\")\n\n try:\n newsegs = toSegments(self.workingDirectory, path)\n except InvalidPath:\n return defer.fail(FileNotFoundError(path))\n\n # XXX For now, just disable the timeout. Later we'll want to\n # leave it active and have the DTP connection reset it\n # periodically.\n self.setTimeout(None)\n\n # Put it back later\n def enableTimeout(result):\n self.setTimeout(self.factory.timeOut)\n return result\n\n def cbOpened(file):\n \"\"\"\n File was open for reading. Launch the data transfer channel via\n the file consumer.\n \"\"\"\n d = file.receive()\n d.addCallback(cbConsumer)\n d.addCallback(lambda ignored: file.close())\n d.addCallbacks(cbSent, ebSent)\n return d\n\n def ebOpened(err):\n \"\"\"\n Called when failed to open the file for reading.\n\n For known errors, return the FTP error code.\n For all other, return a file not found error.\n \"\"\"\n if isinstance(err.value, FTPCmdError):\n return (err.value.errorCode, \"/\".join(newsegs))\n log.err(err, \"Unexpected error received while opening file:\")\n return (FILE_NOT_FOUND, \"/\".join(newsegs))\n\n def cbConsumer(cons):\n \"\"\"\n Called after the file was opended for reading.\n\n Prepare the data transfer channel and send the response\n to the command channel.\n \"\"\"\n if not self.binary:\n cons = ASCIIConsumerWrapper(cons)\n\n d = self.dtpInstance.registerConsumer(cons)\n\n # Tell them what to doooo\n if self.dtpInstance.isConnected:\n self.reply(DATA_CNX_ALREADY_OPEN_START_XFR)\n else:\n self.reply(FILE_STATUS_OK_OPEN_DATA_CNX)\n\n return d\n\n def cbSent(result):\n \"\"\"\n Called from data transport when tranfer is done.\n \"\"\"\n return (TXFR_COMPLETE_OK,)\n\n def ebSent(err):\n \"\"\"\n Called from data transport when there are errors during the\n transfer.\n \"\"\"\n log.err(err, \"Unexpected error received during transfer:\")\n if err.check(FTPCmdError):\n return err\n return (CNX_CLOSED_TXFR_ABORTED,)\n\n d = self.shell.openForWriting(newsegs)\n d.addCallbacks(cbOpened, ebOpened)\n d.addBoth(enableTimeout)\n\n # Pass back Deferred that fires when the transfer is done\n return d",
"title": ""
},
{
"docid": "7042f6a4a26a092bb94d7bdcb0cec5e4",
"score": "0.56409854",
"text": "def _clientServerUploadOptions(self, options, upload_file=None):\n input_path = self.t_640KB\n if not upload_file:\n upload_file = input_path\n\n server = tftpy.TftpServer(self.t_path)\n client = tftpy.TftpClient('localhost', 20001, options)\n server_thread = threading.Thread(group=None, target=server.listen,\n kwargs={'listenip': 'localhost',\n 'listenport': 20001,\n 'timeout': 10})\n server_thread.start()\n server.is_running.wait()\n try:\n time.sleep(1)\n client.upload(\"out.tmp\", upload_file)\n finally:\n server.stop(now=False)\n server_thread.join()",
"title": ""
},
{
"docid": "bf0029fdd558c0d875d604fb562afa86",
"score": "0.56333107",
"text": "def connect(self, host, port=21, user=None, passw=None, path='/', timeout=10):\n\n try:\n\n message = '{0}/{1}@{2}:{3}{4} timeout:{5}'.format(\n user, passw, host, port, path, timeout)\n self._mh.demsg('htk_on_debug_info', self._mh._trn.msg(\n 'htk_ftp_connecting', message), self._mh.fromhere())\n\n ev = event.Event(\n 'ftp_before_connect', host, port, user, passw, path, timeout)\n if (self._mh.fire_event(ev) > 0):\n host = ev.argv(0)\n port = ev.argv(1)\n user = ev.argv(2)\n passw = ev.argv(3)\n path = ev.argv(4)\n timeout = ev.argv(5)\n\n self._host = host\n self._port = port\n self._user = user\n self._passw = passw\n\n if (ev.will_run_default()):\n self._client.connect(self._host, self._port, timeout=timeout)\n\n if (self._user != None):\n self._client.login(self._user, self._passw)\n\n if (self._secured):\n self._client.prot_p()\n\n self._is_connected = True\n\n self._mh.demsg('htk_on_debug_info', self._mh._trn.msg(\n 'htk_ftp_connected'), self._mh.fromhere())\n if (path != None):\n self.change_dir(path)\n\n ev = event.Event('ftp_after_connect')\n self._mh.fire_event(ev)\n\n return True\n\n except all_errors as ex:\n self._mh.demsg(\n 'htk_on_error', 'error: {0}'.format(ex), self._mh.fromhere())\n return False",
"title": ""
},
{
"docid": "f8a957f3a909150e4a01d34be80cc455",
"score": "0.56225544",
"text": "def startServe(self, daemon=False):\n timeHeader = self.giveTime()\n logging.info(timeHeader + \"Proxy listening on \" + self.host + \":\" + str(self.port))\n\n if not daemon:\n self.startHelper()\n else:\n self.TCPserver = threading.Thread(target=self.startHelper)\n self.TCPserver.start()\n pass",
"title": ""
},
{
"docid": "a1eb7b9492ba292b5fc84be1ab6ea83f",
"score": "0.5619557",
"text": "def start(self):\n self.service_info = ServiceInfo(type_ = '_webthing._tcp.local.',\n name = '{}._webthing._tcp.local.'.format(self.name),\n address = socket.inet_aton(self.ip),\n port = self.port,\n properties = {'path': '/', },\n server = '{}.local.'.format(socket.gethostname()))\n self.zeroconf = Zeroconf()\n self.zeroconf.register_service(self.service_info)\n\n self.server.listen(self.port)\n tornado.ioloop.IOLoop.current().start()",
"title": ""
},
{
"docid": "82f1d5fcaaf96cb26c86554b8dc45bba",
"score": "0.5615459",
"text": "def __init__(self, ftpsite, user, password, keepalive=False, debug_lvl=0):\n self._ftpsite = ftpsite\n self._user = user\n self._password = password\n self._debug_lvl = debug_lvl\n self.ftp = self._login()\n self.__keepalive = keepalive\n\n if self.__keepalive:\n self._voidcmd_repeat, self._filetransfer_repeat = self._keepalive()",
"title": ""
},
{
"docid": "e933d004223c7411b1c49717905b08f5",
"score": "0.5613944",
"text": "def start(host=HOST, port=PORT, username='', password='', nickname=\"\"):\n StompClientFactory.username = username\n StompClientFactory.password = password\n StompClientFactory.nickname = nickname\n reactor.connectTCP(host, port, StompClientFactory())\n reactor.run()",
"title": ""
},
{
"docid": "e530d57dcfabd8b468cbd39d4ed224a5",
"score": "0.5609821",
"text": "def run(self):\n self.log.debug(\"Starting up..\")\n self._connect()",
"title": ""
},
{
"docid": "45cf807838074b3b0859748a12d4f227",
"score": "0.55926085",
"text": "def __setup(self):\n # Set to kill on exit to prevent a zombie process\n self.__set(Lftp.__SET_COMMAND_AT_EXIT, \"\\\"kill all\\\"\")\n # Auto-add server to known host file\n self.sftp_auto_confirm = True",
"title": ""
},
{
"docid": "12ee73a77668ead6f15c3e0c03e78419",
"score": "0.5576053",
"text": "def receive_file(self):\n try:\n transfer_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n transfer_socket.bind(('', 0))\n transfer_socket.listen(1)\n transfer_port = transfer_socket.getsockname()[1]\n transfer_port = self.buffer_header(transfer_port)\n except socket.error as e:\n print e\n return\n\n # send transfer port to client\n try:\n self.client_socket.send(transfer_port)\n except socket.error as e:\n self.op_failure_message(const.COMMAND_GET)\n return\n\n while True:\n ftp_transfer_socket, address = transfer_socket.accept()\n print \"Accepted connection from %s\" % str(address)\n\n if ftp_transfer_socket:\n # file name header handling\n file_name_header = self.receive_bytes(ftp_transfer_socket, const.FILENAME_SIZE)\n file_name = file_name_header.translate(None, '0')\n # file size header handling\n file_size_header = self.receive_bytes(ftp_transfer_socket, const.HEADER_SIZE)\n file_size = int(file_size_header)\n # file data handling\n file_data = self.receive_bytes(ftp_transfer_socket, file_size)\n\n # calculate potential home of file in _client_uploads\n file_path = \"%s%s%s\" % (const.CLIENT_UPLOAD_FOLDER, const.FILE_SEPARATOR, file_name)\n\n # allocate file data\n transfer_file = open(file_path, 'w')\n transfer_file.write(file_data)\n transfer_file.close()\n transfer_socket.close()\n self.op_success_message(const.COMMAND_PUT)\n return",
"title": ""
},
{
"docid": "d4a2013681dd053297582d8a274b256d",
"score": "0.5576019",
"text": "def _login(self):\n try:\n if not self.password and self.use_cred_mgr:\n password = keyring.get_password(self._service_name, self.user_name)\n if not password:\n raise FTPUserNameNotFound(\"{self.user_name} not found in credential manager\".format(**locals()))\n else:\n password = self.password\n\n self.ftp.login(user=self.user_name, passwd=password)\n\n except Exception as e:\n logger.exception(\"Authentication Failed\")\n raise FTPAuthenticationFailed\n\n else:\n if self.show_authentication:\n logger.info(\"Authentication succeeded\")\n\n # Update the password in case it changed\n if self.use_cred_mgr:\n keyring.set_password(self._service_name, self.user_name, self.password)\n\n self.authenticated = True\n return True",
"title": ""
},
{
"docid": "567191374cd0d7325f4941cf86938831",
"score": "0.5567827",
"text": "def run(self):\n serversocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n serversocket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n serversocket.bind(('', self._plugin.get_port()))\n serversocket.listen(5)\n while True:\n clientsocket, address = serversocket.accept()\n if self._flag.is_set():\n clientsocket.close()\n break\n else:\n args = clientsocket, address, self._session_factory()\n Thread(target=self._plugin.run, args=args).start()\n serversocket.close()",
"title": ""
},
{
"docid": "915ce88701bb328da996721fb50f2c30",
"score": "0.5566956",
"text": "def start(self):\n self.debug(\"### starting bfx streaming API, trading %s%s\" %\n (self.curr_base, self.curr_quote))\n self.client.start()",
"title": ""
},
{
"docid": "df99f9b76d2e5f3931a530defdaebdc8",
"score": "0.55476975",
"text": "def ftp_RETR(self, path):\n if self.dtpInstance is None:\n raise BadCmdSequenceError(\"PORT or PASV required before RETR\")\n\n try:\n newsegs = toSegments(self.workingDirectory, path)\n except InvalidPath:\n return defer.fail(FileNotFoundError(path))\n\n # XXX For now, just disable the timeout. Later we'll want to\n # leave it active and have the DTP connection reset it\n # periodically.\n self.setTimeout(None)\n\n # Put it back later\n def enableTimeout(result):\n self.setTimeout(self.factory.timeOut)\n return result\n\n # And away she goes\n if not self.binary:\n cons = ASCIIConsumerWrapper(self.dtpInstance)\n else:\n cons = self.dtpInstance\n\n def cbSent(result):\n return (TXFR_COMPLETE_OK,)\n\n def ebSent(err):\n log.msg(\"Unexpected error attempting to transmit file to client:\")\n log.err(err)\n if err.check(FTPCmdError):\n return err\n return (CNX_CLOSED_TXFR_ABORTED,)\n\n def cbOpened(file):\n # Tell them what to doooo\n if self.dtpInstance.isConnected:\n self.reply(DATA_CNX_ALREADY_OPEN_START_XFR)\n else:\n self.reply(FILE_STATUS_OK_OPEN_DATA_CNX)\n\n d = file.send(cons)\n d.addCallbacks(cbSent, ebSent)\n return d\n\n def ebOpened(err):\n if not err.check(\n PermissionDeniedError, FileNotFoundError, IsADirectoryError\n ):\n log.msg(\"Unexpected error attempting to open file for \" \"transmission:\")\n log.err(err)\n if err.check(FTPCmdError):\n return (err.value.errorCode, \"/\".join(newsegs))\n return (FILE_NOT_FOUND, \"/\".join(newsegs))\n\n d = self.shell.openForReading(newsegs)\n d.addCallbacks(cbOpened, ebOpened)\n d.addBoth(enableTimeout)\n\n # Pass back Deferred that fires when the transfer is done\n return d",
"title": ""
},
{
"docid": "8f444016d5d570e82b4ab92a9fb6a5c7",
"score": "0.55407196",
"text": "def startPythonServer(options, pwd):\n command = [os.path.join(pwd, 'src', 'python', 'twt.py')]\n command.append(options.chuck_ip)\n command.append(options.local_word)\n for term in options.terms:\n command.append(term)\n sys.stdout.write(\"Starting tweets server ... \")\n try:\n p = subprocess.Popen(command)\n sys.stdout.write(\"[ok]\\n\")\n except:\n sys.stdout.write(\"[error]\\n\")\n p = None\n finally:\n os.chdir(pwd)\n sys.stdout.flush()\n return p",
"title": ""
},
{
"docid": "8e10d9199d691a4b01142a37220cbb96",
"score": "0.5540484",
"text": "def startup(self):\n args = [OPENOFFICE_BIN,\n '--accept=socket,host=localhost,port=%d;urp;StarOffice.ServiceManager' % self.port,\n '--norestore',\n '--nofirststartwizard',\n '--nologo',\n '--headless',\n ]\n env = {'PATH' : '/bin:/usr/bin:%s' % OPENOFFICE_PATH,\n 'PYTHONPATH' : OPENOFFICE_LIBPATH,\n }\n\n try:\n pid = os.spawnve(os.P_NOWAIT, args[0], args, env)\n except Exception as e:\n print(e, file=sys.stderr)\n raise Exception(\"Failed to start OpenOffice on port %d\" % self.port)\n\n if pid <= 0:\n raise Exception(\"Failed to start OpenOffice on port %d\" % self.port)",
"title": ""
},
{
"docid": "d425375bb28d649c71869732760004ae",
"score": "0.55143577",
"text": "def open(self):\n\n if self.debug: \n print >>sys.stderr, \"Opening host %s:%d ...\" % (self.host,\n self.port)\n \n # self.tcp_conn = telnetlib.Telnet(host = self.host, \n # port = self.port, \n # timeout = self.timeout)\n # DO NOT PROVIDE host / port / etc \n # when you create the Telnet instance\n # -> bug: does not work\n \n self.tcp_conn = telnetlib.Telnet()\n self.tcp_conn.open(host = self.host, port = self.port, \n timeout = self.timeout)\n\n time.sleep(1)\n\n self.purge()\n\n time.sleep(1)\n\n if not(self.echotest()):\n raise IOError((\"Not echoing on TCP/IP %s:%s\") % \n (self.host, self.port))\n \n if self.debug: \n print >>sys.stderr, ( \"Opening socket %s:%s done.\" % \n (self.host, self.port))",
"title": ""
},
{
"docid": "ea62afd1de71e4d8e0663a146b2f2922",
"score": "0.55141145",
"text": "def start(self):\n self.connection_thread = threading.Thread(target=self._run)\n self.connection_thread.daemon = True\n self.connection_thread.start()\n\n self.delegator_thread = threading.Thread(target=self._delegator)\n self.delegator_thread.daemon = True\n self.delegator_thread.start()",
"title": ""
},
{
"docid": "26c632846dd624df4a3887b9cf070684",
"score": "0.54996777",
"text": "def Start(self) -> None:\n self.Stop()\n\n self._isok = True\n self._thread = threading.Thread(target=self._RunThread, name='plcserver')\n self._thread.start()",
"title": ""
},
{
"docid": "931fb6b087e261dacdba6569eeb9d5df",
"score": "0.5497885",
"text": "def start(self):\n log.debug(\"Starting endpoint `%s`\", self.id)\n self._httpd_thread.start()\n self._httpd.startup_done.wait()",
"title": ""
},
{
"docid": "1f0851e6ecfff6d9f8b1dfaf0780135c",
"score": "0.549441",
"text": "def start():\n \n def startServ():\n \"\"\"\n Please don't call this.\n \"\"\"\n global osc_serv_target\n global oscProcess\n osc_serv_target.serve_forever()\n\n oscProcess = Process(\n target = startServ,\n name = \"nimbus_osc_server\",\n daemon = True,\n )\n oscProcess.start()",
"title": ""
},
{
"docid": "5209435dd4ca5c7f47dd1db5fcac4e6f",
"score": "0.5493021",
"text": "def start(self):\n self.mqttc.connect(self._broker_url, self._broker_port, self._broker_keepalive)\n\n # start network loop\n msg = \"starting TC User with id %s\" % (self.id,)\n self.output_log(msg)\n self.mqttc.loop_start()",
"title": ""
},
{
"docid": "b84edcfd044b4c12db2a941f0e67c6f1",
"score": "0.5476911",
"text": "def run_client():\r\n my_client = Client(connect_to_server(), 8820)\r\n my_client.menu_options()",
"title": ""
},
{
"docid": "83ed145571fa601fe1bea624b6248476",
"score": "0.54616207",
"text": "def startServ():\n global osc_serv_target\n global oscProcess\n osc_serv_target.serve_forever()",
"title": ""
},
{
"docid": "0eb89476a9ebf2040b18ce635d910f2b",
"score": "0.54463166",
"text": "def start(self):\n\n self.run_thread.start()",
"title": ""
},
{
"docid": "34e1eb73cb0b03cbc16c61ee38c67659",
"score": "0.54439616",
"text": "def start(self):\n self._sock.connect((IP_ADDRESS, PORT))\n self.start_requesting()",
"title": ""
},
{
"docid": "13f6e0d6091da6ff642ca5f909c83a92",
"score": "0.5430541",
"text": "def start(self):\n super(Controller, self).start()\n self.cmd(self.floodlight, '-cf %s/resources/floodlight.conf &' % emu_config.basedir)",
"title": ""
},
{
"docid": "33b00c0c60d3fc75606a5beee7a83d3c",
"score": "0.5421921",
"text": "def run(self):\n self._connection = self.connect()\n self._connection.ioloop.start()",
"title": ""
},
{
"docid": "4982bcdbb4d83463e1245cb66b34067e",
"score": "0.5419419",
"text": "def StartServer(self):\n self._handle_request_thread.start()",
"title": ""
},
{
"docid": "71283ba341f27fef416f9074a94f38fd",
"score": "0.5414439",
"text": "def run(self):\n\t\tif not self.settings:\n\t\t\traise RuntimeError(\"The server is not configured.\")\n\n\t\tself.transport = TransportFactory(self.settings.get('transport_type'))\n\t\tself.restServer = RestServer()\n\n\t\tself.transport.connect()\n\t\tself.restServer.run()",
"title": ""
},
{
"docid": "498e83c0a393b2dff6dd726769f1f8ab",
"score": "0.5402659",
"text": "def startFrogServer(self, mode):\n\t\tif(mode == 'start'):\n\t\t\tprint \"** Start Frog Server\"\n\t\t\t#os.system(\"mate-terminal -e 'frog -S \" + str(self.PORTNUMBER) + \" > /dev/null 2>&1'\")\n\t\t\tos.system(\"frog -S \" + str(self.PORTNUMBER) + \" > /dev/null 2>&1 &\")\n\t\tif(mode == 'stop'):\n\t\t\tprint \"** Close Frog Server\"\n\t\t\tproc = subprocess.Popen([\"pgrep\", 'frog'], stdout=subprocess.PIPE) \n\t\t\tfor pid in proc.stdout: \n\t\t\t\tos.kill(int(pid), signal.SIGTERM)",
"title": ""
},
{
"docid": "9f7f33daf83a5998e99eee96cc18d54f",
"score": "0.5390237",
"text": "def start(self):\r\n super(Service, self).start()\r\n self.tg.add_thread(self._run, self.application, self._socket)",
"title": ""
},
{
"docid": "478ecc00c458902a70fd2c6b110646b3",
"score": "0.5380135",
"text": "def ftp_connect():\n ftp = FTP(\"ftp.ncbi.nlm.nih.gov\", timeout=None)\n ftp.login(user='anonymous', passwd=\"\")\n return ftp",
"title": ""
},
{
"docid": "edd93d7a371cecc26720fb76d10949f3",
"score": "0.53657466",
"text": "def connect(self):\n\n\t\tself.accept()\n\t\tself.start_sync()",
"title": ""
},
{
"docid": "7f59c17e89f7c11004e4b9d748c579a7",
"score": "0.5353558",
"text": "def run(self):\n cherrypy.engine.SIGHUP = None\n cherrypy.engine.SIGTERM = None\n cherrypy.engine.autoreload_on = False\n\n # User config file if specified\n if self.configFile:\n cherrypy.config.update(self.configFile)\n # Override explicitly passed config options\n cherrypy.config.update(self.configDict)\n cherrypy.log._set_screen = False\n \n cherrypy.tree.mount(self.httpTree)\n cherrypy.server.quickstart()\n cherrypy.engine.start(blocking=False)\n \n cherrypy.log._set_screen = False\n\n # Loop till done\n finished = False\n while not finished:\n time.sleep(5)\n finished = self.exitFlag\n \n # When done, exit gracefully\n self.__suicide__()",
"title": ""
},
{
"docid": "0b8ac3e6293af156471c527b8cea9f40",
"score": "0.5352604",
"text": "def connect_to_server(self):\n self.commandSock.connect(self.server)\n self.start()",
"title": ""
},
{
"docid": "51094efca8a3ca0d00dec72556cafe6e",
"score": "0.5336524",
"text": "def start(self):\n self.session = self.connection.connect()",
"title": ""
},
{
"docid": "7acf656bfffd6088c2cf0d9e5b282a61",
"score": "0.5335576",
"text": "def _start_server(self):\n host, port = hosttuple(self.options.get(\"listen\", \"0.0.0.0\"))\n\n _gdk.threads_init()\n self._server = _xmlrpcserver.SimpleXMLRPCServer(\n (host, port),\n allow_none = True,\n requestHandler = _ft.partial(\n ClipboardExchangeHandler,\n self._destinations,\n ),\n )\n self._server.register_function(self._suggest, \"suggest\")\n #self._server.register_introspection_functions()\n\n t = _threading.Thread(target = self._serve)\n t.daemon = True\n t.start()",
"title": ""
},
{
"docid": "62e2bc9aada11a585754fec5ed11d5da",
"score": "0.533318",
"text": "def run(self):\n if self.is_running:\n return #Maybe add error checking\n self.comm_queue = Queue()\n server = ForkingServerTCP((self.host_name, self.port_number), ForkedClientHandler)\n self.server_process = Process(target=server.queued_serve_forever, args=(self.comm_queue,))\n self.server_process.start()\n self.running = True\n print(\"Server process started on: \" + self.host_name + \":\" + str(self.port_number))\n sys.stdout.flush()",
"title": ""
},
{
"docid": "350c39a1ee4728a2cbff936e8c3aa6c6",
"score": "0.53209066",
"text": "def open(self):\n self._cluster.client.start()",
"title": ""
},
{
"docid": "c751fdfa10c639f5e9c14078125b24b9",
"score": "0.5320184",
"text": "def start(self):\n self._host = self._caller.host\n self._filesystem = self._host.filesystem\n self._port = self._host.port_factory.get(self._options.platform,\n self._options,\n **self._port_kwargs)\n self._driver = self._port.create_driver(self._worker_number)\n self._batch_count = 0",
"title": ""
},
{
"docid": "f4661167f725f5dd9b39ac5eb83900d8",
"score": "0.531993",
"text": "def start(self):\n\n self._execute_command('./start.sh', self.path)",
"title": ""
},
{
"docid": "8313e8c1da3946334130d559658abce6",
"score": "0.53120387",
"text": "def start(self):\n\n self.keep_running = True # Set running flag to true\n self.th = threading.Thread(target=self.listenSocket)\n self.th.daemon = True # Thread will terminate with the main\n self.th.start()\n self.th.join(0)",
"title": ""
},
{
"docid": "ec753e4780e705bed1703697e367453d",
"score": "0.53111696",
"text": "def start_listening(self):\n if self.start:\n print(\"Loop started Already\")\n else:\n self.start = True\n while self.start:\n self.client.loop(timeout=1.0, max_packets=1)",
"title": ""
},
{
"docid": "c3180be4202e77aa973b28850842f584",
"score": "0.53098375",
"text": "def start_http_service(self):\n\n start_http_server(self.port, self.host)",
"title": ""
},
{
"docid": "97276c7885030345dad7162f30baac8b",
"score": "0.5306907",
"text": "def run(self):\n self.listen()\n try:\n # make a nonblocking accept client thread\n Thread(\n target=self.accept_clients,\n args=()\n ).start()\n except:\n # Handle exceptions\n print(\"Failed at threading server: \", sys.exc_info()[0])\n raise",
"title": ""
},
{
"docid": "2622f86e1cf6dd3acde42e185bdbe87d",
"score": "0.5302657",
"text": "def file_upload(self):\n\n self.sock.sendall(\"UPLD\") # Send UPLD command\n file_name = raw_input(\"File to be sent >> Client:files/\")\n self.send_custom(file_name)\n response = self.recv_custom()\n if response == \"File exists\":\n print \"File exists on the server. Use DELF to delete file to upload new.\"\n elif response == \"Ready\":\n print \"Starting file transfer\"\n f = open(os.path.join(self.path, file_name), 'rb')\n data = f.read()\n self.send_custom(data)\n print \"File sent.\"\n f.close()",
"title": ""
},
{
"docid": "f0bc2bb6fdad56d02ce17e4915ee976a",
"score": "0.5301939",
"text": "def do_connect(self, args):\n self.host = args.host\n self.port = args.port\n self.un = args.username\n self.pw = args.password\n\n try:\n log_path = f\"{self.download_dir}{os.path.sep}{self.host}-{datetime.now()}\"\n self.log_file = open(log_path, 'a+')\n\n self.ssh = SSHSession(self.host, self.port, self.un, self.pw, log_file=self.log_file)\n\n print_info(\"Connecting...\")\n self.ssh.connect()\n self.ssh.create_shell()\n self.ssh.create_scp()\n self.run_initial_commmands()\n self.tunnels = []\n self.prompt = ''\n\n print_info(\"Session opened\")\n except Exception as e:\n print_failure(f\"Failed to open session: {e}\")",
"title": ""
},
{
"docid": "d92fc3a0f9b16d0f1f432304f070e6bf",
"score": "0.529915",
"text": "def start():\r\n policy_decision_point = mqsec.PolicyDecisionPoint()\r\n r = policy_decision_point.submit_policy(ruleFile.FILE_NAME)\r\n \r\n try:\r\n # Start listening on the listening_port for client request\r\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n s.bind(('', listening_port))\r\n s.listen(max_conn)\r\n\r\n print(\"[*] Initializing Socket ...Done\")\r\n print(\"[*] Sockets Bind Sucessfully ...\")\r\n print(\"[*] Server Started Successfully [ %d]\\n\" % (listening_port))\r\n except Exception:\r\n print(\"[*] Unable To Initialize Socket\")\r\n sys.exit(2)\r\n while 1:\r\n try:\r\n # Upon reception of a client request create a thread to handle it\r\n connexion_client, addr_client = s.accept()\r\n data = connexion_client.recv(buffer_size)\r\n\r\n thread_server = threadProxyServer(\r\n connexion_client,\r\n data,\r\n addr_client,\r\n policy_decision_point\r\n )\r\n thread_server.start()\r\n\r\n except KeyboardInterrupt:\r\n s.close()\r\n print(\"\\n[*] Proxy Server Shutting Down ...\")\r\n sys.exit(1)\r\n s.close()",
"title": ""
},
{
"docid": "366ddb0e432196e223a05b08adc3c183",
"score": "0.5297054",
"text": "def start(self, host):\n try:\n if not host:\n self.flags[\"ip_port_done\"].wait()\n self.LOG_ON_SCREEN('Trying to connect to the server in ', self.ip, ':', self.port)\n self.connect()\n self.keep_alive() #Creating thread\n self.receive_worker()\n self.send_handshake(host)\n except Exception as exc:\n self.exception_handler(exc)",
"title": ""
},
{
"docid": "745de160cfe20f6bd2a10d59f17d732e",
"score": "0.5288493",
"text": "def run(self):\n self._connection = self.connect()\n self._connection.ioloop.start()",
"title": ""
},
{
"docid": "745de160cfe20f6bd2a10d59f17d732e",
"score": "0.5288493",
"text": "def run(self):\n self._connection = self.connect()\n self._connection.ioloop.start()",
"title": ""
},
{
"docid": "53292fd2fd9b525f44ff7871f59416dd",
"score": "0.5285614",
"text": "def start(self):\n threading.Thread(target=self.__irc.start).start()",
"title": ""
},
{
"docid": "aefea8f5fbec1bc466dc84cd016401dd",
"score": "0.5279714",
"text": "def startChuckServer(options, pwd):\n os.chdir(os.path.join(pwd, 'src', 'chuck'))\n command = ['chuck']\n terms_as_chuck_args = \":\".join(options.terms)\n if len(terms_as_chuck_args):\n terms_as_chuck_args = \":\" + terms_as_chuck_args\n command.append('--bufsize2048')\n command.append('--srate44100')\n command.append('twtNodeSynth3.ck')\n command.append('twtSynthControlLOCAL3.ck')\n command.append(\"twtSynthControlMASTER.ck:%s:%s:%s%s\" % (options.python_ip, options.java_ip, options.local_word, terms_as_chuck_args))\n if options.use_fake_tweets:\n command.append('twtTest5.ck')\n sys.stdout.write(\"Starting sound server ... \")\n try:\n p = subprocess.Popen(command)\n sys.stdout.write(\"[ok]\\n\")\n except:\n sys.stdout.write(\"[error]\\n\")\n p = None\n finally:\n os.chdir(pwd)\n sys.stdout.flush()\n return p",
"title": ""
},
{
"docid": "bb63e7b5197a899836847674c20520b4",
"score": "0.527961",
"text": "def start(self):\n self.should_run = True\n return super(EyeTribeSocket, self).start()",
"title": ""
},
{
"docid": "8c331e36cc10dcc1c912dd6ca2b4cadb",
"score": "0.52766305",
"text": "def start_connector(self):\n self.mq_client.connect(self.t_network_host, port=self._mqtt_client_port, keepalive=self._mqtt_client_keepalive)\n self.mq_client.loop_forever()",
"title": ""
},
{
"docid": "1e44988baf477bcd76983c7647c2ed08",
"score": "0.52715296",
"text": "def start(self):\n self._handle.start()",
"title": ""
},
{
"docid": "1fc19e0e3d64417c0d28a4caa4aa46bb",
"score": "0.5265651",
"text": "def run(self):\n self.connection.connect((self.host, self.server_port))\n\n self.messageReceiver.start()\n \n print 'Client running...\\nType \\'quit\\' to end or \\'help\\' for info'\n \n while True:\n command = raw_input()\n \n if command == 'quit':\n self.disconnect()\n print 'Client closing'\n break\n \n self.send_payload(self.messageParser.parse_dataToSend(command))",
"title": ""
},
{
"docid": "45292700830d03012dad50604b080a72",
"score": "0.5261778",
"text": "def start(self):\n self.log_lines = None\n self._server_thread.daemon = True\n self._server_thread.start()\n return self._waitUntilServerListening()",
"title": ""
},
{
"docid": "e6eab5034ee19b8bdc71da08e5fc968e",
"score": "0.5260719",
"text": "def start(self, overload=None):\n\n self._auto_resume_on_connect = True\n\n if self.running.is_set() or self.syncing.is_set():\n # do nothing if already running\n return\n\n self.connection_thread = Thread(\n target=connection_helper, daemon=True,\n args=(self.client, self.connected, self.syncing, self.running),\n name=\"MaestralConnectionHelper\")\n\n self.local_observer_thread = Observer()\n self.local_observer_thread.schedule(\n self.file_handler, self.sync.dropbox_path, recursive=True)\n\n self.download_thread = Thread(\n target=download_worker, daemon=True,\n args=(self.sync, self.syncing, self.running, self.flagged),\n name=\"MaestralDownloader\")\n\n self.upload_thread = Thread(\n target=upload_worker, daemon=True,\n args=(self.sync, self.syncing, self.running),\n name=\"MaestralUploader\")\n\n self.running.set()\n\n self.connection_thread.start()\n self.local_observer_thread.start()\n self.download_thread.start()\n self.upload_thread.start()\n\n self.connected_signal.connect(self._resume_on_connect)\n self.disconnected_signal.connect(self._pause_on_disconnect)\n\n self.upload_local_changes_after_inactive()\n\n self.syncing.set() # resumes download_thread\n self.file_handler.running.set() # starts local file event handler\n\n logger.info(\"Syncing started\")",
"title": ""
},
{
"docid": "d0ea596fa04689694493e14473e4639c",
"score": "0.5259967",
"text": "def start(self):\n\n args = [\"-c\", self._configfile,]\n cmd = [ \"start-stop-daemon\", \"--start\",\n \"--exec\", self._daemon,\n \"--\"]\n cmd.extend(args)\n (stdout, stderr, rc) = yield twisted_execute(cmd, shell=False)\n if len(stdout):\n debug(stdout)\n if (rc != 0):\n error(\"rinetd.start(): Command failed with RC=%s\", rc)\n for line in stderr.splitlines():\n error(\" %s\" %line)\n # when an error occurs stdout is important too\n if len(stdout):\n stderr = stderr+stdout\n\n yield self._parent._dbpool.startedService(self._config,\n rc, message=stderr)\n defer.returnValue(rc)",
"title": ""
},
{
"docid": "2977c03ded58b5127a64f225f8f7e5c6",
"score": "0.5258457",
"text": "def start(self):\n\n # Loading the Diameter dictionary for messages, codes and AVPs\n LoadDictionary(\"dictDiameter.xml\")\n\n # Create the server, binding to HOST:PORT and set max peers\n socket_connection = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n socket_connection.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n socket_connection.bind((self.host, self.port))\n socket_connection.listen(self.max_clients)\n\n try:\n while True:\n incoming_connection, peer_address = socket_connection.accept()\n logging.info('Connected to ' + str(peer_address))\n thread.start_new(\n self.handle_request, (incoming_connection, peer_address))\n except KeyboardInterrupt:\n print(\"\\nClosing server...\")\n\n # Closing the socket connection\n socket_connection.close()",
"title": ""
},
{
"docid": "9ccfb04851ae3a3c9b17dc9d0b62fd9a",
"score": "0.52552557",
"text": "def run(self):\n \n self._listen()\n self._accept_clients()",
"title": ""
},
{
"docid": "8454deb4e2a2cb205320a9c4efa18e97",
"score": "0.5249779",
"text": "def start(self, port=8080):\n\t\tself.hs_dir = tempfile.mkdtemp(prefix=\"torhs_\")\n\t\tself.port = port\n\t\tif self.test_localhost(port):\n\t\t\t# self.thread = threading.Thread(target=self.connect)\n\t\t\t# self.thread.start()\n\t\t\tself.connect()\n\t\telse:# no localhost server running\n\t\t\tself.status(\"No localhost server is running on port \" + str(port))\n\t\t\treturn False\n\t\treturn True",
"title": ""
},
{
"docid": "88ac5c8712cdc6295c66cd420aa0c7ac",
"score": "0.52494735",
"text": "def send_file(self, file_name, file_path):\n # use with to ensure file is closed after ops\n with open(file_path, 'r') as file_object:\n # establish ephemeral port\n transfer_port = ''\n\n # read file, put file size in padded header, prepend header to data\n data = file_object.read()\n\n try:\n transfer_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n transfer_socket.bind(('', 0))\n transfer_socket.listen(1)\n transfer_port = transfer_socket.getsockname()[1]\n transfer_port = self.buffer_header(transfer_port)\n except socket.error as e:\n print e\n self.op_failure_message(const.COMMAND_GET)\n return\n\n # send transfer port to client\n try:\n self.client_socket.send(transfer_port)\n except socket.error as e:\n self.op_failure_message(const.COMMAND_GET)\n return\n\n while True:\n print \"Listening on %s\" % transfer_port\n ftp_transfer, address = transfer_socket.accept()\n print \"Accepted connection from %s\" % str(address)\n\n if ftp_transfer:\n # byte counter\n bytes_sent = 0\n\n file_name_header = self.buffer_header(file_name, const.FILENAME_SIZE)\n file_size_header = self.buffer_header(str(len(data)), const.HEADER_SIZE)\n\n file_data = file_name_header + file_size_header + data\n\n while len(file_data) > bytes_sent:\n try:\n bytes_sent += ftp_transfer.send(file_data[bytes_sent:])\n except socket.error as e:\n print e\n self.op_failure_message(const.COMMAND_GET)\n return\n\n self.op_success_message(const.COMMAND_GET)\n ftp_transfer.close()\n transfer_socket.close()\n return",
"title": ""
},
{
"docid": "bd5a0784d13016464ae9e00d948df1ab",
"score": "0.52401793",
"text": "def start(self):\n\n\t\tself.conn = serial.Serial(self.port, self.baud)\n\t\tself.conn.open()\n\t\tself.stop = False\n\n\t\tsuper(UARTDaemon, self).start()",
"title": ""
},
{
"docid": "971ec16b3e591e5fac8cc4793ea1117a",
"score": "0.5240173",
"text": "def start(self):\n host = self._get_machine_host()\n start_server = websockets.serve(self.ws_handler, host=host, port=5678)\n logger.info(f\"server created (on {host}), waiting for clients\")\n\n asyncio.get_event_loop().run_until_complete(start_server)\n asyncio.get_event_loop().run_forever()",
"title": ""
},
{
"docid": "b47b725c1d991e8fb46b5795a7e4815a",
"score": "0.5236811",
"text": "def upload_to_corporate_ftp(self):\n csvfile = self._create_csv_extract()\n d = v.LoginDialog(parent=self, title=\"Login to ABQ Corporate FTP server\")\n if d.result is not None:\n username, password = d.result\n try:\n n.upload_to_corporate_ftp(\n filepath=csvfile,\n ftp_host=self.settings[\"abq_ftp_host\"].get(),\n ftp_port=self.settings[\"abq_ftp_port\"].get(),\n ftp_user=username,\n ftp_pass=password,\n )\n except n.ftp.all_errors as e:\n messagebox.showerror(title=\"Error connecting to ftp\", message=str(e))\n else:\n messagebox.showinfo(\n title=\"Success\",\n message=f\"'{csvfile}' successfully uploaded to FTP server.\",\n )",
"title": ""
},
{
"docid": "b75cd7ef7c4d0d347323312b8db47776",
"score": "0.5231645",
"text": "def main():\n factory = protocol.ServerFactory()\n factory.protocol = XmlEcho\n reactor.listenTCP(8000, factory)\n reactor.run()",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="762"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">9ac724d1ba5c72f1a5fe4c4654ab92af</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">1. If value is bigger than Node, go right. If empty, add Node. 2. if value is smaller than Node, go left. If empty, add Node. 3. if value is same as Node, overwrite and return.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "d303aa8d096e0c55418dde460440f6a9",
"score": "0.6986899",
"text": "def insert(self, value): \n if value > self.value:\n if self.right is None:\n self.right = Node(value)\n else:\n #Insert on right node. \n self.right.insert(value)\n\n if value < self.value:\n if self.left == None:\n self.left = Node(value)\n else:\n self.left.insert(value)",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "cc0d4b56d0a03baa884dd027f8872a66",
"score": "0.7236795",
"text": "def add(self, value: object) -> None:\n # make new node\n new_node = TreeNode(value)\n\n\n\n if self.root is None:\n self.root = new_node\n\n else:\n current = self.root\n current_last = None\n while current is not None:\n # keep your place for when the loop breaks\n current_last = current\n if current.value == value:\n current = current.right\n elif current.value > value:\n current = current.left\n elif current.value < value:\n current = current.right\n\n # once out of the loop check to see if\n # its equal or less than the given value\n # if it's equal you must put it on the right\n # if it's less then it also goes on the right, else left\n if current_last.value == value or current_last.value < value:\n current_last.right = new_node\n elif current_last.value > value:\n current_last.left = new_node",
"title": ""
},
{
"docid": "b63560efc32fb269a168b0b5b5b46962",
"score": "0.7153695",
"text": "def add_value(self, value: T) -> None:\n self.len+=1\n z = BSTNode(value)\n y = None\n x = self.root\n while(x!=None):\n y = x\n if(z.value < x.value):\n x = x.left\n else:\n x = x.right\n z.parent = y\n if(y == None):\n self.root = z\n elif (z.value < y.value):\n y.left = z\n else:\n y.right = z\n ...",
"title": ""
},
{
"docid": "52523ed41bed682116b925f26c9eb99b",
"score": "0.67848444",
"text": "def insert(self, value):\n self.size = self.size + 1\n adding = Node(value)\n selector = self.root\n if not self.root:\n self.root = adding\n self.size = self.size + 1\n else:\n while(True):\n if(value < selector.value):\n if not selector.left:\n selector.left = adding\n break\n selector = selector.left\n else:\n if not selector.right:\n selector.right = adding\n break\n selector = selector.right\n adding.parent = selector\n\n \n if selector.balance:\n selector.balance = 0\n else:\n if (selector.left == adding):\n selector.balance = 1\n else:\n selector.balance = -1\n\n climber = selector.parent\n tester = False\n\n while (climber):\n if climber.balance:\n tester = True\n break\n\n if(climber.left == selector):\n climber.balance = 1\n else:\n climber.balance = -1\n\n selector = climber\n climber = climber.parent\n\n if(tester):\n if(climber.balance == 1):\n if(climber.right == selector):\n climber.balance = 0\n elif (selector.balance == -1):\n self.__leftRight(climber)\n else:\n self.__leftLeft(climber)\n else:\n if(climber.left == selector):\n climber.balance = 0\n elif (selector.balance == 1):\n self.__rightLeft(climber)\n else:\n self.__rightRight(climber)",
"title": ""
},
{
"docid": "7ddf9fd44f2a6c464baea904150a16aa",
"score": "0.6771727",
"text": "def add(self, value: object) -> None:\n\n def rec_add(node):\n # create root node if first added value\n if node is None:\n self.root = TreeNode(value)\n # go left!\n elif value < node.value:\n if node.left is not None:\n return rec_add(node.left)\n elif node.left is None and value < node.value:\n node.left = TreeNode(value)\n # go right!\n elif value >= node.value:\n if node.right is not None:\n return rec_add(node.right)\n elif node.right is None and value >= node.value:\n node.right = TreeNode(value)\n\n rec_add(self.root)",
"title": ""
},
{
"docid": "2a4af14be21efd29705ae2b4ad528f4f",
"score": "0.67298836",
"text": "def add(tree, value):\n\n # If the tree is empty, this value becomes the new root.\n # Otherwise, recursively add the value to the left or right\n # sub-tree.\n if tree == None:\n return {'data':value, 'left':None, 'right':None}\n elif value <= tree['data']:\n tree['left'] = add(tree['left'],value)\n return tree\n else: # value > tree['data']\n tree['right'] = add(tree['right'],value)\n return tree",
"title": ""
},
{
"docid": "86fd9ce239e0253d1b3cea0d87d168ce",
"score": "0.66723406",
"text": "def delete(self, val):\n # print \"Enter delete()\"\n if self.head is None:\n return None\n\n # print \"set Booleans\"\n parent = None\n current = self.head\n found = False\n\n # print \"enter while\"\n while (not found):\n if val == current.val:\n found = True\n elif val < current.val:\n if current.left is None:\n return None\n else:\n parent = current\n current = current.left\n else:\n if current.right is None:\n return None\n else:\n parent = current\n current = current.right\n\n # print \"decrement nodeCount\"\n self.nodeCount -= 1\n\n # print \"nodeCount\"\n if self.nodeCount == 0:\n self.head = None\n return None\n\n # print \"check parent\"\n if parent is None:\n # means self.head - the top node - matched the value\n self.head = self._merge(None, self.head.left, self.head.right)\n return None\n\n # print \"check depth\"\n depth_left = self._depth(current.left)\n depth_right = self._depth(current.right)\n\n if depth_left < depth_right:\n # right is deeper, rotate right node up\n if parent.right == current:\n parent.right = current.right\n if ((current.left is not None) and\n (parent.right.left is not None)):\n # collision! parent.right.left and current.left\n parent.right.left = self._merge(parent.right,\n current.left,\n parent.right.left)\n else:\n # parent left == current\n parent.left = current.right\n if ((current.left is not None) and\n (parent.left.left is not None)):\n # collision! parent.left.left and current.left\n parent.left.left = self._merge(parent.left,\n current.left,\n parent.left.left)\n else:\n # left is deeper, or they are equal\n if parent.right == current:\n parent.right = current.left\n if ((current.right is not None) and\n (parent.right.right is not None)):\n # collision! parent.right.right and current.right\n parent.right.right = self._merge(parent.right,\n parent.right.right,\n current.right)\n else:\n # parent.left == current\n parent.left = current.left\n if ((current.right is not None) and\n (parent.left.right is not None)):\n # collision! parent.left.right and current.right\n parent.left.right = self._merge(parent.left,\n parent.left.right,\n current.right)\n return None",
"title": ""
},
{
"docid": "66a5a75113616138d8b8e1186b5bcf12",
"score": "0.6638485",
"text": "def sort_append(self, value):\n\t\tif self.head is None:\n\t\t\tself.head = Node(value)\n\t\t\treturn\n\t\t\n\t\tif value < self.head.value:\n\t\t\tnode = Node(value)\n\t\t\tnode.next = self.head\n\t\t\tself.head = node\n\t\t\treturn\n\t\t\n\t\tnode = self.head\n\t\twhile node.next is not None and value >= node.next.value:\n\t\t\tnode = node.next\n\t\t\t\n\t\tnew_node = Node(value)\n\t\tnew_node.next = node.next\n\t\tnode.next = new_node\n\t\t\n\t\treturn None",
"title": ""
},
{
"docid": "75263f616f94dc7ffe6323f92d8d2bd4",
"score": "0.65912145",
"text": "def insert(self, node, value):\n if not node.value:\n node.value = value\n if value < node.value:\n node.left = node.left or Node()\n self.insert(node.left, value)\n elif value > node.value:\n node.right = node.right or Node()\n self.insert(node.right, value)",
"title": ""
},
{
"docid": "5bd16644213ea4facc3090f169226970",
"score": "0.6586433",
"text": "def __insert(self, value, root):\n if value < root.value:\n if not root.left:\n root.left = BinaryNode(value)\n root._size += 1\n return True\n return self.__insert(value, root.left)\n\n if value > root.value:\n if not root.right:\n root.right = BinaryNode(value)\n root._size += 1\n return True\n return self.__insert(value, root.right)\n\n return False",
"title": ""
},
{
"docid": "efcd071d9e41851fca6245a74104cdec",
"score": "0.6579267",
"text": "def add(self, value: object) -> None:\n # first make a node to store value\n new_node = TreeNode(value)\n # check if the tree is empty\n if self.root == None: \n # add the node as the head\n self.root = new_node\n return # exit\n # otherwise, traverse the BST to find the place to add value\n # define the cur node\n cur = self.root\n # create a pointer that trails behind cur as parent node\n p_cur = None\n while cur is not None: # while the cur doesn't reach the bottom of the BST\n p_cur = cur # trailing behind cur\n if value >= cur.value: # compare value to the current value\n cur = cur.right # the cur will be on the right sub tree\n else: \n cur = cur.left # cur on left sub tree\n # now you are on the place to add the value based on the parent node\n if value >= p_cur.value: # if value is greater than the parent node\n p_cur.right = new_node\n else: # less than parent node\n p_cur.left = new_node",
"title": ""
},
{
"docid": "35874bc90ffb8a213b8b4d43059c268a",
"score": "0.65788686",
"text": "def delete(self, value): \n if not self:\n return self\n\n if value > self.value:\n if self.right is None:\n return self\n else:\n # find on right side. \n # if node found then self.right would act like a parent and get adjusted.\n self.right = self.right.delete(value)\n\n if value < self.value:\n if self.left == None:\n return self\n else:\n # find on left side.\n # if node found then self.left would act like a parent and get adjusted.\n self.left = self.left.delete(value)\n\n if value == self.value:\n \n if self.left == None:\n return self.right\n if self.right == None:\n return self.left\n\n # find the smallest in the right hand side of tree\n smallest_node = self._find_smallest(self.right)\n self.value = smallest_node.value\n self.right = self.right.delete(self.value)\n return self",
"title": ""
},
{
"docid": "ed484a1b8e121695485b1bbb4e244dee",
"score": "0.6520036",
"text": "def sorted_insert(self, value):\n runner = self.__head\n prev = runner\n if runner is None:\n self.__head = Node(value)\n return\n\n if runner.data > value:\n self.__head = Node(value, runner)\n return\n\n while runner is not None and runner.data < value:\n prev = runner\n runner = runner.next_node\n\n prev.next_node = Node(value, runner)",
"title": ""
},
{
"docid": "2a3fd8c3ccdcca701aa679ffe008266b",
"score": "0.65156907",
"text": "def recursive_put(self, node: Node, key: Any, value: Any) -> Node:\n if node is None:\n return self.Node(key, value)\n if node.key > key:\n node.left = self.recursive_put(node.left, key, value)\n elif node.key < key:\n node.right = self.recursive_put(node.right, key, value)\n else:\n node.data = value\n node.count = 1 + self._size(node.left) + self._size(node.right)\n return node",
"title": ""
},
{
"docid": "b1bf27c6442530052c01ea3d478a2550",
"score": "0.64972925",
"text": "def add(self, val):\n\t\tif val <= self.value:\n\t\t\tif self.left:\n\t\t\t\tself.left.add(val)\n\t\t\telse:\n\t\t\t\tself.left = BinaryNode(val)\n\t\telse:\n\t\t\tif self.right:\n\t\t\t\tself.right.add(val)\n\t\t\telse:\n\t\t\t\tself.right = BinaryNode(val)",
"title": ""
},
{
"docid": "f30537cd27cf4c8800258628bd9495ca",
"score": "0.6490018",
"text": "def insert_node(self,current_node, value):\n if value <= current_node.data:\n if current_node.left:\n self.insert_node(current_node.left, value)\n else:\n current_node.left = Node(value)\n current_node.left.parent = current_node\n elif value > current_node.data:\n if current_node.right:\n self.insert_node(current_node.right, value)\n else:\n current_node.right = Node(value)\n current_node.right.parent = current_node",
"title": ""
},
{
"docid": "e59763a1a618868830a44aaf952241b7",
"score": "0.64810973",
"text": "def insert(self, value: int) -> Node:\n node = Node(value)\n\n if self.root is None:\n self.root = node\n return\n else:\n current = self.root\n\n while current:\n if node.value < current.value:\n if current.left is None:\n current.left = node\n break\n else:\n current = current.left\n elif current.value < node.value:\n if current.right is None:\n current.right = node\n break\n else:\n current = current.right\n return node",
"title": ""
},
{
"docid": "53be7c344db4d45d2122705071422475",
"score": "0.6421205",
"text": "def sorted_insert(self, value):\n new_node = Node(value)\n head = self.__head\n\n if head is None:\n self.__head = new_node\n return\n if head.next_node is None:\n if new_node.data <= head.data:\n new_node.next_node = head\n self.__head = new_node\n else:\n head.next_node = new_node\n return\n previous = head\n current = head.next_node\n while True:\n if new_node.data <= head.data:\n new_node.next_node = head\n self.__head = new_node\n return\n if previous.data <= new_node.data <= current.data:\n previous.next_node = new_node\n new_node.next_node = current\n return\n if current.next_node is None:\n if new_node.data >= current.data:\n current.next_node = new_node\n return\n else:\n previous.next_node = new_node\n new_node.next_node = current\n return\n previous = current\n current = current.next_node",
"title": ""
},
{
"docid": "ea7fec6f805c34a69425f58d49bfa42e",
"score": "0.642022",
"text": "def insert(self, val):\n curr_parent, curr = None, self\n\n while curr and curr.val:\n curr_parent = curr\n if curr.val == val:\n return\n if curr.val > val:\n curr = curr.left\n else:\n curr = curr.right\n\n new_node = binary_search_tree(\n node_list=[val, None, None], parent=curr_parent\n )\n\n if not curr_parent:\n self.val = val\n self.parent = None\n self.left = None\n self.right = None\n elif val < curr_parent.val:\n curr_parent.left = new_node\n else:\n curr_parent.right = new_node",
"title": ""
},
{
"docid": "d805687bc5c8979f6ca59dd0e511dbd9",
"score": "0.63973767",
"text": "def _recursive_delete_node(self, node, value):\r\n if node is None:\r\n return None\r\n\r\n if value < node.value:\r\n node.left = self._recursive_delete_node(node.left, value)\r\n elif value > node.value:\r\n node.right = self._recursive_delete_node(node.right, value)\r\n else:\r\n if node.left is None:\r\n return node.right\r\n if node.right is None:\r\n return node.left\r\n\r\n min_node_in_right_subtree = self._get_min_node(node.right)\r\n node.value = min_node_in_right_subtree.value\r\n node.right = self._recursive_delete_node(node.right, min_node_in_right_subtree.value)\r\n\r\n return node",
"title": ""
},
{
"docid": "a426b55765432d34836935342e23e977",
"score": "0.6383498",
"text": "def insert(self, value_):\n\t\tq = list()\n\t\tq.append(self.root)\n\n\t\twhile len(q) > 0:\n\n\t\t\ttemp = q[0]\n\t\t\tq.pop(0)\n\n\t\t\tif not temp.left:\n\t\t\t\ttemp.left = Node(value_)\n\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\tq.append(temp.left)\n\n\t\t\tif not temp.right:\n\t\t\t\ttemp.right = Node(value_)\n\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\tq.append(temp.right)",
"title": ""
},
{
"docid": "8e4e5e0d788bd8f1ccc067e86f3d3553",
"score": "0.6376962",
"text": "def add(self, key, value, node):\n if key < node.key:\n if node.left is not None:\n self.add(key, value, node.left)\n else:\n node.left = Node(key, value)\n else:\n if node.right is not None:\n self.add(key, value, node.right)\n else:\n node.right = Node(key, value)",
"title": ""
},
{
"docid": "ab55c26c3642012e21f9ec6f2e7e3f18",
"score": "0.63647735",
"text": "def insert(self, value):\n if not isinstance(value, (int, float)):\n raise ValueError('Please insert number.')\n\n if self.root is None:\n self.root = Node(value)\n self._size += 1\n return\n elif value == self.root.value:\n raise ValueError('Node already exists.')\n curr = self.root\n while curr:\n if value == curr.value:\n raise ValueError('Node already exists.')\n elif value > curr.value:\n if curr.right:\n curr = curr.right\n else:\n curr.right = Node(value)\n curr.right.parent = curr\n self._size += 1\n self._balance(curr.right)\n break\n elif value < curr.value:\n if curr.left:\n curr = curr.left\n else:\n curr.left = Node(value)\n curr.left.parent = curr\n self._size += 1\n self._balance(curr.left)\n break",
"title": ""
},
{
"docid": "342464a673d9d2eb9026aa6dd09cdf7d",
"score": "0.6349845",
"text": "def insertInOrder(linkedList, value):\r\n\r\n # the first node, the list to be used during search\r\n insertSearch = linkedList\r\n\r\n # let's make the node to be inserted\r\n nodeIn = {\"data\": value, \"next\": None}\r\n\r\n # flag to make sure it's only inserted once, if there are duplicates in the list\r\n inserted = False\r\n\r\n # run until you reach the end\r\n while insertSearch != None:\r\n # have to have the previous so you can link up the new node\r\n previous = insertSearch\r\n # move to the next item in the list\r\n insertSearch = insertSearch['next']\r\n\r\n # if the value needs to be inserted at the start\r\n if previous['data'] >= value:\r\n tempNode = {'data':value, 'next':None}\r\n tempNode['next'] = linkedList\r\n linkedList = tempNode\r\n return linkedList\r\n\r\n # if the previous value is lesser or equal\r\n # and the next value is greater or equal we have found our insert location\r\n if insertSearch != None:\r\n if previous['data'] <= value and insertSearch['data'] >= value:\r\n # first assign the inserted node's next value\r\n nodeIn['next'] = insertSearch\r\n previous['next'] = nodeIn\r\n return linkedList\r\n else:\r\n # you've reached the end\r\n previous['next'] = nodeIn\r\n return linkedList\r\n # you've passed in an empty list, no need to preserve data\r\n # just set it equal to the one node you want to insert and away we go\r\n linkedList = nodeIn\r\n\r\n\r\n return linkedList",
"title": ""
},
{
"docid": "c1d3d837235aab1fbb1445d86b5853db",
"score": "0.63494694",
"text": "def _recursive_add_node(self, node, newnode):\r\n if newnode.value < node.value:\r\n if not node.left:\r\n node.left = newnode\r\n newnode.parent = node\r\n else:\r\n self._recursive_add_node(node.left, newnode)\r\n else:\r\n if not node.right:\r\n node.right = newnode\r\n newnode.parent = node\r\n else:\r\n self._recursive_add_node(node.right, newnode)",
"title": ""
},
{
"docid": "c37ae1c6ecde4d3d3914247c2fff2416",
"score": "0.6348713",
"text": "def _insert_node(self, val, node):\n if val < node.val:\n if node.leftChild:\n self._insert_node(val, node.leftChild)\n else:\n node.leftChild = Node(val)\n else:\n if node.rightChild:\n self._insert_node(val, node.rightChild)\n else:\n node.rightChild = Node(val)",
"title": ""
},
{
"docid": "91622822a8fc17a7533fd527537a5eb7",
"score": "0.6344741",
"text": "def __insert(self, node, key, value):\n if node is None:\n return BSTNode(key, value)\n if key < node.key:\n node.left = self.__insert(node.left, key, value)\n else: # node < key\n node.right = self.__insert(node.right, key, value)\n # Addition!! - needed to maintain count\n node.recount()\n return node",
"title": ""
},
{
"docid": "fa84e0d3735682f393a49ee892220e5d",
"score": "0.63405275",
"text": "def add(self, value, current = None):\n if self.root is None:\n self.root = Node(value)\n self.add_node_count()\n else:\n if current is None:\n current = self.root\n\n if value < current.value:\n if current.left is None:\n current.left = Node(value, current)\n self.add_node_count()\n else:\n self.add(value, current.left)\n else:\n if current.right is None:\n current.right = Node(value, current)\n self.add_node_count()\n else:\n self.add(value, current.right)",
"title": ""
},
{
"docid": "31baa0f4cdc5894d3c67e63eb2d7f07a",
"score": "0.6333356",
"text": "def _insert(self, val, node, parent=None):\n child = None\n if val > node.val:\n if node.right:\n child = self._insert(val, node.right, node)\n else:\n node.right = Node(val)\n child = node.right\n self._length += 1\n elif val < node.val:\n if node.left:\n child = self._insert(val, node.left, node)\n else:\n node.left = Node(val)\n child = node.left\n self._length += 1\n balance = self.balance(node)\n child_balance = self.balance(child)\n if balance not in range(-1, 2):\n self._rotate(\n node, balance, child, child_balance, parent\n )\n return node",
"title": ""
},
{
"docid": "f8dfd9447bf17e918ca029fac74fb6a8",
"score": "0.6319163",
"text": "def insert(self, value):\n def _insert(node):\n if node is None:\n return LLRBT.LLRBTNode(value), True\n if node.value > value:\n node.left, inserted = _insert(node.left)\n elif node.value < value:\n node.right, inserted = _insert(node.right)\n else:\n inserted = False\n node = LLRBT._balance(node)\n return node, inserted\n self._root, inserted = _insert(self._root)\n self._size = self._root.size\n self._root.color = BLACK\n return inserted",
"title": ""
},
{
"docid": "083426d0858df0246d1492811a09a4e7",
"score": "0.6313802",
"text": "def merge(left, right):\n result = None\n\n if left == None: return right\n if right == None: return left\n\n if left.value > right.value:\n result = right\n result.next = merge(left, )",
"title": ""
},
{
"docid": "72f71b6ddc32289e1428df7ea4cb6aea",
"score": "0.6288918",
"text": "def remove(self, value) -> bool:\n if self.root is None:\n return False\n elif self.root.value == value:\n return self.remove_first()\n # check if value is in tree\n elif self.contains(value) is False:\n return False\n\n # having checked if these conditions are met check to see if I can\n # find the node and parent\n\n # find node and parent node\n parent_node = None\n node = self.root\n parent_successor = None\n #successor = None\n child_left = False\n child_right = False\n\n while node is not None and node.value != value:\n parent_node = node\n if node.value >value:\n node = node.left\n child_left = True\n child_right = False\n elif node.value < value:\n node = node.right\n child_right = True\n child_left = False\n\n # with the node found check to see if there is a right value\n if node.right is None:\n if child_right:\n\n parent_node.right = node.left\n #parent_node.left = node.right\n return True\n elif child_left:\n parent_node.left = node.left\n #parent_node.right = node.right\n return True\n\n # if node has a right, but no right.left successor\n # if parent successor == successor\n\n elif node.right.left == None:\n #print(\"ELSH IF WORKED\")\n\n successor = node.right\n\n # print(parent_successor)\n # print(successor)\n\n successor.left = node.left\n\n # direct which branch is being adjusted\n if child_left:\n parent_node.left = successor\n elif child_right:\n parent_node.right = successor\n return True\n else:\n #print(\"final\")\n successor = node.right\n while successor.left is not None:\n parent_successor = successor\n successor = successor.left\n\n if parent_successor is None:\n parent_node.right = successor\n successor.left = node.left\n #successor.left = node.left\n # successor.left = parent_node.left\n # successor.right = current.right\n return True\n else:\n parent_successor.left = successor.right\n if child_right:\n parent_node.right, successor.right = successor, node.right\n successor.left = node.left\n elif child_left:\n parent_node.left, successor.right = successor, node.right\n successor.left = node.left\n return True",
"title": ""
},
{
"docid": "3ee61bac4f9fb2589f4c901abb3fd8b2",
"score": "0.62880903",
"text": "def sorted_insert(self, value):\n new = Node(value, self.__head)\n prev = None\n if new.next_node is None or new.data <= self.__head.data:\n self.__head = new\n else:\n while new.next_node and new.data > new.next_node.data:\n prev = new.next_node\n new.next_node = prev.next_node\n if prev:\n prev.next_node = new",
"title": ""
},
{
"docid": "770fea16c751d44e3793dbdc55860bd4",
"score": "0.6284782",
"text": "def insert(self, added):\n added_node = BST(added)\n if self.val == added:\n return None\n else:\n if added < self.val:\n if self.left is None:\n self.left = added_node\n return\n else:\n self.left.insert(added)\n elif added > self.val:\n if self.right is None:\n self.right = added_node\n return\n else:\n self.right.insert(added)\n else:\n return",
"title": ""
},
{
"docid": "fc346d3eee51abd05eae0414a6f4f411",
"score": "0.6258199",
"text": "def __insert(self, root, node):\n value = node.data\n if value < root.data:\n if not root.left:\n root.left = node\n else:\n self.__insert(root.left, node)\n elif value > root.data:\n if not root.right:\n root.right = node\n else:\n self.__insert(root.right, node)",
"title": ""
},
{
"docid": "ea3937cbe744d6a67a7efe816a57c469",
"score": "0.6249872",
"text": "def __deleteNode(self, root, value):\n if not root:\n return\n if value > root.data:\n root.right = self.__deleteNode(root.right, value)\n elif value < root.data:\n root.left = self.__deleteNode(root.left, value)\n else:\n if not root.left:\n temp = root.right\n root = None\n return temp\n\n if not root.right:\n temp = root.left\n root = None\n return temp\n\n temp = self.findMin(root.right)\n root.data = temp.data\n root.right = self.__deleteNode(root.right, temp.data)\n\n return root",
"title": ""
},
{
"docid": "8f41f4148c6606c53f2517576c4610cf",
"score": "0.6230747",
"text": "def insert(self, val):\n node = self.root\n while node.val != val:\n if node.val > val:\n if not node.left:\n node.left = TreeNode(val)\n node = node.left\n else:\n if not node.right:\n node.right = TreeNode(val)\n node = node.right",
"title": ""
},
{
"docid": "717b21724911a963617ff12323496b81",
"score": "0.62214124",
"text": "def insert(self, value):\n if value == self.value:\n return\n elif value > self.value:\n if self.greater is None:\n self.greater = BinaryTree(value)\n return\n self.greater.insert(value)\n elif value < self.value:\n if self.lesser is None:\n self.lesser = BinaryTree(value)\n return\n self.lesser.insert(value)",
"title": ""
},
{
"docid": "028051e8675258564fdff48268934cbc",
"score": "0.62086636",
"text": "def bst_insert(tree: NodeBST, value: int) -> None:\n if tree.value > value:\n if tree.left is None:\n tree.left = NodeBST(value)\n else:\n bst_insert(tree.left, value)\n else:\n if tree.right is None:\n tree.right = NodeBST(value)\n else:\n bst_insert(tree.right, value)",
"title": ""
},
{
"docid": "e7115c6e40a8c7c98bba45cb7ede3189",
"score": "0.6202272",
"text": "def _add_child(self, val, curr):\n if val < curr.value:\n if not curr.l_child:\n curr.l_child = Node(val)\n else:\n self._add_child(val, curr.l_child)\n else:\n if not curr.r_child:\n curr.r_child = Node(val)\n else:\n self._add_child(val, curr.r_child)",
"title": ""
},
{
"docid": "a835c79d3597851c1cd829bc5641ac26",
"score": "0.6174489",
"text": "def adjust_heap_up(self):\n\n # Current node\n val = self.nodes[len(self.nodes) - 1]\n index = self.nodes.index(val) # Starts as last node\n\n # Parent node\n p_index = self.get_parent_index(index)\n p_val = self.nodes[p_index]\n \n while p_index is not None and p_val > val:\n self.swap_nodes(index, p_index)\n val = p_val\n index = p_index\n p_index = self.get_parent_index(index)\n \n # If unsortable, break\n if not p_index:\n break\n else:\n p_val = self.nodes[p_index]",
"title": ""
},
{
"docid": "94ff36209a22c58ebf76c0282225c339",
"score": "0.6163455",
"text": "def backup_value(node: TreeNode, val: int):\n if (node.parent_node is not None):\n while (node is not None):\n node.N += 1\n node.Q = node.Q - val\n\n node = node.parent_node",
"title": ""
},
{
"docid": "5b1dd7d40ae045f95eb2894207a377a9",
"score": "0.6161687",
"text": "def remove_value(self, value: K) -> None:\n try:\n self.len-=1\n z = self.get_node(value)\n if(z==None):\n raise MissingValueError(\"Miss\")\n return\n if(z.left == None or z.right==None):\n y = z\n else:\n y = successor(z)\n if(y.left!=None):\n x = y.left\n else:\n x = y.right\n if(x!=None):\n x.parent = y.parent\n if(y.parent == None):\n self.root = x\n elif(y == y.parent.left):\n y.parent.left = x\n else:\n y.parent.right = x\n if(y!=z):\n z.value = y.value \n except MissingValueError:\n pass \n ...",
"title": ""
},
{
"docid": "b1556881fce12706c19a079e36645909",
"score": "0.6140181",
"text": "def findValue(self, value):\n reference = self.root\n while( (reference) and (reference.value is not value) ):\n if (value < reference.value):\n reference = reference.left\n else:\n reference = reference.right\n return reference",
"title": ""
},
{
"docid": "70dc5705a06068b040da648e3b73c959",
"score": "0.61079234",
"text": "def insert(value, node=None):\n #If the LinkedList is empty\n if node is None:\n new_node = LinkedNode(value, None)\n return new_node\n #If head is more than value\n elif node.value >= value:\n new_node = LinkedNode(value, None)\n new_node.next_node = node\n return new_node\n #iterating through list\n elif node.value < value:\n node.next_node = insert(value, node.next_node)\n return node",
"title": ""
},
{
"docid": "5878b53e16eb379a73f17c7e7a50a311",
"score": "0.61059606",
"text": "def five_left():\n bst = BinarySearchTree(3)\n bst.insert(2)\n bst.insert(5)\n bst.insert(4)\n bst.insert(7)\n return bst",
"title": ""
},
{
"docid": "d858c9a32bed0a0e3d230cfb4a959ad7",
"score": "0.6094268",
"text": "def insert(self, value):\n node = TreeNode(value)\n if self.size == 0:\n self.root = node\n else:\n n = self.root\n while n:\n p = n\n if value <= n.data:\n n = n.left\n flag = 0\n else:\n n = n.right\n flag = 1\n if flag:\n p.right = node\n else:\n p.left = node\n node.parent = p\n self.update_balance_factor(p)\n\n self.size += 1",
"title": ""
},
{
"docid": "eda304fc09604f3371cfad719012a83b",
"score": "0.60816467",
"text": "def insert(self, value):\n node = _Node(value)\n\n if self._root is None:\n # Tree was empty\n self._root = node\n return\n\n # Find where the new node belongs and insert it there\n parent = self._root\n \n while True:\n if value <= parent.value:\n if parent.left is None:\n parent.left = node\n break\n else:\n parent = parent.left\n else:\n if parent.right is None:\n parent.right = node\n break\n else:\n parent = parent.right",
"title": ""
},
{
"docid": "2d42fc0cb78ed68b85b75237a3272cc2",
"score": "0.6069867",
"text": "def insNode(self, r, x):\n if r is None:\n return x\n if x < r:\n r.left = self.insNode(r.left, x)\n elif r < x:\n r.right = self.insNode(r.right, x)\n else:\n r.val += x.val\n # raise ValueError(\"Same key in RBTree\")\n\n return self.insBalance(r)",
"title": ""
},
{
"docid": "982d7cec7895e3d58ee21b81e232b05d",
"score": "0.60678136",
"text": "def _propagate_values(self, current_node, value):\n\n # Check the children and current node\n subtree_max = current_node.key\n if len(current_node.children) > 0:\n subtree_max = max([current_node.key] + [i.subtree_value for i in current_node.children])\n\n # If it's greater, then we terminate.\n if subtree_max > value:\n value = subtree_max\n\n # Change the value\n current_node.subtree_value = value\n\n # We are at the root\n if current_node.parent is None:\n return\n\n # Recursively propagate upwards\n self._propagate_values(current_node.parent, value)",
"title": ""
},
{
"docid": "d1187852f354d3c405e7302ac52d68f3",
"score": "0.6061363",
"text": "def insert(self, value):\n\t\tto_insert = Node(value)\n\t\tif not self.root:\n\t\t\tself.root = to_insert\n\t\telse:\n\t\t\tcurr_node = self.root\n\t\t\twhile True:\n\t\t\t\tif value <= curr_node.value:\n\t\t\t\t\tif curr_node.left_child:\n\t\t\t\t\t\tcurr_node = curr_node.left_child\n\t\t\t\t\t\tcontinue\n\t\t\t\t\telse:\n\t\t\t\t\t\tcurr_node.left_child = to_insert\n\t\t\t\t\t\tbreak\n\t\t\t\tif value >= curr_node.value:\n\t\t\t\t\tif curr_node.right_child:\n\t\t\t\t\t\tcurr_node = curr_node.right_child\n\t\t\t\t\t\tcontinue\n\t\t\t\t\telse:\n\t\t\t\t\t\tcurr_node.right_child = to_insert\n\t\t\t\t\t\tbreak",
"title": ""
},
{
"docid": "860899574364f89d33310395d5e9ad30",
"score": "0.5993767",
"text": "def _insert_zip(x, root):\n if root is None:\n x.left = x.right = None\n return x\n if x.key < root.key:\n if x is _insert_zip(x, root.left):\n if x.rank < root.rank:\n root.left = x\n else:\n root.left = x.right\n x.right = root\n return x\n else:\n if x is _insert_zip(x, root.right):\n if x.rank <= root.rank:\n root.right = x\n else:\n root.right = x.left\n x.left = root\n return x\n return root",
"title": ""
},
{
"docid": "e6dc508e381c703d8798f2d4d324af03",
"score": "0.5981099",
"text": "def _merge(left, right):\n # Initialize the new Deque.\n if left._value <= right._value:\n new = left\n left = left._next\n else:\n new = right\n right = right._next\n\n # Create a pointer to traverse the new Deque.\n current = new\n\n # Traverse both Deques appending larger value to the end of the Deque.\n while left is not None and right is not None:\n\n if left._value <= right._value:\n current._next = left\n current = current._next\n left = left._next\n else:\n current._next = right\n current = current._next\n right = right._next\n\n # Append the remaining Deque.\n if left is not None:\n current._next = left\n elif right is not None:\n current._next = right\n return new",
"title": ""
},
{
"docid": "b8bbf8bdb06896e62c0818e378006477",
"score": "0.5965125",
"text": "def insert(self, new_data):\n\n # # check the new_data against the current data\n # # if the new data is bigger, go right\n # # if new data is smaller, go left\n\n # current = self\n\n # while current.left is not None or current.right is not None:\n # if new_data < current.data:\n # current = current.left\n # elif new_data > current.data:\n # current = current.right\n\n # if new_data < current.data:\n # current.left = Node(new_data)\n # else:\n # current.right = Node(new_data)\n\n # we can also solve this using recursion\n # we should head left or right depending on whether the new\n # data is greater than or less than the node we are on\n # once we go one direction: say we go right, if the current.right\n # is none, we want to add the node there\n # if the current right is not none, we will call the function recursively\n\n if new_data >= self.data:\n if self.right is None:\n self.right = Node(new_data)\n else:\n self.right.insert(new_data)\n\n else:\n if self.left is None:\n self.left = Node(new_data)\n else:\n self.left.insert(new_data)",
"title": ""
},
{
"docid": "236d69eb4480c4604aecf147446da2b0",
"score": "0.5948784",
"text": "def test_decreasing_insertion():\n tree = BinarySearchTree()\n n = 42\n tree.insert(*reversed(range(n)))\n\n # root case\n assert tree.root is not None\n assert tree.root.key == n - 1\n assert tree.root.right is None\n\n # any other node case\n node = tree.root\n for i in range(n - 2, -1, -1):\n node = node.left\n assert node is not None\n assert node.key == i\n assert node.right is None\n assert node.left is None",
"title": ""
},
{
"docid": "1a34088b18a318be70a89e95866c5fec",
"score": "0.59403616",
"text": "def percolate_down(self, current):\n \n # identify the indices of the end of the heap, and children of the \n # current element\n last = self.heap.length()\n left = 2*current+1\n right = 2*current+2\n \n # percolate the current node down the tree until reached correct spot\n while left < last:\n # CASE 1: left child has lower value than right child and current \n # = swap current with left child\n if right < last and self.heap[left] <= self.heap[right] and self.heap[left] < self.heap[current]:\n self.heap.swap(current, left)\n current = left\n # CASE 2: right child has lower value than left child and current \n # = swap current with right child\n elif right < last and self.heap[left] > self.heap[right] and self.heap[right] < self.heap[current]:\n self.heap.swap(current, right)\n current = right\n # CASE 3: left child has lower value than current, no right child \n # = swap current with left child\n elif self.heap[left] < self.heap[current]:\n self.heap.swap(current, left)\n current = left\n # CASE 4: current value has lower value than any child elements\n else:\n break\n # update child element indices\n left = 2*current+1\n right = 2*current+2",
"title": ""
},
{
"docid": "9ddb564b64db3a0c791e952dca62436b",
"score": "0.5936784",
"text": "def ascending(self):\n nodes = []\n node = self.root\n while True:\n while node is not None:\n nodes.append(node)\n node = node.left\n if nodes:\n node = nodes.pop()\n yield node\n node = node.right\n else:\n break",
"title": ""
},
{
"docid": "a14d588ab2fe0a5cf05bbedff67470a8",
"score": "0.5936595",
"text": "def _add_node(self, curr, node):\n\n if not self.root:\n self.root = node\n if not curr:\n curr = self.root\n if curr.value > node.value:\n if curr.child_left is None:\n curr.child_left = node\n else:\n self._add_node(curr.child_left,node)\n if curr.value < node.value:\n if curr.child_right is None:\n curr.child_right = node\n else:\n self._add_node(curr.child_right,node)",
"title": ""
},
{
"docid": "052d9ec0f620a3ee1b0973621f19fe69",
"score": "0.5932994",
"text": "def adjust_heap_down(self):\n index = 0\n children = self.get_child_indicies(index)\n left = children['left']\n right = children['right']\n\n # If child exists, has to at least exist on the left side\n while left:\n if right and self.nodes[right] < self.nodes[left]:\n smaller_child_index = right\n else:\n smaller_child_index = left\n\n if self.nodes[index] > self.nodes[smaller_child_index]:\n self.swap_nodes(index, smaller_child_index)\n \n index = smaller_child_index\n children = self.get_child_indicies(index)\n\n # Check if we have reached the bottom\n if children is None:\n break\n\n left = children['left']\n right = children['right']",
"title": ""
},
{
"docid": "04925853d7d26b4de5757c5038c05873",
"score": "0.5932006",
"text": "def insert(self, key: K, val: V) -> 'WBBNode[K, V]':\n path = list(self.path(key))\n leaf = path[-1]\n if key in leaf.keys:\n if key in leaf.deleted:\n # Just unmark the key and replace its value!\n # TODO: update global deletion count?\n leaf.deleted.remove(key)\n leaf.vals[leaf.keys.index(key)] = val\n return self\n raise ValueError(f'Key \"{key}\" already in tree')\n\n # Insert the new key-value pair in place.\n inserted = False\n for idx, sibling_key in enumerate(leaf.keys):\n if sibling_key > key:\n leaf.keys.insert(idx, key)\n leaf.vals.insert(idx, val)\n inserted = True\n break\n if not inserted:\n leaf.keys.append(key)\n leaf.vals.append(val)\n for node in path:\n node.weight += 1\n node.size += 1\n\n # Move back up the tree, splitting as necessary.\n for level, child in enumerate(reversed(path)):\n target_weight = self.d**(level + 1)\n if child.weight > 2 * target_weight:\n left, median, right = child.split()\n if level + 1 == len(path):\n # Splitting at the top requires a new root node.\n new_root: WBBNode[K, V] = WBBNode(d=self.d)\n new_root.insert(median[0], median[1])\n new_root.children = [left, right]\n new_root.weight = left.weight + right.weight + 1\n new_root.size = left.size + right.size + 1\n return new_root\n # Otherwise, replace the child node with the new left node and\n # insert the right node next to it.\n parent = path[len(path) - level - 2]\n for idx, node in enumerate(parent.children):\n if node == child:\n parent.children[idx] = left\n parent.keys.insert(idx, median[0])\n parent.vals.insert(idx, median[1])\n parent.children.insert(idx + 1, right)\n break\n return self",
"title": ""
},
{
"docid": "82813b55ab202c913b38ec622415fb47",
"score": "0.59154797",
"text": "def add_node(self, new_value):\n self.n += 1\n self.nth_binary_representing = format(self.n, \"b\")\n\n cur = None\n for i in range(len(self.nth_binary_representing) - 1):\n if self.nth_binary_representing[i] == self.RIGHT_CHILD:\n if not cur: # init for the first iteration\n cur = self.root\n else:\n cur = cur.right_child\n elif self.nth_binary_representing[i] == self.LEFT_CHILD:\n cur = cur.left_child\n\n if self.nth_binary_representing[-1] == self.LEFT_CHILD:\n cur.left_child = Node(new_value, cur)\n return cur.left_child\n elif self.nth_binary_representing[-1] == self.RIGHT_CHILD:\n cur.right_child = Node(new_value, cur)\n return cur.right_child",
"title": ""
},
{
"docid": "4275eb69410aad463ed25b890e4bab5b",
"score": "0.5906915",
"text": "def remove(self, value):\n def _remove(node):\n # Continue the search on the left tree.\n if node.value > value:\n if node.left:\n # If we have two consecutive black links on the left,\n # we move one red node from the right to the left.\n if is_black(node.left) and is_black(node.left.left):\n node = LLRBT._move_red_left(node)\n node.left, removed = _remove(node.left)\n # In this case, the value is not present in the tree.\n else:\n removed = False\n\n # Two things can happen here: the search must continue on the\n # right branch or the value is present in the current node.\n else:\n # In any case, if the left child is red we should move it\n # to the right. This will allow us to eventually delete\n # a node from the right branch.\n if is_red(node.left):\n node = LLRBT._rotate_right(node)\n\n # Node found. Delete it and return True.\n if node.value == value and node.right is None:\n return None, True\n\n # At this point, the search continues on the right sub tree.\n if node.right:\n # If we have a right node on the left, we move it to\n # the right.\n if is_black(node.right) and is_black(node.right.left):\n node = LLRBT._move_red_right(node)\n\n # The value was found, so we need to replace that node\n # with its successor.\n if node.value == value:\n successor, _ = LLRBT._min(node.right)\n node.value = successor.value\n node.right = LLRBT._remove_min(node.right)\n removed = True\n\n # The search continues on the right branch\n else:\n node.right, removed = _remove(node.right)\n\n # The current node doesn't have the value we are looking for\n # and the right branch is empty.\n else:\n removed = False\n return LLRBT._balance(node), removed\n if self._root:\n self._root, removed = _remove(self._root)\n self._size -= int(removed)\n return removed",
"title": ""
},
{
"docid": "5fe3e6028baa879b59378f0dabe3d8fe",
"score": "0.5904875",
"text": "def insert_ele(self, val):\n \n # First consider if the new node could be inserted as the list head.\n q = Node(val)\n if self.head == None or val < self.head.value:\n q.succ = self.head\n self.head = q\n return\n\n # Walk through the list until the next value is None or the next value is larger or equal to the one to be inserted\n p = self.head\n while p.succ != None and p.succ.value < val:\n p = p.succ\n q.succ = p.succ\n p.succ = q",
"title": ""
},
{
"docid": "867a3c950eb0641e5fa8d36a37eeec4a",
"score": "0.5889196",
"text": "def search_pre(self, value, node):\n if (node is not None):\n\n # Check the parent node\n if (node.get_value() == value):\n return node\n\n # Check the left branch\n left_branch = self.search_pre(value, node.get_left())\n if (left_branch is not None):\n return left_branch\n\n # Check the right branch\n right_branch = self.search_pre(value, node.get_right())\n if (right_branch is not None):\n return right_branch\n\n return None",
"title": ""
},
{
"docid": "7e4f46a8cdf2be66f74aa0cfaa973135",
"score": "0.58767587",
"text": "def insertFront(self, value: int) -> bool:\n if not self.isFull():\n new_node = Node(value) # 삽입할 노드 생성\n head_next = self.head.right # head 다음의 노드를 따로 저장 (본래 head <-> head_next)\n self.head.right, head_next.left = new_node, new_node # head->new_node & new_node <- head_next\n new_node.left, new_node.right = self.head, head_next # head<-new_node & new_node -> head_next\n self.cur_size += 1\n return True\n return False",
"title": ""
},
{
"docid": "f854d5d3d023cfef52c86d9874f0eefd",
"score": "0.58718103",
"text": "def insert(self, node):\n if node.key < self.key:\n if self.left is not None:\n return self.left.insert(node)\n node.parent = self\n self.left = node\n return node\n elif node.key > self.key:\n if self.right is not None:\n return self.right.insert(node)\n node.parent = self\n self.right = node\n return node\n return self",
"title": ""
},
{
"docid": "ea3387169fec13a2bac1c1ca8ef42212",
"score": "0.586901",
"text": "def remove(self, value) -> bool:\n # first, check if the BST is empty\n if self.root == None:\n return False\n # find value using BS\n found_left = False # value found in left subtree\n found = False # value found, cur.value = value\n parent = None\n cur = self.root\n while cur is not None and not found: # until the end is found and value is found\n if value == cur.value: # value you found\n found = True\n elif value < cur.value: # value is less than cur.value\n parent = cur\n cur = cur.left # go down left subtree\n found_left = True\n else: # value is greater than cur.value\n parent = cur\n cur = cur.right # go down right subtree\n found_left = False\n # HANDLE SPECIAL CASES BEFORE FINDING IN-ORDER SUCCESSOR\n # cur is the root node, use remove_first()\n if cur == self.root:\n self.remove_first()\n return True\n # value not found, cur is None but found is not true\n if not found:\n return False\n # cur is a leaf, has no children\n if cur.left == None and cur.right == None:\n if found_left:\n parent.left = None\n return True\n if found_left:\n parent.right = None\n return True\n # only left subtree\n if cur.right is None: \n if found_left:\n parent.left = cur.left\n return True\n if not found_left:\n parent.right = cur.left\n return True\n # only right subtree\n if cur.left is None:\n if found_left:\n parent.left = cur.right\n return True\n if not found_left:\n parent.right = cur.right\n return True\n else:\n # two children, need in-order successor\n suc_found_left = False\n succ = cur.right\n succ_parent = cur\n while succ.left is not None:\n succ_parent = succ\n succ = succ.left\n suc_found_left = True\n # remove the successor\n if suc_found_left:\n succ_parent.left = succ.right\n if not suc_found_left:\n succ_parent.right = succ.right\n # place successor in removed node's spot\n if found_left:\n parent.left = succ\n succ.left = cur.left\n succ.right = cur.right\n return True\n if not found_left:\n parent.right = succ\n succ.left = cur.left\n succ.right = cur.right\n return True",
"title": ""
},
{
"docid": "cbe1748de49499c8d366103d780454ea",
"score": "0.58675224",
"text": "def _add_node(self, node, current=None):\r\n if self.root is None:\r\n self.root = node\r\n\r\n if current is None:\r\n current = self.root\r\n\r\n if current:\r\n \"\"\"\r\n If new node value is less than current node value\r\n \"\"\"\r\n if current.value > node.value:\r\n if current.left_child is None:\r\n current.left_child = node\r\n else:\r\n self._add_node(node, current.left_child)\r\n\r\n \"\"\"\r\n If new node value is greater than current node value\r\n \"\"\"\r\n if current.value < node.value:\r\n if current.right_child is None:\r\n current.right_child = node\r\n else:\r\n self._add_node(node, current.right_child)",
"title": ""
},
{
"docid": "e1c004332c63e3267c360500a1b72c5e",
"score": "0.58647496",
"text": "def insert_before(self, val, newVal):\n current = self.head\n previous = None\n while current:\n if current.val == val:\n if previous is None:\n self.insert(newVal)\n else:\n new_node = Node(newVal)\n new_node._next = current\n previous._next = new_node\n self._size += 1\n break\n previous = current\n current = current._next",
"title": ""
},
{
"docid": "f4f8116e27b290aa8d142dd51ec2ffed",
"score": "0.5855117",
"text": "def merge(st1,st2):\n start=ListNode(0)\n curr=start\n while st1!=None and st2!=None:\n if st1.val<st2.val:\n curr.next=st1\n st1=st1.next\n else:\n curr.next=st2\n st2=st2.next\n curr=curr.next\n while st1!=None:\n curr.next=st1\n st1=st1.next\n curr=curr.next\n while st2!=None:\n curr.next=st2\n st2=st2.next\n curr=curr.next\n\n return start.next",
"title": ""
},
{
"docid": "151916e9a5b301be92e67806d8e84c62",
"score": "0.5852492",
"text": "def insert(self, curr, key, value):\n if curr.key == key:\n curr.value = value\n elif curr.key > key:\n if curr.left:\n self.insert(curr.left, key, value)\n else:\n curr.left = TreeNode(key, value)\n self.size += 1\n else:\n if curr.right:\n self.insert(curr.right, key, value)\n else:\n curr.right = TreeNode(key, value)\n self.size += 1",
"title": ""
},
{
"docid": "f656f4413994e228022ad2e333d9a4ee",
"score": "0.58364135",
"text": "def insert_back(self, value):\n curr = self.head\n while curr.next is not None:\n curr = curr.next\n node = Node(value)\n curr.next = node\n return",
"title": ""
},
{
"docid": "f79a92b8593158b1e32e7a28de79ce58",
"score": "0.5827224",
"text": "def insert(self, new_data):\n\n current = self # assume not empty and no dupe\n\n prev_head = None\n # to keep track of where the last head was\n\n while current:\n\n prev_head = current\n \n if new_data < current.data: \n current = current.left\n\n elif new_data > current.data: \n current = current.right\n\n if new_data < prev_head.data:\n prev_head.left = BinaryTreeNode(new_data)\n\n elif new_data > prev_head.data:\n prev_head.right = BinaryTreeNode(new_data)",
"title": ""
},
{
"docid": "402f841c6da47f8880e8f67529c29a82",
"score": "0.58193177",
"text": "def sorted_insert(self, value):\n\n if self.__head is None:\n new_nod = Node(value)\n new_nod.next_node = self.__head\n self.__head = new_nod\n else:\n new_nod = Node(value)\n new_nod.data = value\n new_nod.next_node = self.__head\n self.__head = new_nod",
"title": ""
},
{
"docid": "8e6bb76b771f4dde4d6b7aef809ffabc",
"score": "0.58162725",
"text": "def _insert(self, root_node, curr_node):\n if root_node is None:\n root_node = curr_node\n else:\n if curr_node.get_data() < root_node.get_data():\n if root_node.left is None:\n root_node.left = curr_node\n else:\n self._insert(root_node.left, curr_node)\n\n elif curr_node.get_data() > root_node.get_data():\n if root_node.right is None:\n root_node.right = curr_node\n else:\n self._insert(root_node.right, curr_node)\n\n else:\n print ('Data already exists in the tree')\n return",
"title": ""
},
{
"docid": "df2dccaa0fc9cf2a31b81b3e717c6023",
"score": "0.58162284",
"text": "def sort(nums):\r\n # make yourself a new head to start the sorted array with\r\n sortedList = {'data':nums['data'], 'next': None}\r\n # used to hold a reference to the previous value as we move along\r\n previous = sortedList\r\n # this is the list that maintains its structure so we can return it\r\n ogHead = sortedList\r\n\r\n # a test to see if we are at the first value in the linked list\r\n initValue = nums['data']\r\n\r\n # run until the end\r\n while nums is not None:\r\n # advance nums\r\n nums = nums['next']\r\n # sortedlist gets destroyed as we constantly advance through it\r\n # when you break out of the below loops it is important to reset sortedList to\r\n # the overarching sorted list referenced by ogHead\r\n sortedList = ogHead\r\n\r\n # make sure we don't do an illegal comparison\r\n if nums is not None:\r\n # run until the end\r\n while sortedList is not None:\r\n # generate a temporary node to be slotted in properly\r\n tempNode = {'data':nums['data'], 'next': None}\r\n\r\n if sortedList['data'] > nums['data']:\r\n # we have found a value that is greater than the value we want to insert\r\n # we don't know if it's the first value in the list or later\r\n if sortedList['data'] == initValue:\r\n # if this runs we are at the first value\r\n # simply attach the rest of the list\r\n tempNode['next'] = sortedList\r\n sortedList = tempNode\r\n # because we've edited the head we have to point our 'safe' list to the new start\r\n ogHead = sortedList\r\n # update the initial value so we can continue comparing to the actual head of the list\r\n # (not the initial head of the list)\r\n initValue = sortedList['data']\r\n break\r\n else:\r\n # it's not the first element\r\n # we need to hook up the first half of the list and the second half to the new node\r\n tempNode['next'] = sortedList\r\n previous['next'] = tempNode\r\n break\r\n elif sortedList['next'] is None:\r\n # if this runs we've checked the final node with a value\r\n # we simply have to set the tail end of the linked list equal to the value we're inserting\r\n sortedList['next'] = tempNode\r\n break\r\n else:\r\n # move the values along so that we can continue checking\r\n # this will happen if the value is not larger than the value we're trying to insert\r\n # and if we haven't reached the end of the list where we would just attach the inserted value\r\n previous = sortedList\r\n sortedList = sortedList['next']\r\n\r\n\r\n return ogHead # return the variable tha references the proper head of the\r",
"title": ""
},
{
"docid": "87ea1e36b0cc2a950463988dcdbfa9b9",
"score": "0.581546",
"text": "def binarize_left(self):\n nodes = list(self.bottomup())\n for node in nodes:\n if len(node.children) > 2:\n vlabel = node.label+\"*\"\n children = list(node.children)\n prev = children[0]\n for child in children[1:-1]:\n prev = Node(vlabel, [prev, child])\n node.insert_child(0, prev)",
"title": ""
},
{
"docid": "0f89421395bfc9d1353ce60a746886b7",
"score": "0.5809675",
"text": "def insert_left(self,value):\n if self.left_child == None:\n self.left_child = binaryTree(value)\n else:\n node = binaryTree(value)\n node.left_child = self.left_child\n self.left_child = node",
"title": ""
},
{
"docid": "28c10c2310644ba1c59b5e495bd691ec",
"score": "0.580664",
"text": "def add(self, data):\n if self.data == data:\n return False # As BST cannot contain duplicate data\n\n elif data < self.data:\n if self.leftChild:\n return self.leftChild.add(data)\n else:\n self.leftChild = Node(data)\n return True\n\n else:\n if self.rightChild:\n return self.rightChild.add(data)\n else:\n self.rightChild = Node(data)\n return True",
"title": ""
},
{
"docid": "e03c148188d6238669db444ae094c21b",
"score": "0.58046263",
"text": "def insertion_sort(dll, low, high):\n node = low\n if high is not None:\n while node is not high.get_next():\n next_node = node.get_next()\n if node is not low:\n key = node.get_previous()\n while node.get_value() < key.get_value():\n temp = key.get_value()\n key.set_value(node.get_value())\n node.set_value(temp)\n if key is not low:\n node = key\n key = key.get_previous()\n node = next_node",
"title": ""
},
{
"docid": "aca74443a0f5680f571545abeecd1fad",
"score": "0.5795521",
"text": "def add(self, obj):\r\n # method body goes here\r\n if self._element == obj:\r\n return None\r\n elif obj < self._element:\r\n if not self._leftchild:\r\n node = BSTNode(obj)\r\n self._leftchild = node\r\n node._parent = self\r\n return obj\r\n else:\r\n self._leftchild.add(obj)\r\n else:\r\n if not self._rightchild:\r\n node = BSTNode(obj)\r\n self._rightchild = node\r\n node._parent = self\r\n return obj\r\n else:\r\n self._rightchild.add(obj)",
"title": ""
},
{
"docid": "e15b1655cffd07015ff0deb2efd6d636",
"score": "0.5788769",
"text": "def _insertNode(self, currentNodeidx, val):\n\t\tcurrentNode = self.tree[currentNodeidx]\n\t\tif val <= currentNode['val']:\n\t\t\tleftChild = currentNode['L']\n\t\t\tif leftChild == None:\n\t\t\t\tself.createNode(val)\n\t\t\t\tself.tree[-1]['P'] = currentNodeidx\n\t\t\t\tcurrentNode['L'] = len(self.tree)-1\n\t\t\telse:\n\t\t\t\tself._insertNode(leftChild, val)\n\t\telif val > currentNode['val']:\n\t\t\trightChild = currentNode['R']\n\t\t\tif rightChild == None:\n\t\t\t\tself.createNode(val)\n\t\t\t\tself.tree[-1]['P'] = currentNodeidx\n\t\t\t\tcurrentNode['R'] = len(self.tree)-1\n\t\t\telse:\n\t\t\t\tself._insertNode(rightChild, val)",
"title": ""
},
{
"docid": "e5fa50ff586cabd9718c41f57f0a80c1",
"score": "0.57821983",
"text": "def insert_node(cls, node: BinarySearchTreeNode,\n root: BinarySearchTreeNode) -> BinarySearchTreeNode:\n if root is None:\n return node\n if node.value < root.value:\n root.left = cls.insert_node(node, root.left)\n else:\n root.right = cls.insert_node(node, root.right)\n return root",
"title": ""
},
{
"docid": "72af13acde09e3c16a2af216da100a58",
"score": "0.5778679",
"text": "def bubble_up(self):\n node_index = len(self) - 1\n parent_index = (node_index - 1) // 2\n while self[node_index] < self[parent_index] and node_index > 0:\n self[node_index], self[parent_index] = self[parent_index], self[node_index]\n node_index = parent_index\n parent_index = (node_index - 1) // 2",
"title": ""
},
{
"docid": "88fd6c718583f9e2e06be2c1200089b1",
"score": "0.57774556",
"text": "def delete(self, val):\n\n def helper(node, val):\n if node.val > val:\n node.left = helper(node.left, val)\n elif node.val < val:\n node.right = helper(node.right, val)\n else:\n if not node.left and not node.right:\n return None\n elif node.right:\n node.val = self._successor(node).val\n node.right = helper(node.right, node.val)\n else:\n node.val = self._predecessor(node).val\n node.left = helper(node.left, node.val)\n return node\n\n self.root = helper(self.root, val)",
"title": ""
},
{
"docid": "394078bd4e980120380c36e0e6250e7c",
"score": "0.5774612",
"text": "def insert(self, val):\n if not self.root:\n self.root = Tnode(val)\n self.size += 1\n else:\n node = self.root\n while node:\n if val > node.val:\n if node.right_child:\n node = node.right_child\n else:\n node.right_child = Tnode(val=val,\n parent=node,\n is_right=True)\n self.size += 1\n break\n elif val < node.val:\n if node.left_child:\n node = node.left_child\n else:\n node.left_child = Tnode(val=val,\n parent=node,\n is_left=True)\n self.size += 1\n break\n elif node.val == val:\n break",
"title": ""
},
{
"docid": "f3c9ed39d80ba39c66d039bffecaa905",
"score": "0.57718563",
"text": "def add(self, item):\r\n if self.is_empty():\r\n temp=Node(item)\r\n self.sentinel.next=temp\r\n self.sentinel.prev=temp\r\n temp.prev=self.sentinel\r\n temp.next=self.sentinel\r\n elif item>self.sentinel.prev.item:\r\n temp=Node(item)\r\n temp.prev=self.sentinel.prev\r\n temp.next=self.sentinel\r\n self.sentinel.prev.next=temp\r\n self.sentinel.prev=temp\r\n #figure out how to do middle numbers\r\n else:\r\n temp=Node(item)\r\n next_node=self.sentinel.next\r\n current_node=self.sentinel\r\n while item > next_node.item:\r\n next_node=next_node.next\r\n current_node=current_node.next\r\n next_node.prev=temp\r\n current_node.next=temp\r\n temp.next=next_node\r\n temp.prev=current_node",
"title": ""
},
{
"docid": "21ddc6e1eb5a5b03c3463db7b1520d0d",
"score": "0.5771379",
"text": "def _insert(self, data, node):\n # bust out of the function if data exists in the tree\n if data is node.data:\n return\n if data < node.data:\n # The data belongs on the left side.\n if node.left is None:\n # We found an empty spot\n node.left = BST.Node(data)\n else:\n # Need to keep looking. Call _insert\n # recursively on the left sub-tree.\n self._insert(data, node.left)\n elif data >= node.data:\n # The data belongs on the right side.\n if node.right is None:\n # We found an empty spot\n node.right = BST.Node(data)\n else:\n # Need to keep looking. Call _insert\n # recursively on the right sub-tree.\n self._insert(data, node.right)",
"title": ""
},
{
"docid": "b15b6b7ae1d4159df5e9672bd395b39f",
"score": "0.5765844",
"text": "def delete(self, key):\n\n if self is None:\n return None\n\n if self.value > key: \n self.left = self.left.delete(key)\n elif self.value < key: \n self.right = self.right.delete(key)\n else:\n if self.left is None:\n temp = self.right\n self = None\n return temp\n elif self.right is None:\n temp = self.left\n self = None\n return temp\n\n temp = self.findMin(self.right)\n self.value = temp.value\n self.right = self.right.delete(temp.value)\n\n return self",
"title": ""
},
{
"docid": "937231bd96d0ef10308510b7cda094af",
"score": "0.57524973",
"text": "def bst_insert(t, n):\n if t is not None:\n if n.key < t.key:\n t.left = bst_insert(t.left, n)\n else:\n t.right = bst_insert(t.right, n)\n else:\n t = n\n return t",
"title": ""
},
{
"docid": "9849f282691c9538e6d446dd862fc5af",
"score": "0.5750494",
"text": "def delete(self, root, val, l=0, r=0):\n\n if not root:\n return root\n elif val < root.val:\n l += 1\n r = 0\n root.left = self.delete(root.left, val)\n elif val > root.val:\n r += 1\n l = 0\n root.right = self.delete(root.right, val)\n else:\n if root.left is None:\n temp, root = root.right, None\n return temp\n elif root.right is None:\n temp, root = root.left, None\n return temp\n temp = self.getMinValueNode(root.right)\n root.val = temp.val\n root.right = self.delete(root.right, temp.val)\n if root is None:\n return root\n root.height = 1 + max(self.getHeight(root.left),\n self.getHeight(root.right))\n root = self.__rotate(root, l, r)\n\n return root",
"title": ""
},
{
"docid": "d9896e753bec906292cf44363838db68",
"score": "0.57495576",
"text": "def _insert(self, node, key, data):\n if node == None:\n node = Node(key, data)\n return node\n \n if key > node.key:\n node.right = self._insert(node.right, key, data)\n if node.right.priority < node.priority:\n node = node.rotate_left()\n \n elif key < node.key:\n node.left = self._insert(node.left, key, data)\n if node.left.priority < node.priority:\n node = node.rotate_right()\n \n elif key == node.key:\n self.size -= 1\n \n return node",
"title": ""
},
{
"docid": "cde9506c3f5aee0866ffe5e93d66d4eb",
"score": "0.57495165",
"text": "def insert(self, key, value):\n\n if self.key == key:\n self.val = value\n elif key < self.key:\n if self.left is None:\n self.left = self.__class__(key, value)\n else:\n self.left = self.left.insert(key, value)\n else:\n if self.right is None:\n self.right = self.__class__(key, value)\n else:\n self.right = self.right.insert(key, value)\n\n return self",
"title": ""
},
{
"docid": "effa6195ca25484817f3652d65148dc0",
"score": "0.5743393",
"text": "def insert_after(self, value):\n new_node = ListNode(value)\n current = self \n next_node = current.next\n\n prev_node = current.prev \n new_node.set_prev(current)#current node becomes the previous for new node\n new_node.set_next(next_node) # next node is now next for the new node\n current.next = new_node\n\n # some way go find the value. ",
"title": ""
},
{
"docid": "c6d0c368e47b92d50aa825593b851400",
"score": "0.57340324",
"text": "def inorder_traversal(node):\n\n if node != None:\n #items in left tree are less than node, so print that recursively first\n inorder_traversal(node.left)\n #print node\n print(node.value)\n #items in right tree are greater than node, so print that recursively second\n inorder_traversal(node.right)",
"title": ""
},
{
"docid": "1837fd2243d6e19fabd092d23384a523",
"score": "0.5731491",
"text": "def add(self, data):\n root = self.root\n\n if not root:\n self.root = Node(data)\n return\n else:\n def search_tree(node):\n if data < node.data:\n if not node.left:\n node.left = Node(data)\n return\n node = node.left\n return search_tree(node)\n elif data > node.data:\n if not node.right:\n node.right = Node(data)\n return\n node = node.right\n return search_tree(node)\n\n return search_tree(root)",
"title": ""
},
{
"docid": "bd69f7787431ec6974f3dcf5e50d2d56",
"score": "0.5725863",
"text": "def insert(self, t, NodeType):\n self.size += 1\n if t < self.key:\n if self.left is None:\n self.left = NodeType(self, t) \n return self.left\n else:\n return self.left.insert(t, NodeType)\n else:\n if self.right is None:\n self.right = NodeType(self, t) \n return self.right\n else:\n return self.right.insert(t, NodeType)",
"title": ""
},
{
"docid": "be180de8e99006d4698915b2bf596d05",
"score": "0.57220083",
"text": "def remove(value, node):\n if node is None:\n return None\n if value == node.value:\n node = node.next_node\n return node\n elif node.next_node.next_node is None:\n if value == node.next_node.value:\n node.next_node = None\n return node\n else:\n return node\n elif value == node.next_node.value:\n node.next_node = (node.next_node).next_node\n return node\n else:\n node.next_node = remove(value, node.next_node)\n return node",
"title": ""
},
{
"docid": "0e0245b9ec31946944ef6aaba9814b39",
"score": "0.57202446",
"text": "def add_back(self, value: object) -> None:\n new_link = SLNode(value)\n cur = self.head\n # Checks if the list is empty and, if so, appends the new value between the sentinels.\n if cur.next == self.tail:\n cur = self.head\n new_link = SLNode(value)\n new_link.next = cur.next\n cur.next = new_link\n # Else, it calls the recursive helper function.\n else:\n self.add_back_rec(cur.next, new_link)",
"title": ""
},
{
"docid": "09f191f8cc7cbc06d14936fa6e3508ac",
"score": "0.5713285",
"text": "def _put(self, key, value, current_node):\n if key > current_node.key:\n if current_node.has_right_child():\n self._put(key, value, current_node.right_child)\n else:\n current_node.right_child = WordNode(key, value, parent=current_node)\n else:\n if current_node.has_left_child():\n self._put(key, value, current_node.left_child)\n else:\n current_node.left_child = WordNode(key, value, parent=current_node)",
"title": ""
},
{
"docid": "f41ad36c713073c6c42f2d645e879559",
"score": "0.57002276",
"text": "def delete(self, node, value, parent=None):\n if not node:\n return\n elif value < node.value:\n self.delete(node.left, value, parent=node)\n elif value > node.value:\n self.delete(node.right, value, parent=node)\n else:\n # Case 1: Node has no children, so remove node from its parent\n if not node.left and not node.right:\n if parent.left and node == parent.left:\n parent.left = None\n else:\n parent.right = None\n \n # Case 2: Node has two children, so start with the current node's right child, find that child's leftmost child, replace the current node's value with the leftmost child's value and call the deletion function on the current node's right child\n elif node.left and node.right:\n successor = node.right\n successor_parent = node\n while successor.left:\n successor_parent = successor\n successor = successor.left\n node.value = successor.value\n self.delete(successor, successor.value, parent=successor_parent)\n\n # Case 3: Node has one child, so replace the node with its child\n else:\n if parent.left and node == parent.left:\n if node.left:\n parent.left = node.left\n if node.right:\n parent.left = node.right\n else:\n if node.left:\n parent.right = node.left\n if node.right:\n parent.right = node.right",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="763"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">441d22117b67a3254b48a5d80db5e675</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Searches through the class's attrs for methods with the same name as HTTP ones and adds the bottle.request decorator to them. The route is set via self.route .</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "46576f42443c93c02aa41745f2b78883",
"score": "0.69266075",
"text": "def __new__(cls, name, bases, attrs):\n if 'route' in attrs: # to allow inheritance\n # Get the given route\n # or routes if a list is provided\n routes = attrs['route']\\\n if isinstance(attrs['route'], list)\\\n else [attrs['route']]\n\n # Go through the class's attributes\n for key, value in attrs.iteritems():\n # Only add decorator if HTTP method.\n # Allow uppercase methods but don't recommend it.\n if key.lower() in METHODS:\n # Allows self arg in methods otherwise not passed\n add_cls = lambda *args, **kwargs: value(cls,\n *args, **kwargs)\n # Make it possible for multiple routes\n for route in routes:\n # Add the decorator here, also the 'value' function is modified\n # based on the 2nd to last comment.\n attrs[key] = app.route(route, method=key.upper())(add_cls)\n return super(MethodsMeta, cls).__new__(cls, name, bases, attrs)",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "23abe07965fcb8e4521a1b2d07e3cff9",
"score": "0.6665122",
"text": "def __getattr__(self, name, *args, **kwargs):\n if name not in ('get', 'post', 'put', 'head', 'delete'):\n raise AttributeError\n return partial(self._request, name, *args, **kwargs)",
"title": ""
},
{
"docid": "7f66d4209d125821d4f64d450483f00b",
"score": "0.6346884",
"text": "def route(self, path_regex, methods=['GET'], doc=True):\n def register_func(func):\n \"\"\"\n Decorator implementation\n \"\"\"\n if doc:\n self.env['doc'].append({'url': path_regex, 'methods': ', '.join(methods), 'doc': func.__doc__})\n for method in methods:\n self._handlers[method].append((re.compile(path_regex), func))\n return func # Return the original function\n return register_func # Decorator",
"title": ""
},
{
"docid": "611205ca74aff1ba5e9361d773cbce42",
"score": "0.60822624",
"text": "def route(self, resource: str, httpMethod: str) -> RequestHandlerDecorator:\n def _decorator(handler: ProxyRequestHandler) -> None:\n if resource not in self._routes:\n self._routes[resource] = {}\n self._routes[resource][httpMethod] = handler\n return _decorator",
"title": ""
},
{
"docid": "b66cd21c75a95f6547bd1de2bb9ad8a0",
"score": "0.6028523",
"text": "def route(self, *args, **kwargs):\n def decorator(endpoint):\n ### apply endpoint decorator\n if not hasattr(endpoint, \"_webapp_endpoint\"):\n endpoint = self.endpoint(endpoint)\n\n ### apply flask decorator\n decorator = self.flask.route(*args, **kwargs)\n endpoint = decorator(endpoint)\n\n ### mark\n endpoint._webapp_endpoint = True\n\n return endpoint\n\n return decorator",
"title": ""
},
{
"docid": "20eb194c861d8c6aef9ae6b1b3e4d8f4",
"score": "0.5873895",
"text": "def __getattr__(self, item):\r\n if item.startswith('_'):\r\n raise AttributeError(item)\r\n\r\n def method_proxy(**kw):\r\n return self.do_request(item, **kw)\r\n\r\n return method_proxy",
"title": ""
},
{
"docid": "56ab35f9cdc7be079adc6d9d98a243ba",
"score": "0.58520204",
"text": "def route_request(self, request):\n domain = request.hostname\n path = urllib.unquote_plus(request.path)\n matcher = domain + path\n method = request.method.upper()\n available_methods = set()\n\n request._rule_headers = None\n request._rule_content_type = None\n\n for dmn, dkey, rules in self._route_list:\n # Do basic domain matching.\n if ':' in dmn:\n if not request.host.lower().endswith(dmn):\n continue\n elif not domain.endswith(dmn):\n continue\n\n # Iterate through the available rules, trying for a match.\n for rule, regex, rkey, name, method_table, names, namegen, \\\n converters in rules:\n if not path.startswith(rule):\n continue\n match = regex.match(matcher)\n if match is None:\n continue\n\n # We have a match. Check for a valid method.\n if not method in method_table:\n available_methods.update(method_table.keys())\n continue\n\n # It's a match. Run the method and return the result.\n request.route_name = name\n request.match = match\n\n try:\n func, headers, content_type = method_table[method]\n request._rule_headers = headers\n request._rule_content_type = content_type\n\n return func(request)\n except HTTPException as err:\n request._rule_headers = None\n request._rule_content_type = None\n\n err_handler = getattr(self, \"handle_%d\" % err.status, None)\n if err_handler:\n return err_handler(request, err)\n else:\n return error(err.message, err.status, err.headers,\n request=request)\n except HTTPTransparentRedirect as err:\n request._rule_headers = None\n request._rule_content_type = None\n\n request.uri = err.uri\n request._parse_uri()\n return self.route_request(request)\n except Exception as err:\n request._rule_headers = None\n request._rule_content_type = None\n\n return self.handle_500(request, err)\n\n if available_methods:\n if request.method == 'OPTIONS':\n return '', 200, {'Allow': ', '.join(available_methods)}\n else:\n return error(\n \"The method %s is not allowed for %r.\" % (method, path),\n 405, {'Allow': ', '.join(available_methods)}\n )\n\n elif self.fix_end_slash:\n # If there are no matching routes, and the path doesn't end with a\n # slash, try adding the slash.\n if not path[-1] == \"/\":\n path += \"/\"\n matcher += \"/\"\n for dmn, dkey, rules in self._route_list:\n if ':' in dmn:\n if not request.host.lower().endswith(dmn):\n continue\n elif not domain.endswith(dmn):\n continue\n\n for rule, regex, rkey, name, method_table, names, namegen, \\\n converters in rules:\n if not path.startswith(rule):\n continue\n if regex.match(matcher):\n if request.query:\n return redirect(\"%s?%s\" %\n (path, request.query))\n else:\n return redirect(path)\n\n return self.handle_404(request, None)",
"title": ""
},
{
"docid": "785d206f5246db62f7093087440d9d65",
"score": "0.5849018",
"text": "def request_method_decorator(f):\r\n def request_method(self, *args, **kwargs):\r\n if kwargs.pop('internal', False):\r\n return f(self, *args, **kwargs)\r\n else:\r\n def method_wrapper(*args, **kwargs):\r\n return f(self, *args, **kwargs)\r\n\r\n return self._transport.execute_request_method(method_wrapper,\r\n *args, **kwargs)\r\n\r\n request_method.__name__ = f.__name__\r\n request_method.__doc__ = f.__doc__\r\n request_method.__dict__.update(f.__dict__)\r\n return request_method",
"title": ""
},
{
"docid": "d382dd1b1c5026636396a47148d3700d",
"score": "0.5843218",
"text": "def basic_route(self, rule, name=None, methods=('GET', 'HEAD'),\n headers=None, content_type=None, func=None):\n if not '/' in rule:\n rule = '/' + rule\n\n def decorator(func):\n if not callable(func):\n raise ValueError(\"Request handler must be callable.\")\n\n if name is None:\n if hasattr(func, \"__name__\"):\n _name = func.__name__\n elif hasattr(func, \"__class__\"):\n _name = func.__class__.__name__\n else:\n raise ValueError(\"Cannot find name for rule. Please \"\n \"specify name manually.\")\n else:\n _name = name\n\n # Get the rule table for this rule.\n rule_table = self._routes.setdefault(rule, {})\n if isinstance(rule_table, Module):\n raise ValueError(\"The rule %r is claimed by a Module.\" % rule)\n\n # Now, for each method, store the data.\n for method in methods:\n rule_table[method] = (func, _name, False, False, headers,\n content_type)\n\n # Recalculate routes and return.\n self._recalculate_routes()\n return func\n\n if func:\n return decorator(func)\n return decorator",
"title": ""
},
{
"docid": "8b69caa38c5d2a189da8921b2d7fd581",
"score": "0.5754188",
"text": "def _decorate_request(self, filters, method, url, headers=None, body=None,\n auth_data=None):\n raise NotImplementedError",
"title": ""
},
{
"docid": "013731b504a1d175a9d60d6964065885",
"score": "0.56556445",
"text": "def route(regex, method, name):\n\n def decorator(function):\n function.route = routes.route(\n regex = regex,\n view = function.__name__,\n method = method,\n name = name\n )\n\n @wraps(function)\n def wrapper(self, *args, **kwargs):\n return function(self, *args, **kwargs)\n return wrapper\n\n return decorator",
"title": ""
},
{
"docid": "ed96acb3b17c0abc8787fb7f1d4b11ca",
"score": "0.5610119",
"text": "def _request(self):\n caller_frame = inspect.getouterframes(inspect.currentframe())[1]\n args, _, _, values = inspect.getargvalues(caller_frame[0])\n caller_name = caller_frame[3]\n kwargs = {arg: values[arg] for arg in args if arg != 'self'}\n func = reduce(\n lambda resource, name: resource.__getattr__(name),\n self.mappings[caller_name].split('.'), self)\n return func(**kwargs)",
"title": ""
},
{
"docid": "9757b02a3154423e8ed40a58b741c9f2",
"score": "0.5585204",
"text": "def route(self, rule, name=None, methods=('GET','HEAD'), auto404=False,\n headers=None, content_type=None, func=None):\n if not '/' in rule:\n rule = '/' + rule\n\n def decorator(func):\n if not callable(func):\n raise ValueError(\"Request handler must be callable.\")\n\n if name is None:\n if hasattr(func, \"__name__\"):\n _name = func.__name__\n elif hasattr(func, \"__class__\"):\n _name = func.__class__.__name__\n else:\n raise ValueError(\"Cannot find name for rule. Please \"\n \"specify name manually.\")\n else:\n _name = name\n\n # Get the rule table for this rule.\n rule_table = self._routes.setdefault(rule, {})\n if isinstance(rule_table, Module):\n raise ValueError(\"The rule %r is claimed by a Module.\" % rule)\n\n # Now, for each method, store the data.\n for method in methods:\n rule_table[method] = (func, _name, True, auto404, headers, content_type)\n\n # Recalculate and return.\n self._recalculate_routes()\n return func\n\n if func:\n return decorator(func)\n return decorator",
"title": ""
},
{
"docid": "69624ee51c8d8af2df9dba1c295991bc",
"score": "0.55362064",
"text": "def request(self, route, data):\n pass",
"title": ""
},
{
"docid": "fccc70691f01874e952f491e702fc43a",
"score": "0.551914",
"text": "def add_route(method, path, func):\n ROUTES[method][path] = func",
"title": ""
},
{
"docid": "fb83b940f850c3c2696308ebc6951115",
"score": "0.54985034",
"text": "def handler(self, req):\r\n raise NotImplementedError, self.__class__.__name__ + '.handler'",
"title": ""
},
{
"docid": "76abc00062c5f7758b70707dea4d21ac",
"score": "0.5495282",
"text": "def __init__(self, request):\r\n self.website = None # set in dynamic_resource.py\r\n self.body = request.body\r\n self.headers = request.headers\r\n self.cookie = request.headers.cookie\r\n self.path = request.line.uri.path\r\n self.qs = request.line.uri.querystring\r\n self.request = request\r\n self.channel = None\r\n self.context = self\r\n\r\n # http://www.w3.org/Protocols/rfc2616/rfc2616-sec9.html\r\n for method in ['OPTIONS', 'GET', 'HEAD', 'POST', 'PUT', 'DELETE',\r\n 'TRACE', 'CONNECT']:\r\n self[method] = (method == request.line.method)\r\n setattr(self, method, self[method])",
"title": ""
},
{
"docid": "1272628f719f48c827dfb214379477b0",
"score": "0.5488731",
"text": "def all_methods(self, req):\n for provider in self.method_handlers:\n for candidate in provider.xmlrpc_methods():\n # Expand all fields of method description\n c = Method(provider, *candidate)\n if req.perm.has_permission(c.permission):\n yield c",
"title": ""
},
{
"docid": "d8e9476b0d5919fddd6dbd728046c3ec",
"score": "0.5484383",
"text": "def _map_api_methods(self):\n\n def pass_args(func):\n def wrapper_func(*args, **kwargs):\n\n # Argument collector.\n _kwargs = {}\n\n # If a session request has a cookie_dict, inject the\n # values into the existing CookieJar instead.\n if isinstance(kwargs.get('cookies', None), dict):\n kwargs['cookies'] = add_dict_to_cookiejar(\n self.cookies, kwargs['cookies']\n )\n\n for attr in self.__attrs__:\n # for attr in ['headers',]:\n s_val = self.__dict__.get(attr)\n r_val = kwargs.get(attr)\n\n new_attr = merge_kwargs(r_val, s_val)\n\n # Skip attributes that were set to None.\n if new_attr is not None:\n _kwargs[attr] = new_attr\n\n # Make sure we didn't miss anything.\n for (k, v) in kwargs.items():\n if k not in _kwargs:\n _kwargs[k] = v\n\n return func(*args, **_kwargs)\n\n return wrapper_func\n\n # Map and decorate each function available in requests.api\n map(lambda fn: setattr(self, fn, pass_args(getattr(api, fn))),\n api.__all__)",
"title": ""
},
{
"docid": "27792c4e21b95a3dd1048f23af000c81",
"score": "0.5478163",
"text": "def __getattr__(self, attr: str) -> Callable[[Any, Any], Any]:\n\n if attr in ENDPOINTS:\n\n def endpoint(*args: Any, **kwargs: Any) -> Callable[[Any, Any], Any]:\n return self.ENDPOINT(attr, *args, **kwargs)\n\n return endpoint\n elif attr in NAVIGATION:\n\n def navigate(*args: Any, **kwargs: Any) -> Callable[[Any, Any], Any]:\n return self.NAVIGATE(attr, *args, **kwargs)\n\n return navigate\n\n raise AttributeError",
"title": ""
},
{
"docid": "4d408b19c8624a452799923a0fb2c7d2",
"score": "0.54772764",
"text": "def api_method(methods=('GET',)):\r\n def decorator(f):\r\n def wrapper(request, *args, **kwargs):\r\n if request.method not in methods:\r\n raise MethodNotAllowed(methods)\r\n prepare_api_request(request)\r\n rv = f(request, *args, **kwargs)\r\n return send_api_response(request, rv)\r\n f.is_api_method = True\r\n f.valid_methods = tuple(methods)\r\n return update_wrapper(wrapper, f)\r\n return decorator",
"title": ""
},
{
"docid": "da0ffde4ae1782ab477c3bd0370e4acb",
"score": "0.5471882",
"text": "def __getattr__(self, shortcode):\n\n if shortcode not in conneg.MIMETYPES:\n raise AttributeError(shortcode)\n\n def decorate(method):\n self[shortcode] = resource_method_wrapper(method)\n return self._action\n return decorate",
"title": ""
},
{
"docid": "a9457b6a3330c2f725e8f7da8f39ac94",
"score": "0.5467316",
"text": "def base_action(methods=['POST'], **kwargs):\n def decorator(func):\n func.base_http_methods = methods\n func.kwargs = kwargs\n return func\n return decorator",
"title": ""
},
{
"docid": "feda1b9f11ae890f627854c98ad0bcd0",
"score": "0.53991586",
"text": "def method(cls, func):\n if isinstance(func, str):\n method = func\n\n def wrapper(func):\n \"\"\"Create class with RESTful specific method.\"\"\"\n return type(func.func_name, (RESTfulMethod,), {method: func})\n return wrapper\n return type(func.func_name, (RESTfulMethod,), {'get': func})",
"title": ""
},
{
"docid": "357b736009f3f985b7e4d5c1d1de1a9b",
"score": "0.53860056",
"text": "def monkeypatched_RequestHandler_init(self, *args, **kwargs):\n original_RequestHandler_init(self, *args, **kwargs)\n for method in self.SUPPORTED_METHODS:\n # functio names are method in lower case\n method = method.lower()\n\n # if it has implementation for that function\n if getattr(self, method, None):\n func = getattr(self, method)\n # only if it's not a generator function\n if not func.func_code.co_flags & 0x20:\n # wrap it!\n setattr(self, method, timingwrapper(func))",
"title": ""
},
{
"docid": "1f54ea5ebcf4cb84926473ab99b64b7a",
"score": "0.5383279",
"text": "def set_method(self, method: HttpMethod) -> 'Builder':\n self.http_request.method = method\n return self",
"title": ""
},
{
"docid": "9235af91f5fc29c7eb943fd6a943890b",
"score": "0.5379388",
"text": "def __getattr__(self, _name):\n scope = self\n\n class api_call(object):\n def __call__(selfish, *args, **kwargs):\n return self._call(_name, *args, **kwargs)\n\n def new(self, **kwargs):\n \"\"\"\n Will invoke the new method on the named resource _name, with \n self as scope.\n \"\"\"\n cls = RESTBase.REGISTRY[_name]\n return cls.new(scope, **kwargs)\n\n def append(selfish, resource):\n \"\"\"\n If the current scope is \n \"\"\"\n try:\n self._call(_name, str(resource.id), _alternate_http_method=\"PUT\")\n except AttributeError:\n self._call(_name, str(resource), _alternate_http_method=\"PUT\")\n\n def remove(selfish, resource):\n try:\n self._call(_name, str(resource.id), _alternate_http_method=\"DELETE\")\n except AttributeError:\n self._call(_name, str(resource), _alternate_http_method=\"DELETE\")\n \n if _name in RESTBase.ALL_DOMAIN_CLASSES:\n cls = RESTBase.ALL_DOMAIN_CLASSES[_name]\n\n class ScopeBinder(object):\n def new(self, *args, **data):\n\n d = MultiDict()\n name = cls._singleton()\n\n def unfold_value(key, value):\n if isinstance(value, (basestring, file)):\n d.add(key, value)\n elif isinstance(value, dict):\n for sub_key, sub_value in value.iteritems():\n unfold_value(\"%s[%s]\" % (key, sub_key), sub_value)\n else:\n # assume iteration else\n for sub_value in value:\n unfold_value(key + \"[]\", sub_value)\n \n \n for key, value in data.iteritems():\n unfold_value(\"%s[%s]\" % (name, key), value)\n\n return scope._call(cls.KIND, **d)\n \n def create(self, **data):\n return cls.create(scope, **data)\n\n def get(self, id):\n return cls.get(scope, id)\n\n \n return ScopeBinder()\n return api_call()",
"title": ""
},
{
"docid": "73af080f54501df02f0383cc924c58a3",
"score": "0.5376665",
"text": "def __getattribute__(self, attr):\n requested_attr = super(Client, self).__getattribute__(attr)\n\n if isinstance(requested_attr, types.MethodType) \\\n and not attr.startswith('_') \\\n and hasattr(self, 'credentials'):\n self._refresh_token_if_needed()\n\n return requested_attr",
"title": ""
},
{
"docid": "7c84c39a9231c3708cb9f84c1dc3cf67",
"score": "0.53744537",
"text": "def prepare_request(self, *args, **kw):\n self.http_request = self.request_class(self.path, *args, **kw)",
"title": ""
},
{
"docid": "10187e68bdec2ee4724fd8834f42fb2c",
"score": "0.5363483",
"text": "def _route(path):\n m = getattr(route, path)\n m.side_effect = lambda f : f\n return m",
"title": ""
},
{
"docid": "63494f2ae3f5632f24ac3973b02b6ffd",
"score": "0.53605294",
"text": "def __getattribute__(self, key):\n\n def attr_getter(key):\n return object.__getattribute__(self, key)\n\n def attr_setter(key, value):\n return object.__setattr__(self, key, value)\n\n with attr_getter(\"scope\")():\n if not attr_getter(\"touched\") and attr_getter(\"initialized\"):\n attr_getter(\"evaluate\")()\n attr_setter(\"touched\", True)\n attr = attr_getter(key)\n # check if home url is set, else update.\n if not attr_getter(\"home\"):\n log.debug(\"home url not set, attempting to update.\")\n attr_setter(\"home\", attr_getter(\"driver\").current_url)\n\n if isinstance(attr, types.MethodType):\n\n @wraps(attr)\n def wrap(*args, **kwargs):\n \"\"\"\n fluent wrapper\n \"\"\"\n resp = attr(*args, **kwargs)\n if resp is None:\n resp = self\n return resp\n\n return wrap\n return attr",
"title": ""
},
{
"docid": "1e9ce68d04ea1fcf01b7a5d40d87eba8",
"score": "0.5350271",
"text": "def __getattribute__(self, key):\n with object.__getattribute__(self, \"scope\")():\n attr = object.__getattribute__(self, key)\n # check if home url is set, else update.\n if not object.__getattribute__(self, \"home\"):\n holmium.core.log.debug(\"home url not set, attempting to update.\")\n object.__setattr__(self, \"home\", object.__getattribute__(self,\"driver\").current_url)\n\n if isinstance(attr, types.MethodType):\n @wraps(attr)\n def wrap(*args, **kwargs):\n resp = attr(*args, **kwargs)\n if not resp:\n holmium.core.log.debug(\"method %s returned None, using fluent response\" % attr.func_name)\n resp = self\n return resp\n return wrap\n return attr",
"title": ""
},
{
"docid": "30bab4e93fdae4195ae51b139d9645b3",
"score": "0.53465486",
"text": "def add_route(method, path):\n def func_wrapper(function):\n global ROUTES\n splitpath = []\n elts = path.split('/')\n pos = 0\n root = None\n # Seeking for regexp in the path, because we need to compile it.\n for elt in elts:\n if len(elt) == 0:\n continue\n # regexp have to start with a ( end is terminated by a ).\n if elt[0] == '(' and elt[-1] == ')':\n # Append it compiled.\n splitpath.append(re.compile(elt))\n # string case\n else:\n if pos == 0:\n root = elt\n splitpath.append(elt)\n pos += 1\n # A path can't start with a regexp.\n if root is None:\n raise Exception(\"Wrong route format.\")\n ROUTES.append({\n 'http_method': method,\n 'root': root,\n 'path': path,\n 'splitpath': splitpath,\n 'module': function.__module__,\n 'function': function.__name__})\n return function\n return func_wrapper",
"title": ""
},
{
"docid": "3e5b2945f82970a5ba9e17de0c74a623",
"score": "0.5335357",
"text": "def http_request(wrapped, instance, args, kwargs):\n from colorama import Fore, Back, Style\n import inspect\n\n try:\n sign = inspect.signature(wrapped)\n arguments = sign.bind(*args, **kwargs).arguments\n\n url = '{1[method]} {0.host}:{0.port}{1[url]}'.format(instance, arguments)\n\n print(Fore.YELLOW + 'Oh boy, make me scream with: ' + Style.RESET_ALL, url)\n return wrapped(*args, **kwargs)\n finally:\n print(Fore.YELLOW + 'I am done' + Style.RESET_ALL)",
"title": ""
},
{
"docid": "0a21710d42c6249dfe196fbe3defbde4",
"score": "0.5334582",
"text": "def __getattr__(self, attr):\n try:\n return super(self.__class__, self).__getattr__(attr)\n except AttributeError:\n self.method_name += '.{0}'.format(attr)\n return self",
"title": ""
},
{
"docid": "9925a1a8a7cfb0953dc1dfd1a453f379",
"score": "0.53329194",
"text": "def bind_request(**request_data) -> 'callable':\n\n class Request(Api):\n \"\"\"Request class. Does the actual API request.\"\"\"\n\n model = request_data.get(ClientConst.MODEL)\n api_path = request_data.get(RequestConst.API_PATH)\n formatter = request_data.get(ClientConst.FORMATTER)\n method = request_data.get(RequestConst.METHOD, RequestConst.GET)\n query_parameters = request_data.get(RequestConst.QUERY_PARAMETERS)\n fake_response_path = request_data.get(TestConst.FAKE_RESPONSE_PATH)\n default_parameters = request_data.get(RequestConst.DEFAULT_PARAMETERS, {})\n\n def __init__(self, client, debug: 'Debug',\n *path_params, **query_params):\n client.request = self\n\n self.url = None\n self.debug = debug\n self.client = client\n self.parameters = {RequestConst.QUERY: {},\n RequestConst.PATH: []}\n\n self._timeout = 10\n\n self._set_parameters(*path_params, **query_params)\n\n def _set_parameters(self, *path_params, **query_params) -> None:\n \"\"\"\n Prepares the list of query parameters\n :path_params: list of path parameters\n :query_params: dict of query parameters\n :return: None\n \"\"\"\n\n # take timeout\n try:\n self._timeout = int(query_params.get(RequestConst.TIMEOUT,\n self._timeout))\n except ValueError:\n pass\n try:\n del query_params[RequestConst.TIMEOUT]\n except KeyError:\n pass\n\n # set default API call params\n for key, value in self.default_parameters.items():\n self.parameters[RequestConst.QUERY][key] = value\n\n _query_params = self.query_parameters.get_params()\n\n # set API call params defined during the \"call\" invocation\n for key, value in query_params.items():\n if value is None:\n continue\n\n if key in _query_params.values():\n self.parameters[RequestConst.QUERY][key] = value\n\n elif key in _query_params.keys():\n self.parameters[RequestConst.QUERY][_query_params[key]] = value\n\n # transform all True and False param to 1 and 0\n for key, value in self.parameters[RequestConst.QUERY].items():\n if value is True:\n self.parameters[RequestConst.QUERY][key] = BoolConst.TRUE\n if value is False:\n self.parameters[RequestConst.QUERY][key] = BoolConst.FALSE\n\n # set optional url path params\n for value in path_params:\n self.parameters[RequestConst.PATH].append(value)\n\n def _prepare_url(self) -> str:\n \"\"\"\n Prepares url and query parameters for the request\n :return: URL\n \"\"\"\n\n base_url = '{}://{}{}{}/{}'.format(\n self.client.protocol, self.client.base_url,\n self.client.base_path, self.api_path, self.client.api_key\n )\n\n url_parts = '/'.join([part for part in\n self.parameters[RequestConst.PATH]])\n\n url_query = '/'.join(['{}:{}'.format(key, value)\n for key, value in self.parameters[\n RequestConst.QUERY].items()])\n\n if url_parts:\n final_url = '{}/{}/{}'.format(base_url, url_parts, url_query)\n else:\n final_url = '{}/{}'.format(base_url, url_query)\n\n self.debug.ok('url', '{}{}'.format(base_url, url_parts))\n self.debug.ok(RequestConst.QUERY_PARAMETERS,\n self.parameters[RequestConst.QUERY])\n self.debug.ok('final url', final_url)\n\n return final_url\n\n def _do_request(self, url: str) -> (int, dict):\n \"\"\"\n Makes the request to Marine Traffic Api servers\n :url: Url for the request\n :return: Tuple with two elements, status code and content\n \"\"\"\n\n if self.client.fake_response_path:\n with open(self.client.fake_response_path, 'r') as f:\n return ResponseCode.OK, f.read()\n\n elif self.method == RequestConst.GET:\n try:\n response = requests.get(url, timeout=self._timeout)\n self.debug.ok(ResponseConst.RESPONSE_OBJECT, response)\n return response.status_code, response.text\n except requests.exceptions.ReadTimeout as e:\n return ResponseCode.TIMED_OUT, ''\n \n\n else:\n # For future POST, PUT, DELETE requests\n return ResponseCode.NOT_FOUND, {}\n\n def _process_response(self, status_code: int, response: str) -> 'Response':\n \"\"\"\n Process response using models\n :status_code: Response status code\n :response: Content\n :return: Response object\n \"\"\"\n\n formatter = self.formatter\n if not formatter:\n formatter = FormatterFactory(\n self.parameters[RequestConst.QUERY][RequestConst.PROTOCOL])\\\n .get_formatter()\n\n response = Response(response, status_code, formatter, self)\n\n error_response = response.to_list\n if 'errors' in error_response:\n # error responses have status_code 200 instead of 5xx\n\n self.debug.error(ResponseConst.STATUS_CODE, status_code)\n self.debug.error(ResponseConst.RESPONSE, error_response)\n\n error_codes = ''.join(['code {}: {}'.format(\n error[ResponseConst.CODE], error[ResponseConst.DETAIL])\n for error in error_response['errors']])\n\n msg = 'Request errors: {}'.format(error_codes)\n\n raise MarineTrafficRequestApiException(msg)\n else:\n self.debug.ok(ResponseConst.STATUS_CODE, status_code)\n self.debug.ok(ResponseConst.RESPONSE, response.raw_data)\n\n return response\n\n def call(self) -> 'Response':\n \"\"\"\n Makes the API call\n :return: Return value from self._process_response()\n \"\"\"\n\n self.url = self._prepare_url()\n status_code, response = self._do_request(self.url)\n return self._process_response(status_code, response)\n\n def call(client, *path_params, **query_params) -> Union['Response', None]:\n \"\"\"\n Binded method for API calls\n :path_params: list of path parameters\n :query_params: dict of query parameters\n :return: Return value from Request.call()\n \"\"\"\n\n if MiscConst.PRINT_PARAMS in query_params:\n Request.query_parameters.print_params()\n return\n\n with Debug(client=client) as debug:\n request = Request(client, debug, *path_params, **query_params)\n return request.call()\n\n call.__doc__ = request_data.get(ClientConst.DESCRIPTION)\n\n return call",
"title": ""
},
{
"docid": "5021890916a98570e8e00f2b9b20c998",
"score": "0.5323769",
"text": "def request_to_method(self, request):\n if request.method == \"POST\":\n method = request.POST.get(\"method\", \"\")\n elif request.method == \"GET\":\n method = request.GET.get(\"method\", \"\")\n else:\n return None\n python_method = method.replace(\"-\", \"_\")\n the_method = getattr(self, python_method, None)\n return the_method",
"title": ""
},
{
"docid": "bd884ac3bae4a713630b207e73799ee9",
"score": "0.5304451",
"text": "def expose(url='/', methods=('GET',)):\n def wrap(f):\n if not hasattr(f, '_urls'):\n f._urls = []\n f._urls.append((url, methods))\n return f\n\n return wrap",
"title": ""
},
{
"docid": "2652d5f0130bfb585aa94c1f4a030d48",
"score": "0.5303288",
"text": "def route(path_regex, http_method=\"GET\", data_type=\"json\"):\n def _decorator(func):\n func.callback_spec = (path_regex, http_method.upper(), data_type)\n @functools.wraps(func)\n def _decorated(self, request, context) -> Any:\n for fn in self.middleware:\n result = fn(request, context)\n if context.status_code != 200 or result is not None:\n return result\n match = re.match(path_regex, request.path)\n try:\n return func(self, match, request, context)\n except FakerException as ex:\n context.status_code = ex.status_code\n return ex.as_json()\n return _decorated\n return _decorator",
"title": ""
},
{
"docid": "936d1348131937f81d3c703db23db5fb",
"score": "0.52972424",
"text": "def __call__(self, _handler):\n name = self.name and self.name or _handler.__name__\n self._routes.append(tornado.web.url(self._uri, _handler, name=name))\n return _handler",
"title": ""
},
{
"docid": "936d1348131937f81d3c703db23db5fb",
"score": "0.52972424",
"text": "def __call__(self, _handler):\n name = self.name and self.name or _handler.__name__\n self._routes.append(tornado.web.url(self._uri, _handler, name=name))\n return _handler",
"title": ""
},
{
"docid": "4dad6a8352621b7cd95ce73ba3fa557f",
"score": "0.52896404",
"text": "def request(self, method, url, **kwargs):\n return request(method, url, **kwargs)",
"title": ""
},
{
"docid": "79c94f280d189f10095c347d90792450",
"score": "0.52721167",
"text": "def route(self, rule, **options):\n def decorator(func):\n\n # By default, endpoint name is function name\n endpoint = options.pop('endpoint', func.__name__)\n\n # MethodView (class)\n if isinstance(func, MethodViewType):\n # This decorator may be called multiple times on the same\n # MethodView, but Flask will complain if different views are\n # mapped to the same endpoint, so we should call 'as_view' only\n # once and keep the result in MethodView._view_func\n if not getattr(func, '_view_func', None):\n func._view_func = func.as_view(endpoint)\n view_func = func._view_func\n # Function\n else:\n view_func = func\n\n # Add URL rule in Flask and store endpoint documentation\n self.add_url_rule(rule, endpoint, view_func, **options)\n self._store_endpoint_docs(endpoint, func, **options)\n\n return func\n\n return decorator",
"title": ""
},
{
"docid": "3b4584da662600bbba9b744f6096d6fd",
"score": "0.5271703",
"text": "def dispatch_request(self,*args,**kwargs):\n method = getattr(self,request.method.lower(),None)\n if method is None and request.method == 'HEAD':\n method = getattr(self,'get',None)\n assert method is not None, 'UNimplemented method %r' %request.method\n # head functions is differe\n if isinstance(self.method_decorators,Mapping):\n decorators = self.method_decorators(request.method.lower(),[])\n else:\n decorators = self.method_decorators\n\n for decorator in decorators:\n method = decorator(method)\n\n try:\n resp = method(*args,**kwargs)\n except RestException as e:\n resp = self.handler_error(e)\n\n if isinstance(resp,Response):\n return resp\n\n data,code,headers = RestView.unpack(resp)\n\n if code >= 400 and isinstance(data,dict):\n for key in data:\n if isinstance(data[key],list) and len(data[key]) > 0:\n message = data[key][0]\n else:\n message = data[key]\n data = { 'ok':False,'message':message }\n\n result = dumps(data) + '\\n'\n response = make_response(result,code)\n response.headers.extend(headers)\n\n response.headers['Content-Type'] = self.content_type\n return response",
"title": ""
},
{
"docid": "63755d34ea6360dc80154162e18d8fb2",
"score": "0.52709955",
"text": "def use(self, middleware=None, path='/', method_mask=HTTPMethod.ALL):\n\n # catch decorator pattern\n if middleware is None:\n return lambda mw: self.use(mw, path, method_mask)\n\n if hasattr(middleware, '__growler_router'):\n router = getattr(middleware, '__growler_router')\n if isinstance(router, (types.MethodType,)):\n router = router()\n self.add_router(path, router)\n elif isinstance(type(middleware), RouterMeta):\n router = middleware._RouterMeta__growler_router()\n self.add_router(path, router)\n elif hasattr(middleware, '__iter__'):\n for mw in middleware:\n self.use(mw, path, method_mask)\n else:\n log.info(\"%d Using %s on path %s\" % (id(self), middleware, path))\n self.middleware.add(path=path,\n func=middleware,\n method_mask=method_mask)\n return middleware",
"title": ""
},
{
"docid": "239f29c7b76d228d8617c39eababc5b9",
"score": "0.52538025",
"text": "def route(url):\n def handler(cls):\n cls.url = url\n return cls\n return handler",
"title": ""
},
{
"docid": "3d46244229df87ff5c9dbad95ca1a933",
"score": "0.5242513",
"text": "def contribute_to_class(self, tag_cls, name):\n super(Method, self).contribute_to_class(tag_cls, name)\n\n tag_cls.on_render_tag.connect(self.on_render_tag, dispatch_uid=\"methods\")",
"title": ""
},
{
"docid": "9b2671c28a18a6a5bcedd6b810b60d5b",
"score": "0.5239109",
"text": "def bind(self, method):\n self.method=method",
"title": ""
},
{
"docid": "eb7c6a4880fe5ee60b777b6bc0e7c9ea",
"score": "0.5238172",
"text": "def route(self, method, pattern, fn):\n if self.app:\n raise RuntimeError('Routes may not be added after an APIComponent '\n 'is registered with an APIApplication')\n\n self.rules.append(routing.Rule(pattern, methods=[method], endpoint=fn))",
"title": ""
},
{
"docid": "880f70549b7b0433ebe000d2cc4bde93",
"score": "0.5236311",
"text": "def __getattr__(self, attr):\n self._path.append(attr)\n return self",
"title": ""
},
{
"docid": "1e690c1dff7d39ab0fa6620b63a537b6",
"score": "0.5234645",
"text": "def __call__(self, req):\r\n return self._router",
"title": ""
},
{
"docid": "1e690c1dff7d39ab0fa6620b63a537b6",
"score": "0.5234645",
"text": "def __call__(self, req):\r\n return self._router",
"title": ""
},
{
"docid": "af343fb3863fd206416666731e166bba",
"score": "0.5232845",
"text": "def endpoint(self, functor):\n @wraps(functor)\n def wrapped(*args, **kwargs):\n request = functor()\n return self.__handle_request(request)\n return wrapped",
"title": ""
},
{
"docid": "259d62bd3298af63139ed8bd1a3067dd",
"score": "0.5228788",
"text": "def wrapper(func):\n return type(func.func_name, (RESTfulMethod,), {method: func})",
"title": ""
},
{
"docid": "709800193d5c3dcdcd4cd6b7c2035e5e",
"score": "0.522516",
"text": "def __new__(cls, name, bases, attrs):\n view = attrs.get('view')\n if view:\n attrs['view'] = staticmethod(view)\n return super().__new__(cls, name, bases, attrs)",
"title": ""
},
{
"docid": "6fa2f4524f3a6c688e302ad19e5d5f58",
"score": "0.5215809",
"text": "def apply_method(self, r, **attr):\n\n output = {}\n\n if r.http in (\"GET\", \"POST\"):\n if not r.record:\n r.error(400, current.ERROR.BAD_REQUEST)\n if r.interactive:\n output = self.invite(r, **attr)\n else:\n r.error(415, current.ERROR.BAD_FORMAT)\n else:\n r.error(405, current.ERROR.BAD_METHOD)\n\n return output",
"title": ""
},
{
"docid": "2ab9a9556d24ca2f6d70e0fc2b1ecc94",
"score": "0.5211969",
"text": "def dispatch(self):\n request = self.request\n method_name = request.route.handler_method\n if not method_name:\n method_name = webapp2._normalize_handler_method(request.method)\n\n method = getattr(self, method_name, None)\n if hasattr(self, '__class__'):\n sentry_client.tags_context(\n {'handler': self.__class__.__name__, 'method': method_name}\n )\n\n if method is None:\n # 405 Method Not Allowed.\n valid = b', '.join(webapp2._get_handler_methods(self))\n raise exc.HTTP405_HTTPMethodNotAllowed(\n 'Method not allowed in {}'.format(self.__class__.__name__),\n headers=[(b'Allow', valid)],\n )\n\n # The handler only receives *args if no named variables are set.\n args, kwargs = request.route_args, request.route_kwargs\n if kwargs:\n args = ()\n\n # bind session on dispatch (not in __init__)\n try:\n self.session = gaesessions.get_current_session()\n except AttributeError:\n # probably session middleware not loaded\n self.session = {}\n\n if str(self.session) != 'uninitialized session':\n sentry_client.note(\n 'storage', 'Session loaded', data=dict(session=self.session)\n )\n\n try:\n self._call_all_inherited(\n 'pre_authentication_hook', method_name, *args, **kwargs\n )\n self._call_all_inherited(\n 'authentication_preflight_hook', method_name, *args, **kwargs\n )\n self._call_all_inherited(\n 'authentication_hook', method_name, *args, **kwargs\n )\n self._call_all_inherited('authorisation_hook', method_name, *args, **kwargs)\n self._call_all_inherited(\n 'method_preperation_hook', method_name, *args, **kwargs\n )\n try:\n response = method(*args, **kwargs)\n except TypeError:\n # parameter missmatch is the error we see most often\n # so help to pin down where it happens\n klass = introspection.get_class_that_defined_method(method)\n methname = method.__name__\n sourcepos = '{}:{}'.format(\n os.path.basename(method.__func__.__code__.co_filename),\n method.__func__.__code__.co_firstlineno,\n )\n LOGGER.debug(\n 'method called: %s.%s(%r) from %s',\n klass.__name__,\n methname,\n (args, kwargs),\n sourcepos,\n )\n LOGGER.debug('defined at: %s %s', klass, sourcepos)\n raise\n response = self.response_overwrite(response, method, *args, **kwargs)\n except exc.HTTPException as e:\n # for HTTP exceptions execute `finished_hooks`\n if e.code < 500:\n self._call_all_inherited('finished_hook', method_name, *args, **kwargs)\n return self.handle_exception(e, self.app.debug)\n except BaseException as e:\n return self.handle_exception(e, self.app.debug)\n\n if response and not getattr(self, '_gaetk2_allow_strange_responses', False):\n assert isinstance(response, webapp2.Response)\n\n self._set_cache_headers()\n self._call_all_inherited('finished_hook', method_name, *args, **kwargs)\n self.finished_overwrite(response, method, *args, **kwargs)\n return response",
"title": ""
},
{
"docid": "c675b8f5eb0006c8cb0f1246c90799d5",
"score": "0.5207199",
"text": "def request(self):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "c675b8f5eb0006c8cb0f1246c90799d5",
"score": "0.5207199",
"text": "def request(self):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "0a4ab3a5e3d190a925000843369f7a72",
"score": "0.52038926",
"text": "def dispatch(self, request, *args, **kwargs):\n\n handler = None\n request_method = request.method.lower()\n if request_method in ('get', 'post', 'put', 'delete'):\n if self.method.lower() != request_method:\n ret = {\n 'status_code': 405,\n 'response': {\n 'return': 'error',\n 'message': 'Method not allowed'\n }\n }\n return ret\n if self.func and hasattr(self, self.func):\n handler = getattr(self, self.func)\n if not handler:\n return super(BaseView, self).dispatch(request, *args, **kwargs)\n return handler(request, *args, **kwargs)",
"title": ""
},
{
"docid": "0a8745420fe12b07df841d9e7c9c015b",
"score": "0.5188567",
"text": "def decorator(handler_fn: types.FunctionType):\n nonlocal name\n if name is None:\n name = handler_fn.__name__\n\n if name in routes_dict:\n raise ValueError('Route with name %r exists' % name)\n\n if (method, path) in method_path_set:\n raise ValueError('Route with path %r exists.' % ((method, path),))\n\n routes_dict[name] = Route(method, path, handler_fn)\n method_path_set.add((method, path))\n return handler_fn",
"title": ""
},
{
"docid": "bec2156b570a794e421643b7a96cf30e",
"score": "0.51877487",
"text": "def _request(self, method, url, endpoint=None, **kwargs):\n if not endpoint:\n if not self.endpoint:\n raise ValueError(\n \"Endpoint not set in function call nor in class constructor\"\n )\n endpoint = self.endpoint\n url = \"/\".join([endpoint.rstrip(\"/\"), url.lstrip(\"/\")])\n return self._direct_request(method, url, **kwargs)",
"title": ""
},
{
"docid": "db1df546d749aa2e1cc45fda908dcde8",
"score": "0.5182615",
"text": "def __call__(self, path_info):\n\t\theaders = cherrypy.request.headers\n\t\t\n\t\tif headers.has_key(\"X-Http-Method-Override\"):\n\t\t\tcherrypy.request.method = headers[\"X-Http-Method-Override\"]\n\t\tsuper(OverloadDispatcher, self).__call__(path_info)",
"title": ""
},
{
"docid": "86fbdc4e13ea8e3ec69ab617995a47cf",
"score": "0.51823753",
"text": "def __init__(self, url, method, **kwargs):\n urllib2.Request.__init__(self, url, **kwargs)\n self.method = method",
"title": ""
},
{
"docid": "8996037dbc8638285686f6d728add021",
"score": "0.5176689",
"text": "def send(self, methname, *args):\r\n meth = self.read_attr(methname)\r\n return meth(*args)",
"title": ""
},
{
"docid": "8996037dbc8638285686f6d728add021",
"score": "0.5176689",
"text": "def send(self, methname, *args):\r\n meth = self.read_attr(methname)\r\n return meth(*args)",
"title": ""
},
{
"docid": "8996037dbc8638285686f6d728add021",
"score": "0.5176689",
"text": "def send(self, methname, *args):\r\n meth = self.read_attr(methname)\r\n return meth(*args)",
"title": ""
},
{
"docid": "0e1689c42b7a997c7b53c3d504e25a36",
"score": "0.51721317",
"text": "def _get_request_method(self, method, session):\n\n request_methods = {\n 'post': session.post,\n 'get': session.get,\n 'put': session.put,\n 'delete': session.delete,\n 'patch': session.patch,\n }\n return request_methods[method]",
"title": ""
},
{
"docid": "942e3b2f5318c8b4599b725ac355541e",
"score": "0.51708996",
"text": "def get_handler(self, request):\n verb = request.method.lower()\n if not hasattr(self, verb)\\\n or request.method not in self.allowed_http_methods:\n raise exc.MethodNotAllowed()\n return getattr(self, verb)",
"title": ""
},
{
"docid": "6ded77ebc72620763d95527c666eca26",
"score": "0.5163182",
"text": "def route(self, method, route, fn, template=None, name=None):\n name = name or fn.__name__\n if name in self._routes:\n raise KeyError(name)\n if template:\n fn = self._jinja(fn, template)\n route = self.hook.add_route(method, \"{}/{}\".format(self.prefix, route), fn,\n name=\"{}:{}\".format(self.module, name))\n self._routes[name] = route",
"title": ""
},
{
"docid": "dfd10c007a3e2b6c44aefac4d900e7da",
"score": "0.51627254",
"text": "def _csrf_protect_for_class(cls):\n for name, member in inspect.getmembers(cls):\n # wrap submission handlers\n if name.upper() in SUBMIT_METHODS:\n setattr(cls, name, _csrf_protect_for_function(member))\n return cls",
"title": ""
},
{
"docid": "9a56729cf74abff4788be306a65c8c08",
"score": "0.51615417",
"text": "def o(this='GET', **handlers):\n\n def with_accept(status=200):\n def accept(*args, **kwargs):\n res = d.HttpResponse(status=status)\n res['Accept'] = ', '.join(handlers.keys())\n return res\n return accept\n\n options = with_accept()\n method_not_allowed = with_accept(405) # Method not allowed\n\n # Set default action for options.\n handlers.setdefault('OPTIONS', options)\n\n def decorator(func):\n # The func provided works for 'this'.\n handlers[this] = func\n\n def handle_request(request, *args, **kwargs):\n # Either fetch a method handler, or give back a 'method not allowed'\n handler = handlers.get(request.method, method_not_allowed)\n return handler(request, *args, **kwargs)\n\n return handle_request\n\n return decorator",
"title": ""
},
{
"docid": "f62c27e2faecbbb9ad60b0a0ff62d1a4",
"score": "0.5156604",
"text": "def add_route(self, request: 'DataRequest'):\n\n def _find_route(request):\n for r in request.routes:\n if r.executor == self.name:\n return r\n return None\n\n r = _find_route(request)\n if r is None and self.start_time:\n r = request.routes.add()\n r.executor = self.name\n r.start_time.FromDatetime(self.start_time)\n if self.end_time:\n r.end_time.FromDatetime(self.end_time)\n if self.status:\n r.status.CopyFrom(self.status)\n for outgoing_node in self.outgoing_nodes:\n request = outgoing_node.add_route(request=request)\n return request",
"title": ""
},
{
"docid": "913582c5d69db6ab9a4990bfb3331c3d",
"score": "0.51563966",
"text": "def extend_request_basic(config):\n def furl(request):\n try:\n return request.json_body['furl']\n except ValueError, e:\n return request.params.get(\"furl\") or request.path_qs\n config.add_request_method(furl, 'furl', reify=True)\n def app_globals(request): return request.registry.settings['g']\n config.add_request_method(app_globals, 'globals', reify=True)\n config.add_request_method(attrgetter(\"globals.backend\"), 'backend', reify=True)\n config.add_request_method(fwd_raw)",
"title": ""
},
{
"docid": "136a7054f17518bb2f891333d228cf75",
"score": "0.5155222",
"text": "def requests(self):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "136a7054f17518bb2f891333d228cf75",
"score": "0.5155222",
"text": "def requests(self):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "d047cac224ea92852ae42ab417fc460a",
"score": "0.51478136",
"text": "def _request_with_user_agent(request_method):\n def wrapped_request_method(self, *args, **kwargs):\n if kwargs.get('headers') is not None:\n if kwargs['headers'].get('user-agent'):\n if USER_AGENT not in kwargs['headers']['user-agent']:\n # Save the existing user-agent header and tack on the user-agent.\n kwargs['headers']['user-agent'] = '%s %s' % (USER_AGENT, kwargs['headers']['user-agent'])\n else:\n kwargs['headers']['user-agent'] = USER_AGENT\n else:\n kwargs['headers'] = {'user-agent': USER_AGENT}\n return request_method(self, *args, **kwargs)\n return wrapped_request_method",
"title": ""
},
{
"docid": "3a81e3c081571a2042fd5647081532e0",
"score": "0.51398134",
"text": "def __call__(self, req):\n return self._router",
"title": ""
},
{
"docid": "66f75d346d87ecfdfd6458567b696ec0",
"score": "0.5134525",
"text": "def handle(self, req):\n raise NotImplementedError",
"title": ""
},
{
"docid": "0fc6b305bb270a70c3684908af0b9177",
"score": "0.51228595",
"text": "def __init__(self, router, method: str, route: any):\n self.router = router\n self.method = method\n self.route = route",
"title": ""
},
{
"docid": "5426252920a42380af58022056bdc614",
"score": "0.5119986",
"text": "def __init__(self, environ=None):\n #: The wrapped WSGI environ dictionary. This is the only real attribute.\n #: All other attributes actually are read-only properties.\n self.environ = {} if environ is None else environ\n self.environ['bottle.request'] = self",
"title": ""
},
{
"docid": "881910702d5ae7150a2697f777ee69fe",
"score": "0.51178837",
"text": "def route(self, rule, **options):\n # here is decorator work\n def decorator(f):\n self.add_route(rule, f, **options)\n\n return decorator",
"title": ""
},
{
"docid": "b1e4d39493286055a8b4594a8ff367c3",
"score": "0.5107337",
"text": "def set_route(self, route):\n # Warning: this is ovewriting webob.Request.urlargs\n self.urlargs = route.params\n self.route = route",
"title": ""
},
{
"docid": "6001fcfec78ed8c3f1dd0d11c2fc9685",
"score": "0.5104772",
"text": "def install_mocks(self, requests_mocker) -> None:\n self.requests_mocker = requests_mocker\n for _, method in inspect.getmembers(self, inspect.ismethod):\n if hasattr(method, \"callback_spec\"):\n path_regex, http_method, data_type = method.callback_spec\n self.requests_mocker.register_uri(\n http_method,\n re.compile(fr\"^{self.host}{path_regex}(\\?.*)?$\"),\n **{data_type: method},\n )",
"title": ""
},
{
"docid": "91bfd2d14b63ebfdfe12a1011353f784",
"score": "0.5099969",
"text": "def __getattr__(self, item):\n\n return getattr(base_request, item)",
"title": ""
},
{
"docid": "afc70bf0f8a7191ca3fa159203d206ab",
"score": "0.50925565",
"text": "def accept_method(constraint):\n def decorate(handler):\n if isinstance(constraint, (list, tuple)):\n def one_of(request, *args, **kwargs):\n if request.method not in constraint:\n return method_not_allowed()\n return handler(request, *args, **kwargs)\n return one_of\n else:\n def exact(request, *args, **kwargs):\n if request.method != constraint:\n return method_not_allowed()\n return handler(request, *args, **kwargs)\n return exact\n return decorate",
"title": ""
},
{
"docid": "317b720f637711bd7eee1c12552a0ef4",
"score": "0.5091908",
"text": "def request_method(self, message):",
"title": ""
},
{
"docid": "fa79accc56e140b971a77323c06081a3",
"score": "0.50856775",
"text": "def __getattr__(self, attr):\n fn = getattr(messages_api, attr)\n return curry(fn, self.request)",
"title": ""
},
{
"docid": "1e3a0c98ec49bd1d14d1d37483d412f4",
"score": "0.50848407",
"text": "def __init__(self):\n super(Request, self).__init__()",
"title": ""
},
{
"docid": "6d58f69549a72ea6e4448377671237f6",
"score": "0.507915",
"text": "def route(self, url_rule=None, name=None, root=False, options=None):\n\n def decorator(f):\n view_name = name or f.__name__\n if root:\n url = '/'\n elif not url_rule:\n url = '/' + view_name + '/'\n args = inspect.getargspec(f)[0]\n if args:\n url += '/'.join('%s/<%s>' % (p, p) for p in args)\n else:\n url = url_rule\n self.add_url_rule(url, f, name=view_name, options=options)\n return f\n\n return decorator",
"title": ""
},
{
"docid": "b9144683382ebc58fa751f3fab3b76c6",
"score": "0.50760406",
"text": "def _apply_allowed_methods(rule, on_true, on_false, default=('HEAD', 'OPTIONS', 'GET', )):\n\n def wrapper(fn):\n def inner(obj, request, *args, **kwargs):\n\n obj.allowed_methods = set(default).union(on_true if rule(request.path) else on_false)\n obj.headers['Allow'] = ', '.join(obj.allowed_methods)\n\n if request.method not in obj.allowed_methods:\n return Response({'detail': None,\n 'message': 'method not allowed here'},\n status=status.HTTP_405_METHOD_NOT_ALLOWED)\n\n return fn(obj, request, *args, **kwargs)\n return inner\n return wrapper",
"title": ""
},
{
"docid": "ee24095f91233442a5171e0f41d94360",
"score": "0.5073688",
"text": "def get_method():\n return flask.request.method",
"title": ""
},
{
"docid": "5a908d3a416bed1bf0dd743da0595e26",
"score": "0.50695854",
"text": "def before(method_name):\n def decorator(function):\n @wraps(function)\n def wrapper(self, *args, **kwargs):\n returns = getattr(self, method_name)(*args, **kwargs)\n\n if returns is None:\n return function(self, *args, **kwargs)\n else:\n if isinstance(returns, HttpResponse):\n return returns\n else:\n return function(self, *returns)\n return wrapper\n return decorator",
"title": ""
},
{
"docid": "c4a1094a61b69b5f6107ea84d05fbb65",
"score": "0.50682473",
"text": "def route(method, path, name: str = None, *,\n routes_dict=routes, method_path_set=method_paths) -> types.FunctionType:\n def decorator(handler_fn: types.FunctionType):\n \"\"\"\n Add a route to `routes_dict`.\n\n The decorator does not modify the function.\n\n :raise ValueError: if a route with such name or path exists\n \"\"\"\n nonlocal name\n if name is None:\n name = handler_fn.__name__\n\n if name in routes_dict:\n raise ValueError('Route with name %r exists' % name)\n\n if (method, path) in method_path_set:\n raise ValueError('Route with path %r exists.' % ((method, path),))\n\n routes_dict[name] = Route(method, path, handler_fn)\n method_path_set.add((method, path))\n return handler_fn\n\n return decorator",
"title": ""
},
{
"docid": "477113b18b4b2ddd6b0098d53d252e40",
"score": "0.50578487",
"text": "def __getattr__(self, name, *args, **kwargs):\n if self._is_batch_method(name):\n self._action_list.append({'name': name})\n return self._append_action\n else:\n raise AttributeError(\"%s not found in class %s\" % (name, self.__class__.__name__))",
"title": ""
},
{
"docid": "d58fa66f05e0f1970d6d2f4282ee64ab",
"score": "0.5055626",
"text": "def __getattr__(self, name):\n # If api_commands is not defined, raise NotImplementedError\\\n # If its not defined, _getattr__ will be called with its name\n if name == 'api_commands':\n raise NotImplementedError(\"API command specifications could not be found; use a derived class which defines 'api_commands'.\")\n \n # Is shorthand enabled, and is the called name a command?\n if self.shorthand and name in self.api_commands:\n # If so, simply return a function which passes its arguments\n # to an appropriate send() call\n return lambda **kwargs: self.send(name, **kwargs)\n else:\n raise AttributeError(\"XBee has no attribute '%s'\" % name)",
"title": ""
},
{
"docid": "45dbc0c38ce01c14801670eca77915bc",
"score": "0.5052593",
"text": "def __getattr__(self, path):\n if path.startswith('__'):\n raise AttributeError(path)\n\n if path in AnyAPI.HTTP_METHODS:\n return (lambda params={},\n headers={},\n data={},\n json={},\n auth=(),\n url='':\n self._make_request(path=self._recursive_path,\n method=path,\n params=params,\n headers=headers,\n data=data,\n auth=auth,\n json=json,\n url=url))\n elif path == 'P':\n\n def make_copy(path):\n self_copy = copy.copy(self)\n self_copy._recursive_path = self._recursive_path + '/' + path\n return self_copy\n\n return (lambda path: make_copy(path))\n else:\n self_copy = copy.copy(self)\n self_copy._recursive_path = self._recursive_path + '/' + path\n return self_copy",
"title": ""
},
{
"docid": "a11bbe5b845aeab327e0231ed4890cd3",
"score": "0.5046767",
"text": "def run_match(self):\n \n path = self.request.path\n method = self.request.method\n \n for pattern, handler in self.routes:\n # Default request & response\n handler.request = self.request\n handler.response = self.response\n\n # Looks for a match with the given path\n\n result = re.match('^' + pattern + '$', path)\n if result:\n # Retrieves all params matched\n params = result.groups()\n\n # Checks whether our handler defined the given method\n # ex (get/put..)\n function_name = method.lower()\n try:\n function = getattr(handler, function_name)\n except AttributeError:\n raise HTTPException(\n \"405\", \"Method %s not allowed\"%(method.upper()))\n\n return function(*params)\n \n raise HTTPException(\"404\",\"Path %s is nowhere to be found. \"%(path))",
"title": ""
},
{
"docid": "575f1e4dcbe8cc7735ef4ea761b8123a",
"score": "0.5037825",
"text": "def get(self, resource: str) -> RequestHandlerDecorator:\n return self.route(resource, 'GET')",
"title": ""
},
{
"docid": "c5509634f8dd1771900ffbd3172f8835",
"score": "0.5037233",
"text": "def HandleRequest(self, request):\n return request",
"title": ""
},
{
"docid": "93c85bde7d70c522be582425b94250a2",
"score": "0.50301653",
"text": "def decor(func):\n # wraps the function to be decorated\n wraps(func)\n\n def wrapper(*args, **kwargs):\n \"\"\"\n A wrapper function that performs the necessary request checks\n \"\"\"\n\n # TODO: Refactor and wrap in a try/except block with matching http exception\n if not request.method:\n abort(400)\n \n if str(request.method).lower() != str(method).lower():\n abort(405)\n \n if not request.headers:\n abort(400)\n\n if 'accept' not in request.headers:\n abort(400)\n \n if request.headers['accept'].lower() != 'application/json':\n abort(400)\n \n req_method = str(request.method).lower()\n if req_method == 'post' or req_method == 'put':\n if not request.json:\n abort(400)\n \n if \"payload\" not in request.json:\n abort(400)\n \n if not request.json['payload']:\n abort(400)\n\n\n elif req_method == 'get' or req_method == 'delete':\n # No request checks yet for get and delete\n # so just return the wrapped function\n return func(*args, **kwargs)\n\n else:\n # No other methods are allowed\n abort(405)\n\n return func(*args, **kwargs)\n \n return wrapper",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="764"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">454fdc73af98476e48abd7b0554cd692</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Arms vehicle and fly to aTargetAltitude.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "29bf935b4f5da125599c399f93de029e",
"score": "0.74334264",
"text": "def arm_and_takeoff(aTargetAltitude):\n\n print(\"Basic pre-arm checks\")\n # Don't try to arm until autopilot is ready\n while not vehicle.is_armable:\n print(\" Waiting for vehicle to initialise...\")\n time.sleep(1)\n\n print(\"Arming motors\")\n # Copter should arm in GUIDED mode\n vehicle.mode = VehicleMode(\"GUIDED\")\n vehicle.armed = True\n\n # Confirm vehicle armed before attempting to take off\n while not vehicle.armed:\n print(\" Waiting for arming...\")\n time.sleep(1)\n\n print(\"Taking off!\")\n vehicle.simple_takeoff(aTargetAltitude) # Take off to target altitude\n\n # Wait until the vehicle reaches a safe height before processing the goto\n # (otherwise the command after Vehicle.simple_takeoff will execute\n # immediately).\n while True:\n print(\" Altitude: \", vehicle.location.global_relative_frame.alt)\n # Break and return from function just below target altitude.\n if vehicle.location.global_relative_frame.alt >= aTargetAltitude * 0.95:\n print(\"Reached target altitude\")\n break\n time.sleep(1)",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "af213d26adaf5f294494c15dc4f2d00c",
"score": "0.7867513",
"text": "def arm_and_takeoff(self, aTargetAltitude):\r\n print(\"Basic pre-arm checks\")\r\n # Don't let the user try to arm until autopilot is ready\r\n while not vehicle.is_armable:\r\n print(\" Waiting for vehicle to initialise...\")\r\n time.sleep(1)\r\n\r\n print(\"Arming motors\")\r\n # Copter should arm in GUIDED mode\r\n self.vehicle.mode = VehicleMode(\"GUIDED\")\r\n self.vehicle.armed = True\r\n\r\n while not self.vehicle.armed:\r\n print(\" Waiting for arming...\")\r\n time.sleep(1)\r\n\r\n print(\"Vehicle armed. Waiting 2 secs\")\r\n time.sleep(2)\r\n\r\n print(\"Taking off!\")\r\n self.vehicle.simple_takeoff(aTargetAltitude) # Take off to target altitude\r\n\r\n # Wait until the vehicle reaches a safe height before processing the goto (otherwise the command\r\n # after Vehicle.simple_takeoff will execute immediately).\r\n while True:\r\n print(\" Altitude: \", self.vehicle.location.global_relative_frame.alt)\r\n if self.vehicle.location.global_relative_frame.alt >= aTargetAltitude * 0.95: # Trigger just below target alt.\r\n print(\"Reached target altitude\")\r\n break\r\n time.sleep(1)",
"title": ""
},
{
"docid": "44e463979ca9d0a1799297a30d5c5e73",
"score": "0.78065044",
"text": "def arm_and_takeoff(aTargetAltitude):\n\n print \"Arming motors\"\n vehicle.mode = VehicleMode(\"ALT_HOLD\")\n vehicle.armed = True\n\n # Confirm vehicle armed before attempting to take off\n while not vehicle.armed:\n print \" Waiting for arming...\"\n time.sleep(1)\n\n print \"Taking off!\"\n vehicle.simple_takeoff(aTargetAltitude) # Take off to target altitude\n\n # Wait until the vehicle reaches a safe height before processing the goto (otherwise the command\n # after Vehicle.simple_takeoff will execute immediately).\n while True:\n print \" Altitude: \", vehicle.location.global_relative_frame.alt\n #Break and return from function just below target altitude.\n if vehicle.location.global_relative_frame.alt>=aTargetAltitude*0.95:\n print \"Reached target altitude\"\n break\n time.sleep(1)",
"title": ""
},
{
"docid": "7378c57f51f5a1cddc340c4cf50460cb",
"score": "0.7774095",
"text": "def arm_and_takeoff(self, aTargetAltitude):\n print( \"Basic pre-arm checks\")\n # Don't try to arm until autopilot is ready\n while not self.vehicle.is_armable:\n print( \" Waiting for vehicle to initialise...\")\n sleep(1)\n\n print( \"Arming motors\")\n # Copter should arm in GUIDED mode\n self.vehicle.mode = VehicleMode(\"GUIDED\")\n self.vehicle.armed = True\n\n # Confirm vehicle armed before attempting to take off\n while not self.vehicle.armed:\n print( \"Waiting for arming...\")\n sleep(1)\n\n print( \"Taking off!\")\n self.vehicle.simple_takeoff(aTargetAltitude) # Take off to target altitude\n\n # Wait until the vehicle reaches a safe height before processing the goto (otherwise the command\n # after Vehicle.simple_takeoff will execute immediately).\n while True:\n print( \" Altitude: \", self.vehicle.location.global_relative_frame.alt)\n #Break and return from function just below target altitude.\n if self.vehicle.location.global_relative_frame.alt>=aTargetAltitude*0.95:\n print( \"Reached target altitude\")\n break\n sleep(1)",
"title": ""
},
{
"docid": "e2e4bad45fcd38faaf976958a22d13db",
"score": "0.76360714",
"text": "def arm_and_takeoff(aTargetAltitude):\r\n\r\n print \"Basic pre-arm checks\"\r\n # Don't let the user try to arm until autopilot is ready\r\n while not vehicle.is_armable:\r\n print \" Waiting for vehicle to initialise...\"\r\n time.sleep(1)\r\n\r\n \r\n print \"Arming motors\"\r\n # Copter should arm in GUIDED mode\r\n vehicle.mode = VehicleMode(\"GUIDED\")\r\n vehicle.armed = True\r\n\r\n while not vehicle.armed: \r\n print \" Waiting for arming...\"\r\n time.sleep(1)\r\n\r\n print \"Taking off!\"\r\n vehicle.simple_takeoff(aTargetAltitude) # Take off to target altitude\r\n\r\n # Wait until the vehicle reaches a safe height before processing the goto (otherwise the command \r\n # after Vehicle.simple_takeoff will execute immediately).\r\n while True:\r\n print \" Altitude: \", vehicle.location.global_relative_frame.alt \r\n if vehicle.location.global_relative_frame.alt>=aTargetAltitude*0.95: #Trigger just below target alt.\r\n print \"Reached target altitude\"\r\n break\r\n time.sleep(1)",
"title": ""
},
{
"docid": "b7eae7182cd792d43e6131bcde3721f3",
"score": "0.76024485",
"text": "def arm_and_takeoff(aTargetAltitude):\n\n print(\"Basic pre-arm checks\")\n # Don't try to arm until autopilot is ready\n while not vehicle.is_armable:\n print(\" Waiting for vehicle to initialise...\")\n time.sleep(1)\n\n print(\"Arming motors\")\n # Copter should arm in GUIDED mode\n vehicle.mode = VehicleMode(\"GUIDED\")\n vehicle.armed = True\n\n # Confirm vehicle armed before attempting to take off\n while not vehicle.armed:\n print(\" Waiting for arming...\")\n time.sleep(1)\n\n print(\"Taking off!\")\n vehicle.simple_takeoff(aTargetAltitude) # Take off to target altitude\n\n # Wait until the vehicle reaches a safe height before processing the goto\n # (otherwise the command after Vehicle.simple_takeoff will execute\n # immediately).\n while True:\n print(\" Altitude: \", vehicle.location.global_relative_frame.alt)\n # Break and return from function just below target altitude.\n if vehicle.location.global_relative_frame.alt >= aTargetAltitude * 0.95:\n print(\"Reached target altitude\")\n break\n time.sleep(1)",
"title": ""
},
{
"docid": "3af86cba6240d76d275cb6649452db8e",
"score": "0.7538448",
"text": "def arm_and_takeoff(aTargetAltitude):\n\n print(\"Basic pre-arm checks\")\n # Don't let the user try to arm until autopilot is ready\n while not vehicle.is_armable:\n print(\" Waiting for vehicle to initialise...\")\n time.sleep(1)\n\n print(\"Arming motors\")\n # Copter should arm in GUIDED mode\n vehicle.mode = VehicleMode(\"GUIDED\")\n vehicle.armed = True\n\n while not vehicle.armed:\n print(\" Waiting for arming...\")\n time.sleep(1)\n\n print(\"Taking off!\")\n vehicle.simple_takeoff(aTargetAltitude) # Take off to target altitude\n\n # Wait until the vehicle reaches a safe height before processing the goto (otherwise the command\n # after Vehicle.simple_takeoff will execute immediately).\n while True:\n print(\" Altitude: \", vehicle.location.global_relative_frame.alt)\n # Trigger just below target alt.\n if vehicle.location.global_relative_frame.alt >= aTargetAltitude*0.95:\n print(\"Reached target altitude\")\n break\n time.sleep(1)",
"title": ""
},
{
"docid": "102bf6094f2423db4761bb250953ef65",
"score": "0.753369",
"text": "def arm_and_takeoff(aTargetAltitude):\n\n print \"Basic pre-arm checks\"\n # Don't let the user try to arm until autopilot is ready\n while not vehicle.is_armable:\n print \" Waiting for vehicle to initialise...\"\n time.sleep(1)\n\n \n print \"Arming motors\"\n # Copter should arm in GUIDED mode\n vehicle.mode = VehicleMode(\"GUIDED\")\n vehicle.armed = True\n\n while not vehicle.armed: \n print \" Waiting for arming...\"\n time.sleep(1)\n\n print \"Taking off!\"\n vehicle.simple_takeoff(aTargetAltitude) # Take off to target altitude\n\n # Wait until the vehicle reaches a safe height before processing the goto (otherwise the command \n # after Vehicle.simple_takeoff will execute immediately).\n while True:\n print \" Altitude: \", vehicle.location.global_relative_frame.alt \n if vehicle.location.global_relative_frame.alt>=aTargetAltitude*0.95: #Trigger just below target alt.\n print \"Reached target altitude\"\n break\n time.sleep(1)",
"title": ""
},
{
"docid": "6c00651c3fdcde2e5a5d1c3fb3f101c9",
"score": "0.7464601",
"text": "def arm_and_takeoff(aTargetAltitude):\r\n print(\"\\n Basic pre-arm checks\")\r\n # Don't let the user try to arm until autopilot is ready\r\n while not vehicle.is_armable:\r\n print(\"\\n Waiting for vehicle to initialise...\")\r\n time.sleep(1)\r\n\r\n print(\"\\n Arming motors\")\r\n # Copter should arm in GUIDED mode\r\n vehicle.mode = VehicleMode(\"GUIDED\")\r\n vehicle.armed = True\r\n\r\n # Confirm vehicle armed before attempting to take off\r\n while not vehicle.armed:\r\n print(\"\\n Waiting for arming...\")\r\n time.sleep(1)\r\n\r\n print(\"\\n Taking off!\")\r\n vehicle.simple_takeoff(aTargetAltitude) # Take off to target altitude\r\n\r\n # Wait until the vehicle reaches a safe height before processing the goto (otherwise the command\r\n # after Vehicle.simple_takeoff will execute immediately).\r\n while True:\r\n print(\"\\n Altitude: %s \" % vehicle.location.global_relative_frame.alt)\r\n # Break and return from function just below target altitude.\r\n if vehicle.location.global_relative_frame.alt >= aTargetAltitude * 0.95:\r\n print(\"\\n Reached target altitude\")\r\n break\r\n time.sleep(1)",
"title": ""
},
{
"docid": "86f63e6817ac09ebf50e899d9eec0400",
"score": "0.74461937",
"text": "def arm_and_takeoff(aTargetAltitude):\r\n\r\n print \"Basic pre-arm checks\"\r\n # Don't try to arm until autopilot is ready\r\n if vehicle.mode.name == \"INITIALISING\":\r\n print \"Waiting for vehicle to initialise\"\r\n time.sleep(1)\r\n\r\n print \"Arming motors\"\r\n # Copter should arm in GUIDED mode\r\n vehicle.mode = VehicleMode(\"GUIDED\")\r\n \r\n\r\n vehicle.armed = True\r\n\r\n # Confirm vehicle armed before attempting to take off\r\n while not vehicle.armed:\r\n print \" Waiting for arming...\"\r\n time.sleep(1)\r\n\r\n print \"Taking off!\"\r\n #vehicle.channels.overrides['3']=1550\r\n vehicle.simple_takeoff(aTargetAltitude) # Take off to target altitude\r\n\r\n # Wait until the vehicle reaches a safe height before processing the goto (otherwise the command\r\n # after Vehicle.simple_takeoff will execute immediately).\r\n while True:\r\n print \"Attitude: %s\" % vehicle.attitude\r\n print \" Altitude: \", vehicle.location.global_relative_frame.alt\r\n #Break and return from function just below target altitude.\r\n if vehicle.location.global_relative_frame.alt>=aTargetAltitude*0.95:\r\n print \"Reached target altitude\"\r\n #vehicle.channels.overrides['3']=1550\r\n break\r\n time.sleep(1)",
"title": ""
},
{
"docid": "1ddab547243532c076c5c9ea8d4c2942",
"score": "0.7419982",
"text": "def arm_and_takeoff(aTargetAltitude = 5):\n\n\tprint \"Basic pre-arm checks\"\n\t# Don't try to arm until autopilot is ready\n\twhile not vehicle.is_armable:\n\t\tprint \" Waiting for vehicle to initialise...\"\n\t\ttime.sleep(1)\n\n\tprint \"Arming motors\"\n\t# Copter should arm in GUIDED mode\n\tvehicle.mode = VehicleMode(\"GUIDED\")\n\tvehicle.armed = True \n\n\t# Confirm vehicle armed before attempting to take off\n\twhile not vehicle.armed: \n\t\tprint \" Waiting for arming...\"\n\t\ttime.sleep(1)\n\n\tprint \"Taking off!\"\n\tvehicle.simple_takeoff(aTargetAltitude) # Take off to target altitude\n\n\t# Wait until the vehicle reaches a safe height before processing the goto (otherwise the command \n\t# after Vehicle.simple_takeoff will execute immediately).\n\twhile True:\n\t\tprint \" Altitude: \", vehicle.location.global_relative_frame.alt \n\t\t#Break and return from function just below target altitude. \n\t\tif vehicle.location.global_relative_frame.alt>=aTargetAltitude*0.95: \n\t\t\tprint \"Reached target altitude\"\n\t\t\tbreak\n\t\ttime.sleep(1)",
"title": ""
},
{
"docid": "6fd778940a639eb92e8e97a63189312a",
"score": "0.7401521",
"text": "def arm_and_takeoff(aTargetAltitude):\n\n print \"Basic pre-arm checks\"\n # Don't try to arm until autopilot is ready\n while not vehicle.is_armable:\n print \" Waiting for vehicle to initialise...\"\n time.sleep(1)\n\n print \"home: \" + str(vehicle.location.global_relative_frame.lat)\n \n print \"Arming motors\"\n # Copter should arm in GUIDED mode\n vehicle.mode = VehicleMode(\"GUIDED\")\n vehicle.armed = True \n print \"Mode\" + str(vehicle.mode)\n\n # Confirm vehicle armed before attempting to take off\n while not vehicle.armed: \n print \" Waiting for arming...\"\n time.sleep(1)\n\n print \"Taking off!\"\n vehicle.simple_takeoff(aTargetAltitude) # Take off to target altitude\n\n # Wait until the vehicle reaches a safe height before processing the goto (otherwise the command \n # after Vehicle.simple_takeoff will execute immediately).\n while True:\n print \" Altitude: \", vehicle.location.global_relative_frame.alt \n #Break and return from function just below target altitude. \n if vehicle.location.global_relative_frame.alt>=aTargetAltitude*.95: \n print \"Reached target altitude\"\n break\n time.sleep(1)",
"title": ""
},
{
"docid": "e25d6294e1ea7ce35b6cd3b2f24dfc18",
"score": "0.7297721",
"text": "def arm_and_takeoff(a_target_altitude):\n\n print(\"Basic pre-arm checks\")\n # Don't try to arm until autopilot is ready\n while not vehicle.is_armable:\n print(\" Waiting for vehicle to initialise...\")\n time.sleep(3)\n print(\"Arming motors\")\n vehicle.mode = VehicleMode(\"GUIDED\")\n vehicle.armed = True\n\n while not vehicle.armed:\n print(\" Waiting for arming...\")\n time.sleep(1)\n\n print(\"Vehicle armed!\")\n print(\"Taking off!\")\n vehicle.simple_takeoff(a_target_altitude) # Take off to target altitude\n\n # Wait until the vehicle reaches a safe height before processing the goto\n # (otherwise the command after Vehicle.simple_takeoff will execute\n # immediately).\n while True:\n print(\" Altitude: \", vehicle.location.global_relative_frame.alt)\n # Break and return from function just below target altitude.\n if vehicle.location.global_relative_frame.alt >= a_target_altitude * 0.95:\n print(\"Reached target altitude\")\n break\n time.sleep(1)",
"title": ""
},
{
"docid": "13109f59fbc1265539a7d2ecc59abfb0",
"score": "0.61552215",
"text": "def takeoff_transition(self):\n print(\"takeoff transition\")\n self.target_position[Locations.ALTITUDE] = TARGET_ALTITUDE\n self.takeoff(self.target_position[Locations.ALTITUDE])\n self.flight_state = States.TAKEOFF",
"title": ""
},
{
"docid": "29276b455680a0ed24e725aec2dbca47",
"score": "0.61051506",
"text": "def fix_altitude(self, diff):\n self.goto(self.x, self.y, self.master.z + diff, self.o)",
"title": ""
},
{
"docid": "06e7158d5f8754238482dcb7d966cd0b",
"score": "0.5907058",
"text": "def update(self, target_altitude, t=1):\n\t\t\n\t\t# Update the physical properties of the rocket\n\t\tself.rocket.update()\n\n\t\t# Update PID using current error & velocity\n\t\terror = target_altitude - self.rocket.altitude\n\t\tderivative = self.rocket.velocity\n\t\tself.pid.update(error, derivative, t=t)\n\n\t\t# Follow protocol based on current state\n\t\tif self.state == 'landing-0':\n\t\t\tif self.rocket.estimated_distance() > self.rocket.altitude:\n\t\t\t\tself.rocket.throttle = .95\n\t\t\t\tself.state = 'landing-1'\n\t\t\telse:\n\t\t\t\tself.rocket.throttle = 0\n\n\t\tif self.state == 'landing-1':\n\t\t\tif self.rocket.velocity < 0:\n\t\t\t\tif abs(self.rocket.estimated_distance() - self.rocket.altitude) < 0.1:\n\t\t\t\t\tself.rocket.throttle = .95\n\n\t\t\t\telif self.rocket.altitude - self.rocket.estimated_distance() > 0.1:\n\t\t\t\t\tself.rocket.throttle = 0.9\n\n\t\t\t\telif self.rocket.altitude - self.rocket.estimated_distance() < -0.1:\n\t\t\t\t\tself.rocket.throttle = 1\n\n\t\t\telse:\n\t\t\t\tself.rocket.throttle = 7 / (self.rocket.thrust / self.rocket.total_mass)\n\t\t\t\tself.state = 'landed'\n\n\t\telif self.state == 'hover':\n\t\t\tdesired_acceleration = self.pid.output()\n\t\t\tself.rocket.throttle = desired_acceleration / (self.rocket.thrust / self.rocket.total_mass)\n\n\t\tself.rocket.throttle = min(max(0, self.rocket.throttle), 1)\n\n\t\t# Return rocket data for logging\n\t\treturn self.rocket.dict()",
"title": ""
},
{
"docid": "a6be0cf556edad62e1502312375abf24",
"score": "0.5733334",
"text": "def turnTowardsGoal(self):\n pos = self.client.simGetVehiclePose().position\n dX = self.goal.x_val - pos.x_val \n dY = self.goal.y_val - pos.y_val \n dest = math.atan2(dY, dX)\n yaw = self.yaw\n\n a = dest - yaw\n if a > math.pi:\n a -= math.pi * 2\n elif a < -math.pi:\n a += math.pi * 2\n\n if(a > 0):\n if(self.calculateTooClosePercentage(self.right) < self.percentage):\n yaw = (self.yaw + a / 5)\n else:\n if(self.calculateTooClosePercentage(self.left) < self.percentage):\n yaw = (self.yaw + a / 5)\n\n # Changing altitude\n bot = self.calculateTooClosePercentage(self.bottom)\n top = self.calculateTooClosePercentage(self.top)\n if pos.z_val < self.goal.z_val and bot < self.percentage:\n lidarData = self.client.getLidarData(lidar_name=\"LidarD\")\n if len(lidarData.point_cloud) > 2:\n pc = lidarUtils.parseLidarData(lidarData)\n for p in pc:\n if pos.distance_to(airsim.Vector3r(p[0], p[1], p[2])) > 1:\n self.height = self.height + (self.goal.z_val - pos.z_val) / 5\n break\n elif pos.z_val > self.goal.z_val and top < self.percentage:\n lidarData = self.client.getLidarData(lidar_name=\"LidarT\")\n if len(lidarData.point_cloud) > 2:\n pc = lidarUtils.parseLidarData(lidarData)\n for p in pc:\n if pos.distance_to(airsim.Vector3r(p[0], p[1], p[2])) > 1:\n self.height = self.height + (self.goal.z_val - pos.z_val) / 5\n break\n self.fly(yaw)",
"title": ""
},
{
"docid": "44d73218ba6cc4552cc060e6d202e718",
"score": "0.5569847",
"text": "def altitude_control(self, altitude_cmd, vertical_velocity_cmd, altitude, vertical_velocity, attitude,\n acceleration_ff=0.0):\n\n roll, pitch, yaw = attitude\n rot_mat = euler2RM(roll, pitch, yaw)\n u_bar_1 = self.z_k_p * (altitude_cmd - altitude) + self.z_k_d * (\n vertical_velocity_cmd - vertical_velocity) + acceleration_ff\n c = DRONE_MASS_KG*(u_bar_1 - GRAVITY) / rot_mat[2][2]\n return c\n # return np.array(0.0)",
"title": ""
},
{
"docid": "47b8468d7e8b130221862a9b67591b97",
"score": "0.5562563",
"text": "def test_fixAltitude(self):\n key, value = 'altitude', '545.4'\n altitude = base.Altitude(float(value))\n self._fixerTest({key: value}, {key: altitude})",
"title": ""
},
{
"docid": "831681eff54ce207b019c525e9fe9085",
"score": "0.5555863",
"text": "def altitude(self, altitude: float):\n\n self._altitude = altitude",
"title": ""
},
{
"docid": "00d76541bc5ecd7cd2e79170b80d4386",
"score": "0.5486557",
"text": "def arming_transition(self):\n print(\"arming transition\")\n self.take_control()\n self.arm()\n self.set_home_position(self.global_position[Locations.LATITUDE],\n self.global_position[Locations.LONGITUDE],self.global_position[Locations.ALTITUDE])\n self.flight_state = States.ARMING \n print(\"START \",self.global_position[Locations.LATITUDE],\n self.global_position[Locations.LONGITUDE],self.global_position[Locations.ALTITUDE])",
"title": ""
},
{
"docid": "3bcf2000eac8a62d9389de254dfb2dfb",
"score": "0.5461055",
"text": "def updateTargetArrow():\n [speed] = glob_speed.data[\"val\"] # input/\n [theta] = glob_theta.data[\"val\"] # input/\n [y_0] = glob_y0.data[\"val\"] # input/\n # if speed = 0 then there is no arrow\n if (speed == 0):\n # define xE and yE so that the aim line is updated even if speed = 0\n xE=10*cos(theta)\n yE=10*sin(theta)\n direction_arrow.stream(dict(xS=[0],yS=[0],xE=[0],yE=[0]), rollover=1)\n aim_line.data = dict(x=[x_0,x_0+100*xE],y=[y_0,y_0+100*yE])\n else:\n # else the arrow is proportional to the speed\n xE=speed*cos(theta)\n yE=speed*sin(theta)\n direction_arrow.stream(dict(xS=[x_0], yS=[y_0], xE=[xE+x_0], yE=[yE+y_0]), rollover=1)\n # the dotted line is calculated from cos and sin as numerical errors\n # mean that a solution using tan does not lie on the direction arrow\n aim_line.data = dict(x=[x_0,x_0+100*xE],y=[y_0,y_0+100*yE])",
"title": ""
},
{
"docid": "0f865bed85cc009c47dab69f6798af6d",
"score": "0.54055977",
"text": "def drive_to_cheval(self):\n self.drive.move(.4, 0)\n if self.ultrasonic.getVoltage() < self.targetDistance:\n self.next_state('lower_arms')",
"title": ""
},
{
"docid": "b634475588ebd48f35ec5a977389fde7",
"score": "0.54001355",
"text": "def arming_transition(self):\n print(\"arming transition\")\n self.set_home_position(self.global_position[0],self.global_position[1],self.global_position[2])\n self.take_control()\n self.arm()\n self.flight_state = States.ARMING",
"title": ""
},
{
"docid": "c137d4d489f399d378608538b49081e1",
"score": "0.53965753",
"text": "def targetDistance(self, t, targetAscentRate, flightMode, deviceActivationAltitude,\n floatDuration, balloonNominalBurstDia, returnWeightedSum=True):\n # Find the balloon model with nearest burst diameter to that of the\n # input\n if balloonNominalBurstDia:\n self.balloonModel = min(self.balloonsSelected,\n key=lambda k: abs(self.balloonsSelected.get(k)[1]-balloonNominalBurstDia))\n\n # Convert ascent rate to nozzle lift for the nearest balloon model\n # giving the input burst diameter\n nozzleLift = nozzleLiftFixedAscent(targetAscentRate,\n self._balloonWeight, self.payloadTrainWeight,\n self.environment.inflationTemperature,\n self.environment.getPressure(self.launchSiteLat,\n self.launchSiteLon,\n self.launchSiteElev,\n self.start_dateTime),\n self._gasMolecularMass, self.excessPressureCoeff,\n CD=(0.225 + 0.425)/2.)\n\n log_msg = \"Running flight for datetime {}, nozzleLift={}kg, balloon {}\".format(\n self.start_dateTime + timedelta(hours=t), nozzleLift, self.balloonModel)\n\n if flightMode == 'floating':\n log_msg += \", Floating Altitude {}m, Duration {} seconds\".format(deviceActivationAltitude, floatDuration)\n self.floatingFlight = True\n self.floatingAltitude = deviceActivationAltitude\n self.floatDuration = floatDuration\n\n elif flightMode == 'cutdown':\n log_msg += \", cutdown Altitude {}m\".format(deviceActivationAltitude)\n self.cutdown = True\n self.cutdownAltitude = deviceActivationAltitude\n\n logger.debug(log_msg)\n\n Nobjs = 3\n fitnessArr = np.zeros(Nobjs)\n\n # nozzle lift is an access controlled variable, which may raise an error if\n # lower than the payload lift: return a penalty if this is the case\n try:\n self.nozzleLift = nozzleLift\n except ValueError:\n # Pay a penalty larger than the maximum possible values\n fitnessArr += [5e6] * Nobjs\n if returnWeightedSum:\n return sum(fitnessArr)\n else:\n return fitnessArr\n\n launchDateTime = self.start_dateTime + timedelta(hours=t)\n\n resultProfile, solution = self.fly(0, launchDateTime)\n\n # Penalty for flights not bursting within the time limit\n if not resultProfile.hasBurst:\n fitnessArr += [5e6] * Nobjs\n \n # Distance objective (normalised) - CURRENTLY REMOVING NORMALISATION\n landing_lat = resultProfile.latitudeProfile[-1]\n landing_lon = resultProfile.longitudeProfile[-1]\n distNorm = (tools.haversine(landing_lat, landing_lon, self.targetLat, self.targetLon)) # /\n #self.cutoffDistance)\n\n # Cost related objective (currently evaluates the gas mass required to\n # achieve the nozzle lift used for this profile, normalised by the max\n # )\n if self.balloonsSelected:\n balloonsSelected = self.balloonsSelected\n else:\n # Just use the full dict from available_balloons_parachutes.py\n balloonsSelected = balloons\n\n gasMassNorm = self._gasMassAtInflation # / self.maxGasMass\n\n # Time related objective (could be useful for minimizing cold soak time)\n timeNorm = resultProfile.flightDurationSecs #/ self.maxFlightTime\n\n fitnessArr += [distNorm, gasMassNorm, timeNorm]\n fitness = self.flightFitness(fitnessArr)\n self.fitnesses.append(fitness)\n X = [t, targetAscentRate, flightMode, deviceActivationAltitude,\n floatDuration, balloonNominalBurstDia]\n resultProfile = targetProfile.fromProfile(resultProfile, fitness=fitness, X=X)\n\n # Use the ParetoFront update method to store this profile lexographically\n self.results.update([resultProfile])\n\n if returnWeightedSum:\n return - sum(fitness.wvalues)\n else:\n return fitness.values",
"title": ""
},
{
"docid": "c03eda1d0c66bc8316b66a1ba738f271",
"score": "0.5385241",
"text": "def takeoff_transition(self):\n print(\"takeoff transition\")\n self.target_position[2]=3\n self.takeoff(3)\n self.flight_state = States.TAKEOFF",
"title": ""
},
{
"docid": "80b63522f643245014924398914dddd6",
"score": "0.5312594",
"text": "def bring_band_to_front(self, altitude):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "97ccaa35e1da6e4a46addc4478eb8351",
"score": "0.5305427",
"text": "def point_at_azel(self, az, el):\n self.ephemeris_cmd_location = None\n self.rotor_offsets = (0.0, 0.0)\n self.radio_queue.put((\"soutrack\", f\"azel_{az}_{el}\"))\n new_rotor_destination = (az, el)\n new_rotor_cmd_location = new_rotor_destination\n if self.rotor.angles_within_bounds(*new_rotor_cmd_location):\n self.rotor_destination = new_rotor_destination\n self.rotor_cmd_location = new_rotor_cmd_location\n while not azel_within_range(self.rotor_location, self.rotor_cmd_location):\n sleep(0.1)\n else:\n self.log_message(f\"Object at {new_rotor_cmd_location} Not in Motor Bounds\")",
"title": ""
},
{
"docid": "38db6345729bf9262371316ad2f47909",
"score": "0.5282091",
"text": "def turnToTarget(self, angleLimelight):\n if angleLimelight > 15:\n nspeed = angleLimelight / 50\n self.drive.tankDrive(nspeed+0.1, nspeed+0.1)\n elif 5 < angleLimelight < 15:\n nspeed = angleLimelight / 75\n self.drive.tankDrive(nspeed+0.3, nspeed+0.3)\n elif 2 < angleLimelight < 5:\n self.drive.tankDrive(0.4, 0.4)\n elif -2 < angleLimelight < 2:\n self.drive.stopMotor()\n elif -5 < angleLimelight < 2:\n self.drive.tankDrive(-0.4, -0.4)\n elif -15 < angleLimelight < -5:\n nspeed = angleLimelight / 75\n self.drive.tankDrive(nspeed-0.3, nspeed-0.3)\n elif angleLimelight < -15:\n nspeed = angleLimelight / 50\n self.drive.tankDrive(nspeed-0.1, nspeed-0.1)",
"title": ""
},
{
"docid": "159f01abc1b669ac188b323045f5f0ba",
"score": "0.52740276",
"text": "def unified_powered_flight_guidance(vehicle, target, state, previous):\r\n global g0\r\n \r\n # Block 0\r\n gamma\t= target.angle # Desired inertial flight path angle at terminal (cutoff) position\r\n iy = target.normal # Unit vectors relative to desired trajectory plane: iy is normal to desired trajectory plane.\r\n rdval = target.radius # Desired radius magnitude at terminal (cutoff) position\r\n vdval = target.velocity # Desired velocity magnitude at terminal (cutoff) position\r\n\r\n t = state.time # Time associated with r, v\r\n m = state.mass # Current estimated vehicle mass\r\n r = state.radius # Vehicle position vector\r\n v = state.velocity # Vehicle velocity vector\r\n\r\n cser = previous.cser\r\n rbias = previous.rbias # A position bias to account for effects of a rotating thrust vector\r\n rd = previous.rd # Desired terminal (cutoff) position\r\n rgrav = previous.rgrav # Second integral of central force field gravitational acceleration over thrusting maneuver\r\n tp = previous.time # t of previous guidance cycle\r\n vprev = previous.v\r\n vgo = previous.vgo\r\n\r\n # Block 1\r\n n = len(vehicle) # total number of stages\r\n SM = [1] * n # thrust mode (1=const thrust, 2=const acc)\r\n aL = [0] * n # acceleration limit for const acceleration mode\r\n md = [0] * n # mass flow rate\r\n ve = [0] * n # Effective exhaust velocity for phase i\r\n fT = [0] * n # thrust\r\n aT = [0] * n # acceleration at the beginning of stage\r\n tu = [0] * n # \"time to burn as if the whole stage was fuel\"\r\n tb = [0] * n # Estimated burn time remaining in phase i\r\n\r\n for i in range(n):\r\n SM[i] = vehicle[i].mode\r\n aL[i] = vehicle[i].gLim * g0\r\n fT[i], md[i], ve[i] = get_thrust(vehicle[i].engines, 0, 0)\r\n ve[i] = ve[i] * g0\r\n aT[i] = fT[i] / vehicle[i].m0\r\n tu[i] = ve[i] / aT[i]\r\n tb[i] = vehicle[i].maxT\r\n\r\n # Block 2\r\n # We need to store dt in order to keep track on maneuver time.\r\n dt = t - tp # Guidance cycle time step\r\n\r\n # In the paper, this block assumes the only known thing about vehicle's\r\n # state change since the last iteration is vector of velocity change\r\n # (delta-v_sensed) and time interval (delta t). In this implementation\r\n # however we assume the state is perfectly known and hence the call to\r\n # Powered Flight Navigation Routine is not necessary.\r\n # However, we still decrement vgo here!\r\n dvsensed = v - vprev # Total velocity change accumulated on accelerometers since last reading\r\n vgo = vgo - dvsensed # Velocity to be gained including bias. (vthrust reflects true velocity-to-be-gained)\r\n vgo1 = vgo\r\n\r\n # Calculation of 'remaining time to burn in stage k' (which here means\r\n # current stage) is done differently than in the paper. There, t_b,k is\r\n # decremented by dt every iteration; here this variable is not\r\n # persistent, but re-read from vehicle data at each iteration, and\r\n # instead we remember the current burn time tb, so we simply subtract\r\n # this time from original time-to-burn, obtaining the remaining time.\r\n tb[0] = tb[0] - previous.tb\r\n\r\n # Block 3\r\n # Current vehicle parameters have already been obtained in block 1, the\r\n # only thing different is a_T,k which should be calculated from current\r\n # mass instead of initial, and subsequently tu,k.\r\n # This is done according to theory on pages 33-34 and block diagrams on\r\n # 56-57, although with a small change: original document for the Space\r\n # Shuttle assumed that standard ascent will be finalized with a\r\n # predetermined OMS burn (Orbiter's SSMEs burning fuel from ET will only\r\n # take it so far, then the ET is jettisoned and vehicle coasts for a\r\n # predetermined amount of time (tc), after which the last burn phase\r\n # circularizes). Therefore, active guidance did not calculate the\r\n # integral Li(n). Stages 1..n-2 were assumed to burn out completely\r\n # (hence the logarithmic expression for them), and stage n-1 has burn\r\n # time set accordingly, so the total delta-v expended equals vgo.\r\n # In this application however, there is no such thing as a predetermined\r\n # final stage velocity. Therefore, stages n-1 are assumed to burn out\r\n # completely, and the last one is adjusted, so it burns out only as long\r\n # as needed.\r\n\r\n if SM[0] == 1:\r\n aT[0] = fT[0] / m\r\n elif SM[0] == 2:\r\n aT[0] = aL[0]\r\n\r\n tu[0] = ve[0] / aT[0]\r\n L = 0\r\n Li = [0]*n\r\n for i in range(n-1):\r\n if SM[i] == 1:\r\n Li[i] = ve[i]*np.log(tu[i] / (tu[i]-tb[i]))\r\n elif SM[i] == 2:\r\n Li[i] = aL[i]*tb[i]\r\n\r\n L = L + Li[i]\r\n # If we have more stages than we need to get to orbit, redo the\r\n # whole calculation but skip the last stage.\r\n if L > np.linalg.norm(vgo):\r\n return unified_powered_flight_guidance(vehicle[0:n-1], target, state, previous)\r\n Li[n-1] = np.linalg.norm(vgo) - L\r\n # Now for each stage its remaining time of burn is calculated (tbi) and\r\n # in the same pass accumulated into a total time-to-go of the maneuver.\r\n tgoi = [0]*n # Time-to-go until end of ith phase\r\n for i in range(n):\r\n if SM[i] == 1:\r\n tb[i] = tu[i]*(1-np.exp(-Li[i]/ve[i]))\r\n elif SM[i] == 2:\r\n tb[i] = Li[i] / aL[i]\r\n if i == 0:\r\n tgoi[i] = tb[i]\r\n else:\r\n tgoi[i] = tgoi[i-1] + tb[i]\r\n L1 = Li[0]\r\n tgo = tgoi[n-1]\r\n\r\n # Block 4\r\n L = 0\r\n J = 0\r\n Ji = [0]*n\r\n S = 0\r\n Si = [0]*n\r\n Q = 0\r\n Qi = [0]*n\r\n H = 0\r\n P = 0\r\n Pi = [0]*n\r\n # Major loop of the whole block, almost exactly as in the block diagrams.\r\n for i in range(n):\r\n # Variable tgoi1 represents t_go,i-1 only is determined in a safe\r\n # way (as to not exceed the array).\r\n if i == 0:\r\n tgoi1 = 0\r\n else:\r\n tgoi1 = tgoi[i-1]\r\n\r\n # Constant thrust vs constant acceleration mode\r\n if SM[i] == 1:\r\n Ji[i] = tu[i]*Li[i] - ve[i]*tb[i]\r\n Si[i] = -Ji[i] + tb[i]*Li[i]\r\n Qi[i] = Si[i]*(tu[i]+tgoi1) - 0.5*ve[i]*tb[i]**2\r\n Pi[i] = Qi[i]*(tu[i]+tgoi1) - 0.5*ve[i]*tb[i]**2 * ((1.0/3)*tb[i]+tgoi1)\r\n elif SM[i] == 2:\r\n Ji[i] = 0.5*Li[i]*tb[i]\r\n Si[i] = Ji[i]\r\n Qi[i] = Si[i]*((1.0/3)*tb[i] + tgoi1)\r\n Pi[i] = (1.0/6)*Si[i]*(tgoi[i]**2 + 2*tgoi[i]*tgoi1 + 3*tgoi1**2)\r\n\r\n # Common for both modes\r\n Ji[i] = Ji[i] + Li[i]*tgoi1\r\n Si[i] = Si[i] + L*tb[i]\r\n Qi[i] = Qi[i] + J*tb[i]\r\n Pi[i] = Pi[i] + H*tb[i]\r\n\r\n # No coast period before the last stage.\r\n\r\n L = L + Li[i]\r\n J = J + Ji[i]\r\n S = S + Si[i]\r\n Q = Q + Qi[i]\r\n P = P + Pi[i]\r\n H = J*tgoi[i] - Q\r\n\r\n # Block 5\r\n lambda_vec = unit(vgo) # Unit vector in direction of vgo\r\n # print('lambda %s; vgo = %s' % (lambda_vec, vgo))\r\n rgrav1 = rgrav\r\n if not np.isclose(previous.tgo, 0):\r\n rgrav = (tgo/previous.tgo)**2 * rgrav\r\n rgo = rd - (r + v*tgo + rgrav)\r\n rgo1 = rgo\r\n iz = unit(np.cross(rd, iy))\r\n # print('iz = %s; rd = %s; iy = %s' % (iz, rd, iy))\r\n iz1 = iz\r\n rgoxy = rgo - np.vdot(iz, rgo)*iz\r\n rgoz = (S - np.vdot(lambda_vec, rgoxy)) / np.vdot(lambda_vec, iz)\r\n rgo = rgoxy + rgoz*iz + rbias\r\n lambdade = Q - S*J/L\r\n lambdadot = (rgo - S*lambda_vec) / lambdade # Time derivative of unit vector coincident with lambda_vec, but rotating with desired thrust vector turning rate omega_f\r\n iF = unit(lambda_vec - lambdadot*J/L) # Unit thrust vector\r\n phi = np.arccos(np.vdot(iF, lambda_vec))\r\n phidot = -phi*L/J\r\n vthrust = (L - 0.5*L*phi**2 - J*phi*phidot - 0.5*H*phidot**2)*lambda_vec # First integral of thrust acceleration vector over thrusting maneuver\r\n vthrust = vthrust - (L*phi + J*phidot)*unit(lambdadot)\r\n # print(\"vthrust = %s\" % vthrust)\r\n rthrust = (S - 0.5*S*phi**2 - Q*phi*phidot - 0.5*P*phidot**2)*lambda_vec # Second integral of thrust acceleration vector over thrusting maneuver\r\n rthrust = rthrust - (S*phi + Q*phidot)*unit(lambdadot)\r\n vbias = vgo - vthrust # A velocity bias to account for the effects of a rotating thrust vector (vbias = vgo - vthrust)\r\n rbias = rgo - rthrust # A position bias to account for the effects of a rotating thrust vector (rbias = rgo - rthrust)\r\n\r\n # Block 6 - original document does not contain any implementation\r\n # TODO - pitch and yaw RATES\r\n UP = unit(r)\r\n NORTH = np.array([0, 0, 1])\r\n EAST = unit(np.cross(NORTH, UP))\r\n frame = [UP, NORTH, EAST]\r\n # print('Frame: %s; iF = %s' % (frame, iF))\r\n pitch = get_angle_from_frame(iF, frame, 'pitch')\r\n yaw = get_angle_from_frame(iF, frame, 'yaw')\r\n\r\n # Block 7 - this calls the Conic State Extrapolation Routine\r\n rc1 = r - 0.1*rthrust - (1.0/30)*vthrust*tgo # Vehicle position vector at beginning of gravity computation coast segment\r\n vc1 = v + 1.2*rthrust/tgo - 0.1*vthrust # Vehicle velocity vector at beginning of gravity computation coast segment\r\n # Vehicle velocity vector at end of gravity computation coast segment\r\n # Vehicle position vector at end of gravity computation coast segment\r\n rc2, vc2, cser = conic_state_extrapolation_routine(rc1, vc1, tgo, cser)\r\n vgrav = vc2 - vc1 # First integral of central force field gravity acceleration over thrusting maneuver\r\n rgrav = rc2 - rc1 - vc1*tgo # Second integral of central force field gravitational acceleration over thrusting maneuver\r\n\r\n # Block 8\r\n rho = 0 # Damping factor used in determining the change in dvgo - see 4-20 for a proper definition\r\n rp = r + v*tgo + rgrav + rthrust\r\n rp = rp - np.vdot(rp, iy)*iy\r\n rd = rdval*unit(rp)\r\n ix = unit(rd)\r\n iz = np.cross(ix, iy)\r\n vd = vdval*np.matmul(np.transpose([ix, iy, iz]), [np.sin(gamma), 0, np.cos(gamma)])\r\n vgop = vd - v - vgrav + vbias\r\n dvgo = rho*(vgop-vgo) # big values (0.8+) cause bananas; standard ascent uses 0 (?)\r\n # print('vd = %s; gamma = %f; vgop = %s; v = %s; vgrav = %s; vbias = %s' % (vd, gamma, vgop, v, vgrav, vbias))\r\n vgo = vgop + dvgo # 5-15 shows vgo + dvgo, but 4-21 shows vgop + dvgo\r\n\r\n current = previous\r\n current.cser = cser\r\n current.rbias = rbias\r\n current.rd = rd\r\n current.rgrav = rgrav\r\n current.tb = current.tb + dt\r\n current.time = t\r\n current.tgo = tgo\r\n current.v = v\r\n current.vgo = vgo\r\n\r\n guidance = Guidance(pitch, yaw, 0, 0, tgo)\r\n\r\n debug = DebugState(0, t, r, v, m, dvsensed, vgo1, L1, tgo, L, J, S, Q, P, H,\r\n lambda_vec, rgrav1, rgo1, iz1, rgoxy, rgoz, rgo, lambdade, lambdadot,\r\n iF, phi, phidot, vthrust, rthrust, vbias, rbias, pitch, EAST, yaw,\r\n rc1, vc1, rc2, vc2, cser.dtcp, cser.xcp, cser.A, cser.D, cser.E,\r\n vgrav, rgrav, rp, rd, ix, iz, vd, vgop, dvgo, vgo, 0)\r\n\r\n return current, guidance, debug",
"title": ""
},
{
"docid": "f8497ab96d9473b00ea753bc0e6139e8",
"score": "0.5269227",
"text": "def default_action(self):\r\n parent = self.robot_parent\r\n\r\n self.destination = [ self.local_data['x'], self.local_data['y'], self.local_data['z'] ]\r\n\r\n logger.debug(\"STRAIGHT GOT DESTINATION: {0}\".format(self.destination))\r\n logger.debug(\"Robot {0} move status: '{1}'\".format(parent.bge_object.name, parent.move_status))\r\n\r\n # Vectors returned are already normalised\r\n distance, global_vector, local_vector = self.bge_object.getVectTo(self.destination)\r\n\r\n logger.debug(\"My position: {0}\".format(self.bge_object.position))\r\n logger.debug(\"GOT DISTANCE: {0}\".format(distance))\r\n logger.debug(\"Global vector: {0}\".format(global_vector))\r\n logger.debug(\"Local vector: {0}\".format(local_vector))\r\n\r\n if distance > self._tolerance:\r\n # Set the robot status\r\n parent.move_status = \"Transit\"\r\n \r\n # Scale the speeds to the time used by Blender\r\n try:\r\n if self._type == 'Position':\r\n vx = global_vector[0] * self._speed / self.frequency\r\n vy = global_vector[1] * self._speed / self.frequency\r\n vz = global_vector[2] * self._speed / self.frequency\r\n else:\r\n vx = global_vector[0] * self._speed\r\n vy = global_vector[1] * self._speed\r\n vz = global_vector[2] * self._speed\r\n # For the moment ignoring the division by zero\r\n # It happens apparently when the simulation starts\r\n except ZeroDivisionError:\r\n pass\r\n\r\n # If the target has been reached, change the status\r\n else:\r\n # Reset movement variables\r\n vx, vy, vz = 0.0, 0.0, 0.0\r\n #rx, ry, rz = 0.0, 0.0, 0.0\r\n\r\n parent.move_status = \"Stop\"\r\n logger.debug(\"TARGET REACHED\")\r\n logger.debug(\"Robot {0} move status: '{1}'\".format(parent.bge_object.name, parent.move_status))\r\n\r\n \r\n self.robot_parent.apply_speed(self._type, [vx, vy, vz], [0, 0, 0])",
"title": ""
},
{
"docid": "bf9ef01ef3c9fc6015eb3e1fcdb7d210",
"score": "0.525464",
"text": "def altmTargeting(oe0, oef, simInfo, dt=[], A=[], B=[], DM=[], time='free', manType='bcb', shooter='o2o', searchMode='o2o', lamMinMode='dvt', planMode='nmp', plot=True):\n # -----------------------------------------------------------\n ## Maneuvers ## \n \"\"\"I am only going to tailor this version of ALTM to BC and BCB.\n It seems like Lambert is used for mission planning and single\n burns are used for guidance.\"\"\"\n\n # Finding Impulsive Maneuver\n oe1, oe2, dv1, dv2, dt_lam, t_sk = impMan(oe0, oef, simInfo, searchMode, lamMinMode, dt_bracket=dt, DM=DM, plot=False) #this shows lambert cost function plot for \"st\" stuff\n meoe0 = af.oe2meoe(oe1); meoef = af.oe2meoe(oe2)\n simInfo['tCurr'] += t_sk\n\n # Adjusting Lambert for higher fidelity dynamics (this is a week point IMO)\n \"\"\"Simply targets a position by adjusting dv1 and dt_lam. Messes up mission planning\n for time sensitive targets when it adjusts dt_lam. Need to think on this.\n\n Algorithm is working pretty well with full dynamics without retargeting. I know\n if mission segments get longer, the retargeting will be important.\n\n I have adjusted lambertRetarget to not change ToF when 'time' is fixed.\n \"\"\"\n if planMode == 'singleMan':\n dv1, dv2, dt_lam = lambertRetarget(meoe0, meoef, dv1, dv2, dt_lam, simInfo, time)\n else:\n dv1, dv2, dt_lam = lambertRetarget(meoe0, meoef, dv1, dv2, dt_lam, simInfo, 'free')\n dv1_dim = dv1*simInfo['r_norm']/simInfo['t_norm']\n dv2_dim = dv2*simInfo['r_norm']/simInfo['t_norm']\n simInfo['manTime'] = dt_lam\n\n # Finding Continuous-Thrust Maneuver\n sc_at = simInfo['T']/simInfo['m_current'] # accel, m/s2\n if manType.lower() == 'bc':\n \n dvImp = np.sqrt(dv1.dot(dv1))\n\n # Turn Lambert transfer into BLT Initial Guess \n dt1 = np.sqrt(dv1.dot(dv1))/sc_at # manevuer time guess, s\n A1 = dv1/np.sqrt(dv1.dot(dv1))\n B1 = np.zeros(3)\n dt2 = dt_lam - dt1 # manevuer time guess, s\n if dt2 < 0: # This will be a flag for ALTa\n dt2 = 1/simInfo['t_norm']\n\n # BC Multiple shooter function\n dt = np.array([dt1, dt2])\n A = np.array([A1, np.zeros(3)])\n B = np.array([B1, np.zeros(3)])\n meoe0_fin, meoef_fin, A_fin, B_fin, dt_fin, converge, X_plot, tvec_plot = bcMultipleShooter(meoe0, meoef, A, B, dt, simInfo, time, shooter, True)\n\n print('\\n==============================================================')\n print('BC '+ searchMode+' Lambert Transfer Impulsive Initial Guess')\n print('\\tTransfer Time =', '{:.2f}'.format(sum(dt_fin)*simInfo['t_norm']/60), 'min')\n print('\\tManeuver Time =', '{:.2f}'.format(dt_fin[0]*simInfo['t_norm']/60), 'min')\n print('\\tdV1 =', '{:.2f}'.format(np.sqrt(dv1_dim.dot(dv1_dim))*100), 'cm/s')\n print('==============================================================')\n\n elif manType.lower() == 'bcb':\n\n dvImp = np.sqrt(dv1.dot(dv1)) + np.sqrt(dv2.dot(dv2))\n\n # Maneuvers BLT Initial Guess \n dt1 = np.sqrt(dv1.dot(dv1))/sc_at # manevuer time guess, s\n A1 = dv1/np.sqrt(dv1.dot(dv1))\n B1 = np.zeros(3)\n A3 = dv2/np.sqrt(dv2.dot(dv2))\n B3 = np.zeros(3)\n dt3 = np.sqrt(dv2.dot(dv2))/sc_at # manevuer time guess, s\n dt2 = dt_lam - dt1 - dt3 # manevuer time guess, s\n if dt2 < 0: # This will be a flag for ALTa\n dt2 = 1/simInfo['t_norm']\n\n # BCB Multiple shooter function\n dt = np.array([dt1, dt2, dt3])\n A = np.array([A1, np.zeros(3), A3])\n B = np.array([B1, np.zeros(3), B3])\n meoe0_fin, meoef_fin, A_fin, B_fin, dt_fin, converge, X_plot, tvec_plot = bcbMultipleShooter(meoe0, meoef, A, B, dt, simInfo, time, shooter, True)\n \n print('\\n==============================================================')\n print('BCB '+ searchMode+' Lambert Transfer Impulsive Initial Guess')\n print('\\tTransfer Time =', '{:.2f}'.format(sum(dt_fin)*simInfo['t_norm']/60), 'min')\n print('\\tManeuver Time =', '{:.2f}'.format(dt_fin[0]*simInfo['t_norm']/60), 'min')\n print('\\tManeuver Time =', '{:.2f}'.format(dt_fin[-1]*simInfo['t_norm']/60), 'min')\n print('\\tdV1 =', '{:.2f}'.format(np.sqrt(dv1_dim.dot(dv1_dim))*100), 'cm/s')\n print('\\tdV2 =', '{:.2f}'.format(np.sqrt(dv2_dim.dot(dv2_dim))*100), 'cm/s')\n print('\\tdVt =', '{:.2f}'.format((np.sqrt(dv1_dim.dot(dv1_dim))+np.sqrt(dv2_dim.dot(dv2_dim)))*100), 'cm/s')\n print('==============================================================')\n\n else:\n print('\\n\\nHey, dummy! Pick a correct manType (BC or BCB)!\\n\\n')\n\n oe0_fin = []; oef_fin = []\n for i in range(len(meoe0_fin)):\n oe0_fin.append(np.hstack((af.meoe2oe(meoe0_fin[i][0:6]),meoe0_fin[i][-1])))\n oef_fin.append(np.hstack((af.meoe2oe(meoef_fin[i][0:6]),meoef_fin[i][-1])))\n\n # Need to adjust t_sk here\n if shooter[0] == 'o':\n tAdj = coastTime(oe1[-1], oe0_fin[0][-2], oe1, simInfo['mu_host'], mode='adjust')\n t_sk += tAdj\n simInfo['tCurr'] += tAdj\n\n return oe0_fin, oef_fin, A_fin, B_fin, dt_fin, t_sk, dvImp, converge, X_plot, tvec_plot",
"title": ""
},
{
"docid": "d91b4bfd357e3cda13db28d2839c6191",
"score": "0.5198543",
"text": "def az_update(self, tar_az):\n az_err = self.az_dir(tar_az)\n az_err = abs(az_err)\n if az_err >= 0:\n self.az_motor.set_speed(7)\n elif az_err >= 35:\n self.az_motor.set_speed(7)\n elif az_err >= 50:\n self.az_motor.set_speed(7)\n elif az_err >= 80:\n self.az_motor.set_speed(7)\n elif az_err >= 160:\n self.az_motor.set_speed(7)\n elif az_err >= 240:\n self.az_motor.set_speed(7)",
"title": ""
},
{
"docid": "8522f19a889ec4cf738a70a310208b4e",
"score": "0.5193675",
"text": "def set_target(self, t0, ra=None, dec=None, psi=None, delays=None,\n antenna_patterns=None, duration=None, n_analyze=None):\n tgps = lal.LIGOTimeGPS(t0)\n gmst = lal.GreenwichMeanSiderealTime(tgps)\n delays = delays or {}\n antenna_patterns = antenna_patterns or {}\n for ifo, data in self.data.items():\n # TODO: should we have an elliptical+ftau model?\n if ifo is None or self.model=='ftau':\n dt_ifo = 0\n self.antenna_patterns[ifo] = (1, 1)\n else:\n det = data.detector\n dt_ifo = delays.get(ifo,\n lal.TimeDelayFromEarthCenter(det.location, ra, dec, tgps))\n self.antenna_patterns[ifo] = antenna_patterns.get(ifo,\n lal.ComputeDetAMResponse(det.response, ra, dec, psi, gmst))\n self.start_times[ifo] = t0 + dt_ifo\n self.target = Target(t0, ra, dec, psi)\n # also specify analysis duration if requested\n if duration:\n self._duration = duration\n elif n_analyze:\n self._nanalyze = int(n_analyze)",
"title": ""
},
{
"docid": "bf0aa8bdba7b4ae9bc358dd8ae44cc27",
"score": "0.517993",
"text": "def run(self):\n for target_velocity in self.target_velocities:\n\n # Remove the previous appended values in case of zer crossings to avoid double appending of same value\n if self.target_velocities.index(target_velocity) == 1:\n self.tarray.pop()\n self.varray.pop()\n self.t = self.t - self.ts\n\n while True:\n # Determine direction based on current and target velocities\n if self.current_velocity >= 0 and target_velocity >= 0:\n self.direction_multiplier = 1\n elif self.current_velocity <= 0 and target_velocity <= 0:\n self.direction_multiplier = -1\n\n # append current values in respective lists\n self.tarray.append(self.t)\n self.varray.append(self.current_velocity)\n # Increase Time\n self.t = self.t + self.ts\n\n # Determine to accelerate and decelerate\n sig = np.sign(abs(target_velocity) - abs(self.current_velocity))\n if sig > 0: # accelerate\n\n if np.isclose(abs(self.current_velocity),\n abs(target_velocity), atol=1 * self.ts * self.acceleration):\n self.current_velocity = target_velocity\n\n # Set delay value for flat part\n self.delayValue = self.t + self.flat_part_duration\n else:\n self.current_velocity = self.current_velocity + self.direction_multiplier * self.acceleration\\\n * self.ts\n\n elif sig < 0: # decelerate\n\n if np.isclose(abs(self.current_velocity),\n abs(target_velocity), atol=1 * self.ts * self.deceleration):\n\n self.current_velocity = target_velocity\n\n # Set delay value for flat part\n self.delayValue = self.t + self.flat_part_duration\n else:\n self.current_velocity = self.current_velocity - self.direction_multiplier * self.deceleration\\\n * self.ts\n\n else: # Flat Part after reaching the last target in target_velocities list\n if self.target_velocities.index(target_velocity) == len(self.target_velocities)-1:\n if self.t > self.delayValue:\n self.tarray.append(self.t)\n self.varray.append(self.current_velocity)\n break\n else:\n break",
"title": ""
},
{
"docid": "a69ceb0b04c9b2c8025214cf2edcca0f",
"score": "0.5175943",
"text": "def add_target(self):\n # Temporarily unused, as there is one mode only.\n blueprint = TargetBlueprint()\n blueprint.attributes[AT.TARGET_TYPE] = TargetType.TIMED\n blueprint.attributes[AT.STRENGTH] = 4\n self.target_factory.create(blueprint)",
"title": ""
},
{
"docid": "ee99373a8c7680442f66daf1bcbce50a",
"score": "0.51659304",
"text": "def act(self):\n if not self.overtaken:\n return\n\n if self.crashed:\n return\n\n action = {}\n front_vehicle, rear_vehicle = self.road.neighbour_vehicles(self)\n\n # Lateral: MOBIL\n self.follow_road()\n if self.enable_lane_change:\n self.change_lane_policy()\n action['steering'] = self.steering_control(self.target_lane_index)\n\n # Longitudinal: IDM\n action['acceleration'] = self.acceleration(ego_vehicle=self, front_vehicle=front_vehicle, rear_vehicle=rear_vehicle)\n action['acceleration'] = np.clip(action['acceleration'], -self.ACC_MAX, self.ACC_MAX)\n self.check_collision\n self.action = action",
"title": ""
},
{
"docid": "938b87746c2e639cf3f3837d1c2c7a35",
"score": "0.5164016",
"text": "def find_satellite_alt_az(self):\n print(\"\\nSatellite: \" + self.id)\n while True:\n self.observer.date = datetime.utcnow()\n self.satellite.compute(self.observer)\n print(\"altitude: %4.2f deg, azimuth: %5.2f deg\" %\n (self.satellite.alt*defaults.degrees_per_radian, self.satellite.az*defaults.degrees_per_radian))\n time.sleep(1.0)",
"title": ""
},
{
"docid": "6aa483e780e981f5352a2cc418fed6bc",
"score": "0.51428705",
"text": "def run(self):\n\n if self.vehicle.mode.name != \"BRAKE\" and self.vehicle.mode.name != \"ALT_HOLD\" and self.vehicle.mode.name != \"LAND\" and self.vehicle.mode.name != \"RTL\":\n gps_precision_in_decimal_degrees = Polygonal.centimetersToDecimalDegrees(self.vehicle.gps_0.eph)\n probable_drone_locations = Polygonal.random_coordinates((self.vehicle.location.lat, self.vehicle.location.lon), gps_precision_in_decimal_degrees, self.precision)\n\n # Adaptive fence, predicts the location of the drone according to its velocity, after 1 second\n if self.adaptive_fence:\n def add_velocity(x): return (x[0] + self.vehicle.velocity[0], x[1] + self.vehicle.velocity[1])\n probable_drone_locations = map(add_velocity, probable_drone_locations)\n\n if Polygonal.points_in_poly(probable_drone_locations, self.fence) is False:\n print \"Broke circular fence.\"\n print self.vehicle.location\n print gps_precision_in_decimal_degrees\n\n return SafeBehaviour.SafeBehaviour.halt\n\n if self.vehicle.location.alt >= self.maximum_altitude or self.vehicle.location.alt <= self.minimum_altitude:\n print \"Broke altitude geo-fence.\"\n print self.vehicle.location\n print gps_precision_in_decimal_degrees\n\n return SafeBehaviour.SafeBehaviour.halt\n\n return SafeBehaviour.SafeBehaviour.do_nothing",
"title": ""
},
{
"docid": "0aaa314aa0a86dd8facfa803303e03fc",
"score": "0.5114325",
"text": "def altitude_field_specified(self, altitude_field_specified):\n\n self._altitude_field_specified = altitude_field_specified",
"title": ""
},
{
"docid": "5c3097b9e735176e1740b7cb67e876f1",
"score": "0.5090233",
"text": "def update_control_targets(self, current_vehicle_position_x, current_vehicle_position_y, current_vehicle_speed,\n current_vehicle_track_angle, next_waypoint_x, next_waypoint_y, next_waypoint_v,\n curr_segment_d, curr_segment_a):\n\n # Displacements along the X and Y axis\n dx = next_waypoint_x - current_vehicle_position_x\n dy = next_waypoint_y - current_vehicle_position_y\n\n # Distance and angle to the next waypoint\n distance_to_next_waypoint = np.hypot(dx, dy)\n angle_to_next_waypoint = np.arctan2(dy, dx)\n\n dv = next_waypoint_v - current_vehicle_speed\n da = angle_to_next_waypoint - current_vehicle_track_angle\n while (da > math.pi):\n da -= 2*math.pi\n while (da < -math.pi):\n da += 2*math.pi\n\n # speed_set_point = next_waypoint_v\n\n # Simple rule to slow down at curves and go faster at straight tracks\n #if distance_to_next_waypoint > self.braking_distance:\n # speed_set_point = self.max_straight_track_speed\n #else:\n # speed_set_point = self.max_curving_speed\n\n if curr_segment_d <= self.min_segment_length:\n speed_set_point = self.max_curving_speed\n elif curr_segment_d >= self.max_segment_length:\n speed_set_point = self.max_straight_track_speed\n else:\n speed_set_point = (curr_segment_d - self.min_segment_length) / \\\n (self.max_segment_length - self.min_segment_length) * \\\n (self.max_straight_track_speed - self.max_curving_speed) + self.max_curving_speed\n # print(\"speed sp: \" + str(speed_set_point))\n\n # Speed set point smoother\n if self.last_speed_set_point_init:\n speed_set_point = self.speed_set_point_update_rate * speed_set_point +\\\n (1-self.speed_set_point_update_rate) * self.last_speed_set_point\n self.last_speed_set_point = speed_set_point\n self.last_speed_set_point_init = True\n\n # Simple rule to align the velocity orientation to the next waypoint\n track_angle_set_point = angle_to_next_waypoint\n\n # Track angle set point smoother\n if self.last_track_angle_set_point_init:\n while self.last_track_angle_set_point - track_angle_set_point > math.pi:\n self.last_track_angle_set_point -= 2*math.pi\n while self.last_track_angle_set_point - track_angle_set_point < -math.pi:\n self.last_track_angle_set_point += 2*math.pi\n track_angle_set_point = self.track_angle_set_point_update_rate * track_angle_set_point +\\\n (1 - self.track_angle_set_point_update_rate) * self.last_track_angle_set_point\n self.last_track_angle_set_point = track_angle_set_point\n self.last_track_angle_set_point_init = True\n\n # print(\"speed sp: \" + str(speed_set_point) + \" angle: \"+ str(np.rad2deg(da)) + \" [deg]\")\n # if(abs(np.rad2deg(da)) <= 1.0):\n # speed_set_point = speed_set_point * 1.5\n \n # if(speed_set_point>self.max_straight_track_speed):\n # speed_set_point = self.max_straight_track_speed\n\n return speed_set_point, track_angle_set_point",
"title": ""
},
{
"docid": "d1cd7f1a3a9e8322b1a81ce5b1e3d35e",
"score": "0.50757223",
"text": "def enroute(self):\n self.route = raycasting.ray(self.owner.position[0], self.owner.position[1], self.target[0], self.target[1],\n self.owner.location.width, self.owner.location.height, self.power)\n self.next = iter(self.route)\n self._fly_next() # fly to next cell (or stop if out of power or route end)",
"title": ""
},
{
"docid": "23c333876f58c70b2aa6007aa5285b1d",
"score": "0.50689596",
"text": "def alt_update(self, tar_alt):\n alt_err = self.alt_dir(tar_alt)\n alt_err = abs(alt_err)\n if alt_err > 0 and alt_err < 40:\n self.actuator.turn_motor()\n else:\n self.actuator.kill()",
"title": ""
},
{
"docid": "8f61309cdab103422939a7a714e252be",
"score": "0.50571287",
"text": "def route(self, agent_id: int, target: tuple) -> None:\n if (target is None) or (self.active_agents[agent_id]):\n return\n # clear the agents task queue and blockages if it is idle\n self.agent_tasks[agent_id] = list()\n while len(self.agent_reserved_squares[agent_id]) > 0:\n self.clear_last_task_blockage(agent_id)\n\n # check that the target location is free, and if not, route to a nearby square\n tile_state = self.arena.get_tile_state(target[0], target[1])\n agent_locations = [(agent.location.X, agent.location.Y) for agent in self.agents]\n if tile_state == TileState.RESERVED or tile_state == TileState.AGENT_TARGET or target in agent_locations:\n neighbours = self.arena.get_neighbours(target[0], target[1])\n for neighbour in neighbours:\n if self.arena.get_tile_state(neighbour[0], neighbour[1]) == TileState.FREE:\n target = neighbour\n break\n\n agent = self.agents[agent_id]\n # reset the main routing algorithm and route the agent\n self.routing_algorithm.reset()\n status = self.routing_algorithm.route(agent, target)\n self.agent_routing_state[agent_id] = status\n\n if status == RoutingStatus.SUCCESS:\n # create the path and queue the first routing task\n route_status = self.routing_algorithm.create_path()\n if route_status == RoutingStatus.SUCCESS:\n task = self.routing_algorithm.path[0]\n # check if the move task is less than the max length allowed and truncate if its too large\n direction = np.sign(task.args[1])\n if abs(task.args[1]) >= self.agent_max_distance[agent_id]:\n task.args[1] = self.agent_max_distance[agent_id] * direction\n self.add_agent_task(agent_id, task)\n self.start_new_task(agent_id)",
"title": ""
},
{
"docid": "ee8241a8509338523fd7bebf0524d2cb",
"score": "0.5054423",
"text": "def _update_target(self):\n\t\tself._check_target_edges()\n\t\tself.target.update()",
"title": ""
},
{
"docid": "d34ece52c793ddbcb89be642c970b2df",
"score": "0.5045886",
"text": "def kickBall():\r\n\r\n # Activate Whole Body Balancer\r\n isEnabled = True\r\n motion.wbEnable(isEnabled)\r\n\r\n # Legs are constrained fixed\r\n stateName = \"Fixed\"\r\n supportLeg = \"Legs\"\r\n motion.wbFootState(stateName, supportLeg)\r\n\r\n # Constraint Balance Motion\r\n isEnable = True\r\n supportLeg = \"Legs\"\r\n motion.wbEnableBalanceConstraint(isEnable, supportLeg)\r\n\r\n # Com go to LLeg\r\n supportLeg = \"LLeg\"\r\n duration = 1.0\r\n motion.wbGoToBalance(supportLeg, duration)\r\n\r\n # RLeg is free\r\n stateName = \"Free\"\r\n supportLeg = \"RLeg\"\r\n motion.wbFootState(stateName, supportLeg)\r\n\r\n # RLeg is optimized\r\n effectorName = \"RLeg\"\r\n axisMask = 63\r\n space = mot.FRAME_TORSO\r\n\r\n\r\n # Motion of the RLeg\r\n dx = 0.025 # translation axis X (meters)\r\n dz = 0.02 # translation axis Z (meters)\r\n dwy = 5.0*math.pi/180.0 # rotation axis Y (radian)\r\n\r\n\r\n times = [1.0, 1.4, 2.1]\r\n isAbsolute = False\r\n\r\n targetList = [\r\n [-0.7*dx, 0.0, 1.1*dz, 0.0, +dwy, 0.0],\r\n [+2.2*dx, +dx, dz, 0.0, -dwy, 0.0],\r\n [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]\r\n\r\n motion.positionInterpolation(effectorName, space, targetList,\r\n axisMask, times, isAbsolute)\r\n\r\n\r\n # Example showing how to Enable Effector Control as an Optimization\r\n isActive = False\r\n motion.wbEnableEffectorOptimization(effectorName, isActive)\r\n\r\n time.sleep(1.0)\r\n\r\n # Deactivate Head tracking\r\n isEnabled = False\r\n motion.wbEnable(isEnabled)\r\n\r\n # send robot to Pose Init\r\n posture.goToPosture(\"StandInit\", 0.5)",
"title": ""
},
{
"docid": "d97c3ebcf05b706c69c6b97b7679fe29",
"score": "0.50203586",
"text": "def altitude_field(self, altitude_field):\n\n self._altitude_field = altitude_field",
"title": ""
},
{
"docid": "6d22d29c393e9362465efe2c39177580",
"score": "0.50088006",
"text": "def run_step(self, target_speed=None, debug=False):\n\n if target_speed is not None:\n self._target_speed = target_speed\n else:\n self._target_speed = self._vehicle.get_speed_limit()\n\n # Buffer waypoints\n self._buffer_waypoints()\n\n if len(self._waypoint_buffer) == 0:\n control = carla.VehicleControl()\n control.steer = 0.0\n control.throttle = 0.0\n control.brake = 1.0\n control.hand_brake = False\n control.manual_gear_shift = False\n return control\n\n # Current vehicle waypoint\n self._current_waypoint = self._map.get_waypoint(\n self._vehicle.get_location())\n\n speed = get_speed(self._vehicle) # kph\n look_ahead = max(2, speed / 4.5)\n\n # Target waypoint\n self.target_waypoint, self.target_road_option = self._waypoint_buffer[0]\n\n look_ahead_loc = self._get_look_ahead_location(look_ahead)\n\n if target_speed > 50:\n args_lat = self.args_lat_hw_dict\n args_long = self.args_long_hw_dict\n else:\n args_lat = self.args_lat_city_dict\n args_long = self.args_long_city_dict\n\n if not self._pid_controller:\n self._pid_controller = VehiclePIDController(self._vehicle,\n args_lateral=args_lat,\n args_longitudinal=args_long)\n else:\n self._pid_controller.set_lon_controller_params(**args_long)\n self._pid_controller.set_lat_controller_params(**args_lat)\n\n control = self._pid_controller.run_step(\n self._target_speed, look_ahead_loc)\n\n # Purge the queue of obsolete waypoints\n vehicle_transform = self._vehicle.get_transform()\n max_index = -1\n\n for i, (waypoint, _) in enumerate(self._waypoint_buffer):\n if distance_vehicle(\n waypoint, vehicle_transform) < self._min_distance:\n max_index = i\n if max_index >= 0:\n for i in range(max_index + 1):\n if i == max_index:\n self._prev_waypoint = self._waypoint_buffer.popleft()[0]\n else:\n self._waypoint_buffer.popleft()\n\n if debug:\n carla_world = self._vehicle.get_world()\n\n # Draw current buffered waypoint\n buffered_waypts = [elem[0] for elem in self._waypoint_buffer]\n draw_waypoints(carla_world, buffered_waypts)\n\n # Draw current look ahead point\n look_ahead_loc\n carla_world.debug.draw_line(look_ahead_loc,\n look_ahead_loc+carla.Location(z=0.2),\n color=carla.Color(255, 255, 0),\n thickness=0.2,\n life_time=1.0)\n\n return control",
"title": ""
},
{
"docid": "270e2a462d1d17ff93743c5fce1a8af3",
"score": "0.50043637",
"text": "def step(self, action):\r\n\r\n # Get desired acceleration and check system boundaries.\r\n a_dem = action[0]\r\n assert self.opts.vehicle_a_min <= a_dem <= self.opts.vehicle_a_max, f\"Action {a_dem} m/s² not part of action space!\"\r\n # Clip a_dem according to constraints when specified in opts\r\n if self.opts.clip_a_dem == True:\r\n a_dem = np.clip(a_dem, self.last_a_min, self.last_a_max)\r\n\r\n # Simulate next timesteps of environment and ego_car.\r\n for i in range(self.pyhs_steps_subsample):\r\n a_tar, v_tar, x_tar, scenario_done = self.environment.step(self.t + self.phys_dt * i)\r\n a_ego, v_ego, x_ego = self.ego_car.step(a_dem)\r\n\r\n # Calucate correction velocity to increase distance in Stop&Go scenario.\r\n v_correction = 0\r\n if v_ego < self.opts.stop_n_go_velocity:\r\n v_correction = self.opts.stop_n_go_distance / self.opts.desired_headway * (self.opts.stop_n_go_velocity - v_ego) / self.opts.stop_n_go_velocity\r\n\r\n # Calulate and clip headway and its derivation.\r\n Hw = (x_tar - x_ego) / max(0.001, v_ego + v_correction)\r\n dHw = (Hw - self.last_Hw) / self.dt\r\n if self.last_Hw == -1:\r\n dHw = 0 # Prevent inital value from being to big\r\n self.last_Hw = Hw\r\n Hw = max(0, min(10.01, Hw))\r\n dHw = max(-0.75, min(0.75, dHw))\r\n\r\n # Calculate safe distance. Increase distance for Stop&Go scenario.\r\n safe_distance = self.opts.desired_headway * abs(v_ego)\r\n if v_ego < self.opts.stop_n_go_velocity:\r\n safe_distance += self.opts.stop_n_go_distance * (1 - max(0, v_ego) / self.opts.stop_n_go_velocity)\r\n\r\n # All variables in this dict can be used as observation, in the reward function or can be plotted.\r\n state = {\r\n # Time and raw commanded acceleration by agent.\r\n 't': self.t,\r\n 'a_dem': a_dem,\r\n # Ego vehicle.\r\n 'a_ego': a_ego,\r\n 'v_ego': v_ego,\r\n 'x_ego': x_ego,\r\n 'j_ego': (a_ego - self.last_a_ego) / self.dt,\r\n # Target vehicle.\r\n 'a_tar': a_tar,\r\n 'v_tar': v_tar,\r\n 'x_tar': x_tar,\r\n # Relative values.\r\n 'a_rel': a_tar - a_ego,\r\n 'v_rel': v_tar - v_ego,\r\n 'x_rel': x_tar - x_ego,\r\n # Control setpoints.\r\n 'd_safe': safe_distance,\r\n 'd_err': safe_distance - (x_tar - x_ego),\r\n 'Hw': Hw,\r\n 'dHw': dHw,\r\n 'v_err': v_tar - v_ego,\r\n # misc\r\n 'last_a_dem': self.last_a_dem,\r\n 'last_a_ego': self.last_a_ego,\r\n }\r\n\r\n # Calculation upper and lower constraint for acceleration and add to state.\r\n state[\"a_min\"], state[\"a_max\"] = self.constraints.calculate(state)\r\n\r\n # end episode of ego car crashed in the lead car or car goes backwards fast\r\n # done signal\r\n # done = 0: not done, episode can continue\r\n # done = 1: done, because simulated time ended\r\n # done = 2: done, because agent ended in terminal step (e.g. crash)\r\n done = 1 if scenario_done or (self.steps >= self._max_episode_steps - 1) else 0\r\n done = 2 if (x_tar - x_ego) < -50 or v_ego < -5 else done\r\n state[\"done\"] = done\r\n\r\n # Calculate reward and add to state.\r\n reward = self.reward_function(state, self.opts)\r\n state[\"reward\"] = reward\r\n\r\n # Store state values in buffer for later plotting.\r\n if self.steps < self._max_episode_steps:\r\n # Store all state variables in data_store.\r\n for k, v in state.items():\r\n if k not in self.data_store:\r\n self.data_store[k] = np.zeros(self._max_episode_steps)\r\n self.data_store[k][self.steps] = v\r\n\r\n # Add choosen action to previous timestep in state dict.\r\n if self.steps >= 1:\r\n self.data_store[\"a_dem\"][self.steps - 1] = a_dem\r\n\r\n # Extract observations from state dict.\r\n obs = [state[key] for key in self.opts.observations]\r\n\r\n # Increment counter and time. Store last values.\r\n self.steps += 1\r\n self.t += self.dt\r\n self.last_a_dem = a_dem\r\n self.last_a_ego = a_ego\r\n self.last_a_min = state[\"a_min\"]\r\n self.last_a_max = state[\"a_max\"]\r\n\r\n # OpenAI Gym compatible return: (observations, reward, done, debug_infos)\r\n return np.array(obs, dtype=np.float32), reward, done, {}",
"title": ""
},
{
"docid": "148f4c437464058cabcb055493ec8060",
"score": "0.49866515",
"text": "def arrive_at_destination(self):\n steps = load_route(self.route)['steps']\n self.location = steps[-1]['end_location']\n self.cargo['used'] = 0\n self.route = None\n\n self.send_message('arrive_at_destination')",
"title": ""
},
{
"docid": "f76fe66f2b5a4846dfec202daee91068",
"score": "0.49777296",
"text": "def on_the(self, target: Target) -> \"HoldDown\":\n self.target = target\n return self",
"title": ""
},
{
"docid": "ac1c9eb3824046f8abce9691990a772d",
"score": "0.4977566",
"text": "def attack(self, target_robot):\n self._make_damage(target_robot)\n self._make_damage(target_robot)",
"title": ""
},
{
"docid": "37d2dba79907b57e8486f289fba68c0c",
"score": "0.49629658",
"text": "def _set_new_target(self, ita):\n goal_position = self.goal_pos_list[ita]\n target_msg = ModelState()\n target_msg.model_name = 'target'\n target_msg.pose.position.x = goal_position[0]\n target_msg.pose.position.y = goal_position[1]\n rospy.wait_for_service('gazebo/set_model_state')\n try:\n resp = self.set_model_target(target_msg)\n except rospy.ServiceException as e:\n print(\"Set Target Service Failed: %s\" % e)\n self.pub_action.publish(Twist())\n robot_init_pose = self.robot_init_pose_list[ita]\n robot_init_quat = self._euler_2_quat(yaw=robot_init_pose[2])\n robot_msg = ModelState()\n robot_msg.model_name = 'mobile_base'\n robot_msg.pose.position.x = robot_init_pose[0]\n robot_msg.pose.position.y = robot_init_pose[1]\n robot_msg.pose.orientation.x = robot_init_quat[1]\n robot_msg.pose.orientation.y = robot_init_quat[2]\n robot_msg.pose.orientation.z = robot_init_quat[3]\n robot_msg.pose.orientation.w = robot_init_quat[0]\n rospy.wait_for_service('gazebo/set_model_state')\n try:\n resp = self.set_model_target(robot_msg)\n except rospy.ServiceException as e:\n print(\"Set Target Service Failed: %s\" % e)\n rospy.sleep(0.5)",
"title": ""
},
{
"docid": "0ab6acf0fe78d4db02e577b37c2b167a",
"score": "0.49496445",
"text": "def set_Local_Waypoint(x_Pos, y_Pos, travel_Height, x_Vel, y_Vel, z_Vel, yaw_Angle):\n takeoff_Waypoint = PositionTarget()\n #Timestamp message\n takeoff_Waypoint.header.stamp = rospy.get_rostime()\n\n #Set reference frame as global\n takeoff_Waypoint.header.frame_id = \"1\"\n #Local ENU coordinate system\n takeoff_Waypoint.coordinate_frame = 1\n\n\n #Taken from ardupilot documentation.\n #Sets acceleration and force control bits to 1, or inactive\n #Sets position, velocity, and yaw bits to 0, or active, allowing for\n # control of these elements. Decimal equivalent is 3008.\n takeoff_Waypoint.type_mask = 0b0000101111000000\n\n #set desired positions and travel velocities\n takeoff_Waypoint.position.x = x_Pos\n takeoff_Waypoint.position.y = y_Pos\n takeoff_Waypoint.position.z = travel_Height\n\n takeoff_Waypoint.velocity.x = x_Vel\n takeoff_Waypoint.velocity.y = y_Vel\n takeoff_Waypoint.velocity.z = z_Vel\n\n takeoff_Waypoint.yaw = yaw_Angle\n\n return takeoff_Waypoint",
"title": ""
},
{
"docid": "200ca935f151e6298eaf58eeee089127",
"score": "0.4930247",
"text": "def move_hand(self, diff, tilt):\r\n human = self.bge_object\r\n if human['Manipulate']:\r\n scene = blenderapi.scene()\r\n target = scene.objects['IK_Target_Empty.R']\r\n target.applyMovement([diff, 0.0, 0.0], True)",
"title": ""
},
{
"docid": "e9d567d76fe6d6b67cb482d1273d919a",
"score": "0.4926472",
"text": "def determine_altitude_corr(self, alt: xr.DataArray, raw_att: xr.Dataset, tx_tstmp_idx: xr.DataArray,\n prefixes: str, timestmp: str):\n\n x_lever = float(self.multibeam.xyzrph[prefixes[0] + '_x'][timestmp])\n y_lever = float(self.multibeam.xyzrph[prefixes[0] + '_y'][timestmp])\n z_lever = float(self.multibeam.xyzrph[prefixes[0] + '_z'][timestmp])\n if x_lever or y_lever or z_lever:\n # There exists a lever arm between tx and rp, and the altitude is at the rp\n # - svcorrected offsets are at tx/rx so there will be a correction necessary to use altitude\n rp_to_tx_leverarm = np.array([-x_lever, -y_lever, -z_lever])\n self.logger.info('Applying altitude correction for RP to TX offset: {}'.format(rp_to_tx_leverarm))\n\n # build rotation matrix for attitude at each ping time\n att = interp_across_chunks(raw_att, tx_tstmp_idx)\n tx_att_times, tx_attitude_rotation = return_attitude_rotation_matrix(att)\n\n # compute rotated vector\n rot_lever = xr.DataArray(tx_attitude_rotation.data @ rp_to_tx_leverarm,\n coords={'time': tx_att_times, 'xyz': ['x', 'y', 'z']},\n dims=['time', 'xyz']).compute()\n\n # The only additional z offset that should be included is the induced heave seen when rotating lever arms.\n # Keep that diff by subtracting the original lever arm.\n rot_lever[:, 2] = rot_lever[:, 2] - rp_to_tx_leverarm[2]\n alt = alt + rot_lever[:, 2].values\n else:\n self.logger.info('no altitude correction for RP at TX')\n\n return alt",
"title": ""
},
{
"docid": "24488fc8a788529b4a3365223fc47b88",
"score": "0.4923346",
"text": "def update_target_pos(self):\r\n self.target_ra = self.send_command_to_mount(':U1#:Gr#')\r\n self.target_dec = self.send_command_to_mount(':U2#:Gd#')\r\n if self.target_ra is None:\r\n self.target_ra = '00:00:00.0'\r\n if self.target_dec is None:\r\n self.target_dec = '+00:00:00.0'",
"title": ""
},
{
"docid": "da70f689ca3f52c2f6c93859c9fd46e4",
"score": "0.49073356",
"text": "def telem_track():\n #pos_lat, pos_lon = start_up() #read from Garmin module\n #position of antenna longitude and latitude taken from google maps for testing\n #pos_lat = 43.220746\n #pos_lon = -75.407512\n #pos_lat = 43.219409\n #pos_lon = -75.408620\n pos_lat = 43.219511\n pos_lon = -75.408899\n start = True\n \n while True:\n \n #Method of start (true or false) so that first GPS coordinate received can be stored in the variables for the first point\n #second location can be stored in the variables for the seond point \n #then both points can be used for calculations and comparisons \n if start == True:\n # First GPS location received in order -- Latitude, Longitude, Altitude\n lat_1, lon_1, alt_1 = get_new_GPS() \n target_speed = 1 #can remove, was there for testing. \n #computing the distance between the antenna and the target GPS location\n dist_1 = compute_distance(pos_lat,pos_lon,lat_1,lon_1)\n #computing the elevation angle \n el_1 = compute_elevation(dist_1, alt_1) \n #computing the azimuth angle\n az_1 = compute_azimuth(lon_1,lat_1,pos_lon, pos_lat)\n #casting to float to use in future calculations\n az_1 = float(az_1)\n el_1 = float(el_1) \n print('Lat: {} Lon: {} Alt: {} El: {} Az: {}'.format(lat_1,lon_1,alt_1,el_1,az_1))\n f.write('\\nLat: {} Lon: {} Alt: {} El: {} Az: {}'.format(lat_1,lon_1,alt_1,el_1,az_1))\n #function call to move the antenna to first GPS location\n move_to_position(az_1,el_1)\n h,v = get_degrees('default')\n print('Pointing at Az: {} El: {}'.format(h,v))\n f.write('\\nPointing at Az: {} El: {}'.format(h,v))\n f.write('\\n-------------------------------------------------------------------------------')\n start = False \n \n elif start == False:\n #All other GPS locations are received in order -- Latitude, Longitude, Altitude\n lat_2, lon_2, alt_2 = get_new_GPS() \n target_speed = 1 #can remove, was there for testing. \n #function call from qpt_v2 to get the current azimuth and elevation of the antenna \n pos_az,pos_el = get_degrees('default') \n #computing the distance between the antenna and the target's second GPS location\n dist_2 = compute_distance(pos_lat,pos_lon,lat_2,lon_2)\n #computing the distance between the target's first and second GPS location\n dist_1_to_2 = compute_distance(lat_1, lon_1,lat_2,lon_2)\n #computing the altitude change\n alt_change = float(alt_1) - float(alt_2) \n #condition needed for another calculation: if altitude change is zero, there will be an error for dividing by zero \n if alt_change is 0:\n alt_change = 1 \n #computing the first and second elevation angles (can pull the first elevation angle from above)\n el_2 = compute_elevation(dist_2, alt_2)\n el_1 = compute_elevation(dist_1, alt_1)\n #computing the first and second azimuth angle (can pull the first azimuth angle from above)\n az_1 = compute_azimuth(lon_1,lat_1,pos_lon,pos_lat)\n az_2 = compute_azimuth(lon_2,lat_2,pos_lon,pos_lat) \n print('\\nPoint 2 Data:')\n print('Lat: {}, Lon: {}, Alt: {}, El: {}, Az: {}'.format(lat_2,lon_2,alt_2,el_2,az_2))\n f.write('\\nLat: {} Lon: {} Alt: {} El: {} Az: {}'.format(lat_1,lon_1,alt_1,el_1,az_1))\n #condition to trigger the move pointer function\n move_to_position(round(az_2,1),round(el_2,1))\n h,v = get_degrees('default')\n print('Pointing at Az: {} El: {}'.format(h,v))\n f.write('\\nPointing at Az: {} El: {}'.format(h,v))\n f.write('\\n---------------------------------------------------------------------------------')\n print('------------------------------------------------------------------------------------') \n #making the new position equal the old position for reference to the next calculation when the new coordinate is received \n lat_1 = lat_2\n lon_1 = lon_2\n alt_1 = alt_2\n dist_1 = dist_2",
"title": ""
},
{
"docid": "f3496a3ec2e4d2fe1381780073e25301",
"score": "0.49059814",
"text": "def set_up_to_hold_tray(self):\n speed = 0.2\n self.postureProxy.goToPosture(\"StandInit\", speed)\n time.sleep(0.5)\n\n self.motionProxy.setAngles(self.hip_joints, self.hip_angles, speed) # Make nao lean backwards a bit, helps with keeping tray flat\n time.sleep(0.5)\n\n self.motionProxy.setAngles(self.shoulder_roll_joints, self.shoulder_roll_angles, speed)\n time.sleep(0.5)\n\n self.interpolate_angles_fixed_lr(int(self.num_angles/2), self.num_angles) # Set angles to be middle value\n self.go_to_interpolated_angles_lr(speed=speed)\n time.sleep(0.5)",
"title": ""
},
{
"docid": "085a807202934af9b275d3312096d9f4",
"score": "0.49042267",
"text": "def move_hand(self, diff, tilt):\r\n\r\n human = self.bge_object\r\n if human['Manipulate']:\r\n scene = blenderapi.scene()\r\n target = scene.objects['IK_Target_Empty.R']\r\n target.applyMovement([diff, 0.0, 0.0], True)",
"title": ""
},
{
"docid": "c855a4423eb5ae2f51b8e3ffa5328d39",
"score": "0.4902925",
"text": "def moveTowards(self, destination):\n if self.bounce_count > 0:\n self.bounce()\n return \n distance = self.dist_from(destination)\n if(distance == 0): return\n dist_ratio = self.max_speed/distance\n self.xvel = dist_ratio*self.x_dist_from(destination, False)/32\n self.yvel = dist_ratio*self.y_dist_from(destination, False)/32",
"title": ""
},
{
"docid": "57fb5a4d33afa10095374a4521413a5c",
"score": "0.48854432",
"text": "def default_action(self):\r\n parent = self.robot_parent\r\n speed = self.local_data['speed']\r\n v = 0\r\n rz = 0\r\n\r\n self._destination = [ self.local_data['x'],\r\n self.local_data['y'],\r\n self.local_data['z'] ]\r\n self._projection = [ self.local_data['x'],\r\n self.local_data['y'],\r\n self.bge_object.worldPosition[2] ]\r\n\r\n logger.debug(\"Robot {0} move status: '{1}'\".format(\r\n parent.bge_object.name, parent.move_status))\r\n # Place the target marker where the robot should go\r\n if self._wp_object:\r\n self._wp_object.position = self._destination\r\n\r\n # Vectors returned are already normalized\r\n projection_distance, projection_vector, local_vector = \\\r\n self.bge_object.getVectTo(self._projection)\r\n true_distance, global_vector, local_vector = \\\r\n self.bge_object.getVectTo(self._destination)\r\n # Convert to the Blender Vector object\r\n global_vector = mathutils.Vector(global_vector)\r\n projection_vector = mathutils.Vector(projection_vector)\r\n # if Z is not free, distance is the projection distance\r\n if self._free_z:\r\n distance = true_distance\r\n else:\r\n distance = projection_distance\r\n\r\n logger.debug(\"GOT DISTANCE: xy: %.4f ; xyz: %.4f\" %\r\n (projection_distance, true_distance))\r\n logger.debug(\"Global vector: %.4f, %.4f, %.4f\" %\r\n (global_vector[0], global_vector[1], global_vector[2]))\r\n logger.debug(\"Local vector: %.4f, %.4f, %.4f\" %\r\n (local_vector[0], local_vector[1], local_vector[2]))\r\n logger.debug(\"Projection vector: %.4f, %.4f, %.4f\" %\r\n (projection_vector[0], projection_vector[1], projection_vector[2]))\r\n\r\n # If the target has been reached, change the status\r\n if distance - self.local_data['tolerance'] <= 0:\r\n parent.move_status = \"Arrived\"\r\n\r\n #Do we have a running request? if yes, notify the completion\r\n self.completed(status.SUCCESS, parent.move_status)\r\n\r\n logger.debug(\"TARGET REACHED\")\r\n logger.debug(\"Robot {0} move status: '{1}'\".format(\r\n parent.bge_object.name, parent.move_status))\r\n\r\n else:\r\n # Do nothing if the speed is zero\r\n if speed == 0:\r\n return\r\n\r\n parent.move_status = \"Transit\"\r\n\r\n angle_diff = 0\r\n rotation_direction = 0\r\n\r\n # If the projected distance is not null: else computing the\r\n # target angle is not possible!\r\n if projection_distance - self.local_data['tolerance'] / 2 > 0:\r\n ### Get the angle of the robot ###\r\n robot_angle = parent.position_3d.yaw\r\n\r\n ### Get the angle to the target ###\r\n target_angle = projection_vector.angle(self.world_x_vector)\r\n\r\n # Correct the direction of the turn according to the angles\r\n dot = projection_vector.dot(self.world_y_vector)\r\n logger.debug(\"Vector dot product = %.2f\" % dot)\r\n if dot < 0:\r\n target_angle *= -1\r\n\r\n ### Get the angle that the robot must turn ###\r\n if target_angle < robot_angle:\r\n angle_diff = robot_angle - target_angle\r\n rotation_direction = -1\r\n else:\r\n angle_diff = target_angle - robot_angle\r\n rotation_direction = 1\r\n\r\n # Make a correction when the angles change signs\r\n if angle_diff > math.pi:\r\n angle_diff = (2 * math.pi) - angle_diff\r\n rotation_direction *= -1\r\n\r\n logger.debug(\"Angles: R=%.4f, T=%.4f Diff=%.4f Direction = %d\" %\r\n (robot_angle, target_angle, angle_diff, rotation_direction))\r\n\r\n try:\r\n dt = 1 / self.frequency\r\n if distance < speed * dt:\r\n v = distance / dt\r\n else:\r\n v = speed\r\n\r\n if abs(angle_diff) < speed * dt:\r\n rotation_speed = angle_diff / dt / 2.0\r\n else:\r\n rotation_speed = speed / 2.0\r\n\r\n # Compute the speeds\r\n if self._type == 'Position':\r\n v /= self.frequency\r\n rotation_speed /= self.frequency\r\n # For the moment ignoring the division by zero\r\n # It happens apparently when the simulation starts\r\n except ZeroDivisionError:\r\n pass\r\n\r\n # Allow the robot to rotate in place if the waypoing is\r\n # to the side or behind the robot\r\n if angle_diff >= math.pi/3.0:\r\n logger.debug(\"Turning on the spot!!!\")\r\n v = 0\r\n\r\n # Collision avoidance using the Blender radar sensor\r\n if self._collisions and v != 0 and self._radar_r['Rcollision']:\r\n # No obstacle avoidance when the waypoint is near\r\n if distance + self.local_data['tolerance'] > \\\r\n self._radar_r.sensors[\"Radar\"].distance:\r\n # Ignore obstacles with the properties specified\r\n ignore = False\r\n for prop in self.bge_object['Ignore']:\r\n if prop in self._radar_r.sensors[\"Radar\"].hitObject:\r\n ignore = True\r\n logger.debug(\"Ignoring object '%s' \"\r\n \"with property '%s'\" %\r\n (self._radar_r.sensors[\"Radar\"].hitObject, prop))\r\n break\r\n if not ignore:\r\n rz = rotation_speed\r\n logger.debug(\"Obstacle detected to the RIGHT, \"\r\n \"turning LEFT\")\r\n elif self._collisions and v != 0 and self._radar_l['Lcollision']:\r\n # No obstacle avoidance when the waypoint is near\r\n if distance + self.local_data['tolerance'] > \\\r\n self._radar_l.sensors[\"Radar\"].distance:\r\n # Ignore obstacles with the properties specified\r\n ignore = False\r\n for prop in self.bge_object['Ignore']:\r\n if prop in self._radar_l.sensors[\"Radar\"].hitObject:\r\n ignore = True\r\n logger.debug(\"Ignoring object '%s' \"\r\n \"with property '%s'\" % \\\r\n (self._radar_l.sensors[\"Radar\"].hitObject, prop))\r\n break\r\n if not ignore:\r\n rz = - rotation_speed\r\n logger.debug(\"Obstacle detected to the LEFT, \"\r\n \"turning RIGHT\")\r\n # Test if the orientation of the robot is within tolerance\r\n elif -self._angle_tolerance < angle_diff < self._angle_tolerance:\r\n rz = 0\r\n # If not, rotate the robot in the corresponding direction\r\n else:\r\n rz = rotation_speed * rotation_direction\r\n\r\n if self._free_z:\r\n vx = math.fabs(v * local_vector.dot(self.world_x_vector))\r\n vz = v * local_vector.dot(self.world_z_vector)\r\n else:\r\n vx = v\r\n vz = 0\r\n logger.debug(\"Applying vx = %.4f, vz = %.4f, rz = %.4f (v = %.4f)\" %\r\n (vx, vz, rz, v))\r\n\r\n self.robot_parent.apply_speed(self._type, [vx, 0, vz], [0, 0, rz])",
"title": ""
},
{
"docid": "543f623b5396a721bb63d20ad141dc42",
"score": "0.48820713",
"text": "def set_target_area(self, target_area):\n self.target_area = target_area",
"title": ""
},
{
"docid": "488fdea763389e005a8f375df9b912e6",
"score": "0.48682943",
"text": "def update(self, separation=0.2, cohesion=0.2, alignment=0.6, avoidance=0.6, target=0.2, limit=15.0):\n f = 0.1\n m1, m2, m3, m4, m5 = separation*f, cohesion*f, alignment*f, avoidance*f, target*f\n vx1, vy1, vz1 = self.separation(self.space)\n vx2, vy2, vz2 = self.cohesion(self.sight)\n vx3, vy3, vz3 = self.alignment(self.sight)\n vx4, vy4, vz4 = self.avoidance()\n vx5, vy5, vz5 = self.target and (\n (self.target.x-self.x), \n (self.target.y-self.y), \n (self.target.z-self.z)) or (0,0,0)\n self.velocity.x += m1*vx1 + m2*vx2 + m3*vx3 + m4*vx4 + m5*vx5\n self.velocity.y += m1*vy1 + m2*vy2 + m3*vy3 + m4*vy4 + m5*vy5\n self.velocity.z += m1*vz1 + m2*vz2 + m3*vz3 + m4*vz4 + m5*vz5\n self.velocity.z = self.flock.depth and self.velocity.z or 0 # No z-axis for Flock.depth=0 \n self.limit(speed=limit)\n self.x += self.velocity.x\n self.y += self.velocity.y\n self.z += self.velocity.z",
"title": ""
},
{
"docid": "ade7328ced340526265b27d22c4dd2a7",
"score": "0.48662424",
"text": "def _update_target(self):\n critic_weights = self._tau * self._critic_approximator.get_weights()\n critic_weights += (1 - self._tau) * self._target_critic_approximator.get_weights()\n self._target_critic_approximator.set_weights(critic_weights)\n\n actor_weights = self._tau * self._actor_approximator.get_weights()\n actor_weights += (1 - self._tau) * self._target_actor_approximator.get_weights()\n self._target_actor_approximator.set_weights(actor_weights)",
"title": ""
},
{
"docid": "0a0162aa58b4885f5a33d3e0a8244595",
"score": "0.4864864",
"text": "def start_arp(self):\n # error handling\n if self.target is None or self.victims is None:\n messagebox.showerror(\n \"Error\", \"You have to set a target and/or victims first.\")\n return\n\n if self.target in self.victims:\n messagebox.showerror(\n \"Error\", \"You cannot not set the target as a victim.\")\n return\n\n # if self.ck.get() is not \"0\":\n # self.save_traffic = True\n\n self.button_stop.config(state=tk.NORMAL)\n self.button_start.config(state=tk.DISABLED)\n self.is_poisoning = True\n\n self.target = str(self.target).split(' ', 1)[0]\n\n victims = []\n for victim in self.victims:\n victim = str(victim).split(' ', 1)[0]\n victims.append(victim)\n self.victims = victims\n\n self.arp = ArpPois()\n self.arp.set_time(self.max_value.get())\n self.arp.set_victims(self.victims, self.victims_mac)\n self.arp.set_target(self.target, self.target_mac)\n # self.arp.set_save(self.save_traffic)\n self.arp.start_poisoning()\n\n self.log.update_out('------------------Currently ARP Poisoning----------------------------------------')\n self.log.update_out('Victim: ' + ', '.join(self.victims))\n self.log.update_out('Target: ' + self.target)\n self.log.update_out('--------------------------------------------------------------------------')\n\n self.log.update_stat('ARP Poisoning is active. See above logs for details.')\n\n # only possible to execute dns cache poisoning if two victims are used and the target mac address is\n # the mac address of the attacker\n if len(self.victims) == 2 and self.target == self.attacker_ip:\n for vic in self.victims:\n if vic == self.ns:\n messagebox.showinfo(\"Possible DNS cache poisoning attack\",\n \"It is now possible to execute a DNS cache poisoning attack.\"\n \" Please navigate to the `DNS cache poisoning' tab to execute this attack.\"\n \"\\n\\nSee the log below for more information on the victims and the target.\")\n self.log.update_out('It is now possible to execute')\n self.log.update_out('a DNS cache poisoning attack.')\n self.controller.notebook.tab('.!mainapplication.!notebook.!attackdnsframe', state=\"normal\")\n dis.set_dns_settings(self.victims, self.ns)\n\n return",
"title": ""
},
{
"docid": "21b46bb10065f13288a8ebb2f4aa5239",
"score": "0.48636687",
"text": "def update(self, target_velocity):\n _pal.lib.actuator_angular_motor_update(self.obj, c.c_float(target_velocity))",
"title": ""
},
{
"docid": "a0fdcff360d0bb5b65f7a7b8341888b9",
"score": "0.48417127",
"text": "def fly(self, yaw):\n vx = math.cos(yaw)\n vy = math.sin(yaw)\n self.client.moveByVelocityZAsync(\n vx,\n vy,\n self.height,\n 2,\n airsim.DrivetrainType.ForwardOnly,\n airsim.YawMode(False, 0)\n )",
"title": ""
},
{
"docid": "5fa8d0167e52e5617eb0684a348c8dfc",
"score": "0.4839052",
"text": "def goto(dNorth, dEast, gotoFunction=vehicle.simple_goto):\n \n currentLocation = vehicle.location.global_relative_frame\n targetLocation = get_location_metres(currentLocation, dNorth, dEast)\n targetDistance = get_distance_metres(currentLocation, targetLocation)\n gotoFunction(targetLocation)\n \n #print \"DEBUG: targetLocation: %s\" % targetLocation\n #print \"DEBUG: targetLocation: %s\" % targetDistance\n\n while vehicle.mode.name==\"GUIDED\": #Stop action if we are no longer in guided mode.\n #print \"DEBUG: mode: %s\" % vehicle.mode.name\n remainingDistance=get_distance_metres(vehicle.location.global_relative_frame, targetLocation)\n print \"Distance to target: \", remainingDistance\n if remainingDistance<=targetDistance*0.1: #Just below target, in case of undershoot.\n print \"Reached target\"\n break;\n time.sleep(2)",
"title": ""
},
{
"docid": "a7b7e2fdaf537cce41be84d232d3ee9b",
"score": "0.48352572",
"text": "def act(self):\n if self.state == 'flying':\n if not self._check_if_hit(): # if nothing is hit - fly farther\n self._fly_next() # fly to next cell (or stop if out of power or route end)",
"title": ""
},
{
"docid": "188a954fc36dabf2c437298b6891f8f0",
"score": "0.483146",
"text": "def lunar_altitude(self, tee):\n lamb = Lunar.lunar_longitude(tee)\n beta = Lunar.lunar_latitude(tee)\n alpha = Astro.right_ascension(tee, beta, lamb)\n delta = Astro.declination(tee, beta, lamb)\n theta0 = Astro.sidereal_from_moment(tee)\n cap_H = mod(theta0 + self.longitude - alpha, 360)\n altitude = arcsin_degrees(\n (sin_degrees(self.latitude) * sin_degrees(delta)) +\n (cos_degrees(self.latitude) * cos_degrees(delta) * cos_degrees(cap_H)))\n return mod(altitude + 180, 360) - 180",
"title": ""
},
{
"docid": "d53313bcc8a6d8f0926c7bdf3ce82224",
"score": "0.4830902",
"text": "def turn_on(self, target_velocity):\n self.action = action.Action(\"Actuator \" + str(self.obj),\n self.update, target_velocity)\n self.action.run()",
"title": ""
},
{
"docid": "0aebde9f2c8e7781f7f9246584ac3249",
"score": "0.48218387",
"text": "def go(self):\n dist = self.get_distance(self.goal)\n\n if dist > self.close_range:\n speed_mod = np.sqrt(self.speed[0]**2+self.speed[1]**2)\n # If speed is small, turn in the direction of goal,\n # otherwise, in the direction allowing greater speed vecror change\n if speed_mod < 1:\n t = self.look_dir - abs(self.get_aim_dir(self.goal))\n else:\n ang = np.arctan(self.speed[0]/self.speed[1])\n spe = Object(blanc,\n int(self.rect.centerx+30*np.sin(ang)\n *np.sign(self.speed[1])),\n int(self.rect.centery+30*np.cos(ang)\n *np.sign(self.speed[1])))\n\n true_ang = self.get_aim_dir(self.goal) - self.get_aim_dir(spe)\n spe.kill()\n if true_ang < -180 or true_ang > 180:\n true_ang = -360*np.sign(true_ang) + true_ang\n\n if true_ang < -90 or true_ang > 90:\n t = self.get_aim_dir(self.goal)\n else:\n t = self.get_aim_dir(self.goal) + true_ang\n\n # true_ang = self.get_aim_dir(self.goal) - true_ang\n t = self.look_dir - t\n if t > 360 or t < -360:\n t += -360*np.sign(t)\n\n if abs(t) > self.ROTATION:\n if t < -180 or t > 180:\n t = -t\n self.rotate(-np.sign(t) * self.ROTATION)\n\n if abs(t) < 90:\n if speed_mod < ((self.DEACCELERATION+self.ENV_DEACCELERATION)\n *(dist/max(speed_mod,0.001)) + self.ENV_DEACCELERATION):\n self.accelerate(self.ACCELERATION)\n\n elif speed_mod>1 and abs(true_ang) < 30:\n self.accelerate(-self.DEACCELERATION)\n\n else:\n if speed_mod < ((self.DEACCELERATION+self.ENV_DEACCELERATION)\n *(dist/speed_mod) + self.ENV_DEACCELERATION):\n self.accelerate(-self.DEACCELERATION)\n\n elif speed_mod>1 and true_ang < 30:\n self.accelerate(self.ACCELERATION)\n\n else:\n # self.accelerate(-self.DEACCELERATION)\n self.to_do_list.remove(self.go)",
"title": ""
},
{
"docid": "b5d7fd37b0b8235c48e9f30fbeda229c",
"score": "0.48166823",
"text": "def activate(self, action: actions.ItemAction) -> None:\n consumer = action.entity\n target = None\n closest_distance = self.maximun_range + 1.0\n\n for actor in self.engine.game_map.actors:\n if actor is not consumer and self.parent.gamemap.visible[actor.x, actor.y]:\n distance = consumer.distance(actor.x, actor.y)\n\n if (distance < closest_distance):\n target = actor\n closest_distance = distance\n\n if target:\n self.engine.message_log.add_message(\n f\"A lighting bolt strikes the {target.name} with a loud thunder, for {self.damage} damage!\"\n )\n target.fighter.take_damage(self.damage)\n self.consume()\n else:\n raise Impossible(\"No enemy is close enough to strike.\")",
"title": ""
},
{
"docid": "e7dc5531ce8539b0b183c264e6d75836",
"score": "0.4814621",
"text": "def set_traj_parameters(self, start_time, start_velocity, target_velocity, flat_part_duration=0):\n self.t = start_time\n # Convert to the user defined unit\n self.current_velocity = start_velocity\n self.flat_part_duration = flat_part_duration\n\n if target_velocity == start_velocity:\n self.delayValue = self.t + flat_part_duration\n self.target_velocities.append(target_velocity)\n elif (start_velocity >= 0 and target_velocity >= 0) or (start_velocity <= 0 and target_velocity <= 0):\n self.target_velocities.append(target_velocity)\n else:\n # Target Velocity have different sign from start velocity\n # Split Trajectory in two (first : current velocity to 0 (decel) ; second : 0 to target (acel))\n self.target_velocities.append(0)\n self.target_velocities.append(target_velocity)\n\n if target_velocity > start_velocity and self.acceleration == 0:\n raise Exception(\"Acceleration value can't be zero to generate acceleration profile\")\n\n if target_velocity < start_velocity and self.deceleration == 0:\n raise Exception(\"Deceleration value can't be zero to generate deceleration profile\")",
"title": ""
},
{
"docid": "2d652899d719b2870d4be4cd4dc38c7d",
"score": "0.48131895",
"text": "def goto(dNorth, dEast, gotoFunction=vehicle.simple_goto):\n \n currentLocation = vehicle.location.global_relative_frame\n targetLocation = get_location_metres(currentLocation, dNorth, dEast)\n targetDistance = get_distance_metres(currentLocation, targetLocation)\n gotoFunction(targetLocation)\n \n #print \"DEBUG: targetLocation: %s\" % targetLocation\n #print \"DEBUG: targetLocation: %s\" % targetDistance\n\n while vehicle.mode.name==\"GUIDED\": #Stop action if we are no longer in guided mode.\n #print \"DEBUG: mode: %s\" % vehicle.mode.name\n remainingDistance=get_distance_metres(vehicle.location.global_relative_frame, targetLocation)\n print(\"Distance to target: \", remainingDistance)\n if remainingDistance<=targetDistance*0.01: #Just below target, in case of undershoot.\n print(\"Reached target\")\n break;\n time.sleep(.33)",
"title": ""
},
{
"docid": "67dc940764bbb6256bc0416734b55546",
"score": "0.48080698",
"text": "def engine(self):\n self.speed_x += 0.33 * sin(radians(-self.angle))\n self.speed_y -= 0.33 * cos(radians(self.angle))\n self.fuel -= 5",
"title": ""
},
{
"docid": "a2c8b571a567eff7c12b199fdcedf148",
"score": "0.4804152",
"text": "def lower_arms(self, initial_call):\n if initial_call:\n self.intake.set_arm_bottom()\n if self.intake.on_target():\n self.next_state('drive_on')",
"title": ""
},
{
"docid": "efa0ff52224db1f1df6af25fd0fe08d0",
"score": "0.47983855",
"text": "def goForward(self):\n # TODO - Scale speed with distance to obstacle\n self.fly(self.yaw)",
"title": ""
},
{
"docid": "0a3b0fb92e343da9c904f43dcee1a390",
"score": "0.47952062",
"text": "def drive_straight(self, speed, distance):",
"title": ""
},
{
"docid": "3acbea04b13ffe4d7b4699829a6b2902",
"score": "0.47945142",
"text": "def get_vehicle_apoapsis_altitude(self):\n return self.vessel.orbit.apoapsis_altitude",
"title": ""
},
{
"docid": "60bb35d49fc8a19dd22699188e9d9c74",
"score": "0.47877172",
"text": "def slew(self, tar_az, tar_alt):\n\n cur_az = self.az_encoder.get_degrees()\n cur_alt = self.actuator.get_degrees()\n if cur_az != tar_az:\n self.az_update(tar_az)\n if cur_alt != tar_alt:\n self.alt_update(tar_alt)",
"title": ""
},
{
"docid": "66a8eeab3696323d312f683f73e0b241",
"score": "0.47849157",
"text": "def goto_position(self):\n if self.get_target() is not None:\n target_x, target_y = self.get_target()\n self.set_direction('')\n\n # target north\n if self._y > target_y:\n self._y -= self.get_speed()\n self.add_direction('n')\n if abs(self._x - target_x) > 100:\n self.set_direction('n')\n\n # target east\n if self._x < target_x:\n self._x += self.get_speed()\n self.add_direction('e')\n if abs(self._y - target_y) < 100:\n self.set_direction('e')\n\n # target south\n if self._y < target_y:\n self._y += self.get_speed()\n if 'n' not in self.get_direction():\n self.add_direction('s')\n if abs(self._x - target_x) > 100:\n self.set_direction('s')\n\n # target west\n if self._x > target_x:\n self._x -= self.get_speed()\n if 'e' not in self.get_direction():\n self.add_direction('w')\n if abs(self._y - target_y) < 100:\n self.set_direction('w')\n\n self.at_border()",
"title": ""
},
{
"docid": "eaa71b7d1a49c078db925f84d085fcce",
"score": "0.47758383",
"text": "def __init__(self, vehicle, optional=False, name=\"CheckKeepLane\"):\n super(KeepLaneTest, self).__init__(name, vehicle, 0, None, optional)\n\n world = self.vehicle.get_world()\n blueprint = world.get_blueprint_library().find(\n 'sensor.other.lane_detector')\n self._lane_sensor = world.spawn_actor(\n blueprint, carla.Transform(), attach_to=self.vehicle)\n self._lane_sensor.listen(\n lambda event: self._count_lane_invasion(weakref.ref(self), event))",
"title": ""
},
{
"docid": "d491287a848432ac9817c672f707a3c4",
"score": "0.47751498",
"text": "def attack(self, target_robot):\n self._make_damage(target_robot)",
"title": ""
},
{
"docid": "732be2c53a2a9801517ffc0587eb34ac",
"score": "0.4763007",
"text": "def melee_atk(self, target, hit_func, dmg_func):\n if hit_func(target):\n target.update_hp(-dmg_func(target))",
"title": ""
},
{
"docid": "fc1a600c7812cb27b030768b4fe2db1f",
"score": "0.4753195",
"text": "def optimizeTargetLandingSite(self, flightModes=['standard'],\n flexibleBalloon=False, deviceActivationAltitudeBounds=[np.inf],\n balloonModels=[], method='Nelder-Mead',\n weights=(), seed=None, **kwargs):\n # run the simulation every hour over the time window. Note that we need\n # to also get weather to cover a flight of duration <maxFlightTime>,\n # starting at the end of the window.\n # scipy.optimize.fmin_ \n\n self.results = dptools.ParetoFront()\n # Store all objective scores, for Pareto plotting\n self.fitnesses = []\n\n self.Xs = []\n\n if weights:\n self.weights = weights\n \n # Need to return a tuple of fitness for ga, or a weighted sum for scipy:\n returnWeightedSum = (method.lower() != 'ga')\n\n # # For now, assume the first is the only interesting launch site\n # Estimated maximum bound of nozzle lift for target ascent rates\n self.environment = self.launchSiteForecasts[0]\n\n objective, bounds = self.createObjectiveAndBounds(flightModes=flightModes,\n flexibleBalloon=flexibleBalloon,\n deviceActivationAltitudeBounds=deviceActivationAltitudeBounds,\n balloonModels=balloonModels,\n returnWeightedSum=returnWeightedSum)\n\n if method.lower() in ('nelder-mead', 'l-bfgs-b'):\n try:\n x0 = kwargs.pop('x0')\n except KeyError:\n logger.exception('An initial guess x0 is required for method {}.'.format(method))\n\n try:\n res = opt.minimize(objective, x0=x0, method=method,\n callback=(lambda x: self._callbackStoreResult(x, convergence=None)),\n bounds=bounds, args=(), **kwargs)\n except TypeError:\n # Likely that this method does not support bounded optimisation,\n # so try without that argument\n res = opt.minimize(objective, x0=x0, method=method,\n callback=(lambda x: self._callbackStoreResult(x, convergence=None)),\n args=(), **kwargs)\n\n bestProfile = self.results[0]\n self.bestProfile = bestProfile\n\n elif method.lower() == 'de':\n res = opt.differential_evolution(objective, bounds=bounds,\n callback=self._callbackStoreResult, seed=seed, **kwargs)\n\n bestProfile = self.results[0]\n self.bestProfile = bestProfile\n\n elif method.lower() == 'ga':\n def evaluateIndividualTarget(individual):\n X = interpIndividual(bounds, individual)\n return objective(X)\n creator.create(\"FitnessMin\", base.Fitness, weights=weights)\n creator.create(\"Individual\", list, fitness=self.flightFitness)\n\n toolbox = base.Toolbox()\n\n # Attribute generator\n toolbox.register(\"attr_float\", random.uniform, 0., 1.)\n\n # Structure initializers\n toolbox.register(\"individual\", dptools.initRepeat, creator.Individual, toolbox.attr_float, len(self.variables))\n toolbox.register(\"population\", dptools.initRepeat, list, toolbox.individual)\n\n # define the population to be a list of individuals\n toolbox.register(\"population\", dptools.initRepeat, list, toolbox.individual)\n\n pop = toolbox.population(n=150)\n\n toolbox.register(\"evaluate\", evaluateIndividualTarget)\n toolbox.register(\"mate\", dptools.cxBlend, alpha=1.5)\n toolbox.register(\"mutate\", dptools.mutGaussian, mu=0, sigma=1, indpb=0.3)\n toolbox.register(\"select\", dptools.selNSGA2)\n\n toolbox.decorate(\"mate\", checkBounds(0, 1))\n toolbox.decorate(\"mutate\", checkBounds(0, 1))\n \n if seed:\n random.seed(seed)\n\n MU, LAMBDA = 50, 100\n pop = toolbox.population(n=MU)\n stats = dptools.Statistics(lambda ind: ind.fitness.values)\n stats.register(\"avg\", np.mean, axis=0)\n stats.register(\"std\", np.std, axis=0)\n stats.register(\"min\", np.min, axis=0)\n stats.register(\"max\", np.max, axis=0)\n \n # Limit to 1000 evals (about 25 minutes)\n maxevals = 1500\n cxpb = 0.5\n mutpb = 0.2\n \n # Max number of evaluations is (on average) the probability of replacement\n # times by the population size, for each generation: inverse\n ngen = int(round(maxevals / (MU * (cxpb + mutpb))))\n \n algorithms.eaMuPlusLambda(pop, toolbox, mu=MU, lambda_=LAMBDA, \n cxpb=cxpb, mutpb=mutpb, ngen=ngen, \n stats=stats)\n\n else:\n raise ValueError('No known optimization method for {}'.format(method))\n return res",
"title": ""
},
{
"docid": "748dbdaad61340547a8131cef64b33c7",
"score": "0.475267",
"text": "def waypoint_transition(self):\n print(\"waypoint transition\")\n self.target_position = self.all_waypoints.pop(0)\n print('target position', self.target_position)\n self.cmd_position(self.target_position[Locations.LATITUDE], \n self.target_position[Locations.LONGITUDE], self.target_position[Locations.ALTITUDE], 0.0)\n self.flight_state = States.WAYPOINT",
"title": ""
},
{
"docid": "ca5a158ba6a8ac7514c9fe24eb91ac8a",
"score": "0.4750905",
"text": "def manual_transition(self):\n print(\"manual transition\")\n self.release_control()\n self.stop()\n # Not using Multi thread yet\n # self.in_mission = False\n self.flight_state = States.MANUAL\n print(\"END \",self.global_position[Locations.LATITUDE],\n self.global_position[Locations.LONGITUDE],self.global_position[Locations.ALTITUDE])",
"title": ""
},
{
"docid": "723eded91b031fbe5acfbf32d95e99c7",
"score": "0.47495142",
"text": "def target(self, player):\n #Track targetting attempt, and whether last attempt was successful\n shot = 0\n last_shot_hit = False\n #if last attempt successful (target hit), the targetting repeats.\n while shot < 1 or last_shot_hit:\n battlefield = player.battlefield\n grid = player.battlefield.grid\n #Display user ships, user battlefield,\n rows = battlefield.rows\n columns = battlefield.columns\n player.display_ships()\n input(continue_str)\n battlefield.display_wrapped(\"Your\")\n input(continue_str)\n while True:\n try:\n #Request options from AI algorhythm, and select random\n # option\n options = self.target_options(player)\n if options:\n coordinate = options[\n random.randint(0, len(options)-1)]\n #Should there be no options, fire at a random coordinate.\n else:\n coordinate = (rows[\n random.randint(0, len(rows)-1)], \n random.randint(1, len(columns)))\n #Raise exception if input coordinate has already been targetted.\n #Handle any exceptions (pass). If exceptions occur,\n # targetting is attempted again.\n if grid[coordinate] == Battlefield.states[6] \\\n or grid[coordinate] == Battlefield.states[7]\\\n or grid[coordinate] == Battlefield.states[9]:\n raise TargettedCoordinateException\n break\n except Exception as e:\n pass\n \n shot += 1\n #Add targetted coordinate to a tracking list\n self.targetted_coordinates.append(coordinate)\n \n #If no target hit, update coordinate status on the grid to show a\n # targetted (missed) coordiante, display battlefield, print a\n # status statement, and update attempt count & success tracket\n # (exiting function) \n if not grid[coordinate]:\n grid[coordinate] = Battlefield.states[6]\n self.display_targetting_results(player, coordinate, False)\n last_shot_hit = False\n \n #If a target was hit, update coordinate status on the grid to show\n # a hit coordiante,\\\n # display battlefield, print a status statement, and update attempt\n # count & success tracket (exiting function)\n # Also, add coordinate lists tracking hit coordinates and\n # \"Active targets\"\n elif grid[coordinate] == Battlefield.states[5]:\n grid[coordinate] = Battlefield.states[7]\n print(NL*2 + TargettingStrings.incoming_complete + NL)\n battlefield.display()\n print(NL*2 + Formatting.line_wrap3(\n TargettingStrings.ship_hit_str.format(\n str(coordinate))) + NL*2)\n last_shot_hit = True\n self.hit_coordinates.append(coordinate)\n self.active_targets.append(coordinate)\n\n #Check each enemy ship to see if they've been sunk (updating\n # sunk attribute of the ship). If sunk, remove ship's \n # coordinates from \"active targets\" tracking list.\n #Check if entire fleet has been sunk (updating fleet_sunk\n # attribute of the player)\n #If the fleet has been sunk, break out - to end the game\n for ship in player.fleet.values():\n if coordinate in ship.coordinates:\n ship.check_sunk()\n if ship.sunk:\n for coord in ship.coordinates:\n self.active_targets.remove(coord)\n if player.check_fleet_sunk():\n break\n \n #Print a statement indicating that another turn is coming (\n # and wait for continue inputs).\n input(continue_str)\n print(Formatting.line_str2 + NL*2 + \\\n TargettingStrings.incoming_str)\n input(continue_str)",
"title": ""
},
{
"docid": "353ebbed1ad4468cc00dfeecc32fdfc1",
"score": "0.47472462",
"text": "def ExtrudeMap(source, guideline, target, rem_source, connect_source, guide_distribution, steps, first_height, biasing, factor, factor_dir, side_treatment, angle, snap_to_target, connect_to_target, part, property):",
"title": ""
},
{
"docid": "99e09af07548fddcdf16a68dc40d8315",
"score": "0.4744662",
"text": "def step(self, action):\n\n # on_ramp = False\n\n # Starting reward for current step.\n #reward = 0\n\n if action == 0: # full throttle left\n self.vehicle.apply_control(carla.VehicleControl(\n throttle=1.0, steer=-0.25*self.STEER_AMT, reverse=False, hand_brake=False))\n # print(\"[LOG] Action 0\")\n elif action == 1: # full throttle straight\n self.vehicle.apply_control(\n carla.VehicleControl(throttle=1.0, steer=0, reverse=False, hand_brake=False))\n # print(\"[LOG] Action 1\")\n # reward += 10\n elif action == 2: # full throttle right\n self.vehicle.apply_control(carla.VehicleControl(\n throttle=1.0, steer=0.25*self.STEER_AMT, reverse=False, hand_brake=False))\n # print(\"[LOG] Action 2\")\n elif action == 3: # half throttle straight\n self.vehicle.apply_control(carla.VehicleControl(\n throttle=0.5, steer=0, reverse=False, hand_brake=False))\n # print(\"[LOG] Action 3\")\n # reward += 5\n elif action == 4: # full brake\n self.vehicle.apply_control(carla.VehicleControl(\n throttle=0, steer=0, brake=1, reverse=False, hand_brake=False))\n\n v = self.vehicle.get_velocity()\n kmh = int(3.6 * math.sqrt(v.x**2 + v.y**2 + v.z**2))\n\n #done = False\n\n \"\"\"\n current_lane = self.vehicle.get_world().get_map(\n ).get_waypoint(self.vehicle.get_location())\n if str(current_lane.left_lane_marking.type) == 'Broken' or str(current_lane.right_lane_marking.type) == 'Broken':\n on_ramp = False\n reward += 100\n \"\"\"\n\n \"\"\"\n # Going from ramp to freeway\n new_lane = self.vehicle.get_world().get_map(\n ).get_waypoint(self.vehicle.get_location())\n if on_ramp == True and str(new_lane.left_lane_marking.type) == 'Broken' and str(new_lane.right_lane_marking.type) == 'Solid':\n on_ramp = False\n reward += 600\n self.first_lane_change_on_freeway = False\n print(\"[LOG] Ramp to freeway!\")\n done = False # Phase 1 training\n \"\"\"\n\n # Code for penalizing lane changes\n if len(self.lane_crossings) != 0:\n for x in self.lane_crossings:\n clm = x.crossed_lane_markings # How many events in here?\n for marking in clm:\n # print(str(marking.type))\n # str(marking.type) == 'Solid' or str(marking.type) == 'SolidSolid' or str(marking.type) == 'Curb' or str(marking.type) == 'Other':\n '''\n if str(marking.type) != 'Broken':\n reward += -25\n print(f\"[LOG] {str(marking.type)} Crossed...Penalty\")\n done = False\n\n # Rewarding the first change on the freeway\n elif self.first_lane_change_on_freeway == False and str(marking.type) == 'Broken':\n reward += 500\n self.first_lane_change_on_freeway = True\n print(\"[LOG] First lane change on freeway\")\n done = False\n\n # Rewarding the merging as one single operation\n if on_ramp and str(marking.type) == 'Broken':\n reward += 500\n on_ramp = False\n print(\"[LOG] Merge successful\")\n done = False\n # else: # Penalizing unnecessary lane changes\n # reward += 0 #-10 #Phase 1 ...not penalizing broken changes\n # print(\"[LOG] Lane Change penalty\")\n '''\n\n #print(f\"[LOG] {str(marking.type)} Crossed...Penalty\")\n #reward += -2\n #reward += 0\n pass\n\n if len(self.collision_hist) != 0:\n done = True\n print(\"[LOG] Collided...Done\")\n \"\"\"\n if on_ramp:\n reward += -1000 # penalizing heavily to stop q values from doing only right on onramp\n else:\n reward += -200\n \"\"\"\n reward = -200\n elif kmh < 50:\n done = False\n reward = 1\n else:\n done = False\n reward = 4\n\n \"\"\"\n # Survival reward and speed check\n if not done:\n reward += 2 # +2 for each survival step\n\n if kmh < 50:\n reward += -1\n elif kmh >= 50:\n reward += 1\n \"\"\"\n\n if self.episode_start + SECONDS_PER_EPISODE < time.time():\n done = True\n\n return self.front_camera, reward, done, None",
"title": ""
},
{
"docid": "e6312c126540d297b0a999ce5d6df986",
"score": "0.47403938",
"text": "def run(self):\n try:\n # Create vehicle\n self.vehicle = flightsys.Vehicle()\n\n # Create controller\n self.controller = flightsys.Controller(self.shutdown_flag)\n\n # Reset flight diagnositcs\n self.start_time = 0.0\n self.end_time = 0.0\n self.end_reason = Flight_End_Reason.INITIAL\n\n # Flight starting\n rospy.loginfo(self.log_tag + \"Flight ready. Waiting for OFFBOARD...\")\n\n # Wait for offboard\n while not self.is_running():\n pass\n\n # Set start time\n self.start_time = rospy.get_time()\n\n rospy.loginfo(self.log_tag + \"OFFBOARD entered. Starting flight...\")\n\n # Arm the vehicle\n self.vehicle.arm()\n\n rospy.loginfo(self.log_tag + \"Waiting for full FLOW initialization...\")\n self.sleep(10)\n\n # Start flight\n rospy.loginfo(self.log_tag + \"Starting flight!\")\n self.flight()\n\n # Set end time\n self.end_time = rospy.get_time()\n\n # Set end type\n self.end_reason = Flight_End_Reason.NATURAL\n\n # Flight complete\n rospy.loginfo(self.log_tag + \"Flight complete\")\n\n # Set poison\n self.shutdown_flag.set()\n except Exception as e:\n # Set poison\n self.shutdown_flag.set()\n\n # Record end time\n self.end_time = rospy.get_time()\n\n # Set end state\n self.end_reason = Flight_End_Reason.KILLED\n\n # Log error\n rospy.logerr(self.log_tag + \"%s\" % str(e))",
"title": ""
},
{
"docid": "96a5dcd9cf314adc4ba0207a080f47f2",
"score": "0.4721046",
"text": "def update(self):\n # Update the driving state\n current_time = rospy.Time.now().to_sec()\n current_speed = self.param.speed\n d_time = current_time - self._driving_state.time\n\n if (\n len(self.speeds) > 1\n and self.speeds[1][0] <= self._driving_state.distance_driven\n ):\n # If we reach a new speed zone we delete the old one\n del self.speeds[0]\n\n # Set the current speed\n current_speed = self.speeds[0][1] if len(self.speeds) > 0 else self.param.speed\n current_speed /= 36 # km/h to m/s and model car scale of 1/10\n\n # Check if the car needs to stop\n remaining_stops = self.driving_line[1]\n if len(remaining_stops) > 0:\n if remaining_stops[0][0] < self._driving_state.distance_driven:\n remaining_stops[0][1] -= d_time\n if remaining_stops[0][1] > 0:\n current_speed = 0\n else:\n del remaining_stops[0]\n\n self._driving_state.distance_driven += d_time * current_speed\n\n if (\n not self.param.loop\n and self._driving_state.distance_driven > self.driving_line[0].length\n ):\n rospy.signal_shutdown(\"Finished driving along the road.\")\n return\n self._driving_state.distance_driven %= self.driving_line[0].length\n self._driving_state.time = current_time\n\n rospy.logdebug(f\"Current driving state: {self._driving_state}\")\n\n # Calculate position, speed, and yaw\n position = self.driving_line[0].interpolate(self._driving_state.distance_driven)\n\n # Depending on the align_with_middle_line parameter, the car is always parallel\n # to the middle line or to the driving line.\n alignment_line = (\n self.middle_line if self.param.align_with_middle_line else self.driving_line[0]\n )\n\n # Always let the car face into the direction of the middle line.\n pose = Pose(\n position,\n alignment_line.interpolate_direction(alignment_line.project(position)),\n )\n\n speed = Vector(current_speed, 0) # Ignore y component of speed\n # Yaw rate = curvature * speed\n yaw_rate = (\n alignment_line.interpolate_curvature(\n min(self._driving_state.distance_driven, alignment_line.length)\n )\n * current_speed\n )\n\n # Publish up to date messages!\n self.update_world_vehicle_tf(\n self.initial_tf.inverse * Transform(pose, pose.get_angle())\n )\n self.update_state_estimation(speed, yaw_rate)",
"title": ""
},
{
"docid": "57e3d27835eaab8cc52211d4263856ef",
"score": "0.47160608",
"text": "def drone_altitude_callback(self, data):\n # Acquire semaphore to save new data in cameraTimestamps object\n self.semaphoreAltitude.acquire()\n\n # Calculate message timestamp\n timestamp = data.header.stamp.secs + data.header.stamp.nsecs / float(10 ** 9)\n\n # Only save Altitude Z\n self.droneAltitudes.append(Coordinates(timestamp, 0, 0, abs(data.pose.position.z)))\n # Release semaphore\n self.semaphoreAltitude.release()",
"title": ""
},
{
"docid": "941e3469d01cac9134e06048bad259ba",
"score": "0.47127557",
"text": "def airborne(self) -> \"Flight\":\n altitude = (\n \"baro_altitude\"\n if \"baro_altitude\" in self.data.columns\n else \"altitude\"\n )\n return self.__class__(self.data[self.data[altitude].notnull()])",
"title": ""
},
{
"docid": "4b0612162a2be80b31cefc158cd615da",
"score": "0.47118497",
"text": "def test_altitudes_get(self):\n pass",
"title": ""
},
{
"docid": "78460ad603b3059e6312c65d9d60792b",
"score": "0.4706211",
"text": "def waypoint_transition(self):\n print(\"Waypoint Transition\")\n self.target_position, path = self.calculate_box()\n print(self.target_position)\n time.sleep(2)\n self.cmd_position(self.target_position[0],\n self.target_position[1],\n self.target_position[2],\n self.target_position[3])\n self.check_state['last_path'] = path\n print(self.check_state)\n self.flight_state = States.WAYPOINT",
"title": ""
},
{
"docid": "ea976d25a631de463f995e238a9dd7a5",
"score": "0.4675952",
"text": "def teleopPeriodic(self):\n # drive code\n # Begin by attempting to grab the offset for Hephestus (how far off the target is the robot facing?)\n try:\n rawTargetOffset = self.hephestus.getNumber('targetOffset',0)\n #Convert the offset to a useable motor range using a proportion.\n OldRange = (156 - (-156))\n NewRange = (1 - (-1))\n targetOffset = ((rawTargetOffset/OldRange) * NewRange)\n #If we press button 5, turn to the target.\n if self.stick.getRawButton(5) == True:\n if targetOffset != 0:\n turn = .8 * targetOffset\n self.sd.putNumber('turn',turn)\n else:\n turn = 0\n \n else:\n #Otherwise drive normally.\n turn = (self.stick.getTriggerAxis(Hand.kRight)*((1-self.driveLimit)/1)+self.driveLimit)*(self.stick.getX(Hand.kRight))\n except:\n # If the above fails, drive normally.\n turn = (self.stick.getTriggerAxis(Hand.kRight)*((1-self.driveLimit)/1)+self.driveLimit)*(self.stick.getX(Hand.kRight))\n self.sd.putNumber('turn',turn)\n # Attempt to get the line tracker info...\n try:\n leftLine = self.leftLine.getAverageVoltage()\n centerlLine = self.centerLeftLine.getAverageVoltage()\n centerRLine = self.rightCenterRightLine.getAverageVoltage()\n rightLine = self.RightLine.getAverageVoltage()\n self.sd.putNumberArray('Line Tracker', [leftLine, centerlLine, centerRLine, rightLine])\n if self.stick.getRawButton(6) == True:\n if leftLine >= .8:\n turn = -0.6\n elif centerlLine >= .7 or centerRLine >= .7 :\n turn = 0\n straight = 0\n if self.yaw() < straight:\n turn = 0.03 #* abs(straight - self.yaw() - 0)\n else:\n turn = 0.0 #* abs(straight + self.yaw() - 0) \n elif rightLine >= .8:\n turn = 0.6\n else:\n turn = .8*self.stick.getX()\n except:\n # If the above fails, drive normally.\n turn = (self.stick.getTriggerAxis(Hand.kRight)*((1-self.driveLimit)/1)+self.driveLimit)*(self.stick.getX(Hand.kRight))\n self.sd.putNumber('turn',turn)\n\n #Try to implement the turn controller.\n try:\n rotateToAngle = False\n\n #if self.stick.getRawButton(1):\n # self.pigeon.reset()\n\n if self.stick.getRawButton(1):\n self.turnController.setSetpoint(0.0)\n rotateToAngle = True\n elif self.stick.getRawButton(2):\n self.turnController.setSetpoint(90.0)\n rotateToAngle = True\n elif self.stick.getRawButton(3):\n self.turnController.setSetpoint(179.9)\n rotateToAngle = True\n elif self.stick.getRawButton(4):\n self.turnController.setSetpoint(-90.0)\n rotateToAngle = True\n\n\n if rotateToAngle:\n self.turnController.enable()\n turn = self.rotateToAngleRate\n else:\n self.turnController.disable()\n turn = (self.stick.getTriggerAxis(Hand.kRight)*((1-self.driveLimit)/1)+self.driveLimit)*(self.stick.getX(Hand.kRight))\n except:\n turn = (self.stick.getTriggerAxis(Hand.kRight)*((1-self.driveLimit)/1)+self.driveLimit)*(self.stick.getX(Hand.kRight))\n self.sd.putNumber('turn',turn)\n\n self.drive.arcadeDrive(((self.stick.getTriggerAxis(Hand.kRight)*((1-self.driveLimit)/1))+self.driveLimit)*-1*(self.stick.getY(Hand.kRight)), turn) \n \n # Pneumatics\n if (self.stick2.getRawButton(2) == (1)):\n self.hatchcover.set(1)\n self.sd.putBoolean('Hatch?', True)\n\n elif (self.stick2.getRawButton(3)):\n self.hatchcover.set(2)\n \n else:\n self.hatchcover.set(0)\n if (self.stick.getRawButton(4) == (1)):\n self.doubleSolenoid.set(1)\n elif (self.stick.getRawButton(1)):\n self.doubleSolenoid.set(2)\n else:\n self.doubleSolenoid.set(0)\n\n # intake motor command\n self.left_motor.set((self.stick.getTriggerAxis(Hand.kLeft)*((1-.3)/1)+.3)*(self.stick.getY(Hand.kLeft)))\n self.right_motor.set((self.stick.getTriggerAxis(Hand.kLeft)*((1-.3)/1)+.3)*(self.stick.getY(Hand.kLeft)))\n \n # elevator\n self.eleLeft.set((self.stick2.getY(Hand.kLeft)))\n #self.eleRight.set((self.stick2.getY(Hand.kLeft)))\n #\n self.intake_angle.set(self.stick2.getY(Hand.kRight))\n #Thor <--- We did not implement Thor this season...\n #self.StaLeft.set(0+(self.stick2.getTriggerAxis(Hand.kLeft))-(self.stick2.getTriggerAxis(Hand.kRight)))\n\n # note: Xbox controller 1 controlls the drive base and stablizing/rising pneu. and intake\n # note: Xbox controller 2 controlls the elevator and hatchcover thing",
"title": ""
},
{
"docid": "21a91229f406339cd676c43be6c353aa",
"score": "0.46750912",
"text": "def _init_target(self):\n self._target_actor_approximator.set_weights(\n self._actor_approximator.get_weights())\n self._target_critic_approximator.set_weights(\n self._critic_approximator.get_weights())",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="765"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">f603e37c07d87db035c521a1654b497b</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">currently only suppor one example at a time</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "c0621f075dbd648f6f37506eded4f33b",
"score": "0.0",
"text": "def saliency_expl(expl_method, Xs, ys, model):\n print('Computing explanation by {}...'.format(expl_method))\n\n assert Xs.shape[0] == 1\n assert ys.shape[0] == 1\n\n if expl_method == 'SG':\n sg_r = 0.2\n sg_N = 500\n given_expl = 'Grad'\n else:\n sg_r = None\n sg_N = None\n given_expl = None\n\n anchor_maps, _ = get_explanation_pdt(Xs, model, ys, expl_method, sg_r=sg_r, sg_N=sg_N, given_expl=given_expl, binary_I=False)\n anchor_maps = np.abs(anchor_maps)\n return anchor_maps",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "f84a606a4456d9f6f52b70f06ae01465",
"score": "0.70378304",
"text": "def add_examples(self,src):\n return",
"title": ""
},
{
"docid": "1274e76c0963b153550760a5a6822c52",
"score": "0.6776039",
"text": "def sample(self):\n pass",
"title": ""
},
{
"docid": "2fb8165654dbd510faa8054db7188981",
"score": "0.67337203",
"text": "def _example():\n pass",
"title": ""
},
{
"docid": "b511205786398e2f892adf0f616ffe0e",
"score": "0.6558001",
"text": "def test_multiple_runs(self):\n # TODO: implement\n pass",
"title": ""
},
{
"docid": "1c16fc03b71edf21144cb8e52eb793f2",
"score": "0.65461874",
"text": "def find_examples(examples):\n raise NotImplementedError",
"title": ""
},
{
"docid": "793f8ea535030e52b1f28c7af376caef",
"score": "0.6427932",
"text": "def setup_example(self):\n if self.setup_done:\n raise Exception(\n \"Trying to setup_example on an example that is already setup\"\n )\n self.vw.setup_example(self)\n self.setup_done = True",
"title": ""
},
{
"docid": "2509c952b38aed23ed723b51ea7abe29",
"score": "0.63976955",
"text": "def example():\n\n example = Exam('example')\n example.addQuestion('How many licks does it take to get to the center of the lollipop?', 3)\n example.addQuestion('How much wood could a wood chuck chuck if a wood chuck could chuck wood?', '700 pounds')\n example.addQuestion('What is the air-speed velocity of an unladen swallow?', 'an African or European swallow?')\n student = Student('hello', 'jane', 'jane\\'s address')\n take_test(example, student)",
"title": ""
},
{
"docid": "8c9f56e83fe67caebd57b0fc586bcc6e",
"score": "0.6397048",
"text": "def main():\n example()",
"title": ""
},
{
"docid": "8c9f56e83fe67caebd57b0fc586bcc6e",
"score": "0.6397048",
"text": "def main():\n example()",
"title": ""
},
{
"docid": "14591c7d9aa76f2b2fa6a2850629de2a",
"score": "0.63549733",
"text": "def example():\n astronomy_test = Exam('astronomy_test')\n astronomy_test.add_Question('How many moons does Mars have?', '2')\n astronomy_test.add_Question(\"Which planet is closest to the sun?\", \"Mercury\")\n astronomy_test.add_Question(\"Biggest planet in our solar system?\", \"Jupiter\")\n astronomy_test.add_Question(\"What galaxy are we in?\", \"Milky Way\")\n\n stephanie = Student('Stephanie', 'Song', '1275 University Ave. Berkeley, CA')\n take_test(astronomy_test, stephanie)",
"title": ""
},
{
"docid": "a73651e7629a637e345c67aff4d92d01",
"score": "0.62926656",
"text": "def get_example(self, i):\n raise NotImplementedError",
"title": ""
},
{
"docid": "df2155d4b20154f8b7d25e5d71eeb12c",
"score": "0.6255607",
"text": "def new_example(self, example):\n self.examples.append(example)",
"title": ""
},
{
"docid": "2cd7b0c92c4761147c71eeb869e00fec",
"score": "0.62472284",
"text": "def example(self, mess, args):\n\t\treturn \"Example\"",
"title": ""
},
{
"docid": "832e140974e04c4192d439f288a6edb1",
"score": "0.6144912",
"text": "def process_example(self, example):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "9ba5decacaa58d1930f68cce19ed7f1c",
"score": "0.6138477",
"text": "def test_example_5_6(self):",
"title": ""
},
{
"docid": "b1622d8963606ebac4675378a22243c6",
"score": "0.61363506",
"text": "def _create_examples(self, data, type):\n dataset = data['data']['instance']\n idx = 0\n examples = []\n corrupted_sample = 0\n for i in dataset:\n passage = i['text']\n try:\n if isinstance(i['questions']['question'], list):\n for question in i['questions']['question']:\n query = question['@text']\n options = [item['@text'] for item in question['answer']]\n truth = 0 if question['answer'][0]['@correct'] == 'True' else 1\n examples.append(\n InputExample(\n example_id=idx,\n question=query,\n contexts=[passage, passage], # this is not efficient but convenient\n endings=[options[0], options[1]],\n label=truth))\n idx += 1\n else:\n question = i['questions']['question']\n query = question['@text']\n options = [item['@text'] for item in question['answer']]\n truth = 0 if question['answer'][0]['@correct'] == 'True' else 1\n examples.append(\n InputExample(\n example_id=idx,\n question=query,\n contexts=[passage, passage], # this is not efficient but convenient\n endings=[options[0], options[1]],\n label=truth))\n idx += 1\n except:\n corrupted_sample += 1\n continue\n logger.info(\" Corrupted sample :{}\".format(corrupted_sample))\n return examples",
"title": ""
},
{
"docid": "6fcf65a769da8dd11ac33f3a0fe00d07",
"score": "0.6112587",
"text": "def test_examples_loaded(self):\n all_example_networks()",
"title": ""
},
{
"docid": "b4522b4cdb89c62117021de89279f721",
"score": "0.60988545",
"text": "def test_3(self):\n pass",
"title": ""
},
{
"docid": "1514fbbd81967a28ac6becd1fe17c5bd",
"score": "0.60641235",
"text": "def test_case_01(self):\n\t\tpass",
"title": ""
},
{
"docid": "54e6c05532ecdea0979b8efc0663c391",
"score": "0.6041344",
"text": "def biosamples():",
"title": ""
},
{
"docid": "22b45e621f8531cf10220c8eb6a966e6",
"score": "0.60363597",
"text": "def sample6():",
"title": ""
},
{
"docid": "0645a56473c801c578eebf4001ceec3b",
"score": "0.60306466",
"text": "def test(self):\n # TODO\n pass",
"title": ""
},
{
"docid": "1ac7cb983ea2da48f60ce005921f4aef",
"score": "0.60243654",
"text": "def example_function():\n pass",
"title": ""
},
{
"docid": "7f020af2de737e0ff71c477bb6768541",
"score": "0.6001482",
"text": "def demo():\n ...",
"title": ""
},
{
"docid": "dc8640eeab3aa9ce4404084fc3cbb274",
"score": "0.5999532",
"text": "def test_experiments():\n experiment_one()\n experiment_two()\n experiment_three()\n experiment_four()",
"title": ""
},
{
"docid": "343d0cce8356ac3e92c0de489f4efcd1",
"score": "0.59983265",
"text": "def test(self):\n pass",
"title": ""
},
{
"docid": "343d0cce8356ac3e92c0de489f4efcd1",
"score": "0.59983265",
"text": "def test(self):\n pass",
"title": ""
},
{
"docid": "343d0cce8356ac3e92c0de489f4efcd1",
"score": "0.59983265",
"text": "def test(self):\n pass",
"title": ""
},
{
"docid": "343d0cce8356ac3e92c0de489f4efcd1",
"score": "0.59983265",
"text": "def test(self):\n pass",
"title": ""
},
{
"docid": "343d0cce8356ac3e92c0de489f4efcd1",
"score": "0.59983265",
"text": "def test(self):\n pass",
"title": ""
},
{
"docid": "9a4d0f62757b06f6b2768d3a0b25c1c1",
"score": "0.59929097",
"text": "def three_experiments_with_trials(family_with_trials, single_with_trials):",
"title": ""
},
{
"docid": "69858b0f1b4861f653ba8585e782f2a2",
"score": "0.598832",
"text": "def example():\n\n #create an exam and add questions\n exam = Exam(\"Midterm\")\n exam.add_question(\"What is 2 + 2?\", \"4\")\n exam.add_question(\"What is '2'+'2'?\", \"22\")\n exam.add_question(\"What is an instance?\", \"an object\")\n\n #creates an instance of student\n student = Student(\"Jane\", \"Smith\", \"100 Irving St\")\n\n #take test\n take_test(exam, student)",
"title": ""
},
{
"docid": "3f82b7451e313a4f2d2cc74fb50363a8",
"score": "0.59757984",
"text": "def tests1():",
"title": ""
},
{
"docid": "cc2cb6f3650dbdde8c333a597208dd49",
"score": "0.59739214",
"text": "def test_4(self):\n pass",
"title": ""
},
{
"docid": "b34aa7d8bfc4213db95401d26da1dea7",
"score": "0.5969931",
"text": "def test_demo():",
"title": ""
},
{
"docid": "d31c9ad378dac26bb177274b8d11c04d",
"score": "0.596937",
"text": "def test_examples():\n argv = [\"py.test\", \"-examples\"]\n assert get_sargs(argv) is None",
"title": ""
},
{
"docid": "314c69f44b10bc7f27e69d4615234b38",
"score": "0.59620315",
"text": "def test_example_1():\n example1_dir = join(EXAMPLES, \"01-multiple-data-sources\")\n example1_main = join(example1_dir, \"main.py\")\n # Run it and make sure it doesn't raise an exception or otherwise exit with a non-zero code.\n subprocess.run(example1_main, cwd=example1_dir, check=True)",
"title": ""
},
{
"docid": "12065aff21abf42bbcdd0a1c4134dfdd",
"score": "0.59407955",
"text": "def test_feature_example_1():\n pass",
"title": ""
},
{
"docid": "865646fb7dbb3d7f13bdba09ab788a68",
"score": "0.5935075",
"text": "def setup(self):",
"title": ""
},
{
"docid": "865646fb7dbb3d7f13bdba09ab788a68",
"score": "0.5935075",
"text": "def setup(self):",
"title": ""
},
{
"docid": "865646fb7dbb3d7f13bdba09ab788a68",
"score": "0.5935075",
"text": "def setup(self):",
"title": ""
},
{
"docid": "865646fb7dbb3d7f13bdba09ab788a68",
"score": "0.5935075",
"text": "def setup(self):",
"title": ""
},
{
"docid": "534c271fdd7c1c14f2801cae33570bec",
"score": "0.5924909",
"text": "def test_dataset_specific_examples(self):\n\n other_dataset = corpus_models.Dataset.objects.create(name=\"second test corpus\", description=\"blah\")\n self.generate_some_messages(other_dataset)\n\n filters = {}\n msgs = self.dataset.get_example_messages(filters)\n self.assertEquals(msgs.count(), 2)",
"title": ""
},
{
"docid": "1582333da9bdeb23af670559bd4212b5",
"score": "0.59215033",
"text": "def run(self):",
"title": ""
},
{
"docid": "1582333da9bdeb23af670559bd4212b5",
"score": "0.59215033",
"text": "def run(self):",
"title": ""
},
{
"docid": "1582333da9bdeb23af670559bd4212b5",
"score": "0.59215033",
"text": "def run(self):",
"title": ""
},
{
"docid": "1582333da9bdeb23af670559bd4212b5",
"score": "0.59215033",
"text": "def run(self):",
"title": ""
},
{
"docid": "1582333da9bdeb23af670559bd4212b5",
"score": "0.59215033",
"text": "def run(self):",
"title": ""
},
{
"docid": "1582333da9bdeb23af670559bd4212b5",
"score": "0.59215033",
"text": "def run(self):",
"title": ""
},
{
"docid": "1582333da9bdeb23af670559bd4212b5",
"score": "0.59215033",
"text": "def run(self):",
"title": ""
},
{
"docid": "1582333da9bdeb23af670559bd4212b5",
"score": "0.59215033",
"text": "def run(self):",
"title": ""
},
{
"docid": "1582333da9bdeb23af670559bd4212b5",
"score": "0.59215033",
"text": "def run(self):",
"title": ""
},
{
"docid": "1582333da9bdeb23af670559bd4212b5",
"score": "0.59215033",
"text": "def run(self):",
"title": ""
},
{
"docid": "1582333da9bdeb23af670559bd4212b5",
"score": "0.59215033",
"text": "def run(self):",
"title": ""
},
{
"docid": "1582333da9bdeb23af670559bd4212b5",
"score": "0.59215033",
"text": "def run(self):",
"title": ""
},
{
"docid": "1c87aa34502cc05f9674d0a8837fb71a",
"score": "0.5908477",
"text": "def run( self ) :",
"title": ""
},
{
"docid": "543d02c0fb2f7c11293e9a27a2ee15f2",
"score": "0.5908068",
"text": "def perform_study(self):\n pass",
"title": ""
},
{
"docid": "33dd83ffd5bcc5e4a7b215a6991286c2",
"score": "0.59043336",
"text": "def _behave(self):\n pass",
"title": ""
},
{
"docid": "549a421ad7fac315040450db66389049",
"score": "0.59013486",
"text": "def setup_example(self):\n workspace.ResetWorkspace()\n self.reset_model()",
"title": ""
},
{
"docid": "7c948d070347c04c157c9bc1a32db75f",
"score": "0.5901278",
"text": "def main(self):",
"title": ""
},
{
"docid": "7c948d070347c04c157c9bc1a32db75f",
"score": "0.5901278",
"text": "def main(self):",
"title": ""
},
{
"docid": "b8e94b7d8d71ef960fa925d346b75d37",
"score": "0.58993924",
"text": "def num_examples(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "df00177127dc0e745267828ba5edfcee",
"score": "0.58947456",
"text": "def neptune_example_pipeline(ex_step):\n ex_step()",
"title": ""
},
{
"docid": "92a6c7b08c1eee5adf5d063dd078bf9e",
"score": "0.5894534",
"text": "def test( self ):\r\n pass",
"title": ""
},
{
"docid": "f1f64aa473c56e7dd11a532497a8b443",
"score": "0.5889214",
"text": "def _checkSample(self):",
"title": ""
},
{
"docid": "c20bdc8daacb20112a361ff05944c0fe",
"score": "0.5888078",
"text": "def run(self,):",
"title": ""
},
{
"docid": "e2088d3681e219e4baabb8e581bb8733",
"score": "0.5887474",
"text": "def fake_example_gen_run(mlmd_connection, example_gen, span, version):\n with mlmd_connection as m:\n output_example = types.Artifact(\n example_gen.outputs.outputs['output_examples'].artifact_spec.type)\n output_example.set_int_custom_property('span', span)\n output_example.set_int_custom_property('version', version)\n output_example.uri = 'my_examples_uri'\n contexts = context_lib.register_contexts_if_not_exists(\n m, example_gen.contexts)\n execution = execution_publish_utils.register_execution(\n m, example_gen.node_info.type, contexts)\n execution_publish_utils.publish_succeeded_execution(\n m, execution.id, contexts, {\n 'output_examples': [output_example],\n })",
"title": ""
},
{
"docid": "28592b32b237f540041efbb40f4daa5f",
"score": "0.5879725",
"text": "def three_experiments(two_experiments, one_experiment):",
"title": ""
},
{
"docid": "cf5e182f36df2c193fa33c53b8aca75b",
"score": "0.5877343",
"text": "def _gen_example(i, all_examples):\n example = dataloader.get_example_with_index(i)\n if not example:\n return\n image_seq_stack = _stack_image_seq(example['image_seq'])\n example.pop('image_seq', None) # Free up memory.\n intrinsics = example['intrinsics']\n fx = intrinsics[0, 0]\n fy = intrinsics[1, 1]\n cx = intrinsics[0, 2]\n cy = intrinsics[1, 2]\n save_dir = os.path.join(FLAGS.data_dir, example['folder_name'])\n if not gfile.Exists(save_dir):\n gfile.MakeDirs(save_dir)\n img_filepath = os.path.join(save_dir, '%s.jpg' % example['file_name'])\n scipy.misc.imsave(img_filepath, image_seq_stack.astype(np.uint8))\n cam_filepath = os.path.join(save_dir, '%s_cam.txt' % example['file_name'])\n example['cam'] = '%f,0.,%f,0.,%f,%f,0.,0.,1.' % (fx, cx, fy, cy)\n with open(cam_filepath, 'w') as cam_f:\n cam_f.write(example['cam'])\n\n key = example['folder_name'] + '_' + example['file_name']\n all_examples[key] = example",
"title": ""
},
{
"docid": "e1b0816601c9a6f2222dd7d4fb525583",
"score": "0.5870354",
"text": "def test_recommend_single(self, *args, **kwargs):",
"title": ""
},
{
"docid": "4bc642c388c622a3d238ee0f22f8dcaa",
"score": "0.5857832",
"text": "def test(ctx):",
"title": ""
},
{
"docid": "72e280fde3bc91684ea45af14903ebec",
"score": "0.58569765",
"text": "def _sample_episode(self, *args, **kwargs):\r\n\r\n raise NotImplementedError()",
"title": ""
},
{
"docid": "0daddd67c90d26e70645f2b8a4cbe04b",
"score": "0.5850479",
"text": "def scenario_cool(self):",
"title": ""
},
{
"docid": "87b28960e61c1df5d05595a58d3b350b",
"score": "0.58332443",
"text": "def create_example_books(self):\n # TODO: implement the method to create the example books by using \"add_book\" function and return a string\n # that shows it has been created successfully\n # write your code below\n pass",
"title": ""
},
{
"docid": "b5a2fc76acdfda05b4a92720eec791ea",
"score": "0.5830967",
"text": "def main(ctx):",
"title": ""
},
{
"docid": "2fdc0d1597d23907b12a294df7aded86",
"score": "0.5827748",
"text": "def random_example(lo, hi):\n print \"\\n------------------------RANDOM EXAMPLE----------------------------\"\n f = open(\"random_example.txt\", 'w') \n ex1 = sj.random_jobs(sj.random.randint(lo, hi))\n for job in ex1:\n f.write(str(job[0]) + \" \" + str(job[1]) + \" \")\n f.close()\n run_example(ex1)",
"title": ""
},
{
"docid": "c24e5f5e8eacf5a3e37b51ccb6134ce7",
"score": "0.5826231",
"text": "def tests2():",
"title": ""
},
{
"docid": "24cd9a5d84ba44fa170d1729a2654877",
"score": "0.5811337",
"text": "def get_dev_examples(self):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "cde34c617f0b76e8dd37cc816ff2563f",
"score": "0.57959056",
"text": "def Run(self, test_definition):",
"title": ""
},
{
"docid": "cde34c617f0b76e8dd37cc816ff2563f",
"score": "0.57959056",
"text": "def Run(self, test_definition):",
"title": ""
},
{
"docid": "ddc12f99f53450012b2882aab5764233",
"score": "0.5792049",
"text": "def _create_examples(self, lines, set_type,data_dir):\n examples = []\n \n for (i, row) in tqdm(enumerate(lines),desc=\"Generating\"):\n guid = \"%s-%s\" % (set_type, i)\n text_a = row['para']\n text_b = \"\"\n label = [row['para_label']]+ row['sents_label']\n label = label + [0]*(10-len(label))\n \n examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))\n return examples",
"title": ""
},
{
"docid": "250b33f91b208c6d5ae44b8be398141f",
"score": "0.57880986",
"text": "def Run(self) -> None:",
"title": ""
},
{
"docid": "d1527fd49c8711741bac3a3a59a36bd3",
"score": "0.57775676",
"text": "def sample(self, *args, **kwargs):\n raise NotImplementedError",
"title": ""
},
{
"docid": "8a836b7a6b1b369143ab8da72f0dd898",
"score": "0.57527196",
"text": "def test_spike_all_examples(self):\n play_examples.check_across_all_examples('SPIKE', misc.parse_spike)",
"title": ""
},
{
"docid": "deda02411604445d001ae7044c8f7497",
"score": "0.574347",
"text": "def build_examples(self):\n examples = {\"\"}\n for layer in self.qvars.get(\"layers\"):\n for ex in layer.get_examples() or []:\n examples.add(tuple(ex) if type(ex) is list else ex)\n\n res = []\n use_cat = True\n for ex in examples:\n original = \"\\\" \\\"\".join(ex) if type(ex) is tuple else ex\n res.append(self.format_example(original, '\\n'.join(self.execute(ex)), use_cat))\n use_cat = random.randint(1, 100) > 90\n\n self.qvars[\"examples\"] = \"Examples:\\n\\n%s\\n\" % '\\n'.join(res)\n return self",
"title": ""
},
{
"docid": "0c84f1807a18a5a7557131b3fe7960ad",
"score": "0.57319677",
"text": "def create_examples():\n with sirepo.quest.start() as qcall:\n examples = _get_examples_by_type(qcall)\n for t, s in _iterate_sims_by_users(qcall, examples.keys()):\n for e in examples[t]:\n if e.models.simulation.name not in s[t].keys():\n _create_example(qcall, e)",
"title": ""
},
{
"docid": "196a31b6a749b80fa2fe4d9132110743",
"score": "0.57264894",
"text": "def _create_examples(self, path, docs, set_type):\n if set_type == 'test':\n examples = []\n label_path = os.path.join(path, set_type+'.tsv')\n lines = [] # self._read_tsv(label_path)\n skip_rows = 0\n with codecs.open(label_path, 'r', 'utf-8') as data_fh:\n for _ in range(skip_rows):\n data_fh.readline()\n for row_idx, row in enumerate(data_fh):\n try:\n row = row.strip().split('\\t')\n lines.append(row)\n except Exception as e:\n print(e, \" file: %s, row: %d\" % (label_path, row_idx))\n continue\n for idx, line in enumerate(lines):\n if idx == 0:\n continue\n label = 0\n evidence_temp = []\n text_a = line[1]\n if text_a == '':\n continue\n text_b = line[2]\n if text_b == '':\n continue\n guid = \"%s-%s\" % (set_type, line[0])\n idnum = int(line[0])\n examples.append(\n InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label, evidence=evidence_temp, idnum=idnum))\n else:\n examples = []\n label_path = os.path.join(path, set_type+'.tsv')\n lines = [] # self._read_tsv(label_path)\n skip_rows = 0\n with codecs.open(label_path, 'r', 'utf-8') as data_fh:\n for _ in range(skip_rows):\n data_fh.readline()\n for row_idx, row in enumerate(data_fh):\n try:\n row = row.strip().split('\\t')\n label = int(row[-1])\n lines.append(row)\n except Exception as e:\n print(e, \" file: %s, row: %d\" % (label_path, row_idx))\n continue\n for idx, line in enumerate(lines):\n if idx == 0:\n continue\n if len(line) != 6:\n continue\n label = int(line[-1])\n evidence_temp = []\n text_a = line[3]\n if text_a == '':\n continue\n text_b = line[4]\n if text_b == '':\n continue\n guid = \"%s-%s\" % (set_type, line[0])\n examples.append(\n InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label, evidence=evidence_temp))\n return examples",
"title": ""
},
{
"docid": "e1fe14ff5549736bef77d5b2a1d5f8bd",
"score": "0.5725372",
"text": "def support(self):",
"title": ""
},
{
"docid": "07e76a2dea489b69c96fdd82461f8592",
"score": "0.57194436",
"text": "def main(self):\n pass",
"title": ""
},
{
"docid": "07e76a2dea489b69c96fdd82461f8592",
"score": "0.57194436",
"text": "def main(self):\n pass",
"title": ""
},
{
"docid": "14bf212c3035398ea2b58ef9e60f4696",
"score": "0.5716717",
"text": "def pre_demonstration(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "82d010cc7cb8c85c41c8ef4e37541992",
"score": "0.57047117",
"text": "def get_training_example(self, index):\n pass",
"title": ""
},
{
"docid": "18288b6a1dfa2db937a6455a5089d3c1",
"score": "0.56992954",
"text": "def main():\n start_scenario(3)",
"title": ""
},
{
"docid": "d73e4f4cf2c6bc2e6087ca555d8ec9fa",
"score": "0.56941247",
"text": "def example_exam():\n\n math_test = Exam(\"math\")\n \n math_test.add_question(\"What is two plus two?\", \"4\")\n math_test.add_question(\"what is two minus two?\", \"0\")\n math_test.add_question(\"what is two by two?\", \"8\")\n \n student_1 = Student(\"Juan\", \"Fulanito\")\n\n take_test(math_test, student_1)",
"title": ""
},
{
"docid": "b36dde2ab62775695d88241b2c81a0bc",
"score": "0.5692564",
"text": "def num_examples(self):\n return None",
"title": ""
},
{
"docid": "f4e74228ff6b18b42cbeb8c7d56bd3ee",
"score": "0.56867534",
"text": "def run(self, xs):\n \"*** YOUR CODE HERE ***\"",
"title": ""
},
{
"docid": "f4e74228ff6b18b42cbeb8c7d56bd3ee",
"score": "0.56867534",
"text": "def run(self, xs):\n \"*** YOUR CODE HERE ***\"",
"title": ""
},
{
"docid": "fe2cc7de270405c81c8582adb4662132",
"score": "0.56760144",
"text": "def sample(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "fe2cc7de270405c81c8582adb4662132",
"score": "0.56760144",
"text": "def sample(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "e89d3d6bcd43a0f23bd6bcc4034192dd",
"score": "0.56694543",
"text": "def test_instruments_get(self):\n pass",
"title": ""
},
{
"docid": "5b7a4ee54c2bc6d1f72da9a1378aa056",
"score": "0.5666349",
"text": "def main():\n fetchdata_giosg_api_test1()\n fetchdata_giosg_api_test2()",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="766"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">79741a40c9400c9e7569054ca8891dc6</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">example function to load model The prefix allows the loading of different models</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "fcd7e1764c0ffc983e0b39ea4aff7f8d",
"score": "0.76179326",
"text": "def model_load(prefix='sl',country='all', training=True):\n \n models = [f for f in os.listdir(MODEL_DIR) if re.search(\"sl\",f)]\n\n if len(models) == 0:\n raise Exception(\"Models with prefix '{}' cannot be found did you train?\".format(prefix))\n\n all_models = {}\n for model in models:\n all_models[re.split(\"-\",model)[1]] = joblib.load(os.path.join(MODEL_DIR, model))\n \n return all_models[country]",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "c0fd5de7decb08a60cafccf195b1f345",
"score": "0.76663965",
"text": "def loadmodel(self, nameprefix):\n self.topicmodeler.loadmodel(nameprefix)",
"title": ""
},
{
"docid": "97c0da6e305b6175b926cc9ce18bb622",
"score": "0.76616585",
"text": "def load_model():\n pass",
"title": ""
},
{
"docid": "97c0da6e305b6175b926cc9ce18bb622",
"score": "0.76616585",
"text": "def load_model():\n pass",
"title": ""
},
{
"docid": "5f7f87b01d46aa7fd03f211c64ab3e98",
"score": "0.70979327",
"text": "def load_model(filename):\n pass \n # TODO",
"title": ""
},
{
"docid": "b7234868edad6be85bbfc6ae2fc41516",
"score": "0.70636594",
"text": "def model_load(prefix='sl',data_dir=None,training=True,save_pickle=False):\n\n if not data_dir:\n data_dir = os.path.join(\".\",\"data\",\"cs-train\")\n\n models = [f for f in os.listdir(os.path.join(\".\",\"models\")) if re.search(\"sl\",f)]\n\n if len(models) == 0:\n raise Exception(f\"Models with prefix '{prefix}' cannot be found did you train?\")\n\n all_models = {}\n for model in models:\n all_models[re.split(\"-\",model)[1]] = joblib.load(os.path.join(\".\",\"models\",model))\n\n ## load data\n ts_data = fetch_ts(data_dir)\n all_data = {}\n for country, df in ts_data.items():\n X,y,dates = engineer_features(df,training=training)\n dates = np.array([str(d) for d in dates])\n all_data[country] = {\"X\":X,\"y\":y,\"dates\": dates}\n\n if save_pickle:\n version_ = re.sub(\"\\.\",\"_\",str(MODEL_VERSION))\n pickle.dump((all_data, all_models), open(os.path.join(\"models\",f\"all_data_model-{version_}.pickle\"), \"wb\" ))\n print('Pickle file saved.')\n return(all_data, all_models)",
"title": ""
},
{
"docid": "8aca508aa9581be29cbbc872b7941372",
"score": "0.69391286",
"text": "def load_model(self):\n pass",
"title": ""
},
{
"docid": "8aca508aa9581be29cbbc872b7941372",
"score": "0.69391286",
"text": "def load_model(self):\n pass",
"title": ""
},
{
"docid": "8aca508aa9581be29cbbc872b7941372",
"score": "0.69391286",
"text": "def load_model(self):\n pass",
"title": ""
},
{
"docid": "8aca508aa9581be29cbbc872b7941372",
"score": "0.69391286",
"text": "def load_model(self):\n pass",
"title": ""
},
{
"docid": "cfb1fde4804e9b7e1d6edbe1995f11f9",
"score": "0.6933842",
"text": "def load_model():\n return None",
"title": ""
},
{
"docid": "05bd26a930e7fba7c3804525127134a6",
"score": "0.68454707",
"text": "def load_model(self,from_lwtnn=False):\n pass",
"title": ""
},
{
"docid": "3660654e653726f3dad75ecbeaf543b3",
"score": "0.6806339",
"text": "def _load_model(cls, model_path):\n pass",
"title": ""
},
{
"docid": "47a52e542820fe448283dca1885d1204",
"score": "0.67761624",
"text": "def load_model(self, model):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "ff922928935e2c1880cb11afe16ab3a3",
"score": "0.6768762",
"text": "def load_model(model_name):\n if model_name == 'quartznet':\n print('Using QuartzNet model')\n return stt_nemo.load_model(QUARTZNET_MODEL_PATH)\n elif model_name == 'jasper':\n print('Using Jasper model')\n return stt_nemo.load_model(JASPER_MODEL_PATH)\n elif model_name == 'deepspeech':\n print('Using DeepSpeech model')\n return stt_deepspeech.load_model(DEEPSPEECH_MODEL_PATH, DEEPSPEECH_SCORER_PATH)",
"title": ""
},
{
"docid": "734aa8a23a464b44ec0811ea3cf78e03",
"score": "0.67598957",
"text": "def _load_model(self, model_path: str):\n pass",
"title": ""
},
{
"docid": "e9701a907bec03faab3d526fcb29a5b9",
"score": "0.67537946",
"text": "def loadModel(model_name):\n\n if(model_name=='SSG-LUGIA-F'):\n return SSG_LUGIA_F\n \n elif(model_name=='SSG-LUGIA-R'):\n return SSG_LUGIA_R\n\n elif(model_name=='SSG-LUGIA-P'):\n return SSG_LUGIA_P\n\n else:\n raise ValueError('Invalid Model Name Provided')",
"title": ""
},
{
"docid": "c2d4f1c520c184a3c58aad654b54a4ae",
"score": "0.67424893",
"text": "def loadmodel(self, prefix):\n hyperparameters = json.load(open(prefix+'_s2s_hyperparam.json', 'r'))\n self.vecsize, self.latent_dim = hyperparameters['vecsize'], hyperparameters['latent_dim']\n self.model = load_model(prefix+'.h5')\n self.encoder_model = load_model(prefix+'_encoder.h5')\n self.decoder_model = load_model(prefix+'_decoder.h5')\n self.trained = True",
"title": ""
},
{
"docid": "03e0ab965546c70e6fb4601be854fe22",
"score": "0.669854",
"text": "def load__model():\n print('[INFO] : Model loading ................')\n global model\n # model = tf.keras.models.load_model(MODEL_FOLDER + '/DandelionModel1.h5')\n # model = load_model(MODEL_FOLDER + '/DandelionModel1.h5')\n # model 2\n model = load_model(MODEL_FOLDER + '/DandelionModel2.h5')\n \n\n print('[INFO] : Model loaded')",
"title": ""
},
{
"docid": "1120d231a8cbbd42a1f229e4b3322290",
"score": "0.66162306",
"text": "def load_model(self,model_name):\r\n file_name = \"best_models\\\\\" + model_name + '.pkl'\r\n print(\"Loading model from: \" + file_name)\r\n self.model_name = model_name\r\n self.model = joblib.load(file_name)\r\n print(\"Model loaded successfully!\")",
"title": ""
},
{
"docid": "0aae8a5d2f9418a713faf0d1834cc69a",
"score": "0.6603113",
"text": "def importModel(model_name):\n if model_name == 'en_core_sci_sm':\n import en_core_sci_sm\n elif model_name == 'en_core_sci_lg':\n import en_core_sci_lg\n elif model_name == 'en_ner_bc5cdr_md':\n import en_ner_bc5cdr_md",
"title": ""
},
{
"docid": "d6a568985a3947fc1eb669ab9231101e",
"score": "0.65932494",
"text": "def load_model():\n\n return # return the loaded model object",
"title": ""
},
{
"docid": "043fd84ec290caa41bb84c302793b3b4",
"score": "0.65865594",
"text": "def get_model(model):\n return import_string('rio.models.%s.%s' % (model.lower(), model.capitalize()))",
"title": ""
},
{
"docid": "29a46325992369a32d8b0a05c2be4f99",
"score": "0.65609866",
"text": "def model_fn(model_dir):\n print(\"Loading model.\")\n \n # load using joblib\n model = joblib.load(os.path.join(model_dir, \"model.joblib\"))\n print(\"Done loading model.\")\n \n return model",
"title": ""
},
{
"docid": "29a46325992369a32d8b0a05c2be4f99",
"score": "0.65609866",
"text": "def model_fn(model_dir):\n print(\"Loading model.\")\n \n # load using joblib\n model = joblib.load(os.path.join(model_dir, \"model.joblib\"))\n print(\"Done loading model.\")\n \n return model",
"title": ""
},
{
"docid": "29a46325992369a32d8b0a05c2be4f99",
"score": "0.65609866",
"text": "def model_fn(model_dir):\n print(\"Loading model.\")\n \n # load using joblib\n model = joblib.load(os.path.join(model_dir, \"model.joblib\"))\n print(\"Done loading model.\")\n \n return model",
"title": ""
},
{
"docid": "29a46325992369a32d8b0a05c2be4f99",
"score": "0.65609866",
"text": "def model_fn(model_dir):\n print(\"Loading model.\")\n \n # load using joblib\n model = joblib.load(os.path.join(model_dir, \"model.joblib\"))\n print(\"Done loading model.\")\n \n return model",
"title": ""
},
{
"docid": "29a46325992369a32d8b0a05c2be4f99",
"score": "0.65609866",
"text": "def model_fn(model_dir):\n print(\"Loading model.\")\n \n # load using joblib\n model = joblib.load(os.path.join(model_dir, \"model.joblib\"))\n print(\"Done loading model.\")\n \n return model",
"title": ""
},
{
"docid": "29a46325992369a32d8b0a05c2be4f99",
"score": "0.65609866",
"text": "def model_fn(model_dir):\n print(\"Loading model.\")\n \n # load using joblib\n model = joblib.load(os.path.join(model_dir, \"model.joblib\"))\n print(\"Done loading model.\")\n \n return model",
"title": ""
},
{
"docid": "7973fa98efef7848a4b36b4c560a884c",
"score": "0.65026045",
"text": "def import_model(path):\n split = path.split('.')\n return get_model(split[0], split[-1]) # <app>.contrib[.bla].<model>",
"title": ""
},
{
"docid": "748d4591cc0e91eedbd27f21fe10814b",
"score": "0.6475012",
"text": "def load_model(self, model_name: str) -> None:\n self.client.load_model(model_name)",
"title": ""
},
{
"docid": "c92b79060ee6b774820cf7a2dc085617",
"score": "0.6469755",
"text": "def load_model(filename):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "1df32c47d87787bc4a762e4ac59f7056",
"score": "0.64658505",
"text": "def _load_model():\n return joblib.load('models/sentiment/_model.pkl')",
"title": ""
},
{
"docid": "0f36546f7ddb0f2dbfba6d669342f37c",
"score": "0.64367497",
"text": "def LoadModel(path,cust_obj=None):\n return tf.keras.models.load_model(path,cust_obj)",
"title": ""
},
{
"docid": "f9b75407f69597ad81dae16ffdd4ab9f",
"score": "0.6418851",
"text": "def load_model(self, context, path):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "b705c8d4f773509cc55e89394c03adb5",
"score": "0.64100623",
"text": "def load_model(\n model,\n attributes=None,\n parameter=None,\n kwargs=False,\n permission=None,\n addlperms=None,\n urlcheck=(),\n):\n return load_models(\n (model, attributes, parameter),\n kwargs=kwargs,\n permission=permission,\n addlperms=addlperms,\n urlcheck=urlcheck,\n )",
"title": ""
},
{
"docid": "28b5eff77e0f1f807e0328461ca3fcdc",
"score": "0.63882726",
"text": "def _load_model(self, parameter_dict):\n pass",
"title": ""
},
{
"docid": "f33cea078922aea9d3b908843712d5a4",
"score": "0.6383816",
"text": "def load_model(path):\n res = H2OConnection.post_json(\"Models.bin/\",dir=path,_rest_version=99)\n return get_model(res['models'][0]['model_id']['name'])",
"title": ""
},
{
"docid": "7ee08366e2acb6001e57a43a37213fdf",
"score": "0.63815016",
"text": "def __init__(self, prefix=None):\n if prefix != None:\n self.load(prefix)\n else:\n self.prefix = 'models/ner'\n self.model = None\n self.tag_map = {}",
"title": ""
},
{
"docid": "756e1ede2894fdd40b292adfe217ddb4",
"score": "0.6381106",
"text": "def load(model_dir, **kwargs):\n raise NotImplementedError",
"title": ""
},
{
"docid": "88383cbb8336268bb36a93338ea27382",
"score": "0.6374533",
"text": "def __model_importer(self):\n \n model_location = self._registered_models[self.model]['library_name']\n library_location = 'sans.models.' + model_location\n __import__(library_location)\n model_func = getattr(sys.modules[library_location],\n self._registered_models[self.model]['model_name'])()\n return model_func",
"title": ""
},
{
"docid": "2b39fa4442f78324b2143f53657c086b",
"score": "0.6373997",
"text": "def get_model(self, model_name):\n print('%s model loadind...'% model_name)\n if model_name == 'lsi':\n load_func = models.lsimodel.LsiModel.load\n load_path = self.lsi_path\n elif model_name == 'lda':\n load_func = models.ldamodel.LdaModel.load\n load_path = self.lda_path\n else:\n load_func = models.Word2Vec.load\n load_path = self.w2v_path\n try:\n model = load_func(load_path)\n print('Loaded')\n except:\n print('Модель не найдена.')\n model = None\n return model",
"title": ""
},
{
"docid": "a9341b57187f2152b4832d4f1929b6bd",
"score": "0.63663006",
"text": "def _custom_model_loader_fn(model_path: Text):\n def loader(path):\n model = xgb.Booster()\n model.load_model(path)\n return model\n\n return lambda: loader(model_path)",
"title": ""
},
{
"docid": "7f56adb9bf4cdfb96f48072f4b39d3ec",
"score": "0.63424724",
"text": "def load_model(args):\n config = AutoConfig.from_pretrained(args.model_name_or_path, cache_dir=None)\n\n model = AutoModelWithLMHead.from_pretrained(\n args.model_name_or_path,\n from_tf=bool(\".ckpt\" in args.model_name_or_path),\n config=config,\n cache_dir=None\n )\n \n model.to(args.device)\n return model",
"title": ""
},
{
"docid": "e3495f26240f8d3a1f67fc7402249818",
"score": "0.633554",
"text": "def load_model(model_name):\n if hasattr(single_op, model_name):\n return load_single_op(model_name)\n if hasattr(torchvision.models, model_name):\n return load_torchvision(model_name)\n try:\n if hasattr(pretrainedmodels, model_name):\n return load_pretrainedmodels(model_name)\n except ModuleNotFoundError:\n raise ModuleNotFoundError('Please install pretrainedmodels.pytorch')\n raise RuntimeError('Model not supported')",
"title": ""
},
{
"docid": "7c2dd1a3d53a5ef37e30bf2fe2791594",
"score": "0.6333917",
"text": "def _load_model(self) -> Model:\n return load_model(\n \"models/\" + self._model_name, custom_objects={} # type: ignore\n )",
"title": ""
},
{
"docid": "e878a153442a4ef6727603f593d6c6a4",
"score": "0.6329738",
"text": "def __init__(self,model,load_path=None):\n self.model = model.model\n self.path = load_path",
"title": ""
},
{
"docid": "23f9abfadb93b2d31935711ae0dd1e69",
"score": "0.63234633",
"text": "def model_fn(model_dir):\n logger.info('Loading the model.')\n logger.info(model_dir)\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n logger.info('Current device: {}'.format(device))\n if device == \"cuda\":\n model = torch.load(os.path.join(model_dir, 'model.pth')) #GPU\n else:\n model = torch.load(os.path.join(model_dir, 'model.pth'), map_location=\"cpu\") #CPU\n model.to(device).eval()\n\n logger.info('Loading the classes.')\n global classes\n with open(os.path.join(model_dir, 'class_indices.json'), 'r') as file_handler:\n classes = json.load(file_handler)\n\n return model",
"title": ""
},
{
"docid": "d0a702af8ef3d6ae5b5c0f7f1381bc6a",
"score": "0.6322457",
"text": "def load_model(self, model, postfix):\n\t\t_path_to_model = os.path.join(self.model_checkout_dir, 'model' + '_' + postfix + '.hdf5')\n\t\tmodel.load_weights(_path_to_model)\n\t\treturn model",
"title": ""
},
{
"docid": "064d582579639d280d9f9be719e91cc4",
"score": "0.63106686",
"text": "def _load_model(self):\n self.vectorizer = load('resources/vectorizer.joblib')\n self.model = load('resources/token_SVM.joblib')\n self.dimension_reducer = load('resources/dimension_reducer.joblib')",
"title": ""
},
{
"docid": "a65580f3eabae6ebef205efea0d33ee5",
"score": "0.6304556",
"text": "def load_precomputed_model(self, model_dir=None):\n pass",
"title": ""
},
{
"docid": "a65580f3eabae6ebef205efea0d33ee5",
"score": "0.6304556",
"text": "def load_precomputed_model(self, model_dir=None):\n pass",
"title": ""
},
{
"docid": "8a0a9ceff5253b16aa7d0a1f7503bb60",
"score": "0.6295369",
"text": "def load_model(self, model_name: str) -> None:\n self.model = models.load_model(Path(os.environ[\"MODEL_DATA\"]) / model_name)",
"title": ""
},
{
"docid": "61eb2d2242ebdf397e1f239ca2846679",
"score": "0.6285835",
"text": "def load_model():\n ROOT = \"models/\"\n MODELS = [\"{}{}\".format(ROOT, file) for file in os.listdir(ROOT) if \"nlu\" in file]\n MODEL_PATH = sorted(MODELS, key=os.path.getctime, reverse=True)[0]\n get_interpreter = get_model(MODEL_PATH)\n\n return Agent.load(get_interpreter)",
"title": ""
},
{
"docid": "2ab7e74bc589c2e9e26841055baaf91b",
"score": "0.6280593",
"text": "async def load_model():\n ModelManager.instance().fetch_model()\n download_dict()",
"title": ""
},
{
"docid": "08d9b68c86c1155d0a90b83c1bd2e8f3",
"score": "0.62722564",
"text": "def model_fn(model_dir):\n print('tuki code1:', model_dir)\n \n lr_model = joblib.load(os.path.join(model_dir, \"model.joblib\"))\n return lr_model",
"title": ""
},
{
"docid": "afa683db937f05416e841a4457e13abe",
"score": "0.62712115",
"text": "def load_model(self, import_path, device: str = 'cuda'):\n # TODO: Implement load_model\n pass",
"title": ""
},
{
"docid": "625d96a40c64d2229b65b30b75634c2c",
"score": "0.6270744",
"text": "def load_model(cli, model_name, model_path):\n try:\n req = agent.LoadModelRequest()\n req.url = model_path\n req.name = model_name\n return cli.LoadModel(req) \n except Exception as e:\n print(e) \n return None",
"title": ""
},
{
"docid": "06651f948534c486f234e6e6bedb1e9b",
"score": "0.6262971",
"text": "def load_model_test():\n model_path = pkg_resources.resource_filename('hwrt', 'misc/')\n model_file = os.path.join(model_path, \"model.tar\")\n utils.load_model(model_file)",
"title": ""
},
{
"docid": "96b7b52a87d075f4501d29ec30190a79",
"score": "0.62429786",
"text": "def load_model(self):\n NotImplementedError()",
"title": ""
},
{
"docid": "bb3b886b0cd3cbbf476ae974edd2c5cb",
"score": "0.62409925",
"text": "def load_model(self):\n # Load to continue training or evaluate...\n self.model = load_model(os.path.join('Savings', 'Saved Models', 'SimpleCharRNN'))",
"title": ""
},
{
"docid": "cea578a140cfb24c914ac8d75ea83f2b",
"score": "0.62259597",
"text": "def load_model(model_name):\n\n path = os.path.join(Dirs.MODELS_DIR, model_name, \"model.pkl\")\n model = joblib.load(path)\n return model",
"title": ""
},
{
"docid": "fee40e4abdbadaa4947a3ddaa6bb7549",
"score": "0.6210979",
"text": "def model_fn(model_dir: str):\n print(\"Loading model.\")\n\n # Load Tensorflow Model\n # ----------------------------------\n #\n with open(os.path.join(model_dir, \"model_info.json\"), \"r\") as model_info_file:\n model_info = json.load(model_info_file)\n\n model = AutoEncRec(**model_info['model_init'])\n model.item_idx = joblib.load(os.path.join(model_dir, 'movies_idx.pkl'))\n\n model.load_weights(os.path.join(model_dir, 'model.ckpt')).expect_partial()\n\n return model",
"title": ""
},
{
"docid": "dcf77911fb15712b73ac707976a72550",
"score": "0.62093353",
"text": "def model_fn(model_dir):\n net = gluon.SymbolBlock.imports(\n '%s/model-symbol.json' % model_dir,\n ['data'],\n '%s/model-0000.params' % model_dir,\n )\n \n return net",
"title": ""
},
{
"docid": "aff5b36e61c366cfc734830cf7b590d0",
"score": "0.62064135",
"text": "def load_model(self) -> Net:\n pass",
"title": ""
},
{
"docid": "48f80c77696359002cb31c7c996b33f4",
"score": "0.61982965",
"text": "def load_model(self, model_name):\n #response = self._client_stub.post(\"/api/modelcontrol/load/\" +\n # model_name)\n #self._last_request_id = raise_if_error(response['NV-Status'])\n raise_error('Not implemented')\n return None",
"title": ""
},
{
"docid": "063735b4b053c5a80b5b9dd9073ffdff",
"score": "0.61949223",
"text": "def load_model(filename):\n return K.models.load_model(filename)",
"title": ""
},
{
"docid": "be74699baa262c7643a5b20dce397f0e",
"score": "0.61748606",
"text": "def load_model(model: str) -> str:\n basename, filename = path.split(model)\n uid = filename.replace('.blend', '')\n blendfile = path.join(basename, uid + '.blend')\n section = \"\\\\Object\\\\\"\n object = uid\n\n filepath = uid + '.blend'\n directory = blendfile + section\n filename = object\n\n bpy.ops.wm.append(\n filepath=filepath,\n filename=filename,\n directory=directory)\n\n return uid",
"title": ""
},
{
"docid": "f965e44acfa3ec952c979161f7d3d66e",
"score": "0.6173932",
"text": "def load_model(self, ckpt, prefix='model.'):\n from collections import OrderedDict\n new_dict = OrderedDict()\n len_ = len(prefix)\n for key, value in ckpt['state_dict'].items():\n new_key = key[len_:]\n new_dict[new_key] = value\n self.load_state_dict(new_dict)",
"title": ""
},
{
"docid": "a2175a9bb8165a62badc19efeae38bd1",
"score": "0.6171486",
"text": "def load_model(filename:str):\n return load(filename)",
"title": ""
},
{
"docid": "d854bee179e3347d945940d504b7c575",
"score": "0.61607975",
"text": "def load_models():\n file_name = \"models/model_file.p\"\n with open(file_name, 'rb') as pickled:\n data = pickle.load(pickled)\n model = data['model']\n return model",
"title": ""
},
{
"docid": "1e21690356e97320d1cd2b66aa82e30e",
"score": "0.61416316",
"text": "def restapi_load(files={\"model\": \"dlimg.pkl\"}): # pylint: disable=W0102\n model = files['model']\n here = os.path.dirname(__file__)\n model = os.path.join(here, model)\n if not os.path.exists(model):\n raise FileNotFoundError(\"Cannot find model '{0}' (full path is '{1}')\".format(\n model, os.path.abspath(model)))\n with open(model, \"rb\") as f:\n loaded_model = pickle.load(f)\n return loaded_model",
"title": ""
},
{
"docid": "c2e33531c6d1c82ecb1e5a6a172713e5",
"score": "0.613638",
"text": "def load_pretrain_model():\n model = get_model()\n model = model_preparation(model)\n return model",
"title": ""
},
{
"docid": "7503c3b76cd870f3b16d09db7c30e893",
"score": "0.61306375",
"text": "def load_model():\n ##loading the model from the saved file\n with open(config.root+\"/main/model.bin\", \"rb\") as f_in:\n model = pickle.load(f_in)\n return model",
"title": ""
},
{
"docid": "2bebdf8dc3b6543ae87e444d74b7984e",
"score": "0.612555",
"text": "def load_model(self, fname):\n xglib.XGBoosterLoadModel( self.handle, ctypes.c_char_p(fname.encode('utf-8')) )",
"title": ""
},
{
"docid": "64d2dc62f0b2376c7897d986a346ece1",
"score": "0.6114794",
"text": "def load_model(fp: str):\n raise NotImplementedError",
"title": ""
},
{
"docid": "2e36cd30d1f0c221514634c2857f7d8c",
"score": "0.61091727",
"text": "def load_model(self, **params):\n \t# file_name = params['name']\n # return pickle.load(gzip.open(file_name, 'rb'))\n files=os.listdir()\n file_name=[i for i in files if '.hdf5' in i]\n print(\"#\"*5+\" Using saved model-\"+file_name[0]+\" \"+\"#\"*5)\n model=models.load_model(os.path.join(os.getcwd(),file_name[0]),custom_objects={\"psnr\":self.psnr,\"advance_relu\":self.advance_relu})\n print(\"#\"*5+\" Model Loaded \"+\"#\"*5)\n self.mdl=model",
"title": ""
},
{
"docid": "254fee4bad4aca73a2f217dc00effe9b",
"score": "0.6103432",
"text": "def test_model_load(self):\n model = TestModel().load({\"first\": \"First Name\"})\n self.assertTrue(model.first == 'First Name')",
"title": ""
},
{
"docid": "6d154a6377f12b1af3394f67888f6695",
"score": "0.6101006",
"text": "def load_model(self, model, pretrained_path, load_to_cpu):\r\n def remove_prefix(state_dict, prefix):\r\n ''' Old style model is stored with all names of parameters sharing common prefix 'module.' '''\r\n print('remove prefix \\'{}\\''.format(prefix))\r\n f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x\r\n return {f(key): value for key, value in state_dict.items()}\r\n\r\n def check_keys(model, pretrained_state_dict):\r\n ckpt_keys = set(pretrained_state_dict.keys())\r\n model_keys = set(model.state_dict().keys())\r\n used_pretrained_keys = model_keys & ckpt_keys\r\n unused_pretrained_keys = ckpt_keys - model_keys\r\n missing_keys = model_keys - ckpt_keys\r\n print('Missing keys:{}'.format(len(missing_keys)))\r\n print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys)))\r\n print('Used keys:{}'.format(len(used_pretrained_keys)))\r\n assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint'\r\n return True\r\n\r\n print('Loading pretrained model from {}'.format(pretrained_path))\r\n if load_to_cpu:\r\n pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage)\r\n else:\r\n if self.on_gpu:\r\n device = torch.cuda.current_device()\r\n pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, location: storage.cuda(device))\r\n else:\r\n pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, location: storage)\r\n if \"state_dict\" in pretrained_dict.keys():\r\n pretrained_dict = remove_prefix(pretrained_dict['state_dict'], 'module.')\r\n else:\r\n pretrained_dict = remove_prefix(pretrained_dict, 'module.')\r\n check_keys(model, pretrained_dict)\r\n model.load_state_dict(pretrained_dict, strict=False)\r\n return model",
"title": ""
},
{
"docid": "b00a76f883635aae55333fb7573153ce",
"score": "0.6093356",
"text": "def load(self, prefix=None):\n if prefix != None: self.prefix = prefix\n self.model = load_model(self.prefix+'.h5')\n self.tag_map = json.load(open(self.prefix+'.json', 'r'))",
"title": ""
},
{
"docid": "3fffaa028811bc33e17b745c962d3010",
"score": "0.60927814",
"text": "def load_models(\n tdc: Any, cmc: Any, path: str = \"saves/\", prefix: str = \"best_\"\n) -> None:\n prefix = path + prefix\n tdc.load_state_dict(torch.load(\"{}tdc.pth\".format(prefix)))\n cmc.load_state_dict(torch.load(\"{}cmc.pth\".format(prefix)))",
"title": ""
},
{
"docid": "8adff3f1b53fb1ffb83aed71441ffe3f",
"score": "0.6078114",
"text": "def loadModel(self, name):\n self.model = keras.models.load_model(name)",
"title": ""
},
{
"docid": "ef5869d8378214da5c873ff3f2aec76c",
"score": "0.607232",
"text": "def loadMModel(self) -> None:\n pass",
"title": ""
},
{
"docid": "7e3e2b5a20988167defe49fc05d1884d",
"score": "0.6064967",
"text": "def load_model(self, model):\n sub = model[self.modelroot]\n\n d = {}\n d['family'] = sub['family']\n d['load_style'] = sub['artifact']['format']\n d['load_file'] = sub['artifact']['file']\n load_options = sub['artifact'].get('load_options', None)\n d['symbols'] = sub['symbol']\n\n if load_options is not None:\n d['load_options'] = {}\n load_options_keys = ['key', 'index', 'data_set', 'pbc', 'atom_style',\n 'units', 'prop_info']\n d['load_options'] = termtodict(load_options, load_options_keys)\n if 'index' in d['load_options']:\n d['load_options']['index'] = int(d['load_options']['index'])\n\n self.set_values(**d)",
"title": ""
},
{
"docid": "46b9f47e7835ea8ff050dc50c3a6cdba",
"score": "0.60635734",
"text": "def load_model(self, file_name):\n raise NotImplementedError",
"title": ""
},
{
"docid": "ddbc7501eeacb0bef84834f176586e57",
"score": "0.60629016",
"text": "def model_fn(model_dir):\n net = task.load(model_dir)\n return net",
"title": ""
},
{
"docid": "d2377841e5731521bb874ff9067cb266",
"score": "0.606271",
"text": "def load_model(\n source: Union[str, PathLike, IO[AnyStr]],\n *,\n relative_origin: Optional[str] = None,\n **kwargs,\n) -> Model:\n return load_common(Model, source, relative_origin, **kwargs)",
"title": ""
},
{
"docid": "b99a41ea512db4c930276efac4541953",
"score": "0.605735",
"text": "def load_model(filename):\n model = K.models.load_model(filename)\n return model",
"title": ""
},
{
"docid": "b99a41ea512db4c930276efac4541953",
"score": "0.605735",
"text": "def load_model(filename):\n model = K.models.load_model(filename)\n return model",
"title": ""
},
{
"docid": "98d8c79d78a4b22ee6a14b958413492a",
"score": "0.6055701",
"text": "def loadModel(model):\n # Load the model\n nlp = model.load()\n\n # Add pipe features to pipeline\n linker = UmlsEntityLinker(resolve_abbreviations=True)\n nlp.add_pipe(linker)\n\n logging.info(\"Model and add-ons successfully loaded.\")\n return nlp, linker",
"title": ""
},
{
"docid": "856b1768a64f0027127157b421b508cf",
"score": "0.605529",
"text": "def load_model(self , import_path):\n self.trained_model =load_model(os.path.join(import_path, self.classifier_name + \".h5\"), custom_objects={'BilinearUpSampling2D': BilinearUpSampling2D,\n 'dice_coef_loss': FCN_Classifier.dice_coef_loss,\n 'dice_coef': FCN_Classifier.dice_coef})\n logging.info(\"Loaded Model at : \" + os.path.join(import_path, self.classifier_name + \".h5\"))",
"title": ""
},
{
"docid": "dafff00b8692023ece35f314aa9e174f",
"score": "0.60535765",
"text": "def get_model(self, model_name):\n pass",
"title": ""
},
{
"docid": "0fed14c2e539bc8a419ccaeb40e2053d",
"score": "0.6049982",
"text": "def load(fname: os.PathLike) -> \"Model\":\n fname = pathlib.Path(fname)\n with fname.open(\"rb\") as f:\n model = dill.load(f)\n model.net.device = nn.autodevice()\n return model",
"title": ""
},
{
"docid": "243422b1d1833525bd9ba9e5628190dc",
"score": "0.6046225",
"text": "def model_fn(model_dir):\n net = gluon.SymbolBlock.imports(\n '%s/model-symbol.json' % model_dir,\n ['data'],\n '%s/model-0000.params' % model_dir,\n )\n return net",
"title": ""
},
{
"docid": "c5a9273204aa57b5a850067556f3c15e",
"score": "0.60452366",
"text": "def load_models(load_path=None):\r\n\r\n if load_path is None:\r\n load_path = os.path.join(os.path.dirname(__file__),\r\n 'models_compressed.pkl')\r\n try:\r\n models = pickle.load(open(load_path,'rb'))\r\n\r\n global WORDS_MODEL\r\n WORDS_MODEL = models['words_model']\r\n\r\n global WORD_TUPLES_MODEL\r\n WORD_TUPLES_MODEL = models['word_tuples_model']\r\n\r\n print(\"successfully loaded: models_compressed.pkl\")\r\n except IOError:\r\n print(\"Error in opening pickle object. Training on default corpus text.\")\r\n train_bigtxt()\r\n except KeyError:\r\n print(\"Error in loading both predictve models.\\\r\n Training on default corpus text.\")\r\n train_bigtxt()\r\n except ValueError:\r\n print(\"Corrupted pickle string.\\\r\n Training on default corpus text (big.txt)\")\r\n train_bigtxt()",
"title": ""
},
{
"docid": "715507b254fd8f9f02b8aa45588f9841",
"score": "0.6033404",
"text": "def load_model(name, **overrides):\n data_path = get_data_path()\n if not data_path or not data_path.exists():\n raise IOError(Errors.E049.format(path=path2str(data_path)))\n if isinstance(name, basestring_): # in data dir / shortcut\n if name in set([d.name for d in data_path.iterdir()]):\n return load_model_from_link(name, **overrides)\n if is_package(name): # installed as package\n return load_model_from_package(name, **overrides)\n if Path(name).exists(): # path to model data directory\n return load_model_from_path(Path(name), **overrides)\n elif hasattr(name, 'exists'): # Path or Path-like to model data\n return load_model_from_path(name, **overrides)\n raise IOError(Errors.E050.format(name=name))",
"title": ""
},
{
"docid": "8b0c404ff9a7bb2f13d503604f06d230",
"score": "0.60309684",
"text": "def load_model(self, model_dir, model_name):\n if self.is_numpy():\n self.model = np.load(self.get_path(model_dir, model_name))\n else:\n self.saver.restore(self.sess, self.get_path(model_dir, model_name))\n print (\"Model loaded\", self.get_path(model_dir, model_name))",
"title": ""
},
{
"docid": "275b18ac9c99b86b114b37c85f450eb3",
"score": "0.6030334",
"text": "def load_model(args) :\n\n from keras.models import model_from_json\n\n arch = args.arch_file\n weights = args.weights\n\n json_file = open(arch, 'r')\n loaded_model = json_file.read()\n json_file.close()\n\n loaded_model = model_from_json(loaded_model)\n loaded_model.load_weights(weights)\n #loaded_model.compile\n\n return loaded_model",
"title": ""
},
{
"docid": "a7eddea1349ed581c39cabcad224b797",
"score": "0.6028219",
"text": "def _import_model(module: Any, class_name: str) -> Any:\n return getattr(importlib.import_module(module), class_name)",
"title": ""
},
{
"docid": "1775266cbed2c70900b3ea108c0df582",
"score": "0.6020763",
"text": "def load_model_from_link(name, **overrides):\n path = get_data_path() / name / '__init__.py'\n try:\n cls = import_file(name, path)\n except AttributeError:\n raise IOError(Errors.E051.format(name=name))\n return cls.load(**overrides)",
"title": ""
},
{
"docid": "612c73f48580713c12c4113b661da22e",
"score": "0.60165316",
"text": "def load_models(load_path=None):\r\n if load_path is None:\r\n load_path = os.path.join(config.model_path, config.model_name)\r\n try:\r\n models = pickle.load(open(load_path, \"rb\"))\r\n global WORDS_MODEL\r\n WORDS_MODEL = models[\"words_model\"]\r\n global WORD_TUPLES_MODEL\r\n WORD_TUPLES_MODEL = models[\"word_tuples_model\"]\r\n print(f\"successfully loaded: {config.model_name}\")\r\n except:\r\n print(\"Error in opening pickle object. Training on default corpus text.\")\r\n train_bigtxt()",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="767"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">22309e7e09d72f5df4eb925c102f7779</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">CreateFilmSchema accepts a valid title</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "1ad064f3127428bf4539e89d332f18a6",
"score": "0.6432382",
"text": "def test_title_passes_validation(self):\n result = self.schema.deserialize(self.params)\n self.assertEqual(self.params['title'], result['title'])",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "85eb6fd2eec38707529103ac3078cfba",
"score": "0.63343024",
"text": "def create_movie():\n body = request.get_json()\n validated_body = {}\n required_fields = ['title', 'release_date']\n for field in required_fields:\n resp = body.get(field, None)\n if resp is None:\n abort(400)\n validated_body[field] = resp\n\n try:\n new_movie = Movie(\n title=validated_body['title'],\n release_date=validated_body['release_date'])\n new_movie.insert()\n movie_list = query_all(Movie)\n return jsonify({\n 'success': True,\n 'created': new_movie.id,\n 'movies': movie_list,\n 'total_movies': len(movie_list)\n }), 201\n except DatabaseError:\n abort(422)",
"title": ""
},
{
"docid": "d4316d013528721fc39bf70ca67b9f15",
"score": "0.63081956",
"text": "def test_movie_create_title_missing(self):\n\n mutation = 'mutation { createMovie { movie { id title year } } }'\n\n self.assertMatchSnapshot(self.client.execute(mutation))\n\n self.assertEqual(Movie.objects.all().count(), self.movies_initial_count)",
"title": ""
},
{
"docid": "a79b9386362fa0fa945fd135a5903af9",
"score": "0.61830044",
"text": "def test_title_required(self):\n from colander import Invalid\n with self.assertRaises(Invalid):\n self.schema.deserialize({})",
"title": ""
},
{
"docid": "d09ecd6b3729ba2da0b0895a48102082",
"score": "0.5995201",
"text": "def createSchema(self, uid, title, description, schema):\n self.ExecSQL(sql.InsertSchema, (uid, title, description, str(schema)))\n self.db.commit()\n return uid",
"title": ""
},
{
"docid": "356f93faa57c962b97a75b972ae039b3",
"score": "0.59570664",
"text": "def create_movie(title, overview, release_date, poster_path):\n\n movie = Movie(title = title, overview = overview, release_date = release_date, poster_path = poster_path)\n\n db.session.add(movie)\n db.session.commit()\n\n return movie",
"title": ""
},
{
"docid": "fd261fc63430333dadfb1ee46f122141",
"score": "0.5953896",
"text": "def create_movie(title, type_id, cover=None, description=None, length=None, \r\n year=None):\r\n\r\n new_item = Movie(title=title, \r\n type_id=type_id, # TODO: specify this automatically!\r\n cover=cover, \r\n description=description, \r\n length=length, \r\n created_at=datetime.now(),\r\n year=year)\r\n\r\n db.session.add(new_item)\r\n db.session.commit() \r\n\r\n return new_item",
"title": ""
},
{
"docid": "87971fca7d766c6fec17f19adc1b2eff",
"score": "0.594664",
"text": "def create(self, validated_data):\n movie = Movie()\n movie.title = validated_data['title']\n movie.release_year = validated_data['release_year']\n movie.save()\n movie.producers.set(validated_data['producers'])\n movie.directors.set(validated_data['directors'])\n movie.casting.set(validated_data['casting'])\n return movie",
"title": ""
},
{
"docid": "d108c4c49ba4e3e72ce4dd6705476eeb",
"score": "0.59267753",
"text": "def create_movie(title, genre, rating):\r\n\r\n if title and genre and rating:\r\n movies = {\r\n \"title\": title,\r\n \"genre\": genre,\r\n \"rating\": rating\r\n }\r\n return movies\r\n else:\r\n return None",
"title": ""
},
{
"docid": "3adfad41e10d551cef5c40fbf32e06ed",
"score": "0.57562876",
"text": "def test_video_serializer_validate_title(mocker):\n mocker.patch(\"ui.serializers.get_moira_client\")\n video = factories.VideoFactory()\n video.title = \"\"\n serialized_data = serializers.VideoSerializer(video).data\n with pytest.raises(ValidationError) as exc:\n serializers.VideoSerializer(data=serialized_data).is_valid(raise_exception=True)\n assert exc.value.detail == {\"title\": [\"This field may not be blank.\"]}",
"title": ""
},
{
"docid": "ee794ea572cd05ff725672cac848657a",
"score": "0.5741217",
"text": "def title_type_v(app, title_type):\n vocabulary_service.create(\n system_identity,\n {\n \"id\": \"subtitle\",\n \"props\": {\"datacite\": \"Subtitle\"},\n \"title\": {\"en\": \"Subtitle\"},\n \"type\": \"titletypes\",\n },\n )\n\n vocab = vocabulary_service.create(\n system_identity,\n {\n \"id\": \"alternative-title\",\n \"props\": {\"datacite\": \"AlternativeTitle\"},\n \"title\": {\"en\": \"Alternative title\"},\n \"type\": \"titletypes\",\n },\n )\n\n Vocabulary.index.refresh()\n\n return vocab",
"title": ""
},
{
"docid": "92f3bd6743fcba27fccc8e6b8202cd9d",
"score": "0.5722184",
"text": "def test_empty_title(self):\n post = self.create_post(title='', author=self.author)\n self.assertRaisesRegex(ValidationError, 'title', post.full_clean)\n self.assertFalse(post.save())",
"title": ""
},
{
"docid": "1532537871515270ba065f17515c7eac",
"score": "0.56489146",
"text": "def test_missing_title_sets_default(self):\n result = self.schema.deserialize({})\n self.assertEqual(None, result['title'])",
"title": ""
},
{
"docid": "6472b7120ec57a56bba6b6ca4e225deb",
"score": "0.5578253",
"text": "def test_title():\n program = ProgramFactory.create(title=\"test title of the program\")\n course = CourseFactory.create(title=\"test title of the course\")\n run = CourseRunFactory.create(course=course)\n\n program_product = ProductFactory.create(content_object=program)\n assert program_product.title == \"test title of the program\"\n run_product = ProductFactory.create(content_object=run)\n assert run_product.title == \"test title of the course\"",
"title": ""
},
{
"docid": "f5037903434f9fba2db5d4da16acc409",
"score": "0.556812",
"text": "def createSchema():\n cursor = conn.cursor()\n\n # drop the table if already exists\n\n cursor.execute(\"DROP TABLE IF EXISTS MOVIE_GENRE\")\n cursor.execute(\"DROP TABLE IF EXISTS GENRE\")\n cursor.execute(\"DROP TABLE IF EXISTS ACTOR_MOVIE_ROLE\")\n cursor.execute(\"DROP TABLE IF EXISTS MOVIE_ACTOR\")\n cursor.execute(\"DROP TABLE IF EXISTS MOVIE_WRITER\")\n cursor.execute(\"DROP TABLE IF EXISTS MOVIE_DIRECTOR\")\n cursor.execute(\"DROP TABLE IF EXISTS MOVIE_PRODUCER\")\n cursor.execute(\"DROP TABLE IF EXISTS ROLE\")\n cursor.execute(\"DROP TABLE IF EXISTS MEMBER\")\n cursor.execute(\"DROP TABLE IF EXISTS MOVIE\")\n\n cursor.execute(\"\"\"CREATE TABLE MOVIE\n (\n id INTEGER PRIMARY KEY,\n type varchar(512),\n title text,\n originalTitle text,\n startYear INTEGER,\n endYear INTEGER,\n runtime INTEGER,\n avgRating FlOAT,\n numVotes INTEGER\n )\"\"\"\n )\n cursor.execute(\"\"\"CREATE TABLE Genre \n ( \n id SERIAL PRIMARY KEY,\n genre varchar(150) \n ) \"\"\"\n )\n\n cursor.execute(\n '''CREATE TABLE MOVIE_GENRE\n (\n genre INTEGER REFERENCES Genre(id) ON DELETE CASCADE,\n movie INTEGER REFERENCES MOVIE(id) ON DELETE CASCADE\n )'''\n )\n cursor.execute(\n \"\"\"CREATE TABLE MEMBER\n (\n id INTEGER PRIMARY KEY,\n name text NOT NULL,\n birthYear integer,\n deathYear integer\n )\"\"\"\n )\n\n cursor.execute(\n '''CREATE TABLE Movie_Actor\n (\n actor INTEGER REFERENCES MEMBER(id) ON DELETE CASCADE,\n movie INTEGER REFERENCES MOVIE(id) ON DELETE CASCADE\n )'''\n )\n\n cursor.execute(\n '''CREATE TABLE Movie_Writer\n (\n writer INTEGER REFERENCES MEMBER(id) ON DELETE CASCADE,\n movie INTEGER REFERENCES MOVIE(id) ON DELETE CASCADE\n )'''\n )\n\n cursor.execute(\n '''CREATE TABLE Movie_Director\n (\n director INTEGER REFERENCES MEMBER(id) ON DELETE CASCADE,\n movie INTEGER REFERENCES MOVIE(id) ON DELETE CASCADE\n )'''\n )\n\n cursor.execute(\n '''CREATE TABLE Movie_Producer\n (\n producer INTEGER REFERENCES MEMBER(id) ON DELETE CASCADE,\n movie INTEGER REFERENCES MOVIE(id) ON DELETE CASCADE\n )'''\n )\n cursor.execute('''CREATE TABLE ROLE\n (\n id SERIAL PRIMARY KEY,\n role text\n )'''\n )\n cursor.execute(\"\"\"CREATE TABLE ACTOR_MOVIE_ROLE\n (\n movie INTEGER,\n actor INTEGER,\n role INTEGER REFERENCES ROLE(id) on DELETE CASCADE\n )\"\"\"\n )\n conn.commit()",
"title": ""
},
{
"docid": "fc8d1062c60ebd86d455ca2a258bbc79",
"score": "0.5533216",
"text": "def valid_title(text):\n if text == P_TITLE:\n raise ValidationError('Please change the default title.')\n if len(text) < 5:\n raise ValidationError('Your title appears to be shorter than the minimum of five characters.')\n if len(text) > 150:\n raise ValidationError('Your title appears to be longer than the maximum of 150 characters.')",
"title": ""
},
{
"docid": "560d66c76e8e256f11a7435f83316515",
"score": "0.55241495",
"text": "def test_movie_create_all_required_fields_present(self):\n\n mutation = 'mutation { createMovie(title: \"Test\") { movie { id title year } } }'\n\n self.assertMatchSnapshot(self.client.execute(mutation))\n\n self.assertEqual(Movie.objects.all().count(), self.movies_initial_count + 1)",
"title": ""
},
{
"docid": "e30bbd9eb8e592bdab55b2e8660ba572",
"score": "0.54874253",
"text": "def test_title(title, expected_title):\n actual_title = EntityParser(data={\"title\": title}, parser=JSONParser()).parse_title()\n assert actual_title == expected_title, \"Wrong title\"",
"title": ""
},
{
"docid": "6a3f872525861d48632d5898fabaadde",
"score": "0.54870343",
"text": "def test_create_post_form_invalid_title(self):",
"title": ""
},
{
"docid": "836769598cfe780a4b07c91cea073630",
"score": "0.53986496",
"text": "def create_model(ApiId=None, ContentType=None, Description=None, Name=None, Schema=None):\n pass",
"title": ""
},
{
"docid": "2066108c271be81281de3f2881d7b5dd",
"score": "0.5382494",
"text": "def add_movie(payload):\n body = request.get_json()\n if not body:\n # posting an empty json should return a 400 error.\n abort(400, 'JSON passed is empty')\n if 'title' not in body.keys() or 'release_date' not in body.keys():\n abort(400, 'Invalid JSON, \"title\" or \"release_date\" key is not present')\n\n if Movie.query.filter_by(title=body['title']).first():\n abort(409, 'Movie with name ' + body['title'] + ' already exists.')\n try:\n movie = Movie(\n title=body['title'],\n release_date=body['release_date'])\n movie.insert()\n return jsonify(movie.serialize())\n except Exception as e:\n db.session.rollback()\n abort(422, str(e))\n finally:\n db.session.close()",
"title": ""
},
{
"docid": "61196d36e23cbf91083e240d6cde9d02",
"score": "0.53771734",
"text": "def test_create_movie_invalid_payload(self):\n empty_field_payload = {\n 'title': 'Batman',\n 'release_date': '2021-06-24T14:18:35Z',\n 'imdb_ranking': 9.7,\n 'director': ''\n }\n\n self.client.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)\n url = reverse('general_models', kwargs={'database_name':'ine_database','table_name': 'Movie'})\n\n response = self.client.post(\n url, \n data=json.dumps(empty_field_payload), \n content_type='application/json'\n )\n result = json.loads(response.content)\n\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\n self.assertEqual(result, {'director': ['This field may not be null.'], 'status_code': 400})",
"title": ""
},
{
"docid": "317487b9082d1e5d34aa3e17b873332f",
"score": "0.53507113",
"text": "def test_workflow_create_not_valid_name(create_yaml_workflow_schema):\n env = {\"REANA_SERVER_URL\": \"localhost\"}\n illegal_workflow_name = \"workflow.name\"\n runner = CliRunner(env=env)\n result = runner.invoke(cli, [\"create\", \"-n\", illegal_workflow_name])\n message = 'Workflow name {} contains illegal character \"{}\"'.format(\n illegal_workflow_name, \".\"\n )\n assert message in result.output\n assert result.exit_code == 1",
"title": ""
},
{
"docid": "801e1b859642772e7e259fe6872110fe",
"score": "0.5313177",
"text": "def test_movie_create_additional_fields_present(self):\n\n mutation = 'mutation { createMovie(title: \"Test\", year: 2005) { movie { id title year } } }'\n\n self.assertMatchSnapshot(self.client.execute(mutation))\n\n self.assertEqual(Movie.objects.all().count(), self.movies_initial_count + 1)",
"title": ""
},
{
"docid": "e2635e905897907ec380fe6b8014be8b",
"score": "0.5308444",
"text": "def test_create_entity_with_name_too_short_raises_exception(self):\n request_to_validate = {'name': ''}\n self.assertRaises(exception.SchemaValidationError,\n self.create_schema_validator.validate,\n request_to_validate)",
"title": ""
},
{
"docid": "d7959a6bf713641d09a9e51f0a5d0991",
"score": "0.5285373",
"text": "def test_create_entity_with_only_required_valid_parameters_validates(self):\n request_to_validate = {'name': self.resource_name}\n self.create_schema_validator.validate(request_to_validate)",
"title": ""
},
{
"docid": "66c5dda6db51fd8788caefbb7f3461c4",
"score": "0.5276886",
"text": "def __addMovie(self):\r\n self.__idM = input(\"Dati id-ul filmului: \")\r\n self.__titleM = input(\"Dati titlul filmului: \")\r\n self.__descriptionM = input(\"Dati descrierea filmului: \")\r\n self.__genreM = input(\"Dati tipul filmului: \")\r\n try:\r\n self.__movieController.addMovie(self.__idM, self.__titleM, self.__descriptionM, self.__genreM)\r\n print(\"Film \" + self.__titleM + \" adaugat.\")\r\n except RepositoryException as errors:\r\n self.__showErrors(errors.getErrors())\r\n except ValidatorException as errors:\r\n self.__showErrors(errors.getErrors())",
"title": ""
},
{
"docid": "e38c49499a010d99dc6873c8989e557d",
"score": "0.5276552",
"text": "def test_create_movie_valid_payload(self):\n\n director = Director.objects.get_or_create(name='Campanella')[0]\n\n valid_payload = {\n 'title': 'Batman',\n 'release_date': '2021-06-24T14:18:35Z',\n 'imdb_ranking': 9.7,\n 'director': director.id\n }\n\n self.client.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)\n url = reverse('general_models', kwargs={'database_name':'ine_database','table_name': 'Movie'})\n \n response = self.client.post(\n url, \n data=json.dumps(valid_payload), \n content_type='application/json'\n )\n\n result = json.loads(response.content)\n\n if 'id' in result:\n del result['id']\n\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n self.assertEqual(result, valid_payload)",
"title": ""
},
{
"docid": "bcb9e06ec9e9ce77b1b27d3eb7ade00f",
"score": "0.52689576",
"text": "def test_create_entity_with_null_string_succeeds(self):\n request_to_validate = {'name': self.resource_name,\n 'id_string': None}\n self.create_schema_validator.validate(request_to_validate)",
"title": ""
},
{
"docid": "0b8beb5ae62ad783eb4d33e54ba3daa7",
"score": "0.52366227",
"text": "def test_missing_title():\n actual_title = EntityParser(data={}, parser=JSONParser()).parse_title()\n assert actual_title is None, \"Wrong title\"",
"title": ""
},
{
"docid": "f3f3ef07eacb2aaf92962d461de778ba",
"score": "0.5226737",
"text": "def test_blog_post_has_the_correct_title(self):\n blog_post = BlogPostFactory.create(title=\"Here is a blog post\")\n assert blog_post.title == \"Here is a blog post\"",
"title": ""
},
{
"docid": "a3cc7c36a98399c69080319e324122d9",
"score": "0.5202243",
"text": "def check_title(mol, infile):\n extract_fname = os.path.splitext(os.path.basename(infile))[0]\n extract_fname = extract_fname.replace(\"-\", \"\")\n extract_fname = extract_fname.replace(\".\", \"\")\n if mol.GetTitle() == \"\":\n mol.SetTitle(extract_fname)\n\n return mol",
"title": ""
},
{
"docid": "1f8d95eea9f29993b1b9a658631ac1f4",
"score": "0.5183001",
"text": "def test_conformance_v1_0_schemadef_req_wf_param(self):\n self.cwl_populator.run_conformance_test(\"\"\"v1.0\"\"\", \"\"\"Test SchemaDefRequirement definition used in workflow parameter\n\"\"\")",
"title": ""
},
{
"docid": "667c9369b3d30e8aa0eab47b6d01ab50",
"score": "0.51668197",
"text": "def add_movie(token):\n\n data = request.get_json()\n\n if not data.get(\"title\"):\n return jsonify({\n \"success\": False,\n \"error\": 422,\n \"message\": \"Movie's title is not provided.\"\n }), 422\n\n if not data.get(\"rating\"):\n return jsonify({\n \"success\": False,\n \"error\": 422,\n \"message\": \"Movie's rating is not provided.\"\n }), 422\n\n try:\n movie = Movie(\n title=data[\"title\"],\n rating=data[\"rating\"],\n release_date=data.get(\"release_date\"),\n desc=data.get(\"desc\"))\n movie.insert()\n except Exception:\n abort(500)\n\n return jsonify({\n \"success\": True,\n \"movie\": movie.format()\n })",
"title": ""
},
{
"docid": "a16be31d58eb258b2af5047908722631",
"score": "0.5165649",
"text": "def submit_title():\n\n title = title_entry.get().strip()\n\n if title:\n conn = sqlite3.connect(_DBPATH)\n cur = conn.cursor()\n cur.execute(\"INSERT INTO dvd_collection VALUES(?)\", (title,))\n conn.commit()\n conn.close()\n\n title_entry.delete(0, END)\n show_collection()",
"title": ""
},
{
"docid": "dfcbeedacd36db01a17712196faaed8b",
"score": "0.5162177",
"text": "def test_autogenerated_title(self):\n control_title = self.control.title\n audit_title = self.audit.title\n response = self.assessment_post()\n title = response.json[\"assessment\"][\"title\"]\n self.assertIn(audit_title, title)\n self.assertIn(control_title, title)",
"title": ""
},
{
"docid": "276cbbe0f940292b45b076cbd3c80b4d",
"score": "0.513228",
"text": "def test_construct(self):\n\n # This should construct a valid movie\n params = {\"title\" : \"title\",\n \"stub\" : \"stub\",\n \"path\" : \"path\",\n \"image_type\" : \"png\",\n \"variable\" : \"density\",\n \"mode\" : \"profile : contrast\",\n \"window_x_lo\" : -1.2,\n \"window_x_hi\" : 1.2,\n \"window_y_lo\" : -1,\n \"window_y_hi\" : 1,\n \"window_z_lo\" : -1,\n \"window_z_hi\" : 1,\n \"time_lo\" : 13,\n \"time_hi\" : 73,\n \"value_lo\" : 1.5,\n \"value_hi\" : 12.5,\n \"make_movie\" : True,\n \"fps\" : 60,\n \"movie_type\" : \"avi\",\n \"final_pause\" : \"16.5s\",\n \"masks\" : [],\n \"mask_method\" : \"force low\",\n \"xlines\" : [-1, -0.5, 0, 0.5, 1]\n }\n movie = MovieDescriptor(params, [])\n\n # Use defaults where allowed\n p = copy.deepcopy(params)\n del p[\"title\"]\n del p[\"path\"]\n del p[\"mode\"]\n del p[\"window_x_lo\"]\n del p[\"window_x_hi\"]\n del p[\"window_y_lo\"]\n del p[\"window_y_hi\"]\n del p[\"window_z_lo\"]\n del p[\"window_z_hi\"]\n del p[\"time_lo\"]\n del p[\"time_hi\"]\n del p[\"value_lo\"]\n del p[\"value_hi\"]\n del p[\"fps\"]\n del p[\"movie_type\"]\n del p[\"final_pause\"]\n del p[\"masks\"]\n del p[\"mask_method\"]\n movie = MovieDescriptor(params, [])\n\n # Require a stub\n p = copy.deepcopy(params)\n del p[\"stub\"]\n self.assertRaises(KeyError, MovieDescriptor, p, [])\n\n # Require image type\n p = copy.deepcopy(params)\n del p[\"image_type\"]\n self.assertRaises(KeyError, MovieDescriptor, p, [])\n\n # Require variable\n p = copy.deepcopy(params)\n del p[\"variable\"]\n self.assertRaises(KeyError, MovieDescriptor, p, [])\n\n # Require window_x_lo < window_x_hi\n p = copy.deepcopy(params)\n p[\"window_x_lo\"] = p[\"window_x_hi\"]\n self.assertRaises(DescriptorError, MovieDescriptor, p, [])\n\n # Require window_y_lo < window_y_hi\n p = copy.deepcopy(params)\n p[\"window_y_lo\"] = p[\"window_y_hi\"]\n self.assertRaises(DescriptorError, MovieDescriptor, p, [])\n\n # Require window_z_lo < window_z_hi\n p = copy.deepcopy(params)\n p[\"window_z_lo\"] = p[\"window_z_hi\"]\n self.assertRaises(DescriptorError, MovieDescriptor, p, [])\n\n # Require time_lo < time_hi\n p = copy.deepcopy(params)\n p[\"time_lo\"] = p[\"time_hi\"]\n self.assertRaises(DescriptorError, MovieDescriptor, p, [])\n\n # Require value_lo < value_hi\n p = copy.deepcopy(params)\n p[\"value_lo\"] = p[\"value_hi\"]\n self.assertRaises(DescriptorError, MovieDescriptor, p, [])\n\n # Allow make_movie to take default\n p = copy.deepcopy(params)\n del p[\"make_movie\"]\n movie = MovieDescriptor(p, [])\n\n # Require fps only if make_movie is true\n p = copy.deepcopy(params)\n del p[\"fps\"]\n del p[\"final_pause\"]\n p[\"make_movie\"] = False\n movie = MovieDescriptor(p, [])\n p[\"make_movie\"] = True\n self.assertRaises(KeyError, MovieDescriptor, p, [])\n\n # Require movie_type only if make_movie is true\n p = copy.deepcopy(params)\n del p[\"movie_type\"]\n p[\"make_movie\"] = False\n movie = MovieDescriptor(p, [])\n p[\"make_movie\"] = True\n self.assertRaises(KeyError, MovieDescriptor, p, [])\n\n # Require fps if final pause specified in seconds\n p = copy.deepcopy(params)\n del p[\"fps\"]\n p[\"make_movie\"] = False\n p[\"final_pause\"] = \"16f\"\n movie = MovieDescriptor(p, []) # prove it works up to here\n p[\"final_pause\"] = \"13.6s\"\n self.assertRaises(DescriptorError, MovieDescriptor, p, [])\n\n # Try bad value for mode\n p = copy.deepcopy(params)\n p[\"mode\"] = \"bad mode\"\n self.assertRaises(DescriptorError, MovieDescriptor, p, [])\n\n # Try bad values for window limits\n p = copy.deepcopy(params)\n p[\"window_x_lo\"] = \"yes\"\n self.assertRaises(ValueError, MovieDescriptor, p, [])\n p = copy.deepcopy(params)\n p[\"window_x_hi\"] = \"yes\"\n self.assertRaises(ValueError, MovieDescriptor, p, [])\n p = copy.deepcopy(params)\n p[\"window_y_lo\"] = \"yes\"\n self.assertRaises(ValueError, MovieDescriptor, p, [])\n p = copy.deepcopy(params)\n p[\"window_y_hi\"] = \"yes\"\n self.assertRaises(ValueError, MovieDescriptor, p, [])\n p = copy.deepcopy(params)\n p[\"window_z_lo\"] = \"yes\"\n self.assertRaises(ValueError, MovieDescriptor, p, [])\n p = copy.deepcopy(params)\n p[\"window_z_hi\"] = \"yes\"\n self.assertRaises(ValueError, MovieDescriptor, p, [])\n\n # Try bad values for time limits\n p = copy.deepcopy(params)\n p[\"time_lo\"] = \"yes\"\n self.assertRaises(ValueError, MovieDescriptor, p, [])\n p = copy.deepcopy(params)\n p[\"time_hi\"] = \"yes\"\n self.assertRaises(ValueError, MovieDescriptor, p, [])\n\n # Try bad values for value limits\n p = copy.deepcopy(params)\n p[\"value_lo\"] = \"yes\"\n self.assertRaises(ValueError, MovieDescriptor, p, [])\n p = copy.deepcopy(params)\n p[\"value_hi\"] = \"yes\"\n self.assertRaises(ValueError, MovieDescriptor, p, [])\n\n # Try bad value for fps\n p = copy.deepcopy(params)\n p[\"fps\"] = \"what?\"\n self.assertRaises(ValueError, MovieDescriptor, p, [])\n\n # Try bad values for final pause\n p = copy.deepcopy(params)\n p[\"final_pause\"] = \"what?\"\n self.assertRaises(DescriptorError, MovieDescriptor, p, [])\n p[\"final_pause\"] = \"13\"\n self.assertRaises(DescriptorError, MovieDescriptor, p, [])\n p[\"final_pause\"] = \"13.5f\"\n self.assertRaises(DescriptorError, MovieDescriptor, p, [])\n\n # Try bad value for mask method\n p = copy.deepcopy(params)\n p[\"mask_method\"] = \"what?\"\n self.assertRaises(DescriptorError, MovieDescriptor, p, [])\n\n # Try bad value for xlines\n p = copy.deepcopy(params)\n p[\"xlines\"] = [0, 0.0, \"zero\"]\n self.assertRaises(ValueError, MovieDescriptor, p, [])",
"title": ""
},
{
"docid": "f0059714252a46f245f9067e9f0e2644",
"score": "0.51295537",
"text": "def test_add_post_with_title(self):\n response = self.client.post(\n reverse(\"wagtaildocs:add_multiple\"),\n {\n \"title\": \"(TXT) test title\",\n \"files[]\": SimpleUploadedFile(\"test.txt\", b\"Simple text document\"),\n },\n )\n\n # Check response\n self.assertEqual(response.status_code, 200)\n self.assertEqual(response[\"Content-Type\"], \"application/json\")\n self.assertTemplateUsed(\n response, \"wagtailadmin/generic/multiple_upload/edit_form.html\"\n )\n\n # Check document\n self.assertIn(\"uploaded_document\", response.context)\n self.assertIn(\".txt\", response.context[\"uploaded_document\"].file.name)\n\n # Check JSON\n response_json = json.loads(response.content.decode())\n self.assertIn(\"uploaded_document_id\", response_json)\n self.assertIn(\"form\", response_json)\n self.assertEqual(\n response_json[\"uploaded_document_id\"],\n response.context[\"uploaded_document\"].id,\n )\n self.assertTrue(response_json[\"success\"])",
"title": ""
},
{
"docid": "9f608bc74547132d240524f63d28de8d",
"score": "0.510398",
"text": "def __init__(self, title, year, genre):\n\t\tself.title = title\n\t\tself.year = year\n\t\tself.genre = genre",
"title": ""
},
{
"docid": "df407cac19148314cd7ed361237749c2",
"score": "0.50992376",
"text": "def generate_title(self, required = True):\r\n\r\n #-----------------------------------------------------generate title\r\n if self.title:\r\n self.title_fragment = \"%s\" % (self.title)\r\n if self.type == \"thesis\":\r\n self.title_fragment += \" (Master's thesis)\"\r\n elif self.type == \"thesis-unpub\":\r\n self.title_fragment += \" (Unpublished master's thesis)\"\r\n elif self.type == \"dissertation\":\r\n self.title_fragment += \" (Doctoral dissertation)\"\r\n elif self.type == \"dissertation-unpub\":\r\n self.title_fragment += \" (Unpublished doctoral dissertation)\"\r\n\r\n self.title_fragment += \".\"\r\n if required and self.title_fragment == \"\":\r\n raise Exception(\"No Title Included\")\r\n #-----------------------------------------------------generate title\r",
"title": ""
},
{
"docid": "068b0a0e82b5c0151793575f2b821933",
"score": "0.50957114",
"text": "def test_collection_serializer_validate_title(mocker):\n mocker.patch(\"ui.serializers.get_moira_client\")\n collection = factories.CollectionFactory(title=\"\")\n serialized_data = serializers.CollectionSerializer(collection).data\n with pytest.raises(ValidationError) as exc:\n serializers.CollectionSerializer(data=serialized_data).is_valid(\n raise_exception=True\n )\n assert exc.value.detail == {\"title\": [\"This field may not be blank.\"]}",
"title": ""
},
{
"docid": "283dc64dd382994842c57848ceffc89d",
"score": "0.50809306",
"text": "def test_create_entity_with_null_id_string(self):\n request_to_validate = {'name': self.resource_name,\n 'id_string': None}\n self.create_schema_validator.validate(request_to_validate)",
"title": ""
},
{
"docid": "0150abff4438d41887a0c94bd8911ca3",
"score": "0.50784457",
"text": "def test_title(self):\n title_in = \"title\"\n meta, title = _set_title(dict(), title=title_in)\n self.assertEqual(title_in, title)\n self.assertEqual(title_in, meta[\"customTitle\"])\n self.assertEqual(WidgetTitleValue.CUSTOM_TITLE, meta[\"showTitleValue\"])\n\n title_in = False\n meta, title = _set_title(dict(), title=title_in)\n self.assertIsNone(title)\n self.assertIsNone(meta[\"customTitle\"])\n self.assertEqual(WidgetTitleValue.DEFAULT, meta[\"showTitleValue\"])\n\n title_in = None\n meta, title = _set_title(dict(), title=title_in)\n self.assertIsNone(title)\n self.assertIsNone(meta[\"customTitle\"])\n self.assertEqual(WidgetTitleValue.NO_TITLE, meta[\"showTitleValue\"])",
"title": ""
},
{
"docid": "be687b1ed00bdb1442f510245dde6601",
"score": "0.50766283",
"text": "def test_create_entity_with_unicode_name_validates(self):\n request_to_validate = {'name': u'αβγδ'}\n self.create_schema_validator.validate(request_to_validate)",
"title": ""
},
{
"docid": "c555f9010e290ce30e59eed29a41e2d4",
"score": "0.5061101",
"text": "def testTitleArgument(self):\n\n path = pathDatafile(self.pdb['file'])\n title = 'small protein'\n self.assertEqual(parseMMCIF(path, title=title).getTitle(),\n title, 'parseMMCIF failed to set user given title')\n\n name = 1999\n self.assertEqual(parseMMCIF(path, title=name).getTitle(),\n str(name), 'parseMMCIF failed to set user given non-string name')",
"title": ""
},
{
"docid": "7aa72cf2f52469c5ea00ae9055f623ad",
"score": "0.5060484",
"text": "def test_create_failure_400(app, client):\n\n data = {\n \"audioFileType\":\"song\",\n \"audioFileMetadata\":{\n \"name\":\"Fast Lane\"\n }\n }\n\n response = client.post('/', data=json.dumps(data), headers=headers)\n assert response.status_code == 400",
"title": ""
},
{
"docid": "0f4244f71eb4b8384ea0f08b18e391f9",
"score": "0.5056029",
"text": "def test_missing_schema():\n with pytest.raises(exceptions.SchemaNotFoundError):\n model_factory.model_factory(name=\"Missing\", base=None, schemas={})",
"title": ""
},
{
"docid": "f1c2a331a700a89ba8e33c4fac6398e8",
"score": "0.50544006",
"text": "def test_title_is_none_on_no_title_placeholder(self):\n # setup ------------------------\n shapes = test_shapes.empty_shape_collection\n # verify -----------------------\n assert_that(shapes.title, is_(None))",
"title": ""
},
{
"docid": "82f79928af5c06ecf33332f8cd5badc8",
"score": "0.5035332",
"text": "def __init__(self, movie_id: str, title: str, release_year: Set[int], genre: Set[str],\n duration: Set[int], language: Set[str], rating: float) -> None:\n self.movie_id = movie_id\n self.title = title\n self.release_year = release_year\n self.genre = genre\n self.duration = duration\n self.language = language\n self.rating = rating",
"title": ""
},
{
"docid": "2f9445bfa47fe9fbd684de57795d9a5e",
"score": "0.50273865",
"text": "def create_schema(augur_app):\n check_pgpass_credentials(augur_app.config.get_raw_config())\n run_psql_command_in_database(augur_app, '-f', 'schema/create_schema.sql')",
"title": ""
},
{
"docid": "0381aa8f591fd5eba805987d6708f097",
"score": "0.50263643",
"text": "def test_sanitize_multi_word_title(self):\n title = 'Game of Thrones'\n self.assertEqual(\n sanitize_title(title),\n 'game of thrones',\n )",
"title": ""
},
{
"docid": "da2b77b936f35c1b367e9861cb67d4dd",
"score": "0.5022477",
"text": "def f(self, title, doc):\n self.addl(frame(doc_main=doc, title=title))",
"title": ""
},
{
"docid": "519714b8ad17ad8a3f557bcb96fe18ba",
"score": "0.5020081",
"text": "def test_schema_name_should_be_alphanumeric():\n # Escaping spaces\n event = Event(name=\" name \", description=\"description\")\n schema = SchemaGen.gen_schema_from_record(event)\n assert schema.__name__ == \"name\"\n # Escaping symbols\n event = Event(name=\"name@!,.123name?`~\", description=\"description\")\n schema = SchemaGen.gen_schema_from_record(event)\n assert schema.__name__ == \"name123name\"",
"title": ""
},
{
"docid": "5255d1ea6a5379a296229a4d557868f0",
"score": "0.5003132",
"text": "def _check_title_is_not_blank(self):\n for record in self:\n if not record.title:\n msg = \"\"\"Invalid agenda title! The event title should not be\\\n blank.\"\"\"\n raise ValidationError(msg)",
"title": ""
},
{
"docid": "68d4f6c0d2ecaa69eaf974d3959c80be",
"score": "0.50025874",
"text": "def test_api_video_update_detail_token_user_title_empty(self):\n video = factories.VideoFactory(title=\"my title\")\n jwt_token = InstructorOrAdminLtiTokenFactory(playlist=video.playlist)\n data = {\"title\": \" \"}\n response = self.client.put(\n f\"/api/videos/{video.id}/\",\n data,\n HTTP_AUTHORIZATION=f\"Bearer {jwt_token}\",\n content_type=\"application/json\",\n )\n self.assertEqual(response.status_code, 400)\n self.assertEqual(response.json(), {\"title\": [\"This field may not be blank.\"]})\n video.refresh_from_db()\n self.assertEqual(video.title, \"my title\")",
"title": ""
},
{
"docid": "ba7069a9b66d5cd8e9378322a7951af4",
"score": "0.49903286",
"text": "def create_categories():\n request_body = request.get_json()\n print(\"Whats coming in from FE? \", request_body) # coming from the FE: {'title': 'Produce'} now {'category_title': 'Produce'}\n\n if \"category_title\" not in request_body:\n return make_response({\"details\": \"Enter valid category name\"}, 400)\n new_category = Category(category_title=request_body[\"category_title\"])\n \n db.session.add(new_category)\n db.session.commit()\n return {\"category\": new_category.json_formatted()}, 201",
"title": ""
},
{
"docid": "c7e33151ff2083a56e701f5d497af39e",
"score": "0.49871504",
"text": "def create_by_file(self, local_file, title=None):\n with open(local_file) as pf:\n create_data = json.loads(pf.read())\n if title:\n create_data['title'] = title\n return self.create_by_data(create_data)",
"title": ""
},
{
"docid": "49f371b7dc2df5f2e41abb44695fa74e",
"score": "0.498674",
"text": "def _parse_title(it, current, recipe):\n current = _skip_empty(it, current)\n is_metadata, attribute, value = _test_metadata(current)\n if is_metadata and attribute.lower() == 'title':\n recipe.title = value\n current = next(it)\n return current",
"title": ""
},
{
"docid": "c49a842e2a1bd59d00fd2950210871d9",
"score": "0.4982823",
"text": "def create_schema_validate(self, event=EVENT, name=\"schema\"):\n return self.save(SchemaValidate(event=event, name=name))",
"title": ""
},
{
"docid": "661b87408f05c437c3837bc17ccd12d4",
"score": "0.4980257",
"text": "def create(self, validated_data):\n\n # Create movie instance after popping genre list from dict\n genre_list = validated_data.pop('genres', list())\n movie_obj = Movie.objects.create(**validated_data)\n\n # Associates list of genre objects to movie obj\n for genre in genre_list:\n movie_obj.genres.add(genre)\n movie_obj.save()\n\n return movie_obj",
"title": ""
},
{
"docid": "4e88e3e92f7b6c751ac24805400085f4",
"score": "0.4967314",
"text": "def test_required(self):\n movie = Movie(year=2012)\n self.assertRaises(TypeError, movie.save)",
"title": ""
},
{
"docid": "8f28b184450d545dc835e882d2427dc4",
"score": "0.49665275",
"text": "def test_create_entity_with_name_too_long_raises_exception(self):\n invalid_name = 'a' * 256\n request_to_validate = {'name': invalid_name}\n self.assertRaises(exception.SchemaValidationError,\n self.create_schema_validator.validate,\n request_to_validate)",
"title": ""
},
{
"docid": "76a511a1b2734027ddc770c2217e9acd",
"score": "0.49446046",
"text": "def make_movie(movie_data):\n\n # if poster exists make movie, else skip\n if movie_data[\"poster_path\"]:\n title = movie_data[\"title\"]\n plot = movie_data[\"overview\"].encode('utf-8')\n movie_id = movie_data[\"id\"]\n year = movie_data['release_date'][:4]\n youtube_link = get_youtube(movie_id)\n poster = \"https://image.tmdb.org/t/p/w600_and_h900_bestv2/\" + str(movie_data[\"poster_path\"])\n movie = movie_class.Movie(title, poster, plot, year, youtube_link)\n else:\n movie = False\n\n return movie",
"title": ""
},
{
"docid": "6e67cb7777ad5e15b96e19736fdfb79b",
"score": "0.49263716",
"text": "def test__is_title_true_for_title_placeholder(self):\n # setup ------------------------\n xpath = './p:cSld/p:spTree/p:sp'\n title_placeholder_sp = self.sld.xpath(xpath, namespaces=nsmap)[0]\n base_shape = _BaseShape(title_placeholder_sp)\n # verify -----------------------\n actual = base_shape._is_title\n msg = \"expected True, got %s\" % (actual)\n self.assertTrue(actual, msg)",
"title": ""
},
{
"docid": "09621091738ca015526bc1b36106ad4a",
"score": "0.49252534",
"text": "def test_create_movie_incomplete_payload(self):\n\n incomplete_payload = {\n 'title': 'Batman',\n 'release_date': '2021-06-24T14:18:35Z',\n 'imdb_ranking': 9.7,\n }\n\n self.client.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)\n url = reverse('general_models', kwargs={'database_name':'ine_database','table_name': 'Movie'})\n \n response = self.client.post(\n url, \n data=json.dumps(incomplete_payload), \n content_type='application/json'\n )\n\n result = json.loads(response.content)\n\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\n self.assertEqual(result, {'detail': 'IntegrityError(\\'null value in column \"director_id\" violates not-null constraint\\\\nDETAIL: Failing row contains (1, Batman, 2021-06-24 14:18:35+00, 9.7, null).\\\\n\\')', 'status_code': 400})",
"title": ""
},
{
"docid": "2b74a5fc68f2034ea9d5d3db8d07f493",
"score": "0.49246928",
"text": "def test_valid_fields_work(self):\n from ..models import Film\n field_choices = Film.FIELD_CHOICES\n params = {'fields': field_choices}\n from colander import Invalid\n try:\n self.schema.deserialize(params)\n except Invalid as err:\n self.fail(err.msg)",
"title": ""
},
{
"docid": "6249a13b68fccb0bb0fe0e2025379e54",
"score": "0.49218458",
"text": "def addSchema(schema_id):",
"title": ""
},
{
"docid": "1479ab64fcd9653d8ed0dc20f77ac7a6",
"score": "0.4918597",
"text": "def test_object_create(self):\n\n # test the creation of a dataset instance\n venue = Venue()\n venue.name = \"paper\"\n time = dt.date(2019, 4, 17)\n venue.publication_date = time\n venue.type = \"J\"\n venue.publisher = \"CSIRO\"\n venue.keywords = \"J\"\n venue.peer_reviewed = \"N\"\n venue.id = \"01\"\n\n self.assertEquals(venue.publication_date, time)\n self.assertEquals(venue.type, \"J\")\n self.assertEquals(venue.keywords, \"J\")\n self.assertEquals(venue.peer_reviewed, \"N\")\n self.assertEquals(venue.__str__(), \"paper by CSIRO on 2019-04-17\")\n url = venue.get_absolute_url()\n\n # test name is required\n venue.name = None\n try: # It is missing required property name\n venue.save()\n except RequiredProperty:\n assert True\n else:\n assert False\n\n # test publication_date is required\n venue.name = \"paper\"\n venue.publication_date = None\n try: # It is missing required property publication_date\n venue.save()\n except RequiredProperty:\n assert True\n else:\n assert False\n\n # test type is required\n venue.publication_date = time\n venue.type = None\n try: # It is missing required property type\n venue.save()\n except RequiredProperty:\n assert True\n else:\n assert False\n\n # test publisher is required\n venue.type = \"J\"\n venue.publisher = None\n try: # It is missing required property publisher\n venue.save()\n except RequiredProperty:\n assert True\n else:\n assert False\n\n # test keywords is required\n venue.publisher = \"CSIRO\"\n venue.keywords = None\n try: # It is missing required property keywords\n venue.save()\n except RequiredProperty:\n assert True\n else:\n assert False\n\n # test peer_reviewed is required\n venue.keywords = \"J\"\n venue.peer_reviewed = None\n try: # It is missing required property peer-reviewed\n venue.save()\n except RequiredProperty:\n assert True\n else:\n assert False",
"title": ""
},
{
"docid": "8ef7a5964d57c1e3acff46b62c321ae8",
"score": "0.49142265",
"text": "def _make_unique_title(self, title):\n unique_title = title\n\n for counter in itertools.count():\n unique_title = \"{}-{}\".format(title, counter)\n if not idaapi.find_tform(unique_title):\n break\n\n return unique_title",
"title": ""
},
{
"docid": "aaeef998d83a0009817371697cabef6b",
"score": "0.49110955",
"text": "def test_200_create_movie_header(self):\n res = self.client().post('/movies', json=self.movie, headers=headers)\n data = json.loads(res.data)\n self.assertEqual(res.status_code, 200)\n self.assertEqual(data['success'], True)\n self.assertTrue(len(data['movie']))",
"title": ""
},
{
"docid": "c3bf121d294ddf25d9acbf9b07ed0368",
"score": "0.49070024",
"text": "def Title():",
"title": ""
},
{
"docid": "d3e799a6c9b5728f3fbaa117d2140871",
"score": "0.48949102",
"text": "def test_xform_title(self):\n xform = mommy.make('ona.XForm', title=\"test\")\n cattle_task = mommy.make(\n 'main.Task',\n name='Cattle Price',\n target_content_object=xform\n )\n\n self.assertEqual(xform.title, cattle_task.xform_title)",
"title": ""
},
{
"docid": "68cafa386c60d174398c1859e7607dbb",
"score": "0.4891682",
"text": "def test_post_duplicate_movie(self):\n name = \"Movie\" + str(random.randint(0, 1000000))\n desc = str(random.randint(0, 1000000))\n r = self.post_movie(name=name, description=desc)\n self.assertTrue(r.status_code == 200,\n 'Expected 200, received ' + str(r.status_code))\n r = self.post_movie(name=name, description=desc)\n self.assertTrue(r.status_code == 200,\n 'Expected 200, received ' + str(r.status_code))",
"title": ""
},
{
"docid": "907f44d3a423fc62fcab2f8dd0fbcd75",
"score": "0.48908553",
"text": "def set_Title(self, value):\n super(CreatePeopleInputSet, self)._set_input('Title', value)",
"title": ""
},
{
"docid": "2f13b676411fff597f81834eb1ad610b",
"score": "0.4888249",
"text": "def assert_title(driver, title):\n if not title_is(title)(driver):\n raise AssertionError('title no matches `%s`' % str(title))",
"title": ""
},
{
"docid": "964c26f770e54d39ecd8f8f29e3bc11d",
"score": "0.48834032",
"text": "def _parse_title(self, doc):\n # title\n title = None\n\n if (title is None\n and 'title' in doc\n and isinstance(doc['title'], list)\n and len(doc['title']) > 0\n and len(doc['title'][0]) > 0\n ):\n title = doc['title'][0]\n\n return title",
"title": ""
},
{
"docid": "3140c15ae00321663b369f277f72b066",
"score": "0.48699915",
"text": "def prepare_title_form(self) -> StringField:\n title_form = StringField(\"Title\", default=self._default_title, validators=[DataRequired()])\n return title_form",
"title": ""
},
{
"docid": "94e8739f1d0d6e046c58ee5c73039ec0",
"score": "0.48653615",
"text": "def test_add_post_with_title(self):\n response = self.client.post(\n reverse(\"wagtaildocs:add_multiple\"),\n {\n \"title\": \"(TXT) test title\",\n \"files[]\": SimpleUploadedFile(\"test.txt\", b\"Simple text document\"),\n },\n )\n\n # Check response\n self.assertEqual(response.status_code, 200)\n self.assertEqual(response[\"Content-Type\"], \"application/json\")\n self.assertTemplateUsed(\n response, \"wagtailadmin/generic/multiple_upload/edit_form.html\"\n )\n\n # Check document\n self.assertIn(\"doc\", response.context)\n self.assertEqual(response.context[\"doc\"].title, \"(TXT) test title\")\n self.assertIn(\".txt\", response.context[\"doc\"].filename)\n self.assertTrue(response.context[\"doc\"].file_size)\n self.assertTrue(response.context[\"doc\"].file_hash)\n self.assertEqual(\n response.context[\"edit_action\"],\n \"/admin/documents/multiple/%d/\" % response.context[\"doc\"].id,\n )\n self.assertEqual(\n response.context[\"delete_action\"],\n \"/admin/documents/multiple/%d/delete/\" % response.context[\"doc\"].id,\n )\n\n # check that it is in the root collection\n doc = get_document_model().objects.get(title=\"(TXT) test title\")\n root_collection = Collection.get_first_root_node()\n self.assertEqual(doc.collection, root_collection)\n\n # Check form\n self.assertIn(\"form\", response.context)\n self.assertEqual(\n set(response.context[\"form\"].fields),\n set(get_document_model().admin_form_fields) - {\"file\", \"collection\"},\n )\n self.assertEqual(response.context[\"form\"].initial[\"title\"], \"(TXT) test title\")\n\n # Check JSON\n response_json = json.loads(response.content.decode())\n self.assertIn(\"doc_id\", response_json)\n self.assertIn(\"form\", response_json)\n self.assertIn(\"success\", response_json)\n self.assertEqual(response_json[\"doc_id\"], response.context[\"doc\"].id)\n self.assertTrue(response_json[\"success\"])\n\n # form should not contain a collection chooser\n self.assertNotIn(\"Collection\", response_json[\"form\"])",
"title": ""
},
{
"docid": "64305847d27eb1379d895866663165e8",
"score": "0.48575357",
"text": "def test_good_episode_title(self):\n self.assertEqual(self.episode.title, 'Winter Is Coming')",
"title": ""
},
{
"docid": "7ac52f5ab4889a0aa119f07f8e4f1278",
"score": "0.48472223",
"text": "def build_song_schema():\n # specify schema for dataframe\n song_schema = T.StructType([\n T.StructField('song_id', T.StringType()),\n T.StructField('num_songs', T.IntegerType()),\n T.StructField('title', T.StringType()),\n T.StructField('artist_name', T.StringType()),\n T.StructField('artist_latitude', T.DoubleType()),\n T.StructField('year', T.IntegerType()),\n T.StructField('duration', T.DoubleType()),\n T.StructField('artist_id', T.StringType()),\n T.StructField('artist_longitude', T.DoubleType()),\n T.StructField('artist_location', T.StringType())\n ]) \n return song_schema",
"title": ""
},
{
"docid": "691b42c61294ece00abc86ab049079b9",
"score": "0.48447487",
"text": "def add(data):\n title = Title(data[\"title_name\"])\n return db.Database.add(\"titles\", title.db_data())",
"title": ""
},
{
"docid": "ece3d781aa6ae566f951e9b96dd1e7e0",
"score": "0.48360124",
"text": "def _validate_constructor(title: str, artist: str, runtime: str, rating: int, album: str,\n genre: str) -> None:\n if not ((type(title) != str) or (type(artist) != str) or (type(runtime) != str) or\n (type(rating) != int) or (type(album) != str) or\n ((type(genre) != str) and type(genre) is None)):\n pass\n else:\n raise ValueError('Values must be a string and \"rating\" must be an int.')",
"title": ""
},
{
"docid": "3687617daa185932a98528fb805f0c26",
"score": "0.48318827",
"text": "def schema():",
"title": ""
},
{
"docid": "3cb0df488ea66068b2523adaef5fe744",
"score": "0.4831116",
"text": "def add(**kwargs):\n kwargs.pop('_id', None)\n kwargs = Movies.sanitize_insert_arguments(**kwargs)\n if Movies.can_add_movie(**kwargs):\n mongo.db.movies.insert( kwargs )\n return {'success': True}\n else:\n return {'success': False, 'errMsg': 'Movie name already exists'}",
"title": ""
},
{
"docid": "874281cbe8cf149ee8fff93329a7bf1a",
"score": "0.48296887",
"text": "def test_create_entity_with_valid_id_strings(self):\n valid_id_strings = [str(uuid.uuid4()), uuid.uuid4().hex, 'default']\n for valid_id in valid_id_strings:\n request_to_validate = {'name': self.resource_name,\n 'id_string': valid_id}\n self.create_schema_validator.validate(request_to_validate)",
"title": ""
},
{
"docid": "8d62eb3772a53abf68e05bb99a726ec0",
"score": "0.4828241",
"text": "def _validate_schema(query, collection, schema, document):\n for key in schema:\n if key not in document:\n document[key] = schema[key]\n\n collection.replace_one(query, document)",
"title": ""
},
{
"docid": "aa29b8c1b877765f4fe6c0aeac185a08",
"score": "0.48219767",
"text": "def test_post_long_movie(self):\n r = self.post_movie(name=\"\", description=\"\")\n self.assertTrue(r.status_code == 200,\n 'Expected 200, received ' + str(r.status_code))",
"title": ""
},
{
"docid": "a11607c133632f81799b8d184d6a7a31",
"score": "0.48219237",
"text": "def _gen_movies(self, df):\n bc = list(map(lambda x: Movies(id=x[1][0],\n original_title=x[1][1],\n overview=x[1][2],\n tagline=x[1][3],\n title=x[1][4]), df.iterrows()))\n\n Movies.objects.bulk_create(bc, ignore_conflicts=True)",
"title": ""
},
{
"docid": "28eb4b3287f08654ae7316f76a21c3e1",
"score": "0.48084953",
"text": "def __init__(self, title, year_of_released, director, type, story_line, poster, trailer):\n self.title = title\n self.year_of_released = year_of_released\n self.director = director\n self.type = type\n self.story_line = story_line\n self.poster = poster\n self.trailer = trailer",
"title": ""
},
{
"docid": "009c8fa448b7269f05c6b53827814161",
"score": "0.48060307",
"text": "def _id_sanitize(title):\n _validate_type(title, str, \"title\")\n # replace any whitespace runs with underscores\n title = re.sub(r\"\\s+\", \"_\", title)\n # replace any non-alphanumeric (plus dash and underscore) with underscores\n # (this is very greedy but should be safe enough)\n title = re.sub(\"[^a-zA-Z0-9_-]\", \"_\", title)\n return title",
"title": ""
},
{
"docid": "cfe648fa8c71f5fcdc6e875208021494",
"score": "0.4800133",
"text": "def test_unobtainable_title():\n with pytest.raises(ValueError) as error_info:\n EntityParser(data=None, parser=JSONParser()).parse_title()\n\n assert error_info.value.args[0] == \"Failed to get title from entity data\", \"Wrong error\"",
"title": ""
},
{
"docid": "4af934c9f11602dddd1190c493e90ebe",
"score": "0.47873044",
"text": "def title():",
"title": ""
},
{
"docid": "efc081fd4b60282f1bd944e112de515b",
"score": "0.47846428",
"text": "def set_title(self):\n self.title = None\n raw_title = self.raw_data[1].get(\"hasTitle\")\n if not raw_title:\n return\n if len(raw_title) > 1:\n for title in raw_title:\n if title.get(\"@type\") in [\"Title\", \"CoverTitle\"]:\n self.title = title.get(\"mainTitle\")\n else:\n if raw_title[0].get(\"@type\") == \"Title\":\n self.title = raw_title[0].get(\"mainTitle\")\n\n if not self.lang_wikidata:\n language = \"und\"\n else:\n language = self.lang_wikidata\n if self.title:\n wd_title = utils.package_monolingual(\n self.title, language)\n self.add_statement(\"title\", wd_title, ref=self.source)",
"title": ""
},
{
"docid": "827e22194c37ad68a89aa0714c7b7729",
"score": "0.47839814",
"text": "def make_movie(entry):\n\t\treturn Movie(entry[0], entry[1], entry[2])",
"title": ""
},
{
"docid": "371763fcaddd13de81cdb0e7a7170840",
"score": "0.47740352",
"text": "def test_missing_tablename():\n with pytest.raises(exceptions.MalformedSchemaError):\n model_factory.model_factory(\n name=\"MissingTablename\", base=None, schemas={\"MissingTablename\": {}}\n )",
"title": ""
},
{
"docid": "39b9deab00b9f6daaa5131d38c768564",
"score": "0.47713456",
"text": "def title(self, title):\n if title is None:\n raise ValueError(\"Invalid value for `title`, must not be `None`\")\n\n self._title = title",
"title": ""
},
{
"docid": "9339bb86af9c806a7729f9988d7c2cc6",
"score": "0.476243",
"text": "def test_post_long_movie(self):\n r = self.post_movie(name=\"LongName\"*100000, description=\"LongDesc\"*100000)\n self.assertTrue(r.status_code == 200,\n 'Expected 200, received ' + str(r.status_code))",
"title": ""
},
{
"docid": "1992308dbd3f5078fceeb16ab4719d64",
"score": "0.47607005",
"text": "def test_validate_service_create_fails_when_type_too_short(self):\n request_to_validate = {'type': ''}\n self.assertRaises(exception.SchemaValidationError,\n self.create_service_validator.validate,\n request_to_validate)",
"title": ""
},
{
"docid": "d08bdbdb0dc4da002e4ad13aaca89b63",
"score": "0.4760509",
"text": "def title(self, title):\n if title is None:\n raise ValueError(\"Invalid value for `title`, must not be `None`\") # noqa: E501\n if title is not None and len(title) > 255:\n raise ValueError(\"Invalid value for `title`, length must be less than or equal to `255`\") # noqa: E501\n if title is not None and len(title) < 1:\n raise ValueError(\"Invalid value for `title`, length must be greater than or equal to `1`\") # noqa: E501\n\n self._title = title",
"title": ""
},
{
"docid": "ef25cdb5653fdd34616e9289648743a3",
"score": "0.4758486",
"text": "def test_api_video_update_detail_token_user_title_null(self):\n video = factories.VideoFactory(title=\"my title\")\n jwt_token = InstructorOrAdminLtiTokenFactory(playlist=video.playlist)\n data = {\"title\": None}\n response = self.client.put(\n f\"/api/videos/{video.id}/\",\n data,\n HTTP_AUTHORIZATION=f\"Bearer {jwt_token}\",\n content_type=\"application/json\",\n )\n self.assertEqual(response.status_code, 400)\n self.assertEqual(response.json(), {\"title\": [\"This field may not be null.\"]})\n video.refresh_from_db()\n self.assertEqual(video.title, \"my title\")",
"title": ""
},
{
"docid": "a97838b67e88f6459468dfc9dd82f3e6",
"score": "0.47580773",
"text": "def test_explicit_name(self):\n self._verify_schema1_content(load_raw_schema(env.input_path(\"schema2.yaml\")), \"schema2\")",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="768"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">daa1ef20b040cb30b2c08026e8c2b7d8</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Draw a pixel at (x,y) on the given canvas</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "0995007b44a80ad80146cc37fd0a1fff",
"score": "0.7908712",
"text": "def draw_pixel(canvas, x, y, color='#FF0000'):\n x1, y1 = x - 1, y - 1\n x2, y2 = x + 1, y + 1\n canvas.create_oval(x1, y1, x2, y2, fill=color, outline=color)",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "eb9d7b8bdc51c63d8b12792d5b48a0d3",
"score": "0.7462939",
"text": "def draw_pixel(self, x, y):\n # TODO Support for colors\n if (x, y) not in self.coord2rectangle:\n r = self.canvas.create_rectangle(x, y, x+1, y+1, fill=\"black\")\n self.coord2rectangle[(x, y)] = r",
"title": ""
},
{
"docid": "a42a6a8cf4875b19a9e5a1950f6e5340",
"score": "0.71367854",
"text": "def draw_pixel(self, x, y, r, g, b):\n if 0 <= x < self.width and 0 <= y < self.height:\n self[x, y] = r, g, b",
"title": ""
},
{
"docid": "7d61a5edacaec8fa2bd58e2e69e205ce",
"score": "0.68805146",
"text": "def set_pixel(self, x, y, c):\n self.pixels[x + self.width*y] = c",
"title": ""
},
{
"docid": "7d61a5edacaec8fa2bd58e2e69e205ce",
"score": "0.68805146",
"text": "def set_pixel(self, x, y, c):\n self.pixels[x + self.width*y] = c",
"title": ""
},
{
"docid": "b88718001b3d86e864f2bebd7af663ad",
"score": "0.68289745",
"text": "def draw_point(self, p):\n\tself._check_image()\n self._apply_image()\n if self.m_color is not None:\n x,y = self.to_device(p)\n self.m_image.setPixel(x, y, self.m_color)",
"title": ""
},
{
"docid": "d22f903caef01e19c3c4ec6a798d8b74",
"score": "0.66654515",
"text": "def draw_at(self, x, y):\n\n if not pysketch.draw_lines:\n self.draw_point(x, y)\n else:\n global lastx\n global lasty\n if lastx is not None:\n if lastx != x or lasty != y:\n self.create_line(lastx, lasty, x, y,\n fill=fgcolor, width=pysketch.pen_size,\n capstyle=ROUND)\n self.pil_draw.line((lastx, lasty, x, y),\n fill=fgcolor, width=pysketch.pen_size)\n # Mimicking ROUND join/butt style, by drawing two additional circles\n # at the end points...\n self.pil_draw.ellipse(((x - rect_left,\n y - rect_left),\n (x + rect_right,\n y + rect_right)),\n fill=fgcolor, outline=fgcolor)\n self.pil_draw.ellipse(((lastx - rect_left,\n lasty - rect_left),\n (lastx + rect_right,\n lasty + rect_right)),\n fill=fgcolor, outline=fgcolor)\n\n else:\n self.draw_point(x, y, False)\n\n # store the current position\n lastx = x\n lasty = y",
"title": ""
},
{
"docid": "f968611dd8e07205ec2879898b20cad3",
"score": "0.6650123",
"text": "def PutPixle(win, x, y):\n pt = Point(x, y)\n pt.draw(win)",
"title": ""
},
{
"docid": "5d43d3b4f99daa26010511c956c5b7be",
"score": "0.6627509",
"text": "def draw_pixel(board, values):\n [column, row, color] = values\n board[int(row) - 1][int(column) - 1] = color",
"title": ""
},
{
"docid": "66755b827f2abfc96b5ce8ec3810d6ba",
"score": "0.6603328",
"text": "def draw(self, x, y):\n data.draw_buffer.append(self.rendered(x,y))",
"title": ""
},
{
"docid": "36c6bb44fe12f26c11ef847e400f2d98",
"score": "0.654807",
"text": "def PutPixle(win, x, y): \n pt = Point(x, y) \n pt.draw(win) \n return pt",
"title": ""
},
{
"docid": "83b8babcc46a6fcf147696a212a4b53b",
"score": "0.6484395",
"text": "def draw_point ( canvas, point, color='black') :\n# canvas doesn't have a point method so I am going to simulate a point by drawing a 1x1 rectangle \n assert isinstance( point, Point ), \"argument point is not a Point\"\n bbox = [[point.x, point.y],[point.x+1, point.y+1] ]\n canvas.rectangle(bbox, fill=color, outline=color, width=1 )",
"title": ""
},
{
"docid": "65ecf5d266584832e0018a41f4afb5e7",
"score": "0.6393965",
"text": "def draw(self, y: tuple, x: tuple, color: tuple):\n if self.drawing_copy is not None:\n self.drawing_copy[y[0]:y[1], x[0]:x[1]] = color",
"title": ""
},
{
"docid": "7b4b3cf5fdd531d7c42bf2090bc5bffb",
"score": "0.63691276",
"text": "def plotPixel(self, x, y, color=\"000000\"):\n intX = int(x)\n intY = int(y)\n # print('plot:', intX, intY)\n\n if (intX >= self.width): return False\n if (intY >= self.height): return False\n if (intX < 0): return False\n if (intY < 0): return False\n\n self.image[intY, intX] = colorname_to_rgb(color)",
"title": ""
},
{
"docid": "462e472dffc52979c1e544ace1b8cb53",
"score": "0.63431966",
"text": "def set_pixel(self, x, y, rgb):\n self.pixels[x][y] = rgb",
"title": ""
},
{
"docid": "fdd1b809a660fbb263c33a1b162be0b4",
"score": "0.63317096",
"text": "def draw_at_pos(self, event, z):\n\n \"\"\"Get the x,y coords of the mouse\"\"\"\n self.x = event.x\n self.y = event.y\n\n \"\"\"Get the corresponding coord on the numpy pixel array for where the\n window-based x,y coordinate was drawn\"\"\"\n arrX = self.x // self.canvas.wSpl\n arrY = self.y // self.canvas.hSpl\n\n \"\"\"Draw the new pixel\"\"\"\n self.drawToArr(arrX, arrY, z)",
"title": ""
},
{
"docid": "f8e719b4ca675f232d79265a5c558f96",
"score": "0.63034916",
"text": "def pixelChange(self, x, y, clr):\r\n painter = QtGui.QPainter(self.controller_map)\r\n painter.setPen(clr)\r\n painter.drawPoint(x,y)\r\n self.redraw()",
"title": ""
},
{
"docid": "99be9c4f6d15132a8ec5dbfd0b431a71",
"score": "0.62633437",
"text": "def draw(self, color = Color.GREEN):\n self.image[self.x, self.y] = color",
"title": ""
},
{
"docid": "66fb3bb73f31ea459b63933ae70bc365",
"score": "0.6254106",
"text": "def fresh(self, canvas, x, y):\n canvas.coords(self.circle, self.rect(x, y))",
"title": ""
},
{
"docid": "c06db347cb157059e651f5ee34121ff6",
"score": "0.6178478",
"text": "def drawMe(self):\r\n self.root = Tk()\r\n self.mycanvas = Canvas(self.root, width = 600, height = 600)\r\n self.mycanvas.pack()\r\n drawboard(self.mycanvas,self.board)\r\n drawPoint(self.mycanvas,self.start,\"red\")\r\n drawPoint(self.mycanvas,self.end,\"green\")",
"title": ""
},
{
"docid": "8b7548e438129814f0b28a4eeaa807a1",
"score": "0.61364657",
"text": "def set_pixel(self, x, y, color):\n if type(color) == int:\n self.data[y][x] = color\n else:\n self.data[y][x] = color[1] # expect \"\\x1C0\" constants from colors module.",
"title": ""
},
{
"docid": "44c03cdc0c50d9f3f76cb0a66ccb1aa2",
"score": "0.6119728",
"text": "def draw(sprite, x, y):\r\n Config.screen.blit(sprite, (round(x,0) - sprite.get_width()/2,\r\n round(y,0) - sprite.get_height()/2))",
"title": ""
},
{
"docid": "f278da5acb16e81111b1e0ab0d016887",
"score": "0.6058983",
"text": "def draw_point(self, board, x,y, color, r, type='o'):\n if type == 'o':\n self.render.draw_circle(board, color, (x, y), r)\n elif type == 'x':\n self.render.draw_line(board, color, [(x - r, y - r), \\\n (x + r, y + r)], 3)\n self.render.draw_line(board, color, [(x + r, y - r), \\\n (x - r, y + r)], 3)",
"title": ""
},
{
"docid": "23f474815b96aa867ef69b7be3b5f4f4",
"score": "0.6038877",
"text": "def draw(self):\n self.screen.blit(self.image, (self.x, self.y))",
"title": ""
},
{
"docid": "b734bd928aa054fe7ff970881a8a2316",
"score": "0.6035779",
"text": "def draw_offset(self, screen, x, y):\n screen.blit(self.image, self.rect.move(x,y))",
"title": ""
},
{
"docid": "5422debec8d4bf6a1b5598d01b1b9bc2",
"score": "0.6027333",
"text": "def updatePixel(self, y, x, colour):\r\n self.image = self.controller_map.toImage()\r\n self.image.setPixel(x, y,colour.rgb())\r\n self.controller_map = QtGui.QPixmap.fromImage(self.image)",
"title": ""
},
{
"docid": "e3ce8d769a45d340cd1d0e197d209010",
"score": "0.59984493",
"text": "def drawpoint(im,xy,c_index = 'green',radius = 5):\n draw = ImageDraw.Draw(im)\n color = fromIDgetcolor(c_index)\n draw.pieslice([xy[0]-radius,xy[1]-radius,xy[0]+radius,xy[1]+radius],0,360,\n color) #first give a bouding box of a circle\n del draw",
"title": ""
},
{
"docid": "2906d90309f1225277a555a443c99779",
"score": "0.5963505",
"text": "def pixel(self, x, y):\n\n # Pixel data is unsigned char (8bit unsigned integer),\n # and there are four (blue,green,red,alpha)\n data_format = \"BBBB\"\n\n\n #self.screen_width = len(self.data) / (self.sqrt_num_neurons * self.screen_height)\n #print self.screen_width\n\n # Calculate offset, based on\n # http://www.markj.net/iphone-uiimage-pixel-color/\n # REALLY WEIRD +10 OFFSET. NO IDEA WHY\n offset = 4 * (((len(self.data) / (self.screen_height * 4))*int(round(y))) + int(round(x)))\n\n # Unpack data from string into Python'y integers\n b, g, r, a = struct.unpack_from(data_format, self.data, offset=offset)\n\n # Return BGRA as RGBA\n return (r, g, b, a)\n\n\n\n #return [PIL.ImageGrab.grab(bbox=(pos[0], pos[1], pos[0] + 1, pos[1] + 1)).load()[0, 0] for pos in self.positions]",
"title": ""
},
{
"docid": "4b4a811f3ca8ea5cec77a20baee79dbc",
"score": "0.59542876",
"text": "def draw(self,screen):\n screen.blit(self.__image,(self.__xPosition,self.__yPosition))",
"title": ""
},
{
"docid": "2be9b55e49879dbf06f8cc5de668e2d6",
"score": "0.5921094",
"text": "def add_pixel(self, x, y, r, g, b):\n if 0 <= x < self.width and 0 <= y < self.height:\n r1, g1, b1 = self[x, y]\n self[x, y] = r1 + r, g1 + g, b1 + b",
"title": ""
},
{
"docid": "77ecd8b582e25cbd1f9d23a1f5a9e8fb",
"score": "0.59121585",
"text": "def _set_pix_(self, x, y, pix):\n self.px[x, y] = pix",
"title": ""
},
{
"docid": "ddda426ea333025bb3b97d1eb262af40",
"score": "0.5901022",
"text": "def draw(board):\n #TODO\n pass",
"title": ""
},
{
"docid": "99d6091d9dd0c26c1f197ca88dc8b68c",
"score": "0.5900802",
"text": "def display(self, canvas, x, y, width, height):\n # Do we need this?\n pass",
"title": ""
},
{
"docid": "d52ba28a0be282ef9e00b1a3459279bf",
"score": "0.5865279",
"text": "async def put_pixel(request: Request, pixel: Pixel) -> Message:\n log.info(f\"{request.state.user_id} is setting {pixel.x}, {pixel.y} to {pixel.rgb}\")\n await request.state.canvas.set_pixel(request.state.db_conn, pixel.x, pixel.y, pixel.rgb, request.state.user_id)\n return Message(message=f\"Set pixel at x={pixel.x},y={pixel.y} to color {pixel.rgb}.\")",
"title": ""
},
{
"docid": "29306a0b819185dfd3e5a23a3a37075a",
"score": "0.58301944",
"text": "def draw(self):\n # TODO: implement using matplotlib\n# StdDraw.point(self.x, self.y)\n pass",
"title": ""
},
{
"docid": "902e02ad5bea63484fc51208d01c4c32",
"score": "0.5826663",
"text": "def draw(self, canvas):\n self.circle = canvas.create_oval(self.rect(self.x, self.y),\n fill = self.fill_color,\n width = self.outline,\n outline = self.outline_color)",
"title": ""
},
{
"docid": "ed126000fd1c0553191cd20852404334",
"score": "0.58227795",
"text": "def draw(self):\n (xpt, ypt) = (self.xpt, self.ypt)\n self.canvas.create_oval(xpt-2, ypt-2, \\\n xpt+2, ypt+2, fill='SkyBlue2')",
"title": ""
},
{
"docid": "b1e7934f06c776c1edaeb08a4cfa4382",
"score": "0.58124703",
"text": "def draw(self, screen):\n pygame.draw.rect(screen, self.color, [self.x_point, self.y_point, self.width, self.height])",
"title": ""
},
{
"docid": "cdd067c69aba0b9a4be360d4529d0081",
"score": "0.581073",
"text": "def set_pixel(self, x: int, y: int, color: list) -> None:\n if self.pixel_is_inside_field(x, y):\n self.field[y][x] = color",
"title": ""
},
{
"docid": "8150604a249df171a426daccb666cf88",
"score": "0.578564",
"text": "def pixel(pos, color):\n r,g,b = color\n x,y = pos\n image.put(\"#%02x%02x%02x\" % (r,g,b), (x, y))",
"title": ""
},
{
"docid": "09dd2e1dd61ec24c5f98fb68ab5ae64e",
"score": "0.57704705",
"text": "def draw_stamp(canvas, x, y, color):\n rect = canvas.create_rectangle(x - STAMP_SIZE / 2, y - STAMP_SIZE / 2,\n x + STAMP_SIZE / 2, y + STAMP_SIZE / 2)\n canvas.set_color(rect, color)\n return rect",
"title": ""
},
{
"docid": "2a9900da038fb914e88c270999d73910",
"score": "0.57539225",
"text": "def draw_image(canvas,\n img,\n x,\n y,\n width=None,\n height=None,\n proportional=True,\n scale=None,\n halign=None,\n valign=None,\n ):\n\n if hasattr(img, \"seek\"):\n is_buffer = True\n img.seek(0)\n else:\n is_buffer = False\n\n try:\n from PIL import Image as pImage\n except ImportError:\n current.log.error(\"Image rendering failed: PIL not installed\")\n return\n\n pimg = pImage.open(img)\n img_size = pimg.size\n\n if not img_size[0] or not img_size[1]:\n # This image has at least one dimension of zero\n return\n\n # Compute drawing width/height\n if scale:\n width = img_size[0] * scale\n height = img_size[1] * scale\n elif width and height:\n if proportional:\n scale = min(float(width) / img_size[0], float(height) / img_size[1])\n width = img_size[0] * scale\n height = img_size[1] * scale\n elif width:\n height = img_size[1] * (float(width) / img_size[0])\n elif height:\n width = img_size[0] * (float(height) / img_size[1])\n else:\n width = img_size[0]\n height = img_size[1]\n\n # Compute drawing position from alignment options\n hshift = vshift = 0\n if halign == \"right\":\n hshift = width\n elif halign == \"center\":\n hshift = width / 2.0\n\n if valign == \"top\":\n vshift = height\n elif valign == \"middle\":\n vshift = height / 2.0\n\n # Draw the image\n if is_buffer:\n img.seek(0)\n ir = ImageReader(img)\n\n canvas.drawImage(ir,\n x - hshift,\n y - vshift,\n width = width,\n height = height,\n preserveAspectRatio = proportional,\n mask = \"auto\",\n )",
"title": ""
},
{
"docid": "1eacbcd550c0baadd6348598fdd08b77",
"score": "0.57322997",
"text": "def draw(self, screen):\n screen.blit(self.imageSprite, (self.xDraw, self.yDraw))",
"title": ""
},
{
"docid": "9e1ae5b144a1bdabe6baf5b878a9aecd",
"score": "0.5722362",
"text": "def draw(self, surface):\n\t\tsurface.blit(self.image, (self.x, self.y))",
"title": ""
},
{
"docid": "414ad06f86dc68a3b2189a20f39fb548",
"score": "0.5712287",
"text": "async def get_pixel(x: int, y: int, request: Request) -> Pixel:\n if x >= Sizes.WIDTH or y >= Sizes.HEIGHT:\n raise HTTPException(400, \"Pixel is out of the canvas bounds.\")\n pixel_data = await request.state.canvas.get_pixel(x, y)\n\n return Pixel(x=x, y=y, rgb=''.join(f\"{x:02x}\" for x in pixel_data))",
"title": ""
},
{
"docid": "62ac39a1f6424862e48241372eb2e684",
"score": "0.571013",
"text": "def __draw_sensor(self, canvas=None):\r\n if canvas:\r\n canvas.create_oval(self.x - 3, self.y - 3,\\\r\n self.x + 3, self.y + 3,\\\r\n width=10)",
"title": ""
},
{
"docid": "fded35874f077e28d8c0b19b82abc08e",
"score": "0.56953335",
"text": "def draw(self, screen):\n r = pygame.Rect(self.x-self.radius,\n self.y-self.radius,\n 2*self.radius,\n 2*self.radius)\n screen.blit(self.image, r)",
"title": ""
},
{
"docid": "458bde18c4180f09033c044c971b0d3e",
"score": "0.5694221",
"text": "def draw(self):\n pygame.draw.rect(screen, self.color, [self.x_point, self.y_point, self.width , self.height])",
"title": ""
},
{
"docid": "affd7a435a34daaf20c53b268ae84a34",
"score": "0.56926554",
"text": "def Pixel(self, x, y, r, g, b, a=255): # pylint: disable=C0103\n try:\n if a == 255:\n color = (r*256*256) + (g*256) + b\n if self.factor>1:\n for w in xrange(0, self.factor):\n for h in xrange(0, self.factor):\n self.pixels[(x*self.factor) + w][(y*self.factor) + h] = color\n else:\n self.pixels[x][y] = color\n else:\n old = self.pixels[x][y]\n oldr = old >> 16\n oldg = (old & 0x00ff00) / 256\n oldb = old & 0x0000ff\n red = (r * a) + (oldr * (1.0 - a))\n green = (g * a) + (oldg * (1.0 - a))\n blue = (b * a) + (oldb * (1.0 - a))\n self.pixels[x][y] = (red*256*256) + (green*256) + blue\n except IndexError:\n pass",
"title": ""
},
{
"docid": "ac2e225f290ac2e7089b0284fdba5739",
"score": "0.5688069",
"text": "def draw(self, surface):\n surface.blit(self.image, (self.x, self.y))",
"title": ""
},
{
"docid": "0dc4b5d9e7bed8f319ead07305b465c5",
"score": "0.5680878",
"text": "def draw(self, window):\r\n window.blit(self.image, (self.x, self.y))",
"title": ""
},
{
"docid": "a14cc020175c2d483c1412145ce07325",
"score": "0.5678831",
"text": "def draw(self):\n circle(screen, self.color, (int(self.x), int(self.y)), self.r)",
"title": ""
},
{
"docid": "955e7776f456b8485a967b0cef67f597",
"score": "0.566129",
"text": "def get_pixel(self, x, y):\n return self.pixels[x + self.width*y]",
"title": ""
},
{
"docid": "955e7776f456b8485a967b0cef67f597",
"score": "0.566129",
"text": "def get_pixel(self, x, y):\n return self.pixels[x + self.width*y]",
"title": ""
},
{
"docid": "8027c3a0fa363b665037f62d606fcde8",
"score": "0.5656514",
"text": "def draw_on(self, canvas, offset):\n raise NotImplementedError('subclasses should implement this')",
"title": ""
},
{
"docid": "9d4145a82583681b5f1fd6595d52e8e9",
"score": "0.5635233",
"text": "def render( self, surface, x, y, bright ): \n \n if not WU_PIXELS:\n b= int( min( 255., bright * 255. ) )\n surface.set_at( ( int(x), int(y) ), ( b, b, b ) )\n return\n \n if x >= 0. and x < SCREEN_WIDTH and y >= 0. and y < SCREEN_HEIGHT:\n # Get the fractional, and the whole part of the coordinates\n fx, ix = math.modf( x )\n fy, iy = math.modf( y )\n ix = int(ix)\n iy = int(iy)\n \n # Scale brightness (a value between 0 and 1), to a colour value (0-255)\n bright= min( bright*255., 255. ) \n \n # Calculate the brightness of each sub pixel, see the link above\n btl = int( (1.-fx) * (1.-fy) * bright )\n btr = int( fx * (1.-fy) * bright )\n bbl = int( (1.-fx)* fy * bright )\n bbr = int( fx * fy * bright )\n \n # Plot the pixel on screen\n surface.set_at( ( ix, iy ), ( btl, btl, btl ) )\n surface.set_at( ( ix+1, iy ), ( btr, btr, btr ) )\n surface.set_at( ( ix, iy+1 ), ( bbl, bbl, bbl ) )\n surface.set_at( ( ix+1, iy+1 ), ( bbr, bbr, bbr ) )",
"title": ""
},
{
"docid": "7c7d496c8a07ca1d9a2db7811b9a1aae",
"score": "0.5630312",
"text": "def get_pixel(self, x, y):\n return self.data[y][x]",
"title": ""
},
{
"docid": "03f0e7deb008cded1cb624a09ffa5cc6",
"score": "0.5587571",
"text": "def test():\n # I think that the image will have half the pixels as the original if we change the line.\n width = 300\n height = 200\n image = PNGImage(width, height)\n\n # create a loop in order to draw some pixels\n\n for col in range(width):\n for row in range(height):\n if weWantThisPixel(col, row):\n image.plotPoint(col, row)\n\n # we looped through every image pixel; we now write the file\n\n image.saveFile()",
"title": ""
},
{
"docid": "1aff25484c68532b61e15a6e20e43a59",
"score": "0.5581076",
"text": "def draw(self,x,y,scale=1):\n self.x = x\n self.y = y\n self.scale = scale\n pyglet.sprite.Sprite.draw(self)",
"title": ""
},
{
"docid": "7199e330a6bd2a0c504a31528125ecc1",
"score": "0.5568812",
"text": "def _DrawEntireBitmap(self, dc , WorldToPixel, ScaleWorldToPixel, HTdc):\n XY = WorldToPixel(self.XY)\n H = int(round(ScaleWorldToPixel(self.Height)[0]))\n W = int(round(H * (self.bmpWidth / self.bmpHeight)))\n if W == 0 or H == 0: # nothing to draw\n return\n else:\n if (self.ScaledBitmap is None) or (self.ScaledBitmap[0] != (0, 0, self.bmpWidth, self.bmpHeight, W, H) ):\n #if True: #fixme: (self.ScaledBitmap is None) or (H <> self.ScaledHeight) :\n self.ScaledHeight = H\n Img = self.Image.Scale(W, H, quality=self._scale_quality)\n bmp = wx.BitmapFromImage(Img)\n self.ScaledBitmap = ((0, 0, self.bmpWidth, self.bmpHeight , W, H), bmp)# this defines the cached bitmap\n else:\n bmp = self.ScaledBitmap[1]\n XY = self.ShiftFun(XY[0], XY[1], W, H)\n dc.DrawBitmapPoint(bmp, XY, True)\n if HTdc and self.HitAble:\n HTdc.SetPen(self.HitPen)\n HTdc.SetBrush(self.HitBrush)\n HTdc.DrawRectanglePointSize(XY, (W, H) )",
"title": ""
},
{
"docid": "7500a6a6291b7be103139794dbaeeebb",
"score": "0.5566554",
"text": "def draw(self, surface):\n\n x = self._r[0] - self._image.get_width() / 2\n y = self._r[1] - self._image.get_height() / 2\n surface.blit(self._image, (x, y))",
"title": ""
},
{
"docid": "b83c1f9989dda61840b0099e6cfd28f8",
"score": "0.55539834",
"text": "def draw(self):\n return self._myCanvas.draw()",
"title": ""
},
{
"docid": "266706e649ca015745fa5ec3767d8036",
"score": "0.5551536",
"text": "def clear_pixel(self, x, y):\n if (x, y) in self.coord2rectangle:\n self.canvas.delete(self.coord2rectangle[(x, y)])\n del(self.coord2rectangle[(x, y)])",
"title": ""
},
{
"docid": "318a8ab405ce092fa8a6345a84244908",
"score": "0.5519273",
"text": "def coordToPixel(self, x, y):\n xPix = self.padx + (x + 0.5) * self.squareSize\n yPix = self.pady + (self.boardSize - 1 - y + 0.5) * self.squareSize\n return xPix, yPix",
"title": ""
},
{
"docid": "df61ec7cc897dfeb61e85b0f99e35d6c",
"score": "0.5512152",
"text": "def draw_point(self, x, y, rectshape=True):\n\n if rectshape:\n self.create_rectangle((x - rect_left,\n y - rect_left,\n x + rect_right,\n y + rect_right),\n fill=fgcolor, outline=fgcolor)\n self.pil_draw.rectangle(((x - rect_left,\n y - rect_left),\n (x + rect_right,\n y + rect_right)),\n fill=fgcolor, outline=fgcolor)\n else:\n self.create_oval((x - rect_left,\n y - rect_left,\n x + rect_right,\n y + rect_right),\n fill=fgcolor, outline=fgcolor)\n self.pil_draw.ellipse(((x - rect_left,\n y - rect_left),\n (x + rect_right,\n y + rect_right)),\n fill=fgcolor, outline=fgcolor)",
"title": ""
},
{
"docid": "31b6a9f0732322840398006ff81a3582",
"score": "0.5503087",
"text": "def markCoordinate(self,event = None):\n x1,y1 = event.x,event.y\n idtag = self.mycanvas.create_oval((x1 -2),(y1-2),(x1 +2),(y1 + 2),fill = 'black')\n # self.mycanvas_items_tags.append(idtag)",
"title": ""
},
{
"docid": "d5645ab7d7fc901686bb033153fbbd45",
"score": "0.5501955",
"text": "def draw(self) -> None:\n self.WIN.blit(self.current_image, (self.x, self.y))",
"title": ""
},
{
"docid": "95c4862505d056a60ca07413e77798e0",
"score": "0.54932195",
"text": "def draw_pinky(x, y):\n pinky_surf = pygame.transform.scale(pygame.image.load(r'Images\\Pinky.png'), [14 * scale, 14 * scale])\n screen.blit(pinky_surf, (x*scale, y*scale))",
"title": ""
},
{
"docid": "4f2da4d5eb5df5323a08e64df2cacfee",
"score": "0.5487918",
"text": "def draw(self):\n screen.blit(playerImg, (self.x, self.y))",
"title": ""
},
{
"docid": "3dac082ef4e24ce5f953beb071d7dc38",
"score": "0.54872626",
"text": "def draw_square(canvas, row, col, mode):\n x = col * SIZE\n y = row * SIZE\n if mode in CHASE_MODES[1:5] or mode == 2: # grey is used to allow subsequent cleaning of the canvas\n color = \"grey\"\n else:\n color = get_color(row, col) # get colour to draw a checkerboard pattern\n canvas.create_rectangle(x, y, x + SIZE, y + SIZE, fill=color, outline=color)",
"title": ""
},
{
"docid": "1454a4c3a35e9161feb24fdfbad33447",
"score": "0.546675",
"text": "def paintToCanvasWithSource(canvas, stroke, source):\n x = stroke['x']\n y = stroke['y']\n r = stroke['R']\n color = (int(source[y,x,0]), int(source[y,x,1]), int(source[y,x,2]))\n canvas = cv2.circle(canvas, (x,y), r, color, -1)\n return canvas",
"title": ""
},
{
"docid": "ddbb74bff5900d6fae9511e636aa2da6",
"score": "0.54659605",
"text": "def draw(self, screen):\n\n self.draw_image()\n \n return None",
"title": ""
},
{
"docid": "a753fce668a0829553605d474a848685",
"score": "0.5460119",
"text": "def draw(self, canvas):\n self.line = canvas.create_line(self.x0, self.y0, self.x1, self.y1,\n width = self.width_, fill = self.color)",
"title": ""
},
{
"docid": "fe266f365e8d5a60024537b38bccb4b6",
"score": "0.54338586",
"text": "def draw_flag(self, x, y):\n xval = x + self.square_width / 4\n yval = y + self.square_height / 4\n pg.draw.rect(self.screen, cfg.RED, [xval, yval, self.square_width / 2, self.square_height / 2])\n xval = x + self.square_width / 3\n yval = y + self.square_height / 3\n pg.draw.rect(self.screen, cfg.BLACK, [xval, yval, self.square_width / 3, self.square_height / 3])",
"title": ""
},
{
"docid": "174015cbbb6cf1dcb00314475f7423f9",
"score": "0.5429889",
"text": "def set_pixel(self, row: int, col: int, colour: Colour) -> NoReturn:\n self.pixels[col][row] = colour",
"title": ""
},
{
"docid": "ee03c9db425a361494ff589b0d7f9c9a",
"score": "0.54185474",
"text": "def draw_square(x, y, x_size=1, y_size=1):\r\n gl.glRectf(x, y, x + x_size, y + y_size)",
"title": ""
},
{
"docid": "7e092dcac0f5643a2cb52f9b7491589e",
"score": "0.54032445",
"text": "def draw(self, win):\n\t\twin.blit(self.image, (self.x1, self.y))\n\t\twin.blit(self.image, (self.x2, self.y))",
"title": ""
},
{
"docid": "e0e69b33d7d953a9aa5ae2d681b5d6ff",
"score": "0.54021275",
"text": "def drawSquare(myturtle, x, y, a):\n\n #Start drawing the square with visible pointer\n myturtle.penup()\n myturtle.setposition(x, y)\n myturtle.pendown()\n myturtle.setposition(x+a, y)\n myturtle.setposition(x+a, y+a)\n myturtle.setposition(x, y+a)\n myturtle.setposition(x, y)",
"title": ""
},
{
"docid": "50e0d52f94a6f16eb535d93851aa69d1",
"score": "0.54004943",
"text": "def test(): \n width = 300 \n height = 200 \n image = PNGImage(width, height) \n # create a loop in order to draw some pixels \n for col in range(width): \n for row in range(height): \n if weWantThisPixel( col, row ) == True: \n image.plotPoint(col, row) \n # we looped through every image pixel; we now write the file \n image.saveFile()",
"title": ""
},
{
"docid": "1a9bb6e4a9e4b804cbba0406e91c1bcc",
"score": "0.5400239",
"text": "def drawPoint(self, point, color):\n pygame.draw.line(\n self._display,\n color,\n point,\n point)\n pygame.display.flip()",
"title": ""
},
{
"docid": "7ff03bba238799390635890d426e3a13",
"score": "0.5397216",
"text": "def draw(*args, **kwargs):\n \n pass",
"title": ""
},
{
"docid": "37374621b951f15f8a13420af6398298",
"score": "0.5394875",
"text": "def draw_eye(x, y, r):\n circle(screen, RED, (x, y), 2*r)\n circle(screen, BLACK, (x, y), r)",
"title": ""
},
{
"docid": "50fb232b86eb075a2d3695815cc76396",
"score": "0.538462",
"text": "def draw_pixel(self, i):\n if self.should_be_on(i):\n # ON\n self.sense.set_pixel(i, self.row, self.foreground)\n else:\n # OFF\n self.sense.set_pixel(i, self.row, self.background)",
"title": ""
},
{
"docid": "f555277eab586afdc68ea36a4e6f9778",
"score": "0.53816265",
"text": "def draw(self, x, y):\n canvas = self._canvas\n\n # Add label on canvas and store handle\n if self.style[\"label\"] != \"\":\n self._labelhandle = canvas.create_text(x, y,\n text=self.style[\"label\"])\n bbox = canvas.bbox(self._labelhandle)\n else:\n self._labelhandle = None\n bbox = (x, y, x, y)\n\n # Draw on canvas and store handle\n self._handle = self.style[\"shape\"].draw(canvas,\n bbox,\n self.style.shape_style)\n\n if self._labelhandle is not None:\n canvas.tag_raise(self._labelhandle)\n\n if self.tooltip is not None:\n for handle in self.handles:\n CanvasToolTip(canvas, handle, follow_mouse=1,\n text=self.tooltip)\n\n self.refresh()",
"title": ""
},
{
"docid": "8877700e9e9859478487c2a96f19eab0",
"score": "0.5381092",
"text": "def draw_sprite(self, x, y, width, height, sprite):\n sprite.position = x + width // 2, y + height // 2\n sprite.width = width\n sprite.height = height\n sprite.draw()",
"title": ""
},
{
"docid": "a644e09a444949120702f4ca4cf20a42",
"score": "0.53796524",
"text": "def scatter(self, x, y, pen=None, **kwargs):\n\n # XXX: this should use markers (as in ``line`` below)\n\n picture = self._picture(**kwargs)\n pen = self._pen(pen, **kwargs)\n\n self._filter_and_slurp2(x, y, **kwargs)\n\n self.asy.send('''for (int i=0; i<X.length; ++i)\n { dot(%s, (X[i], Y[i]), %s); }'''\n % (picture, pen))",
"title": ""
},
{
"docid": "5266529fe5d8f67d57669a533f367832",
"score": "0.53674376",
"text": "def paint_pixel_transparent(im, x_index, y_pos):\n pixdata = im.load()\n #print(str(x_index) + \" \" + str(y_pos))\n pixdata[x_index, y_pos] = (255, 255, 255, 0)",
"title": ""
},
{
"docid": "9f1674a80dc51192572c0381f6c4baee",
"score": "0.53577286",
"text": "def draw_point(self, point, color, r=3):\n cv2.circle(self._frame, (int(point.x), int(point.y)), r, color, -1)",
"title": ""
},
{
"docid": "bbec0de5e540a0c7559f3dc533c5c768",
"score": "0.5337999",
"text": "def draw_rectangle(x, y, color):\n global screen\n global margin\n global width\n global height\n pygame.draw.rect(screen, color, [((margin + width) * x + margin),\n ((margin + height) * y + margin),\n width, height])",
"title": ""
},
{
"docid": "dec01ef9d9a57c210a8b54f4d29a658b",
"score": "0.53358155",
"text": "def DrawBitmap(self, *args, **kwargs):\n pass",
"title": ""
},
{
"docid": "d88eddb6e864a8496a7ab97e78ad308e",
"score": "0.53328747",
"text": "def set_pixel(image, c):\n image['pixels'].append(c)",
"title": ""
},
{
"docid": "f471475ddc961393cd14236ca728b190",
"score": "0.5332671",
"text": "def draw():\n pass",
"title": ""
},
{
"docid": "bbe9e28ca6b23a595fc3ca892c1cc79e",
"score": "0.5327743",
"text": "def drawCircle(self, x0, y0, r, color=None):\n f = 1 - r\n ddF_x = 1\n ddF_y = -2 * r\n x = 0\n y = r\n\n self.set(x0, y0 + r, color)\n self.set(x0, y0 - r, color)\n self.set(x0 + r, y0, color)\n self.set(x0 - r, y0, color)\n\n while x < y:\n if f >= 0:\n y -= 1\n ddF_y += 2\n f += ddF_y\n x += 1\n ddF_x += 2\n f += ddF_x\n\n self.set(x0 + x, y0 + y, color)\n self.set(x0 - x, y0 + y, color)\n self.set(x0 + x, y0 - y, color)\n self.set(x0 - x, y0 - y, color)\n self.set(x0 + y, y0 + x, color)\n self.set(x0 - y, y0 + x, color)\n self.set(x0 + y, y0 - x, color)\n self.set(x0 - y, y0 - x, color)",
"title": ""
},
{
"docid": "c47d453be372e00ac966936baf6a46e7",
"score": "0.53272706",
"text": "def map_pixel(x, y):\n # find the horizontal coordinate\n q, r = divmod(x, maze.scaling + maze.lw)\n if left % 2 == 0:\n dx = 2 * q if r < maze.scaling else 2 * q + 1\n else:\n dx = 2 * q if r < maze.lw else 2 * q + 1\n\n # find the vertical coordinate\n q, r = divmod(y, maze.scaling + maze.lw)\n if top % 2 == 0:\n dy = 2 * q if r < maze.scaling else 2 * q + 1\n else:\n dy = 2 * q if r < maze.lw else 2 * q + 1\n\n return colormap[maze.get_cell((left + dx, top + dy))]",
"title": ""
},
{
"docid": "f4525140ded06ad02042489a031d144e",
"score": "0.53212357",
"text": "def drawImage(self):\n self.clearCanvas()\n for raw_y, row in enumerate(self.image):\n for raw_x, pixel in enumerate(row):\n x = raw_x * self.scaleSize\n y = raw_y * self.scaleSize\n color = self.pixelToHexString(pixel)\n self.canvas.create_rectangle(x,y, x+self.scaleSize, y+self.scaleSize, fill=color, outline=color)",
"title": ""
},
{
"docid": "0412dbc27b0693d164a49cb0e8725079",
"score": "0.53149873",
"text": "def _circle(self, x, y, r):\n x0 = x - r\n y0 = y - r\n x1 = x + r\n y1 = y + r\n self.canvas.create_oval(x0, y0, x1, y1, width=4)",
"title": ""
},
{
"docid": "c0f3c4585bd1a63f14eea41e82bc4fad",
"score": "0.5308701",
"text": "def pixel_edit():\r\n img = Image.open('sunset.png')\r\n numxpixels = 2000\r\n numypixels = 10\r\n for i in range(numxpixels):\r\n for j in range(numypixels):\r\n changepixelcolortogreen = img.putpixel([i,j],(0, 255, 94)) #accesses pixels (numxpixels and numypixels) and changes the RGB value to 0, 255, 94. \r\n img.save('changedpixel.png')",
"title": ""
},
{
"docid": "391de59f5701848fea4fc5efc83d433f",
"score": "0.53060806",
"text": "def grid_to_pixel(self, y_coord: int, x_coord: int):\n y = self.gui_vars.margin + y_coord * self.gui_vars.cell_size\n x = self.gui_vars.margin + x_coord * self.gui_vars.cell_size\n return y, x",
"title": ""
},
{
"docid": "42443c8be5d44886cf3cd94e2bd08ca0",
"score": "0.530565",
"text": "def draw(self, screen):\n screen.blit(self.image, self.rect)",
"title": ""
},
{
"docid": "42443c8be5d44886cf3cd94e2bd08ca0",
"score": "0.530565",
"text": "def draw(self, screen):\n screen.blit(self.image, self.rect)",
"title": ""
},
{
"docid": "b50cd58ac6aee7f5e71da5ae8b3345c2",
"score": "0.5304264",
"text": "def draw_figure(canvas, figure, loc=(0, 0)):\n figure_canvas_agg = FigureCanvasAgg(figure)\n figure_canvas_agg.draw()\n figure_x, figure_y, figure_w, figure_h = figure.bbox.bounds\n figure_w, figure_h = int(figure_w), int(figure_h)\n photo = Tk.PhotoImage(master=canvas, width=figure_w, height=figure_h)\n canvas.create_image(loc[0] + figure_w / 2, loc[1] + figure_h / 2, image=photo)\n tkagg.blit(photo, figure_canvas_agg.get_renderer()._renderer, colormode=2)\n return photo",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="769"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">4ff1bc27f33dfb14c2e012b3e212a2a1</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Ensure virtual machine endpoint returns a valid response</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "defb997169eda472584e5d8d9cd0b157",
"score": "0.6642315",
"text": "def test_endpoint_virtual_machine(self):\n\n for i in range(60):\n utils.build_vm_full(f\"api-test-vm-{i}.example.com\")\n\n resp = self.client.get(\"/api/plugins/prometheus-sd/virtual-machines/\")\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\n data = json.loads(resp.content)\n\n self.assertIsNotNone(data[0][\"targets\"])\n self.assertIsNotNone(data[0][\"labels\"])\n self.assertEqual(len(data), 60)",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "67a12dd4e5a05e76ce0ee3a18313dd21",
"score": "0.6154137",
"text": "def verify(self, response):",
"title": ""
},
{
"docid": "41f9543e83d54ae65b2a593f033a6b4d",
"score": "0.60561776",
"text": "def check(self, response):\n pass",
"title": ""
},
{
"docid": "97ecfb2169d7fa7f11123398a1c085cc",
"score": "0.60550433",
"text": "def _check_response_for_request_errors(self):\n\n pass",
"title": ""
},
{
"docid": "8f33f313e88ab4bfe178235c3e5012c6",
"score": "0.60378504",
"text": "def validate_response(rsp):\n assert rsp.status_code in [200, 201]\n assert 'application/json' in rsp.headers['content-type']\n data = json.loads(rsp.content)\n assert 'message' in data.keys()\n assert 'status' in data.keys()\n assert 'result' in data.keys()\n assert 'version' in data.keys()\n return data['result']",
"title": ""
},
{
"docid": "73e60bcd5daa8c82130bfbf822b17e80",
"score": "0.5988535",
"text": "def test_server_control_http_code(endpoint):\n res = requests.get(url_for(endpoint, _external=True))\n assert res.status_code == 200",
"title": ""
},
{
"docid": "1c9b03ac9d8df9a921875986193e53f2",
"score": "0.5977692",
"text": "def test_valid_multisession(self):\n response = self.client.get('/b/')\n self._check_hello(response)\n self._check_profile(response)\n return response",
"title": ""
},
{
"docid": "faf09fd22229cf1dc0e329e7c49acd09",
"score": "0.59760654",
"text": "def test_invalid_multisession(self):\n response = self.client.get('/c/')\n self._check_hello(response)\n self.assertFalse(hasattr(response, 'profile'))\n return response",
"title": ""
},
{
"docid": "eb444fd35b30b5e46f8426e9fe75bec3",
"score": "0.5969429",
"text": "def check_error(self, response):\n pass",
"title": ""
},
{
"docid": "484d180ee26c5bc308ae26e8e753152b",
"score": "0.59663576",
"text": "def test_vehicle_view_set(self):\n response = self.client.get(self.api_url)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(len(response.data), 1)",
"title": ""
},
{
"docid": "d16f949390040774f2e18f58599f070b",
"score": "0.5863489",
"text": "def __call__(self):\n try:\n return {'status': 'OK'}\n except Exception: # pragma: no cover\n raise self.prepare_exception(HTTPServiceUnavailable())",
"title": ""
},
{
"docid": "e9dcb1883c69bc4dcde8b44a60538132",
"score": "0.5853631",
"text": "def testGetResponse(self):\n api_response = self.client.get(\n self.video_url,\n content_type=\"application/vnd.api+json\",\n headers=self.headers,\n )\n self.assertEqual(api_response.status_code, status.HTTP_405_METHOD_NOT_ALLOWED)",
"title": ""
},
{
"docid": "c3b009bd2f3db7f31b077bd0d381928d",
"score": "0.5848819",
"text": "def test_api_versioning_invalid_version(self):\n # TODO: Test with a more simple SODAR API view once implemented\n\n response = self.client.get(\n reverse(\n 'projectroles:api_remote_get',\n kwargs={'secret': REMOTE_SITE_SECRET},\n ),\n HTTP_ACCEPT='{};version={}'.format(\n SODAR_API_MEDIA_TYPE, SODAR_API_VERSION_INVALID\n ),\n )\n\n self.assertEqual(response.status_code, 406)",
"title": ""
},
{
"docid": "171a53a0f15472dbeb6e046ca5a12271",
"score": "0.5831408",
"text": "def test_client_parse_req_bad_http(message, result):\n assert client(message) == result",
"title": ""
},
{
"docid": "416838f2e8c455c45c5b90d988447969",
"score": "0.5797074",
"text": "def __valid_response(response_to_validate):\n\n assert 200 <= response_to_validate.status_code <= 299",
"title": ""
},
{
"docid": "2584a913255619c1643b6a883d971303",
"score": "0.5794685",
"text": "def test_service(self):\n\n response = requests.get('http://127.0.0.1:5000/'.format(platform.node()))\n self.assertEqual(200, response.status_code,\n response.status_code)",
"title": ""
},
{
"docid": "70908cf7d5bbecef97131dd5c32e8b1c",
"score": "0.5783925",
"text": "def test_valid_response_get(self):\r\n data = {'identity': '1112223333', 'text': 'hi there'}\r\n response = self.client.get(self.http_backend_url, data)\r\n self.assertEqual(response.status_code, 200)",
"title": ""
},
{
"docid": "a02673addff376a486b9d3fb98381e60",
"score": "0.5744733",
"text": "def test_response(self):\n operation, _ = self.app.resolve_obj(\n '#/paths/~1a/get', from_spec_version='2.0')\n\n self.assertEqual(\n deref(operation.responses['default']).description, 'void, r1')",
"title": ""
},
{
"docid": "f0e512141b4bb629e4d1a9aae265d64d",
"score": "0.57414216",
"text": "def test_virt(self):\n resp = self.client.get(\"/virt/\")\n self.assertEqual(resp.status_code, 200)\n self.assertTrue(\"<title>Kitchen</title>\" in resp.content)",
"title": ""
},
{
"docid": "f9510ac9b7a36a0ea72d40a90aa597f0",
"score": "0.57313323",
"text": "def test_client_parse_req_bad_host(message, result):\n assert client(message) == result",
"title": ""
},
{
"docid": "ebcd943812547d633e559e40587f7af5",
"score": "0.5705372",
"text": "def test_valid_response_post(self):\r\n response = self.client.post(reverse('vumi-backend'),\r\n json.dumps(self.valid_data),\r\n content_type='text/json')\r\n self.assertEqual(response.status_code, 200)",
"title": ""
},
{
"docid": "36d82853a39b1c83c0be7fd030c16e62",
"score": "0.56990784",
"text": "def test_version_successfull_operation(self):\n resp = json.loads(bolt.version())\n self.assertEqual(resp[\"success\"], self.SUCCESS_RESPONSE)\n self.assertTrue(resp[\"value\"] != None)",
"title": ""
},
{
"docid": "fb3453f62d789496febbbd945c2f61a8",
"score": "0.5687995",
"text": "def check_rsp(_rsp):\n if _rsp.code == 200:\n return _rsp.read()\n else:\n raise Exception(str(_rsp))",
"title": ""
},
{
"docid": "96ed4ded972d458b1ea1c8d065904b75",
"score": "0.568593",
"text": "def test_invalid_representation_request(self):\n with self.assertRaises(HTTPError):\n request('Morphine', 'ogiuewrgpw')",
"title": ""
},
{
"docid": "e62fdabae3d8b9593489f8341e95ab3f",
"score": "0.56783336",
"text": "def test_422(self, response_GET):\n assert response_GET.status_code == 422",
"title": ""
},
{
"docid": "e62fdabae3d8b9593489f8341e95ab3f",
"score": "0.56783336",
"text": "def test_422(self, response_GET):\n assert response_GET.status_code == 422",
"title": ""
},
{
"docid": "c5cb7e94c6c8aae03e2b2e881187c7b0",
"score": "0.5667097",
"text": "def test_response_okay(self):\n assert (\n self.response.status_code == 200\n ), f\"Request to {self.request_str} failed: {self.response.content}\"",
"title": ""
},
{
"docid": "83cc276f45e84538495fe0800d8186ab",
"score": "0.5651921",
"text": "def check(self, response, payload):\n pass",
"title": ""
},
{
"docid": "9bcae9a3d43cc2528d9d368add49f5cf",
"score": "0.5651378",
"text": "def ping_check():\n return Response(status=const.HTTP_200_OK)",
"title": ""
},
{
"docid": "1cb45c57e4a834e776550755fe6b2580",
"score": "0.56495965",
"text": "def _check_resp(self, expected, command, typ, data):\n if typ != expected:\n raise self.Error('%s failed: %r' % (command, data[0]))",
"title": ""
},
{
"docid": "1ef8c028d853234e5d6dbc5a3da7bd34",
"score": "0.56363493",
"text": "def __check_response_for_bluedart_error(self):\n\n pass",
"title": ""
},
{
"docid": "5ce604a7a6770dbc6103086a7ded9d7f",
"score": "0.5626736",
"text": "def test_theres_something_to_use(self):\n self.assertIsNotNone(self.response.content)",
"title": ""
},
{
"docid": "a44222271b9aaed6a1b254e6c2385bdf",
"score": "0.5614778",
"text": "def getErrorResponse(self):",
"title": ""
},
{
"docid": "7281e263df700c2f63904f9b07bb9738",
"score": "0.5609904",
"text": "def test_get_farmers_with_wrong_endpoint(self):\n response = self.client.get('/farmerss')\n data = json.loads(response.data.decode())\n\n self.assertEqual(response.status_code, 404)\n self.assertFalse(data['success'])",
"title": ""
},
{
"docid": "e30d26a7671b7fbf63b7ebcbd7748fbb",
"score": "0.55992496",
"text": "def test_invalid_endpoint(client):\n\n result = client.get('/nonsense')\n assert result.status_code == 404",
"title": ""
},
{
"docid": "94232c2c6522fcca55dfb14f519e0223",
"score": "0.55955476",
"text": "def test_post_vessel_bad_request(self):\n info = {}\n\n res = self.client().post('/vessels', data=json.dumps(info), headers={'Content-Type': 'application/json'})\n self.assertEqual(res.status_code, 400)\n self.assertEqual(res.get_json()['success'], False)\n self.assertEqual(res.get_json()['message'], 'bad request')",
"title": ""
},
{
"docid": "87ae74c37484a4db65da62c2b0fb5cd8",
"score": "0.5594477",
"text": "def test_invalid_response(self):\r\n data = {'invalid-phone': '1112223333', 'message': 'hi there'}\r\n response = self.client.get(reverse('kannel-backend'), data)\r\n self.assertEqual(response.status_code, 400)",
"title": ""
},
{
"docid": "521c66064ff79540fd760959f756b0aa",
"score": "0.55905485",
"text": "def test_bad_gateway_details(self):\n setup_routing(self.edr_api_app, func=response_code)\n setup_routing(self.edr_api_app, path='/1.0/subjects/2842335', func=bad_gateway_details)\n response = self.app.get('/verify?id=14360570', expect_errors=True)\n self.assertEqual(response.content_type, 'application/json')\n if SANDBOX_MODE:\n self.assertEqual(response.status, '404 Not Found')\n self.assertEqual(response.json['errors'][0]['description'][0]['error']['errorDetails'],\n \"Couldn't find this code in EDR.\")\n else:\n self.assertEqual(response.status, '403 Forbidden')\n self.assertEqual(response.json['errors'][0]['description'],\n [{u'message': u'Service is disabled or upgrade.'}])",
"title": ""
},
{
"docid": "89e0cf77a03812caa4d62a20fa8bb8a0",
"score": "0.5587534",
"text": "def test_server_error(self):\n setup_routing(self.edr_api_app, func=server_error)\n response = self.app.get('/verify?id=123', status=403)\n self.assertEqual(response.content_type, 'application/json')\n self.assertEqual(response.status, '403 Forbidden')\n self.assertEqual(response.json['errors'][0]['description'], [{u'message': u'Internal error.', u'code': 20}])",
"title": ""
},
{
"docid": "6cd74ac8bc68d188c2cabe5e2e32aa4c",
"score": "0.558574",
"text": "def test_client_parse_req_bad_get(message, result):\n assert client(message) == result",
"title": ""
},
{
"docid": "813883b73d766beba25ce66fa315947c",
"score": "0.55846286",
"text": "def test_adapts_response(self):\n \n handler = self._make_one('', 'resp', '')\n self.assertTrue(handler.response == 'resp')",
"title": ""
},
{
"docid": "9571157fa4e8ea7d8a0f1edc9e4e561c",
"score": "0.55796945",
"text": "def test_invalid_response_get(self):\r\n data = {'invalid-phone': '1112223333', 'message': 'hi there'}\r\n response = self.client.get(self.http_backend_url, data)\r\n self.assertEqual(response.status_code, 400)",
"title": ""
},
{
"docid": "ce7795170339b8c760d78b62eb6165cb",
"score": "0.55778116",
"text": "def test_status_good(self):\n response = self.simulate_get(\"/v1/healthchecks/ping\")\n self.assertEqual(200, response.status_code)",
"title": ""
},
{
"docid": "348562df92b783340e27d344a30bd1bb",
"score": "0.55754715",
"text": "def test_validate_missing_identity(self):\n response = self._get_hosts({})\n self.assertEqual(401, response.status_code)",
"title": ""
},
{
"docid": "a62c5bafadf506bf2cc2580df644a4db",
"score": "0.55564964",
"text": "def test_un_available_end_point(self):\n response = self.client.get(\"/v1/shop\")\n\n self.assertEqual(True, self.is_json(response.data))",
"title": ""
},
{
"docid": "148feb418f7b5b315085486a5d58b822",
"score": "0.55550694",
"text": "def test_warmup_request_responds_200(self):\n response = self.client.get('/_ah/warmup')\n self.assertEqual('200 OK', response.status)",
"title": ""
},
{
"docid": "522b509d76d2e4c5c1ae5c98124cfca4",
"score": "0.55489707",
"text": "def _http_precondition_failed(start_response):\n start_response(falcon.HTTP_412, [('Content-Length', '0')])\n return []",
"title": ""
},
{
"docid": "b5bea1d123694ed8a10f1fda342f35e4",
"score": "0.5544173",
"text": "def api_check():\n return jsonify({'success': True})",
"title": ""
},
{
"docid": "cff092229521aa00227d8cef1d639a13",
"score": "0.5538141",
"text": "def test_remote_fuzzy_vies():\n try:\n valid, response = vat.check_details(test_numbers[1][0],\n test_numbers[1][1])\n assert valid is True\n\n valid, response = vat.check_details(test_numbers[1][0], bad_info)\n assert valid is False\n except vat.VIESHTTPException as e:\n if 500 <= e.code <= 599:\n pytest.skip('EU VIES server is malfunctioning, so skipping test')\n else:\n raise",
"title": ""
},
{
"docid": "a4270dd8bce8cafceb23202616c5af91",
"score": "0.5532226",
"text": "def test_minimal_response(self):\n response = self.run_daphne_response(\n [\n {\"type\": \"http.response.start\", \"status\": 200},\n {\"type\": \"http.response.body\", \"body\": b\"hello world\"},\n ]\n )\n self.assertEqual(response.status, 200)\n self.assertEqual(response.read(), b\"hello world\")",
"title": ""
},
{
"docid": "e82aa363ca64d2debb4a0a98632947e5",
"score": "0.5518671",
"text": "def test_empty_results_for_public_resource(test_client):\n response = test_client.get('/api/search/vehicles')\n assert response.status_code == 200\n assert len(_get_json_from_resopnse(response)) == 0",
"title": ""
},
{
"docid": "8c03ef6206049d9abd0207555f86b033",
"score": "0.55077064",
"text": "def test_api_returns_error_when_downstream_server_returns_an_unusable_content_type(self):\n self.client.force_login(self.user)\n\n # Prepare mock response\n mock_response_headers = {\n 'content-type':'application/json',\n }\n mock_response_content = b'{\"hello\":\"world\"}'\n mock_response = MockHTTPResponse(\n status.HTTP_200_OK,\n mock_response_content,\n headers=mock_response_headers)\n self.mock_requests_get.return_value = mock_response\n\n target_url = f'https://foobar.com/robots.txt'\n data = {\n 'target_url':target_url,\n 'analysis_mode':WebAnalysis.ANALYSIS_MODE_STATIC,\n }\n api_url = reverse(\"api-create-web-analysis\")\n response = self.client.post(api_url, data, format=\"json\")\n self.assertEqual(response.status_code, status.HTTP_502_BAD_GATEWAY)\n self.assertEqual(response.data, {'error': 'Received invalid content type: application/json'})",
"title": ""
},
{
"docid": "63f13d675c98806164c84af4822e0ee4",
"score": "0.5507409",
"text": "def vAppReq(request):\n \n ret = None\n body = request.read()\n print body\n print \"CLIENTSRV_PORT %s\" % CLIENTSRV_PORT\n jsobj = json.loads(body)\n req = jsobj[0]\n\n if req == CMDClientAgent.reqinstance:\n type = jsobj[1]['type']\n reqins = CMDClientAgent.cmd_reqinstance(type)\n soc = socket.socket(type = socket.SOCK_DGRAM)\n soc.sendto(reqins, ('192.168.1.187', CLIENTSRV_PORT))\n\n # None VM Instance\n ret = CMDClientAgent.ack_reqinstance(type, None, 0)\n\n ack_reqins = soc.recv(512)\n if ack_reqins:\n ret = ack_reqins\n\n return HttpResponse(ret, mimetype = 'application/json')\n\n ##############################################\n ##############################################\n \"\"\"\n # just for test\n jsobj = CMDClientAgent.ack_reqinstance('winxp', '192.168.1.187', 6000)\n #return HttpResponse(jsobj, mimetype = 'application/json')\n #return HttpResponse(\"%s\" % request)\n return HttpResponse(\"%s\\n\\n%s\" % (request.read(), request))\n \"\"\"\n\n\n ##############################################\n ##############################################\n \"\"\" Temp test for POST\n \"\"\"\n \"\"\"\n cmd = request.POST['CMD']\n type = request.POST['TYPE']\n reqins = CMDClientAgent.cmd_reqinstance(type)\n soc = socket.socket(type = socket.SOCK_DGRAM)\n soc.sendto(reqins, ('192.168.1.187', CLIENTSRV_PORT))\n\n ret = CMDClientAgent.ack_reqinstance(type, None, 0)\n\n ack_reqins = soc.recv(512)\n if ack_reqins:\n ret = ack_reqins\n\n return HttpResponse(ret, mimetype = 'application/json')\n \"\"\"",
"title": ""
},
{
"docid": "89d2ce6ae7a73f123652de830755e185",
"score": "0.5505376",
"text": "def health_check(self, request):\n return Response()",
"title": ""
},
{
"docid": "ae677f52b2bdf678604828a739baeee7",
"score": "0.5504166",
"text": "def test_vf_create_verify(self):\n\n new_vf2 = {\n 'element-id': '2',\n 'name': 'vf_2',\n }\n\n # the function to be tested:\n resp = self.urihandler.post(self.hmc,\n '/api/partitions/1/virtual-functions',\n new_vf2, True, True)\n\n assert len(resp) == 1\n assert 'element-uri' in resp\n new_vf2_uri = resp['element-uri']\n assert new_vf2_uri == '/api/partitions/1/virtual-functions/2'\n\n # the function to be tested:\n vf2 = self.urihandler.get(self.hmc,\n '/api/partitions/1/virtual-functions/2',\n True)\n\n exp_vf2 = {\n 'element-id': '2',\n 'element-uri': '/api/partitions/1/virtual-functions/2',\n 'class': 'virtual-function',\n 'parent': '/api/partitions/1',\n 'name': 'vf_2',\n 'device-number': vf2['device-number'], # auto-generated\n }\n\n assert vf2 == exp_vf2",
"title": ""
},
{
"docid": "6330ff6f33189c2cd5065af00defa0a6",
"score": "0.55011123",
"text": "def test_vs_get(self):\n\n # the function to be tested:\n vswitch1 = self.urihandler.get(self.hmc, '/api/virtual-switches/1',\n True)\n\n exp_vswitch1 = {\n 'object-id': '1',\n 'object-uri': '/api/virtual-switches/1',\n 'class': 'virtual-switch',\n 'parent': '/api/cpcs/2',\n 'name': 'vswitch_osa_1',\n 'description': 'Vswitch for OSA #1 in CPC #2',\n 'connected-vnic-uris': [], # auto-generated\n }\n assert vswitch1 == exp_vswitch1",
"title": ""
},
{
"docid": "a44afc385526ea90431cd507b2d7e5ce",
"score": "0.5488537",
"text": "def _is_valid_response(response):\n return len(response['results']['bindings']) > 0",
"title": ""
},
{
"docid": "94518794b2237515fb405c42a9c3af62",
"score": "0.5479386",
"text": "def test_uri_parsing_minimal(self):\n\n vmw = self.get_vmware('vpx://example.com')\n\n self.assertIsNotNone(vmw)\n self.assertEqual(vmw.user,\n 'administrator@vsphere.local')\n self.assertEqual(vmw.server, 'example.com')\n self.assertEqual(vmw.port, None)",
"title": ""
},
{
"docid": "5551f4de1fc5e6154e79c77a236b380d",
"score": "0.54783934",
"text": "def test_not_acceptable(self):\n setup_routing(self.edr_api_app, func=not_acceptable)\n response = self.app.get('/verify?id=123', status=403)\n self.assertEqual(response.content_type, 'application/json')\n self.assertEqual(response.status, '403 Forbidden')\n self.assertEqual(response.json['errors'][0]['description'], [{u'message': u'Message.'}])",
"title": ""
},
{
"docid": "520220b4a182bcd4c01a40e6b420c3d3",
"score": "0.5475951",
"text": "def test_uri_parsing(self):\n\n vmw = self.get_vmware('vpx://some.server:12345')\n\n self.assertIsNotNone(vmw)\n self.assertEqual(vmw.user,\n 'administrator@vsphere.local')\n self.assertEqual(vmw.server, 'some.server')\n self.assertEqual(vmw.port, 12345)",
"title": ""
},
{
"docid": "90a056ad555b1eafdf602eeb52bf7b6a",
"score": "0.5474884",
"text": "def test_api_status_page():\n res = requests.get('http://localhost:8000/status')\n assert res.ok == True",
"title": ""
},
{
"docid": "8283197bf32207c835a0027275052fa4",
"score": "0.5472093",
"text": "def test_invalid_response(self):\n data = {}\n response = self.client.post(self.backend_url, data)\n self.assertEqual(response.status_code, 400)",
"title": ""
},
{
"docid": "78eefe42cedc32cfb2cb6ba2108e201c",
"score": "0.5463732",
"text": "def test_check_machine_exists_existing_machine(conn, vm_data):\n context = conn.return_value.__enter__.return_value\n context.compute.find_server.return_value = NonCallableMock()\n found = check_machine_exists(vm_data)\n\n conn.assert_called_once_with()\n context.compute.find_server.assert_called_with(vm_data.virtual_machine_id)\n assert isinstance(found, bool) and found",
"title": ""
},
{
"docid": "87050d1daf93d95514a06f7a9afb2246",
"score": "0.54534465",
"text": "def test_restart_successfull_operation(self):\n resp = json.loads(bolt.restart())\n try:\n self.assertEqual(resp[\"value\"], self.RESTART_RESPONSE)\n except AssertionError:\n self.assertEqual(resp[\"value\"], self.RESTART_ALTERNATIVE_RESPONSE)",
"title": ""
},
{
"docid": "16dc84aa565b00e21002e62ed26efa3e",
"score": "0.5446946",
"text": "def test_get_instance_missing(self):\n r = self.client.get('/minimals/1')\n self.assertEquals(r.status_code, 404)",
"title": ""
},
{
"docid": "2449af4e33f524da238b3472b3b6a6a2",
"score": "0.54451525",
"text": "def test_check_machine_exists_deleted_machine(conn, vm_data):\n context = conn.return_value.__enter__.return_value\n context.compute.find_server.return_value = None\n found = check_machine_exists(vm_data)\n\n conn.assert_called_once_with()\n context = conn.return_value.__enter__.return_value\n context.compute.find_server.assert_called_with(vm_data.virtual_machine_id)\n assert isinstance(found, bool) and not found",
"title": ""
},
{
"docid": "5375e759f47a68580e40cb2012ded2d7",
"score": "0.5444239",
"text": "def test_empty_request(self):\n setup_routing(self.edr_api_app, func=response_passport)\n response = self.app.get('/verify', status=403)\n self.assertEqual(response.status, '403 Forbidden')\n self.assertEqual(response.content_type, 'application/json')\n self.assertEqual(response.json['errors'][0]['description'], [{u'message': u'Wrong name of the GET parameter'}])",
"title": ""
},
{
"docid": "8b5881e28218430efa550054653fcbf6",
"score": "0.5437207",
"text": "def check_status(response: requests.Response):\n response.raise_for_status()",
"title": ""
},
{
"docid": "12488d3a961f39df05d9aa27be6dd9a7",
"score": "0.54369724",
"text": "def test_http_contradiction_error(self):\n with settings.runtime_values(\n host='http://33.33.33.33', verify_ssl=True):\n with self.assertRaises(exc.TowerCLIError):\n client.prefix",
"title": ""
},
{
"docid": "6f14113d825e20a027a9530adb8e9a7f",
"score": "0.5434841",
"text": "def test_invalid_response(self):\r\n data = {'invalid-phone': '1112223333', 'message': 'hi there'}\r\n response = self.client.post(reverse('vumi-backend'), json.dumps(data),\r\n content_type='text/json')\r\n self.assertEqual(response.status_code, 400)",
"title": ""
},
{
"docid": "7f363e0ce5423d2c00fa4bbb1679a21b",
"score": "0.54269844",
"text": "def test_valid_response_get(self):\r\n data = {'id': '1112223333', 'text': 'hi there'}\r\n response = self.client.get(reverse('kannel-backend'), data)\r\n self.assertEqual(response.status_code, 200)",
"title": ""
},
{
"docid": "d40e9b05260bea8c467ffae7d14254db",
"score": "0.5424007",
"text": "def test_already_response() -> None:\n actual = _view_function_response()\n expected = Response(dumps({\"message\": \"This is a JSON.\"}),\n status=200, content_type='application/json')\n assert actual.response == expected.response\n assert actual.status_code == expected.status_code\n assert actual.content_type == expected.content_type",
"title": ""
},
{
"docid": "901ef6eb88378fc7d5cc6f025d2bf65a",
"score": "0.54239625",
"text": "def check_vm(self, vm):\n if 'name' not in vm:\n raise ValueError(\"Can not create a virtual machine without a name\")\n\n if 'iso_path' not in vm:\n raise ValueError(\"Can not create a virtual machine without an iso image\")",
"title": ""
},
{
"docid": "70feb6568a8f7b8c1c8be1486996d3f6",
"score": "0.5423182",
"text": "def test_unexpected_response(requests_mock_get, invalid_response):\n _, response = requests_mock_get\n response.status_code = 200\n response.json = lambda: invalid_response\n\n with raises(TemperatureSourceException):\n NoaaTemperatureSource.get_current_temperature(1.0, 2.0)",
"title": ""
},
{
"docid": "66856f07757274f14ea1dfde102dd145",
"score": "0.5420256",
"text": "def test_api_versioning_invalid_media_type(self):\n # TODO: Test with a more simple SODAR API view once implemented\n\n response = self.client.get(\n reverse(\n 'projectroles:api_remote_get',\n kwargs={'secret': REMOTE_SITE_SECRET},\n ),\n HTTP_ACCEPT='{};version={}'.format(\n SODAR_API_MEDIA_TYPE_INVALID, SODAR_API_VERSION\n ),\n )\n\n self.assertEqual(response.status_code, 406)",
"title": ""
},
{
"docid": "cfb42ab252839d146e8ef129afcb9d6e",
"score": "0.5418866",
"text": "def test_api_route_is_status_ok(self):\n response = self.client.get(reverse_lazy('api_list'))\n self.assertTrue(response.status_code == 200)",
"title": ""
},
{
"docid": "8ecdd1cd09b82cb7c54b693ea0239790",
"score": "0.54183984",
"text": "def testPostResponseNeedSignature(self):\n self.assertEqual(Video.objects.count(), 0)\n api_response = self.client.post(\n self.video_url,\n self.video_data_json,\n content_type=\"application/vnd.api+json\",\n )\n self.assertEqual(api_response.status_code, status.HTTP_401_UNAUTHORIZED)\n self.assertEqual(Video.objects.count(), 0)",
"title": ""
},
{
"docid": "e49182debe6d995e0be3ef1628d1827f",
"score": "0.5412201",
"text": "def test_new_incident_invalid_video(self):\n #no body\n response = self.post_incident(self.redflag_invalid_video)\n self.assertEqual(response.status_code, 400)\n data = json.loads(response.get_data())\n self.assertEqual(data['error'], 'Video link is invalid')",
"title": ""
},
{
"docid": "1128bc86e551bddf3c9376cbb76a43c7",
"score": "0.5411965",
"text": "def check_response(self, response):\n\n status = response.status_code\n\n if status == 204:\n return\n elif status == 401 or status == 403:\n raise APIException(response)\n elif status == 404:\n json = response.json()\n raise ResourceNotFoundException(json.get('errors'))\n elif status == 422:\n json = response.json()\n raise ValidationException(json.get('errors'))\n elif status < 200 or status >= 300:\n text = response.text\n raise APIException(response)\n\n return response.json()",
"title": ""
},
{
"docid": "3a64fe9e9d2a44eb5a8e176c768a18c6",
"score": "0.54092234",
"text": "def check_response(self, response):\n if response.status_code == 401:\n raise D4S2Error(UNAUTHORIZED_MESSAGE)\n if not 200 <= response.status_code < 300:\n raise D4S2Error(\"Request to {} failed with {}:\\n{}.\".format(response.url, response.status_code,\n response.text))",
"title": ""
},
{
"docid": "a570bcd4f35616ea428874981a6f47a5",
"score": "0.54003346",
"text": "def test_get_bad_args(self, fake_args_valid):\n fake_args_valid.return_value = False\n resp = self.app.get('/api/1/ipam/addr',\n headers={'X-Auth': self.token})\n\n expected = 400\n\n self.assertEqual(resp.status_code, expected)",
"title": ""
},
{
"docid": "ee241f9b5aa4908c5384e5ca65388441",
"score": "0.5400166",
"text": "def test_response_200(self):\n expected = 200\n response = self.client.get(reverse('booking_list'))\n\n self.AssertEqual(response.code, expected)",
"title": ""
},
{
"docid": "775ccd7d239fd7df26220dd21033491a",
"score": "0.5396227",
"text": "def test_answer(self):\n response = hug.test.get(app, '/')\n assert '404' in response.status",
"title": ""
},
{
"docid": "5fae1c9c592668d94637efeca3d52040",
"score": "0.5394412",
"text": "def test_server_parse_request_ok(message, result):\n assert parse_request(message) == result",
"title": ""
},
{
"docid": "682f067c3d3f6df04c2602039923e101",
"score": "0.5394092",
"text": "def test_fake_val_json_returns_200(testapp_route):\n response = testapp_route.get('/fake-validated', status=200)\n assert response.status_code == 200",
"title": ""
},
{
"docid": "68dda1b65cf9acc1a41a682a8baf4a6b",
"score": "0.5388438",
"text": "def test_get(self):\n self.assertEqual(200, self.resp.status_code)",
"title": ""
},
{
"docid": "68dda1b65cf9acc1a41a682a8baf4a6b",
"score": "0.5388438",
"text": "def test_get(self):\n self.assertEqual(200, self.resp.status_code)",
"title": ""
},
{
"docid": "68dda1b65cf9acc1a41a682a8baf4a6b",
"score": "0.5388438",
"text": "def test_get(self):\n self.assertEqual(200, self.resp.status_code)",
"title": ""
},
{
"docid": "68dda1b65cf9acc1a41a682a8baf4a6b",
"score": "0.5388438",
"text": "def test_get(self):\n self.assertEqual(200, self.resp.status_code)",
"title": ""
},
{
"docid": "68dda1b65cf9acc1a41a682a8baf4a6b",
"score": "0.5388438",
"text": "def test_get(self):\n self.assertEqual(200, self.resp.status_code)",
"title": ""
},
{
"docid": "68dda1b65cf9acc1a41a682a8baf4a6b",
"score": "0.5388438",
"text": "def test_get(self):\n self.assertEqual(200, self.resp.status_code)",
"title": ""
},
{
"docid": "68dda1b65cf9acc1a41a682a8baf4a6b",
"score": "0.5388438",
"text": "def test_get(self):\n self.assertEqual(200, self.resp.status_code)",
"title": ""
},
{
"docid": "68dda1b65cf9acc1a41a682a8baf4a6b",
"score": "0.5388438",
"text": "def test_get(self):\n self.assertEqual(200, self.resp.status_code)",
"title": ""
},
{
"docid": "68dda1b65cf9acc1a41a682a8baf4a6b",
"score": "0.5388438",
"text": "def test_get(self):\n self.assertEqual(200, self.resp.status_code)",
"title": ""
},
{
"docid": "68dda1b65cf9acc1a41a682a8baf4a6b",
"score": "0.5388438",
"text": "def test_get(self):\n self.assertEqual(200, self.resp.status_code)",
"title": ""
},
{
"docid": "68dda1b65cf9acc1a41a682a8baf4a6b",
"score": "0.5388438",
"text": "def test_get(self):\n self.assertEqual(200, self.resp.status_code)",
"title": ""
},
{
"docid": "68dda1b65cf9acc1a41a682a8baf4a6b",
"score": "0.5388438",
"text": "def test_get(self):\n self.assertEqual(200, self.resp.status_code)",
"title": ""
},
{
"docid": "14a3c3b6dc7a31df2f4bc77229196858",
"score": "0.53880656",
"text": "def test_bad_gateway(self):\n setup_routing(self.edr_api_app, func=bad_gateway)\n response = self.app.get('/verify?id=123', status=403)\n self.assertEqual(response.content_type, 'application/json')\n self.assertEqual(response.status, '403 Forbidden')\n self.assertEqual(response.json['errors'][0]['description'], [{u'message': u'Service is disabled or upgrade.'}])",
"title": ""
},
{
"docid": "380a3b94ef9bc9c8fbb795c81388460a",
"score": "0.5386735",
"text": "def test_non_json_response() -> None:\n with raises(TypeError):\n _view_function_response_failure()",
"title": ""
},
{
"docid": "53074d61fbc7717bd1b3b318dca1c666",
"score": "0.5384225",
"text": "def test_valid_session(self):\n response = self.client.get('/a/')\n self._check_hello(response)\n self._check_profile(response)\n return response",
"title": ""
},
{
"docid": "f4e30b113409d39b4300fafd866b6716",
"score": "0.5382633",
"text": "def test_healthcheck(self):\r\n resp = self.app.get('/healthcheck')\r\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\r\n data = json.loads(resp.data)\r\n self.assertEqual(data['message'], \"Healthy\")",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="770"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">e99ea12864f0f0d98f66a723a580ef33</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Find all water touching point (i,j) and set to ii. The recursive version works for teeny, tiny grids only, so we need another way.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "1a6ad161e90035cd4a4d77e21a0f1e1a",
"score": "0.67142487",
"text": "def flood_fill_water(imask, i, j, ii):\n Mp, Lp = imask.shape\n Lm = Lp-2\n Mm = Mp-2\n jj = imask[j,i]\n\n llist = []\n llist.append((j,i))\n while len(llist) > 0:\n (j,i) = llist.pop()\n imask[j,i] = ii\n if ( imask[j,i-1] == jj and i > 1 ):\n llist.append((j, i-1))\n if ( imask[j-1,i] == jj and j > 1 ):\n llist.append((j-1, i))\n if ( imask[j,i+1] == jj and i < Lm ):\n llist.append((j, i+1))\n if ( imask[j+1,i] == jj and j < Mm ):\n llist.append((j+1, i))",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "419f98b652d4ab344feb2043d0c6ed25",
"score": "0.5981496",
"text": "def swimInWater(self, grid: List[List[int]]) -> int:\n\n n = len(grid)\n\n to_visit = []\n\n def add_to_visit(i, j):\n heapq.heappush(to_visit, (grid[i][j], i, j))\n\n def neighbors(i, j):\n if i > 0:\n yield i - 1, j\n if j > 0:\n yield i, j - 1\n if i < n - 1:\n yield i + 1, j\n if j < n - 1:\n yield i, j + 1\n\n min_depth = 0\n visited = set()\n add_to_visit(0, 0)\n while to_visit:\n depth, i, j = heapq.heappop(to_visit)\n if (i, j) in visited:\n continue\n\n min_depth = max(min_depth, depth)\n if (i, j) == (n - 1, n - 1):\n break\n\n visited.add((i, j))\n for x, y in neighbors(i, j):\n if (x, y) not in visited:\n add_to_visit(x, y)\n return min_depth",
"title": ""
},
{
"docid": "e5482a124e9c6c9abfe38ef9f73464d6",
"score": "0.5955794",
"text": "def search_basin(i: int, j: int, visited: Set[str]):\n adjacent_alternatives = [\n [i - 1, j] if i > 0 and \"{}x{}\".format(i - 1, j) not in visited and map[i - 1][j] != 9 else None,\n [i + 1, j] if i < height - 1 and map[i + 1][j] != 9 and \"{}x{}\".format(i + 1, j) not in visited else None,\n [i, j - 1] if j > 0 and map[i][j - 1] != 9 and \"{}x{}\".format(i, j - 1) not in visited else None,\n [i, j + 1] if j < width - 1 and map[i][j + 1] != 9 and \"{}x{}\".format(i, j + 1) not in visited else None,\n ]\n adjacent_points = [p for p in adjacent_alternatives if p is not None]\n\n for (pi, pj) in adjacent_points:\n visited.add(\"{}x{}\".format(pi, pj))\n\n for (pi, pj) in adjacent_points:\n search_basin(pi, pj, visited)",
"title": ""
},
{
"docid": "b5e503a8b4950955b2b197fbbdb68a45",
"score": "0.5751172",
"text": "def interior(imask, ilist, iwat, iland):\n Mp, Lp = imask.shape\n for i in range(2,Lp-2):\n for j in range(2,Mp-2):\n if ((imask[j,i] == iwat) and (imask[j+1,i] == iland)):\n island(imask, ilist, i, j+1, 'east', iwat, iland)",
"title": ""
},
{
"docid": "2e86548edb3f6861e5f84d9c8957c3a8",
"score": "0.5704806",
"text": "def flood_fill_land(imask, i, j, ii):\n Mp, Lp = imask.shape\n Lm = Lp-2\n Mm = Mp-2\n jj = imask[j,i]\n\n llist = []\n llist.append((j,i))\n while len(llist) > 0:\n (j,i) = llist.pop()\n imask[j,i] = ii\n if ( imask[j,i-1] == jj and i > 1 ):\n llist.append((j, i-1))\n if ( imask[j-1,i] == jj and j > 1 ):\n llist.append((j-1, i))\n if ( imask[j,i+1] == jj and i < Lm ):\n llist.append((j, i+1))\n if ( imask[j+1,i] == jj and j < Mm ):\n llist.append((j+1, i))\n# now do the diagonals\n if ( imask[j-1,i-1] == jj and i > 1 ):\n llist.append((j-1, i-1))\n if ( imask[j-1,i+1] == jj and j > 1 ):\n llist.append((j-1, i+1))\n if ( imask[j+1,i+1] == jj and i < Lm ):\n llist.append((j+1, i+1))\n if ( imask[j+1,i-1] == jj and j < Mm ):\n llist.append((j+1, i-1))",
"title": ""
},
{
"docid": "b05fd4d7135aa54cbe4268482c33bf37",
"score": "0.56482804",
"text": "def draw_cells(i, j):\n if i>j: return\n for (ii, jj) in ((i, j), (j, i), (-i-j, i)):\n for xm in (-1, 1):\n for ym in (-1, 1):\n draw_point(xm*(jj+ii/2), ym*ii*r3/2)",
"title": ""
},
{
"docid": "28be7b360cd63a32c30e4ba71122ea28",
"score": "0.56219655",
"text": "def chording(self, i, j):\n if self.flags_nearby(i, j) == self.mines_nearby(i, j):\n l = self.neighbors(i, j)\n for (x, y) in l:\n if not self.flagged(x, y):\n self.reveal(x, y)",
"title": ""
},
{
"docid": "3d8d11ad64bd8a09b2311cfbef789315",
"score": "0.5619303",
"text": "def island(imask, ilist, i, j, dir, iwat, iland):\n ilist.append((-1, -1))\n p = (i,j)\n pstart = p\n\n # add to list of i,j Points\n# assert(!p.isEdge());\n ilist.append(p)\n # Now we change p so the exit condition doesn't happen yet...\n p = (0,0)\n\n if (dir == 'east'):\n seed = (i,j)\n elif (dir == 'west'):\n seed = (i-1,j-1)\n elif (dir == 'south'):\n seed = (i,j-1)\n elif (dir == 'north'):\n seed = (i-1,j)\n# warning(\"Island at\", seed, dir)\n\n # Trace the edge of the island, keeping track of the psi\n # points in a linked list. Also keep track of the east and west\n # edge segments so that we can later change the peninsula mask\n # points to water.\n while True:\n if (p == pstart):\n break\n if (dir == 'east'):\n i += 1\n p = (i,j)\n if ((imask[j-1,i] == iland) and (imask[j-1,i-1] == iwat)):\n dir = 'south'\n elif ((imask[j,i] == iland) and (imask[j-1,i] == iwat)):\n dir = 'east'\n elif ((imask[j,i-1] == iland) and (imask[j,i] == iwat)):\n dir = 'north'\n else:\n warning(\"Problem in island at \", i, j)\n exit(1)\n elif (dir == 'north'):\n j += 1\n p = (i,j)\n if ((imask[j,i] == iland) and (imask[j-1,i] == iwat)):\n dir = 'east'\n elif ((imask[j,i-1] == iland) and (imask[j,i] == iwat)):\n dir = 'north'\n elif ((imask[j-1,i-1] == iland) and (imask[j,i-1] == iwat)):\n dir = 'west'\n else:\n warning(\"Problem in island at \", i, j)\n exit(1)\n elif (dir == 'west'):\n i -= 1\n p = (i,j)\n if ((imask[j,i-1] == iland) and (imask[j,i] == iwat)):\n dir = 'north'\n elif ((imask[j-1,i-1] == iland) and (imask[j,i-1] == iwat)):\n dir = 'west'\n elif ((imask[j-1,i] == iland) and (imask[j-1,i-1] == iwat)):\n dir = 'south'\n else:\n warning(\"Problem in island at \", i, j)\n exit(1)\n elif (dir == 'south'):\n j -= 1\n p = (i,j)\n if ((imask[j-1,i-1] == iland) and (imask[j,i-1] == iwat)):\n dir = 'west'\n elif ((imask[j-1,i] == iland) and (imask[j-1,i-1] == iwat)):\n dir = 'south'\n elif ((imask[j,i] == iland) and (imask[j-1,i] == iwat)):\n dir = 'east'\n else:\n warning(\"Problem in island at \", i, j)\n exit(1)\n\n ilist.append(p)\n\n # time to flood_fill to show we are done with this island\n i = seed[0];\n j = seed[1];\n flood_fill_land(imask, i, j, iwat);",
"title": ""
},
{
"docid": "59842f84ad8d5499bbc91a1fd0d3ea11",
"score": "0.55495906",
"text": "def color_water(imask):\n count = 2\n\n Mp, Lp = imask.shape\n for j in range(1,Mp-1):\n for i in range(1,Lp-1):\n if (imask[j,i] == 1):\n flood_fill_water(imask, i, j, count)\n warning(\"New body!\", i, j)\n count += 1\n\n warning(\"There are\", count-2, \" bodies of water.\")\n return count-2",
"title": ""
},
{
"docid": "3d2372e3ddf176d51fd1fa10fe1f5c50",
"score": "0.5543924",
"text": "def e_1(self,r_i,temp_wall,i,j):\n all_walls = self.walls[:,:,:]\n wall_norm = (temp_wall[0,:]-temp_wall[1,:])/np.linalg.norm(temp_wall[0,:]-temp_wall[1,:])\n check_close_corner = np.zeros((all_walls.shape[0],2))\n point = self.seg_intersect(r_i,self.r_D,temp_wall[0,:],temp_wall[1,:])\n t_or_f = self.is_between(temp_wall[0,:],temp_wall[1,:],point)\n \n for k in range(all_walls.shape[0]):\n if k == j:\n continue\n check_close_corner[k,0] = self.is_between(all_walls[k,0,:],all_walls[k,1,:],temp_wall[0,:])\n check_close_corner[k,1] = self.is_between(all_walls[k,0,:],all_walls[k,1,:],temp_wall[1,:])\n \n if np.sum(check_close_corner) == 1:\n for k in range(all_walls.shape[0]):\n if (check_close_corner[k,0] == 1 and check_close_corner[k,1] == 0) and self.is_between(all_walls[k,0,:],all_walls[k,1,:],self.seg_intersect(r_i,self.r_D,all_walls[k,0,:],all_walls[k,1,:])) == 1:\n e = self.nearest_path(temp_wall,t_or_f,point,wall_norm,r_i,i)\n break\n elif (check_close_corner[k,0] == 1 and check_close_corner[k,1] == 0) and self.is_between(all_walls[k,0,:],all_walls[k,1,:],self.seg_intersect(r_i,self.r_D,all_walls[k,0,:],all_walls[k,1,:])) == 0:\n if np.linalg.norm(r_i-point)<self.radii[i]:\n e = self.nearest_path(temp_wall,t_or_f,point,wall_norm,r_i,i)\n elif (t_or_f == 1 and np.linalg.norm(point-r_i)<2*self.radii[i]):\n e = - wall_norm \n break\n else:\n p = temp_wall[1,:] - wall_norm*2*self.radii[i]\n e = (-r_i+p)/np.linalg.norm(-r_i+p)\n break\n elif check_close_corner[k,0] == 0 and check_close_corner[k,1] == 1 and self.is_between(all_walls[k,0,:],all_walls[k,1,:],self.seg_intersect(r_i,self.r_D,all_walls[k,0,:],all_walls[k,1,:])) == 1:\n e = self.nearest_path(temp_wall,t_or_f,point,wall_norm,r_i,i)\n break\n \n elif check_close_corner[k,0] == 0 and check_close_corner[k,1] == 1 and self.is_between(all_walls[k,0,:],all_walls[k,1,:],self.seg_intersect(r_i,self.r_D,all_walls[k,0,:],all_walls[k,1,:])) == 0: \n if np.linalg.norm(r_i-point)<self.radii[i]:\n e = self.nearest_path(temp_wall,t_or_f,point,wall_norm,r_i,i)\n elif (t_or_f == 1 and np.linalg.norm(point-r_i)<2*self.radii[i]):\n e = wall_norm \n break\n else: \n p = temp_wall[0,:] + wall_norm*2*self.radii[i]\n e = (-r_i+p)/np.linalg.norm(-r_i+p)\n break\n else:\n e = self.nearest_path(temp_wall,t_or_f,point,wall_norm,r_i,i)\n return e",
"title": ""
},
{
"docid": "656ee03fce3debc02f7a53e55b02eeb1",
"score": "0.5402994",
"text": "def surrounding_points(self, i, j):\n return [\n (i - 1, j + 1),\n (i, j + 1),\n (i + 1, j + 1),\n (i - 1, j),\n (i + 1, j),\n (i - 1, j - 1),\n (i, j - 1),\n (i + 1, j - 1),\n ]",
"title": ""
},
{
"docid": "962a4cc4691efe180c5957ac91b0a299",
"score": "0.5388439",
"text": "def place_mine(self, i, j):\n if not self.mined(i, j):\n self.square_at(i, j).mined = True\n l = self.neighbors(i, j)\n for (x, y) in l:\n self.square_at(x, y).mines_nearby += 1\n self.nmines += 1",
"title": ""
},
{
"docid": "09623fed4147979114eb9431699d6e85",
"score": "0.53872585",
"text": "def stamp_building(x,y,s,m):\n for sy in range(len(s)):\n for sx in range(len(s[0])):\n if on_map(x + sx, y + sy, m): \n new_tile = s[sy][sx]\n if m[y + sy][x + sx] == 0:\n m[y + sy][x + sx] = 0\n else:\n m[y + sy][x + sx] = new_tile",
"title": ""
},
{
"docid": "84c1e672fbc1a58707f97b326007ed4d",
"score": "0.535187",
"text": "def fill_in_points(self, canvas, left_x, top_y, tile_width, tile_height):\n for j in range(top_y, top_y - tile_height, -1):\n for i in range(left_x, left_x + tile_width):\n distances = canvas.closest_points(i, j, 4)\n weighted = list(color_weighting(distances))\n self[(i, j)] = ThemeColor.average(weighted)",
"title": ""
},
{
"docid": "2d80ac10013cb50f28ccdad00acf65ec",
"score": "0.5335353",
"text": "def neighbors(i, j):\n if i>j: return\n if (i, j) == (0, 0):\n for _ in range(6): yield (0, 1)\n return\n if (i, j) == (0, 1):\n for r in ((0, 2), (1, 1), (0, 1), (0, 0), (0, 1), (1, 1)):\n yield r\n return\n if i == 0:\n for r in ((0, j+1), (1, j), (1, j-1), (0, j-1), (1, j-1), (1, j)):\n yield r\n return\n if i == j:\n for r in ((i, j+1), (i, j+1), (i-1, j+1), (i-1, j), (i-1, j), (i-1, j+1)):\n yield r\n return\n if i == j-1:\n for r in ((i, j+1), (i+1, j), (i, j), (i, j-1), (i-1, j), (i-1, j+1)):\n yield r\n return\n for r in ((i, j+1), (i+1, j), (i+1, j-1), (i, j-1), (i-1, j), (i-1, j+1)):\n yield r\n\n if i+1 <= j: yield (i+1, j)\n if i+1 <= j-1: yield (i+1, j-1)\n if i <= j-1: yield (i, j-1)\n if i-1 <= j: yield (i-1, j)\n if i-1 <= j+1: yield (i-1, j+1)\n if i <= j+1: yield (i, j+1)",
"title": ""
},
{
"docid": "71048c0908a8d1b7647b0c7c96d5c743",
"score": "0.52873176",
"text": "def dfs_maze(self, start_i, start_j):\n self.start = [start_i, start_j]\n visited = ndarray((self.n, self.m), dtype=int)\n visited[:,:] = 0\n visited[start_i, start_j] = 1\n visited_count = 1\n current_i, current_j = start_i, start_j\n trail = []\n trail.append((current_i, current_j))\n while visited_count < self.n*self.m:\n cand_neighbors = list(filter(lambda x: x[0] >= 0 and x[1] >= 0 and x[0] < self.n and x[1] < self.m and visited[x[0],x[1]] == 0,\n [(current_i+1, current_j), (current_i-1, current_j), (current_i, current_j+1), (current_i, current_j-1)]))\n if len(cand_neighbors) > 0:\n pick = cand_neighbors[randint(0, len(cand_neighbors)-1)]\n diff = (pick[0]-current_i, pick[1]-current_j)\n assert abs(diff[0]) + abs(diff[1]) == 1\n if diff[1] == 1: # south\n self.wall_y[current_i, current_j+1] = 0\n elif diff[1] == -1: # north\n self.wall_y[current_i, current_j] = 0\n elif diff[0] == 1: # east\n self.wall_x[current_i+1, current_j] = 0\n elif diff[0] == -1: # west\n self.wall_x[current_i, current_j] = 0\n trail.append(pick)\n visited[pick[0],pick[1]] = 1\n visited_count += 1\n (current_i, current_j) = pick\n else:\n if len(trail) > 0:\n (current_i, current_j) = trail.pop()",
"title": ""
},
{
"docid": "383db48d227a27dc18554b2d740cd34c",
"score": "0.527942",
"text": "def flags_nearby(self, i, j):\n nr=0\n l = self.neighbors(i, j)\n for (x, y) in l:\n if self.square_at(x, y).flagged:\n nr += 1\n return nr",
"title": ""
},
{
"docid": "cf92612c1bb2de3f086b51575aa2599a",
"score": "0.52774817",
"text": "def boundary_search(i, j):\n if not (i < 0 or j < 0 or i >= len(grid) or j >= len(grid[0]) or grid[i][j] != '1'):\n grid[i][j] = EXPLORED\n boundary_search(i, j + 1)\n boundary_search(i + 1, j)\n boundary_search(i, j - 1)\n boundary_search(i - 1, j)",
"title": ""
},
{
"docid": "33176bc8af49e3fadf48275e784051f3",
"score": "0.5265446",
"text": "def peninsula(imask, plist, i, j, dir, iwat, iland):\n Mp, Lp = imask.shape\n plist.append((-3, -3))\n p = (i,j)\n\n # set up new linked list of Points\n# assert(isEdge(p));\n plist.append(p)\n\n if (dir == 'east'):\n seed = (i,j)\n elif (dir == 'west'):\n seed = (i-1,j-1)\n elif (dir == 'south'):\n seed = (i,j-1)\n elif (dir == 'north'):\n seed = (i-1,j)\n# warning(\"Peninsula at\", seed, dir)\n\n # Trace the edge of the peninsula, keeping track of the psi\n # points in a linked list. The seed point is kept for the\n # flood_fill, setting the land to match the current water\n # value.\n while True:\n if (dir == 'east'):\n i += 1\n p = (i,j)\n if ((imask[j-1,i] == iland) and (imask[j-1,i-1] == iwat)):\n dir = 'south'\n elif ((imask[j,i] == iland) and (imask[j-1,i] == iwat)):\n dir = 'east'\n elif ((imask[j,i-1] == iland) and (imask[j,i] == iwat)):\n dir = 'north'\n elif (i==1 or j==1 or i==Lp-1 or j==Mp-1):\n break\n else:\n warning(\"Problem in peninsula at \", i, j)\n exit(1)\n elif (dir == 'north'):\n j += 1\n p = (i,j)\n if ((imask[j,i] == iland) and (imask[j-1,i] == iwat)):\n dir = 'east'\n elif ((imask[j,i-1] == iland) and (imask[j,i] == iwat)):\n dir = 'north'\n elif ((imask[j-1,i-1] == iland) and (imask[j,i-1] == iwat)):\n dir = 'west'\n elif (i==1 or j==1 or i==Lp-1 or j==Mp-1):\n break\n else:\n warning(\"Problem in peninsula at \", i, j)\n exit(1)\n elif (dir == 'west'):\n i -= 1\n p = (i,j)\n if ((imask[j,i-1] == iland) and (imask[j,i] == iwat)):\n dir = 'north'\n elif ((imask[j-1,i-1] == iland) and (imask[j,i-1] == iwat)):\n dir = 'west'\n elif ((imask[j-1,i] == iland) and (imask[j-1,i-1] == iwat)):\n dir = 'south'\n elif (i==1 or j==1 or i==Lp-1 or j==Mp-1):\n break\n else:\n warning(\"Problem in peninsula at \", i, j)\n exit(1)\n elif (dir == 'south'):\n j -= 1\n p = (i,j)\n if ((imask[j-1,i-1] == iland) and (imask[j,i-1] == iwat)):\n dir = 'west'\n elif ((imask[j-1,i] == iland) and (imask[j-1,i-1] == iwat)):\n dir = 'south'\n elif ((imask[j,i] == iland) and (imask[j-1,i] == iwat)):\n dir = 'east'\n elif (i==1 or j==1 or i==Lp-1 or j==Mp-1):\n break\n else:\n warning(\"Problem in peninsula at \", i, j)\n exit(1)\n\n plist.append(p)\n plist.append(p)\n\n # time to flood_fill to show we are done with this peninsula\n i = seed[0];\n j = seed[1];\n flood_fill_land(imask, i, j, iwat);",
"title": ""
},
{
"docid": "ffc3a7d6b5afc1d2dfe13f2fffd756b6",
"score": "0.5242948",
"text": "def cell_ijk(cell_id,nx,ny):\n k=math.ceil(cell_id/(nx*ny))-1\n j=math.ceil((cell_id-(nx*ny)*k)/nx)-1\n i=math.ceil(cell_id-(nx*ny*k)-nx*j)\n return i,j,k",
"title": ""
},
{
"docid": "16d027c1816face21b291befa133b673",
"score": "0.52218753",
"text": "def get_unknown_neighbors_idx(self, i, j):\n\n\n neighbors = [(i+1, j+1), (i+1, j), (i, j+1), (i-1, j+1), \\\n (i-1, j-1), (i-1, j), (i, j-1), (i+1, j-1)]\n\n out = []\n\n for neighbor in neighbors: \n # The neighbor is within bounds of the board \n if (0 <= neighbor[0] < self.dim) and (0 <= neighbor[1] < self.dim): \n\n # The neighbor is covered and not a flag \n if self.cells[neighbor].covered and not self.cells[neighbor].flag: \n out.append((neighbor[0], neighbor[1], self.cells[neighbor].idx)) \n \n return out",
"title": ""
},
{
"docid": "d932d4acd0d55d91b1b49c870cb4292c",
"score": "0.52177817",
"text": "def draw_cell(i, j):\n draw_point(j + i/2, i*r3/2)",
"title": ""
},
{
"docid": "3b319e4b997c5cf9125aad1ff0975d4f",
"score": "0.52050185",
"text": "def set_all_points_for_tile(self, left_x, top_y, tile_width, tile_height, get_color):\n for j in range(top_y, top_y - tile_height, -1):\n for i in range(left_x, left_x + tile_width):\n color = get_color(i - left_x, (tile_height - 1) - (j - top_y + tile_height - 1))\n if color is not None:\n self[(i, j)] = color",
"title": ""
},
{
"docid": "69260829669a3964bc295435f4240b28",
"score": "0.51734066",
"text": "def simulate_night():\n\n def infected_neighbour(r, c):\n \"\"\"Returns True if an orthogonally adjacent square is infected.\"\"\"\n return any(grid[nr][nc] == 1 for nr, nc in neighbours(r, c))\n\n for r, c in cells():\n # Find clean cells that are next to infected cells\n if grid[r][c] == 0 and infected_neighbour(r, c):\n # Set them temporarily to 2, so that further iterations do not\n # count them as infected (otherwise it would spread endlessly\n # in one night).\n grid[r][c] = 2\n\n # Go over a second time and set the temporarily marked newly infected cells\n # to permanently infected.\n for r, c in cells():\n if grid[r][c] == 2:\n grid[r][c] = 1",
"title": ""
},
{
"docid": "9e90588268dc08cee3412613a089f05c",
"score": "0.51573163",
"text": "def mark_the_spot(grid):\n size = len(grid)\n for row in range(size):\n for i in range(size):\n if i == row:\n grid[row][i] = 'X'\n elif (size - 1 - i) == row:\n grid[row][i] = 'X'\n return grid",
"title": ""
},
{
"docid": "5ac133850775e22828cbe0b2b97b54be",
"score": "0.5138004",
"text": "def countIslandsDFS(self, i, j, visited):\n visited.add(i * self.ncols + j)\n for d in ([0, 1], [1, 0], [0, -1], [-1, 0]):\n new_i = i+d[0]\n new_j = j+d[1]\n if ((new_i * self.ncols + new_j) not in visited) and self.isIsland(new_i, new_j):\n self.countIslandsDFS(new_i, new_j, visited)\n return",
"title": ""
},
{
"docid": "1a67953d899321452ab2de3c0f5fbc55",
"score": "0.51323897",
"text": "def turn_pixel_and_neighbors_black(image, xy):\n \n# print(xy)\n \n ### Turn this pixel black\n image.putpixel(xy, (0, 0, 0))\n \n ### Make this function call itself for every neighbor that is white\n \n (x, y) = xy\n \n ## Check the neighbor to the north\n if (y - 1) >= 0 and image.getpixel((x, y - 1)) == (255, 255, 255):\n turn_pixel_and_neighbors_black(image, (x, y - 1))\n \n ## Check the neighbor to the south\n if (y + 1) < image.height and image.getpixel((x, y + 1)) == (255, 255, 255):\n turn_pixel_and_neighbors_black(image, (x, y + 1))\n \n ## Check the neighbor to the east\n if (x + 1) < image.width and image.getpixel((x + 1, y)) == (255, 255, 255):\n turn_pixel_and_neighbors_black(image, (x + 1, y))\n \n ## Check the neighbor to the west\n if (x - 1) >= 0 and image.getpixel((x - 1, y)) == (255, 255, 255):\n turn_pixel_and_neighbors_black(image, (x - 1, y))",
"title": ""
},
{
"docid": "8704b5172a1b09b9425820649e968eb8",
"score": "0.5123769",
"text": "def uniquePathsIII(self, grid: List[List[int]]) -> int:\n\n def DFS(i: int, j: int, end: tuple, unvisited: set, grid: List[List[int]]) -> None:\n \"\"\"\n DFS - traverse all paths, stopping if:\n - i or j is OOB\n - (i,j) has been visited already in the current path\n - (i,j) is the endpoint (if all squares have been visited, increment output)\n \"\"\"\n if (\n i < 0 or\n j < 0 or\n i >= len(grid) or\n j >= len(grid[0]) or\n (i,j) not in unvisited\n ):\n return\n\n unvisited.remove((i,j)) # we are now at (i,j)\n\n if (i,j) == end:\n if len(unvisited) == 0:\n self.output += 1\n unvisited.add((i,j)) # end is unvisited again\n return\n\n DFS(i+1, j, end, unvisited, grid) # up\n DFS(i, j+1, end, unvisited, grid) # right\n DFS(i-1, j, end, unvisited, grid) # down\n DFS(i, j-1, end, unvisited, grid) # left\n\n unvisited.add((i,j)) # (i,j) is now available to be used in a different path\n\n\n # set the start/end points and mark all squares that can be traversed\n self.output = 0\n unvisited = set()\n start = None\n end = None\n for i in range(len(grid)):\n for j in range(len(grid[0])):\n if grid[i][j] == -1:\n continue\n if grid[i][j] == 1:\n start = (i,j)\n elif grid[i][j] == 2:\n end = (i,j)\n unvisited.add((i,j))\n\n # run the DFS and return the output\n DFS(start[0], start[1], end, unvisited, grid)\n return self.output",
"title": ""
},
{
"docid": "7c4af3468bb9c3d50b163989e2fefc0d",
"score": "0.5092911",
"text": "def floodFill(self, ox, oy, dx, dy):\r\n q = []\r\n q.append((ox, oy))\r\n points = []\r\n\r\n update = False\r\n # este update deve ser feito no mapa do agente\r\n world.World.updateWorldObject(world.World, ox, oy, 'marked', 0) # mark origin with 0\r\n world.World.updateWorldObject(world.World, ox, oy, 'visited', True)\r\n self.marked[ox-1][oy-1] = 0 # não é necessário, só para testes. Remover depois\r\n points.append({'x': ox, 'y': oy})\r\n mark = 1\r\n while len(q) > 0:\r\n (x, y) = q.pop()\r\n\r\n if x == dx and y == dy: # if coods of destination return points so far\r\n print(\"Destiny: (x: {}, y: {})\".format(dx, dy))\r\n return points\r\n if ox < 1 or oy < 1 or ox > 10 or oy > 10: # check limits\r\n print(\"No more points to move to.\")\r\n return points\r\n\r\n if self.checkIfItsFree(x+1, y): # right cell\r\n update = True\r\n q.append((x+1, y))\r\n self.updateMark(x+1, y, mark)\r\n points.append({'x': x+1, 'y': y})\r\n if self.checkIfItsFree(x-1, y): # left cell\r\n update = True\r\n q.append((x-1, y))\r\n self.updateMark(x-1, y, mark)\r\n points.append({'x': x-1, 'y': y})\r\n if self.checkIfItsFree(x, y+1): # down cell\r\n update = True\r\n q.append((x, y+1))\r\n self.updateMark(x, y+1, mark)\r\n points.append({'x': x, 'y': y+1})\r\n if self.checkIfItsFree(x, y-1): # upper cell\r\n update = True\r\n q.append((x, y-1))\r\n self.updateMark(x, y-1, mark)\r\n points.append({'x': x, 'y': y-1})\r\n\r\n if update:\r\n mark+=1\r\n update = False\r\n\r\n return points",
"title": ""
},
{
"docid": "cbdc4488fdf88b3b83defc59a94214d2",
"score": "0.50708735",
"text": "def getCornerPointCellIdx(self,i,j,k):\r\n nx,ny,nz=2*self.NX,2*self.NY,2*self.NZ\r\n\r\n p1_id,p2_id=getIJK(2*i,2*j,2*k,nx,ny,nz),getIJK(2*i+1,2*j,2*k,nx,ny,nz)\r\n p3_id,p4_id=getIJK(2*i,2*j+1,2*k,nx,ny,nz),getIJK(2*i+1,2*j+1,2*k,nx,ny,nz)\r\n\r\n p5_id,p6_id=getIJK(2*i,2*j,2*k+1,nx,ny,nz),getIJK(2*i+1,2*j,2*k+1,nx,ny,nz)\r\n p7_id,p8_id=getIJK(2*i,2*j+1,2*k+1,nx,ny,nz),getIJK(2*i+1,2*j+1,2*k+1,nx,ny,nz)\r\n\r\n #print(p1_id,p2_id,p3_id,p4_id)#Top Layer\r\n #print(p5_id,p6_id,p7_id,p8_id)#Bottom Layer\r\n\r\n return p1_id,p2_id,p3_id,p4_id,p5_id,p6_id,p7_id,p8_id",
"title": ""
},
{
"docid": "1453fca0baac558bd15e30149a407148",
"score": "0.5066737",
"text": "def initialize_neighbors(i, j, h=9, w=9):\n neighbors_indexes = list()\n # indexes of the neighbors along the vertical line\n for i_ in range(9):\n if i_ != i:\n index = i_*w + j \n neighbors_indexes.append(index)\n\n # indexes of the neighbors along the horizontal line\n for j_ in range(9):\n if j_ != j:\n index = i*w + j_ \n neighbors_indexes.append(index)\n \n i_center, j_center = 3*(i//3) + 1, 3*(j//3) +1\n \n # indexes of the neighbors for the coressponding cube(3*3) with center (i_center, j_center)\n for i_ in range(9):\n for j_ in range(9):\n if abs(i_center - i_) <= 1 and abs(j_center - j_) <= 1:\n index = i_*w + j_\n neighbors_indexes.append(index)\n # delete repeated indexes\n neighbors_indexes = list(set(neighbors_indexes))\n # delete index with coressponding to position (i, j)\n neighbors_indexes.remove(i*w + j)\n return neighbors_indexes",
"title": ""
},
{
"docid": "c72af55d8d5466e963b5a180c72f304b",
"score": "0.50648713",
"text": "def DFS(self, i, j, ccl):\n self.coverDFS[i,j] = ccl\n if i>0 and self.coverDFS[i-1, j] == 1:\n self.DFS(i-1, j, ccl)\n if i<self.height-1 and self.coverDFS[i+1, j] == 1:\n self.DFS(i+1, j, ccl)\n if j>0 and self.coverDFS[i, j-1] == 1:\n self.DFS(i, j-1, ccl)\n if j<self.width-1 and self.coverDFS[i, j+1] == 1:\n self.DFS(i, j+1, ccl)",
"title": ""
},
{
"docid": "635379245b0df41a87dbf115e7b19344",
"score": "0.5060932",
"text": "def fill_b(self, T, b):\n\n # Compute b\n for j in range(self.Nz):\n for i in range(self.Nx): # i.e. i = 1:79\n\n if i == 0:\n im1 = 0\n a_w = 0\n else:\n im1 = i - 1\n a_w = self.get_a_e(self.x[i], j) / (\n self.rho(self.z[j])\n * self.get_kappa(T[i, j], self.rho(self.z[j]))\n * self.dx\n )\n\n if i == self.Nx - 1:\n ip1 = self.Nx - 1\n else:\n ip1 = i + 1\n if j == 0:\n a_s = 0.0\n # a_s_2 = self.Fbase * self.get_a_s(self.x[i + 1], self.x[i])\n # a_s = self.get_a_s(self.x[i + 1], self.x[i]) / (\n # self.rho(self.z[j])\n # * self.get_kappa(T[i, j], self.rho(self.z[j]))\n # * self.dz\n # )\n a_s_2 = a_s * self.Tcrust ** 4\n\n else:\n a_s = self.get_a_s(self.x[i + 1], self.x[i]) / (\n self.rho(self.z[j])\n * self.get_kappa(T[i, j], self.rho(self.z[j]))\n * self.dz\n )\n a_s_2 = a_s * T[i, j - 1]\n\n if j == self.Nz - 1:\n hotspoty = np.arange(0, int(self.hsize / 2))\n if i in hotspoty:\n a_n = self.get_a_s(self.x[i + 1], self.x[i]) / (\n self.rho(self.z[j])\n * self.get_kappa(T[i, j], self.rho(self.z[j]))\n * self.dz\n )\n a_n_2 = a_n * self.Thotspot ** 4\n\n else:\n if self.bb_flag == 'bb':\n # a_n = self.sigma * self.get_a_s(self.x[i+1], self.x[i])\n # a_n = 0.0\n # a_n_2 = (\n # self.sigma\n # * self.Ttop ** 4\n # * self.get_a_s(self.x[i + 1], self.x[i])\n # )\n\n # a_n = self.sigma * self.get_a_s(self.x[i + 1], self.x[i])\n a_n = 0.0\n a_n_2 = (\n self.sigma\n * (self.Ttop ** 4)\n * self.get_a_s(self.x[i + 1], self.x[i])\n )\n\n else:\n a_n = self.get_a_s(self.x[i + 1], self.x[i]) / (\n self.rho(self.z[j])\n * self.get_kappa(T[i, j], self.rho(self.z[j]))\n * self.dz\n )\n a_n_2 = a_n * self.Ttop ** 4\n\n else:\n a_n = self.get_a_s(self.x[i + 1], self.x[i]) / (\n (self.rho(self.z[j + 1]))\n * self.get_kappa(T[i, j], self.rho(self.z[j]))\n * self.dz\n )\n a_n_2 = a_n * T[i, j + 1]\n\n a_e = self.get_a_e(self.x[i + 1], j) / (\n self.rho(self.z[j])\n * self.get_kappa(T[i, j], self.rho(self.z[j]))\n * self.dx\n )\n\n S = (\n self.rho(self.z[j])\n * (self.get_h(j))\n * self.get_a_s(self.x[i + 1], self.x[i])\n * self.eps(T[i, j], self.rho(self.z[j]))\n * 3\n / (self.a * self.c)\n )\n\n a_p = -a_e - a_w - a_s - a_n\n\n p = self.m(i, j)\n b[p] = (\n S\n - a_p * T[i, j]\n - a_w * T[im1, j]\n - a_e * T[ip1, j]\n - a_n_2\n - a_s_2\n )\n\n\n return b",
"title": ""
},
{
"docid": "26c1e277fe8e707d89f23b15de012daa",
"score": "0.5042707",
"text": "def find_neighbours_with_same_color_as_starting_point(self, pixel_s, pixel_t):\r\n for s,t in self.neighbours(pixel_s, pixel_t):\r\n if self.board[s,t] == self.board[pixel_s,pixel_t] and [s,t] not in self.where_to_color:\r\n self.where_to_color.append([s,t])\r\n self.find_neighbours_with_same_color_as_starting_point(s, t)\r\n self.where_to_color = [list(x) for x in set(tuple(x) for x in self.where_to_color)]",
"title": ""
},
{
"docid": "8563e5c1a522025847181ff1611361b6",
"score": "0.50414836",
"text": "def flagged(self, i, j):\n return self.square_at(i, j).flagged",
"title": ""
},
{
"docid": "eb99045d14231741f8bed4664d1e8fe0",
"score": "0.50381595",
"text": "def grow(cells, i, j):\n if cells[i][j] == 0: return ({}, 0)\n flake = set()\n boundary = {(i, j)}\n size = 0\n\n while len(boundary) > 0:\n (i, j) = boundary.pop()\n if (i, j) in flake: continue\n\n flake.add((i, j))\n if (i, j) == (0, 0): size += 1\n elif i == 0: size += 6\n elif i == j: size += 6\n else: size += 12\n\n for (ni, nj) in neighbors(i, j):\n if cells[ni][nj] > 0 and (ni, nj) not in flake:\n boundary.add((ni, nj))\n\n return (flake, size)",
"title": ""
},
{
"docid": "61fdfd97c9ed8f09a58b8b93b68e0413",
"score": "0.50375104",
"text": "def reveal(self, i, j):\n if self.revealed(i, j) or self.flagged(i, j):\n pass\n elif not self.revealed(i, j):\n self.squares_revealed += 1\n self.square_at(i, j).revealed = True\n if not self.mined(i, j) and self.mines_nearby(i, j) == 0:\n for (x, y) in self.neighbors(i, j):\n self.reveal(x, y)",
"title": ""
},
{
"docid": "860557716bc66ade292f8b2729f0d83e",
"score": "0.503615",
"text": "def get_neighbors(size_i, size_j, ij):\n i,j = ij\n neighbors = set()\n if i>0:\n neighbors.add((i-1, j))\n if j>0:\n neighbors.add((i, j-1))\n if i<size_i-1:\n neighbors.add((i+1, j))\n if j<size_j-1:\n neighbors.add((i, j+1))\n return neighbors",
"title": ""
},
{
"docid": "bad12934b9f76a8febd855f535b897d6",
"score": "0.5033498",
"text": "def _mask(i, j):\n mask = [\n # Row 1\n [i, j],\n [i, j + 1],\n [i, j + 2],\n # Row 2\n [i + 1, j + 1],\n # Row 3\n [i + 2, j],\n [i + 2, j + 1],\n [i + 2, j + 2]\n ]\n return mask",
"title": ""
},
{
"docid": "99f2d5b753c53f160f435d63bd30bf27",
"score": "0.50296795",
"text": "def __setAdjacent_square__(self, pos):\n self.__checkIndices__(pos)\n i, j = pos; adjacent = []\n # Function to filter out nonexistent cells.\n def filterfn(p):\n do_not_filter = 0 <= p[0] < self.__numrows__ and 0 <= p[1] < self.__numcols__\n return do_not_filter and not self.__isdisabled__[p[0]][p[1]]\n for cell in filter(filterfn, ( (i+1,j), (i-1,j), (i,j+1), (i,j-1) )):\n adjacent += [cell]\n self.__adjacent__[i][j] = adjacent",
"title": ""
},
{
"docid": "ff96762ee2eb10c9a7314fceb9d310c1",
"score": "0.5019842",
"text": "def update(self, i, j): \n neighbors = self.getNeighbors(i, j)\n total,difference = self.evaluateNeighbours(neighbors,self.grid[i][j].getStatusOfHousehold()) \n #if self.evaluateNeighborhoodLeaving(neighborhood, neighbors, self.grid[i][j]):\n # self.leave(i,j)\n p = difference/total\n \n \n if (np.random.rand()<=p):\n self.leave(i,j,p)",
"title": ""
},
{
"docid": "d293559c887b9678000206bc08bf61a0",
"score": "0.500969",
"text": "def backtrack(self, i, j, ali1, ali2):",
"title": ""
},
{
"docid": "99ce55e8d5157736d1eb0fe1aadc0e6e",
"score": "0.5000956",
"text": "def _fill_proximity(self):\n for row in self._cells:\n for cell in row:\n if not cell.is_mine:\n self._fill_cell_proximity(cell)",
"title": ""
},
{
"docid": "0878b9c1c2b543fc63aa4691a42b05dd",
"score": "0.4997323",
"text": "def update(self, seti, nested=T):\n sets = self.sets.copy()\n if len(sets) == 0:\n sets.append( seti )\n else:\n if not np.any( [ seti <= setj for setj in sets ] ):\n intersected = [ seti.intersection(set_j) for set_j in sets ]\n istats = np.array([ si != set() for si in intersected ])\n nset = len(sets)\n idxs = np.arange( nset )\n if np.any(istats):\n #assert istats.astype(np.int).sum() == 1\n for iset in idxs:\n if istats[iset]:\n sets[iset] = seti.union( sets[iset] )\n else:\n sets.append( seti )\n self.sets = sets",
"title": ""
},
{
"docid": "07a41b0b00de6eebf10596e2f5dae9c9",
"score": "0.49972573",
"text": "def Get_Neighbours2D(ii_0, jj_0, n_rows, n_cols):\r\n neighbours = []\r\n\r\n\r\n for ii in range(ii_0 - 1, ii_0+2, 1):\r\n for jj in range(jj_0 -1, jj_0 +2, 1):\r\n condition_inside = (ii >= 0) & (ii < n_rows) & (jj >= 0) & (jj < n_cols)\r\n if ((ii,jj) != (ii_0, jj_0)) & condition_inside:\r\n neighbours.append((ii,jj))\r\n \r\n return neighbours",
"title": ""
},
{
"docid": "b8c42f4c96989ae58bc56d422846adcb",
"score": "0.49960622",
"text": "def FillWater(self):\n for row in range(self.nrows):\n if not self._row_counts[row]:\n self._FillRowWithWater(row)\n\n for col in range(self.ncols):\n if not self._col_counts[col]:\n self._FillColumnWithWater(col)",
"title": ""
},
{
"docid": "763a400121da8c7644d7bb8afb07272c",
"score": "0.49910706",
"text": "def surrounding_cells(self, cell):\n i, j = cell\n neighbors = set()\n for a in range(max(0, i-1), min(i+2, self.height)):\n for b in range(max(0, j-1), min(j+2, self.width)):\n if (a, b) != (i, j):\n neighbors.add((a, b))\n return neighbors",
"title": ""
},
{
"docid": "85068abd1595b4d9cef0c89d9d05e582",
"score": "0.49664626",
"text": "def find_edges(self, i, j):\n potential_edges = [\n (i - 1, j + 1),\n (i, j + 1),\n (i + 1, j + 1),\n (i - 1, j),\n # (i, j),\n (i + 1, j),\n (i - 1, j - 1),\n (i, j - 1),\n (i + 1, j - 1)\n ]\n\n real_edges = []\n for x, y in potential_edges:\n if self.imax > x >= self.imin \\\n and self.jmax > y >= self.jmin\\\n and (x, y) not in real_edges:\n real_edges += [(x, y)]\n\n return real_edges",
"title": ""
},
{
"docid": "48432c12ea5a92e4ac4d515e4096e3e5",
"score": "0.4961008",
"text": "def _index(self, i, j, k):\n if i < 0 or j < 0 or k < 0 or k >= self.nz or j >= self.ny or i >= self.nx:\n return -1\n return k * self.nxy + j * self.nx + i",
"title": ""
},
{
"docid": "dd5d745b02a1356131200177b6c6f77f",
"score": "0.49528313",
"text": "def step_to_unknown(x,y):\n seen = set()\n q = deque()\n q.append((0,x,y))\n\n backtrack = dict()\n\n while len(q) > 0:\n d, tx, ty = q.popleft()\n seen.add((tx,ty))\n for (nx, ny) in neighbors(tx, ty):\n if (nx, ny) in seen:\n continue\n backtrack[(nx,ny)] = (tx,ty)\n if tile_kb[(nx,ny)] == MAP_UNKNOWN:\n while backtrack[(nx,ny)] != (x,y):\n nx, ny = backtrack[(nx,ny)]\n return (nx,ny)\n elif tile_kb[(nx,ny)] == MAP_FREE:\n q.append((d+1,nx,ny))\n return None",
"title": ""
},
{
"docid": "01354a87c3bdc4bc86d4d770910ca407",
"score": "0.49441138",
"text": "def test_neighbors(self,i,j):\n\t\tcell=0\n\t\tif len(self.board) >= i and len(self.board[i]) >= j:\n\t\t\tcell \t= self.board[i][j] \n\n\t\ttry:\n\t\t\tone = self.board[i-1][j+1]\n\t\texcept:\n\t\t\tone\t= False\n\t\t\t\n\t\ttry:\n\t\t\ttwo\t= self.board[i][j+1]\n\t\texcept:\n\t\t\ttwo\t= False\n\t\t\n\t\ttry:\n\t\t\tthree\t= self.board[i+1][j+1]\n\t\texcept:\n\t\t\tthree\t= False\n\t\t\n\t\ttry:\n\t\t\tfour\t= self.board[i+1][j]\n\t\texcept:\n\t\t\tfour\t= False\n\t\t\t\n\t\ttry:\n\t\t\tfive\t= self.board[i+1][j-1]\n\t\texcept:\n\t\t\tfive\t= False\n\t\t\n\t\ttry:\n\t\t\tsix\t= self.board[i][j-1]\n\t\texcept:\n\t\t\tsix\t= False\n\t\t\n\t\ttry:\n\t\t\tseven\t= self.board[i-1][j-1]\n\t\texcept:\n\t\t\tseven\t= False\n\t\t\t\n\t\ttry:\n\t\t\teight\t= self.board[i-1][j]\n\t\texcept:\n\t\t\teight\t= False\n\t\t\t\n\t\tneighbors = [one,two,three,four,five,six,seven,eight]\n\t\tresult = self.test_cell(neighbors,cell)\n\n\t\tif result != -1:\n\t\t\tif result == True:\n\t\t\t\tcell = 1\n\t\t\telse:\n\t\t\t\tcell = 0\n\t\t\tself.set_cell(i,j,cell,True)\n\t\tpass",
"title": ""
},
{
"docid": "be7d2e5ab2a8f4e59b420c4a4724977a",
"score": "0.49421033",
"text": "def cell_id(i,j,k,nx,ny):\n cell = (nx*j+i)+k*nx*ny\n\n return cell",
"title": ""
},
{
"docid": "6e7de70c76ad9b47f96a097c183699a0",
"score": "0.49334875",
"text": "def jump_to_end(i, j):\n while j < len(grid[i]):\n if grid[i][j] == SEA:\n return j\n j += 1\n return j",
"title": ""
},
{
"docid": "35fde1d8247009c8245c55051112d604",
"score": "0.4931361",
"text": "def my_cell(self, xyz: np.array):\n start = time.time()\n rn = np.array(xyz)\n # first check if point is within the grid\n\n # crit = 0\n # if np.all(rn >= np.array([self.xo, self.yo, self.zo])) and np.all(\n # rn <= np.array([self.x_lim, self.y_lim, self.z_lim])\n # ):\n # crit = 1\n\n crit = 1\n if crit: # if point inside\n if self.parent.point_set.dimension == 3: # if 3D\n # minimum distance under which a point is in a cell\n dmin = np.min([self.dx, self.dy, self.dz]) / 2\n else: # if 2D\n # minimum distance under which a point is in a cell\n dmin = np.min([self.dx, self.dy]) / 2\n\n blocks = blocks_from_rc(\n self.along_c, self.along_r, self.along_l, self.xo, self.yo, self.zo\n ) # cell generator\n\n # mapping data points to cells:\n # slow but memory-effective method\n vmin = np.inf\n cell = None\n for b in blocks:\n c = b[2]\n dc = np.linalg.norm(rn - c) # Euclidean distance\n if dc <= dmin: # If point is inside cell\n logger.info(\"found 1 cell id in {} s\".format(time.time() - start))\n return b[0]\n if dc < vmin:\n vmin = dc\n cell = b[0]\n logger.info(\"found 1 cell id in {} s\".format(time.time() - start))\n return cell\n else:\n return self.parent.nodata",
"title": ""
},
{
"docid": "6b8ce0c862e6d5767e2b37289b289e3f",
"score": "0.49238217",
"text": "def random_J(n):\r\n J_ij = {}\r\n for i in range(n):\r\n for j in range(n):\r\n for pos in nearest_neighbours(i, j, n):\r\n num = random.choice([1, -1])\r\n J_ij[((i, j), pos)] = num\r\n J_ij[(pos, (i, j))] = num\r\n \r\n return J_ij",
"title": ""
},
{
"docid": "300bc87b91d460ff8610a2c13f67af8f",
"score": "0.491882",
"text": "def iterNeighborFull(self, point):\n for shift in FULL_SHIFT_LIST:\n x = point[0] + shift[0]\n y = point[1] + shift[1]\n if self.pointIndex[x].get(y, False):\n yield self.pointIndex[x].get(y, False)\n else:\n continue",
"title": ""
},
{
"docid": "23535ffb281b5a1dfa29ace5f4aabdc2",
"score": "0.491618",
"text": "def neighbors(self, idx):\n (x, y) = idx\n results = [(x+1, y), (x, y-1), (x-1, y), (x, y+1), (x+1, y+1), (x+1, y-1), (x-1, y+1), (x-1, y-1)]\n results = filter(self.in_bounds, results)\n results = filter(self.passable, results)\n return results",
"title": ""
},
{
"docid": "48ec6232bd9b14ffb3fa04b7d911f094",
"score": "0.48998088",
"text": "def mines_nearby(self, i, j):\n return self.square_at(i, j).mines_nearby",
"title": ""
},
{
"docid": "fe7af75bd6b9bcc58cc37ab7dfce52fa",
"score": "0.48979288",
"text": "def point_to_ji(point : Point, board_size : int):\n j = board_size - point.y - 1\n i = point.x\n return j, i",
"title": ""
},
{
"docid": "7c19bd9116fcd47eeb89be91c4de646b",
"score": "0.4897531",
"text": "def I(i, j, k):\r\n return k*(i-1) + (j-1)",
"title": ""
},
{
"docid": "381bd8620e60b22db8a80263d3d97473",
"score": "0.48899344",
"text": "def _set_neighborhood(self):\n if self.rows % self.k == 0 and self.cols % self.k == 0:\n return self._simple_neighbor\n else:\n return self._complex_neighbor",
"title": ""
},
{
"docid": "5d6e8708974ef61bf0f308442ecc723a",
"score": "0.4876152",
"text": "def neighbors(self, i, j):\n if i==0 and j==0:\n return [(i+1, j), (i, j+1), (i+1, j+1)]\n elif i==0 and j==self.ncols-1:\n return [(i+1, j), (i, j-1), (i+1, j-1)]\n elif i==self.nrows-1 and j==0:\n return [(i-1, j), (i, j+1), (i-1, j+1)]\n elif i==self.nrows-1 and j==self.ncols-1:\n return [(i-1, j), (i, j-1), (i-1, j-1)]\n elif i==0:\n return [(i, j-1), (i, j+1), (i+1, j-1), (i+1, j), (i+1, j+1)]\n elif j==0:\n return [(i-1, j), (i+1, j), (i-1, j+1), (i, j+1), (i+1, j+1)]\n elif i==self.nrows-1:\n return [(i-1, j-1), (i-1, j), (i-1, j+1), (i, j-1), (i, j+1)]\n elif j==self.ncols-1:\n return [(i-1, j-1), (i, j-1), (i+1, j-1), (i-1, j), (i+1, j)]\n else:\n return [(i-1, j-1), (i-1, j), (i-1, j+1), (i, j-1), (i, j+1), (i+1, j-1), (i+1, j), (i+1, j+1)]",
"title": ""
},
{
"docid": "436e39dce4dd95b5377c41f4331e9212",
"score": "0.48653796",
"text": "def place_flag(self, i, j):\n if not self.flagged(i, j):\n self.square_at(i, j).flagged = True\n self.nflags += 1",
"title": ""
},
{
"docid": "aadb537cc19cd92c823abd3caae26dfd",
"score": "0.48596513",
"text": "def setPixel(self, i, j, val):\n\t\tn = len(self.arr)\n\t\tif self.arr[i][j] != 0:\n\t\t\treturn False\n\t\tif (i == j == 0) or (i == j == n-1) or (i == 0 and j == n-1) or (i == n-1 and j == 0):\n\t\t\tself.arr[0][0] = val\n\t\t\tself.arr[0][-1] = val\n\t\t\tself.arr[-1][0] = val\n\t\t\tself.arr[-1][-1] = val\n\t\t\t#self.__writePixel(0,0,val)\n\t\t\t#self.__writePixel(0,-1,val)\n\t\t\t#self.__writePixel(-1,0,val)\n\t\t\t#self.__writePixel(-1,-1,val)\n\t\telif i == 0:\n\t\t\tself.arr[-1][j] = val\n\t\t\t#self.__writePixel(-1,j,val)\n\t\telif i == n-1:\n\t\t\tself.arr[0][j] = val\n\t\t\t#self.__writePixel(0,j,val)\n\t\telif j == 0:\n\t\t\tself.arr[i][-1] = val\n\t\t\t#self.__writePixel(i,-1,val)\n\t\telif j == n-1:\n\t\t\tself.arr[i][0] = val\n\t\t\t#self.__writePixel(i,0,val)\n\t\tself.arr[i][j] = val\n\t\t#self.__writePixel(i,j,val)\n\t\treturn True",
"title": ""
},
{
"docid": "fe99fc56b9a882867d241708ab7ff30c",
"score": "0.48595273",
"text": "def find(root, i):\r\n while root[i] != -1:\r\n i = root[i]\r\n\r\n return i",
"title": ""
},
{
"docid": "b044ab73ff1645f2157fb85958f35f91",
"score": "0.4845958",
"text": "def getNeighbors(cellAlive,x,y):\n\n count = 0\n for j in range((y-1),(y+2)):\n for i in range((x-1),(x+2)):\n if not ((i == x) and (j == y)):\n if cellAlive[i%COLS][j%ROWS]:\n count += 1\n return count",
"title": ""
},
{
"docid": "8e5ddda4d3e9cfa9643b3633e66986c7",
"score": "0.4843123",
"text": "def index_cleaner(i,j,row,column):\n if i>=row:\n i=i-row\n if j>=column:\n j=j-column\n return (i,j)",
"title": ""
},
{
"docid": "3f4df27250bb3cf1538ecf49aa74fa2d",
"score": "0.48357844",
"text": "def check_neighbours(self, i, j):\n for x in range(-1,2):\n for y in range(-1,2):\n if not (x == 0 and y == 0):\n if i+x >= 0 and i+x < self.rows and j+y >= 0 and j+y < self.cols:\n self.board[i+x][j+y].surrounding_bombs += 1",
"title": ""
},
{
"docid": "fa58e1a7e074c232ba8ea7e2f29db885",
"score": "0.4833783",
"text": "def intensity(self, j, w, t, r, s):\n idxs = self.indices(j, self.fwhm * w, t, r, s)\n return map_coordinates(self.grid[j, 0], idxs, order=1, mode='nearest')",
"title": ""
},
{
"docid": "d235a7a518bab7e219fadd3da74aeb6d",
"score": "0.48283464",
"text": "def dfs(i, j):\n if i < 0 or i >= m or j < 0 or j >= n or grid[i][j] == '0':\n return\n\n grid[i][j] = '0'\n\n for d in range(4):\n dfs(i+dr[d], j+dc[d])",
"title": ""
},
{
"docid": "34b5bf3e4e41d7b525221b97d661223b",
"score": "0.48269436",
"text": "def s_i(t, i, m=None, mutable=True):\n if not mutable:\n t = t.clone()\n i_count = i_plus_one_count = 0\n o_i, o_i_plus_one = a.Ordinary(i), a.Ordinary(i+1)\n b_i, b_i_plus_one = a.Barred(i), a.Barred(i+1)\n for x in range(len(t.body)):\n for y in range(len(t.body[x])):\n if t.body[x][y] == o_i:\n i_count += 1\n elif t.body[x][y] == o_i_plus_one:\n i_plus_one_count += 1\n elif t.body[x][y] == b_i:\n i_count -= 1\n elif t.body[x][y] == b_i_plus_one:\n i_plus_one_count -= 1\n k = i_count - i_plus_one_count\n if k >= 0:\n task = f_i\n else:\n task = e_i\n k = -k\n for _ in range(k):\n task(t, i, m)\n return t",
"title": ""
},
{
"docid": "9b653592ac2c9fc4204dbde641bf5d5f",
"score": "0.4826304",
"text": "def traverse_component(i, j):\n grid[i][j] = -1\n result = 1\n \n # Check all four neighbours\n if i > 0 and (grid[i-1][j]==1):\n result += traverse_component(i-1, j)\n if j > 0 and (grid[i][j-1]==1):\n result += traverse_component(i, j-1)\n if i < len(grid)-1 and (grid[i+1][j]==1):\n result += traverse_component(i+1, j)\n if j < len(grid[0])-1 and (grid[i][j+1]==1):\n result += traverse_component(i, j+1)\n return result",
"title": ""
},
{
"docid": "13839971d3340338aeb4304531a659f9",
"score": "0.4821486",
"text": "def positive_loc(i,j,row,column):\n if i<0:\n i=i+row\n if j<0:\n j=j+column\n return (i,j)",
"title": ""
},
{
"docid": "e0aa5fef87546e2e18c63bc81259fba4",
"score": "0.4812136",
"text": "def mark_all_mine_cells(self, log=False):\n\n success = False \n\n for i in range(self.dim): \n for j in range(self.dim): \n\n if self.cells[i, j].covered and (self.cells[i, j].safe == False) and (not self.cells[i, j].flag):\n self.toggle_flag(i, j, log)\n success = True \n\n return success",
"title": ""
},
{
"docid": "a3e0ddbd60d3c38f2ca4640259fbfa7d",
"score": "0.48109877",
"text": "def mysetZeroes(self, matrix):\n\n \"\"\"\n be straight forward, simple\n \"\"\"\n i_and_j_sets = []\n\n for i in range(0, len(matrix)):\n for j in range(0, len(matrix[i])):\n if matrix[i][j] == 0:\n i_and_j_sets.append([i, j])\n\n print(i_and_j_sets)\n\n for i_and_j in i_and_j_sets:\n print(i_and_j)\n for j in range(0, len(matrix[0])):\n matrix[i_and_j[0]][j] = 0\n for i in range(0, len(matrix)):\n matrix[i][i_and_j[1]] = 0",
"title": ""
},
{
"docid": "e466523cf56448bc674d86ed74dcd5cb",
"score": "0.47778496",
"text": "def site2D_id (i,j,n) :\n return i*n + j",
"title": ""
},
{
"docid": "c7e352faba7d156744d50fd98a248e5f",
"score": "0.47772226",
"text": "def point_within_grid(self, ind, geo_coordinate, bb_coordinate, in_tmp_array, item_id, in_geo):\n if self.bb_collection[ind][3] == self.initial_bb[3] and self.bb_collection[ind][2] == self.initial_bb[2]:\n if (geo_coordinate[0] >= bb_coordinate[0] and geo_coordinate[0] <= bb_coordinate[2] and\n geo_coordinate[1] >= bb_coordinate[1] and geo_coordinate[1] <= bb_coordinate[3]):\n\n if not (in_tmp_array is None):\n in_tmp_array[ind] += 1\n \n else:\n self.histogram[ind] += 1\n self.gridid_collection[item_id + '-' + in_geo] = ind + self.id_start\n \n elif self.bb_collection[ind][3] == self.initial_bb[3] and self.bb_collection[ind][2] != self.initial_bb[2]:\n if (geo_coordinate[0] >= bb_coordinate[0] and geo_coordinate[0] < bb_coordinate[2] and\n geo_coordinate[1] >= bb_coordinate[1] and geo_coordinate[1] <= bb_coordinate[3]):\n\n if not (in_tmp_array is None):\n in_tmp_array[ind] += 1\n \n else:\n self.histogram[ind] += 1\n self.gridid_collection[item_id + '-' + in_geo] = ind + self.id_start\n \n elif self.bb_collection[ind][3] != self.initial_bb[3] and self.bb_collection[ind][2] == self.initial_bb[2]:\n if (geo_coordinate[0] >= bb_coordinate[0] and geo_coordinate[0] <= bb_coordinate[2] and\n geo_coordinate[1] >= bb_coordinate[1] and geo_coordinate[1] < bb_coordinate[3]):\n\n if not (in_tmp_array is None):\n in_tmp_array[ind] += 1\n \n else:\n self.histogram[ind] += 1\n self.gridid_collection[item_id + '-' + in_geo] = ind + self.id_start\n \n elif self.bb_collection[ind][3] != self.initial_bb[3] and self.bb_collection[ind][2] != self.initial_bb[2]:\n if (geo_coordinate[0] >= bb_coordinate[0] and geo_coordinate[0] < bb_coordinate[2] and\n geo_coordinate[1] >= bb_coordinate[1] and geo_coordinate[1] < bb_coordinate[3]):\n\n if not (in_tmp_array is None):\n in_tmp_array[ind] += 1\n \n else:\n self.histogram[ind] += 1\n self.gridid_collection[item_id + '-' + in_geo] = ind + self.id_start",
"title": ""
},
{
"docid": "796a6e7eb0e60f22fa967cf1c35df534",
"score": "0.47764277",
"text": "def cell_neighbors(arr, i, j, d):\n w = sliding_window(arr, 2*d+1)\n\n ix = np.clip(i - d, 0, w.shape[0]-1)\n jx = np.clip(j - d, 0, w.shape[1]-1)\n\n i0 = max(0, i - d - ix)\n j0 = max(0, j - d - jx)\n i1 = w.shape[2] - max(0, d - i + ix)\n j1 = w.shape[3] - max(0, d - j + jx)\n\n return w[ix, jx][i0:i1,j0:j1].ravel()",
"title": ""
},
{
"docid": "796a6e7eb0e60f22fa967cf1c35df534",
"score": "0.47764277",
"text": "def cell_neighbors(arr, i, j, d):\n w = sliding_window(arr, 2*d+1)\n\n ix = np.clip(i - d, 0, w.shape[0]-1)\n jx = np.clip(j - d, 0, w.shape[1]-1)\n\n i0 = max(0, i - d - ix)\n j0 = max(0, j - d - jx)\n i1 = w.shape[2] - max(0, d - i + ix)\n j1 = w.shape[3] - max(0, d - j + jx)\n\n return w[ix, jx][i0:i1,j0:j1].ravel()",
"title": ""
},
{
"docid": "0e49375c6a5ca3acf0955eeaa0159c51",
"score": "0.47735775",
"text": "def fill(self, point):\n if self.status[point[1]][point[0]]:\n raise Exception('Point already filled')\n self.status[point[1]][point[0]] = True\n farest_point = (1, point)\n while True:\n directions = ((0, -1), (1, 0), (0, 1), (-1, 0))\n walldirections = ((0, -1, 1), (0, 0, 0), (0, 0, 1), (-1, 0, 0))\n possible_neighbors = []\n corresponding_walls = []\n for dir in range(4):\n x = point[0] + directions[dir][0]\n y = point[1] + directions[dir][1]\n if x >= 0 and x < len(self.status[0]) and y >= 0 and \\\n y < len(self.status) and not self.status[y][x]:\n possible_neighbors += [(x, y)]\n corresponding_walls += [(point[0] + walldirections[dir][0],\n point[1] + walldirections[dir][1],\n walldirections[dir][2])]\n if len(possible_neighbors) == 0:\n return farest_point\n next = random.randrange(len(possible_neighbors))\n self.walls[corresponding_walls[next][1]] \\\n [corresponding_walls[next][0]] \\\n [corresponding_walls[next][2]] = False\n newpoint = self.fill(possible_neighbors[next])\n if newpoint[0] >= farest_point[0]:\n farest_point = (newpoint[0] + 1, newpoint[1])",
"title": ""
},
{
"docid": "4bfb71d3abdbac34c0079cf806027f45",
"score": "0.47695708",
"text": "def flood_fill_iterative(array, index, target_val, replacement_val):\n if target_val == replacement_val:\n return\n if not index_in_array(array, index):\n return\n i, j = index\n if array[i][j] != target_val:\n return\n array[i][j] = replacement_val\n q = Queue()\n q.add(index)\n while not q.is_empty():\n a, b = q.remove()\n pairs = [\n (a, b - 1),\n (a, b + 1),\n (a - 1, b),\n (a + 1, b),\n ]\n for (c, d) in pairs:\n if index_in_array(array, (c, d)):\n if array[c][d] == target_val:\n array[c][d] = replacement_val\n q.add((c, d))\n return",
"title": ""
},
{
"docid": "faf0c531bbcab42ca59262f719785e83",
"score": "0.4768663",
"text": "def generate_neighbourhood(row, col, H, W):\n return list(filter(lambda coordinate: inside_image(coordinate, H, W), \n [\n (row - 1, col + 1), (row, col + 1), (row + 1, col + 1), \n (row - 1, col), (row + 1, col), \n (row - 1, col - 1), (row, col - 1), (row + 1, col - 1)\n ]\n ))",
"title": ""
},
{
"docid": "f7b2a490916726e7dbd34ea335cb018b",
"score": "0.4762994",
"text": "def fill_known_row_column_values():\n\t\t\tnonlocal change_made\n\t\t\tfor n in self.number_set:\n\t\t\t\tfor i in range(self.side_length):\n\t\t\t\t\t# * n = a value from the number set\n\t\t\t\t\t# * i = an index along the row/column\n\t\t\t\t\tif n not in self.get_row(i):\n\t\t\t\t\t\t# take all indexes in the row\n\t\t\t\t\t\tvalid_row_indexes = set(range(self.side_length))\n\t\t\t\t\t\t# remove indexes where there is already a value\n\t\t\t\t\t\tvalid_row_indexes = valid_row_indexes.difference(\n\t\t\t\t\t\t\t[x for x in range(self.side_length) if self.get_row(i)[x] in self.number_set])\n\t\t\t\t\t\t# remove indexes where n conflicts with itself in that column\n\t\t\t\t\t\tvalid_row_indexes = valid_row_indexes.difference(\n\t\t\t\t\t\t\t[x for x in range(self.side_length) if n in self.get_column(x)])\n\t\t\t\t\t\t# remove indexes where n conflicts with itself in that subgrid\n\t\t\t\t\t\tvalid_row_indexes = valid_row_indexes.difference(\n\t\t\t\t\t\t\t[x for x in range(self.side_length) if n in [y for z in self.get_subgrid(i, x) for y in z]])\n\t\t\t\t\t\tif len(valid_row_indexes) == 0:\n\t\t\t\t\t\t\traise Exception(f\"Unable to place {n} in row: {i}. It is impossible\")\n\t\t\t\t\t\telif len(valid_row_indexes) == 1:\n\t\t\t\t\t\t\tindex = valid_row_indexes.pop()\n\t\t\t\t\t\t\tself.__set_tile(i, index, n)\n\t\t\t\t\t\t\tchange_made = True\n\n\t\t\t\t\tif n not in self.get_column(i):\n\t\t\t\t\t\t# take all indexes in the column\n\t\t\t\t\t\tvalid_column_indexes = set(range(self.side_length))\n\t\t\t\t\t\t# remove indexes where there is already a value\n\t\t\t\t\t\tvalid_column_indexes = valid_column_indexes.difference(\n\t\t\t\t\t\t\t[x for x in range(self.side_length) if self.get_column(i)[x] in self.number_set])\n\t\t\t\t\t\t# remove indexes where n conflicts with itself in that column\n\t\t\t\t\t\tvalid_column_indexes = valid_column_indexes.difference(\n\t\t\t\t\t\t\t[x for x in range(self.side_length) if n in self.get_row(x)])\n\t\t\t\t\t\t# remove indexes where n conflicts with itself in that subgrid\n\t\t\t\t\t\tvalid_column_indexes = valid_column_indexes.difference(\n\t\t\t\t\t\t\t[x for x in range(self.side_length) if n in [y for z in self.get_subgrid(x, i) for y in z]])\n\t\t\t\t\t\tif len(valid_column_indexes) == 0:\n\t\t\t\t\t\t\traise Exception(f\"Unable to place {n} in column: {i}. It is impossible\")\n\t\t\t\t\t\telif len(valid_column_indexes) == 1:\n\t\t\t\t\t\t\tindex = valid_column_indexes.pop()\n\t\t\t\t\t\t\tself.__set_tile(index, i, n) # probably where the error is\n\t\t\t\t\t\t\tchange_made = True",
"title": ""
},
{
"docid": "1463bf3e2e574f658a9ecad075b7b3a0",
"score": "0.47574636",
"text": "def lf_batch_idx(grid, i: int, j: int):\n return i * grid[1] + j",
"title": ""
},
{
"docid": "e6c0af07718b4e56b560b6f27c75534d",
"score": "0.47562897",
"text": "def set_values(imask, k, val):\n Mp, Lp = imask.shape\n for j in range(1,Mp-1):\n for i in range(1,Lp-1):\n if (imask[j,i] == k): imask[j,i] = val",
"title": ""
},
{
"docid": "9379c3616b8c45349ba10f78bf24ab3b",
"score": "0.474916",
"text": "def maptoneighborhood(ind, ts, sz, mn_x, mx_x, mn_y, mx_y):\n rng_x = range(-sz, sz+1, 1)\n rng_y = range(-sz, sz+1, 1)\n out = list()\n for x in rng_x:\n for y in rng_y:\n new_x = clip(ind[0] + x, mn_x, mx_x)\n new_y = clip(ind[1] + y, mn_y, mx_y)\n newind = (new_x, new_y, ind[2])\n out.append((newind, ts))\n return out",
"title": ""
},
{
"docid": "b0dd7da915297813e1ff06cda5a493bc",
"score": "0.474486",
"text": "def Veli(x,y,Grid,u,v):\n# 1 fastest, \n# find nearest barycenter\n kf=nearxy(Grid['xc'],Grid['yc'],x,y)\n# but the point may be in the neighboring triangle \n#timing [s] per step: 0.0493136494444 0.0309618651389\n\n# 2 slower \n# find the triangle to which point x,y truely belongs\n# kf,lamb0,lamb1,lamb2=find_kf(Grid,x,y)\n# by means of calculating baricentric coordinates for all triangles in the grid\n#timing [s] per step: 0.482606426944 0.148569285694\n\n# 3 fasterthan 2\n# find the closest vertex and closest barycenter\n# and calculate barycentric coordinates \n# in the small neighborhood of those points\n# kf,lamb0,lamb1,lamb2=find_kf2(Grid,x,y)\n#timing [s] per step: 0.0725187981944 0.0322402066667\n\n\n# nearest neighbor interpolation \n ui=u[kf]\n vi=v[kf]\n \n return ui,vi",
"title": ""
},
{
"docid": "ff0c0c2e092538724cd2ae0004092b9a",
"score": "0.47414067",
"text": "def erase_island(self, a, i, j, r, c):\n if i != 0:\n # Search UP one cell\n if a[i - 1][j] == \"1\":\n a[i - 1][j] = \"0\"\n a = self.erase_island(a, i - 1, j, r, c)\n if i != r - 1:\n # Search DOWN one cell\n if a[i + 1][j] == \"1\":\n a[i + 1][j] = \"0\"\n a = self.erase_island(a, i + 1, j, r, c)\n if j != 0:\n # Search LEFT one cell\n if a[i][j - 1] == \"1\":\n a[i][j - 1] = \"0\"\n a = self.erase_island(a, i, j - 1, r, c)\n if j != c - 1:\n # Search RIGHT one cell\n if a[i][j + 1] == \"1\":\n a[i][j + 1] = \"0\"\n a = self.erase_island(a, i, j + 1, r, c)\n return a",
"title": ""
},
{
"docid": "3464c1ee966dc50a7b1a9214abb16f5f",
"score": "0.47412178",
"text": "def apply_S(self, i):\n c, d = self.__list[i].tuple()\n t, j = search(self.__list, self.normalize(d, -c))\n return j",
"title": ""
},
{
"docid": "c5ecd4120edfd73d501bcdcf34c740b1",
"score": "0.47363293",
"text": "def find_holes(self):\n\t\t#We go through the list level\n\t\tholes_list = []\n\t\tline_nb = 0\n\t\tfor line in self.structure:\n\t\t\tcase_nb = 0\n\t\t\tfor sprite in line:\n\t\t\t\t#Calculating the position of each sprite (in pixels)\n\t\t\t\tx = case_nb * sprite_size\n\t\t\t\ty = line_nb * sprite_size\n\t\t\t\tif sprite == 'h':\n\t\t\t\t\tcoord = (x, y)\n\t\t\t\t\tholes_list.append(coord)\n\t\t\t\tcase_nb += 1\n\t\t\tline_nb += 1\n\t\t#Generating 3 random coordinates for our elements and saving them in a list\n\t\telements_list = []\n\t\tfor i in range(3):\n\t\t\thole = randint(0, len(holes_list)-1)\n\t\t\tcoord_hole = holes_list[hole]\n\t\t\telements_list.append(coord_hole)\n\t\t\tdel holes_list[hole]\n\t\tself.elements_list = elements_list",
"title": ""
},
{
"docid": "5d7ae84ee8b4399effb5b26d57037574",
"score": "0.47333413",
"text": "def neighbors_func(self, maze, p_orient, p_y, p_x):",
"title": ""
},
{
"docid": "e8a05ce4c703d835b4c4857a2efa1236",
"score": "0.47269243",
"text": "def hit(row, column, fleet):\n for ship in fleet:\n if (ship[0], ship[1]) == (row, column):\n x = ship\n elif ship[2] is True:\n for i in range(ship[3]):\n if row == ship[0] and column == ship[1] + i:\n x = ship\n elif ship[2] is False:\n for i in range(ship[3]):\n if row == ship[0] + i and column == ship[1]:\n x = ship\n x[4].add((row, column))\n fleet1 = []\n for el in fleet:\n if el != x:\n fleet1.append(el)\n else:\n fleet1.append(x)\n return fleet1, x",
"title": ""
},
{
"docid": "d05bab3c89cb5786b5f632dea348003e",
"score": "0.47268173",
"text": "def get_neighbour_suares(n):\n if n == 1: # right on the spot\n return 1\n \n grid = []\n s = int(math.ceil(math.sqrt(n)))\n \n if s % 2 == 0:\n s += 1\n \n for i in xrange(s):\n grid.append([0] * s)\n \n grid[(s - 1) / 2][(s - 1) / 2] = 1\n \n # We represent co-ordinates as complex numbers, because it allows us to do a couple of neat tricks\n # Complex numbers consist of a *real* and an *imaginary* part. We see these as our x and y co-ordinates respectively.\n # If you add 2 complex numbers together, you create a ew number with the sum of the two real parts of the others, and the sum of the imaginary parts, just like vector addition.\n # If you multiply a complex number with I (I is a complex number with real part 0 and imaginary part 1), you're basically rotating it by 90 degrees around the origin counterclockwise.\n I = icomplex(0, 1)\n pos = icomplex((s-1) / 2 + 1, (s-1) / 2) # one cell to the right of center.\n print (pos.real, pos.imag)\n dir = icomplex(1, 0)\n neighdirs = list(itertools.starmap(complex, [(1, 1), (1, 0), (0, 1), (-1, 0), (0, -1), (-1, -1), (1, -1), (-1, 1)]))\n \n for i in xrange(2, n+1):\n t = 0\n for nd in neighdirs:\n neigh = pos + nd\n if neigh.real < 0 or neigh.real >= s or neigh.imag < 0 or neigh.imag >= s:\n continue\n t += grid[neigh.real][neigh.imag]\n \n if t > n:\n return t\n \n grid[pos.real][pos.imag] = t\n \n left = pos + (dir * I)\n if grid[left.real][left.imag] == 0:\n dir *= I\n \n if i < n:\n pos = pos + dir\n \n return grid[pos.real][pos.imag]",
"title": ""
},
{
"docid": "848cde39b1c4e74470cd0df44283b876",
"score": "0.4726665",
"text": "def get_neighbours(i, j, shape_):\n list_indexes = [(i, j)]\n if i - 1 >= 0:\n if j - 1 >= 0:\n list_indexes.append((i - 1, j - 1))\n if j >= 0:\n list_indexes.append((i - 1, j))\n if j + 1 < shape_[1]:\n list_indexes.append((i - 1, j + 1))\n if j - 1 >= 0:\n list_indexes.append((i, j - 1))\n if j + 1 < shape_[1]:\n list_indexes.append((i, j + 1))\n if i + 1 < shape_[0]:\n if j - 1 >= 0:\n list_indexes.append((i + 1, j - 1))\n if j >= 0:\n list_indexes.append((i + 1, j))\n if j + 1 < shape_[1]:\n list_indexes.append((i + 1, j + 1))\n return list_indexes",
"title": ""
},
{
"docid": "5d688cbcf1893c752da9d00f524adf81",
"score": "0.47255757",
"text": "def fill_pixel(board, col, row, new_color):\n coord = (col, row)\n old_color = get_item(board, coord)\n\n def bound(coord):\n return contains(board, coord)\n\n def same_color(neighbor):\n return get_item(board, neighbor) == old_color\n\n set_many(board, flood(coord, inside=bound, key=same_color), new_color)",
"title": ""
},
{
"docid": "d342531f58d20a0c155ac0011758266d",
"score": "0.47215727",
"text": "def neighbors(self, coord):\n result = []\n for (delta_i, delta_j) in [(1,0), (0,1), (0,-1), (-1,0)]:\n neighbor = (coord[0] + delta_i, coord[1] + delta_j)\n if neighbor in self.coords:\n result.append(neighbor)\n return result",
"title": ""
},
{
"docid": "5319d861961996851aa9466ae5fd2628",
"score": "0.47212723",
"text": "def mark_cells(self):\n # print \"Beginning Marking pass\"\n # print \"\\tCompleted RN:{}\".format(self.completed)\n for location in self.game.revealed:\n # location = (height, width)\n if location not in self.completed: # if we've already done all we can with the spot\n height, width = location\n info = self.check_info(location)\n marked = info['marked']\n unknowns = info['unknowns']\n adjacents = info['adjacents']\n mines = self.game.grid[height][width].value\n remaining_mines = mines - len(marked)\n if len(unknowns) == 0:\n # nothing to do\n self.completed.add(location)\n elif len(unknowns) == remaining_mines:\n # if all the unknowns are mines\n for target in unknowns:\n self.game.mark_cell(*target)\n self.completed.add(location)\n else: # check adjacency\n for adjacent in adjacents:\n if adjacent not in self.completed:\n other_info = self.check_info(adjacent)\n if len(other_info['unknowns']) == 0:\n self.completed.add(adjacent)\n else:\n other_mines = self.game.grid[adjacent[0]][adjacent[1]].value\n other_remaining_mines = other_mines - len(other_info['marked'])\n if other_remaining_mines > remaining_mines:\n self.mark_adjacency(other_info, other_remaining_mines, info, remaining_mines)\n elif remaining_mines > other_remaining_mines:\n self.mark_adjacency(info, remaining_mines, other_info, other_remaining_mines)",
"title": ""
},
{
"docid": "c9dc18b19393516bf4ce56409beccd7e",
"score": "0.47194016",
"text": "def _getTile(self, i, j):\n tiles = self.map.getTiles()\n width, height = self.map.getSize()\n model_id, orientation = 0, 0\n if ((i == 0 or i == width - 1) and not (j == 0 or j == height - 1)) \\\n or ((j == 0 or j == height - 1) and not (i == 0 or i == width - 1)):\n if tiles[i][j] == TileType.LAND.value:\n model_id = TileModel.CLIFF_TOP_DIRT\n elif tiles[i][j] == TileType.WATER.value:\n model_id = TileModel.CLIFF_TOP_WATERFALL_DIRT\n if i == 0:\n orientation = 0\n elif j == 0:\n orientation = 90\n elif i == width - 1:\n orientation = 180\n elif j == height - 1:\n orientation = 270\n\n elif (i == 0 or i == width - 1) and (j == 0 or j == height - 1):\n model_id = TileModel.CLIFF_TOP_CORNER_DIRT\n if i == 0 and j == 0:\n orientation = 90\n elif i == 0 and j == height - 1:\n orientation = 0\n elif i == width - 1 and j == 0:\n orientation = 180\n elif i == width - 1 and j == height - 1:\n orientation = 270\n else:\n if tiles[i][j] == TileType.LAND.value:\n model_id = TileModel.FLAT_DIRT\n elif tiles[i][j] == TileType.WATER.value:\n neighbours = tiles[i][j - 1], tiles[i + 1][j], tiles[i][j + 1], tiles[i - 1][j] # n, e, s, w\n if neighbours == (0, 0, 0, 0):\n model_id = TileModel.RIVER_END_DIRT # TODO create water pool\n elif neighbours == (1, 0, 0, 0):\n model_id = TileModel.RIVER_END_DIRT\n orientation = 0\n elif neighbours == (0, 1, 0, 0):\n model_id = TileModel.RIVER_END_DIRT\n orientation = 90\n elif neighbours == (0, 0, 1, 0):\n model_id = TileModel.RIVER_END_DIRT\n orientation = 180\n elif neighbours == (0, 0, 0, 1):\n model_id = TileModel.RIVER_END_DIRT\n orientation = 270\n elif neighbours == (1, 0, 1, 0):\n model_id = TileModel.RIVER_STRAIGHT_DIRT\n elif neighbours == (0, 1, 0, 1):\n model_id = TileModel.RIVER_STRAIGHT_DIRT\n orientation = 90\n elif neighbours == (1, 1, 0, 0):\n model_id = TileModel.RIVER_CORNER_DIRT\n orientation = 180\n elif neighbours == (0, 1, 1, 0):\n model_id = TileModel.RIVER_CORNER_DIRT\n orientation = 270\n elif neighbours == (0, 0, 1, 1):\n model_id = TileModel.RIVER_CORNER_DIRT\n orientation = 0\n elif neighbours == (1, 0, 0, 1):\n model_id = TileModel.RIVER_CORNER_DIRT\n orientation = 90\n elif neighbours == (1, 1, 1, 0):\n model_id = TileModel.RIVER_TJUNCTION_DIRT\n orientation = 180\n elif neighbours == (0, 1, 1, 1):\n model_id = TileModel.RIVER_TJUNCTION_DIRT\n orientation = 270\n elif neighbours == (1, 0, 1, 1):\n model_id = TileModel.RIVER_TJUNCTION_DIRT\n orientation = 0\n elif neighbours == (1, 1, 0, 1):\n model_id = TileModel.RIVER_TJUNCTION_DIRT\n orientation = 90\n elif neighbours == (1, 1, 1, 1):\n model_id = TileModel.RIVER_JUNCTION_DIRT\n return model_id, orientation",
"title": ""
},
{
"docid": "34dc345ff0f57b116660c10bf632e71e",
"score": "0.47175282",
"text": "def neighborhood(*position):\n for diff in product([-1, 0, 1], repeat=len(position)):\n neighbor = tuple(pos + diff[i] for i, pos in enumerate(position))\n yield neighbor",
"title": ""
},
{
"docid": "b112a08351f26902f88da6c86d5c6067",
"score": "0.4708016",
"text": "def find_tiles():\n pass",
"title": ""
},
{
"docid": "de75b11e79a7f0fd4560610601ff93a3",
"score": "0.47063208",
"text": "def ijPixToxy(self, i: Number, j: Number) -> Tuple[Number, Number]:\r\n\r\n return ijPixToxyGeogr(self.gt, i, j)",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="771"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">00f6d034adfcd3beaf86e7505d0ae22f</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Start the agent after initialisation.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "1491c91070d75bdf257c2ff5acaeafb3",
"score": "0.7184218",
"text": "def on_initialized(self) -> None:\n self.start()",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "3b4be7c1b90499b9dfd73790ef730100",
"score": "0.78729284",
"text": "def agent_start(self, state):\n pass",
"title": ""
},
{
"docid": "232a87f307a67be276259c654c633733",
"score": "0.7734188",
"text": "def init_agent(self):",
"title": ""
},
{
"docid": "4ebe9206865c2fd51d9a459474c303c6",
"score": "0.73794717",
"text": "def start(self, time, agents, env):\n pass",
"title": ""
},
{
"docid": "e7228297c2399db53c8eed410fb7e018",
"score": "0.73787016",
"text": "def agent_init(self, agent_info= {}):",
"title": ""
},
{
"docid": "b0cbf18a13101a82a59b3893c57af793",
"score": "0.69831955",
"text": "def pre_main_loop(self):\n self.obs = self.prev_obs = self.env.reset()\n self.agent.initialize()",
"title": ""
},
{
"docid": "8198cf878a27148f3631dc00eb5c67c9",
"score": "0.6864005",
"text": "def start(self) -> None:\n self.status = \"init\"\n self.init()\n self.run()",
"title": ""
},
{
"docid": "1182a3fd4667be033e37d3d5ab059430",
"score": "0.6831519",
"text": "def autonomousInit(self):\n\n #self.autonomousProgram.start()",
"title": ""
},
{
"docid": "bd7032c89741ee61471f5dbe6362aeaf",
"score": "0.6809839",
"text": "def agent_start(self, observation):",
"title": ""
},
{
"docid": "f767273a4eb99c4c47e67d34390c748a",
"score": "0.6740275",
"text": "def setup(self) -> None:\n self.advance_state(AgentState.SETUP)",
"title": ""
},
{
"docid": "c1b95eb0b24d6e8756219f753cc2426e",
"score": "0.6687562",
"text": "def initialize(self):\n self.sys_id = self.dbi.get_system_id()\n if not self.sys_id:\n self.logger.warning(f\"No fue posible obtener 'sys_id' de la base de datos. Usando valor '{self.sys_id}'.\")\n self.change_state(States.STARTING)\n if os.name == 'posix':\n python_exec = os.path.join(sys.exec_prefix, 'bin', 'python')\n elif os.name == 'nt':\n python_exec = os.path.join(sys.exec_prefix, 'Scripts', 'pythonw.exe')\n else:\n self.logger.error(\"No se pudo determinar sistema operativo\")\n sys.exit(1)\n agents_working_dir = os.path.dirname(os.path.abspath(__file__)) + os.sep + \"agents\"\n self.logger.info(f\"Manager ejecutandose con PID {os.getpid()}\")\n for agt in self.get_enabled_agents():\n pid = Popen([python_exec, f\"{agents_working_dir}{os.sep}agent_{agt.name}.py\"], stdin=DEVNULL, stdout=DEVNULL, stderr=STDOUT).pid\n self.logger.info(f\"Agente {agt.name} ejecutandose con PID {pid}\")\n self.flags.quit.wait(1) # Les da tiempo para partir antes de intentar conexión\n for agt in self.get_enabled_agents():\n agt.connect()\n\n # Espera a que agentes se conecten\n self.logger.info(\"Esperando conexión a agentes\")\n for agt in self.get_enabled_agents():\n self.logger.info(f\"Conectando a agente {agt.name}\")\n while not agt.is_connected():\n self.flags.quit.wait(0.01)\n self.logger.info(f\"Agente {agt.name} conectado\")\n self.logger.info(\"Conectado a todos los agentes habilitados\")\n\n self.logger.info(\"Iniciando thread de reporte de estado de hardware\")\n Thread(target=self.check_hw, name=\"check_hw\", daemon=True).start()\n\n self.logger.info(\"Iniciando thread de reporte de estado de agentes\")\n Thread(target=self.check_critical_agents_ready, name=\"check_agents_ready\", daemon=True).start()\n\n self.logger.info(\"Esperando que agentes críticos esten listos para capturar\")\n self.flags.critical_agents_ready.wait()\n self.logger.info(\"Agentes críticos están listos\")\n\n self.logger.info(\"Iniciando thread de lectura de teclado\")\n Thread(target=self.get_keyboard_input, name=\"get_keyboard_input\", daemon=True).start()\n\n if self.agents.ATMEGA.enabled:\n self.logger.info(\"Iniciando thread de lectura de botones\")\n Thread(target=self.get_buttons, name=\"get_buttons\", daemon=True).start()\n if self.agents.DATA_COPY.enabled:\n self.logger.info(\"Iniciando thread de estado de copia a pendrive\")\n Thread(target=self.check_data_copy, name=\"check_data_copy\", daemon=True).start()\n\n self.logger.info(\"--Iniciando thread de monitoreo de avance y tiempo\")\n Thread(target=self.check_spacetime, name=\"check_spacetime\", daemon=True).start()",
"title": ""
},
{
"docid": "51493548f1486a8c98ccba5305a6b3f9",
"score": "0.66345406",
"text": "def start(self):\n self.ae.start()",
"title": ""
},
{
"docid": "5d960a197ed2900739a94e3ebba97911",
"score": "0.66317767",
"text": "def start(self):\r\n\r\n pass\r\n # TODO: start a process\r\n \"\"\"\r\n global agent\r\n try:\r\n LOGGER.info(\"Agent_Service start\")\r\n python_path = os.path.dirname(util.get_script_path()) + os.sep + r'tool' + os.sep + r'python' + os.sep + r'install' + os.sep + r'python.exe'\r\n agent_py = util.get_script_path() + os.sep + r'agent.py'\r\n exc_path = python_path + ' ' + agent_py\r\n agent = subprocess.Popen(exc_path, stdin=subprocess.PIPE, \r\n\t\t\t\t\t\t\t\t\t\t\t\tstdout=subprocess.PIPE, \r\n\t\t\t\t\t\t\t\t\t\t\t\tstderr=subprocess.PIPE, \r\n\t\t\t\t\t\t\t\t\t\t\t\tshell = False, \r\n\t\t\t\t\t\t\t\t\t\t\t\tcwd=worker_cwd)\r\n except Exception, e:\r\n LOGGER.error(\"Agent_Service, cmd: %s start agent.py failed: %s\" %(exc_path, str(e)))\r\n if agent is None:\r\n sys.exit()\r\n \"\"\"",
"title": ""
},
{
"docid": "7814432fa4f4f2040ec690393a4f5eda",
"score": "0.6571248",
"text": "def start(self):\n\n pass",
"title": ""
},
{
"docid": "2e543d77322cb02ff15d5343d688517c",
"score": "0.6565455",
"text": "def start_episode(self):\n\n self.total_reward = 0\n logger.info(\"Initializing the world environment of the game\")\n\n self.hybrid_wumpus_agent = HuntWumpusHybridAgent()",
"title": ""
},
{
"docid": "92021d91b7ffec08fab1e0b7b3ecda02",
"score": "0.65416384",
"text": "def initialize(self, env: VecEnv, agent: BaseAgent) -> None:\n pass",
"title": ""
},
{
"docid": "33e943548bb1ea9916704a8a26c4d4be",
"score": "0.6526539",
"text": "def startup(self) -> None:\n ...",
"title": ""
},
{
"docid": "2a2d592f2982aab221974d1cc98c8727",
"score": "0.65194976",
"text": "def setup_class(cls):\n cls.runner = CliRunner()\n cls.agent_name = \"myagent\"\n cls.cwd = os.getcwd()\n cls.t = tempfile.mkdtemp()\n # copy the 'packages' directory in the parent of the agent folder.\n shutil.copytree(Path(ROOT_DIR, \"packages\"), Path(cls.t, \"packages\"))\n\n os.chdir(cls.t)\n result = cls.runner.invoke(cli, [*CLI_LOG_OPTION, \"init\", \"--author\", AUTHOR])\n assert result.exit_code == 0\n\n result = cls.runner.invoke(\n cli,\n [*CLI_LOG_OPTION, \"create\", \"--local\", cls.agent_name],\n standalone_mode=False,\n )\n assert result.exit_code == 0\n\n Path(cls.t, cls.agent_name, DEFAULT_AEA_CONFIG_FILE).write_text(\"\")\n\n os.chdir(Path(cls.t, cls.agent_name))\n\n cls.result = cls.runner.invoke(\n cli, [*CLI_LOG_OPTION, \"run\"], standalone_mode=False\n )",
"title": ""
},
{
"docid": "bd1c7287f1fc9f895288bd2c05546a5c",
"score": "0.6519244",
"text": "def start(self):\n pass",
"title": ""
},
{
"docid": "bd1c7287f1fc9f895288bd2c05546a5c",
"score": "0.6519244",
"text": "def start(self):\n pass",
"title": ""
},
{
"docid": "bd1c7287f1fc9f895288bd2c05546a5c",
"score": "0.6519244",
"text": "def start(self):\n pass",
"title": ""
},
{
"docid": "bd1c7287f1fc9f895288bd2c05546a5c",
"score": "0.6519244",
"text": "def start(self):\n pass",
"title": ""
},
{
"docid": "bd1c7287f1fc9f895288bd2c05546a5c",
"score": "0.6519244",
"text": "def start(self):\n pass",
"title": ""
},
{
"docid": "bd1c7287f1fc9f895288bd2c05546a5c",
"score": "0.6519244",
"text": "def start(self):\n pass",
"title": ""
},
{
"docid": "bd1c7287f1fc9f895288bd2c05546a5c",
"score": "0.6519244",
"text": "def start(self):\n pass",
"title": ""
},
{
"docid": "bd1c7287f1fc9f895288bd2c05546a5c",
"score": "0.6519244",
"text": "def start(self):\n pass",
"title": ""
},
{
"docid": "bd1c7287f1fc9f895288bd2c05546a5c",
"score": "0.6519244",
"text": "def start(self):\n pass",
"title": ""
},
{
"docid": "bd1c7287f1fc9f895288bd2c05546a5c",
"score": "0.6519244",
"text": "def start(self):\n pass",
"title": ""
},
{
"docid": "bd1c7287f1fc9f895288bd2c05546a5c",
"score": "0.6519244",
"text": "def start(self):\n pass",
"title": ""
},
{
"docid": "bd1c7287f1fc9f895288bd2c05546a5c",
"score": "0.6519244",
"text": "def start(self):\n pass",
"title": ""
},
{
"docid": "bd1c7287f1fc9f895288bd2c05546a5c",
"score": "0.6519244",
"text": "def start(self):\n pass",
"title": ""
},
{
"docid": "ff5451b8b4a577f756a063574da4d828",
"score": "0.65086436",
"text": "def autonomousInit(self):\n self.globalInit()\n self.autonomous.start()",
"title": ""
},
{
"docid": "c7fee80dd7c67f518f1fa4d1844275b1",
"score": "0.64965785",
"text": "def run():\n logging.info(\"Starting an RL Agent.\")\n config = RLConfig()\n agent = simple_rl_agent.RLAgent(config)\n asyncio.run(agent.start_agent())",
"title": ""
},
{
"docid": "6dec644bec1d75e51cee97d6082da145",
"score": "0.64729255",
"text": "def setup_class(cls):\n cls.runner = CliRunner()\n cls.agent_name = \"myagent\"\n cls.cwd = os.getcwd()\n cls.t = tempfile.mkdtemp()\n # copy the 'packages' directory in the parent of the agent folder.\n shutil.copytree(Path(ROOT_DIR, \"packages\"), Path(cls.t, \"packages\"))\n\n os.chdir(cls.t)\n result = cls.runner.invoke(cli, [*CLI_LOG_OPTION, \"init\", \"--author\", AUTHOR])\n assert result.exit_code == 0\n\n result = cls.runner.invoke(\n cli,\n [*CLI_LOG_OPTION, \"create\", \"--local\", cls.agent_name],\n standalone_mode=False,\n )\n assert result.exit_code == 0\n Path(cls.t, cls.agent_name, DEFAULT_AEA_CONFIG_FILE).unlink()\n\n os.chdir(Path(cls.t, cls.agent_name))\n\n cls.result = cls.runner.invoke(\n cli,\n [\"--skip-consistency-check\", *CLI_LOG_OPTION, \"run\"],\n standalone_mode=False,\n )",
"title": ""
},
{
"docid": "6adca61d5fd574533e0617ef2b16f295",
"score": "0.6455056",
"text": "async def startup(self) -> None:\n ...",
"title": ""
},
{
"docid": "0e4343051b0f14676e2ec10b5f731638",
"score": "0.64517725",
"text": "def start(self):\n self.started = True",
"title": ""
},
{
"docid": "6c516f5aa85f98b4bf2b5c4dc8ab6100",
"score": "0.6450202",
"text": "def startup(self):\n pass",
"title": ""
},
{
"docid": "6c516f5aa85f98b4bf2b5c4dc8ab6100",
"score": "0.6450202",
"text": "def startup(self):\n pass",
"title": ""
},
{
"docid": "6c516f5aa85f98b4bf2b5c4dc8ab6100",
"score": "0.6450202",
"text": "def startup(self):\n pass",
"title": ""
},
{
"docid": "6c516f5aa85f98b4bf2b5c4dc8ab6100",
"score": "0.6450202",
"text": "def startup(self):\n pass",
"title": ""
},
{
"docid": "8093fb0f7839c495b7798c8c7fff6b5d",
"score": "0.64480084",
"text": "def start(self):\n super().start()",
"title": ""
},
{
"docid": "14d45201e49f6550ee485d159a0c74a9",
"score": "0.64129114",
"text": "def start(self):\n\n if not self.is_running:\n logger.info('Party not registered yet.')\n if self.register_party():\n logger.info('Listening for commands from Aggregator')\n self.is_running = True\n self.initialize_model_config()\n else:\n logger.info('Party already running.')",
"title": ""
},
{
"docid": "a96412a83e55345027a8a68bb00a1bd1",
"score": "0.6407468",
"text": "def start(self):\n if not self._init:\n self.init()\n\n self.mainLoop()\n self.cleanExit()",
"title": ""
},
{
"docid": "725838b8f71539ae40ef5d539f824b27",
"score": "0.64017546",
"text": "def initialize(self):\n self.manager.initialize()\n self.worker.initialize()\n self.meta_reward = 0",
"title": ""
},
{
"docid": "a641b04a5f12943963c144c1005a48e3",
"score": "0.63892263",
"text": "def start(self) -> None:\n pass",
"title": ""
},
{
"docid": "a641b04a5f12943963c144c1005a48e3",
"score": "0.63892263",
"text": "def start(self) -> None:\n pass",
"title": ""
},
{
"docid": "ea68e9ab53080f8d28812b587a9affdf",
"score": "0.63798875",
"text": "async def start(self, *args, **kwargs):\n\n await super().start(*args, **kwargs)\n\n self.alerts.set_actor(self)\n await self.alerts.start()\n\n if self.chiller:\n self.chiller.set_actor(self)\n await self.chiller.start()",
"title": ""
},
{
"docid": "d4557a0d599fcfb610d5472809351f31",
"score": "0.63715714",
"text": "def start(self):\n\n LOG.debug(\"Starting bot\")\n super().start()",
"title": ""
},
{
"docid": "91860fab2790d231c58acf0081a3d5f4",
"score": "0.63684726",
"text": "def __startup(self):\n self.__log.info('Starting spawner')\n\n # Set the effective user and group to an unprivileged user and group.\n self.__set_unprivileged_user()\n\n # Install signal handlers.\n self.__install_signal_handlers()\n\n # Read all user names under which the controller is allowed to start jobs.\n JobHandler.read_allowed_users()",
"title": ""
},
{
"docid": "017c0289832b149b7694161317f24fd5",
"score": "0.6360426",
"text": "def start(self):\n self.start(None)",
"title": ""
},
{
"docid": "03e4c796737db824227955e84f3abbdd",
"score": "0.6352929",
"text": "def Start(self):\n LOG.info(\"Started.\")",
"title": ""
},
{
"docid": "80194c670b8ed30cdd25569bfe6d178c",
"score": "0.63370734",
"text": "def start(cls):\n pass",
"title": ""
},
{
"docid": "42420ba20cdf5a81a6f861a66874e54f",
"score": "0.6310121",
"text": "def doStart(self):\n self._wrappedFactory.doStart()",
"title": ""
},
{
"docid": "7ffc6d4b233d87b371372f97b181800f",
"score": "0.6308182",
"text": "def run(self) -> None:\n if self.current_state != AgentState.SETUP:\n if self.config.raise_on_state_mismatch:\n self.advance_state(AgentState.ABORTED)\n raise AgentError(\"Agent state is not in SETUP state. Bailing out\")\n\n warnings.warn(\n \"Agent is currently not in the SETUP state. Proceeding anyway\"\n )\n\n if not self.is_runnable():\n raise AgentError(\"Agent not in executable state. Bailing out\")\n self.advance_state(AgentState.RUNNING)",
"title": ""
},
{
"docid": "b212c0d233ff8802ffa32d219f15e0c4",
"score": "0.6296529",
"text": "def start(self):\n pass # pragma: no cover",
"title": ""
},
{
"docid": "c4b6f24c9d99fcf80df99bbe9f7746c3",
"score": "0.62899935",
"text": "def main():\n\n logging.info(\"Starting RL Agent's process.\")\n if mp.get_start_method(allow_none=True) != 'spawn':\n mp.set_start_method('spawn', force=True)\n\n proc = mp.Process(target=run)\n proc.start()\n\n logging.info(\"Starting RL Environment's process.\")\n server = simple_rl_server.RLServer()\n server.run()",
"title": ""
},
{
"docid": "650ba6958ea4aa02d2021df22d32a60d",
"score": "0.6277244",
"text": "def agent():\n agent_module, agent_name = FLAGS.agent.rsplit(\".\", 1)\n agent_cls = getattr(importlib.import_module(agent_module), agent_name)\n\n logging.info(\"Starting agent:\")\n with lan_sc2_env.LanSC2Env(\n host=FLAGS.host,\n config_port=FLAGS.config_port,\n race=sc2_env.Race[FLAGS.agent_race],\n step_mul=FLAGS.step_mul,\n agent_interface_format=sc2_env.parse_agent_interface_format(\n feature_screen=FLAGS.feature_screen_size,\n feature_minimap=FLAGS.feature_minimap_size,\n rgb_screen=FLAGS.rgb_screen_size,\n rgb_minimap=FLAGS.rgb_minimap_size,\n action_space=FLAGS.action_space,\n use_feature_units=FLAGS.use_feature_units),\n visualize=FLAGS.render) as env:\n agents = [agent_cls()]\n logging.info(\"Connected, starting run_loop.\")\n try:\n run_loop.run_loop(agents, env)\n except lan_sc2_env.RestartException:\n pass\n logging.info(\"Done.\")",
"title": ""
},
{
"docid": "f0b3238992f06cc9dc5a649d8374041d",
"score": "0.6271742",
"text": "def will_start(self):\n pass",
"title": ""
},
{
"docid": "f0b3238992f06cc9dc5a649d8374041d",
"score": "0.6271742",
"text": "def will_start(self):\n pass",
"title": ""
},
{
"docid": "893579338ef116cab50ac66da00d6734",
"score": "0.6271327",
"text": "def rl_init(self, agent_init_info={}, env_init_info={}):\n self.environment.env_init(env_init_info)\n self.agent.agent_init(agent_init_info)\n\n self.total_reward = 0.0\n self.num_steps = 0\n self.num_episodes = 0",
"title": ""
},
{
"docid": "eeed22be0637128eeae19a2f6d1a8b16",
"score": "0.6253038",
"text": "def start_agent() -> None:\n app = create_app()\n with app.app_context():\n database.await_connection()\n if not database.tables_exist():\n database.create_all()\n process_stream(app)",
"title": ""
},
{
"docid": "5ef645be9021ce41bf8590d37c28494e",
"score": "0.624618",
"text": "def initialize(self):\n\n if self.common.trace:\n log.debug(\"XenActions.initialize()\")\n \n if self.conf.xendpath:\n # run even in evaluate mode, tests in evaluate() will run xm etc.\n self.testXend()\n\n self.initialized = True\n \n # For log message tailoring only -- not for functionality difference.\n # For that, use descendant class XenKillNine(XenActions).\n self.killninemode = False",
"title": ""
},
{
"docid": "2416bdcc3e5d6935822b3ddd64c0a575",
"score": "0.6233326",
"text": "async def _on_start(self) -> None:\n # Nothing to do in the general case.",
"title": ""
},
{
"docid": "fd3e99ddfefb7d06690a9a87ffef67c7",
"score": "0.62225217",
"text": "def __init__(self, args):\n\n # First of all, we need to create the client that will send the requests\n # to the simulator. Here we'll assume the simulator is accepting\n # requests in the localhost at port 2000.\n self.client = carla.Client(args.host, int(args.port))\n self.client.set_timeout(self.client_timeout)\n\n dist = pkg_resources.get_distribution(\"carla\")\n if LooseVersion(dist.version) < LooseVersion('0.9.6'):\n raise ImportError(\n \"CARLA version 0.9.6 or newer required. CARLA version found: {}\".format(dist))\n\n # Load additional scenario definitions, if there are any\n # If something goes wrong an exception will be thrown by importlib (ok here)\n if args.additionalScenario != '':\n module_name = os.path.basename(\n args.additionalScenario).split('.')[0]\n sys.path.insert(0, os.path.dirname(args.additionalScenario))\n self.additional_scenario_module = importlib.import_module(\n module_name)\n\n # Load agent if requested via command line args\n # If something goes wrong an exception will be thrown by importlib (ok here)\n if args.agent is not None:\n module_name = os.path.basename(args.agent).split('.')[0]\n sys.path.insert(0, os.path.dirname(args.agent))\n self.module_agent = importlib.import_module(module_name)\n\n # Create the ScenarioManager\n self.manager = ScenarioManager(args.debug)\n\n self._start_wall_time = datetime.now()",
"title": ""
},
{
"docid": "6cd6c9c2a04feaf54de6275d53fe11fa",
"score": "0.62209684",
"text": "def on_start(self):\n # print('Starting the test here - first user')\n pass",
"title": ""
},
{
"docid": "a627e89804f411034ce5847bcfcf6c01",
"score": "0.6212409",
"text": "def start(self):\n self._supervisor = self.main_loop.create_task(self._supervise())",
"title": ""
},
{
"docid": "c9686d053319752c3915c6375dd3bc87",
"score": "0.6202408",
"text": "def env_start(self):",
"title": ""
},
{
"docid": "f1794152db9a51c16dcc023b31abe0b2",
"score": "0.6200127",
"text": "def activate(self):\n debug.virtual('RecogStartMgr.activate')",
"title": ""
},
{
"docid": "3106c31c2e1ee12dbc711c50ce2fe4ca",
"score": "0.6199368",
"text": "def start(self):\n\n self.moisture_sensor.start()\n\n super().start()",
"title": ""
},
{
"docid": "241e78e7ebb2d5f9a9a4bdc64f4c4d07",
"score": "0.6197708",
"text": "def setup_class(cls):\n cls.runner = CliRunner()\n cls.agent_name = \"myagent\"\n cls.cwd = os.getcwd()\n cls.t = tempfile.mkdtemp()\n # copy the 'packages' directory in the parent of the agent folder.\n shutil.copytree(Path(ROOT_DIR, \"packages\"), Path(cls.t, \"packages\"))\n\n os.chdir(cls.t)\n result = cls.runner.invoke(cli, [*CLI_LOG_OPTION, \"init\", \"--author\", AUTHOR])\n assert result.exit_code == 0\n\n result = cls.runner.invoke(\n cli,\n [*CLI_LOG_OPTION, \"create\", \"--local\", cls.agent_name],\n standalone_mode=False,\n )\n assert result.exit_code == 0\n\n Path(cls.t, cls.agent_name, DEFAULT_AEA_CONFIG_FILE).write_text(\n \"invalid_attribute: 'foo'\\n\"\n )\n\n os.chdir(Path(cls.t, cls.agent_name))\n\n cls.result = cls.runner.invoke(\n cli, [*CLI_LOG_OPTION, \"run\"], standalone_mode=False\n )",
"title": ""
},
{
"docid": "af91a89644fbf93f61d5517cf8bdb027",
"score": "0.6169803",
"text": "def start(self, **kwargs):\n pass",
"title": ""
},
{
"docid": "907d518e93f4ff9624905ed0581dd474",
"score": "0.61604196",
"text": "def startup(event):\n abode.start_listener()",
"title": ""
},
{
"docid": "443b1664f00ca1ed57ceb87e7062f8ef",
"score": "0.6158222",
"text": "def do_initialize(self):\n if not self.logger.get_setup_state() == 'systemReady':\n if self.logger.get_setup_state() == 'sessionRunning':\n self.do_stop_session()\n elif self.logger.get_setup_state() == 'stimRunning':\n self.do_stop_stim()\n else:\n pass\n self.logger.update_setup_state('systemReady')",
"title": ""
},
{
"docid": "6effe471937ef2f553ada0053be21d96",
"score": "0.6157977",
"text": "def run(self):\n self.ae.start()",
"title": ""
},
{
"docid": "ac879a067d36599d7c57137fa0d4bcfe",
"score": "0.6144972",
"text": "def on_lifecycle_start(self, ch, mthd, prop, msg):\n super(self.__class__, self).on_lifecycle_start(ch, mthd, prop, msg)\n LOG.info(\"Stress Mano plugin started and operational.\")\n\n self.start_next_test()",
"title": ""
},
{
"docid": "1e44988baf477bcd76983c7647c2ed08",
"score": "0.6136337",
"text": "def start(self):\n self._handle.start()",
"title": ""
},
{
"docid": "71d42ff157aa43f5e39d6429314b888b",
"score": "0.6135054",
"text": "def start(self) -> None:",
"title": ""
},
{
"docid": "f487eaa5cb984062deb56a1d8ed07e0d",
"score": "0.6130484",
"text": "def start(self): #{\n pass",
"title": ""
},
{
"docid": "86e7438a0f458d14379693ba0a62cf5c",
"score": "0.61289376",
"text": "def _sim_start_callback(self, msg):\n\n if not self._sim_started: # init only once here\n\n rospy.loginfo(self._agent_name + \" started\")\n\n # creating the actual RHBP model\n self._initialize_behaviour_model()\n\n self._sim_started = True",
"title": ""
},
{
"docid": "1a700bf80d68b7951b2b463707dfdac2",
"score": "0.61229026",
"text": "def initialize(self):\n\n if self.common.trace:\n log.debug(\"XenKillNine.initialize()\")\n \n if self.conf.xendpath:\n # run even in evaluate mode, tests in evaluate() will run xm etc.\n self.testXend()\n\n self.initialized = True\n self.killninemode = True",
"title": ""
},
{
"docid": "22f5c0e1c40178b4f561b091041555bf",
"score": "0.61057365",
"text": "def doStart():",
"title": ""
},
{
"docid": "a74dc21ee52ab6b82ed7da79f06d2570",
"score": "0.6102199",
"text": "def on_startup(args):\n excludes = []\n # eventually grab values from the commandline args here\n # this throws exceptions if it doesn't find anything\n wireless = NetworkDeviceList.wireless(exclude=excludes)\n # set our little state token\n congiured_mesh = False\n # walk through all the interfaces until we find a working one or run out\n for intface in wireless:\n mesh = iface.MeshIFace(intface)\n if mesh.configure():\n congiured_mesh = True\n break\n # else continue to the next and try there\n # if nothing is configured stop here\n logger.warn(\"No interfaces configured. Exiting.\")\n if not configured_mesh: return None\n # else load up the mesh interface\n mesh.load()\n # and setup the client interface.\n client = iface.ClientIFace(mesh.device, client_number)\n client.load()\n # add the commotion-wireless route\n mesh.add_commotion_route()\n # Start the captive portal daemon on the client interface.\n client.start_captive_portal()\n logger.info(\"Started captive portal daemon.\")\n # Make some config files\n client.make_hosts_file()\n dnsmasq = rc.DNSMasq(mesh, client)\n dnsmasq.make_include()\n # Poke some services we changed configs for\n dnsmasq.rc(action=\"restart\")\n olsrd = rc.OLSRD(mesh)",
"title": ""
},
{
"docid": "a907c875de6958203ed58b0d6778a8b3",
"score": "0.6095568",
"text": "def initAgent(agentNum):\n Sales.agents = int(\"0\" + agentNum)",
"title": ""
},
{
"docid": "504be96883aacab4272b2c0d0c04b30f",
"score": "0.60786283",
"text": "def on_start(locust):\n pass",
"title": ""
},
{
"docid": "325bbfa884308700316c24335db945f8",
"score": "0.607754",
"text": "def start(self):\n\t\tprint(\"Initializing MBus handler..\")\n\t\tself.parse_devices()\n\t\tself.loop.run_forever()",
"title": ""
},
{
"docid": "ab98fa2e804cd0fb38f9e2d74e74500c",
"score": "0.60705423",
"text": "def start(self):\n\n self._setup_logging()\n\n if self.generate_config:\n self.write_config()\n\n self.run()",
"title": ""
},
{
"docid": "6eb22f20ecc9f7b311f9bc6f04eeca4d",
"score": "0.6067596",
"text": "def start(self):",
"title": ""
},
{
"docid": "6eb22f20ecc9f7b311f9bc6f04eeca4d",
"score": "0.6067596",
"text": "def start(self):",
"title": ""
},
{
"docid": "6eb22f20ecc9f7b311f9bc6f04eeca4d",
"score": "0.6067596",
"text": "def start(self):",
"title": ""
},
{
"docid": "44bf1d6c34fdf5801cb59a988a4e7c83",
"score": "0.6066073",
"text": "def __init__(self):\n self._gearman_client = GearmanAdapter()\n logging.debug(\"{name}: processor successfully initiated\".format(name=self.__class__))",
"title": ""
},
{
"docid": "dcacc10f6f4f1780a355c55b833c5ddb",
"score": "0.60622793",
"text": "def start(self):\n try:\n self.connection.start()\n except Exception as ex:\n logger.error(\"Error occurred during start\")\n logger.error(ex)\n else:\n logger.info(\"Party start successful\")",
"title": ""
},
{
"docid": "dfbb0b81975a5223c5c98ebd0207b9df",
"score": "0.60563576",
"text": "def on_first_start(self):\n self.monsters.start()",
"title": ""
},
{
"docid": "646e6fcba70a930448a494721c83b3c9",
"score": "0.60553074",
"text": "def do_initialize(self, *args):\n self.params = args\n self.log.append(\n \"{0} version {1} initialized\".format(\n self.description, self.version\n )\n )\n self.result = True",
"title": ""
},
{
"docid": "563e04c035e4233c88f7166ebf0627c2",
"score": "0.6052791",
"text": "def __init__(self, agent: Agent):\n self.agent: Agent = agent",
"title": ""
},
{
"docid": "5eee73fd628c2639a6ae9f1fb75c15f3",
"score": "0.60516375",
"text": "def Start(self):\n print('Reactor is now running.')\n LOG.info(\"\\n======================== Starting ======================== Version: {}\\n\".format(__version__))\n LOG.info('Starting - Reactor is now running.')\n self.m_utility.start_all_components()\n self.m_pyhouse_obj._Twisted.Reactor.callLater(INITIAL_DELAY, self.m_utility._config_save_loop, self.m_pyhouse_obj)\n LOG.info(\"\\n======================== Started ======================== Version: {}\\n\".format(__version__))\n LOG.info(\"\\n======================== Opperational ========================\")",
"title": ""
},
{
"docid": "f7ff41a683e13c000f6deaa9b3119cd1",
"score": "0.6049648",
"text": "def start(self):\n self.plugin_manager.load_plugins()",
"title": ""
},
{
"docid": "dd3544f6c8ba671551be9513b451981f",
"score": "0.60423285",
"text": "def autonomousInit(self):\n pass",
"title": ""
},
{
"docid": "dd3544f6c8ba671551be9513b451981f",
"score": "0.60423285",
"text": "def autonomousInit(self):\n pass",
"title": ""
},
{
"docid": "dd3544f6c8ba671551be9513b451981f",
"score": "0.60423285",
"text": "def autonomousInit(self):\n pass",
"title": ""
},
{
"docid": "ce5bc999b60fe302c2d64c82db28f549",
"score": "0.6041876",
"text": "def init(self):\n self.kill_server()\n return self.start_server()",
"title": ""
},
{
"docid": "c8ccbf5b8ab8e85eaf08cba8fb77dfa4",
"score": "0.60413474",
"text": "def start(self):\n self._do_vm_action('create', 'started')",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="772"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">398a8a80ee96b0ca0e45d459aeaefb5d</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">BGR format, packed into 2 bytes by dropping LSBs.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "7ffc6cbaac3fab19fdefcecb63cfc96c",
"score": "0.53539056",
"text": "def save_bgr565(pixels: Array, data: bytearray, width: int, height: int) -> None:\n for offset in range(width * height):\n data[2*offset], data[2 * offset + 1] = compress565(\n pixels[4 * offset + 2],\n pixels[4 * offset + 1],\n pixels[4 * offset],\n )",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "06a6a687383b0f339c9d45e713944124",
"score": "0.648248",
"text": "def rgb2bgr(tpl):\n return (tpl[2], tpl[1], tpl[0])",
"title": ""
},
{
"docid": "187a8dd5a5e69a8eec16f24f10e9f27a",
"score": "0.637679",
"text": "def from_rgb2bgr(im):\n return cv2.cvtColor(im, cv2.COLOR_RGB2BGR)",
"title": ""
},
{
"docid": "88808453968a952a558478a6899dedb2",
"score": "0.6316284",
"text": "def to_bgr(img):\n return cv2.cvtColor(img, cv2.COLOR_HSV2BGR)",
"title": ""
},
{
"docid": "64df51e9739663ca787df73fc7c40993",
"score": "0.6300245",
"text": "def get_BGR_img(self):\n img = self.img.copy()\n # Convert BGR to HSV\n hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n # define range of BGR color in HSV\n threshold_blue = np.array([[100,43,46], [124,255,255]])\n threshold_green = np.array([[35,43,46], [77,255,255]])\n threshold_red1 = np.array([[0,43,46], [10,255,255]])\n threshold_red2 = np.array([[156,43,46], [180,255,255]])\n # Threshold the HSV image to get only BGR colors\n mask_blue = cv2.inRange(hsv, threshold_blue[0], threshold_blue[1])\n mask_green = cv2.inRange(hsv, threshold_green[0], threshold_green[1])\n mask_red1 = cv2.inRange(hsv, threshold_red1[0], threshold_red1[1])\n mask_red2 = cv2.inRange(hsv, threshold_red2[0], threshold_red2[1])\n mask_red = mask_red1 | mask_red2\n # Bitwise-AND mask and original image\n self.blue = cv2.bitwise_and(img, img, mask=mask_blue)\n self.green = cv2.bitwise_and(img, img, mask=mask_green)\n self.red = cv2.bitwise_and(img, img, mask=mask_red)\n # 返回 bgr 三通道的分量合成的图片\n return np.stack((self.blue[:, :, 0], self.green[:, :, 1], self.red[:, :, 2]), axis=2)",
"title": ""
},
{
"docid": "36f0be4a8eb86f720f80abe2d65333c4",
"score": "0.61182624",
"text": "def load_bgr888_bluescreen(pixels: Array, data: bytes, width: int, height: int) -> None:\n for offset in range(width * height):\n r = data[3 * offset + 2]\n g = data[3 * offset + 1]\n b = data[3 * offset]\n if r == g == 0 and b == 255:\n pixels[4*offset] = pixels[4*offset+1] = 0\n pixels[4*offset+2] = pixels[4*offset+3] = 0\n else:\n pixels[4 * offset] = r\n pixels[4 * offset + 1] = g\n pixels[4 * offset + 2] = b\n pixels[4 * offset + 3] = 255",
"title": ""
},
{
"docid": "07f89bb4bfd9a3a59ceba80ea76e4058",
"score": "0.6107117",
"text": "def test_rgb_ybr_rgb_multi_frame(self):\r\n ds = dcmread(RGB_8_3_2F)\r\n\r\n arr = ds.pixel_array\r\n assert (255, 0, 0) == tuple(arr[0, 5, 50, :])\r\n assert (255, 128, 128) == tuple(arr[0, 15, 50, :])\r\n assert (0, 255, 0) == tuple(arr[0, 25, 50, :])\r\n assert (128, 255, 128) == tuple(arr[0, 35, 50, :])\r\n assert (0, 0, 255) == tuple(arr[0, 45, 50, :])\r\n assert (128, 128, 255) == tuple(arr[0, 55, 50, :])\r\n assert (0, 0, 0) == tuple(arr[0, 65, 50, :])\r\n assert (64, 64, 64) == tuple(arr[0, 75, 50, :])\r\n assert (192, 192, 192) == tuple(arr[0, 85, 50, :])\r\n assert (255, 255, 255) == tuple(arr[0, 95, 50, :])\r\n # Frame 2 is frame 1 inverted\r\n assert np.array_equal((2**ds.BitsAllocated - 1) - arr[1], arr[0])\r\n\r\n ybr = convert_color_space(arr, 'RGB', 'YBR_FULL')\r\n assert (76, 85, 255) == tuple(ybr[0, 5, 50, :])\r\n assert (166, 107, 192) == tuple(ybr[0, 15, 50, :])\r\n assert (150, 44, 21) == tuple(ybr[0, 25, 50, :])\r\n assert (203, 86, 75) == tuple(ybr[0, 35, 50, :])\r\n assert (29, 255, 107) == tuple(ybr[0, 45, 50, :])\r\n assert (142, 192, 118) == tuple(ybr[0, 55, 50, :])\r\n assert (0, 128, 128) == tuple(ybr[0, 65, 50, :])\r\n assert (64, 128, 128) == tuple(ybr[0, 75, 50, :])\r\n assert (192, 128, 128) == tuple(ybr[0, 85, 50, :])\r\n assert (255, 128, 128) == tuple(ybr[0, 95, 50, :])\r\n # Frame 2\r\n assert (179, 171, 1) == tuple(ybr[1, 5, 50, :])\r\n assert (89, 149, 65) == tuple(ybr[1, 15, 50, :])\r\n assert (105, 212, 235) == tuple(ybr[1, 25, 50, :])\r\n assert (52, 170, 181) == tuple(ybr[1, 35, 50, :])\r\n assert (226, 1, 149) == tuple(ybr[1, 45, 50, :])\r\n assert (113, 65, 138) == tuple(ybr[1, 55, 50, :])\r\n assert (255, 128, 128) == tuple(ybr[1, 65, 50, :])\r\n assert (191, 128, 128) == tuple(ybr[1, 75, 50, :])\r\n assert (63, 128, 128) == tuple(ybr[1, 85, 50, :])\r\n assert (0, 128, 128) == tuple(ybr[1, 95, 50, :])\r\n\r\n # Round trip -> rounding errors get compounded\r\n rgb = convert_color_space(ybr, 'YBR_FULL', 'RGB')\r\n # All pixels within +/- 1 units\r\n assert np.allclose(rgb, arr, atol=1)\r\n assert rgb.shape == arr.shape",
"title": ""
},
{
"docid": "351436f32b65e2c270a6fdc5d0907c86",
"score": "0.6105011",
"text": "def to_msx2_rgb(r, g, b):\n return g | r >> 3 | b >> 6",
"title": ""
},
{
"docid": "b8559285794d89d5961cf6f84e31667c",
"score": "0.6104921",
"text": "def unpack_r5g6b5(value: int) -> \"RGBA\":\n r = get_bits(value, 15, 11)\n g = get_bits(value, 10, 5)\n b = get_bits(value, 4, 0)\n return (r / 31, g / 63, b / 31, 1)",
"title": ""
},
{
"docid": "836ae529ae58ffe4c811d1b39ed70c85",
"score": "0.6052053",
"text": "def save_bgr888_bluescreen(pixels: Array, data: bytearray, width: int, height: int) -> None:\n for offset in range(width * height):\n if pixels[4 * offset + 3] < 128:\n data[3 * offset + 2] = 0\n data[3 * offset + 1] = 0\n data[3 * offset] = 255\n else:\n data[3 * offset + 2] = pixels[4 * offset]\n data[3 * offset + 1] = pixels[4 * offset + 1]\n data[3 * offset] = pixels[4 * offset + 2]",
"title": ""
},
{
"docid": "6444a3237067403d0bd3e91955bacd0a",
"score": "0.6052019",
"text": "def load_bgr565(pixels: Array, data: bytes, width: int, height: int) -> None:\n for offset in range(width * height):\n b, g, r = decomp565(data[2 * offset], data[2 * offset + 1])\n\n pixels[4 * offset] = r\n pixels[4 * offset + 1] = g\n pixels[4 * offset + 2] = b\n pixels[4 * offset + 3] = 255",
"title": ""
},
{
"docid": "606ce43583950829f8cf374c7f5ec713",
"score": "0.6050047",
"text": "def from_bgr2rgb(im):\n return cv2.cvtColor(im, cv2.COLOR_BGR2RGB)",
"title": ""
},
{
"docid": "ed55b1dafdaacf7a28dcf3908da399f4",
"score": "0.59716886",
"text": "def bgr2rgb(img):\n if len(img.shape) == 3 and img.shape[2] == 3:\n b, g, r = cv2.split(img)\n out = cv2.merge([r, g, b])\n else:\n out = img\n return out",
"title": ""
},
{
"docid": "d944844767cda2dd6a5d675652010821",
"score": "0.594047",
"text": "def test_rgb_ybr_rgb_single_frame(self):\r\n ds = dcmread(RGB_8_3_1F)\r\n\r\n arr = ds.pixel_array\r\n assert (255, 0, 0) == tuple(arr[5, 50, :])\r\n assert (255, 128, 128) == tuple(arr[15, 50, :])\r\n assert (0, 255, 0) == tuple(arr[25, 50, :])\r\n assert (128, 255, 128) == tuple(arr[35, 50, :])\r\n assert (0, 0, 255) == tuple(arr[45, 50, :])\r\n assert (128, 128, 255) == tuple(arr[55, 50, :])\r\n assert (0, 0, 0) == tuple(arr[65, 50, :])\r\n assert (64, 64, 64) == tuple(arr[75, 50, :])\r\n assert (192, 192, 192) == tuple(arr[85, 50, :])\r\n assert (255, 255, 255) == tuple(arr[95, 50, :])\r\n\r\n ybr = convert_color_space(arr, 'RGB', 'YBR_FULL')\r\n assert (76, 85, 255) == tuple(ybr[5, 50, :])\r\n assert (166, 107, 192) == tuple(ybr[15, 50, :])\r\n assert (150, 44, 21) == tuple(ybr[25, 50, :])\r\n assert (203, 86, 75) == tuple(ybr[35, 50, :])\r\n assert (29, 255, 107) == tuple(ybr[45, 50, :])\r\n assert (142, 192, 118) == tuple(ybr[55, 50, :])\r\n assert (0, 128, 128) == tuple(ybr[65, 50, :])\r\n assert (64, 128, 128) == tuple(ybr[75, 50, :])\r\n assert (192, 128, 128) == tuple(ybr[85, 50, :])\r\n assert (255, 128, 128) == tuple(ybr[95, 50, :])\r\n\r\n # Round trip -> rounding errors get compounded\r\n rgb = convert_color_space(ybr, 'YBR_FULL', 'RGB')\r\n # All pixels within +/- 1 units\r\n assert np.allclose(rgb, arr, atol=1)\r\n assert rgb.shape == arr.shape",
"title": ""
},
{
"docid": "ede0fa95d8c55f4b76e3908fe3c69335",
"score": "0.5928817",
"text": "def decode_bc1(w: int, h: int, data: bytes) -> List[\"RGBA\"]: # noqa: E501, pylint:disable=line-too-long\n assert w % 4 == 0\n assert h % 4 == 0\n blocks_per_row = w // 4\n blocks_per_col = h // 4\n num_blocks = blocks_per_row * blocks_per_col\n num_bytes = num_blocks * 8\n assert len(data) >= num_bytes\n pixels: List[RGBA] = [(0.0, 0.0, 0.0, 0.0) for _ in range(w * h)]\n\n # Decode blocks\n for block_idx in range(num_blocks):\n block_y = (block_idx // blocks_per_row) * 4\n block_x = (block_idx % blocks_per_row) * 4\n block_data = data[(block_idx * 8) : (block_idx * 8 + 8)]\n\n c0_raw, c1_raw, indices = struct.unpack(\"<HHI\", block_data[0:8])\n alpha_enabled = c0_raw <= c1_raw\n c0, c1 = unpack_r5g6b5(c0_raw), unpack_r5g6b5(c1_raw)\n\n if alpha_enabled:\n mixed = mix(c0, c1, 1 / 2)\n mixed2 = (0.0, 0.0, 0.0, 0.0)\n else:\n mixed = mix(c0, c1, 1 / 3)\n mixed2 = mix(c0, c1, 2 / 3)\n colors = (c0, c1, mixed, mixed2)\n\n for y in range(4):\n for x in range(4):\n bit_off = y * 8 + x * 2\n color_idx = get_bits(indices, bit_off + 1, bit_off)\n addr = (block_y + y) * w + (block_x + x)\n pixels[addr] = colors[color_idx]\n\n return pixels",
"title": ""
},
{
"docid": "0b9a91e0fd0f03dbe0d611532739779a",
"score": "0.5928062",
"text": "def bgr_to_rgb(im):\n tmp = np.empty(im.shape, dtype=np.uint8)\n tmp[..., 0] = im[..., 2]\n tmp[..., 1] = im[..., 1]\n tmp[..., 2] = im[..., 0]\n return tmp",
"title": ""
},
{
"docid": "cc6511cbad3208d0df6e0fec4afbefc3",
"score": "0.5925262",
"text": "def to_bgra_array(image):\n array = np.frombuffer(image.raw_data, dtype=np.dtype(\"uint8\"))\n array = np.reshape(array, (image.height, image.width, 4))\n return array",
"title": ""
},
{
"docid": "cc6511cbad3208d0df6e0fec4afbefc3",
"score": "0.5925262",
"text": "def to_bgra_array(image):\n array = np.frombuffer(image.raw_data, dtype=np.dtype(\"uint8\"))\n array = np.reshape(array, (image.height, image.width, 4))\n return array",
"title": ""
},
{
"docid": "c0179c258fd925b4defa197d58833e45",
"score": "0.5915684",
"text": "def rawtorgb(rawimg, width = 32, height = 32):\r\n img = []\r\n for rgb in xrange(3):\r\n single = []\r\n for h_i in xrange(height):\r\n single.append(rawimg[rgb*width*height+h_i*width:rgb*width*height+(h_i+1)*(width)])\r\n img.append(single)\r\n img = np.array(img)\r\n return np.swapaxes(np.swapaxes(img, 0, 2), 0, 1)",
"title": ""
},
{
"docid": "7b69356f47bbffa2b1556acbe273327d",
"score": "0.59077233",
"text": "def color565(r, g, b):\n return ((r & 0xF8) << 8) | ((g & 0xFC) << 3) | (b >> 3)",
"title": ""
},
{
"docid": "fc94e729a754130e2e00765cae43345e",
"score": "0.5873065",
"text": "def cvt_uint8(img, is_bgr=True):\r\n nimg = np.round(np.clip(img, 0, 255)).astype(np.uint8)\r\n if not is_bgr:\r\n nimg[:, :, 0] = np.clip(nimg[:, :, 0], 0, 179)\r\n return nimg",
"title": ""
},
{
"docid": "1db07e44ce4682e570a20f7070711b76",
"score": "0.58409727",
"text": "def test_rgb_to_bgr_int(self):\n self.assertEqual(util.rgb_to_bgr_int((255, 128, 64)), 4227327)",
"title": ""
},
{
"docid": "b2ac204ed99963f737ef486b6bf53c7d",
"score": "0.582907",
"text": "def _color565(self, r, g, b):\n return (((r & 0xF8) << 8) | ((g & 0xFC) << 3) | (b >> 3))",
"title": ""
},
{
"docid": "25a2c5ea39e074c2ce71d7b644370c4d",
"score": "0.5829013",
"text": "def rgb(self):\n # type: () -> bytes\n if not self.__rgb:\n rgb = bytearray(self.height * self.width * 3)\n raw = self.raw\n rgb[0::3] = raw[2::4]\n rgb[1::3] = raw[1::4]\n rgb[2::3] = raw[0::4]\n self.__rgb = bytes(rgb)\n return self.__rgb",
"title": ""
},
{
"docid": "95dd80c4687cc790b3766d7c0472515c",
"score": "0.5810056",
"text": "def b(self):\n return _pymaxwell5.Crgba8Tbyte_b(self)",
"title": ""
},
{
"docid": "5b278e94ea6c04122979d31f03b09914",
"score": "0.58075047",
"text": "def _color565(self, r, g, b):\n return ((r & 0xF8) << 8) | ((g & 0xFC) << 3) | (b >> 3)",
"title": ""
},
{
"docid": "f30cf5186b4bdbbfacf8188b1266302e",
"score": "0.5803132",
"text": "def convertYUV_ToBGR_cv2( image, nSizeX, nSizeY ):\n #~ timeBegin = time.time();\n numpyBuf = (numpy.reshape(numpy.frombuffer(image, dtype='%iuint8' % 1), ( nSizeX, nSizeY, 2)))\n rgb = cv2.cvtColor(numpyBuf, cv2.COLOR_YUV2BGR_YUYV); # COLOR_YUV2RGB_Y422 # cv2.COLOR_YUV2RGB_YUYV\n imageRgb = rgb.tostring();\n #~ rDuration = time.time() - timeBegin;\n #~ print( \"rDuration: %s\" % rDuration ); # ~1ms per conversion!\n return imageRgb;",
"title": ""
},
{
"docid": "42bdf959c23b2fc527568c90ab8621fb",
"score": "0.58001995",
"text": "def get_rgbc_raw(self):\n self.setup()\n colour_data = self._bh1745.get('COLOUR_DATA')\n r, g, b, c = colour_data.red, colour_data.green, colour_data.blue, colour_data.clear\n\n if self._enable_channel_compensation:\n cr, cg, cb, cc = self._channel_compensation\n r, g, b, c = r * cr, g * cg, b * cb, c * cc\n\n return (r, g, b, c)",
"title": ""
},
{
"docid": "d401ae2ac668d255346efd31a1386b86",
"score": "0.5772383",
"text": "def rgb_to_bgr(input, name=None):\n rgb = tf.unstack(input, axis=-1)\n r, g, b = rgb[0], rgb[1], rgb[2]\n return tf.stack([b, g, r], axis=-1)",
"title": ""
},
{
"docid": "7fc502b665451f2017b4cd6a7297a4b8",
"score": "0.57688963",
"text": "def gray2bgr(img):\n img = img[..., None] if img.ndim == 2 else img\n out_img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)\n return out_img",
"title": ""
},
{
"docid": "62b1ee8e8526298e5f6acb0e99121300",
"score": "0.5765475",
"text": "def bgr_to_rgb(bgr):\n return (bgr[2], bgr[1], bgr[0])",
"title": ""
},
{
"docid": "83a9b7c2acf8581a075ccc51b2bd80ef",
"score": "0.57600075",
"text": "def get_blue_image(self, image:np.uint8)->np.uint8:\n imageDetails = image.shape # shape is in the order ROWS,COLS,CHANNELS\n row=0;col=0\n new_image = np.zeros((imageDetails[0], imageDetails[1], 3), np.uint8)\n print('just created the new image')\n while row < imageDetails[0]:\n col = 0\n while col < imageDetails[1]:\n new_image[row,col] =[image[row,col,0],0,0]\n col = col + 1\n row = row +1\n print('just finished the while loop')\n return new_image",
"title": ""
},
{
"docid": "1354f4efa01c88fdfffa8dac12b79a67",
"score": "0.57054824",
"text": "def cvimage_to_pygame(image):\n\n return pygame.image.frombuffer(image_rgb.tostring(), image_rgb.shape[1::-1], \"RGB\")",
"title": ""
},
{
"docid": "706e121c4b8bd7f6d37cfd369d86c5af",
"score": "0.57006776",
"text": "def load_bgra4444(pixels: Array, data: bytes, width: int, height: int) -> None:\n for offset in range(width * height):\n a = data[2 * offset]\n b = data[2 * offset + 1]\n pixels[4 * offset+1] = (a & 0b11110000) | (a & 0b11110000) >> 4\n pixels[4 * offset+2] = (a & 0b00001111) | (a & 0b00001111) << 4\n pixels[4 * offset] = (b & 0b00001111) | (b & 0b00001111) << 4\n pixels[4 * offset+3] = (b & 0b11110000) | (b & 0b11110000) >> 4",
"title": ""
},
{
"docid": "bcbf86cee618b972a95a87347531061a",
"score": "0.56827456",
"text": "def pixel(argb):\n return argb[::-1]",
"title": ""
},
{
"docid": "78a9afa22de7fa8366c9c7524224c271",
"score": "0.5679972",
"text": "def packed_uchar(num):\r\n return pack('>B', num)",
"title": ""
},
{
"docid": "cd2c6bf028cdc89390a1b019dfcb4002",
"score": "0.5655687",
"text": "def save_bgra4444(pixels: Array, data: bytearray, width: int, height: int) -> None:\n for offset in range(width * height):\n r = pixels[4 * offset]\n g = pixels[4 * offset + 1]\n b = pixels[4 * offset + 2]\n a = pixels[4 * offset + 3]\n\n data[2 * offset] = (g & 0b11110000) | (b >> 4)\n data[2 * offset + 1] = (a & 0b11110000) | (r >> 4)",
"title": ""
},
{
"docid": "60d32dfebf7613f21a8a4e77a5f871af",
"score": "0.5650772",
"text": "def binary_to_uint8(img):\n rimg = (img * 255).round().astype(np.uint8)\n return rimg",
"title": ""
},
{
"docid": "86d58ee273bf1eebfef0e3b3f10573ce",
"score": "0.56470793",
"text": "def b(self):\n return _pymaxwell5.Crgb8Tbyte_b(self)",
"title": ""
},
{
"docid": "f76a75ff403d4067f103103a4ce6b823",
"score": "0.5637872",
"text": "def sixteen2eightB(self,img16):\r\n a = np.array(img16.getdata(),dtype='uint16')\r\n b=256.0*a/a.max()\r\n array8= np.reshape(b,(img16.size[1],img16.size[0]))\r\n img8 = Image.fromarray(array8)\r\n \r\n return img8",
"title": ""
},
{
"docid": "c725c6e8b4b3ce5c8978a7fe58b2ff1d",
"score": "0.5620584",
"text": "def save_rgb888_bluescreen(pixels: Array, data: bytearray, width: int, height: int) -> None:\n for offset in range(width * height):\n if pixels[4 * offset + 3] < 128:\n data[3 * offset] = 0\n data[3 * offset + 1] = 0\n data[3 * offset + 2] = 255\n else:\n data[3 * offset] = pixels[4 * offset]\n data[3 * offset + 1] = pixels[4 * offset + 1]\n data[3 * offset + 2] = pixels[4 * offset + 2]",
"title": ""
},
{
"docid": "92f6262876657d0e34d8f41627112b7c",
"score": "0.56127524",
"text": "def test_rgb_bgr_roundtrip(self):\n rgb_tuples = [\n (0, 0, 0),\n (64, 64, 64),\n (128, 128, 128),\n (255, 255, 255),\n (255, 0, 0),\n (0, 255, 0),\n (0, 0, 255),\n (255, 128, 0),\n (0, 255, 128),\n (128, 0, 255),\n ]\n for rgb in rgb_tuples:\n bgr_int = util.rgb_to_bgr_int(rgb)\n got_rgb = util.bgr_int_to_rgb(bgr_int)\n self.assertEqual(rgb, got_rgb)",
"title": ""
},
{
"docid": "15a787b7280c8cbb1215e9b1fade1035",
"score": "0.56085724",
"text": "def colour(self, r, g, b, w=0):\n return np.array((b, g, r, w), dtype=self.frame_dtype)",
"title": ""
},
{
"docid": "ea6d35c903da7c71e3cb7b1e483c260c",
"score": "0.560671",
"text": "def load_rgb888_bluescreen(pixels: Array, data: bytes, width: int, height: int) -> None:\n for offset in range(width * height):\n r = data[3 * offset]\n g = data[3 * offset + 1]\n b = data[3 * offset + 2]\n if r == g == 0 and b == 255:\n pixels[4*offset] = pixels[4*offset+1] = 0\n pixels[4*offset+2] = pixels[4*offset+3] = 0\n else:\n pixels[4 * offset] = r\n pixels[4 * offset + 1] = g\n pixels[4 * offset + 2] = b\n pixels[4 * offset + 3] = 255",
"title": ""
},
{
"docid": "0ad73513af5a8ccf481205f50173324e",
"score": "0.56020766",
"text": "def tobgr(x):\n if isinstance(x, basestring):\n if x.startswith('0x'):\n return int(x, 0)\n try:\n (r,g,b) = ImageColor.getrgb(x)\n return r + g*256 + b*256*256\n except :\n pass\n try:\n return int(x)\n except ValueError:\n pass\n raise ValueError(\"Unknown color specifier: '%s'. Colors must be specified as 0xBBGGRR, #RRGGBB, or standard color names.\" % x)\n return x",
"title": ""
},
{
"docid": "594989b1eb274f3f8f438d081e6cc24d",
"score": "0.5586245",
"text": "def GetRGB(self, r, g, b):\r\n\t\treturn (r & 255) + (g & 255) * 256 + (b & 256) * 256**2",
"title": ""
},
{
"docid": "94153176f251be1a070ea164d0f9e490",
"score": "0.5575341",
"text": "def save_bgrx5551(pixels: Array, data: bytearray, width: int, height: int) -> None:\n for offset in range(width * height):\n r = pixels[4 * offset]\n g = pixels[4 * offset + 1]\n b = pixels[4 * offset + 2]\n # GGGBBBBB XRRRRRGG\n data[2 * offset + 0] = ((g << 2) & 0b11100000) | (b >> 3)\n data[2 * offset + 1] = ((r >> 1) & 0b01111100) | (g >> 6)",
"title": ""
},
{
"docid": "268a82c458f2c5f8d947b2c36761a804",
"score": "0.5568412",
"text": "def btc_ours_decode_0_1(image_btc, block_size):\n width = ba2int(image_btc[-32:-16])\n height = ba2int(image_btc[-16:])\n image_decoded = np.zeros((width, height), dtype=\"uint8\")\n n1 = 0\n n2 = 8\n for w in range(0, height * width // 2, height * block_size // 2):\n for b in range(0, height * block_size // 2, block_size ** 2 // 2):\n block = np.zeros((block_size, block_size), dtype=int)\n block_checker = np.zeros((4, 4), dtype=int)\n block_checker[1::2, ::2] = 1\n block_checker[::2, 1::2] = 1\n bit_list = image_btc[w + b: w + b + (block_size ** 2 // 2)].tolist()\n for i in range(block_size):\n for j in range(block_size):\n if block_checker[i, j] == 1:\n if bit_list[0]:\n block[i, j] = 1\n else:\n block[i, j] = 0\n bit_list.pop(0)\n else:\n block[i, j] = 256\n # bit_list.pop(0)\n\n block = np.where(block == 1,\n ba2int(image_btc[height * width // 2 + n2: height * width // 2 + n2 + 8]),\n np.where(block == 0,\n ba2int(image_btc[height * width // 2 + n1: height * width // 2 + n2]),\n 256))\n\n image_decoded[int(w * 2 / height): int(w * 2 / height) + block_size,\n int(b * 2 / block_size): int(b * 2 / block_size) + block_size] = block\n n1 += 16\n n2 += 16\n for i in range(width):\n for j in range(height):\n if i % 2 == 0 and j % 2 == 0:\n if i == 0 and j == 0:\n image_decoded[i, j] = (int(image_decoded[i + 1, j]) + int(image_decoded[i, j + 1])) // 2\n elif i == 0 and j > 0:\n image_decoded[i, j] = (int(image_decoded[i, j - 1]) + int(image_decoded[i, j + 1])\n + int(image_decoded[i + 1, j])) // 3\n elif i > 0 and j == 0:\n image_decoded[i, j] = (int(image_decoded[i + 1, j]) + int(image_decoded[i - 1, j])\n + int(image_decoded[i, j + 1])) // 3\n else:\n image_decoded[i, j] = (int(image_decoded[i + 1, j]) + int(image_decoded[i - 1, j])\n + int(image_decoded[i, j + 1]) + int(image_decoded[i, j - 1])) // 4\n elif i % 2 == 1 and j % 2 == 1:\n if i == width - 1 and j == height - 1:\n image_decoded[i, j] = (int(image_decoded[i - 1, j]) + int(image_decoded[i, j - 1])) // 2\n elif i == width - 1 and j < height - 1:\n image_decoded[i, j] = (int(image_decoded[i, j - 1]) + int(image_decoded[i, j + 1])\n + int(image_decoded[i - 1, j])) // 3\n elif i < width - 1 and j == height - 1:\n image_decoded[i, j] = (int(image_decoded[i - 1, j]) + int(image_decoded[i + 1, j])\n + int(image_decoded[i, j - 1])) // 3\n elif i < width - 1 and j < height - 1:\n image_decoded[i, j] = (int(image_decoded[i + 1, j]) + int(image_decoded[i - 1, j])\n + int(image_decoded[i, j + 1]) + int(image_decoded[i, j - 1])) // 4\n return image_decoded",
"title": ""
},
{
"docid": "5fb9fc8656eae9899e96b23d9cf5e666",
"score": "0.5562856",
"text": "def _convert_images_to_uint8(self, image_r, image_g, image_b):\n image_r = image_r - self.minimum[0] # n.b. makes copy\n image_g = image_g - self.minimum[1]\n image_b = image_b - self.minimum[2]\n\n fac = self.map_intensity_to_uint8(self.intensity(image_r, image_g, image_b))\n\n image_rgb = [image_r, image_g, image_b]\n for c in image_rgb:\n c *= fac\n with np.errstate(invalid=\"ignore\"):\n c[c < 0] = 0 # individual bands can still be < 0, even if fac isn't\n\n pixmax = self._uint8Max\n # copies -- could work row by row to minimise memory usage\n r0, g0, b0 = image_rgb\n\n # n.b. np.where can't and doesn't short-circuit\n with np.errstate(invalid=\"ignore\", divide=\"ignore\"):\n for i, c in enumerate(image_rgb):\n c = np.where(\n r0 > g0,\n np.where(\n r0 > b0,\n np.where(r0 >= pixmax, c * pixmax / r0, c),\n np.where(b0 >= pixmax, c * pixmax / b0, c),\n ),\n np.where(\n g0 > b0,\n np.where(g0 >= pixmax, c * pixmax / g0, c),\n np.where(b0 >= pixmax, c * pixmax / b0, c),\n ),\n ).astype(np.uint8)\n c[c > pixmax] = pixmax\n\n image_rgb[i] = c\n\n return image_rgb",
"title": ""
},
{
"docid": "c1436bc081a903ec6288f4891fcfcd59",
"score": "0.555034",
"text": "def r(self):\n return _pymaxwell5.Crgba8Tbyte_r(self)",
"title": ""
},
{
"docid": "47938511827adb938a8b918273d4f57a",
"score": "0.5543817",
"text": "def test_rgb_to_ycbcr(self):\n red_uint8 = numpy.array([[1, 214, 23], [45, 43, 0]], dtype=numpy.uint8)\n green_uint8 = numpy.array([[255, 255, 23], [0, 13, 0]], dtype=numpy.uint8)\n blue_uint8 = numpy.array([[100, 255, 0], [0, 0, 0]], dtype=numpy.uint8)\n rgb_uint8 = numpy.stack((red_uint8, green_uint8, blue_uint8), axis=2)\n ycbcr_uint8 = tls.rgb_to_ycbcr(rgb_uint8)\n print('Red channel:')\n print(red_uint8)\n print('Green channel:')\n print(green_uint8)\n print('Blue channel:')\n print(blue_uint8)\n print('Luminance computed by the function:')\n print(ycbcr_uint8[:, :, 0])\n print('Luminance computed by hand:')\n print(numpy.array([[155, 224, 34], [28, 34, 16]], dtype=numpy.uint8))\n print('Blue chrominance computed by the function:')\n print(ycbcr_uint8[:, :, 1])\n print('Blue chrominance computed by hand:')\n print(numpy.array([[98, 134, 118], [121, 118, 128]], dtype=numpy.uint8))\n print('Red chrominance computed by the function:')\n print(ycbcr_uint8[:, :, 2])\n print('Red chrominance computed by hand:')\n print(numpy.array([[28, 110, 130], [148, 142, 128]], dtype=numpy.uint8))",
"title": ""
},
{
"docid": "1dc360f0243c97137dab729ed500bff6",
"score": "0.55428964",
"text": "def ycbcr2bgr(img):\n img_type = img.dtype\n img = _convert_input_type_range(img) * 255\n out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0.00791071, -0.00153632, 0],\n [0, -0.00318811, 0.00625893]]) * 255.0 + [-276.836, 135.576, -222.921] # noqa: E126\n out_img = _convert_output_type_range(out_img, img_type)\n return out_img",
"title": ""
},
{
"docid": "ea5963dbbbc1b92a46d9f1e0edc7ab9c",
"score": "0.5538706",
"text": "def btc_ours_decode_1_0(image_btc, block_size):\n width = ba2int(image_btc[-32:-16])\n height = ba2int(image_btc[-16:])\n image_decoded = np.zeros((width, height), dtype=\"uint8\")\n n1 = 0\n n2 = 8\n for w in range(0, height * width // 2, height * block_size // 2):\n for b in range(0, height * block_size // 2, block_size ** 2 // 2):\n block = np.zeros((block_size, block_size), dtype=int)\n block_checker = np.zeros((4, 4), dtype=int)\n block_checker[1::2, ::2] = 1\n block_checker[::2, 1::2] = 1\n bit_list = image_btc[w + b: w + b + (block_size ** 2 // 2)].tolist()\n for i in range(block_size):\n for j in range(block_size):\n if block_checker[i, j] == 0:\n if bit_list[0]:\n block[i, j] = 1\n else:\n block[i, j] = 0\n bit_list.pop(0)\n else:\n block[i, j] = 256\n # bit_list.pop(0)\n\n block = np.where(block == 1,\n ba2int(image_btc[height * width // 2 + n2: height * width // 2 + n2 + 8]),\n np.where(block == 0,\n ba2int(image_btc[height * width // 2 + n1: height * width // 2 + n2]),\n 256))\n\n image_decoded[int(w * 2 / height): int(w * 2 / height) + block_size,\n int(b * 2 / block_size): int(b * 2 / block_size) + block_size] = block\n n1 += 16\n n2 += 16\n for i in range(width):\n for j in range(height):\n if i % 2 == 1 and j % 2 == 0:\n if i == width - 1 and j == 0:\n image_decoded[i, j] = (int(image_decoded[i - 1, j]) + int(image_decoded[i, j + 1])) // 2\n elif i < width - 1 and j == 0:\n image_decoded[i, j] = (int(image_decoded[i + 1, j]) + int(image_decoded[i - 1, j])\n + int(image_decoded[i, j + 1])) // 3\n elif i == width - 1 and 0 < j < height - 1:\n image_decoded[i, j] = (int(image_decoded[i - 1, j]) + int(image_decoded[i, j + 1])\n + int(image_decoded[i, j - 1])) // 3\n elif i < width - 1 and 0 < j < height - 1:\n image_decoded[i, j] = (int(image_decoded[i + 1, j]) + int(image_decoded[i - 1, j])\n + int(image_decoded[i, j + 1]) + int(image_decoded[i, j - 1])) // 4\n elif i % 2 == 0 and j % 2 == 1:\n # print(image_decoded[i, j])\n if i == 0 and j == height - 1:\n image_decoded[i, j] = (int(image_decoded[i + 1, j]) + int(image_decoded[i, j - 1])) // 2\n elif i == 0 and j < height - 1:\n image_decoded[i, j] = (int(image_decoded[i + 1, j]) + int(image_decoded[i, j + 1])\n + int(image_decoded[i, j - 1])) // 3\n elif 0 < i < width - 1 and j == height - 1:\n image_decoded[i, j] = (int(image_decoded[i - 1, j]) + int(image_decoded[i + 1, j])\n + int(image_decoded[i, j - 1])) // 3\n elif 0 < i < width - 1 and j < height - 1:\n image_decoded[i, j] = (int(image_decoded[i + 1, j]) + int(image_decoded[i - 1, j])\n + int(image_decoded[i, j + 1]) + int(image_decoded[i, j - 1])) // 4\n\n return image_decoded",
"title": ""
},
{
"docid": "a57a4167f73d425f0273ea9da7701d24",
"score": "0.55189794",
"text": "def compress565(r: int, g: int, b: int) -> Tuple[int, int]:\n # RRRRRGGG GGGBBBBB\n return (\n (g << 3) & 0b11100000 | (b >> 3),\n (r & 0b11111000) | (g >> 5),\n )",
"title": ""
},
{
"docid": "110318740c85e90b019824b22d2c02ea",
"score": "0.55163354",
"text": "def ycbcr2bgr(img):\n img_type = img.dtype\n img = _convert_input_type_range(img) * 255\n out_img = np.matmul(\n img,\n [\n [0.00456621, 0.00456621, 0.00456621],\n [0.00791071, -0.00153632, 0],\n [0, -0.00318811, 0.00625893],\n ],\n ) * 255.0 + [-276.836, 135.576, -222.921]\n out_img = _convert_output_type_range(out_img, img_type)\n return out_img",
"title": ""
},
{
"docid": "a0e440be6e53402445012ecce2f71491",
"score": "0.551029",
"text": "def img4save(data):\n data_ = as_np(data).transpose(1, 2, 0) * 255.\n return data_.astype(np.uint8)[:, :, ::-1]",
"title": ""
},
{
"docid": "ca5920c8a56c4965f2b21775b81b23e7",
"score": "0.5491085",
"text": "def rgb_reverse_lookup(index):\n if index in GRAYSCALE_REVERSE_LOOKUP:\n gray = GRAYSCALE_REVERSE_LOOKUP[index]\n return (gray,gray,gray)\n index -= 16\n remainder1 = index % 36\n remainder2 = remainder1 % 6\n pos = np.array([\n (index - remainder1) / 36,\n (remainder1 - remainder2) / 6,\n remainder2\n ], dtype=np.int)\n return CHANNEL_VALUES[pos].flatten()",
"title": ""
},
{
"docid": "647e46b90f15cca0828e50affaada046",
"score": "0.54710686",
"text": "def right_rgb(r, g, b):\n\n set(LED_R_R, r)\n set(LED_R_B, b)\n set(LED_R_G, g)\n update()",
"title": ""
},
{
"docid": "035f6fbb2295aaf2bd22a7431b902799",
"score": "0.5465873",
"text": "def color(self, r=0, g=0, b=0):\r\n\t\t\tif (r > 255 or g > 255 or b > 255 or r < 0 or g < 0 or b <0):\r\n\t\t\t\tr = 0\r\n\t\t\t\tg = 0\r\n\t\t\t\tb = 0\r\n\t\t\treturn bytes([b, g, r])",
"title": ""
},
{
"docid": "6706dc4d65040b5fdf8cfeb7d7535519",
"score": "0.546006",
"text": "def ycbcr2rgb(img: T.Tensor):\n\n if len(img.shape) != 4:\n raise ValueError('Input images must have four dimensions, not %d' % len(img.shape))\n\n R = img[:, 0] + 1.4 * (img[:, 2] - 128.)\n G = img[:, 0] - .343 * (img[:, 1] - 128.) - .711 * (img[:, 2] - 128.)\n B = img[:, 0] + 1.765 * (img[:, 1] - 128.)\n return T.cat((R.unsqueeze(1), G.unsqueeze(1), B.unsqueeze(1)), 1)",
"title": ""
},
{
"docid": "1e3aca7e100af891248cb39a509206e3",
"score": "0.5456905",
"text": "def switch_RGB(img):\n return img[:, :, ::-1]",
"title": ""
},
{
"docid": "8a1dcda6c8c79d29c394b7ca6e08242d",
"score": "0.545601",
"text": "def ycbcr2rgb(img):\n img = np.float64(img)\n img = img - np.array([16, 128, 128])\n img = np.dot(img, ycbcr_to_rgb.T) * 255.0\n return img",
"title": ""
},
{
"docid": "11cff8872cefaeb704d58c13b545fbef",
"score": "0.5449477",
"text": "def b(self):\n return _pymaxwell5.Crgba8Tword_b(self)",
"title": ""
},
{
"docid": "a6a930005f6b2ab952346ba7dadf179f",
"score": "0.54425",
"text": "def hex_to_bgr(value):\n value = value.lstrip('#')\n lv = len(value)\n retval = tuple(int(value[i:i + lv // 3], 16) for i in range(0, lv, lv // 3))\n return retval[::-1]",
"title": ""
},
{
"docid": "4ce0721f020bbe78d37aed10ec40445b",
"score": "0.54269",
"text": "def test_uint08_16_2frame(self):\r\n ds = dcmread(PAL_08_256_0_16_2F)\r\n assert 8 == ds.BitsStored\r\n assert 16 == ds.RedPaletteColorLookupTableDescriptor[2]\r\n arr = ds.pixel_array\r\n orig = arr.copy()\r\n rgb = apply_color_lut(arr, ds)\r\n assert (2, 600, 800, 3) == rgb.shape\r\n assert [9472, 15872, 24064] == list(rgb[0, 0, 0, :])\r\n assert [34816, 43520, 54016] == list(rgb[0, 12, 12, :])\r\n assert [65280, 65280, 65280] == list(rgb[0, 17, 110, :])\r\n assert [0, 0, 0] == list(rgb[0, 77, 103, :])\r\n assert [23040, 52480, 65280] == list(rgb[0, 478, 793, :])\r\n\r\n # 2nd frame is inverse of 1st, so won't be coloured correctly\r\n ref = np.asarray(\r\n [[26112, 26112, 26112],\r\n [54528, 54528, 54528],\r\n [54528, 54528, 54528],\r\n [16640, 16640, 16640],\r\n [49152, 45056, 22016],\r\n [34816, 43520, 54016],\r\n [5632, 9984, 14848],\r\n [62464, 2816, 2816],\r\n [3072, 5632, 8192],\r\n [3072, 5632, 8192]]\r\n )\r\n assert np.array_equal(ref, rgb[1, 143:153, 355, :])\r\n\r\n # original `arr` is unchanged\r\n assert np.array_equal(orig, arr)",
"title": ""
},
{
"docid": "f4a7ae3e3ea355d10363389a3f59b345",
"score": "0.5406163",
"text": "def load_uv88(pixels: Array, data: bytes, width: int, height: int) -> None:\n for offset in range(width * height):\n pixels[4*offset] = data[2*offset]\n pixels[4*offset+1] = data[2*offset+1]\n pixels[4*offset+2] = 0\n pixels[4*offset+3] = 255",
"title": ""
},
{
"docid": "b226e7d680dae4135a596ac2dac54fca",
"score": "0.54026234",
"text": "def bgr_to_rgb(self, frame):\n c_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n return c_frame",
"title": ""
},
{
"docid": "2ccfc3514f481dbf11f6ae72d532ce57",
"score": "0.5402162",
"text": "def save_bgra5551(pixels: Array, data: bytearray, width: int, height: int) -> None:\n for offset in range(width * height):\n r = pixels[4 * offset]\n g = pixels[4 * offset + 1]\n b = pixels[4 * offset + 2]\n a = pixels[4 * offset + 3]\n # GGGBBBBB ARRRRRGG\n data[2 * offset + 0] = ((g << 2) & 0b11100000) | (b >> 3)\n data[2 * offset + 1] = (a & 0b10000000) | ((r >> 1) & 0b01111100) | (g >> 6)",
"title": ""
},
{
"docid": "653c215e1f373192c57453bb2b415385",
"score": "0.539009",
"text": "def make_rgb_image(self, image_r, image_g, image_b):\n image_r = np.asarray(image_r)\n image_g = np.asarray(image_g)\n image_b = np.asarray(image_b)\n\n if (image_r.shape != image_g.shape) or (image_g.shape != image_b.shape):\n msg = \"The image shapes must match. r: {}, g: {} b: {}\"\n raise ValueError(msg.format(image_r.shape, image_g.shape, image_b.shape))\n\n return np.dstack(\n self._convert_images_to_uint8(image_r, image_g, image_b)\n ).astype(np.uint8)",
"title": ""
},
{
"docid": "b773ac5edc6b97597c477ba5486f8254",
"score": "0.5386602",
"text": "def to_rgb_array(image):\n array = to_bgra_array(image)\n array = array[:, :, :3]\n array = array[:, :, ::-1]\n return array",
"title": ""
},
{
"docid": "c2040e5a040f09a5e314f929375a5caf",
"score": "0.53756875",
"text": "def g(self):\n return _pymaxwell5.Crgb8Tbyte_g(self)",
"title": ""
},
{
"docid": "a74e39aed8cb0563331a0e4cda2e9192",
"score": "0.537337",
"text": "def rgb(r,g,b):\n r = int(r)\n g = int(g)\n b = int(b)\n return '#%02x%02x%02x' % (r, g, b)",
"title": ""
},
{
"docid": "214688b65097b7e5b4fea7e6be61dfbb",
"score": "0.53717935",
"text": "def fill_top_right(self, b):\n p = b.GetWidth()\n for y in range(0, p/4):\n for x in range(p - y * 2, p):\n b.SetRGB(x, p/2 + p/4 - y, 0, 0, 0)\n \n for y in range(0, p/2 + 1):\n for x in range(p/2, p):\n b.SetRGB(x, y, 0, 0, 0)\n\n return b",
"title": ""
},
{
"docid": "c9fbbcd8fce56bf0c35e229812cda251",
"score": "0.53710335",
"text": "def binaryImg(image):\n # For now the \"blue\" channel has the best information from the maskPipeline\n outImg = image[:,:,2]\n return outImg",
"title": ""
},
{
"docid": "fb1baec4a64dda9a133e0c60e5c4ce67",
"score": "0.53675455",
"text": "def load_bgrx5551(pixels: Array, data: bytes, width: int, height: int) -> None:\n for offset in range(width * height):\n a = data[2 * offset]\n b = data[2 * offset + 1]\n pixels[4 * offset] = upsample(5, (b & 0b01111100) << 1)\n pixels[4 * offset+1] = upsample(5, (a & 0b11100000) >> 2 | (b & 0b00000011) << 6)\n pixels[4 * offset+2] = upsample(5, (a & 0b00011111) << 3)\n pixels[4 * offset+3] = 255",
"title": ""
},
{
"docid": "594bc893a23f89a3808940053742b184",
"score": "0.53637105",
"text": "def bgr_to_grey(self, frame):\n c_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n return c_frame",
"title": ""
},
{
"docid": "4b3bb2eb1dbe249dd480877d37465307",
"score": "0.5363243",
"text": "def np_img_float32_to_uint8(np_img):\n return np.uint8(np_img * 255)",
"title": ""
},
{
"docid": "9fb6e83f6ac915ece8a292e1dd54a7b4",
"score": "0.5361148",
"text": "def bands_to_rgb(rgb_array):\n r = normalize(rgb_array[:,:,0])\n g = normalize(rgb_array[:,:,1])\n b = normalize(rgb_array[:,:,2])\n return np.dstack((r,g,b))",
"title": ""
},
{
"docid": "ec753eca8cc43acb0f6b5343d7d7011f",
"score": "0.5342239",
"text": "def get_frame(self):\n return np.flipud(np.asarray(self.orig_image, np.uint8))",
"title": ""
},
{
"docid": "46173eda88e942c53b9c0ed42880ae12",
"score": "0.5339836",
"text": "def read_image_bgr(path):\n # We deliberately don't use cv2.imread here, since it gives no feedback on errors while reading the image.\n image = np.ascontiguousarray(Image.open(path).convert('RGB'))\n return image[:, :, ::-1]",
"title": ""
},
{
"docid": "aac55db8afea44e4beff65433043d028",
"score": "0.5338901",
"text": "def gray_to_rgb(arr):\n\n return np.dstack([arr, arr, arr])",
"title": ""
},
{
"docid": "faa61e9a14fd58feeff5f78380c00ced",
"score": "0.53361845",
"text": "def conv_to_bandslast(img):\n return _np.transpose(img,(1,2,0))",
"title": ""
},
{
"docid": "af3840444dc01f7a53a25fa3f85f42db",
"score": "0.53328127",
"text": "def rgb_from_encoding(encoding):\n rgb_filter = (1 << RGB_LENGTH) - 1\n R = encoding >> 2*RGB_LENGTH\n G = (encoding >> RGB_LENGTH) & rgb_filter\n B = encoding & rgb_filter\n return (R,G,B)",
"title": ""
},
{
"docid": "ed1650f81d620511b95c605f32e8193d",
"score": "0.5332488",
"text": "def numpy_flip(im):\n frame = np.array(im, dtype=np.uint8)\n return np.flip(frame[:, :, :3], 2).tobytes()",
"title": ""
},
{
"docid": "9371b12d0c4f2c4dc1360100ea0d7154",
"score": "0.5331136",
"text": "def g(self):\n return _pymaxwell5.Crgba8Tbyte_g(self)",
"title": ""
},
{
"docid": "107385db5b05a359e34e6c77af77d304",
"score": "0.5329843",
"text": "def save_uv88(pixels: Array, data: bytearray, width: int, height: int) -> None:\n for offset in range(width * height):\n data[2*offset] = pixels[4*offset]\n data[2*offset+1] = pixels[4*offset+1]",
"title": ""
},
{
"docid": "36b8b7b860220a277cd8a14da500cff6",
"score": "0.5327078",
"text": "def get_rgb_clamped(self):\n r, g, b, c = self.get_rgbc_raw()\n\n div = max(r, g, b)\n\n if div > 0:\n r, g, b = [int((x / float(div)) * 255) for x in (r, g, b)]\n return (r, g, b)\n\n return (0, 0, 0)",
"title": ""
},
{
"docid": "aaa3b97f66a2134b5943e64f75e23ae1",
"score": "0.53256243",
"text": "def getBGR32(dc, bitmap):\n\tbmpInfo = bitmap.GetInfo()\n\twidth, height = bmpInfo['bmWidth'], bmpInfo['bmHeight']\n\n\tbmi = BITMAPINFO()\n\tctypes.memset(ctypes.byref(bmi), 0x00, ctypes.sizeof(bmi))\n\tbmi.bmiHeader.biSize = ctypes.sizeof(BITMAPINFOHEADER)\n\tbmi.bmiHeader.biWidth = width\n\tbmi.bmiHeader.biHeight = height\n\tbmi.bmiHeader.biBitCount = 24\n\tbmi.bmiHeader.biPlanes = 1\n\n\tbufferLen = height * ((width * 3 + 3) & -4)\n\tpbBits = ctypes.create_string_buffer(bufferLen)\n\n\tret = ctypes.windll.gdi32.GetDIBgetits(\n\t\tdc.GetHandleAttrib(),\n\t\tbitmap.GetHandle(),\n\t\t0,\n\t\theight,\n\t\tctypes.byref(pbBits),\n\t\tctypes.pointer(bmi),\n\t\twin32con.DIB_RGB_COLORS)\n\tif ret == 0:\n\t\traise DIBFailed(\"Return code 0 from GetDIBits\")\n\n\tassert len(pbBits.raw) == bufferLen, len(pbBits.raw)\n\n\treturn pbBits.raw, (width, height)",
"title": ""
},
{
"docid": "bcf28fc4bc2b25f5e2d3fc9c388f0b56",
"score": "0.53129995",
"text": "def make_rgb_fractions(r, g, b):\n m = max(r, g, b)\n r = r/m\n g = g/m\n b = b/m\n\n return (r, g, b)",
"title": ""
},
{
"docid": "9e454224b840a3e11260e9402b8a72ee",
"score": "0.5302391",
"text": "def get_video_frame(self):\n image_buffer = glReadPixels(\n 0, 0, self.w_width, self.w_height, GL_BGR, GL_UNSIGNED_BYTE\n )\n image = np.frombuffer(image_buffer, dtype=np.uint8).reshape(\n self.w_width, self.w_height, 3\n )\n return np.flipud(image)",
"title": ""
},
{
"docid": "f5b3e8fa888e81a8d568b9e302c7c48d",
"score": "0.5299741",
"text": "def rgb_to_hsb(r, g, b):\n h, s, v = 0, 0, max(r, g, b)\n d = v - min(r, g, b)\n if v != 0:\n s = d / float(v)\n if s != 0:\n if r == v: h = 0 + (g-b) / d\n elif g == v: h = 2 + (b-r) / d\n else : h = 4 + (r-g) / d\n h = h / 6.0 % 1\n return h, s, v",
"title": ""
},
{
"docid": "cd851ad342f7f4b2c9bc5296f711bc72",
"score": "0.5298458",
"text": "def merge_rgb(col0, col1, k):\n\tr0, g0, b0 = col0\n\tr1, g1, b1 = col1\n\tr = max(0, min(255, int(r0*(1-k) + r1*k)))\n\tg = max(0, min(255, int(g0*(1-k) + g1*k)))\n\tb = max(0, min(255, int(b0*(1-k) + b1*k)))\n\treturn r, g, b",
"title": ""
},
{
"docid": "db113d32dfefaf50dde2199dbe47aa47",
"score": "0.5297081",
"text": "def decode_raw_color(value: int) -> Tuple[int, Union[int, RGB]]:\n flags = (value >> 24) & 0xff\n if flags == COLOR_TYPE_BY_BLOCK:\n return COLOR_TYPE_BY_BLOCK, const.BYBLOCK\n elif flags == COLOR_TYPE_BY_LAYER:\n return COLOR_TYPE_BY_LAYER, const.BYLAYER\n elif flags == COLOR_TYPE_ACI:\n return COLOR_TYPE_ACI, value & 0xff\n elif flags == COLOR_TYPE_RGB:\n return COLOR_TYPE_RGB, int2rgb(value)\n elif flags == COLOR_TYPE_WINDOW_BG:\n return COLOR_TYPE_WINDOW_BG, 0\n else:\n raise ValueError(f'Unknown color type: 0x{flags:02x}')",
"title": ""
},
{
"docid": "0ef7b78dfed0019a86bdc66f264a7d84",
"score": "0.529593",
"text": "def b(self):\n return _pymaxwell5.Crgb8Tword_b(self)",
"title": ""
},
{
"docid": "5e064896cc50e8e0956805f72ecf9af6",
"score": "0.5295194",
"text": "def bgr_to_rgb(input, name=None):\n bgr = tf.unstack(input, axis=-1)\n b, g, r = bgr[0], bgr[1], bgr[2]\n return tf.stack([r, g, b], axis=-1)",
"title": ""
},
{
"docid": "a77e23cccc4cf6f210028f25ff1cbdbd",
"score": "0.5292751",
"text": "def get_rgb(rgb_stream, h, w):\n bgr = np.fromstring(rgb_stream.read_frame().get_buffer_as_uint8(), dtype=np.uint8).reshape(h, w, 3)\n rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)\n return rgb",
"title": ""
},
{
"docid": "7e70c3e48a217ae97eb8236ab71256b5",
"score": "0.5290482",
"text": "def cvimage_to_pygame(image):\r\n return pygame.image.frombuffer(image.tostring(), image.shape[:2],\"RGB\")",
"title": ""
},
{
"docid": "45e7ad1c76ac945dee8151c20f3bc03b",
"score": "0.52821064",
"text": "def rgb(r, g, b):\n\n left_rgb(r, g, b)\n mid_rgb(r, g, b)\n right_rgb(r, g, b)",
"title": ""
},
{
"docid": "7a07e8cbaf3c2a1b2bff5dd40839f1b0",
"score": "0.52791816",
"text": "def wxBitmap2np_v2(wxBmp, is_rgb=True):\n total_t = time.time()\n \n w, h = wxBmp.GetSize()\n\n npimg = np.zeros(h*w*3, dtype='uint8')\n wxBmp.CopyToBuffer(npimg, format=wx.BitmapBufferFormat_RGB)\n npimg = npimg.reshape(h,w,3)\n\n total_dur = time.time() - total_t\n #print \"==== wxBitmap2np_v2: {0:.6f}s\".format(total_dur)\n return npimg",
"title": ""
},
{
"docid": "2e21320d725d586004b06fc9c3caeb15",
"score": "0.5275676",
"text": "def test_bgr_int_to_rgb(self):\n self.assertEqual(util.bgr_int_to_rgb(4227327), (255, 128, 64))",
"title": ""
},
{
"docid": "2acbb76b3066dc925b53e8e796f91377",
"score": "0.5275091",
"text": "def blue_channel(image: Image) -> Image:\n new_image = copy(image)\n for x, y, (r, g, b) in image:\n blue = create_color(0, 0, b)\n set_color(new_image, x, y, blue)\n return new_image",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="773"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">e12dde588b95e7dc2285219334173982</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Creates the logistical function for percent increase in exp.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "70f7cf4e4f10fc320823fa801ad7d3b2",
"score": "0.6326081",
"text": "def exp_algorithm(self):\n exp_logistic = 1 / (\n 3 + math.exp(-(3 / 4) * (self.playerLevel / 16) - 4)) + 1\n return exp_logistic",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "f1bc095663e2201c1472cfae0a3b0016",
"score": "0.7222441",
"text": "def log(x):\n return 1 / ( 1 + math.exp(-x))",
"title": ""
},
{
"docid": "427a834b19c5cab5d04c7074822f5036",
"score": "0.70191973",
"text": "def exp(x):\n return F.exp(x)",
"title": ""
},
{
"docid": "f70f4309b22827a04b89d664d43799aa",
"score": "0.6930326",
"text": "def log_1_plus_exp_safe(z):\n if z > 100:\n return z\n else:\n return np.log(1+np.exp(z))",
"title": ""
},
{
"docid": "19878e529ed74d6b657e71d8fe5fa016",
"score": "0.6889261",
"text": "def func_exp_growth(x, prefactor, exp_factor):\n return -prefactor * np.exp(-exp_factor * x) + 1",
"title": ""
},
{
"docid": "6bcd9c304a6bd198fcf705d5344df391",
"score": "0.68320197",
"text": "def exp(self, log_L: np.ndarray) -> np.ndarray:",
"title": ""
},
{
"docid": "324130e342ff1463f9f42acb6a130027",
"score": "0.6799026",
"text": "def logistic(x):\n return 1/(1+exp(-x))",
"title": ""
},
{
"docid": "372a3be4a65bf6bd6b92ad236544059f",
"score": "0.67851824",
"text": "def from_log(n):\n return np.exp(n)",
"title": ""
},
{
"docid": "028f0aede19978c583318a1c89350394",
"score": "0.6755656",
"text": "def _logistic(x):\n return 1.0 / (1.0 + np.exp(-x))",
"title": ""
},
{
"docid": "034d23f10e59365a70e79bad7c19a197",
"score": "0.66202134",
"text": "def exp(x):\n return math.exp(x)",
"title": ""
},
{
"docid": "24817aaaff871c61146d02440f8a9f83",
"score": "0.65837955",
"text": "def exponential(lambda_):\n if lambda_ == 0:\n \treturn 0\n rand = random.random()\n return -1 / lambda_ * math.log(1-rand)",
"title": ""
},
{
"docid": "cd784034b341e0286840beea9d734731",
"score": "0.6558222",
"text": "def default_exp_func( round_num ):\n return math.log( math.log( round_num + 3 ) )",
"title": ""
},
{
"docid": "5a885af1e87a98b7335a8ab588464b34",
"score": "0.6534256",
"text": "def F(w, x):\n x0, ymax, a, b = w\n x0 = exp(x0) # x0 is positive\n return ymax*expit(b + a*np.log(x + x0))",
"title": ""
},
{
"docid": "e4afc4be68a298c3601dbb1b719ba2e0",
"score": "0.65314037",
"text": "def log_sum_exp_stable(z):\n a = np.max(z)\n return a + np.log(np.sum(np.exp(z-a)))",
"title": ""
},
{
"docid": "3c645a9aad2f97153b388a7ed0be8d68",
"score": "0.650226",
"text": "def Probability(self, x):\n logprob_given_x = self.LogProbability(x)\n return lambda y: math.exp(logprob_given_x(y))",
"title": ""
},
{
"docid": "fff2cc4388f33ba110ab1f82d7e28e1d",
"score": "0.64794004",
"text": "def _deriv_exp2(x):\n return _log2 * np.exp2(x)",
"title": ""
},
{
"docid": "b9df0d419bf7bbd4db3cb3ee198b47e9",
"score": "0.645374",
"text": "def log_sum_exp(a, b):\n return max(a, b) + math.log1p(math.exp(-abs(a-b)))",
"title": ""
},
{
"docid": "77274d49b3645097584a52bf4c052aad",
"score": "0.64248353",
"text": "def _func_pct(x, pct, npuls):\n log_e = np.log10(np.e)\n f = (\n 2. * np.power(\n 10.,\n np.log10(x) * (npuls - 1) - log_e * (x + gammaln(npuls)) +\n np.log10(gammainc(npuls, x / pct)) - log_e *\n (gammaln(npuls + 1) + x / pct) +\n np.log10(x / pct) * npuls) -\n np.power(\n 10.,\n np.log10(x) * (npuls - 1) - log_e * (x + gammaln(npuls)) +\n 2. * (np.log10(gammainc(npuls, x / pct)) -\n log_e * (gammaln(npuls + 1) + (x / pct)) + np.log10(x / pct) * npuls)))\n return f",
"title": ""
},
{
"docid": "159d8553cebfe8c43782e7db19d9b584",
"score": "0.64046276",
"text": "def exp(self):",
"title": ""
},
{
"docid": "97a9f1dfdad2576a1a72a78ebe223b08",
"score": "0.6399865",
"text": "def exp(self, log_L: np.ndarray) -> np.ndarray:\n if log_L.size > 0:\n log_L[np.isnan(log_L)] = -np.inf\n log_L[log_L>=100.0] = 100.0 \n L = np.exp(log_L - np.nanmax(log_L, axis=(0, 1)))\n return L / L.sum(axis=(0,1))\n else:\n return log_L.copy()",
"title": ""
},
{
"docid": "dbbeea5fa4b14bc66feba16a67c4304d",
"score": "0.6394869",
"text": "def log_sum_exp(x):\n x_max = x.data.max()\n return torch.log(torch.sum(torch.exp(x - x_max), 1)) + x_max",
"title": ""
},
{
"docid": "130c93f3aaad78cea3cfef75eb4b8b73",
"score": "0.6394174",
"text": "def log(x):\n return F.log(x)",
"title": ""
},
{
"docid": "303ad7e9aac3aa912422900a8f65fc1a",
"score": "0.63932955",
"text": "def stable_log_sum_exp(x, N=None):\n a = np.max(x)\n if N is None:\n y = a + np.log(np.sum(np.exp(x-a)))\n else:\n y = a + np.log(np.sum(np.exp(x-a)) / N)\n return y",
"title": ""
},
{
"docid": "ffc39dfb8a1b964eb4e4acd0b3826bf1",
"score": "0.6365588",
"text": "def exp(self, log_L: np.ndarray) -> np.ndarray:\n if log_L.size > 0:\n log_L[np.isnan(log_L)] = -np.inf\n log_L[log_L>=100.0] = 100.0\n L = np.exp(log_L - np.nanmax(log_L, axis=(0,1,2)))\n return L / L.sum(axis=(0,1,2))\n else:\n return log_L.copy()",
"title": ""
},
{
"docid": "1583de7e60d650e7abf3e3a7a1424645",
"score": "0.6336044",
"text": "def log_sum_exp(x):\n x_max = x.data.max()\n return torch.log(torch.sum(torch.exp(x-x_max), 1, keepdim=True)) + x_max",
"title": ""
},
{
"docid": "1583de7e60d650e7abf3e3a7a1424645",
"score": "0.6336044",
"text": "def log_sum_exp(x):\n x_max = x.data.max()\n return torch.log(torch.sum(torch.exp(x-x_max), 1, keepdim=True)) + x_max",
"title": ""
},
{
"docid": "86f9dad4af9ba64170117d03ed54a96d",
"score": "0.6335515",
"text": "def exp(value):\n\t\n\treturn numpy.exp(value)",
"title": ""
},
{
"docid": "2be55dd2b2701886dd839cfb433a9819",
"score": "0.6330194",
"text": "def evalExpLogIntegrand(*args):\n theta = np.array(args[:-2]).reshape(1, -1)\n integrandLogFuncs = args[-2]\n integrandLogVal = args[-1]\n for logFunc, argIndices in integrandLogFuncs.items():\n integrandLogVal += logFunc(theta[argIndices])\n return np.exp(integrandLogVal)",
"title": ""
},
{
"docid": "2c3d2f844459871a564f279e747be3b1",
"score": "0.62999064",
"text": "def e_logpi(self):\n return (torch.digamma(torch.FloatTensor([self.alpha / self.K])) - torch.digamma(torch.FloatTensor([self.alpha]))).repeat(self.K)",
"title": ""
},
{
"docid": "ed3b4a4c7b7086f6a0d8abe17c5b0041",
"score": "0.6293198",
"text": "def exp(x):\n return intrinsic('exp', x)",
"title": ""
},
{
"docid": "4d1c9178fa3d0ef93593fa750a52aee3",
"score": "0.62819475",
"text": "def expon_icdf(p, lambd=1):\n return -np.log(1-p)/lambd",
"title": ""
},
{
"docid": "6fd73a424d4924eb42e9c90dc2af17c3",
"score": "0.627232",
"text": "def growth_exp(t, args):\n r, a_0 = args['r'], args['a_0']\n a = np.zeros(len(t))\n i = np.searchsorted(t,0,side='left')\n a[i:] = a_0*np.exp(r*t[i:])\n return a",
"title": ""
},
{
"docid": "da9190e76bb331819b9e5bd479772659",
"score": "0.6247152",
"text": "def log_sum_exp(x):\n x_max = x.max()\n return np.log(np.sum(np.exp(x - x_max), 1, keepdim=True)) + x_max",
"title": ""
},
{
"docid": "fe57183d8fff14c641f925e121297000",
"score": "0.62425244",
"text": "def tf_log_var_exp(x):\n mu = tf_log_mean_exp(x)\n x_max = tf.reduce_max(x)\n x_prime = x - x_max\n mu_prime = mu - x_max\n summand = tf.exp(2 * x_prime) - tf.exp(2 * mu_prime)\n n = tf.cast(shape_list(x)[0], dtype=tf.float32)\n return 2 * x_max + tf.log(tf.reduce_sum(summand)) - tf.log(n)",
"title": ""
},
{
"docid": "1617442e7470fb87ce7d42e1a439c032",
"score": "0.6226347",
"text": "def exp(space, w_x):\n return math1(space, math.exp, w_x)",
"title": ""
},
{
"docid": "c8f9eaade624fbf449c3bfcd21ae4580",
"score": "0.62236094",
"text": "def exp(var):\r\n return unary(var,np.exp)",
"title": ""
},
{
"docid": "adf87a1a4c8d2dbf802b24fbd17cebbf",
"score": "0.61995995",
"text": "def log_density_expconcrete(logalphas, logsample, temp):\n exp_term = logalphas + logsample.mul(-temp)\n log_prob = exp_term + np.log(temp) - 2. * F.softplus(exp_term)\n return log_prob",
"title": ""
},
{
"docid": "3be0915c5061a53cdfff3eaadb897295",
"score": "0.61913127",
"text": "def exp(self, log_L: np.ndarray) -> np.ndarray:\n if log_L.size > 0:\n L = np.exp(log_L - log_L.max(axis=0))\n L = L / L.sum(axis=0)\n return L\n else:\n return log_L.copy()",
"title": ""
},
{
"docid": "3be0915c5061a53cdfff3eaadb897295",
"score": "0.61913127",
"text": "def exp(self, log_L: np.ndarray) -> np.ndarray:\n if log_L.size > 0:\n L = np.exp(log_L - log_L.max(axis=0))\n L = L / L.sum(axis=0)\n return L\n else:\n return log_L.copy()",
"title": ""
},
{
"docid": "aa42dd4e6a9b89455e51dfe3d1844cab",
"score": "0.6161713",
"text": "def log_sum_exp(x):\n x_max = tf.reduce_max(x)\n return tf.add(x_max, tf.log(tf.reduce_sum(tf.exp(tf.sub(x, x_max)))))",
"title": ""
},
{
"docid": "4845bc81268a2ab9eec2821c0b7da5b8",
"score": "0.61434054",
"text": "def exp(self, X, U):",
"title": ""
},
{
"docid": "e3f6f1de077d88a338df8d9df1b83d16",
"score": "0.6138953",
"text": "def logmeanexp(x: to.Tensor, dim: int = 0) -> to.Tensor:\n return to.logsumexp(x, dim=dim) - to.log(to.tensor(x.shape[dim], dtype=to.get_default_dtype()))",
"title": ""
},
{
"docid": "6b8e305abe5e3b71be4768a61f62c118",
"score": "0.6119436",
"text": "def exponential(value):\n return math.exp(value)",
"title": ""
},
{
"docid": "e82b22502a57b82b7696f21ee7991a87",
"score": "0.61186904",
"text": "def log_sum_exp(x):\n max_value, _ = torch.max(x, dim=-1, keepdim=True)\n\n return max_value.squeeze(-1) + torch.log(torch.sum(torch.exp(x - max_value), dim=-1))",
"title": ""
},
{
"docid": "4c649ae5bead0921fce5510248dc2c18",
"score": "0.61120546",
"text": "def exp(z) -> exponential:\n pass",
"title": ""
},
{
"docid": "ce2871eefd288fb373c788f82f8ad14d",
"score": "0.6111095",
"text": "def log_sum_exp(x):\n # TF ordering\n axis = len(x.size()) - 1\n m, _ = torch.max(x, dim=axis)\n m2, _ = torch.max(x, dim=axis, keepdim=True)\n return m + torch.log(torch.sum(torch.exp(x - m2), dim=axis))",
"title": ""
},
{
"docid": "7b2a4144a758ea51125c3fbb02e1294c",
"score": "0.6098223",
"text": "def _log_cdf(x, mean, log_scale):\n\tz = (x - mean) * torch.exp(-log_scale)\n\tlog_p = F.logsigmoid(z)\n\n\treturn log_p",
"title": ""
},
{
"docid": "27a5faa93d070e8b114a4486277369de",
"score": "0.6095797",
"text": "def _log_pdf(x, mean, log_scale):\n\tz = (x - mean) * torch.exp(-log_scale)\n\tlog_p = z - log_scale - 2 * F.softplus(z)\n\n\treturn log_p",
"title": ""
},
{
"docid": "64c7b552b322064a1814088866e7349e",
"score": "0.6094654",
"text": "def get_logistic_probability(intercept, beta_1, unit_prices):\n logistic_probability = np.exp(intercept + beta_1 * unit_prices) / (1 + np.exp(intercept + beta_1 * unit_prices))\n return logistic_probability",
"title": ""
},
{
"docid": "ca292f770fcbe09c0f90f5becc16099e",
"score": "0.6080775",
"text": "def logtrapzexp(lnf, dx):\n\n lnfdx1 = lnf[:-1]\n lnfdx2 = lnf[1:]\n if isinstance(dx, (int, float)):\n C = np.log(dx / 2.0)\n elif isinstance(dx, (list, np.ndarray)):\n if len(dx) != len(lnf) - 1:\n raise ValueError(\n \"Step size array must have length one less than the function length\"\n )\n\n lndx = np.log(dx)\n lnfdx1 = lnfdx1.copy() + lndx\n lnfdx2 = lnfdx2.copy() + lndx\n C = -np.log(2.0)\n else:\n raise TypeError(\"Step size must be a single value or array-like\")\n\n return C + logsumexp([logsumexp(lnfdx1), logsumexp(lnfdx2)])",
"title": ""
},
{
"docid": "b225add8d4ee2151aa38a5da56c801d6",
"score": "0.60779047",
"text": "def f(x):\n return 10 * math.log(x) + 0.2 * pow(x, 2) - 4.5 * x + 1",
"title": ""
},
{
"docid": "c77e6bae0558cc002d212ffe2160cda0",
"score": "0.60752755",
"text": "def LogProbability(self, x):\n zeta_x = self.LogZ(x)\n score_given_x = self.Score(x)\n return lambda y: score_given_x(y) - zeta_x",
"title": ""
},
{
"docid": "e7ac07c18db211aa3f2f53b242206857",
"score": "0.6073191",
"text": "def logsubexp(a, b):\n if a < b:\n raise Exception(\"Computing the log of a negative number.\")\n elif a == b:\n return np.log(ZERO)\n return a + np.log1p(-np.exp(b - a))",
"title": ""
},
{
"docid": "7854a2a20227050ecba2aa6f16f25fa9",
"score": "0.60497",
"text": "def logistic_function(X):\n \n y = np.zeros(len(X))\n for i in range(len(X)):\n if X[i] > 30:\n y[i] = 1\n elif X[i] < -30:\n y[i] = 0\n else:\n y[i] = 1/(1 + np.exp(-X[i]))\n\n return y",
"title": ""
},
{
"docid": "6959b646fa10d42159e7d378c1bac405",
"score": "0.60484076",
"text": "def exp2(x):\n return 2**x",
"title": ""
},
{
"docid": "5fbf38d8d9b7eaf69f5c2a4b2c95f15a",
"score": "0.60425186",
"text": "def exp(num):\n return 2 ** num",
"title": ""
},
{
"docid": "322876a46996241cee0d06465a08b930",
"score": "0.60402894",
"text": "def logaddexp(x1, x2):\n return log(exp(x1) + exp(x2))",
"title": ""
},
{
"docid": "61931bc84b3585ecda73c01bfefa37bb",
"score": "0.6028994",
"text": "def log(x):\n return intrinsic('log', x)",
"title": ""
},
{
"docid": "81a8c732e4e63ed8609aa62ffa016216",
"score": "0.6025429",
"text": "def calc_half_life(prefactor, exp_factor):\n return np.log(2 * prefactor) / exp_factor",
"title": ""
},
{
"docid": "a8b393052e16159b6512375b0d7d4738",
"score": "0.6016713",
"text": "def simple2log(ret):\n return np.log(ret + 1)",
"title": ""
},
{
"docid": "9eb69aad0da2e142e02df991a1632b53",
"score": "0.5990171",
"text": "def _deriv_log2(x):\n return 1.0 / (_log2 * x)",
"title": ""
},
{
"docid": "5943b704d9f85d9d05f7756b68d1d6d1",
"score": "0.59778875",
"text": "def log_sum_exp(x, y):\n larger_values = tf.maximum(x, y)\n return larger_values + tf.log(tf.exp(x - larger_values) + tf.exp(y - larger_values))",
"title": ""
},
{
"docid": "5bfe0f275a37c4a3038ddc642fef0171",
"score": "0.59772086",
"text": "def exponential(base_score: float) -> float:\n return (np.exp(base_score) - 1) / (np.exp(1) - 1)",
"title": ""
},
{
"docid": "1a891ff7a52d0ca378bfe7a6e3b5490d",
"score": "0.5971593",
"text": "def _deriv_log1p(x):\n return 1.0 / (1.0 + x)",
"title": ""
},
{
"docid": "68bc72a46c0153f476677bd0bb2b7730",
"score": "0.596801",
"text": "def log(x):\n if isinstance(x, FuncInput):\n new_vals = np.log(x.val_)\n new_ders = [x.ders_[i] * (1/x.val_) for i in range(len(x.ders_))]\n return FuncInput(new_vals, new_ders)\n elif isinstance(x, numbers.Real):\n return np.log(x)",
"title": ""
},
{
"docid": "dec6946530dde9cc68f8ae2c50237315",
"score": "0.59641474",
"text": "def line_loglog(x, m, n):\n return x ** m * np.exp(n)",
"title": ""
},
{
"docid": "a109f3b4378da8c0da118b796201734b",
"score": "0.5951329",
"text": "def growth_exp(t, args):\n # for i in np.arange(len(t)):\n # if t[i] >= 0:\n # t_0 = i\n # break\n r, a_0 = args['r'], args['a_0']\n a=np.zeros_like(t)\n i = np.searchsorted(t,0,side='left')\n a[i:] = a_0*np.exp(r*t[i:])\n return a",
"title": ""
},
{
"docid": "da52074ca456c45c53ab4531efc22d67",
"score": "0.5945504",
"text": "def G(x, *arg):\n alpha, mean = arg\n return np.sqrt(np.log(2) / np.pi) / alpha* np.exp(-((x-mean) / alpha)**2 * np.log(2))",
"title": ""
},
{
"docid": "42fa4cc35ac2b210c60e49ebfc8f7917",
"score": "0.59401095",
"text": "def stable_log(x):\n eps = 1e-15\n return np.log(x + eps)",
"title": ""
},
{
"docid": "b460deb536438cc7c2d839953f01e7fc",
"score": "0.59312433",
"text": "def log_gamma(vals,hyperpars):\n return np.sum((hyperpars[:,0]-1)*np.log(vals) - vals/hyperpars[:,1])",
"title": ""
},
{
"docid": "f0debee299470291932145e5ef665ace",
"score": "0.5928244",
"text": "def exp(x ,n):\n if n == 0:\n return 1\n if n % 2 == 0:\n return exp(x*x, n/2)\n return x * exp(x*x, (n-1)/2)",
"title": ""
},
{
"docid": "befe4e9be2df0d02d7b6db9672103d1a",
"score": "0.5926149",
"text": "def log1p_exp(input_tensor):\n x = input_tensor * input_tensor.ge(0).to(torch.float32)\n res = x + torch.log1p(\n torch.exp(-torch.abs(input_tensor)))\n return res",
"title": ""
},
{
"docid": "55bd347aa7bf262f3b1edf52495fc4f8",
"score": "0.59168255",
"text": "def exp_func(values, lc, a):\n lc_prop = int((values == lc).sum()) / values.size\n exp_value = np.exp(a*lc_prop)\n es_value = (exp_value - np.exp(a*0)) / (np.exp(a*1) - np.exp(a*0))\n return es_value",
"title": ""
},
{
"docid": "1684ca98e5fae282618d1aba546a07ab",
"score": "0.5916192",
"text": "def _deriv_log10(x):\n return 1.0 / (_log10 * x)",
"title": ""
},
{
"docid": "4d7414784abf385095483711a7bbb664",
"score": "0.5914015",
"text": "def expDecay(v, x):\n return v[0] * np.exp(-x / v[1]) #+ v[2]",
"title": ""
},
{
"docid": "2804c484a6d8055159eda927f6c60e3b",
"score": "0.5913434",
"text": "def expm1(x):\n return exp(x) - 1",
"title": ""
},
{
"docid": "42e1fe8cbe403ace1404f03bf59a1bfa",
"score": "0.5911702",
"text": "def logaddexp(x: Tensor, y: Tensor) -> Tensor:\n return _elwise(x, y, mode=Elemwise.Mode.LOG_SUM_EXP)",
"title": ""
},
{
"docid": "e9156da5b4394cf91369a9869413aa7d",
"score": "0.59064806",
"text": "def logisticFormula(k, m, a, t):\n return k / (1 + m * np.exp(-a*t))",
"title": ""
},
{
"docid": "f68e855382709ba203b9c5fba039cd3d",
"score": "0.58966744",
"text": "def compute_prior_exponential(blens, rate=10.0):\n exp = Exponential(rate=rate)\n ln_blens = torch.sum(exp.log_prob(blens))\n\n n_branch = int(len(blens))\n n_leaf = int(n_branch / 2 + 1)\n if n_leaf <= 2:\n ln_topo = 0.0\n else:\n ln_topo = torch.sum(torch.log(torch.arange(n_leaf * 2 - 5, 0, -2)))\n return ln_blens + ln_topo",
"title": ""
},
{
"docid": "aa5f762513a761621b82ab07d1dad33c",
"score": "0.5892039",
"text": "def pdf(self, x):\n return np.exp(self.logpdf(x))",
"title": ""
},
{
"docid": "44eb8da01ecd938adca91b73067dd4de",
"score": "0.5891505",
"text": "def evalLogLExpLogIntegrand(*args):\n theta = np.array(args[:-3]).reshape(1, -1)\n integrandLogFuncs = args[-3]\n integrandLogVal = args[-2]\n LLhoodFunc = args[-1]\n LLhoodFuncArgs = integrandLogFuncs[LLhoodFunc] # should be all of theta\n for logFunc, argIndices in integrandLogFuncs.items():\n integrandLogVal += logFunc(theta[argIndices])\n return LLhoodFunc(theta[LLhoodFuncArgs]) * np.exp(integrandLogVal)",
"title": ""
},
{
"docid": "a03b7c2e195db3431da624826245ac13",
"score": "0.5891316",
"text": "def _compute_log_value(self):",
"title": ""
},
{
"docid": "12319a0f3311472e901992714e159dc8",
"score": "0.58887976",
"text": "def exp_(self):\n return self.no_params_func(\"exp_\")",
"title": ""
},
{
"docid": "887fbe4f63ad85ed6b14dfa4eef921c5",
"score": "0.5886893",
"text": "def g(z):\r\n return 1/(1 + np.exp(-z))",
"title": ""
},
{
"docid": "51151a92d9890727abfc78aab7063c86",
"score": "0.5883293",
"text": "def log_probability( self, symbol ):\n\t\t\n\t\treturn super(InverseGammaDistribution, self).log_probability(\n\t\t\t1.0 / symbol)",
"title": ""
},
{
"docid": "a459ec9ec0d806a0a772d86edb229662",
"score": "0.5882596",
"text": "def evalExponentialPDF(x,lam):\n return lam * math.exp(-lam * x)",
"title": ""
},
{
"docid": "3bd25b806bfb117b2a94dcbe8eae0717",
"score": "0.58802557",
"text": "def exp(x):\n if isinstance(x, FuncInput):\n new_vals = np.exp(x.val_)\n new_ders = [x.ders_[i] * np.exp(x.val_) for i in range(len(x.ders_))]\n return FuncInput(new_vals, new_ders)\n elif isinstance(x, numbers.Real):\n return np.exp(x)",
"title": ""
},
{
"docid": "1cdf497eb0b9dc7c4b4fb42e4c5cbd4e",
"score": "0.5879837",
"text": "def log_sum_exp(self, a, b):\n bound = float('-inf')\n if bound in a or bound in b: \n return np.log(np.sum(np.exp(a) * np.exp(b)))\n c = a + b\n max_value = np.max(c)\n return max_value + np.log(np.sum(np.exp(c - max_value)))",
"title": ""
},
{
"docid": "ca444b8ebb6004b59c79724d597a30fa",
"score": "0.58683085",
"text": "def source_term(x):\n\t\n\t\treturn 100*np.exp(-10*x)",
"title": ""
},
{
"docid": "5798821aad7b656b947e24ec8ff0c1fe",
"score": "0.5863583",
"text": "def logistic_reg(w, x):\r\n return 1/(1+np.exp(-1 * (w.T.dot(x[0]))))",
"title": ""
},
{
"docid": "22f4cc37e9f69705d46aad83703ec254",
"score": "0.58632183",
"text": "def log_probability(value, mean, variance):\n\n # Your code here\n a = log(1/(2*kPI*variance)**(1/2))\n b = -((value-mean)**2)/(2*variance)\n return a+b",
"title": ""
},
{
"docid": "d722112cbe371e6a0187425959458a60",
"score": "0.58622545",
"text": "def z(x):\n return 1.0/(1.0 + np.exp(-x))",
"title": ""
},
{
"docid": "bf65cab416f889061f4e47caade50dd0",
"score": "0.586219",
"text": "def logsumexp(arr):\r\n b=arr.max()\r\n return b + np.log((np.exp(arr-b)).sum())",
"title": ""
},
{
"docid": "97a5db689b2ae1607e4a92c163aa6fea",
"score": "0.586213",
"text": "def logistic(z):\n logi = np.zeros(z.shape)\n ### YOUR CODE HERE 1-5 lines \n logi = 1/(1+np.exp(-z))\n ### END CODE\n \n assert logi.shape == z.shape\n return logi",
"title": ""
},
{
"docid": "9f8a3ff3e3f46b1bee07c0da8c0b4a91",
"score": "0.585756",
"text": "def sigmoid(t):\n logfn = np.divide(1,1+np.exp(-t)) \n return logfn",
"title": ""
},
{
"docid": "6cdbdeb1bf49b4176d5a29247b396809",
"score": "0.5853146",
"text": "def scaled_exp(field, c):\n def func(edges):\n return {field: T.exp((edges.data[field] / c).clamp(-10, 10))}\n return func",
"title": ""
},
{
"docid": "d3cdf4fd428c8582e9460442a9760eef",
"score": "0.58505446",
"text": "def growth_log(t, args):\n k, a_0, amax = args['k'], args['log_0'], args['amax']\n a = np.zeros(len(t))\n for i in np.arange(len(t)):\n if t[i]>0:\n a[i:] = a_0*amax/((a_0+(amax-a_0)*np.exp(-amax*k*t[i:])))**2*(amax-a_0)*(amax)*k*np.exp(-amax*k*t[i:])\n break\n return a",
"title": ""
},
{
"docid": "d3cdf4fd428c8582e9460442a9760eef",
"score": "0.58505446",
"text": "def growth_log(t, args):\n k, a_0, amax = args['k'], args['log_0'], args['amax']\n a = np.zeros(len(t))\n for i in np.arange(len(t)):\n if t[i]>0:\n a[i:] = a_0*amax/((a_0+(amax-a_0)*np.exp(-amax*k*t[i:])))**2*(amax-a_0)*(amax)*k*np.exp(-amax*k*t[i:])\n break\n return a",
"title": ""
},
{
"docid": "ad33cace3b500b0cc2891cd11b7c100f",
"score": "0.5844503",
"text": "def log_inv_gamma(vals,hyperpars):\n return np.sum(-(hyperpars[:,0]+1)*np.log(vals) - hyperpars[:,1]/vals)",
"title": ""
},
{
"docid": "d421692190da416ae4498ba539cdfb75",
"score": "0.58427554",
"text": "def func_exp_decay(x, prefactor, exp_factor):\n return prefactor * np.exp(-exp_factor * x)",
"title": ""
},
{
"docid": "55d515444927cf2b7cd47d96f2feb392",
"score": "0.58402574",
"text": "def _one_over_log(t):\n return 1/math.log(t)",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="774"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">76d8a4a962caf2411134a7ab3da14128</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Sort the segments in blue and red</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "2546b0ed2ad9cad8b4b2ae20845f8f6f",
"score": "0.8025422",
"text": "def sort_segments(case):\n blue = []\n red = []\n for segment in case:\n\n if segment[-1] == 'B':\n blue.append(int(segment[:-1]))\n else:\n red.append(int(segment[:-1]))\n\n # sort from big to small\n blue.sort(reverse=True)\n red.sort(reverse=True)\n\n return blue, red",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "1355250a07537a8dcc0629f5dc7e0575",
"score": "0.6966242",
"text": "def sortColors(self, nums) -> None:\n end, end_0, start_1, start_2, count_1 = len(nums) - 1, -1, -1, -1, 0\n for i in range(end, -1, -1):\n if nums[i] == 2:\n start_2 = i\n else:\n break\n if start_2 == 0:\n return\n for i in range(len(nums)):\n if start_2 != -1 and i >= start_2:\n break\n if nums[i] == 2:\n if start_2 == -1:\n nums[i], nums[end] = nums[end], nums[i]\n start_2 = end\n else:\n while nums[start_2 - 1] == 2:\n start_2 -= 1\n if start_2 - 1 > end_0 and start_2 - 1 > start_1 + count_1:\n start_2 -= 1\n else:\n break\n nums[i], nums[start_2] = nums[start_2], nums[i]\n if nums[i] == 1:\n count_1 += 1\n if start_1 == -1:\n start_1 = i\n if nums[i] == 0:\n end_0 += 1\n if start_1 != -1:\n nums[i], nums[start_1] = nums[start_1], nums[i]\n if nums[start_1 + 1] == 1:\n start_1 += 1\n else:\n start_1 = -1",
"title": ""
},
{
"docid": "e14145f31fa5f4cd6db0fcc0f7560fdf",
"score": "0.68927705",
"text": "def sortColors(self, nums: List[int]) -> None:\n red = 0\n blue = len(nums)-1\n i = 0\n while i<=blue:\n if nums[i] == 2:\n nums[i],nums[blue] = nums[blue],nums[i]\n blue -= 1\n elif nums[i] ==0 and i>red:\n nums[i],nums[red] = nums[red],nums[i]\n red += 1\n else:\n i+=1\n return nums",
"title": ""
},
{
"docid": "d1e3598b8c49814c3aba477ec1a3ffe4",
"score": "0.6675524",
"text": "def sortColors(nums):\n n=len(nums)\n \n lt=-1\n gt=n\n i=0\n while i<gt:\n if nums[i]==1:\n i+=1\n elif nums[i]<1:\n if i!=lt+1:\n nums[lt+1],nums[i]=nums[i],nums[lt+1]\n lt+=1\n i+=1\n elif nums[i]>1:\n if i!=gt-1:\n nums[gt-1],nums[i]=nums[i],nums[gt-1]\n gt-=1",
"title": ""
},
{
"docid": "70fc153b0455294e8e88a286655d9a4b",
"score": "0.6652458",
"text": "def sortColors(self, nums: List[int]) -> None:\n red, white, blue = 0, 0, len(nums)-1\n while white <= blue:\n if nums[white] == 0:\n nums[red], nums[white] = nums[white], nums[red]\n white += 1\n red += 1\n elif nums[white] == 1:\n white += 1\n else:\n nums[blue], nums[white] = nums[white], nums[blue]\n blue -= 1",
"title": ""
},
{
"docid": "f19d8bfe5725d19b6009deee659fe7a0",
"score": "0.6636578",
"text": "def sortColors(self, nums: List[int]) -> None:\n\n \"\"\"\n Double-pass with counting colors separately.\n Single-pass implementation here.\n - Keep track of start for red, white, blue\n - compare what the white index is at every point, and use that\n to manipulate red, white, blue pointers\n \"\"\"\n\n red = 0\n white = 0\n blue = len(nums)\n\n index = 0\n\n while white < blue:\n if nums[white]==0:\n nums[red],nums[white] = nums[white], nums[red]\n red+=1\n white+=1\n elif nums[white]==1:\n white+=1\n elif nums[white]==2:\n blue-=1\n nums[blue],nums[white] = nums[white], nums[blue]\n\n\n # print(nums)",
"title": ""
},
{
"docid": "2e8244ca405faa84cb840d11de4fe4dc",
"score": "0.663346",
"text": "def sortColors(self, nums: List[int]) -> None:\n index, start, end = 0, 0, len(nums) - 1\n while index <= end:\n if nums[index] == 2:\n nums[index] = nums[end]\n nums[end] = 2\n end -= 1\n elif nums[index] == 0:\n nums[index] = 1\n nums[start] = 0\n start += 1\n index += 1\n else:\n index += 1\n\n # def swap(arr: List[int], start: int, end: int):\n # temp1 = arr.pop(start)\n # temp2 = arr.pop(end)\n # arr.insert(end, temp1)\n # arr.insert(start, temp2)\n #\n # first, last = 0, len(nums) - 1\n # for i in range(len(nums)):\n # if nums[i] == 0:\n # if nums[first] == 0:\n # while nums[first] == 0:\n # first += 1\n # if first != i:\n # swap(nums, first, i)\n # elif nums[first] == 2:\n # while nums",
"title": ""
},
{
"docid": "c8c6350058093848b5c6dd54dc653ca3",
"score": "0.66190356",
"text": "def _sort_sides(self, sides, first_line):",
"title": ""
},
{
"docid": "f25606f897340782d8599f9e47dd7bc4",
"score": "0.65900403",
"text": "def sortColors(self, nums: List[int]) -> None:\n \n \n # 1 0 0 1 1 0\n # l r \n # 0 0 0 1 1 1\n # r\n # l\n \n # 0 1 2 1\n # r \n # l \n # m\n \n # swap zero if 1 or 2 are smaller\n # swap one if 2 is smaller\n \n \n zero, one, two = 0, 0, 0\n \n while zero < len(nums) and nums[zero] != 0:\n zero+=1\n \n while one < len(nums) and nums[one] != 1:\n one+=1\n \n while two < len(nums) and nums[two] != 2:\n two+=1\n \n while zero < len(nums):\n if one < two < zero or one < zero < two:\n nums[one], nums[zero] = nums[zero], nums[one]\n while one < len(nums) and nums[one] != 1:\n one+=1\n elif two < one < zero or two < zero < one:\n nums[zero], nums[two] = nums[two], nums[zero]\n while two < len(nums) and nums[two] != 2:\n two+=1\n else:\n zero+=1\n while zero < len(nums) and nums[zero] != 0:\n zero+=1\n \n \n \n while one < len(nums):\n if two < one:\n nums[one], nums[two] = nums[two], nums[one]\n while two < len(nums) and nums[two] != 2:\n two+=1\n else:\n one+=1\n while one < len(nums) and nums[one] != 1:\n one+=1",
"title": ""
},
{
"docid": "66b831f8e18d31cbc3dfaa388e0e8efc",
"score": "0.6588373",
"text": "def sortColors(self, nums: List[int]) -> None:\n lo=0\n mid=0\n hi= len(nums)-1\n while mid <= hi:\n if nums[mid]==0:\n nums[lo],nums[mid]=nums[mid],nums[lo]\n lo+=1\n mid+=1\n elif nums[mid] == 1:\n mid+=1\n else:\n nums[mid],nums[hi]=nums[hi],nums[mid]\n hi-=1\n \n return nums",
"title": ""
},
{
"docid": "d0f3a6e43e968f7d382e860cb336d8cd",
"score": "0.65854543",
"text": "def sortColors(self, nums) -> None:\n\n ll = len(nums)\n i= 0\n left = 0\n right = ll-1\n\n while i <= right:\n if nums[i] == 2:\n nums[right], nums[i] = nums[i], nums[right]\n right -= 1\n elif nums[i] == 0:\n nums[left], nums[i] = nums[i], nums[left]\n left += 1\n i += 1\n\n else:\n i += 1",
"title": ""
},
{
"docid": "8ea7537f1c918f36706261de1c07f10f",
"score": "0.65770024",
"text": "def sortColors(self, nums: List[int]) -> None:\n RED, WHITE = 0, 1\n red = 0\n white = 0\n blue = len(nums) - 1\n\n while white <= blue:\n if nums[white] == RED:\n nums[red], nums[white] = nums[white], nums[red]\n white += 1\n red += 1\n elif nums[white] == WHITE:\n white += 1\n else:\n nums[white], nums[blue] = nums[blue], nums[white]\n blue -= 1",
"title": ""
},
{
"docid": "2d806033199c3cfe0554a78ad69c4382",
"score": "0.6575849",
"text": "def sortColors(self, nums: List[int]) -> None:\n if nums == []:\n return None\n \n new_start_idx = self.partition(nums, 0, len(nums) - 1, 0)\n self.partition(nums, new_start_idx, len(nums) - 1, 1)",
"title": ""
},
{
"docid": "bb3c3f10a23cf787c25525353cc94c9d",
"score": "0.65743333",
"text": "def sortColors(self, nums: List[int]) -> None:\n rightOfZero = currIndex = 0\n leftOfTwo = len(nums)-1\n while currIndex <= leftOfTwo:\n if(nums[currIndex] == 0):\n nums[rightOfZero], nums[currIndex] = nums[currIndex], nums[rightOfZero]\n rightOfZero += 1\n currIndex += 1\n continue\n if(nums[currIndex] == 2):\n nums[leftOfTwo], nums[currIndex] = nums[currIndex], nums[leftOfTwo]\n leftOfTwo -= 1\n continue\n currIndex += 1\n \n \n \n \n \n \n \n Sort Colors",
"title": ""
},
{
"docid": "13eed23e8d55bba722a2f60a50147a7c",
"score": "0.65439963",
"text": "def sortColors(self, nums: List[int]) -> None:\n def even_ex(nums,size):#定义两个函数，第一个函数是偶数对遍历，从下标0开始\n if size <2: #当长度小于2时我们直接返回\n return nums\n first = 0\n second = 1\n while second < size:\n if nums[first]>nums[second]: #每当找到一对数前面的大于后面的，则调换顺序\n nums[first], nums[second] = nums[second], nums[first]\n\n first += 2 #跳两位继续循环\n second +=2\n return nums\n def odd_ex(nums,size): # 第二个为奇数对遍历，从下标1开始\n if size <= 2: #当长度小于等于2时，我们不用进行奇数位遍历\n return nums\n first = 1\n second = 2 \n while second < size:\n if nums[first] > nums[second]:\n nums[first], nums[second] = nums[second], nums[first]\n first += 2\n second += 2 \n return nums\n size = len(nums)\n count = 0\n while True: #循环遍历\n nums = odd_ex(even_ex(nums, size),size) #不断进行偶数位遍历再进行奇数位遍历，这两个遍历不分前后顺序，直到排序完成位置\n count += 1 \n nums_c = nums.copy() #注意⚠️这里判断时需要copy数组，而不是直接等于，直接等于还是指向原内存地址，再次调用函数肯定是相等的\n if nums_c == odd_ex(even_ex(nums, size),size): #如果相等说明已经排好序，退出返回nums\n break\n \n return nums",
"title": ""
},
{
"docid": "bb63f08af9046f281a20f592ac603d27",
"score": "0.65366995",
"text": "def sortColors(self, nums: List[int]) -> None:\n left = 0\n right = len(nums)-1\n index = 0\n while index<=right:\n if nums[index]==0 :\n nums[index],nums[left]=nums[left],nums[index]\n index+=1\n left+=1\n elif nums[index]==2:\n nums[index],nums[right]=nums[right],nums[index]\n right-=1\n else:\n index+=1",
"title": ""
},
{
"docid": "0afe056251ff7371e025de2ebde4a1fe",
"score": "0.6526098",
"text": "def sortColors(self, nums):\r\n\r\n p, q = 0, 0 \r\n k = len(nums) - 1 \r\n\r\n while q <= k:\r\n if p < q and nums[q] == 0:\r\n nums[p], nums[q] = nums[q], nums[p]\r\n p += 1\r\n elif nums[q] == 2:\r\n nums[q], nums[k] = nums[k], nums[q]\r\n k -= 1 \r\n else:\r\n q += 1",
"title": ""
},
{
"docid": "6e5522b252ef515a25a4c798ed403c9e",
"score": "0.652499",
"text": "def sortColors(self, nums: List[int]) -> None:\n nums.sort()",
"title": ""
},
{
"docid": "6e5522b252ef515a25a4c798ed403c9e",
"score": "0.652499",
"text": "def sortColors(self, nums: List[int]) -> None:\n nums.sort()",
"title": ""
},
{
"docid": "addfa3d3da6cbcfa75aef448422c28c6",
"score": "0.65207136",
"text": "def sortColors(self, nums: List[int]) -> None:\n zero,two=0,len(nums)-1\n i=0\n while i<len(nums):\n if nums[i]==0:\n temp=nums[i]\n nums[i]=nums[zero]\n nums[zero]=temp\n while nums[zero]==0:\n zero+=1\n elif nums[i]==2:\n temp=nums[i]\n nums[i]=nums[two]\n nums[two]=temp\n while nums[two]==0:\n two+=1\n \n i+=1",
"title": ""
},
{
"docid": "a36760e2a39a1c5138d1eec5b370e46c",
"score": "0.6478503",
"text": "def sortColors(self, nums: List[int]) -> None:\n low , high , i = 0 , len(nums)-1 , 0\n while i <= high:\n if(nums[i]==0):\n nums[low], nums[i] = nums[i], nums[low]\n low+=1\n i+=1\n elif nums[i] == 2:\n nums[high] , nums[i] = nums[i] , nums[high]\n high -= 1\n else:\n i+=1",
"title": ""
},
{
"docid": "f8bc2b5b4e79f471c7f778803a2737fd",
"score": "0.64570874",
"text": "def sortColors(self, nums: List[int]) -> None:\n idx = [0, 0, len(nums)-1]\n while idx[1] <= idx[2]:\n if nums[idx[1]] == 0:\n nums[idx[0]], nums[idx[1]] = nums[idx[1]], nums[idx[0]]\n idx[0] += 1\n if nums[idx[1]] == 2:\n nums[idx[2]], nums[idx[1]] = nums[idx[1]], nums[idx[2]]\n idx[2] -= 1\n else:\n idx[1] += 1",
"title": ""
},
{
"docid": "921bab4d88761ce609a790e3cfadc1ff",
"score": "0.6441648",
"text": "def sortColors(self, nums: List[int]) -> None:\n \n if (len(nums) == 0 or len(nums) == 1):\n return \n \n start = 0\n end = len(nums) - 1\n current = 0\n \n while (current <= end and start < end):\n if nums[current] == 0:\n nums[current] = nums[start]\n nums[start] = 0\n start += 1\n current += 1\n \n elif nums[current] == 2:\n nums[current] = nums[end]\n nums[end] = 2\n end -= 1\n \n else:\n current += 1",
"title": ""
},
{
"docid": "4d150e0f28618d4905a00c66bb13743f",
"score": "0.64383554",
"text": "def sortColors(self, nums: List[int]) -> None:\n self.qSort(nums, 0, len(nums)-1)",
"title": ""
},
{
"docid": "04605d7cd59ba165bb6aae5bee71ff40",
"score": "0.64378613",
"text": "def sortColors(self, nums: List[int]) -> None:\n # for all idx < p0 : nums[idx < p0] = 0\n # curr is an index of element under consideration\n p0 = curr = 0\n # for all idx > p2 : nums[idx > p2] = 2\n p2 = len(nums) - 1\n\n while curr <= p2:\n if nums[curr] == 0:\n nums[p0], nums[curr] = nums[curr], nums[p0]\n p0 += 1\n curr += 1\n elif nums[curr] == 2:\n nums[curr], nums[p2] = nums[p2], nums[curr]\n p2 -= 1\n else:\n curr += 1",
"title": ""
},
{
"docid": "de263e88e68a9c12dcdfa64e9fe9f897",
"score": "0.64362895",
"text": "def sortColors(self, nums: List[int]) -> None:\n n = len(nums)\n # pointer left on 0\n i = 0\n while i < n and nums[i] == 0:\n i += 1\n # pointer right on 2\n k = n - 1\n while k > 0 and nums[k] == 2:\n k -= 1\n # move around 0, 1, 2\n j = i\n while j <= k:\n if nums[j] == 0:\n if j == i:\n j += 1\n else:\n nums[i], nums[j] = nums[j], nums[i]\n i += 1\n elif nums[j] == 2:\n nums[j], nums[k] = nums[k], nums[j]\n k -= 1\n else:\n j += 1\n return None",
"title": ""
},
{
"docid": "9cfd6f74761e7798e5aea4f123cf5745",
"score": "0.6398047",
"text": "def sortColors(self, nums) -> None:\n nums_two = []\n print(nums)\n import bisect\n for num in nums:\n bisect.insort_left(nums_two, num)\n\n print(nums_two)\n nums[:] = nums_two[:]\n return nums",
"title": ""
},
{
"docid": "6840b556bdd71e0cc9f7fdc125bc55ce",
"score": "0.63923675",
"text": "def refactor_and_sort_data(color_data):\n return sorted(color_data)",
"title": ""
},
{
"docid": "9adb075b30face9716c60b5009384417",
"score": "0.637082",
"text": "def rgb_sort(colours, reverse=False):\n sorted_col = sorted(colours, reverse=reverse)\n return sorted_col",
"title": ""
},
{
"docid": "350a6c28d21cba69fc822d9ba3d88808",
"score": "0.6365573",
"text": "def sortColors(self, nums: List[int]) -> None:\n if len(nums) <= 1:\n return\n \n curr = 1\n \n while curr < len(nums):\n stop = False\n temp = curr\n while stop != True and temp > 0:\n if nums[temp] < nums[temp-1]:\n #swap\n nums[temp], nums[temp-1] = nums[temp-1], nums[temp]\n temp -= 1\n else:\n stop = True\n \n curr += 1",
"title": ""
},
{
"docid": "a861756a0fb344a95586d07ec523022f",
"score": "0.634927",
"text": "def sortColors(self, nums: List[int]) -> None:\n left = i = 0\n right = len(nums) - 1\n while i <= right:\n if nums[i] == 0:\n nums[i], nums[left] = nums[left], nums[i]\n left += 1\n i += 1\n elif nums[i] == 1:\n i += 1\n else:\n nums[i], nums[right] = nums[right], nums[i]\n right -= 1",
"title": ""
},
{
"docid": "c892f53d9e8c3ae38cae1c8ed0be1b3b",
"score": "0.6340334",
"text": "def sortColorsEasy(self, nums: List[int]) -> None:\n red = white = blue = 0\n for o in nums:\n if o == 0:\n red += 1\n elif o == 1:\n white += 1\n elif o == 2:\n blue += 1\n for i in range(len(nums)):\n if i < red:\n nums[i] = 0\n elif i < red + white:\n nums[i] = 1\n else:\n nums[i] = 2\n return nums",
"title": ""
},
{
"docid": "e9f574e1c8cc12e4444a1f383c0f3dbd",
"score": "0.6331082",
"text": "def sortColors(self, nums: List[int]) -> None:\n if not nums:\n return []\n \n left = 0\n right = len(nums) - 1 # 5 \n curr = 0\n n = nums\n\n while curr <= right: # 2, 4\n if n[curr] == 0: # \n n[curr], n[left] = n[left], n[curr]\n left += 1 # 1\n curr += 1 # 2\n elif n[curr] == 1:\n curr += 1\n else:\n n[curr], n[right] = n[right], n[curr] \n right -= 1 # 4\n \n return n",
"title": ""
},
{
"docid": "057269395f5e58bbcaa08d25cfbba734",
"score": "0.63001716",
"text": "def sortColors(self, nums: List[int]) -> None:\n a, b = 0, 0\n for item in nums:\n if item == 0:\n a += 1\n if item == 1:\n b += 1\n for i in range(len(nums)):\n if i < a:\n nums[i] = 0\n elif i >= a and i < a + b:\n nums[i] = 1\n else:\n nums[i] = 2",
"title": ""
},
{
"docid": "b08ff483d9b59ea1e78cad74ba4b0861",
"score": "0.6297325",
"text": "def sortColors(nums):\n n = len(nums)\n for i in range(n):\n for j in range(0, n-1):\n if nums[j] > nums[j+1]:\n nums[j], nums[j+1] = nums[j+1], nums[j]\n return nums",
"title": ""
},
{
"docid": "491d3236bb1026f903ca790f041e7bbf",
"score": "0.629117",
"text": "def sortColors(self, nums: List[int]) -> None:\n # Two pointers\n i = j = 0\n k = len(nums) - 1\n\n while i <= j <= k:\n if nums[j] == 0:\n nums[i], nums[j] = nums[j], nums[i]\n i += 1\n j += 1\n elif nums[j] == 2:\n nums[j], nums[k] = nums[k], nums[j]\n k -= 1\n else:\n j += 1\n # print((i, j, k), nums)",
"title": ""
},
{
"docid": "9c814debe056420ebcbdd4fb32af2d57",
"score": "0.62824494",
"text": "def sortColors(self, nums: List[int]) -> None:\n count_0 = 0\n count_1 = 0\n count_2 = 0\n for i in nums:\n if i == 0:\n count_0 += 1\n elif i == 1:\n count_1 += 1\n elif i == 2:\n count_2 += 1\n length = count_0 + count_1 + count_2\n for i in range(count_0):\n nums[i] = 0\n for i in range(count_0,count_0 + count_1):\n nums[i] = 1\n for i in range(count_0 + count_1,count_0 + count_1 + count_2):\n nums[i] = 2",
"title": ""
},
{
"docid": "e6e061f04ea200c412ba0060b956e3ed",
"score": "0.6280019",
"text": "def sortColors(self, nums: List[int]) -> None:\n zero, one, two = 0, 0, len(nums)-1\n while one <= two:\n if nums[one] == 0:\n nums[one], nums[zero] = nums[zero], nums[one]\n one += 1\n zero += 1\n elif nums[one] == 1:\n one += 1\n else:\n nums[one], nums[two] = nums[two], nums[one]\n two -= 1",
"title": ""
},
{
"docid": "4f5e25d4798c4e0aef8d17e8306f627d",
"score": "0.6270304",
"text": "def sortColors(self, nums: List[int]) -> None:\n\n # all in [0, zero] = 0\n # all in (zero, i) = 1\n # all in (two, len - 1] = 2\n\n def swap(nums, index1, index2):\n nums[index1], nums[index2] = nums[index2], nums[index1]\n\n size = len(nums)\n if size < 2:\n return\n\n zero = -1\n two = size - 1\n\n i = 0\n\n while i <= two:\n if nums[i] == 0:\n zero += 1\n swap(nums, i, zero)\n i += 1\n elif nums[i] == 1:\n i += 1\n else:\n swap(nums, i, two)\n two -= 1",
"title": ""
},
{
"docid": "c74751e78e6b0585f4984a31bf5135d6",
"score": "0.62580085",
"text": "def sortColors(self, nums: List[int]) -> None:\n p0 = 0\n p1 = 0\n p2 = len(nums) - 1\n\n while p1 <= p2:\n if nums[p1] == 0:\n nums[p0], nums[p1] = nums[p1], nums[p0]\n p0 += 1\n p1 += 1\n elif nums[p1] == 1:\n p1 += 1\n else:\n nums[p0], nums[p2] = nums[p2], nums[p0]\n p2 -= 1",
"title": ""
},
{
"docid": "a972dae7a6249f7aeaacb6cba3858aab",
"score": "0.621943",
"text": "def sortColors(self, nums: List[int]) -> None:\n from collections import Counter\n counter = Counter(nums)\n \n idx_1 = counter[0]\n idx_2 = counter[0] + counter[1]\n \n for idx in range(len(nums)):\n if idx < idx_1:\n nums[idx] = 0\n elif idx_1 <= idx < idx_2:\n nums[idx] = 1\n else:\n nums[idx] = 2",
"title": ""
},
{
"docid": "6c5d04cef93dceb54949f39aed1ea8de",
"score": "0.6208972",
"text": "def sortColors(self, nums: List[int]) -> None:\n \n if len(nums) <= 1:\n return nums\n \n p0 = curr = 0\n p2 = len(nums) - 1\n \n while curr <= p2:\n \n #if currVal = 0, swap p0 and curr and increment both\n if nums[curr] == 0:\n nums[p0],nums[curr] = nums[curr], nums[p0]\n curr += 1\n p0 += 1\n # currVal = 1, increment curr\n elif nums[curr] == 1:\n curr += 1\n # currVal = 2, swap curr and p2 then decrement p2\n else:\n nums[p2],nums[curr] = nums[curr], nums[p2]\n p2 -= 1",
"title": ""
},
{
"docid": "4b9e60d09a4b5f6001cb5b237236c3f7",
"score": "0.61643505",
"text": "def sortColors(nums) -> None:\n low = cur = 0\n high = len(nums) - 1\n\n while cur <= high:\n if nums[cur] == 0:\n nums[low], nums[cur] = nums[cur], nums[low]\n low += 1\n cur += 1\n elif nums[cur] == 2:\n nums[high], nums[cur] = nums[cur], nums[high]\n high -= 1\n elif nums[cur] == 1:\n cur += 1\n else:\n raise ValueError(f\"Invalid input found: {nums[cur]}\")",
"title": ""
},
{
"docid": "e1c31d9a0b498c3945a2c8ace99949a9",
"score": "0.6118577",
"text": "def sortColors(self, nums: List[int]) -> None:\n c = Counter(nums)\n nums[:] = [0] * c[0] + [1] * c[1] + [2] * c[2]",
"title": ""
},
{
"docid": "3ecc4e9fc3590d598ce911a203384a20",
"score": "0.6116772",
"text": "def sortColors(self, nums: List[int]) -> None:\n zeros, ones, twos = 0, 0, 0\n for x in nums:\n if x == 0:\n zeros += 1\n elif x == 1:\n ones += 1\n else:\n twos += 1\n \n i = 0 \n while zeros > 0:\n nums[i] = 0\n zeros -= 1\n i += 1\n \n while ones > 0:\n nums[i] = 1\n ones -= 1\n i += 1\n \n while twos > 0:\n nums[i] = 2\n twos -= 1\n i += 1\n \n return nums",
"title": ""
},
{
"docid": "7447336c54d1a5edd30322ebfdb1b38f",
"score": "0.610931",
"text": "def sortColors(self, nums: List[int]) -> None:\n cnt_0, cnt_1, cnt_2 = 0, 0, 0\n for num in nums:\n if num == 0:\n cnt_0 += 1\n if num == 1:\n cnt_1 += 1\n if num == 2:\n cnt_2 += 1\n\n for i in range(len(nums)):\n if i < cnt_0:\n nums[i] = 0\n elif i >= cnt_0 and i < cnt_0 + cnt_1:\n nums[i] = 1\n else:\n nums[i] = 2",
"title": ""
},
{
"docid": "1ea92760b3082a3e38f9befac29d9e82",
"score": "0.61074936",
"text": "def sortColors(self, nums: List[int]) -> None:\n color = {0:0, 1:0, 2:0}\n sortColor = []\n for i in nums:\n color[i] +=1\n \n for i in color:\n for j in range(color[i]):\n sortColor.append(i)\n for i in range(len(nums)):\n nums[i] = sortColor[i]",
"title": ""
},
{
"docid": "9d40e1c38115622a7a0fa2e6351be8a0",
"score": "0.6106",
"text": "def sortColors(self, nums: List[int]) -> None:\n head = 0 #设置一个头边界，一开始为0，因为头边界还没有任何值\n for i in range(len(nums)): \n if nums[i] == 0: #每当找到一个0 就把0放到head的位置，并同时head向后走一位\n nums[head], nums[i] = nums[i], nums[head]\n head += 1 \n\n \n\n if head < len(nums):\n for i in range(head, len(nums)): #找1，从head开始往后遍历，每当找到1就把1 放到head的位置，并往后遍历\n if nums[i] == 1:\n nums[head], nums[i] = nums[i], nums[head]\n head += 1 \n\n \n\n \n return nums",
"title": ""
},
{
"docid": "b483d375565d51b8b0581005cd3961e4",
"score": "0.6103135",
"text": "def sortColors_onepass(self, nums: List[int]) -> None:\n n = len(nums)\n ren = [1] * n\n zeropoint = 0\n twopoint = n-1\n \n for i in range(n):\n if nums[i] == 0:\n ren[zeropoint] = 0\n zeropoint += 1\n elif nums[i] == 2:\n ren[twopoint] = 2\n twopoint -= 1\n \n nums[:] = ren[:]",
"title": ""
},
{
"docid": "455d2bd8e021679217e958f53d634918",
"score": "0.60957396",
"text": "def get_sorted_colors():\n\n colors = mcolors.CSS4_COLORS\n\n list_colors = sorted((tuple(mcolors.rgb_to_hsv(mcolors.to_rgb(color))), name) for name, color in colors.items())\n sorted_colors = [x[1] for x in list_colors]\n\n return sorted_colors",
"title": ""
},
{
"docid": "c75a590487a7b2ff15bca37cdda819ec",
"score": "0.6029276",
"text": "def actual_pixel_sort(color_data):\n color_data = [tuple(rgb_shift(i) for i in toop) for toop in color_data]\n return color_data",
"title": ""
},
{
"docid": "f58dc6acddbc77ab2b46c3d1b8e15a53",
"score": "0.60143024",
"text": "def sortColors(self, nums: List[int]) -> None:\n # approach 1 worst case 2 pass\n i = j = 0\n nums_len = len(nums)\n\n while (i < nums_len):\n\n if nums[i] == 0:\n nums[i], nums[j] = nums[j], nums[i]\n i += 1\n j += 1\n\n else:\n i += 1\n\n i = j\n\n while (i < nums_len):\n if nums[i] == 1:\n nums[i], nums[j] = nums[j], nums[i]\n i += 1\n j += 1\n\n else:\n i += 1\n\n # approach 2 one pass (time and space same as approach 1)\n i = j = 0\n k = len(nums) - 1\n\n while (i <= k): # ******************** Notice the condition. its 'k' not len_nums ********************\n\n if nums[i] == 0:\n nums[i], nums[j] = nums[j], nums[i]\n i += 1\n j += 1\n\n elif nums[i] == 2:\n nums[i], nums[k] = nums[k], nums[i]\n k -= 1 # ******************** Notice you are only decr 'k' and not incrementing i ********************\n\n else:\n i += 1\n\n return nums",
"title": ""
},
{
"docid": "a8a1212b1490c7a69596e4ea3dd5dd2f",
"score": "0.59752405",
"text": "def sortColors(self, nums: List[int]) -> None:\n _0,_1,_2 = 0,0,0\n \n for i in nums:\n if i == 0:\n _0 += 1\n elif i == 1:\n _1 += 1\n else:\n _2 += 1\n nums.clear() \n nums += [0] * _0 + [1] * _1 + [2] * _2",
"title": ""
},
{
"docid": "6a6d4e47619b3bab5ce76dc66b9e2b33",
"score": "0.5940246",
"text": "def sortColors2(self, nums: List[int]) -> None:\n # two pass overwrite\n counts = [0, 0, 0]\n for num in nums:\n counts[num] += 1\n \n \n i = 0\n while (i < len(nums)):\n for j in range(len(counts)):\n for _ in range(counts[j]):\n nums[i] = j\n i += 1",
"title": ""
},
{
"docid": "2dbda583b2ebed3f71038f4ac18044ba",
"score": "0.5910891",
"text": "def sortColors(nums):\n if len(nums) == 0 or len(nums) == 1:\n return\n \n '''\n (2) (0) 2 1 1 0 0 1 2\n i = 2, i+1 = 0\n\n check for 1 or 2 values\n i == 2: 2s = 0, 2e = 0\n i == 0: none\n\n 2 > 0 = swap i, i+1 = i+1, i\n (0) (2) 2 1 1 0 0 1 2\n\n 0 (2) (2) 1 1 0 0 1 2 \n i = 2, i+1 = 2\n check for 1 or 2 values\n i == 2 and i+1 == 2: 2s = 0, 2e = 1\n equal, no swap\n 2 == 2: continue\n\n 0 2 (2) (1) 1 0 0 1 2 \n i = 2, i+1 = 1\n check for 1 or 2 values\n i == 2\n i+1 == 1\n\n 2 > 1: swap with 2s, reset 2s/2e and 1s/1e\n 0 (1) 2 (2) 1 0 0 1 2 \n\n 0 1 2 (2) (1) 0 0 1 2 \n i = 2, i+1 = 1\n check for 1 or 2 values\n i == 2\n i+1 == 1\n 2 > 1: swap\n 0 1 (1) 2 (2) 0 0 1 2\n\n 0 1 1 2 (2) (0) 0 1 2\n 2 > 0: swap with 2s\n 0 1 1 (0) (2) (2) 0 1 2\n\n 0 1 1 0 2 (2) (0) 1 2\n 2 > 0: swap with 2s\n 0 1 1 0 (0) 2 (2) 1 2\n \n 0 1 1 0 0 2 (2) (1) 2 [key]\n i = 2, swap = i+1 = 1 ind = 6, swapInd = 6+1 = 7\n 2 > 1: swap with 2s swap values nums[swapInd], nums[2s=5]... swap indexes swapInd, 2s\n 0 1 1 0 0 (1) 2 (2) 2\n\n swap = 1, swap-1 = 0 != 1; swap 1e+1 if nums[swapInd] != nums[swapInd - 1], not adjacent to own kind, swap again with 1e\n 0 1 1 (0) 0 (1) 2 2 2 = 0 1 1 (1) 0 (0) 2 2 2 \n\n at the end, deal with the 0's\n 0 1 1 1 0 0 2 2 2\n 2 will always be at end because comparing 2 > 1\n for 1s=3 to 1e=1: \n 2s=6 to 2e=8:\n 1startInd = 6 - 1 - (3-1) = start -> prevSpace -> spaceFor1's\n for 1s to 1e, set to 0\n\n ex) start with 1\n (1) 0 2 2 0 1\n 0 (1) 2 2 0 1 \n 0 1 (2) 2 0 1\n 0 1 2 (2) 0 1\n 0 1 0 2 (2) 1\n 0 1 0 1 2 (2) + swap 1 to 1's\n '''\n\n s2 = None # start 2\n e2 = None\n\n if nums[0] == 2: # found first 2\n s2 = 0\n e2 = 0\n\n for i in range(len(nums) - 1):\n # else: \n first = nums[i]\n second = nums[i+1]\n # print(\"elements: \", first, second)\n # print((s2, e2))\n \n if second == 2 and e2 is None:\n s2 = i+1\n e2 = i+1\n\n if first == 2 and second == 1:\n # print(nums)\n nums[s2], nums[i+1] = nums[i+1], nums[s2] # values\n # print(nums)\n\n # shift the 2's over right\n s2 += 1 # indexes\n e2 += 1\n \n if first == second:\n if first == 2:\n e2 += 1\n\n # simple swap\n if second == 0: \n if first == 1:\n nums[i], nums[i+1] = nums[i+1], nums[i] # values\n if first == 2:\n nums[s2], nums[i+1] = nums[i+1], nums[s2] # values\n s2 += 1\n e2 += 1\n print(nums)\n\n # # shift over the 1's next to the 0's\n # if 1 not in nums:\n # return \n\n # print\n # print(nums[::-1])\n # [1,2,0]\n \n\n # '[0, 1, 1, 0, 2, 2]\n # '[1, 0, 2]\n # '[0, 1, 2]\n # while there is a blank space right\n # if one element\n # print(numberofOnes)\n\n # if only 1 and 0:\n # numbOnes = endInd - startInd + 1\n # while (numbOnes > 0):\n # nums[len(nums) - 1 - numbOnes] = 1\n # numbOnes -= 1\n\n '''\n # even number of 0's\n # odd number of 0's - radiating is fine\n numbZero = s2-1-endInd\n # numbOnes = endInd - startInd + 1\n print(\"zeros to right: \", s2 - 1 - endInd)\n print(\"ones: \", numbOnes)\n # if numbOnes % 2 == 1:\n # nums[endInd] = 1\n for i in range(0, s2-1-endInd):\n print(endInd - i - 1, endInd + i + 1)\n if endInd - i - 1 >= 0: # process\n nums[endInd - i - 1] = 0\n print(\"1\")\n nums[endInd + i + 1] = 1\n print(\"2\")\n '''\n if 1 not in nums:\n return\n\n startInd = nums.index(1)\n endInd = len(nums) - 1 - nums[::-1].index(1)\n\n # if 0, 1\n if s2 is None: # no 2's\n s2 = len(nums)\n\n # if 0, 1, 2\n numbOnes = endInd - startInd + 1\n numbZero = s2-1-endInd\n # numbOnes = endInd - startInd + 1\n print(\"zeros to right: \", s2 - 1 - endInd)\n print(\"ones: \", numbOnes)\n\n # if numbOnes % 2 == 1:\n # nums[endInd] = 1\n\n # for i in range(0, s2-1-endInd):\n # print(endInd - i - 1, endInd + i + 1)\n # if endInd - i - 1 >= 0: # process\n # nums[endInd - i - 1] = 0\n # print(\"1\")\n # nums[endInd + i + 1] = 1\n # print(\"2\")\n\n for i in range(s2 - 1, -1, -1):\n if numbOnes > 0:\n nums[i] = 1\n numbOnes -= 1\n # if nums[i] == 0: \n # break\n # print(i)\n else:\n nums[i] = 0\n \n\n # if 0, 2: nothing\n # if 1, 2: nothing\n \n \n # if more than one element\n # no blank spaces to the right\n print(\"=================================================NUMS: \", nums)",
"title": ""
},
{
"docid": "531b2acdc643a44cc8bcb8e8541574d6",
"score": "0.5877082",
"text": "def sortColors(self, nums: List[int]) -> None:\n\n zero_list = []\n one_list = []\n two_list = []\n nums_counter = Counter(nums)\n\n if 0 in nums_counter:\n zero_list =[0]*nums_counter[0]\n\n if 1 in nums_counter:\n one_list = [1] * nums_counter[1]\n\n if 2 in nums_counter:\n two_list = [2] * nums_counter[2]\n return zero_list + one_list + two_list",
"title": ""
},
{
"docid": "cf51df00c8d108cea0f48f494edc5d88",
"score": "0.58550376",
"text": "def sortColors(self, nums: List[int]) -> None:\n counts = Counter(nums)\n result = [0]*counts[0] + [1]*counts[1] + [2]*counts[2]\n \n for idx, num in enumerate(result):\n nums[idx] = num",
"title": ""
},
{
"docid": "44bfb9255d3d55c796e8c97eb60de0a1",
"score": "0.5835337",
"text": "def _topological_sort_r(self, v):\n \n # TODO: Fill in this function\n self.colour[v] = 'grey' # Visited vertices are coloured 'grey'\n for w in self.adj_list[v]: # Let's visit all outgoing edges from v\n if self.colour[w] == 'white': # To avoid loops, we check if the next vertex hasn't been visited yet\n self._topological_sort_r(w)\n self.colour[v] = 'black' # When we finish the for loop, we know we have visited all nodes from v. It is time to turn it 'black'\n self.stack.append(v)",
"title": ""
},
{
"docid": "e5e2eb7ed1bce492bb7d5e733a3a4b77",
"score": "0.5821784",
"text": "def sort_hsvs(hsv_list):\n bars_with_indexes = []\n for index, hsv_val in enumerate(hsv_list):\n bars_with_indexes.append((index, hsv_val[0], hsv_val[1], hsv_val[2]))\n bars_with_indexes.sort(key=lambda elem: (elem[1], elem[2], elem[3]))\n return [item[0] for item in bars_with_indexes]",
"title": ""
},
{
"docid": "97c0551a27ecce0d6ee53908403ad0db",
"score": "0.5787651",
"text": "def sort_group(self):\r\n if self.orientation == 'Horizontal':\r\n self.lamp_list.sort(key=lambda x: x[1])\r\n else:\r\n self.lamp_list.sort(key=lambda x: x[2])",
"title": ""
},
{
"docid": "3c8286cba993aee46dfac61c6dadbbbd",
"score": "0.5709738",
"text": "def sortColors(self, nums: List[int]) -> None:\r\n \r\n my_dict = dict()\r\n \r\n idx = 0\r\n l = len(nums)\r\n while idx < l:\r\n if nums[idx] in my_dict:\r\n nums.insert(my_dict[nums[idx]]+1,nums[idx])\r\n for k in range(nums[idx+1],4):\r\n if k in my_dict.keys():\r\n my_dict[k] += 1\r\n del nums[idx+1]\r\n idx += 1\r\n else:\r\n if nums[idx] == 0:\r\n for k in my_dict.keys():\r\n my_dict[k] += 1\r\n my_dict[nums[idx]] = 0\r\n elif nums[idx] == 1:\r\n if 2 in my_dict.keys():\r\n my_dict[2] += 1\r\n if 0 in my_dict.keys():\r\n my_dict[1] = my_dict[0]+1\r\n else:\r\n my_dict[1] = 0\r\n else:\r\n if 1 in my_dict.keys():\r\n my_dict[2] = my_dict[1]+1\r\n elif 0 in my_dict.keys():\r\n my_dict[2] = my_dict[0]+1\r\n else:\r\n my_dict[2] = 0\r\n \r\n nums.insert(my_dict[nums[idx]],nums[idx])\r\n del nums[idx+1]\r\n idx += 1\r\n #print(\"For idx:\",idx-1,nums,my_dict)\r\n \r\n #print(my_dict)\r\n #print(nums) ",
"title": ""
},
{
"docid": "c8ea5b4efb1ac5555207612a42ad92b9",
"score": "0.56693304",
"text": "def reading_order(lines, highlight=None, debug=0):\n order = zeros((len(lines), len(lines)), 'B')\n\n def x_overlaps(u, v):\n return u[1].start < v[1].stop and u[1].stop > v[1].start\n\n def above(u, v):\n return u[0].start < v[0].start\n\n def left_of(u, v):\n return u[1].stop < v[1].start\n\n def separates(w, u, v):\n if w[0].stop < min(u[0].start, v[0].start): return 0\n if w[0].start > max(u[0].stop, v[0].stop): return 0\n if w[1].start < u[1].stop and w[1].stop > v[1].start: return 1\n\n if highlight is not None:\n clf()\n title(\"highlight\")\n imshow(binary)\n ginput(1, debug)\n for i, u in enumerate(lines):\n for j, v in enumerate(lines):\n if x_overlaps(u, v):\n if above(u, v):\n order[i, j] = 1\n else:\n if [w for w in lines if separates(w, u, v)] == []:\n if left_of(u, v): order[i, j] = 1\n if j == highlight and order[i, j]:\n print((i, j), end=' ')\n y0, x0 = sl.center(lines[i])\n y1, x1 = sl.center(lines[j])\n plot([x0, x1 + 200], [y0, y1])\n if highlight is not None:\n print()\n ginput(1, debug)\n return order",
"title": ""
},
{
"docid": "abe54d4e4913a2903dc6cbbe8b48aba3",
"score": "0.5589601",
"text": "def flowchart_sort(\n bounding_box: Rectangle,\n ) -> typing.List[typing.Tuple[Decimal, Decimal]]:\n half_width = bounding_box.width / Decimal(2)\n half_height = bounding_box.height / Decimal(2)\n return [\n (bounding_box.x, bounding_box.y + half_height),\n (bounding_box.x + bounding_box.width, bounding_box.y + half_height),\n (bounding_box.x, bounding_box.y + half_height),\n (bounding_box.x + half_width, bounding_box.y + bounding_box.height),\n (bounding_box.x + bounding_box.width, bounding_box.y + half_height),\n (bounding_box.x + half_width, bounding_box.y),\n (bounding_box.x, bounding_box.y + half_height),\n ]",
"title": ""
},
{
"docid": "f09946724162180acb26ac726d87a515",
"score": "0.5581442",
"text": "def dijkstra(dutch: list):\n red, mid, blue = 0, 0, len(dutch)\n while mid < blue:\n # Element is red, sort it to the beginning of the list\n if dutch[mid] == \"red\":\n dutch[mid], dutch[red] = dutch[red], dutch[mid]\n red, mid = red + 1, mid + 1\n\n # Element is white, leave it\n elif dutch[mid] == \"white\":\n mid += 1\n\n # Element is blue, sort it to the end of the list\n elif dutch[mid] == \"blue\":\n blue -= 1\n dutch[mid], dutch[blue] = dutch[blue], dutch[mid]",
"title": ""
},
{
"docid": "5f238cdab2780cef998710133da07407",
"score": "0.5578445",
"text": "def _sort_plots(self):\n pass",
"title": ""
},
{
"docid": "0afc8db7235fe5af6adaff576d55f2e9",
"score": "0.5562389",
"text": "def sort_two_colourable_strips(self):\n\n\t\tstrip_connectivity = self.strip_connectivity()\n\t\tkey_to_colour = is_network_two_colorable(strip_connectivity)\n\n\t\tif key_to_colour is None:\n\t\t\treturn None\n\n\t\telse:\n\t\t\tred_strips = [key for key, colour in key_to_colour.items() if colour == 0]\n\t\t\tblue_strips = [key for key, colour in key_to_colour.items() if colour == 1]\n\t\t\treturn red_strips, blue_strips",
"title": ""
},
{
"docid": "846f8bf29ec94f7bc7c29d0f09d57d03",
"score": "0.5560389",
"text": "def _sortTraces(\n self,\n rdt,\n cdt\n ):\n\n tmp_rdt = []\n tmp_cdt = []\n\n if(len(rdt) > 0):\n # first, find background trace: (max 'x')\n rdt.sort(key=lambda t: -1*max(list(t['x'])))\n tmp_rdt.append(rdt[0])\n # then, sort top-to-bottom\n r = rdt[1:]\n r.sort(key=lambda t: -1*min(list(t['y'])))\n tmp_rdt += r\n if(len(cdt) > 0):\n # background trace has max 'y'\n cdt.sort(key=lambda t: -1*max(list(t['y'])))\n tmp_cdt.append(cdt[0])\n # sort left to right\n c = cdt[1:]\n c.sort(key=lambda t: min(list(t['x'])))\n tmp_cdt += c\n\n return(tmp_rdt, tmp_cdt)",
"title": ""
},
{
"docid": "58614cb35b02728fd11d32d03052d2b5",
"score": "0.5548507",
"text": "def _sort(self) -> None:\n self.intervals.sort()",
"title": ""
},
{
"docid": "8060d64719f72234e3a2dfcf6322ba44",
"score": "0.554085",
"text": "def drawSegments(self):\n\n colordict = {}\n for track in self:\n for s in track:\n try:\n p = s.plot()\n p[0].set_color(colordict[s.stack])\n except:\n colordict[s.stack] = [random(), random(), random()]\n p[0].set_color(colordict[s.stack])\n show()",
"title": ""
},
{
"docid": "25a38756971634ce992b541e7ba8bcee",
"score": "0.5527703",
"text": "def _geom_kf_sort(kf):\n frame, shape, opacity = kf\n return frame",
"title": ""
},
{
"docid": "7690bb2190743088a2d4a12490d39d9b",
"score": "0.55044585",
"text": "def selection_sort(nums):\n\n for i in range(len(nums)):\n min = i\n for j in range(i+1, len(nums)):\n if nums[min] > nums[j]:\n min = j\n\n nums[i], nums[min] = nums[min], nums[i]\n clock.tick(SPEED)\n draw_bars(nums, nums[i], nums[min])",
"title": ""
},
{
"docid": "98af01597a34773e9df1fb062960bed7",
"score": "0.54755366",
"text": "def sortColors_countingsort(self, nums: List[int]) -> None:\n count = [0 for x in range(3)]\n for num in nums:\n count[num] += 1\n \n for i in range(len(nums)):\n if i < count[0]:\n nums[i] = 0\n elif i < count[0] + count[1]:\n nums[i] = 1\n else:\n nums[i] = 2",
"title": ""
},
{
"docid": "6852c6d248cb0b043f0e3889e9e24098",
"score": "0.54668874",
"text": "def sorting(triangles_list):\n sorted_list = sorted(triangles_list, key=lambda x: x.area, reverse=True)\n return sorted_list",
"title": ""
},
{
"docid": "168fb5d67bd0441c188dd8ce03415ba8",
"score": "0.54454",
"text": "def sort(self):\n index_child = -1\n index_parent = (len(self.data) - 2) // 2\n while self.data[index_child] < self.data[index_parent]:\n self.data[index_child], self.data[index_parent] = self.data[index_parent], self.data[index_child]\n index_child, index_parent = index_parent, (index_parent - 1) // 2\n if self.data[index_child] == self.data[0]:\n break",
"title": ""
},
{
"docid": "bafdc1492fdbebaac564c7aa08511009",
"score": "0.54001844",
"text": "def create_loop(segments):\n blue, red = segments\n loop = []\n\n while len(blue) is not 0 and len(red) is not 0:\n loop.append((blue[0], red[0]))\n del blue[0]\n del red[0]\n\n return loop",
"title": ""
},
{
"docid": "baff772a8b30a02b0dbafe0f55f74f22",
"score": "0.5393696",
"text": "def vertsort():\n args = load_args()\n int_cl = []\n verts = []\n for i in range(len(args.f)):\n fragment = o3d.io.read_point_cloud(args.f[i])\n index = args.k[i]\n cloud = o3d.io.read_point_cloud(args.og[i])\n core_cl, vert = visualize_interest_pts(cloud,index)\n verts.append(vert)\n# int_cl.append(visualize_interest_pts(cloud,index)[0])\n# visualize_multiple([cloud,visualize_interest_pts(cloud,index)])\n for vert in verts:\n vert_sort = vert[vert[:,1].argsort()]",
"title": ""
},
{
"docid": "09517bae7483d4777084b102bf5ec8ac",
"score": "0.5381861",
"text": "def sorted_shreds(shred_list):\n sorted_shred_list = []\n shreds, start_value = shred_list\n print \"\\n\", \"Unsorted Shreds:\\n %r\" % shreds\n print \"\\n\"\n for i in range(len(shreds)):\n if i == 0:\n sorted_shred_list.append(shreds[start_value - 1])\n nextstrip = shreds[start_value - 1][1]\n else:\n sorted_shred_list.append(shreds[nextstrip - 1])\n nextstrip = shreds[nextstrip - 1][1]\n print \"Sorted Shreds: \\n %r\" % sorted_shred_list, \"\\n\"\n\n return sorted_shred_list",
"title": ""
},
{
"docid": "f948afdee7069be4aa9fdbdf9a536f1f",
"score": "0.5302121",
"text": "def get_super_pixels(segments, seg):\r\n colors = []\r\n for row, i in enumerate(segments):\r\n for column, val in enumerate(i):\r\n if seg == val:\r\n colors.append((row,column))\r\n return colors",
"title": ""
},
{
"docid": "1d489461598fdca82aa23ba48a3e5b85",
"score": "0.5289428",
"text": "def _dorder(self, raveled):\n unique, counts = np.unique(raveled, return_counts=True)\n pairs = np.array([unique, counts]).T\n sorting = np.argsort(pairs[:, 0])\n orders = pairs[sorting, 1]\n return tuple(orders)",
"title": ""
},
{
"docid": "9579f4c3d76a3f69e3140e31a7f21c9d",
"score": "0.5284862",
"text": "def lower_covers(partition):\n lc = []\n for i in range(0,len(partition)-1):\n for j in range(i+1,len(partition)):\n if partition[i] != partition[j]:\n new_partition = partition[:]\n del new_partition[j]\n del new_partition[i]\n new_partition.append(partition[i]+partition[j])\n new_partition.sort(reverse=True)\n tup = tuple(new_partition)\n if tup not in lc:\n lc.append(tup)\n return lc",
"title": ""
},
{
"docid": "52cd4df56bb0055077ab67142378e37a",
"score": "0.52798176",
"text": "def topological_sort(self):\n # We start with an empty stack\n self.stack = []\n # Colour is dictionary that associates node keys to colours. The colours are 'white', 'grey' and 'black'.\n self.colour = {node: 'white' for node in self.adj_list.keys()}\n # We call the recursive function to visit a first node. When the function returns, if there are any white \n # nodes remaining, we call the function again for these white nodes\n for start in self.adj_list.keys():\n # If the node is 'white' we call the recursive function to vists the nodes connected to it in DFS order\n if self.colour[start] == 'white':\n # This is a call to topologicalSort_r\n self._topological_sort_r(start)\n # We need to reverse the list, we use a little trick with list slice\n return self.stack[::-1]",
"title": ""
},
{
"docid": "fa19a5905cd556f8ba85a2200677ad83",
"score": "0.52714187",
"text": "def sort(self):\r\n \r\n for i in range(1, len(self.chords)):\r\n j = i\r\n while(j >= 1 and self.chords[j-1].time > self.chords[j].time):\r\n temp_chord = self.chords[j]\r\n self.chords[j] = self.chords[j-1]\r\n self.chords[j-1] = temp_chord\r\n j -= 1",
"title": ""
},
{
"docid": "82f9817cacfb3f5ac77ea3b7215b7abf",
"score": "0.52423507",
"text": "def lower_covers(partition):\n lc = []\n for i in range(0,len(partition)-1):\n for j in range(i+1,len(partition)):\n new_partition = partition[:]\n del new_partition[j]\n del new_partition[i]\n new_partition.append(partition[i]+partition[j])\n new_partition.sort(reverse=True)\n tup = tuple(new_partition)\n if tup not in lc:\n lc.append(tup)\n return lc",
"title": ""
},
{
"docid": "a33c8a8ade324c242bbc19dd439eec34",
"score": "0.5227981",
"text": "def _sort_contour(self, contour: np.ndarray) -> np.ndarray:\n cont = contour.astype(np.int)\n sort = sorted(cont, key=lambda x: (x[1],x[0]))\n index = np.where((cont[:, 0] == sort[0][0]) &\n (cont[:, 1] == sort[0][1]))[0]\n out = np.roll(cont, -index, axis=0)\n return out",
"title": ""
},
{
"docid": "a45f1077e58668c850e95b43ba64819f",
"score": "0.5218186",
"text": "def orderPixels(unorderdPixels):\n xList = []\n yList = []\n for tuple in unorderdPixels:\n xList.append(tuple[0])\n yList.append(tuple[1])\n \n orderedList = [0, 0, 0, 0] \n xList.sort() \n yList.sort()\n for tuple in unorderdPixels:\n if (tuple[0] == xList[0] or tuple[0] == xList[1]) and \\\n (tuple[1] == yList[2] or tuple[1] == yList[3]):\n orderedList[0] = tuple\n elif (tuple[0] == xList[0] or tuple[0] == xList[1]) and \\\n (tuple[1] == yList[0] or tuple[1] == yList[1]):\n orderedList[1] = tuple \n elif (tuple[0] == xList[2] or tuple[0] == xList[3]) and \\\n (tuple[1] == yList[0] or tuple[1] == yList[1]):\n orderedList[2] = tuple \n elif (tuple[0] == xList[2] or tuple[0] == xList[3]) and \\\n (tuple[1] == yList[2] or tuple[1] == yList[3]):\n orderedList[3] = tuple \n return orderedList",
"title": ""
},
{
"docid": "8464f2962368b0c50901750f45998b69",
"score": "0.5197775",
"text": "def random_pixel_sort(color_data):\n rand_num = random.randint(0, 255)\n # rand_num2 = random.randint(0, 255)\n # rand_num3 = random.randint(0, 255)\n color_data = [tuple(rand_shift(i, rand_num) for i in toop) for toop in color_data]\n print(color_data[:2])\n # color_data = [tuple(rand_shift(a, rand_num), rand_shift(b, rand_num2), rand_shift(c, rand_num3) for a,b,c in toop) for toop in color_data]\n # color_data = [tuple((10, 125, 250) for (a, b, c) in toop) for toop in color_data]\n print(rand_num)\n return color_data",
"title": ""
},
{
"docid": "17e7f814bc8489412cbca8a00809bc7e",
"score": "0.51886004",
"text": "def ordered_segments(hash_points, points):\n def segment_length(points):\n \"\"\"\n return the length of the given segment (couple of two points)\n \"\"\"\n return points[0].distance_to(points[1])\n\n # avec hashage\n if hash_points:\n precision = 100.0 # valeur arbitraire\n tables_groups = [Hash(points, precision)]\n while True: # pour simuler un do while\n collision = False\n for index in range(len(tables_groups[0].tables)):\n # \"-1\" car dernier jeu de tables\n if tables_groups[-1].has_collision(index):\n collision = True\n break\n if collision:\n precision = precision/2\n # on pouvait faire en sorte de ne hasher que les tables\n # qui possèdent une collision et pas les 4 toujours\n tables_groups.append(Hash(points, precision))\n else:\n break\n # inversion de la liste des jeux de tables\n tables_groups = iter(reversed(tables_groups))\n # pour éviter d'itérer dans le jeu de table le plus précis car pas de combinaisons\n next(tables_groups)\n # pour éviter de proposer des doublons de segment (représenté par un couple de 2 points ici)\n # on considère également ici que le segment [a,b] est un doublon du segment [b,a]\n seen_segments = set()\n for hash_tables in tables_groups:\n for hash_table in hash_tables.tables:\n for hash_points in hash_table.values():\n if len(hash_points) > 1:\n # toutes les combinaisons de points\n for point1, point2 in combinations(hash_points, 2):\n # on va payer plus cher dans cet itérateur à tester l'appartenance\n # mais moins cher dans les fonctions du graphe car on va éviter\n # des parcours inutiles\n if ((point1, point2) not in seen_segments and\n (point2, point1) not in seen_segments):\n seen_segments.add((point1, point2))\n seen_segments.add((point2, point1))\n yield Segment([point1, point2])\n # sans hachage (itérateur quadratique)\n else:\n for point1, point2 in sorted(combinations(points, 2), key=segment_length):\n yield Segment([point1, point2])",
"title": ""
},
{
"docid": "a7ca35882df457fadfd3232d302050a3",
"score": "0.518572",
"text": "def get_segments(self):",
"title": ""
},
{
"docid": "aab021ed73db0514470085c6738bb0ab",
"score": "0.5166568",
"text": "def pixSort(image, startW=0, startH=0,\n endW=IMAGE_WIDTH, endH=IMAGE_HEIGHT, p=0.8):\n for y in range(startH, endH): # for each line of the pic\n if probability(p):\n line = []\n for x in range(startW, endW):\n try:\n # make list of every pixel RGB value on the line as tuple\n line.append(image.getpixel((y, x)))\n except IndexError: # if out of bounds of the picture\n break\n # backup of the line before sort, to unglitch a channel later\n originalLine = list(line)\n # if we broke out of the previous loop we might get out of bounds\n try:\n line = partialSort(line)\n except ValueError:\n pass # My code is bad, and I should feel bad.\n # restore one of the original channels at random (looks colourful)\n if probability(p * 0.75):\n colour = randrange(3) # 0 = R, 1 = G, 2 = B\n for px in range(len(line)):\n line[px] = ((originalLine[px][0]\n if colour == 0 else line[px][0]),\n (originalLine[px][1]\n if colour == 1 else line[px][1]),\n (originalLine[px][2]\n if colour == 2 else line[px][2]))\n # make the actual changes to the image object\n for x in range(len(line)):\n try:\n image.putpixel((y + (randrange(1, 3)\n if probability(0.1) and DITHER\n else 1),\n startW + x),\n (line[x]))\n except IndexError: # out of bounds of the picture\n break\n return image",
"title": ""
},
{
"docid": "1ed43e0d50c4e528fa2fa0993ec67ef8",
"score": "0.516619",
"text": "def cd_color_segmentation(img, show_image=False):\n # convert from rgb to hsv color space (it might be BGR)\n new_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n new_img = new_img[40:, :]\n # new_img = new_img[220:260, :]\n\n # define lower and upper bound of image values\n # TO DO!\n low_range = np.array( [-50, 70, 250] )\n high_range = np.array( [50, 245, 255] )\n\n # create mask for image with overlapping values\n mask = cv2.inRange(new_img, low_range, high_range)\n\n # filter the image with bitwise and\n filtered = cv2.bitwise_and(new_img, new_img, mask=mask)\n\n # find the contours in the image\n contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n\n x1, y1, x2, y2 = 0, 0, 0, 0\n if len(contours) != 0:\n\t# find contour with max area, which is most likely the cone\n # Solution note: max uses an anonymous function in this case, we can also use a loop...\n contours_max = max(contours, key = cv2.contourArea)\n\n\t# Find bounding box coordinates\n x1, y1, x2, y2 = cv2.boundingRect(contours_max)\n\n\t# Draw the bounding rectangle\n cv2.rectangle(img, (x1, y1), (x1 + x2, y1 + y2), (0, 255, 0), 2)\n\n if show_image:\n cv2.imshow(\"Color segmentation\", img)\n key = cv2.waitKey()\n if key == 'q':\n cv2.destroyAllWindows()\n\n # Return bounding box\n return ((x1, y1), (x1 + x2, y1 + y2))",
"title": ""
},
{
"docid": "e6f945a78ce55d2833d751ff882703a1",
"score": "0.5151514",
"text": "def sort_points(self):\n base = self.points[0]\n del self.points[0]\n self.points.sort(\n key=cmp_to_key(lambda x, y: Point.get_rotation(base, x, y)))\n self.points.insert(0, base)",
"title": ""
},
{
"docid": "65781889d2bf1d8f5ed72eb9399d237d",
"score": "0.51480025",
"text": "def bubble_sort(nums):\n\n for i in range(len(nums)):\n for j in range(len(nums)-1-i):\n if nums[j] > nums[j+1]:\n nums[j], nums[j+1] = nums[j+1], nums[j]\n clock.tick(SPEED)\n draw_bars(nums, nums[j], nums[j+1])",
"title": ""
},
{
"docid": "e4a325381d8abab36087c865213a31ff",
"score": "0.51439965",
"text": "def sort_to_line(vals):\n\tpoint_list = [vals[0]]\n\tvals = np.delete(vals, 0, axis=0)\n\twhile len(vals) > 0:\n\t\tdists = ((point_list[-1]-vals)**2).sum(axis=-1)\n\t\tidx = np.argmin(dists)\n\t\tif vals[idx,0]!=point_list[-1][0] and vals[idx,1]!=point_list[-1][1]:\n\t\t\tpoint_list.append(vals[idx])\n\t\tvals = np.delete(vals, idx, axis=0)\n\tpoint_list = np.array(point_list)\n\treturn point_list",
"title": ""
},
{
"docid": "4a751a2d958d37164ee9ae3cd337cdf3",
"score": "0.5119224",
"text": "def sort_angles_to_components(self):\n angles = self.raw_data[self.orient_name]\n coords = self.raw_data[[self.x_name, self.y_name]]\n component_labels = self.clf.predict(coords)\n ret = [[] for i in range(self.clf.n_components)]\n for angle, component_label in zip(angles, component_labels):\n ret[component_label].append(angle)\n return ret",
"title": ""
},
{
"docid": "15274cbcd0678a37a04d00d35c9ea13a",
"score": "0.51132613",
"text": "def optimal_points(segments):\n points = []\n sorted_segments = sorted(segments, key=lambda x: x.start)\n end = sorted_segments[0].end\n\n for current in sorted_segments:\n if current.start > end:\n points.append(end)\n end = current.end\n elif current.end < end:\n end = current.end\n points.append(end)\n return points",
"title": ""
},
{
"docid": "c029b865a5785c18daad368f14a9cc45",
"score": "0.50969875",
"text": "def _randomize_segmentation_colors(self) -> None:\r\n\r\n black = np.array([0, 0, 0])\r\n\r\n root_dir = Path(self.output_dir)\r\n # Randomize each segmentation color per frame.\r\n for f in root_dir.glob(\"id_*.png\"):\r\n frame = np.array(Image.open(str(f.resolve())))\r\n unique = np.unique(frame.reshape(-1, frame.shape[2]), axis=0)\r\n unique = np.delete(unique, black, axis=0)\r\n replace = unique.copy()\r\n for i in range(len(replace)):\r\n replace[i] = np.array((random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)))\r\n for i in range(len(unique)):\r\n frame = np.where(frame == unique[i], replace[i], frame)\r\n im = Image.fromarray(frame)\r\n\r\n # Save a copy of the image.\r\n image_path = str(root_dir.joinpath(\"id_random_\" + f.stem.split(\"_\")[1] + \".png\").resolve())\r\n im.save(image_path)",
"title": ""
},
{
"docid": "d93958d756bf117032b6668e1c028815",
"score": "0.50712514",
"text": "def sort_cycles(cycles):\n return sorted(cycles, key=lambda cycle: -cycle[1])",
"title": ""
},
{
"docid": "22346b4e17ae0c9d715e8b4f4e2b44a4",
"score": "0.50711644",
"text": "def sepia_transform(red, green, blue):\n new_red = min(math.floor(red * .393 + green * .769 + blue * .189), 255)\n new_green = min(math.floor(red * .349 + green * .686 + blue * .168), 255)\n new_blue = min(math.floor(red * .272 + green * .534 + blue * .131), 255)\n\n return [new_red, new_green, new_blue]",
"title": ""
},
{
"docid": "ff5769504160c9532a13b54b6c472fa1",
"score": "0.50676584",
"text": "def numerositySort(self, cl):\r\n return cl.numerosity",
"title": ""
},
{
"docid": "8a64657a438a3d15629af2743db403d3",
"score": "0.506708",
"text": "def test_groups_are_sorted(self):\n self.assertEqual(\"r1c1,r10c10\", encode(\n ((0, 0), (9, 9)), width=2, height=5, use_boxes=False))\n self.assertEqual(\"r1c1,r10c10\", encode(\n ((9, 9), (0, 0)), width=2, height=5, use_boxes=False))\n self.assertEqual(\"b1p1,b10p10\", encode(\n ((0, 0), (9, 9)), width=2, height=5, use_boxes=True))\n self.assertEqual(\"b1p1,b10p10\", encode(\n ((9, 9), (0, 0)), width=2, height=5, use_boxes=True))",
"title": ""
},
{
"docid": "7f6de07128b439cfcc863b6a68cde786",
"score": "0.5060111",
"text": "def sort_split_names(legend):\n split_position_list = []\n sorted_split_names_list = []\n split_name = \"\"\n with open(legend, \"r\") as f:\n for split_name in f:\n split_name = split_name.strip()\n split = Split(split_name)\n split_position_list.append(\n (int(split.split_pos[-3]), (int(split.split_pos[-2])), (int(split.split_pos[-1]))))\n\n # sort the last element first in the tuple\n split_position_list = sorted(split_position_list, key=lambda t: t[::-1])\n for position in split_position_list:\n sorted_split_names_list.append(regenerate_split_name_from_position(split_name, position))\n return sorted_split_names_list",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="775"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">1ab6d1d0f03396b3d9278e24a23c3d29</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Use the given status structure and upate all of the redis variables with the current values.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "373481df6af4d56f205a3446b94fda97",
"score": "0.7114745",
"text": "def updateRedis(self,status):\n\n self.redis.set('pumpcall',status['PC'])\n self.redis.set('pumpon',status['P'])\n self.redis.set('northcall',status['NC'])\n self.redis.set('northon',status['N'])\n self.redis.set('southcall',status['SC'])\n self.redis.set('southon',status['S'])\n self.redis.set('ditch',status['Ditch'])\n self.redis.set('sump',status['Sump'])\n self.redis.set('ditch_inches',self.loggr.ditchInches(status['Ditch']))\n self.redis.set('sump_inches',self.loggr.sumpInches(status['Sump']))\n\n try:\n self.loggr.logSystemStatus(status['P'] != '0',\n status['N'] != '0',\n status['S'] != '0')\n\n except:\n pass",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "13f3868cf43499654a670d77e85c5e99",
"score": "0.6291984",
"text": "def updateStatus(self):\n\n try :\n st = self.api.getSystemStatus()\n\n if st:\n self.updateRedis(st)\n\n bFast = False\n for key in ['P','PC','N','NC','S','SC']:\n if st[key] != '0':\n bFast = True\n\n if bFast:\n self.dbLogInterval = 5\n self.cosmLogInterval = 10\n else:\n self.dbLogInterval = 60\n self.cosmLogInterval = 60\n except:\n self.lprint(\"Exception updating status\")",
"title": ""
},
{
"docid": "ff333605d38d813c32ff5a249c71bcc4",
"score": "0.6256841",
"text": "def _update_from_status(self, status: str) -> None:\n if len(status) != 4:\n return\n state = status[0]\n mute = status[2]\n\n self._attr_state = STATE_STANDBY if state == \"1\" else STATE_ON\n self._attr_is_volume_muted = mute == \"0\"",
"title": ""
},
{
"docid": "e3eb2eb75935547d7b76ef33fbf77936",
"score": "0.6101226",
"text": "def touch_status(status):\n print \"Updating status: %s\" % status\n timestamp = int(time.time())\n dbclient = boto3.resource('dynamodb')\n table = dbclient.Table(DYNAMO_NAME)\n table.put_item(\n Item={\n 'ip': INSTANCE_IP,\n 'time': timestamp,\n 'status': status\n }\n )",
"title": ""
},
{
"docid": "e0389ebe3a002b016cc7636961157ead",
"score": "0.6098345",
"text": "def update_status_information(self, _status):\r\n pass\r\n #self._update(info_values={}})\r",
"title": ""
},
{
"docid": "232fab56830d3e686f570c3f3d0a4692",
"score": "0.5990322",
"text": "def update_status(self, status):\n self.status = status\n self.notified = False",
"title": ""
},
{
"docid": "fa9419d50508641d64755585be026751",
"score": "0.59634167",
"text": "def update(self, status):\n self.status = status\n print(self.status)",
"title": ""
},
{
"docid": "6e894155ed62ea8172b26ee838ff2083",
"score": "0.5922209",
"text": "async def update_status(self):\n async with self.session.get(self._base_url + \"smobot\") as response:\n full_state = await response.json()\n print(full_state)\n self._status = SmobotStatus(**full_state)",
"title": ""
},
{
"docid": "f64f99e8db1fa84366671b576a5221f4",
"score": "0.58967453",
"text": "async def read(self, internal_status):\n self.deviceUpdate()\n await asyncio.sleep(random.random()) # FUZZING\n self.looptime = time.time()\n\n for key in internal_status.keys():\n internal_status[key] = self.__dict__[key]",
"title": ""
},
{
"docid": "4735883ce23fd483619cf04dfce9bec0",
"score": "0.5849444",
"text": "def assign_status(self, status):\n if status == \"t\":\n self.is_evidence = True\n self.evidence_value = True\n self.value = True\n elif status == \"f\":\n self.is_evidence = True\n self.evidence_value = False\n self.value = False\n elif status == \"?\" or status == \"q\":\n self.is_query = True",
"title": ""
},
{
"docid": "f26a17f1607bb93261ef913c75c87b11",
"score": "0.5841516",
"text": "def _set_status(self, s):\n temp = self.status\n self.status = s\n return temp",
"title": ""
},
{
"docid": "8e67febd7b6bd3c737d9de78248e61a4",
"score": "0.58198625",
"text": "def set_autelion(status: dict) -> None:\n try:\n autelion = Autelion(status=status, updated_at=datetime.utcnow())\n\n logger.info('Updating cache')\n\n RedisCache().mset({\n 'autelion_status': json.dumps(autelion.status),\n 'autelion_updated_at': autelion.updated_at.isoformat(),\n })\n\n except TypeError as ex:\n logger.error('Unable to format status as JSON')\n except redis.RedisError as ex:\n logger.error('Could not write to Redis: %s', ex)",
"title": ""
},
{
"docid": "1222842376059c32197be52c8b9b8cd8",
"score": "0.58145714",
"text": "def _set_result_and_status(self, result, status):\n\t\tself.result = result\n\t\tself.status = status",
"title": ""
},
{
"docid": "83e7d05bca52c641efb80cf59033a052",
"score": "0.57646626",
"text": "def update_status(status, session):\n try:\n session.query(orm.Statuses).filter_by(status_id=status['id']). \\\n update({\"reposts_count\": status['reposts_count'], \\\n \"comments_count\": status['comments_count'], \\\n \"attitudes_count\": status['attitudes_count'], \\\n }, \\\n synchronize_session=False \\\n )\n except:\n logger.error('update_status() error..')",
"title": ""
},
{
"docid": "53021bcdd625a833d13060b416098f28",
"score": "0.57518685",
"text": "async def update_status(self):\n self._status = await self.http_request(\"get\", \"status\")",
"title": ""
},
{
"docid": "455618a78464ae8527d30e9f1d5ebe3d",
"score": "0.573571",
"text": "def update_total_stats(self, servers, status):\n \n # which servers are OK\n members = servers.keys()\n members_not_deleted = [i for i in members\n if servers.get(i) and servers.get(i).get('deleted') != True]\n members_ok = [i for i in members_not_deleted\n if status.get(i) and status.get(i).get('status') == 'ok' and status.get(i).get('score') != None\n and servers.get(i).get('out_of_service') != True]\n \n self.store_rotate_stats('total', members_ok, members_not_deleted, status)",
"title": ""
},
{
"docid": "689455695e8d2f880b0bf85856d67f46",
"score": "0.5730154",
"text": "def __getitem__(self, status):\n return self.status[status]",
"title": ""
},
{
"docid": "9576d240b0e5dd5df2ae706b9db2f253",
"score": "0.5711132",
"text": "def __update_status(self, c_id, status):\n try:\n entry = yaml.load(self.__db[c_id], yaml.RoundTripLoader)\n except KeyError:\n raise ValueError(\"Command \" + repr(c_id) + \" has not been submitted yet.\")\n entry[\"status\"] = status\n self.__db[c_id] = yaml.dump(entry, Dumper=yaml.RoundTripDumper)\n self.__db.sync()",
"title": ""
},
{
"docid": "8e895eba78b29bdb7db242c189b53479",
"score": "0.5692924",
"text": "def _update_status(self):\n\n status_resp = requests.get(\"%s/status-json.xsl\" % \n self.config['server_url'], \n headers={'User-Agent': USERAGENT})\n\n \n if status_resp.status_code == 200:\n status_text = re.sub(\"([^\\\\\\\\])([\\\"\\']): *- *,([\\\"\\'])\",\n \"\\\\1\\\\2:\\\"-\\\",\\\\3\", status_resp.text)\n \n status_obj = json.loads(status_text)\n if ('icestats' in status_obj \n and 'source' in status_obj['icestats']):\n # when multiple mountpoints are present, they're \n # collected into a JSON array\n\n status = status_obj['icestats']['source']\n self.server_status = (status \n if type(status) == type([]) else [status]\n self.status_timestamp = time.time()\n\n else: \n FileLogger.error(\"Error querying Icecast server status:\n %s\" % status_resp.text)",
"title": ""
},
{
"docid": "add4a7b9f3722d980975c7616fd5aca9",
"score": "0.5657394",
"text": "def resetStatuses(self):\n if self.status[0] == \"Badly Poisoned\":\n self.status[1] = 14\n self.vStatus = {}",
"title": ""
},
{
"docid": "ea6494b91f5e0fd346c3b6eb08b4b19f",
"score": "0.56184345",
"text": "def update_status(self, new_status: str) -> None:\n if self.db_status == new_status:\n return # Noop, this is already the case\n logger.debug(f\"Updating {self} to {new_status}\")\n if self.db_status in AgentState.complete():\n logger.info(f\"Updating {self} from final status to {new_status}\")\n\n old_status = self.db_status\n self.db.update_agent(self.db_id, status=new_status)\n self.db_status = new_status\n if self.agent_in_active_run():\n live_run = self.get_live_run()\n live_run.loop_wrap.execute_coro(live_run.worker_pool.push_status_update(self))\n if new_status in [\n AgentState.STATUS_RETURNED,\n AgentState.STATUS_DISCONNECT,\n AgentState.STATUS_TIMEOUT,\n ]:\n # Disconnect statuses should free any pending acts\n self.has_live_update.set()\n self.did_submit.set()\n if old_status == AgentState.STATUS_WAITING:\n # Waiting agents' unit can be reassigned, as no work\n # has been done yet.\n unit = self.get_unit()\n logger.debug(f\"Clearing {self} from {unit} for update to {new_status}\")\n unit.clear_assigned_agent()\n\n # Metrics changes\n ACTIVE_AGENT_STATUSES.labels(status=old_status, agent_type=\"main\").dec()\n ACTIVE_AGENT_STATUSES.labels(status=new_status, agent_type=\"main\").inc()\n if old_status not in AgentState.complete() and new_status in AgentState.complete():\n ACTIVE_WORKERS.labels(worker_id=self.worker_id, agent_type=\"main\").dec()",
"title": ""
},
{
"docid": "0f77848ca96e245a91157f9ddd44347c",
"score": "0.56041956",
"text": "def evolve_status(stone_pos):\n stone = stone_db.get_stone(*stone_pos)\n stone_db.update_status(stone[\"id\"], {\n \"Locked\": \"Pending\",\n \"Pending\": \"Unlocked\",\n \"Unlocked\": \"Unlocked\",\n }[stone[\"status\"]])",
"title": ""
},
{
"docid": "12b510105ea517e8f56615f3e1fd3365",
"score": "0.5602532",
"text": "def setup_status_variables(self, controller, **kwargs):\n where = [\"S\" if 'comm' in kwargs.keys() else \"MS\", \"status\"]\n self.add_variable(controller, name='restart', where=where, init=False)\n self.add_variable(controller, name='restarts_in_a_row', where=where, init=0)",
"title": ""
},
{
"docid": "2b080310e2831d87d74613a0cb15cdf5",
"score": "0.5597029",
"text": "def unite_statuses(statuses, update):\n result = {}\n for key, value in statuses.iteritems():\n if key in update:\n upd_status = update[key]\n res_status = {\n \"exitstatus\" : max(value[\"exitstatus\"], upd_status[\"exitstatus\"]),\n \"log\" : value[\"log\"] + \"\\n\" + upd_status[\"log\"]\n }\n result[key] = res_status\n else:\n result[key] = value\n return result",
"title": ""
},
{
"docid": "e19182fd964fb4dbc9f7ab7fd70bbf38",
"score": "0.5595016",
"text": "def build_status():\n\n Mapping.set_db(settings.db)\n Feed.set_db(settings.db)\n Route.set_db(settings.db)\n RouteStop.set_db(settings.db)\n Vehicle.set_db(settings.db)\n Stop.set_db(settings.db)\n\n # Mappings cache in the application server.\n mappings = {}\n\n # Remove all damaged feed documents; these cannot be processed.\n docs = feed.remove_damaged( settings.db, settings.db.view( \"feed/new\" ) )\n print( \"Cleanup\", docs )\n\n # If the change notification is a mapping...\n # Or. Do all new mappings.\n counts= mapping.refresh_mapping_cache(mappings, Mapping.view('mapping/new', descending=True))\n print( \"Mapping\", dict(counts) )\n\n # If the change notification is a feed...\n docs= status.remove_old(settings.db)\n print( \"Status Removal\", docs )\n\n start= datetime.datetime.now()\n counts= feed.transform_new( mappings, feed.new_feed_iter(), status.track_arrival, status.track_location )\n end= datetime.datetime.now()\n print( \"Transform {0} reports in {1}\".format( dict(counts), end-start ) )\n\n # Not every time we receive a feed; only once per day.\n docs= feed.remove_old( settings.db )\n print( \"Feed Removal\", docs )",
"title": ""
},
{
"docid": "56eea406236ce41074a12c402a01fae2",
"score": "0.55837697",
"text": "async def _status(self, ctx: commands.Context, status: str = None):\n statuses = {\n \"online\": [discord.Status.online, \"Online\"],\n \"idle\": [discord.Status.idle, \"Idle\"],\n \"dnd\": [discord.Status.dnd, \"Dnd\"],\n \"invisible\": [discord.Status.invisible, \"Invisible\"],\n \"offline\": [discord.Status.invisible, \"Invisible\"],\n }\n if status.lower() in statuses:\n chosenstatus = statuses.get(status.lower())\n elif status is None:\n return await ctx.send(\"Nothing set.\")\n else:\n return await ctx.send(f\"Invalid status: `{status}`\")\n status = chosenstatus[0]\n game = ctx.bot.guilds[0].me.activity if len(ctx.bot.guilds) > 0 else None\n try:\n await ctx.bot.change_presence(status=status, activity=game)\n except Exception as e:\n await ctx.send(f\"Failed to set status. {e}\")\n await ctx.send(f\"Set status to `{chosenstatus[1]}`\")",
"title": ""
},
{
"docid": "d47d1a0a58e66bc64a029d19902c8349",
"score": "0.5576187",
"text": "def update_status_information(self, status):\r\n info_values = {}\r\n relays = status.get(STATUS_RESPONSE_RELAYS)\r\n if relays:\r\n relay = relays[self._channel]\r\n if relay.get(STATUS_RESPONSE_RELAY_OVER_POWER) is not None:\r\n info_values[INFO_VALUE_OVER_POWER] = \\\r\n relay.get(STATUS_RESPONSE_RELAY_OVER_POWER)\r\n\r\n self._update(info_values=info_values)",
"title": ""
},
{
"docid": "6b7c5c0c37cdafc0e8dc66e10470596a",
"score": "0.5565295",
"text": "def initRedisValues(self):\n\n pRequest = self.getSystemValue('pumprequest','0')\n nRequest= self.getSystemValue('northrequest','0')\n sRequest= self.getSystemValue('southrequest','0')\n\n self.currCommandValues['pump'] = pRequest != '0'\n self.currCommandValues['north'] = nRequest != '0'\n self.currCommandValues['south'] = sRequest != '0'\n\n for key in ['pump','north','south']:\n self.api.sendBool(key,self.currCommandValues[key])\n self.lprint(\"%s set to %s\" % (key,self.currCommandValues[key]))",
"title": ""
},
{
"docid": "39863df706eaca9066f0c2c18d5072aa",
"score": "0.5560727",
"text": "def __set_status(self, status):\n self.__status = status",
"title": ""
},
{
"docid": "8ff0427c42bd6a7756171b32f5208f94",
"score": "0.55580986",
"text": "def get_status(dummy_status):\n raw_status = dummy_status\n return raw_status",
"title": ""
},
{
"docid": "97b60b46c7a764e702ed19e0a3ff9bfe",
"score": "0.55552596",
"text": "def update_progress_to_db(req,role_id_list,status,hosts_list,host_ip=None):\n for role_id in role_id_list:\n role_hosts = daisy_cmn.get_hosts_of_role(req, role_id)\n for host_id_ip in hosts_list:\n host_ip_tmp=host_id_ip.values()[0]\n host_id_tmp=host_id_ip.keys()[0]\n if host_ip:\n for role_host in role_hosts:\n if (host_ip_tmp == host_ip and\n role_host['host_id']== host_id_tmp):\n role_host_meta = {}\n if 0 == cmp(status, tecs_state['UPDATING']):\n role_host_meta['progress'] = 10\n role_host_meta['messages'] = 'TECS upgrading'\n if 0 == cmp(status, tecs_state['UPDATE_FAILED']):\n role_host_meta['messages'] = 'TECS upgraded failed'\n elif 0 == cmp(status, tecs_state['ACTIVE']):\n role_host_meta['progress'] = 100\n role_host_meta['messages'] = 'TECS upgraded successfully'\n if role_host_meta:\n role_host_meta['status'] = status\n daisy_cmn.update_role_host(req,\n role_host['id'],\n role_host_meta)\n else:\n role = {}\n if 0 == cmp(status, tecs_state['UPDATING']):\n for role_host in role_hosts:\n role_host_meta = {}\n role_host_meta['status'] = status\n role_host_meta['progress'] = 0\n role_host_meta['messages'] = 'TECS upgrading'\n daisy_cmn.update_role_host(req,\n role_host['id'],\n role_host_meta)\n role['progress']=0\n role['messages'] = 'TECS upgrading'\n if 0 == cmp(status, tecs_state['UPDATE_FAILED']):\n role['messages'] = 'TECS upgraded failed'\n elif 0 == cmp(status, tecs_state['ACTIVE']):\n role['progress'] = 100\n role['messages'] = 'TECS upgraded successfully'\n if role:\n role['status'] = status\n daisy_cmn.update_role(req, role_id, role)",
"title": ""
},
{
"docid": "28d4041a89371d7c8e39a85f5ae3848a",
"score": "0.5540759",
"text": "def _update_status(self):\n status_line = self._bridge._make_request(\"ls2 \" + self._uid)\n\n # Status line looks like\n # L7.001 ON 073.0F 077.7F Low Cool OK - 0\n fields = re.split(r\"\\s+\", status_line.strip())\n if len(fields) != 9:\n raise Exception(\"Unexpected status line format: \" + str(fields))\n\n self._is_on = fields[1] == \"ON\"\n self._unit = \"imperial\" if fields[2][-1] == \"F\" else \"celsius\"\n self._thermostat = float(fields[2][:-1])\n self._temperature = float(fields[3][:-1])\n self._fan_speed = fields[4].lower()\n self._mode = fields[5].lower()\n\n swing_line = self._bridge._make_request(\"query {} s\".format(self._uid))\n self._swing_mode = _SWING_CHAR_TO_NAME[swing_line.strip()]\n\n self._last_refresh_time = time.time()",
"title": ""
},
{
"docid": "2bfcb96602468ff1f701fbb9c2edbd81",
"score": "0.5538246",
"text": "def put_status(self, duration):",
"title": ""
},
{
"docid": "b6af59cd2200ce633826c5aa139cb36b",
"score": "0.5526064",
"text": "def collect_status_update(self, status):\r\n\r\n self._previous_collect_status = self._actual_collect_status\r\n self._actual_collect_status = status\r\n if self._collecting:\r\n if self._actual_collect_status == \"error\":\r\n self.emit_collection_failed()\r\n elif self._actual_collect_status == \"collecting\":\r\n self.store_image_in_lims_by_frame_num(1)\r\n if self._previous_collect_status is None:\r\n if self._actual_collect_status == 'busy':\r\n logging.info(\"Preparing collecting...\") \r\n elif self._previous_collect_status == 'busy':\r\n if self._actual_collect_status == 'collecting':\r\n self.emit(\"collectStarted\", (self.owner, 1))\r\n elif self._previous_collect_status == 'collecting':\r\n if self._actual_collect_status == \"ready\":\r\n self.emit_collection_finished()\r\n elif self._actual_collect_status == \"aborting\":\r\n logging.info(\"Aborting...\")\r\n self.emit_collection_failed()",
"title": ""
},
{
"docid": "8272368bd4cc874db5ff89466205ae4b",
"score": "0.5512881",
"text": "def updStatus(self, status):\n self.eel.updStatus(StatusDict[status])",
"title": ""
},
{
"docid": "28dda79ba6067e917e475851dfda68b7",
"score": "0.55028355",
"text": "def setStatus(self, status):\n\n neutralColor = (68, 68, 68)\n visualStatusData = {\n 'default': {\n 'baseColor': (80, 80, 80),\n 'colorAnimation': None,\n 'cursor': QC.Qt.PointingHandCursor,\n 'enabled': True \n },\n\n 'waiting': {\n 'baseColor': neutralColor,\n 'colorAnimation': {\n 0.00: neutralColor,\n 0.50: (110, 110, 110),\n 1.00: neutralColor \n }, \n 'cursor': QC.Qt.WaitCursor,\n 'enabled': True \n },\n\n 'warning': {\n 'baseColor': neutralColor,\n 'colorAnimation': {\n 0.00: neutralColor,\n 0.65: neutralColor,\n 0.70: (200, 200, 100), \n 1.00: neutralColor \n }, \n 'cursor': QC.Qt.PointingHandCursor,\n 'enabled': True \n },\n\n 'fatality': {\n 'baseColor': (0, 0, 0),\n 'colorAnimation': {\n 0.00: neutralColor,\n 0.30: (255, 100, 100),\n 0.50: (255, 180, 100),\n 1.00: neutralColor \n }, \n 'cursor': QC.Qt.PointingHandCursor, \n 'enabled': True \n },\n\n 'disabled': {\n 'baseColor': neutralColor,\n 'colorAnimation': None, \n 'cursor': QC.Qt.ArrowCursor, \n 'enabled': False\n },\n\n 'success': {\n 'baseColor': (0, 0, 0),\n 'colorAnimation': {\n 0.00: neutralColor,\n 0.30: (100, 155, 100),\n 0.50: (200, 255, 120),\n 1.00: neutralColor \n }, \n 'cursor': QC.Qt.PointingHandCursor, \n 'enabled': True\n } \n }\n \n\n if status not in visualStatusData:\n MC.error('[FATAL] The status \"{0}\" is unknown!\\nPossible choices: {1}!'.format(status, ', '.join(visualStatusData)))\n\n self._status = status\n statusData = visualStatusData[status]\n\n if self._pulsation:\n # There's already an animation object; by using the start policy\n # 'QC.QAbstractAnimation.DeleteWhenStopped' when stopped it should be destroyed\n self._pulsation.stop()\n self._pulsation = None\n\n\n # Set the cursor\n self.setCursor(statusData['cursor'])\n \n # Enable/disable\n self.setEnabled(statusData['enabled'])\n\n if statusData['colorAnimation']:\n # The status requires a color pulsation\n self._pulsation = QC.QPropertyAnimation(self, '_pulsationProperty') \n\n animationCurve = statusData['colorAnimation'] \n for key in animationCurve:\n self._pulsation.setKeyValueAt(key, QG.QColor(*animationCurve[key]))\n\n self._pulsation.setDuration(1000)\n self._pulsation.setLoopCount(-1)\n #??? self._pulsation.setEasingCurve(QC.Qt.QEasingCurve.Linear)\n self._pulsation.start(policy=QC.QAbstractAnimation.DeleteWhenStopped)\n \n else:\n # No color pulsation: set the base color\n colorString = str(QG.QColor(*statusData['baseColor']).getRgb())\n self.setStyleSheet('background-color: rgb' + colorString)",
"title": ""
},
{
"docid": "7d066abaf3e1bfef2f603db57373c1a5",
"score": "0.55017895",
"text": "def hive_status_updates():\n \n rospy.Service('hive_status_updates', Status, handle_hive_status_requests)",
"title": ""
},
{
"docid": "f65e9a73203206c7ce4c3674c7109f40",
"score": "0.5495111",
"text": "def __on_status(self, status):\n self.__last_status[0] = status\n if status['state'] == 'stopped' or status['state'] == 'halted':\n self.__launched = False\n self.__sim = None",
"title": ""
},
{
"docid": "c71e286a23c0f25ea51875164ce8a887",
"score": "0.54902077",
"text": "def status(self, status):\n self._status = status",
"title": ""
},
{
"docid": "c71e286a23c0f25ea51875164ce8a887",
"score": "0.54902077",
"text": "def status(self, status):\n self._status = status",
"title": ""
},
{
"docid": "c71e286a23c0f25ea51875164ce8a887",
"score": "0.54902077",
"text": "def status(self, status):\n self._status = status",
"title": ""
},
{
"docid": "c71e286a23c0f25ea51875164ce8a887",
"score": "0.54902077",
"text": "def status(self, status):\n self._status = status",
"title": ""
},
{
"docid": "c71e286a23c0f25ea51875164ce8a887",
"score": "0.54902077",
"text": "def status(self, status):\n self._status = status",
"title": ""
},
{
"docid": "c71e286a23c0f25ea51875164ce8a887",
"score": "0.54902077",
"text": "def status(self, status):\n self._status = status",
"title": ""
},
{
"docid": "c71e286a23c0f25ea51875164ce8a887",
"score": "0.54902077",
"text": "def status(self, status):\n self._status = status",
"title": ""
},
{
"docid": "c71e286a23c0f25ea51875164ce8a887",
"score": "0.54902077",
"text": "def status(self, status):\n self._status = status",
"title": ""
},
{
"docid": "c71e286a23c0f25ea51875164ce8a887",
"score": "0.54902077",
"text": "def status(self, status):\n self._status = status",
"title": ""
},
{
"docid": "8895cc4e90679d973f44627ba94d6e84",
"score": "0.54824764",
"text": "def update_status(self) -> str:\n data = (\n self._cognite_client.__getattribute__(self._JOB_TYPE.value)._get(f\"{self._status_path}{self.job_id}\").json()\n )\n self.status = data[\"status\"]\n self.status_time = data.get(\"statusTime\")\n self.start_time = data.get(\"startTime\")\n self.created_time = self.created_time or data.get(\"createdTime\")\n self.error_message = data.get(\"errorMessage\")\n self._result = {k: v for k, v in data.items() if k not in self._COMMON_FIELDS}\n return self.status",
"title": ""
},
{
"docid": "3882392290db9f9ad81c4bd6b2802274",
"score": "0.5455646",
"text": "def update_status(self):\n self._clear_status()\n self._update_status()",
"title": ""
},
{
"docid": "e9d504bf4d2c9e0f5efe9b87796276a7",
"score": "0.5439403",
"text": "def statusPrint(self):\n\n r = self.redis",
"title": ""
},
{
"docid": "1b7b132a9e63c8c9bbdee809845531d6",
"score": "0.5409085",
"text": "def get_status(self):\n status = {}\n for _, bt_manager in self.tasks.iteritems():\n pretty_hash = bt_manager.metainfo.pretty_info_hash\n speed = bt_manager.get_speed()\n num_connections = bt_manager.get_num_connections()\n\n status[pretty_hash] = {\n \"state\": bt_manager.status,\n \"speed_up\": speed[\"up\"],\n \"speed_down\": speed[\"down\"],\n \"num_seeds\": num_connections[\"server\"],\n \"num_peers\": num_connections[\"client\"],\n }\n try:\n status[\"all\"][\"speed_up\"] += status[pretty_hash][\"speed_up\"] \n status[\"all\"][\"speed_down\"] += status[pretty_hash][\"speed_down\"] \n except KeyError:\n status[\"all\"] = {\n \"speed_up\": status[pretty_hash][\"speed_up\"], \n \"speed_down\": status[pretty_hash][\"speed_down\"]\n }\n\n\n return status",
"title": ""
},
{
"docid": "aa24dc2675ea8ac33de6707c6b79fe82",
"score": "0.54028684",
"text": "def update_status(self, status):\n self.table.update_item(\n Key={\n \"releaseName\": self.release_name\n },\n UpdateExpression=\"SET currentState = :state\",\n ExpressionAttributeValues={\n \":state\": status\n }\n )",
"title": ""
},
{
"docid": "8b372b33bca28e3502d9558bf4fc6e8f",
"score": "0.5388374",
"text": "def update_status(self, status) -> NoReturn:\n self.status = self.status and status",
"title": ""
},
{
"docid": "4b8c6e94b8d6cf26fa043716754bddc7",
"score": "0.537686",
"text": "def _infere_status_from_data(self, *args: Any, **kwargs: Any) -> None:\n\n data = kwargs.get('data') or args[0] if args else {}\n if 'status' in data:\n self.status = data['status']\n elif 'success' in data:\n smap = {True: 'succeeded', False: 'failed'}\n self.status = smap[data['success']]\n else:\n self.status = 'succeeded' # almost safe, trust me",
"title": ""
},
{
"docid": "930d6976af5e6618ab419a0d341c21a1",
"score": "0.5369409",
"text": "def thread_status(self, status): # general function to get datas/infos from all threads back to the main\n\n if status.command == \"Update_Status\":\n if len(status.attributes) > 2:\n self.update_status(status.attributes[0], wait_time=self.wait_time, log_type=status.attributes[1])\n else:\n self.update_status(status.attributes[0], wait_time=self.wait_time)\n\n elif status.command == \"ini_stage\":\n # status.attributes[0]=edict(initialized=bool,info=\"\", controller=)\n self.update_status(\"Stage initialized: {:} info: {:}\".format(status.attributes[0]['initialized'],\n status.attributes[0]['info']),\n wait_time=self.wait_time)\n if status.attributes[0]['initialized']:\n self.controller = status.attributes[0]['controller']\n self.set_enabled_move_buttons(enable=True)\n self.ui.Ini_state_LED.set_as_true()\n self.initialized_state = True\n else:\n self.initialized_state = False\n if self.initialized_state:\n self.get_position()\n self.init_signal.emit(self.initialized_state)\n\n elif status.command == \"close\":\n try:\n self.update_status(status.attributes[0], wait_time=self.wait_time)\n self.stage_thread.exit()\n self.stage_thread.wait()\n finished = self.stage_thread.isFinished()\n if finished:\n pass\n delattr(self, 'stage_thread')\n else:\n self.update_status('thread is locked?!', self.wait_time, 'log')\n except Exception as e:\n self.logger.exception(str(e))\n self.initialized_state = False\n self.init_signal.emit(self.initialized_state)\n\n elif status.command == \"check_position\":\n self.ui.Current_position_sb.setValue(status.attributes[0])\n self.move_moving_signal.emit(self.title, status.attributes[0])\n self.current_position = status.attributes[0]\n if self.settings.child('main_settings', 'tcpip', 'tcp_connected').value() and self.send_to_tcpip:\n self.command_tcpip.emit(ThreadCommand('position_is', status.attributes))\n\n elif status.command == \"move_done\":\n self.ui.Current_position_sb.setValue(status.attributes[0])\n self.current_position = status.attributes[0]\n self.move_done_bool = True\n self.ui.Move_Done_LED.set_as_true()\n self.move_done_signal.emit(self.title, status.attributes[0])\n if self.settings.child('main_settings', 'tcpip', 'tcp_connected').value() and self.send_to_tcpip:\n self.command_tcpip.emit(ThreadCommand('move_done', status.attributes))\n\n elif status.command == \"Move_Not_Done\":\n self.ui.Current_position_sb.setValue(status.attributes[0])\n self.current_position = status.attributes[0]\n self.move_done_bool = False\n self.ui.Move_Done_LED.set_as_false()\n self.command_stage.emit(ThreadCommand(command=\"move_Abs\", attributes=[self.target_position]))\n\n elif status.command == 'update_main_settings':\n # this is a way for the plugins to update main settings of the ui (solely values, limits and options)\n try:\n if status.attributes[2] == 'value':\n self.settings.child('main_settings', *status.attributes[0]).setValue(status.attributes[1])\n elif status.attributes[2] == 'limits':\n self.settings.child('main_settings', *status.attributes[0]).setLimits(status.attributes[1])\n elif status.attributes[2] == 'options':\n self.settings.child('main_settings', *status.attributes[0]).setOpts(**status.attributes[1])\n except Exception as e:\n self.logger.exception(str(e))\n\n elif status.command == 'update_settings':\n # ThreadCommand(command='update_settings',attributes=[path,data,change]))\n try:\n self.settings.sigTreeStateChanged.disconnect(\n self.parameter_tree_changed) # any changes on the settings will update accordingly the detector\n except Exception:\n pass\n try:\n if status.attributes[2] == 'value':\n self.settings.child('move_settings', *status.attributes[0]).setValue(status.attributes[1])\n elif status.attributes[2] == 'limits':\n self.settings.child('move_settings', *status.attributes[0]).setLimits(status.attributes[1])\n elif status.attributes[2] == 'options':\n self.settings.child('move_settings', *status.attributes[0]).setOpts(**status.attributes[1])\n elif status.attributes[2] == 'childAdded':\n child = Parameter.create(name='tmp')\n child.restoreState(status.attributes[1][0])\n self.settings.child('move_settings', *status.attributes[0]).addChild(status.attributes[1][0])\n\n except Exception as e:\n self.logger.exception(str(e))\n self.settings.sigTreeStateChanged.connect(\n self.parameter_tree_changed) # any changes on the settings will update accordingly the detector\n\n elif status.command == 'raise_timeout':\n self.raise_timeout()\n\n elif status.command == 'outofbounds':\n self.bounds_signal.emit(True)\n\n elif status.command == 'show_splash':\n self.ui.settings_tree.setEnabled(False)\n self.splash_sc.show()\n self.splash_sc.raise_()\n self.splash_sc.showMessage(status.attributes[0], color=Qt.white)\n\n elif status.command == 'close_splash':\n self.splash_sc.close()\n self.ui.settings_tree.setEnabled(True)\n\n elif status.command == 'set_allowed_values':\n if 'decimals' in status.attributes:\n self.ui.Current_position_sb.setDecimals(status.attributes['decimals'])\n self.ui.Abs_position_sb.setDecimals(status.attributes['decimals'])\n self.ui.Abs_position_sb_bis.setDecimals(status.attributes['decimals'])\n if 'minimum'in status.attributes:\n self.ui.Current_position_sb.setMinimum(status.attributes['minimum'])\n self.ui.Abs_position_sb.setMinimum(status.attributes['minimum'])\n self.ui.Abs_position_sb_bis.setMinimum(status.attributes['minimum'])\n if 'maximum'in status.attributes:\n self.ui.Current_position_sb.setMaximum(status.attributes['maximum'])\n self.ui.Abs_position_sb.setMaximum(status.attributes['maximum'])\n self.ui.Abs_position_sb_bis.setMaximum(status.attributes['maximum'])\n if 'step'in status.attributes:\n self.ui.Current_position_sb.setSingleStep(status.attributes['step'])\n self.ui.Abs_position_sb.setSingleStep(status.attributes['step'])\n self.ui.Abs_position_sb_bis.setSingleStep(status.attributes['step'])",
"title": ""
},
{
"docid": "e2fede95e8e96fe70e812edf0bd33a4c",
"score": "0.53677565",
"text": "def load_status(self, status2load):\n\n self.status_dict = self._load_status(status2load)\n\n # if not isinstance(self.status_dict, dict):\n # self.status_dict = dict()\n\n # if not isinstance(self.status_dict, dict):\n # logger.error(' The loaded object was not a dictionary.')",
"title": ""
},
{
"docid": "f8a7c0a4fdc4a255c167ae8789679311",
"score": "0.53608286",
"text": "def _write_status(self, status):",
"title": ""
},
{
"docid": "2ad05aa057a7f785ea28cfd776a932ad",
"score": "0.53589004",
"text": "def DecodeStatus1(self, data, timestamp):\n self.status1['timestamp'] = timestamp\n CurrentList = self.GetCurrent(data)\n keys = ['chan_0', 'chan_1','chan_2','chan_3','chan_4','chan_5']\n self.status1.update(dict(zip(keys, CurrentList)))",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "d892fab9580da11953f1cf694d468f0b",
"score": "0.5350294",
"text": "def status(self, status):\n\n self._status = status",
"title": ""
},
{
"docid": "fa0099821e9372c6658a6bb38868d81e",
"score": "0.5349482",
"text": "def set_status(status: SensorStatus) -> None:\n g.status = status",
"title": ""
},
{
"docid": "761b1b8170e69339d463ececd2c4a017",
"score": "0.53384125",
"text": "def update_status_information(self):\r\n self._last_update_status_info = datetime.now()\r\n\r\n status = self.http_get('/status', False)\r\n if status == {}:\r\n return\r\n\r\n \"\"\"Put status in info_values\"\"\"\r\n info_values = {}\r\n for name, attr in BLOCK_INFO_VALUES.items():\r\n data = status\r\n path = attr[ATTR_PATH] \r\n for key in path.split('/'):\r\n data = data.get(key,None) if data is not None else None\r\n if data is not None:\r\n info_values[name] = data\r\n\r\n if info_values.get(INFO_VALUE_CLOUD_ENABLED)==True:\r\n if info_values.get(INFO_VALUE_CLOUD_CONNECTED):\r\n info_values[INFO_VALUE_CLOUD_STATUS] = 'connected'\r\n else:\r\n info_values[INFO_VALUE_CLOUD_STATUS] = 'disconnected'\r\n else:\r\n info_values[INFO_VALUE_CLOUD_STATUS] = 'disabled'\r\n\r\n self.info_values = info_values\r\n self._raise_updated()\r\n\r\n for dev in self.devices:\r\n dev.update_status_information(status)",
"title": ""
},
{
"docid": "65dfe02b9f6900e136cc220265833c57",
"score": "0.5334551",
"text": "def gotStatus(self, version, status, message):\n self.version, self.status, self.message = version, status, message",
"title": ""
},
{
"docid": "60a340431fb27447152dca0d32abb72a",
"score": "0.53324383",
"text": "def process_status(self, result):\n data = result[7:-4]\n self.state.set_state(GMCentred = int(data[0:4], 16) & 1,\n GMInbeam = int(data[0:4], 16) >> 1 & 1,\n GMMoving = int(data[0:4], 16) >> 2 & 1,\n GMFailure = int(data[0:4], 16) >> 3 & 1,\n FilterInit = int(data[0:4], 16) >> 4 & 1,\n FilterCentred = int(data[0:4], 16) >> 5 & 1,\n FilterMoving = int(data[0:4], 16) >> 6 & 1,\n FilterFailure = int(data[0:4], 16) >> 7 & 1,\n ARCMirror = int(data[0:4], 16) >> 8 & 1,\n ARC1 = int(data[0:4], 16) >> 9 & 1,\n ARC2 = int(data[0:4], 16) >> 10 & 1,\n SlitShutter = int(data[0:4], 16) >> 11 & 1,\n SlitIllumination = int(data[0:4], 16) >> 12 & 1,\n RearOfSlitMirror = int(data[0:4], 16) >> 13 & 1,\n HartmanA = int(data[0:4], 16) >> 14 & 1,\n HartmanB = int(data[0:4], 16) >> 15 & 1,\n\n SlitWidthInitPos = int(data[4:8], 16) >> 0 & 1,\n SlitWidthAtLimits = int(data[4:8], 16) >> 1 & 1,\n SlitWidthMoving = int(data[4:8], 16) >> 2 & 1,\n SlitWidthFailure = int(data[4:8], 16) >> 3 & 1,\n GratingAngleInit = int(data[4:8], 16) >> 4 & 1,\n GratingAngleLimit1 = int(data[4:8], 16) >> 5 & 1,\n GratingAngleLimit2 = int(data[4:8], 16) >> 6 & 1,\n GratingAngleMoving = int(data[4:8], 16) >> 7 & 1,\n GratingAngleFailure = int(data[4:8], 16) >> 8 & 1,\n CameraFocusInit = int(data[4:8], 16) >> 9 & 1,\n CameraFocusLimit1 = int(data[4:8], 16) >> 10 & 1,\n CameraFocusLimit2 = int(data[4:8], 16) >> 11 & 1,\n CameraFocusMoving = int(data[4:8], 16) >> 12 & 1,\n CameraFocusFailure = int(data[4:8], 16) >> 13 & 1,\n SlitWidthInitReq = int(data[4:8], 16) >> 14 & 1,\n AngleInitReq = int(data[4:8], 16) >> 15 & 1,\n\n FilterwheelPosition = int(data[11],16),\n GratingID = int(data[9:11], 16) & 0b11111, \n\n GratingInserted = int(data[8:12], 16) >> 9 & 1,\n GratingHatchClosed = int(data[8:12], 16) >> 10 & 1,\n SlitShutterFailure = int(data[8:12], 16) >> 12 & 1,\n ARCMirrorFailure = int(data[8:12], 16) >> 13 & 1,\n RoSMirrorFailure = int(data[8:12], 16) >> 14 & 1,\n HartmanFailure = int(data[8:12], 16) >> 15 & 1,\n\n # The following results from:\n # Interpret the two words (8 hex nibbles) as two numbers directly.\n # The number is sign * (10000 * high word + low word )\n # The MSB of the two words gives the sign.\n # So if data[19] = 80 (=1000) => (int(data[19]) - 4)/(-1/4) = -1\n # So if data[19] = 00 (=0000) => (int(data[19]) - 4)/(-1/4) = +1\n GratingAngleSteps = ((int(data[12:16]) + 10000 * int(data[19:20]))\n * (int(data[16]) - 4)*(-1/4.0)),\n \n FocusPositionPot = int(data[20:24]),\n\n FocusPosition = int(data[24:28])/10000.0 + int(data[31]),\n\n SlitWidthPosition = int(data[32:36]),\n\n TopCrateInterlock = int(data[36:40], 16) >> 0 & 1,\n FilterInterlock = int(data[36:40], 16) >> 1 & 1,\n PneumaticsInterlock = int(data[36:40], 16) >> 2 & 1,\n ARCInterlock = int(data[36:40], 16) >> 3 & 1,\n SlitWidthInterlock = int(data[36:40], 16) >> 4 & 1,\n BottomSignalInterlock = int(data[36:40], 16) >> 5 & 1,\n BottomDriveInterlock = int(data[36:40], 16) >> 6 & 1,\n\n SlitIlluminationValue = int(data[36]),\n\n SlitWidthErrorCodes = int(data[40:44]),\n\n GratingAngleErrorCodes = int(data[44:48]),\n\n FocusError = int(data[48:52], 16) >> 0 & 1,\n \n FocusMotorOn = int(data[48:52], 16) >> 4 & 1,\n FocusMoving = int(data[48:52], 16) >> 5 & 1,\n FocusReferencing = int(data[48:52], 16) >> 6 & 1,\n FocusAtPosition = int(data[48:52], 16) >> 7 & 1,\n FocusNegativeLimit = int(data[48:52], 16) >> 8 & 1,\n FocusReference = int(data[48:52], 16) >> 9 & 1,\n FocusPositiveLimit = int(data[48:52], 16) >> 10 & 1,\n\n FocusIn1 = int(data[48:52], 16) >> 12 & 1,\n FocusIn2 = int(data[48:52], 16) >> 13 & 1,\n FocusIn3 = int(data[48:52], 16) >> 14 & 1,\n FocusIn4 = int(data[48:52], 16) >> 15 & 1\n )",
"title": ""
},
{
"docid": "6be9f9e01e53eee5859e3bd5945e47a2",
"score": "0.5318702",
"text": "def get_status():\r\n return ({\"status\": \"running\"})",
"title": ""
},
{
"docid": "bbd389ffba6ad41c63d3faf0bb798661",
"score": "0.53118736",
"text": "def do_update_status(args):\n \n opts = parse_sel(args)\n db.set_status(opts['status'], opts['fn'])",
"title": ""
},
{
"docid": "6324a249067f2f362de5eafe31f3509c",
"score": "0.53110313",
"text": "def status(self, status: str):\n\n self._status = status",
"title": ""
},
{
"docid": "6324a249067f2f362de5eafe31f3509c",
"score": "0.53110313",
"text": "def status(self, status: str):\n\n self._status = status",
"title": ""
},
{
"docid": "b03347aa8dc4c4356a15fa5a15f34e35",
"score": "0.5306215",
"text": "def update_status(self, new_status: str) -> None:\n if self.db_status == new_status:\n return # Noop, this is already the case\n\n logger.debug(f\"Updating {self} to {new_status}\")\n if self.db_status in AgentState.complete():\n logger.info(f\"Updating {self} from final status to {new_status}\")\n\n old_status = self.db_status\n self.db.update_onboarding_agent(self.db_id, status=new_status)\n self.db_status = new_status\n if self.agent_in_active_run():\n if new_status not in [\n AgentState.STATUS_APPROVED,\n AgentState.STATUS_REJECTED,\n ]:\n live_run = self.get_live_run()\n live_run.loop_wrap.execute_coro(live_run.worker_pool.push_status_update(self))\n if new_status in [AgentState.STATUS_RETURNED, AgentState.STATUS_DISCONNECT]:\n # Disconnect statuses should free any pending acts\n self.has_live_update.set()\n self.did_submit.set()\n\n # Metrics changes\n ACTIVE_AGENT_STATUSES.labels(status=old_status, agent_type=\"onboarding\").dec()\n ACTIVE_AGENT_STATUSES.labels(status=new_status, agent_type=\"onboarding\").inc()\n if old_status not in AgentState.complete() and new_status in AgentState.complete():\n ACTIVE_WORKERS.labels(worker_id=self.worker_id, agent_type=\"onboarding\").dec()",
"title": ""
},
{
"docid": "d6e88f99b44b4d65a8acc00b7dff6d6e",
"score": "0.5301704",
"text": "def status():\n time_now = str(datetime.utcnow())\n click.echo(f'It is {time_now}')\n click.echo('')\n\n low_q = Queue('low', connection=conn)\n default_q = Queue('default', connection=conn)\n high_q = Queue('high', connection=conn)\n total_low = len(low_q)\n total_default = len(default_q)\n total_high = len(high_q)\n click.echo('Current queue status:')\n click.echo(f' Low: {total_low} items')\n click.echo(f' Default: {total_default} items')\n click.echo(f' High: {total_high} items')\n click.echo('')\n\n total_failed_low = len(low_q.failed_job_registry)\n total_failed_default = len(default_q.failed_job_registry)\n total_failed_high = len(high_q.failed_job_registry)\n click.echo('Failed queue status:')\n click.echo(f' Low: {total_failed_low} items')\n click.echo(f' Default: {total_failed_default} items')\n click.echo(f' High: {total_failed_high} items')",
"title": ""
},
{
"docid": "30059658a289be32d33169351b98b14b",
"score": "0.5285334",
"text": "def update_info(self):\n result = uploading_request('POST', 'from_url/status/',\n data={'token': self.token})\n if 'status' not in result:\n raise APIError(\n 'could not find status in result: {0}'.format(result)\n )\n self._info_cache = result\n return result",
"title": ""
},
{
"docid": "daa7f03ce27f67a0a21b1e60532a28cd",
"score": "0.5282011",
"text": "def _set_status(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=RestrictedClassType(base_type=six.text_type, restriction_type=\"dict_key\", restriction_arg={'preferred': {'value': 1}, 'inaccessible': {'value': 4}, 'unknown': {'value': 5}, 'tentative': {'value': 6}, 'duplicate': {'value': 7}},), is_leaf=True, yang_name=\"status\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:huawei:yang:huawei-ip', defining_module='huawei-ip', yang_type='ipv6-status-type', is_config=False)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"status must be of a type compatible with ipv6-status-type\"\"\",\n 'defined-type': \"huawei-ip:ipv6-status-type\",\n 'generated-type': \"\"\"YANGDynClass(base=RestrictedClassType(base_type=six.text_type, restriction_type=\"dict_key\", restriction_arg={'preferred': {'value': 1}, 'inaccessible': {'value': 4}, 'unknown': {'value': 5}, 'tentative': {'value': 6}, 'duplicate': {'value': 7}},), is_leaf=True, yang_name=\"status\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:huawei:yang:huawei-ip', defining_module='huawei-ip', yang_type='ipv6-status-type', is_config=False)\"\"\",\n })\n\n self.__status = t\n if hasattr(self, '_set'):\n self._set()",
"title": ""
},
{
"docid": "32a3437f4d25c849d2fbd887907b649b",
"score": "0.5278441",
"text": "def update_status(self) -> str:\n headers = {\"X-Job-Token\": self.job_token} if self.job_token else {}\n data = (\n getattr(self._cognite_client, self._JOB_TYPE.value)\n ._get(f\"{self._status_path}{self.job_id}\", headers=headers)\n .json()\n )\n self.status = data[\"status\"]\n self.status_time = data.get(\"statusTime\")\n self.start_time = data.get(\"startTime\")\n self.created_time = self.created_time or data.get(\"createdTime\")\n self.error_message = data.get(\"errorMessage\")\n self._result = {k: v for k, v in data.items() if k not in self._COMMON_FIELDS}\n assert self.status is not None\n return self.status",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="776"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">3f40489d1f123e20b33fb7002aa842da</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">get the last execution result of the EoX API auto sync</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "f09a18a493f4a14a124658503b9a2d05",
"score": "0.795451",
"text": "def get_cisco_eox_api_auto_sync_last_execution_result(self):\n return self._config_options[ConfigOption.CISCO_EOX_CRAWLER_LAST_EXECUTION_RESULT]",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "b6025beb0654ff0149e33ae1f554e28d",
"score": "0.68530107",
"text": "def get_cisco_eox_api_auto_sync_last_execution_time(self):\n return self._config_options[ConfigOption.CISCO_EOX_CRAWLER_LAST_EXECUTION_TIME]",
"title": ""
},
{
"docid": "32229a5cc40f87b900278b6fac6d9fa3",
"score": "0.6731058",
"text": "def set_cisco_eox_api_auto_sync_last_execution_result(self, value):\n co, _ = ConfigOption.objects.get_or_create(key=ConfigOption.CISCO_EOX_CRAWLER_LAST_EXECUTION_RESULT)\n co.value = value\n co.save()\n self._rebuild_config_cache()",
"title": ""
},
{
"docid": "35513396943b872f2cdb0ac5e10245ac",
"score": "0.66467154",
"text": "def last_sync_success(self):\n return self._last_sync_success",
"title": ""
},
{
"docid": "beec5ca0df835517ebc95a1106b65812",
"score": "0.65931463",
"text": "def last_result(self):\n\n url = \"{0}/{1}?lastresult=true\".format(API_URL, self.checkid)\n url = _utils.create_url(self.token, url, self.customerid)\n\n return _query_nodeping_api.get(url)",
"title": ""
},
{
"docid": "84e5ca2e6b78f6785840c12ca6fe173c",
"score": "0.65709865",
"text": "def response(self):\n return self.__last_response",
"title": ""
},
{
"docid": "072bc617ef9072d9875d92e87a8c9445",
"score": "0.6561068",
"text": "def get_task_execution_full(self):\n path = 'qrs/executionresult/full'\n return json.loads(self.get(path).text)",
"title": ""
},
{
"docid": "6d624b9e2fc90aeb8bc846cc8ec3b0a3",
"score": "0.64260906",
"text": "def last_sync_failure(self):\n return self._last_sync_failure",
"title": ""
},
{
"docid": "f25f7f55bf6cddd80209e24574e95b9e",
"score": "0.624835",
"text": "def get_result(self):\n pass",
"title": ""
},
{
"docid": "08a1ee683e3a29674e70a826aafdcae1",
"score": "0.6226722",
"text": "async def fetch_result(self) -> T:",
"title": ""
},
{
"docid": "db071faa407e2203dae80be6fda50510",
"score": "0.61955374",
"text": "def latest_created_execution(self) -> 'outputs.ExecutionReferenceResponse':\n return pulumi.get(self, \"latest_created_execution\")",
"title": ""
},
{
"docid": "8fe98e7fbdad596bf4c94822f50b46f2",
"score": "0.61951655",
"text": "def _get_last_update(self):\n return self.__last_update",
"title": ""
},
{
"docid": "110f8adb1215f9951f25aa6b3d671ba2",
"score": "0.6185151",
"text": "def lastsave(self):\n return self.execute(b'LASTSAVE')",
"title": ""
},
{
"docid": "b52ba3fd57737101c73c273a6405084f",
"score": "0.6147553",
"text": "def get_result(self):\n return self.result",
"title": ""
},
{
"docid": "b52ba3fd57737101c73c273a6405084f",
"score": "0.6147553",
"text": "def get_result(self):\n return self.result",
"title": ""
},
{
"docid": "668b3bd8f499cd7eb08324ac60988dd7",
"score": "0.608723",
"text": "def last_response(self):\n return self.__last_response",
"title": ""
},
{
"docid": "03e90e868866675cad8b9945f1adf196",
"score": "0.60475993",
"text": "def last_sync_start(self):\n return self._last_sync_start",
"title": ""
},
{
"docid": "772095bb90241347f3d6225368677373",
"score": "0.6023226",
"text": "def last_operation_end(self):\n return self._last_operation_end",
"title": ""
},
{
"docid": "faba8f9d491737c4f9884a6fd510f284",
"score": "0.6016055",
"text": "def get_last_sync(self):\n es_query = {\n \"size\": 1,\n \"sort\": [{\"@timestamp\": {\"order\": \"desc\"}}],\n \"query\": {\"bool\": {\"filter\": [{\"term\": {\"iplist.name\": \"tor\"}}]}},\n }\n\n es_results = raw_search(es_query, index=\"redelk-*\")\n\n self.logger.debug(es_results)\n\n # Return the latest hit or False if not found\n if es_results and len(es_results[\"hits\"][\"hits\"]) > 0:\n dt_str = get_value(\"_source.@timestamp\", es_results[\"hits\"][\"hits\"][0])\n dtime = datetime.datetime.strptime(dt_str, \"%Y-%m-%dT%H:%M:%S.%f\")\n return dtime\n\n return datetime.datetime.fromtimestamp(0)",
"title": ""
},
{
"docid": "7e09cf41cc3e884b3f47026869668f03",
"score": "0.601061",
"text": "def get_result(self):\n\t\treturn self.success",
"title": ""
},
{
"docid": "309675f3d981898771d7362d4fe43f47",
"score": "0.5976368",
"text": "def last_result_details(self):\n return self._last_result_details",
"title": ""
},
{
"docid": "422b729ae99a62ffc9e4e44f7d89c693",
"score": "0.59745467",
"text": "def getResult(self):\n self.lockTask();\n sStatus = self.sStatus;\n oTaskRc = self.oTaskRc;\n self.unlockTask();\n if sStatus != \"\":\n return None;\n return oTaskRc;",
"title": ""
},
{
"docid": "9c8e1471120ef6389cc25f6446f38e8d",
"score": "0.59149015",
"text": "def get_result(self):\n return self._result",
"title": ""
},
{
"docid": "897211fcdbe97d4ce421b2674ce14bd8",
"score": "0.5909386",
"text": "def last_move_result(self):\n return self._last_move_result",
"title": ""
},
{
"docid": "43bcdc8bd81fac4420c94cc9160b2e64",
"score": "0.5905729",
"text": "def return_last_value(retry_state):\n return retry_state.outcome.result()",
"title": ""
},
{
"docid": "dbff31cf3df20f8d679a34d82fca3750",
"score": "0.58643377",
"text": "def last_job(self):\n return self._data.get('last_job')",
"title": ""
},
{
"docid": "45714195f600f817f325572f0276aa15",
"score": "0.5818749",
"text": "def get_last_execution_info(job_name):\n method, endpoint = EXECUTION_STATE\n endpoint = endpoint.format(project_id=util.project_id(), job_name=job_name)\n return rest_rpc._http(endpoint, method=method)",
"title": ""
},
{
"docid": "d2b0028948ccc37757ec1a3d7c2f738e",
"score": "0.5810724",
"text": "def getResult(self):\r\n\t\treturn self.__result",
"title": ""
},
{
"docid": "0773d5cffd832d2837a7cc0727310f32",
"score": "0.579678",
"text": "def last_operation_begin(self):\n return self._last_operation_begin",
"title": ""
},
{
"docid": "1754f06c8c54d117ac4c65d09e495e37",
"score": "0.5794521",
"text": "def last_update(self):\n return self._last_update",
"title": ""
},
{
"docid": "0bcdab2470c9d9d1ab62eecba66ffdda",
"score": "0.5748801",
"text": "def _get_sync(self):\n return self.__sync",
"title": ""
},
{
"docid": "6b484b8d0cc01e816f66d25c7577f487",
"score": "0.57459736",
"text": "def result(self):\r\n return self._result",
"title": ""
},
{
"docid": "122e34004baad5fc0baec721a2ea5181",
"score": "0.5742182",
"text": "def latest_created_execution(self) -> pulumi.Output['outputs.GoogleCloudRunV2ExecutionReferenceResponse']:\n return pulumi.get(self, \"latest_created_execution\")",
"title": ""
},
{
"docid": "495dc0a1720fbbd36c107e36e78e1c2b",
"score": "0.5711194",
"text": "def get_result( self ):\n self.join() #wait end of run()\n if isinstance( self.result, Exception ):\n raise Exception( self.result )\n else:\n return self.result",
"title": ""
},
{
"docid": "5c04a75d098424ebbd4019e2b5092b99",
"score": "0.57085013",
"text": "def last_output(self) -> Tuple[Tensor, StatDict]:\n return self._last_output",
"title": ""
},
{
"docid": "98ba0540b4ee32e2d75fa1224156501c",
"score": "0.56936914",
"text": "def last_execute_time(self) -> str:\n return pulumi.get(self, \"last_execute_time\")",
"title": ""
},
{
"docid": "05b2a0cb1ddc79301a5febb4f667d349",
"score": "0.5685125",
"text": "def getLast(self):\n return self.__lastValue",
"title": ""
},
{
"docid": "9d0590abf5b96c75e753a91892ca5f7f",
"score": "0.56844777",
"text": "def result(self) -> str:\n\t\treturn self.scope.result",
"title": ""
},
{
"docid": "76cfde214b16d91c4bbc6797dfd871da",
"score": "0.5672138",
"text": "def result(self):\n if not hasattr(self, '_result'):\n self._result = None\n return self._result",
"title": ""
},
{
"docid": "36dea028256321e1e3357b0594e877e3",
"score": "0.566913",
"text": "def last_commit(self):\n return self.commit_response.data[0] if self.commit_response.data else None",
"title": ""
},
{
"docid": "d6ea12f7885693012c20fddcd96cc919",
"score": "0.5662084",
"text": "def get_auto_commit(self):\n return self.__aceQLHttpApi.get_auto_commit()",
"title": ""
},
{
"docid": "65f6e34f6c379c403425f059d51741d5",
"score": "0.5660062",
"text": "def result(self):\n return self._result",
"title": ""
},
{
"docid": "65f6e34f6c379c403425f059d51741d5",
"score": "0.5660062",
"text": "def result(self):\n return self._result",
"title": ""
},
{
"docid": "65f6e34f6c379c403425f059d51741d5",
"score": "0.5660062",
"text": "def result(self):\n return self._result",
"title": ""
},
{
"docid": "65f6e34f6c379c403425f059d51741d5",
"score": "0.5660062",
"text": "def result(self):\n return self._result",
"title": ""
},
{
"docid": "88567355a4fe7db3ed736a45815a9f59",
"score": "0.5649645",
"text": "def last_update(self):\n return timestamp_to_index(self.pi_point.CurrentValue().Timestamp.UtcTime)",
"title": ""
},
{
"docid": "3a0cf67d1e1bfca7386887494700c61e",
"score": "0.5649202",
"text": "def get_async_token(self):\r\n\t\treturn (self.last_command_id, self.last_executed_command)",
"title": ""
},
{
"docid": "76fc43ab160aa44c464f55d0d5c9c97f",
"score": "0.56490725",
"text": "def getResult(self):\r\n return self.__result",
"title": ""
},
{
"docid": "980cf1d5ab5a1c387271c6726925cf36",
"score": "0.56439716",
"text": "def getLast(self) -> object:\n ...",
"title": ""
},
{
"docid": "c504438900288eeeb25f0981c47c76c5",
"score": "0.56321806",
"text": "def _LastRunResults(bm_spec: benchmark_spec.BenchmarkSpec) -> str:\n vm = bm_spec.vms[0]\n stdout, _ = vm.RobustRemoteCommand(\n f'{bm_spec.env_cmd} && grep -l \\'\\\\\"result_validity\\\\\": \\\\\"VALID\\\\\"\\''\n ' build/logs/*/*/*/*/metadata.json | xargs ls -t | head -n 1 | xargs cat'\n )\n return stdout",
"title": ""
},
{
"docid": "dca058abb0240994c7651b259be31b65",
"score": "0.56179756",
"text": "def backend_result(self):\n return self._backend_result",
"title": ""
},
{
"docid": "8517f0d67b02871d1a0a240a36bad816",
"score": "0.56114703",
"text": "def getResult(self):\n return self.resultQueue.get()",
"title": ""
},
{
"docid": "0b4bae2fad43ac33a57117e13198dcfb",
"score": "0.5605905",
"text": "def onComplete(self, result, config):\n\t\treturn result",
"title": ""
},
{
"docid": "9ce4f99ea6cbed065c1277c8535320a9",
"score": "0.56008834",
"text": "def last_executed_on(self) -> str:\n return pulumi.get(self, \"last_executed_on\")",
"title": ""
},
{
"docid": "bf1789cfd2d9a34996e9e90bd28e535e",
"score": "0.5596954",
"text": "def get_last_click():\r\n return done_clicks[-1]",
"title": ""
},
{
"docid": "4271689e89c1369a7ed802bc5711d1c0",
"score": "0.5587079",
"text": "def last(self):\n return self._statcmd('LAST')",
"title": ""
},
{
"docid": "a81ad97dd8a95c789a5c29df15b2ff32",
"score": "0.557597",
"text": "def result(self):\n pass",
"title": ""
},
{
"docid": "a81ad97dd8a95c789a5c29df15b2ff32",
"score": "0.557597",
"text": "def result(self):\n pass",
"title": ""
},
{
"docid": "28bd45b77649552e96713b6aa4767a30",
"score": "0.55739266",
"text": "def execution(self):\n if self._execution is None:\n raise Exception('execution is not set on this object')\n return self._execution",
"title": ""
},
{
"docid": "7bdc568f32717326122a331c45be8622",
"score": "0.55736095",
"text": "def last_operation_error(self):\n return self._last_operation_error",
"title": ""
},
{
"docid": "e5fab362f1356d88a512bafb67d3088c",
"score": "0.55580264",
"text": "def set_cisco_eox_api_auto_sync_last_execution_time(self, value):\n co, _ = ConfigOption.objects.get_or_create(key=ConfigOption.CISCO_EOX_CRAWLER_LAST_EXECUTION_TIME)\n co.value = value\n co.save()\n self._rebuild_config_cache()",
"title": ""
},
{
"docid": "f9c4441ca1e96060cf06a0dcc59960ad",
"score": "0.5554923",
"text": "def finish(self):\n return self.data.get('finish')",
"title": ""
},
{
"docid": "369395b454de98370b656d807b2d999e",
"score": "0.5533212",
"text": "def get_execution_results(cls):\n return cls._execution_results",
"title": ""
},
{
"docid": "ace27c70e58952c07200db10508f17ec",
"score": "0.5524969",
"text": "def fetch(self):\n return 0",
"title": ""
},
{
"docid": "2eec155ece8de1e9bf415f7772eb0b31",
"score": "0.55170494",
"text": "def execute(self) -> Response:\n response: Response = self.storage_.retry_with_new_sid(\n self.execute_once,\n )\n return response # NOQA:WPS331",
"title": ""
},
{
"docid": "80196bead2b90594e7b03b3d90855c38",
"score": "0.5516315",
"text": "def get_last_run() -> str:\n if last_run := demisto.getIntegrationContext().get('last_run'):\n demisto.info(f'get last_run: {last_run}')\n params = f'date:{last_run}+'\n else:\n params = ''\n return params",
"title": ""
},
{
"docid": "27e6413430c530b36e82ae00d855c783",
"score": "0.5516096",
"text": "def get_last_inserted(self): \n \n #print v.raw\n try:\n v = self.client.query('select pkey from %s' %(self.last_id)) \n pkey = v.raw['series'][0]['values'][0][1]\n except Exception as ex:\n #print ex.message\n pkey = None\n return pkey",
"title": ""
},
{
"docid": "839c57406cf4b2cb9b65a3ffb11e1f92",
"score": "0.5505431",
"text": "def send_result(self):\n\t\t\n\t\tpass",
"title": ""
},
{
"docid": "fc5878c1bbde5fe48fb395e1e819aacc",
"score": "0.5484103",
"text": "def push_task_response(self, result):\n pass\n #self.log.info(\"Task send to computation framework\")",
"title": ""
},
{
"docid": "a2dc565aeed4d859b7a62b6a416836ac",
"score": "0.5477977",
"text": "def get_last_update(): # get last update date of the database\n # client can access this\n last_update = open(datapath(False, 'Spyder', 'datefile.txt')).readlines()[0]\n print(\"Last update time: \" + last_update)\n return last_update",
"title": ""
},
{
"docid": "4e912017174184c986fb72e6d3e6d1ca",
"score": "0.54723716",
"text": "def result(self) -> None:\n pass",
"title": ""
},
{
"docid": "b90c836bc03678c057e4794b2567fbd5",
"score": "0.5462542",
"text": "def get_output(self):\n return self.outputs[-1]",
"title": ""
},
{
"docid": "b90c836bc03678c057e4794b2567fbd5",
"score": "0.5462542",
"text": "def get_output(self):\n return self.outputs[-1]",
"title": ""
},
{
"docid": "f97c79bc02f8ebb187244bb257825acf",
"score": "0.54588056",
"text": "def _get_result():\r\n\r\n result = ivoire.current_result\r\n if result is None:\r\n raise ValueError(\r\n \"ivoire.current_result must be set to a TestResult before \"\r\n \"execution starts!\"\r\n )\r\n return result",
"title": ""
},
{
"docid": "37edc29e2c6a04d591687c004833fb7b",
"score": "0.5454232",
"text": "def sync(self):\n return self.execute(b'SYNC')",
"title": ""
},
{
"docid": "0deba911fb9e7bb3b6f6138bf4a34525",
"score": "0.54540277",
"text": "def last_run_time(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"last_run_time\")",
"title": ""
},
{
"docid": "0297f3afd90d62c443fcde272b4b476e",
"score": "0.5451215",
"text": "def get_results(self):\r\n return self.res",
"title": ""
},
{
"docid": "f638aee553c31dcb3fcd8cebf12c8fbc",
"score": "0.54435134",
"text": "def last_change(self):\n return self._last_change",
"title": ""
},
{
"docid": "7105c72d4d2cfed1805b1e5202d1de2a",
"score": "0.54349273",
"text": "def reset_last_run():\n demisto.setIntegrationContext({})\n return CommandResults(readable_output='Fetch history deleted successfully')",
"title": ""
},
{
"docid": "55734b6975c55c5835cc9c4a1a40eb72",
"score": "0.5432674",
"text": "def update_execution_time(self):",
"title": ""
},
{
"docid": "368848b96f8b5a4c1b77ea40ad10cf06",
"score": "0.5429311",
"text": "def get_results(self):\n return self.result",
"title": ""
},
{
"docid": "7c020858594e1355ee26d3957ae07bfd",
"score": "0.5428073",
"text": "def op_result(self) -> Any:\n if self.use == SessionScopeArgs.SINGLE_USE:\n result = self.pop_result()\n else:\n result = self.peek_result()\n return result",
"title": ""
},
{
"docid": "93542a58c7b43edcd9774b83946134da",
"score": "0.5423819",
"text": "def sync(self):\n return 0",
"title": ""
},
{
"docid": "a3ac3783bee5be23e858942c3d198956",
"score": "0.54210305",
"text": "def last_updated(self):\n return self._last_updated",
"title": ""
},
{
"docid": "00814078c5801b5216b0b1701019ad69",
"score": "0.5420256",
"text": "def _get_result(self, idx, timeout=None):\n res = self._results[idx]\n res.wait(timeout)\n return res",
"title": ""
},
{
"docid": "ce549061eadee7b82d779878b74ba29f",
"score": "0.5418198",
"text": "def get_response(self):\n return self.cmd_response",
"title": ""
},
{
"docid": "a85a1805441354050dbe7b7f5198f653",
"score": "0.54070544",
"text": "def get_result(self):\n return self.buffer_res",
"title": ""
},
{
"docid": "c0d4fc128c49126f988b3be809f620f2",
"score": "0.54053783",
"text": "def last_refresh(self) -> str:\n return pulumi.get(self, \"last_refresh\")",
"title": ""
},
{
"docid": "a4f66183cb67c08d27a716df7ade5639",
"score": "0.5404288",
"text": "def last_update(self):\n as_performer = self.performer.order_by('-updated')\n as_composer = self.composer.order_by('-updated')\n if not as_performer.count() and as_composer.count():\n return as_composer[0]\n elif not as_composer.count() and as_performer.count():\n return as_performer[0]\n elif as_composer.count() and as_performer.count():\n if as_composer[0].updated < as_performer[0].updated:\n return as_performer[0]\n else:\n return as_composer[0]\n return None",
"title": ""
},
{
"docid": "cf34c4e31c15abdd5303becda26f1524",
"score": "0.5399527",
"text": "def _GetResult(self, poll_result):\n return None",
"title": ""
},
{
"docid": "7accac64dedc94b4b663d0cf11ed981e",
"score": "0.53978145",
"text": "def result(self) -> Sequence[str]:\n return pulumi.get(self, \"result\")",
"title": ""
},
{
"docid": "85909ab6c0251e67534da15370d98193",
"score": "0.53900373",
"text": "def synchronous(self):\n ...",
"title": ""
},
{
"docid": "76ec176ecb93566cbc70a4be45df2700",
"score": "0.53888905",
"text": "def execute(self):",
"title": ""
},
{
"docid": "76ec176ecb93566cbc70a4be45df2700",
"score": "0.53888905",
"text": "def execute(self):",
"title": ""
},
{
"docid": "37b6929963d6081b74ce3f7aa490d355",
"score": "0.5379688",
"text": "def current_execution(self):\n return str(self._current_execution).zfill(2)",
"title": ""
},
{
"docid": "e852b0634d3dd9d409bcead4b055962b",
"score": "0.53732085",
"text": "def ret_value(self):\n return self._ret_value",
"title": ""
},
{
"docid": "205cf99ece87d23144aee8fb6f0bf377",
"score": "0.5367592",
"text": "def _update_result(self, engine, state, result=None):\n if state == states.PENDING:\n engine.storage.reset(self.uuid)\n else:\n engine.storage.save(self.uuid, result, state)\n engine.on_task_state_change(self, state, result)",
"title": ""
},
{
"docid": "0536b011fd72806dd96f5f9ae44bb708",
"score": "0.5365974",
"text": "def sync_status(self):\n return self._sync_status",
"title": ""
},
{
"docid": "ecfa6fd43af1d51b892d238e9223ff0d",
"score": "0.5354878",
"text": "def get_result(self, state):\r\n pass",
"title": ""
},
{
"docid": "bff96048288778cbc8426cae55a9bf48",
"score": "0.5354874",
"text": "def last_update(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"last_update\")",
"title": ""
},
{
"docid": "7c2e93cfaf8563c73209544c34c20c34",
"score": "0.53530777",
"text": "def last_changed(self):\n return self._last_changed",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="777"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">6358dfa57d11a64dec215b90d16ad948</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">generated source for method parseAndGetClassForMessage</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "35bae810b20ef8b26a8550ad4e6b7e87",
"score": "0.7376051",
"text": "def parseAndGetClassForMessage(cls, message):\n try:\n json_obj = json.loads(message)\n if json_obj is None:\n # Nothing found\n raise Exception(\"Could not find [\" + cls.TYPE_IDENTIFIER + \"] in message: \" + message)\n clazz = cls.getClassForIdentifier(json_obj)\n clazz.setVersion(json_obj[\"version\"])\n\n return clazz\n except Exception as e:\n # JSON exception\n raise Exception(\"Could not parse message: \" + message, e)",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "9693b8b81c159ca8ea2de795504711e8",
"score": "0.6514559",
"text": "def _parse_msg(self, msg):\n raise NotImplementedError(\"Implement!\")",
"title": ""
},
{
"docid": "5594dba28b3e43f98d8eba50df9e984e",
"score": "0.650433",
"text": "def generate_message_classes(message_path, msg_list):\r\n \r\n #Create an empty list for parsed messages\r\n parsed_msg_list = []\r\n \r\n #Generate a class for each message entry\r\n for msg in msg_list.findall('message'):\r\n parsed_msg = generate_class_from_msg(message_path, msg)\r\n parsed_msg_list.insert(int(parsed_msg['msgid']), parsed_msg)\r\n \r\n return parsed_msg_list",
"title": ""
},
{
"docid": "53e41954e4d8fe362ecb916127586e38",
"score": "0.6250373",
"text": "def parse_ws_message(raw_msg):\n\n msg_klasses = [\n WebsocketMessageRequest,\n WebsocketMessageError,\n WebsocketMessageResponse,\n WebsocketMessageEmittedItem\n ]\n\n for klass in msg_klasses:\n try:\n msg_instance = klass.from_raw(raw_msg)\n return msg_instance\n except WebsocketMessageException:\n pass\n\n raise WebsocketMessageException(\"Invalid message: {}\".format(raw_msg))",
"title": ""
},
{
"docid": "f27416a6feb7ab5c7752b50d0dc12966",
"score": "0.6242583",
"text": "def GetMessageClass(self, msg_str):\n return GetApiMessage(msg_str=msg_str,\n api_version=self.version,\n msg_module=self.messages)",
"title": ""
},
{
"docid": "db6004a63e8eca5920246825ecd43386",
"score": "0.6222061",
"text": "def deserialize(self, ctx, message):",
"title": ""
},
{
"docid": "26d80e158a87d93036d317210dafe2f5",
"score": "0.6205122",
"text": "def parse(self, parsed_class, class_dict):\n try:\n self.logger.debug(\"Parsing {0} ...\".format(class_dict))\n return parsed_class(**class_dict)\n\n except JSONDecodeError:\n self.logger.exception(\"An error occurred when parsing {0} with {1}\".\n format(parsed_class, class_dict))\n\n except TypeError:\n self.logger.exception(\"An error occurred when parsing {0} with {1}\".\n format(parsed_class, class_dict))",
"title": ""
},
{
"docid": "9121bf911d578654dda5a70f2233a3f5",
"score": "0.61600614",
"text": "def _parse_classes(self):\n\n while self.text and self.text.startswith(TOKEN_CLASS):\n class_name = regex_class_id.match(self.text)\n class_name = class_name.group(1)\n\n if not class_name:\n raise ParseError(\"Encountered an empty class name\")\n\n self.tag.classes.append(class_name)\n self.text = self.text[len(class_name)+1:]",
"title": ""
},
{
"docid": "acb56e92ce2c43b011d177b01196eed2",
"score": "0.6095472",
"text": "def test_receive_message_class(self):\n class TestIncomingMessage(IncomingMessage):\n pass\n msg = self.receive(\"echo hello\", self.create_connection(),\n class_=TestIncomingMessage)\n self.assertTrue(isinstance(msg, TestIncomingMessage))",
"title": ""
},
{
"docid": "9379c92e39cc37716fcc6f2ea7f8fc99",
"score": "0.6084361",
"text": "def _get_payload_class(self, item):\n event = item[\"event\"]\n\n enum_list = []\n field_list = []\n\n # process all fields\n for field in event.payload[\"fields\"]:\n field_name = field.keys()[0]\n\n # generate code for each field which is not an enum\n if field[field_name][\"type\"] != \"enum\":\n field_list.append(\n field[field_name][\"type\"]\n + \" \" + field_name\n + \";\"\n )\n\n # generate code for enum fields\n if field[field_name][\"type\"] == \"enum\":\n field_list.append(\n field_name\n + \"_Enum \"\n + field_name\n + \";\"\n )\n enum_list.append(\n \"enum \"\n + field_name\n + \"_Enum{\\n\\t\\t\"\n + \", \".join(field[field_name][\"values\"])\n + \"\\n\\t};\"\n )\n\n # wrap the field and enum definitions in a class definition\n generated_class = \\\n \"class \" \\\n + event.metadata[\"id\"] \\\n + \"_Payload\" \\\n + \"{\\n\\t\" \\\n + \"\\n\\t\".join(enum_list) \\\n + \"\\n\\t\" \\\n + \"\\n\\t\".join(field_list) \\\n + \"\\n};\"\n\n #######\n # TODO: remove temporary fix as soon as content-based routing is properly integrated\n generated_class = \"typedef std::vector<unsigned char> \" + event.metadata[\"id\"] + \"_Payload;\"\n #######\n\n return generated_class",
"title": ""
},
{
"docid": "19668ddf4e78133dafeaef2a5dca9054",
"score": "0.6077876",
"text": "def __parse_class(self, frameup):\n classframe = re.sub(r\"(<[\\w#-]+)\\.([\\w.-]+)(#|>|/|\\s+)\", \"\\\\1 class=\\x22\\\\2\\x22\\\\3\", frameup)\n parsed = re.findall(r\"class=\\x22.+?\\x22\", classframe)\n for each in parsed:\n each_ = re.sub(r\"\\.\", \" \", each)\n classframe = re.sub(r\"%s\"%each, each_, classframe)\n return classframe",
"title": ""
},
{
"docid": "51593703563a89c33e00e8eb34f15402",
"score": "0.6067488",
"text": "def _get_msg_class(code):\n msg_classes = {}\n msg_classes = _add_msg_class(msg_classes,\n MESSAGE_STANDARD_MESSAGE_RECEIVED_0X50,\n StandardReceive)\n msg_classes = _add_msg_class(msg_classes,\n MESSAGE_EXTENDED_MESSAGE_RECEIVED_0X51,\n ExtendedReceive)\n msg_classes = _add_msg_class(msg_classes,\n MESSAGE_X10_MESSAGE_RECEIVED_0X52,\n X10Received)\n msg_classes = _add_msg_class(msg_classes,\n MESSAGE_ALL_LINKING_COMPLETED_0X53,\n AllLinkComplete)\n msg_classes = _add_msg_class(msg_classes,\n MESSAGE_BUTTON_EVENT_REPORT_0X54,\n ButtonEventReport)\n msg_classes = _add_msg_class(msg_classes,\n MESSAGE_USER_RESET_DETECTED_0X55,\n UserReset)\n msg_classes = _add_msg_class(msg_classes,\n MESSAGE_ALL_LINK_CEANUP_FAILURE_REPORT_0X56,\n AllLinkCleanupFailureReport)\n msg_classes = _add_msg_class(msg_classes,\n MESSAGE_ALL_LINK_RECORD_RESPONSE_0X57,\n AllLinkRecordResponse)\n msg_classes = _add_msg_class(msg_classes,\n MESSAGE_ALL_LINK_CLEANUP_STATUS_REPORT_0X58,\n AllLinkCleanupStatusReport)\n msg_classes = _add_msg_class(msg_classes,\n MESSAGE_GET_IM_INFO_0X60,\n GetImInfo)\n msg_classes = _add_msg_class(msg_classes,\n MESSAGE_SEND_ALL_LINK_COMMAND_0X61,\n SendAllLinkCommand)\n msg_classes = _add_msg_class(msg_classes,\n MESSAGE_SEND_STANDARD_MESSAGE_0X62,\n StandardSend)\n msg_classes = _add_msg_class(msg_classes,\n MESSAGE_X10_MESSAGE_SEND_0X63,\n X10Send)\n msg_classes = _add_msg_class(msg_classes,\n MESSAGE_START_ALL_LINKING_0X64,\n StartAllLinking)\n msg_classes = _add_msg_class(msg_classes,\n MESSAGE_CANCEL_ALL_LINKING_0X65,\n CancelAllLinking)\n msg_classes = _add_msg_class(msg_classes,\n MESSAGE_RESET_IM_0X67,\n ResetIM)\n msg_classes = _add_msg_class(msg_classes,\n MESSAGE_GET_FIRST_ALL_LINK_RECORD_0X69,\n GetFirstAllLinkRecord)\n msg_classes = _add_msg_class(msg_classes,\n MESSAGE_GET_NEXT_ALL_LINK_RECORD_0X6A,\n GetNextAllLinkRecord)\n msg_classes = _add_msg_class(msg_classes,\n MESSAGE_MANAGE_ALL_LINK_RECORD_0X6F,\n ManageAllLinkRecord)\n msg_classes = _add_msg_class(msg_classes,\n MESSAGE_SET_IM_CONFIGURATION_0X6B,\n SetIMConfiguration)\n msg_classes = _add_msg_class(msg_classes,\n MESSAGE_GET_IM_CONFIGURATION_0X73,\n GetImConfiguration)\n\n return msg_classes.get(code, None)",
"title": ""
},
{
"docid": "c6ddd5087beb8cba69a7f8d10aba5846",
"score": "0.6065375",
"text": "def parse( self, data, vm ):\n\n\t\tsuper( self.__class__, self ).parse( data )\n\n\t\tdata = StringIO.StringIO( self.data )\n\n\t\tnum_classes = struct.unpack( '>I', data.read( 4 ) )[0]\n\n\t\tfrom jdwp.misc import TypeTagConstants, ClassStatusConstants, JavaClass\n\n\t\tfor i in xrange( num_classes ):\n\t\t\tjclass = JavaClass()\n\t\t\tjclass.type = TypeTagConstants.get( ord( data.read( 1 ) ), None )\n\t\t\tid = data.read( vm.reference_size )\n\t\t\tif ( vm.reference_size == 8 ):\n\t\t\t\tjclass.id = struct.unpack( '>q', id )[0]\n\t\t\telse:\n\t\t\t\traise NotImplementedError()\n\t\t\tjclass.signature = data.read( struct.unpack( '>I', data.read( 4 ) )[0] )\n\t\t\tstatus = struct.unpack( '>I', data.read( 4 ) )[0]\n\t\t\tif jclass.type in ( 'CLASS', 'INTERFACE' ):\n\t\t\t\tstatuses = [ _status for code, _status in [ i for i in ClassStatusConstants.items() if isinstance( i, int ) ] if code & status ]\n\t\t\t\tjclass.status = statuses\n\t\t\tself.classes.append( jclass )",
"title": ""
},
{
"docid": "7352108b37d38ed34fa2baa742277b0b",
"score": "0.6042212",
"text": "def parse( self, data, vm ):\n\n\t\tsuper( self.__class__, self ).parse( data )\n\n\t\tdata = StringIO.StringIO( self.data )\n\n\t\tnum_classes = struct.unpack( '>I', data.read( 4 ) )[0]\n\n\t\tfrom jdwp.misc import TypeTagConstants, ClassStatusConstants, JavaClass\n\n\t\tfor i in xrange( num_classes ):\n\t\t\tjclass = JavaClass()\n\t\t\tjclass.type = TypeTagConstants.get( ord( data.read( 1 ) ), None )\n\t\t\tid = data.read( vm.reference_size )\n\t\t\tif ( vm.reference_size == 8 ):\n\t\t\t\tjclass.id = struct.unpack( '>q', id )[0]\n\t\t\telse:\n\t\t\t\traise NotImplementedError()\n\t\t\tjclass.signature = data.read( struct.unpack( '>I', data.read( 4 ) )[0] )\n\t\t\tjclass.generic = data.read( struct.unpack( '>I', data.read( 4 ) )[0] )\n\t\t\tstatus = struct.unpack( '>I', data.read( 4 ) )[0]\n\t\t\tif jclass.type in ( 'CLASS', 'INTERFACE' ):\n\t\t\t\tstatuses = [ _status for code, _status in [ i for i in ClassStatusConstants.items() if isinstance( i, int ) ] if code & status ]\n\t\t\t\tjclass.status = statuses\n\t\t\tself.classes.append( jclass )",
"title": ""
},
{
"docid": "2dd1fffaff795cc41cd6a44b9f6d6f65",
"score": "0.60406524",
"text": "def resolve_message_class(self, message_type: str) -> type:\n msg_cls = self._typemap.get(message_type)\n if isinstance(msg_cls, str):\n msg_cls = ClassLoader.load_class(msg_cls)\n return msg_cls",
"title": ""
},
{
"docid": "25115343ccba09b87a7d03781faad746",
"score": "0.60391283",
"text": "def generate_class_from_msg(msg_path, msg):\r\n \r\n #Get top level message attributes\r\n msgid = msg.attrib.get('id')\r\n name = str.lower(msg.attrib.get('name'))\r\n class_name = 'msg_' + name\r\n \r\n #Create the message class file and generate MATLAB code\r\n with open('%s/%s.m' % (msg_path, class_name), 'w') as fo:\r\n \r\n #Get message description if available\r\n if msg.find('description') != None:\r\n desc = msg.find('description').text.replace('\\n',' ')\r\n else:\r\n desc = \"None\"\r\n \r\n #Generate the class header and summary\r\n fo.write('''\\\r\n\\\r\nclassdef %s < mavlink_message\r\n %%MAVLINK Message Class\r\n %%Name: %s\\tID: %s\r\n %%Description: %s\r\n\\\r\n ''' % (class_name, name, msgid, desc))\r\n \r\n #Look-up dictionaries\r\n type_size = {'double' : 8, 'int64_t' : 8, 'uint64_t': 8, 'int32_t' : 4, 'uint32_t': 4,\r\n 'float' : 4, 'int16_t' : 2, 'uint16_t': 2, 'int8_t' : 1, 'uint8_t': 1,\r\n 'char' : 1, 'uint8_t_mavlink_version' : 1}\r\n \r\n sort_mapping = {'double' : 0, 'int64_t' : 0, 'uint64_t': 0, 'int32_t' : 1, 'uint32_t': 1,\r\n 'float' : 1, 'int16_t' : 3, 'uint16_t': 3, 'int8_t' : 4, 'uint8_t' : 4,\r\n 'char' : 4, 'uint8_t_mavlink_version' : 4}\r\n \r\n #Get message fields\r\n fields = []\r\n msglen = 0;\r\n \r\n for field in msg.findall('field'):\r\n \r\n field_type = field.attrib.get('type').split('[')[0]\r\n if '[' in field.attrib.get('type'):\r\n array_size = int(field.attrib.get('type').split('[')[1].split(']')[0])\r\n else:\r\n array_size = 1\r\n \r\n if field_type == 'uint8_t_mavlink_version':\r\n field_type = 'uint8_t'\r\n \r\n msglen += type_size[field_type]*array_size\r\n \r\n fields.append({'type' : field_type, \r\n 'name' : field.attrib.get('name'), \r\n 'desc' : field.text.replace('\\n',' '),\r\n 'size' : array_size})\r\n \r\n #Sort message fields\r\n fields.sort(key = lambda k: sort_mapping[k['type']])\r\n \r\n #Calculate the XML checksum for this message using original XML\r\n crc = 0xffff\r\n crc = accumulate(crc,name.upper() + ' ')\r\n \r\n for field in fields: \r\n crc = accumulate(crc,field['type'] + ' ')\r\n crc = accumulate(crc,field['name'] + ' ')\r\n if field['size'] > 1:\r\n crc = accumulate(crc,chr(field['size']))\r\n \r\n crc = (crc&0xFF) ^ (crc>>8)\r\n \r\n #Re-format field strings\r\n for field in fields:\r\n field['type'] = field['type'].split('_')[0]\r\n field['name'] = field['name'].lower()\r\n \r\n if field['type'] == 'char':\r\n field['type'] = 'uint8'\r\n \r\n if field['type'] == 'float':\r\n field['type'] = 'single'\r\n \r\n #Generate class properties\r\n fo.write('''\\\r\n \r\n properties(Constant)\r\n ID = %s\r\n LEN = %s\r\n end\r\n \r\n properties\\\r\n \\\r\n ''' % (msgid, msglen))\r\n \r\n #Insert a variable per field\r\n for field in fields:\r\n if field['size'] == 1:\r\n fo.write('\\n\\t\\t%s\\t%%%s (%s)' % (field['name'], field['desc'], field['type']))\r\n else:\r\n fo.write('\\n\\t\\t%s\\t%%%s (%s[%s])' % (field['name'], field['desc'], field['type'], field['size']))\r\n fo.write('\\n\\tend\\n')\r\n \r\n #Generate class constructors\r\n fo.write('''\\\r\n \r\n methods\r\n \r\n %%Constructor: %s\r\n %%packet should be a fully constructed MAVLINK packet\\\r\n \\\r\n ''' % class_name)\r\n \r\n fo.write('\\n\\t\\tfunction obj = %s(packet' % class_name)\r\n for field in fields:\r\n fo.write(',%s' % field['name'])\r\n fo.write(')')\r\n \r\n fo.write('''\r\n \r\n obj.msgid = obj.ID;\r\n obj.sysid = mavlink.SYSID;\r\n obj.compid = mavlink.COMPID;\r\n\r\n if nargin == 1\r\n \r\n if isa(packet,'mavlink_packet')\r\n obj.sysid = packet.sysid;\r\n obj.compid = packet.compid;\r\n obj.unpack(packet.payload);\r\n else\r\n mavlink.throwTypeError('packet','mavlink_packet');\r\n end\r\n \r\n elseif nargin == %s\r\n \\\r\n ''' % str(len(fields)+1))\r\n \r\n for field in fields:\r\n fo.write('\\n\\t\\t\\t\\tobj.%s = %s;' % (field['name'],field['name']))\r\n \r\n fo.write('''\r\n \r\n elseif nargin ~= 0\r\n mavlink.throwCustomError('The number of constructor arguments is not valid');\r\n end\r\n \r\n end\r\n \\\r\n ''')\r\n \r\n #Generate message pack function\r\n fo.write('''\\\r\n \r\n %%Function: Packs this MAVLINK message into a packet for transmission\r\n function packet = pack(obj)\r\n \r\n errorField = obj.verify();\r\n if errorField == 0\r\n \r\n packet = mavlink_packet(%s.LEN);\r\n packet.sysid = mavlink.SYSID;\r\n packet.compid = mavlink.COMPID;\r\n packet.msgid = %s.ID;\r\n \\\r\n ''' % (class_name, class_name))\r\n \r\n for field in fields:\r\n \r\n if field['size'] > 1:\r\n fo.write('''\\\r\n \r\n for i = 1:%s\r\n packet.payload.put%s(obj.%s(i));\r\n end\r\n \\\r\n ''' % (field['size'], field['type'].upper(), field['name']))\r\n else:\r\n fo.write('\\n\\t\\t\\t\\tpacket.payload.put%s(obj.%s);\\n' % (field['type'].upper(), field['name']))\r\n \r\n fo.write('''\\\r\n \r\n else\r\n packet = [];\r\n mavlink.throwPackingError(errorField);\r\n end\r\n \r\n end\r\n \\\r\n ''')\r\n \r\n #Generate message unpack function\r\n fo.write('''\\\r\n \r\n %Function: Unpacks a MAVLINK payload and stores the data in this message\r\n function unpack(obj, payload)\r\n \r\n payload.resetIndex();\r\n ''')\r\n \r\n for field in fields:\r\n \r\n if field['size'] > 1:\r\n fo.write('''\\\r\n \r\n for i = 1:%s\r\n obj.%s(i) = payload.get%s();\r\n end\r\n \\\r\n ''' % (field['size'], field['name'], field['type'].upper()))\r\n else:\r\n fo.write('\\n\\t\\t\\tobj.%s = payload.get%s();\\n' % (field['name'], field['type'].upper()))\r\n \r\n fo.write('\\n\\t\\tend\\n')\r\n \r\n #Generate verification function\r\n fo.write('''\\\r\n \r\n %Function: Returns either 0 or the name of the first encountered empty field.\r\n function result = verify(obj)\r\n \\\r\n ''')\r\n \r\n for i in range(0,len(fields)):\r\n field = fields[i]\r\n if i == 0:\r\n fo.write('''\\\r\n \r\n if size(obj.%s,2) ~= %s\r\n result = '%s';\\\r\n \\\r\n ''' % (field['name'], field['size'], field['name']))\r\n else:\r\n fo.write('''\\\r\n \r\n elseif size(obj.%s,2) ~= %s\r\n result = '%s';\\\r\n \\\r\n '''% (field['name'], field['size'], field['name']))\r\n \r\n fo.write('''\r\n else\r\n result = 0;\r\n end\r\n \r\n end\r\n \\\r\n ''')\r\n \r\n #Generate setters for integer message fields\r\n for field in fields:\r\n \r\n if field['type'] == 'double' or field['type'] == 'single':\r\n fo.write('''\\\r\n \r\n function set.%s(obj,value)\r\n obj.%s = %s(value);\r\n end\r\n \\\r\n ''' % (field['name'], field['name'], field['type'])) \r\n \r\n else:\r\n \r\n fo.write('''\\\r\n \r\n function set.%s(obj,value)\r\n if value == %s(value)\r\n obj.%s = %s(value);\r\n else\r\n mavlink.throwTypeError('value','%s');\r\n end\r\n end\r\n \\\r\n ''' % (field['name'], field['type'], field['name'], field['type'], field['type']))\r\n \r\n #End of class\r\n fo.write('\\n\\tend\\nend')\r\n \r\n #Return a parsed message\r\n parsed_msg = {'name' : name, 'msgid' : msgid, 'crc' : crc}\r\n return parsed_msg",
"title": ""
},
{
"docid": "e026468671bfda96f7102e0440c7edef",
"score": "0.59701777",
"text": "def test_parse_infer_type(self):\n factory_strings = (\"look\", \"dig 5 2\", \"flag 6 2\", \"deflag 3 6\",\n \"help\", \"bye\")\n message_classes = UTSMessage.message_types\n\n for string, mclass in zip(factory_strings, message_classes):\n o = UTSMessage.parse_infer_type(string)\n\n self.assertIsInstance(\n o,\n mclass\n )",
"title": ""
},
{
"docid": "f49f77bbf2a52271bb0461dcbc678175",
"score": "0.59089833",
"text": "def deserialize(self, msg):\n return self.serializer.deserialize(msg)",
"title": ""
},
{
"docid": "6cb332f79032b4fa773347db58b5263f",
"score": "0.5885095",
"text": "def message_class(self) -> str:\n return self._message_class",
"title": ""
},
{
"docid": "76dfeaf46c79f915ca3ad6ba3c8d8f73",
"score": "0.58044106",
"text": "def classify(message):",
"title": ""
},
{
"docid": "7f31b3939f0f6b501d38563c6ee3c627",
"score": "0.57920605",
"text": "def _ParseMessage(self, message):\n # Record this message in the object state for debugging purposes.\n self._last_message = message\n # Attempt to parse this message by matching it against expected\n # messages and recording pertinent state information.\n m = re.match('INFO: Attempting to flash (.*) segment on target'\n ' (.*) \\\\[(.*), .*\\\\]\\\\.', message)\n if m:\n self._target_ip = m.group(3)\n return\n\n m = re.match('INFO: Binary size: (.*) bytes; target IP: (.*)\\\\.', message)\n if m:\n self._total_size = int(m.group(1))\n self._target_ip = m.group(2)\n return\n\n m = re.match('INFO: Sent (.*) bytes\\\\.\\\\.\\\\.\\n', message)\n if m:\n self._sent_size = int(m.group(1))\n return\n\n m = re.match('INFO: Got an acknowledgement from target; starting upload\\\\.',\n message)\n if m:\n self._target_ack = True\n return\n\n m = re.match('INFO: Successfully transferred (.*) bytes; cleaning up\\\\.',\n message)\n if m:\n self._bootloader_declared_success = True\n self._sent_size = int(m.group(1))\n return\n\n # Messages that we simply ignore.\n if re.match('INFO: Target hardware type: (.*)\\\\.', message): return\n if re.match('INFO: Flashing file (.*)\\\\.', message): return\n if re.match('Another Bazel command .*\\\\.\\\\.\\\\.', message): return\n\n self._unparsed_messages += message",
"title": ""
},
{
"docid": "c9cd0dfa052d3cdf05cde4a24d67916d",
"score": "0.57739234",
"text": "def test_parse_class():\n from arg_scraper import parse\n assert parse('.target') == ('class', 'target')",
"title": ""
},
{
"docid": "f1ece9ba9651c564f3e15b9731363a47",
"score": "0.5744354",
"text": "def parse(self):\n raise NotImplementedError('Should be subclassed')",
"title": ""
},
{
"docid": "2b245a80a12d0714870412c1ccec7635",
"score": "0.57247514",
"text": "def get_msg_cls(cls, jobj):\n if cls in cls.TYPES.itervalues():\n # cls is already registered Message type, force to use it\n # so that, e.g Revocation.from_json(jobj) fails if\n # jobj[\"type\"] != \"revocation\".\n return cls\n\n if not isinstance(jobj, dict):\n raise errors.ValidationError(\n \"{0} is not a dictionary object\".format(jobj))\n try:\n msg_type = jobj[\"type\"]\n except KeyError:\n raise errors.ValidationError(\"missing type field\")\n\n try:\n msg_cls = cls.TYPES[msg_type]\n except KeyError:\n raise errors.UnrecognizedMessageTypeError(msg_type)\n\n return msg_cls",
"title": ""
},
{
"docid": "e7d25826087740069b16cabf0b715617",
"score": "0.5713601",
"text": "def _parse(self):\n pass",
"title": ""
},
{
"docid": "bea2cd6cb0d3460d186e23fe2ccc6048",
"score": "0.5705066",
"text": "def parse(self):\n # Each message is structured as:\n # <length prefix><message ID><payload>\n #\n # The `length prefix` is a four byte big-endian value\n # The `message ID` is a decimal byte\n # The `payload` is the value of `length prefix`\n #\n # The message length is not part of the actual length. So another\n # 4 bytes needs to be included when slicing the buffer.\n header_length = 4\n\n if len(self.buffer) > 4: # 4 bytes is needed to identify the message\n message_length = struct.unpack('>I', self.buffer[0:4])[0]\n\n if message_length == 0:\n return KeepAlive()\n\n if len(self.buffer) >= message_length:\n message_id = struct.unpack('>b', self.buffer[4:5])[0]\n\n def _consume():\n \"\"\"Consume the current message from the read buffer\"\"\"\n self.buffer = self.buffer[header_length + message_length:]\n\n def _data():\n \"\"\"\"Extract the current message from the read buffer\"\"\"\n return self.buffer[:header_length + message_length]\n\n if message_id is PeerMessage.BitField:\n data = _data()\n _consume()\n return BitField.decode(data)\n elif message_id is PeerMessage.Interested:\n _consume()\n return Interested()\n elif message_id is PeerMessage.NotInterested:\n _consume()\n return NotInterested()\n elif message_id is PeerMessage.Choke:\n _consume()\n return Choke()\n elif message_id is PeerMessage.Unchoke:\n _consume()\n return Unchoke()\n elif message_id is PeerMessage.Have:\n data = _data()\n _consume()\n return Have.decode(data)\n elif message_id is PeerMessage.Piece:\n data = _data()\n _consume()\n return Piece.decode(data)\n elif message_id is PeerMessage.Request:\n data = _data()\n _consume()\n return Request.decode(data)\n elif message_id is PeerMessage.Cancel:\n data = _data()\n _consume()\n return Cancel.decode(data)\n else:\n logging.info('Unsupported message!')\n else:\n logging.debug('Not enough in buffer in order to parse')\n return None",
"title": ""
},
{
"docid": "98b52c056f10821eb6466196ab556ee2",
"score": "0.5704917",
"text": "def __decide_parse(self):",
"title": ""
},
{
"docid": "5962456706254cfe90ec3eefae15f0f1",
"score": "0.5663202",
"text": "def parse(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "5962456706254cfe90ec3eefae15f0f1",
"score": "0.5663202",
"text": "def parse(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "17a813592ee7eee54c82e633b7b65c91",
"score": "0.5661448",
"text": "def parse(self):\n raise NotImplementedError('not done yet')",
"title": ""
},
{
"docid": "8c2796c1215f2059f60c1db99e83f77f",
"score": "0.56608844",
"text": "def visit_known_class(self, cls: \"KnownClass\"):\n return self.visit_class(cls)",
"title": ""
},
{
"docid": "6e1af4ff2a5f503b09bc9ccfeb499ced",
"score": "0.5647483",
"text": "def get_class(self, message_type, reply_to):\n if message_type == \"request\":\n return self._get_request_cls(reply_to)\n elif message_type == \"response\":\n return self._get_response_cls()\n elif message_type == \"notification\":\n return self._get_notification_cls()\n elif message_type == \"error\":\n return self._get_error_cls()",
"title": ""
},
{
"docid": "c5b7c322de9ef579f8022c22c9491436",
"score": "0.56321806",
"text": "def parse_incoming(message):\n pass",
"title": ""
},
{
"docid": "99f388203331dfe79191d10850748526",
"score": "0.56273216",
"text": "def parseProtobuf(self):\n\t\tcurrentType = -1\n\t\tfeildType = -1\n\n\t\twhile self.parsePos < len(self.binary):\n\t\t\tif currentType == -1:\n\t\t\t\t(feildType, currentType) = self._parseProtobufHeader()\n\n\t\t\t\t#Sanity Check for CurrentType\n\t\t\t\tname = self.getNameofType(currentType)\n\n\t\t\t#Varint\n\t\t\telif currentType == 0:\n\t\t\t\tself._parseVarint(feildType)\n\t\t\t\tcurrentType = -1\n\n\t\t\t#64-bit\n\t\t\telif currentType == 1:\n\t\t\t\tself._parse64Bit(feildType)\n\t\t\t\tcurrentType = -1\n\n\t\t\t#Length-delimited\n\t\t\telif currentType == 2:\n\t\t\t\ttry:\n\t\t\t\t\tself._parseLengthDelimited(feildType)\n\t\t\t\texcept Exception as e:\n\t\t\t\t\traise Exception(\"Invalid Recursive Message: %s\" % e.message)\n\n\t\t\t\tcurrentType = -1\n\t\t\t\n\t\t\t#32-bit\n\t\t\telif currentType == 5:\n\t\t\t\tself._parse32Bit(feildType)\n\t\t\t\tcurrentType = -1\n\n\t\t\telse:\n\t\t\t\traise Exception(\"Invalid Message Type\")",
"title": ""
},
{
"docid": "ed1fd8b4d502c61d9f9b84da3d06936d",
"score": "0.56229925",
"text": "def parse_message_file(self, message_file):\n pass",
"title": ""
},
{
"docid": "aa794fa997f0e5ee3d167b3677526f5d",
"score": "0.5602757",
"text": "def load_class(self, _class):",
"title": ""
},
{
"docid": "1022d15ac8f50285162824021cf822dc",
"score": "0.5544541",
"text": "def deserialize(self, str):\n try:\n if self.classes_with_probabilities is None:\n self.classes_with_probabilities = None\n end = 0\n start = end\n end += 4\n (length,) = _struct_I.unpack(str[start:end])\n self.classes_with_probabilities = []\n for i in range(0, length):\n val1 = automated_driving_msgs.msg.ClassWithProbability()\n _x = val1\n start = end\n end += 5\n (_x.classification, _x.probability,) = _get_struct_Bf().unpack(str[start:end])\n self.classes_with_probabilities.append(val1)\n return self\n except struct.error as e:\n raise genpy.DeserializationError(e) #most likely buffer underfill",
"title": ""
},
{
"docid": "e6cad75e461f7f60b724a74cfd243f35",
"score": "0.55298424",
"text": "def receive_class(self):\n return self._get_class(self.receiver)",
"title": ""
},
{
"docid": "08e853b2a350987a79e4ef058a081fec",
"score": "0.55223966",
"text": "def message_parser(self, message):\n attributes = [\n 'username',\n 'full_message',\n 'action',\n 'text',\n 'channel',\n ]\n\n message_obj = namedtuple('Message', attributes)\n username, *full_message = message.split()\n\n if full_message[0] in self.keywords:\n action, *rest_message = full_message\n else:\n action, rest_message = None, full_message\n\n parsed_message = {\n 'username': username[:-1],\n 'full_message': message,\n 'action': action,\n 'text': ' '.join(rest_message),\n 'channel': 'text_channel',\n }\n\n msg = message_obj(**parsed_message)\n return msg",
"title": ""
},
{
"docid": "b82372dd32dc763fb44b23a67153deab",
"score": "0.55154413",
"text": "def decodeMessage(cls, msg):\n message = cls.parseAndGetClassForMessage(msg)\n try:\n if not cls.currentVersion == message.getVersion():\n cls.log.warn(\"The node you are communicating with is at version: [%s] and this client is at version: [%s]\" % (message.getVersion(), cls.currentVersion))\n return message\n except Exception as e:\n cls.log.error(msg)\n raise e",
"title": ""
},
{
"docid": "c375c672db6623120d29479679d56e97",
"score": "0.5509761",
"text": "def class_(self, state, ast):\n ast.next() #'class keyword'\n state['classname'] = ast.next_val()\n ast.next() #'{'\n state['sym_tbl'] = SymbolTable()\n while(ast.get_key() == 'classVarDec'):\n self.classVarDec(state, ast.next_sec())\n while(ast.get_key() == 'subroutineDec'):\n self.subroutineDec(state, ast.next_sec())\n ast.next() #'}'\n return",
"title": ""
},
{
"docid": "12d829df31706ad7a7429fa56dec88eb",
"score": "0.5505378",
"text": "def read_message(cls, data):\n return cls(*data.split(':'))",
"title": ""
},
{
"docid": "732cfb872b0d46e1a68498573e8772b2",
"score": "0.5497529",
"text": "def parse(self):",
"title": ""
},
{
"docid": "f1ed3a374c2f73928af0f8ddb9ceed6a",
"score": "0.5496349",
"text": "def __deserialize(self, data, klass):\n if data is None:\n return None\n\n if type(klass) == str:\n if klass.startswith('list['):\n sub_kls = re.match('list\\[(.*)\\]', klass).group(1)\n return [self.__deserialize(sub_data, sub_kls)\n for sub_data in data]\n\n if klass.startswith('dict('):\n sub_kls = re.match('dict\\(([^,]*), (.*)\\)', klass).group(2)\n return {k: self.__deserialize(v, sub_kls)\n for k, v in six.iteritems(data)}\n\n # convert str to class\n if klass in self.NATIVE_TYPES_MAPPING:\n klass = self.NATIVE_TYPES_MAPPING[klass]\n else:\n klass = getattr(Telstra_Messaging.models, klass)\n\n if klass in self.PRIMITIVE_TYPES:\n return self.__deserialize_primitive(data, klass)\n elif klass == object:\n return self.__deserialize_object(data)\n elif klass == datetime.date:\n return self.__deserialize_date(data)\n elif klass == datetime.datetime:\n return self.__deserialize_datatime(data)\n else:\n return self.__deserialize_model(data, klass)",
"title": ""
},
{
"docid": "9fd0a8af056f9a8f33ea93ec13a9d294",
"score": "0.54943496",
"text": "def deserialize(self, serialized: str) -> Message:\n pass",
"title": ""
},
{
"docid": "bc1e6ad05a9057784cf33ea3f8462417",
"score": "0.5492093",
"text": "def parse(self):\n pass",
"title": ""
},
{
"docid": "bc1e6ad05a9057784cf33ea3f8462417",
"score": "0.5492093",
"text": "def parse(self):\n pass",
"title": ""
},
{
"docid": "bc1e6ad05a9057784cf33ea3f8462417",
"score": "0.5492093",
"text": "def parse(self):\n pass",
"title": ""
},
{
"docid": "cc2040deb7e290f12ababc20c39690ae",
"score": "0.5483615",
"text": "def test_parse_class_small():\n from arg_scraper import parse\n assert parse('.t') == ('class', 't')",
"title": ""
},
{
"docid": "d15f51dd520b3f2721c674a0ccea64a1",
"score": "0.548037",
"text": "def make_message(self, serialized_msg: dict) -> AgentMessage:\n\n msg_type = serialized_msg.get(\"@type\")\n if not msg_type:\n raise MessageParseError(\"Message does not contain '@type' parameter\")\n\n msg_cls = self.resolve_message_class(msg_type)\n if not msg_cls:\n raise MessageParseError(f\"Unrecognized message type {msg_type}\")\n\n try:\n instance = msg_cls.deserialize(serialized_msg)\n except BaseModelError as e:\n raise MessageParseError(f\"Error deserializing message: {e}\") from e\n\n return instance",
"title": ""
},
{
"docid": "d81ca1898bd02af15adc4d1974422600",
"score": "0.54744613",
"text": "def _parse(self, msg, level=None):\n if msg.is_multipart() and msg.get_content_maintype() != \"message\":\n cpt = 1\n for part in msg.get_payload():\n nlevel = level is None and (\"%d\" % cpt) \\\n or \"%s.%d\" % (level, cpt)\n self._parse(part, nlevel)\n cpt += 1\n return\n\n if level is None:\n level = \"1\"\n try:\n getattr(self, \"_parse_%s\" % msg.get_content_maintype())(msg, level)\n except AttributeError:\n self._parse_default(msg, level)",
"title": ""
},
{
"docid": "99c008b098a8683ec16c65fae6df68d9",
"score": "0.5469361",
"text": "def _parse_suspected_cls(predator_result):\n if not predator_result:\n return None\n\n # The raw result contains some additional information that we don't need here.\n # Everything we're concerned with is a part of the \"result\" object included\n # with the response.\n predator_result = predator_result['result']\n return {\n 'found': predator_result.get('found'),\n 'suspected_project': predator_result.get('suspected_project'),\n 'suspected_components': predator_result.get('suspected_components'),\n 'changelists': predator_result.get('suspected_cls'),\n 'feedback_url': predator_result.get('feedback_url'),\n 'error_message': predator_result.get('error_message'),\n }",
"title": ""
},
{
"docid": "8713a294003057e67ba8f22ef0373e6b",
"score": "0.5457736",
"text": "def message_class(self, message_class: str):\n self._message_class = message_class",
"title": ""
},
{
"docid": "4a5362dc3ad3d593eafc839b23bb4865",
"score": "0.54523456",
"text": "def parse(self, raw_message):\n _params = {'raw_message': raw_message}\n return self.master.call('messages/parse', _params)",
"title": ""
},
{
"docid": "623ca4d90f058bf5cea605b882ba2a86",
"score": "0.5450521",
"text": "def _proto2object(\n proto: SearchUsersMessage_PB,\n ) -> \"SearchUsersMessage\":\n\n return SearchUsersMessage(\n msg_id=_deserialize(blob=proto.msg_id),\n address=_deserialize(blob=proto.address),\n content=json.loads(proto.content),\n reply_to=_deserialize(blob=proto.reply_to),\n )",
"title": ""
},
{
"docid": "ae61536fdc1ec6941a62c13f93a78da0",
"score": "0.54497355",
"text": "def parseClassExpression(self, manchester_exp):\n self.parser.setStringToParse(manchester_exp);\n\n return self.parser.parseClassExpression()",
"title": ""
},
{
"docid": "9c51844c5fda4b4ebe67e0c3f4c81ed1",
"score": "0.544371",
"text": "def unpack(cls, msg):\n ...",
"title": ""
},
{
"docid": "9c51844c5fda4b4ebe67e0c3f4c81ed1",
"score": "0.544371",
"text": "def unpack(cls, msg):\n ...",
"title": ""
},
{
"docid": "758ef9613841becfef308789d19162ee",
"score": "0.54122514",
"text": "def _PPIGuessPayloadClass(p, **kargs):\n if len(p) >= 4:\n t,pfh_len = struct.unpack(\"<HH\", p[:4])\n # Find out if the value t is in the dict _ppi_types.\n # If not, return the default TLV class\n cls = getPPIType(t, \"default\")\n pfh_len += 4\n out = cls(p[:pfh_len], **kargs)\n if (out.payload):\n out.payload = conf.raw_layer(out.payload.load)\n if (len(p) > pfh_len):\n out.payload.payload = conf.padding_layer(p[pfh_len:])\n elif (len(p) > pfh_len):\n out.payload = conf.padding_layer(p[pfh_len:])\n \n else:\n out = conf.raw_layer(p, **kargs)\n return out",
"title": ""
},
{
"docid": "0d04b78efbfab5d8baf64f13a5cd8838",
"score": "0.5400379",
"text": "def test_responseType(self):\n class SuppliedClass(object):\n id = 1\n queries = []\n\n expectedClass = dns.Message\n\n self.assertIsInstance(\n dns._responseFromMessage(responseConstructor=expectedClass,\n message=SuppliedClass()),\n expectedClass\n )",
"title": ""
},
{
"docid": "a15b0805091afc7fd466c6549cc101f9",
"score": "0.54002815",
"text": "def test_class_no_body(self):\n tokens = (\n Token('keyword', 'class'), \n Token('identifier', 'Main'), \n Token('symbol', '{'),\n Token('symbol', '}'),\n )\n\n expected = Token('class', [\n Token('keyword', 'class'), \n Token('identifier', 'Main'), \n Token('symbol', '{'),\n Token('symbol', '}'),\n ])\n\n actual = Parser(tokens).parse_class()\n self.assertEqual(expected, actual)",
"title": ""
},
{
"docid": "ff1007828efe7af13790faa9539fd7da",
"score": "0.5398521",
"text": "def processMessage(msg): #@NoSelf",
"title": ""
},
{
"docid": "e6d8be62b04e8299992bc93071f63272",
"score": "0.539516",
"text": "def parse(self) -> None:\n pass",
"title": ""
},
{
"docid": "02858a86ca31537a2299d6ae3f97d3be",
"score": "0.53911483",
"text": "def parse(self, data: bytes):\n\n message = Message.parse(data)\n self.obj_sha = message.get_header(\"object\")\n self.obj_type = message.get_header(\"type\")\n self.name = message.get_header(\"tag\")\n self.tagger = message.get_author(key=\"tagger\")\n self.message = message.get_text()",
"title": ""
},
{
"docid": "20d7edc7ebe07d709f200b891c51265f",
"score": "0.5390818",
"text": "def deserialize(self, str):\n try:\n if self.cls is None:\n self.cls = zzz_perception_msgs.msg.ObjectClass()\n if self.state is None:\n self.state = zzz_driver_msgs.msg.RigidBodyState()\n if self.ffstate is None:\n self.ffstate = zzz_driver_msgs.msg.FrenetSerretState2D()\n if self.shape is None:\n self.shape = geometry_msgs.msg.Polygon()\n if self.dimension is None:\n self.dimension = zzz_perception_msgs.msg.DimensionWithCovariance()\n end = 0\n _x = self\n start = end\n end += 20\n (_x.uid, _x.confidence, _x.cls.classid, _x.cls.score,) = _get_struct_QfIf().unpack(str[start:end])\n start = end\n end += 4\n (length,) = _struct_I.unpack(str[start:end])\n start = end\n end += length\n if python3:\n self.cls.comments = str[start:end].decode('utf-8')\n else:\n self.cls.comments = str[start:end]\n start = end\n end += 4\n (length,) = _struct_I.unpack(str[start:end])\n start = end\n end += length\n if python3:\n self.state.child_frame_id = str[start:end].decode('utf-8')\n else:\n self.state.child_frame_id = str[start:end]\n _x = self\n start = end\n end += 56\n (_x.state.pose.pose.position.x, _x.state.pose.pose.position.y, _x.state.pose.pose.position.z, _x.state.pose.pose.orientation.x, _x.state.pose.pose.orientation.y, _x.state.pose.pose.orientation.z, _x.state.pose.pose.orientation.w,) = _get_struct_7d().unpack(str[start:end])\n start = end\n end += 288\n self.state.pose.covariance = _get_struct_36d().unpack(str[start:end])\n _x = self\n start = end\n end += 48\n (_x.state.twist.twist.linear.x, _x.state.twist.twist.linear.y, _x.state.twist.twist.linear.z, _x.state.twist.twist.angular.x, _x.state.twist.twist.angular.y, _x.state.twist.twist.angular.z,) = _get_struct_6d().unpack(str[start:end])\n start = end\n end += 288\n self.state.twist.covariance = _get_struct_36d().unpack(str[start:end])\n _x = self\n start = end\n end += 48\n (_x.state.accel.accel.linear.x, _x.state.accel.accel.linear.y, _x.state.accel.accel.linear.z, _x.state.accel.accel.angular.x, _x.state.accel.accel.angular.y, _x.state.accel.accel.angular.z,) = _get_struct_6d().unpack(str[start:end])\n start = end\n end += 288\n self.state.accel.covariance = _get_struct_36d().unpack(str[start:end])\n _x = self\n start = end\n end += 12\n (_x.ffstate.s, _x.ffstate.d, _x.ffstate.psi,) = _get_struct_3f().unpack(str[start:end])\n start = end\n end += 36\n self.ffstate.pose_covariance = _get_struct_9f().unpack(str[start:end])\n _x = self\n start = end\n end += 12\n (_x.ffstate.vs, _x.ffstate.vd, _x.ffstate.omega,) = _get_struct_3f().unpack(str[start:end])\n start = end\n end += 36\n self.ffstate.twist_covariance = _get_struct_9f().unpack(str[start:end])\n _x = self\n start = end\n end += 12\n (_x.ffstate.sa, _x.ffstate.ad, _x.ffstate.epsilon,) = _get_struct_3f().unpack(str[start:end])\n start = end\n end += 36\n self.ffstate.accel_covariance = _get_struct_9f().unpack(str[start:end])\n _x = self\n start = end\n end += 22\n (_x.lane_index, _x.lane_anglediff, _x.lane_dist_left_t, _x.lane_dist_right_t, _x.lane_dist_s, _x.static, _x.shape_type,) = _get_struct_5f2B().unpack(str[start:end])\n self.static = bool(self.static)\n start = end\n end += 4\n (length,) = _struct_I.unpack(str[start:end])\n self.shape.points = []\n for i in range(0, length):\n val1 = geometry_msgs.msg.Point32()\n _x = val1\n start = end\n end += 12\n (_x.x, _x.y, _x.z,) = _get_struct_3f().unpack(str[start:end])\n self.shape.points.append(val1)\n _x = self\n start = end\n end += 24\n (_x.dimension.length_x, _x.dimension.length_y, _x.dimension.length_z,) = _get_struct_3d().unpack(str[start:end])\n start = end\n end += 72\n self.dimension.covariance = _get_struct_9d().unpack(str[start:end])\n start = end\n end += 4\n (length,) = _struct_I.unpack(str[start:end])\n pattern = '<%sf'%length\n start = end\n end += struct.calcsize(pattern)\n self.shape_uncertainty = struct.unpack(pattern, str[start:end])\n _x = self\n start = end\n end += 2\n (_x.behavior, _x.priority,) = _get_struct_2B().unpack(str[start:end])\n return self\n except struct.error as e:\n raise genpy.DeserializationError(e) #most likely buffer underfill",
"title": ""
},
{
"docid": "4ac5865d5116439fc469db2210e5092c",
"score": "0.5388804",
"text": "def _class_for_nxm_header (raw):\r\n t,has_mask,length = nxm_entry.unpack_header(raw, 0)\r\n c = _nxm_type_to_class.get(t)\r\n if c: return c\r\n\r\n # Need to generate a new nxm_entry type.\r\n # This code is totally untested.\r\n vendor = (t >> 7) & 0xffff\r\n field = t & 0x7f\r\n typename = \"NXM_UNKNOWN_\"\r\n typename += \"%04x_%02x\" % (vendor,field)\r\n if has_mask: typename += \"_MASKABLE\"\r\n types = [_nxm_raw]\r\n if has_mask:\r\n types.append(_nxm_maskable)\r\n return _make_nxm(typename, vendor, field, length, types)",
"title": ""
},
{
"docid": "cac2b65ed86c6d083b9b524b66d2b3c5",
"score": "0.5370653",
"text": "def _proto2object(\n proto: GetUserMessage_PB,\n ) -> \"GetUserMessage\":\n\n return GetUserMessage(\n msg_id=_deserialize(blob=proto.msg_id),\n address=_deserialize(blob=proto.address),\n content=json.loads(proto.content),\n reply_to=_deserialize(blob=proto.reply_to),\n )",
"title": ""
},
{
"docid": "109bf97738a981637ff90203fa701a22",
"score": "0.5366171",
"text": "def receive_classical_msg(self, msg: \"ClassicalMessage\", **kwargs) -> None:\n pass",
"title": ""
},
{
"docid": "7c6e790bc8e49dd06434c947dd94f508",
"score": "0.5357033",
"text": "def test_implemented_message_class(self):\r\n msg = messages.StringMessage()\r\n self.assertIsInstance(msg, messages.Message)",
"title": ""
},
{
"docid": "1e49de8b6d498f66675b2c25e4e10523",
"score": "0.5353519",
"text": "def _class_definition(self, name: str) -> dict:\n for definition in self._amqp_json['classes']:\n if definition['name'] == name:\n for method in definition['methods']:\n for index, ar in enumerate(method['arguments']):\n method['arguments'][index].update(\n self._lookup_field(\n name, method['name'], ar['name']))\n return definition",
"title": ""
},
{
"docid": "040641cb5695d76369516d5efb4cb353",
"score": "0.5350358",
"text": "def parse(message: bytes) -> LspMessage:\n # Its important to check Request before Notification, because Notification\n # contains a subset of Request's necessary fields.\n try:\n return LspRequest.parse(message)\n except ValueError:\n pass\n try:\n return LspNotification.parse(message)\n except ValueError:\n pass\n try:\n return LspResponse.parse(message)\n except ValueError:\n pass\n raise ValueError(f\"Could not parse `message` as any LspMessage: {message}\")",
"title": ""
},
{
"docid": "ba706a64798f8a808fb4a20a8b6b7127",
"score": "0.53476083",
"text": "def _ParseClassNode(class_node, classIndex: int,\n annotations: Dict[int, Annotations]):\n methods = []\n for child in class_node:\n if child.tag == 'method' and child.attrib['visibility'] == 'public':\n methods.append(child.attrib['name'])\n return {\n 'methods':\n methods,\n 'superclass':\n class_node.attrib['extends'],\n 'is_abstract':\n class_node.attrib.get('abstract') == 'true',\n 'annotations':\n annotations.get(classIndex,\n Annotations(classAnnotations={}, methodsAnnotations={}))\n }",
"title": ""
},
{
"docid": "20b1d0ab6dacb320514cdd3294687dcc",
"score": "0.53349054",
"text": "def modality_based_class_importer(self, modality):\n try:\n module = importlib.import_module(\"protofiles.sensor_pb2\")\n classtype = getattr(module, modality.name)\n return classtype\n except Exception as e:\n traceback.print_exc()",
"title": ""
},
{
"docid": "c541641883bac27dc8b8d17fec2b8ef9",
"score": "0.5327817",
"text": "def __class_to_proto(self,\n class_to_prop: Dict[str, Set[PropertyToParent]],\n enumerations: Set[str]):\n proto_class = '// Definition of classes begin here.\\n\\n'\n\n for x in sorted(class_to_prop.keys()):\n if ((x not in enumerations) and (x not in constants.schema_datatypes) and (\n x not in constants.schema_primitives)):\n\n comment = ''\n\n for _, _, c in self.graph.triples(\n (utils.add_url(x), constants.schema_constants['Comment'], None)):\n comment += c\n\n soup = BeautifulSoup(comment, 'html.parser')\n comment = soup.get_text()\n\n proto_class += class_descriptor.ClassDescriptor(\n x, list(class_to_prop[x])).to_proto(comment)\n proto_class += '\\n'\n\n return proto_class",
"title": ""
},
{
"docid": "a6ff964cb598f502b0c13f2ee9fc8f37",
"score": "0.5327099",
"text": "def parse(class_name, module_name=None):\n if module_name is None:\n module_name, class_name = class_name.rsplit(\".\", 1)\n\n cls = etau.get_class(class_name, module_name=module_name)\n config_cls = etau.get_class(\n class_name + \"Config\", module_name=module_name\n )\n return cls, config_cls",
"title": ""
},
{
"docid": "401a3588dbad17564957c371b8b0f11a",
"score": "0.5321055",
"text": "def parse(self):\n return",
"title": ""
},
{
"docid": "e2e3626609288d26f375de6b1f8faa9a",
"score": "0.53113353",
"text": "def test_send_message_class(self):\n class TestIncomingMessage(OutgoingMessage):\n pass\n msg = self.send(\"hello\", self.create_connection(),\n class_=TestIncomingMessage)\n self.assertTrue(isinstance(msg, TestIncomingMessage))",
"title": ""
},
{
"docid": "029f71fbd2414077a9758545fd4a4cf1",
"score": "0.5306129",
"text": "def deserialize(self, msg, **kwargs):\n return msg, {}",
"title": ""
},
{
"docid": "66d93a182a6dd6d0d90d786f7d3e366c",
"score": "0.53019243",
"text": "def parse_message(self, message):\n\t\t\n\t\twords = repr(message).strip(string.punctuation).split(',')\n\t\t\n\t\t# take appropriate action depending on the type of message\n\t\tif (words[1] == \"genes\"):\n\t\t\tself.process_genes_message(message)\n\t\telif (words[1] == \"display\"):\n\t\t\tself.process_display_message(message)\n\t\t\n\t\t# other message types...\t\t",
"title": ""
},
{
"docid": "225167bce72f6d96cc63bee914edaa56",
"score": "0.5299948",
"text": "def _parseMessage(self, message, array):\n if message.startswith('codec='):\n result = dict(self._parsePartialResult(m)\n for m in re.finditer(self.MESSAGE_PATTERN + '(?: |$)', message))\n if result.get('decodeto') == 'surface':\n self.codec = result['codec']\n fmt = result['DecOutputFormat']\n self.size = Size(fmt['width'], fmt['height'])\n self.mime = result['mime']\n self._rates_from_message.append(1000000. / result['min'])",
"title": ""
},
{
"docid": "d0556e55fd7ec2aca9cb633b3cd19b84",
"score": "0.5297314",
"text": "def test_class_with_body(self):\n tokens = (\n Token('keyword', 'class'), \n Token('identifier', 'Main'), \n Token('symbol', '{'),\n Token('keyword', 'static'), # Dummy class var declaration\n Token('keyword', 'function'), # Dummy subroutine declaration\n Token('symbol', '}'),\n )\n\n expected = Token('class', [\n Token('keyword', 'class'), \n Token('identifier', 'Main'), \n Token('symbol', '{'),\n Token('dummy', 'dummy'),\n Token('dummy', 'dummy'),\n Token('symbol', '}'),\n ])\n\n parser = Parser(tokens)\n parser.parse_class_var_declaration = self._mock_parse(parser)\n parser.parse_subroutine_declaration = self._mock_parse(parser)\n\n actual = parser.parse_class()\n self.assertEqual(expected, actual)",
"title": ""
},
{
"docid": "cf37d96e6d4a0dc590b72866e097539c",
"score": "0.5296471",
"text": "def parse(self):\n raise NotImplementedError(\"parse() method not implemented.\")",
"title": ""
},
{
"docid": "08a960b2b9b1212de5aa6c68302215ac",
"score": "0.529646",
"text": "def _proto2object(\n proto: GetUsersMessage_PB,\n ) -> \"GetUsersMessage\":\n\n return GetUsersMessage(\n msg_id=_deserialize(blob=proto.msg_id),\n address=_deserialize(blob=proto.address),\n content=json.loads(proto.content),\n reply_to=_deserialize(blob=proto.reply_to),\n )",
"title": ""
},
{
"docid": "a987d552821362dbb5fdb15839dd3231",
"score": "0.52619565",
"text": "def _deserialize_class(cls, input_cls_name, trusted, strict):\n if not input_cls_name or input_cls_name == cls.__name__:\n return cls\n if trusted and input_cls_name in cls._REGISTRY:\n return cls._REGISTRY[input_cls_name]\n if strict:\n raise ValueError(\n 'Class name {} from deserialization input dictionary does '\n 'not match input class {}'.format(input_cls_name, cls.__name__)\n )\n return cls",
"title": ""
},
{
"docid": "312df24be6893e4b5fa941b52511356b",
"score": "0.525977",
"text": "def parse(cls, data: bytes) -> MessageContent:\n lines = cls._find_lines(data)\n view = memoryview(data)\n return cls._parse(data, view, lines)",
"title": ""
},
{
"docid": "160eb1bd157fb070ad293e7f5638c1b4",
"score": "0.52536815",
"text": "async def parser(content: str, **kwargs) -> List[Message]:\n\n messages = [] # type: List[Message]\n\n for message in full_message_pattern.finditer(content):\n messageId = int(message.group(1))\n userId = int(message.group(2))\n userName = message.group(3).strip()\n\n dateStr = message.group(4).strip()\n\n try:\n date = datetime.strptime(dateStr, \"%A, %B %d, %Y, %I:%M%p\")\n except ValueError:\n raise UnknownError(\"Found a date that we couldn't parse in a kmail\")\n\n rawText = message.group(5).strip()\n index = rawText.find(\"<center\")\n if index >= 0:\n text = rawText[:index].strip()\n else:\n text = rawText.strip()\n\n # Get rid of extraneous spaces, tabs, or new lines.\n text = text.replace(\"\\r\\n\", \"\\n\")\n text = whitespace_pattern.sub(\" \", text)\n text = text.replace(\"<br />\\n\", \"\\n\")\n text = text.replace(\"<br/>\\n\", \"\\n\")\n text = text.replace(\"<br>\\n\", \"\\n\")\n text = text.replace(\"\\n<br />\", \"\\n\")\n text = text.replace(\"\\n<br/>\", \"\\n\")\n text = text.replace(\"\\n<br>\", \"\\n\")\n text = text.replace(\"<br />\", \"\\n\")\n text = text.replace(\"<br/>\", \"\\n\")\n text = text.replace(\"<br>\", \"\\n\")\n text = text.strip()\n\n # KoL encodes all of the HTML entities in the message. Let's decode them to get the real text.\n text = unescape(text)\n\n # Handle special messages.\n if \"brokewin.gif\" in content or \"bigbrick.gif\" in content:\n type = \"brick\"\n elif \"/heart/cuptop.gif\" in content:\n type = \"coffeeCup\"\n elif \"/heart/hearttop.gif\" in content:\n type = \"candyHeart\"\n else:\n type = \"normal\"\n\n messages += [\n Message(\n id=messageId,\n user_id=userId,\n username=userName,\n date=date,\n text=text,\n items=await parsing.item(rawText),\n meat=parsing.meat(rawText),\n type=type,\n )\n ]\n\n return messages",
"title": ""
},
{
"docid": "f7d8d9dd90d16622812bcda85aac4925",
"score": "0.52531856",
"text": "def parse_for(self):\n pass",
"title": ""
},
{
"docid": "c711a7bdea0ad7acf81da6f0ae59f288",
"score": "0.52431387",
"text": "def _parseMessage(self, message, array):\n if message.startswith('codec='):\n result = dict(self._parsePartialResult(m)\n for m in re.finditer(self.MESSAGE_PATTERN + '(?: |$)', message))\n if 'EncInputFormat' in result:\n self.codec = result['codec']\n fmt = result['EncInputFormat']\n self.size = Size(fmt['width'], fmt['height'])\n self.mime = result['EncOutputFormat']['mime']\n self._rates_from_message.append(1000000./result['min'])",
"title": ""
},
{
"docid": "904dcc53835ed140ca315f1d9ac98d5d",
"score": "0.52369505",
"text": "def process_type(self):\n return 'message'",
"title": ""
},
{
"docid": "27823a7c415035d8be78defc66179bd7",
"score": "0.5235687",
"text": "def parse_for_class_method(self, section):\n id_tag = section.find('dt').get('id')\n if id_tag:\n tag_parts = id_tag.split('.')\n\n # if it doesnt fit the pattern\n # module.class.method\n # then concat the remaining parts into the method name\n # ex: email.message.EmailMessage.is_attachment\n if len(tag_parts) == 3:\n return tag_parts\n elif len(tag_parts) > 3:\n return tag_parts[0], tag_parts[1], '.'.join(tag_parts[2:])\n return ['','','']",
"title": ""
},
{
"docid": "d0a283a511c00d62164ca13f928330ee",
"score": "0.52321124",
"text": "def get_by_name(name):\n for message_class in classes:\n if message_class.name==name:\n return message_class\n raise ValueError(\"Unknown MessageClass of name \\\"{}\\\".\".format(name))",
"title": ""
},
{
"docid": "7e6a71fdd7df3e11fa8020b13732d64e",
"score": "0.5225357",
"text": "def build(cls, msg):\n\n msg = super().parse(msg)\n event_type = msg['args'].pop('type')\n event_class = get_event_class_by_type(event_type)\n\n args = msg['args'] if 'args' in msg else {}\n args['id'] = msg['id'] if 'id' in msg else cls._generate_id()\n args['target'] = msg['target'] if 'target' in msg else Config.get('device_id')\n args['origin'] = msg['origin'] if 'origin' in msg else Config.get('device_id')\n args['timestamp'] = msg['_timestamp'] if '_timestamp' in msg else time.time()\n return event_class(**args)",
"title": ""
},
{
"docid": "931a3d0d91055324a161a3872d3cec83",
"score": "0.5219071",
"text": "def parse_message(self, raw_message: str):\n self.messages.append(message.parse_from_raw(self, raw_message))",
"title": ""
},
{
"docid": "3f07a8466a281392923cec209c45c19d",
"score": "0.52158785",
"text": "def HisparcMessageFactory(buff):\n if len(buff) == 0:\n return None\n\n for cls in HisparcMessage.__subclasses__():\n if cls.is_message_for(buff):\n return cls(buff)\n raise NotImplementedError(\"Message type not implemented\")",
"title": ""
},
{
"docid": "bb4bbb793829332108a10de214963348",
"score": "0.5213787",
"text": "def decode(cls, msg):\n return msg",
"title": ""
},
{
"docid": "7a646fc6c74a73b50d0a9ce2a3e1a1b8",
"score": "0.52117586",
"text": "def from_msg(cls, msg):\r\n raise NotImplementedError()",
"title": ""
},
{
"docid": "fb660e64ce84d5fa352ccf14c0233194",
"score": "0.52115744",
"text": "def _parse_message(self, message):\n try:\n obj = json.loads(message)\n except ValueError:\n obj = {'message': message}\n\n return obj",
"title": ""
},
{
"docid": "c3fcb6e214e9d0385c246f8a2f2c7d3f",
"score": "0.5208099",
"text": "def test_get_message_class_from_descriptor_returns_message_class(self):\n actual = proto_utils.get_message_class_from_descriptor(\n patient_pb2.Patient.DESCRIPTOR\n )\n self.assertTrue(\n proto_utils.are_same_message_type(\n actual.DESCRIPTOR, patient_pb2.Patient.DESCRIPTOR\n )\n )",
"title": ""
},
{
"docid": "e4238cc43725d387c9de49972f511aef",
"score": "0.5204824",
"text": "def process(self, message):",
"title": ""
},
{
"docid": "f2ebe25be77d501a1cbf21e30f253dd1",
"score": "0.520309",
"text": "def deserialize(self, str):\n codecs.lookup_error(\"rosmsg\").msg_type = self._type\n try:\n if self.header is None:\n self.header = std_msgs.msg.Header()\n if self.goal_id is None:\n self.goal_id = actionlib_msgs.msg.GoalID()\n if self.goal is None:\n self.goal = object_grabber.msg.object_grabberGoal()\n end = 0\n _x = self\n start = end\n end += 12\n (_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs,) = _get_struct_3I().unpack(str[start:end])\n start = end\n end += 4\n (length,) = _struct_I.unpack(str[start:end])\n start = end\n end += length\n if python3:\n self.header.frame_id = str[start:end].decode('utf-8', 'rosmsg')\n else:\n self.header.frame_id = str[start:end]\n _x = self\n start = end\n end += 8\n (_x.goal_id.stamp.secs, _x.goal_id.stamp.nsecs,) = _get_struct_2I().unpack(str[start:end])\n start = end\n end += 4\n (length,) = _struct_I.unpack(str[start:end])\n start = end\n end += length\n if python3:\n self.goal_id.id = str[start:end].decode('utf-8', 'rosmsg')\n else:\n self.goal_id.id = str[start:end]\n _x = self\n start = end\n end += 40\n (_x.goal.action_code, _x.goal.object_id, _x.goal.grasp_option, _x.goal.approach_strategy, _x.goal.lift_object_strategy, _x.goal.dropoff_strategy, _x.goal.dropoff_withdraw_strategy, _x.goal.object_frame.header.seq, _x.goal.object_frame.header.stamp.secs, _x.goal.object_frame.header.stamp.nsecs,) = _get_struct_7i3I().unpack(str[start:end])\n start = end\n end += 4\n (length,) = _struct_I.unpack(str[start:end])\n start = end\n end += length\n if python3:\n self.goal.object_frame.header.frame_id = str[start:end].decode('utf-8', 'rosmsg')\n else:\n self.goal.object_frame.header.frame_id = str[start:end]\n _x = self\n start = end\n end += 64\n (_x.goal.object_frame.pose.position.x, _x.goal.object_frame.pose.position.y, _x.goal.object_frame.pose.position.z, _x.goal.object_frame.pose.orientation.x, _x.goal.object_frame.pose.orientation.y, _x.goal.object_frame.pose.orientation.z, _x.goal.object_frame.pose.orientation.w, _x.goal.speed_factor,) = _get_struct_8d().unpack(str[start:end])\n start = end\n end += 4\n (length,) = _struct_I.unpack(str[start:end])\n pattern = '<%sd'%length\n start = end\n s = struct.Struct(pattern)\n end += s.size\n self.goal.gripper_test_params = s.unpack(str[start:end])\n return self\n except struct.error as e:\n raise genpy.DeserializationError(e) # most likely buffer underfill",
"title": ""
},
{
"docid": "73a1a9bae30a83c920c2fedacf08f711",
"score": "0.5202862",
"text": "def loads(cls, serialised):\n class_fullname = \".\".join([cls.__module__, cls.__name__])\n if serialised[\"class\"] != class_fullname:\n raise TypeError(\"Expecting calss {}, but got {}\".format(class_fullname, serialised[\"class\"]))\n return cls.deserialise(serialised[\"params\"])",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="778"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">a098c3b6f3646c90f7b6e6f563f3ac81</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Write values from row of septwq table using cursor and sql, using num for the first element.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "4a18dea0a97aaebfd50360a841c4eb2d",
"score": "0.75467116",
"text": "def writeSeptRow(self, num: int, row: List[Any], cursor: Any, sql: str) -> None:\n data = (num, row[1].lower()) + tuple(row[4:7]) + tuple(row[8:12]) + tuple(row[13:16]) + (row[2],)\n cursor.execute(sql, data)",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "aaafeb3175ba43a527c8dfb69b771262",
"score": "0.71464074",
"text": "def writePestRow(self, num: int, row: List[Any], cursor: Any, sql: str) -> None:\n data = (num, row[2].lower()) + tuple(row[3:7]) + (row[8], 0, 0, 0, 0, 0, 0, 0, 0, row[10])\n cursor.execute(sql, data)",
"title": ""
},
{
"docid": "78aa2717e9af02d40f0c3f2068832711",
"score": "0.6968553",
"text": "def writeTillRow(self, num: int, row: List[Any], cursor: Any, sql: str) -> None:\n data = (num, row[2].lower()) + tuple(row[3:5]) + (row[7], 0, 0, row[5])\n cursor.execute(sql, data)",
"title": ""
},
{
"docid": "763a3c48a167cb3aa0fb0fe7a39e8ed5",
"score": "0.6608491",
"text": "def writeUrbanRow(self, num: int, row: List[Any], cursor: Any, sql: str) -> None:\n # columns 2-10 of new table same as columns 4-12 of urban\n data = (num, row[2].lower()) + tuple(row[4:13]) + (row[18], row[3])\n cursor.execute(sql, data)",
"title": ""
},
{
"docid": "fb859a6fab8679709232885e5873e68d",
"score": "0.633848",
"text": "def writePlantRow(self, num: int, row: List[Any], cursor: Any, sql: str) -> None:\n idc = int(row[3])\n daysMat = 120 if idc == 4 or idc == 5 else 365\n data = (num, row[2].lower(), idc, 'temp_gro', 0, daysMat) + tuple(row[5:13]) + (1,) + \\\n tuple(row[13:34]) + tuple(row[40:45]) + (12, 3, row[45], 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, row[4])\n cursor.execute(sql, data)",
"title": ""
},
{
"docid": "312038c5385aecd918badc47e8f88bb7",
"score": "0.616627",
"text": "def writeFertRow(self, num: int, row: List[Any], cursor: Any, sql: str) -> None:\n # columns 2-7 of new table same as columns 2:7 of fert, plus FERTNAME for description\n data = (num, row[2].lower()) + tuple(row[3:8]) + ('', row[11])\n cursor.execute(sql, data)",
"title": ""
},
{
"docid": "6365632b8e44e76fd5e42e2c5fa4a613",
"score": "0.5678015",
"text": "def writeT(self,dbName,column,value,muti = False): \n if muti:\n for i in value:\n insertStrs = ''\n for item in i:\n if 'str' in type(item).__name__:\n insertStrs += \"'\"+str(item)+\"',\"\n else:\n insertStrs += str(item)+','\n insertStrs = insertStrs[0:-1]\n sql = 'insert into '+str(dbName)+'('+','.join(column)+') values('+insertStrs+\")\"\n try:\n self.__cursor.execute(sql)\n self.__db.commit()\n except:\n self.__db.rollback()\n return['error in sql:',sql]\n else:\n insertStrs = ''\n for item in value:\n if 'str' in type(item).__name__:\n insertStrs += \"'\"+str(item)+\"',\"\n else:\n insertStrs += str(item)+','\n insertStrs = insertStrs[0:-1]\n sql = 'insert into '+str(dbName)+'('+','.join(column)+') values('+insertStrs+\")\"\n try:\n self.__cursor.execute(sql)\n self.__db.commit()\n except:\n self.__db.rollback()\n return['error in sql:',sql]\n return ['success']",
"title": ""
},
{
"docid": "f7b9cc304135fe869f24770fe7717392",
"score": "0.5606956",
"text": "def insert_into_db(cur, insert_rows):\r\n # print str(insert_rows)\r\n # Special handling for CLOB data type\r\n str_sql_stmt = cur.var(cx_Oracle.CLOB)\r\n # Original line, Table had 11 columns\r\n # str_sql_stmt.setvalue(0, insert_rows[10])\r\n # Need to change this line if you add a column\r\n # Table currently has 19 columns\r\n str_sql_stmt.setvalue(0, insert_rows[17])\r\n # Insert into SQL Table\r\n # Original statement WORKS!\r\n # cur.execute(\"INSERT INTO PSPPPARSEAET (PROCESS_INSTANCE, SEQ_NBR, AE_APPLID, SQLID, PM_SQL_TYPE, COUNTER, SQLROWS_2, START_TIME, END_TIME, CALC_TIME, SQLTEXT2) values (:1, :2, :3, :4, :5, :6, :7, TO_TIMESTAMP(:8, 'HH24:MI:SS.FF'), TO_TIMESTAMP(:9, 'HH24:MI:SS.FF'), TO_TIMESTAMP(:10, 'HH24:MI:SS.FF'), :11)\", [insert_rows[0], insert_rows[1], insert_rows[2], insert_rows[3], insert_rows[4], insert_rows[5], insert_rows[6], insert_rows[7], insert_rows[8], insert_rows[9], str_sql_stmt])\r\n # Added BIND Variables, Buffers, COMMIT, and CheckPoint information\r\n # str_array_sql_stmt = [insert_rows[0], insert_rows[1], insert_rows[2], insert_rows[3], insert_rows[4], insert_rows[5], insert_rows[6], insert_rows[7], insert_rows[8], insert_rows[9], insert_rows[10], insert_rows[11], insert_rows[12], insert_rows[13], insert_rows[14], insert_rows[15], insert_rows[16], str_sql_stmt]\r\n # print \"Insert Stmt \" + str(str_array_sql_stmt)\r\n # cur.execute(\"SELECT * FROM PSPPPARSEAET WHERE 1 = 0\")\r\n # print \"Cur.desc \" + str(cur.description)\r\n cur.execute(\"INSERT INTO PSPPPARSEAET (PROCESS_INSTANCE, SEQ_NBR, AERP, AE_APPLID, SQLID, ACTION_PLAN_DESCR, PM_SQL_TYPE, PF_ITERATION_NBR, COUNTER, SQLROWS_2, DP_RESTART_CONTROL, COMMIT_ACTN_STRNG, START_TIME, END_TIME, CALC_TIME, FIELD_LIST_AET, BINDNAME, SQLTEXT2, AE_RUN_DATA) VALUES (:1, :2, :3, :4, :5, :6, :7, :8, :9, :10, :11, :12, TO_TIMESTAMP(:13, 'HH24:MI:SS.FF'), TO_TIMESTAMP(:14, 'HH24:MI:SS.FF'), TO_TIMESTAMP(:15, 'HH24:MI:SS.FF'), :16, :17, :18, :19)\", [insert_rows[0], insert_rows[1], insert_rows[2], insert_rows[3], insert_rows[4], insert_rows[5], insert_rows[6], insert_rows[7], insert_rows[8], insert_rows[9], insert_rows[10], insert_rows[11], insert_rows[12], insert_rows[13], insert_rows[14], insert_rows[15], insert_rows[16], str_sql_stmt, insert_rows[18]])",
"title": ""
},
{
"docid": "41ebf26dba4f4a1b82c9d2fbe2a82c74",
"score": "0.541328",
"text": "def mySQLquery13(cur, conn, table, runNum):\n commands = \"\"\"\n CREATE TEMPORARY TABLE query13 (\n unique1 integer NOT NULL,\n unique2 integer NOT NULL,\n two integer NOT NULL,\n four integer NOT NULL,\n ten integer NOT NULL,\n twenty integer NOT NULL,\n onepercent integer NOT NULL,\n tenpercent integer NOT NULL,\n twentypercent integer NOT NULL,\n fiftypercent integer NOT NULL,\n unique3 integer NOT NULL,\n evenonepercent integer NOT NULL,\n oddonepercent integer NOT NULL,\n stringu1 varchar(52) NOT NULL,\n stringu2 varchar(52) NOT NULL,\n string4 varchar(52) NOT NULL,\n tunique1 integer NOT NULL,\n tunique2 integer NOT NULL,\n ttwo integer NOT NULL,\n tfour integer NOT NULL,\n tten integer NOT NULL,\n ttwenty integer NOT NULL,\n tonepercent integer NOT NULL,\n ttenpercent integer NOT NULL,\n ttwentypercent integer NOT NULL,\n tfiftypercent integer NOT NULL,\n tunique3 integer NOT NULL,\n tevenonepercent integer NOT NULL,\n toddonepercent integer NOT NULL,\n tstringu1 varchar(52) NOT NULL,\n tstringu2 varchar(52) NOT NULL,\n tstring4 varchar(52) NOT NULL\n )\n \"\"\"\n try:\n cur.execute(commands)\n cur.execute(\"INSERT INTO query13 \"\n \"SELECT * \"\n \"FROM %s as t1, bprime \"\n \"WHERE (t1.unique2 = bprime.unique2)\" % table)\n cur.execute(\" show profiles\")\n row = cur.fetchall()\n with open(\"mySQLquery13%s.txt\"%table, mode='a', newline='') as query_file:\n query_file.write(\"Run: %d\\n\"%runNum)\n json.dump(row, query_file, sort_keys=True, indent=4)\n query_file.close()\n except mysql.connector.Error as err:\n print(\"Something went wrong: {}\".format(err))",
"title": ""
},
{
"docid": "07fa52d50d28323daca5b4224fa9d16a",
"score": "0.53648347",
"text": "def sql(self, txn):\n inserts = 0\n for ob in self.data:\n inserts += 1\n txn.execute(\n \"INSERT into scp_alldata(station, valid, mid, high, cldtop1, \"\n \"cldtop2, eca, source) \"\n \"VALUES (%s, %s, %s, %s, %s, %s, %s, %s)\",\n (\n ob.station,\n ob.valid,\n ob.mid,\n ob.high,\n ob.cldtop1,\n ob.cldtop2,\n ob.eca,\n self.afos[-1],\n ),\n )\n return inserts",
"title": ""
},
{
"docid": "d2e329b0bdaf68df0beb419035075cf8",
"score": "0.5339764",
"text": "def query13(cur, conn, table, runNum):\n commands = \"\"\"\n CREATE TEMP TABLE query13 (\n unique1 integer NOT NULL,\n unique2 integer NOT NULL,\n two integer NOT NULL,\n four integer NOT NULL,\n ten integer NOT NULL,\n twenty integer NOT NULL,\n onepercent integer NOT NULL,\n tenpercent integer NOT NULL,\n twentypercent integer NOT NULL,\n fiftypercent integer NOT NULL,\n unique3 integer NOT NULL,\n evenonepercent integer NOT NULL,\n oddonepercent integer NOT NULL,\n stringu1 varchar(52) NOT NULL,\n stringu2 varchar(52) NOT NULL,\n string4 varchar(52) NOT NULL,\n tunique1 integer NOT NULL,\n tunique2 integer NOT NULL,\n ttwo integer NOT NULL,\n tfour integer NOT NULL,\n tten integer NOT NULL,\n ttwenty integer NOT NULL,\n tonepercent integer NOT NULL,\n ttenpercent integer NOT NULL,\n ttwentypercent integer NOT NULL,\n tfiftypercent integer NOT NULL,\n tunique3 integer NOT NULL,\n tevenonepercent integer NOT NULL,\n toddonepercent integer NOT NULL,\n tstringu1 varchar(52) NOT NULL,\n tstringu2 varchar(52) NOT NULL,\n tstring4 varchar(52) NOT NULL\n )\n \"\"\"\n try:\n\n cur.execute(commands)\n cur.execute(\"EXPLAIN (ANALYZE, BUFFERS) \"\n \"INSERT INTO query13 \"\n \"SELECT * \"\n \"FROM %s as t1, bprime \"\n \"WHERE (t1.unique2 = bprime.unique2)\" % table)\n conn.commit()\n row = cur.fetchall()\n with open(\"pgQuery13%s.txt\"%table, mode='a', newline='') as query_file:\n query_file.write(\"Run: %d\\n\"%runNum)\n json.dump(row, query_file, sort_keys=True, indent=4)\n query_file.close()\n except (Exception, psycopg2.DatabaseError) as error:\n print(error)",
"title": ""
},
{
"docid": "eec8206284703aa01c33672c7ab8575d",
"score": "0.5337123",
"text": "def update_row(self, tname, col, val, valtuple):\n i = len(valtuple) / 2\n if i == 0:\n i = 1\n qs = ['%s = \"%s\"'] * i\n qs = \",\".join(qs)\n s0 = 'UPDATE %s SET' % tname\n s1 = ' %s WHERE ' % qs\n s2 = '%s=\"%s\"' % (col, val)\n s1 = s1 % valtuple\n #print s0 + s1 + s2\n self.c.execute(s0 + s1 + s2)\n self.commit()",
"title": ""
},
{
"docid": "0b8f48ea6cfd09da6faddf43ec10100c",
"score": "0.5308891",
"text": "def write(self, ddb_row):\n pass",
"title": ""
},
{
"docid": "905fb94d54837782a318734dc28dbe1d",
"score": "0.52762",
"text": "def write_TLEs_to_db(self):\n assert(self._dbtype == \"sqlserver\")\n if(len(self._TLEentryList) > 0):\n try:\n self.c_addTLE_query.executemany(self.addTLE_query,self._TLEentryList)\n self._TLEentryList = []\n except Exception as e:\n log.error(\"MYSQL ERROR: {}\".format(e))\n return self.c_addTLE_query.lastrowid",
"title": ""
},
{
"docid": "fd2fbba6cb3c73bf382879f0b6239110",
"score": "0.5217024",
"text": "def populate_table(conn, row):\n sql = ''' INSERT INTO data_points(n,p,data)\n VALUES(?,?,?) '''\n cur = conn.cursor()\n cur.execute(sql, row)\n conn.commit()\n return cur.lastrowid",
"title": ""
},
{
"docid": "1611797100c7bd9e817557559da9c0d7",
"score": "0.5213431",
"text": "def fetch_and_write_sqlite(con, df, start, end, collection=\"COPERNICUS/S2_SR\", i=0, i_max=None):\n\n if i_max is None:\n i_max = df.shape[0]\n err_cnt = 0\n retrieved = None\n with tqdm(total=i_max - i) as pbar:\n pbar.update(i)\n while i < i_max:\n retrieved, err_cnt = retrieve_single_point(retrieved, i, err_cnt, df, collection, start, end)\n\n if retrieved is not None and i % 10 == 0:\n retrieved.to_sql('sentinel', con, if_exists='append', index=False)\n retrieved = None\n if err_cnt == 0:\n i += 1\n pbar.update()\n\n if retrieved is not None and i % 10 != 0:\n retrieved.to_sql('sentinel', con, if_exists='append', index=False)",
"title": ""
},
{
"docid": "38be24b5ba6441af413c62bee06a0b43",
"score": "0.51907414",
"text": "def createSeptTable(self, cursor: Any, refCsvDir: str) -> None:\n septwqFile = os.path.join(refCsvDir, 'septwq.csv')\n if not os.path.isfile(septwqFile):\n return\n table = 'arc_septwq'\n cursor.execute('DROP TABLE IF EXISTS {0}'.format(table))\n cursor.execute('CREATE TABLE ' + table + ConvertFromArc._SEPTICTABLE)\n sql = 'INSERT INTO {0} VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?)'.format(table)\n num = 0\n with open(septwqFile, 'r', newline='') as f:\n reader = csv.reader(f)\n next(reader) # skip headers\n for row in reader:\n num += 1\n self.writeSeptRow(num, row, cursor, sql)",
"title": ""
},
{
"docid": "85fdf6687cd6952b0a8a6508b6b6da72",
"score": "0.51434857",
"text": "def chunk_insert(conn, project):\n global values_string\n sql = ''' INSERT INTO celeb(ident,file_path,score,check_flag,face_embeddings)\n VALUES(?,?,?,?,?) '''\n\n cur = conn.cursor()\n cur.execute(\"begin;\")\n for i in range(len(project)):\n proj = project[i]\n cur.execute(sql, proj)\n cur.execute(\"commit;\")\n return cur.lastrowid",
"title": ""
},
{
"docid": "278ab3e267d7b21c8a5c056d0a4fa726",
"score": "0.512945",
"text": "def writeUsedSoilRow(sid: int, lid: int, name: str, row: List[Any], cursor: Any, insert: str, insertLayer: str) -> int:\n cursor.execute(insert, (sid, name) + tuple(row[7:12]) + (None,))\n startLayer1 = 12 # index of SOL_Z1\n layerWidth = 12 # number of entries per layer\n startCal = 132 # index of SOL_CAL1\n startPh = 142 # index of SOL_PH1\n for i in range(int(row[6])):\n lid += 1 \n startLayer = startLayer1 + i*layerWidth\n cursor.execute(insertLayer, (lid, sid, i+1) + tuple(row[startLayer:startLayer+layerWidth]) + (row[startCal+i], row[startPh+i]))\n return lid",
"title": ""
},
{
"docid": "514fb82e44166862bec9d711066129d0",
"score": "0.5014584",
"text": "def store(event, t0, s, kind, table):\n row = table.row\n for i, val in enumerate(s):\n row['event'] = event\n row['kind'] = kind\n row['t'] = t0 + i\n row['value'] = val\n row.append()\n table.flush()",
"title": ""
},
{
"docid": "da5832870ee4875c5974d2c915c0eb7b",
"score": "0.4988264",
"text": "def freight_sql(lng_lst, frt_index, invoice_no, invoice_date, year, month, day, file_name):\n freight_price = lng_lst[frt_index].replace('$', '').strip()\n sql_freight = '''INSERT INTO freight_test(invoice_no, invoice_date, year, month, day, price, source, file, \n date_added)\n VALUES('{0}', '{1}', {2}, {3}, {4}, {5}, '{6}', '{7}', '{8}');''' \\\n .format(invoice_no, invoice_date, year, month, day, freight_price, 'Krueger', file_name,\n now.strftime(\"%Y-%m-%d %H:%M\"))\n c.execute(sql_freight)",
"title": ""
},
{
"docid": "02dd96ba614d05989ba901941752fcfd",
"score": "0.49810675",
"text": "def write(self, row, column, value, format=None):\n pass",
"title": ""
},
{
"docid": "7224dfa047954147638001b6edbfa658",
"score": "0.49561715",
"text": "def read_csv_and_insert_product_sql(self):\r\n \r\n csv_reader = csv.DictReader(open('%s/tbl_Products.csv' %(self._root_dir)))\r\n \r\n nb_rows = 0\r\n \r\n lookup_dict = Lookup(RoddExtractor.PRODUCT_MAPPER)\r\n \r\n # for each line of data create an insert line\r\n\r\n insert_line = \"INSERT INTO %s.%s (%s) VALUES (%s)\"\r\n \r\n \r\n columns = self._create_sql_columns(RoddExtractor.PRODUCT_TABLE_ORDER)\r\n \r\n #file = open(\"/tmp/insert_products.sql\",\"w+\")\r\n\r\n for row in csv_reader:\r\n cpt_keys = 0\r\n values = \"\"\r\n has_changed = False\r\n \r\n for elem in RoddExtractor.PRODUCT_TABLE_ORDER:\r\n \r\n #get list of matching keys\r\n key = lookup_dict.get_key(elem)\r\n \r\n if not key:\r\n raise Exception(\"Error: %s as no matching keys in %s\" %(elem, RoddExtractor.PRODUCT_MAPPER))\r\n \r\n val = row.get(key[0], None)\r\n \r\n # and elem == \"resources_1\"\r\n if nb_rows == 200 and (\"%\" in val):\r\n print(\"This is the break\")\r\n \r\n has_changed, val = self._transform_product_table_data(elem, val)\r\n \r\n \r\n \r\n # if no transformations performed apply the standard rule taht considers the value as a string\r\n if has_changed:\r\n val = val if val else \"null\"\r\n else:\r\n val = \"%s\" % ( \"'%s'\" % (val) if val else \"NULL\")\r\n \r\n # add in values\r\n if cpt_keys == 0:\r\n values += \"%s\" % ( val )\r\n else:\r\n values += \", %s\" % ( val )\r\n \r\n \r\n cpt_keys += 1\r\n \r\n insert = insert_line %(\"rodd\", \"products\", columns, values)\r\n \r\n print('[r%d]:insert = %s\\n' %(nb_rows, insert) )\r\n #file.write(\"%s;\\n\" %(insert))\r\n result = self._conn.execute(\"%s;\" %(insert))\r\n \r\n nb_rows += 1",
"title": ""
},
{
"docid": "f33188ac962c053aa583014b85fd6f11",
"score": "0.49558327",
"text": "def write(self, bucket, rows):\n pass",
"title": ""
},
{
"docid": "fcdc8e74b78a14790484a42d460f5cd1",
"score": "0.49498433",
"text": "def insert_time():\n files = list_files()\n conn = psycopg2.connect(host=\"postgres_udacity\", dbname=\"udacity\", user=\"udacity\", password=\"udacity\")\n conn.autocommit = True\n for file in files:\n df = return_df_file(file)\n print(df.head())\n df_used = df[['ts', 'page']]\n df_used = df_used[df_used['page'] == 'NextSong']\n df_used = df_used[['ts']]\n\n df_final = pd.DataFrame(columns=['start_time', 'hour', 'day', 'week', 'month', 'year', 'weakday'])\n list_final = []\n list_year = []\n list_month = []\n list_day = []\n list_hour = []\n list_week = []\n list_weekday = []\n\n for i, line in df_used.iterrows():\n a = pd.to_datetime(line.ts, unit='ms')\n hour = a.strftime('%H')\n day = a.strftime('%d')\n month = a.strftime('%m')\n year = a.strftime('%Y')\n week = datetime.date(int(year), int(month), int(day)).isocalendar()[1]\n weekday = a.strftime('%A')\n list_final.append(a)\n list_year.append(int(year))\n list_month.append(int(month))\n list_day.append(int(day))\n list_hour.append(int(hour))\n list_week.append(int(week))\n list_weekday.append(weekday)\n \n df_final['start_time'] = list_final\n df_final['hour'] = list_hour\n df_final['day'] = list_day\n df_final['week'] = list_week\n df_final['month'] = list_month\n df_final['year'] = list_year\n df_final['weakday'] = list_weekday\n\n sql_insert = '''INSERT INTO sparkifydb.time\n (start_time, hour, day, week, month, year, weakday) \n values (%s,%s,%s,%s,%s,%s,%s)\n ON CONFLICT (start_time) \n DO \n UPDATE SET\n hour = excluded.hour,\n day = excluded.day,\n week = excluded.week,\n month = excluded.month,\n year = excluded.year,\n weakday = excluded.weakday;'''\n\n try:\n with conn.cursor() as cursor:\n cursor.executemany(sql_insert,df_final.values.tolist())\n print('Commit')\n except Exception as e:\n print(e)",
"title": ""
},
{
"docid": "d81d9145496051b32869e8f5626f3294",
"score": "0.4938468",
"text": "def write_to_db(vals, db_c):\n tries = 0\n maxtries = 3\n while(1):\n tries += 1\n if tries > maxtries:\n msg = 'Exceeded {} tries to write to db. Giving up'\n logging.warning(msg.format(maxtries))\n break\n try:\n db_c.executemany('INSERT INTO disk_usage VALUES (?,?,?,?,?,?)', vals) \n break\n except sqlite3.OperationalError:\n time.sleep(0.5)\n continue",
"title": ""
},
{
"docid": "34c9c5b30383b2655b9499aa301af322",
"score": "0.49228352",
"text": "def insert(query, records, cur):\n for record in records:\n cur.execute(query, record)",
"title": ""
},
{
"docid": "da61b3e3de75817ba25831889c575a5c",
"score": "0.49144936",
"text": "def add_row(tablename, data, conn):\n sql = \"INSERT INTO %s\" % tablename\n columns, vals = zip(*data)\n colstr =\"(\"\n valstr = \"(\"\n for i, column in enumerate(columns):\n if i > 0:\n colstr += \", \"\n valstr += \", \"\n colstr += \"%s\" % column\n valstr += \"?\" \n sql = sql + colstr +\") VALUES\" + valstr + \");\"\n cur = conn.cursor()\n try:\n res = cur.execute(sql, tuple(vals))\n except Error as e:\n print \"Error in add_row:\", (e), \". SQL:\", sql, vals\n return None\n print \"SQL:\", sql, vals\n rowid = cur.lastrowid \n conn.commit()\n return rowid",
"title": ""
},
{
"docid": "b1e58df91497d7946542ab2247812455",
"score": "0.49107462",
"text": "def insert_comments(comments, keyword):\n sql = \"\"\"INSERT INTO sentiment(source, keyword, date, sentiment)\n VALUES(%s, %s, %s, %s);\"\"\"\n conn = None\n try:\n # connect to the PostgreSQL database\n conn = pg.connect(dbname=DBNAME, user=USER, password=PASSWORD, host=DBHOST)\n # create a new cursor\n cur = conn.cursor()\n # execute the INSERT statement\n for i in range(len(comments.index)):\n\n if i == 0:\n print(comments.index[i], comments.iloc[i])\n\n cur.execute(sql, (\"reddit\", keyword, comments.index[i], comments.iloc[i]))\n\n # commit the changes to the database\n conn.commit()\n # close communication with the database\n cur.close()\n except (Exception, pg.DatabaseError) as error:\n print(error)\n finally:\n if conn is not None:\n conn.close()",
"title": ""
},
{
"docid": "82b146cab3171188c811824dd462b6e7",
"score": "0.49068365",
"text": "def update_db(self, rank):\n if not self._db_conn :\n return \n \n cur = self._db_conn.cursor()\n\t\t\n try :\n for id in self._word_dict :\n str = self._word_dict[id]\n cur.execute('INSERT INTO lexicon_Table(wordid, value) VALUES(?, ?);', (id, str[0]))\n \n for id in self._doc_dict :\n str = pk.dumps(self._doc_dict[id])\n cur.execute('INSERT INTO docIndex_Table(docid, value) VALUES(%d, \"%s\");'% (id, str))\n\n for id in self._inverted_index :\n str = pk.dumps(self._inverted_index[id])\n cur.execute('INSERT INTO invertedindex_Table(invertedid, value) VALUES(?, ?);', (id, str))\n\n if rank :\n for id in rank :\n str = pk.dumps(rank[id])\n cur.execute('INSERT INTO pagerank_Table(pageid, value) VALUES(?, ?);', (id, str))\n except Exception as e:\n print e\n \n self._db_conn.commit()",
"title": ""
},
{
"docid": "aad4861bb108ecb53d7a2be1ca87972f",
"score": "0.49065563",
"text": "def put(table, **kargs):\n #from utils import ustring\n print('im in outer put')\n row_id = None\n fields = ''\n values = ''\n fields_values = ''\n for f in kargs:\n if f not in ('id', 'user', 'user_change', 'date_change', 'active'):\n if kargs[f] not in ['None', '', None]:\n fields += f + ','\n values += \"'{field_value}',\".format(field_value=kargs[f])\n fields_values += \"{field} = '{field_value}',\".format(field=f, field_value=kargs[f])\n fields = fields[:-1]\n values = values[:-1]\n fields_values = fields_values[:-1]\n #print(1)\n if 'id' in kargs:\n fields_values += \",user_change='{user}',date_change='{date}'\".format(user=str(kargs['user']), date=str(datetime.datetime.today()))\n sql = \"UPDATE {table} SET {values} where id='{key}'\".format(table=table, values=fields_values, key=kargs['id'])\n row_id = kargs['id']\n else:\n fields += \",user_create,date_create,id,active\"\n row_id = get_identity(table=table, user=str(kargs['user']))\n values += \",'{user}','{date}','{new_id}', True\".format(user=str(kargs['user']), date=str(datetime.datetime.today()),new_id=row_id)\n sql = \"INSERT INTO {table} ({fields}) VALUES ({values})\".format(table=table, fields=fields, values=values)\n #print(sql)\n with getcursor() as cursor:\n cursor.execute(sql)\n #print('fim de put')\n return row_id",
"title": ""
},
{
"docid": "ac73ee72beccb5f76f5432ea01bc9d84",
"score": "0.48740086",
"text": "def sql(self, cmd):\n try:\n self.cursor.execute(cmd)\n except:\n print(\"Invalue SQL commnd...\")\n return None\n \n rows = self.cursor.fetchall()\n return rows",
"title": ""
},
{
"docid": "cf571f756f74e509ca9f293e67a5d0b8",
"score": "0.48734766",
"text": "def sqlinserter(self):\n\n print('Starting the data insertion from the queue')\n\n #We're using a list to make appends because append in python has complexity O(1)\n index=0\n valuelst=[]\n alreadypresent=self.db.get_coins_and_timevalues()\n while self.myqueue.empty()==False:\n \n #converts the string in the queue to a list for each crypto\n cryptohistory=str(self.myqueue.get())\n cryptohistory=literal_eval(cryptohistory[3:-2])\n cryptohistory=list(cryptohistory)\n\n for i in cryptohistory:\n\n # Here we format the list element\n coin=i[0]\n timevalue=i[1]\n price=i[2]\n deleted=0\n formattedtuple=tuple([coin,timevalue,price,deleted])\n #print(formattedtuple)\n \n # Our API, by default, sends us their most recent price data.\n # This checks that our data actually comes from midnight.\n # Works in GMT +01:00\n if str(timevalue)[-5:]!='00000':\n continue\n\n dt_object = datetime.datetime.fromtimestamp(timevalue//1000)\n if not((dt_object.hour==1 or dt_object.hour==0 or dt_object.hour==2) and dt_object.minute==0 and dt_object.second==00):\n continue\n\n #IF the value is already inserted in the SQL, don't add it again, otherwise, add it.\n #print(alreadypresent)\n if tuple([coin,timevalue]) in alreadypresent:\n continue \n\n #IF the value is already present in valuelst, don't add it to the query.\n if str(formattedtuple) in valuelst:\n continue\n \n # We noticed our SQL INSERT query has some problems after 1000 values, as such we're splitting\n # the task and doing multiple inserts of 950 values.\n if index<950: \n index+=1\n valuelst.append(str(formattedtuple))\n else:\n index=0\n print('Inserted 950 data points')\n self.db.insert_price_values(valuelst)\n valuelst=[]\n alreadypresent=self.db.get_coins_and_timevalues()\n\n # Given that our queue rarely has multiple of 980 elements, we'll do a final push by \n # selecting the remaining messages.\n if len(valuelst)>0:\n print('Inserted data in the SQL')\n self.db.insert_price_values(valuelst)\n valuelst=[]",
"title": ""
},
{
"docid": "4ba5efcb2400589088862da4c024d4ec",
"score": "0.48700768",
"text": "def _writeInTable(self, issues, table_name):\r\n\t\tself.log.debug('')\r\n\r\n\t\tcreate_table = True # \t\tif conf.conf['db']['ISSUE_TABLE_CREATE']: # TODO mettre en conf, mettre en UI ...\r\n\t\tif (create_table):\r\n\t\t\tself._createTable(table_name)\r\n\r\n\t\t# 1. On récupère les noms des champs de la table \"issue\" :\r\n\t\t# (on aurait pu le faire à partir du fichier schema de création de cette table mais cela aurait\r\n\t\t# nécéssité un parsing compliqué de la requête de création\r\n\t\t# => la solution la plus simple est de faire une requête à la DB)\r\n\r\n\t\t# ATTENTION ! Psycopg ne propose apparemment pas de construction de requête avec échappement en ce qui concerne\r\n\t\t# le nom des tables\r\n\t\t# => être sûr de la provenance de la valeur que nous prenons\r\n\t\t# Proposons quand même une validation :\r\n\r\n\t\t# Requête ...\r\n\t\tself.db_connector.securityCheckTableName(table_name)\r\n\t\tself.db_connector.execute('select * from %s LIMIT 1' % table_name)\r\n\r\n\t\t# d'où la liste des champs de la table :\r\n\t\ttable_fields = [description[0] for description in self.db_connector.cur.description]\r\n\r\n\t\t# ... et la liste des champs de la classe JiraIssue :\r\n\t\tissue_fields = table_fields # Le mécanisme actuel impose l'identité des champs en DB et dans le modèle JiraIssue\r\n\r\n\t\t# 2. On insère ...\r\n\r\n\t\tsql_template = \"INSERT into %s ( %s ) VALUES ( %s ) \" % \\\r\n\t\t\t\t\t\t\t\t\t (table_name,\r\n\t\t\t\t\t\t\t\t\t\t', '.join(table_fields),\r\n\t\t\t\t\t\t\t\t\t\t', '.join([\"%s\"] * len(table_fields))\r\n\t\t\t\t\t\t\t\t\t\t)\r\n\r\n\t\tlog.debug(\"sql_template=\" + sql_template)\r\n\r\n\t\tnb_inserted_rows = 0\r\n\t\tfor iss in issues:\r\n\t\t\t#fields = [getFieldVal(iss, field, case_insensitive=True) for field in issue_fields]\r\n\t\t\t#log.debug(\"fields=\" + pprint.pformat(fields))\r\n\t\t\t# cur.execute(sql_template, [ str(vars(iss)[field]) for field in issue_fields])\r\n\r\n\t\t\tself.db_connector.execute(sql_template, [getFieldVal(iss, field, case_insensitive=True, raise_excp_if_not_found=True) for field in issue_fields])\r\n\r\n\r\n\t\t\t# TODO approche pas forcément optimale (à vérifier) - Pê mieux si on pouvait passer toutes les requêtes\r\n\t\t\t# INSERT à execute en une seule fois/\r\n\r\n\t\t\tnb_inserted_rows += self.db_connector.cur.rowcount\r\n\r\n\t\t# Make the changes to the database persistent\r\n\t\tself.db_connector.commit()\r\n\r\n\t\tself.log.debug(\"Insertion complete : %d rows inserted\" % (nb_inserted_rows,))\r\n\t\treturn nb_inserted_rows",
"title": ""
},
{
"docid": "6101602ecaef0bd689d59cd7566f2bdf",
"score": "0.48684865",
"text": "def execute(self, sqlstr):",
"title": ""
},
{
"docid": "6101602ecaef0bd689d59cd7566f2bdf",
"score": "0.48684865",
"text": "def execute(self, sqlstr):",
"title": ""
},
{
"docid": "6101602ecaef0bd689d59cd7566f2bdf",
"score": "0.48684865",
"text": "def execute(self, sqlstr):",
"title": ""
},
{
"docid": "6101602ecaef0bd689d59cd7566f2bdf",
"score": "0.48684865",
"text": "def execute(self, sqlstr):",
"title": ""
},
{
"docid": "e4e05dcd26f30955e12e00893cf1b74d",
"score": "0.48652503",
"text": "def _bulk_write(self, df, model_class):\n\n def insert(data):\n conn = db.engine.connect()\n # TODO: This sytanx is specific to sqlite\n conn.execute(\n model_class.__table__.insert().prefix_with(\"OR REPLACE\"), data)\n\n data = []\n BATCH_SIZE = 1000\n CURSOR = 0\n for _, row in df.iterrows():\n data.append(dict(row))\n CURSOR += 1\n\n if CURSOR % BATCH_SIZE == 0 and CURSOR != 0 and data:\n insert(data)\n data = []\n CURSOR = 0\n\n if data:\n insert(data)\n\n app.logger.debug(\"Number of rows inserted : {0}\".format(df.shape[0]))",
"title": ""
},
{
"docid": "aea529a5f3a7e80e547340b211246483",
"score": "0.48644304",
"text": "def teryt():\n pytanie = \"\"\"SELECT\n gmina, numer, woje, powiat\n FROM\n system\"\"\"\n ewopis_cursor.execute(pytanie)\n dane = ewopis_cursor.fetchall()\n return dane",
"title": ""
},
{
"docid": "3c35f3a1405eea52b373e83ed9b8ac11",
"score": "0.48608625",
"text": "def synapse_table(cur,data):\n sql = (\"insert into synapse \"\n \"(idsynapse,idcontin,idpre,idpost) \"\n \"values (%s,%s,%s,%s)\")\n cur.executemany(sql,data)",
"title": ""
},
{
"docid": "732d7f85e52e93db9751d6194d3cd250",
"score": "0.4846855",
"text": "def get_data(self, sql):\n try: \n self.tableWidget.setSortingEnabled(False)\n\n mydb = mc.connect(\n host=\"localhost\",\n user=\"admin\",\n password=\"Coincard2@\",\n database=self.dbname\n )\n mycursor = mydb.cursor()\n mycursor.execute(sql)\n \n result = mycursor.fetchall()\n self.tableWidget.setRowCount(0)\n for row_number, row_data in enumerate(result):\n # print(row_number)\n self.tableWidget.insertRow(row_number)\n for column_number, data in enumerate(row_data):\n self.tableWidget.setEditTriggers(QtWidgets.QTableWidget.NoEditTriggers)\n # print(column_number) \n self.tableWidget.setItem(row_number, column_number, QTableWidgetItem(str(data)))\n if column_number==6: \n self.tableWidget.setItem(row_number, column_number, QTableWidgetItem(str(data)))\n self.tableWidget.item(row_number, column_number).setForeground(QtGui.QColor(0,0,255))\n except mc.Error as e:\n print(\"Error:\", e)\n self.tableWidget.setSortingEnabled(True)",
"title": ""
},
{
"docid": "bdcc00bfd47dbcffa0f28bd39ea7408b",
"score": "0.4826331",
"text": "def add_row(self, tname, vallist, commit=True):\n s = u'INSERT INTO {0:s} values'.format(tname)\n qs = ['?'] * len(vallist)\n qs = \",\".join(qs)\n\n try:\n self.c.execute('''%s (%s)''' % (s, qs), vallist)\n except Exception, e:\n self.logger.error(\"Failed to add row: %s\" % e)\n # print '''%s (%s)''' % (s, qs)\n # print \"vallist\", vallist\n return {'mesg': \"Failed to add row, rollback: %s\" % e, 'id': None}\n else:\n if commit:\n self.commit()\n return {'mesg': \"ok\", 'id': self.c.lastrowid}",
"title": ""
},
{
"docid": "41767ff327515f5abfa55fd5ace24a30",
"score": "0.4812182",
"text": "def import_penyakit(conn, gj):\n cursor = conn.cursor()\n\n for g in gj:\n print(g[0])\n cursor.execute(\"INSERT INTO penyakit(nama_penyakit) VALUES('\"+g[0]+\"')\")\n conn.commit()\n return g",
"title": ""
},
{
"docid": "3c3ee032313e7c95b9c2cad82369686f",
"score": "0.48120952",
"text": "def write(self, sql=None, values=None):\n rows = []\n err_msg = self._connect()\n if err_msg:\n return err_msg\n try:\n if self.local_env:\n statement = connection.SimpleStatement(\n sql, consistency_level=CL.ONE)\n else:\n statement = connection.SimpleStatement(\n sql, consistency_level=CL.LOCAL_QUORUM)\n if values:\n resultset = self.session.execute(statement, values)\n else:\n resultset = self.session.execute(statement)\n if len(resultset.current_rows) == 0:\n return CassandraWrite.object_created()\n else:\n uuid_columns = self._get_uuid_columns(values)\n while True:\n if not resultset.has_more_pages:\n rows = self._format_row_data(resultset, uuid_columns)\n break\n rows.append(self._format_row_data(resultset, uuid_columns))\n resultset.next()\n err_msg = self._disconnect()\n if err_msg:\n return err_msg\n if len(rows) == 1:\n return CassandraWrite.one_row_found(rows[0])\n else:\n return CassandraWrite.many_rows_found(rows)\n except connection.NoHostAvailable as e:\n return CassandraError.no_host_available(self.hosts)\n except OperationTimedOut as e:\n return CassandraError.operation_timeout(str(e))\n except InvalidRequest as e:\n return CassandraError.invalid_request(self.keyspace, str(e))\n except Exception as e:\n (type_e, value, traceback_prev) = exc_info()\n backtrace = extract_tb(traceback_prev)\n return CassandraWriteError.unknown_exception(sql,\n values,\n backtrace,\n str(e))",
"title": ""
},
{
"docid": "ce5cda637786aa76693be3f8a903ac6c",
"score": "0.48044273",
"text": "def insert_row(conn, value):\n\n sql = ''' INSERT or REPLACE into SCORES(name,score) VALUES (?,?) '''\n cur = conn.cursor()\n cur.execute(sql, value)\n conn.commit()",
"title": ""
},
{
"docid": "36ac26df3806532479937b95e8527914",
"score": "0.47847366",
"text": "def insert_rec_to_db(mydb, mycursor, weather_info, container_version):\n try:\n sql = \"INSERT INTO actual (\" \\\n \"ts_local, \" \\\n \"ts_utc, \" \\\n \"julian, \" \\\n \"hour_utc, \" \\\n \"location, \" \\\n \"main, \" \\\n \"description, \" \\\n \"pressure, \" \\\n \"wind_speed, \" \\\n \"wind_deg, \" \\\n \"wind_quadrant, \" \\\n \"wind_rose, \" \\\n \"wind_strength, \" \\\n \"wind_gust, \" \\\n \"temp, \" \\\n \"feels_like, \" \\\n \"dew_point, \" \\\n \"uvi, \" \\\n \"humidity, \" \\\n \"visibility, \" \\\n \"rain, \" \\\n \"snow, \" \\\n \"coverage, \" \\\n \"met_source, \" \\\n \"lat, \" \\\n \"lon, \" \\\n \"location_code, \" \\\n \"condition_code, \" \\\n \"synopsis, \" \\\n \"synopsis_code, \" \\\n \"light, \" \\\n \"light_condition, \" \\\n \"alert_sender, \" \\\n \"alert_event, \" \\\n \"tz, \" \\\n \"tz_offset, \" \\\n \"ts_epoch, \" \\\n \"sunrise_local, \" \\\n \"sunset_local,\" \\\n \"image_name, \" \\\n \"video_name, \" \\\n \"container_version\" \\\n \") \" \\\n \"VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)\"\n\n val = (\n weather_info['ts_local'],\n weather_info['ts_utc'],\n weather_info['julian'],\n weather_info['hour_utc'],\n weather_info['location'],\n weather_info['main'],\n weather_info['description'],\n weather_info['pressure'],\n weather_info['wind_speed'],\n weather_info['wind_deg'],\n weather_info['wind_quadrant'],\n weather_info['wind_rose'],\n weather_info['wind_strength'],\n weather_info['wind_gust'],\n weather_info['temp'],\n weather_info['feels_like'],\n weather_info['dew_point'],\n weather_info['uvi'],\n weather_info['humidity'],\n weather_info['visibility'],\n weather_info['rain'],\n weather_info['snow'],\n weather_info['coverage'],\n weather_info['met_source'],\n weather_info['lat'],\n weather_info['lon'],\n weather_info['location_code'],\n weather_info['condition_code'],\n weather_info['synopsis'],\n weather_info['synopsis_code'],\n weather_info['light'],\n weather_info['light_condition'],\n weather_info['alert_sender'],\n weather_info['alert_event'],\n weather_info['tz'],\n weather_info['tz_offset'],\n weather_info['ts_epoch'],\n weather_info['sunrise_local'],\n weather_info['sunset_local'],\n weather_info['image_name'],\n weather_info['video_name'],\n container_version\n )\n\n mycursor.execute(sql, val)\n mydb.commit()\n print('uuid=' + weather_info['uuid'] + ', ' + mycursor.rowcount.__str__() + ' record inserted into MetMini Actual table OK')\n\n except Exception as e:\n log_msg = 'insert_rec_to_db() : uuid=' + weather_info['uuid'] + ', error : ' + e.__str__()\n print(log_msg)",
"title": ""
},
{
"docid": "6d8d3298524d4dac04c73a9bf994fbb5",
"score": "0.47718042",
"text": "def insert_row(batch, row):\n row = row.rstrip()\n line = row.split(\"\\t\");\n word = line[0].split(\"#\"); \n #print(word[0],line[1])\n batch.put(word[0], {\"data:polarity\":line[1]})",
"title": ""
},
{
"docid": "7fcad9d7db5e3ac7d41804ab68545549",
"score": "0.4769189",
"text": "def addRows():\n if Virus.query.first() == None:\n df = Etl().data\n df.to_sql('viruses', engine, if_exists=\"replace\", index=False)\n print(\"done adding rows\")",
"title": ""
},
{
"docid": "469b937b93fefb4035a234851fd5a533",
"score": "0.47642264",
"text": "def insert(self):\n self.cursor.executemany(self.sql_insert, self.val)",
"title": ""
},
{
"docid": "740a9b808c7cca6058a2c72ae61a76ae",
"score": "0.47561455",
"text": "def write_row(row):\n # for field in row:\n # print('\"{}\",'.format(escapify(field)), end=\"\")\n # print()\n writer.writerow(row)",
"title": ""
},
{
"docid": "78f8e9df3bb815a0a73d7fb609690545",
"score": "0.47547886",
"text": "def save_set(self, new_set):#{{{\n position = 1\n for par in new_set[\"parameters\"]:\n query = '''INSERT INTO %s (prot_id, prot_ver, ps, variable, type, value, position)\n VALUES ( '%s', '%s', '%s', '%s', '%s', '%s', '%s' )''' %(self.tableName,\n new_set[\"prot_id\"],\n new_set[\"prot_ver\"],\n new_set[\"ps\"],\n par[\"variable\"],\n par[\"type\"],\n par[\"value\"],\n str(position))\n #print query\n con.execute(query)\n position += 1\n db.commit()\n return True\n #}}}",
"title": ""
},
{
"docid": "e748efc6341f2c3e8137288adf9283b0",
"score": "0.47508883",
"text": "def turbo_write(mydb, df, table):\n start = time.time()\n # preparing columns\n colunas = '('\n colunas += ', '.join(df.columns)\n colunas += ')'\n\n # preparing value place holders\n val_place_holder = ['?' for col in df.columns]\n sql_val = '('\n sql_val += ', '.join(val_place_holder)\n sql_val += ')'\n\n # writing sql query for turbodbc\n sql = f\"\"\"\n INSERT INTO {mydb}.dbo.{table} {colunas}\n VALUES {sql_val}\n \"\"\"\n\n # writing array of values for turbodbc\n valores_df = [df[col].values for col in df.columns]\n\n # cleans the previous head insert\n with connection.cursor() as cursor:\n cursor.execute(f\"delete from {mydb}.dbo.{table}\")\n connection.commit()\n\n # inserts data, for real\n with connection.cursor() as cursor:\n try:\n cursor.executemanycolumns(sql, valores_df)\n connection.commit()\n except Exception:\n connection.rollback()\n print('something went wrong')\n\n stop = time.time() - start\n return print(f'finished in {stop} seconds')",
"title": ""
},
{
"docid": "dd8b4748c32570c5fd3ea0ca2ba88b7f",
"score": "0.47464663",
"text": "def test_insert_int(self):\n self.tbl.insert(25)",
"title": ""
},
{
"docid": "5fc0dd4c3b510eb5b8e777280c11d35f",
"score": "0.4743067",
"text": "def populateTable(where, name):\n\n class Indexed(IsDescription):\n var1 = StringCol(itemsize=4, dflt=b\"\", pos=1)\n var2 = BoolCol(dflt=0, pos=2)\n var3 = IntCol(dflt=0, pos=3)\n var4 = FloatCol(dflt=0, pos=4)\n\n nrows = minRowIndex\n table = where._v_file.create_table(where, name, Indexed, \"Indexed\",\n None, nrows)\n for i in range(nrows):\n table.row['var1'] = str(i)\n\n # table.row['var2'] = i > 2\n table.row['var2'] = i % 2\n table.row['var3'] = i\n table.row['var4'] = float(nrows - i - 1)\n table.row.append()\n table.flush()\n\n # Index all entries:\n indexrows = table.cols.var1.create_index()\n indexrows = table.cols.var2.create_index()\n indexrows = table.cols.var3.create_index()\n\n # Do not index the var4 column\n # indexrows = table.cols.var4.create_index()\n if common.verbose:\n print(\"Number of written rows:\", nrows)\n print(\"Number of indexed rows:\", table.cols.var1.index.nelements)\n print(\"Number of indexed rows(2):\", indexrows)",
"title": ""
},
{
"docid": "de2320bb4663e30b1fde248ff0ab4145",
"score": "0.47347358",
"text": "def get_amount():\n conn = None\n try:\n params = config()\n conn = psycopg2.connect(**params)\n cur = conn.cursor()\n\n #tas\n jenis_value={};\n jenis_index={};\n tas={};\n cur.execute(\"SELECT DISTINCT coalesce(jc.name,'') \\\n FROM vw_cps_last_3_day dy \\\n LEFT JOIN tta_coal_prod_stat_coal_index AS ci \\\n ON dy.generate_series=ci.date \\\n LEFT JOIN tta_coal_prod_stat_jenis_coal_index AS jc \\\n ON ci.jenis=jc.id \\\n WHERE coalesce(jc.name,'') <> '' \\\n limit 3 \")\n rowcount=cur.rowcount\n print(\"The number of row: \", rowcount)\n row = cur.fetchone()\n counter=0\n item={}\n\n if rowcount>0:\n f=open('../../data_coal_index.csv','w')\n f.write('date,')\n while row is not None:\n #print(row)\n jenis_index[counter]=row[0]\n f.write(jenis_index[counter])\n counter+=1\n if counter<rowcount:\n f.write(\",\")\n row = cur.fetchone()\n f.write(\"\\n\")\n\n cur.execute(\"SELECT dy.generate_series::date date,coalesce(jc.name,'') \\\n ,coalesce(ci.coal_index,0) coal_index \\\n FROM vw_cps_last_3_day dy \\\n LEFT JOIN tta_coal_prod_stat_coal_index AS ci \\\n ON dy.generate_series=ci.date \\\n LEFT JOIN tta_coal_prod_stat_jenis_coal_index AS jc \\\n ON ci.jenis=jc.id \\\n limit 3 \")\n rowcount=cur.rowcount\n print(\"The number of row: \", rowcount)\n row = cur.fetchone()\n\n if rowcount>0:\n while row is not None:\n #f.write(str(row[3])+',')\n date=str(row[0])\n jenis=str(row[1])\n val=str(row[2])\n if date not in item:\n item_2={}\n for ah in jenis_index:\n item_2[jenis_index[ah]]=\"0\"\n item[date]=item_2\n else:\n item_2=item[date]\n item_2[jenis]=val\n row = cur.fetchone()\n\n for i in item:\n f.write(i+',')\n counter=0\n for idx in item[i]:\n tmp=item[i]\n f.write(tmp[idx])\n if counter < len(item[i])-1:\n f.write(',')\n counter+=1\n f.write(\"\\n\")\n f.close()\n cur.close()\n\n print(str(datetime.datetime.now())+' '+str(rowcount)+' row updated')\n except (Exception, psycopg2.DatabaseError) as error:\n print(str(datetime.datetime.now())+' '+str(error))\n finally:\n if conn is not None:\n conn.close()",
"title": ""
},
{
"docid": "266759b689bf2b39f3ba61bd354040f5",
"score": "0.47346246",
"text": "def insert_data(statbank_table, rows, conv):\n insert_errmsg = 'Encountered a database error when inserting row with value: {} at time: {}'\n table = make_table(statbank_table)\n\n with create_session() as session:\n for i, row in enumerate(rows):\n try:\n for variable_id, value in row.variables.items():\n row.variables[variable_id] = conv[variable_id][value.id]\n session.execute(table.insert().values(value=row.value, time=row.time, **row.variables))\n except SQLAlchemyError as err:\n logging.exception(inserterr.format(row.value, row.time))\n raise err\n break\n finally:\n subprogress.send(dict(\n max=100,\n value=i % 100,\n ))",
"title": ""
},
{
"docid": "00d9706d198d56707cfeee2463e5dd3e",
"score": "0.47266757",
"text": "def job_data_sql (starttime, endtime, job_type, expected_rows, inserted_rows, null_values, completed):\n \n sql_query = (\"\"\"\n insert into job_tracker (\n starttime\n ,endtime\n ,job_type\n ,expected_rows\n ,inserted_rows\n ,null_values\n ,completed\n )\n values \n (\n null,?,?,?,?,?,?,?\n )\n \"\"\").format{starttime, endtime, job_type, expected_rows, inserted_rows, null_values, completed}\n \n return sql_query",
"title": ""
},
{
"docid": "6b9d95d84461ba07fd50500e25bc3f80",
"score": "0.47208992",
"text": "def insert_data(conn, time, git_branch, git_commit_id, jenkins_job_id, db_version, environment, benchmark_type, query_mode, scale_factor, terminals, client_time, weights, metrics, incremental_metrics):\n\n INSERT_SQL = \"\"\"\n INSERT INTO %s (\n time, git_branch, git_commit_id, jenkins_job_id, db_version, environment, benchmark_type, query_mode, scale_factor, terminals, client_time, weights, metrics, incremental_metrics\n ) VALUES (\n to_timestamp(%s/1000), '%s', '%s', '%s', '%s', '%s', '%s', '%s', %s, \n %s, %s, '%s', '%s', '%s'\n );\n \"\"\" % (TABLENAME, time, git_branch, git_commit_id, jenkins_job_id, db_version, environment, benchmark_type, query_mode, scale_factor, terminals, client_time, weights, metrics, incremental_metrics)\n\n cur = conn.cursor()\n try:\n cur.execute(INSERT_SQL)\n except (Exception, psycopg2.Error) as error:\n print(error.pgerror)\n conn.commit()\n cur.close()",
"title": ""
},
{
"docid": "66e4a6e242c169895432fc6972d8d86f",
"score": "0.47168422",
"text": "def write_to_db(self) -> None:\n records_in = 0\n config.CURSOR.execute('SELECT DATE FROM UQ')\n dates = config.CURSOR.fetchall()\n for date, uq in self.schedule.items():\n try:\n config.CURSOR.execute(\n 'INSERT INTO UQ VALUES (?, ?, ?, ?)',\n (str(date), uq, self.title, self.url)\n )\n records_in += 1\n except sqlite3.IntegrityError:\n config.LOGGER.info(\n f'{date} (UQ: {uq}) was found in the DB! Skipped.'\n )\n continue\n\n if self.is_url:\n config.DB.commit()\n config.LOGGER.info(f'Wrote {records_in} records into database.')\n else:\n print('Example results:', self.schedule)",
"title": ""
},
{
"docid": "197401a75ec5346ff31d4771f68944a8",
"score": "0.46977594",
"text": "def inserter_dynamic(a, b, c, d, e, f):\r\n\r\n num = a\r\n status = b\r\n bikestands = c\r\n avail = d\r\n availbikes = e\r\n last = f\r\n sql = ('INSERT INTO onyourbikemysql.JCD_dynamic_data'\r\n '(number, status, bike_stands, available_bike_stands, available_bikes, last_update)'\r\n 'VALUES (\"%d\", \"%s\", \"%d\", \"%d\", \"%d\", \"%s\" )' %\r\n (num, status, bikestands, avail, availbikes, last))\r\n try:\r\n cur.execute(sql)\r\n cnx.commit()\r\n # print(\"Dynamic data - SQL statement executed\")\r\n except:\r\n cnx.rollback()\r\n pass",
"title": ""
},
{
"docid": "2c911ff21d64ee6afb746fc6c117cf4b",
"score": "0.4694495",
"text": "def updatingDatabase(self, step, stepNum, stepCount):",
"title": ""
},
{
"docid": "a59a839b52f3fb4f087ca13c6ce99e8f",
"score": "0.46924427",
"text": "def output_inv_info(fh, cp_tbl_name, cl_tbl_name, csv_file, model):\n fh.write(\n f'''\nDROP TABLE IF EXISTS inv_info;\nCREATE TABLE inv_info AS\nSELECT DISTINCT\n {cp_tbl_name}.{columns.assg_prdn.name},\n {cp_tbl_name}.{columns.grant_yr.name},\n {cp_tbl_name}.{columns.app_yr.name},\n {cp_tbl_name}.{columns.cw_yr.name},\n {cp_tbl_name}.{columns.assg_seq.name},\n {cp_tbl_name}.{columns.assg_firmid.name},\n {cp_tbl_name}.{columns.emp_yr.name},\n {cp_tbl_name}.{columns.inv_seq.name},\n {cp_tbl_name}.{columns.pik.name},\n {cp_tbl_name}.{columns.pik_ein.name}\nFROM \n {cp_tbl_name},\n {cl_tbl_name}\nWHERE\n {cp_tbl_name}.{columns.assg_prdn.name} = {cl_tbl_name}.{columns.assg_prdn.name},\n {cp_tbl_name}.{columns.cw_yr.name} = {cl_tbl_name}.{columns.cw_yr.name},\n {cp_tbl_name}.{columns.assg_seq.name} = {cl_tbl_name}.{columns.assg_seq.name},\n {cp_tbl_name}.{columns.assg_firmid.name} = {cl_tbl_name}.{columns.assg_firmid.name};\n ''')\n shared_code.output_data(fh, f'inv_info', csv_file)",
"title": ""
},
{
"docid": "4f580f84e7c8a6defcbcc8066e20e4c3",
"score": "0.46877512",
"text": "def save_public_trade_df(conn, public_trade_df):\n\n for row in public_trade_df.iterrows():\n vals = row[1].values\n indx = row[1].name\n trades_as_array = np.insert(vals, 0, vals[-1])[:-1]\n trades_as_array = np.append(trades_as_array, indx.replace(microsecond=0).to_pydatetime())\n trades_as_tuple = tuple(trades_as_array)\n\n sql = ''' \n INSERT OR IGNORE INTO public_trades(sequence, instrument_code,price,amount,volume,taker_side, DateTime_UTC)\n VALUES(?,?,?,?,?,?,?) '''.format()\n cur = conn.cursor()\n cur.execute(sql, trades_as_tuple)\n conn.commit()\n return cur.lastrowid",
"title": ""
},
{
"docid": "9210d9f1682bacfbaa99a247a1da1085",
"score": "0.46822748",
"text": "def write_records(self):",
"title": ""
},
{
"docid": "34ff5f7fdde9b49064dc658b2955c498",
"score": "0.46812287",
"text": "def insert(item, quantity, price, conn):\n cur = conn.cursor()\n cur.execute(\"INSERT INTO store VALUES(?,?,?)\", (item, quantity, price))\n conn.commit()",
"title": ""
},
{
"docid": "ae454b32ca44f3c9ec8d823448b488da",
"score": "0.46776038",
"text": "def update_department_development(cursor, row, item):\n sql = \"\"\"\n UPDATE reports SET \n pp_misc_mfd_1_kuerzel = %s\n WHERE\n unters_schluessel = %s\n \"\"\"\n cursor.execute(sql, (item, row))",
"title": ""
},
{
"docid": "f4d46b93dccf172768a6aef468b77ca5",
"score": "0.46648768",
"text": "def get_tuple_query(self, f_tuple):\n\n query = str(\"INSERT INTO \" + self.name + \" VALUES (\")\n\n for each_cell in f_tuple: #add each cell to query\n query = query + \"'\" + str(each_cell) + \"', \"\n #\n\n query = query[:-2] #remove last comma and space\n query = query + \");\\n\"\n\n return query",
"title": ""
},
{
"docid": "9b56f644eecb1cc9cd8070c3b47804ca",
"score": "0.46626136",
"text": "def insert(cls, conn, count):\n query = f\"INSERT INTO counts (val) VALUES('{int(count)}')\"\n conn.execute(query)",
"title": ""
},
{
"docid": "29ee72e812291d777490e5153bfec96f",
"score": "0.46605125",
"text": "def create_sightings(conn, project):\n sql = \"INSERT INTO SIGHTINGS(NAME, PERSON, LOCATION, SIGHTED) VALUES(?, ?, ?, ?)\"\n cur = conn.cursor()\n cur.execute(sql, project)\n return cur.lastrowid",
"title": ""
},
{
"docid": "947af5c1ef4a1f7a659af9a2be644301",
"score": "0.4658577",
"text": "def write_live(self):\n last_row = self._price_history.get_values()[-1].tolist()\n last_time = [self._price_history.index[-1]]\n last_time.extend(last_row)\n # last_row.insert(1, self.ticker)\n last_row = [tuple(last_time)]\n try:\n self.gquery.write(last_row)\n except (ConnectionError, SystemError):\n # logging.exception(e)\n pass",
"title": ""
},
{
"docid": "44f995d0356899b8c7f57119e2ee4304",
"score": "0.46465826",
"text": "def sql_postgres_query_to_csv(\n sqlr=\"SELECT ticker,shortratio,sector1_id, FROM stockfundamental\", csv_out=\"\"\n):\n import psycopg2\n\n con = psycopg2.connect(database=mydatabase, user=myusername, password=mypassword)\n cur = con.cursor()\n if con:\n print((\"Connected: %s\" % con))\n else:\n print(\"Connection lost\")\n return -1\n\n output_query = \"COPY ({0}) TO STDOUT WITH CSV HEADER\".format(sqlr)\n with open(csv_out, \"w\") as f:\n cur.copy_expert(output_query, f)\n print((\"Successfully submitted results to: %s\" % csv_out))\n con.close()",
"title": ""
},
{
"docid": "079f9bc36e58e0cd541eaf5a7292e74a",
"score": "0.46353108",
"text": "def insertfromdata(tablename, records, multiple=True):\n if multiple:\n lst = records[0]\n else:\n lst = records\n s = 'INSERT INTO ' + str(tablename) + ' VALUES '\n s += \"( \" + \", \".join([\"?\"]*len(lst)) + \")\"\n return s",
"title": ""
},
{
"docid": "816d6e091a91de9eba49e8d9f5bfdb2d",
"score": "0.46301907",
"text": "def _db_update(self, s_sql, t_data = ()):\n t_data= tuple([TO_UNICODE(_) for _ in t_data])\n self._logger.debug(\n \"Executing '\" + s_sql + \"' with data '\"\n + \"', '\".join(t_data) + \"'\"\n )\n try:\n result = self.dbh.execute(s_sql, t_data)\n if not self.__db_lock:\n self.dbh.commit()\n except sqlite3.OperationalError:\n self.dbh.rollback()\n self._logger.error(\n \"Error executing '\" + s_sql + \"' with data '\"\n + \"', '\".join(t_data) + \"'\"\n )\n raise\n return result",
"title": ""
},
{
"docid": "6181e9d1d1df7163239d33df827ee878",
"score": "0.46279156",
"text": "def executemany(self, sql, values):\r\n self.cur.execute(sql, values)",
"title": ""
},
{
"docid": "0433a91f6800af569e67131df2363b59",
"score": "0.4626792",
"text": "def uvozi_trenutek(conn):\n conn.execute(\"DELETE FROM trenutek;\")\n with open('podatki/trenutek.csv') as datoteka:\n podatki = csv.reader(datoteka)\n \n i = 1\n poizvedba = \"\"\"\n INSERT INTO trenutek VALUES (?,?)\n \"\"\"\n for vrstica in podatki:\n conn.execute(poizvedba, vrstica)",
"title": ""
},
{
"docid": "06013f7a7fb5c72700afcc4efc508f50",
"score": "0.46231562",
"text": "def insert(res):\n #初始化\n query=\"insert into purchase (item_name,date,quantity,price,store) values (%s,%s,%s,%s,%s)\"\n param=[]\n result={\"param\":\"\",\"row\":0}\n row=0\n #从网页获得数据\n if res['bg']: param.append(('bg',res['p_date'],res['bg'],res['bg_p'],res['store']))\n if res['gn']: param.append(('gn',res['p_date'],res['gn'],res['gn_p'],res['store']))\n if res['hm']: param.append(('hm',res['p_date'],res['hm'],res['hm_p'],res['store']))\n if res['hty']: param.append(('hty',res['p_date'],res['hty'],res['hty_p'],res['store']))\n if res['mt']: param.append(('mt',res['p_date'],res['mt'],res['mt_p'],res['store']))\n if res['nf']: param.append(('nf',res['p_date'],res['nf'],res['nf_p'],res['store']))\n if res['pf']: param.append(('pf',res['p_date'],res['pf'],res['pf_p'],res['store']))\n if res['day']: param.append(('day',res['p_date'],res['day'],res['day_p'],res['store']))\n if res['sgm']: param.append(('sgm',res['p_date'],res['sgm'],res['sgm_p'],res['store']))\n if res['night']: param.append(('night',res['p_date'],res['night'],res['night_p'],res['store']))\n if res['ydl']: param.append(('ydl',res['p_date'],res['ydl'],res['ydl_p'],res['store']))\n if res['mf']: param.append(('mf',res['p_date'],res['mf'],res['mf_p'],res['store']))\n #print(param)\n #增加到数据库\n if len(param)>0:\n sqlConnection()\n row=sqlInsert(query,param)\n sqlDisconnection()\n \n #记录结果 \n result={\"param\":param,\"row\":row}\n return result",
"title": ""
},
{
"docid": "88d6221ed709d17c7cad7075361dc272",
"score": "0.46227482",
"text": "def _carto_insert_values(self, n_batch=10000, debug=False):\n n_items = len(self)\n if debug: print(\"self has {} rows\".format(n_items))\n row_vals = []\n char_count = 0\n insert_stem = (\"INSERT INTO {tablename}({cols}) \"\n \"VALUES \").format(tablename=self.get_carto_tablename(),\n cols=','.join(self.columns))\n colnames = list(self.columns)\n if debug: print(\"insert_stem: {}\".format(insert_stem))\n\n for row_num, row in enumerate(self.iterrows()):\n row_vals.append('({rowitems})'.format(\n rowitems=utils.format_row(row[1],\n self.dtypes,\n colnames)))\n char_count += len(row_vals[-1])\n if debug: print(\"row_num: {0}, row: {1}\".format(row_num, row))\n # run query if at batch size, end of dataframe, or near POST limit\n if (len(row_vals) == n_batch or\n row_num == n_items - 1 or char_count > 900000):\n\n query = ''.join([insert_stem, ', '.join(row_vals), ';'])\n if debug: print(\"insert query: {}\".format(query))\n resp = self.carto_sql_client.send(query)\n if debug: print(\"insert response: {}\".format(resp))\n\n # reset batch\n row_vals = []\n char_count = 0\n\n\n return None",
"title": ""
},
{
"docid": "655d6f6de3471d068089c4a88461e4c9",
"score": "0.46202412",
"text": "def test_data():\n \n temp = 22.0\n humi = 76.9\n itmp = 21.9\n pres = 1016\n lcur = 0.2\n batv = 13.0\n wspeed = 0.0\n wdir = 180.0\n rain = 0.0\n pcur = 0.51\n\n cnx = open_database()\n \n cursor = cnx.cursor()\n data = (time.strftime(\"%Y-%m-%d\"), temp,humi,itmp,pres,lcur,batv,wspeed,wdir,rain,pcur)\n print(data)\n cursor.execute(INSERT_DEF, data)\n cnx.commit()\n\n query = (\"SELECT ts, temperature FROM GardenLabData\")\n cursor.execute(query)\n for( ts, temperature) in cursor:\n print( ts, temperature)\n \n cursor.close()\n cnx.close()",
"title": ""
},
{
"docid": "412ce511dddce47d46ce86b2d8fcb071",
"score": "0.4619653",
"text": "def write_to_mySQL(self, tempSeriesData):\n # re-Create table if already exists\n if self.Gwritten == 0:\n print('Creating new Google Trend SQL table: %s' % (self.path))\n# else: # else it exists so append without writing the header\n # Adjust dataframe and write to sql table\n tempSeriesData.to_sql(self.path, self.engine, if_exists='append', index=False)\n return",
"title": ""
},
{
"docid": "af9e029d7f61b269f2ba81880d4ea787",
"score": "0.4618785",
"text": "def execQuery(query,out2file):\n \n server='localhost'\n conn = None\n results=()\n try:\n # host user password db\n conn = MySQLdb.Connection(server, 'saguinag', 'dsg1!0xB', 'wikipediagame')\n cursor = conn.cursor()\n\n cursor.execute(query)\n conn.commit()\n #print cursor.rowcount\n #print results\n results = cursor.fetchall()\n if (out2file):\n fn_datetime='outputFiles/'+datetime.date.today().strftime(\"%d%b%y\")+datetime.datetime.now().strftime(\"_%I%M%p\")\n f = open(fn_datetime,'w')\n for row in results:\n csv.writer(f).writerow(row) #f.write(row)\n f.close()\n else:\n for row in results:\n print row;\n\n except MySQLdb.Error, e:\n print \"Error %d: %s\" % (e.args[0], e.args[1])\n sys.exit(1)\n\n finally:\n\n if conn:\n conn.close()\n\n return",
"title": ""
},
{
"docid": "103034a066f845ee22c2f380283a2952",
"score": "0.4618027",
"text": "def studies2db(study_xml, cur):\n # xmldb_queries structure:\n # each row includes information for one column in the table\n # 1st: name of the column in the table of the database,\n # 2nd: keyword used to extract data from xml object\n # 3rd: obsolete for now, may be useful in the future\n # 4th: type of the data, default %s, if other, data will be converted accordingly.\n # e.g., %d indicate the extracted data will be first converted into a number and then write into the database\n table_name = \"studies\" # name of the table\n xmldb_queries = [[\"nct_id\", \"id_info/nct_id\", False, \"%s\"],\n [\"official_title\", \"official_title\", False, \"%s\"],\n [\"start_month_year\", \"start_date\", False, \"%s\"],\n [\"start_date\", \"start_date\", False, \"%my\"],\n [\"completion_month_year\", \"completion_date\", False, \"%s\"],\n [\"completion_date\", \"completion_date\", False, \"%my\"],\n [\"primary_completion_month_year\", \"primary_completion_date\", False, \"%s\"],\n [\"primary_completion_date\", \"primary_completion_date\", False, \"%my\"],\n [\"verification_month_year\", \"verification_date\", False, \"%s\"],\n [\"verification_date\", \"verification_date\", False, \"%my\"],\n [\"study_type\", \"study_type\", False, \"%s\"],\n [\"acronym\", \"acronym\", False, \"%s\"],\n [\"baseline_population\", \"clinical_results/baseline/population\", False, \"%s\"],\n [\"overall_status\", \"overall_status\", False, \"%s\"],\n [\"last_known_status\", \"last_known_status\", False, \"%s\"],\n [\"phase\", \"phase\", False, \"%s\"],\n [\"enrollment\", \"enrollment\", False, \"%d\"],\n [\"enrollment_type\", \"enrollment\", False, \"%s\"],\n [\"source\", \"source\", False, \"%s\"],\n [\"number_of_arms\", \"number_of_arms\", False, \"%d\"],\n [\"number_of_groups\", \"number_of_groups\", False, \"%d\"],\n [\"limitations_and_caveats\", \"clinical_results/limitations_and_caveats\", False, \"%s\"],\n [\"last_changed_date\", \"lastchanged_date\", False, \"%my\"],\n [\"brief_title\", \"brief_title\", False, \"%s\"],\n [\"why_stopped\", \"why_stopped\", False, \"%s\"],\n [\"has_expanded_access_type\", \"has_expanded_access\", False, \"%s\"],\n [\"has_expanded_access\", \"has_expanded_access\", False, \"%bool\"],\n [\"first_received_results_date\", \"firstreceived_results_date\", False, \"%s\"]\n ]\n col_names, data = simple_query_list(study_xml, xmldb_queries)\n query = generate_query(table_name, col_names) # query string used by cursor object (psycopg2) to write to the database\n cur.execute(query, data)\n return(cur)",
"title": ""
},
{
"docid": "55f9d33f6480c1eb3e4f5e7d1d160f2d",
"score": "0.46170175",
"text": "def sql_db_updater(lat, lon, db='url_imagen', col='ind', val=''):\n t = (lat, lon)\n C.execute(f'Update {db} set {col} = {val} WHERE lat=? AND lon=?', t)\n conn.commit()",
"title": ""
},
{
"docid": "7c742e46597a0410f6d62f9353483095",
"score": "0.46152425",
"text": "def update(self, row):\n ...",
"title": ""
},
{
"docid": "fbb89dcf10fcb7132c82818af78547cc",
"score": "0.4614965",
"text": "def append_row(self):\r\n values = []\r\n vals_to_insert = ''\r\n \r\n for key in Output.COLUMNS:\r\n values.append(str(self[key]))\r\n\r\n # Replace any Quotes in parsed record with double quotes\r\n for i in values:\r\n vals_to_insert += i.replace('\"', '\"\"') + '\",\"'\r\n\r\n vals_to_insert = '\"' + vals_to_insert[:-3] + '\"'\r\n insert_sqlite_db(vals_to_insert)",
"title": ""
},
{
"docid": "71ea65cfdaa9c2af97efae19b413b71a",
"score": "0.4609924",
"text": "def _insert_items(self, items):\n sql = \"INSERT INTO doge(ts, price) VALUES(%s, %s) ON CONFLICT DO NOTHING;\"\n conn = None\n try:\n conn = psycopg2.connect(f\"dbname={_NAME} user={_NAME} host={_HOST} password={_PASS} port={_PORT}\")\n cur = conn.cursor()\n # execute the INSERT statement\n cur.executemany(sql,items)\n # commit the changes to the database\n conn.commit()\n # close communication with the database\n cur.close()\n except (Exception, psycopg2.DatabaseError) as error:\n print(f\"Psql error: {error}\")\n finally:\n if conn is not None:\n conn.close()",
"title": ""
},
{
"docid": "82e65ad10a48cc9fadf35f5b41f49f51",
"score": "0.46081418",
"text": "def insert_into_db(phenny, sqlite_data):\n\n if not bash.conn:\n bash.conn = sqlite3.connect(phenny.bash_db)\n\n c = bash.conn.cursor()\n\n c.execute('''INSERT INTO quotes\n (channel, nick, quote, time)\n VALUES(\n :channel,\n :nick,\n :quote,\n CURRENT_TIMESTAMP\n );''', sqlite_data)\n\n c.execute('''INSERT OR REPLACE INTO stats\n (channel, nick, lines)\n VALUES(\n :channel,\n :nick,\n COALESCE((SELECT lines FROM stats WHERE channel=:channel AND nick=:nick) + 1, 1)\n );''', sqlite_data)\n\n\n c.close()\n bash.conn.commit()\n\n c = bash.conn.cursor()\n\n c.execute('''SELECT id FROM quotes ORDER BY id DESC LIMIT 1''')\n last_id = c.fetchall()[0][0] - 99\n\n c.execute('''SELECT channel, nick FROM quotes WHERE id < ?''', (last_id,))\n rows = c.fetchall()\n for row in rows:\n channel = row[0]\n nick = row[1]\n\n c.execute('''SELECT lines FROM stats WHERE channel=? AND nick=?''', (channel, nick))\n lines = 0\n lines = c.fetchall()[0][0]\n\n if lines - 1 == 0:\n c.execute('''DELETE FROM stats WHERE channel=? AND nick=?''', (channel, nick))\n else:\n c.execute('''REPLACE INTO stats\n (channel, nick, lines)\n VALUES(\n ?,\n ?,\n (SELECT lines FROM stats WHERE channel=? AND nick=?) - 1\n );''', (channel, nick, channel, nick))\n\n c.execute('''DELETE FROM quotes WHERE id < ?''', (last_id,))\n\n c.close()\n bash.conn.commit()",
"title": ""
},
{
"docid": "baa5984589ec0107e73d23feb9cf212c",
"score": "0.46008945",
"text": "def _insert_data(self):\n cur = self.conn.cursor()\n for row in self.data.data:\n cur.execute(\n f'INSERT INTO {self.schema}.{self.table} ({self._prepare_header()}) VALUES ( {self._prepare_row(row)} )')\n\n self.conn.commit()\n cur.close()\n self.conn.close()",
"title": ""
},
{
"docid": "25bfdce9c3ea29c936c51642d487e178",
"score": "0.46002275",
"text": "def write(self, cr, uid, ids, vals, context=None):\n if isinstance(ids, (list, tuple, dict, )):\n select = list(ids)\n else:\n select = [ids]\n\n rv = super(stock_move, self).write(cr, uid, select, vals, context=context)\n\n for _obj in self.browse(cr, uid, select, context=context):\n if _obj.prodlot_id and _obj.purchase_line_id:\n _price = self.calculate_lot_cost_price_per_unit(cr, uid, prodlot_id=_obj.prodlot_id.id, purchase_line_id=_obj.purchase_line_id.id, context=context)\n _obj.prodlot_id.write({'cost_price_per_unit': _price}, context=context)\n\n return rv",
"title": ""
},
{
"docid": "e8d85f089e1692c7ca89e8639177ff33",
"score": "0.4599512",
"text": "def insert_songplays():\n files = list_files()\n conn = psycopg2.connect(host=\"postgres_udacity\", dbname=\"udacity\", user=\"udacity\", password=\"udacity\")\n conn.autocommit = True\n for file in files:\n df = return_df_file(file)\n print(df.head())\n df_used = df[df['page'] == 'NextSong']\n df_used = df_used[['ts', 'userId', 'level', 'song', 'artist', 'sessionId', 'location', 'userAgent']]\n columns = ['start_time', 'user_id', 'level', 'session_id', 'location', 'user_agent', 'song_id', 'artist_id']\n list_song_id = []\n list_artist_id = []\n list_ts = []\n for i, line in df_used.iterrows():\n print(line.song)\n if (\"'\" in line.song):\n a = str(line.song)\n a = a.replace(\"'\",\"´\")\n else:\n a = line.song\n df = return_song_id(a, conn)\n print(df)\n if df.empty:\n list_song_id.append('0')\n list_artist_id.append('0')\n else:\n list_song_id.append(df['song_id'].values[0])\n list_artist_id.append(df['artist_id'].values[0])\n\n time = pd.to_datetime(line.ts, unit='ms')\n list_ts.append(time)\n \n df_used = df_used[['ts', 'userId', 'level', 'sessionId', 'location', 'userAgent']]\n df_used['song_id'] = list_song_id\n df_used['artist_id'] = list_artist_id\n df_used['ts'] = list_ts\n \n df_used.columns = columns\n print(df_used.head())\n \n sql_insert = '''INSERT INTO sparkifydb.songplays\n (start_time, user_id, level, session_id, location, user_agent, song_id, artist_id) \n values (%s,%s,%s,%s,%s,%s,%s,%s);'''\n\n try:\n with conn.cursor() as cursor:\n cursor.executemany(sql_insert,df_used.values.tolist())\n print('Commit')\n except Exception as e:\n print(e)",
"title": ""
},
{
"docid": "566bc371cb2a1f82704c4f8f3408358e",
"score": "0.45923266",
"text": "def insert_raw_data(date_found_unix, found_at_date, found_at_time, duration,\n hash_rate, block_no, block_value):\n try:\n conn = sqlite3.connect(get_current_db_file_path())\n c = conn.cursor()\n c.execute(\"INSERT INTO raw_data VALUES (?, ?, ?, ?, ?, ?, ?);\",\n [date_found_unix, found_at_date, found_at_time, duration, hash_rate, block_no, block_value]\n )\n conn.commit()\n finally:\n conn.close()",
"title": ""
},
{
"docid": "6c9104defcfe458f5448d377c55e3bd9",
"score": "0.4591124",
"text": "def no_desc_sql(invoice_no, invoice_date, year, month, day, qty, itm, item, item_type, price, price_total, taxable,\n file_name):\n sql_five = '''INSERT INTO item_test(invoice, date, year, month, day, source, qty, itm, item, type, price, \n price_total, taxable, file, date_added)\n VALUES('{0}', '{1}', {2}, {3}, {4}, '{5}', {6}, '{7}', '{8}', '{9}', {10}, {11}, {12}, '{13}', '{14}');''' \\\n .format(invoice_no, invoice_date, year, month, day, 'Krueger', qty, itm, item, item_type, price,\n price_total, taxable, file_name, now.strftime(\"%Y-%m-%d %H:%M\"))\n c.execute(sql_five)",
"title": ""
},
{
"docid": "2d52d477124a39a485e46b6efa83f1d3",
"score": "0.45908946",
"text": "def write_row(worksheet, lno, columns, encoding='utf-8'):\n cno = 0\n for column in columns:\n worksheet.write(lno, cno, column.decode(encoding))\n cno = cno + 1",
"title": ""
},
{
"docid": "2f13f6dc725b7e512a9122f6ecec99c7",
"score": "0.4578129",
"text": "def write_to_postgres(all_files, folder, cursor, connection):\n global DEBUG\n\n log('Entering write_to_postgres')\n # how many rows we are planning to write to db\n rows_number = len(all_files)\n table_name = folder\n # this form is for queries , otherwise doesn't work. 'echelon_in.field'\n table_n = 'echelon_in.'+table_name\n data = [] # list of dictionaries\n empty_count = 0\n # To support data lake 2 https://agrium.atlassian.net/browse/CO-2128\n tables_dict = {'grower', 'farm', 'field',\n 'country', 'county', 'state', 'organization'}\n for raw_j in all_files:\n j = raw_j.encode('utf-8')\n if DEBUG:\n log('DEBUG: processing string {}'.format(j))\n # only process not-empty strings, by comparing str(j) to str '\"\"', both not unicode\n if j != '\"\"':\n try:\n tmp_dict = {\"id\": json.loads(j)[folder]['id'],\n \"json\": j,\n # dict is a representation of record in postgres\n \"appended_at\": datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"),\n # dict is a representation of record in postgres\n \"updated_at\": datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"),\n \"nutrien_id\": None, # dict is a representation of record in postgres\n \"npi_reconciled_at\": None, # dict is a representation of record in postgres\n \"is_rejected_by_npi\": False} # dict is a representation of record in postgres\n data.append(tmp_dict)\n if DEBUG:\n log('DEBUG: Successfully added tmp_dict to dictionaries list')\n except ValueError:\n log('ERROR: ValueError building json; source string was {}'.format(j))\n\n else:\n if DEBUG:\n log('DEBUG: empty string in all_files')\n empty_count += 1\n\n if DEBUG:\n log('DEBUG: Building query for insertion into postgres db')\n if table_name in tables_dict:\n query = \"\"\"\n\t\t\t\tINSERT into \"\"\" + table_n + \"\"\"\n\t\t\t\t\t(\"id\", \"json\", \"appended_at\", \"updated_at\",\n\t\t\t\t\t\t\"nutrien_id\" , \"npi_reconciled_at\", \"is_rejected_by_npi\")\n\t\t\t\tVALUES\n\t\t\t\t\t(%(id)s, %(json)s, %(appended_at)s, %(updated_at)s, %(nutrien_id)s, %(npi_reconciled_at)s, %(is_rejected_by_npi)s)\n\t\t\t\tON CONFLICT (id) DO UPDATE SET\n id=EXCLUDED.id, json=EXCLUDED.json, appended_at=EXCLUDED.appended_at, updated_at = now(), nutrien_id=EXCLUDED.nutrien_id, npi_reconciled_at=EXCLUDED.npi_reconciled_at, is_rejected_by_npi=EXCLUDED.is_rejected_by_npi;\n\t\t\t\"\"\"\n else:\n query = \"\"\"\n\t\t\t\tINSERT into \"\"\" + table_n + \"\"\"\n\t\t\t\t\t(\"id\", \"json\", \"appended_at\")\n\t\t\t\tVALUES\n\t\t\t\t\t(%(id)s, %(json)s, %(appended_at)s)\n\t\t\t\tON CONFLICT (id) DO NOTHING ;\n\t\t\t\t\"\"\"\n if DEBUG:\n log('DEBUG: formed the query')\n log('DEBUG: query: {}'.format(query))\n\n try:\n log('Executing query to write to postgres')\n cursor.executemany(query, data)\n log('Successful execution of query')\n except Exception as e:\n log('ERROR: Exception in write_to_postgres for {}: {}'.format(folder, e))\n log('Rolling back...')\n connection.rollback()\n log('Successful execution of the query; committing')\n connection.commit()\n log('Successful commit')\n if empty_count != 0:\n log('\\t{} has {} empty files, skip them'.format(folder, empty_count))\n log(\"\\tWrote to db {} rows, while all_files has {} rows, found empty {} rows\".format(\n cursor.rowcount, rows_number, empty_count))\n log('Exiting write_to_postgres')",
"title": ""
},
{
"docid": "10b90515e6738d3bf6144c1c68b0fa4c",
"score": "0.45713007",
"text": "def desc_sql(c_list, invoice_no, invoice_date, year, month, day, qty, itm, item, item_type, price, price_total, taxable,\n file_name):\n desc = c_list[5]\n sql_desc = '''INSERT INTO item_test(invoice, date, year, month, day, source, qty, itm, item, type, price, \n price_total, taxable, desc, file, date_added)\n VALUES('{0}', '{1}', {2}, {3}, {4}, '{5}', {6}, '{7}', '{8}', '{9}', {10}, {11}, {12}, '{13}', '{14}', '{15}');''' \\\n .format(invoice_no, invoice_date, year, month, day, 'Krueger', qty, itm, item, item_type, price,\n price_total, taxable, desc, file_name, now.strftime(\"%Y-%m-%d %H:%M\"))\n c.execute(sql_desc)",
"title": ""
},
{
"docid": "0693b678901b757bb2a8d0957b6b0dfe",
"score": "0.4570846",
"text": "def write_pogo_into_db(cursor, matlab_engine, pogo_name):\n\t# data_info has elements representing data at every single time frame, \n\t# with structure [time, emitter_magnitude, receiver_magnitude]\n\tdata_info = matlab_engine.view_hist(pogo_name)\n\n\tcursor.execute(\n\t\t\"CREATE TABLE {}\"\n\t\t\"(time real, emitter real, receiver real)\"\n\t\t.format(pogo_name)\n\t)\n\n\tfor i in data_info:\n\t\tcursor.execute(\n\t\t\t\"INSERT INTO {} (time, emitter, receiver) VALUES (?,?,?)\"\n\t\t\t\t.format(pogo_name), \n\t\t\t(i[0], i[1], i[2])\n\t\t)",
"title": ""
},
{
"docid": "bdddfdc4382d2033674278a9ffd1f2b5",
"score": "0.4570053",
"text": "def sql_update_table(table_title, columns, data): \r\n col = \"\"\r\n val = \"\"\r\n for i, j in zip(columns, data):\r\n col+=f'{i},' \r\n val+=f\"\"\"?,\"\"\" \r\n sql = f\"\"\"INSERT INTO {table_title} ({col[:-1]}) VALUES ({val[:-1]});\"\"\" \r\n return sql",
"title": ""
},
{
"docid": "53f354febafbf5ba06864536ea3f333d",
"score": "0.4566683",
"text": "def save_to_db(df, index_name, table):\n for item in df.to_dict(orient='records'):\n result_name = 'result_' + index_name\n table.put_item(\n Item={\n 'pk': result_name,\n 'sk': item[index_name],\n 'yes': item['yes'],\n 'no': item['no']\n }\n )",
"title": ""
},
{
"docid": "c0a4733dc42cfacd46d7ece49baeac53",
"score": "0.45581508",
"text": "def sendDataToExternal(self, lst_of_lines, external_table, external_db):\r\n\t\t# set proper RFS_USED table\r\n\t\tself.clear_rfs_used()\r\n\t\texternal_db.clear_rfs_used()\r\n\t\tfor line in lst_of_lines:\r\n\t\t\tself.set_rfs_used(line)\r\n\t\t\texternal_db.set_rfs_used(line)\r\n\r\n\t\t# get data\r\n\t\tdata = self.__execute_query(\"SELECT DATA_RAW.RFS, DATA_RAW.SUBLINE, DATA_RAW.MONTH, DATA_RAW.CONTRIB, DATA_RAW.FLOW, DATA_RAW.REV, DATA_RAW.REV_EX_ROX, DATA_RAW.RPK, DATA_RAW.ASK \\\r\n\t\t\t\tFROM DATA_RAW INNER JOIN RFS_USED ON DATA_RAW.RFS = RFS_USED.RFS \\\r\n\t\t\t\tGROUP BY DATA_RAW.RFS, DATA_RAW.SUBLINE, DATA_RAW.MONTH, DATA_RAW.CONTRIB, DATA_RAW.FLOW, DATA_RAW.REV, DATA_RAW.REV_EX_ROX, DATA_RAW.RPK, DATA_RAW.ASK;\")\r\n\r\n\t\t# for d in data:\r\n\t\t\t# print(d)\r\n\r\n\r\n\t\t# remove data for selected scope\r\n\t\texternal_db.__commit_query(\"DELETE * FROM \" + external_table + \" WHERE RFS IN (SELECT RFS FROM RFS_USED)\")\r\n\r\n\r\n\t\t# put data into the remote database\r\n\t\ttry:\r\n\t\t\tif (self.debug):\r\n\t\t\t\tprint(\"Writing data into TABLE \" + external_table + \" FROM \" + external_db.dbname)\r\n\t\t\texternal_db.cnx.cursor().executemany(\"INSERT INTO \" + external_table + \" (RFS, SUBLINE, MONTH, CONTRIB, FLOW, REV, REV_EX_ROX, RPK, ASK ) VALUES (?, ?, ?, ?,?,?,?,?,?)\" , data)\r\n\t\t\texternal_db.cnx.commit()\r\n\t\texcept:\r\n\t\t\texternal_db.cnx.rollback()\r\n\t\treturn 0",
"title": ""
},
{
"docid": "dfd99530feededdd47d33e8020108322",
"score": "0.45572424",
"text": "def insert_data(session, insert_query, df, df_cols):\n for i, row in df.iterrows():\n data = tuple([row[col] for col in df_cols])\n # print(data)\n session.execute(insert_query, data)",
"title": ""
},
{
"docid": "b15360cad263870c727071567867f581",
"score": "0.45569274",
"text": "def import_gejala(conn, gj):\n cursor = conn.cursor()\n\n for g in gj:\n print(g[0])\n cursor.execute(\"INSERT INTO gejala(nama_gejala) VALUES('\"+g[0]+\"')\")\n conn.commit()\n return g",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="779"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">b2c5a670fd92894d410e96d40abdcf0e</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Return a dictionary mapping package to recipe name.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "dd851b24009a5f0216ea3f9c824a09c4",
"score": "0.534241",
"text": "def _pkgmap(d):\n\n target_os = d.getVar(\"TARGET_OS\", True)\n target_vendor = d.getVar(\"TARGET_VENDOR\", True)\n basedir = os.path.dirname(d.getVar(\"PKGDATA_DIR\", True))\n\n dirs = (\"%s%s-%s\" % (arch, target_vendor, target_os)\n for arch in d.getVar(\"PACKAGE_ARCHS\", True).split())\n\n pkgmap = {}\n for pkgdatadir in (os.path.join(basedir, sys) for sys in dirs):\n try:\n files = os.listdir(pkgdatadir)\n except OSError:\n continue\n\n for pn in filter(lambda f: not os.path.isdir(os.path.join(pkgdatadir, f)), files):\n try:\n pkgdata = read_pkgdatafile(os.path.join(pkgdatadir, pn))\n except OSError:\n continue\n\n for pkg in pkgdata[\"PACKAGES\"].split():\n pkgmap[pkg] = pn\n\n return pkgmap",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "15852747fb8674970bb432c0bdfb20d2",
"score": "0.61707795",
"text": "def recipes():\n return {'recipes': [recipe(), recipe()]}",
"title": ""
},
{
"docid": "40cf200d1b8aefffb7a98b88a7f76b4d",
"score": "0.61030656",
"text": "def packages_from_entry(self, entry):\r\n return [entry.get(\"name\")]",
"title": ""
},
{
"docid": "4d4122a1d2cc982de0fbb09115f11976",
"score": "0.6102812",
"text": "def load_recipes():\n recipes = {}\n for filename in os.listdir(const.recipe_folder):\n recipe = Recipe(filename)\n recipes[recipe.name] = recipe\n return recipes",
"title": ""
},
{
"docid": "e205fcc0d3a8783ae33c9981e1fc643c",
"score": "0.6052566",
"text": "def recipename(pkg, d):\n\n return pkgmap(d).get(pkg)",
"title": ""
},
{
"docid": "19f6769a3599b3487010dfb3e70a1656",
"score": "0.58453065",
"text": "def get_package_versions() -> Dict[str, str]:\n import pkg_resources\n\n package_dict = pkg_resources.working_set.by_key # type: ignore\n package_version_dict = {key: val.version for key, val in package_dict.items()}\n return package_version_dict",
"title": ""
},
{
"docid": "53e13f4146c327fc74939517cf1fe74e",
"score": "0.572421",
"text": "def pipinstalled(self):\n\n packages_dict = {}\n installed_packages = pkg_resources.working_set\n sorted_packages = sorted([\"%s==%s\" % (i.key, i.version) for i in installed_packages])\n for pypipreq in sorted_packages:\n\n if pypipreq and pypipreq != '':\n\n if \"=\" in pypipreq:\n pypipreq = pypipreq.split(\"=\")\n\n elif \">\" in pypipreq:\n pypipreq = pypipreq.split(\">\")\n\n elif \"<\" in pypipreq:\n pypipreq = pypipreq.split(\"<\")\n\n else:\n pypipreq = [pypipreq, None]\n\n packages_dict[pypipreq[0]] = pypipreq[-1]\n\n return packages_dict",
"title": ""
},
{
"docid": "d7831ac34d03855cfc3e92be097f15d3",
"score": "0.5720363",
"text": "def get_package(self, package_name):\n return package_key(package_name).get()",
"title": ""
},
{
"docid": "68b95c63d7165e95a4d6d3c084517677",
"score": "0.5700001",
"text": "def build_yname(pkgname, inst):\r\n rv = {}\r\n if isinstance(inst, yum.packages.PackageObject):\r\n for i in ['name', 'epoch', 'version', 'release', 'arch']:\r\n rv[i] = getattr(inst, i)\r\n else:\r\n rv['name'] = pkgname\r\n if inst.get('version') != 'any':\r\n rv['version'] = inst.get('version')\r\n if inst.get('epoch', False):\r\n rv['epoch'] = inst.get('epoch')\r\n if inst.get('release', False) and inst.get('release') != 'any':\r\n rv['release'] = inst.get('release')\r\n if inst.get('arch', False) and inst.get('arch') != 'any':\r\n rv['arch'] = inst.get('arch')\r\n return rv",
"title": ""
},
{
"docid": "157c33276d4765d7d39c308cb7c9ee04",
"score": "0.56484914",
"text": "def get_package_data(package):\n walk = [(dirpath.replace(package + os.sep, '', 1), filenames)\n for dirpath, dirnames, filenames in os.walk(package)\n if not os.path.exists(os.path.join(dirpath, '__init__.py'))]\n\n filepaths = []\n for base, filenames in walk:\n filepaths.extend([os.path.join(base, filename)\n for filename in filenames])\n return {package: filepaths}",
"title": ""
},
{
"docid": "157c33276d4765d7d39c308cb7c9ee04",
"score": "0.56484914",
"text": "def get_package_data(package):\n walk = [(dirpath.replace(package + os.sep, '', 1), filenames)\n for dirpath, dirnames, filenames in os.walk(package)\n if not os.path.exists(os.path.join(dirpath, '__init__.py'))]\n\n filepaths = []\n for base, filenames in walk:\n filepaths.extend([os.path.join(base, filename)\n for filename in filenames])\n return {package: filepaths}",
"title": ""
},
{
"docid": "0f4d50383a431e93556dfdcc3b319f8d",
"score": "0.5609704",
"text": "def get_package_names_with_component_types():\n return list(get_resources(COMPONENTS_RESOURCE_TYPE).keys())",
"title": ""
},
{
"docid": "816a9c5c551eed742d1bd71e61d2e6d8",
"score": "0.5595933",
"text": "def name(self):\n return self.recipe_name",
"title": ""
},
{
"docid": "5d6ed6f51b72e60f14339f19e8a7d9e4",
"score": "0.55345887",
"text": "def package_name(self):\n return self.key.parent().string_id()",
"title": ""
},
{
"docid": "5d6ed6f51b72e60f14339f19e8a7d9e4",
"score": "0.55345887",
"text": "def package_name(self):\n return self.key.parent().string_id()",
"title": ""
},
{
"docid": "9d8583919a417be60bce843e58af7cc2",
"score": "0.55316776",
"text": "def package_name(self):\n return self.payload.package_name",
"title": ""
},
{
"docid": "e2f3d8e894e08e6d0c696cbd97af57eb",
"score": "0.5526514",
"text": "def package_name(self):\n return self.key.parent().parent().string_id()",
"title": ""
},
{
"docid": "feedf41c1aa3b6cfe35ab9bac53c1bca",
"score": "0.5520685",
"text": "def package_name(self):\n return self.key.string_id()",
"title": ""
},
{
"docid": "dee85cd4f56944f277c3f51aabfe0e53",
"score": "0.5512602",
"text": "def get_package_info(pkg_name):\n global package_info\n if pkg_name in package_info:\n return package_info.get(pkg_name)\n else:\n try:\n yaml_stream = check_output(['apt-cache','show',pkg_name])\n except:\n print \"Unable to find info for package: '%s'\" % pkg_name\n package_info[pkg_name] = {}\n return {}\n d = Deb822(yaml_stream)\n package_info[pkg_name] = d\n return d",
"title": ""
},
{
"docid": "5b21ba27ee6eabb8f1bae37de7b90545",
"score": "0.5510991",
"text": "def get_packages_details(self):\n return self._package_color_dic",
"title": ""
},
{
"docid": "0399b5110992654af9d9ad412ec59196",
"score": "0.5505371",
"text": "def get_package_names_with_component_types(self):\n return list(get_resources(self.component_resource_type).keys())",
"title": ""
},
{
"docid": "c48095e764de03689a0ca651565e34df",
"score": "0.55027646",
"text": "def build_package_dict(files):\n settings = context.get_settings()\n package_dict = {}\n for f in files:\n # Ignore folder\n if not os.path.isfile(f): continue\n\n # Ignore \"-meta.xml\"\n if f.endswith(\"-meta.xml\"): continue\n\n # Get meta_type and code name\n base, name = os.path.split(f)\n name, extension = name.split(\".\")\n base, folder = os.path.split(base)\n meta_type = settings[folder][\"type\"]\n file_dict = {\n \"name\": name,\n \"dir\": f,\n \"folder\": folder,\n \"extension\": \".\"+extension\n }\n\n # Build dict\n if meta_type in package_dict:\n package_dict[meta_type].append(file_dict)\n else:\n package_dict[meta_type] = [file_dict]\n\n return package_dict",
"title": ""
},
{
"docid": "734347e562d74ac3e69d1955aa889ba0",
"score": "0.5464882",
"text": "def package_name(self) -> str:\n return pulumi.get(self, \"package_name\")",
"title": ""
},
{
"docid": "99feadacbe7c5f32633d90cabebf689e",
"score": "0.5458396",
"text": "def get_package_name(self):\n return __package__",
"title": ""
},
{
"docid": "99feadacbe7c5f32633d90cabebf689e",
"score": "0.5458396",
"text": "def get_package_name(self):\n return __package__",
"title": ""
},
{
"docid": "292039fb47d38463d884c0a0c036e623",
"score": "0.54456836",
"text": "def get_pkgs(rpmdir):\r\n pkgs = {}\r\n \"\"\"\r\npkgs structure:\r\n* pkgs is a dict of package name, rpmblob list pairs:\r\n pkgs = {name:[rpmblob,rpmblob...], name:[rpmblob,rpmblob...]}\r\n* rpmblob is a dict describing an rpm file:\r\n rpmblob = {'file':'foo-0.1-5.i386.rpm', 'name':'foo', 'version':'0.1', 'release':'5', 'subarch':'i386'},\r\n\r\nexample:\r\npkgs = {\r\n'foo' : [\r\n {'file':'foo-0.1-5.i386.rpm', 'name':'foo', 'version':'0.1', 'release':'5', 'subarch':'i386'},\r\n {'file':'foo-0.2-3.i386.rpm', 'name':'foo', 'version':'0.2', 'release':'3', 'subarch':'i386'}],\r\n'bar' : [\r\n {'file':'bar-3.2a-12.mips.rpm', 'name':'bar', 'version':'3.2a', 'release':'12', 'subarch':'mips'},\r\n {'file':'bar-3.7j-4.mips.rpm', 'name':'bar', 'version':'3.7j', 'release':'4', 'subarch':'mips'}]\r\n}\r\n\"\"\"\r\n rpms = [item for item in os.listdir(rpmdir) if item.endswith('.rpm')]\r\n for filename in rpms:\r\n (name, version, release, subarch) = parse_rpm_filename(rpmdir, filename)\r\n rpmblob = {'file': filename,\r\n 'name': name,\r\n 'version': version,\r\n 'release': release,\r\n 'subarch': subarch}\r\n if name in pkgs:\r\n pkgs[name].append(rpmblob)\r\n else:\r\n pkgs[name] = [rpmblob]\r\n return pkgs",
"title": ""
},
{
"docid": "12ddf0bdca8bc7a216d9787a55e2fc0c",
"score": "0.54414946",
"text": "def get_cookicutters():\n return {\n \"cookiecutters\": {\n \"audreyr/cookiecutter-pypackage\": []\n }\n }",
"title": ""
},
{
"docid": "2f44850f46b1dd0c7c3356300b0de0fe",
"score": "0.54048055",
"text": "def _makeRecipes(self):\n if self.helper.plan.dumpRecipes:\n recipeDir = self.helper.plan.recipeDir\n else:\n recipeDir = tempfile.mkdtemp(prefix='bob-')\n # Dump all the recipes out at once in case they have interdependencies\n finalRecipes = []\n for package in self.packages:\n recipe = package.getRecipe()\n finalRecipe = mangle(package, recipe)\n package.recipeFiles[package.getRecipeName()] = finalRecipe\n with open(os.path.join(recipeDir, package.getRecipeName()\n ), 'w') as fobj:\n fobj.write(finalRecipe)\n finalRecipes.append(finalRecipe)\n for package, finalRecipe in zip(self.packages, finalRecipes):\n recipeObj = _loadRecipe(self.helper, package,\n os.path.join(recipeDir, package.getRecipeName()))\n self.recipes.append((finalRecipe, recipeObj))\n if not self.helper.plan.dumpRecipes:\n shutil.rmtree(recipeDir)",
"title": ""
},
{
"docid": "57c03288335173592891f6c4074650f2",
"score": "0.5402503",
"text": "def get_metadata(module_path):\n matches = re.finditer(\n r\"^__(\\w+?)__ *= *'(.*?)'$\",\n read(module_path),\n re.MULTILINE)\n return dict(\n (match.group(1), match.group(2).decode('unicode_escape'))\n for match in matches)",
"title": ""
},
{
"docid": "a82335798247baa37d29b1e9a4c46b57",
"score": "0.53675145",
"text": "def listRecipes():\n recipeNames = list(map(lambda recipe: recipe['title'], recipes.getRecipeList()))\n return jsonify(recipeNames)",
"title": ""
},
{
"docid": "6f71d91a2f35e3a907801da46c2d67b3",
"score": "0.53643185",
"text": "def to_dict(self):\n return {\n \"name\": self.name,\n \"packages\": [package.to_dict() for package in self.packages],\n \"files\": [_file.to_dict() for _file in self.files],\n }",
"title": ""
},
{
"docid": "1451f5e3b3a9cf6e6abceff44a66da4f",
"score": "0.5327946",
"text": "def _package(self) -> dict:\n if self._cached_package:\n return self._cached_package\n\n module = importlib.import_module(self._python_package)\n assert hasattr(module, '__queenbee__'), \\\n 'Failed to find __queenbee__ info in __init__.py'\n self._cached_package = getattr(module, '__queenbee__')\n return self._cached_package",
"title": ""
},
{
"docid": "1cd2c1fcef0eeaf005ed615f62166298",
"score": "0.5299345",
"text": "def get_package_name():\n import os.path\n with open(\"PACKAGE_NAME\") as f:\n package_name = f.readline().strip()\n dir_name = package_name.replace(\"-\", \"_\") # reverse PyPI name normalization\n package_exists = os.path.exists(os.path.join(dir_name, \"__init__.py\"))\n assert package_exists, \"Cannot get package name automatically\" # package name should be in the current dir as well!\n return package_name, dir_name",
"title": ""
},
{
"docid": "5e1d839b5a09c6a231cc883b1140dbc9",
"score": "0.5270784",
"text": "def package_view(self):\n package_name = self.request.matchdict.get('package_name', None)\n package_id = self.request.matchdict.get('id', None)\n\n packages = Package.get_packages_by_name(package_name)\n requires = None\n other_versions = False\n\n if package_id:\n package = packages.filter(Package.id == package_id).first()\n if package and package.requires:\n requires = package.requires\n else:\n package = None\n\n if packages.count() > 1:\n other_versions = True\n\n return {'packages': packages.all(), 'package': package,\n 'package_name': package_name, 'main': self.main,\n 'other_versions': other_versions,\n 'requires': requires}",
"title": ""
},
{
"docid": "72a55a440b84ecc6cce00ca5db5ddd4e",
"score": "0.52563757",
"text": "def core_debs(snap):\n # type: (str) -> Dict[str, str]\n pkgs = {} # type: Dict[str, str]\n with tmpdir() as tmp:\n unsquashfs(tmp, snap, \"/usr/share/snappy/dpkg.list\")\n with open(os.path.join(tmp, \"usr/share/snappy/dpkg.list\")) as fp:\n for line in fp.readlines():\n line = line.strip()\n if not line.startswith(\"ii\"):\n continue\n l = re.split(r'\\s+',line)\n name = l[1]\n ver = l[2]\n pkgs[name] = ver\n return pkgs",
"title": ""
},
{
"docid": "aee4063280f5ca12a50d06a1c52bb4ad",
"score": "0.5250498",
"text": "def initconfig_package_entries(self):\n return []",
"title": ""
},
{
"docid": "7686bd7f83cc6cb452004b7ba6109145",
"score": "0.5250227",
"text": "def _get_package_dict(starting_path, exclude: typing.List[str] = None) -> typing.Dict:\n package_dict = {}\n exclude = exclude or [\"__pycache__\"]\n\n for dir_path, dir_names, _ in os.walk(starting_path):\n key_path = dir_path.replace(starting_path, \"\")\n sub_package_dict = package_dict\n for sub_package in key_path.split(\"/\"):\n if sub_package and sub_package not in exclude:\n sub_package_dict = sub_package_dict[sub_package]\n\n for dir_name in dir_names:\n if dir_name not in exclude:\n sub_package_dict[dir_name] = {}\n\n return package_dict",
"title": ""
},
{
"docid": "080cad83c5be33cbf87a2b5c4cd12810",
"score": "0.5243155",
"text": "def getPkgInfo(module_dir):\n\t# Specify which pkginfo get key / value pairs for from the PKG-INFO file\n\tkeys = ('Name', 'Version', 'Summary', 'Author')\n\tmodule_pkginfo = module_dir + '/' + module_dir.split('/')[-1] + '/PKG-INFO'\n\t# Extract the lines from the PKG-INFO into a list\n\tlines = [line.rstrip('\\n') for line in open(module_pkginfo)]\n\t# Get the specified key / value pairs from the list of lines in dictionary form\n\tpkginfo = {line.split(':')[0]: line.split(':')[1].strip(' ') for line in lines if line.split(':')[0] in keys}\n\treturn pkginfo",
"title": ""
},
{
"docid": "aa31d2e32d410dcf86b86fdcd0d42529",
"score": "0.52352154",
"text": "def get_mod(mod_name, root_dotpath=SERVICES_DOTPATH):\n out = {}\n ns = {}\n exec('from ' + root_dotpath + ' import ' + mod_name + ' as mod', ns)\n mod = ns['mod']\n\n for name in dir(mod):\n val = getattr(mod, name)\n out[name] = val\n return out",
"title": ""
},
{
"docid": "31d11eb7631c5737c6c5f17347ec3241",
"score": "0.52305025",
"text": "def get_metadata(self):\n return self.client._perform_json(\n \"GET\", \"/projects/%s/recipes/%s/metadata\" % (self.project_key, self.recipe_name))",
"title": ""
},
{
"docid": "b9c4b8194209793f4261177891e1d229",
"score": "0.5209526",
"text": "def info():\n if env.flags[\"pkg_mgmt\"] == \"pkg\":\n args = pkg.info()\n elif env.flags[\"pkg_mgmt\"] == \"pkgng\":\n args = pkgng.info()\n else:\n assert not \"Unknown pkg_mgmt\"\n\n pkg_info = subprocess.Popen(args, stdin=subprocess.PIPE,\n stdout=subprocess.PIPE, stderr=subprocess.STDOUT, close_fds=True)\n pkg_info.stdin.close()\n\n pkgdb = {}\n if pkg_info.wait() == 0:\n for pkg_port in pkg_info.stdout.readlines():\n pkgname, origin = pkg_port.split(':')\n origin = origin.strip()\n if origin in pkgdb:\n pkgdb[origin].add(pkgname)\n else:\n pkgdb[origin] = set((pkgname,))\n return pkgdb",
"title": ""
},
{
"docid": "50878952abea254755dd0d46c958ee51",
"score": "0.5198599",
"text": "def __call__(self):\n packages = Package.by_name()\n unused = [{'id': package.id,\n 'name': package.name,\n 'version': package.version.version} for package in\n packages if not package.buildouts and\n package.version.version != 'stdlib']\n return {'packages': packages,\n 'project': 'whiskers',\n 'unused': unused,\n 'main': self.main}",
"title": ""
},
{
"docid": "9dcdaf9657d99f09f5d9fa483dbe5268",
"score": "0.5188871",
"text": "def __get_package_name(self, path):\n\t\tpath = os.path.valid(path, 'package.json')\n\t\tif not os.path.exists(path):\n\t\t\treturn False\n\t\treturn json_decode(path, True)['name']",
"title": ""
},
{
"docid": "175b94bb5329f643d91dd4df4067f7ad",
"score": "0.5179145",
"text": "def find_package_data(modules):\n result = {}\n for module in modules:\n result.update({\n module: [\n '*.js',\n ]})\n return result",
"title": ""
},
{
"docid": "bca77fe81472d6828fdef66fc5c003ff",
"score": "0.5175167",
"text": "def pyre_resolveDependencies(self):\n # initialize the map\n dependencies = {}\n\n # do the easy thing, for now\n for category in self.requirements:\n # ask the external manager for a matching package\n package = self.pyre_host.packager.locate(category=category)\n # store the instance\n dependencies[category] = package\n\n # all done\n return dependencies",
"title": ""
},
{
"docid": "83f1129bca32224bb6ea585bdabd28f5",
"score": "0.51679367",
"text": "def package(self):\n return self._root.get(\"package\", \"\")",
"title": ""
},
{
"docid": "f8550bc1d08b6c615c8bf3572efb09c1",
"score": "0.5160732",
"text": "def recipe_words(recipe):\n return [ingredients[i] for i in recipe]",
"title": ""
},
{
"docid": "7410bca0fe54f2022cb4f220aa00c5a9",
"score": "0.515933",
"text": "def package_data(pkg, roots):\n data = []\n for root in roots:\n for dirname, _, files in os.walk(os.path.join(pkg, root)):\n for fname in files:\n data.append(os.path.relpath(os.path.join(dirname, fname), pkg))\n\n return {pkg: data}",
"title": ""
},
{
"docid": "824adf91c80d9e9d878ad35a36cb28b6",
"score": "0.51524985",
"text": "def metadata(argv):\n\tif (len(argv) < 4):\n\t\tprint >> sys.stderr, \"ERROR: insufficient parameters!\"\n\t\tsys.exit(2)\n\n\troot, pkgtype, pkgspec = argv[0:3]\n\tmetakeys = argv[3:]\n\ttype_map = {\n\t\t\"ebuild\":\"porttree\",\n\t\t\"binary\":\"bintree\",\n\t\t\"installed\":\"vartree\"}\n\tif pkgtype not in type_map:\n\t\tprint >> sys.stderr, \"Unrecognized package type: '%s'\" % pkgtype\n\t\tsys.exit(1)\n\ttrees = portage.db\n\tif os.path.realpath(root) == os.path.realpath(portage.settings[\"ROOT\"]):\n\t\troot = portage.settings[\"ROOT\"] # contains the normalized $ROOT\n\ttry:\n\t\t\tvalues = trees[root][type_map[pkgtype]].dbapi.aux_get(\n\t\t\t\tpkgspec, metakeys)\n\t\t\tfor value in values:\n\t\t\t\tprint value\n\texcept KeyError:\n\t\tprint >> sys.stderr, \"Package not found: '%s'\" % pkgspec\n\t\tsys.exit(1)",
"title": ""
},
{
"docid": "6aa02684e858e1e9bd5968eac74290f2",
"score": "0.5147736",
"text": "def _get_modules_names(package):\n\n return sorted(\n map(operator.itemgetter(1),\n pkgutil.walk_packages(package.__path__,\n '{0}.'.format(package.__name__))))",
"title": ""
},
{
"docid": "f89afa11d7fd804b137d070450d6b3b5",
"score": "0.51403534",
"text": "def _get_bundler_metadata(module):\n m = import_item(module)\n if not hasattr(m, '_jupyter_bundlerextension_paths'):\n raise KeyError('The Python module {} does not contain a valid bundlerextension'.format(module))\n bundlers = m._jupyter_bundlerextension_paths()\n return m, bundlers",
"title": ""
},
{
"docid": "19413b4d9776b896df515266b306b82a",
"score": "0.51393265",
"text": "def get_package_name(self):\n\n return self._get_version_metadata()['packageName']",
"title": ""
},
{
"docid": "da26cef4144d3689ae2670161e574ebb",
"score": "0.51320714",
"text": "def make_recipe(self):\n return '\\n'.join([v for k, v in self.__dict__.items()])",
"title": ""
},
{
"docid": "7fe847f68bfe99136e728041c3d409f5",
"score": "0.51237595",
"text": "def PACKAGE():\n # Module package name (Used in code so MUST equal name of parent package)\n package = 'SpirouDRS'\n return package",
"title": ""
},
{
"docid": "612fb209971b50f14442506d3c4ff154",
"score": "0.51195675",
"text": "def package_with_components_name_completer(prefix, parsed_args, **kwargs):\n return get_package_names_with_component_types()",
"title": ""
},
{
"docid": "f4d53fcbd6bb3814bb81da35d6629e9a",
"score": "0.5100698",
"text": "def get_recipe_by_name(cls, recipe_name):\n return cls.recipes_by_name[recipe_name]",
"title": ""
},
{
"docid": "ff764e3fec53f1b32a771387186b4352",
"score": "0.50963396",
"text": "def package_data(pkg, roots):\n data = []\n for root in roots:\n for dirname, _, files in os.walk(os.path.join(pkg, root)):\n for fname in files:\n data.append(os.path.relpath(os.path.join(dirname, fname), pkg))\n\n return {pkg: data}",
"title": ""
},
{
"docid": "1c5ab9e01b40689b105ae7915bb7af21",
"score": "0.50926936",
"text": "def get_package_component_types(self, package_name=None):\n if not has_resource(self.component_resource_type, package_name):\n return []\n component_registry, _ = get_resource(self.component_resource_type, package_name)\n return [line.split(';')[0] for line in component_registry.splitlines()]",
"title": ""
},
{
"docid": "8b26029689f1d776ccf31ea1dc603859",
"score": "0.50876343",
"text": "def get_dpkg_data (file_name, pkg_name):\n\n data = {'components': []}\n with gzip.open(file_name, 'rt') as sources:\n name_found = False\n files_found = False\n to_download = []\n for line in sources:\n if files_found:\n if line.startswith(' '):\n component = line.split()[2]\n data['components'].append(component)\n if component.endswith('.dsc'):\n data['dsc'] = component\n else:\n files_found = False\n if line.startswith('Package:'):\n if name_found:\n name_found = False\n break\n read_name = line.split()[1]\n if read_name == pkg_name:\n name_found = True\n elif name_found and line.startswith('Files:'):\n files_found = True\n elif name_found and line.startswith('Directory:'):\n data['directory'] = line.split()[1]\n return(data)",
"title": ""
},
{
"docid": "e1fbaedacfe427b74ab6eb175b387831",
"score": "0.5083799",
"text": "def _from_npm_registry(self, package_name=str):\n data_dict = None\n api_url = \"https://registry.npmjs.org/\" + str(package_name)\n try:\n response = requests.get(api_url)\n json_data = response.json()\n latest_version = json_data.get(\"dist-tags\", {}).get(\"latest\", None)\n if latest_version:\n latest_version_data = json_data.get(\"versions\", {}).get(latest_version, {})\n data_dict = {\n \"name\": json_data.get(\"name\", \"\"),\n \"description\": json_data.get(\"description\", \"\"),\n \"version\": latest_version,\n \"keywords\": latest_version_data.get(\"keywords\", []),\n \"dependencies\":\n list(latest_version_data.get(\"dependencies\", {}).keys()),\n \"homepage\": json_data.get(\"homepage\", \"\"),\n \"repositoryurl\": json_data.get(\"repository\", {}).get(\"url\", \"\"),\n \"updated_timestamp\": int(datetime.datetime.now().timestamp()),\n }\n # Other fields that were present in past, but not used for training model are\n # below. Removing this fields saves lot of space while storing pacakge data in\n # S3.\n # \"devDependencies\":\n # list(latest_version_data.get(\"devDependencies\", {}).keys()),\n # \"peerDependencies\":\n # list(latest_version_data.get(\"peerDependencies\", {}).keys()),\n # \"readme\": json_data.get(\"readme\", \"\"),\n\n self._track_stats('fetched_from_npm', 1)\n except Exception as e:\n self._track_stats('npm_fetch_errors', 1)\n logger.error(\"Can't fetch the keywords for %s from NPM Registry, it throws %s\",\n package_name, e)\n\n return data_dict",
"title": ""
},
{
"docid": "fc282934fd25438eff1f34f696f636b8",
"score": "0.50715363",
"text": "def get_package_component_types(*, package_name=None):\n if not has_resource(COMPONENTS_RESOURCE_TYPE, package_name):\n return []\n component_registry, _ = get_resource(COMPONENTS_RESOURCE_TYPE, package_name)\n return [line.split(';')[0] for line in component_registry.splitlines()]",
"title": ""
},
{
"docid": "edcdbb292b0e5dafccca9609512302b2",
"score": "0.5066961",
"text": "def render_recipe(recipe: str, ingredients: dict) -> str:\n ingredients_used = []\n file_lines = recipe.splitlines()\n\n # Scan the file to used ingredients\n for line in file_lines:\n match = INGREDIENT_FILL.match(line)\n if match:\n ingredients_used.append(ingredients[match.group(1)])\n\n simple_imports_used = set()\n for ingredient in ingredients_used:\n for simple_import in ingredient.simple_imports:\n simple_imports_used.add(simple_import)\n\n from_imports_used = defaultdict(set)\n for ingredient in ingredients_used:\n for import_from in ingredient.imports_from:\n from_imports_used[import_from[0]].add(import_from[1])\n\n import_lines = set()\n for simple_import in simple_imports_used:\n if simple_import.asname:\n import_lines.add(f\"import {simple_import.name} as {simple_import.asname}\")\n else:\n import_lines.add(f\"import {simple_import.name}\")\n\n for module, from_imports in from_imports_used.items():\n names = set()\n for from_import in from_imports:\n if from_import.asname:\n name = f\"{from_import.name} as {from_import.asname}\"\n else:\n name = from_import.name\n names.add(name)\n names = \", \".join(names)\n import_lines.add(f\"from {module} import {names}\")\n\n import_lines = isort.code(\n \"\\n\".join(import_lines), config=isort.Config(profile=\"google\")\n )\n\n output_file = []\n header_added = False\n for line in file_lines:\n if IMPORTS_FILL.search(line):\n output_file.append(import_lines)\n elif INGREDIENT_FILL.search(line):\n match = INGREDIENT_FILL.search(line)\n output_file.append(ingredients[match.group(1)].text)\n elif REGION_START.search(line):\n # The string has to be broken up, so that the snippet\n # machine doesn't recognize it as a valid start of a region\n output_file.append(REGION_START.sub(\"# [\" + \"START \\\\1]\", line))\n elif REGION_END.search(line):\n # The string has to be broken up, so that the snippet\n # machine doesn't recognize it as a valid start of a region\n output_file.append(REGION_END.sub(\"# [\" + \"END \\\\1]\", line))\n else:\n output_file.append(line)\n continue\n if not header_added:\n end = output_file[-1]\n output_file[-1] = \"\"\n output_file.append(HEADER)\n output_file.append(\"\")\n output_file.append(end)\n header_added = True\n\n if output_file and not output_file[-1].endswith(\"\\n\"):\n output_file.append(\"\")\n\n return os.linesep.join(output_file)",
"title": ""
},
{
"docid": "926c3340ab7008d1a15bef9c502fcf18",
"score": "0.50530154",
"text": "def kernel_deb_package():\n import apt\n\n boot_image = kernel_cmdline().get('BOOT_IMAGE')\n if not boot_image:\n return\n\n class FileFilter(apt.cache.Filter):\n def apply(self, pkg):\n return pkg.is_installed and boot_image in pkg.installed_files\n\n cache = apt.cache.FilteredCache(apt.Cache())\n cache.set_filter(FileFilter())\n kernel_deb = list(cache)\n if kernel_deb:\n kernel_package = kernel_deb[0].installed\n return {\n 'name': kernel_package.package.name,\n 'version': kernel_package.version,\n 'source_name': kernel_package.source_name,\n 'source_version': kernel_package.source_version,\n 'arch': kernel_package.architecture,\n }",
"title": ""
},
{
"docid": "fe652940b1ecdf3cacfcaf7fd11e326c",
"score": "0.5049365",
"text": "def get_package_locations():\n p = subprocess.Popen(['rosdep', 'db'], stdout=subprocess.PIPE)\n package_lines = p.stdout.read().splitlines()\n package_map = map((lambda x: x.split(' -> ')), package_lines)\n return package_map",
"title": ""
},
{
"docid": "6d0c913ec53fc77893bd107ad1b120ca",
"score": "0.50439864",
"text": "def get_label(self):\n return _(\"Package:\")",
"title": ""
},
{
"docid": "6729f66852487c1abaedb8c098a046fe",
"score": "0.50376594",
"text": "def recipes(self, args):\n ctx = self.ctx\n if args.compact:\n print(\" \".join(set(Recipe.list_recipes(ctx))))\n else:\n for name in sorted(Recipe.list_recipes(ctx)):\n try:\n recipe = Recipe.get_recipe(name, ctx)\n except (IOError, ValueError):\n warning('Recipe \"{}\" could not be loaded'.format(name))\n except SyntaxError:\n import traceback\n traceback.print_exc()\n warning(('Recipe \"{}\" could not be loaded due to a '\n 'syntax error').format(name))\n version = str(recipe.version)\n print('{Fore.BLUE}{Style.BRIGHT}{recipe.name:<12} '\n '{Style.RESET_ALL}{Fore.LIGHTBLUE_EX}'\n '{version:<8}{Style.RESET_ALL}'.format(\n recipe=recipe, Fore=Out_Fore, Style=Out_Style,\n version=version))\n print(' {Fore.GREEN}depends: {recipe.depends}'\n '{Fore.RESET}'.format(recipe=recipe, Fore=Out_Fore))\n if recipe.conflicts:\n print(' {Fore.RED}conflicts: {recipe.conflicts}'\n '{Fore.RESET}'\n .format(recipe=recipe, Fore=Out_Fore))\n if recipe.opt_depends:\n print(' {Fore.YELLOW}optional depends: '\n '{recipe.opt_depends}{Fore.RESET}'\n .format(recipe=recipe, Fore=Out_Fore))",
"title": ""
},
{
"docid": "8d92c2c6cc3247fe470ef3b45bed67d7",
"score": "0.5035626",
"text": "def package_name(self) -> str:\n return self.command",
"title": ""
},
{
"docid": "a8f53e79e3b32844bf4814535dc42c3b",
"score": "0.5030516",
"text": "async def get_version_map(self, recipe: Recipe):\n\n sources = recipe.meta.get(\"source\")\n if not sources:\n raise self.Metapackage(recipe)\n\n if isinstance(sources, Sequence):\n source_iter = iter(sources)\n versions = await self.get_versions(recipe, next(source_iter), 0)\n for num, source in enumerate(source_iter):\n add_versions = await self.get_versions(recipe, source, num+1)\n for vers, files in add_versions.items():\n for fname, data in files.items():\n versions[vers][fname] = data\n else:\n versions = await self.get_versions(recipe, sources, 0)\n\n if not versions:\n raise self.NoReleases(recipe)\n return versions",
"title": ""
},
{
"docid": "fb5d17c57948018f3af0c439e03ad039",
"score": "0.50230455",
"text": "def get_name() -> str:\n package_name = os.path.basename(PACKAGE_DIR)\n return package_name",
"title": ""
},
{
"docid": "8e02548a29a7f3a06f9650d889064c5c",
"score": "0.5022055",
"text": "def extract_pkgs(req_file=None, pkg_reqs=None):\n if req_file is not None:\n with open(req_file, \"r\") as req_fh:\n pkg_reqs = req_fh.readlines()\n if not pkg_reqs:\n return {}\n pkg_dict = {}\n for line in pkg_reqs:\n req_match = re.match(PKG_VER_PATTERN, line)\n if not req_match:\n print(f\"Failed on {line}\")\n pkg_dict[req_match.groups()[0]] = (req_match.groups()[1], req_match.groups()[2])\n return pkg_dict",
"title": ""
},
{
"docid": "d59ad996d4024733762194fd056b9c6a",
"score": "0.50191",
"text": "def get_names_of_packages(packages_info, without_rpmem):\n packages = []\n types = ['-', '-debug-', '-devel-', '-debuginfo-', '-debug-debuginfo-']\n for elem in packages_info:\n # checks if rpmem and rpmemd packages should be built\n # skips creating names of packages for rpmemd and librpmem\n if without_rpmem:\n if elem in ['rpmemd', 'librpmem']:\n continue\n sets_of_information = zip(packages_info[elem], types)\n for kit in sets_of_information:\n if kit[0]:\n package_name = elem + kit[1] + PMDK_VERSION + '.' +\\\n SYSTEM_ARCHITECTURE + '.rpm'\n packages.append(package_name)\n return packages",
"title": ""
},
{
"docid": "92699d8f90107170206f9d62e04ea621",
"score": "0.5016364",
"text": "def make_module_req_guess(self):\n return {\n 'PATH': [os.path.join('SASFoundation', self.version)],\n }",
"title": ""
},
{
"docid": "34cfeafa39fc1c9215edb42a3fb0c3f2",
"score": "0.5016319",
"text": "def package_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"package_name\")",
"title": ""
},
{
"docid": "7a8cdf4fff5b3408a5f6c89330e8e050",
"score": "0.50085044",
"text": "def get_package_name():\n\n # getting git repo top level\n project_root = get_generated_project_top_level()\n get_name_cmd = \"cd %s \" \\\n \" && cat setup.py | grep 'setup(name=\\\"'\" \\\n % project_root\n\n name = os.popen(get_name_cmd).read().strip(\"setup(name=\")\n name = name.strip().strip(',').strip('\"')\n\n if name == \"\":\n print(Fore.RED + \"Error getting package name: %s (%s) 😢\"\n % (name, get_name_cmd)\n + Style.RESET_ALL)\n\n exit(1)\n\n return name",
"title": ""
},
{
"docid": "ab97848d81fc5fe721bdc3c120905bd7",
"score": "0.50068325",
"text": "def make_module_req_guess(self):\n return {\n 'PATH':['bin', 'bin/linux64', 'bin64'],\n 'LD_LIBRARY_PATH':['lib', 'lib/linux64', 'lib64'],\n }",
"title": ""
},
{
"docid": "cb6a9c9b091c9c48dda8263ad5eedd17",
"score": "0.5005103",
"text": "def package_name(self) -> str:\n if self._package_name is not None:\n return self._package_name\n else:\n return self.name",
"title": ""
},
{
"docid": "918a7975c3f8c4be754cdf4b1c66b569",
"score": "0.5001492",
"text": "def GetAllModsByNamespace () -> typing.Dict[str, Mod]:\n\n\treturn dict(_allMods)",
"title": ""
},
{
"docid": "38c1bdc8748967e2f692824c4eee98b1",
"score": "0.49969646",
"text": "def find_package(self):\n\n for i in self.channel:\n key = ums.defaults.REDIS_PREFIX + i.replace('/', '_').upper()\n key += ums.defaults.PACKAGES_INFIX + self.package.upper()\n\n data = self.redis.exists(key)\n if data:\n return key\n\n return ''",
"title": ""
},
{
"docid": "e403bae37f2b6b34cf0d26f12a468ffc",
"score": "0.49909917",
"text": "def names(self, package, release, arch):\n c = self.udd.psql.cursor(cursor_factory=psycopg2.extras.DictCursor)\n\n packagesql = package.replace(\"*\", \"%\")\n packagesql = packagesql.replace(\"?\", \"_\")\n\n if package.startswith('src:'):\n packagesql = packagesql[4:]\n sql = r\"\"\"SELECT DISTINCT version, source AS package, component\n FROM sources\n WHERE source LIKE %(package)s AND\n release=%(release)s\n ORDER BY source\"\"\"\n else:\n searchsource = False\n sql = r\"\"\"SELECT DISTINCT version, package, component\n FROM packages\n WHERE package LIKE %(package)s AND\n (architecture=%(arch)s OR architecture='all') AND\n release=%(release)s\n ORDER BY package\"\"\"\n\n c.execute(sql,\n dict(package=packagesql,\n arch=arch,\n release=release))\n return c.fetchall()",
"title": ""
},
{
"docid": "924d4f50fa7ffdf806117b6dd852999e",
"score": "0.49809697",
"text": "def manifest_json(self) -> Iterable[Dict[str, Union[str, bool]]]:\n\n for tag in self.tags:\n tag_suffixes = \" \".join([f\"-{arch}\" for arch in self.archs])\n archs = \" \".join(self.archs)\n yield {\n \"benchmark\": self.benchmark,\n \"image_name\": self.image_name,\n \"dockerfile\": self.dockerfile,\n \"tag\": tag,\n \"tag_suffixes\": tag_suffixes,\n \"changed\": self.changed,\n \"archs\": archs,\n }",
"title": ""
},
{
"docid": "bbd6e0e0a8b4c7cca51bc79d9bff12f9",
"score": "0.49788994",
"text": "def _get_package_name(module):\n try:\n # if __package__ is defined, use it\n package_name = module.__package__\n except AttributeError:\n package_name = None \n \n if package_name is None:\n # if __path__ is defined, the package name is the module name\n package_name = module.__name__\n if not hasattr(module, '__path__'):\n # if __path__ is not defined, the package name is the\n # string before the last \".\" of the fully-qualified module name\n package_name = package_name.rpartition('.')[0]\n \n return package_name",
"title": ""
},
{
"docid": "c306c2849f40439534753a6351a8fe4f",
"score": "0.49772495",
"text": "def _fetch(self, package_name=str):\n package_metadata = self._from_npm_registry(package_name)\n\n # If key words are not found in repository, get it from github.\n if package_metadata and len(package_metadata.get(\"keywords\", [])) == 0 and \\\n len(package_metadata.get(\"repositoryurl\", \"\")) > 0:\n package_metadata[\"keywords\"] = self._from_github(package_metadata[\"repositoryurl\"])\n\n return package_metadata",
"title": ""
},
{
"docid": "1ead16793f18729ffea3b4ec6b41f1d6",
"score": "0.4973024",
"text": "def rpmpackagelist(rts):\r\n return [{'name':header[rpm.RPMTAG_NAME],\r\n 'epoch':header[rpm.RPMTAG_EPOCH],\r\n 'version':header[rpm.RPMTAG_VERSION],\r\n 'release':header[rpm.RPMTAG_RELEASE],\r\n 'arch':header[rpm.RPMTAG_ARCH],\r\n 'gpgkeyid':header.sprintf(\"%|SIGGPG?{%{SIGGPG:pgpsig}}:{None}|\").split()[-1]}\r\n for header in rts.dbMatch()]",
"title": ""
},
{
"docid": "1e2c79f921942e9c77045bedef0ee4b6",
"score": "0.49705163",
"text": "def all_pkgs_by_name(self, name):\n return fnmatch.filter(self.all_pkgs().keys(), name)",
"title": ""
},
{
"docid": "2043547f24b2e777e4480ac01e4978d9",
"score": "0.49657035",
"text": "def get_available_recipes():\n return True, runtime.get_available_recipes()",
"title": ""
},
{
"docid": "e5827156d6a50a864578cf60d46aef71",
"score": "0.49615538",
"text": "def get_provides(self, metadata, package):\r\n for arch in self.get_arches(metadata):\r\n if package in self.provides[arch]:\r\n return self.provides[arch][package]\r\n return []",
"title": ""
},
{
"docid": "cea4692595a714a8e04e0185fc6bb071",
"score": "0.4954542",
"text": "def findDeps(self, pkgs):\n results = {}\n\n for pkg in pkgs:\n results[pkg] = {} \n reqs = pkg.requires\n reqs.sort()\n pkgresults = results[pkg] # shorthand so we don't have to do the\n # double bracket thing\n \n for req in reqs:\n (r,f,v) = req\n if r.startswith('rpmlib('):\n continue\n \n satisfiers = []\n\n for po in self.whatProvides(r, f, v):\n satisfiers.append(po)\n\n pkgresults[req] = satisfiers\n \n return results",
"title": ""
},
{
"docid": "96c0a91caf6637a3305b2df34e4b30d1",
"score": "0.49393058",
"text": "def discovery_package(package, attribute_filter):\n package_path = package.__path__[0]\n modules = glob(\"{}/*.so\".format(package_path)) + glob(\"{}/*.py\".format(package_path))\n\n contents = []\n for module in modules:\n module_name = os.path.splitext(os.path.basename(module))[0]\n full_module_name = package.__name__ + \".\" + os.path.splitext(os.path.basename(module))[0]\n try:\n m = import_module(full_module_name)\n\n if attribute_filter(module_name):\n contents.append(m)\n except:\n # todo log this error.\n pass\n\n return contents",
"title": ""
},
{
"docid": "78c33302135e07445cdece672b8708ac",
"score": "0.49387923",
"text": "def test_get_idname_from_metarecipe():\n\n accession_id = \"GSE123\"\n meta_recipe = \"meta-recipe-geo-accession-geo-v1\"\n ggd_jdict = {u'channeldata_version': 1, u'subdirs': [u'noarch'], u'packages': {u'meta-recipe-geo-accession-geo-v1': {u'activate.d': \n False, u'version': u'1', u'tags': {u'cached': [], u'ggd-channel': u'genomics', u'data-version': \n u'', u'data-provider': u'GEO'}, u'post_link': True, u'binary_prefix': False, u'run_exports': {}, u'pre_unlink': \n False, u'subdirs': [u'noarch'], u'deactivate.d': False, u'reference_package': \n u'noarch/meta-recipe-geo-accession-geo-v1-1-0.tar.bz2', u'pre_link': False, u'keywords': [u'GEO', u'Gene Expression Omnibus'], \n u'summary': u'GEO Meta-Recipe', u'text_prefix': False, u'identifiers': {u'genome-build': \n u'meta-recipe', u'species': u'meta-recipe'}}}}\n\n new_name = install.get_idname_from_metarecipe(accession_id, meta_recipe, ggd_jdict)\n\n ## This method does not change case\n assert new_name != \"gse123-geo-v1\"\n assert new_name == \"GSE123-geo-v1\"\n\n\n accession_id = \"gds456\"\n meta_recipe = \"meta-recipe-geo-accession-geo-v1\"\n ggd_jdict = {u'channeldata_version': 1, u'subdirs': [u'noarch'], u'packages': {u'meta-recipe-geo-accession-geo-v1': {u'activate.d': \n False, u'version': u'1', u'tags': {u'cached': [], u'ggd-channel': u'genomics', u'data-version': \n u'', u'data-provider': u'GEO'}, u'post_link': True, u'binary_prefix': False, u'run_exports': {}, u'pre_unlink': \n False, u'subdirs': [u'noarch'], u'deactivate.d': False, u'reference_package': \n u'noarch/meta-recipe-geo-accession-geo-v1-1-0.tar.bz2', u'pre_link': False, u'keywords': [u'GEO', u'Gene Expression Omnibus'], \n u'summary': u'GEO Meta-Recipe', u'text_prefix': False, u'identifiers': {u'genome-build': \n u'meta-recipe', u'species': u'meta-recipe'}}}}\n\n new_name = install.get_idname_from_metarecipe(accession_id, meta_recipe, ggd_jdict)\n\n ## This method does not change case\n assert new_name == \"gds456-geo-v1\"\n\n\n accession_id = \"GsM99890\"\n meta_recipe = \"meta-recipe-geo-accession-geo-v1\"\n ggd_jdict = {u'channeldata_version': 1, u'subdirs': [u'noarch'], u'packages': {u'meta-recipe-geo-accession-geo-v1': {u'activate.d': \n False, u'version': u'1', u'tags': {u'cached': [], u'ggd-channel': u'genomics', u'data-version': \n u'', u'data-provider': u'GEO'}, u'post_link': True, u'binary_prefix': False, u'run_exports': {}, u'pre_unlink': \n False, u'subdirs': [u'noarch'], u'deactivate.d': False, u'reference_package': \n u'noarch/meta-recipe-geo-accession-geo-v1-1-0.tar.bz2', u'pre_link': False, u'keywords': [u'GEO', u'Gene Expression Omnibus'], \n u'summary': u'GEO Meta-Recipe', u'text_prefix': False, u'identifiers': {u'genome-build': \n u'meta-recipe', u'species': u'meta-recipe'}}}}\n\n new_name = install.get_idname_from_metarecipe(accession_id, meta_recipe, ggd_jdict)\n\n ## This method does not change case\n assert new_name == \"GsM99890-geo-v1\"\n\n\n accession_id = \"GsM99890\"\n meta_recipe = \"meta-recipe-geo-accession-geo-v1\"\n ggd_jdict = {u'channeldata_version': 1, u'subdirs': [u'noarch'], u'packages': {u'meta-recipe-geo-accession-geo-v1': {u'activate.d': \n False, u'version': u'1', u'tags': {u'cached': [], u'ggd-channel': u'genomics', u'data-version': \n u'', u'data-provider': u'THE-DATA-PROVIDER'}, u'post_link': True, u'binary_prefix': False, u'run_exports': {}, u'pre_unlink': \n False, u'subdirs': [u'noarch'], u'deactivate.d': False, u'reference_package': \n u'noarch/meta-recipe-geo-accession-geo-v1-1-0.tar.bz2', u'pre_link': False, u'keywords': [u'GEO', u'Gene Expression Omnibus'], \n u'summary': u'GEO Meta-Recipe', u'text_prefix': False, u'identifiers': {u'genome-build': \n u'meta-recipe', u'species': u'meta-recipe'}}}}\n\n new_name = install.get_idname_from_metarecipe(accession_id, meta_recipe, ggd_jdict)\n\n ## Test that the data provider is changed to lower case \n assert new_name == \"GsM99890-the-data-provider-v1\"\n\n\n accession_id = \"GsM99890\"\n meta_recipe = \"meta-recipe-geo-accession-geo-v1\"\n ggd_jdict = {u'channeldata_version': 1, u'subdirs': [u'noarch'], u'packages': {u'meta-recipe-geo-accession-geo-v1': {u'activate.d': \n False, u'version': u'THE-VERSION', u'tags': {u'cached': [], u'ggd-channel': u'genomics', u'data-version': \n u'', u'data-provider': u'geo'}, u'post_link': True, u'binary_prefix': False, u'run_exports': {}, u'pre_unlink': \n False, u'subdirs': [u'noarch'], u'deactivate.d': False, u'reference_package': \n u'noarch/meta-recipe-geo-accession-geo-v1-1-0.tar.bz2', u'pre_link': False, u'keywords': [u'GEO', u'Gene Expression Omnibus'], \n u'summary': u'GEO Meta-Recipe', u'text_prefix': False, u'identifiers': {u'genome-build': \n u'meta-recipe', u'species': u'meta-recipe'}}}}\n\n new_name = install.get_idname_from_metarecipe(accession_id, meta_recipe, ggd_jdict)\n\n ## Test that the version is properly used\n assert new_name == \"GsM99890-geo-vTHE-VERSION\"",
"title": ""
},
{
"docid": "22bf2cc12cddb6ac19b2e1cf2d36332d",
"score": "0.49384657",
"text": "def getinfo(self, packname: str, complete: bool=False) -> dict:\n\t\tinfo = None\n\n\t\tif packname not in self.__root['packs']:\n\t\t\tinfo = {\n\t\t\t\t\"NOT INSTALLED\": \"PACKAGE NOT INSTALLED\"\n\t\t\t}\n\t\t\tinfo['available-versions'] = dmutils.getversions(packname)\n\t\telif complete:\n\t\t\tinfo = dmutils.getpackinfo(packname)\n\t\t\tinfo['head'] = self.__root['packs'][packname]['head']\n\t\t\tinfo['dev'] = self.__root['packs'][packname]['dev']\n\t\t\tinfo['available-versions'] = dmutils.getversions(packname)\n\t\telse:\n\t\t\tinfo = self.__root['packs'][packname].copy()\n\n\t\treturn info",
"title": ""
},
{
"docid": "e5a094256b77cc079dadf4eb54dabf28",
"score": "0.49359626",
"text": "def config_dict(name: str) -> Dict[str, Any]:\n try:\n content = resource_string(PACKAGE, DATADIR.format(name)).decode()\n except DistributionNotFound as error:\n LOGGER.warning(\"Cannot load %s from packages: %s\", name, error)\n content = DATA_FALLBACK.joinpath(name).read_text()\n\n return cast(Dict[str, Any], json.loads(content))",
"title": ""
},
{
"docid": "60b5ae14198c2a88d6061d6ce03e0b40",
"score": "0.4924606",
"text": "def namespace_modules(ns):\n return {name: f'{ns.__name__}.{name}'\n for _, name, _ in pkgutil.iter_modules(ns.__path__)}",
"title": ""
},
{
"docid": "413ec711526281355a0dbc829ec90aaf",
"score": "0.49229595",
"text": "def info(self):\n res = dict(\n timestamp=0,\n prefixes=[\n dict(idx=\"Z\", qp=None, type=None,\n desc=\"Stemmed forms of keywords\",\n ldesc=\"This contains the stemmed forms of keywords as generated by\"\n \" TermGenerator and matched by QueryParser\"),\n ],\n )\n if not HAS_APT: return res\n if not hasattr(apt_pkg, \"config\"): return res\n fname = apt_pkg.config.find_file(\"Dir::Cache::pkgcache\")\n if not os.path.exists(fname): return res\n res[\"sources\"] = [dict(path=fname, desc=\"APT index\")]\n res[\"timestamp\"] = os.path.getmtime(fname)\n return res",
"title": ""
},
{
"docid": "81e3105f8b365cf1eae2fd5691af4740",
"score": "0.49222255",
"text": "def get_package_names(root_map):\n # It has to be ensured that if more roots are loaded, packages under a\n # subsequent root will neither override, nor extend with subpackage the\n # trees found in earlier roots.\n #\n # This dict will save all the packages that have been found during the\n # search.\n package_tree = _PackageTree()\n\n for root, root_path in root_map.items():\n packages_in_current_root = _PackageTree()\n\n for dirpath, _, files in os.walk(root_path):\n for match in fnmatch.filter(files, 'package.yaml'):\n logical_package_name = Package.data_file_to_package_name(\n root_path, os.path.join(dirpath, match))\n if package_tree.has_any_non_namespace_parents(\n logical_package_name) or \\\n package_tree.is_registered(logical_package_name):\n # If the to-be-registered package or a parent name has\n # already been shadowed by a package from a previous\n # root, do not register it.\n continue\n packages_in_current_root.register_package(logical_package_name)\n yield logical_package_name\n\n for package in packages_in_current_root.packages:\n package_tree.register_package(package)",
"title": ""
},
{
"docid": "c996e3e1f8bb534f0d85f51b126fa781",
"score": "0.49221414",
"text": "def get_package_info(package_name):\n log_helper = logging_helper.logging_helper.Logger()\n log_helper.logger.debug(\"Getting additional package info for %s\" % package_name)\n command = \"smart info \" + package_name\n output = shell_ops.run_command(command)\n description = ''\n version = ''\n if output.count('Name:') > 1:\n # Multiple versions available. Narrow down smart info scope to get accurate info for the current version\n response = shell_ops.run_command(\"smart query --installed \" + package_name + \" --show-format=$version\")\n version = response[response.index('[100%]') + 6:response.index('@')].replace('\\n', '')\n if 'not' in version: # Workaround for \"(not installed)\" case\n version = 'Unknown'\n\n output = output[output.rindex(version):]\n\n if 'Name' in output:\n if output.index('Name') > output.index('Description'):\n # Additional entry after description\n description = output[output.rindex(\"Description:\") + 14: output.index(\"Name\")].replace('\\n', '').strip()\n else:\n description = output[output.rindex(\"Description:\") + 14:].replace('\\n', '').strip()\n else:\n version = output[output.index(\"Version:\") + 9: output.index(\"Priority:\")].replace('\\n', '')\n version = version[:version.index('@')]\n if 'not' in version: # Workaround for \"(not installed)\" case\n version = 'Unknown'\n description = output[output.rindex(\"Description:\") + 14:].replace('\\n', '').strip()\n\n url = output[output.index(\"Reference URLs:\") + 16: output.index(\"Flags:\")].replace('\\n', '')\n my_license = output[output.index(\"License:\") + 9: output.index(\"Installed Size:\")].replace('\\n', '')\n size = output[output.index(\"Installed Size:\") + 16: output.index(\"Reference URLs:\")].replace('\\n', '')\n group = output[output.index(\"Group:\") + 7: output.index(\"License:\")].replace('\\n', '')\n summary = output[output.index(\"Summary:\") + 9: output.index(\"Description:\")].replace('\\r\\n', '')\n\n # escape special JSON charater (\") if any in description and summary\n summary = summary.replace('\"', '\\\\\"')\n description = description.replace('\"', '\\\\\"')\n\n package = {\n 'url': url,\n 'license': my_license,\n 'size': size,\n 'description': description,\n 'summary': summary,\n 'group': group,\n 'version': version\n }\n log_helper.logger.debug(\"Returning package info: \" + str(package))\n return json.dumps(package)",
"title": ""
},
{
"docid": "bb17184e847674f8664e769fc345e810",
"score": "0.49126816",
"text": "def get_recipe_by_name(self, name):\n pass",
"title": ""
},
{
"docid": "5c0b11ea0c82093e7c60e0cd174610be",
"score": "0.49113357",
"text": "def names(self):\n return [\n {\"name\": m.Plugin.display_name, \"module\": k}\n for k, m in self.plugin_modules.items()\n ]",
"title": ""
},
{
"docid": "6ae836ee3c4b15142c4044cf329f197f",
"score": "0.49047828",
"text": "def get_node_metadata(self):\n return {\n class_json_consts.PACKAGE: self.package,\n class_json_consts.CLASS: self.class_name,\n class_json_consts.BUILD_TARGETS: sorted(self.build_targets),\n class_json_consts.NESTED_CLASSES: sorted(self.nested_classes),\n }",
"title": ""
},
{
"docid": "67ffede57706dbdec3d947ef6e4dd535",
"score": "0.4903101",
"text": "def list_pkg_info(pkg_names, pkgs_dict, env_vars, conda_list, prefix, prefix_set=False):\n\n ## Create a 2d list for string formatting\n formatted_list = [\n [\" Name\", \"Pkg-Version\", \"Pkg-Build\", \"Channel\", \"Environment-Variables\"]\n ]\n\n missing_in_conda = False\n missing_message = \" [WARNING: Present in GGD but missing from Conda]\"\n ## Iterate over each package in pkg_names\n for pkg in pkg_names:\n\n version = pkgs_dict[pkg][\"version\"]\n\n ## If package is present in both ggd metadata and conda metadata\n if pkg in conda_list:\n assert version == conda_list[pkg][\"version\"]\n build = conda_list[pkg][\"build\"]\n channel = \"ggd-\" + pkgs_dict[pkg][\"tags\"][\"ggd-channel\"]\n assert channel == conda_list[pkg][\"channel\"]\n\n ## If package is missing from conda metadata\n else:\n missing_in_conda = True\n build = missing_message\n channel = \"\"\n\n ## Get env_vars\n env_variables = []\n if (\n \"ggd_\" + pkg.replace(\"-\", \"_\").replace(\".\", \"_\") + \"_dir\"\n ) in env_vars.keys():\n env_variables.append(\n \" $ggd_\" + pkg.replace(\"-\", \"_\").replace(\".\", \"_\") + \"_dir\"\n )\n if (\n \"ggd_\" + pkg.replace(\"-\", \"_\").replace(\".\", \"_\") + \"_file\"\n ) in env_vars.keys():\n env_variables.append(\n \" $ggd_\" + pkg.replace(\"-\", \"_\").replace(\".\", \"_\") + \"_file\"\n )\n\n formatted_list.append([pkg, version, build, channel, \",\".join(env_variables)])\n\n ## Print data pkg list\n print(\"\\n\\n# Packages in environment: {p}\\n#\".format(p=prefix))\n\n dash = \"-\" * 120\n for i in range(len(formatted_list)):\n if i == 0:\n print(dash)\n print(\n \"{:<40s}{:>5s}{:>10s}{:>10s}{:>30s}\".format(\n formatted_list[i][0],\n formatted_list[i][1],\n formatted_list[i][2],\n formatted_list[i][3],\n formatted_list[i][4],\n )\n )\n print(dash)\n else:\n print(\n \"-> {:<40s}{:>5s}{:>10s}{:>15s}{:^60s}\\n\".format(\n formatted_list[i][0],\n formatted_list[i][1],\n formatted_list[i][2],\n formatted_list[i][3],\n formatted_list[i][4],\n )\n )\n\n ## Print environment variables info\n if prefix_set:\n print(\n \"# The environment variables are only available when you are using the '{p}' conda environment.\".format(\n p=prefix\n )\n )\n else:\n print(\"# To use the environment variables run `source activate base`\")\n print(\n \"# You can see the available ggd data package environment variables by running `ggd show-env`\\n\"\n )\n\n ## Print message if a package is missing from conda metadata\n if missing_in_conda:\n print(\n (\n \"#\\n# NOTE: Packages with the '{}' messages represent packages where the ggd\"\n \" package(s) are installed, but the package metadata has been removed from conda storage. This\"\n \" happens when one of the following happen: \\n 1) The package represents an ID specific meta-\"\n \"recipe installed by GGD. \\n 2) When the recipe is built locally using 'ggd check-recipe' and\"\n \" has not been uninstalled. (Commonly for private data packages).\\n Or \\n 3) The package is\"\n \" uninstalled using conda rather then ggd. The package is still available for use and is in\"\n \" the same state as before the 'conda uninstall'. To fix the problem on conda's side, uninstall\"\n \" the package with 'ggd uninstall' and re-install with 'ggd install'.\\n\"\n ).format(missing_message.strip())\n )",
"title": ""
},
{
"docid": "718bd6393bde7cc0e019c028986d26e1",
"score": "0.49017423",
"text": "def find_package_data(where='.', package='',\n exclude=standard_exclude,\n exclude_directories=standard_exclude_directories,\n only_in_packages=True,\n show_ignored=False):\n\n out = {}\n stack = [(convert_path(where), '', package, only_in_packages)]\n while stack:\n where, prefix, package, only_in_packages = stack.pop(0)\n for name in os.listdir(where):\n fn = os.path.join(where, name)\n if os.path.isdir(fn):\n bad_name = False\n for pattern in exclude_directories:\n if (fnmatchcase(name, pattern)\n or fn.lower() == pattern.lower()):\n bad_name = True\n if show_ignored:\n sys.stderr.write(\n \"Directory %s ignored by pattern %s\\n\"\n % (fn, pattern))\n break\n if bad_name:\n continue\n if os.path.isfile(os.path.join(fn, '__init__.py')):\n if not package:\n new_package = name\n else:\n new_package = package + '.' + name\n stack.append((fn, '', new_package, False))\n else:\n stack.append(\n (fn, prefix + name + '/', package, only_in_packages)\n )\n elif package or not only_in_packages:\n # is a file\n bad_name = False\n for pattern in exclude:\n if (fnmatchcase(name, pattern)\n or fn.lower() == pattern.lower()):\n bad_name = True\n if show_ignored:\n sys.stderr.write(\n \"File %s ignored by pattern %s\\n\"\n % (fn, pattern))\n break\n if bad_name:\n continue\n out.setdefault(package, []).append(prefix + name)\n return out",
"title": ""
},
{
"docid": "7dc01b7b9d0d05fa3628a82dfa369146",
"score": "0.4899758",
"text": "def get_deps_versions() -> dict[str, Version | None]:\n result: dict[str, Version | None] = {}\n\n for name in [\"ansible-core\", \"ansible-compat\", \"ruamel-yaml\", \"ruamel-yaml-clib\"]:\n try:\n result[name] = Version(version(name))\n except PackageNotFoundError:\n result[name] = None\n return result",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="780"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">923aa58fcc2054b3c100e2f5d852d0ad</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">This tests to see if the function that removed quotes worked and that there are no remaining quotations in the data</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "b4f723e2ff61fcd43c3cec1e918d1837",
"score": "0.8019426",
"text": "def test_replace_quotes():\n df_result_quotes = replace_quotes(output_not_cleaned)\n assert '\"' not in df_result_quotes.values\n print('TEST PASSED, for replace_quotes')",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "5d27fa81384f85a59c3d6ccc79418bb4",
"score": "0.6825093",
"text": "def test_remove_quotes(self):\n # Set up\n sample_sheet = CasavaSampleSheet(fp=cStringIO.StringIO(\"\"\"FCID,Lane,SampleID,SampleRef,Index,Description,Control,Recipe,Operator,SampleProject\n\"D190HACXX\",1,\"PB\",\"PB\",\"CGATGT\",\"RNA-seq\",\"N\",,,\"Peter Briggs\"\n\"\"\"))\n self.assertEqual(sample_sheet[0]['FCID'],'D190HACXX')\n self.assertEqual(sample_sheet[0]['Lane'],1)\n self.assertEqual(sample_sheet[0]['SampleID'],'PB')\n self.assertEqual(sample_sheet[0]['SampleRef'],'PB')\n self.assertEqual(sample_sheet[0]['Index'],'CGATGT')\n self.assertEqual(sample_sheet[0]['Description'],'RNA-seq')\n self.assertEqual(sample_sheet[0]['Control'],'N')\n self.assertEqual(sample_sheet[0]['Recipe'],'')\n self.assertEqual(sample_sheet[0]['Operator'],'')\n self.assertEqual(sample_sheet[0]['SampleProject'],'Peter Briggs')",
"title": ""
},
{
"docid": "7f8f59813f9fe612a19cc5deab0c3338",
"score": "0.641688",
"text": "def test_complete_sentences_quote_only(self):\n\n cleaner = Cleaner(complete_sentences=True)\n\n text = '\"'\n self.assertEqual(text, cleaner.clean(text))",
"title": ""
},
{
"docid": "260689d5b665bd31d45b0e7788930a86",
"score": "0.6379068",
"text": "def _unquote(data):\r\n if not data.startswith(b'\"') or not data.endswith(b'\"'):\r\n return data\r\n return QUOTE_RE.sub(b\"\\\\1\", data[1:-1])",
"title": ""
},
{
"docid": "cc7690ce58e93dce56a97ee78e7c10be",
"score": "0.61906886",
"text": "def _remove_quotes(value):\n return value.strip(\" '\\\"\")",
"title": ""
},
{
"docid": "21a0a038d377bc473186f30f1eaec23b",
"score": "0.6120836",
"text": "def remove_quotes(x):\n if x is None:\n return None\n if x.startswith('``') and x.endswith('``') and len(x) > 3:\n return x[2:-2]\n elif x.startswith('``') or x.endswith('``'):\n msg = 'Malformed quoting in string %r.' % x\n raise ContractException(msg)\n else:\n return x",
"title": ""
},
{
"docid": "1faa3b2637983a31ca331a301e1f2331",
"score": "0.61193484",
"text": "def test_strip_str(self):\r\n artist = 'Walter Bishop Jr.'\r\n expected = 'walter bishop jr'\r\n actual = utils.strip_str(artist)\r\n self.assertEqual(actual, expected)",
"title": ""
},
{
"docid": "66de1cbb08b86ef8b1b9e6c2b47610c6",
"score": "0.6108535",
"text": "def clean(s):",
"title": ""
},
{
"docid": "0244246fd92ea8a1d8a7273e43ff2ab5",
"score": "0.6067782",
"text": "def test_strip_after_processing(self):\n\n cleaner = Cleaner(remove_alt_codes=True)\n\n text = ' Our prediction based on #FIFA Rankings, Country Risk Ratings &'\n self.assertEqual('Our prediction based on #FIFA Rankings, Country Risk Ratings', cleaner.clean(text))",
"title": ""
},
{
"docid": "7526e8301aced39d867356d0dfc883d0",
"score": "0.6062872",
"text": "def _unquote(self, value):\r\n if (value[0] == value[-1]) and (value[0] in ('\"', \"'\")):\r\n value = value[1:-1]\r\n return value",
"title": ""
},
{
"docid": "1168f48669a2aff9ff0e19c613fca21d",
"score": "0.6035481",
"text": "def remove_simple_quotes(self,cell):\n if isinstance(cell,str):\n if cell.find(\"'\") != -1:\n cell = cell.replace(\"'\",\"''\")\n\n return cell",
"title": ""
},
{
"docid": "6fa4123330d422b5be1732c676d8cd1a",
"score": "0.5997706",
"text": "def test_complete_sentences_single_quote_only(self):\n\n cleaner = Cleaner(complete_sentences=True)\n\n text = '\\''\n self.assertEqual(text, cleaner.clean(text))",
"title": ""
},
{
"docid": "ff49edd637f29b51759784b7dcf165c6",
"score": "0.5997262",
"text": "def test_remove_quotes_and_comments(self):\n # Set up\n sample_sheet = CasavaSampleSheet(fp=cStringIO.StringIO(\"\"\"FCID,Lane,SampleID,SampleRef,Index,Description,Control,Recipe,Operator,SampleProject\n\"D190HACXX\",1,\"PB\",\"PB\",\"CGATGT\",\"RNA-seq\",\"N\",,,\"Peter Briggs\"\n\"#D190HACXX\",2,\"PB\",\"PB\",\"ACTGAT\",\"RNA-seq\",\"N\",,,\"Peter Briggs\"\n\"\"\"))\n self.assertEqual(len(sample_sheet),1)",
"title": ""
},
{
"docid": "f78e6c8be329f5b592f6b069e535a083",
"score": "0.5996065",
"text": "def test_unquoted_strings(self):\n log_line = 'hello world'\n self.assertEqual(\n list(s3logparse.raw_fields(log_line)),\n ['hello', 'world']\n )",
"title": ""
},
{
"docid": "549c972c3bc119499b550e347cf2341e",
"score": "0.5989835",
"text": "def _unquote(self, val):\r\n if (len(val) >= 2) and (val[0] in (\"'\", '\"')) and (val[0] == val[-1]):\r\n val = val[1:-1]\r\n return val",
"title": ""
},
{
"docid": "9e605874636f591b8836f04de3266d60",
"score": "0.5953961",
"text": "def scrub_data(in_data, remove):\n for i in range(len(in_data)):\n record = in_data[i].split(\",\")\n for j in range(len(record)):\n record[j] = record[j].strip(remove)\n in_data[i] = \",\".join(record)\n while in_data.remove(\"\") != None:\n continue",
"title": ""
},
{
"docid": "4fea5958b86293b5a03c730f2ab43110",
"score": "0.5951054",
"text": "def test_quoted_string(self):\n log_line = 'hello \"hello world\" world'\n self.assertEqual(\n list(s3logparse.raw_fields(log_line))[1],\n 'hello world'\n )",
"title": ""
},
{
"docid": "5029c05b1660d06b178ee1ec38457396",
"score": "0.5947107",
"text": "def check_quotes(logical_line, noqa=False):\n if noqa:\n return\n\n in_string = False\n in_multiline_string = False\n single_quotas_are_used = False\n\n check_tripple = (\n lambda line, i, char: (\n i + 2 < len(line)\n and (char == line[i] == line[i + 1] == line[i + 2])\n )\n )\n\n i = 0\n while i < len(logical_line):\n char = logical_line[i]\n\n if in_string:\n if char == \"\\\"\":\n in_string = False\n if char == \"\\\\\":\n i += 1 # ignore next char\n\n elif in_multiline_string:\n if check_tripple(logical_line, i, \"\\\"\"):\n i += 2 # skip next 2 chars\n in_multiline_string = False\n\n elif char == \"#\":\n break\n\n elif char == \"'\":\n single_quotas_are_used = True\n break\n\n elif char == \"\\\"\":\n if check_tripple(logical_line, i, \"\\\"\"):\n in_multiline_string = True\n i += 3\n continue\n in_string = True\n\n i += 1\n\n if single_quotas_are_used:\n yield i, \"N350 Remove Single quotes\"",
"title": ""
},
{
"docid": "31372ebe595ec40a1bb77672a455df87",
"score": "0.5921402",
"text": "def test_complete_sentences_ends_with_quote(self):\n\n cleaner = Cleaner(complete_sentences=True)\n\n text = '\"Sounds a lot like the last one\"'\n self.assertEqual('\"Sounds a lot like the last one.\"', cleaner.clean(text))",
"title": ""
},
{
"docid": "424ccd2e591dc53293da757fdcd520e6",
"score": "0.59083706",
"text": "def test_with_no_loose_commas(self):\r\n self.assertEquals(parse_tag_input('one two \"thr,ee\"'), [u'one', u'thr,ee', u'two'])",
"title": ""
},
{
"docid": "bee8453a9ef45b16361b4af9aff31331",
"score": "0.5897252",
"text": "def test_clean_no_configuration(self):\n\n cleaner = Cleaner()\n\n text = 'Our prediction based on #FIFA Rankings, & Country Risk Ratings'\n self.assertEqual(text, cleaner.clean(text))",
"title": ""
},
{
"docid": "88bf913446a40e07215e0a36e830da41",
"score": "0.58515286",
"text": "def clean_data(quotes_data):\n quotes_list = quotes_data['data']\n formatted_quotes = []\n for quote in quotes_list:\n quote_formatted = f\"{quote['quoteText']} - {quote['quoteAuthor']}\"\n formatted_quotes.append(quote_formatted)\n\n return formatted_quotes",
"title": ""
},
{
"docid": "90fb4649b74c533c1a284d484059bf01",
"score": "0.58479375",
"text": "def quote_stripper(string):\n\n #check if the string meets the conditions for a stripping\n if string[0] == string[-1] and (string[0] == '\"' or string[0] == \"'\"):\n\n #construct and return the new string\n newstring = string[1:-1]\n else:\n newstring = string\n return newstring",
"title": ""
},
{
"docid": "e06af2dacbfad87c6d14d883cd471e68",
"score": "0.58205414",
"text": "def stripQuote(self, text):\n return text.replace('\"', '')",
"title": ""
},
{
"docid": "c9872025a560ce2d0e5adcaf8e8fe11e",
"score": "0.5819792",
"text": "def test_clean_strip(self):\n\n cleaner = Cleaner()\n\n text = ' Our prediction based on #FIFA Rankings, & Country Risk Ratings '\n self.assertEqual('Our prediction based on #FIFA Rankings, & Country Risk Ratings', cleaner.clean(text))",
"title": ""
},
{
"docid": "8f7377424f3a9ae59a31064ccc3707fa",
"score": "0.5814914",
"text": "def remove_inside_single_quote(string):\n if type(string) == pd.Timestamp:\n return string\n\n if pd.notna(string):\n if isinstance(string, str):\n if string.startswith(\"'\"):\n return string[1:]\n return string",
"title": ""
},
{
"docid": "b92284ade4065c1b2a356b3bb81f025a",
"score": "0.5771279",
"text": "def test_mixed_quote_types_unsafe(state: State):\n\n out, count = code_editor.fstringify_code_by_line(s_in_mixed_quotes_unsafe, state)\n assert out == s_in_mixed_quotes_unsafe",
"title": ""
},
{
"docid": "7cd95a6ede689cfb6f1f1efdb5475657",
"score": "0.57389265",
"text": "def stripQuotes(string):\r\n single = string.startswith(\"'\") and string.endswith(\"'\")\r\n double = string.startswith('\"') and string.endswith('\"')\r\n if single or double:\r\n return string[1:-1]\r\n return string",
"title": ""
},
{
"docid": "56638625a0bdddf43548288b3bf32bfd",
"score": "0.5730555",
"text": "def remove_inside_quotes(string):\n\n # pandas often parses timestamp values obtained from SQL as objects\n if type(string) == pd.Timestamp:\n return string\n\n if pd.notna(string):\n if isinstance(string, str):\n if string.find(\"'\") != -1:\n first_quote_loc = string.find(\"'\")\n if string.find(\"'\", first_quote_loc + 1) != -1:\n second_quote_loc = string.find(\"'\", first_quote_loc + 1)\n string_cleaned = (\n string[:first_quote_loc]\n + string[first_quote_loc + 1 : second_quote_loc]\n + string[second_quote_loc + 1 :]\n )\n return string_cleaned\n return string",
"title": ""
},
{
"docid": "a2f577afa089891c5899152ab8f07bc1",
"score": "0.57125485",
"text": "def test_escape_single_quote(self) -> None:\n account_name = '''Dr. Evil's Giant\\\\' \"Laser\", LLC'''\n account = Account(Name=account_name)\n account.save()\n try:\n self.assertTrue(Account.objects.filter(Name=account_name).exists())\n finally:\n account.delete()",
"title": ""
},
{
"docid": "f593ef134207d55b84586cd893ed7c1d",
"score": "0.56992257",
"text": "def test_remove_alt_codes(self):\n\n cleaner = Cleaner(remove_alt_codes=True)\n\n text = 'Our prediction based on #FIFA Rankings, & Country Risk Ratings'\n self.assertEqual('Our prediction based on #FIFA Rankings, Country Risk Ratings', cleaner.clean(text))",
"title": ""
},
{
"docid": "3370f07573bfed62ebfed90e683d8b47",
"score": "0.5697099",
"text": "def strip_quotes(s):\n if not s or len(s) < 2:\n return s\n if s[0] == s[-1]:\n if s[0] in '\"\\'':\n s = s[1:-1]\n return s",
"title": ""
},
{
"docid": "c0c4532b26b0b2343e3eaaed7b16eea6",
"score": "0.5685424",
"text": "def test_noncontainer_and_string(self):\n result = _unwrap_data(3)\n self.assertEqual(result, 3)\n\n result = _unwrap_data('abc')\n self.assertEqual(result, 'abc')",
"title": ""
},
{
"docid": "6185f24697b99e5e5671f29a4738670c",
"score": "0.5684807",
"text": "def test_escape_empty_input(self):\n self.assertEqual(unescape(''), '')",
"title": ""
},
{
"docid": "6a5dc9e17dcf3a351c8f84ef035e112f",
"score": "0.5683477",
"text": "def test_shellquote(self):\n test_data = (\n (None, None, \"None ⇒ None\"),\n (\"\", \"\", \"(empty string) ⇒ (empty string)\"),\n (\" \", '\" \"', '␢ ⇒ \" \"'),\n (\" \", '\" \"', '␢␢␢␢␢ ⇒ \" \"'),\n (\"foobar\", \"foobar\", \"foobar ⇒ foobar\"),\n (\"foo bar\", '\"foo bar\"', 'foo bar ⇒ \"foo bar\"'),\n ('\"foobar\"', '\\'\"foobar\"\\'', '\"foobar\" ⇒ \\'\"foobar\"\\''),\n (\"'foobar'\", \"'foobar'\", \"'foobar' ⇒ 'foobar'\"),\n (\"foo 'bar'\", '\"foo \\'bar\\'\"', \"foo 'bar' ⇒ \\\"foo 'bar'\\\"\"),\n ('foo\"bar', '\\'foo\"bar\\'', 'foo\"bar ⇒ \\'foo\"bar\\''),\n (\"foo.bar\", '\"foo.bar\"', 'foo.bar ⇒ \"foo.bar\"'),\n (\"foo(bar)\", '\"foo(bar)\"', 'foo(bar) ⇒ \"foo(bar)\"'),\n (\"[foobar]\", '\"[foobar]\"', '[foobar] ⇒ \"[foobar]\"'),\n (\"foo[bar\", '\"foo[bar\"', 'foo[bar ⇒ \"foo[bar\"'),\n (\"/foo/bar\", \"/foo/bar\", \"/foo/bar ⇒ /foo/bar\"),\n (\"-f\", \"-f\", \"-f ⇒ -f\"),\n (\"--foobar\", \"--foobar\", \"--foobar ⇒ --foobar\"),\n (\"(\", r\"\\(\", r\"( ⇒ \\(\"),\n (\")\", r\"\\)\", r\"( ⇒ \\)\"),\n (\"'\", '\"\\'\"', '\\' ⇒ \"\\'\"'),\n )\n for original, expected, description in test_data:\n with self.subTest(msg=description):\n self.assertEqual(ShellCommand.shellquote(original), expected)",
"title": ""
},
{
"docid": "917cdae509071df9c19395ba7227d20b",
"score": "0.5681021",
"text": "def test_empty(self):\n self.assertEqual(\n self.clean(u''),\n u'',\n 'empty string is not preserved',\n )",
"title": ""
},
{
"docid": "be2fba617cc979da78d1024192229f4d",
"score": "0.56772774",
"text": "def test_sentence_input(self, sentence):\n\t\temote_pat = re.compile(r\"\\[.*?\\]\\(\\/.+?\\)\")\n\t\treject_pat = re.compile(r\"(^')|('$)|\\s'|'\\s|([\\\"(\\(\\)\\[\\])])\")\n\t\t# Decode unicode, mainly to normalize fancy quotation marks\n\t\tdecoded = unidecode(sentence)\n\t\t# Sentence shouldn't contain problematic characters\n\t\tfiltered_str = re.sub(emote_pat, '', decoded).replace(' ',' ')\n\t\t# Filtered sentence will have neither emotes nor double spaces\n\t\tif re.search(reject_pat, filtered_str):\n\t\t\t# Not counting emotes, there are no awkward characters.\n\t\t\treturn False\n\t\treturn True",
"title": ""
},
{
"docid": "19491c56e7a76a0610ebfb6db663c6be",
"score": "0.5673544",
"text": "def test_complete_sentences_ends_with_single_quote(self):\n\n cleaner = Cleaner(complete_sentences=True)\n\n text = '\\'Sounds a lot like the last one\\''\n self.assertEqual('\\'Sounds a lot like the last one.\\'', cleaner.clean(text))",
"title": ""
},
{
"docid": "5d82ba7d6026799c65c722b2a024a132",
"score": "0.5671139",
"text": "def _untouched_string(quote):\n return originalTextFor(Literal(quote) + SkipTo(quote) + Literal(quote))",
"title": ""
},
{
"docid": "b2b354d60480f79ade98670c4b7ae8c3",
"score": "0.5664184",
"text": "def test_garbage(self):\r\n self.input = ['hotdog', 'burger', 'pizza', 'pineapple', 'crab', 'cup']\r\n self.parsed = glados._validate_filter_input(self.input)\r\n self.expected = []\r\n self.assertListEqual(sorted(self.parsed), sorted(self.expected))",
"title": ""
},
{
"docid": "59d80de668d9959651380902cd88ad38",
"score": "0.566142",
"text": "def _clean_string(self, text):\n pass",
"title": ""
},
{
"docid": "f6f041581b796efa93872d335172fb4b",
"score": "0.5649939",
"text": "def test_clean_data_qqwing_format():\n s = Sudoku(test_data[\"valid_data\"])\n assert len(s.data) == 81 and \".\" not in s.data",
"title": ""
},
{
"docid": "5ecab50ef440925048ac8022369f628c",
"score": "0.56401235",
"text": "def removeDialogue(text):\n #double = checkForBritishQuoteMarks(text)\n #if double:\n text = re.sub(\"(?m)^.*[\\\"].*$\", \"\", text) # Remove all paragraphs with double quotes...\n # TODO: Handle British single quotes\n return text",
"title": ""
},
{
"docid": "5e12552fe0bb8b66b568a841832d3447",
"score": "0.5623431",
"text": "def test_with_loose_commas(self):\r\n self.assertEquals(parse_tag_input('\"one\", two three'), [u'one', u'two three'])",
"title": ""
},
{
"docid": "f72ca9c816af05512954f6ffc525ba92",
"score": "0.5622649",
"text": "def test_replace_arguments_invalid_quote_posix(self):\n self.assertEqual(replace_arguments('\"\\\\\"', [], posix=False),\n ['\"\\\\\"'])",
"title": ""
},
{
"docid": "ffa0cbf0bb8839030894f2652aee32ee",
"score": "0.5615867",
"text": "def clean_up_string(dirty_string):\n ds = dirty_string\n ds = ds.replace(\"\\n\", \"\")\n ds = ds.replace(\"'\", \"\")\n clean_string = re.split('[;,./]', ds)\n return clean_string",
"title": ""
},
{
"docid": "c3c946cd9649581176c2f75c5ec5ee3f",
"score": "0.55974793",
"text": "def unquote(value):\n if isinstance(value, str) and len(value) > 1 and value[0] == '\"' and value[-1] == '\"':\n return value[1:-1]\n return value",
"title": ""
},
{
"docid": "9cd4d59f4bffd5a6356be0e9691d43d7",
"score": "0.55910176",
"text": "def strip_quotes(val):\n return val[1:-1] if QUOTE_REGEX.match(val) else val",
"title": ""
},
{
"docid": "6cc0d580c3bbebdb83c4288aeff1c5b4",
"score": "0.5589566",
"text": "def clean_data(data):\n\tif not data.endswith(\"\\n\"):\n\t\tdata += \"\\n\"\n\treturn data",
"title": ""
},
{
"docid": "d32bd945c36bd0307f64b4d62ca654bf",
"score": "0.55829096",
"text": "def destring(value):\n return value.strip('\"\\'')",
"title": ""
},
{
"docid": "69d38d3914a5bed47b32c69e404788e3",
"score": "0.55713946",
"text": "def strip_unwanted(data_str):\n # Right now, this just requires stripping out commas\n return data_str.replace(',', '')",
"title": ""
},
{
"docid": "88cbb61f658cf6fd144bea277d800ebe",
"score": "0.55702394",
"text": "def remove_quotes(input_string):\n if not input_string:\n return ''\n\n # strip off double and single quotes\n return input_string.strip('\"').strip(\"'\")",
"title": ""
},
{
"docid": "41908b272116a723964ed25e65ee7f8a",
"score": "0.55696195",
"text": "def test_mixed_quote_types(state: State):\n\n expected = \"\"\"f'one is {one} and two is {two}'\"\"\"\n\n out, count = code_editor.fstringify_code_by_line(s_in_mixed_quotes, state)\n assert out == expected",
"title": ""
},
{
"docid": "b7b20b77cc011f937e6d81e47edfa018",
"score": "0.55691874",
"text": "def clean_unit_vectorized(column:pd.Series) -> pd.Series:\n \"\"\"Takes a dataframe and a column and cleans the strings including removing periods, apostrophies, weird spaces\"\"\"\n if column.isnull().all() == True:\n return column\n if pd.api.types.is_string_dtype(column) == False:\n write_to_log(\"column is not of type object/string\")\n return column\n\n column = column.str.lower()\n\n column = column.str.replace(r'\\.|!|@|\\$|~|\\(|\\)|\\\\|\\||\\*|/|\"|`', \"\", regex=True)\n\n column = column.str.replace(r\"'\", \"\", regex=True)\n\n column = column.str.strip()\n\n column = column.str.replace(r'^(.+)([&,]\\s?)$', r'\\g<1>', regex=True)\n\n column = column.str.replace(r'\\s{2,}', \" \", regex=True,flags=re.IGNORECASE)\n\n column = column.str.replace(r\"([0-9]+)(\\s-\\s|\\s-|-\\s)([0-9]+)\",r\"\\g<1>-\\g<3>\", regex=True,flags=re.IGNORECASE)\n\n column = column.str.replace(\n r'([\\s^-])(rm\\s|space\\s|room\\s|units?\\b|suite\\b|apt\\b|un\\s|ste\\b|number\\b|no\\b)', r'\\g<1>#', regex=True)\n\n column = column.str.replace(\n r'\\s(rm|room|unit|suite|apt|un|ste|number\\b|no\\b)(\\s)([0-9a-zA-Z]+)', r'#\\g<3>', regex=True)\n\n # column = column.str.replace(\n # r'(rm|space|room|unit|suite|apt|un|ste|no)(\\s)([0-9a-zA-Z]+)', r'#\\g<3>', regex=True)\n\n column = column.str.replace(r'##', r'#', regex=True)\n column = column.str.replace(r'#\\s', '#', regex=True, flags=re.IGNORECASE)\n\n column = column.str.replace(\n r'([^#])([0-9]{1,})([abcefgijklmopquvwxyz]{2,})',r'\\g<1>\\g<2> \\g<3>', regex=True,flags=re.IGNORECASE)\n\n column = column.str.replace(r'([a-z])(#)',r'\\g<1> \\g<2>', regex=True,flags=re.IGNORECASE)\n\n column = column.str.replace(r'(no\\s?)([0-9-]+)',r'#\\g<2>', regex=True,flags=re.IGNORECASE)\n\n column = column.str.replace(r'([^ ])(,|&)([ ])', r'\\g<1>\\g<2> \\g<3>',regex=True, flags=re.IGNORECASE)\n\n column = column.str.replace(r'([^ ])(,|&)([^\\s])', r'\\g<1>\\g<2> \\g<3>',regex=True, flags=re.IGNORECASE)\n\n column = column.str.replace(r'([a-z])(,)(a-z])', r'\\g<1>\\g<2> \\g<3>', regex=True, flags=re.IGNORECASE)\n\n column = column.str.replace(r'([a-z])(&)(a-z])', r'\\g<1> \\g<2> \\g<3>',regex=True, flags=re.IGNORECASE)\n\n column = column.str.replace(r'(&)(a-z])', r'\\g<1> \\g<2>',regex=True, flags=re.IGNORECASE)\n\n column = column.str.replace(r'([a-z])(&)', r'\\g<1> \\g<2>',regex=True, flags=re.IGNORECASE)\n\n column = column.str.replace(r'([^#])([a-z]{1,})([0-9]{2,})',r'\\g<1>\\g<2> \\g<3>', regex=True,flags=re.IGNORECASE)\n\n column = column.str.replace(r'([a-z]{2,})([#])', r'\\g<1> \\g<2>', regex=True, flags=re.IGNORECASE)\n\n column = column.str.replace(r'\\(.+?\\)', \"\", regex=True, flags=re.IGNORECASE)\n\n column = column.str.replace(r'##', r\"#\", regex=True, flags=re.IGNORECASE)\n column = column.str.replace(r'(.+)[\\s,\\s]{1,3}baltimore,?\\s?(md|maryland)?$', r\"\\g<1>\", regex=True,\n flags=re.IGNORECASE)\n\n # delete parenthesis\n column = column.str.replace(r'\\(.+?\\)', r\"\", regex=True)\n column = column.str.replace(r'\\s{2,}', \" \", regex=True, flags=re.IGNORECASE)\n column = column.str.replace(r\"([0-9]+)(\\s-\\s|\\s-|-\\s)([0-9]+)\",r\"\\g<1>-\\g<3>\", regex=True,flags=re.IGNORECASE)\n column = column.str.replace(\n r'\\s(rm|room|space|unit|suite|apt|un|ste|number\\b)(\\s)([0-9a-zA-Z]+)', r'#\\g<3>', regex=True)\n column = column.str.replace(r'##', r\"#\", regex=True, flags=re.IGNORECASE)\n\n column = column.str.replace(r'#\\s', '#', regex=True, flags=re.IGNORECASE)\n\n column = column.str.replace(r'(#)([a-z]{1,2})(\\s)([0-9]+)',r\"\\g<1>\\g<2>\\g<4>\", regex=True, flags=re.IGNORECASE)\n\n column = column.str.replace(r'(\\s$|^\\s)', '', regex=True,flags=re.IGNORECASE)\n\n column = column.str.replace(r'(p\\s?o)\\s?(box)', 'po box', regex=True, flags=re.IGNORECASE)\n column = column.str.replace(r'(p\\.?o\\.?)\\s?(box)', 'po box', regex=True,flags=re.IGNORECASE)\n column = column.replace(r'', np.nan, regex=True )\n column = column.replace(r'nan', np.nan, regex=False)\n\n column = column.str.replace(r'(.+)[\\s,\\s]{1,3}baltimore,?\\s?(md|maryland)?$', r\"\\g<1>\",regex=True,\n flags=re.IGNORECASE)\n column = column.str.replace(r'(\\s(1st|2nd|3rd)\\sfloor)$', r\"\",regex=True, flags=re.IGNORECASE)\n\n column = column.str.strip()\n return column",
"title": ""
},
{
"docid": "cbcf544c77c9678933784ee4c19b9277",
"score": "0.5565953",
"text": "def test_clean():\n mystring = script.CleanQuery(\"Donnée vers Paris\")\n assert mystring.clean() == \"donnee paris\"",
"title": ""
},
{
"docid": "6cfbdf44d0d5795cab5d677c9feb0606",
"score": "0.5553528",
"text": "def test_escape_empty_input(self):\n self.assertEqual(escape(''), '')",
"title": ""
},
{
"docid": "cdd010df5fef870370275eac62ea8771",
"score": "0.55473834",
"text": "def unquote(strg):\n while strg.startswith(\"\\'\") or strg.startswith(\"\\\"\"):\n strg = strg[1:]\n while strg.endswith(\"\\'\") or strg.endswith(\"\\\"\"):\n strg = strg[:-1]\n return strg",
"title": ""
},
{
"docid": "49b81f517c530deda199ac1db5f436cf",
"score": "0.5533874",
"text": "def test_code_block_str(dummy_code_block):\n assert (\n str(dummy_code_block)\n == dedent(\n \"\"\"\n return_true()\n return_true()\n return_true()\n \"\"\"\n ).strip()\n )",
"title": ""
},
{
"docid": "62ef6af93ac00ebde7a651fe60ca7436",
"score": "0.55338395",
"text": "def test_clean_strip_end(self):\n\n cleaner = Cleaner()\n\n text = 'Our prediction based on #FIFA Rankings, & Country Risk Ratings '\n self.assertEqual('Our prediction based on #FIFA Rankings, & Country Risk Ratings', cleaner.clean(text))",
"title": ""
},
{
"docid": "8989e13623bcb4c5fb7d1d7e4feb7157",
"score": "0.55299973",
"text": "def stripQuotes(s):\n \"\"\" stripQuotes(\" \\\"This is the intended use\\\" \") yields \"This is the intended use\" \"\"\"\n \"\"\" stripQuotes(\" \\\"This is the \\\"intended\\\" use \") yields \"This is the \\\"intended\\\" use\" \"\"\"\n return s[::-1].strip().replace('\"',\"\",1)[::-1].replace('\"',\"\",1)",
"title": ""
},
{
"docid": "d24a0241e258008b7ab706f83def12e4",
"score": "0.55144864",
"text": "def test_clean_address(self):\n\n assert clean_address(self.good_string) == \"186+rue+du+faubourg+saint+antoine+75012+paris\"\n assert clean_address(self.upper_string) == \"186+rue+du+faubourg+saint+antoine+75012+paris\"\n assert clean_address(self.symbole_string) == \"'++++++++++++++++++++++++++++++++++++++++++\"\n assert clean_address(self.empty_string) == \"\"",
"title": ""
},
{
"docid": "1d589560fde226a73fc93406d228464b",
"score": "0.5507586",
"text": "def test_case_8(self):\n result = sort_str_to_sql(sort_expression='')\n self.assertEqual(result, '')",
"title": ""
},
{
"docid": "f55b311875d00f9e4266a59729b4fb92",
"score": "0.5499993",
"text": "def test_quoted_argument():\n _test_option_parsing(\"a 'foo'\", ['foo'])",
"title": ""
},
{
"docid": "efbaa56b3cc81f0757415b8a5fcb7ea3",
"score": "0.54972094",
"text": "def not_string(stuff):",
"title": ""
},
{
"docid": "ed5075ae7fed494b52ac6a32c7c66f16",
"score": "0.54968375",
"text": "def sanitize(string) -> str:\n return '\"'+string+'\"'",
"title": ""
},
{
"docid": "7abc07363f74abfafc4166ff6626d14c",
"score": "0.54907155",
"text": "def test_remove_special_chars(self):\r\n document1 = \"\"\"Tf-idf stands for term frequency-inverse document frequency, and the tf-idf weight is a weight often used in information retrieval and text mining. This weight is a statistical measure used to evaluate how important a word is to a document in a collection or corpus. The importance increases proportionally to the number of times a word appears in the document but is offset by the frequency of the word in the corpus. Variations of the tf-idf weighting scheme are often used by search engines as a central tool in scoring and ranking a document's relevance given a user query.\"\"\"\r\n cleaned_text = \"\"\"Tf idf stands for term frequency inverse document frequency and the tf idf weight is a weight often used in information retrieval and text mining This weight is a statistical measure used to evaluate how important a word is to a document in a collection or corpus The importance increases proportionally to the number of times a word appears in the document but is offset by the frequency of the word in the corpus Variations of the tf idf weighting scheme are often used by search engines as a central tool in scoring and ranking a document s relevance given a user query\"\"\"\r\n clean_text = remove_special_chars(document1)\r\n self.assertTrue(len(clean_text) > 0)\r\n self.assertEquals(clean_text, cleaned_text)",
"title": ""
},
{
"docid": "ce201e278c2ae1af563106a932b3fd71",
"score": "0.54812366",
"text": "def test_delete_space(self):\n pass",
"title": ""
},
{
"docid": "1dba0d6261d3149651326d2948216856",
"score": "0.54804516",
"text": "def unmagicquotes(payload, **kwargs):\n\n retVal = payload\n\n if payload:\n found = False\n retVal = \"\"\n\n for i in xrange(len(payload)):\n if payload[i] == '\\'' and not found:\n retVal += \"%bf%27\"\n found = True\n else:\n retVal += payload[i]\n continue\n\n if found:\n _ = re.sub(r\"(?i)\\s*(AND|OR)[\\s(]+([^\\s]+)\\s*(=|LIKE)\\s*\\2\", \"\", retVal)\n if _ != retVal:\n retVal = _\n retVal += \"-- \"\n elif not any(_ in retVal for _ in ('#', '--', '/*')):\n retVal += \"-- \"\n return retVal",
"title": ""
},
{
"docid": "9bec0a2566ba7febb1c699888bb95ed9",
"score": "0.54757977",
"text": "def test_clean_key(self):\n self.assertEqual(parser.clean_key(' Earthshaker! <i>test</i>(test2)'), 'earthshaker')",
"title": ""
},
{
"docid": "2952902e9f24c57c1c62c82fda1d8a80",
"score": "0.54580754",
"text": "def test_clean_input_not_empty_list(monkeypatch):\n\n def mock_input(prompt):\n return \"1, 2, 3\"\n\n monkeypatch.setattr('retriever.lib.datapackage.input', mock_input)\n assert clean_input(\"\", ignore_empty=True, split_char=',', dtype=None) == \\\n [\"1\", \"2\", \"3\"]",
"title": ""
},
{
"docid": "78cf0cbf3527fc3cc878fd4cca87a6c3",
"score": "0.5457831",
"text": "def normalize_string_quotes(leaf: black.Leaf) -> None:\n single_quotes = True\n preferred_quote = \"'\" if single_quotes else '\"'\n other_quote = '\"' if single_quotes else \"'\"\n\n value = leaf.value.lstrip('furbFURB')\n if value[:3] == '\"\"\"':\n return\n\n elif value[:3] == \"'''\":\n orig_quote = \"'''\"\n new_quote = '\"\"\"'\n elif value[0] == preferred_quote:\n orig_quote = preferred_quote\n new_quote = other_quote\n else:\n orig_quote = other_quote\n new_quote = preferred_quote\n first_quote_pos = leaf.value.find(orig_quote)\n if first_quote_pos == -1:\n return # There's an internal error\n\n prefix = leaf.value[:first_quote_pos]\n unescaped_new_quote = re.compile(rf'(([^\\\\]|^)(\\\\\\\\)*){new_quote}')\n escaped_new_quote = re.compile(rf'([^\\\\]|^)\\\\((?:\\\\\\\\)*){new_quote}')\n escaped_orig_quote = re.compile(rf'([^\\\\]|^)\\\\((?:\\\\\\\\)*){orig_quote}')\n body = leaf.value[first_quote_pos + len(orig_quote) : -len(orig_quote)]\n if 'r' in prefix.casefold():\n if unescaped_new_quote.search(body):\n # There's at least one unescaped new_quote in this raw string\n # so converting is impossible\n return\n # Do not introduce or remove backslashes in raw strings\n new_body = body\n else:\n # remove unnecessary escapes\n new_body = black.sub_twice(\n escaped_new_quote, rf'\\1\\2{new_quote}', body\n )\n if body != new_body:\n # Consider the string without unnecessary escapes as the original\n body = new_body\n leaf.value = f'{prefix}{orig_quote}{body}{orig_quote}'\n new_body = black.sub_twice(\n escaped_orig_quote, rf'\\1\\2{orig_quote}', new_body\n )\n new_body = black.sub_twice(\n unescaped_new_quote, rf'\\1\\\\{new_quote}', new_body\n )\n if 'f' in prefix.casefold():\n matches = re.findall(\n r\"\"\"\n (?:[^{]|^)\\{ # start of the string or a non-{ followed by a single {\n ([^{].*?) # contents of the brackets except if begins with {{\n \\}(?:[^}]|$) # A } followed by end of the string or a non-}\n \"\"\",\n new_body,\n re.VERBOSE,\n )\n for m in matches:\n if '\\\\' in str(m):\n # Do not introduce backslashes in interpolated expressions\n return\n\n if new_quote == '\"\"\"' and new_body[-1:] == '\"':\n # edge case:\n new_body = new_body[:-1] + '\\\\\"'\n orig_escape_count = body.count('\\\\')\n new_escape_count = new_body.count('\\\\')\n if new_escape_count > orig_escape_count:\n return # Do not introduce more escaping\n\n if new_escape_count == orig_escape_count and orig_quote == preferred_quote:\n return\n\n leaf.value = f'{prefix}{new_quote}{new_body}{new_quote}'",
"title": ""
},
{
"docid": "8d02e48297a5718638964cd1e17fb70d",
"score": "0.54485285",
"text": "def test_mapr_clean_str():\n test_mapr_data = \"key1\\tval_a, val_b, val_c\\nkey2\\tval_d, val_e, val_f\"\n expected_result = pd.DataFrame(index=[\"key1\", \"key2\"], data={\"col_1\": [\"val_a\", \"val_d\"],\n \"col_2\": [\"val_b\", \"val_e\"],\n \"col_3\": [\"val_c\", \"val_f\"]})\n test_result = clean_mapr_results(test_mapr_data, col_names=[\"col_1\", \"col_2\", \"col_3\"])\n assert test_result.equals(expected_result)",
"title": ""
},
{
"docid": "cc1c056a1f52adecccf7e4d2e9777f67",
"score": "0.5448163",
"text": "def test_empty(self):\n self.assertNotIn('', self.f)\n self.assertNotIn(b'', self.f)",
"title": ""
},
{
"docid": "fc39c3de72d63804f6d98a902c42c9b1",
"score": "0.5446737",
"text": "def testEmptyIsFalse(self):\n self.assertFalse(MultilineString())",
"title": ""
},
{
"docid": "ea025fd570224af000fa5c9649e34b7c",
"score": "0.54401714",
"text": "def removeQuotes(s):\n return ''.join(i for i in s if i!='\"')",
"title": ""
},
{
"docid": "c00f3758baf4555cf22441d61c55d28e",
"score": "0.5428964",
"text": "def __clean_data(self, text):\r\n if text in self.all_letters: return text\r\n return \"\"",
"title": ""
},
{
"docid": "c00f3758baf4555cf22441d61c55d28e",
"score": "0.5428964",
"text": "def __clean_data(self, text):\r\n if text in self.all_letters: return text\r\n return \"\"",
"title": ""
},
{
"docid": "07d7bc77da410235027410c717dc47c5",
"score": "0.5428084",
"text": "def clean_data(self, raw_json):\n pass",
"title": ""
},
{
"docid": "470d10213614b178efdf5e253ceab991",
"score": "0.54264444",
"text": "def test_replace_arguments_invalid_quote(self):\n self.assertRaises(\n ValueError,\n lambda: replace_arguments('\"foo', [], posix=True))\n\n self.assertRaises(\n ValueError,\n lambda: replace_arguments('\"foo', [], posix=False))",
"title": ""
},
{
"docid": "dce98cc3d3dbfbd27ff1c31f2d7ed7b2",
"score": "0.5426033",
"text": "def test_replace_arguments_invalid_quote_non_posix(self):\n self.assertRaises(\n ValueError,\n lambda: replace_arguments('\"\\\\\"', [], posix=True))",
"title": ""
},
{
"docid": "d17345edd7c894e33fefbd85b44e4336",
"score": "0.5414083",
"text": "def test_ignore_garbage(self):\n log_input = textwrap.dedent(\"\"\"\\\n 2019-4-1 13:32:40 No pid\n \"\"\")\n\n expected = \"\"\n\n self.assertLog(log_input, expected)",
"title": ""
},
{
"docid": "909a893a7b994b598a86e082c1016887",
"score": "0.5411936",
"text": "def test_clean_strip_start(self):\n\n cleaner = Cleaner()\n\n text = ' Our prediction based on #FIFA Rankings, & Country Risk Ratings'\n self.assertEqual('Our prediction based on #FIFA Rankings, & Country Risk Ratings', cleaner.clean(text))",
"title": ""
},
{
"docid": "3daa631ea0ef296e5d3970a842cbf7c8",
"score": "0.5401127",
"text": "def sanitize(original):\n new = original.strip('\\n')\n new = new.rstrip('\\x00')\n new = new.strip()\n new = new.replace(' X ', ' & ') # Ex.: DJ Muggs X Bambu\n new = new.replace(' x ', ' & ')\n if new[len(new) - 3: len(new)] == '12\"': # Ex.: Keep It Going 12\"\n print(new + \" was changed to 'Single'.\")\n return 'Single'\n return new",
"title": ""
},
{
"docid": "5663f95bd0fc5149bce2dc97e1b55b6d",
"score": "0.5394814",
"text": "def test_remove_donor_from_database1():\n\n assert mr.remove_donor_from_database('Douglas')\\\n == '\\nDouglas removed from database'\n assert 'Douglas' not in mr.mailroom_db",
"title": ""
},
{
"docid": "80984bdf8e24f22c52e3fa23545ee1bb",
"score": "0.5392014",
"text": "def test_valid(self):\n self.assertEquals(self.field.clean(\"260953-11667\"), \"260953-11667\")",
"title": ""
},
{
"docid": "c3836c53510132ba1c9eff2c852058c8",
"score": "0.539012",
"text": "def fix_double_quotes(text):\n ans = text.replace(\"''\", '\" ')\n ans = ans.replace(\"``\", '\" ')\n return ans",
"title": ""
},
{
"docid": "680b92a237b77f7a5e527989f8b82323",
"score": "0.53832066",
"text": "def dequote(value):\n if not isinstance(value, basestring):\n return value\n\n if value[0] in '\\'\"`' and value[-1] == value[0]:\n return value[1:-1]\n return value",
"title": ""
},
{
"docid": "8df6f94c739c3c5a0dd095e0c3422f47",
"score": "0.5380306",
"text": "def imap_unquote(quoted):\n if not (quoted.startswith('\"') and quoted.endswith('\"')):\n unquoted = quoted\n else:\n unquoted = re.sub(r'\\\\(\\\\|\")', r'\\1', quoted[1:-1])\n return unquoted",
"title": ""
},
{
"docid": "be3c99ea605423f97e26258c8dc6f63d",
"score": "0.5367466",
"text": "def cleanData(self, ls):\n return ls.strip().lower().replace('?',' XXQMARKXX').replace('!',' XXEXMARK')",
"title": ""
},
{
"docid": "e553c625ecb43ebbfbfd5e64254d80c7",
"score": "0.53633386",
"text": "def _clean_dummy_val(self, cval):\n cval_clean = cval.replace(' ','_').replace('-','_').replace('\"','').replace(\"'\",'')\n return re.sub('[^a-zA-Z\\d\\s:]', '', cval_clean)",
"title": ""
},
{
"docid": "e00391f34c25d3a7eb4905e5150b3555",
"score": "0.535826",
"text": "def test_single_quote_in_double_quoted_string(self):\n lines = [\"test_fn('My string arg\" + '\"' + \"is cool.');\",\n 'test_fn2();']\n plugin = Plugin(\"test.nasl\")\n pt = plugin._parse(lines)\n assert len(pt) == 2, \"The encapsulated double quote was not ignored.\"",
"title": ""
},
{
"docid": "3e3c8de25abe5db6192a1f130af88088",
"score": "0.53511816",
"text": "def cleandata(data, x):",
"title": ""
},
{
"docid": "eabac93d5cfed6abcfdec1005613ceee",
"score": "0.53476775",
"text": "def test_clean_input_empty_list_ignore_empty(monkeypatch):\n\n def mock_input(prompt):\n return \", , ,\"\n\n monkeypatch.setattr('retriever.lib.datapackage.input', mock_input)\n assert clean_input(\"\", ignore_empty=True, split_char=\",\") == []",
"title": ""
},
{
"docid": "1ade48242fd7c5d9cd2273e0248a83c5",
"score": "0.53441435",
"text": "def test_wrongmethod4(self):\n code = 'd = dict()\\nd[42] = False\\n{0}'\n good, typo1, typo2 = 'del d[42]', 'd.remove(42)', 'd.discard(42)'\n good_code, bad_code1, bad_code2 = format_str(code, good, typo1, typo2)\n self.runs(good_code)\n sugg = \"'__delitem__'\"\n self.throws(bad_code1, ATTRIBUTEERROR, sugg)\n self.throws(bad_code2, ATTRIBUTEERROR, sugg)",
"title": ""
},
{
"docid": "fb3c1bfcaf722d00d969eddd13f2b837",
"score": "0.5325706",
"text": "def _has_quote(self, tokens):\n return '\"' in tokens or \"'\" in tokens",
"title": ""
},
{
"docid": "36c6ce2553154788898367784325b3ab",
"score": "0.53156936",
"text": "def test_with_double_quoted_multiple_words(self):\r\n \r\n self.assertEquals(parse_tag_input('\"one'), [u'one'])\r\n self.assertEquals(parse_tag_input('\"one two'), [u'one', u'two'])\r\n self.assertEquals(parse_tag_input('\"one two three'), [u'one', u'three', u'two'])\r\n self.assertEquals(parse_tag_input('\"one two\"'), [u'one two'])\r\n self.assertEquals(parse_tag_input('a-one \"a-two and a-three\"'),\r\n [u'a-one', u'a-two and a-three'])",
"title": ""
},
{
"docid": "6d940e637b98274781b643c776f1f6dd",
"score": "0.53121793",
"text": "def dequote(string):\n\tif ( ( string.startswith('\"') and string.endswith('\"'))\n\t\t or (string.startswith(\"'\") and string.endswith(\"'\")) ):\n\t\treturn string[1:-1]\n\telse:\n\t\treturn string",
"title": ""
},
{
"docid": "d256eec733f1c8298f8b9a61066d41be",
"score": "0.5309168",
"text": "def preCleaning(self, data):\n ret = [x for x in data if x[0] !=\"-\" and x[1]!=\"-\"]\n return ret",
"title": ""
},
{
"docid": "21f0a8dcb590e2f35efc0f1de35476ed",
"score": "0.53002614",
"text": "def test_deletion_from_string(self):\n code = \"s = 'abc'\\ndel s[1]\"\n good_code = \"s = 'abc'\\nl = list(s)\\ndel l[1]\\ns = ''.join(l)\"\n sugg = 'convert to list to edit the list and use \"join()\" on the list'\n self.runs(good_code)\n self.throws(code, OBJECTDOESNOTSUPPORT, sugg)",
"title": ""
},
{
"docid": "f8769d3f986bfc90224b8539d74dfc42",
"score": "0.5290395",
"text": "def test_capitalize_first_quote_only(self):\n\n cleaner = Cleaner(capitalize_first=True)\n\n text = '\"'\n self.assertEqual(text, cleaner.clean(text))",
"title": ""
},
{
"docid": "144b72e6eff9c3f5f0c63c47d2e05b46",
"score": "0.5288785",
"text": "def paranoid_clean(query_value):\n if not query_value:\n return ''\n remove = ['{', '}', '<', '>', '\"', \"'\", \";\"]\n for item in remove:\n query_value = query_value.replace(item, '')\n query_value = query_value.rstrip().lstrip().strip()\n return query_value",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="781"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">4ce8dae4ac4053b4ab3ca20a68802c00</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Calculate average distance of two clusters</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "f67bd78a9d76772e123e3a2250a45b7b",
"score": "0.69087136",
"text": "def mean(average_dis1, num_of_distance1, average_dis2, num_of_distance2, num_of_distances):\r\n return (average_dis1*num_of_distance1 + average_dis2*num_of_distance2)/num_of_distances",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "397ef63681c0bead1b04d694faf2c1b2",
"score": "0.7762529",
"text": "def _calculate_average_distance(matrix, clusters, c_x, c_y):\n\n pass",
"title": ""
},
{
"docid": "3ab2226ee7caf41831be5816611eb289",
"score": "0.7740244",
"text": "def avg_cluster_distance(self, matrix, cluster1, cluster2):\n dist_list = []\n\n if isinstance(cluster1, int):\n \tcluster1 = [cluster1]\n elif isinstance(cluster2, int):\n \tcluster2 = [cluster2]\n for point1 in cluster1:\n \tfor point2 in cluster2:\n \t\tdist = matrix[point1][point2]\n \t\tif dist > 0:\n \t\t\tdist_list.append(dist)\n\n return sum(dist_list) / ((len(cluster1) * len(cluster2)))",
"title": ""
},
{
"docid": "f86c11a10c84965de2592a8e1a9ab8ef",
"score": "0.732256",
"text": "def average_distance(x_rdd,y_rdd):\n joined = x_rdd.join(y_rdd)\n return cv.total_rss(joined)**2",
"title": ""
},
{
"docid": "0066a2b6e76cdbca61ab4da086ff1bce",
"score": "0.7070983",
"text": "def average_euclidean_distance(a, b):\n\n total = 0\n for a_el in a:\n for b_el in b:\n total += euclidean_distance(a_el, b_el)\n\n n_combinations = len(a) * len(b)\n return total / n_combinations",
"title": ""
},
{
"docid": "a7f259d5efd15532fb0a3b0cd5c4379d",
"score": "0.6985862",
"text": "def compute(self, cluster, other):\n dist_list = []\n for this_sample in cluster.samples:\n for other_sample in other.samples:\n dist_list.append(this_sample.get_dist(other_sample))\n return min(dist_list)",
"title": ""
},
{
"docid": "0ccef3f22bd9d48092cdde1b41edf6da",
"score": "0.69777274",
"text": "def Distance(a, b):\n return ((a - b)**2).mean(axis=-1)",
"title": ""
},
{
"docid": "182d3359707ba190bd380cef9cdcede0",
"score": "0.69545054",
"text": "def cluster_distance(cluster1, cluster2, distance_agg=min):\n return distance_agg(\n [\n distance(input1, input2)\n for input1 in get_values(cluster1)\n for input2 in get_values(cluster2)\n ]\n )",
"title": ""
},
{
"docid": "7c533a8f70a7d425d399dabab025bf5c",
"score": "0.6937587",
"text": "def m_distance_cluster(cluster_1, cluster_2):\n centroid_1 = cluster_1[\"SUM\"] / len(cluster_1[\"N\"])\n centroid_2 = cluster_2[\"SUM\"] / len(cluster_2[\"N\"])\n sig_1 = cluster_1[\"SUMSQ\"] / len(cluster_1[\"N\"]) - (cluster_1[\"SUM\"] / len(cluster_1[\"N\"]))**2\n sig_2 = cluster_2[\"SUMSQ\"] / len(cluster_2[\"N\"]) - (cluster_2[\"SUM\"] / len(cluster_2[\"N\"]))**2\n z_1 = (centroid_1 - centroid_2) / sig_1\n z_2 = (centroid_1 - centroid_2) / sig_2\n m_1 = np.dot(z_1, z_1) ** (1/2)\n m_2 = np.dot(z_2, z_2) ** (1/2)\n return min(m_1, m_2)",
"title": ""
},
{
"docid": "e4113212b1adf6c7035b12e64ef03b37",
"score": "0.68892837",
"text": "def compute_means(self,clusters,docs):\n total1 = defaultdict(lambda: Counter())#Total of all clusters\n total2 = defaultdict(lambda: 0)#Number of clusters\n self.error_value = 0.0\n for cluster in clusters:\n d_id,c_id,distance = cluster\n total1[c_id].update(docs[d_id])\n total2[c_id] += 1\n self.e = self.error(clusters)\n return self.display(total1,total2)\n pass",
"title": ""
},
{
"docid": "6dcb36a9f5372cda9c2f30e041e3cdc2",
"score": "0.68366635",
"text": "def mean_contour_distance(\n s1: ndarray,\n s2: ndarray,\n voxelspacing: VOXELSPACING_TYPE = None,\n edt_method: Callable = distance_transform_edt_float32,\n) -> float:\n s1_c_dist = __directed_contour_distances(\n s1, s2, voxelspacing, edt_method=edt_method\n )\n s2_c_dist = __directed_contour_distances(\n s2, s1, voxelspacing, edt_method=edt_method\n )\n\n return max(s1_c_dist.mean(), s2_c_dist.mean())",
"title": ""
},
{
"docid": "a8ae637cdbd1836ffc80b506917e9b26",
"score": "0.68055916",
"text": "def compute(self, this_cluster, other_cluster):\n dist_List = []\n for this_sample in this_cluster.samples:\n for other_sample in other_cluster.samples:\n dist_List.append(this_sample.compute_euclidean_distance(other_sample))\n return max(dist_List)",
"title": ""
},
{
"docid": "ff08a54c83ba0ea6ac7e5052595a1c3e",
"score": "0.6713744",
"text": "def distance(self, other_cluster):\r\n vert_dist = self._vert_center - other_cluster.vert_center()\r\n horiz_dist = self._horiz_center - other_cluster.horiz_center()\r\n return math.sqrt(vert_dist ** 2 + horiz_dist ** 2)",
"title": ""
},
{
"docid": "367d919517106734b1e0862633ec4439",
"score": "0.6634883",
"text": "def avg_to_cent(cluster, centroid):\n\n dist = []\n for site in cluster:\n dist.append(compute_similarity(location(site), centroid))\n return np.mean(dist)",
"title": ""
},
{
"docid": "4f0e1c176bd7a583d04dc042f0923e42",
"score": "0.66267014",
"text": "def distance(self, other_cluster):\n vert_dist = self._vert_center - other_cluster.vert_center()\n horiz_dist = self._horiz_center - other_cluster.horiz_center()\n return math.sqrt(vert_dist ** 2 + horiz_dist ** 2)",
"title": ""
},
{
"docid": "3d07edf8d545183577474a8d2e6f8d2a",
"score": "0.660504",
"text": "def _define_distance_to_clusters(self, data):\n # TODO(xavigonzalvo): reuse (input - mean) * cov^-1 * (input -\n # mean) from log probability function.\n self._all_scores = []\n for shard in data:\n all_scores = []\n shard = array_ops.expand_dims(shard, 0)\n for c in xrange(self._num_classes):\n if self._covariance_type == FULL_COVARIANCE:\n cov = self._covs[c, :, :]\n elif self._covariance_type == DIAG_COVARIANCE:\n cov = array_ops.diag(self._covs[c, :])\n inverse = linalg_ops.matrix_inverse(cov + self._min_var)\n inv_cov = array_ops.tile(\n array_ops.expand_dims(inverse, 0),\n array_ops.stack([self._num_examples, 1, 1]))\n diff = array_ops.transpose(shard - self._means[c, :, :], perm=[1, 0, 2])\n m_left = math_ops.matmul(diff, inv_cov)\n all_scores.append(\n math_ops.sqrt(\n math_ops.matmul(\n m_left, array_ops.transpose(\n diff, perm=[0, 2, 1]))))\n self._all_scores.append(\n array_ops.reshape(\n array_ops.concat(all_scores, 1),\n array_ops.stack([self._num_examples, self._num_classes])))\n\n # Distance to the associated class.\n self._all_scores = array_ops.concat(self._all_scores, 0)\n assignments = array_ops.concat(self.assignments(), 0)\n rows = math_ops.to_int64(math_ops.range(0, self._num_examples))\n indices = array_ops.concat(\n [array_ops.expand_dims(rows, 1), array_ops.expand_dims(assignments, 1)],\n 1)\n self._scores = array_ops.gather_nd(self._all_scores, indices)",
"title": ""
},
{
"docid": "80324a6b065002d4be25a520eea33968",
"score": "0.6539736",
"text": "def _mean_distance(self, other=None):\n try:\n return abs(self.mean - other.mean) \\\n / ((self.all_valid_variance + other.all_valid_variance) / 2.0)\n except ZeroDivisionError:\n return float(\"inf\")",
"title": ""
},
{
"docid": "5b2719126c199c06255ac17cb334087f",
"score": "0.6538746",
"text": "def centroid_sum_distance(cluster):\n total_sum = 0\n for point in cluster:\n total_sum = total_sum + sum(point.vector)\n return total_sum",
"title": ""
},
{
"docid": "92522446fa5b60d1fa60f88a675479d5",
"score": "0.6486524",
"text": "def avg_min_euclidean_dist(a, b):\n diff = np.tile(b, (a.shape[0], 1)) - np.repeat(a, b.shape[0], axis=0)\n dist_vector = np.sqrt(np.sum(np.power(diff, 2), axis=1))\n return np.mean(np.min(dist_vector.reshape(a.shape[0], b.shape[0]), axis=1))",
"title": ""
},
{
"docid": "a304969d683cd9469f4153c4309061f7",
"score": "0.64548403",
"text": "def _means_part(X,Y,labels):\n part_X = []\n for lb in labels:\n ith_class_X = X[Y == lb]\n part_X.append(np.mean(ith_class_X, axis=0))\n euclids = euclidean_distances(part_X, part_X)\n mean_distance = np.sum(euclids)/2\n return mean_distance",
"title": ""
},
{
"docid": "5aeb0f27a4033b9bd8e58c4a1d485f1a",
"score": "0.6447648",
"text": "def average_distance(outputs, targets):\n\n # Check that the two signals have\n # the same number of dimensions.\n if outputs.shape != targets.shape:\n return -1.0\n\n out = 0\n total = 0.0\n for target in targets:\n count = 0.0\n for i in range(len(target)):\n count += math.pow(target[i] - outputs[out][i], 2)\n # endfor\n total += math.sqrt(count)\n out += 1\n # endfor\n\n return total / float(len(targets))",
"title": ""
},
{
"docid": "c4ec96ae7178d118474c04174c795912",
"score": "0.64428496",
"text": "def average_distance(self,x,y):\n self._available = False\n rv = (x-y).mean()\n print rv\n self._available = True\n return rv",
"title": ""
},
{
"docid": "43d94b1a49c84a08a1c74a9e5db1f507",
"score": "0.6434606",
"text": "def average_clustering(G):\n order=G.order()\n s=sum(clustering(G))\n return s/float(order)",
"title": ""
},
{
"docid": "9ec09cfff265740ddce1bcd9843fc7d9",
"score": "0.63956314",
"text": "def compute(self, cluster, other):\n max_dist = 0\n for this_sample in cluster.samples:\n for other_sample in other.samples:\n if this_sample.get_dist(other_sample) > max_dist:\n max_dist = this_sample.get_dist(other_sample)\n return max_dist",
"title": ""
},
{
"docid": "01def1187f491a7574ecef8b888fea91",
"score": "0.6375127",
"text": "def calculate_average_distance(self):\n average_distance = 0\n for flight in self.edges.values():\n average_distance += flight.distance\n\n return average_distance / len(self.edges)",
"title": ""
},
{
"docid": "58eb9d3ff51898ff295c2ef5a02de5a2",
"score": "0.63711196",
"text": "def compute_euclidean_distance(self, other):\n sum_difference = 0\n for gene1, gene2 in zip(self.genes, other.genes):\n sum_difference += (gene1-gene2)**2\n return sum_difference**0.5",
"title": ""
},
{
"docid": "69eb3c89a4d168e4fb50624ced3efe0c",
"score": "0.6346163",
"text": "def compute_avg_clustering(self):\n if self.network:\n subgraph = biggest_subgraph(self.network)\n return nx.algorithms.average_clustering(subgraph)\n else:\n return None",
"title": ""
},
{
"docid": "c0fbaeda8febe4910afcb74b9e462c1d",
"score": "0.6341573",
"text": "def agg_dist(docs, kmeans):\n agg_d = 0\n labels = kmeans.labels_\n centroids = kmeans.cluster_centers_\n\n # print docs.shape # 35\n\n for d in range(docs.shape[0]):\n #print docs[d].toarray().reshape(docs[d].shape[1]).shape # 2382\n #print centroids[d].shape # 2382\n doc = docs[d].toarray().reshape(docs[d].shape[1])\n dist = np.linalg.norm(doc-centroids[labels[d]])\n agg_d += dist\n\n return agg_d",
"title": ""
},
{
"docid": "e07a58ff34d1e9ccd1238777becddbc1",
"score": "0.62998766",
"text": "def assign_to_current_mean(img, result, clustermask):\n # closest distance to cluster\n distances,clusters = zip(*[closest_cluster(img[yp,xp],current_cluster_centers) for yp in range(h1) for xp in range(w1)])\n\n # update cluster mask\n clustermask = np.asarray(clusters).reshape([h1,w1,1])\n \n # calculate overall distance\n overall_dist = np.sum(distances)\n \n return overall_dist,clustermask",
"title": ""
},
{
"docid": "b97cad64546f4bf915e1aa5912f3a145",
"score": "0.62998205",
"text": "def ddpg_distance_metric(actions1, actions2):\n diff = np.array(actions1)-np.array(actions2)\n mean_diff = np.mean(np.square(diff), axis=0)\n dist = np.sqrt(np.mean(mean_diff))\n return dist",
"title": ""
},
{
"docid": "b6cb3f7f13a57d3e760616dd12e9ff9b",
"score": "0.627095",
"text": "def ddpg_distance_metric(actions1, actions2):\n diff = actions1-actions2\n mean_diff = np.mean(np.square(diff), axis=0)\n dist = sqrt(np.mean(mean_diff))\n return dist",
"title": ""
},
{
"docid": "4831eaaf518f9e85082c3977362f884d",
"score": "0.6265303",
"text": "def ddpg_distance_metric(actions1, actions2):\n diff = actions1 - actions2\n mean_diff = np.mean(np.square(diff), axis=0)\n dist = sqrt(np.mean(mean_diff))\n return dist",
"title": ""
},
{
"docid": "54d1abc31d91923e28c6c8692c8b0571",
"score": "0.6247573",
"text": "def compute_cluster_mean(cluster):\n mean_element = list(cluster[0])\n for dim in range(dimensions):\n for element in cluster:\n mean_element[dim] += element[dim] # Sum of elements' \"dim\" dimension\n\n # Computing Average for each dimension (dividing by num elements)\n mean_element[dim] /= len(cluster)\n return mean_element # return average",
"title": ""
},
{
"docid": "36b4b89c3169a9ae24b81a70da7a5fa9",
"score": "0.62295914",
"text": "def get_average_distance(self, transform_df, real_df):\n return euclidean_distances(transform_df, real_df).mean()",
"title": ""
},
{
"docid": "070b5d1cde27d32e9448f97390e1ced5",
"score": "0.621884",
"text": "def centroid_by_distance(G, keywords):\n return _sorted_average_distance(G, keywords)",
"title": ""
},
{
"docid": "479882cff1e7cde67bf87aa5045bfd56",
"score": "0.620912",
"text": "def compute_avg_dist(X):\n x = X[:, 0:1, :] # Center point\n K = X[:, 1:, :] # Neighborhood\n\n if hasattr(X, \"numpy\"): # Torch tensors\n return torch.mean(torch.abs(torch.subtract(x, K)), axis=1)\n elif hasattr(X, \"shape\"): # NumPy arrays\n return np.mean(np.abs(np.subtract(x, K)), axis=1)",
"title": ""
},
{
"docid": "fcff75f3c4ac431f3189eb20c3d3e772",
"score": "0.6188748",
"text": "def _single_linkage_distance(y, cluster1, cluster2):\n\n return min(y[i, j] for (i, j) in product(cluster1, cluster2))",
"title": ""
},
{
"docid": "56d02c7770baba4711015b1cb68efd58",
"score": "0.61874855",
"text": "def merge_clusters(self, other_cluster):\r\n if len(other_cluster.fips_codes()) == 0:\r\n return self\r\n else:\r\n self._fips_codes.update(set(other_cluster.fips_codes()))\r\n \r\n # compute weights for averaging\r\n self_weight = float(self._total_population) \r\n other_weight = float(other_cluster.total_population())\r\n self._total_population = (self._total_population \r\n + other_cluster.total_population())\r\n self_weight /= self._total_population\r\n other_weight /= self._total_population\r\n \r\n # update center and risk using weights\r\n self._vert_center = (self_weight * self._vert_center \r\n + other_weight * other_cluster.vert_center())\r\n self._horiz_center = (self_weight * self._horiz_center \r\n + other_weight * other_cluster.horiz_center()) \r\n self._averaged_risk = (self_weight * self._averaged_risk \r\n + other_weight * other_cluster.averaged_risk())\r\n return self",
"title": ""
},
{
"docid": "5d4e97ee57f69f27e0ae5e76a9bc3e30",
"score": "0.61817724",
"text": "def merge_clusters(self, other_cluster):\n if len(other_cluster.fips_codes()) == 0:\n return self\n else:\n self._fips_codes.update(set(other_cluster.fips_codes()))\n \n # compute weights for averaging\n self_weight = float(self._total_population) \n other_weight = float(other_cluster.total_population())\n self._total_population = self._total_population + other_cluster.total_population()\n self_weight /= self._total_population\n other_weight /= self._total_population\n \n # update center and risk using weights\n self._vert_center = self_weight * self._vert_center + other_weight * other_cluster.vert_center()\n self._horiz_center = self_weight * self._horiz_center + other_weight * other_cluster.horiz_center()\n self._averaged_risk = self_weight * self._averaged_risk + other_weight * other_cluster.averaged_risk()\n return self",
"title": ""
},
{
"docid": "180cf70878aaf48a90d33eb468488101",
"score": "0.6178146",
"text": "def compute_clusters(self, documents):\n res = []\n cluster = []\n for doc in self.mean_vector:\n dp = doc.values()\n res.append(sum(starmap(mul,izip(dp,dp)))) \n for doc in range(0,len(documents)):\n cluster.append((doc,)+ self.find_min_dist(documents[doc],res))\n return cluster",
"title": ""
},
{
"docid": "a02d80c3275f8c319d4e9a08360d6840",
"score": "0.6177282",
"text": "def mean_distance(data):\n\tdistances = []\n\tfor i in range(data.shape[0]):\n\t for j in range(data.shape[0]):\n\t if i != j:\n\t distances.append(euclidean(data[j,:].flatten(), data[i,:].flatten()))\n\tmean_dist = sum(distances) / len(distances)\n\treturn mean_dist",
"title": ""
},
{
"docid": "93a16a31cf976e85c9581ef513037ad6",
"score": "0.6170197",
"text": "def SSE(cluster, dataset):\n\n SSE_cluster = 0\n for point in cluster.pointStore:\n SSE_cluster += np.linalg.norm(cluster.centroid-dataset[point - 1])**2\n return SSE_cluster",
"title": ""
},
{
"docid": "d7ac9369093bc0d9d842f9d766e64ac5",
"score": "0.61591256",
"text": "def getDistance(self, a: np.ndarray, b: np.ndarray) -> float:\n acc: np.ndarray = np.abs(a - b)\n return np.sum(acc) / (2 * acc.size)",
"title": ""
},
{
"docid": "0c163e55b2bf85e0db6d1a95041315fd",
"score": "0.6158793",
"text": "def _distance(self, data, centroids):\r\n return np.sqrt(((data - centroids[:, np.newaxis]) ** 2).sum(axis=2)) #togliere sqrt?\r",
"title": ""
},
{
"docid": "33422cc0781014062e0be00ac70fdb91",
"score": "0.61322075",
"text": "def custom_dist(a,b):\n a = a.fillna(0)\n b = b.fillna(0)\n return(distance.euclidean(a,b))",
"title": ""
},
{
"docid": "0733df093c6ce37ddd53a75c6d6f383b",
"score": "0.6131744",
"text": "def calculate_distance_mean(self):\n\n distance = 0\n self.count = len(self.readings)\n for reading in self.readings:\n distance = distance + reading.distance\n self.distance = int(distance / self.count)",
"title": ""
},
{
"docid": "a61bc365ab2940918b0c93d29ad36acb",
"score": "0.61070704",
"text": "def average_of(self, distance: float) -> np.ndarray:\n return self.average[self._idx_of_dist[distance]]",
"title": ""
},
{
"docid": "763c4d9db3b7c1d34e33ee8b6d776f64",
"score": "0.61001307",
"text": "def compute_dist(self, a, b):\n return abs(b[0] - a[0]) + abs(b[1] - a[1])",
"title": ""
},
{
"docid": "2041c1a38fbd73172806dbf453578cca",
"score": "0.6089617",
"text": "def euclidean_distance(x1, y1, x2, y2):",
"title": ""
},
{
"docid": "eb5dd62f75e68afeced98bb3d4a94c74",
"score": "0.60689175",
"text": "def dist(c1, c2):\n return abs(c1[0] - c2[0]) + abs(c1[1] - c2[1])",
"title": ""
},
{
"docid": "3a0896ef1c35f02993b7a5dc35eea5d2",
"score": "0.60609925",
"text": "def _compute_euclidean_dist(a, b):\n\n assert len(a) == len(b)\n\n return math.sqrt(sum(map(lambda x, y: (x - y) ** 2, a, b)))",
"title": ""
},
{
"docid": "ba830b82b12c0f5f6d5724b01f8080b1",
"score": "0.60467875",
"text": "def getDistance(self, a: np.ndarray, b: np.ndarray) -> float:\n acc = (a - b) ** 2\n return np.sqrt(np.sum(acc)) / (2 * np.sqrt(acc.size))",
"title": ""
},
{
"docid": "735a500ee687a720af505f2eb53df6da",
"score": "0.6037984",
"text": "def getDistanceFromCluster(self, otherCluster):\n\n otherClusterItems = otherCluster.items\n \n minDistance = 999999\n for other_item in otherClusterItems:\n for item in self.items:\n minDistance = min(minDistance, other_item.getDistance(item))\n\n return minDistance",
"title": ""
},
{
"docid": "0e974be36df85c14c5052e0debcedd52",
"score": "0.6030014",
"text": "def mean(cluster, dataset):\n if not cluster.pointAssign:\n return 0\n else:\n index = np.array(cluster.pointAssign) - 1\n cluster_array = dataset[index]\n cluster_mean = np.mean(cluster_array, axis=0)\n return cluster_mean",
"title": ""
},
{
"docid": "d3e992e390ccfafd04cb17c1d93ca0a4",
"score": "0.6028471",
"text": "def squared_clustering_errors(inputs, k):\n clusterer = KMeans(k)\n clusterer.train(inputs)\n means = clusterer.means\n assignments = list(map(clusterer.classify, inputs))\n\n return sum(\n squared_distance(inputs, means[cluster_])\n for inputs, cluster_ in zip(inputs, assignments)\n )",
"title": ""
},
{
"docid": "a5a81e022210f971873a7221097a4cf7",
"score": "0.602696",
"text": "def get_mean_distances(self) -> float:\n return self._get_distances().mean()",
"title": ""
},
{
"docid": "2f8979a8e49355a479bede1b7562e5b7",
"score": "0.60225093",
"text": "def dist(c1, c2):\n\n\td0 = c1[0]-c2[0]\n\td1 = c1[1]-c2[1]\n\td2 = c1[2]-c2[2]\n\treturn d0*d0+d1*d1+d2*d2",
"title": ""
},
{
"docid": "8360558a46f2fab976537261c70291c9",
"score": "0.6014496",
"text": "def point_avg(points):\n dimensions = len(points[0])\n new_centroid = []\n\n for dimension in range(dimensions): #dimension 120\n dim_sum = 0 # dimension sum\n for p in points: # for all point withn the same cluster\n dim_sum += p[dimension]\n\n # average of each dimension\n new_centroid.append(dim_sum / float(len(points)))\n return new_centroid",
"title": ""
},
{
"docid": "3746353972d78c386c7383d9447adc32",
"score": "0.60113657",
"text": "def euclidean_distance(self,other) :\r\n \r\n latitude1,longitude1,latitude2,longitude2=self.latitude,self.longitude,other.latitude,other.longitude\r\n latitude_difference,longitude_difference=latitude2-latitude1,longitude2-longitude1\r\n return math.sqrt(latitude_difference*latitude_difference+longitude_difference*longitude_difference)",
"title": ""
},
{
"docid": "324918bf255a1c5e07703ae276b32293",
"score": "0.6001541",
"text": "def _dist(self, a, b):\n # TODO: expand beyond 2D space. Sum of squares still works, so\n # this can be done in a fairly trivial list comprehension.\n x1, x2 = a[0], b[0]\n y1, y2 = a[1], b[1]\n return math.sqrt(math.pow(x2 - x1, 2) + math.pow(y2 - y1, 2))",
"title": ""
},
{
"docid": "fc265f55fe317559659aed6236591caa",
"score": "0.59918225",
"text": "def centers_displacement(p1, p2):\n # geometric center\n c1 = numpy.mean(numpy.array(p1), axis=0)\n c2 = numpy.mean(numpy.array(p2), axis=0)\n return c2 - c1",
"title": ""
},
{
"docid": "6294cab897460092c5f23c54c2e845db",
"score": "0.596792",
"text": "def calcClusterSimilarity(labels1, labels2):\n \n # initialize varaiables for Jaccard Index calcuation\n f_11 = 0 # \"True positive\"\n f_10 = 0 # \"False positive\"\n f_01 = 0 # \"False negative\"\n \n # check whether each pair of items found in each cluster\n # Jaccard index = f11 / f01 + f10 + f11 = same / total (except negatives)\n for i in range(len(labels1)):\n for j in range(len(labels2)):\n pair_1 = labels1[i] == labels1[j]\n pair_2 = labels2[i] == labels2[j]\n \n if pair_1 and pair_2: # pairs of points are in same cluster in both 1 & 2\n f_11 += 1 \n elif pair_1 and not pair_2: # pairs of points are in same cluster in 1 but not 2\n f_10 +=1\n elif pair_2 and not pair_1: # pairs of points are in same cluster in 2 but not 1\n f_01 +=1\n \n # Return 1 if there are no common pairs between the samples. Sklearn's Jaccard metric allows\n # user input for either returning 0 or 1 in this case but I just chose 1 for simplicity and\n # the clusters are technically very similar in that they don't share any overlap\n # https://github.com/scikit-learn/scikit-learn/blob/95119c13a/sklearn/metrics/_classification.py#L642\n if (f_01 + f_10 + f_11) == 0: \n return 1\n \n return f_11 / (f_01 + f_10 + f_11)",
"title": ""
},
{
"docid": "a10a4b433cc0ddbb7657f355f60d3c53",
"score": "0.5966511",
"text": "def compute_cluster_metrics(A, B):\n assert len(A) == len(B)\n\n metrics = {}\n metrics['adj_rank'] = adjusted_rand_score(A, B)\n metrics['AMI'] = adjusted_mutual_info_score(A, B)\n metrics['NMI'] = normalized_mutual_info_score(A, B)\n metrics['v_measure'] = v_measure_score(A, B)\n return pd.Series(metrics)",
"title": ""
},
{
"docid": "3bc68cb6dd0660274d505f4e8d36e394",
"score": "0.5961636",
"text": "def relocate_clusters():\n for i in range(len(cc_list)):\n for j in range(len(cc_list[i])):\n sum_list = []\n n = 0\n mean = 0\n for k in range(len(dp_list)):\n if i == dp_list[k][0]:\n sum_list.append(dp_list[k][1][j])\n n += 1\n if n > 0:\n mean = sum(sum_list) / n\n cc_list[i][j] = mean\n return cc_list",
"title": ""
},
{
"docid": "2da06e35d0a7d7f923d64f5bf4b04e74",
"score": "0.5954641",
"text": "def get_euclidian_dist(self, node_a, node_b):\n\n na_coords = np.array([node_a['lat'], node_a['lng']])\n nb_coords = np.array([node_b['lat'], node_b['lng']])\n\n return np.linalg.norm(na_coords - nb_coords)",
"title": ""
},
{
"docid": "4723ec6f0d172d963815cab37965e935",
"score": "0.59491515",
"text": "def _dist(coor1, coor2, noshift=True):\n last = (2 if noshift else 3)\n return np.linalg.norm(coor1[:last] - coor2[:last])",
"title": ""
},
{
"docid": "ccbb5ad86b9b2cd932f243136e13323a",
"score": "0.594771",
"text": "def dist(a, b):\n a = numpy.array(a)\n b = numpy.array(b)\n return numpy.sqrt(numpy.sum(numpy.power(a - b, 2)))",
"title": ""
},
{
"docid": "6f1648619aa92060bcfba0c9db0c53ff",
"score": "0.5947318",
"text": "def euclidean_dist(r1, r2):\n # there's three xyzs in each coord tuple, corresponding to the n,\n # ca, c backbone atoms. We calculate the distance between n1 and\n # n2, ca1 and ca2, c1 and c2; and sum those distances.\n coords1 = get_backbone_coords(r1)\n coords2 = get_backbone_coords(r2)\n s = 0\n for i in range(3):\n s = s + (((coords1[i][0] - coords2[i][0])**2 +\n (coords1[i][1] - coords2[i][1])**2 + \n (coords1[i][2] - coords2[i][2])**2)**(0.5))\n return s",
"title": ""
},
{
"docid": "37977d5dac1fe5f235047af3848966c9",
"score": "0.5943313",
"text": "def __calc_sse(self):\r\n sse = 0\r\n for i in range(self.n_cluster):\r\n sse += np.sum((self.data[self.idx==i] - self.centroid[i])**2)\r\n return sse",
"title": ""
},
{
"docid": "61022c07d9f77e1692cafd36f85523c6",
"score": "0.5942908",
"text": "def euclidean_similarity(person1, person2):\r\n if person1 in gd.groups.keys():\r\n user_data_1 = gd.get_group(person1)\r\n else:\r\n return 0\r\n if person2 in gd.groups.keys():\r\n user_data_2 = gd.get_group(person2)\r\n else:\r\n return 0\r\n pivot_1 = user_data_1.pivot(index='userid', columns='topic_id', values='rating')\r\n pivot_2 = user_data_2.pivot(index='userid', columns='topic_id', values='rating')\r\n topic_1 = pivot_1.columns\r\n topic_2 = pivot_2.columns\r\n common_topics = np.intersect1d(topic_1, topic_2)\r\n if len(common_topics) != 0:\r\n rating_1 = np.zeros(len(common_topics))\r\n rating_2 = np.zeros(len(common_topics))\r\n idx = 0\r\n for ii in common_topics:\r\n rating_1[idx] = pivot_1[ii].iloc[0]\r\n rating_2[idx] = pivot_2[ii].iloc[0]\r\n idx += 1\r\n score = np.sqrt(np.sum(np.power(rating_1 - rating_2, 2)))\r\n return 1 / (1 + score)\r\n return 0",
"title": ""
},
{
"docid": "a92b3df03b4f1e70e97cba0f664dc210",
"score": "0.5927397",
"text": "def compute_distance(a, b):\n sums = 0\n for key in a.keys():\n if key in b.keys():\n sums += pow(a[key][0] - b[key][0], 2)\n return sums **.5",
"title": ""
},
{
"docid": "0f017bc51aa2561f615af7bc386d53d6",
"score": "0.59173715",
"text": "def avgOverlapCoeff(data1, data2):\n r = []\n weights = ones(min(shape(data1)), dtype=float)\n weights[0] = 0.1\n weights[-1] = 0.1\n for i in range(0, min(shape(data1))):\n nd1 = squeeze(asarray(data1[i], dtype=float128))\n if sum(nd1) < 1:\n nd1[0, 0] = 1.0\n nd2 = squeeze(asarray(data2[i], dtype=float128))\n if sum(nd2) < 1:\n nd2[0, 0] = 1.0\n if (sum(nd1) + sum(nd2)) > 0:\n r.append(sum(multiply(nd1, nd2)) / sqrt(multiply(sum(square(nd1)), sum(square(nd2)))))\n else:\n print('Note: both equal only as blank')\n r.append(1.0)\n print(r)\n print(weights)\n print(average(r, weights=weights))\n return average(r, weights=weights)",
"title": ""
},
{
"docid": "fa2b8884fc2da8d0b7e10d30ba446b79",
"score": "0.59146065",
"text": "def update_centroids_weighted_distance(self):\n\n t1 = time.time()\n\n for k in self.clusters:\n\n if len(self.clusters[k][\"lower\"]) == len(self.clusters[k][\"upper\"]) and \\\n len(self.clusters[k][\"lower\"]) != 0:\n # Get lower approximation vectors and distance weights\n weights = np.asarray([self.d_weights[k][str(l)] for l in self.clusters[k][\"lower\"]])\n weights /= np.sum(weights)\n self.centroids[str(k)] = \\\n np.sum([weights[m] * self.data_array[l,:]\n for m,l in enumerate(self.clusters[k][\"lower\"])], axis=0)\n\n elif len(self.clusters[k][\"lower\"]) == 0 and len(self.clusters[k][\"upper\"]) != 0:\n # Get upper approximation vectors\n weights = np.asarray(\n [self.d_weights[k][str(l)] for l in self.clusters[k][\"upper\"]])\n weights /= np.sum(weights)\n self.centroids[str(k)] = \\\n np.sum([weights[m] * self.data_array[l, :]\n for m,l in enumerate(self.clusters[k][\"upper\"])], axis=0)\n\n else:\n # Get both upper-exclusive and lower approximation sets\n exclusive_set = \\\n list(set(self.clusters[k][\"upper\"]).difference(set(self.clusters[k][\"lower\"])))\n weights1 = np.asarray(\n [self.d_weights[k][str(l)] * self.data_array[l, :]\n for l in self.clusters[k][\"lower\"]])\n weights1 /= np.sum(weights1)\n weights2 = np.asarray(\n [self.d_weights[k][str(l)] * self.data_array[l, :] for l in exclusive_set])\n weights2 /= np.sum(weights2)\n self.centroids[str(k)] = \\\n self.wght_lower * np.sum([weights1[m] * self.data_array[l, :]\n for m,l in enumerate(self.clusters[k][\"lower\"])], axis=0) \\\n + self.wght_upper * np.sum([weights2[m] * self.data_array[l, :]\n for m,l in enumerate(exclusive_set)], axis=0)\n\n if self.debug_update is True:\n print \"\"\"###Cluster\"\"\", k, self.clusters[k][\"lower\"], self.clusters[k][\"upper\"]\n\n if self.timing is True:\n t3 = time.time()\n print \"update_centroids Time\", t3 - t1\n\n return",
"title": ""
},
{
"docid": "9ca879d23513c48ad661c8843443f407",
"score": "0.591186",
"text": "def e_score(self, other):\n self.gnode.grinch.num_e_scores += 1\n return -_fast_norm_diff(self.centroid, other.centroid)",
"title": ""
},
{
"docid": "6452e95136b771b4f3b213eb62e422ad",
"score": "0.5903836",
"text": "def cluster_data():\n distance_list = []\n euclidean_d = []\n for i in range(len(dp_list)):\n for j in range(len(cc_list)):\n for k in range(len(cc_list[0])):\n distance_list.append((dp_list[i][1][k] - cc_list[j][k]) ** 2)\n euclidean_d.append(math.sqrt(sum(distance_list)))\n distance_list.clear()\n dp_list[i][0] = euclidean_d.index(min(euclidean_d))\n euclidean_d.clear()\n return dp_list",
"title": ""
},
{
"docid": "d4740a03412748f69375a9814eb7ab99",
"score": "0.5903079",
"text": "def directed_distance(A, B):\n Na = len(A)\n sum = 0\n for a in A:\n sum += distance_between_point_and_set(a, B)\n return sum/Na",
"title": ""
},
{
"docid": "fa4ac1926f424dddb559e34807a4969a",
"score": "0.58978266",
"text": "def dist_centroid(self, arff):\n self.compute_centroids(arff)\n n = len(self.labels)\n dist = np.zeros((n,n))\n \n for i in range(0,n) :\n for j in range(i+1,n):\n cent_i = self.centroids[i]\n cent_j = self.centroids[j]\n dist[i,j] = np.linalg.norm(cent_i - cent_j)\n \n return dist",
"title": ""
},
{
"docid": "9b247bc6844a09acdeea38a8f4de60f9",
"score": "0.58847165",
"text": "def update_means(clusters):\n sums = [np.array(c).sum(axis=0) for c in clusters]\n means = [sum/len(c) for c, sum in zip(clusters, sums)]\n return means",
"title": ""
},
{
"docid": "66c42c1f5da9efcf48b629794ce2da33",
"score": "0.5882879",
"text": "def calcDist(self,gal1,gal2):\n from math import sqrt as sqrt\n\n d = 0.0;\n for i in range(self.dim):\n d += (self.pos[gal1][i]-self.pos[gal2][i])**2\n return sqrt(d)",
"title": ""
},
{
"docid": "45dde8455e3caa3e99865142119f3d7d",
"score": "0.58799577",
"text": "def average_neighbor_distance_map(labels : Image, distance_map : Image = None) -> Image:\n\n from .._tier9 import centroids_of_labels\n from .._tier1 import generate_distance_matrix\n from .._tier1 import generate_touch_matrix\n from .._tier1 import average_distance_of_touching_neighbors\n\n centroids = centroids_of_labels(labels)\n distance_matrix = generate_distance_matrix(centroids,centroids)\n touch_matrix = generate_touch_matrix(labels)\n\n value_vector = average_distance_of_touching_neighbors(distance_matrix, touch_matrix)\n\n distance_map = replace_intensities(labels, value_vector, distance_map)\n return distance_map",
"title": ""
},
{
"docid": "73c6d1f4d4390355f686e5d03557de50",
"score": "0.58796996",
"text": "def cluster_accuracy(assigments, labels):\n clusters = np.unique(assigments)\n classes = np.unique(labels)\n\n num_hit = 0\n for c in clusters:\n subassignments = assigments[assigments==c]\n sublabels = labels[assigments==c]\n counts = np.zeros(len(classes))\n for l in range(len(classes)):\n counts[l] = np.sum(sublabels==classes[l])\n cluster_label = classes[np.argmax(counts)]\n num_hit += np.sum(sublabels==cluster_label)\n\n # ave_acc = ave_acc/(len(clusters))\n ave_acc = 1.0*num_hit/len(assigments)\n return ave_acc",
"title": ""
},
{
"docid": "e7eb206827cfe4f51c5ff52c37a2d949",
"score": "0.5869067",
"text": "def UpdateClusters(self,c0,c1,b):\n if len(c0.sites) > len(c1.sites):\n c_tmp = c1\n c1 = c0\n c0 = c_tmp\n \"\"\"\n Remove c0 from clusters list.\n \"\"\"\n self.clusters.pop(c0.index)\n c1.sites += c0.sites\n \"\"\"\n The more efficient way to calculate P/A.\n \"\"\"\n c1.perimeter += c0.perimeter-2.*b.length\n c1.perimeterToArea = c1.perimeter/len(c1.sites)\n \"\"\"\n All existing boundaries of c1 are updated with recalculated costs. \n \"\"\"\n for b in c1.boundaries:\n cc0,cc1 = b.clusterPair\n b.areaToPerimeterOfCombinedCluster = (len(cc0.sites)+len(cc1.sites))/(cc0.perimeter+cc1.perimeter-2.*b.length)\n b.deltaPerimeter = 2.*b.length\n cc0,cc1 = b.clusterPair\n if (cc0 == c1):\n cc1,cc0 = b.clusterPair\n self.costs[b.index] = (max([cc0.perimeterToArea,cc1.perimeterToArea])-self.J,\\\n b.deltaPerimeter,b.areaToPerimeterOfCombinedCluster) \n return c0,c1",
"title": ""
},
{
"docid": "be991492989edb0ebdadfbd3b5e10787",
"score": "0.5868576",
"text": "def _conductivity_average(self, k1, k2):\n\t\t# Geometric mean\n\t\t# return (k1 * k2) ** 0.5\n\t\t# Harmonic mean\n\t\t# return 2 * k1 * k2 / (k1 + k2)\n\t\t# Arithmetic mean\n\t\treturn 0.5 * (k1 + k2)",
"title": ""
},
{
"docid": "c5a29b4cc8589221eb55d6e022b8bc3a",
"score": "0.5867241",
"text": "def dist_a_b(a, b):\n return sum([(y - x) ** 2 for (x, y) in zip(a, b)])",
"title": ""
},
{
"docid": "51a273e8c4b6445f1ad327f35a9bb25c",
"score": "0.5859607",
"text": "def euclideanDistance(A,B):\n\t\n\t#spatial.distance.cdist(A, B, metric = 'euclidean')\n\treturn np.sqrt(np.sum((np.array(A)[None, :] - np.array(B)[:, None])**2, -1)).T",
"title": ""
},
{
"docid": "6f98474bafeb6f33222c8c1b50f6ec6a",
"score": "0.5842861",
"text": "def distance_calculate(sample1, sample2):\n dist = []\n for i in range(len(sample1)):\n for j in range(len(sample2)):\n try:\n dist.append(np.linalg.norm(\n np.array(sample1[i])-np.array(sample2[j])))\n except TypeError:\n dist.append(intersampledist(sample1[i], sample2[j]))\n return min(dist)",
"title": ""
},
{
"docid": "b810ecbd2fff810dd0ed88c073cc6052",
"score": "0.583533",
"text": "def compute_distance(self, tensor1, tensor2, metric):\n if metric == 'l1':\n return torch.abs(tensor1 - tensor2).mean(dim=(2,))\n elif metric == 'l2':\n return torch.pow(tensor1 - tensor2, 2).mean(dim=(2,))\n elif metric == 'cosine':\n return 1 - self.cosine(tensor1, tensor2)\n else:\n raise ValueError(metric)",
"title": ""
},
{
"docid": "9732d314746760ca1dd0b9332e6b2dc6",
"score": "0.5826812",
"text": "def get_euclidean_distance(self, lista, listb):\n return sum( (b - a) ** 2 for a,b in zip(lista, listb) ) ** .5",
"title": ""
},
{
"docid": "e1a7ac60cfa3c96352a2d2cc71c1c5d6",
"score": "0.58216935",
"text": "def loss(self, x, y):\n\n # Compute distance of each example to each cluster centroid (euclid without the root)\n distances = self.calculate_distance(self.centroids, x)\n\n # Compute the mask selecting the distances related to each class(r)=class(cluster/rep)\n intra_cluster_mask = self.comparison_mask(y, torch.from_numpy(self.cluster_classes).cuda())\n\n # Compute variance of intra-cluster distances\n # N = x.shape[0]\n # variance = min_match.sum() / float((N - 1))\n variance = 0.5 # hard code 0.5 [as suggested in paper]\n var_normalizer = -1 / (2 * variance**2)\n\n if not self.avg_variance:\n self.avg_variance = variance\n else:\n self.avg_variance = (self.avg_variance + variance) / 2\n\n # Compute numerator\n numerator_pre_mask = torch.exp(var_normalizer * distances)\n numerator = (intra_cluster_mask.float() * numerator_pre_mask).sum(1)\n\n # Compute denominator\n denominator = numerator_pre_mask.sum(1)\n\n # Compute example losses and total loss\n epsilon = 1e-8\n\n # Compute example losses and total loss\n losses = F.relu(-torch.log(numerator / (denominator + epsilon) + epsilon) + self.alpha)\n\n total_loss = losses.mean()\n\n _, preds = distances.min(1)\n preds = ensure_tensor(self.cluster_classes[preds]).cuda() # convert from cluster ids to class ids\n acc = torch.eq(y, preds).float().mean()\n\n return total_loss, losses, acc",
"title": ""
},
{
"docid": "844ec1a58b08d3fdbb12f3161643424e",
"score": "0.58143556",
"text": "def avg_cluster_coefficient(G):\n number_of_nodes = nx.number_of_nodes(G)\n sum_of_cluster_coefficients = 0.0\n for node in list(G.nodes):\n sum_of_cluster_coefficients += cluster_coefficient_of_node(G, node)\n avg_cluster_coefficient = sum_of_cluster_coefficients / number_of_nodes\n return avg_cluster_coefficient",
"title": ""
},
{
"docid": "794e8b13a23dcff23edfa4e5c8f564cd",
"score": "0.5812002",
"text": "def euclidean_dist_centred(x, y):\n diff = np.mean(x) - np.mean(y)\n return np.dot(diff, diff)",
"title": ""
},
{
"docid": "aa89da77766faf47a5fd5ef6ceba5f08",
"score": "0.58119434",
"text": "def _distance(data, centers, var):\n weight_euclidean = np.ndarray(shape=(len(centers), len(data)), dtype=float)\n# print('the weight euclidean ', weight_euclidean.shape)\n for i in range(len(centers)):\n# print('center[i] is ', centers[i])\n# print('data is ',data)\n im_data = data - centers[i]\n# print('im_data is ', im_data) #x^power / var\n im_data = np.power(im_data, 2)/var\n# print('var is ', var)\n im_data = np.sum(im_data, axis=1)\n# print('final data is ', im_data)\n im_data = np.sqrt(im_data)\n for j in range(len(data)):\n weight_euclidean.itemset((i,j), im_data[j])\n# print('the new method dist is ', weight_euclidean)\n return weight_euclidean",
"title": ""
},
{
"docid": "0a3d98a8e8b4a97d638b2b96f0956c0c",
"score": "0.5802523",
"text": "def plot_mean_distances_in_clusters(coords, labels, epsilon):\n\n number_of_clusters = len(set(labels)) - 1\n\n x_axis = [] # cluster label\n y_axis = [] # mean distances\n\n for cluster in range(0, number_of_clusters):\n # Get coordinates of points in this cluster\n coords_cluster = coords[labels == cluster]\n n_points = len(coords_cluster)\n \n # Set up knn and get distances\n knn = KNeighborsClassifier(n_points - 1).fit(coords_cluster, np.zeros(n_points)) #Fit the KNeighboursClassifier to this cluster points\n distances = knn.kneighbors()[0] #Obtain the distances of the points inside this cluster with KNeighborsClassifier\n\n x_axis.append(cluster) #Add the current cluster to the x_axis\n y_axis.append(np.mean(distances)) # Add mean to list\n \n plt.close()\n plt.figure(figsize = (15, 8))\n plt.title('Mean intra-cluster distance')\n plt.bar(x = x_axis, height = y_axis)\n plt.xlabel(\"Cluster Labels\")\n plt.ylabel(\"Mean distance within cluster\")\n plt.xticks(x_axis[::5], x_axis[::5])\n plt.yticks(range(int(np.max(y_axis)))[::10], range(int(np.max(y_axis)))[::10] )\n\n file_name = f\"dist_within_cluster_eps={round(epsilon, 1)}.png\"\n plt.savefig(__image_dir + \"/\" + file_name, dpi=300)\n plt.savefig(__image_dir + \"/\" + file_name[0:-3]+\"eps\", dpi=300)\n plt.show()\n plt.close()\n\n # Plot histogram\n plt.figure(figsize = (15, 8))\n font = {\n 'weight' : 'regular',\n 'size' : 24}\n plt.rc('font', **font)\n plt.title(\"Histogram of Mean intra-cluster distance\")\n bins2 = np.linspace(0, max(y_axis), num=1+(max(y_axis))/10)\n plt.hist(y_axis, bins=bins2)\n plt.grid(True)\n plt.xlabel(\"Mean intra-cluster distance\")\n plt.ylabel(\"Frequency of clusters\")\n file_name = f\"hist_dist_within_cluster_eps={round(epsilon, 1)}.png\"\n plt.savefig(__image_dir + \"/\" + file_name, dpi=300)\n plt.savefig(__image_dir + \"/\" + file_name[0:-3]+\"eps\", dpi=300)\n plt.show()\n plt.close()",
"title": ""
},
{
"docid": "18736fa38ab8d98d1b95a6d360880ed3",
"score": "0.58024585",
"text": "def compute(self, this_cluster, other_cluster):\n pass",
"title": ""
},
{
"docid": "08abc2438bc3b7cf9ec4e5fe53f84dc7",
"score": "0.57994497",
"text": "def compute_similarity(site_a, site_b):\n\n # Euclidean distance\n \n return math.sqrt(sum([(x - y)**2 for x, y in zip(site_a, site_b)]))",
"title": ""
},
{
"docid": "4ea498a0dcad24590e560ffc37e533d6",
"score": "0.57969093",
"text": "def distance(self, samples):\n return self.kl_divergence(samples)",
"title": ""
},
{
"docid": "2a9799013198d57e69cac7d1ba848fb8",
"score": "0.5793544",
"text": "def cluster_grid_distance(clusters, grid, shape, affine):\n\n\tif isinstance(grid, Path):\n\t\tgrid = str(grid)\n\tif isinstance(grid, str):\n\t\tgrid = gpd.read_file(grid)\n\n\tgrid = grid.to_crs(crs=clusters.crs)\n\tgrid = grid.loc[grid['geometry'].length > 0]\n\n\tgrid_raster = rasterize(grid.geometry, out_shape=shape, fill=1,\n\t default_value=0, all_touched=True, transform=affine)\n\tdist_raster = ndimage.distance_transform_edt(grid_raster) * affine[0]\n\n\tdists = zonal_stats(vectors=clusters, raster=dist_raster, affine=affine, stats='min', nodata=1000)\n\tclusters['grid_dist'] = [x['min'] for x in dists]\n\n\treturn clusters",
"title": ""
},
{
"docid": "92910800de6e646afdbdaa545bea6a80",
"score": "0.57910913",
"text": "def image_dist(g1, g2, alpha=1.0):\n g1_center = 0.5 * (g1[:, 0:2] + g1[:, 2:4])\n g1_axis = g1[:, 2:4] - g1[:, 0:2]\n g1_axis = g1_axis / np.linalg.norm(g1_axis)\n\n g2_center = 0.5 * (g2[:, 0:2] + g2[:, 2:4])\n g2_axis = g2[:, 2:4] - g2[:, 0:2]\n g2_axis = g2_axis / np.linalg.norm(g2_axis)\n\n point_dist = np.linalg.norm(g1_center - g2_center, axis=-1)\n axis_dist = np.arccos(np.sum(g1_axis * g2_axis, axis=-1))\n\n return point_dist + alpha * axis_dist",
"title": ""
},
{
"docid": "3803e2b2659f53fa80d916af82e7d2b0",
"score": "0.5788278",
"text": "def calculate_distance(atom1,atom2):\n\n x_dist = float(atom1[0]) - float(atom2[0])\n y_dist = float(atom1[1]) - float(atom2[1])\n z_dist = float(atom1[2]) - float(atom2[2])\n distance = np.sqrt(x_dist**2 + y_dist**2 + z_dist**2)\n return (distance)",
"title": ""
},
{
"docid": "6a114df2b3e44f9f0908cbb5ea123e48",
"score": "0.5786679",
"text": "def _global_cluster_coefficient(self):\n return nx.average_clustering(self.g)",
"title": ""
},
{
"docid": "36ce44ee0984ee92f7d3136dc1da82e7",
"score": "0.57857674",
"text": "def rdist(X1, X2):\n dim = X1.shape[-1]\n d_sq = 0.0\n for j in range(dim):\n d_sq += (X1[j] - X2[j]) ** 2\n return d_sq",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="782"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">f634af849b1a0f84d7a22d842504efad</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Set up a blob selection survey</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "77bcf48d7ed0a69707ea80974df3ea53",
"score": "0.58597565",
"text": "def testBlobs(self):\n nside = 32\n survey_length = 2.0 # days\n\n surveys = []\n # Set up the DD\n dd_surveys = generate_dd_surveys(nside=nside)\n surveys.append(dd_surveys)\n\n surveys.append(gen_blob_surveys(nside))\n surveys.append(gen_greedy_surveys(nside))\n\n scheduler = Core_scheduler(surveys, nside=nside)\n observatory = Model_observatory(nside=nside)\n observatory, scheduler, observations = sim_runner(observatory, scheduler,\n survey_length=survey_length,\n filename=None)\n\n # Make sure some blobs executed\n assert('blob, gg, b' in observations['note'])\n assert('blob, gg, a' in observations['note'])\n # assert('blob, u' in observations['note'])\n\n # Make sure some greedy executed\n assert('' in observations['note'])\n # Check that the a DD was observed\n assert('DD:ELAISS1' in observations['note'])\n # Make sure a few different filters were observed\n assert(len(np.unique(observations['filter'])) > 3)\n # Make sure lots of observations executed\n assert(observations.size > 1000)\n # Make sure nothing tried to look through the earth\n assert(np.min(observations['alt']) > 0)",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "4a9668099875beb4a9c885505b4d2517",
"score": "0.53254974",
"text": "def test_bokeh_selector(self):\n from straxen.analyses.bokeh_waveform_plot import DataSelectionHist\n p = self.st.get_array(nt_test_run_id, 'peak_basics')\n ds = DataSelectionHist('ds')\n fig = ds.histogram2d(p,\n p['area'],\n p['area'],\n bins=50,\n hist_range=((0, 200), (0, 2000)),\n log_color_scale=True,\n clim=(10, None),\n undeflow_color='white')\n\n import bokeh.plotting as bklt\n save_as = 'test_data_selector.html'\n bklt.save(fig, save_as)\n self.assertTrue(os.path.exists(save_as))\n os.remove(save_as)\n self.assertFalse(os.path.exists(save_as))\n # Also test if we can write it to the wiki\n straxen.bokeh_to_wiki(fig)\n straxen.bokeh_to_wiki(fig, save_as)\n self.assertTrue(os.path.exists(save_as))\n os.remove(save_as)\n self.assertFalse(os.path.exists(save_as))",
"title": ""
},
{
"docid": "ee2c0af9756ef803bd714f495285cfb6",
"score": "0.51698744",
"text": "def test_create_single_poll_choice(self):\n # This method utilises the POST request method and will make changes to the Canvas instance. This needs consideration.\n pass",
"title": ""
},
{
"docid": "66045d8830743cdbbaf232b7f67493b0",
"score": "0.5135461",
"text": "def run(self, selection=False, references=False):",
"title": ""
},
{
"docid": "f9fabad0944204d506a1e89ddfc05aae",
"score": "0.51213545",
"text": "def __init__(self, rs):\n self.rs = rs\n self.selectedOpts = set()",
"title": ""
},
{
"docid": "9fc349dd885b3fd57a896df7656ca2b5",
"score": "0.51057255",
"text": "def gen_blob_surveys(nside):\n target_map = standard_goals(nside=nside)\n norm_factor = calc_norm_factor(target_map)\n\n filter1s = ['u', 'g'] # , 'r', 'i', 'z', 'y']\n filter2s = [None, 'g'] # , 'r', 'i', None, None]\n filter1s = ['g'] # , 'r', 'i', 'z', 'y']\n filter2s = ['g'] # , 'r', 'i', None, None]\n\n pair_surveys = []\n for filtername, filtername2 in zip(filter1s, filter2s):\n detailer_list = []\n bfs = []\n bfs.append(bf.M5_diff_basis_function(filtername=filtername, nside=nside))\n if filtername2 is not None:\n bfs.append(bf.M5_diff_basis_function(filtername=filtername2, nside=nside))\n bfs.append(bf.Target_map_basis_function(filtername=filtername,\n target_map=target_map[filtername],\n out_of_bounds_val=np.nan, nside=nside,\n norm_factor=norm_factor))\n if filtername2 is not None:\n bfs.append(bf.Target_map_basis_function(filtername=filtername2,\n target_map=target_map[filtername2],\n out_of_bounds_val=np.nan, nside=nside,\n norm_factor=norm_factor))\n bfs.append(bf.Slewtime_basis_function(filtername=filtername, nside=nside))\n bfs.append(bf.Strict_filter_basis_function(filtername=filtername))\n # Masks, give these 0 weight\n bfs.append(bf.Zenith_shadow_mask_basis_function(nside=nside, shadow_minutes=60., max_alt=76.))\n bfs.append(bf.Moon_avoidance_basis_function(nside=nside, moon_distance=30.))\n bfs.append(bf.Clouded_out_basis_function())\n # feasibility basis fucntions. Also give zero weight.\n filternames = [fn for fn in [filtername, filtername2] if fn is not None]\n bfs.append(bf.Filter_loaded_basis_function(filternames=filternames))\n bfs.append(bf.Time_to_twilight_basis_function(time_needed=22.))\n bfs.append(bf.Not_twilight_basis_function())\n bfs.append(bf.Planet_mask_basis_function(nside=nside))\n\n weights = np.array([3.0, 3.0, .3, .3, 3., 3., 0., 0., 0., 0., 0., 0., 0.])\n if filtername2 is None:\n # Need to scale weights up so filter balancing still works properly.\n weights = np.array([6.0, 0.6, 3., 3., 0., 0., 0., 0., 0., 0., 0.])\n if filtername2 is None:\n survey_name = 'blob, %s' % filtername\n else:\n survey_name = 'blob, %s%s' % (filtername, filtername2)\n if filtername2 is not None:\n detailer_list.append(detailers.Take_as_pairs_detailer(filtername=filtername2))\n pair_surveys.append(Blob_survey(bfs, weights, filtername1=filtername, filtername2=filtername2,\n survey_note=survey_name, ignore_obs='DD', detailers=detailer_list))\n return pair_surveys",
"title": ""
},
{
"docid": "9a65f394aabdf3d9bd6037f5b706866e",
"score": "0.5030312",
"text": "def selected_fun():\n\n global ques_index\n global labelQuestion1, r1, r2, r3, r4\n global choice\n global score\n\n\n choice_var.set(-1)\n\n\n if ques_index == 21:\n question_label.destroy()\n query_fun()\n\n\n elif ques_index < 21:\n choice = [anser[ques_index-1], option1[ques_index-1], option2[ques_index-1], option3[ques_index-1]]\n random.shuffle(choice)\n\n labelQuestion1.config(text=f\" {ques_index}) {question[ques_index-1]}\")\n r1['text'] = choice[0]\n r2['text'] = choice[1]\n r3['text'] = choice[2]\n r4['text'] = choice[3]\n\n ques_index += 1\n\n else:\n pass",
"title": ""
},
{
"docid": "2a1db7632c33cd0e6b3bdd73b6a73907",
"score": "0.50106955",
"text": "def choice(face_locations,small_frame,known_faces,res):\n if(easygui.ynbox('Do you want to be in the dataset ?', 'Face recognition', ('Yeah', 'Hell no !')) == True):\n takeFace(face_locations,small_frame,known_faces,res)\n else:\n \"\"\"To avoid crash, it's needed to return the known_faces\"\"\"\n res.put(known_faces)\n return",
"title": ""
},
{
"docid": "727bba9e05ec8d4e92db78916f832784",
"score": "0.49803796",
"text": "def test_nhanes_fulldesign_subset_continuous(standardize):\n # Load the data\n df = clarite.load.from_csv(DATA_PATH / \"nhanes_data_subset.csv\", index_col=None)\n # Process data\n df = clarite.modify.make_binary(df, only=[\"HI_CHOL\", \"RIAGENDR\"])\n df = clarite.modify.make_categorical(df, only=[\"race\", \"agecat\"])\n design = clarite.survey.SurveyDesignSpec(\n df,\n weights=\"WTMEC2YR\",\n cluster=\"SDMVPSU\",\n strata=\"SDMVSTRA\",\n fpc=None,\n nest=True,\n drop_unweighted=True,\n )\n design.subset(df[\"subset\"] > 0)\n df = df.drop(columns=[\"subset\"])\n df = clarite.modify.colfilter(df, only=[\"HI_CHOL\", \"RIAGENDR\", \"race\", \"agecat\"])\n # Get Results\n surveylib_result_file = RESULT_PATH / \"nhanes_complete_result_subset_cont.csv\"\n # Run analysis and comparison\n regression_kinds = [\"weighted_glm\", \"r_survey\"]\n results = dict()\n for rk in regression_kinds:\n results[rk] = pd.concat(\n [\n clarite.analyze.association_study(\n outcomes=\"HI_CHOL\",\n covariates=[\"agecat\", \"RIAGENDR\"],\n data=df,\n regression_kind=rk,\n survey_design_spec=design,\n standardize_data=standardize,\n ),\n clarite.analyze.association_study(\n outcomes=\"HI_CHOL\",\n covariates=[\"race\", \"RIAGENDR\"],\n data=df,\n regression_kind=rk,\n survey_design_spec=design,\n standardize_data=standardize,\n ),\n clarite.analyze.association_study(\n outcomes=\"HI_CHOL\",\n covariates=[\"race\", \"agecat\"],\n data=df,\n regression_kind=rk,\n survey_design_spec=design,\n standardize_data=standardize,\n ),\n ],\n axis=0,\n )\n # Compare\n if not standardize:\n compare_loaded(results[regression_kinds[0]], surveylib_result_file, rtol=1e-04)\n assert_frame_equal(\n results[regression_kinds[0]], results[regression_kinds[1]], rtol=1e-04\n )\n else:\n assert_frame_equal(\n results[regression_kinds[0]], results[regression_kinds[1]], rtol=1e-04\n )",
"title": ""
},
{
"docid": "3ff55fea400dd939d8b79722b305164a",
"score": "0.49732262",
"text": "def subset(config):\n\n samples_data = create_dataframe(config)\n subset_type = \"\"\n\n while subset_type == \"\":\n subset_type = input(constants.msg_subset)\n\n if subset_type == 'l':\n load_labeled(samples_data)\n\n elif subset_type == 'k':\n load_samples(samples_data)\n\n elif subset_type == 'f':\n family = input(constants.msg_family)\n load_family(family, samples_data)\n\n elif subset_type == 's':\n load_balanced(samples_data, 100, 50)\n\n elif subset_type == 'b':\n load_balanced(samples_data, 100, 1000)\n\n elif subset_type == 'q':\n exit()\n\n else:\n subset_type = \"\"\n print(constants.msg_invalid)\n\n return samples_data",
"title": ""
},
{
"docid": "b690c3dd73fd3d885b97b8384e87e3ed",
"score": "0.4874731",
"text": "def import_picking_selection_data (self, widget): \n atom1 = self.vm_session.picking_selections.picking_selections_list[0]\n atom2 = self.vm_session.picking_selections.picking_selections_list[1]\n atom3 = self.vm_session.picking_selections.picking_selections_list[2]\n atom4 = self.vm_session.picking_selections.picking_selections_list[3]\n \n if atom1:\n self.vobject = atom1.vm_object\n else:\n return None\n \n\n if atom1:\n self.builder.get_object('entry_atom1_index_coord1').set_text(str(atom1.index-1) )\n self.builder.get_object('entry_atom1_name_coord1' ).set_text(str(atom1.name) )\n else: print('use picking selection to chose the central atom') \n #-------\n if atom2:\n self.builder.get_object('entry_atom2_index_coord1').set_text(str(atom2.index-1) )\n self.builder.get_object('entry_atom2_name_coord1' ).set_text(str(atom2.name) )\n else: print('use picking selection to chose the central atom')\n #-------\n if atom3:\n self.builder.get_object('entry_atom3_index_coord1').set_text(str(atom3.index-1) )\n self.builder.get_object('entry_atom3_name_coord1' ).set_text(str(atom3.name) )\n \n if atom3.symbol == atom1.symbol:\n self.builder.get_object('mass_restraints1').set_active(False)\n else:\n self.builder.get_object('mass_restraints1').set_active(True)\n \n else: print('use picking selection to chose the central atom')\n #-------\n if atom4:\n self.builder.get_object('entry_atom4_index_coord1').set_text(str(atom4.index-1) )\n self.builder.get_object('entry_atom4_name_coord1' ).set_text(str(atom4.name) )\n else: print('use picking selection to chose the central atom')\n \n self.refresh_dmininum( coord1 = True)",
"title": ""
},
{
"docid": "d8becdece2b935ba44968fabfc8f3847",
"score": "0.48739973",
"text": "def OnActualVerbSelect(self, event):\n self.ChangeActualVerb(self.actual_verb_cb.GetValue())\n ix = self.actual_verb_cb.GetSelection()\n actual_verb = model.actual_verbs[ix]\n self.AddActualVerbWidgets(actual_verb)\n \n # Refresh the GUI\n self.Layout()\n self.Refresh()",
"title": ""
},
{
"docid": "e6ff7b13e5f6a2d70f1048615a8839f8",
"score": "0.48475847",
"text": "def setUp(self):\n question = \"What language did you first learn to speak?\" \n self.my_survey = AnonymousSurvey(question)\n self.responses = ['English', 'Spanish', 'Mandarin']",
"title": ""
},
{
"docid": "76e758951dedb2c2a51e183776d222e7",
"score": "0.48371542",
"text": "def choice(self):",
"title": ""
},
{
"docid": "7e4bb2962f1d79304ae557215eeec199",
"score": "0.48099133",
"text": "def select_one(ansys, setup_data):\n ansys.ksel(\"S\", \"KP\", \"\", setup_data['k'].selected)\n ansys.lsel(\"S\", \"LINE\", \"\", setup_data['l'].selected)\n ansys.asel(\"S\", \"AREA\", \"\", setup_data['a'].selected)\n ansys.vsel(\"S\", \"VOLU\", \"\", setup_data['v'].selected)\n ansys.nsel(\"S\", \"NODE\", \"\", 1)\n ansys.esel(\"S\", \"ELEM\", \"\", 1)",
"title": ""
},
{
"docid": "8d4ddb14c9592f3eb80d160e71bc5c59",
"score": "0.48078218",
"text": "def select(self):\n if self.GUI==None: return\n self.GUI.current_fit = self\n if self.tmax != None and self.tmin != None:\n self.GUI.update_bounds_boxes()\n if self.PCA_type != None:\n self.GUI.update_PCA_box()\n try: self.GUI.zijplot\n except AttributeError: self.GUI.draw_figure(self.GUI.s)\n self.GUI.fit_box.SetStringSelection(self.name)\n self.GUI.get_new_PCA_parameters(-1)",
"title": ""
},
{
"docid": "02caf668bb69e5af0e7d4e9f17aead84",
"score": "0.4805751",
"text": "def __init__(self, X_train, X_test, y_train, y_test, labels, model, viz_selection, upsampled=False):\n\n self.labels = labels\n self.model = model\n self.viz_selection = viz_selection\n self.upsampled = upsampled\n self.X_train, self.X_test, self.y_train, self.y_test = X_train, X_test, y_train, y_test\n\n if self.viz_selection == 'ClassificationReport':\n self.visualizer = ClassificationReport(self.model, classes=self.labels, support=True)\n elif self.viz_selection == 'ROCAUC':\n self.visualizer = ROCAUC(self.model, classes=self.labels, support=True)\n elif self.viz_selection == 'PrecisionRecallCurve':\n self.visualizer = PrecisionRecallCurve(self.model)\n elif self.viz_selection == 'ConfusionMatrix':\n self.visualizer = ConfusionMatrix(model, classes=self.labels)\n else:\n return print(\"Error: viz_selection does not match accepted values. View Visualizer Class for accepted values.\")",
"title": ""
},
{
"docid": "7c1ce3fdd006b2d85339d5bb85e6c626",
"score": "0.47923017",
"text": "def pushdataview(clicks, username, selecteddata, rawdata):\n if clicks != 0:\n datasets.download_data(selecteddata.split('*')[0], username) \n overview = process.getsets(username)\n expname = list(overview.keys())[0]\n samples = overview[expname].keys()\n output = []\n index = 1\n for element in samples:\n opts = []\n for sets in overview[expname][element]:\n opts.append({'label': sets, 'value': expname + '/' + element + '/' + sets})\n output.append(html.Details(children=[\n html.Summary([\n element\n ]),\n html.Div([\n dcc.Checklist(\n id = 'sample'+str(index),\n options=opts,\n labelStyle={'display': 'block', 'margin':'0%'},\n value=[]\n ),\n html.Button('All', id='samplebutton'+str(index), n_clicks=0),\n ], style={'padding-left':'10%'})\n ], style={'padding-left': '5%'}))\n index += 1 \n\n return html.Div(id='dataoverview', children=output)\n else:\n return dcc.RadioItems(\n id = 'availabledatasets',\n options=[],\n labelStyle={'display': 'block', 'margin':'0%'},\n )",
"title": ""
},
{
"docid": "0b07ffe76dc072e1b26ce3ba8a18a0b5",
"score": "0.47917664",
"text": "def question_selection():\r\n print (\"Select a query. 1. What are the most popular three articles of \"\r\n \"all time? 2. Who are the most popular articles authors of all \"\r\n \"time? 3. On which days did more than 1% of requests lead to \"\r\n \"errors? \")\r\n selection = raw_input(\"Choose a query 1, 2, or 3. \")\r\n if __name__ == '__main__':\r\n if selection == \"1\":\r\n get_query(q1_query, title_1)\r\n elif selection == \"2\":\r\n get_query(q2_query, title_2)\r\n else:\r\n get_query(q3_query, title_3)",
"title": ""
},
{
"docid": "f4706d6fb9bf2f672a168d2855ff660e",
"score": "0.47864944",
"text": "def setup(self):\n self.ds = Dataset()\n #self.ds.SOPClassUID = PatientRootQueryRetrieveInformationModelGet.UID\n self.ds.PatientName = '*'\n self.ds.QueryRetrieveLevel = \"PATIENT\"\n\n self.good = Dataset()\n self.good.file_meta = Dataset()\n self.good.file_meta.TransferSyntaxUID = ImplicitVRLittleEndian\n self.good.SOPClassUID = CTImageStorage\n self.good.SOPInstanceUID = '1.1.1'\n self.good.PatientName = 'Test'\n\n self.scp = None",
"title": ""
},
{
"docid": "f4e561ce96e564c043ece82bf007f046",
"score": "0.47735164",
"text": "def select_bands(self, input_bands, output_bands):\n self.bands = [input_bands, output_bands]\n\n # Populate image table\n for n, slug in enumerate(self.slugs):\n \n dataset_id = self.datasets[self.datasets['slug'] == slug].index[0]\n\n if self.norm_type == 'geostore':\n df = self.images[(self.images['dataset_id'] == dataset_id) & \n (self.images['scale'] == self.scale) & \n (self.images['init_date'] == self.init_date) & \n (self.images['end_date'] == self.end_date) & \n (self.images['norm_type'] == self.norm_type) & \n (self.images['geostore_id'] == self.geostore_id)\n ].copy()\n else:\n df = self.images[(self.images['dataset_id'] == dataset_id) & \n (self.images['scale'] == self.scale) & \n (self.images['init_date'] == self.init_date) & \n (self.images['end_date'] == self.end_date) & \n (self.images['norm_type'] == self.norm_type)\n ].copy()\n\n # Take rows where bands_selections column is empty\n df1 = df[df['bands_selections'] == ''].copy()\n\n if df1.any().any():\n # Take first index\n index = df1.index[0]\n self.images.at[index, 'bands_selections'] = str(self.bands[n])\n else:\n if not self.images[['dataset_id', 'bands_selections', 'scale', 'init_date', 'end_date', 'norm_type']].isin(\n [dataset_id, str(self.bands[n]), self.scale, self.init_date, self.end_date, self.norm_type]).all(axis=1).any():\n\n df2 = df.iloc[0:1].copy()\n df2.at[df2.index[0], 'bands_selections'] = str(self.bands[n])\n self.images = self.images.append(df2, ignore_index = True)",
"title": ""
},
{
"docid": "0738d3dafd0e6e6ba82e6ce93148a51a",
"score": "0.4751879",
"text": "def set_film_buttons(self):\n\n\t\tw1=self.tree.get_object(\"fileaccept\")\n\t\tw=self.tree.get_object(\"moviefile\")\n\t\tcfile=w.get_filename()\n\t\t\n\t\tw=self.tree.get_object(\"preview_film\")\n\t\tif (cfile==\"\") or (cfile==None):\n\t\t\tw.set_sensitive(False)\n\t\t\tw1.set_sensitive(False)\n\t\telse:\n\t\t\tw.set_sensitive(True)\n\t\t\tw1.set_sensitive(True)\n\n\t\tw0=self.tree.get_object(\"ismpeg\")\n\t\tw0s=w0.get_active()\n\t\tif w0s:\n\t\t\tgrupo2=False\n\t\telse:\n\t\t\tgrupo2=True\n\n\t\tw1=self.tree.get_object(\"copy_audio\")\n\t\tw1s=w1.get_active()\n\t\tif w1s:\n\t\t\tcopy_audio=False\n\t\telse:\n\t\t\tcopy_audio=grupo2\n\n\t\tw2=self.tree.get_object(\"isvob\")\n\t\tw2s=w2.get_active()\n\t\tif w2s:\n\t\t\tisvob=False\n\t\telse:\n\t\t\tisvob=grupo2\n\n\t\tif (self.disctocreate!=\"dvd\") and (self.disctocreate!=\"divx\"):\n\t\t\tw1.set_active(False)\n\t\t\tw1.set_sensitive(False)\n\t\t\tsound51=False\n\t\telse:\n\t\t\tw1.set_sensitive(grupo2)\n\t\t\tsound51=grupo2\n\n\t\tif w0s:\n\t\t\tw2.set_sensitive(False)\n\t\t\tw1.set_sensitive(False)\n\t\telse:\n\t\t\tw2.set_sensitive(True)\n\t\t\tw1.set_sensitive(True)\n\t\t\tif w1s:\n\t\t\t\tw2.set_sensitive(False)\n\t\t\telse:\n\t\t\t\tw2.set_sensitive(True)\n\t\t\t\tif w2s:\n\t\t\t\t\tw0.set_sensitive(False)\n\t\t\t\t\tw1.set_sensitive(False)\n\t\t\t\telse:\n\t\t\t\t\tw1.set_sensitive(True)\n\t\t\t\t\tw0.set_sensitive(True)\n\n\t\tif self.disctocreate ==\"vcd\":\n\t\t\tgrupo1=False\n\t\telse:\n\t\t\tgrupo1=grupo2\n\t\t\n\t\tif (self.disctocreate==\"dvd\") or (self.disctocreate==\"divx\"):\n\t\t\tw=self.tree.get_object(\"sound51\")\n\t\t\tif w.get_active():\n\t\t\t\tw=self.tree.get_object(\"audio_rate_adj\")\n\t\t\t\tw.set_lower(384)\n\t\t\t\tw.set_upper(448)\n\t\t\t\tif w.get_value()<384:\n\t\t\t\t\tw.set_value(384)\n\t\t\t\t#w.set_range(384,448)\t\t\n\t\t\telse:\n\t\t\t\tw=self.tree.get_object(\"audio_rate_adj\")\n\t\t\t\tw.set_lower(128)\n\t\t\t\tw.set_upper(448)\n\t\t\t\t#w.set_range(128,448)\n\n\t\tgrupo3=grupo2\n\t\ttry:\n\t\t\tif self.file_properties[\"olength\"]<60:\n\t\t\t\tgrupo3=False\n\t\texcept:\n\t\t\tgrupo3=False\n\n\t\tw=self.tree.get_object(\"video_rate\")\n\t\tw.set_sensitive(grupo1 and isvob)\n\t\tw=self.tree.get_object(\"audio_rate\")\n\t\tw.set_sensitive(grupo1 and copy_audio and isvob)\n\t\tw=self.tree.get_object(\"swap_fields\")\n\t\tw.set_sensitive(grupo1 and isvob)\n\t\tw=self.tree.get_object(\"gop12\")\n\t\tw.set_sensitive(grupo2 and isvob)\n\t\tw=self.tree.get_object(\"volume_scale\")\n\t\tw.set_sensitive(grupo2 and isvob and copy_audio)\n\t\tw=self.tree.get_object(\"volume_level\")\n\t\tw.set_sensitive(grupo2 and isvob and copy_audio)\n\t\tw=self.tree.get_object(\"reset_volume\")\n\t\tw.set_sensitive(grupo2 and isvob and copy_audio)\n\t\tw=self.tree.get_object(\"sound51\")\n\t\tif grupo1==False:\n\t\t\tw.set_active(False)\n\t\tw.set_sensitive(sound51)\n\t\tw=self.tree.get_object(\"resauto\")\n\t\tw.set_sensitive(grupo1 and isvob)\n\t\tif (w.get_active()) and (self.disctocreate==\"divx\"):\n\t\t\tset_aspect=False\n\t\telse:\n\t\t\tset_aspect=True\n\t\tw=self.tree.get_object(\"res352x240\")\n\t\tw.set_sensitive(grupo1 and isvob)\n\t\tw=self.tree.get_object(\"res352x480\")\n\t\tw.set_sensitive(grupo1 and isvob)\n\t\tw=self.tree.get_object(\"res480x480\")\n\t\tw.set_sensitive(grupo1 and isvob)\n\t\tw=self.tree.get_object(\"res704x480\")\n\t\tw.set_sensitive(grupo1 and isvob)\n\t\tw=self.tree.get_object(\"res720x480\")\n\t\tw.set_sensitive(grupo1 and isvob)\n\t\t\n\t\tw=self.tree.get_object(\"full_length\")\n\t\tw.set_sensitive(grupo3)\n\t\tw=self.tree.get_object(\"first_half\")\n\t\tw.set_sensitive(grupo3)\n\t\tw=self.tree.get_object(\"second_half\")\n\t\tw.set_sensitive(grupo3)\n\t\tw=self.tree.get_object(\"video_pal\")\n\t\tw.set_sensitive(grupo2 and isvob)\n\t\tw=self.tree.get_object(\"video_ntsc\")\n\t\tw.set_sensitive(grupo2 and isvob)\n\t\tw=self.tree.get_object(\"audiodelay\")\n\t\tw.set_sensitive(copy_audio and isvob)\n\t\tw=self.tree.get_object(\"blackbars\")\n\t\tw.set_sensitive(grupo2 and isvob)\n\t\tw=self.tree.get_object(\"scalepict\")\n\t\tw.set_sensitive(grupo2 and isvob)\n\t\tw=self.tree.get_object(\"custom_params\")\n\t\tw.set_sensitive(grupo2 and isvob)\n\t\tw=self.tree.get_object(\"custom_params_vf\")\n\t\tw.set_sensitive(grupo2 and isvob)\n\t\tw=self.tree.get_object(\"custom_params_lavcopts\")\n\t\tw.set_sensitive(grupo2 and isvob)\n\t\tw=self.tree.get_object(\"custom_params_lameopts\")\n\t\tw.set_sensitive(copy_audio and isvob)\n\t\tw2=self.tree.get_object(\"lameopts_label\")\n\t\tif self.disctocreate==\"divx\":\n\t\t\tw.show()\n\t\t\tw2.show()\n\t\telse:\n\t\t\tw.hide()\n\t\t\tw2.hide()\n\n\t\tw=self.tree.get_object(\"trell\")\n\t\tw.set_sensitive(grupo2 and isvob)\n\t\t\n\t\tw=self.tree.get_object(\"rotation0\")\n\t\tw.set_sensitive(grupo2 and isvob)\n\t\tw=self.tree.get_object(\"rotation90\")\n\t\tw.set_sensitive(grupo2 and isvob)\n\t\tw=self.tree.get_object(\"rotation180\")\n\t\tw.set_sensitive(grupo2 and isvob)\n\t\tw=self.tree.get_object(\"rotation270\")\n\t\tw.set_sensitive(grupo2 and isvob)\n\t\tw=self.tree.get_object(\"hmirror\")\n\t\tw.set_sensitive(grupo2 and isvob)\n\t\tw=self.tree.get_object(\"vmirror\")\n\t\tw.set_sensitive(grupo2 and isvob)\n\t\t\n\t\tw=self.tree.get_object(\"mbd\")\n\t\tw.set_sensitive(grupo2 and isvob)\n\t\tw=self.tree.get_object(\"mbd1\")\n\t\tw.set_sensitive(grupo2 and isvob)\n\t\tw=self.tree.get_object(\"mbd2\")\n\t\tw.set_sensitive(grupo2 and isvob)\n\t\tw=self.tree.get_object(\"twopass\")\n\t\tw.set_sensitive(grupo2 and isvob)\n\t\ttp = w.get_active()\n\t\tw=self.tree.get_object(\"turbo1stpass\")\n\t\tw.set_sensitive(grupo2 and isvob and tp)\n\t\tw=self.tree.get_object(\"deinterlace\")\n\t\tw.set_sensitive(grupo2 and isvob)\n\t\tw=self.tree.get_object(\"deinterlace_lb\")\n\t\tw.set_sensitive(grupo2 and isvob)\n\t\tw=self.tree.get_object(\"deinterlace_md\")\n\t\tw.set_sensitive(grupo2 and isvob)\n\t\tw=self.tree.get_object(\"deinterlace_fd\")\n\t\tw.set_sensitive(grupo2 and isvob)\n\t\tw=self.tree.get_object(\"deinterlace_l5\")\n\t\tw.set_sensitive(grupo2 and isvob)\n\t\tw=self.tree.get_object(\"deinterlace_yadif\")\n\t\tw.set_sensitive(grupo2 and isvob)\n\t\tw=self.tree.get_object(\"audiotrack\")\n\t\tw.set_sensitive(grupo2)\n\t\tw1=self.tree.get_object(\"aspect_ratio_4_3\")\n\t\tw2=self.tree.get_object(\"aspect_ratio_16_9\")\n\t\tif (self.disctocreate == \"dvd\") or (self.disctocreate == \"divx\"):\n\t\t\tw1.set_sensitive(grupo2 and isvob and set_aspect)\n\t\t\tw2.set_sensitive(grupo2 and isvob and set_aspect)\n\t\telse:\n\t\t\tw1.set_sensitive(False)\n\t\t\tw2.set_sensitive(False)\n\n\t\tw1=self.tree.get_object(\"sub_remove\")\n\t\tw2=self.tree.get_object(\"sub_add\")\n\t\ttry:\n\t\t\tsub_number=len(self.file_properties[\"sub_list\"])\n\t\t\tif sub_number==0:\n\t\t\t\tw1.set_sensitive(False)\n\t\t\telse:\n\t\t\t\tw1.set_sensitive(True)\n\t\t\t\n\t\t\tif sub_number>=32:\n\t\t\t\tw2.set_sensitive(False)\n\t\t\telse:\n\t\t\t\tw2.set_sensitive(True)\n\t\texcept:\n\t\t\tw1.set_sensitive(False)\n\t\t\tw2.set_sensitive(False)",
"title": ""
},
{
"docid": "cbb017e60b7a384aa34006886fc69108",
"score": "0.47454613",
"text": "def add_blob(self, blob, blob_set, pos_set, title, credit, dest, blob_vr):\n res = False\n conn = None\n try:\n conn = lite.connect(self.database_file)\n mime = self.get_blob_mime(blob.file)\n\n blob_format, ext = self.get_format_and_extension(mime)\n # ext = \".\" + ext\n\n blob_hash = self.get_hash(blob.file)\n blob_id = self.store_blob(conn, blob.file, credit, blob_hash, ext, blob_format)\n\n try:\n if blob_vr:\n # If the user specified to use a new VR, then we use it\n _, vr_ext = self.get_format_and_extension(self.get_blob_mime(blob_vr.file))\n blob_file = self.copy_blob(blob_vr.file, blob_hash, vr_ext, self.vr_dir)\n self.create_thumbnail(blob_file, blob_hash)\n else:\n # The user didn't give any new VR.\n # We'll update the thumbnail only if it doesn't have already a VR\n if not self.blob_has_VR(blob_hash):\n blob_file = os.path.join(self.blob_dir, self.get_subdir(blob_hash))\n blob_file = os.path.join(blob_file, blob_hash + ext)\n self.create_thumbnail(blob_file, blob_hash)\n\n except IPOLBlobsThumbnailError as ex:\n # An error in the creation of the thumbnail doesn't stop the execution of the method\n self.logger.exception(\"Error creating the thumbnail\")\n print(\"Couldn't create the thumbnail. Error: {}\".format(ex))\n\n # If the set is empty the module generates an unique set name\n if not blob_set:\n blob_set = self.generate_set_name(blob_id)\n pos_set = 0\n\n if dest[\"dest\"] == \"demo\":\n demo_id = dest[\"demo_id\"]\n # Check if the pos is empty\n if database.is_pos_occupied_in_demo_set(conn, demo_id, blob_set, pos_set):\n editor_demo_id = database.get_demo_id(conn, demo_id)\n pos_set = database.get_available_pos_in_demo_set(conn, editor_demo_id, blob_set)\n\n # if blob_set, then check if this blobset has zero blob in the database, if yes then set pos=0\n if blob_set:\n if not database.get_demo_blobs(conn, demo_id, blob_set):\n pos_set = 0\n\n self.do_add_blob_to_demo(conn, demo_id, blob_id, blob_set, pos_set, title)\n res = True\n elif dest[\"dest\"] == \"template\":\n template_id = dest[\"template_id\"]\n # Check if the pos is empty\n if database.is_pos_occupied_in_template_set(conn, template_id, blob_set, pos_set):\n pos_set = database.get_available_pos_in_template_set(conn, template_id, blob_set)\n\n self.do_add_blob_to_template(conn, template_id, blob_id, blob_set, pos_set, title)\n res = True\n else:\n self.logger.error(\"Failed to add the blob in add_blob. Unknown dest: {}\".format(dest[\"dest\"]))\n\n except IOError as ex:\n self.logger.exception(\"Error copying uploaded blob\")\n print(\"Couldn't copy uploaded blob. Error: {}\".format(ex))\n except IPOLBlobsDataBaseError as ex:\n self.logger.exception(\"Error adding blob info to DB\")\n print(\"Couldn't add blob info to DB. Error: {}\".format(ex))\n except IPOLBlobsTemplateError as ex:\n print(\"Couldn't add blob to template. blob. Template doesn't exists. Error: {}\".format(ex))\n except Exception as ex:\n self.logger.exception(\"*** Unhandled exception while adding the blob\")\n print(\"*** Unhandled exception while adding the blob. Error: {}\".format(ex))\n finally:\n if conn is not None:\n conn.close()\n\n return res",
"title": ""
},
{
"docid": "9b7c37a40af350d58e480ca84c0f66b8",
"score": "0.4741057",
"text": "def selectFile(self):\r\n\r\n\t\tself.dataFile = QFileDialog.getOpenFileName()\t\t\t# Opens the file browser, the selected file is saved in self.dataFile\r\n\t\tprint(self.dataFile)\r\n\t\t# Try to determine if this is a prediction or review based on file type\r\n\t\tif self.dataFile.split('.')[-1] == 'kml':\r\n\t\t\tself.predictionCheckbox.setChecked(True)\r\n\t\tif self.dataFile.split('.')[-1] == 'csv' or self.dataFile.split('.')[-1] == 'txt':\r\n\t\t\tself.reviewCheckbox.setChecked(True)\r\n\r\n\t\tself.fileLabel.setText(self.dataFile)\t\t\t\t\t# Display the file path\r\n\r\n\t\t# Handle the file\r\n\t\tself.makePlots()",
"title": ""
},
{
"docid": "1bc750018d2e09a9c00c960f39adabbd",
"score": "0.4739686",
"text": "def test_flagging_question(self):\n # This method utilises the PUT request method and will make changes to the Canvas instance. This needs consideration.\n pass",
"title": ""
},
{
"docid": "5931c83f918538c8ca762d8f9b8c7cc4",
"score": "0.47194025",
"text": "def test_survey(self):\n sra_surveyor = SraSurveyor(self.survey_job)\n sra_surveyor.discover_experiment_and_samples()\n\n samples = Sample.objects.all()\n\n # We are expecting this to discover 1 sample.\n self.assertEqual(samples.count(), 1)\n # Confirm the sample's protocol_info\n experiment = Experiment.objects.all().first()\n self.assertEqual(\n samples.first().protocol_info[0][\"Description\"], experiment.protocol_description\n )",
"title": ""
},
{
"docid": "181d805467e555231b24a5d5fdbb5b2c",
"score": "0.4719264",
"text": "def test_spector_init_autochoose_survey_spec(obj_dirobj):\n\tobj = obj_dirobj\n\n\ts = spector.Spector(obj=obj, survey='hsc')\n\n\tassert s.survey_spec == 'boss'",
"title": ""
},
{
"docid": "c9b8e472d3acef1cf31aad61c73eeaf1",
"score": "0.469785",
"text": "def line_select_callback(clk, rls):\n global OBJECT\n extents = toggle_selector.RS.extents\n OBJECT = BoundingBox.create_from_points(\n Point(int(extents[0]), int(extents[2])), Point(int(extents[1]), int(extents[3])))\n print(OBJECT)",
"title": ""
},
{
"docid": "9a7df3e0150351b4aaae471f697690c1",
"score": "0.46891364",
"text": "def cSelectionSet(type, *args): \n\n gShelfTopLevel = mel.eval(\"$tmpVar=$gShelfTopLevel\")\n currentTab = cmds.tabLayout(gShelfTopLevel, q=1, st=1)\n setName = cmds.textField('tSelectionSet', tx=1, q=1)\n iconColor = cmds.radioCollection('rcIconColors', q=1, sl=1)\n if iconColor == 'rbRed':\n iconColor = 'red'\n if iconColor == 'rbGreen':\n iconColor = 'green'\n if iconColor == 'rbBlue':\n iconColor = 'blue'\n\n mySel = cmds.ls(os=1, fl=1)\n\n imageName = 'ss_' + iconColor + '_' + str(type) + '.png'\n cmd = 'cmds.select(\"'\n for i in range(0, len(mySel), 1):\n cmd += mySel[i]\n if i < len(mySel)-1:\n cmd += '\", \"'\n cmd += '\", ' + type + '=1)'\n\n cmds.shelfButton(p=currentTab, rpt=1, i1=imageName, iol=setName, c=cmd, ann=cmd)",
"title": ""
},
{
"docid": "78aeac2182674e805e790835e2befc31",
"score": "0.46787342",
"text": "def revote_3(self, revote_data):\r\n self.is_button_pressed = False\r\n self.c.create_rectangle(0, 65, 800, 550, fill=\"#212121\")\r\n for widget in self.activity_frame.winfo_children():\r\n widget.destroy()\r\n self.c.create_text(40, 70, text=\"You have been voted as the murderer.\\n\\nSelect a statement to defend yourself.\", anchor=NW, fill=\"white\", font=\"Arial 20\")\r\n self.all_statements = list.copy(revote_data)\r\n shuffle(self.all_statements)\r\n self.statement_options = []\r\n for index in range(0, 3):\r\n self.statement_options.append(self.all_statements.pop())\r\n self.statement_box = ttk.Combobox(self.activity_frame, value=self.statement_options, font=\"Arial 18\", width=30)\r\n self.statement_box.grid(row=0, column=0, ipadx=10, padx=10)\r\n self.statement_box.delete(0, END)\r\n self.statement_box.insert(0, \"Select Statement\")\r\n self.statement_box.bind(\"<<ComboboxSelected>>\", self.enable_statement_btn)\r\n self.statement_btn = Button(self.activity_frame, text=\"Confirm\", font=\"Arial 18 bold\", bg=\"#666666\", fg=\"white\", command=self.proceed, state=DISABLED)\r\n self.statement_btn.grid(row=0, column=1, ipadx=10, padx=10)",
"title": ""
},
{
"docid": "60754ae6569ea55248fe4a2c0445226d",
"score": "0.46614414",
"text": "def __init__(self, varlist, classer, scaler1):\n self.varlist = varlist\n # self.varlist1 = varlist1\n\n \"\"\"\n This is the variable list, it contains all of the variables for the long answer, it contains a total of 30 variables all of which have some degree of weightage and \n play roles in the determination of whether or not someone has breast cancer. \n \"\"\"\n self.varlist1 = [\"radius_mean\", \"texture_mean\", \"perimeter_mean\", \"area_mean\", \"smoothness_mean\", \"compactness_mean\",\n \"concavity_mean\", \"concave points_mean\", \"symmetry_mean\", \"fractal_dimension_mean\", \"radius_se\",\n \"texture_se\", \"perimeter_se\", \"area_se\", \"smoothness_se\", \"compactness_se\", \"concavity_se\",\n \"concave points_se\", \"symmetry_se\", \"fractal_dimension_se\", \"radius_worst\", \"texture_worst\",\n \"perimeter_worst\", \"area_worst\", \"smoothness_worst\", \"compactness_worst\", \"concavity_worst\",\n \"concave points_worst\", \"symmetry_worst\", \"fractal_dimension_worst\"]\n self.classer = classer\n self.scaler1 = scaler1\n\n\n\n\n \"\"\"\n This is the first GUI object, it is the opening page that offers the option between the long answer response and the short answer response. \n \"\"\"\n guiObj = Tk()\n guiObj.title('BREAST CANCER PREDICTOR')\n guiObj.configure(background='#ff00c3')\n guiObj.geometry('600x300')\n guiObj.resizable(width = False, height = False)\n\n\n \"\"\"FIRST PAGE\"\"\"\n shortbuttonp1 = Button(guiObj, text=\"Click here for the short option\", height = 5, fg='#1e00ff', command=self.shortsurvey)\n shortbuttonp1.grid(row=0, column=0, padx=20, pady=100)\n \"------------------------------------------\"\n Longbuttonp1 = Button(guiObj, text=\"Click here for a long option\", height=5, fg='#1e00ff', command = self.longsurvey)\n Longbuttonp1.grid(row=0, column=3, padx=125, pady=15)\n\n\n\n guiObj.mainloop()",
"title": ""
},
{
"docid": "d70377e0e0c0efe35049b2b22ecc939e",
"score": "0.46378887",
"text": "def test_01_ERP5BankingClassificationSurvey(self, quiet=QUIET, run=RUN_ALL_TEST):\n if not run:\n return\n sequence_list = SequenceList()\n # define the sequence\n sequence_string = 'Tic CheckObjects Tic CheckInitialInventory CheckSource CheckDestination ' \\\n + 'CreateClassificationSurvey ' \\\n + 'CreateTwoValidIncomingLine CheckSubTotal ' \\\n + 'CreateValidOutgoingLineForInternalBanknote ' \\\n + 'CreateValidOutgoingLineForExternalBanknote ' \\\n + 'Tic CheckTotal ' \\\n + 'CheckSource CheckDestination ' \\\n + 'ConfirmClassificationSurvey Tic ' \\\n + 'CheckSourceDebitPlanned CheckDestinationCreditPlanned ' \\\n + 'ResetSourceInventory Tic ' \\\n + 'DeliverClassificationSurveyFails Tic ' \\\n + 'DeleteResetInventory Tic ' \\\n + 'DeliverClassificationSurvey Tic ' \\\n + 'CheckSourceDebit CheckDestinationCredit '\n sequence_list.addSequenceString(sequence_string)\n # play the sequence\n sequence_list.play(self)",
"title": ""
},
{
"docid": "af7085cd9d8f06e0b941527911703850",
"score": "0.4629225",
"text": "def device_choice_click(self):\n self.my_spotify.choice_device(self.avaiable.text())\n # current playlist initializer\n\n self.gui_bottom_init()",
"title": ""
},
{
"docid": "73aa7002fe0414716360508501f11f81",
"score": "0.46287608",
"text": "def setup(self):\n self.ds = Dataset()\n self.ds.PatientName = '*'\n self.ds.QueryRetrieveLevel = \"PATIENT\"\n\n self.good = Dataset()\n self.good.file_meta = Dataset()\n self.good.file_meta.TransferSyntaxUID = ImplicitVRLittleEndian\n self.good.SOPClassUID = CTImageStorage\n self.good.SOPInstanceUID = '1.1.1'\n self.good.PatientName = 'Test'\n\n self.scp = None\n self.scp2 = None",
"title": ""
},
{
"docid": "ff325b6597d2544518f16a31eb9a6dfc",
"score": "0.4626999",
"text": "def select(self, competences):\n pass",
"title": ""
},
{
"docid": "d7ddecee711939a012d85ef5891aef20",
"score": "0.46263063",
"text": "def onRbStudyClicked(self, button):\n self.StudyId = self.studyIds[button.text] \n self.txtOtherStudy.visible = (button.text == \"Other\")\n if (self.txtOtherStudy.visible):\n self.StudyId = self.txtOtherStudy.text.strip()\n #self.checkDownloadButtonEnabled()",
"title": ""
},
{
"docid": "207e44c355a32ece912bae054434c6fb",
"score": "0.46252",
"text": "def setup(self, *args):\n\n responses = [\n ('Yes.', 'eq'),\n ('How should I know?', 'eq'),\n ('Try asking a human', 'eq/10'),\n ('Eww.', 'eq'),\n ('You\\'d just do the opposite of whatever I tell you', 'eq/50'),\n ('No.', 'eq'),\n ('Nope.', 'eq'),\n ('Maybe.', 'eq'),\n ('Possibly.', 'eq'),\n ('It could be.', 'eq'),\n (\"No. No, I don't think so.\", 'eq/2'),\n ('Without a doubt.', 'eq/2'),\n ('I think... Yes.', 'eq/2'),\n ('Heck yes!', 'eq/2'),\n ('Maybe. Possibly. It could be.', 'eq/2'),\n ('Ask again later.', 'eq/3'),\n (\"I don't know.\", 'eq/3'),\n (\"I'm sorry, I was thinking of bananas\", 'eq/100'),\n ]\n responses += [(i.strip(), 'eq') for i in affirmatives]\n responses += [(i.strip(), 'eq') for i in negatories]\n self.advices = [(x, 1) for x in obliques]\n total_prob = 0\n real_resp = []\n evens = []\n for resp, prob in responses:\n if isinstance(prob, str):\n if prob.startswith('eq'):\n sp = prob.split('/')\n if len(sp) == 1:\n evens.append((resp, 1))\n else:\n div = int(sp[1])\n evens.append((resp, 1.0 / div))\n\n else:\n real_resp.append((resp, prob))\n total_prob += prob\n\n # Share is the probability of a \"eq\" probability. Share/2 would be the\n # probability of a \"eq/2\" probability.\n share = (1 - total_prob) / sum(div for _, div in evens)\n for resp, divisor in evens:\n real_resp.append((resp, share * divisor))\n\n self.responses = real_resp\n self.is_question = re.compile('.*\\?(\\?|!)*$')",
"title": ""
},
{
"docid": "6319a9fa3a2efae3751a2de0fa908c92",
"score": "0.46059477",
"text": "def __init__(self, eqs_dataset, limiter=None, cocos=3):\n self.eqs_dataset = eqs_dataset\n self.limiter = limiter\n self.cocos = cocos",
"title": ""
},
{
"docid": "8365f485852d3de1210e84d01d8dd032",
"score": "0.46008262",
"text": "def detect_blob(self, in_img):\n img_cv = self.bridge.imgmsg_to_cv2(in_img , desired_encoding=\"passthrough\")\n img = np.asarray(img_cv)[:, :, ::-1]\n\n im_rgb = img\n im_gray = cv2.cvtColor(im_rgb,cv2.COLOR_BGR2GRAY)\n \n params = cv2.SimpleBlobDetector_Params()\n \n\n # Segmentation Thresholds\n params.minThreshold = 100\n params.maxThreshold = 400\n \n # Filter by color\n params.filterByColor = True\n params.blobColor = 0\n \n # Filter by size of the blob.\n params.filterByArea = True\n params.minArea = 20\n params.maxArea = 150\n \n # Filter by Circularity\n params.filterByCircularity = True\n params.minCircularity = 0.7\n \n # Filter by Convexity\n params.filterByConvexity = False\n params.minConvexity = 0.87\n \n # Filter by Inertia\n params.filterByInertia = False\n params.minInertiaRatio = 0.01\n\n\n \n # Create a detector with the parameters\n ver = (cv2.__version__).split('.')\n if int(ver[0]) < 3:\n detector = cv2.SimpleBlobDetector(params)\n else:\n detector = cv2.SimpleBlobDetector_create(params)\n \n # Detect blobs.\n keypoints = detector.detect(im_gray)\n \n # Draw the key points \n im_with_keypoints = cv2.drawKeypoints(im_rgb, keypoints, np.array([]), (255, 0, 0), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n \n # Show blobs\n\n #cv2.imwrite(\"/root/catkin_ws/src/o2as_blob_detection/img_res/blob_detection_\"+param_part_id+\"_results.png\", cv2.cvtColor(im_with_keypoints, cv2.COLOR_BGR2RGB))\n #print(\"before publish\")\n\n try:\n self.pub_img_w_blob.publish(self.bridge.cv2_to_imgmsg(im_with_keypoints, \"rgb8\"))\n except CvBridgeError as e:\n print(e)\n\n self.current_image_blob = self.bridge.cv2_to_imgmsg(im_with_keypoints, \"rgb8\")\n\n blob_array = []",
"title": ""
},
{
"docid": "fe1a3afb1b0c620d6d0df9d0251886b2",
"score": "0.46007222",
"text": "def exercise_2(self):\n survey_data = self.survey_data\n\n # Drop Age NaN\n survey_data = survey_data[survey_data['Mathematics'].notna()]\n # create a list of our conditions\n conditions = [\n (survey_data['Mathematics'] < 4.0),\n (survey_data['Mathematics'] >= 4.0)]\n # create a list of the values we want to assign for each condition\n values = [False, True]\n\n # create a new column and use np.select to assign values to it using our lists as arguments\n survey_data['Interested in Math'] = np.select(conditions, values)\n print(survey_data.head())\n\n # Create a bar plot of interest in math, separated by gender\n sns.catplot(x=\"Gender\" , y= \"Interested in Math\", data= survey_data, kind=\"bar\")\n\n # Show plot\n plt.show()",
"title": ""
},
{
"docid": "b0750adab9796dbe5440bf2530f2572c",
"score": "0.4599479",
"text": "def write_selector_file(\n simple_al, sources, predictions, inference_input_s3_ref, inference_input, auto_annotations\n):\n logger.info(\"Selecting input for next manual annotation\")\n selection_data = StringIO()\n selections = simple_al.select_for_labeling(predictions, auto_annotations)\n selections_set = set(selections)\n for line in inference_input:\n data = json.loads(line)\n if data[\"id\"] in selections_set:\n selection_data.write(json.dumps(data) + \"\\n\")\n inference_input.seek(0)\n selection_dest = create_ref_at_parent_key(inference_input_s3_ref, \"selection.manifest\")\n upload(selection_data, selection_dest)\n logger.info(\"Uploaded selections to {}.\".format(selection_dest.get_uri()))\n return selection_dest.get_uri(), selections",
"title": ""
},
{
"docid": "f0003254b36961e0c0cc083bb87dc560",
"score": "0.4595298",
"text": "def step_select_action(self):\n # Check whether a local DB has already been created.\n while True:\n try:\n # Check whether a connection with an existing local DB is OK.\n self.queries = sql.ORMConnection()\n break\n except Exception:\n # Creation of a connexion, iot prepare the DB creation\n self.itf.clear_window(\"right\")\n self.itf.right_display_info(cfg.WARNING_MESSAGE_4, \"warning\")\n self.__create_cnx_parameters()\n # Creation of the DB through a method hosted in this module\n self.__initialize_DB()\n self.itf.title_bar(cfg.TITLE_2)\n # Display a drop down menu to navigate in the application\n self.itf.clear_window()\n self.itf.left_display_string(0, cfg.INFO_LINE_1)\n self.itf.display_result(cfg.INFO_DISPLAY_RESULTS)\n answer = self.itf.set_up_drop_down(cfg.OPERATE_ON_DB,\n cfg.SELECT_ANSWER)\n # Here start the work on the DB to find, select and record a product\n y = 0\n while True:\n # Open a session with the ORM iot work with the DB.\n self.queries.open_session()\n result = self.queries.total_items()\n # check that the DB is not empty\n if result == 0:\n self.itf.right_display_info(cfg.EMPTY_DB, \"warning\")\n answer = cfg.OPERATE_ON_DB[2]\n # Look for a product !!\n if answer == cfg.OPERATE_ON_DB[0]:\n self.itf.title_bar(cfg.TITLE_3)\n self.itf.clear_window()\n # list of recorded categories is displayed on the right window.\n top_categories = self.queries.get_categories()\n rank_categories_dict = \\\n self.__display_top_categories(top_categories)\n self.itf.display_guide(cfg.USER_GUIDE)\n # Avoid that a too narrow request leads to an empty selection.\n while True:\n y = 0\n self.itf.left_display_string(y, cfg.KEYPAD_INSTRUCTION_1)\n y += 1\n answer_category, y = self.__check_valid_answer(y, 1, 3,\n cfg.SELECT_CATEGORY, rank_categories_dict)\n # Input the parameters to search for a food item.\n answer_name, y = self.itf.display_string_textpad(\n y, 1, 25, cfg.ITEM_NAME)\n # Launch the query in the local DB.\n brand_name, y = self.itf.display_string_textpad(\n y, 1, 25, cfg.ITEM_BRAND)\n item_search = [answer_category, answer_name, brand_name]\n refer_products = self.queries.refer_products(item_search)\n # If the criterion are too specific, avoid a null outcome.\n if len(refer_products) > 0:\n break\n else:\n self.itf.right_display_info(cfg.WARNING_MESSAGE_2,\n \"warning\")\n # Display a selection of products.\n rank_item_dict = dict()\n rank_counter = 0\n self.itf.clear_window(\"right\")\n self.itf.display_result(cfg.ITEM_SEARCH_OUTCOME)\n for product in refer_products:\n rank_counter += 1\n self.itf.display_result(\n cfg.PRODUCT_RANK_NAME.format(rank_counter,\n product.name))\n self.itf.display_result(\n cfg.PRODUCT_BRAND_NUTR_GR.\n format(product.brand, product.nutrition_grade))\n self.itf.display_result(cfg.EMPTY_LINE)\n # Create key_value of rank and product for further check\n rank_item_dict[rank_counter] = product.code\n # Requests the user to select a food item to be compared with.\n while True:\n code_ref_prod, y = self.__check_valid_answer(y, 1, 3,\n cfg.COMPARE_FOOD_ITEMS, rank_item_dict)\n # New keywords are demanded if to restrictive.\n while True:\n # The user to enter keywords iot broaden the search\n keywords_item, y = self.itf.display_string_textpad(\n y, 1, 25, cfg.ADD_KEYWORDS)\n # Create tuple with features of reference product\n selected_prod = answer_category, keywords_item, \\\n code_ref_prod\n selected_prod = tuple(selected_prod)\n list_top_products = self.queries.top_products(\n selected_prod)\n # Make sure that user's choice isn't too restrictive\n if len(list_top_products) > 0:\n break\n else:\n self.itf.right_display_info(\n cfg.WARNING_MESSAGE_1, \"warning\")\n y -= 4\n break\n self.itf.clear_window()\n self.itf.display_guide(cfg.USER_GUIDE)\n # Display the products matching the best user's request.\n top_products_dict = self.__display_top_products(\n list_top_products)\n while True:\n y = 0\n # The user can view the item in a browser and to record it.\n self.itf.left_display_string(\n y, cfg.CHECK_DETAILED_RESULT)\n answer, y = self.itf.display_string_textpad(\n y+1, 1, 2, cfg.SELECT_Y_N)\n answer = self.__ascii_to_string(answer).upper()\n if answer == \"Y\":\n code_best_prod, y = self.__check_valid_answer(y, 1, 3,\n cfg.USE_BROWSER, top_products_dict)\n self.OFF.open_product_file_OFF(code_best_prod)\n self.itf.right_display_info(\n cfg.RECORD_SELECTED_ITEM)\n date_time = self.__record_current_DTG()\n compared_prods = code_best_prod, date_time, \\\n code_ref_prod\n # Record automatically both selected and ref. products.\n self.queries.record_comparred_products(compared_prods)\n self.itf.right_display_info(cfg.PROCESSING_RECORD)\n break\n elif answer == \"N\":\n break\n else:\n answer = self.itf.left_error_input()\n y -= 4\n # Used to quit this loop an return to the main menu\n self.itf.clear_window()\n answer = self.itf.set_up_drop_down(\n cfg.OPERATE_ON_DB, cfg.SELECT_ANSWER)\n\n # Step where the user looks into the best products he has recorded.\n elif answer == cfg.OPERATE_ON_DB[1]:\n self.itf.clear_window()\n last_compared_products = self.queries.get_compared_products()\n # Display the last compared product, reference and best.\n best_products_dict = self.__display_compared_products(\n last_compared_products)\n # The user can see a product in detail.\n if len(best_products_dict) > 0:\n self.itf.display_guide(cfg.USER_GUIDE)\n # User to confirm he wants to see the item in the browser.\n no_further_check = False\n while True:\n y = 0\n answer, y = self.itf.display_string_textpad(\n y, 1, 2, cfg.CHECK_AGAIN_ITEMS_Y_N)\n answer = self.__ascii_to_string(answer).upper()\n if answer == \"Y\":\n # Display the product in the default browser\n code_product, y =\\\n self.__check_valid_answer(y, 1,\n 3, cfg.USE_BROWSER,\n best_products_dict)\n self.OFF.open_product_file_OFF(code_product)\n elif answer == \"N\":\n no_further_check = True\n break\n else:\n answer = self.itf.left_error_input()\n y -= 4\n else:\n # Inform the user that he has no best product yet\n self.itf.right_display_info(\n cfg.WARNING_MESSAGE_3, \"warning\")\n no_further_check = True\n # Return to the main menu, end of this loop.\n if no_further_check:\n self.itf.clear_window()\n answer = self.itf.set_up_drop_down(\n cfg.OPERATE_ON_DB, cfg.SELECT_ANSWER)\n\n # Import products from one of the most popular categories.\n elif answer == cfg.OPERATE_ON_DB[2]:\n y = 0\n self.itf.clear_window()\n # Import a short sample of OFF categories and displayed it.\n categories = self.queries.display_categories()\n categories_dict = dict()\n rank_counter = 0\n for category in categories:\n rank_counter += 1\n self.itf.display_result(\n cfg.CAT_RANK_NAME.format(rank_counter, category.name))\n categories_dict[rank_counter] = category.name\n\n self.itf.display_guide(cfg.USER_GUIDE)\n # The user is requested to designate a category to be uploaded.\n y = 0\n selected_category, y = \\\n self.__check_valid_answer(y, 1, 3, cfg.ADD_CATEGORY,\n categories_dict)\n display_chosen_category = cfg.NAME_IMPORTED_CATEGORY + \\\n selected_category\n self.itf.right_display_info(display_chosen_category)\n # This methods fetches a range of data from Open Food Facts.\n nb_imported, left_apart, list_items = \\\n self.OFF.import_products_list(selected_category)\n # Pieces of info from the downloaded data are given for info.\n self.itf.right_display_info(\n coff.NUMBER_REJECTED_ITEMS.format(left_apart))\n self.itf.right_display_info(\n coff.NUMBER_DOWNLOADED_ITEMS.format(nb_imported))\n self.itf.right_display_info(cfg.BE_PATIENT)\n # This is where the excerpt of OFF is uploaded in the local DB.\n self.queries.upload_products(list_items)\n nb_rows = self.queries.total_items()\n self.itf.right_display_info(\n cfg.ROWS_LOCAL_DB.format(nb_rows))\n # Used to quit this step\n self.itf.clear_window()\n answer = self.itf.set_up_drop_down(\n cfg.OPERATE_ON_DB, cfg.SELECT_ANSWER)\n\n # Close properly the program and reinitialize the shell.\n elif answer == cfg.OPERATE_ON_DB[3]:\n self.itf.clear_window()\n self.queries.close_session()\n self.decision = \"Quit\"\n break\n return self.decision",
"title": ""
},
{
"docid": "6e7e8577237c9706bded70d3da604ed4",
"score": "0.45923108",
"text": "def _button_connect(self):\n\n def button_browse_h_model():\n v1, v2 = QFileDialog.getOpenFileName(self,\n \"Select one file to open\",\n \"./\",\n \"All Files (*);;\")\n self.ui.lineEdit_h_model.setText(v1)\n\n def button_browse_r_model():\n v1, v2 = QFileDialog.getOpenFileName(self,\n \"Select one file to open\",\n \"./\",\n \"All Files (*);;\")\n self.ui.lineEdit_r_model.setText(v1)\n\n self.ui.pushButton_predict.clicked.connect(self._button_predict)\n self.ui.toolButton_filedialog_h.clicked.connect(button_browse_h_model)\n self.ui.toolButton_filedialog_r.clicked.connect(button_browse_r_model)",
"title": ""
},
{
"docid": "d74b9e1a24d44cad75a6d043ba1015cb",
"score": "0.45903692",
"text": "def WhatKnow():\r\n known = [focal_select.get(), object_select.get(),\r\n image_select.get(), magnification_select.get()]\r\n return known",
"title": ""
},
{
"docid": "e2c5cbd68ac701523e864bd6c360a420",
"score": "0.458885",
"text": "def setUp(self): # creates 1) survey instance 2) list of responses\n question = \"What language did you first learn?\"\n # self prefix means following can be used anywhere in class\n # methods simpler as neither has to make a survey instance or response\n self.my_survey = AnonymousSurvey(question) # survey instance\n self.responses = ['English', 'French', 'Latin'] # list of responses",
"title": ""
},
{
"docid": "ca6fc9ae8a013b6c5ba8fa998719e272",
"score": "0.45885468",
"text": "def getBlobSelectionAreas(self, fileContents):\n SELECTION_SIZE = Vector2(112, 112)\n BLOB_SIZE = Vector2(64, 64)\n fileStuff = fileContents.split(\"\\n\")\n selectionCount = 0\n for line in fileStuff:\n info = line.split(\",\")\n if info[0] == \"selection\":\n if self._filename == \"blobmenu.txt\":\n self._selectionAreas.append(Drawable(\"blob_selection.png\", Vector2(int(info[1]), int(info[2])), (selectionCount,0)))\n blobXpos = int(info[1]) + SELECTION_SIZE.x//2 - BLOB_SIZE.x//2\n blobYpos = int(info[2]) + SELECTION_SIZE.y//2 - BLOB_SIZE.y//2 - 14\n self._blobs.append(Drawable(\"menu_blobs.png\", Vector2(blobXpos, blobYpos), (selectionCount + 1,0)))\n selectionCount += 1",
"title": ""
},
{
"docid": "6de87c38d14e62bb87774eda7e9b3545",
"score": "0.4582993",
"text": "def default_blob_detector_params() -> object:\n # Setup SimpleBlobDetector parameters.\n params = cv2.SimpleBlobDetector_Params()\n\n # Change thresholds\n params.minThreshold = 20\n params.maxThreshold = 150\n\n # Filter by Area.\n params.filterByArea = True\n params.minArea = 15\n params.maxArea = 60\n params.minDistBetweenBlobs = 1.0\n\n # Turn off other filters\n params.filterByCircularity = False\n params.filterByConvexity = False\n params.filterByInertia = False\n\n return params",
"title": ""
},
{
"docid": "f2ed6c44ef26f2528f1c5de94458fcc7",
"score": "0.4576794",
"text": "def on_the_add_dataset_slide_input_name_my_ad_dataset_and_share_type_smb(driver, dataset_name):\n assert wait_on_element(driver, 5, xpaths.add_Dataset.title)\n assert wait_on_element_disappear(driver, 15, xpaths.progress.progressbar)\n assert wait_on_element(driver, 5, xpaths.add_Dataset.name_Textarea, 'inputable')\n driver.find_element_by_xpath(xpaths.add_Dataset.name_Textarea).clear()\n driver.find_element_by_xpath(xpaths.add_Dataset.name_Textarea).send_keys(dataset_name)\n rsc.Scroll_To(driver, xpaths.add_Dataset.share_Type_Select)\n assert wait_on_element(driver, 5, xpaths.add_Dataset.share_Type_Select, 'clickable')\n driver.find_element_by_xpath(xpaths.add_Dataset.share_Type_Select).click()\n assert wait_on_element(driver, 5, xpaths.add_Dataset.share_Type_SMB_Option, 'clickable')\n driver.find_element_by_xpath(xpaths.add_Dataset.share_Type_SMB_Option).click()",
"title": ""
},
{
"docid": "0bac0b029601713ab2e85ea6b88ca36d",
"score": "0.4575863",
"text": "def evaluate(check=False):\r\n survey = Survey(directory, questions, header, off_x, off_y, lower, upper)\r\n\r\n print(\"check positions, see check.png\")\r\n survey.check_positions(original=True)\r\n\r\n print(\"find answers and store to csv\")\r\n ans = survey.write_answers_to_csv(csv_fn, log=\"log.html\")\r\n stats = survey.statistics(ans)\r\n\r\n print(\"store statistics for LaTex report\")\r\n write_tex(stats, \"report/data.tex\")\r\n\r\n print(\"store boxes to analyze\")\r\n boxes = survey.get_box_data()\r\n np.save(\"boxes\", boxes)\r\n\r\n if check:\r\n print(\"mark all boxes in the forms, see scan directory\")\r\n survey.check_all()",
"title": ""
},
{
"docid": "04723b36303a02c325d9e52d71949bf9",
"score": "0.45754704",
"text": "def set_widgets(self):\n\t\t\n\t\tdsize,minvid,maxvid=devede_other.get_dvd_size(None,self.disctocreate)\n\n\t\tif self.disctocreate==\"vcd\":\n\t\t\tw=self.tree.get_object(\"video_rate_adj\")\n\t\t\tw.set_value(1152)\n\t\t\tw=self.tree.get_object(\"audio_rate_adj\")\n\t\t\tw.set_value(224)\n\t\telif (self.disctocreate==\"svcd\") or (self.disctocreate==\"cvd\"):\n\t\t\tw=self.tree.get_object(\"audio_rate_adj\")\n\t\t\tw.set_lower(64)\n\t\t\tw.set_upper(384)\n\t\t\t#w.set_range(64,384)\n\t\telse:\n\t\t\tprint \"entro en parte critica\"\n\t\t\tw=self.tree.get_object(\"audio_rate_adj\")\n\t\t\tprint \"paso por set_lower\"\n\t\t\tw.set_lower(128)\n\t\t\tw.set_upper(448)\n\t\t\t#w.set_range(128,448)\n\t\t\t\n\t\tif self.disctocreate!=\"vcd\":\n\t\t\tw=self.tree.get_object(\"video_rate_adj\")\n\t\t\tw.set_lower(minvid)\n\t\t\tw.set_upper(maxvid)\n\t\t\t#w.set_range(minvid,maxvid)\n\n\t\tif self.file_properties==None:\n\t\t\treturn\n\t\t\n\t\tw=self.tree.get_object(\"force_subs\")\n\t\tw.set_active(self.file_properties[\"force_subs\"])\n\n\t\tw1=self.tree.get_object(\"aspect_ratio_4_3\")\n\t\tw2=self.tree.get_object(\"aspect_ratio_16_9\")\n\t\tw1.set_active(True)\n\t\tprint \"Activo ASPECT_RATIO\"\n\t\tprint self.disctocreate\n\t\tif (self.disctocreate==\"dvd\") or (self.disctocreate==\"divx\"):\n\t\t\tw1.set_sensitive(True)\n\t\t\tw2.set_sensitive(True)\n\t\t\tif self.file_properties[\"aspect\"]>1.6:\n\t\t\t\tw2.set_active(True)\n\t\t\telse:\n\t\t\t\tw1.set_active(True)\n\t\telse:\n\t\t\tw1.set_sensitive(False)\n\t\t\tw2.set_sensitive(False)\n\n\t\tif self.file_properties[\"resolution\"]==0: # auto resolution\n\t\t\tw=self.tree.get_object(\"resauto\")\n\t\telif self.file_properties[\"resolution\"]==1: # 720x480\n\t\t\tw=self.tree.get_object(\"res720x480\")\n\t\telif self.file_properties[\"resolution\"]==2: # 704x480\n\t\t\tw=self.tree.get_object(\"res704x480\")\n\t\telif self.file_properties[\"resolution\"]==3: # 480x480\n\t\t\tw=self.tree.get_object(\"res480x480\")\n\t\telif self.file_properties[\"resolution\"]==4: # 352x480\n\t\t\tw=self.tree.get_object(\"res352x480\")\n\t\telif self.file_properties[\"resolution\"]==6: # 1280x720\n\t\t\tw=self.tree.get_object(\"res1280x720\")\n\t\telif self.file_properties[\"resolution\"]==7: # 1920x1080\n\t\t\tw=self.tree.get_object(\"res1920x1080\")\n\t\telif self.file_properties[\"resolution\"]==8: # 160x128\n\t\t\tw=self.tree.get_object(\"res160x128\")\n\t\telse:\n\t\t\tw=self.tree.get_object(\"res352x240\")\n\t\n\t\tw.set_active(True)\n\t\n\t\tw=self.tree.get_object(\"trell\")\n\t\tw.set_active(self.file_properties[\"trellis\"])\n\t\n\t\tw=self.tree.get_object(\"twopass\")\n\t\tw.set_active(self.file_properties[\"twopass\"])\n\t\t\n\t\tw=self.tree.get_object(\"turbo1stpass\")\n\t\tw.set_active(self.file_properties[\"turbo1stpass\"])\n\t\t\n\t\tw.set_sensitive(self.file_properties[\"twopass\"])\n\t\t\n\t\tif self.file_properties[\"mbd\"]==0:\n\t\t\tw=self.tree.get_object(\"mbd\")\n\t\telif self.file_properties[\"mbd\"]==1:\n\t\t\tw=self.tree.get_object(\"mbd1\")\n\t\telse:\n\t\t\tw=self.tree.get_object(\"mbd2\")\n\t\tw.set_active(True)\n\t\n\t\tif self.file_properties[\"deinterlace\"]==\"none\":\n\t\t\tw=self.tree.get_object(\"deinterlace\")\n\t\telse:\n\t\t\tw=self.tree.get_object(\"deinterlace_\"+self.file_properties[\"deinterlace\"])\n\t\tw.set_active(True)\n\t\n\t\tw=self.tree.get_object(\"volume_adj\")\n\t\tw.set_value(self.file_properties[\"volume\"])\n\t\n\t\tw=self.tree.get_object(\"ismpeg\")\n\t\tw.set_active(self.file_properties[\"ismpeg\"])\n\n\t\tw=self.tree.get_object(\"copy_audio\")\n\t\tw.set_active(self.file_properties[\"copy_audio\"])\n\n\t\tw=self.tree.get_object(\"isvob\")\n\t\tw.set_active(self.file_properties[\"isvob\"])\n\t\n\t\tw=self.tree.get_object(\"sound51\")\n\t\tw.set_active(self.file_properties[\"sound51\"])\n\t\t\n\t\tw=self.tree.get_object(\"gop12\")\n\t\tw.set_active(self.file_properties[\"gop12\"])\n\t\t\n\t\tw=self.tree.get_object(\"subfont_size\")\n\t\tw.set_value(self.file_properties[\"subfont_size\"])\n\t\t\n\t\tw=self.tree.get_object(\"swap_fields\")\n\t\tw.set_active(self.file_properties[\"swap_fields\"])\n\t\n\t\tw=self.tree.get_object(\"custom_params\")\n\t\tw.set_text(self.file_properties[\"params\"])\n\n\t\tw=self.tree.get_object(\"custom_params_lavcopts\")\n\t\tw.set_text(self.file_properties[\"params_lavc\"])\n\t\t\n\t\tw=self.tree.get_object(\"custom_params_vf\")\n\t\tw.set_text(self.file_properties[\"params_vf\"])\n\t\t\n\t\tif (self.disctocreate==\"divx\"):\n\t\t\tw=self.tree.get_object(\"custom_params_lameopts\")\n\t\t\tw.set_text(self.file_properties[\"params_lame\"])\n\t\n\t\tvrate=self.tree.get_object(\"video_rate\")\n\t\tvrate.set_value(self.file_properties[\"vrate\"])\n\t\tarate=self.tree.get_object(\"audio_rate\")\n\t\tarate.set_value(self.file_properties[\"arate\"])\n\t\tw=self.tree.get_object(\"audiodelay\")\n\t\tw.set_value(self.file_properties[\"adelay\"])\n\t\tif self.file_properties[\"blackbars\"]==0:\n\t\t\tw=self.tree.get_object(\"blackbars\")\n\t\telse:\n\t\t\tw=self.tree.get_object(\"scalepict\")\n\t\tw.set_active(True)\n\t\t\n\t\tw=self.tree.get_object(\"do_chapters\")\n\t\tif self.file_properties[\"lchapters\"]==0:\n\t\t\tw.set_active(False)\n\t\t\tw=self.tree.get_object(\"chapter_long\")\n\t\t\tw.set_sensitive(False)\n\t\telse:\n\t\t\tw.set_active(True)\n\t\t\tw=self.tree.get_object(\"chapter_long\")\n\t\t\tw.set_sensitive(True)\n\t\t\tw.set_value(self.file_properties[\"lchapters\"])\n\t\t\n\t\tif self.file_properties[\"fps\"]==25:\n\t\t\tw=self.tree.get_object(\"video_pal\")\n\t\telse:\n\t\t\tw=self.tree.get_object(\"video_ntsc\")\n\t\tw.set_active(True)\n\t\n\t\tif self.file_properties[\"cutting\"]==0:\n\t\t\tw=self.tree.get_object(\"full_length\")\n\t\telif self.file_properties[\"cutting\"]==1:\n\t\t\tw=self.tree.get_object(\"first_half\")\n\t\telse:\n\t\t\tw=self.tree.get_object(\"second_half\")\n\t\tw.set_active(True)\n\t\n\t\tprint \"Rotate: \"+str(self.file_properties[\"rotate\"])\n\t\tif self.file_properties[\"rotate\"]==0:\n\t\t\tw=self.tree.get_object(\"rotation0\")\n\t\telif self.file_properties[\"rotate\"]==90:\n\t\t\tw=self.tree.get_object(\"rotation90\")\n\t\telif self.file_properties[\"rotate\"]==180:\n\t\t\tw=self.tree.get_object(\"rotation180\")\n\t\telif self.file_properties[\"rotate\"]==270:\n\t\t\tw=self.tree.get_object(\"rotation270\")\n\t\tw.set_active(True)\n\t\t\n\t\tw=self.tree.get_object(\"hmirror\")\n\t\tw.set_active(self.file_properties[\"hmirror\"])\n\t\tw=self.tree.get_object(\"vmirror\")\n\t\tw.set_active(self.file_properties[\"vmirror\"])\n\n\t\tself.audio_model.clear()\n\t\tif len(self.file_properties[\"audio_list\"])>0:\n\t\t\tw=self.tree.get_object(\"hasaudiotracks\")\n\t\t\tw.show()\n\t\t\tw=self.tree.get_object(\"noaudiotracks\")\n\t\t\tw.hide()\n\t\t\tposition=0\n\t\t\tfor track in self.file_properties[\"audio_list\"]:\n\t\t\t\tprint \"Meto pista \"+str(track)\n\t\t\t\titerator=self.audio_model.insert(position)\n\t\t\t\tself.audio_model.set_value(iterator,0,track)\n\t\t\t\tposition+=1\n\t\t\tprint \"Pista seleccionada: \"+str(self.file_properties[\"audio_stream\"])\n\t\t\tw=self.tree.get_object(\"audiotrack\")\n\t\t\tw.set_active(self.file_properties[\"audio_list\"].index(self.file_properties[\"audio_stream\"]))\n\t\telse:\n\t\t\tw=self.tree.get_object(\"hasaudiotracks\")\n\t\t\tw.hide()\n\t\t\tw=self.tree.get_object(\"noaudiotracks\")\n\t\t\tw.show()\n\n\t\tself.refresh_subtitles()\n\t\t\n\t\tif ((self.global_vars[\"encoder_video\"]==\"ffmpeg\") or (self.global_vars[\"encoder_video\"]==\"avconv\")):\n\t\t\tuse_ffmpeg=False\n\t\telse:\n\t\t\tuse_ffmpeg=True\n\t\t\n\t\tself.tree.get_object(\"turbo1stpass\").set_visible(use_ffmpeg)\n\t\tself.tree.get_object(\"deinterlace_md\").set_visible(use_ffmpeg)\n\t\tself.tree.get_object(\"deinterlace_l5\").set_visible(use_ffmpeg)\n\t\tself.tree.get_object(\"deinterlace_lb\").set_visible(use_ffmpeg)\n\t\tself.tree.get_object(\"lavcopts_label\").set_visible(use_ffmpeg)\n\t\tself.tree.get_object(\"lameopts_label\").set_visible(use_ffmpeg)\n\t\tself.tree.get_object(\"custom_params_lavcopts\").set_visible(use_ffmpeg)\n\t\tself.tree.get_object(\"custom_params_lameopts\").set_visible(use_ffmpeg)\n\t\t#self.tree.get_object(\"volume_frame\").set_visible(use_ffmpeg)\n\t\tself.tree.get_object(\"audiotrack_box\").set_visible(use_ffmpeg)\n\t\tself.tree.get_object(\"frame_field_order\").set_visible(use_ffmpeg)",
"title": ""
},
{
"docid": "902a683a229a171f17fe631782553b91",
"score": "0.45690173",
"text": "def accept(self):\r\n linkerpad = self.browse1.input.currentText()\r\n rechterpad = self.browse2.input.currentText()\r\n selectiontype = ''\r\n for ix, sel in enumerate(self.sel):\r\n print(' ', ix)\r\n if sel[0].isChecked():\r\n selectiontype = sel[1]\r\n break\r\n mld = self.master.check_input(linkerpad, rechterpad, selectiontype)\r\n if mld:\r\n qtw.QMessageBox.critical(self, self.master.parent.apptitel, mld)\r\n return\r\n self.master.parent.lhs_path = linkerpad\r\n self.master.parent.rhs_path = rechterpad\r\n self.master.parent.comparetype = selectiontype\r\n super().accept()",
"title": ""
},
{
"docid": "3e6bcfa93eff8459d532b87f91e3aa18",
"score": "0.4567321",
"text": "def setTrackSelection(self, arg1: hiero.core.TrackBase):\n ...",
"title": ""
},
{
"docid": "882460fb456640038008dec1e362eb29",
"score": "0.45637542",
"text": "def __init__(self):\n self.dataset = None # dataframe with columns {ID, Description, Tag}.\n self.tags = None # all possible tags for a document.\n self.sgd_pipeline = self.create_sgd_pipeline()",
"title": ""
},
{
"docid": "e60b983d10b215e9a270fede226accd2",
"score": "0.45600757",
"text": "def create_option_subset(superset, subset):\n return None",
"title": ""
},
{
"docid": "e60b983d10b215e9a270fede226accd2",
"score": "0.45600757",
"text": "def create_option_subset(superset, subset):\n return None",
"title": ""
},
{
"docid": "aa9d9425a3a8defccf746869646125ad",
"score": "0.45596603",
"text": "def setChoicePool(self, pool):\n self.pool = pool",
"title": ""
},
{
"docid": "42e7c2e2d4a1e3dcb642371181e102c2",
"score": "0.4552315",
"text": "def selection(self):\n\n self.pop_size = len(self.model_list)\n self.n_elite = int(self.pop_size*self.elite_frac)\n self.n_fill = self.pop_size - self.n_elite\n self.elitism() # Does not do anything if elite_frac = 0\n if self.selection_type == \"fit\":\n self.fitness_proportional()\n elif self.selection_type == \"rank\":\n self.rank_selection()",
"title": ""
},
{
"docid": "104077be8206d82b8a87c48e42abedce",
"score": "0.4551419",
"text": "def _getspectra_selected( self, name, tbsel={} ):\n isthere=os.path.exists(name)\n self.assertEqual(isthere,True,\n msg='file %s does not exist'%(name)) \n tb.open(name)\n if len(tbsel) == 0:\n sp=tb.getcol('SPECTRA').transpose()\n else:\n command = ''\n for key, val in tbsel.items():\n if len(command) > 0:\n command += ' AND '\n command += ('%s in %s' % (key, str(val)))\n newtb = tb.query(command)\n sp=newtb.getcol('SPECTRA').transpose()\n newtb.close()\n\n tb.close()\n return sp",
"title": ""
},
{
"docid": "e6980dc20942eb7375884c77e75127f3",
"score": "0.45513213",
"text": "def pool_select(ui, layout, active):\n ui.label(\"\", layout)\n ui.prop(bpy.context.scene.batchapps_submission, \"pool\", layout.row(),\n label=None, expand=True, active=active)\n\n if bpy.context.scene.batchapps_submission.pool == {\"reuse\"}:\n ui.label(\"Use an existing persistant pool by ID\", layout.row(), active=active)\n ui.prop(bpy.context.scene.batchapps_submission, \"pool_id\",\n layout.row(), active=active)\n\n elif bpy.context.scene.batchapps_submission.pool == {\"create\"}:\n ui.label(\"Create a new persistant pool\", layout.row(), active=active)\n ui.prop(bpy.context.scene.batchapps_pools, \"pool_size\",\n layout.row(), \"Number of instances:\", active=active)\n\n else:\n ui.label(\"Auto provision a pool for this job\", layout.row(),\n active=active)\n ui.prop(bpy.context.scene.batchapps_submission, \"pool_size\",\n layout.row(), \"Number of instances:\", active=active)",
"title": ""
},
{
"docid": "cb257c00d8e4645a64443bdda94619f5",
"score": "0.45465818",
"text": "def OnButtonClick(self):\n self.choice()",
"title": ""
},
{
"docid": "9c0acd37e1bfc3de4247de652c6f7617",
"score": "0.45339665",
"text": "def on_hiquality_clicked(self):\n plogger.info(\"Posting a \" + ( \"high\" if self.hi_quality.isChecked() else \"medium\" ) + \" quality image\")\n self.wimg.setHiQualityImage(self.hi_quality.isChecked())\n pass",
"title": ""
},
{
"docid": "81aa7147a80b31f28d0b802c0a696068",
"score": "0.45250195",
"text": "def __init__(self, topic_choices, **kwargs):\n super(EditObjective, self).__init__(**kwargs)\n self.topic_id.choices = topic_choices",
"title": ""
},
{
"docid": "5850eaf82e8fec585d09e0443771445e",
"score": "0.45190576",
"text": "def _entry_band(self):\n self.ALBUMARTIST = self.band_entry_input.text()\n if self.ALBUMARTIST:\n self.start_preload()",
"title": ""
},
{
"docid": "46fca5b9b6c87cc23d3f4cf132f524a2",
"score": "0.45173883",
"text": "def find_blobs(f : str,\n ax,\n area : float = 2,\n minTh : float = 200.0,\n figsize : float = 5) :\n # Read image and convert from BGR (cv2) to RGB (python)\n img = cv2.imread(f, cv2.IMREAD_COLOR)\n\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n\n # Flip image vertically (acquired inverted from microscope)\n img = cv2.flip( img, 0 )\n\n hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n\n # Fill any white/yellowish spots with black\n lower_white = np.array([0,200,0])\n upper_white = np.array([255,255,255])\n mask = cv2.inRange(hsv, lower_white, upper_white)\n imfil = cv2.bitwise_and(img, img, mask=mask)\n\n # Invert image for easier contrast in blob finding\n inv = cv2.bitwise_not(imfil)\n\n ################ Define blob parameters and initialize detector\n params = cv2.SimpleBlobDetector_Params()\n\n # Change thresholds\n params.minThreshold = minTh\n params.maxThreshold = 700\n\n # Change size\n params.filterByArea = True\n params.minArea = area\n\n params.filterByInertia = False\n# params.minInertiaRatio = 0.001\n\n params.filterByConvexity = False\n params.filterByCircularity = False\n\n detector = cv2.SimpleBlobDetector_create(params)\n keypoints = detector.detect(inv)\n# print(len(keypoints))\n font = cv2.FONT_HERSHEY_SIMPLEX\n ##### Plot marker on keypoints\n for j, marker in enumerate(keypoints):\n img2 = cv2.drawMarker(imfil, tuple(int(i) for i in marker.pt),\n (255, 0, 0), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n img2 = cv2.putText(img2, str(j), tuple(int(x) for x in marker.pt),\n fontFace=font, fontScale=0.6,\n color=(255,255,255), lineType=cv2.LINE_AA)\n\n if len(keypoints) != 0:\n ax.imshow(img2)\n else:\n ax.imshow(img)\n fig = plt.gcf()\n fig.set_figwidth(figsize)\n fig.set_figheight(figsize)\n sz = [i for i in keypoints]\n return sz",
"title": ""
},
{
"docid": "27e653176bc34cdd5a45f01ade53a48e",
"score": "0.45167223",
"text": "def initiate_classification(self):\n\t\tanswers = ClassificationAnswer.objects.filter(classification=self)\n\n\t\t#Check we're not running the function twice\n\t\tif len(answers) == 0:\n\n\t\t\t# for TSC\n\t\t\tif self.sample.analysis_performed.panel[0:4].lower() == 'tsc_':\n\n\t\t\t\tquestions = ClassificationQuestion.objects.all()\n\n\t\t\telse:\n\n\t\t\t\tquestions = ClassificationQuestion.objects.all().exclude(category = 'Familial Cancer Specific')\n\n\n\t\t\tquestions = questions.order_by('order')\n\n\t\t\tfor question in questions:\n\n\t\t\t\tnew_answer = ClassificationAnswer.objects.create(\n\t\t\t\t\tclassification = self,\n\t\t\t\t\tclassification_question=question,\n\t\t\t\t\tselected_first = False,\n\t\t\t\t\tselected_second = False,\n\t\t\t\t\tstrength_first = question.default_strength,\n\t\t\t\t\tstrength_second = question.default_strength,\n\t\t\t\t\t)\n\t\t\t\tnew_answer.save()\n\n\t\telse:\n\t\t\treturn HttpResponseForbidden()\n\n\t\treturn None",
"title": ""
},
{
"docid": "33cf6edf9e2f97ab8f384a26fd945e52",
"score": "0.45123348",
"text": "def __init__(self, config):\n params.__init__(self)\n self.surveyId = config[\"id\"]\n self.surveyName = config[\"name\"]\n self.active = config[\"active\"]\n self.endpoint = surveyEndpoint(self.dataCenter, self.surveyId)\n self.data = {\"name\": self.surveyName,\n \"isActive\": self.active}\n self.headers = {\"content-type\": \"application/json\"}\n self.headers.update(self.authHeader)",
"title": ""
},
{
"docid": "a3139434d27a637941a16ce9e6916e8e",
"score": "0.451171",
"text": "def test(expert_arxiv, size, plot):\n dset = DemoDataset(expert_arxiv, size=size)\n if plot:\n dset.plot()",
"title": ""
},
{
"docid": "68bf88a8749741353a84dd91afa3422f",
"score": "0.45097113",
"text": "def choice1():\n \n # Save info\n try:\n dct_interaction = file_presistance(PATH_USER + '/' + file_name + '.json', \"json\", None, \"load\")\n dct_interaction['sonnet_selected'] = str(self.sonnet1_type.get())\n dct_interaction['id_sonnet'] = str(self.key1.get())\n except:\n dct_interaction = {'sonnet_selected':str(self.sonnet1_type.get()),\n 'id_sonnet':str(self.key1.get())}\n \n # Save results \n file_presistance(PATH_USER + '/' + file_name + '.json', \"json\", dct_interaction, \"save\")\n # Reset variables\n self.text_box_1.config(state=\"disabled\")\n self.text_box_2.config(state=\"disabled\")\n self.button_sel1.config(state=\"disabled\")\n self.button_sel2.config(state=\"disabled\")\n self.text_box_1.delete(1.0, END)\n self.text_box_2.delete(1.0, END)\n self.query_text.delete(1.0, END)\n self.choice.set(\"1\")\n self.sonnet1_type.set(\"1\")\n self.sonnet2_type.set(\"1\")\n # Change window\n controller.show_frame(\"RateApp\")",
"title": ""
},
{
"docid": "99a21744467cd884f574d8dbc1077a93",
"score": "0.45089412",
"text": "def sirt1():\n s=cybert_collection();\n p=1e-2;\n s.loaddb(\"./cybert_WT1vsWT2cybert.db\");\n#load the WT comparison as background\n assert len(s.samples)==2;\n s.samples[0].name=\"WT1(OE)\"\n s.samples[1].name=\"WT2(WT)\"\n print s.samples[0].entries[0].genesym;\n wt1_up=s.find_differential_entries(s.samples[0],p=p);\n wt1_down=s.find_differential_entries(s.samples[0],True,p=p);\n temp=s.find_differential_entries(s.samples[1],p=p);\n print \"number of entries that are up in WT1 is\", len (wt1_up)\n print \"number of entries that are up in WT2 is\", len (wt1_down)\n print \"number of entries that are up in WT2 is (test)\", len (temp)\n assert len(wt1_down)==len(temp);\n#QC\n se=cybert_collection()\n KOs=se.loaddb(\"./cybert_KOvsWT2cybert.db\")\n #kos_up=se.find_differential_entries(se.samples[1])\n kos_up=se.find_differential_entries(se.samples[1],p=p);\n print \"number of entries that are up in KO is\", len (kos_up)\n kos_down=se.find_differential_entries(se.samples[0],p=p)\n print \"number of entries that are DOWN in KO is\", len (kos_down)\n sel=cybert_collection();\n OEs=sel.loaddb(\"./cybert_OEvsWT1cybert.db\")\n oes_up=sel.find_differential_entries(sel.samples[1],p=p)\n print \"number of entries that are up in OE is\", len (oes_up)\n oes_down=sel.find_differential_entries(sel.samples[0],p=p)\n print \"number of entries that are DOWN in OE is\", len (oes_down)\n #out1=oes_up&kos_down;\n out1=kos_up-oes_up;\n #print \"number of entries that are up in OE and down in KO is\", len (out1)\n out2=oes_down-kos_down;\n\n pp=[\"lepr\",\"sirt1\"];\n m=set(sel.samples[0].entry_by_genesym2(pp));\n print \"number of added additional entries are \", len(m);\n #out1=m;\n #out2=m;\n #out2=oes_up&kos_down-wt1_up;\n print \"number of entries that are up in KO and not up in OE is\", len (out1)\n print \"number of entries that are DOWN in OE and not down in KO is\", len (out2)\n #print \"number of entries that are up in OE and down in KO but not up in WT1 compared to WT2 is\", len (out2)\n #print \"number of entries that are up in OE and up in KO is\", len (oes_up&kos_up)\n #print \"number of entries that are up in OE and up in KO but not up in WT1 compared to WT2 is\", len (oes_up&kos_up-wt1_up)\n out3=oes_up-kos_up;\n print \"number of entries that are up in OE and not up in KO is\", len (out3)\n out4=kos_down-oes_down;\n print \"number of entries that are down in KO and not down in OE\", len (out4)\n\n #out3=m;\n #out4=m;\n #pvals(out1,\"./csv/OE+KO-.csv\")\n #pvals(out2,\"./csv/OE+KO-NOWT.csv\")\n pvals(out3,\"./csv/OE+.csv\")\n pvals(out4,\"./csv/KO-.csv\")\n \n pvals(out1,\"./csv/KO+.csv\")\n pvals(out2,\"./csv/OE-.csv\")\n #pvals(out3,\"./csv/OE-KO+.csv\")\n #pvals(out4,\"./csv/OE-KO+NOWT.csv\")\n print \"These have a total intersection of {0:d}(KO+OE-), {1:d}(KO-OE+) and a total union of length {2:d}\".\\\n format(len(out1&out2),len(out3&out4),len(out1|out2|out3|out4));",
"title": ""
},
{
"docid": "44201764ee52bb06441ad7c12dc60ffb",
"score": "0.4506443",
"text": "def analysis_page(self):\n cp.session['download_files']['Config_default'] = fig.DEFAULT_CONFIG\n cp.session['download_files']['Config_example'] = fig.CONFIG_EXAMPLE\n study_html = ''' <tr class=\"w3-hover-blue\">\n <th>\n <a href=\"{select_specimen_page}?access_code={access_code}\"> {study_name} </a>\n </th>\n <th>{date_created}</th>\n </tr> '''\n\n id_study_html = '<option value=\"{study_name}\">{study_name}</option>'\n\n # Get all the studies that are available to the current user\n with Database(testing=self.testing, owner=self.get_user()) as db:\n studies = db.get_all_user_studies(self.get_user())\n\n # Build an HTML list of the studies\n study_list = []\n id_study_list = []\n for study in studies:\n study_list.append(study_html.format(study_name=study.study_name,\n select_specimen_page=SERVER_PATH + 'query/select_specimen',\n access_code=study.access_code,\n date_created=study.created,\n ))\n id_study_list.append(id_study_html.format(study_name=study.study_name))\n\n # Add unhide any privileged options\n if check_privileges(self.get_user(), self.testing):\n page = self.load_webpage('analysis_select_tool',\n title='Select Analysis',\n user_studies='\\n'.join(study_list),\n id_user_studies='\\n'.join(id_study_list),\n privilege=''\n )\n else:\n page = self.load_webpage('analysis_select_tool',\n title='Select Analysis',\n user_studies='\\n'.join(study_list),\n id_user_studies='\\n'.join(id_study_list)\n )\n return page",
"title": ""
},
{
"docid": "18d8f350526306d84aa2a85346ca899c",
"score": "0.45056507",
"text": "def __init__(self, parent, mainframe=None):\n self.init_common('select', parent, 'Selection tool', mainframe, info='Select objects in cancvas')",
"title": ""
},
{
"docid": "10e009dfa9afecb3251ae78835aea9a1",
"score": "0.4505262",
"text": "def __create_selection_from_simba(self,**kwargs):\n\n p = copy.copy(params)\n\n # Open galaxy sample extracted\n galsample = pd.read_pickle(p.d_data+'galaxy_selection/%s_galsample_%i_%s%s' % (p.z1,p.nGal,p.sim_name,p.sim_run))\n \n # Sort according to stellar mass from caesar\n galsample = galsample.sort_values(['M_star_caesar'], ascending=True).reset_index(drop=True)\n nGal_or = len(galsample)\n # Convert sample info to dictionary\n GR = {}\n for key in galsample.keys():\n GR[key] = galsample[key].values\n\n # Let's assume that all galaxies are at the same redshift\n GR['zreds'] = np.zeros(len(galsample)) + p.zred\n\n # Add global info\n SFR = np.zeros(len(galsample))\n Zsfr = np.zeros(len(galsample))\n M_dust = np.zeros(len(galsample))\n M_gas = np.zeros(len(galsample))\n M_star = np.zeros(len(galsample))\n R_max = np.zeros(len(galsample))\n R2_gas = np.zeros(len(galsample))\n R2_star = np.zeros(len(galsample))\n SFRsd = np.zeros(len(galsample))\n for i in range(len(galsample)):\n\n # Galaxy object\n rawgalname = 'z%.2f_' % p.zred + p.sim_run + '_%i' % galsample.gal_num[i]\n gal_ob = dict( galname=rawgalname, zred=p.zred, gal_index=i, gal_num=galsample.gal_num[i]) # dummy gal object\n \n # Stars\n simstar = aux.load_temp_file(gal_ob=gal_ob, data_type='rawsimstar', gal_ob_present=True)\n M_star[i] = np.sum(simstar['m'].values)\n SFR[i] = np.sum(simstar['m'].values[simstar['age']*1e9 < 100e6])/100e6\n\n # Gas and dust\n simgas = aux.load_temp_file(gal_ob=gal_ob, data_type='rawsimgas', gal_ob_present=True)\n\n simgas['nH'] = 0.75 * simgas['nH'].values # So gas density > H density\n M_dust[i] = np.sum(simgas['m_dust'].values)\n M_gas[i] = np.sum(simgas['m'].values)\n Zsfr[i] = np.sum(simgas['Z'].values*simgas['SFR'].values)/np.sum(simgas['SFR'].values)\n radii = np.sqrt(simgas.x.values**2 + simgas.y.values**2 + simgas.z.values**2)\n m_gas = simgas.m.values[np.argsort(radii)]\n radii = radii[np.argsort(radii)]\n m_cum = np.cumsum(m_gas)\n R2_gas[i] = radii[m_cum > 0.5*M_gas[i]][0]\n radii = np.sqrt(simstar.x.values**2 + simstar.y.values**2 + simstar.z.values**2)\n m_star = simstar.m.values[np.argsort(radii)]\n radii = radii[np.argsort(radii)]\n m_cum = np.cumsum(m_star)\n R2_star[i] = radii[m_cum > 0.5*M_star[i]][0]\n radii = np.sqrt(simstar.x.values**2 + simstar.y.values**2 + simstar.z.values**2)\n SFRsd[i] = np.sum(simstar['m'].values[(radii < R2_star[i]) &\\\n (simstar['age']*1e9 < 100e6)])/100e6/(np.pi*R2_star[i]**2)\n # print(R2_gas[i],R2_star[i])\n\n # Max radius\n r_star = np.sqrt(simstar.x**2 + simstar.y**2 + simstar.z**2)\n r_gas = np.sqrt(simgas.x**2 + simgas.y**2 + simgas.z**2)\n R_max[i] = np.max(np.append(r_star,r_gas))\n\n # Move dataframe to particle_data folder\n aux.save_temp_file(simgas, gal_ob=gal_ob, data_type='simgas', gal_ob_present=True)\n aux.save_temp_file(simstar, gal_ob=gal_ob, data_type='simstar', gal_ob_present=True)\n\n GR['M_star'] = M_star\n GR['M_gas'] = M_gas\n GR['M_dust'] = M_dust\n GR['SFR'] = SFR\n GR['Zsfr'] = Zsfr\n GR['R_max'] = R_max\n GR['R2_gas'] = R2_gas\n GR['R2_star'] = R2_star\n GR['SFRsd'] = SFRsd\n\n # convert dictionary to DataFrame\n GR = pd.DataFrame(GR)\n GR = GR[GR.SFR > 0].reset_index(drop=True)\n print('Final number of galaxies:')\n print(len(GR))\n\n print('Check min and max of SFR (<100 Myr):')\n print(np.min(GR.SFR), np.max(GR.SFR))\n\n # Add names\n GR['galnames'] = ['G%i' % i for i in np.arange(len(GR))]\n\n for line in p.lines:\n GR = self.__set_attr(GR,'L_'+line)\n\n for attr in ['lum_dist', 'Zmw']:\n print('get attributes: %s' %attr)\n GR = self.__set_attr(GR,attr)\n\n # Save DF of global results\n filename = self.__get_file_location(nGal=len(GR))\n print(\"Filename in global results __create_file: \")\n print(filename)\n GR.to_pickle(filename)\n\n if len(GR) != nGal_or:\n print('Stop and update parameter.txt to reflect new nGal')\n sys.exit() \n\n return GR",
"title": ""
},
{
"docid": "40a8e56856147841a5bcbb555be62f4b",
"score": "0.44971666",
"text": "def test_kit():\n output_path = _TempDir()\n data_path = op.join(base_path, 'kit', 'tests', 'data')\n raw_fname = op.join(data_path, 'test.sqd')\n events_fname = op.join(data_path, 'test-eve.txt')\n hpi_fname = op.join(data_path, 'test_mrk.sqd')\n electrode_fname = op.join(data_path, 'test_elp.txt')\n headshape_fname = op.join(data_path, 'test_hsp.txt')\n event_id = dict(cond=1)\n\n raw_to_bids(subject_id=subject_id, session_id=session_id, run=run,\n task=task, raw_file=raw_fname, events_data=events_fname,\n event_id=event_id, hpi=hpi_fname, electrode=electrode_fname,\n hsp=headshape_fname, output_path=output_path,\n overwrite=True)\n cmd = ['bids-validator', output_path]\n run_subprocess(cmd, shell=shell)",
"title": ""
},
{
"docid": "9cb4f5ecd80d41529bd706a8bf8ac4da",
"score": "0.44933814",
"text": "def choice(dataset: str, source: str, options: List[str], multiple: bool = False):\n # Load the stream from a JSONL file and return a generator that yields a\n # dictionary for each example in the data.\n stream = JSONL(source)\n\n # Add the options to all examples in the stream\n stream = add_options(stream, options)\n\n return {\n \"view_id\": \"choice\", # Annotation interface to use\n \"dataset\": dataset, # Name of dataset to save annotations\n \"stream\": stream, # Incoming stream of examples\n \"config\": { # Additional config settings\n # Allow multiple choice if flag is set\n \"choice_style\": \"multiple\" if multiple else \"single\",\n # Automatically accept and \"lock in\" selected answers if only\n # single choice is allowed\n \"choice_auto_accept\": False if multiple else True,\n },\n }",
"title": ""
},
{
"docid": "73f7a5c5416a53bfc5bc42b52be8ad6c",
"score": "0.4492823",
"text": "def testDataSelection(self):\n # Populate the model with 1d and 2d data\n filename = [\"cyl_400_20.txt\", \"P123_D2O_10_percent.dat\"]\n self.form.readData(filename)\n\n # Wait a moment for data to load\n time.sleep(1)\n # Unselect all data\n self.form.cbSelect.activated.emit(1)\n # Test the current selection\n item1D = self.form.model.item(0)\n item2D = self.form.model.item(1)\n\n self.assertTrue(item1D.checkState() == Qt.Unchecked)\n self.assertTrue(item2D.checkState() == Qt.Unchecked) \n\n # Select all data\n self.form.cbSelect.activated.emit(0)\n\n # Test the current selection\n self.assertTrue(item1D.checkState() == Qt.Checked)\n self.assertTrue(item2D.checkState() == Qt.Checked) \n\n # select 1d data\n self.form.cbSelect.activated.emit(2)\n # Test the current selection\n self.assertTrue(item1D.checkState() == Qt.Checked)\n self.assertTrue(item2D.checkState() == Qt.Checked)\n\n # unselect 1d data\n self.form.cbSelect.activated.emit(3)\n\n # Test the current selection\n self.assertTrue(item1D.checkState() == Qt.Unchecked)\n self.assertTrue(item2D.checkState() == Qt.Checked)\n\n # select 2d data\n self.form.cbSelect.activated.emit(4)\n\n # Test the current selection\n self.assertTrue(item1D.checkState() == Qt.Unchecked)\n self.assertTrue(item2D.checkState() == Qt.Checked) \n\n # unselect 2d data\n self.form.cbSelect.activated.emit(5)\n\n # Test the current selection\n self.assertTrue(item1D.checkState() == Qt.Unchecked)\n self.assertTrue(item2D.checkState() == Qt.Unchecked)",
"title": ""
},
{
"docid": "77740bf17b35c2ee49132dc0305ea79d",
"score": "0.44909778",
"text": "def OnLoadDemoDataset1(self, event):\n\n self.page1.OnLoadDemoDataset1(event)\n self.notebook.SetSelection(1)",
"title": ""
},
{
"docid": "25c185f6cc7d4e492bfb093992a4ce87",
"score": "0.44849783",
"text": "def quiz_maker(level):\n\n if level == \"mix it up\":\n animalrows = db.execute(\"SELECT animal, unsplash FROM animals\")\n return random.sample(animalrows, 10)\n\n\n else:\n # Imports all animals from the database in selected 'level'\n animalrows = db.execute(\"SELECT animal, unsplash FROM animals WHERE domain = :domain\", domain=level)\n\n # Returns a random selection of 10 animals\n return random.sample(animalrows, 10)",
"title": ""
},
{
"docid": "cd45868cadb6130640736d69dea40368",
"score": "0.4478949",
"text": "def select(self):\n\t\tpass",
"title": ""
},
{
"docid": "7a651c342c3356d042ccf2e6b614b585",
"score": "0.44778532",
"text": "def test_nhanes_subset_singleclusters(standardize):\n # Load the data\n df = clarite.load.from_tsv(\n DATA_PATH.parent / \"test_data_files\" / \"nhanes_subset\" / \"data.txt\"\n )\n survey_df = clarite.load.from_tsv(\n DATA_PATH.parent / \"test_data_files\" / \"nhanes_subset\" / \"design_data.txt\"\n )\n survey_df = survey_df.loc[df.index]\n # Process data\n df = clarite.modify.make_binary(df, only=[\"LBXHBC\", \"black\", \"female\"])\n df = clarite.modify.make_categorical(df, only=[\"SES_LEVEL\", \"SDDSRVYR\"])\n # Create design\n design = clarite.survey.SurveyDesignSpec(\n survey_df,\n weights=\"WTMEC4YR\",\n cluster=\"SDMVPSU\",\n strata=\"SDMVSTRA\",\n fpc=None,\n nest=True,\n )\n design.subset(df[\"black\"] == 1)\n df = df.drop(columns=\"black\")\n # Get Results\n surveylib_result_file = RESULT_PATH / \"nhanes_subset_result.csv\"\n covariates = [\"female\", \"SES_LEVEL\", \"RIDAGEYR\", \"SDDSRVYR\", \"BMXBMI\"]\n # Run analysis and comparison\n regression_kinds = [\"weighted_glm\", \"r_survey\"]\n results = dict()\n for rk in regression_kinds:\n results[rk] = clarite.analyze.association_study(\n outcomes=\"LBXLYPCT\",\n covariates=covariates,\n data=df,\n regression_kind=rk,\n survey_design_spec=design,\n min_n=50,\n standardize_data=standardize,\n )\n # Compare\n if not standardize:\n compare_loaded(results[regression_kinds[0]], surveylib_result_file, rtol=1e-04)\n assert_frame_equal(\n results[regression_kinds[0]], results[regression_kinds[1]], rtol=1e-04\n )\n else:\n assert_frame_equal(\n results[regression_kinds[0]], results[regression_kinds[1]], rtol=5e-03\n )",
"title": ""
},
{
"docid": "4e230b1b78d63df03230e3ef878739fc",
"score": "0.4476087",
"text": "def ConvertSelection(self, vtkDataRepresentation, vtkSelection):\n ...",
"title": ""
},
{
"docid": "13d4ecd4ddd09594d5265ddeaee24a16",
"score": "0.44730246",
"text": "def __init__(self, question, answer):\n\t\tself.question = question\n\t\tself.answer = answer",
"title": ""
},
{
"docid": "bcfee537454ba61f6758098f4ec67904",
"score": "0.44698718",
"text": "def __init__(self, **kwargs):\n\t\t\n\t\tsuper(Spector, self).__init__(**kwargs)\n\n\t\t# set survey\n\t\tif hasattr(self.obj, 'survey'):\n\t\t\tdefault_survey = self.obj.survey\n\t\t\tself.survey = kwargs.pop('survey', default_survey)\n\t\telse: \n\t\t\tself.survey = kwargs.pop('survey')\n\n\t\t# set survey_spec\n\t\tself.survey_spec = kwargs.pop('survey_spec', 'auto')\n\n\t\tif self.survey_spec in ['sdss', 'boss', 'eboss', 'auto']:\n\t\t\tself.obj.add_sdss(toload_photoobj=False)\n\t\t\tif self.survey_spec == 'auto':\n\t\t\t\tself.survey_spec = self.obj.sdss.instrument.lower()\n\n\t\t\t# sanity check - spec name consistent\n\t\t\tif (self.survey_spec != self.obj.sdss.instrument.lower()):\n\t\t\t\traise Exception(\"[spector] survey_spec inconsistent with sdss_xid.csv:instrument\")\n\n\t\t# set z\n\t\tif hasattr(self.obj, 'z'):\n\t\t\tself.z = kwargs.pop('z', self.obj.z)\n\t\telif self.survey_spec in ['sdss', 'boss', 'boss', 'auto']:\n\t\t\tself.obj.add_sdss(toload_photoobj=False)\n\t\t\tself.z = kwargs.pop('z', self.obj.sdss.z) \n\t\telse: \n\t\t\tself.z = kwargs.pop('z') \n\n\t\t# set others\n\t\tself.bands = filters.filtertools.surveybands[self.survey]\n\t\tself.waverange = filters.filtertools.waverange[self.survey]\n\n\t\t# define paths\n\t\tself.fp_spec_decomposed = self.dir_obj+'spec_decomposed.ecsv'\n\t\tself.fp_spec_contextrp = self.dir_obj+'spec_contextrp.ecsv'\n\t\tself.fp_spec_mag = self.dir_obj+'spec_mag.csv'",
"title": ""
},
{
"docid": "a2a27f0d7a89ebb20bdbc4117f72f3ba",
"score": "0.44672063",
"text": "def testoptionChoice5(self):\n #test option 5\n import Histogram\n res = Histogram.Histogram.optionChoice5(self)\n exp = Histogram.Histogram.optionChoiceR(self)\n self.assertEqual(res,exp)",
"title": ""
},
{
"docid": "2c0037554016960b71cfb120a32c27b3",
"score": "0.44647366",
"text": "def set_animal(self):\n self.animalName = str(self.cbAnimal.currentText())\n if self.animalName != '--Select Animal--':\n self.txtUpdates.append(self.animalName + ' selected!')\n self.btnChooseVid.setEnabled(True)\n self.dataDir = self.baseDataPath + os.sep + self.animalName + os.sep",
"title": ""
},
{
"docid": "76eb67f755489cf5634a63177175cfaf",
"score": "0.4462906",
"text": "def test_init(radio):\n assert len(radio.choices) == 2\n assert radio.selected == \"update\"\n\n # Change selection\n radio.selected = \"repair\"\n assert radio.selected == \"repair\"",
"title": ""
},
{
"docid": "8944b796b020de9b657e53f1df0a89fa",
"score": "0.44612396",
"text": "def select(self):\n pass",
"title": ""
},
{
"docid": "0e6bae6550f74a65120126ba681c2eb3",
"score": "0.4460005",
"text": "def setUp(self):\n question = \"What language did you first learn to speak?\"\n self.my_survey = AnonymousServery(question)\n self.responses = ['English', 'Spanish', 'Korean']",
"title": ""
},
{
"docid": "241a92a86b0d6f8f593e93a4f415c870",
"score": "0.44593355",
"text": "def _handle_preannot_selection(self, basename):\n abspath = os.path.join(self.file_lists.preannot_list.dirpath, basename)\n pval = self.paint_form.slider_to_p_val(\n self.paint_form._sliders[-1].value())\n self.graphics_view.preannot_from_path(\n abspath, self.preannot_color)",
"title": ""
},
{
"docid": "b3182029846de3b5674d20fc114eba05",
"score": "0.44569832",
"text": "def setSelection(self, arg1: hiero.core.TrackItem):\n ...",
"title": ""
},
{
"docid": "b3182029846de3b5674d20fc114eba05",
"score": "0.44569832",
"text": "def setSelection(self, arg1: hiero.core.TrackItem):\n ...",
"title": ""
},
{
"docid": "3e6f737dbcf3c506135678dc7f21598a",
"score": "0.4456356",
"text": "def on_sample(self, action, multiple):\n dialog = gtk.Dialog('Stack Sample Preferences', self)\n vbox = gtk.VBox()\n self.pack_sample_options(vbox, multiple)\n dialog.vbox.pack_start(vbox, False, False, 0)\n hbox = gtk.HButtonBox()\n button = gtk.Button(stock=gtk.STOCK_CANCEL)\n button.connect(\"clicked\", lambda w: dialog.destroy())\n hbox.pack_start(button, False, True, 0)\n button = gtk.Button(stock=gtk.STOCK_OK)\n if multiple is True:\n button.connect(\"clicked\", lambda w: self.update_prefs_and_sample_cb(self.sample_multiple, w, dialog, 'OK'))\n else:\n button.connect(\"clicked\", lambda w: self.update_prefs_and_sample_cb(self.sample, w, dialog, 'OK'))\n hbox.pack_start(button, False, True, 0)\n dialog.vbox.pack_start(hbox, False, False, 0)\n dialog.show_all()\n dialog.run()",
"title": ""
},
{
"docid": "d84f0f6839cb3048f5253b178956cf2b",
"score": "0.44557506",
"text": "def main():\r\n\tst.title(\"Contraceptive Method Choice Prediction\")\r\n\tst.subheader(\"Predicting Contraceptive Choice with ML and Streamlit\")\r\n\r\n\t# Load Our Dataset\r\n\tdf = pd.read_csv(\"cmc_dataset.csv\")\r\n\r\n\tif st.checkbox(\"Show DataSet\"):\r\n\t\tst.dataframe(df.head(10))\r\n\r\n\tif st.button(\"Columns Names\"):\r\n\t\tst.write(df.columns)\r\n\r\n\tif st.checkbox(\"Shape of Dataset\"):\r\n\t\tst.write(df.shape)\r\n\t\tdata_dim = st.radio(\"Show Dimension by\",(\"Rows\",\"Columns\"))\r\n\t\tif data_dim == 'Rows':\r\n\t\t\tst.text(\"Number of Rows\")\r\n\t\t\tst.write(df.shape[0])\r\n\t\telif data_dim == 'Columns':\r\n\t\t\tst.text(\"Number of Columns\")\r\n\t\t\tst.write(df.shape[1])\r\n\r\n\tif st.checkbox(\"Select Columns To Show\"):\r\n\t\tall_columns = df.columns.tolist()\r\n\t\tselected_columns = st.multiselect('Select',all_columns)\r\n\t\tnew_df = df[selected_columns]\r\n\t\tst.dataframe(new_df)\r\n\r\n\tif st.button(\"Data Types\"):\r\n\t\tst.write(df.dtypes)\r\n\r\n\tif st.button(\"Value Counts\"):\r\n\t\tst.text(\"Value Counts By Target/Class\")\r\n\t\tst.write(df.iloc[:,-1].value_counts())\r\n\r\n\tst.subheader(\"Data Visualization\")\r\n\t# Show Correlation Plots\r\n\t# Matplotlib Plot\r\n\tif st.checkbox(\"Correlation Plot [Matplotlib]\"):\r\n\t\tplt.matshow(df.corr())\r\n\t\tst.pyplot()\r\n\t# Seaborn Plot\r\n\tif st.checkbox(\"Correlation Plot with Annotation[Seaborn]\"):\r\n\t\tst.write(sns.heatmap(df.corr(),annot=True))\r\n\t\tst.pyplot()\r\n\r\n\t# Counts Plots\r\n\tif st.checkbox(\"Plot of Value Counts\"):\r\n\t\tst.text(\"Value Counts By Target/Class\")\r\n\r\n\t\tall_columns_names = df.columns.tolist()\r\n\t\tprimary_col = st.selectbox('Select Primary Column To Group By',all_columns_names)\r\n\t\tselected_column_names = st.multiselect('Select Columns',all_columns_names)\r\n\t\tif st.button(\"Plot\"):\r\n\t\t\tst.text(\"Generating Plot for: {} and {}\".format(primary_col,selected_column_names))\r\n\t\t\tif selected_column_names:\r\n\t\t\t\tvc_plot = df.groupby(primary_col)[selected_column_names].count()\t\t\r\n\t\t\telse:\r\n\t\t\t\tvc_plot = df.iloc[:,-1].value_counts()\r\n\t\t\tst.write(vc_plot.plot(kind='bar'))\r\n\t\t\tst.pyplot()\r\n\r\n\tif st.checkbox(\"Pie Plot\"):\r\n\t\tall_columns_names = df.columns.tolist()\r\n\t\t# st.info(\"Please Choose Target Column\")\r\n\t\t# int_column = st.selectbox('Select Int Columns For Pie Plot',all_columns_names)\r\n\t\tif st.button(\"Generate Pie Plot\"):\r\n\t\t\t# cust_values = df[int_column].value_counts()\r\n\t\t\t# st.write(cust_values.plot.pie(autopct=\"%1.1f%%\"))\r\n\t\t\tst.write(df.iloc[:,-1].value_counts().plot.pie(autopct=\"%1.1f%%\"))\r\n\t\t\tst.pyplot()\r\n\r\n\t# Prediction\r\n\tst.subheader(\"Options For Prediction\")\r\n\tst.subheader(\"Attributes To Select from\")\r\n\r\n\tdef get_value(val,my_dict):\r\n\t\tfor key ,value in my_dict.items():\r\n\t\t\tif val == key:\r\n\t\t\t\treturn value\r\n\r\n\tage = st.slider(\"Select Age\",16,60)\r\n\twife_education = st.number_input(\"Wife's Education Level(low2High) [1,4]\",1,4)\r\n\thusband_education = st.number_input(\"Husband's Education Level(low2High) [1,4]\",1,4)\r\n\tnum_of_children_ever_born = st.number_input(\"Number of Children\")\r\n\r\n\twife_reg = {\"Non_Religious\":0,\"Religious\":1}\r\n\tchoice_wife_reg = st.radio(\"Wife's Religion\",tuple(wife_reg.keys()))\r\n\tresult_wife_reg = get_value(choice_wife_reg,wife_reg)\r\n\t# st.text(result_wife_reg)\r\n\r\n\r\n\twife_working = {\"Yes\":0,\"No\":1}\r\n\tchoice_wife_working = st.radio(\"Is the Wife Currently Working\",tuple(wife_working.keys()))\r\n\tresult_wife_working = get_value(choice_wife_working,wife_working)\r\n\t# st.text(result_wife_working)\r\n\r\n\r\n\thusband_occupation = st.number_input(\"Husband Occupation(low2High) [1,4]\",1,4)\r\n\tstandard_of_living = st.slider(\"Standard of Living (low2High) [1,4]\",1,4)\r\n\r\n\tmedia_exposure = {\"Good\":0,\"Not Good\":1}\r\n\tchoice_media_exposure = st.radio(\"Media Exposure\",tuple(media_exposure.keys()))\r\n\tresult_media_exposure = get_value(choice_media_exposure,media_exposure)\r\n\r\n\r\n\t# Result and in json format\r\n\tresults = [age,wife_education,husband_education,num_of_children_ever_born,result_wife_reg,result_wife_working,husband_occupation,standard_of_living,result_media_exposure]\r\n\tdisplayed_results = [age,wife_education,husband_education,num_of_children_ever_born,choice_wife_reg,choice_wife_working,husband_occupation,standard_of_living,choice_media_exposure]\r\n\tprettified_result = {\r\n\t\t\"age\":age,\r\n\t\t\"wife_education\":wife_education,\r\n\t\t\"husband_education\":husband_education,\r\n\t\t\"num_of_children_ever_born\":num_of_children_ever_born,\r\n\t\t\"result_wife_reg\":choice_wife_reg,\r\n\t\t\"result_wife_working\":choice_wife_working,\r\n\t\t\"husband_occupation\":husband_occupation,\r\n\t\t\"standard_of_living\":standard_of_living,\r\n\t\t\"media_exposure\":choice_media_exposure\r\n\t}\r\n\tsample_data = np.array(results).reshape(1, -1)\r\n\t\r\n\t\r\n\tif st.checkbox(\"Your Inputs Summary\"):\r\n\t\tst.json(prettified_result)\r\n\t\tst.text(\"Vectorized as ::{}\".format(results))\r\n\r\n\tst.subheader(\"Prediction\")\r\n\tif st.checkbox(\"Make Prediction\"):\r\n\t\tall_ml_dict = {'LR':LogisticRegression(),\r\n\t\t'CART':DecisionTreeClassifier(),\r\n\t\t'RForest':RandomForestClassifier(),\r\n\t\t'NB':GaussianNB(),\r\n\t\t'MultNB':MultinomialNB()}\r\n\t\t# models = []\r\n\t\t# model_choice = st.multiselect('Model Choices',list(all_ml_dict.keys()))\r\n\t\t# for key in all_ml_dict:\r\n\t\t# \tif 'RForest' in key:\r\n\t\t# \t\tst.write(key)\r\n\r\n\t\t# Find the Key From Dictionary\r\n\t\tdef get_key(val,my_dict):\r\n\t\t\tfor key ,value in my_dict.items():\r\n\t\t\t\tif val == value:\r\n\t\t\t\t\treturn key\r\n\r\n\t\t# Load Models\r\n\t\tdef load_model_n_predict(model_file):\r\n\t\t\tloaded_model = joblib.load(open(os.path.join(model_file),\"rb\"))\r\n\t\t\treturn loaded_model\r\n\r\n\t\t# Model Selection\r\n\t\tmodel_choice = st.selectbox('Model Choice',list(all_ml_dict.keys()))\r\n\t\tprediction_label = {\"No-use\": 1,\"Long-term\": 2,\"Short-term\":3}\r\n\t\tif st.button(\"Predict\"):\r\n\t\t\tif model_choice == 'RForest':\r\n\t\t\t\tloaded_model = joblib.load(open(\"contraceptives_rf_model.pkl\",\"rb\"))\r\n\t\t\t\tprediction = loaded_model.predict(sample_data)\r\n\t\t\t\t# final_result = get_key(prediction,prediction_label)\r\n\t\t\t\t# st.info(final_result)\r\n\t\t\telif model_choice == 'LR':\r\n\t\t\t\tmodel_predictor = load_model_n_predict(\"models/contraceptives_logit_model.pkl\")\r\n\t\t\t\tprediction = model_predictor.predict(sample_data)\r\n\t\t\t\t# st.text(prediction)\r\n\t\t\telif model_choice == 'CART':\r\n\t\t\t\tmodel_predictor = load_model_n_predict(\"models/contraceptives_dcTree_model.pkl\")\r\n\t\t\t\tprediction = model_predictor.predict(sample_data)\r\n\t\t\t\t# st.text(prediction)\r\n\t\t\telif model_choice == 'NB':\r\n\t\t\t\tmodel_predictor = load_model_n_predict(\"models/contraceptives_nv_model.pkl\")\r\n\t\t\t\tprediction = model_predictor.predict(sample_data)\r\n\t\t\t\t# st.text(prediction)\r\n\t\t\t\r\n\t\t\tfinal_result = get_key(prediction,prediction_label)\r\n\t\t\tst.success(final_result)\r\n\r\n\r\n\tst.sidebar.subheader(\"About\")\r\n\tst.sidebar.info(\"ML App with Streamlit\")\r\n\tst.sidebar.text(\"Streamlit Is Awesome\")",
"title": ""
},
{
"docid": "31480e19531f960b820a4e4792e133aa",
"score": "0.44557306",
"text": "def UpdateChoice(self):\n b_name = model.behaviour.name.lower()\n select = self.file_ch.GetCurrentSelection()\n self.file_ch.SetString(0, \"b_%s\" % b_name)\n self.file_ch.SetString(1, \"z_b_%s\" % b_name)\n self.file_ch.SetSelection(select)",
"title": ""
},
{
"docid": "7589c2010667b683f527e084202e58df",
"score": "0.4453588",
"text": "def __init__(self, sentence):\r\n self.sentence = sentence\r\n self.answer = None\r\n\r\n self._create_question_simple()",
"title": ""
},
{
"docid": "a1d1a19ce15aa7eecdaf764e3548deee",
"score": "0.44495338",
"text": "def __init__(self, notebook):\n ogl.ShapeEvtHandler.__init__(self)\n self.notebook = notebook\n \"\"\"The notebook that contains the VerbCanvas.\n Keeping track of this object allows us to change the currently selected tab.\n @type: wx.NoteBook\n \"\"\"",
"title": ""
},
{
"docid": "ade7444da3c12d95759a840cd350db72",
"score": "0.44477096",
"text": "def study_collection(self, button, collection=None):\n\n def study_prompt(button, slide):\n \"\"\" Render the menu for the first half of the study menu: reading\n the prompt.\n \"\"\"\n menu = GenericMenu(\"Studying \" + collection.name + \".\")\n menu.add_widget(urwid.Text(slide.prompt))\n menu.add_widget(urwid.GridFlow(\n [\n self._button(\"Flip\", study_answer, slide)\n ],\n 40, 1, 1, 'center'))\n\n self._update_widget(menu)\n\n def study_answer(button, slide):\n \"\"\" Render the menu for the first half of the study menu: reading\n the answer and determining how difficult the question was.\n \"\"\"\n menu = GenericMenu(\"Studying \" + collection.name + \".\")\n menu.add_widget(urwid.Text(slide.answer))\n menu.add_widget(urwid.GridFlow(\n [\n self._button(\"Easy\", study_process_difficulty, (slide, 1))\n ,self._button(\"Medium\", study_process_difficulty, (slide, 2))\n ,self._button(\"Hard\", study_process_difficulty, (slide, 3))\n ,self._button(\"Back\", self.main)\n ],\n 20, 1, 1, 'center'))\n\n self._update_widget(menu)\n\n def study_process_difficulty(button, data):\n \"\"\" Determine when the slide should be reviewed next based on the\n subjective difficulty of the question.\n\n The \"data\" parameter should be a data structure with a slide in\n the first position (data[0]) and an integer representing\n difficulty (generally from 1 to 3) in the second position\n (data[1]).\n \"\"\"\n # TODO: Find a better way to pass the slide and difficulty in.\n slide = data[0]\n difficulty = data[1]\n\n # How long should the user wait to review this slide again?\n if difficulty == 1:\n # If it's easy, double the time\n multiplier = 2\n elif difficulty == 3:\n # If it's hard, halve the time\n multiplier = 0.5\n else:\n # If it's medium or anything else, leave the time alone\n multiplier = 1\n\n slide.set_new_wait_time(slide.previous_wait_time * multiplier)\n\n self.study_collection(None, collection)\n # ----------------------------------------\n\n if collection == None:\n self.choose_collection(self.study_collection)\n else:\n slide = collection.get_next_slide()\n if slide == None:\n slide = collection.get_random_slides(1)[0]\n\n study_prompt(None, slide)",
"title": ""
},
{
"docid": "2b18a508bd1c1a99d18c59cb2e1705d1",
"score": "0.44455856",
"text": "def Pick(self, vtkAbstractPicker, vtkObject):\n ...",
"title": ""
},
{
"docid": "48ddea962e0c1a1d79d3ad68815629b4",
"score": "0.44427353",
"text": "def add_question(self):\n # adding a new top level to our tk - on this level the the fill form will be represented.\n self.self_add = Toplevel(self.student_Lecturer_top)\n self.self_add.geometry('240x340')\n\n # identifier key\n\n # default answer is No:\n self.combo_answers = 'No'\n key = 0\n\n # opening the dialog to choose the path to the wanted question\n self.self_add.filename = filedialog.askopenfilename(initialdir=\"/\", title=\"Select file\", filetypes=((\"Jpeg files\", \"*.Jpeg\"), (\"Pdf files\", \"*.Pdf\"), (\"Jpg files\", \"*.Jpg\"), (\"Docx files\", \"*.Docx\")))\n\n # we need to check the question's format:\n self.question_file_format = self.self_add.filename.rpartition('.')[-1]\n #print(question_file_format)\n\n if self.question_file_format == 'jpeg' or self.question_file_format == 'jpg':\n size = (350, 350)\n thumb = ImageOps.fit(Image.open(self.self_add.filename), size, Image.ANTIALIAS)\n thumb.save(self.self_add.filename.format(self.self_add.filename[:self.self_add.filename.rfind('.')]), \"JPEG\")\n\n img = ImageOps.fit(Image.open(self.self_add.filename), size, Image.ANTIALIAS)\n self.img_question = ImageTk.PhotoImage(img)\n\n with open(self.self_add.filename, \"rb\") as imageFile:\n self.str_img_question = base64.b64encode(imageFile.read())\n\n elif self.question_file_format == 'pdf':\n #if the file is in a PDF format:\n self.pdf_question = \"\"\n pdfFileObj = open(self.self_add.filename, 'rb')\n\n # creating a pdf reader object\n pdfReader = PyPDF2.PdfFileReader(pdfFileObj)\n\n # printing number of pages in pdf file\n # print(pdfReader.numPages)\n\n # creating a page object\n\n for i in range(0, pdfReader.numPages):\n pageObj = pdfReader.getPage(i)\n\n # extracting text from page\n self.pdf_question = self.pdf_question + str(pageObj.extractText())\n\n # organizing the text which is extracted from the pdf to a: 10 words per line format:\n count = 0\n for i in range(0, len(self.pdf_question)):\n if self.pdf_question[i] == ' ':\n if count < 9:\n count += 1\n else:\n self.pdf_question = self.pdf_question[0: i] + \"\\n\" + self.pdf_question[i:]\n count = 0\n\n # print(pdf_question)\n\n # closing the pdf file object\n pdfFileObj.close()\n\n elif self.question_file_format == 'docx':\n text = docx2txt.process(self.self_add.filename)\n self.docx_question = text\n # organizing the text which is extracted from the pdf to a: 10 words per line format:\n count = 0\n for i in range(0, len(self.docx_question)):\n if self.docx_question[i] == ' ':\n if count < 9:\n count += 1\n else:\n self.docx_question = self.docx_question[0: i] + \"\\n\" + self.docx_question[i:]\n count = 0\n\n # /////////////////////////////////// #\n # ------ creating the fill form : #\n # /////////////////////////////////// #\n\n Label(self.self_add, text='Fill form:').place(x=80, y=0)\n\n # creating a Label for courses:\n Label(self.self_add, text='Course: ').place(x=2, y=40)\n\n # creating combobox for the courses:\n self.combo_course = Combobox(self.self_add, width=14)\n self.combo_course['values'] = ('Calculus1', 'Linear algebra', 'Pre computer science', 'Architecture', \"Logic 1\")\n self.combo_course.current(0) # set the selected item\n self.combo_course.place(x=80, y=40)\n\n # creating a button for courses:\n if self.combo_course.get() == None:\n Button(self.self_add, text='Apply', width=4, state=DISABLED).place(x=192, y=35)\n else:\n Button(self.self_add, text='Apply', width=4, command=lambda: self.sub_subject_check(key)).place(x=192, y=35)\n\n # creating a Label for courses:\n Label(self.self_add, text='Sub subject: ').place(x=2, y=70)\n\n # creating combobox for the sub-subjects:\n self.combo_sub_subject = Combobox(self.self_add)\n self.combo_sub_subject['values'] = ('not yet')\n self.combo_sub_subject.current(0) # set the selected item\n self.combo_sub_subject.place(x=80, y=70)\n\n # creating a Label for Difficulty:\n Label(self.self_add, text='Difficulty: ').place(x=2, y=100)\n\n # creating combobox for the difficulty:\n self.combo_difficulty = Combobox(self.self_add)\n self.combo_difficulty['values'] = ('Easy', 'Moderate', 'Hard')\n self.combo_difficulty.current(0) # set the selected item\n self.combo_difficulty.place(x=80, y=100)\n\n # creating a Label and a Button for Answers:\n Label(self.self_add, text='Answers: ').place(x=2, y=130)\n self.self_add.browseButton = Button(self.self_add, command=self.add_answer, text='...', width=2, height=1).place(x=80, y=125)\n self.self_add.browseText = tk.Text(self.self_add, height=1, width=15, state=\"disabled\").place(x=110, y=125)\n\n # creating a Label for Years:\n Label(self.self_add, text='Year:').place(x=2, y=160)\n\n # creating spinbox for the Years:\n var = IntVar()\n var.set(2019)\n self.spin_years = Spinbox(self.self_add, from_=1995, to=2020, width=21, textvariable=var)\n self.spin_years.place(x=80, y=160)\n\n # creating Label for the semester:\n Label(self.self_add, text='Semester:').place(x=2, y=190)\n\n # creating a combobox for the Semester:\n self.combo_semester = Combobox(self.self_add)\n self.combo_semester['values'] = ('A', 'B', 'Summer')\n self.combo_semester.current(0) # set the selected item\n self.combo_semester.place(x=80, y=190)\n\n # creating a Label for Format:\n Label(self.self_add, text='Format:').place(x=2, y=220)\n\n # creating a combobox for Format:\n self.combo_format = Combobox(self.self_add)\n self.combo_format['values'] = ('Docx', 'Pdf', 'Jpeg')\n self.combo_format.current(0) # set the selected item\n self.combo_format.place(x=80, y=220)\n\n # creating Label for the Exam/Quiz:\n Label(self.self_add, text='From:').place(x=2, y=250)\n\n # creating a combobox for the Exam/Quiz:\n self.combo_from = Combobox(self.self_add)\n self.combo_from['values'] = ('Exam', 'Quiz')\n self.combo_from.current(0) # set the selected item\n self.combo_from.place(x=80, y=250)\n\n # crating an Accept button:\n Button(self.self_add, text='Accept', command=self.add_question_to_db, width=20).place(x=40, y=290)",
"title": ""
},
{
"docid": "0c790170e3833e59e7bdd7b6955bb710",
"score": "0.44412005",
"text": "def __init__(self, opt):\n # save the option and dataset root\n BaseDataset.__init__(self, opt)\n\n remote_handler = RemoteHandler('Minimal Working Example', force_work='scratch', user='temirlan')\n label_parserA = LabelParser(groups=[self.opt.lp_nameA])\n label_parserB = LabelParser(groups=[self.opt.lp_nameB])\n\n self.transform = [Rotation(), RandomCrop(offset=-1, patch_size=256), TransformToTensor(normalise=True)]\n # self.transform2 = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n self.segm_datasetA = SegmentationDataset(size_minibatches=1,\n\t\t\t\t\t\t\tnum_subsets=6,\n\t\t\t\t\t\t\tpatchsize=256,\n\t\t\t\t\t\t\tlabel_parser=label_parserA,\n\t\t\t\t\t\t\ttransform=self.transform,\n\t\t\t\t\t\t\treturn_parameters=['image', 'segmap_NCWH'],\n\t\t\t\t\t\t\tremote_handler=remote_handler)\n self.segm_datasetA.load_sample_list(load_from=os.path.join('slide002_6x1000x400x200_centred.npy'))\n self.segm_datasetA.keep_in_memory()\n \n self.segm_datasetB = SegmentationDataset(size_minibatches=1,\n\t\t\t\t\t\t\tnum_subsets=6,\n\t\t\t\t\t\t\tpatchsize=256,\n\t\t\t\t\t\t\tlabel_parser=label_parserB,\n\t\t\t\t\t\t\ttransform=self.transform,\n\t\t\t\t\t\t\treturn_parameters=['image', 'segmap_NCWH'],\n\t\t\t\t\t\t\tremote_handler=remote_handler)\n self.segm_datasetB.load_sample_list(load_from=os.path.join('slide002_6x1000x400x200_centred.npy'))\n self.segm_datasetB.keep_in_memory()\n\n if self.opt.isTrain:\n self.segm_datasetA.subset = [i for i in range(self.segm_datasetA.num_subsets) if i != self.opt.test_subset]\n self.segm_datasetB.subset = [i for i in range(self.segm_datasetA.num_subsets) if i != self.opt.test_subset]\n else:\n self.segm_datasetA.subset = self.opt.test_subset\n self.segm_datasetB.subset = self.opt.test_subset",
"title": ""
},
{
"docid": "3a7325699570a01d3a1351c079543a26",
"score": "0.4437901",
"text": "def overlaySelectionChanged(self, selection):\n indices = selection.indexes()\n if len(indices) > 0:\n model = self.view.overlay_treeview.model()\n source_index = model.mapToSource(indices[0])\n filename = str(model.sourceModel().filePath(source_index))\n if filename.lower().endswith('.hdf5'):\n file = h5py.File(filename)\n data = np.array(file['Capture000001'])\n is_full_image = data.size == IMAGE_WIDTH_FULL * IMAGE_HEIGHT_FULL\n dims = IMAGE_DIMS_FULL if is_full_image else IMAGE_DIMS_VELA\n self.OverlayImage.setImage(np.flip(np.transpose(data.reshape(dims)), 1))\n max_level = np.percentile(data, 99.9) # self.view.max_level_spin.maximum()\n self.OverlayImage.setLevels([0, max_level], update=True)\n conv = self.pix2mm()\n self.OverlayImage.setRect(QtCore.QRect(0, 0, dims[1] * conv, dims[0] * conv))\n if not self.OverlayImage in self.ImageBox.items:\n self.view.overlay_checkbox.setEnabled(True)\n self.view.overlay_checkbox.setChecked(True)\n else:\n self.view.overlay_checkbox.setChecked(False)\n else:\n self.view.overlay_checkbox.setChecked(False)",
"title": ""
},
{
"docid": "75181d515b65f8bcfe843935ffa0a9f7",
"score": "0.44353533",
"text": "def test_answering_questions(self):\n # This method utilises the POST request method and will make changes to the Canvas instance. This needs consideration.\n pass",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="783"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">caba96953d0db8ab81c413e65e5f9dde</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Returns the loom input tensor, used for building feed dictionaries. May be fed a single result or a sequence of results from `Compiler.build_loom_inputs()` or `Compiler.build_loom_input_batched()`.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "a4a7142f25ab9382865e61641721e6ce",
"score": "0.82956433",
"text": "def loom_input_tensor(self):\n self._check_loom_initialized()\n return self._wet.input_tensor",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "b1e438b5032c09b23af1bd83e8f00646",
"score": "0.63694286",
"text": "def input_tensor(interpreter):\r\n tensor_index = interpreter.get_input_details()[0]['index']\r\n return interpreter.tensor(tensor_index)()[0]",
"title": ""
},
{
"docid": "8978a692cfa1077b16d93793236519fb",
"score": "0.6358351",
"text": "def input_tensor(interpreter):\n tensor_index = interpreter.get_input_details()[0]['index']\n return interpreter.tensor(tensor_index)()[0]",
"title": ""
},
{
"docid": "5111f524586d440fc8fe972383f76d5b",
"score": "0.62802005",
"text": "def input_tensor(self):\n if not self._input_tensor:\n raise RuntimeError('Compiler has no input tensor.')\n return self._input_tensor",
"title": ""
},
{
"docid": "8f254e59e2a40d71629098e5a8a60af0",
"score": "0.59578973",
"text": "def get_input(self, state):\n with torch.no_grad():\n state = torch.from_numpy(state.reshape(1,1,state.shape[0])).to(torch.float32)\n inp = self.current_algo.forward(state)[0][0].numpy()\n return inp",
"title": ""
},
{
"docid": "62357b4fe84bb33ed838828228aa4ed0",
"score": "0.58380306",
"text": "def __call__(self, input_tensor):\n return self.build(input_tensor)",
"title": ""
},
{
"docid": "727d4a0c9763789680ce97eda8f66afe",
"score": "0.56931156",
"text": "def get_input_tensor(self, islot, iname):\n return self._islot_to_itensor[islot][iname]",
"title": ""
},
{
"docid": "65d4190326145130df56bc4882146ff9",
"score": "0.56182665",
"text": "def get_input(self, time_step: Optional[int]) -> Tensor:\n return self.inputs[time_step]",
"title": ""
},
{
"docid": "fe4deface298d5e3b525408c6895d2e2",
"score": "0.56179",
"text": "def input(self):\n if self.layers:\n return self.layers[0].input\n return None",
"title": ""
},
{
"docid": "3974f95af8f6084a6a9f3286c13b039e",
"score": "0.5515112",
"text": "def forward(self, input: Tensor) -> Tensor:\n return self.model(input)",
"title": ""
},
{
"docid": "064dc67964df30012835b8d95de5642d",
"score": "0.55131733",
"text": "def __call__(self, x_input):\n reuse = True if self.built else None\n x_input = image_normalize(x_input, 'default')\n with slim.arg_scope(inception.inception_v4_arg_scope()):\n logit, end_points = inception.inception_v4(\n x_input, num_classes=self.num_classes, is_training=True,\n reuse=reuse)\n self.built = True\n output = end_points['Predictions']\n # Strip off the extra reshape op at the output\n probs = output.op.inputs[0]\n return logit",
"title": ""
},
{
"docid": "ea8818ce18f54e33b8f9a61c4552329c",
"score": "0.5438823",
"text": "def get_input_matrix(self):\n return self._sess.run(self._input_mat)",
"title": ""
},
{
"docid": "f62eca6aa619312595e41ff3238b79b2",
"score": "0.5412484",
"text": "def get_output_tensor(self, oname): \n return self._oslot_to_otensor[oname]",
"title": ""
},
{
"docid": "59f00e8316b8b11b42f12a7694803fd9",
"score": "0.53018624",
"text": "def forward(self, input):\n # TODO: batched evaluation\n if not self.operator.is_linear:\n # Only needed for nonlinear operators\n self.save_for_backward(input)\n\n # TODO: use GPU memory directly if possible\n input_arr = input.cpu().detach().numpy()\n if any(s == 0 for s in input_arr.strides):\n # TODO: remove when Numpy issue #9165 is fixed\n # https://github.com/numpy/numpy/pull/9177\n input_arr = input_arr.copy()\n\n op_result = self.operator(input_arr)\n if np.isscalar(op_result):\n # For functionals, the result is funnelled through `float`,\n # so we wrap it into a Numpy array with the same dtype as\n # `operator.domain`\n op_result = np.array(op_result, ndmin=1,\n dtype=self.operator.domain.dtype)\n tensor = torch.from_numpy(np.array(op_result, copy=False, ndmin=1))\n tensor = tensor.to(input.device)\n return tensor",
"title": ""
},
{
"docid": "ae6d879a6871e8077256c2a6d4c9e197",
"score": "0.5273065",
"text": "def _ensure_tensor(input):\n if isinstance(input, (int, float)):\n input = torch.tensor(input)\n return input",
"title": ""
},
{
"docid": "6ca2e26965c0bb37e3e8054bfd4d773a",
"score": "0.52303904",
"text": "def to_input_tensor(self, embed, pos, head, r_deps, l_deps):\n\n embed, pos, head, r_deps, l_deps, masks = self.input_transpose(embed, pos, head, r_deps, l_deps)\n\n\n embed_t = torch.tensor(embed, dtype=torch.float32, requires_grad=False, device=self.device)\n pos_t = torch.tensor(pos, dtype=torch.long, requires_grad=False, device=self.device)\n head_t = torch.tensor(head, dtype=torch.long, requires_grad=False, device=self.device)\n r_deps_t = torch.tensor(r_deps, dtype=torch.long, requires_grad=False, device=self.device)\n l_deps_t = torch.tensor(l_deps, dtype=torch.long, requires_grad=False, device=self.device)\n masks_t = torch.tensor(masks, dtype=torch.float32, requires_grad=False, device=self.device)\n\n return embed_t, pos_t, head_t, r_deps_t, l_deps_t, masks_t",
"title": ""
},
{
"docid": "76d8bbc06c913ef0d2670e3bdce457f0",
"score": "0.52295107",
"text": "def Input(shape: Tuple[int], dtype: str): # pylint: disable=invalid-name\n x = SymbolicTensor(shape, dtype)\n return InputModule(shape, dtype)(x)",
"title": ""
},
{
"docid": "8ad9e4916035cf6790133a6c18ff6975",
"score": "0.5219318",
"text": "def GetInput(self, *args):\n return _ITKLabelMapBasePython.itkImageToImageFilterLM2IUL2_GetInput(self, *args)",
"title": ""
},
{
"docid": "1cf540d80a19d331ece06d1cd72315ec",
"score": "0.5211619",
"text": "def _get_inputs(input_dim: int = 8) -> torch.tensor:\n # Prepare random tensor as test cases.\n shapes = (\n # Forward succeeded.\n (1, input_dim, 5, 7, 7),\n (2, input_dim, 5, 7, 7),\n (4, input_dim, 5, 7, 7),\n (4, input_dim, 5, 7, 7),\n (4, input_dim, 7, 7, 7),\n (4, input_dim, 7, 7, 14),\n (4, input_dim, 7, 14, 7),\n (4, input_dim, 7, 14, 14),\n # Forward failed.\n (8, input_dim * 2, 3, 7, 7),\n (8, input_dim * 4, 5, 7, 7),\n )\n for shape in shapes:\n yield torch.rand(shape)",
"title": ""
},
{
"docid": "c8d3510a37b6113372bdee5f93c9d616",
"score": "0.5203342",
"text": "def GetInput(self, *args):\n return _ITKLabelMapBasePython.itkImageToImageFilterLM2LM2_GetInput(self, *args)",
"title": ""
},
{
"docid": "fd0e8343626bad19624db285cca657f2",
"score": "0.51869524",
"text": "def call(self, inputs):\n unpacked_inputs = tf_utils.unpack_inputs(inputs)\n pooled_output = unpacked_inputs[0]\n sequence_output = unpacked_inputs[1]\n masked_lm_positions = unpacked_inputs[2]\n\n mask_lm_input_tensor = gather_indexes(sequence_output, masked_lm_positions)\n lm_output = self.lm_dense(mask_lm_input_tensor)\n lm_output = self.lm_layer_norm(lm_output)\n lm_output = tf.matmul(lm_output, self.embedding_table, transpose_b=True)\n lm_output = tf.nn.bias_add(lm_output, self.output_bias)\n lm_output = tf.nn.log_softmax(lm_output, axis=-1)\n\n return lm_output",
"title": ""
},
{
"docid": "877affc873b83f5393c179b96cd896de",
"score": "0.51753575",
"text": "def input_layer(shape: Tuple):\n return layers.Input(shape)",
"title": ""
},
{
"docid": "6df080d007f3b3ed26f07df8cb4efc9e",
"score": "0.51684725",
"text": "def GetInput(self, *args):\n return _ITKLabelMapBasePython.itkImageToImageFilterIUL2LM2_GetInput(self, *args)",
"title": ""
},
{
"docid": "d9b5e2f4941c8cd50ad3b0110ea0e207",
"score": "0.51577914",
"text": "def input_variable(self, word2id):\n tensor = torch.LongTensor(self.size, self.max_length)\n\n for i, doc in enumerate(self.input):\n ids = get_train_ids(doc, word2id)\n # Pad the difference with symbol\n ids = ids + [word2id[self.symbol]] * (self.max_length - len(doc))\n tensor[i] = torch.LongTensor(ids)\n\n self.input = autograd.Variable(tensor)\n return self.input",
"title": ""
},
{
"docid": "f461602e6f36b3a30c09ee588aacced1",
"score": "0.51476866",
"text": "def GetInput(self, *args):\n return _ITKLabelMapBasePython.itkImageToImageFilterLM3IUL3_GetInput(self, *args)",
"title": ""
},
{
"docid": "ba35460b661bc06a44dfe6a11d50d1b3",
"score": "0.512338",
"text": "def GetInput(self, *args):\n return _ITKLabelMapBasePython.itkImageToImageFilterLM3LM3_GetInput(self, *args)",
"title": ""
},
{
"docid": "aff49a1e8f7fd7edc42bf36ed4f05cbd",
"score": "0.5117733",
"text": "def input():\n return list_of_inputs.pop(0)",
"title": ""
},
{
"docid": "a969b7142d7d6baa9c9176b9f7199df6",
"score": "0.5117551",
"text": "def _get_inputs(input_dim: int = 8) -> torch.tensor:\n # Prepare random tensor as test cases.\n shapes = (\n # Forward succeeded.\n (1, input_dim, 5, 7, 7),\n (2, input_dim, 5, 7, 7),\n (4, input_dim, 5, 7, 7),\n (4, input_dim, 5, 7, 7),\n (4, input_dim, 7, 7, 7),\n (4, input_dim, 7, 7, 14),\n (4, input_dim, 7, 14, 7),\n (4, input_dim, 7, 14, 14),\n # Forward failed.\n (8, input_dim * 2, 3, 7, 7),\n (8, input_dim * 4, 5, 7, 7),\n )\n for shape in shapes:\n input_tensor = torch.rand(shape)\n bboxes = [[i, 1, 2, 3, 4] for i in range(input_tensor.shape[0])]\n bboxes = torch.Tensor(bboxes)\n yield (input_tensor, bboxes)",
"title": ""
},
{
"docid": "fa86632ba1bc618807832b1ef62044d5",
"score": "0.508071",
"text": "def forward(self, input: torch.Tensor, ilens: torch.Tensor):\n return input, ilens",
"title": ""
},
{
"docid": "ed1dbcb74c51976594a4669136bc3a24",
"score": "0.50804025",
"text": "def input_builder(self):\n input_shape = [None] + self.input_shape[1:]\n self._input_placeholder = array_ops.placeholder(\n dtypes.as_dtype(self._input_dtype),\n input_shape,\n name='input')\n if self.output_shape is None:\n self._output_placeholder = None\n else:\n output_shape = [None] + self.output_shape[1:]\n self._output_placeholder = array_ops.placeholder(\n dtypes.as_dtype(self._output_dtype),\n output_shape,\n name='output')\n return self._input_placeholder, self._output_placeholder",
"title": ""
},
{
"docid": "af0e3f1d81c3943f09f3e00328d0bdd0",
"score": "0.50742704",
"text": "def get_input_vector(self, ind):\n return self._sess.run(self._input_mat[ind])",
"title": ""
},
{
"docid": "1e2fedee41c843cb68bb59bd4b3ae22a",
"score": "0.5066573",
"text": "def generate_latent_code(self, input_repr: torch.Tensor): # pylint: disable=W0221\n raise NotImplementedError",
"title": ""
},
{
"docid": "05c2c3ed4b27a84a2adb4ffde02e68f4",
"score": "0.5052144",
"text": "def build_input(self, observations, actions, rewards):\n if self.io_type == 1:\n return observations\n elif self.io_type == 2:\n return torch.cat((torch.unsqueeze(rewards, 2), actions, observations), dim=2)",
"title": ""
},
{
"docid": "74a2eaa3c8945009a94307e93cdd9eaa",
"score": "0.5044912",
"text": "def forward(self, input):\r\n hidden, state = self._initHidden(input.shape[0])\r\n embeddings = self.pos_embed(input.long())\r\n embeddings = embeddings.view((input.shape[0], self.cfg.sequence_length, -1))\r\n output, (_, _) = self.lstm(embeddings, (hidden, state))\r\n output = self.out(output.reshape(input.shape[0], -1))\r\n output = self.sigm(output)\r\n return output, embeddings",
"title": ""
},
{
"docid": "556cd55021288aabd82e6ddb9d033b7e",
"score": "0.50443023",
"text": "def get_corrupted_input(self, input, corruption_level):\n return self.theano_rng.binomial(size=input.shape, n=1,\n p=1 - corruption_level,\n dtype=theano.config.floatX) * input",
"title": ""
},
{
"docid": "e08b12a649158cbc308a69b20d40e5a9",
"score": "0.504372",
"text": "def GetInput(self, *args):\n return _ITKLabelMapBasePython.itkImageToImageFilterLM2IUC2_GetInput(self, *args)",
"title": ""
},
{
"docid": "86bec93e2b4e3c5dbd68fca49c1527c9",
"score": "0.50412405",
"text": "def input_train():\n batch = mnist.train.next_batch(100)\n return batch[0], batch[1]",
"title": ""
},
{
"docid": "3ccb2fa77d7bef26befc55f76ab01af0",
"score": "0.50333333",
"text": "def output_tensors(self):\n self._check_loom_initialized()\n return self._output_tensors",
"title": ""
},
{
"docid": "e853607bbc6212135ab5cdf8dd570b5d",
"score": "0.5032512",
"text": "def getInput(self, name):\n\n if name not in self.inputs:\n raise Exception(\"Input with name '\" + name +\n \"' was not found in operator: \" +\n self.getName() + \".\")\n\n return self.inputs[name]",
"title": ""
},
{
"docid": "913ad1404ae22a194144acdd2119ebd7",
"score": "0.50264394",
"text": "def GetInput(self, *args):\n return _ITKLabelMapBasePython.itkImageToImageFilterID2LM2_GetInput(self, *args)",
"title": ""
},
{
"docid": "5db3d0d0d374fed86376a4dd0cead288",
"score": "0.5016531",
"text": "def single_forward(model, inp):\n with torch.no_grad():\n model_output = model(inp)\n if isinstance(model_output, list) or isinstance(model_output, tuple):\n output = model_output[0]\n else:\n output = model_output\n output = output.data.float().cpu()\n return output",
"title": ""
},
{
"docid": "0313f0300356470eb142da2c41f80518",
"score": "0.50127906",
"text": "def GetInput(self, *args):\n return _ITKLabelMapBasePython.itkImageToImageFilterLM2ID2_GetInput(self, *args)",
"title": ""
},
{
"docid": "c07f201f31d4c48ea1f1fe8daf81dbe1",
"score": "0.5010178",
"text": "def GetInput(self, *args):\n return _ITKLabelMapBasePython.itkImageToImageFilterLM3IUS3_GetInput(self, *args)",
"title": ""
},
{
"docid": "3d6236999bd20dfeb28442615d35663a",
"score": "0.5008481",
"text": "def forward(self, input):\n return input.view(input.size(0), -1)",
"title": ""
},
{
"docid": "501036e910796c334538a2c7510ad42c",
"score": "0.50083566",
"text": "def forward(self, input: torch.Tensor) -> torch.Tensor:\n\n l = tv(input, **self.kwargs)\n\n return self.reduce(l)",
"title": ""
},
{
"docid": "4036e5e0bcf000d7810be82e2a53caa4",
"score": "0.5004871",
"text": "def GetInput(self, *args):\n return _ITKLabelMapBasePython.itkImageToImageFilterLM2IUS2_GetInput(self, *args)",
"title": ""
},
{
"docid": "c7d59800cd5af8d2b79c8c4e2e392589",
"score": "0.5003701",
"text": "def GetInput(self, *args):\n return _ITKLabelMapBasePython.itkImageToImageFilterIUS2LM2_GetInput(self, *args)",
"title": ""
},
{
"docid": "528c1554bace6fb2feb23561604cc1d9",
"score": "0.5002966",
"text": "def GetInput(self, *args):\n return _ITKLabelMapBasePython.itkImageToImageFilterLM3IUC3_GetInput(self, *args)",
"title": ""
},
{
"docid": "2b863ba7ded43c6f4f1d58a0472cc89c",
"score": "0.49924773",
"text": "def predict(self, input: torch.Tensor) -> torch.Tensor:",
"title": ""
},
{
"docid": "52992346f9d08374f044f814cacaac05",
"score": "0.49838197",
"text": "def forward(ctx, input):\n return input",
"title": ""
},
{
"docid": "ba0d0d64f262256cdf7f658060187474",
"score": "0.49818256",
"text": "def forward(self, input):\n output = self.mlp(input) # (bs, L, output_size)\n if self.clamping:\n output = torch.clamp(output, min=self.clamping_min_actions, max=self.clamping_max_actions)\n return output",
"title": ""
},
{
"docid": "ee57c625580e46c8d3e9b433cb749710",
"score": "0.49741396",
"text": "def GetInput(self, *args):\n return _ITKLabelMapBasePython.itkImageToImageFilterIF2LM2_GetInput(self, *args)",
"title": ""
},
{
"docid": "d5d4fc7078d39e124c5c867ede9b6e8e",
"score": "0.49734145",
"text": "def build_loom_inputs(self, examples, metric_labels=False,\n chunk_size=100, ordered=False):\n self._check_build('build_loom_inputs', examples)\n return _map_maybe_parallel(\n self.pool, _subprocess_build_single, self._build_single, examples,\n ordered, chunk_size, metric_labels=bool(metric_labels))",
"title": ""
},
{
"docid": "57cf5593ab74e729255c3c19ebbff9f0",
"score": "0.497197",
"text": "def get_input_trajectory(self):\n try:\n trajectory = numpy.array(self.u.value[:]).flatten()\n return trajectory\n except TypeError:\n print('MPC returning acc = 0')\n return numpy.zeros(self.h*self.nu)",
"title": ""
},
{
"docid": "9dbed0254dadd65a95566768f865bbb7",
"score": "0.4960122",
"text": "def GetInput(self, *args):\n return _ITKLabelMapBasePython.itkImageToImageFilterLM2IF2_GetInput(self, *args)",
"title": ""
},
{
"docid": "e57df1578ba7a059ac599d7066881d16",
"score": "0.495905",
"text": "def simple_readout(input_tensor, reuse=None):\n pass",
"title": ""
},
{
"docid": "6b871b1af7d4f13fa9cdd2f3cfe1a283",
"score": "0.49337435",
"text": "def GetInput(self, *args):\n return _ITKLabelMapBasePython.itkImageToImageFilterLM3IF3_GetInput(self, *args)",
"title": ""
},
{
"docid": "de9d2440eb668db4cd3f5b9dad661770",
"score": "0.49308977",
"text": "def GetInput(self, *args):\n return _ITKLabelMapBasePython.itkImageToImageFilterIUL3LM3_GetInput(self, *args)",
"title": ""
},
{
"docid": "dcc3f8d70f052a9b0e4621f0a93a9385",
"score": "0.49240306",
"text": "def input_layer(self):\n return self.layers[0]",
"title": ""
},
{
"docid": "430f8fc1906d167d2dab9b2891f7a78a",
"score": "0.49209413",
"text": "def load_input(self, context: InputContext, table_slice: TableSlice) -> T:",
"title": ""
},
{
"docid": "eb55840b3c07e8cf29dbb7e3e3f440f0",
"score": "0.49194962",
"text": "def GetInput(self, *args):\n return _ITKLabelMapBasePython.itkImageToImageFilterIUC2LM2_GetInput(self, *args)",
"title": ""
},
{
"docid": "c463abf4cd9f8de4820e521ee5c184c2",
"score": "0.49066356",
"text": "def GetInput(self, *args):\n return _ITKLabelMapBasePython.itkImageToImageFilterLM3ID3_GetInput(self, *args)",
"title": ""
},
{
"docid": "dbe319020f3d794ae844c635eacc4a6a",
"score": "0.4903892",
"text": "def __call__(self, x_input):\n reuse = True if self.built else None\n with slim.arg_scope(inception.inception_v3_arg_scope()):\n _, end_points = inception.inception_v3(\n x_input, num_classes=self.num_classes, is_training=False,\n reuse=reuse)\n self.built = True\n output = end_points['Predictions']\n # Strip off the extra reshape op at the output\n probs = output.op.inputs[0]\n return probs",
"title": ""
},
{
"docid": "a040691fdf81f7b3160cb44e7af0ea8f",
"score": "0.48951092",
"text": "def get_input(self):\n\t\traise NotImplementedError()",
"title": ""
},
{
"docid": "750afab6af065060b847fdfba4e5d39c",
"score": "0.48901474",
"text": "def _net_input(self, X):\n return np.dot(X, self.w_[1:]) + self.w_[0]",
"title": ""
},
{
"docid": "e7b5520bba2f286b64be16ff9db72797",
"score": "0.48878554",
"text": "def _infer(self, input_):\n return self._session.run(\n self._outputs, feed_dict={self._inputs[0]: input_}\n )",
"title": ""
},
{
"docid": "a5a83cf56a345eed5bd41c7390bf2521",
"score": "0.48876998",
"text": "def flatten_tensor(input_):\n output = tensor2numpy(input_)\n\n return output.flatten()",
"title": ""
},
{
"docid": "6c58c39d00653faf02d815d5533fa65b",
"score": "0.4882471",
"text": "def _get_tensors_or_ops(inputs):\n\n def _get_fn(element):\n if element is None:\n return None\n if ':' in element.name:\n return updated_graph.get_tensor_by_name(element.name)\n return updated_graph.get_operation_by_name(element.name)\n\n if isinstance(inputs, (list, dict, tuple)):\n return nest.map_structure(_get_fn, inputs)\n else:\n return _get_fn(inputs)",
"title": ""
},
{
"docid": "79537275214a9b507371bfbc5c69dcb4",
"score": "0.4874047",
"text": "def get_dummy_input(self, input_type):\n\n if input_type == 'image_tensor':\n return np.zeros((1, 20, 20, 3), dtype=np.uint8)\n if input_type == 'float_image_tensor':\n return np.zeros((1, 20, 20, 3), dtype=np.float32)\n elif input_type == 'encoded_image_string_tensor':\n image = Image.new('RGB', (20, 20))\n byte_io = io.BytesIO()\n image.save(byte_io, 'PNG')\n return [byte_io.getvalue()]\n elif input_type == 'tf_example':\n image_tensor = tf.zeros((20, 20, 3), dtype=tf.uint8)\n encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).numpy()\n example = tf.train.Example(\n features=tf.train.Features(\n feature={\n 'image/encoded':\n dataset_util.bytes_feature(encoded_jpeg),\n 'image/format':\n dataset_util.bytes_feature(six.b('jpeg')),\n 'image/source_id':\n dataset_util.bytes_feature(six.b('image_id')),\n })).SerializeToString()\n return [example]",
"title": ""
},
{
"docid": "7e8570011ac6f602810eb9ceb98f835e",
"score": "0.4867013",
"text": "def GetInput(self, *args):\n return _ITKLabelMapBasePython.itkImageToImageFilterID3LM3_GetInput(self, *args)",
"title": ""
},
{
"docid": "c35cb400dfd0e8cc56e4d854bb64bb01",
"score": "0.48613647",
"text": "def provide_input(self) -> List[tf.Tensor]:\n with tf.name_scope(\"loading\"):\n prediction_input, expected_result = get_data_from_tfrecord(\n \"./data/test.tfrecord\", self.BATCH_SIZE\n ).get_next()\n\n with tf.name_scope(\"pre-processing\"):\n prediction_input = tf.reshape(\n prediction_input,\n shape=(self.BATCH_SIZE, ModelTrainer.IN_DIM, ModelTrainer.IN_DIM, 1),\n )\n expected_result = tf.reshape(expected_result, shape=(self.BATCH_SIZE,))\n\n return [prediction_input, expected_result]",
"title": ""
},
{
"docid": "492803eb95add0d3049e50b1393b763c",
"score": "0.4844574",
"text": "def GetInput(self, *args):\n return _ITKLabelMapBasePython.itkImageToImageFilterIUS3LM3_GetInput(self, *args)",
"title": ""
},
{
"docid": "360907a63772d5fa9097ac70526c95b7",
"score": "0.48442572",
"text": "def build_inputs(self):\n ret = []\n for param in self.circuit_ctx[-1].parameters:\n ret.append(IO(IOModifier.input, Identifier(\"float[32]\"), Identifier(param.name)))\n return ret",
"title": ""
},
{
"docid": "e96962187bbb1389e0efb1b432531a6e",
"score": "0.48383296",
"text": "def tensorsFromInput(pair, lang):\n input_tensor = tensorFromDocument(lang, pair)\n return (input_tensor, input_tensor)",
"title": ""
},
{
"docid": "ed46246df4a6c151805720d38e29abc9",
"score": "0.48366207",
"text": "def input(self):\n return self._input",
"title": ""
},
{
"docid": "deac71c2cceb08678794f85ca47e35d1",
"score": "0.48257944",
"text": "def get_bert_input(\n examples: List[tuple],\n) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n input_ids = examples[0]\n token_type_ids = examples[1]\n max_seq_len = min(max(len(input_id) for input_id in input_ids), MAX_SEQ_LEN)\n input_ids_tensor = torch.zeros((len(input_ids), max_seq_len), dtype=torch.long)\n token_type_ids_tensor = torch.zeros_like(input_ids_tensor)\n attention_mask = torch.ones_like(input_ids_tensor)\n\n for i, input_id in enumerate(input_ids):\n cur_seq_len = len(input_id)\n if cur_seq_len <= max_seq_len:\n input_ids_tensor[i, :cur_seq_len] = torch.tensor(input_id, dtype=torch.long)\n token_type_ids_tensor[i, :cur_seq_len] = torch.tensor(\n token_type_ids[i], dtype=torch.long\n )\n attention_mask[i, cur_seq_len:] = 0\n else:\n input_ids_tensor[i] = torch.tensor(\n input_id[: max_seq_len - 1] + [102], dtype=torch.long\n )\n token_type_ids_tensor[i] = torch.tensor(\n token_type_ids[i][:max_seq_len], dtype=torch.long\n )\n\n return attention_mask, input_ids_tensor, token_type_ids_tensor",
"title": ""
},
{
"docid": "bb0556900a31a6cc70ff019033572625",
"score": "0.4815999",
"text": "def __call__(self, inputs):\n if not jnp.issubdtype(inputs.dtype, jnp.integer):\n raise ValueError('Input type must be an integer or unsigned integer.')\n # Use take because fancy indexing numpy arrays with JAX indices does not work correctly.\n return jnp.take(self.embedding, inputs, axis=0)",
"title": ""
},
{
"docid": "fbe8c08d1f256a9684698985f4d18c35",
"score": "0.481335",
"text": "def GetInput(self, *args):\n return _ITKLabelMapBasePython.itkImageToImageFilterLM3IRGBUS3_GetInput(self, *args)",
"title": ""
},
{
"docid": "e19c189cc6b93769c191c0ffa833acf8",
"score": "0.48111212",
"text": "def input_fn():\n dataset = mnist_dataset(DatasetKeys.TRAIN)\n dataset = dataset.map(get_corrupt_fn(drop_prob))\n dataset = dataset.shuffle(10000).repeat().batch(batch_size)\n images, labels = dataset.make_one_shot_iterator().get_next()\n images = tf.expand_dims(images, axis=-1)\n return images, labels",
"title": ""
},
{
"docid": "bb28ff6f1e5bd1f2bb6a5a85a7c86a47",
"score": "0.48090324",
"text": "def forward(\n self,\n inputs: List[torch.Tensor],\n masks: Optional[torch.Tensor] = None,\n memories: Optional[torch.Tensor] = None,\n ) -> Tuple[Union[int, torch.Tensor], ...]:\n pass",
"title": ""
},
{
"docid": "1874c2e3103b8a4f0c9854ab280d00a3",
"score": "0.48042944",
"text": "def tensor2numpy(input_):\n if isinstance(input_, torch.Tensor):\n try:\n output = input_.numpy()\n\n except RuntimeError:\n try:\n output = input_.detach().numpy()\n except TypeError:\n output = input_.detach().cpu().numpy()\n\n except TypeError:\n try:\n output = input_.cpu().numpy()\n except RuntimeError:\n output = input_.cpu().detach().numpy()\n else:\n pass\n\n return output",
"title": ""
},
{
"docid": "a6a19e8b06810258dbe392faf11632d4",
"score": "0.48001778",
"text": "def GetInput(self, *args):\n return _ITKLabelMapBasePython.itkImageToImageFilterIUC3LM3_GetInput(self, *args)",
"title": ""
},
{
"docid": "99bf2ff43b00cffc19427bb949910a8c",
"score": "0.47994098",
"text": "def to_input_tensor(self, sents: List[List[str]], device = 'cpu'):\n word_ids = self.words2indices(sents)\n sents_t = self.pad_sents(word_ids, self['<pad>'])\n sents_var = torch.tensor(sents_t, dtype=torch.long, device=device)\n return torch.t(sents_var)",
"title": ""
},
{
"docid": "ad1e991b70dcee52fb6658a8d96af550",
"score": "0.4797589",
"text": "def single_forward(model, inp, nmap):\n with torch.no_grad():\n model_output = model(inp, nmap)\n if isinstance(model_output, list) or isinstance(model_output, tuple):\n output = model_output[0]\n else:\n output = model_output\n output = output.data.float().cpu()\n return output",
"title": ""
},
{
"docid": "1f6a37363f588901e2c8d14feab2bf46",
"score": "0.47973415",
"text": "def forward(self,input): \n output = input\n n = input.size()\n if self.train:\n self.binomial_distribution = torch.distributions.Binomial(probs=1-self.p)\n self.binomial_val = self.binomial_distribution.sample(n * (1./(1-self.p)))\n output = input * self.binomial_val\n return output",
"title": ""
},
{
"docid": "795dd5effb5552d1dbcf2a4c08d10205",
"score": "0.47956547",
"text": "def net_input(self, X):\n return np.dot(X, self.w_[1:]) + self.w_[0]",
"title": ""
},
{
"docid": "7a0fdbfc09a955be1ff559f6f6b3f80d",
"score": "0.47946978",
"text": "def forward(self, input):\n seq_len = input.size()[0]\n batch_size = input.size()[1]\n # we reuse initial_state and initial_cell, if they havent changed\n # since last time.\n if self.initial_state is None or self.initial_state.size()[1] != batch_size:\n self.initial_state = autograd.Variable(torch.zeros(\n self.num_layers * 2,\n batch_size,\n self.num_hidden\n ))\n self.initial_cell = autograd.Variable(torch.zeros(\n self.num_layers * 2,\n batch_size,\n self.num_hidden\n ))\n if input.is_cuda:\n self.initial_state = self.initial_state.cuda()\n self.initial_cell = self.initial_cell.cuda()\n x = self.embedding(input)\n x, _ = self.lstm(x, (self.initial_state, self.initial_cell))\n x = self.linear(x)\n x = F.sigmoid(x)\n rationale_selected_node = torch.bernoulli(x)\n rationale_selected = rationale_selected_node.view(seq_len, batch_size)\n rationale_lengths = rationale_selected.sum(dim=0).int()\n max_rationale_length = rationale_lengths.max()\n # if self.rationales is None or self.rationales.shape[1] != batch_size:\n rationales = torch.LongTensor(max_rationale_length.data[0], batch_size)\n if input.is_cuda:\n rationales = rationales.cuda()\n rationales.fill_(self.pad_id)\n for n in range(batch_size):\n this_len = rationale_lengths[n].data[0]\n rationales[:this_len, n] = torch.masked_select(\n input[:, n].data, rationale_selected[:, n].data.byte()\n )\n return rationale_selected_node, rationale_selected, rationales, rationale_lengths",
"title": ""
},
{
"docid": "7586146cf62bc6dac3c5a3e040df9cd3",
"score": "0.479467",
"text": "def GetInput(self, *args):\n return _ITKLabelMapBasePython.itkImageToImageFilterIF3LM3_GetInput(self, *args)",
"title": ""
},
{
"docid": "26d4c58582bbd64996e8ba08ff6889ec",
"score": "0.47939306",
"text": "def _make_input_fn(ground_truth_data, seed, num_batches=None):\n\n def load_dataset(params):\n \"\"\"TPUEstimator compatible input fuction.\"\"\"\n dataset = util.tf_data_set_from_ground_truth_data(ground_truth_data, seed)\n batch_size = params[\"batch_size\"]\n # We need to drop the remainder as otherwise we lose the batch size in the\n # tensor shape. This has no effect as our data set is infinite.\n dataset = dataset.batch(batch_size, drop_remainder=True)\n if num_batches is not None:\n dataset = dataset.take(num_batches)\n return dataset.make_one_shot_iterator().get_next()\n\n return load_dataset",
"title": ""
},
{
"docid": "192a268efbded961500b16fa07045142",
"score": "0.47891495",
"text": "def get_job_input(self, job_id):\n return join(self.get_job(job_id), 'input')",
"title": ""
},
{
"docid": "bfac2ca5a760eeeeec217ba614f3b683",
"score": "0.47885728",
"text": "def __call__(self, x_input):\n reuse = True if self.built else None\n with slim.arg_scope(inception.inception_v2_arg_scope()):\n _, end_points = inception.inception_v2(\n x_input, num_classes=self.num_classes, is_training=False,\n reuse=reuse)\n self.built = True\n output = end_points['Predictions']\n # Strip off the extra reshape op at the output\n probs = output.op.inputs[0]\n return probs",
"title": ""
},
{
"docid": "ec0682f58aba306d2adc8d3debc3183c",
"score": "0.4780097",
"text": "def net_input(self, X):\n return np.dot(X, self.w_[1:])+self.w_[0]",
"title": ""
},
{
"docid": "65599aa7a0469c47c8365a0cc3c30e52",
"score": "0.47780952",
"text": "def build_inputs(self, params, input_context=None):\n return data_loader_factory.get_data_loader(params).load(input_context)",
"title": ""
},
{
"docid": "c42d1014951f2ab7f87439c49798dbc7",
"score": "0.4774573",
"text": "def get_tensor_linop_backend():\n return _TENSOR_LINOP_BACKEND",
"title": ""
},
{
"docid": "5f8a5e3102428963bfb494dcedc84453",
"score": "0.4773948",
"text": "def target2tensor(line_input):\n letter_indexes = [all_letters.find(line_input[i]) for i in range(1, len(line_input))]\n letter_indexes.append(n_letters - 1) # EOS\n return torch.LongTensor(letter_indexes)",
"title": ""
},
{
"docid": "96698496f98235d611e5970374c75c4d",
"score": "0.4765432",
"text": "def _get_model():\n root = autotrackable.AutoTrackable()\n kernel_in = np.array([-2, -1, 1, 2], dtype=np.float32).reshape((2, 2, 1, 1))\n\n @tf.function(\n input_signature=[tf.TensorSpec(shape=[1, 3, 3, 1], dtype=tf.float32)])\n def func(inp):\n kernel = tf.constant(kernel_in, dtype=tf.float32)\n conv = tf.nn.conv2d(inp, kernel, strides=1, padding='SAME')\n output = tf.nn.relu(conv, name='output')\n return output\n\n root.f = func\n to_save = root.f.get_concrete_function()\n return (root, to_save)",
"title": ""
},
{
"docid": "140419e53c8555eecc0a525fe68c7b08",
"score": "0.47634083",
"text": "def linop(self) -> pn.linops.LinearOperator:",
"title": ""
},
{
"docid": "16d4edb23490e18f816357e4a0ab4abc",
"score": "0.47630465",
"text": "def forward(self, input):\n mean, covar = input.representation()\n eye_lv = DiagLazyVariable(torch.ones(covar.size(-1) // self.n_tasks, device=self.log_noise.device))\n task_var_lv = DiagLazyVariable(self.log_task_noises.exp())\n diag_kron_lv = KroneckerProductLazyVariable(task_var_lv, eye_lv)\n noise = covar + diag_kron_lv\n noise = add_diag(noise, self.log_noise.exp())\n return input.__class__(mean, noise)",
"title": ""
},
{
"docid": "f8267035c4f5a38d232a75ad5bee48a5",
"score": "0.4760949",
"text": "def load_input(self, context: \"InputContext\") -> object:",
"title": ""
},
{
"docid": "0ebf3644a1d61c131934840473eb15fa",
"score": "0.47544077",
"text": "def generate(self, x: torch.Tensor, **kwargs) -> torch.Tensor:\n\n return self.forward(x)[0]",
"title": ""
},
{
"docid": "05a0cd790657215aa813db527082dead",
"score": "0.4752112",
"text": "def set_input_tensor(interpreter, image):\n tensor_index = interpreter.get_input_details()[0]['index']\n input_tensor = interpreter.tensor(tensor_index)()[0]\n input_tensor[:, :] = image",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="784"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">a65c1835300f919c202a781fd914cf72</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Builds a layer which connects to the input_layer according to the given parameters.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "77f3d7c659762805d395fbc0fee40fbb",
"score": "0.61406976",
"text": "def create_output_layer(input_layer, weights_tuple, delta, layer_name, refrac):\n# print('Number of output neurons {} for size {}x{}'.format(\\\n# total_output_neurons, t_n, t_m))\n n, m = how_many_squares_in_shape(input_layer.shape, weights_tuple[1], delta)\n total_output_neurons = n * m\n print('Layer:', layer_name)\n print('Output layer has shape', n, m)\n output_layer = Layer(sim.Population(total_output_neurons,\n sim.IF_curr_exp(tau_refrac=refrac),\n structure=space.Grid2D(aspect_ratio=m/n),\n label=layer_name), (n, m))\n connect_layer_to_layer(input_layer, output_layer, weights_tuple[1], delta,\n weights_tuple[0])\n\n return output_layer",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "3a81c94c57960a2260aafdee1e162199",
"score": "0.6962431",
"text": "def create_layer(input_layer, layer):\n\n # Common to all layers\n layer_type = layer[0]\n name = layer[1]\n\n\n if layer_type == CONV:\n weights = weight_variable(layer[2], name+'_conv_weights')\n stride = layer[3]\n padding = layer[4]\n return tf.nn.conv2d(input_layer, weights, stride, padding, name=name+'_conv')\n \n\n elif layer_type == POOL:\n pool_shape = layer[2]\n stride = layer[3]\n padding = layer[4]\n return tf.nn.max_pool(input_layer, pool_shape, stride, padding, name=name+'_pool')\n\n\n elif layer_type == RELU:\n # What is the shape of the activiation function?\n activation_size = input_layer.get_shape()[-1].value\n\n # Make a bias for the activation\n bias = bias_variable([activation_size], name+'_bias')\n return tf.nn.relu(input_layer + bias, name=name+'_relu')\n\n\n elif layer_type == FULL:\n # What's the shape of the previous layer?\n input_size = input_layer.get_shape()[-1].value\n output_size = layer[2]\n\n weights = weight_variable([input_size, output_size], name+'_weights')\n return tf.matmul(input_layer, weights)\n \n\n elif layer_type == FLAT:\n # Simply flatten the previous layer\n input_layer_shape = input_layer.get_shape()[1:].dims\n flat_dim = reduce(lambda x,y: x*y, input_layer_shape, tf.Dimension(1))\n\n return tf.reshape(input_layer, [-1, flat_dim.value])",
"title": ""
},
{
"docid": "55e0e24a0252c9c1c17a453147a4bc4c",
"score": "0.68159527",
"text": "def _make_layer(layer_dict, layer_id, prev_layer, param_dict):\n if len(layer_dict.keys()) > 1:\n raise ValueError('Layer with multiple types.')\n layer_type = next(layer_dict.keys().__iter__())\n l = layer_dict[layer_type]\n if l is None:\n l = {}\n\n for (k, v) in l.items():\n if isinstance(v, str) and v in param_dict.keys():\n l[k] = param_dict[v]\n\n inputs = [prev_layer]\n if layer_type == 'Input':\n inputs = []\n if 'name' in l:\n layer_id = l['name']\n if 'inputs' in l:\n inputs = l['inputs']\n del l['inputs']\n if isinstance(inputs, (int, str)):\n inputs = [inputs]\n\n return (layer_id, _LayerWrapper(layer_type, layer_id, inputs, l))",
"title": ""
},
{
"docid": "d4a466183280285ac41699ca2060ac07",
"score": "0.6807971",
"text": "def build(self, input_shape):\n params = self.get_config()\n for lid in range(params[\"num_layers\"]):\n self._stacking_layers.append([\n build_transformer_component({\n \"base_layer.class\": LightConvolutionLayer.__name__,\n \"base_layer.params\": dict(\n kernel_size=params[\"conv_kernel_size_list\"][lid],\n num_heads=params[\"num_conv_heads\"],\n conv_type=params[\"conv_type\"],\n conv_dim=params[\"conv_hidden_size\"],\n use_glu=params[\"glu_after_proj\"],\n weight_dropout_rate=params[\"conv_weight_dropout_rate\"],\n name=\"light_conv\"\n )},\n dropout_rate=params[\"layer_postprocess_dropout_rate\"],\n epsilon=params[\"layer_postprocess_epsilon\"]),\n build_transformer_component({\n \"base_layer.class\": TransformerFFN.__name__,\n \"base_layer.params\": dict(\n filter_size=params[\"filter_size\"],\n output_size=input_shape[-1],\n dropout_rate=params[\"ffn_dropout_rate\"],\n activation=params[\"ffn_activation\"],\n name=\"ffn\")},\n dropout_rate=params[\"layer_postprocess_dropout_rate\"],\n epsilon=params[\"layer_postprocess_epsilon\"])\n ])\n self._output_norm_layer = tf.keras.layers.LayerNormalization(\n epsilon=params[\"layer_postprocess_epsilon\"],\n dtype=\"float32\", name=\"output_ln\")\n super(LightConvolutionEncoder, self).build(input_shape)",
"title": ""
},
{
"docid": "41c834be11ec70576599d4dbbf987a7a",
"score": "0.6784931",
"text": "def build(self):\n # keep_prob = tf.cond(tf.equal(self.is_training, tf.constant(True)), lambda: self.k_prob, lambda: 1.0)\n\n with tf.variable_scope(self.name):\n incoming = layers.flatten(self.incoming)\n\n input_layer = layers.dense(incoming, self.n_in, kernel_initializer=he_init, bias_initializer=b_init)\n input_layer = tf.layers.batch_normalization(input_layer)\n input_layer = tf.nn.relu(input_layer)\n\n hidden_layer = layers.dense(input_layer, self.n_hidden, kernel_initializer=he_init, bias_initializer=b_init)\n hidden_layer = tf.layers.batch_normalization(hidden_layer)\n hidden_layer = tf.nn.relu(hidden_layer)\n\n output_layer = layers.dense(hidden_layer, self.n_out, bias_initializer=b_init)\n output_layer = tf.layers.batch_normalization(output_layer)\n\n # final activation: linear\n return output_layer",
"title": ""
},
{
"docid": "892c12b2e6b094255bf9449cfe295bb8",
"score": "0.6717162",
"text": "def build_input_layer(self):\r\n # We could e.g. pass them through a dense layer\r\n if self.input_hidden_size is not None:\r\n with tf.variable_scope(\"input_layer\", reuse=self.reuse):\r\n self.inputs_hidden = tf.layers.dense(self.prediction_inputs, self.input_hidden_size,\r\n tf.nn.relu, reuse=self.reuse)\r\n else:\r\n self.inputs_hidden = self.prediction_inputs",
"title": ""
},
{
"docid": "064303ae560acc2cb25ed93038504b4e",
"score": "0.6695",
"text": "def build_layer(self, input_x, shape, layer_id, activation_fn=tf.nn.relu):\n w_mu = self.build_mu_variable(shape)\n w_sigma = self.sigma_transform(self.build_sigma_variable(shape))\n w_noise = tf.random_normal([self.n_sample] + shape)\n w = w_mu + w_sigma * w_noise\n\n b_mu = self.build_mu_variable([1, shape[1]])\n # b_sigma = self.sigma_transform(self.build_sigma_variable([1, shape[1]]))\n b = b_mu\n\n # Create outputs\n output_h = activation_fn(tf.matmul(input_x, w) + b)\n return output_h",
"title": ""
},
{
"docid": "de5a54e37b63409236d24d0269fb8cc4",
"score": "0.6666327",
"text": "def build_input_layer(self):\r\n self.inputs_hidden = self.prediction_inputs\r\n drop_rate = self.config.get(\"input_dropout_rate\", 0)\r\n\r\n if drop_rate > 0:\r\n with tf.variable_scope('input_dropout', reuse=self.reuse):\r\n self.inputs_hidden = tf.layers.dropout(self.inputs_hidden,\r\n rate=drop_rate,\r\n seed=self.config[\"seed\"],\r\n training=self.is_training)\r\n\r\n hidden_layers = self.config.get(\"input_hidden_layers\", 0)\r\n hidden_size = self.config.get(\"input_hidden_size\", 0)\r\n\r\n for layer_idx in range(hidden_layers):\r\n with tf.variable_scope(\"inp_dense_\" + str(layer_idx), reuse=self.reuse):\r\n self.inputs_hidden = tf.layers.dense(inputs=self.inputs_hidden,\r\n units=self.input_hidden_size,\r\n activation=tf.nn.relu,\r\n reuse=self.reuse)",
"title": ""
},
{
"docid": "0b82c777bb7554631c7ba958db1d40e7",
"score": "0.6662574",
"text": "def build_input_layer(self):\n # We could e.g. pass them through a dense layer\n self.inputs_hidden = tf.constant([0])",
"title": ""
},
{
"docid": "df95e61c8bae0a194b0b83fd0e43ec91",
"score": "0.6589434",
"text": "def build_input_layer(self):\n # We could e.g. pass them through a dense layer\n if self.input_hidden_size is not None:\n with tf.variable_scope(\"input_layer\", reuse=self.reuse):\n self.inputs_hidden = tf.layers.dense(self.prediction_inputs, self.input_hidden_size,\n tf.nn.relu, reuse=self.reuse)\n else:\n self.inputs_hidden = self.prediction_inputs\n\n print(\"inputs_hidden:\\t\", self.inputs_hidden.get_shape())",
"title": ""
},
{
"docid": "c62564b1ffb3bb119a3c9cb54c646bed",
"score": "0.6541359",
"text": "def generateLayer(self, input_tensor):\n\n\t\t# Get the previous tensor shape\n\t\tinput_shape = input_tensor.get_shape().as_list()[1:]\n\n\t\t# Create the convolution weights and bias\n\t\tfilter_shape = self.kernel_shape + (input_shape[2], self.num_kernels)\n\t\tweights = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.05))\n\t\tbias = tf.Variable(tf.constant(0.0, shape=(self.num_kernels,)))\n\n\t\ttry:\n\t\t\tself.tensor = self.activation(tf.nn.conv2d(input_tensor, weights, (1,) + self.stride + (1,), 'VALID') + bias)\n\t\texcept:\n\t\t\tprint \"Error!\"\n\t\t\tprint \"Input Shape:\", input_tensor.get_shape()\n\t\t\tprint \"Kernel Shape:\", self.kernel_shape\n\t\t\tprint \"Num Kernels:\", self.num_kernels\n\n\n\t\treturn self.tensor",
"title": ""
},
{
"docid": "36af64d49f1ef6b8767025c72155adfd",
"score": "0.6526699",
"text": "def build_input_layer(self):\n # We could e.g. pass them through a dense layer\n if self.input_hidden_size is not None:\n if self.weight_sharing == \"w/o\": # no weight sharing in linear layer between encoder and decoder\n if not self.sampling_loss:\n with tf.variable_scope(\"input_layer_decoder\", reuse=self.reuse, regularizer=self.regularizer):\n self.inputs_hidden = tf.layers.dense(self.prediction_inputs, self.input_hidden_size,\n activation=self.activation_fn_in, reuse=self.reuse)\n if self.dropout_lin:\n self.inputs_hidden = tf.layers.dropout(self.inputs_hidden, rate=self.dropout_lin,\n training=not self.reuse)\n\n with tf.variable_scope(\"input_layer_encoder\", reuse=self.reuse, regularizer=self.regularizer):\n self.inputs_hidden_encoder = tf.layers.dense(self.inputs_encoder, self.input_hidden_size,\n activation=self.activation_fn_in, reuse=self.reuse)\n if self.dropout_lin:\n self.inputs_hidden_encoder = tf.layers.dropout(self.inputs_hidden_encoder,\n rate=self.dropout_lin,\n training=not self.reuse)\n\n else: # weight sharing between encoder and decoder only (s2s), or between all (all)\n if not self.sampling_loss:\n with tf.variable_scope(\"input_layer_shared\", reuse=self.reuse, regularizer=self.regularizer):\n self.inputs_hidden = tf.layers.dense(self.prediction_inputs, self.input_hidden_size,\n activation=self.activation_fn_in, reuse=self.reuse)\n if self.dropout_lin:\n self.inputs_hidden = tf.layers.dropout(self.inputs_hidden, rate=self.dropout_lin,\n training=not self.reuse)\n\n with tf.variable_scope(\"input_layer_shared\", reuse=True, regularizer=self.regularizer):\n self.inputs_hidden_encoder = tf.layers.dense(self.inputs_encoder, self.input_hidden_size,\n activation=self.activation_fn_in)\n if self.dropout_lin:\n self.inputs_hidden_encoder = tf.layers.dropout(self.inputs_hidden_encoder,\n rate=self.dropout_lin,\n training=not self.reuse)\n else:\n with tf.variable_scope(\"input_layer_shared\", reuse=self.reuse, regularizer=self.regularizer):\n self.inputs_hidden_encoder = tf.layers.dense(self.inputs_encoder, self.input_hidden_size,\n activation=self.activation_fn_in, reuse=self.reuse)\n\n if self.dropout_lin:\n self.inputs_hidden_encoder = tf.layers.dropout(self.inputs_hidden_encoder,\n rate=self.dropout_lin,\n training=not self.reuse)\n\n else:\n self.inputs_hidden = self.prediction_inputs\n self.inputs_hidden_encoder = self.inputs_encoder",
"title": ""
},
{
"docid": "4d6f72349a0c39b571f259acc4fb1b46",
"score": "0.6480997",
"text": "def _build_layers(self):\n layers = {}\n rate = self._dropout_rate\n\n # Create an input layer using shape of data\n layers['input'] = keras.layers.Input(shape=self._input_shape)\n\n # Loop over all layers and construct layer relationships using\n # Keras functional API for complex networks.\n for layer in self._topology:\n # Unpack relevant items in params and meta to reduce visual noise\n layer_type = layer['meta']['layer_type']\n layer_id = layer['meta']['layer_id']\n parent_ids = layer['meta']['parent_ids']\n params = layer['params']\n\n # Construct layer to be built from string 'meta' which is\n # part of topology list.\n try:\n layer_cls = getattr(keras.layers, layer_type)\n except AttributeError:\n layer_cls = getattr(custom_layers, layer_type)\n\n # In order to get the accepteble arguments for each layer we need\n # to use inspect because of keras' legacy support decorators.\n try:\n args = getargspec(layer_cls.__init__._original_function)[0]\n except AttributeError:\n args = getargspec(layer_cls)[0]\n\n # Add other input arguments to params dict when\n # necessary. For example, MaxPooling1D does not accept the\n # argument 'kernel_initializer'.\n if 'kernel_initializer' in args:\n params['kernel_initializer'] = self._rnd_init\n\n # Create the Keras layer using a switch for MC Dropout after\n # every layer.\n parents = []\n for parent_id in parent_ids:\n next_id = parent_id\n\n if self._uncertainty:\n # If MC Dropout, aka the Bayesian approximation to Neural\n # Networks should be used, we add Dropout after each layer,\n # even at test time.\n dropout_id = next_id + '_' + layer_id + '_dropout'\n if self._concreteDropout:\n layers[dropout_id] = custom_layers.ConcreteDropout(\n layers[next_id]\n )\n else:\n layers[dropout_id] = custom_layers.MCDropout(rate)(\n layers[next_id]\n )\n next_id = dropout_id\n\n parents.append(layers[next_id])\n\n # Many layers don't expect a list of tensors as input but just\n # a single tensor, but the Concat layer expects a list of input\n # tensors, so we need to deal with this case.\n if len(parents) < 2:\n parents = parents[0]\n\n layers[layer_id] = layer_cls(**params)(parents)\n\n # Need to handle the output layer and reshaping for multi-step\n # forecasting.\n if self._uncertainty:\n dropout_id = layer_id + '_dense_dropout'\n if self._concreteDropout:\n layers[dropout_id] = custom_layers.ConcreteDropout(\n layers[layer_id]\n )\n else:\n layers[dropout_id] = custom_layers.MCDropout(rate)(\n layers[layer_id]\n )\n layer_id = dropout_id\n\n layer_cls = keras.layers.Dense\n params = {'units': np.prod(self._output_shape)}\n layers['_dense'] = layer_cls(**params)(layers[layer_id])\n\n layer_cls = keras.layers.Reshape\n params = {'target_shape': self._output_shape}\n layers['output'] = layer_cls(**params)(layers['_dense'])\n\n return layers['input'], layers['output']",
"title": ""
},
{
"docid": "23c112149ac64f14eed254417698dbe4",
"score": "0.64676744",
"text": "def make_mlp_layer_from_conv_layer(layer_params, input_bounds):\n assert isinstance(layer_params, dict)\n assert layer_params['padding'] == 'SAME'\n assert layer_params['stride'] == 1\n assert layer_params['n_cin'] == 1\n # only 'SAME' padding supported for now with stride (1,1)\n # to be used for unit-test support only\n # TODO: Add support for 'VALID'\n\n inp_shape = (layer_params['input_shape'], layer_params['input_shape'])\n w, b = layer_params['W'], layer_params['b']\n op_shape = int(np.ceil(inp_shape[0] / layer_params['stride']))\n pad_h = max((op_shape - 1) * layer_params['stride'] + layer_params['n_h'] -\n inp_shape[0], 0)\n pad_t = pad_h // 2\n pad_b = pad_h - pad_t\n pad_inp_shape = [inp_shape[0] + pad_h, inp_shape[1] + pad_h]\n padded_bounds = jnp.zeros(pad_inp_shape)\n lb = jax.ops.index_add(padded_bounds, jax.ops.index[pad_t:-pad_b,\n pad_t:-pad_b],\n input_bounds.lb[0, :, :, 0])\n ub = jax.ops.index_add(padded_bounds, jax.ops.index[pad_t:-pad_b,\n pad_t:-pad_b],\n input_bounds.ub[0, :, :, 0])\n pad_filter_shape = pad_inp_shape + [inp_shape[0], inp_shape[1], w.shape[-1]]\n pad_filter = jnp.zeros(pad_filter_shape)\n pad_bias = jnp.zeros(inp_shape + (w.shape[-1],))\n n_h, n_w = w.shape[0], w.shape[1]\n\n # unrolling the conv into an FC layer, stride=(1,1)\n for i in range(inp_shape[0]):\n for j in range(inp_shape[1]):\n pad_filter = jax.ops.index_add(\n pad_filter, jax.ops.index[i:i + n_h, j:j + n_w, i, j, 0], w[:, :, 0,\n 0])\n pad_bias = jax.ops.index_add(pad_bias, jax.ops.index[i, j, 0], b[0])\n pad_filter = jax.ops.index_add(\n pad_filter, jax.ops.index[i:i + n_h, j:j + n_w, i, j, 1], w[:, :, 0,\n 1])\n pad_bias = jax.ops.index_add(pad_bias, jax.ops.index[i, j, 1], b[1])\n pad_filter_lin = jnp.reshape(\n pad_filter,\n (pad_inp_shape[0] * pad_inp_shape[1], inp_shape[0] * inp_shape[1] * 2))\n pad_bias_lin = jnp.reshape(pad_bias, inp_shape[0] * inp_shape[1] * 2)\n\n return lb, ub, pad_filter_lin, pad_bias_lin",
"title": ""
},
{
"docid": "be8df6c03fc5f8113d52e23042988a47",
"score": "0.6464587",
"text": "def build_output_layer(self, inputs):\r\n\r\n # add residual connection here\r\n if self.joint_prediction_layer == \"plain\":\r\n # Create a number of hidden layers and predict the full pose vector.\r\n with tf.variable_scope('output_layer', reuse=self.reuse):\r\n hidden_layers = self.config.get(\"output_hidden_layers\", 0)\r\n current_layer = inputs\r\n for layer_idx in range(hidden_layers):\r\n with tf.variable_scope('out_dense_all_' + str(layer_idx), reuse=self.reuse):\r\n current_layer = tf.layers.dense(inputs=current_layer, units=self.config[\"output_hidden_size\"],\r\n activation=tf.nn.relu)\r\n with tf.variable_scope('out_dense_all_' + str(hidden_layers), reuse=self.reuse):\r\n pose_prediction = tf.layers.dense(inputs=current_layer, units=self.HUMAN_SIZE, activation=None)\r\n\r\n else:\r\n # Predict the pose vector by composing a hierarchy of joint specific networks.\r\n with tf.variable_scope('output_layer', reuse=self.reuse):\r\n spl_sparse = True if self.joint_prediction_layer == \"spl_sparse\" else False\r\n sp_layer = SPL(hidden_layers=self.config[\"output_hidden_layers\"],\r\n hidden_units=self.config[\"output_hidden_size\"],\r\n joint_size=self.JOINT_SIZE,\r\n sparse=spl_sparse,\r\n config=self.config,\r\n is_training=self.is_training,\r\n reuse=self.reuse)\r\n pose_prediction = sp_layer.build(inputs)\r\n if self.residual_velocity:\r\n pose_prediction += self.prediction_inputs[:, 0:tf.shape(pose_prediction)[1], :self.HUMAN_SIZE]\r\n\r\n return pose_prediction",
"title": ""
},
{
"docid": "824efd7bac4ffc9d57730ea2bd9cc63a",
"score": "0.63370085",
"text": "def build(self, input_shape):\n super(CircuitLayer, self).build(input_shape)\n self.built = True",
"title": ""
},
{
"docid": "7b895beda296c394383d1886fe35e976",
"score": "0.63083494",
"text": "def connect_layer_to_layer(input_layer, output_layer, feature_shape, delta,\n weights, stdp=False, initial_weight=0,\n ndicts=None, ondicts=None, omdicts=None)\\\n -> List[sim.Projection]:\n # Go through the lines of the image and connect input neurons to the\n # output layer according to delta\n t_n, t_m = input_layer.shape\n f_n, f_m = feature_shape\n overfull_n = (t_n - f_n) % delta > 0 # True for vertical overflow\n overfull_m = (t_m - f_m) % delta > 0 # True for horizontal overflow\n k_out = 0\n projections = []\n i = 0\n while i + f_n <= t_n:\n j = 0\n while j + f_m <= t_m:\n projections.append(connect_layers(input_layer, output_layer,\n weights, i, j, i + f_n, j + f_m,\n k_out, stdp=stdp,\n initial_weight=initial_weight,\n label_dicts=ndicts))\n k_out += 1\n j += delta\n if overfull_m:\n projections.append(connect_layers(input_layer, output_layer,\n weights, i, t_m - f_m, i + f_n,\n t_m, k_out, stdp=stdp,\n initial_weight=initial_weight,\n label_dicts=omdicts))\n k_out += 1\n i += delta\n if overfull_n:\n j = 0\n while j + f_m <= t_m:\n projections.append(connect_layers(input_layer, output_layer,\n weights, t_n - f_n, j, t_n,\n j + f_m, k_out, stdp=stdp,\n initial_weight=initial_weight,\n label_dicts=ondicts))\n k_out += 1\n j += delta\n if overfull_m:\n projections.append(connect_layers(input_layer, output_layer,\n weights, t_n - f_n, t_m - f_m,\n t_n, t_m, k_out, stdp=stdp,\n initial_weight=initial_weight,\n label_dicts=None))\n k_out += 1\n return projections",
"title": ""
},
{
"docid": "920007b182dc41eca63029bbe4c35101",
"score": "0.6301503",
"text": "def build(input_shape, num_outputs):\n # Load function from str if needed.\n inputs = Input(input_shape)\n\n # conv1 = Conv2D(64, 3, strides=(1,1), padding='SAME', activation='relu',\n # kernel_initializer=tf.keras.initializers.TruncatedNormal(stddev=0.01))(inputs)\n # pool1 = MaxPooling2D(pool_size=(2,2))(conv1)\n\n # conv2 = Conv2D(128, 3, strides=(1,1), padding='SAME', activation='relu',\n # kernel_initializer=tf.keras.initializers.TruncatedNormal(stddev=0.01))(pool1)\n # pool2 = MaxPooling2D(pool_size=(2,2))(conv2)\n\n # conv3 = Conv2D(256, 3, strides=(1,1), padding='SAME', activation='relu',\n # kernel_initializer=tf.keras.initializers.TruncatedNormal(stddev=0.01))(pool2)\n # pool3 = MaxPooling2D(pool_size=(2,2))(conv3)\n pool3_flat = tf.keras.layers.Flatten()(inputs)\n\n dense1 = tf.keras.layers.Dense(256, activation='relu')(pool3_flat)\n dense2 = tf.keras.layers.Dense(num_outputs, activation='softmax')(dense1)\n\n model = Model(inputs=inputs, outputs=dense2)\n return model",
"title": ""
},
{
"docid": "0a6a063d74b2ba72cae6a96e5affdb83",
"score": "0.62959397",
"text": "def build(self, input_shape):\n assert len(input_shape) == 4, \"input shape is not supported\"\n super().build(input_shape)\n # Build the keras layer\n self.feature_Conv2D.build((input_shape[0], input_shape[1], input_shape[2], input_shape[3] - self.mask_depth))\n self.mask_Conv2D.build((input_shape[0], input_shape[1], input_shape[2], self.mask_depth))\n self.mask_MaxPooling2D.build((input_shape[0], input_shape[1], input_shape[2], self.mask_depth))",
"title": ""
},
{
"docid": "976ba89bec477f16b6c16f2670459250",
"score": "0.6274579",
"text": "def fully_connect_layer(input, input_nodes, output_nodes, name):\n with tf.variable_scope(name) as scope:\n W = tf.Variable(tf.truncated_normal([input_nodes, output_nodes], stddev=0.05))\n b = tf.Variable(tf.constant(0.05, shape=[output_nodes]))\n\n # y = Wx + b\n # rows[0] == columns[1]\n layer = tf.matmul(input, W) + b\n return layer",
"title": ""
},
{
"docid": "5c3e7f8640394e8ea7251161954092dd",
"score": "0.62698966",
"text": "def _build_layers_v2(self, input_dict, num_outputs, options):\r\n xy_encode = slim.fully_connected(input_dict[\"obs\"][0], 256)\r\n t_encode = slim.fully_connected(input_dict[\"obs\"][2], 128)\r\n fc1 = tf.concat([xy_encode, input_dict[\"obs\"][1], t_encode], 1)\r\n fc2 = slim.fully_connected(fc1, 512)\r\n fc3 = slim.fully_connected(fc2, 256)\r\n print('Im hereeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee')\r\n #if 'constrain_outputs' in options:\r\n #constraints = tf.convert_to_tensor([0.01, 0.01, 0.01, 0.01])#options['constrain_outputs'])\r\n #outputs = slim.fully_connected(fc3, num_outputs, \r\n # weights_initializer=normc_initializer(0.01), \r\n # activation_fn=tf.nn.tanh)\r\n \r\n #outputs = tf.math.multiply(constraints, self.common_layer)\r\n \r\n #else:\r\n outputs = slim.fully_connected(fc3, num_outputs, \r\n weights_initializer=normc_initializer(0.01),\r\n activation_fn=None)\r\n \r\n return outputs, fc3",
"title": ""
},
{
"docid": "414519966ac0087c450f549f9ae6a1bd",
"score": "0.62567586",
"text": "def build(self,input_shape): \n self.dim = input_shape.as_list()[-1]\n \n # this has to be called for any tensorflow custom layer\n super(Param_to_Signal_Layer,self).build(input_shape)",
"title": ""
},
{
"docid": "9272f3d730415d975033a95017c6927a",
"score": "0.625306",
"text": "def __init__(self, shape, input_var=None):\n\n self.output = layers.InputLayer(shape, input_var=input_var)",
"title": ""
},
{
"docid": "13f669b4e878a0a17a786d6a40464552",
"score": "0.6226912",
"text": "def build_neural(self):\n input_size = 773\n\n input_layer1 = Input(shape=(input_size,))\n input_layer2 = Input(shape=(input_size,))\n\n encoder_left = self.encoder(input_layer1)\n encoder_right = self.encoder(input_layer2)\n\n combined = concatenate([encoder_left, encoder_right])\n\n layer1 = Dense(400, activation='relu', name='layer1')(combined)\n layer2 = Dense(200, activation='relu', name='layer2')(layer1)\n layer3 = Dense(100, activation='relu', name='layer3')(layer2)\n output_layer = Dense(2, activation='sigmoid', name='layerOutput')(layer3)\n\n self.model = Model(inputs=[input_layer1, input_layer2], outputs=output_layer)\n self.model.summary()",
"title": ""
},
{
"docid": "dcec2daf6d5af3bc8fa9102245d328b1",
"score": "0.62156093",
"text": "def build_model(self) -> None:\n\n self.add(layers.Input(self.input_dim, name=\"input\"))\n\n for i, dim in enumerate(self.hidden_dims):\n layer_name = f\"hidden_{i}\"\n layer = layers.Dense(\n dim, activation=\"relu\" if i > 0 else \"sigmoid\", name=layer_name\n )\n self.add(layer)\n\n # add layer mask\n self.layer_masks[layer_name] = self.get_mask(layer)\n\n layer_name = \"output\"\n output_layer = layers.Dense(\n self.input_dim, activation=\"sigmoid\", name=layer_name\n )\n self.add(output_layer)\n self.layer_masks[layer_name] = self.get_mask(output_layer)",
"title": ""
},
{
"docid": "b63117ba09309fdf62ffc6d394a5d6fd",
"score": "0.6213187",
"text": "def hyper_layer(layer_input, n_channel, drate=0.0, name=None):\n\n layer = Dropout(rate=drate, seed=888)(layer_input)\n layer = Conv2D(n_channel, (3, 3), strides=(1, 1), padding='same', activation='relu',\n dilation_rate=1, kernel_initializer=xavier(), name=name+'_1')(layer)\n layer = Dropout(rate=drate, seed=777)(layer)\n layer_output = Conv2D(n_channel, (3, 3), strides=(1, 1), padding='same', activation='relu',\n dilation_rate=1, kernel_initializer=xavier(), name=name+'_2')(layer)\n return layer_output",
"title": ""
},
{
"docid": "e734f13bbedb21bc1004def808933ef0",
"score": "0.62026036",
"text": "def create_layer():\r\n pass",
"title": ""
},
{
"docid": "719de11743aa4631244748b3adfdf5e5",
"score": "0.6181684",
"text": "def internal_layer(self, input, _id):\n bn = self.bottleneck(input, _id)\n comp = self.composite(bn, _id)\n output = tf.concat(values=(input, comp), axis=3)\n # print('internal layer id: %d, shape:' % _id, output.shape)\n return output",
"title": ""
},
{
"docid": "b07b95525dd1d28c8f02e9a8865ec776",
"score": "0.6180107",
"text": "def build_model(self,input_dim, output_dim,\n batch_size=BATCH_SIZE):\n print(\"input_dim\",input_dim, \"output_dim\",output_dim)\n l_in = lasagne.layers.InputLayer(\n shape=(batch_size, input_dim),\n )\n\n l_hidden = lasagne.layers.DenseLayer(\n l_in,\n num_units=200,\n nonlinearity=lasagne.nonlinearities.tanh,\n )\n\n\n l_out = lasagne.layers.DenseLayer(\n l_hidden,\n num_units=output_dim,\n nonlinearity=lasagne.nonlinearities.linear,\n )\n\n return l_out",
"title": ""
},
{
"docid": "80e65717b733d37891f9fc13c31723a5",
"score": "0.61766833",
"text": "def _build(self, inputs):\n # A module like Linear would require the final dimension to be known in\n # order to construct weights.\n assert inputs.get_shape().as_list()[-1] is not None\n batch_size = tf.shape(inputs)[0]\n result_shape = [batch_size] + [1] * (self._output_rank - 1)\n return tf.zeros(result_shape, dtype=inputs.dtype)",
"title": ""
},
{
"docid": "5084710a8a9abb804262e4eaee2e414b",
"score": "0.614964",
"text": "def construct_network_computation_graph(self, input_layer = None, batch_size = 0, shared_weight = None):\n if (shared_weight is None):\n parameters = self.construct_parameters(save_to_this = False)\n else:\n parameters = shared_weight\n layers = {}\n if (input_layer is None):\n layers[\"image_input\"] = tf.placeholder(tf.float32, shape = [\\\n batch_size, self.image_height, self.image_width, self.image_channel])\n else:\n layers[\"image_input\"] = input_layer\n layers[\"conv1_1\"] = tf.nn.bias_add(tf.nn.conv2d(layers[\"image_input\"],\n parameters[\"w_conv1_1\"], strides=[1, 1, 1, 1], padding='SAME'), parameters[\"b_conv1_1\"])\n layers[\"relu1_1\"] = tf.nn.relu(layers[\"conv1_1\"])\n layers[\"conv1_2\"] = tf.nn.bias_add(tf.nn.conv2d(layers[\"relu1_1\"],\n parameters[\"w_conv1_2\"], strides=[1, 1, 1, 1], padding='SAME'), parameters[\"b_conv1_2\"])\n layers[\"relu1_2\"] = tf.nn.relu(layers[\"conv1_2\"])\n layers[\"pool1\"] = tf.nn.max_pool(layers[\"relu1_2\"], ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')\n layers[\"conv2_1\"] = tf.nn.bias_add(tf.nn.conv2d(layers[\"pool1\"], parameters[\"w_conv2_1\"], strides = [1, 1 ,1, 1],\\\n padding = 'SAME'), parameters[\"b_conv2_1\"])\n layers[\"relu2_1\"] = tf.nn.relu(layers[\"conv2_1\"])\n layers[\"conv2_2\"] = tf.nn.bias_add(tf.nn.conv2d(layers[\"relu2_1\"], parameters[\"w_conv2_2\"], strides = [1, 1, 1, 1], \\\n padding = 'SAME'), parameters[\"b_conv2_2\"])\n layers[\"relu2_2\"] = tf.nn.relu(layers[\"conv2_2\"])\n layers[\"pool2\"] = tf.nn.max_pool(layers[\"relu2_2\"], ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')\n layers[\"conv3_1\"] = tf.nn.bias_add(tf.nn.conv2d(layers[\"pool2\"], parameters[\"w_conv3_1\"], strides = [1, 1, 1, 1], \\\n padding = 'SAME'), parameters[\"b_conv3_1\"])\n layers[\"relu3_1\"] = tf.nn.relu(layers[\"conv3_1\"])\n layers[\"conv3_2\"] = tf.nn.bias_add(tf.nn.conv2d(layers[\"relu3_1\"], parameters[\"w_conv3_2\"], strides = [1, 1, 1, 1], \\\n padding = 'SAME'), parameters[\"b_conv3_2\"])\n layers[\"relu3_2\"] = tf.nn.relu(layers[\"conv3_2\"])\n layers[\"conv3_3\"] = tf.nn.bias_add(tf.nn.conv2d(layers[\"relu3_2\"], parameters[\"w_conv3_3\"], strides = [1, 1, 1, 1], \\\n padding = 'SAME'), parameters[\"b_conv3_3\"])\n layers[\"relu3_3\"] = tf.nn.relu(layers[\"conv3_3\"])\n layers[\"pool3\"] = tf.nn.max_pool(layers[\"relu3_3\"], ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')\n layers[\"conv4_1\"] = tf.nn.bias_add(tf.nn.conv2d(layers[\"pool3\"], parameters[\"w_conv4_1\"], strides = [1, 1, 1, 1], \\\n padding = 'SAME'), parameters[\"b_conv4_1\"])\n layers[\"relu4_1\"] = tf.nn.relu(layers[\"conv4_1\"])\n layers[\"conv4_2\"] = tf.nn.bias_add(tf.nn.conv2d(layers[\"relu4_1\"], parameters[\"w_conv4_2\"], strides = [1, 1, 1, 1], \\\n padding = 'SAME'), parameters[\"b_conv4_2\"])\n layers[\"relu4_2\"] = tf.nn.relu(layers[\"conv4_2\"])\n layers[\"conv4_3\"] = tf.nn.bias_add(tf.nn.conv2d(layers[\"relu4_2\"], parameters[\"w_conv4_3\"], strides = [1, 1, 1, 1], \\\n padding = 'SAME'), parameters[\"b_conv4_3\"])\n layers[\"relu4_3\"] = tf.nn.relu(layers[\"conv4_3\"])\n layers[\"pool4\"] = tf.nn.max_pool(layers[\"relu4_3\"], ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')\n layers[\"conv5_1\"] = tf.nn.bias_add(tf.nn.conv2d(layers[\"pool4\"], parameters[\"w_conv5_1\"], strides = [1, 1, 1, 1], \\\n padding = 'SAME'), parameters[\"b_conv5_1\"])\n layers[\"relu5_1\"] = tf.nn.relu(layers[\"conv5_1\"])\n layers[\"conv5_2\"] = tf.nn.bias_add(tf.nn.conv2d(layers[\"relu5_1\"], parameters[\"w_conv5_2\"], strides = [1, 1, 1, 1], \\\n padding = 'SAME'), parameters[\"b_conv5_2\"])\n layers[\"relu5_2\"] = tf.nn.relu(layers[\"conv5_2\"])\n layers[\"conv5_3\"] = tf.nn.bias_add(tf.nn.conv2d(layers[\"relu5_2\"], parameters[\"w_conv5_3\"], strides = [1, 1, 1, 1], \\\n padding = 'SAME'), parameters[\"b_conv5_3\"])\n layers[\"relu5_3\"] = tf.nn.relu(layers[\"conv5_3\"])\n layers[\"pool5\"] = tf.nn.max_pool(layers[\"relu5_3\"], ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')\n pool5_size = int(layers[\"pool5\"].shape[1]) * int(layers[\"pool5\"].shape[2]) * int(layers[\"pool5\"].shape[3])\n layers[\"flat\"] = tf.reshape(layers[\"pool5\"], [-1, pool5_size])\n layers[\"fc6\"] = tf.nn.bias_add(tf.matmul(layers[\"flat\"], parameters[\"w_fc6\"]), parameters[\"b_fc6\"])\n layers[\"relu6\"] = tf.nn.relu(layers[\"fc6\"])\n layers[\"fc7\"] = tf.nn.bias_add(tf.matmul(layers[\"relu6\"], parameters[\"w_fc7\"]), parameters[\"b_fc7\"])\n layers[\"relu7\"] = tf.nn.relu(layers[\"fc7\"])\n layers[\"fc8\"] = tf.nn.bias_add(tf.matmul(layers[\"relu7\"], parameters[\"w_fc8\"]), parameters[\"b_fc8\"])\n layers[\"output\"] = tf.nn.softmax(layers[\"fc8\"])\n return [layers, parameters]",
"title": ""
},
{
"docid": "4691222aab72822984f9e4ba4ff686ae",
"score": "0.6109898",
"text": "def layer_input(self, layer_i):\n\t\t# v(1) layer\n\t\tif layer_i == 0:\n\t\t\tw = self.w[layer_i];\n\t\t\tif self.USE_DROPOUT:\n\t\t\t\tw *= self.dropout_matrix[layer_i]\n\t\t\t_input_ = sigmoid(tf.matmul(self.layer[layer_i+1], w,transpose_b=True) + self.bias[layer_i], self.temp_tf)\n\n\t\t# v(3) layer \n\t\telif layer_i == self.n_layers-1:\n\t\t\t### add all termns together to an buffer \n\t\t\t# first connection between label and context layer \n\t\t\tw = self.w[layer_i-1]\n\t\t\tif self.USE_DROPOUT:\n\t\t\t\tw *= self.dropout_matrix[layer_i-1]\n\t\t\t_input_buff_ = tf.matmul(self.layer[layer_i-1], w)\n\t\t\t# every extra layer\n\t\t\tfor i,l in enumerate(self.layers_to_connect):\n\t\t\t\tindex = i + self.n_layers-1\n\t\t\t\tw = self.w[index]\n\t\t\t\tif self.USE_DROPOUT:\n\t\t\t\t\tw *= self.dropout_matrix[index]\n\t\t\t\t_input_buff_ += tf.matmul(self.layer[l], w)\n\t\t\t# bias \n\t\t\t_input_buff_ += self.bias[layer_i]\n\n\t\t\t# sigmoid \n\t\t\t_input_ = sigmoid(_input_buff_, self.temp_tf)\n\n\t\t# hidden and v(2) layer \n\t\telse:\n\t\t\t# normal layer bottom up and top down adjacend \n\t\t\tw0 = self.w[layer_i-1];\n\t\t\tw1 = self.w[layer_i];\n\t\t\tif self.USE_DROPOUT:\n\t\t\t\tw0 *= self.dropout_matrix[layer_i-1]\n\t\t\t\tw1 *= self.dropout_matrix[layer_i]\n\t\t\t_input_buff_ = (tf.matmul(self.layer[layer_i-1],w0)\n\t\t\t\t\t\t\t\t+ tf.matmul(self.layer[layer_i+1],w1,transpose_b=True)\n\t\t\t\t\t\t\t\t+ self.bias[layer_i])\n\n\t\t\t# extra layer input :\n\t\t\tcorrect_extra_index = self.layers_to_connect+len(DBM.SHAPE)\n\t\t\tif layer_i in correct_extra_index:\n\t\t\t\tw_index = DBM.n_layers-1 + np.where(correct_extra_index == layer_i)[0][0]\n\t\t\t\tw2 = self.w[w_index]\n\t\t\t\tif self.USE_DROPOUT:\n\t\t\t\t\tw2 *= self.dropout_matrix[w_index]\n\t\t\t\t_input_buff_ += tf.matmul(self.layer[-1], w2, transpose_b = True)\t\t\t\t\n\n\t\t\t_input_ = sigmoid(_input_buff_, self.temp_tf)\t\t\t\n\n\t\treturn _input_",
"title": ""
},
{
"docid": "abfbf079f29e3f653d627683b85db7d5",
"score": "0.6085865",
"text": "def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):\n if fc_dims is None:\n self.feature_dim = input_dim\n return None\n\n assert isinstance(fc_dims, (list, tuple)), 'fc_dims must be either list or tuple, but got {}'.format(type(fc_dims))\n\n layers = []\n for dim in fc_dims:\n layers.append(nn.Linear(input_dim, dim))\n layers.append(nn.BatchNorm1d(dim))\n layers.append(nn.ReLU(inplace=True))\n if dropout_p is not None:\n layers.append(nn.Dropout(p=dropout_p))\n input_dim = dim\n\n self.feature_dim = fc_dims[-1]\n\n return nn.Sequential(*layers)",
"title": ""
},
{
"docid": "8a3f688ff782a75b75495d888cc2cde1",
"score": "0.60810477",
"text": "def add_internal_layer(self, _input, growth_rate):\n # call composite function with 3x3 kernel\n if not self.bc_mode:\n comp_out = self.composite_function(\n _input, out_features=growth_rate, kernel_size=3)\n elif self.bc_mode:\n bottleneck_out = self.bottleneck(_input, out_features=growth_rate)\n comp_out = self.composite_function(\n bottleneck_out, out_features=growth_rate, kernel_size=3)\n # concatenate _input with out from composite function\n if TF_VERSION >= 1.0:\n output = tf.concat(axis=3, values=(_input, comp_out))\n else:\n output = tf.concat(3, (_input, comp_out))\n return output",
"title": ""
},
{
"docid": "be41f2e809ad688c3bb915f2fb93ad02",
"score": "0.6078957",
"text": "def get_input_layer(dim_input, dim_output):\n net_input = tf.placeholder(\"float\", [None, dim_input], name='nn_input')\n action = tf.placeholder('float', [None, dim_output], name='action')\n precision = tf.placeholder('float', [None, dim_output, dim_output], name='precision')\n return net_input, action, precision",
"title": ""
},
{
"docid": "45738bf69d072417b55b2b4e9ce546dd",
"score": "0.60751307",
"text": "def build_model(self, img_input: TensorType) -> TensorType:\n filters = int(32 * self.alpha)\n shape = (-1, 1, 1, int(1024 * self.alpha))\n\n # Conv 1 block\n x = layers.zero_padding(img_input, padding=((0, 1), (0, 1)), name='conv1_pad')\n x = layers.conv(x, filters_out=filters, kernel_size=3, padding='valid', add_bias=False, stride=2, name='conv1')\n x = layers.norm(x, axis=-1, name='conv1_bn')\n x = layers.relu(x, name='conv1_relu')\n\n # Depthwise convolutions\n x = self._depthwise_conv_block(x, 64, self.alpha, depth_multiplier=1, block_id=1)\n x = self._depthwise_conv_block(x, 128, self.alpha, depth_multiplier=1, strides=2, block_id=2)\n x = self._depthwise_conv_block(x, 128, self.alpha, depth_multiplier=1, block_id=3)\n x = self._depthwise_conv_block(x, 256, self.alpha, depth_multiplier=1, strides=2, block_id=4)\n x = self._depthwise_conv_block(x, 256, self.alpha, depth_multiplier=1, block_id=5)\n x = self._depthwise_conv_block(x, 512, self.alpha, depth_multiplier=1, strides=2, block_id=6)\n x = self._depthwise_conv_block(x, 512, self.alpha, depth_multiplier=1, block_id=7)\n x = self._depthwise_conv_block(x, 512, self.alpha, depth_multiplier=1, block_id=8)\n x = self._depthwise_conv_block(x, 512, self.alpha, depth_multiplier=1, block_id=9)\n x = self._depthwise_conv_block(x, 512, self.alpha, depth_multiplier=1, block_id=10)\n x = self._depthwise_conv_block(x, 512, self.alpha, depth_multiplier=1, block_id=11)\n x = self._depthwise_conv_block(x, 1024, self.alpha, depth_multiplier=1, strides=2, block_id=12)\n x = self._depthwise_conv_block(x, 1024, self.alpha, depth_multiplier=1, block_id=13)\n\n # Include top\n x = layers.global_avg_pool(x)\n x = layers.reshape(x, shape=shape, name='reshape_1')\n x = layers.conv(x, filters_out=self.num_classes, kernel_size=1, padding='same', name='conv_preds',\n add_bias=False)\n x = layers.reshape(x, shape=(-1, self.num_classes), name='reshape_2')\n x = layers.softmax(x, name='act_softmax')\n return x",
"title": ""
},
{
"docid": "8dbcb1e4855f99065c10c87f425b78eb",
"score": "0.6071676",
"text": "def new_fc_layer(self,inputt, #input layer and we assumed that it's shpa will be 2D [num of images , num of inputs]\n numOfInputs,\n numOfOutputs,\n relu=True):\n #shape of wieghts \n shape = [numOfInputs,numOfOutputs]\n #create weights and biases \n weights = self.new_weights(shape= shape)\n biases = self.new_biases(numOfOutputs)\n \n #compute our output layer \n layer = tf.matmul(inputt,weights) + biases\n if(relu):\n layer = tf.nn.relu(layer)\n \n return layer",
"title": ""
},
{
"docid": "14a7a63f3626a86ee7d71fc1ce2e6bca",
"score": "0.6067665",
"text": "def layer(self, layer_dict):\n if self._layer is not None:\n return self._layer\n inputs = []\n for k in self._inputs:\n if isinstance(k, tensorflow.Tensor):\n inputs.append(k)\n continue\n if k not in layer_dict:\n raise ValueError('Input layer ' + str(k) + ' not found.')\n inputs.append(layer_dict[k].layer(layer_dict))\n if inputs:\n if len(inputs) == 1:\n inputs = inputs[0]\n self._layer = self._layer_constructor(inputs)\n else:\n self._layer = self._layer_constructor\n return self._layer",
"title": ""
},
{
"docid": "dca6f09a25ca8669fd8b6c4bb1662423",
"score": "0.6054492",
"text": "def add_layer(inputs, in_size, out_size, active_fun = lambda x: x):\n weights = tf.Variable(tf.random_normal([in_size, out_size]))\n biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)\n linear_struct = tf.matmul(inputs, weights) + biases\n outputs = active_fun(linear_struct)\n return outputs",
"title": ""
},
{
"docid": "5bd117ba00b59c1aba5d84b8db094b35",
"score": "0.60505694",
"text": "def add_layer(self, input_layer):\n\n # Layers must provide froward_prop and back_prop\n check_forward = getattr(input_layer, 'forward_prop', None)\n if not callable(check_forward):\n print('Error: Provided layer does not have forward_prop! ')\n return None\n check_backward = getattr(input_layer, 'back_prop', None)\n if not callable(check_backward):\n print('Error: Provided layer does not have back_prop! ')\n return None\n # Add the layer at the end of the networks list of layers\n self.layers.append(input_layer)",
"title": ""
},
{
"docid": "68e56ca209cc62f5e3a6c174432b5d71",
"score": "0.6049663",
"text": "def addLayers(self, *arg, **kw):\n netType = \"serial\"\n if \"type\" in kw:\n netType = kw[\"type\"]\n self.addLayer('input', arg[0])\n hiddens = []\n if len(arg) > 3:\n hcount = 0\n for hidc in arg[1:-1]:\n name = 'hidden%d' % hcount\n self.addLayer(name, hidc)\n hiddens.append(name)\n hcount += 1\n elif len(arg) == 3:\n name = 'hidden'\n self.addLayer(name, arg[1])\n hiddens.append(name)\n elif len(arg) == 2:\n pass\n else:\n raise AttributeError, \"not enough layers! need >= 2\"\n self.addLayer('output', arg[-1])\n lastName = \"input\"\n for name in hiddens:\n if netType == \"parallel\":\n self.connect('input', name)\n self.connect(name, 'output')\n else: # serial\n self.connect(lastName, name)\n lastName = name\n if netType == \"serial\" or lastName == \"input\":\n self.connect(lastName, \"output\")",
"title": ""
},
{
"docid": "3eda1217c1ec355a598f8309fe90baff",
"score": "0.60468584",
"text": "def build(\n network_spec: Dict,\n ):\n # --------------------------------------------------------------------------------\n # Build layers in the network\n # --------------------------------------------------------------------------------\n inference_layers, objective_layers = \\\n SequentialNetwork._build_network_layers(\n network_spec=network_spec\n )\n\n # --------------------------------------------------------------------------------\n # Wire the layers to function as a network\n # --------------------------------------------------------------------------------\n *layers, = SequentialNetwork._wire_network_layers(\n network_spec=network_spec,\n inference_layers=inference_layers,\n objective_layers=objective_layers,\n )\n layer_inference, layer_objective = layers\n\n return layer_inference, layer_objective",
"title": ""
},
{
"docid": "7d7f1db177f133f76e3ed7aa0154aa54",
"score": "0.6046636",
"text": "def build_call(self, inputs, training=True):\n\n e1x, x = self.e1(inputs, training=training)\n e2x, x = self.e2(x, training=training)\n e3x, x = self.e3(x, training=training)\n e4x, x = self.e4(x, training=training)\n \n x = self.d4(x, training=training)\n\n x = layers.concatenate([x, e4x], axis=self.concat_axis)\n x = self.d3(x, training=training)\n \n x = layers.concatenate([x, e3x], axis=self.concat_axis)\n x = self.d2(x, training=training)\n\n x = layers.concatenate([x, e2x], axis=self.concat_axis)\n x = self.d1(x, training=training)\n\n x = layers.concatenate([x, e1x], axis=self.concat_axis)\n x, _ = self.output_block(x, training=training)\n x = self.conv_output(x)\n\n return x",
"title": ""
},
{
"docid": "be474a797bfc4d098b0be0e480d1e23c",
"score": "0.6035846",
"text": "def build_model(input, output_dim):\n model = slim.fully_connected(input, 512, scope='fc1')\n model = slim.dropout(model)\n model = slim.fully_connected(model, 512, scope='fc2')\n model = slim.fully_connected(model, output_dim, scope='output')\n\n # l_in = lasagne.layers.InputLayer(\n # shape=(1, input_dim)\n # )\n #\n # l_hidden1 = lasagne.layers.DenseLayer(\n # l_in,\n # num_units=512,\n # nonlinearity=lasagne.nonlinearities.rectify\n # )\n #\n # l_hidden1_dropout = lasagne.layers.DropoutLayer(\n # l_hidden1)\n #\n # l_hidden2 = lasagne.layers.DenseLayer(\n # l_hidden1_dropout,\n # num_units=512,\n # nonlinearity=lasagne.nonlinearities.rectify\n # )\n # l_hidden2_dropout = lasagne.layers.DropoutLayer(\n # l_hidden2)\n #\n # l_out = lasagne.layers.DenseLayer(\n # l_hidden2_dropout,\n # num_units=output_dim,\n # nonlinearity=lasagne.nonlinearities.softmax)\n\n return model",
"title": ""
},
{
"docid": "72dcca2309127385247e09d08194a7f1",
"score": "0.60353947",
"text": "def highway_layer(input_data, dim, init, name='', reuse=None):\n trans = linear(input_data, dim, init, name='trans_{}'.format(name),\n reuse=reuse)\n trans = tf.nn.relu(trans)\n gate = linear(input_data, dim, init, name='gate_{}'.format(name),\n reuse=reuse)\n gate = tf.nn.sigmoid(gate)\n if (dim != input_data.get_shape()[-1]):\n input_data = linear(input_data, dim, init, name='trans2_{}'.format(name),\n reuse=reuse)\n output = gate * trans + (1 - gate) * input_data\n return output",
"title": ""
},
{
"docid": "cf8be62280c5fd97bd45aaa95a0899fa",
"score": "0.600594",
"text": "def build_network(self):\n self.build_input_layer()\n\n self.initial_states = tf.constant([0])\n self.rnn_outputs = tf.constant([0])\n self.rnn_state = tf.constant([0])\n\n self.build_output_layer()\n self.build_loss()",
"title": ""
},
{
"docid": "3f9e81c889c21dd203547d8b06626460",
"score": "0.59287363",
"text": "def create_input_layer(self, initial):\n #todo use populations of size > 1\n self.neurons_input = nest.Create(\"poisson_generator\", self.num_input)\n # introduce parrot neuron to fix limitation of devices with STDP synapses\n self.parrots = nest.Create(\"parrot_neuron\", self.num_input)\n self.neur_ids_parrot = self.parrots.tolist()\n if initial:\n self.neur_ids_ex.extend(self.neur_ids_parrot)\n # add each parrot as a population\n for i in range(self.num_input):\n self.populations_nest.append(self.parrots[i])\n # connect without adding to front-end\n nest.Connect(self.neurons_input, self.parrots, \"one_to_one\")\n\n self.spike_detector = nest.Create('spike_detector')\n nest.Connect(self.parrots, self.spike_detector, 'all_to_all')",
"title": ""
},
{
"docid": "2aa3c52af631304a3eb805c002c3d521",
"score": "0.5927486",
"text": "def build_tower_cnn_model(input_shape):\n\n x0 = Input(input_shape, name='Input')\n\n x = x0\n\n tower_1 = Conv1D(16, 3, padding='same', activation='relu')(x)\n tower_2 = Conv1D(16, 5, padding='same', activation='relu')(x)\n tower_3 = MaxPooling1D(3, strides=1, padding='same')(x)\n tower_3 = Conv1D(16, 1, padding='same', activation='relu')(tower_3)\n\n x = keras.layers.concatenate([tower_1, tower_2, tower_3], axis=2)\n x = GlobalAveragePooling1D()(x)\n x = BatchNormalization()(x)\n x = Activation(\"relu\")(x)\n # x = Flatten()(x)\n\n y = Dense(EMB_SIZE, name='dense_encoding')(x)\n y = Lambda(lambda x: K.l2_normalize(x, axis=1))(y)\n\n model = Model(inputs=x0, outputs=y)\n\n return model",
"title": ""
},
{
"docid": "173cc90313256ab7d83b66a32557e178",
"score": "0.5926356",
"text": "def fully_connected(name, layer_input, \n input_length = None, output_length = None,\n add_bias = True, activation_param = None, add_dropout = False,\n weight_initializer = None, bias_initializer = None,\n param = None, ops = None):\n weight_name = weight_name_fc(name)\n bias_name = bias_name_fc(name)\n if (param is not None and weight_name in param.keys()):\n weight = param[weight_name]\n else:\n weight = None\n if (param is not None and add_bias and bias_name in param.keys()):\n bias = param[bias_name]\n else:\n bias = None\n fc_op, fc_param = fc_layer(name, layer_input,\n input_length, output_length,\n weight, bias, add_bias,\n weight_initializer, bias_initializer)\n if (ops is not None):\n ops[name] = fc_op\n if (param is not None):\n param[weight_name] = fc_param['weight']\n if (add_bias):\n param[bias_name] = fc_param['bias']\n if (activation_param is not None):\n fc_op, activation_type = activation_function(fc_op,\n activation_param)\n if (ops is not None):\n ops[activation_op_name(name, activation_type)] = fc_op\n if (add_dropout):\n dropout_rate_id = dropout_rate_name(name)\n dropout_rate_ph = tf.placeholder(tf.float32, [], dropout_rate_id)\n if (ops is not None):\n ops[dropout_rate_id] = dropout_rate_ph\n fc_op = tf.nn.dropout(fc_op, 1 - dropout_rate_ph)\n dropout_name = dropout_op_name(name)\n if (ops is not None):\n ops[dropout_name] = fc_op\n return fc_op, fc_param",
"title": ""
},
{
"docid": "ef88f358fcc0887d49831a768ba5823c",
"score": "0.59167314",
"text": "def build(self, input_shape):\n \n if input_shape[0][-1] is None:\n raise ValueError('The channel dimension of the inputs should be defined. Found `None`.')\n \n self.input_dim = input_shape[0][-1]\n \n # Image kernel\n kernel_shape = (self.kernel_size, self.kernel_size, self.input_dim, self.filters)\n self.kernel = self.add_weight(shape=kernel_shape,\n initializer=self.kernel_initializer,\n name='img_kernel',\n regularizer=self.kernel_regularizer)\n # Mask kernel\n self.kernel_mask = K.ones(shape=kernel_shape)\n\n # Calculate padding size to achieve zero-padding\n self.pconv_padding = (\n (int((self.kernel_size-1)/2), int((self.kernel_size-1)/2)), \n (int((self.kernel_size-1)/2), int((self.kernel_size-1)/2)), \n )\n\n # Window size - used for normalization\n self.window_size = self.kernel_size ** 2 \n \n if self.use_bias:\n self.bias = self.add_weight(shape=(self.filters,),\n initializer=self.bias_initializer,\n name='bias',\n regularizer=self.bias_regularizer)\n else:\n self.bias = None\n self.built = True",
"title": ""
},
{
"docid": "5569fd98a90b42bd779226b43a9f8873",
"score": "0.5916666",
"text": "def build(self, input_shape):\n self.kernel = self.add_weight(name='kernel', \t\t\n shape=(input_shape[1], \t\n self.output_dim), \n initializer='uniform',\t#Make weights random.\n trainable=True)\t\t#We need trainable weights.\n\n self.bias = None\n if self.use_bias:\n self.bias = self.add_weight(name='bias',\n shape=(self.output_dim,),\n initializer='zeros',\t#Initialize bias to be all 0's. Maybe try 'uniform'.\n trainable=True)\t#Bias must also be trainable.\n\n super(Dense, self).build(input_shape)\t#Have to be called in the end.",
"title": ""
},
{
"docid": "1f56b6e671679ef4ee0d3707a15ced94",
"score": "0.5912598",
"text": "def link(self, input):\n\n # convolutional layer\n self.conv_out = T.nnet.conv2d(\n input=input,\n filters=self.filters,\n # input_shape=None, _TODO_ might be faster\n filter_shape=self.filter_shape,\n border_mode=self.border_mode,\n subsample=self.stride,\n filter_flip=False,\n # image_shape=None\n )\n\n # bias + squash function\n self.linear_output = self.conv_out + self.bias.dimshuffle('x', 0, 'x', 'x')\n self.output = T.nnet.relu(self.linear_output)\n\n return self.output",
"title": ""
},
{
"docid": "43d2f08666c2b814be8e87bb48098110",
"score": "0.5910952",
"text": "def build_inception_layer(self, x, imap, omap, reduce1x1):\n with tf.name_scope('inception_layer'):\n W_conv1_1x1_1 = self.createWeight([1,1,imap,omap],'W_conv1_1x1_1')\n b_conv1_1x1_1 = self.createBias([omap],'b_conv1_1x1_1') \n \n W_conv1_1x1_2 = self.createWeight([1,1,imap,reduce1x1],'W_conv1_1x1_2')\n b_conv1_1x1_2 = self.createBias([reduce1x1],'b_conv1_1x1_2')\n \n W_conv1_1x1_3 = self.createWeight([1,1,imap,reduce1x1],'W_conv1_1x1_3')\n b_conv1_1x1_3 = self.createBias([reduce1x1],'b_conv1_1x1_3')\n\n W_conv1_3x3 = self.createWeight([3,3,reduce1x1,omap],'W_conv1_3x3')\n b_conv1_3x3 = self.createBias([omap],'b_conv1_3x3')\n\n W_conv1_5x5 = self.createWeight([5,5,reduce1x1,omap],'W_conv1_5x5')\n b_conv1_5x5 = self.createBias([omap],'b_conv1_5x5')\n \n W_conv1_1x1_4= self.createWeight([1,1,imap,omap],'W_conv1_1x1_4')\n b_conv1_1x1_4= self.createBias([omap],'b_conv1_1x1_4')\n\n conv1_1x1_1 = self.conv2d_s1(x,W_conv1_1x1_1)+b_conv1_1x1_1\n conv1_1x1_2 = tf.nn.relu(self.conv2d_s1(x,W_conv1_1x1_2)+b_conv1_1x1_2)\n conv1_1x1_3 = tf.nn.relu(self.conv2d_s1(x,W_conv1_1x1_3)+b_conv1_1x1_3)\n conv1_3x3 = self.conv2d_s1(conv1_1x1_2,W_conv1_3x3)+b_conv1_3x3\n conv1_5x5 = self.conv2d_s1(conv1_1x1_3,W_conv1_5x5)+b_conv1_5x5\n maxpool1 = self.max_pool_3x3_s1(x)\n conv1_1x1_4 = self.conv2d_s1(maxpool1,W_conv1_1x1_4)+b_conv1_1x1_4\n \n return tf.nn.relu(tf.concat([conv1_1x1_1,conv1_3x3,conv1_5x5,conv1_1x1_4], 3))",
"title": ""
},
{
"docid": "d8eb426c82c9647cbb88ed7bc09eed62",
"score": "0.59054786",
"text": "def build(self, input_shape):\r\n \r\n if self.data_format == 'channels_first':\r\n channel_axis = 1\r\n else:\r\n channel_axis = -1\r\n \r\n if input_shape[0][channel_axis] is None:\r\n raise ValueError('The channel dimension of the inputs should be defined. Found `None`.')\r\n \r\n self.input_dim = input_shape[0][channel_axis]\r\n \r\n # Image kernel\r\n kernel_shape = self.kernel_size + (self.input_dim, self.filters)\r\n self.kernel = self.add_weight(shape=kernel_shape,\r\n initializer=self.kernel_initializer,\r\n name=self.name+'img_kernel',\r\n regularizer=self.kernel_regularizer,\r\n constraint=self.kernel_constraint)\r\n # Mask kernel\r\n self.kernel_mask = K.ones(shape=self.kernel_size + (1, 1))\r\n\r\n kk = np.zeros(shape=self.kernel_size + (self.input_dim, self.input_dim))\r\n for ii in range(self.input_dim):\r\n kk[:,:,:,ii,ii] = 1\r\n \r\n self.image_mask = K.variable(kk, name=self.name+'image_mask')\r\n self.image_mask._trainable = False\r\n \r\n \r\n if self.use_bias:\r\n self.bias = self.add_weight(shape=(self.filters,),\r\n initializer=self.bias_initializer,\r\n name=self.name+'bias',\r\n regularizer=self.bias_regularizer,\r\n constraint=self.bias_constraint)\r\n else:\r\n self.bias = None\r\n \r\n self.built = True",
"title": ""
},
{
"docid": "3c6a135e24363ddf9f73f4105b70d802",
"score": "0.5894312",
"text": "def link(self, input):\n if self.k_max is None:\n raise Exception(\"k_max has not been defined in the layer %s!\" % self.name)\n\n self.input = input\n\n # 2D convolutional layer\n self.conv2d_layer.link(self.input)\n self.conv_out = self.conv2d_layer.conv_out\n\n # k-max pooling\n k_max_layer = pooling.KMaxPoolingLayer1(self.k_max)\n self.pooled_out = k_max_layer.link(self.conv_out)\n\n # bias + squash function\n self.linear_output = self.pooled_out + self.conv2d_layer.bias.dimshuffle('x', 0, 'x', 'x')\n self.output = T.tanh(self.linear_output)\n\n return self.output",
"title": ""
},
{
"docid": "76910609145ae1eacbaab81ce376b545",
"score": "0.5894224",
"text": "def build_layers(self, data_dim, nClasses, hidden_dims, lamda, W=None, b=None, verbose=True, par_seed=None, alpha=0.9):\n\n n_layers = len(hidden_dims)\n\n self.alpha = alpha\n self.layer_dims = hidden_dims\n self.lamda = lamda\n\n if W is None:\n W = []\n for i in range(n_layers):\n W.append(None)\n\n if b is None:\n b = []\n for i in range(n_layers):\n b.append(None)\n \n if verbose:\n print(\"\\n{0}\\n\".format(20*\"-\"))\n print(\"-- Building Network with parameters --\\n\")\n print(\"- Input dimension : %d \\n- Number of classes : %d\\n\" % (data_dim,nClasses))\n print(\"- Number of hidden layers: %d\" % (len(hidden_dims)))\n print(\"\\t- Dims: \", end='')\n for dim in hidden_dims:\n print(dim, end=\" \")\n print(\"\\n{0}\\n\".format(20*\"-\"))\n\n\n # Adding layers to network according to [dims]\n\n self.add_layer( FCLayer(input_size=data_dim, \n output_size=hidden_dims[0],\n init_func=self.init_func, \n lamda=self.lamda, \n W=W[0], b=b[0], \n seed=par_seed, \n normalize=self.normalize, \n alpha=self.alpha), \n verbose=verbose)\n\n self.add_layer( ActLayer(self.act_func), \n verbose=verbose)\n\n for i in range(1,n_layers):\n self.add_layer( FCLayer(input_size=hidden_dims[i-1],\n output_size=hidden_dims[i],\n init_func=self.init_func,\n lamda=self.lamda,\n W=W[i], b=b[i],\n seed=par_seed,\n normalize=self.normalize,\n alpha=self.alpha),\n verbose=verbose)\n\n self.add_layer( ActLayer(self.act_func),\n verbose=verbose)\n\n self.add_layer( FCLayer(input_size=hidden_dims[-1],\n output_size=nClasses,\n init_func=self.init_func,\n lamda=self.lamda,\n W=W[-1], b=b[-1],\n seed=par_seed,\n normalize=False,\n alpha=self.alpha),\n verbose=verbose)\n \n if verbose:\n print(\"\\n{0}DONE{0}\\n\".format(8*\"-\"))",
"title": ""
},
{
"docid": "2e45536aa93e60ca0757fed1343281f4",
"score": "0.58764404",
"text": "def add_layer(_input, in_size, out_size, activation_fun=None):\n # random_normal: mean=0, var=1\n Weights = tf.Variable(tf.random_normal([in_size, out_size]))\n biases = tf.Variable(tf.zeros([1, out_size]))\n Wx_plus_b = tf.matmul(_input, Weights) + biases\n\n if activation_fun is None:\n output = Wx_plus_b\n else:\n output = activation_fun(Wx_plus_b)\n return output",
"title": ""
},
{
"docid": "5d1099261bcd48ff7d1460ebe5077356",
"score": "0.58753544",
"text": "def build_model(self, ):\n\n input = k.layers.Input(shape=self.input_shape)\n\n conv1 = conv3D(input=input, output=64, kernel_size=3, strides=1)\n conv1_bn = k.layers.BatchNormalization(epsilon=1e-5, scale=True, momentum=0.9)(conv1)\n conv1_relu = k.layers.ReLU()(conv1_bn)\n pool1 = k.layers.MaxPool3D(pool_size=2, strides=2)(conv1_relu)\n\n conv2 = conv3D(input=pool1, output=128, kernel_size=3, strides=1)\n conv2_bn = k.layers.BatchNormalization(epsilon=1e-5, scale=True, momentum=0.9)(conv2)\n conv2_relu = k.layers.ReLU()(conv2_bn)\n pool2 = k.layers.MaxPool3D(pool_size=2, strides=2)(conv2_relu)\n\n conv3 = conv3D(input=pool2, output=256, kernel_size=3, strides=1)\n conv3_bn = k.layers.BatchNormalization(epsilon=1e-5, scale=True, momentum=0.9)(conv3)\n conv3_relu = k.layers.ReLU()(conv3_bn)\n conv3_2 = conv3D(input=conv3_relu, output=256, kernel_size=3, strides=1)\n conv3_bn_2 = k.layers.BatchNormalization(epsilon=1e-5, scale=True, momentum=0.9)(conv3_2)\n conv3_relu_2 = k.layers.ReLU()(conv3_bn_2)\n pool3 = k.layers.MaxPool3D(pool_size=2, strides=2)(conv3_relu_2)\n\n conv4 = conv3D(input=pool3, output=512, kernel_size=3, strides=1)\n conv4_bn = k.layers.BatchNormalization(epsilon=1e-5, scale=True, momentum=0.9)(conv4)\n conv4_relu = k.layers.ReLU()(conv4_bn)\n conv4_2 = conv3D(input=conv4_relu, output=512, kernel_size=3, strides=1)\n conv4_bn_2 = k.layers.BatchNormalization(epsilon=1e-5, scale=True, momentum=0.9)(conv4_2)\n conv4_relu_2 = k.layers.ReLU()(conv4_bn_2)\n pool4 = k.layers.MaxPool3D(pool_size=2, strides=2)(conv4_relu_2)\n\n conv5 = conv3D(input=pool4, output=512, kernel_size=3, strides=1)\n conv5_bn = k.layers.BatchNormalization(epsilon=1e-5, scale=True, momentum=0.9)(conv5)\n conv5_relu = k.layers.ReLU()(conv5_bn)\n conv5_2 = conv3D(input=conv5_relu, output=512, kernel_size=3, strides=1)\n conv5_bn_2 = k.layers.BatchNormalization(epsilon=1e-5, scale=True, momentum=0.9)(conv5_2)\n conv5_relu_2 = k.layers.ReLU()(conv5_bn_2)\n\n deconv1 = deconv3D(input=conv5_relu_2, output=512)\n deconv1_bn = k.layers.BatchNormalization(epsilon=1e-5, scale=True, momentum=0.9)(deconv1)\n deconv1_relu = k.layers.ReLU()(deconv1_bn)\n\n concat1 = k.layers.concatenate([deconv1_relu, conv4_2])\n deconv1_2 = conv3D(input=concat1, output=256, kernel_size=3, strides=1)\n deconv1_bn_2 = k.layers.BatchNormalization(epsilon=1e-5, scale=True, momentum=0.9)(deconv1_2)\n deconv1_relu_2 = k.layers.ReLU()(deconv1_bn_2)\n\n deconv2 = deconv3D(input=deconv1_relu_2, output=256)\n deconv2_bn = k.layers.BatchNormalization(epsilon=1e-5, scale=True, momentum=0.9)(deconv2)\n deconv2_relu = k.layers.ReLU()(deconv2_bn)\n\n concat2 = k.layers.concatenate([deconv2_relu, conv3])\n deconv2_2 = conv3D(input=concat2, output=128, kernel_size=3, strides=1)\n deconv2_bn_2 = k.layers.BatchNormalization(epsilon=1e-5, scale=True, momentum=0.9)(deconv2_2)\n deconv2_relu_2 = k.layers.ReLU()(deconv2_bn_2)\n\n deconv3 = deconv3D(input=deconv2_relu_2, output=128)\n deconv3_bn = k.layers.BatchNormalization(epsilon=1e-5, scale=True, momentum=0.9)(deconv3)\n deconv3_relu = k.layers.ReLU()(deconv3_bn)\n\n concat3 = k.layers.concatenate([deconv3_relu, conv2])\n deconv3_2 = conv3D(input=concat3, output=64, kernel_size=3, strides=1)\n deconv3_bn_2 = k.layers.BatchNormalization(epsilon=1e-5, scale=True, momentum=0.9)(deconv3_2)\n deconv3_relu_2 = k.layers.ReLU()(deconv3_bn_2)\n\n deconv4 = deconv3D(input=deconv3_relu_2, output=64)\n deconv4_bn = k.layers.BatchNormalization(epsilon=1e-5, scale=True, momentum=0.9)(deconv4)\n deconv4_relu = k.layers.ReLU()(deconv4_bn)\n\n concat4 = k.layers.concatenate([deconv4_relu, conv1])\n deconv4_2 = conv3D(input=concat4, output=32, kernel_size=3, strides=1)\n deconv4_bn_2 = k.layers.BatchNormalization(epsilon=1e-5, scale=True, momentum=0.9)(deconv4_2)\n deconv4_relu_2 = k.layers.ReLU()(deconv4_bn_2)\n\n output = k.layers.Conv3D(8, kernel_size=1, strides=1)(deconv4_relu_2)\n prob = k.layers.Softmax()(output)\n\n model = k.models.Model(inputs=input, outputs=prob)\n\n print(model.summary())\n\n return model",
"title": ""
},
{
"docid": "63be2c433dba5022fc19b6cde39fa29a",
"score": "0.5872169",
"text": "def make_layer(self, layer_index):\n\n layer = nn.ModuleList([None for i in range(self.ns)]) # initialise the layer\n\n for row in range(layer_index, self.ns):\n convs = nn.ModuleList([]) # stores the convolutions\n\n # === strided convolution h_hat ===\n h_hat_num_channels_in = (self.k0 * self.gr ** (row - 1)) + (layer_index - 1) * (2 * self.k0 * self.gr ** (row - 1))\n h_hat = self.h_hat(\n n_channels_in = h_hat_num_channels_in,\n scale_index = row\n )\n\n # === regular convolution h ===\n h_num_channels_in = (self.k0 * self.gr ** row) + (layer_index - 1) * (2 * self.k0 * self.gr ** row)\n h = self.h(\n n_channels_in = h_num_channels_in,\n scale_index = row\n )\n\n convs.append(h_hat)\n convs.append(h)\n\n layer.insert(row, convs)\n\n return layer",
"title": ""
},
{
"docid": "39e94254dd00fe58fbfb939d522ff535",
"score": "0.58720887",
"text": "def build(self, input_shape):\n _, height, width, channel = input_shape\n if self._positional_encoding_type.lower() == '2d':\n self._embeddings = self.add_weight(\n shape=(1, height, width, channel),\n initializer=self._initializer, trainable=True,\n name='embeddings',\n regularizer=self._kernel_regularizer)\n self._batch_norm = self._bn_layer(axis=-1, name='batch_norm')\n elif self._positional_encoding_type.lower() == '1d':\n # Generate separable positional encodings for the height axis and the\n # width axis.\n self._height_axis_embeddings = self.add_weight(\n shape=(1, height, 1, channel),\n initializer=self._initializer, trainable=True,\n name='height_axis_embeddings',\n regularizer=self._kernel_regularizer)\n self._height_axis_batch_norm = self._bn_layer(\n axis=-1, name='height_axis_batch_norm')\n self._width_axis_embeddings = self.add_weight(\n shape=(1, height, 1, channel),\n initializer=self._initializer, trainable=True,\n name='width_axis_embeddings',\n regularizer=self._kernel_regularizer)\n self._width_axis_batch_norm = self._bn_layer(\n axis=-1, name='width_axis_batch_norm')",
"title": ""
},
{
"docid": "4444dc0511ca79b5ceb731fd494ad067",
"score": "0.5862088",
"text": "def _generator_network(self, layer_input, layer_dim):\n for layer_i, n_output in enumerate(reversed(layer_dim[1:])):\n n_input = int(layer_input.get_shape()[1])\n W = tf.Variable(\n self.W_init_fct([n_input, n_output]), dtype=tf.float32\n )\n b = tf.Variable(self.b_init_fct([n_output]), dtype=tf.float32)\n output = self.transfer_fct(tf.add(tf.matmul(layer_input, W), b))\n layer_input = output\n\n n_dims = self.net_arch[\"hidden_dim\"][0]\n\n W_out_mean = tf.Variable(\n self.W_init_fct([n_dims, self.net_arch[\"n_output\"]])\n )\n b_out_mean = tf.Variable(\n self.b_init_fct([self.net_arch[\"n_output\"]], dtype=tf.float32)\n )\n\n x_reconstr_mean = tf.nn.sigmoid(\n tf.add(tf.matmul(layer_input, W_out_mean), b_out_mean)\n )\n return x_reconstr_mean",
"title": ""
},
{
"docid": "c49dbb58041189eb261e3c5261218958",
"score": "0.58608097",
"text": "def build(self, input_shape, num_levels, num_filters, kernel_size, padding, activation, pool_size, num_convs, dropout_rate, num_classes):\n X = tf.keras.Input(input_shape)\n inputs = X\n X_concat = []\n \n # Linear projection to match dimensions\n X = tf.keras.layers.Conv3D(filters=num_filters[0], kernel_size=1, padding='same', activation=None)(X)\n \n # Encoder\n for i in range(num_levels):\n X_residual = X\n X = tf.keras.layers.BatchNormalization()(X)\n\n for j in range(num_convs[i]):\n X = tf.keras.layers.Conv3D(filters=num_filters[i], kernel_size=kernel_size[i], padding=padding[i], activation=activation[i])(X)\n\n X = tf.keras.layers.Add()([X, X_residual])\n\n # Pooling Replaced by Stride 2 Conv\n if i != num_levels-1:\n X_concat.append(X)\n X = tf.keras.layers.Conv3D(filters=num_filters[i+1], kernel_size=kernel_size[i], padding=padding[i], strides=pool_size[i], activation=activation[i])(X)\n\n # Decoder\n for i in range(num_levels-1, 0, -1):\n X = tf.keras.layers.Conv3DTranspose(filters=num_filters[i], kernel_size=kernel_size[i], padding=padding[i], strides=pool_size[i-1], activation=activation[i])(X)\n X_residual = X\n X = tf.keras.layers.Concatenate()([X, X_concat.pop()])\n\n for j in range(num_convs[i]):\n X = tf.keras.layers.Conv3D(filters=num_filters[i], kernel_size=kernel_size[i], padding=padding[i], activation=activation[i])(X)\n \n X = tf.keras.layers.Add()([X, X_residual])\n\n X = tf.keras.layers.Dropout(dropout_rate)(X)\n outputs = tf.keras.layers.Conv3D(filters=num_classes, kernel_size=1, strides=1, padding='same', activation='softmax')(X)\n\n self.loss_options = {}\n model = tf.keras.Model(inputs, outputs)\n return model",
"title": ""
},
{
"docid": "a06b3ff44f0bc95761ce61c31632eabe",
"score": "0.58590233",
"text": "def _build_model(input_dim, num_classes, num_hidden_layers=0, \n hidden_dimension=128,\n normalize_inputs=False, dropout=0):\n inpt = tf.keras.layers.Input((input_dim))\n net = inpt\n \n # if we're normalizing inputs:\n if normalize_inputs:\n norm = tf.keras.layers.Lambda(lambda x:K.l2_normalize(x,axis=1))\n net = norm(net)\n \n # for each hidden layer\n for _ in range(num_hidden_layers):\n if dropout > 0:\n net = tf.keras.layers.Dropout(dropout)(net)\n net = tf.keras.layers.Dense(hidden_dimension, activation=\"relu\")(net)\n \n # final layer\n if dropout > 0:\n net = tf.keras.layers.Dropout(dropout)(net)\n net = tf.keras.layers.Dense(num_classes, activation=\"relu\")(net)\n \n return tf.keras.Model(inpt, net)",
"title": ""
},
{
"docid": "8703dec506d901c3468cfb425133ad6d",
"score": "0.5848629",
"text": "def link(self, input):\n if self.k_max is None:\n raise Exception(\"k_max has not been defined in the layer %s!\" % self.name)\n\n self.input = input\n\n # 1D convolutional layer\n self.conv1d_layer.link(self.input)\n self.conv_out = self.conv1d_layer.conv_out\n\n # k-max pooling\n k_max_layer = pooling.KMaxPoolingLayer1(self.k_max)\n self.pooled_out = k_max_layer.link(self.conv_out)\n\n # bias + squash function\n self.linear_output = self.pooled_out + self.conv1d_layer.bias.dimshuffle('x', 0, 'x', 1)\n self.output = T.tanh(self.linear_output)\n\n return self.output",
"title": ""
},
{
"docid": "7d7a97c48f12bd4763ab77d797b56265",
"score": "0.5846188",
"text": "def _build_network(self, h_size=10, l_rate=0.01) -> None:\n # Input\n self.X = tf.placeholder(dtype=tf.float32,\n shape=[None, self.input_size],\n name='X')\n self.Y = tf.placeholder(dtype=tf.float32,\n shape=[None, self.output_size],\n name='Y')\n\n # Layer 1\n w1 = tf.get_variable(name=\"W1\",\n shape=[self.input_size, h_size],\n initializer=tf.contrib.layers.xavier_initializer())\n l1 = tf.nn.tanh(tf.matmul(self.X, w1))\n\n # Layer 2\n w2 = tf.get_variable(name=\"W2\",\n shape=[h_size, self.output_size],\n initializer=tf.contrib.layers.xavier_initializer())\n l2 = tf.matmul(l1, w2)\n\n # Output\n self.Y_ = l2\n\n # cost\n self.cost = tf.reduce_mean(tf.square(self.Y - self.Y_))\n self.train = tf.train.AdamOptimizer(learning_rate=l_rate).minimize(self.cost)",
"title": ""
},
{
"docid": "24067b3331cbb845cd91384936629aa4",
"score": "0.58461857",
"text": "def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):\r\n # Adding a name scope ensures logical grouping of the layers in the graph.\r\n with tf.name_scope(layer_name):\r\n # This Variable will hold the state of the weights for the layer\r\n with tf.name_scope('weights'):\r\n weights = weight_variable([input_dim, output_dim])\r\n with tf.name_scope('biases'):\r\n biases = bias_variable([output_dim])\r\n with tf.name_scope('Wx_plus_b'):\r\n preactivate = tf.matmul(input_tensor, weights) + biases\r\n if act is not None:\r\n activations = act(preactivate, name='activation')\r\n return activations\r\n else:\r\n return preactivate",
"title": ""
},
{
"docid": "2d81e4315e2433fcb13bd287d52b6b7c",
"score": "0.58339506",
"text": "def build_network(input_shape,\n base_depth=16,\n depth_growth=1.25,\n stride_2_layers=4,\n stride_1_layers=1,\n kernel_size=3):\n\n if len(input_shape) != 4:\n raise ValueError('Expecting 4D input')\n\n model = tf.keras.Sequential()\n\n # Create CNN for image patch feature extraction\n cnn = build_cnn(input_shape[1:], base_depth, depth_growth, stride_2_layers,\n stride_1_layers, kernel_size)\n\n # Run CNN on each image patch\n model.add(TimeDistributed(cnn, input_shape=input_shape))\n\n # Copmute average of image patch features per example\n model.add(GlobalAveragePooling1D())\n\n # Compute risk scores\n model.add(Dense(1))\n\n return model",
"title": ""
},
{
"docid": "444806af8a337f9044ca8bac04b90775",
"score": "0.5819715",
"text": "def layerloop(name, input, shapes):\n if len(shapes) == 0:\n return input\n else:\n Wi, bi, out = gen_layer(name, input, shapes[0])\n return layerloop(name, out, shapes[1:])",
"title": ""
},
{
"docid": "9b4a08b1e040205e35f4ae416c98388f",
"score": "0.58168435",
"text": "def build_cnn(input_shape, base_depth, depth_growth, stride_2_layers,\n stride_1_layers, kernel_size):\n model = tf.keras.Sequential()\n\n # Configure base layer\n base_layer = Conv2D(\n base_depth,\n kernel_size,\n strides=kernel_size,\n activation='relu',\n padding='same',\n input_shape=input_shape)\n model.add(base_layer)\n model.add(BatchNormalization())\n\n # Depthwise separable convolution sequence\n for i in range(stride_2_layers):\n depth = int(base_depth * depth_growth**i)\n model.add(\n SeparableConv2D(\n depth, kernel_size, strides=2, activation='relu', padding='same'))\n model.add(BatchNormalization())\n for _ in range(stride_1_layers):\n model.add(\n SeparableConv2D(\n depth, kernel_size, strides=1, activation='relu', padding='same'))\n model.add(BatchNormalization())\n\n # Spatial Pooling\n model.add(GlobalAveragePooling2D())\n\n return model",
"title": ""
},
{
"docid": "88e8decd6e2b7e553c141fb058ea789e",
"score": "0.5814399",
"text": "def add_layer(self, in_size, out_size, input_w=None, activation_function=None):\n new_layer = Layer(in_size, out_size, input_w, activation_function)\n self.__layers.append(new_layer)\n return",
"title": ""
},
{
"docid": "353b96e059a56aa7c7fe8f41d6770e7d",
"score": "0.5814152",
"text": "def inject_additional_input(self, layer, inputs, name):\n inputs = common.to_float(inputs)\n layer_shape = self.shape_list(layer)\n emb = layers.encode_to_shape(inputs, layer_shape, name)\n layer = tf.concat(values=[layer, emb], axis=-1)\n return layer",
"title": ""
},
{
"docid": "aa13a109b2c10dcacc3196988efab161",
"score": "0.58094865",
"text": "def build_module(spec, inputs, channels, is_training):\n num_vertices = np.shape(spec.matrix)[0]\n\n if spec.data_format == 'channels_last':\n channel_axis = 3\n elif spec.data_format == 'channels_first':\n channel_axis = 1\n else:\n raise ValueError('invalid data_format')\n\n input_channels = inputs.get_shape()[channel_axis].value\n # vertex_channels[i] = number of output channels of vertex i\n vertex_channels = compute_vertex_channels(\n input_channels, channels, spec.matrix)\n\n # Construct tensors from input forward\n tensors = [tf.identity(inputs, name='input')]\n\n final_concat_in = []\n for t in range(1, num_vertices - 1):\n with tf.variable_scope('vertex_{}'.format(t)):\n # Create interior connections, truncating if necessary\n add_in = [truncate(tensors[src], vertex_channels[t], spec.data_format)\n for src in range(1, t) if spec.matrix[src, t]]\n\n # Create add connection from projected input\n if spec.matrix[0, t]:\n add_in.append(projection(\n tensors[0],\n vertex_channels[t],\n is_training,\n spec.data_format))\n\n if len(add_in) == 1:\n vertex_input = add_in[0]\n else:\n vertex_input = tf.add_n(add_in)\n\n # Perform op at vertex t\n op = base_ops.OP_MAP[spec.ops[t]](\n is_training=is_training,\n data_format=spec.data_format)\n vertex_value = op.build(vertex_input, vertex_channels[t])\n\n tensors.append(vertex_value)\n if spec.matrix[t, num_vertices - 1]:\n final_concat_in.append(tensors[t])\n\n # Construct final output tensor by concating all fan-in and adding input.\n if not final_concat_in:\n # No interior vertices, input directly connected to output\n assert spec.matrix[0, num_vertices - 1]\n with tf.variable_scope('output'):\n outputs = projection(\n tensors[0],\n channels,\n is_training,\n spec.data_format)\n\n else:\n if len(final_concat_in) == 1:\n outputs = final_concat_in[0]\n else:\n outputs = tf.concat(final_concat_in, channel_axis)\n\n if spec.matrix[0, num_vertices - 1]:\n outputs += projection(\n tensors[0],\n channels,\n is_training,\n spec.data_format)\n\n outputs = tf.identity(outputs, name='output')\n return outputs",
"title": ""
},
{
"docid": "cc6a28f1faac769ab474c563bb17812a",
"score": "0.5796551",
"text": "def build_model(self, input_shape):\n # build network topology\n model = keras.Sequential()\n\n # 1st conv layer\n model.add(keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))\n model.add(keras.layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same'))\n model.add(keras.layers.BatchNormalization())\n\n # 2nd conv layer\n model.add(keras.layers.Conv2D(32, (3, 3), activation='relu'))\n model.add(keras.layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same'))\n model.add(keras.layers.BatchNormalization())\n\n # 3rd conv layer\n model.add(keras.layers.Conv2D(32, (2, 2), activation='relu'))\n model.add(keras.layers.MaxPooling2D((2, 2), strides=(2, 2), padding='same'))\n model.add(keras.layers.BatchNormalization())\n\n # flatten output and feed it into dense layer\n model.add(keras.layers.Flatten())\n model.add(keras.layers.Dense(64, activation='relu'))\n model.add(keras.layers.Dropout(0.2))\n\n # output layer\n model.add(keras.layers.Dense(2, activation='softmax'))\n\n optimizer = keras.optimizers.Adam(learning_rate=self.config[\"model\"][\"learning_rate\"])\n model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n model.summary()\n return model",
"title": ""
},
{
"docid": "74ab23e5d868f2ce60cfd248a9f081d4",
"score": "0.5796425",
"text": "def buildModel(self):\n self.hiddenLayerPremise = LSTMLayer(self.dimInput, self.dimHidden,\n self.dimEmbedding, \"premiseLayer\",\n self.dropoutMode, self.initializer)\n\n # Need to make sure not differentiating with respect to Wcat of premise\n # May want to find cleaner way to deal with this later\n del self.hiddenLayerPremise.params[\"weightsCat_premiseLayer\"]\n del self.hiddenLayerPremise.params[\"biasCat_premiseLayer\"]\n\n self.hiddenLayerHypothesis = LSTMLayer(self.dimInput, self.dimHidden,\n self.dimEmbedding, \"hypothesisLayer\",\n self.dropoutMode, self.initializer)\n\n # TODO: add above layers to self.layers\n self.layers.extend((self.hiddenLayerPremise, self.hiddenLayerHypothesis))",
"title": ""
},
{
"docid": "5fb2d32738c1dd415915c4d65d918080",
"score": "0.57877713",
"text": "def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):\n # Adding a name scope ensures logical grouping of the layers in the graph.\n with tf.name_scope(layer_name):\n # This Variable will hold the state of the weights for the layer\n with tf.name_scope(\"weights\"):\n weights = weight_variable([input_dim, output_dim])\n with tf.name_scope(\"biases\"):\n biases = bias_variable([output_dim])\n with tf.name_scope(\"Wx_plus_b\"):\n preactivate = tf.matmul(input_tensor, weights) + biases\n\n activations = act(preactivate)\n return activations",
"title": ""
},
{
"docid": "f6dd078d554efc076d621924e97d1f93",
"score": "0.5787049",
"text": "def __init__(self,\n in_dim,\n layer_name=\"Highway Layer\",\n W_h=None,\n W_t=None,\n bias_h=None,\n bias_t=None,\n gate_bias=-5,\n use_bias=True,\n **kwargs):\n \n self.in_dim=in_dim;\n self.out_dim=in_dim;\n self.W_h=W_h;\n self.W_t=W_t;\n self.bias_h=bias_h;\n self.bias_t=bias_t;\n self.gate_bias=gate_bias;\n self.use_bias=use_bias;\n \n self.initialize();",
"title": ""
},
{
"docid": "b91358bf6acebab4db4de99f1a807d66",
"score": "0.578618",
"text": "def make_initial_layer(self):\n\n layer = nn.ModuleList([nn.ModuleList([]) for i in range(self.ns)]) # stores the strided convolutions\n\n for scale in range(self.ns):\n if scale == 0:\n \"\"\" first scale is produced by a convolution with stride of 0 \"\"\"\n conv = nn.Conv2d(\n in_channels = self.n_channels_in,\n out_channels = self.k0 * self.gr ** scale,\n kernel_size = (3,3),\n stride = 1,\n padding = 1\n )\n\n\n bn = nn.BatchNorm2d(conv.out_channels)\n\n else:\n \"\"\" subsequent scales are produced by convolutions with scale 'self.sf ** i' \"\"\"\n prev_out_channels = layer[scale - 1][0][-1].num_features\n conv = nn.Conv2d(\n in_channels = prev_out_channels,\n out_channels = self.k0 * self.gr ** scale,\n kernel_size = (3,3),\n padding = 1,\n stride = self.sf #** scale\n )\n\n bn = nn.BatchNorm2d(conv.out_channels)\n\n operations = nn.ModuleList([conv, bn])\n layer[scale].append(operations)\n return layer",
"title": ""
},
{
"docid": "c4a42d39cf5c2c2dea0726a4b830d04d",
"score": "0.5781898",
"text": "def convolution_block(net, input_layer, base_name, layers, k=3, pad=1,\\\n planes=(64,64,64), lr_mult=1, decay_mult=1, reverse=False):\n if reverse:\n range_ = range(1, layers + 1)[::-1]\n else:\n range_ = range(1, layers + 1)\n\n for idx, i in enumerate(range_):\n if idx == 0:\n in_ = input_layer\n conv, bn, scale, relu = convolution_unit(in_, k, pad, planes[3-i], lr_mult=lr_mult, decay_mult=decay_mult)\n name = base_name.format(i)\n net[name] = conv\n net[name + \"_bn\"] = bn\n net[name + \"_scale\"] = scale\n net[name + \"_relu\"] = relu\n in_ = conv",
"title": ""
},
{
"docid": "9b6e8f647636350200267e5a4c30e0ba",
"score": "0.57759506",
"text": "def _create_hidden_layer(self, input, name, out_size, activation_fn=None):\n\n with tf.name_scope(name):\n in_size = input.get_shape().as_list()[1]\n W = tf.Variable(\n tf.truncated_normal((in_size, out_size),\n stddev=1.0 / math.sqrt(float(in_size))),\n name='Weights')\n biases = tf.Variable(tf.zeros([out_size]), name='biases')\n h_layer = tf.matmul(input, W) + biases\n\n if activation_fn:\n h_layer = activation_fn(h_layer)\n\n return h_layer",
"title": ""
},
{
"docid": "be2f377af43a41ab729f56b0bcb82d5a",
"score": "0.5774955",
"text": "def build_network(x, y):\n nn = tf.layers.dense(x, x.shape[1], tf.nn.tanh)\n nn = tf.layers.dense(nn, 23, tf.nn.tanh)\n nn = tf.layers.dense(nn, 28, tf.nn.tanh)\n nn = tf.layers.dense(nn, 28, tf.nn.tanh)\n nn = tf.layers.dense(nn, 28, tf.nn.tanh)\n nn = tf.layers.dense(nn, 28, tf.nn.tanh)\n\n return tf.layers.dense(nn, y.shape[1], tf.nn.tanh, name='Output_layer')",
"title": ""
},
{
"docid": "8a5385b59f5f41b319a36ddad30a2520",
"score": "0.57744795",
"text": "def build(self, input_shape):\n self.W = self.add_weight(\n shape=(input_shape[-1], ),\n initializer=\"glorot_uniform\",\n # initializer=\"random_normal\",\n # initializer=\"zeros\",\n regularizer=custom_regularizer_to_zero,\n # constraint=tensorflow.keras.constraints.MinMaxNorm(min_value=-1.01, max_value=1.01, rate=0.00001, axis=0),\n trainable=True\n )\n\n # print('build, input_shape: ', input_shape)\n # print('build, self.W.shape:', self.W.shape)\n # print(self.W)\n\n super(CustomContextLayer, self).build(input_shape)",
"title": ""
},
{
"docid": "dd7264c9da7c068d3e5c0ef49a1cd06c",
"score": "0.57731974",
"text": "def get_conform_to_output_layer_by_lod(self, input_lod) -> tfkl.Layer:",
"title": ""
},
{
"docid": "6176094869846cf011073fa0ba656fd2",
"score": "0.57651687",
"text": "def construct_network(self):\n self._encoder(\"encoder\")\n self._decoder(\"decoder\")\n\n self.custom_objects = {'mean_activation': ACTIVATIONS['mean_activation'],\n 'disp_activation': ACTIVATIONS['disp_activation'],\n 'ColwiseMultLayer': LAYERS['ColWiseMultLayer'],\n 'FirstLayer': LAYERS['FirstLayer']}\n\n # Building the model via calling it with a random input\n input_arr = [tf.random.uniform((1, self.x_dim)), tf.ones((1, self.n_conditions)),\n tf.ones((1, self.n_conditions))]\n self(input_arr)\n\n get_custom_objects().update(self.custom_objects)\n print(f\"{self.class_name}'s network has been successfully constructed!\")",
"title": ""
},
{
"docid": "4c635300e2542177aea381be81339225",
"score": "0.57583505",
"text": "def create_conv_layer(input_, n_in, n_out, name='conv', bias=True,\n norm=False):\n with tf.variable_scope(name):\n W = weight_variable([3, 1, n_in, n_out])\n if bias:\n b = bias_variable([n_out])\n\n conv = conv2d(input_, W)\n if bias:\n conv = conv + b\n if norm:\n relu = tf.nn.relu(batch_norm(conv, n_out), name='relu')\n else:\n relu = tf.nn.relu(conv, name='relu')\n return W, relu",
"title": ""
},
{
"docid": "76d7f40c91b71c0ab879a60343e37b76",
"score": "0.5756249",
"text": "def build_model(self):\n # construct CNN model\n self.CNN = self.build_base_model()\n inputs = keras.layers.Input(shape=self._input_shape, name='x')\n x = self.CNN(inputs)\n x = keras.layers.Flatten()(x)\n\n # number of output heads\n outputs = []\n for i in range(self._heads):\n name = \"z_head%d\" % i\n outputs.append(keras.layers.Dense(self._z_dimension,\n activation='softmax',\n name=name)(x))\n # add aux layer\n if self._aux_cluster:\n name = \"aux_cluster_layer\"\n outputs.append(keras.layers.Dense(self._z_dimension*3,\n activation='softmax',\n name=name)(x))\n \n self._model = keras.models.Model([inputs], outputs, name='IIC')\n optimizer = keras.optimizers.Adam(lr=1e-3)\n self._model.compile(optimizer=optimizer, loss=self.mi_loss)\n self._model.summary()",
"title": ""
},
{
"docid": "afba6f7a8a24733b89104f4127fabfa0",
"score": "0.5756116",
"text": "def build(self, input_shape):\n self.current_iteration = 0\n self.map = self.add_weight(name='map',\n shape=(self.m * self.n, input_shape[1]),\n initializer=self.initializer,\n trainable=False)\n super(SOMLayer, self).build(input_shape)",
"title": ""
},
{
"docid": "5df812fd153e8abc34228ac43c9cb164",
"score": "0.57491773",
"text": "def build_model(input_shape: tuple, output_shape: tuple):\n n_layers = 2\n loss = 'mse'\n optimizer = 'adam'\n n_actions = max(output_shape)\n input_layer = Input(shape=input_shape)\n fc = Dense(60, activation='relu')(input_layer)\n for i in range(n_layers):\n fc = Dense(40, activation='relu')(fc)\n final = Dense(n_actions)(fc)\n model = Model(inputs=input_layer, outputs=final)\n model.compile(optimizer=optimizer, loss=loss)\n return model",
"title": ""
},
{
"docid": "b11281c931de34a2ca187946c58b2d10",
"score": "0.5743315",
"text": "def _nnLayer(self,\n input_tensor,\n input_dim,\n output_dim,\n layer_name,\n set_dropout=False,\n act=tf.nn.relu):\n # Adding a name scope ensures logical grouping of the layers in the\n # graph.\n with tf.name_scope(layer_name):\n # This Variable will hold the state of the weights for the layer\n with tf.name_scope('weights'):\n weights = self._weightVariable([input_dim, output_dim])\n with tf.name_scope('biases'):\n biases = self._biasVariable([output_dim])\n with tf.name_scope('ridge_transform'):\n preactivate = tf.matmul(input_tensor, weights) + biases\n activations = act(preactivate, name='activation')\n if (self._dropout) & (set_dropout):\n return tf.nn.dropout(activations, self._dropout_pl)\n else:\n return activations",
"title": ""
},
{
"docid": "4df9a05f09794e858b42738c1a015926",
"score": "0.57424384",
"text": "def layer(\n fn: Callable = None,\n n_in: int = None,\n n_out: int = None,\n inputs: Union[str, Tuple[str, ...], List[str]] = None,\n outputs: Union[str, Tuple[str, ...], List[str]] = None,\n):\n # pylint: disable=protected-access,invalid-name\n def _create_layer_class(fn: Callable) -> Type[Layer]:\n \"\"\"Decorator that creates a Layer constructor.\"\"\"\n parameters = inspect.signature(fn).parameters\n signature = inspect.Signature([param for key, param in parameters.items() if key not in {\"tensors\", \"mode\"}])\n\n # Check parameters\n if list(parameters.keys())[0] != \"tensors\":\n raise TypeError(f\"'tensors' should be the first parameter of {fn.__name__}\")\n if \"mode\" in parameters:\n if list(parameters.keys())[1] != \"mode\":\n raise TypeError(f\"'mode' should be the second parameter of {fn.__name__}\")\n\n @functools.wraps(fn)\n def _init(self, *args, **kwargs):\n _n_in = kwargs.pop(\"n_in\") if \"n_in\" in kwargs else n_in\n _n_out = kwargs.pop(\"n_out\") if \"n_out\" in kwargs else n_out\n _inputs = kwargs.pop(\"inputs\") if \"inputs\" in kwargs else inputs\n _outputs = kwargs.pop(\"outputs\") if \"outputs\" in kwargs else outputs\n _name = kwargs.pop(\"name\") if \"name\" in kwargs else None\n Layer.__init__(self, n_in=_n_in, n_out=_n_out, inputs=_inputs, outputs=_outputs, name=_name)\n signature.bind(*args, **kwargs)\n self._args = args\n self._kwargs = kwargs\n\n if \"mode\" in parameters:\n\n def _forward(self, tensors, mode: str = None):\n return fn(tensors, mode, *self._args, **self._kwargs)\n\n else:\n\n def _forward(self, tensors, mode: str = None):\n # pylint: disable=unused-argument\n return fn(tensors, *self._args, **self._kwargs)\n\n attributes = {\"__module__\": fn.__module__, \"__doc__\": fn.__doc__, \"__init__\": _init, \"forward\": _forward}\n return type(fn.__name__, (Layer,), attributes)\n\n if fn is not None:\n return _create_layer_class(fn)\n else:\n return _create_layer_class",
"title": ""
},
{
"docid": "a65cc50f2af1ff3c50419159d27e3ecb",
"score": "0.5741774",
"text": "def build(self, input_shape):\r\n super(Conv3D, self).build(input_shape)\r\n\r\n # TODO(b/177662019): tf.nn.conv3d with depthwise kernels on CPU\r\n # in eager mode may produce incorrect output or cause a segfault.\r\n # To avoid this issue, compile the op to TF graph using tf.function.\r\n self._convolution_op = tf.function(\r\n self._convolution_op, experimental_compile=True)",
"title": ""
},
{
"docid": "708bc55ac6a5ebd5453f2e30ffe49562",
"score": "0.5741465",
"text": "def create_fully_connected_layer(input_, n_in, n_out=1, relu=False,\n name='fully_connected'):\n with tf.variable_scope(name):\n flat = tf.reshape(input_, [-1, n_in])\n W = weight_variable([n_in, n_out])\n b = bias_variable([n_out])\n\n h_fc = tf.matmul(flat, W) + b\n if relu:\n h_fc = tf.nn.relu(h_fc, name='relu')\n return W, h_fc",
"title": ""
},
{
"docid": "69dcad453c03a21aad29a5586b20ccb2",
"score": "0.5740355",
"text": "def _add_projection_network(model, from_layer, channels):\n # get all layers to process on\n layers = [layer for layer in model.original_layers if not isinstance(layer, (LeakyReLU, Dropout))]\n\n def proj_func(input, layer: Layer):\n if isinstance(layer, (Dense, Conv2D)):\n input = tf.nn.relu(input)\n # input = input - layer.bias # biased?\n output = tf.gradients(layer.output, layer.input, grad_ys=input)\n return output\n\n from_idx = layers.index(from_layer)\n to_idx = layers.index(model.get_layer('my_input'))\n x = from_layer.output\n if channels is not None and channels != []:\n new_layer = Lambda(lambda x: tf.gather(x, channels, axis=-1), name='gather_channel')\n x = new_layer(x)\n from_idx += 1\n layers[from_idx] = new_layer\n\n for idx in range(from_idx, to_idx, -1):\n new_layer = Lambda(proj_func, arguments={'layer': layers[idx]}, name=layers[idx-1].name+'_proj')\n x = new_layer(x)\n layers[idx-1].related_projection = new_layer",
"title": ""
},
{
"docid": "472cab901183192496d1cccd4c48d746",
"score": "0.5739241",
"text": "def _gen_cnn_network_increase(\n self,\n input_dim: Tuple[int, ...] = None,\n output_dim: Tuple[int, ...] = None,\n activation: str = None,\n name: str = None,\n **kwargs,\n ) -> dict:\n\n\n assert type(input_dim) == tuple\n assert type(output_dim) == tuple\n assert type(activation) == str\n\n layers_list = list()\n ref_dim = input_dim\n channels = input_dim[self.channels_position]\n\n layer_count = 0\n\n while not (sum(ref_dim[2:]) >= int(sum(output_dim[2:]) / self.multiplier)):\n layer = self._gen_cnn_layer_increase_dimensionality(channels_in=channels)\n\n channels = layer[\"out_channels\"]\n\n layers_list.append(layer)\n\n ref_dim = self._multiply_cnn_dims(ref_dim)\n\n layer_count += 1\n\n layer = self._gen_cnn_layer_increase_dimensionality(\n channels_in=channels, channels_out=self.channels\n )\n layers_list.append(layer)\n\n config_dict = {\n \"layers\": layers_list,\n \"activations\": activation,\n \"case\": self.dim,\n \"name\": name,\n }\n\n config_dict.update(kwargs)\n\n return config_dict",
"title": ""
},
{
"docid": "eb642caabd993ba17ac8b6b67979b8b5",
"score": "0.57375914",
"text": "def config(self, length, layers, optimizer, loss, activation, compile=True, verbose=False):\n\n # Input layer\n input_layer = Input(shape=(length[0], length[1] + length[2]), name='input')\n input_num = length[0] * (length[1] + length[2])\n output_num = length[3] * (length[1] + length[2])\n\n # flatten layer\n current_input = Flatten(name='flatten')(input_layer)\n\n # Dense layer(s)\n dense_id = 1\n dropout_id = 1\n hidden_num = math.floor((input_num + output_num) / 2)\n\n for layer in layers:\n current_input = Dense(hidden_num, name='dense_' + str(dense_id),\n activation=layer['activation'].lower())(current_input)\n dense_id += 1\n\n if layer['dropout'] > 0:\n current_input = Dropout(layer['dropout'], name='dropout_' + str(dropout_id))(current_input)\n dropout_id += 1\n\n # Last layer\n current_input = Dense(output_num, name='dense_' + str(dense_id),\n activation=activation.lower())(current_input)\n\n # Output layer (Reshape)\n output_layer = Reshape((length[3], length[1] + length[2]), input_shape=(output_num,),\n name='output')(current_input)\n\n # Create model\n self.model = Model(inputs=input_layer, outputs=output_layer)\n if compile:\n self.model.compile(optimizer.lower(), loss.lower())\n\n # Print summary to console\n if verbose:\n self.model.summary()\n\n return self.model",
"title": ""
},
{
"docid": "b545c844f85874b3084b8b8dc20c6733",
"score": "0.57280624",
"text": "def call(self, inputs):\n\t\tlayer_1_out = self.layer_1(inputs)\n\t\tlayer_2_out = self.layer_2(layer_1_out)\n\t\treturn layer_2_out",
"title": ""
},
{
"docid": "7b6205633fb3f8acdefa10077c633b10",
"score": "0.5727607",
"text": "def build_pl(mid_size,\n drate,\n filters):\n\n layers = list()\n for layer_id, filters_ in enumerate(filters):\n if layer_id == 0: # Input layer\n layers.append(nn.Conv3d(mid_size, filters_,\n kernel_size=3,\n padding=1))\n layers.append(nn.Dropout(drate))\n layers.append(nn.LeakyReLU(negative_slope=0.1))\n elif layer_id < len(filters) - 1:\n layers.append(nn.Conv3d(previous, filters_,\n kernel_size=3,\n padding=1))\n layers.append(nn.Dropout(drate))\n layers.append(nn.LeakyReLU(negative_slope=0.1))\n else:\n layers.append(nn.Conv3d(previous, filters_,\n kernel_size=3,\n padding=1))\n previous = filters_\n return nn.Sequential(*layers)",
"title": ""
},
{
"docid": "12ed4fbbf9b511ff52d77c40b85ae2d4",
"score": "0.5724175",
"text": "def connect_layers(input_layer, output_layer, weights, i_s, j_s, i_e, j_e,\n k_out, stdp=False, initial_weight=0, label_dicts=None):\n m = input_layer.shape[1]\n view_elements = []\n i = i_s\n while i < i_e:\n j = j_s\n while j < j_e:\n view_elements.append(m * i + j)\n j += 1\n i += 1\n\n if stdp:\n w_max = initial_weight * 15\n stdp_shared = sim.native_synapse_type('stdp_synapse')\\\n (Wmax=w_max * 1000, mu_plus=0.0, mu_minus=1.0)\n proj = sim.Projection(input_layer.population[view_elements],\n output_layer.population[[k_out]],\n sim.AllToAllConnector(), stdp_shared)\n ol = int(output_layer.population.label)\n il = input_layer.population.label\n out_neuron = output_layer.population[k_out]\n if label_dicts == None:\n for i in range(len(view_elements)):\n label = '{}_{}_{}'.format(ol, il, i)\n in_neuron = input_layer.population[view_elements[i]]\n conn = nest.GetConnections(source=[in_neuron],\n target=[out_neuron])\n nest.SetStatus(conn, {'label': label, 'weight': weights[i][0]})\n else:\n for i in range(len(view_elements)):\n label = '{}_{}_{}'.format(ol, il, i)\n if not label in label_dicts[ol]:\n label_dicts[ol][label] = ([], [])\n in_neuron = input_layer.population[view_elements[i]]\n label_dicts[ol][label][0].append(in_neuron)\n label_dicts[ol][label][1].append(out_neuron)\n else:\n proj = sim.Projection(input_layer.population[view_elements],\n output_layer.population[[k_out]],\n sim.AllToAllConnector(),\n sim.StaticSynapse(weight=weights))\n return proj",
"title": ""
},
{
"docid": "822dca9328b4d21f8c3ab951ad1d88d5",
"score": "0.57209414",
"text": "def build_model(data_tensor, reuse, training, output_shape):\n if isinstance(output_shape, list):\n output_shape = output_shape[0]\n with tf.variable_scope('cnn', reuse=reuse):\n with tf.variable_scope('input', reuse=reuse):\n x = tf.layers.conv2d(\n inputs=data_tensor,\n filters=24,\n kernel_size=11,\n name='l0',\n strides=(1, 1),\n padding='same',\n activation=tf.nn.relu,\n trainable=training,\n use_bias=True)\n layer_hgru = hgru.hGRU(\n 'hgru_1',\n x_shape=x.get_shape().as_list(),\n timesteps=8,\n h_ext=15,\n strides=[1, 1, 1, 1],\n padding='SAME',\n aux={'reuse': False, 'constrain': False},\n train=training)\n h2 = layer_hgru.build(x)\n h2 = normalization.batch(\n bottom=h2,\n renorm=True,\n name='hgru_bn',\n training=training)\n\n with tf.variable_scope('readout_1', reuse=reuse):\n activity = conv.conv_layer(\n bottom=h2,\n name='readout_conv',\n num_filters=1,\n kernel_size=1,\n trainable=training,\n use_bias=True)\n extra_activities = {\n 'activity': h2\n }\n return activity, extra_activities",
"title": ""
},
{
"docid": "49b5cb5d7b67d529f45b233e2c6c5982",
"score": "0.5717752",
"text": "def _make_layer(self, block, out_channels, num_blocks, stride):\n\n # we have num_block blocks per layer, the first block \n # could be 1 or 2, other blocks would always be 1\n strides = [stride] + [1] * (num_blocks - 1)\n layers = []\n for stride in strides:\n layers.append(block(self.in_channels, out_channels, stride))\n self.in_channels = out_channels * block.expansion\n \n return nn.Sequential(*layers)",
"title": ""
},
{
"docid": "36f98fd98e16c95918658b6b2691ac3d",
"score": "0.5717057",
"text": "def build(self, input_shape):\n # create the trainable variables for entity embeddings\n if self._has_enough_args_to_build_ent_emb:\n self.ent_emb = self.add_weight(\n \"ent_emb\",\n shape=[self._max_ent_size_internal, self.k],\n initializer=self.ent_init,\n regularizer=self.ent_regularizer,\n dtype=tf.float32,\n trainable=True,\n )\n\n if self.ent_partition is not None:\n paddings_ent = [\n [\n 0,\n self._max_ent_size_internal\n - self.ent_partition.shape[0],\n ],\n [0, 0],\n ]\n self.ent_emb.assign(\n np.pad(\n self.ent_partition,\n paddings_ent,\n \"constant\",\n constant_values=0,\n )\n )\n del self.ent_partition\n self.ent_partition = None\n\n else:\n raise TypeError(\n \"Not enough arguments to build Encoding Layer. Please set max_ent_size property.\"\n )\n\n # create the trainable variables for relation embeddings\n if self._has_enough_args_to_build_rel_emb:\n self.rel_emb = self.add_weight(\n \"rel_emb\",\n shape=[self._max_rel_size_internal, self.k],\n initializer=self.rel_init,\n regularizer=self.rel_regularizer,\n dtype=tf.float32,\n trainable=True,\n )\n\n if self.rel_partition is not None:\n paddings_rel = [\n [\n 0,\n self._max_rel_size_internal\n - self.rel_partition.shape[0],\n ],\n [0, 0],\n ]\n self.rel_emb.assign(\n np.pad(\n self.rel_partition,\n paddings_rel,\n \"constant\",\n constant_values=0,\n )\n )\n del self.rel_partition\n self.rel_partition = None\n else:\n raise TypeError(\n \"Not enough arguments to build Encoding Layer. Please set max_rel_size property.\"\n )\n\n self.built = True",
"title": ""
},
{
"docid": "0e2bd418b51ebe15bd16832be86519de",
"score": "0.5712064",
"text": "def __call__(\n self, input_layer, activation_fn=None, bias=tf.zeros_initializer(), phase=prettytensor.Phase.train,\n parameter_modifier=parameters.identity, name=PROVIDED\n ):\n\n if input_layer.get_shape().ndims != 4:\n raise ValueError(\n 'pixel_bias requires a rank 4 Tensor with known second '\n 'dimension: %s' % input_layer.get_shape())\n if input_layer.shape[1] is None or input_layer.shape[2] is None or input_layer.shape[3] is None:\n raise ValueError('input size must be known.')\n\n x = input_layer.tensor\n dtype = input_layer.dtype\n books = input_layer.bookkeeper\n b = parameter_modifier(\n 'bias',\n self.variable('bias', input_layer.shape[2:], bias, dt=dtype),\n phase)\n y = x + tf.expand_dims(b, axis=0)\n\n if activation_fn is not None:\n if not isinstance(activation_fn, collections.Sequence):\n activation_fn = (activation_fn,)\n y = layers.apply_activation(books,\n y,\n activation_fn[0],\n activation_args=activation_fn[1:])\n books.add_histogram_summary(y, '%s/activations' % y.op.name)\n\n return input_layer.with_tensor(y, parameters=self.vars)",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="785"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">4c9e79ec6f0453fbd09830ae83effa3f</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Return a dict description of the Operation</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "28710bff765d22500247aae734742e28",
"score": "0.5967061",
"text": "def describe(self) -> dict:\n\n description = super().describe()\n\n description.update(\n {\n \"args\": {\"keys\": self.keys}\n }\n )\n\n return description",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "edb73633f6a56bc0d0a4cd54c806823b",
"score": "0.84622484",
"text": "def describe(self) -> dict:\n\n return {\n \"operation\": type(self).__name__\n }",
"title": ""
},
{
"docid": "084ed9cb0338e34f3fc6cb6189dc59d2",
"score": "0.73279786",
"text": "def get_operation(self):\n\n operation = OrderedDict(\n tags=self._get_tags(),\n summary=self._get_summary(),\n description=self._get_description(),\n parameters=self._get_parameters(),\n produces=self._get_produces(),\n consumes=self._get_consumes(),\n responses=self.responses,\n security=self._get_security()\n )\n\n # TODO: SECURITY OBJECT SECURITY DEFINITIONS\n for key, value in list(operation.items()):\n # Remove empty keys\n if not value:\n operation.pop(key)\n\n return operation",
"title": ""
},
{
"docid": "86c47411ec317a4e11d3a241c374f97f",
"score": "0.6960272",
"text": "def ifOperStatus_description(self):",
"title": ""
},
{
"docid": "8e55d2c9d73c4a090153130d34078185",
"score": "0.6799102",
"text": "def get_operation(self) -> str:\n return self.__op",
"title": ""
},
{
"docid": "03c386bd7571ba5005adf54eaf2da512",
"score": "0.64994544",
"text": "def op_name(self) -> str:",
"title": ""
},
{
"docid": "02a96d27d36c41df34d3429c65d03250",
"score": "0.6483325",
"text": "def get_operation_data_mapping(self) -> Dict[str, OperationData]:\n pass",
"title": ""
},
{
"docid": "aff65bcaed38649268fa102b876924d2",
"score": "0.644737",
"text": "def operation_status_info(self):\n return self._location.operation_status_info",
"title": ""
},
{
"docid": "c99ac43d19b112f896d77e2f254615ed",
"score": "0.6438134",
"text": "def operation(self):\n if not (operation := self._get_operation()):\n return None\n return operation.name",
"title": ""
},
{
"docid": "2d08bdefcef1e4b4d0841f743e5c711a",
"score": "0.640963",
"text": "def parse(self):\n self.get_device_target_filename()\n self.get_framework_summary()\n self.get_cpu_op_detail_info()\n self.get_activity_op_info()\n if isinstance(self.op_names, str):\n self.combine_performance_data(self.op_names)\n elif isinstance(self.op_names, list):\n for op_name in self.op_names:\n self.combine_performance_data(op_name)\n self.operation_info[\"device_id\"] = self._dev_id\n return json.dumps(self.operation_info)",
"title": ""
},
{
"docid": "0ed9223f535ff66642906c9caf1b7f13",
"score": "0.6353754",
"text": "def fresh_operation(op_id):\n operation = {'path': '', 'headers': {}, 'header_params': {},\n 'path_params': {}, 'query_params': {}, 'params': {},\n 'files': None, 'form_data': None, 'json': None, 'id': op_id,\n 'dl_path': None, 'auth_settings': 'access_token'}\n\n return operation",
"title": ""
},
{
"docid": "e701a3027c6070a8e98b8060983b5a5e",
"score": "0.63516676",
"text": "def operation_list(self):\n return self.operations",
"title": ""
},
{
"docid": "0079c22e7ac4754220a852770cc32bab",
"score": "0.6249494",
"text": "def operation(self) -> pulumi.Input['OperationType']:\n return pulumi.get(self, \"operation\")",
"title": ""
},
{
"docid": "d57b8a57f3ef4a21daeb2676080e2056",
"score": "0.6249248",
"text": "def show(self):\n print '\\n> ' + self.name\n print 'ID: ' + str(self.id)\n \n print 'Operations:'\n for idx, op in enumerate(self.operations):\n print ' ' + op + '(' + str(idx) + ')'",
"title": ""
},
{
"docid": "7af14efbfa3e7fcef4ed546a95f679f7",
"score": "0.6232654",
"text": "def _get_info(self):\n return {\"Control Parameters\": self.ControlParameters, \"Actions\": self.action_space}",
"title": ""
},
{
"docid": "c133d3c64d4ae2a6d7689f5cdb03ccbc",
"score": "0.6226585",
"text": "def get_description():\n desc = dict()\n desc['data'] = True\n desc['report'] = True\n desc['description'] = \"\"\" \"\"\"\n desc['arguments'] = [\n dict(type='station', name='station', default='IATDSM',\n label='Select Station', network='IACLIMATE'),\n ]\n return desc",
"title": ""
},
{
"docid": "5672fc34b2c22060c856f24002b368ec",
"score": "0.6213066",
"text": "def operation(self): # pragma: no cover\n\n pass",
"title": ""
},
{
"docid": "96c5bf8fb83e1326315f7818554550e4",
"score": "0.6198662",
"text": "def spec(self):\n return {'TyName' : self.name,\n 'TyOps' : [tc.spec() for tc in self.ops ] }",
"title": ""
},
{
"docid": "ca6f27ba892fbf48192966a53e32535b",
"score": "0.61944467",
"text": "def GetOperationResourceSpec():\n return concepts.ResourceSpec(\n 'managedidentities.projects.locations.global.operations',\n resource_name='operation',\n disable_auto_completers=False,\n projectsId=concepts.DEFAULT_PROJECT_ATTRIBUTE_CONFIG,\n operationsId=OperationAttributeConfig(),\n )",
"title": ""
},
{
"docid": "2819feb97f090681f5ac343c8c4663e6",
"score": "0.61844754",
"text": "def operations(self, raw):\n return self.operations_list",
"title": ""
},
{
"docid": "dedc0991fe54b0a0c16c117a6ea20f70",
"score": "0.6169841",
"text": "def _get_ops(self):\n graph = self._graph\n props_and_names = {\n 'energy': 'Output/Energy/energy:0',\n 'forces': 'Output/Forces/forces:0',\n 'stress': 'Output/Stress/Voigt/stress:0',\n }\n ops = {}\n for prop, name in props_and_names.items():\n try:\n ops[prop] = graph.get_tensor_by_name(name)\n except KeyError:\n continue\n self._predict_properties = list(ops.keys())\n return ops",
"title": ""
},
{
"docid": "77de23d5c5f611c9806e44002b87110b",
"score": "0.61418074",
"text": "def get_description():\n desc = {\"description\": __doc__, \"data\": True, \"cache\": 86400}\n desc[\"arguments\"] = [\n dict(\n type=\"zstation\",\n name=\"zstation\",\n default=\"AMW\",\n label=\"Select Station:\",\n network=\"IA_ASOS\",\n ),\n {\n \"type\": \"select\",\n \"options\": PDICT2,\n \"default\": \"tmpf\",\n \"label\": \"Select Variable\",\n \"name\": \"v\",\n },\n dict(type=\"int\", name=\"hours\", label=\"Number of Hours:\", default=24),\n dict(\n type=\"select\",\n name=\"month\",\n default=\"all\",\n label=\"Month Limiter\",\n options=MDICT2,\n ),\n dict(\n type=\"select\",\n name=\"dir\",\n default=\"warm\",\n label=\"Direction:\",\n options=MDICT,\n ),\n dict(\n type=\"select\",\n name=\"how\",\n default=\"exact\",\n label=\"How to compute change over given time window:\",\n options=PDICT,\n ),\n ]\n return desc",
"title": ""
},
{
"docid": "fd47f35d13fca4777875fe53ae5f6188",
"score": "0.6137055",
"text": "def _DisplayOps(self, name, operations):\n def _DisplayExtents(extents, name):\n \"\"\"Show information about extents.\"\"\"\n num_blocks = sum([ext.num_blocks for ext in extents])\n ext_str = ' '.join(\n '(%s,%s)' % (ext.start_block, ext.num_blocks) for ext in extents)\n # Make extent list wrap around at 80 chars.\n ext_str = '\\n '.join(textwrap.wrap(ext_str, 74))\n extent_plural = 's' if len(extents) > 1 else ''\n block_plural = 's' if num_blocks > 1 else ''\n print(' %s: %d extent%s (%d block%s)' %\n (name, len(extents), extent_plural, num_blocks, block_plural))\n print(' %s' % ext_str)\n\n op_dict = update_payload.common.OpType.NAMES\n print('%s:' % name)\n for op, op_count in itertools.izip(operations, itertools.count()):\n print(' %d: %s' % (op_count, op_dict[op.type]))\n if op.HasField('data_offset'):\n print(' Data offset: %s' % op.data_offset)\n if op.HasField('data_length'):\n print(' Data length: %s' % op.data_length)\n if op.src_extents:\n _DisplayExtents(op.src_extents, 'Source')\n if op.dst_extents:\n _DisplayExtents(op.dst_extents, 'Destination')",
"title": ""
},
{
"docid": "073c0f4c170f2522b6075d069a01805f",
"score": "0.6132287",
"text": "def GetResult(self, operation):\n return operation",
"title": ""
},
{
"docid": "5b0b1d98ddab6d9f20194c1b5864e27e",
"score": "0.61297846",
"text": "def __str__(self):\n return \"OP: {}; Cliente: {}; Descripción: {}\".format(\n self.order_op_number,\n self.order_client,\n self.order_description)",
"title": ""
},
{
"docid": "6c00fb9018167f78b2b791be868537e3",
"score": "0.6118538",
"text": "def operations(self):\n return self._operations",
"title": ""
},
{
"docid": "579b553c0d797b8132c9c6257b247721",
"score": "0.6109443",
"text": "def operation(self) -> Operation:\n return self.__operation",
"title": ""
},
{
"docid": "0015cc9901a52ca87acd9ae9ca6c66d2",
"score": "0.61022246",
"text": "def running_ops(self):\n if self._is_inference:\n return {\n \"predictions\": self.predictions,\n \"predictions_raw\": self.predictions\n }\n else:\n if self._is_training:\n return {\n \"accuracy\": self.accuracy,\n \"optimizer\": self.optimizer,\n \"label_pred\": self._label_pred,\n \"label_true\": self._label_true,\n \"cross_entropy\": self.cross_entropy\n }\n else:\n return {\n \"accuracy\": self.accuracy,\n \"label_pred\": self._label_pred,\n \"label_true\": self._label_true\n }",
"title": ""
},
{
"docid": "c300db3beadd9531a5a08a0c9b027f84",
"score": "0.61001647",
"text": "def operation_list(self):\r\n return self._operation_list",
"title": ""
},
{
"docid": "25946f55a4e23762df3bf8cb2b7287f7",
"score": "0.6093984",
"text": "def ops_to_run(self):\n if self._ops_to_run is None:\n self._ops_to_run = {}\n assert isinstance(self._ops_to_run, dict), \\\n 'ops to run should be a dictionary'\n return self._ops_to_run",
"title": ""
},
{
"docid": "5b4b0d65c07b61913f9888f91f30655c",
"score": "0.6069525",
"text": "def get_description():\n desc = {\"description\": __doc__, \"data\": True}\n today = datetime.date.today()\n desc[\"arguments\"] = [\n dict(\n type=\"station\",\n name=\"station\",\n default=\"IATDSM\",\n label=\"Select Station:\",\n network=\"IACLIMATE\",\n ),\n dict(\n type=\"month\",\n name=\"month\",\n default=today.month,\n label=\"Select Month:\",\n ),\n dict(\n type=\"select\",\n name=\"dir\",\n default=\"cold\",\n label=\"Select variable to plot:\",\n options=PDICT,\n ),\n ]\n return desc",
"title": ""
},
{
"docid": "0ce54dccd7e70d5d4995136de621756e",
"score": "0.6067061",
"text": "def spec(self):\n\n spec = {'TyOpName': self.name, 'TyOpAr': self.arity}\n if self.infix:\n spec['TyFixity'] = self.infix\n return spec",
"title": ""
},
{
"docid": "8c594a690e0249a3fb9cfba006c35091",
"score": "0.60541373",
"text": "def list_operations(cls):\n return OperationRegistry.registry.keys()",
"title": ""
},
{
"docid": "bd5514c3785a46eb5625a748ca2adf7e",
"score": "0.60454977",
"text": "def get_description():\n desc = {\"description\": __doc__, \"data\": True}\n desc[\"arguments\"] = [\n dict(\n type=\"station\",\n name=\"station\",\n default=\"IATDSM\",\n label=\"Select Station\",\n network=\"IACLIMATE\",\n ),\n dict(\n type=\"select\",\n name=\"var\",\n default=\"high\",\n label=\"Which Daily Variable:\",\n options=PDICT,\n ),\n ]\n return desc",
"title": ""
},
{
"docid": "0c7c14f7c79ed12e177ebd494302a63a",
"score": "0.6038793",
"text": "def OperationAttributeConfig():\n return concepts.ResourceParameterAttributeConfig(\n name='operation',\n help_text='Name of the Managed Microsoft AD operation.',\n )",
"title": ""
},
{
"docid": "b28b0bc6f456b29e878025b5fa682a90",
"score": "0.60299003",
"text": "def getOperation(self):\n # Extracts the operation part of the command:\n op = self.__curr_command.split(SPACE)[OPERATION].strip()\n\n # Classify the command:\n if op in VM_OPERATIONS_2_COMMANDS:\n return VM_OPERATIONS_2_COMMANDS[op]\n\n elif op in VM_OPERATIONS_2_ARITHMETIC:\n return VM_OPERATIONS_2_ARITHMETIC[op]\n\n else:\n raise ValueError(NOT_AN_OPERATION_MSG)",
"title": ""
},
{
"docid": "180dcfc5ae8d210b7be14d3a6befd9cb",
"score": "0.6029524",
"text": "def describe(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "dfecc6da717daeda0d394f78a48a56b3",
"score": "0.60074824",
"text": "def describe(self) -> dict:\n\n description = super().describe()\n\n description.update(\n {\n \"args\": {\n \"start_key\": self.start_key,\n \"start_inclusive\": self.start_inclusive\n }\n }\n )\n\n return description",
"title": ""
},
{
"docid": "bafb2b874ed83218d77a6c08d9a6bd3a",
"score": "0.6004166",
"text": "def operations(self) -> typing.List[str]:\n return typing.cast(\n typing.List[str],\n self._properties.get(\"operations\"),\n )",
"title": ""
},
{
"docid": "7a1984433dd6859c6594baecfc28608b",
"score": "0.59988266",
"text": "def encoded(self):\n return {\n 'blocking': self.blocking,\n 'docstring': self.docstring,\n 'op_type': 'process',\n }",
"title": ""
},
{
"docid": "feaca71c2529f33286a48f80e57ca309",
"score": "0.5994416",
"text": "def operation_list(self):\n return self._operation_list",
"title": ""
},
{
"docid": "feaca71c2529f33286a48f80e57ca309",
"score": "0.5994416",
"text": "def operation_list(self):\n return self._operation_list",
"title": ""
},
{
"docid": "feaca71c2529f33286a48f80e57ca309",
"score": "0.5994416",
"text": "def operation_list(self):\n return self._operation_list",
"title": ""
},
{
"docid": "b10d69b6615147dc91de2cd6449faf0b",
"score": "0.59863967",
"text": "def _construct_dr_orchestration_operation_json(self):\n dr_orchestration_json = {\n \"taskInfo\": {\n \"task\": self._json_task,\n \"subTasks\": [\n {\n \"subTaskOperation\": 1,\n \"subTask\": self._json_dr_orchestration_subtasks,\n \"options\": {\n \"adminOpts\": {\n \"drOrchestrationOption\": self._json_dr_orchestration\n }\n }\n }\n ]\n }\n }\n\n return dr_orchestration_json",
"title": ""
},
{
"docid": "ef7154b00494748f6f737045196dfdea",
"score": "0.5982532",
"text": "def get_operations(base_url: str = \"\") -> dict[str, DictStr]:\n return {\n \"List files in storage\": dict(\n url_templ=\"{}\".format(base_url),\n context=BaseFilesResource,\n permission=\"kp_view_files\",\n accept=\"application/json\",\n request_method=\"GET\",\n renderer=\"json\",\n view=list_files,\n ),\n \"Upload a single file\": dict(\n url_templ=\"{}/@@single\".format(base_url),\n context=BaseFilesResource,\n name=\"single\",\n permission=\"kp_upload\",\n accept=\"application/json\",\n request_method=\"POST\",\n renderer=\"json\",\n view=upload_single_file,\n ),\n \"Delete a file\": dict(\n url_templ=\"{}/:md5\".format(base_url),\n context=BaseFileResource,\n permission=\"kp_upload\",\n request_method=\"DELETE\",\n view=delete_file_and_its_versions,\n ),\n \"Update file metadata\": dict(\n url_templ=\"{}/:file_id/@@metadata\".format(base_url),\n context=BaseFileResource,\n name=\"metadata\",\n permission=\"kp_upload\",\n accept=\"application/json\",\n request_method=\"PUT\",\n renderer=\"json\",\n view=update_metadata,\n ),\n }",
"title": ""
},
{
"docid": "7c299f0adf16af775f747e1644052484",
"score": "0.59636265",
"text": "def get_json(self) -> Dict[str, str]:\n return {\n 'cmd': func_to_str(self.cmd),\n 'name': self.name,\n 'description': self.description\n }",
"title": ""
},
{
"docid": "f9583d95b65ef8d4efd5a13354605705",
"score": "0.5960384",
"text": "def describe(self):\n\n pass",
"title": ""
},
{
"docid": "d0adf72013da910016116b4ef7a73f87",
"score": "0.59407073",
"text": "def get_description():\n desc = {\"description\": __doc__, \"data\": True, \"cache\": 86400}\n desc[\"defaults\"] = {\"_r\": None} # disables\n desc[\"arguments\"] = [\n dict(\n type=\"networkselect\",\n name=\"station\",\n network=\"WFO\",\n default=\"DMX\",\n label=\"Select WFO:\",\n all=True,\n )\n ]\n return desc",
"title": ""
},
{
"docid": "9dd6aab553a428d7407cde33304d4be4",
"score": "0.5939824",
"text": "def operationType(*args, **kwargs):\n \n pass",
"title": ""
},
{
"docid": "f767d0adf8e4eb207fb63941f17edbce",
"score": "0.59391224",
"text": "def get_op_file_info():\n op_file_info = {\n \"tab\": cmds.optionVar(q='op_currOpenTab'),\n \"cat\": cmds.optionVar(q='op_currOpenCategory'),\n \"level1\": cmds.optionVar(q='op_currOpenLevel1'),\n \"level2\": cmds.optionVar(q='op_currOpenLevel2'),\n \"level3\": cmds.optionVar(q='op_currOpenLevel3'),\n \"version\": cmds.optionVar(q='op_currOpenVersion'),\n }\n return op_file_info",
"title": ""
},
{
"docid": "eb79fbd532fd8d45ebf2957c3ea2d3f9",
"score": "0.5935591",
"text": "def operations(self):\n if self.only_ops or self.only_virtual_ops:\n return self[\"operations\"]\n ops = []\n trxs = self[\"transactions\"]\n for tx in trxs:\n for op in tx[\"operations\"]:\n # Replace opid by op name\n # op[0] = getOperationNameForId(op[0])\n ops.append(op)\n return ops",
"title": ""
},
{
"docid": "3294f6b7429d784b0f83c6e8654f34ad",
"score": "0.5925356",
"text": "def get_instruction(self):\n\t\treturn self.description",
"title": ""
},
{
"docid": "9f65fcc917ad4ca013af8899294cea7b",
"score": "0.59147364",
"text": "def entity_description(self, entity):\n desc = {}\n desc[\"Name\"] = entity.name\n desc[\"Tool\"] = entity.tool\n desc[\"Parameters\"] = entity.parameters\n desc[\"Note\"] = entity.note\n return desc",
"title": ""
},
{
"docid": "033ca435d6286e136fe6b3537dbde262",
"score": "0.58961743",
"text": "def encoded(self):\n return {\n 'blocking': self.blocking,\n 'abortable': (self.aborter is not None),\n 'docstring': self.docstring,\n 'op_type': 'task',\n }",
"title": ""
},
{
"docid": "452e9bcc315baae0db99b909df9609e7",
"score": "0.5859901",
"text": "def operation_status(self):\n return self._location.operation_status",
"title": ""
},
{
"docid": "cb7d07a60b1c789de30c1d40ae2a8a70",
"score": "0.5827218",
"text": "def describe_me(self):\n #description_list = []\n #for action in self.allowable_actions:\n # description_list.append(getattr(self, '_' + self.__class__.__name__ + '__' + action)(describe=True))\n #return description_list\n return list(self.command_definitions.values())",
"title": ""
},
{
"docid": "83788fcdb2210ed828fe2dddbbf78683",
"score": "0.5826472",
"text": "def _summary_info(self):\n info = dict()\n info['creation'] = str(datetime.datetime.now())\n info['size_batches'] = len(self.data_train)\n info['batch_size'] = self.batch_size\n string=\"\"\n for number,contents in enumerate(self.data_train.index_file_list):\n string += \"\\n content :\" + self.data_train.index_file_content[number]\n for elements in contents:\n string += \" \"\n string += \" %s,\" % elements\n info['details'] = string\n info['optimiser']=str(self.optimizer)\n info['loss']=str(self.loss.__class__.__name__)\n info['sampling'] = str(self.data_train.sampling)\n return info",
"title": ""
},
{
"docid": "1d9b34bf5c88ad49a31baf1a1af7cff8",
"score": "0.5820188",
"text": "def description(self):\n response = {\n 'type': self.type,\n 'retrievable': self.retrievable,\n }\n parameters = self.parameters()\n if parameters is not None:\n response['parameters'] = parameters\n\n return response",
"title": ""
},
{
"docid": "93c28c8f8a9384d5124133c0444b471c",
"score": "0.5816711",
"text": "def operation_post(operation_name=None):\n if connexion.request.is_json:\n operation_name = OperationName.from_dict(connexion.request.get_json())\n\n name = operation_name.name or \"default operation\"\n public_name = operation_name.public_name or \"default operation - public\"\n\n key_value = uuid.uuid4().hex\n\n qKey = application_map.Key\n qOperation = application_map.Operation\n session = application_map.Session()\n\n q_operation = qOperation(\n name=name,\n public_name=public_name)\n\n q_operation.master_key = qKey(\n value=key_value,\n permission='master',\n name='Master Key')\n\n operation = Operation(\n master_key=key_value,\n name=name,\n public_name=public_name,\n tower_count=0,\n sub_key_count=1)\n\n session.add(q_operation)\n session.commit()\n\n # let sqlite decide what the operation id should be, then make sure they match\n q_operation.master_key.operation_id = q_operation.id\n session.commit()\n\n return operation, 200",
"title": ""
},
{
"docid": "d63252859cadeb39c9ca5731a7306c3d",
"score": "0.58062166",
"text": "def get_description():\n desc = dict()\n desc['data'] = True\n desc['cache'] = 86400\n desc['description'] = \"\"\"This chart presents the largest changes in\n temperature over a given number of hours. This is based on available\n hourly temperature reports. It also requires an exact match in time of\n day between two observations.\n \"\"\"\n desc['arguments'] = [\n dict(type='zstation', name='zstation', default='DSM',\n label='Select Station:', network='IA_ASOS'),\n dict(type='int', name='hours', label='Number of Hours:',\n default=24),\n dict(type='select', name='month', default='all',\n label='Month Limiter', options=MDICT2),\n dict(type='select', name='dir', default='warm',\n label='Direction:', options=MDICT),\n ]\n return desc",
"title": ""
},
{
"docid": "ea6f5ed3193c87cea478049b44799514",
"score": "0.57980895",
"text": "def get_description():\n desc = {}\n desc[\"data\"] = True\n desc[\n \"description\"\n ] = \"\"\"This chart compares yearly summaries between two\n long term climate sites.\"\"\"\n desc[\"arguments\"] = [\n dict(\n type=\"select\",\n options=PDICT,\n name=\"var\",\n label=\"Select Variable to Plot\",\n default=\"avg_temp\",\n ),\n dict(\n type=\"station\",\n name=\"station1\",\n default=\"IATDSM\",\n label=\"Select First Station:\",\n network=\"IACLIMATE\",\n ),\n dict(\n type=\"station\",\n name=\"station2\",\n default=\"IATAME\",\n label=\"Select Secont Station:\",\n network=\"IACLIMATE\",\n ),\n ]\n return desc",
"title": ""
},
{
"docid": "f104fc5b91f66710c143b1649a74f9b0",
"score": "0.5784668",
"text": "def _build_command_dict(self):\n self._cmd_dict.add(Capability.START_AUTOSAMPLE, display_name=\"Start Autosample\")\n self._cmd_dict.add(Capability.STOP_AUTOSAMPLE, display_name=\"Stop Autosample\")\n self._cmd_dict.add(Capability.GET, display_name=\"Get\")\n self._cmd_dict.add(Capability.SET, display_name=\"Set\")\n self._cmd_dict.add(Capability.DISCOVER, display_name=\"Discover\")\n self._cmd_dict.add(Capability.CLEAR_WRITE_ERROR, display_name=\"Clear Write Error\")",
"title": ""
},
{
"docid": "23ebdb97f98e8798418f0bbe66a77b5c",
"score": "0.5756997",
"text": "def show_op_stat(self):\n self.policy.show_op_stat()",
"title": ""
},
{
"docid": "81be95f8ce87b0ab65ecbb1be238d932",
"score": "0.57464105",
"text": "def get_description(self, command):\n return self.command_description[command]",
"title": ""
},
{
"docid": "e782f2e4c5088e86fcf5d0ced16b9fae",
"score": "0.57390743",
"text": "def __repr__(self):\n representation_string = '\"DROrchestrationOperations: instance for commcell: \"{1}\"'\n return representation_string.format(\n self._commcell_object.commserv_name)",
"title": ""
},
{
"docid": "1b55986aadacf7bf886275edd92ac5b0",
"score": "0.5719401",
"text": "def describe(self, req=None, resp=None, **kwargs):\n description = {\n 'params': OrderedDict([\n (name, param.describe())\n for name, param in self.params.items()\n ]),\n 'details':\n inspect.cleandoc(\n self.__class__.__doc__ or\n \"This resource does not have description yet\"\n ),\n 'name': self.__class__.__name__,\n 'methods': self.allowed_methods()\n }\n # note: add path to resource description only if request object was\n # provided in order to make auto-documentation engines simpler\n if req:\n description['path'] = req.path\n\n description.update(**kwargs)\n return description",
"title": ""
},
{
"docid": "5e30ef8018ee49267debc25181bfc0b8",
"score": "0.5712427",
"text": "def operations(self):\n gap3_session = self._check_valid()\n if not hasattr(self,\"_gap_operations\"):\n s = str(gap3_session.eval(\"RecFields(%s.operations)\" % self._name))\n s = s.strip('[] ').replace('\\n','')\n self._gap_operations = [ss.strip('\" ') for ss in s.split(',')]\n return getattr(self,\"_gap_operations\")",
"title": ""
},
{
"docid": "b3d11f00bb02f6bf190c17ab841754ba",
"score": "0.57115316",
"text": "def list_ops(self):\n names = [op.name for op in self.graph.get_operations()]\n return names",
"title": ""
},
{
"docid": "cce52ad142667413b103f70c9bef12ea",
"score": "0.57059544",
"text": "def meta(self) -> Dict[str, str]:\n return {\n 'run_id': str(self.id),\n 'operator': self.operator,\n 'run_added_at': self.added_at,\n 'run_edited_at': self.edited_at,\n 'run_measured_at': self.measured_at,\n 'IC_HV': self.IC_HV,\n }",
"title": ""
},
{
"docid": "b2510ed7b17656fef16fffc2f24b890f",
"score": "0.5702769",
"text": "def describe(self):\n print(self.description.format())",
"title": ""
},
{
"docid": "9d856ff45ca4212c3a3e0d2d3f7d8668",
"score": "0.5687439",
"text": "def to_task_id_full_op_name_dict(self):\n return self._task_id_full_op_name_dict",
"title": ""
},
{
"docid": "cef4748ec80f89ed90f94f75161fd048",
"score": "0.5675128",
"text": "def _operands_repr(self) -> str:",
"title": ""
},
{
"docid": "a5bbd995963238d479f7dfedc564b55e",
"score": "0.56721324",
"text": "def _get_description(self, args: Tuple, kwargs: Dict[str, Any]) -> Dict[str, Any]:\n return {\n 'id': uuid1().hex,\n 'args': args,\n 'kwargs': kwargs,\n 'module': self._module_name,\n 'function': self.f.__name__,\n 'sender_hostname': socket.gethostname(),\n 'sender_pid': os.getpid(),\n 'sender_cmd': ' '.join(sys.argv),\n 'sender_timestamp': datetime.utcnow().isoformat()[:19],\n }",
"title": ""
},
{
"docid": "6ad841900cb6d0b522edd88acb502ff1",
"score": "0.5652869",
"text": "def get_description():\n desc = dict()\n desc['data'] = True\n desc['description'] = \"\"\"This application generates a map displaying the\n number of LSRs issued between a period of your choice by NWS Office. These\n are the preliminary reports and not official totals of events.\"\"\"\n today = datetime.date.today() + datetime.timedelta(days=1)\n jan1 = today.replace(month=1, day=1)\n desc['arguments'] = [\n dict(type='datetime', name='sdate',\n default=jan1.strftime(\"%Y/%m/%d 0000\"),\n label='Start Date / Time (UTC, inclusive):',\n min=\"2006/01/01 0000\"),\n dict(type='datetime', name='edate',\n default=today.strftime(\"%Y/%m/%d 0000\"),\n label='End Date / Time (UTC):',\n min=\"2006/01/01 0000\"),\n dict(type='select', name='filter', default='NONE', options=MDICT,\n label='Local Storm Report Type Filter'),\n dict(type='select', name='by', default='wfo',\n label='Aggregate Option:', options=PDICT),\n dict(type='cmap', name='cmap', default='plasma', label='Color Ramp:'),\n ]\n return desc",
"title": ""
},
{
"docid": "681a5bf74232593a29be11e0fc718967",
"score": "0.56477886",
"text": "def operation(self, key):\n return self._operation_sequence[_cast_to_int(key, 'key')]",
"title": ""
},
{
"docid": "c1a7b768a9d1f4647041a864521ec643",
"score": "0.564273",
"text": "def get_command_description(cls):\n class_commands = cls.get_commands()\n commands = {}\n for c in class_commands:\n commands[c] = class_commands[c].get_description()\n return commands",
"title": ""
},
{
"docid": "805c4ab0027cc1254520404385b7ed46",
"score": "0.56351805",
"text": "def description(self):",
"title": ""
},
{
"docid": "805c4ab0027cc1254520404385b7ed46",
"score": "0.56351805",
"text": "def description(self):",
"title": ""
},
{
"docid": "805c4ab0027cc1254520404385b7ed46",
"score": "0.56351805",
"text": "def description(self):",
"title": ""
},
{
"docid": "e03e61dc041ce04a1b1dc4cc64ed98b8",
"score": "0.5633803",
"text": "def show_method():\n i = 0\n while i < len(Operation_History):\n print(\" Id you'r operation :\"+str((i+1)))\n print(\" Sign you'r operation :\"+Operation_History[i]['sign'])\n Sum = \" Summary you'r operation :\"\n print(Sum+str(Operation_History[i]['summary']))\n print(\" Day you'r operation :\"+str(Operation_History[i]['dd']))\n print(\" Mounth you'r operation :\"+str(Operation_History[i]['mm']))\n print(\" Year you'r operation :\"+str(Operation_History[i]['yy']))\n Com = \" Comment to you'r operation :\"\n print(Com+Operation_History[i]['comment']+\"\\n\")\n i += 1",
"title": ""
},
{
"docid": "f3940fc5c318d83f3dead44b37b997e7",
"score": "0.5631586",
"text": "def description (self) :\r\n\t\tpass",
"title": ""
},
{
"docid": "0fd04a94fd6762cbfd2917ba112ae60f",
"score": "0.56308866",
"text": "def operation_list(self):\n return [STATE_HEAT, STATE_AUTO, STATE_ECO]",
"title": ""
},
{
"docid": "44501be74597856c029bb5cea07a02dd",
"score": "0.5630864",
"text": "def get_cmd_info(self, action):\n if action == b'complete':\n return vim.Dictionary(\n cmd=self.format_cmd(),\n ftype=self.filetype,\n is_daemon=self.daemon,\n is_sync=self.sync)\n return vim.Dictionary()",
"title": ""
},
{
"docid": "9199f10aa4d1cda248cdc50d68db4f8e",
"score": "0.562991",
"text": "def _GetActionDescriptions(self):\n action_names = self.actions.keys()\n action_names.sort()\n desc = ''\n for action_name in action_names:\n desc += ' %s: %s\\n' % (action_name, self.actions[action_name].short_desc)\n return desc",
"title": ""
},
{
"docid": "8c27b1cb5f1c68979d16aebf0f2e22a4",
"score": "0.56292826",
"text": "def description(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "d8e749446d2c1e31c3dc7302b646abdf",
"score": "0.56253123",
"text": "def current_operation(self):\r\n return self._current_operation",
"title": ""
},
{
"docid": "cf276f2765933f6e0a4f390e95398c3a",
"score": "0.5612336",
"text": "def __repr__(self):\n result = self.name\n result += \" | \" + self.startDescription\n result += \" | \" + self.description\n result += \" | \"\n for i in self.expectedCommands:\n result += \" \"\n result += i\n return result",
"title": ""
},
{
"docid": "9ea18b34af65d2a153443e0a74b3029a",
"score": "0.56111753",
"text": "def operation_get(api_key):\n session = application_map.Session()\n qOperation = application_map.Operation\n qKey = application_map.Key\n\n q_operation = session.query(qOperation).filter(qOperation.id == api_key.operation_id).one()\n master_key = q_operation.master_key\n \n\n operation = Operation(\n master_key=master_key.value,\n name=q_operation.name,\n public_name=q_operation.public_name,\n tower_count=len(q_operation.towers),\n sub_key_count=len(q_operation.keys))\n\n return operation, 200",
"title": ""
},
{
"docid": "762146afa9dea5dec6ab0f1109e0186f",
"score": "0.5608612",
"text": "def describe(self):\n if self._description is None:\n new_description = DotDict()\n for k in self._counters.keys():\n new_description[k.name] = k.description\n self._description = new_description\n return self._description",
"title": ""
},
{
"docid": "e1957897d1f7e8c33d7ecdede9ecd922",
"score": "0.55984294",
"text": "def operation_list(self):\r\n return self.HiveObjects.Get_Heating_Operation_Mode_List()",
"title": ""
},
{
"docid": "b7d8e05470c7187221833aa4c897be35",
"score": "0.5591979",
"text": "def description():",
"title": ""
},
{
"docid": "b7d8e05470c7187221833aa4c897be35",
"score": "0.5591979",
"text": "def description():",
"title": ""
},
{
"docid": "3a5d20071520511aa8468bbdc8867f38",
"score": "0.55913377",
"text": "def _construct_task_id_full_op_name_dict(task_desc_info):\n task_id_full_op_name = {}\n for task_desc in task_desc_info:\n task_id = combine_stream_task_id(task_desc['streamId'], task_desc['taskId'])\n task_id_full_op_name[task_id] = task_desc['opName']\n return task_id_full_op_name",
"title": ""
},
{
"docid": "ae754293fba52089073cba913c5ce254",
"score": "0.55610675",
"text": "def summary(self):\n to_return = self.name + \":\\n\"\n for k, v in self.__dict__.items():\n to_return += \"\\t\" + k + \":\\t\" + str(v) + \"\\n\"\n return to_return",
"title": ""
},
{
"docid": "0fd467247630a1a4fbe5eb539bb7d63f",
"score": "0.5559402",
"text": "def get_description():\n desc = dict()\n desc['data'] = True\n desc['description'] = \"\"\"This plot displays the distribution of observed\n daily high and low temperatures for a given day and given state. The\n dataset is fit with a simple normal distribution based on the simple\n population statistics.\n \"\"\"\n desc['arguments'] = [\n dict(type='state', name='state', default='IA',\n label='Which state?'),\n dict(type='month', name='month', default='10',\n label='Select Month:'),\n dict(type='day', name='day', default='7',\n label='Select Day:'),\n ]\n return desc",
"title": ""
},
{
"docid": "c5082e5163672cdc7f64098b88fb1751",
"score": "0.5547345",
"text": "def _build_command_dict(self):\n\n self._cmd_dict.add(Capability.START_AUTOSAMPLE,\n timeout=300,\n display_name=\"start autosample\",\n description=\"Place the instrument into autosample mode\")\n self._cmd_dict.add(Capability.STOP_AUTOSAMPLE,\n display_name=\"stop autosample\",\n description=\"Exit autosample mode and return to command mode\")\n self._cmd_dict.add(Capability.CLOCK_SYNC,\n display_name=\"sync clock\")\n self._cmd_dict.add(Capability.GET_CALIBRATION,\n display_name=\"get calibration\")\n self._cmd_dict.add(Capability.GET_CONFIGURATION,\n timeout=300,\n display_name=\"get configuration\")\n self._cmd_dict.add(Capability.GET_INSTRUMENT_TRANSFORM_MATRIX,\n display_name=\"get instrument transform matrix\")\n self._cmd_dict.add(Capability.SAVE_SETUP_TO_RAM,\n display_name=\"save setup to ram\")\n self._cmd_dict.add(Capability.SEND_LAST_SAMPLE,\n display_name=\"send last sample\")\n self._cmd_dict.add(Capability.GET_ERROR_STATUS_WORD,\n display_name=\"get error status word\")\n self._cmd_dict.add(Capability.CLEAR_ERROR_STATUS_WORD,\n display_name=\"clear error status word\")\n self._cmd_dict.add(Capability.GET_FAULT_LOG,\n display_name=\"get fault log\")\n self._cmd_dict.add(Capability.CLEAR_FAULT_LOG,\n display_name=\"clear fault log\")\n self._cmd_dict.add(Capability.RUN_TEST_200,\n display_name=\"run test 200\")\n self._cmd_dict.add(Capability.POWER_DOWN,\n display_name=\"Power Down\")",
"title": ""
},
{
"docid": "7efd2d605d6ff992d3090e8c73a4b7c4",
"score": "0.5546303",
"text": "def __repr__(self):\n\t\treturn self.func.__doc__",
"title": ""
},
{
"docid": "347a232fa750c570146d567ad34c4e46",
"score": "0.5541942",
"text": "def __repr__(self):\r\n\t\treturn self.func.__doc__",
"title": ""
},
{
"docid": "5194c4f7cab5e9ff3434f290935aaed5",
"score": "0.5538498",
"text": "def op_code(self):\n if self.status is None:\n return OpCode.NONE\n elif self.status in ['starting', 'running', 'stopping']:\n return {'starting': OpCode.STARTING, 'running': OpCode.RUNNING,\n 'stopping': OpCode.STOPPING}[self.status]\n elif self.success:\n return OpCode.SUCCEEDED\n else:\n return OpCode.FAILED",
"title": ""
},
{
"docid": "781a0a83af0e71c872a9c5a7db451e14",
"score": "0.55371183",
"text": "def calc_description(self):\n pass",
"title": ""
},
{
"docid": "359e8654850d051f23911354291930d5",
"score": "0.55358785",
"text": "def get_op_types(self):\n return self.cur_config['ops']",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="786"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">e02c3b6520f5cfe70a6eeb8665f0faab</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">"To ensure the robustness of the model, five random datasets were created to repeat the training and testing of the CNN classifiers (5fold crossvalidation)"</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "35b086822916871823d5cf5d63c5a3bd",
"score": "0.5881104",
"text": "def run(self, *inputs, **kwargs) -> None:\n validation_folds = self.config.training_crossval_folds\n split_ratios = [1.0 / validation_folds] * validation_folds\n\n \"\"\"\n \"All baseline MR data were expanded to up to 7,200 slices (4,800 for training, 2,400 for testing) for 150 NC\n subjects, 7,200 slices (4,800 for training, 2,400 for testing) for 150 patients with sMCI, and 7,536 slices\n (5,024 for training, 2,512 for testing) for 157 patients with cMCI.\"\n\n Around 2/3 for training and 1/3 for testing.\n \"\"\"\n shuffled_mapping = self.mapping.shuffle()\n train_split, test_split = shuffled_mapping.split_by_ratio([0.7, 0.3])\n results = []\n\n for fold_idx in range(validation_folds):\n self.logger.info(f\"Running {fold_idx + 1}th fold.\")\n \"\"\"\n \"Due to the limited data set in this study, this technique was employed to learn the appropriate salient \n features for MR-based imaging classification, where all CNN layers except for the last were fine-tuned with \n a learning rate using 1/10 of the default learning rate. The last fully-connected layer was randomly \n initialized and freshly trained, in order to accommodate the new object categories in this study. Its \n learning rate was set to 1/100 of the default value.\"\n \n Default learning rate is 0.001, therefore setting all layers except the last one to 0.0001 and last layer \n 0.00001.\n \"\"\"\n # building a new model for each fold\n self.model = self.provide_model()\n parameters = list(self.model.parameters())\n parameters = [\n {\"params\": parameters[:-2], \"lr\": 0.0001}, # two sets of parameters because weight + bias\n {\"params\": parameters[-2:], \"lr\": 0.00001}\n ]\n self.optimizer = self.build_optimizer(parameters, optimizer_type=\"sgd\")\n\n copied_splits: List[Mapping] = train_split.split_by_ratio(split_ratios)\n fold_valid_split = copied_splits.pop(fold_idx)\n fold_train_split = Mapping.merge(copied_splits)\n\n self.train(num_epochs=self.config.train_epochs,\n train_mapping=fold_train_split,\n valid_mapping=fold_valid_split,\n ith_fold=fold_idx)\n\n test_result = self.test(ith_fold=fold_idx, test_mapping=test_split)\n results.append(test_result)",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "8b71a66a734b075d1133dcee50313951",
"score": "0.66847914",
"text": "def train_model():\r\n x_train = []\r\n x_test = []\r\n x_valid = []\r\n\r\n y_train = []\r\n y_test = []\r\n y_valid = []\r\n\r\n # reading train\r\n read_images(fpath_train, x_train, y_train, FEMALE)\r\n read_images(mpath_train, x_train, y_train, MALE)\r\n\r\n # reading test\r\n read_images(fpath_test, x_test, y_test, FEMALE)\r\n read_images(mpath_test, x_test, y_test, MALE)\r\n\r\n # reading valid\r\n read_images(fpath_valid, x_valid, y_valid, FEMALE)\r\n read_images(mpath_valid, x_valid, y_valid, MALE)\r\n\r\n print('x_train - ', len(x_train))\r\n print('y_train - ', len(y_train))\r\n\r\n print('x_test - ', len(x_test))\r\n print('y_test - ', len(y_test))\r\n\r\n print('x_valid - ', len(x_valid))\r\n print('y_valid - ', len(y_valid))\r\n\r\n # x_train, x_test, y_train, y_test = train_test_split(images, labels, test_size=0.10, random_state=42)\r\n x_train = np.asarray(x_train)\r\n x_train = x_train.astype('float32')\r\n x_train /= 255\r\n y_train = np.asarray(y_train)\r\n # x_train, y_train = shuffle_2_lists(x_train, y_train)\r\n\r\n x_test = np.asarray(x_test)\r\n x_test = x_test.astype('float32')\r\n x_test /= 255\r\n y_test = np.asarray(y_test)\r\n\r\n x_valid = np.asarray(x_valid)\r\n x_valid = x_valid.astype('float32')\r\n x_valid /= 255\r\n y_valid = np.asarray(y_valid)\r\n # x_valid, y_valid = shuffle_2_lists(x_valid, y_valid)\r\n\r\n # build model\r\n model = Sequential()\r\n\r\n model.add(Conv2D(32, (5, 5), strides=(1, 1), name = 'conv0', input_shape=(112, 112, 1)))\r\n\r\n model.add(BatchNormalization(axis=3, name = 'bn0'))\r\n model.add(Activation('relu'))\r\n\r\n model.add(MaxPooling2D((2, 2), name='max_pool'))\r\n model.add(Conv2D(64, (3, 3), strides=(1, 1), name=\"conv1\"))\r\n model.add(Activation('relu'))\r\n model.add(AveragePooling2D((3, 3), name='avg_pool'))\r\n\r\n model.add(GlobalAveragePooling2D())\r\n model.add(Dense(300, activation=\"relu\", name='rl'))\r\n model.add(Dropout(0.5))\r\n model.add(Dense(1, activation='sigmoid', name='sm'))\r\n\r\n model.compile(loss='binary_crossentropy',\r\n optimizer=Adam(lr=1e-4),\r\n metrics=['accuracy'])\r\n\r\n batch_size = 32\r\n nb_epoch = 98\r\n\r\n # Train model\r\n history = model.fit(x_train, y_train,\r\n batch_size=batch_size,\r\n epochs=nb_epoch,\r\n validation_data=(x_valid, y_valid),\r\n shuffle=True,\r\n )\r\n\r\n loss, accuracy = model.evaluate(x_test, y_test)\r\n print('loss = ', loss, 'accuracy = ', accuracy)\r\n save_model(model, model_name)",
"title": ""
},
{
"docid": "06bd04c7701270b855cad2d685903c15",
"score": "0.6546097",
"text": "def step_7_train_models(self):\n title_line = [\"#\", \"accuracy\", \"MCC\", \"precision\", \"recall\", \"f1\", \"auc\", \"kappa\", \"prevalence\",\n \"bias\", \"pickel-File\"]\n self.csv_text = [title_line]\n\n TL_list = []\n property_list_list = []\n directory = os.getcwd().split(\"/\")[-2:]\n dir_string = ';'.join(directory)\n for cpd in self.sd_entries:\n property_list = []\n property_name_list = []\n prop_name = cpd.GetPropNames()\n for property in prop_name:\n if property not in ['TL', 'value']:\n try:\n f = float(cpd.GetProp(property))\n if math.isnan(f) or math.isinf(f):\n print(\"invalid: %s\" % property)\n\n except ValueError:\n print(\"valerror: %s\" % property)\n continue\n property_list.append(f)\n property_name_list.append(property)\n elif property == 'TL':\n TL_list.append(int(cpd.GetProp(property)))\n else:\n print(property)\n pass\n property_list_list.append(property_list)\n dataDescrs_array = np.asarray(property_list_list)\n dataActs_array = np.array(TL_list)\n\n for randomseedcounter in range(1, 11):\n if self.verbous:\n print(\"################################\")\n print(\"try to calculate seed %d\" % randomseedcounter)\n X_train, X_test, y_train, y_test = cross_validation.train_test_split(\n dataDescrs_array, dataActs_array, test_size=.4, random_state=randomseedcounter)\n # try:\n clf_RF = RandomForestClassifier(n_estimators=100, random_state=randomseedcounter)\n clf_RF = clf_RF.fit(X_train, y_train)\n\n cv_counter = 5\n\n scores = cross_validation.cross_val_score(clf_RF, X_test, y_test, cv=cv_counter,\n scoring='accuracy')\n\n accuracy_CV = round(scores.mean(), 3)\n accuracy_std_CV = round(scores.std(), 3)\n\n calcMCC = make_scorer(metrics.matthews_corrcoef, greater_is_better=True,\n needs_threshold=False)\n scores = cross_validation.cross_val_score(clf_RF, X_test, y_test, cv=cv_counter,\n scoring=calcMCC)\n\n MCC_CV = round(scores.mean(), 3)\n MCC_std_CV = round(scores.std(), 3)\n\n scores = cross_validation.cross_val_score(clf_RF, X_test, y_test, cv=cv_counter, scoring='f1')\n scores_rounded = [round(x, 3) for x in scores]\n f1_CV = round(scores.mean(), 3)\n f1_std_CV = round(scores.std(), 3)\n\n scores = cross_validation.cross_val_score(clf_RF, X_test, y_test, cv=cv_counter,\n scoring='precision')\n scores_rounded = [round(x, 3) for x in scores]\n precision_CV = round(scores.mean(), 3)\n precision_std_CV = round(scores.std(), 3)\n\n scores = cross_validation.cross_val_score(clf_RF, X_test, y_test, cv=cv_counter,\n scoring='recall')\n scores_rounded = [round(x, 3) for x in scores]\n recall_CV = round(scores.mean(), 3)\n recall_std_CV = round(scores.std(), 3)\n\n scores = cross_validation.cross_val_score(clf_RF, X_test, y_test, cv=cv_counter,\n scoring='roc_auc')\n scores_rounded = [round(x, 3) for x in scores]\n auc_CV = round(scores.mean(), 3)\n auc_std_CV = round(scores.std(), 3)\n\n y_predict = clf_RF.predict(X_test)\n conf_matrix = metrics.confusion_matrix(y_test, y_predict)\n # coh_kappa = cohenskappa.cohens_kappa(conf_matrix)\n coh_kappa = cohens_kappa(conf_matrix)\n kappa = round(coh_kappa['kappa'], 3)\n kappa_stdev = round(coh_kappa['std_kappa'], 3)\n\n tp = conf_matrix[0][0]\n tn = conf_matrix[1][1]\n fp = conf_matrix[1][0]\n fn = conf_matrix[0][1]\n n = tn + fp\n p = tp + fn\n kappa_prevalence = round(float(abs(tp - tn)) / float(n), 3)\n kappa_bias = round(float(abs(fp - fn)) / float(n), 3)\n\n if self.verbous:\n print(\"test:\")\n print(\"\\tpos\\tneg\")\n print(\"true\\t%d\\t%d\" % (tp, tn))\n print(\"false\\t%d\\t%d\" % (fp, fn))\n print(conf_matrix)\n print(\"\\ntrain:\")\n y_predict2 = clf_RF.predict(X_train)\n conf_matrix2 = metrics.confusion_matrix(y_train, y_predict2)\n tp2 = conf_matrix2[0][0]\n tn2 = conf_matrix2[1][1]\n fp2 = conf_matrix2[1][0]\n fn2 = conf_matrix2[0][1]\n print(\"\\tpos\\tneg\")\n print(\"true\\t%d\\t%d\" % (tp2, tn2))\n print(\"false\\t%d\\t%d\" % (fp2, fn2))\n print(conf_matrix2)\n\n result_string_cut = [\n randomseedcounter, str(accuracy_CV) + \"_\" + str(accuracy_std_CV),\n str(MCC_CV) + \"_\" + str(MCC_std_CV), str(precision_CV) + \"_\" + str(precision_std_CV),\n str(recall_CV) + \"_\" + str(recall_std_CV), str(f1_CV) + \"_\" + str(f1_std_CV),\n str(auc_CV) + \"_\" + str(auc_std_CV), str(kappa) + \"_\" + str(kappa_stdev), kappa_prevalence,\n kappa_bias, \"model_file.pkl\"\n ]\n\n self.model.append(clf_RF)\n self.csv_text.append(result_string_cut)\n\n# except Exception as e:\n# print \"got %d models\" % len(self.model)\n# print e\n# sys.exit(-1)\n# break\n return True if len(self.model) > 0 else False",
"title": ""
},
{
"docid": "f857825a9e86686fd720c226f85e0674",
"score": "0.6482086",
"text": "def train():\n # Build the data generators\n train_generator, val_generator, eval_generator = build_data_pipelines()\n # Get the classes mapping dictionnairy from the data generators\n classes_dict = train_generator.class_indices\n # Print the classes mapping\n print(\"classes_dict : \\n \", classes_dict)\n # Build the model to train\n model = build_model(output_layer_dim=len(classes_dict))\n # Prepare the callbacks\n early_stopping, ckpt_saver = build_callbacks()\n # Train the mdoel on the training data generator indicating the validation \n # data generator to use at the end on each epochs.\n model.fit(\n train_generator,\n steps_per_epoch=get_nb_step(DATA_DIR, 'training'),\n validation_data=val_generator,\n validation_steps=get_nb_step(DATA_DIR, 'validation'),\n epochs=cfg.NB_EPOCHS,\n callbacks=[early_stopping, ckpt_saver]\n )\n # Once the training is finished:\n # Load best model\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n model.load_weights(os.path.join(OUTPUT_DIR, 'checkpoints'))\n # Evaluate the model\n print(\"[INFO] Evaluation phase...\")\n # Make the predictions on the test images from the evaluation dataset\n predictions = model.predict_generator(eval_generator)\n predictions_idxs = np.argmax(predictions, axis=1)\n # Build a classification report\n my_classification_report = classification_report(eval_generator.classes,\n predictions_idxs, target_names=eval_generator.class_indices.keys())\n # Build a confusion matrix\n my_confusion_matrix = confusion_matrix(eval_generator.classes,\n predictions_idxs)\n # Print the classifiyer performances\n print(\"[INFO] Classification report : \")\n print(my_classification_report)\n\n print(\"[INFO] Confusion matrix : \")\n print(my_confusion_matrix)\n\n # Save the best model and it's results and config\n model.save(os.path.join(OUTPUT_DIR, \"model.h5\"))\n pickle.dump(my_classification_report, open(os.path.join(OUTPUT_DIR,\n \"classification_report.pkl\"), \"wb\" ))\n pickle.dump(my_confusion_matrix, open(os.path.join(OUTPUT_DIR,\n \"confusion_matrix.pkl\"), \"wb\" ))\n pickle.dump(classes_dict, open(os.path.join(OUTPUT_DIR,\n \"classes_dict.pkl\"), \"wb\" ))\n config_dict = cfg.get_config_dict() \n pickle.dump(config_dict, open(os.path.join(OUTPUT_DIR,\n \"config_dict.pkl\"), \"wb\" ))",
"title": ""
},
{
"docid": "17da67f37e8ffd1adb60d71627c0ce49",
"score": "0.6469292",
"text": "def test_kaggle_classification_data():\n torch.manual_seed(100)\n n_episodes = 20\n a_space = _algorithm_space()\n for dataset_name in [\n \"kaggle.homesite_quote_conversion\",\n \"kaggle.santander_customer_satisfaction\",\n \"kaggle.bnp_paribas_cardif_claims_management\",\n \"kaggle.poker_rule_induction\",\n \"kaggle.costa_rican_household_poverty_prediction\",\n ]:\n t_env = _task_environment(\n env_sources=[\"KAGGLE\"],\n target_types=[\"BINARY\", \"MULTICLASS\"],\n dataset_names=[dataset_name],\n n_samples=500)\n controller = _metalearn_controller(a_space, t_env)\n reinforce = _metalearn_reinforce(controller, t_env)\n reinforce.fit(\n n_episodes=n_episodes,\n **_fit_kwargs())\n history = pd.DataFrame(reinforce.history)\n assert history.shape[0] == n_episodes\n assert history[\"n_successful_mlfs\"].sum() > 0",
"title": ""
},
{
"docid": "4d86f3ba0c1c9b84474430f78e7c1af7",
"score": "0.64522827",
"text": "def get_train_test_valid_dataloaders(data_path, test_data_path, seed, image_size, batch_size):\n def build_data(data_path):\n content_list = []\n labels_list = []\n\n for image in tqdm(os.listdir(data_path)):\n if \".jpg\" in image:\n content = cv2.imread(data_path + image)\n content_list.append(content)\n elif \".txt\" in image:\n with open(data_path + image, \"r\") as f:\n labels = f.read()\n labels = np.array(labels.split(\" \"), dtype=int)\n labels[0] = 0 if labels[0] == 1 else 1\n labels = np.roll(labels, -1)\n labels_list.append(labels)\n data = np.array([list(a) for a in zip(content_list, labels_list)])\n\n return data\n\n train_data = build_data(data_path=data_path)\n test_data = build_data(data_path=test_data_path)\n\n train_data, valid_data = train_test_split(train_data, shuffle=True, test_size=0.1, random_state=seed)\n\n train_clf_labels = [a[-1] for a in train_data[:, 1]]\n\n transform = Compose(\n [\n Resize(width=image_size, height=image_size),\n HorizontalFlip(p=0.4),\n # ShiftScaleRotate(p=0.3),\n MedianBlur(blur_limit=7, always_apply=False, p=0.3),\n IAAAdditiveGaussianNoise(scale=(0, 0.15 * 255), p=0.5),\n HueSaturationValue(hue_shift_limit=0.2, sat_shift_limit=0.2, val_shift_limit=0.2, p=0.4),\n RandomBrightnessContrast(brightness_limit=(-0.1, 0.1), contrast_limit=(-0.1, 0.1), p=0.5),\n # in this implementation imagenet normalization is used\n Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], max_pixel_value=255.0, p=1.0),\n Cutout(p=0.4),\n ToTensorV2(p=1.0),\n ],\n p=1.0,\n bbox_params=A.BboxParams(format=\"pascal_voc\"),\n )\n\n test_transform = Compose(\n [\n # only resize and normalization is used for testing\n # no TTA is implemented in this solution\n Resize(width=image_size, height=image_size),\n Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], max_pixel_value=255.0, p=1.0),\n ToTensorV2(p=1.0),\n ],\n p=1.0,\n bbox_params=A.BboxParams(format=\"pascal_voc\"),\n )\n\n train_dataset = Dataset(train_data, transforms=transform)\n valid_dataset = Dataset(valid_data, transforms=transform)\n test_dataset = Dataset(test_data, transforms=test_transform)\n\n train_dataloader = DataLoader(\n train_dataset,\n # balanced sampler is used to minimize harmful effects of dataset not being fully balanced\n sampler=BalanceClassSampler(labels=train_clf_labels, mode=\"upsampling\"),\n batch_size=batch_size,\n )\n test_dataloader = DataLoader(test_dataset, sampler=SequentialSampler(test_dataset), batch_size=1)\n valid_dataloader = DataLoader(valid_dataset, sampler=SequentialSampler(valid_dataset), batch_size=batch_size)\n\n return train_dataloader, test_dataloader, valid_dataloader",
"title": ""
},
{
"docid": "c39bc838fe64492c50e98365a188c9f1",
"score": "0.643714",
"text": "def train_model(model):\n # Add your code here\n\n #Preprocessing \n # Data augmentation - creation of more images to train on\n train_datagen = ImageDataGenerator(\n rescale = 1./255,\n shear_range=0.2,\n zoom_range=0.2,\n width_shift_range=0.2,\n\t\theight_shift_range=0.2,\n rotation_range=20,\n horizontal_flip=True)\n\n test_datagen = ImageDataGenerator(rescale = 1./255)\n\n training_set = train_datagen.flow_from_directory( #how can I check if I am actually making many more instances of pictures.\n '/Users/harryrodger/Desktop/data',\n target_size=(64,64),\n batch_size=32,\n class_mode='categorical'\n )\n\n test_set = test_datagen.flow_from_directory( #how can I check if I am actually making many more instances of pictures.\n '/Users/harryrodger/Desktop/ProjectCOMP309/ProjectCode/data/test',\n target_size=(64,64),\n batch_size=32,\n class_mode='categorical'\n )\n\n print('Data augmentation complete')\n\n model.fit_generator( \n training_set,\n steps_per_epoch=8000, \n epochs = 50,\n validation_data=test_set,\n validation_steps = 15) \n\n return model",
"title": ""
},
{
"docid": "ddc1d590f7f0d9c927c217dc46001088",
"score": "0.6421587",
"text": "def test_nn_models(num_models, raw_data, preprocess_tech, save_path, eval_tech = 'k-fold', list_of_models= None):\n model_hist = [] ; model_results = pd.DataFrame(columns=['Hidden', 'Activations', 'Learning rate', 'Train Accuracy', 'Test Accuracy', 'Train AUC', 'Test AUC']) # TODO: Add tensorboard and model saving capabilities to this function\n result_index = 0\n if eval_tech not in ['k-fold', 'boot']: # must be a valid evaluation technique\n raise NameError\n\n for i in range(num_models):\n if not list_of_models: # if a list of models to test isn't provided\n model_info = generate_random_model() # randomly generate models\n model_info[\"Preprocessing\"] = preprocess_tech\n model_info[\"Input size\"] = raw_data.shape[1] - 1 # set input data size\n else:\n assert num_models == len(list_of_models) # if we provide models, ensure the num_models param lines up\n model_info = list_of_models[i] # get the ith model's info\n\n model_info[\"Model type\"] = 'NN' # this is for k-fold cross validation, since k-fold is able to evaluate both neural nets and logistic regression models\n model = build_nn(model_info) # build & compile NN\n\n if eval_tech == 'k-fold':\n results = k_fold_cross_validation(model_info, raw_data, preprocess_tech, model=model) # do k-fold cross validation on model\n else:\n print(\"currently not supporting bootstrap evaluation due to computational constraints\")\n\n train_acc, test_acc, train_auc, test_auc = zip(*results) # separate results into lists\n\n model_info[\"Avg Test AUC\"] = round(np.mean(test_auc), 3) # compute average metrics for each fold of the evaluation\n model_info[\"Avg Train AUC\"] = round(np.mean(train_auc), 3)\n model_info[\"Avg Train Accuracy\"] = round(np.mean(train_acc), 3) # these are averages from k-fold cross validation\n model_info[\"Avg Test Accuracy\"] = round(np.mean(test_acc), 3)\n model_hist.append(model_info) # add this model's info to the list\n\n model_results.loc[result_index] = (model_info['Hidden layers'], model_info['Activations'],\\\n model_info['Learning rate'], model_info['Avg Train Accuracy'],\\\n model_info[\"Avg Test Accuracy\"], \\\n model_info[\"Avg Train AUC\"], model_info[\"Avg Test AUC\"]) # write model results to df\n result_index += 1\n\n model_results.to_csv(save_path + \"K-fold NN Results.csv\") # save results to disk\n display_k_fold_results(model_hist, top_models=1) # display the top models' performance",
"title": ""
},
{
"docid": "665d6aca3d23932c887e6fb47be6e04d",
"score": "0.6421472",
"text": "def k_fold_cv(dataset, nbr_folds=5, m=10, alpha=0.0, feature_extraction=\"k-means distances\"):\n # copy the data to avoid affecting reference\n data = dataset.copy()\n\n # divide into k-folds\n folds = assign_randomly(data, nbr_folds)\n\n accuracy_train, accuracy_test = [], []\n MSE_train, MSE_test = [], []\n\n # train models for the folds\n for i in range(nbr_folds):\n\n # distribute into train and test data\n test_data, train_data = folds[i], []\n for j in range(nbr_folds):\n if i != j:\n train_data.extend(folds[j])\n\n train_data = np.array(train_data)\n test_data = np.array(test_data)\n\n # separate X (inputs) and Y (labels) for the training and testing data\n x_train, y_train = train_data[:, :-TOTAL_DIGIT_CLASSES], train_data[:, -TOTAL_DIGIT_CLASSES:]\n x_test, y_test = test_data[:, :-TOTAL_DIGIT_CLASSES], test_data[:, -TOTAL_DIGIT_CLASSES:]\n\n # Dimensionality reduction\n if feature_extraction == \"k-means distances\":\n clusters = k_means(x_train, m)\n codebooks = get_codebooks(clusters)\n x_train = select_features(x_train, codebooks)\n x_test = select_features(x_test, codebooks)\n elif feature_extraction == \"pca\":\n components, x_bar = get_pca_components(x_train, m)\n x_train = project_to_pca_comps(x_train, components)\n x_test = project_to_pca_comps(x_test, components)\n\n # compute models for training data\n model = compute_linreg(x_train, y_train, alpha)\n\n # prediction on training and testing data\n pred_train = predict_hypothesis_matrix(model, x_train)\n pred_test = predict_hypothesis_matrix(model, x_test)\n\n # assess accuracy\n _acc_train = check_accuracy(pred_train, y_train)\n _acc_test = check_accuracy(pred_test, y_test)\n\n accuracy_train.append(_acc_train)\n accuracy_test.append(_acc_test)\n\n # assesss MSE\n _MSE_train = check_MSE(pred_train, y_train)\n _MSE_test = check_MSE(pred_test, y_test)\n\n MSE_train.append(_MSE_train)\n MSE_test.append(_MSE_test)\n\n cv_accuracy_train = np.mean(accuracy_train)\n cv_accuracy_test = np.mean(accuracy_test)\n cv_MSE_train = np.mean(MSE_train)\n cv_MSE_test = np.mean(MSE_test)\n\n return cv_accuracy_train, cv_accuracy_test, cv_MSE_train, cv_MSE_test",
"title": ""
},
{
"docid": "abac45c5186b9632817485064a2cda0e",
"score": "0.6410815",
"text": "def random_labeled_data(data_size,randomness):\n\n with open('config.yml') as ymlfile:\n cgf = yaml.load(ymlfile, Loader=yaml.SafeLoader);\n n = cgf['DATASET_TRAIN']['arguments']['grid_size']\n input_shape = (n,n,1)\n output_shape = (1)\t\n\n weights_path = \"model/7/keras_model_files.h5\"\t\n model = mb.build_model_fc2(input_shape, output_shape,cgf['MODEL']['arguments'])\n model.load_weights(weights_path)\n\n optimizer = cgf['TRAIN']['optim']['type']\n loss_type= cgf['TRAIN']['loss']['type']\n metric_list = list(cgf['TRAIN']['metrics'].values())\n\n model.compile(optimizer=optimizer,\n loss=loss_type,\n metrics= metric_list)\n \n if randomness == \"gaussian\":\n #Gaussian with total mean 96 \n data = np.random.normal(0.09375, size=(data_size, n, n, 1))\n elif randomness == \"uniform\":\t\t\t\t\n #data = np.random.uniform(low=0, high=0.1875, size=(data_size, n, n, 1)) \n data = np.random.uniform(low=-0.5, high=2, size=(data_size, n, n, 1)) \n\n elif randomness == \"stripes\":\n data_loader_test = Data_loader_stripe_test(cgf['DATASET_TEST']['arguments'])\n data, _ = data_loader_test.load_data() \n\n sum_pixels = [i.sum() for i in data[:]]\n\n labels = model.predict(data)\n return data, labels, sum_pixels",
"title": ""
},
{
"docid": "d5770646ac9e05493f3a0a76c930a5fd",
"score": "0.6401645",
"text": "def cross_validate(model, cv_set, cv_target, n=..., shuffle=..., n_jobs=...):\n ...",
"title": ""
},
{
"docid": "a36652f9b73c547a618ff155e8f9161f",
"score": "0.6391487",
"text": "def main():\n\n configure_model_dir()\n base = os.environ['DATA_ROOT']\n\n dp = dataset.DatasetProvider(\n os.path.join(base, cfg.get('data', 'train')),\n cfg.get('args', 'max_files'),\n cfg.getint('args', 'max_cuis'),\n cfg.getint('args', 'samples_per_doc'),\n cfg.getint('bow', 'batch'),\n cfg.getboolean('args', 'make_alphabet'),\n cfg.getboolean('args', 'verbose'))\n\n max_cuis = int(cfg.get('args', 'max_cuis'))\n model = get_model(max_cuis, max_cuis - 1)\n optim = getattr(optimizers, cfg.get('bow', 'optimizer'))\n\n model.compile(\n loss='binary_crossentropy',\n optimizer=optim(lr=10**cfg.getint('bow', 'log10lr')),\n metrics=['accuracy'])\n\n callback = ModelCheckpoint(\n 'Model/model.h5',\n verbose=1,\n save_best_only=True)\n\n # load validation data\n val_x, val_y = dp.load(os.path.join(base, cfg.get('data', 'dev')))\n print('dev x, y shapes:', val_x.shape, val_y.shape)\n\n steps = math.ceil(dp.train_size / cfg.getint('bow', 'batch'))\n print('steps per epoch:', steps)\n\n model.fit_generator(\n dp.stream(),\n\t validation_data=(val_x, val_y),\n epochs=cfg.getint('bow', 'epochs'),\n steps_per_epoch=steps,\n verbose=0,\n callbacks=[callback])\n\n # save final model\n model.save('Model/final.h5')\n\n # probability for each class; (test size, num of classes)\n distribution = model.predict(val_x)\n\n # turn into an indicator matrix\n distribution[distribution < 0.5] = 0\n distribution[distribution >= 0.5] = 1\n\n f1 = f1_score(val_y, distribution, average='macro')\n p = precision_score(val_y, distribution, average='macro')\n r = recall_score(val_y, distribution, average='macro')\n print(\"\\nmacro: p: %.3f - r: %.3f - f1: %.3f\" % (p, r, f1))\n\n f1 = f1_score(val_y, distribution, average='micro')\n p = precision_score(val_y, distribution, average='micro')\n r = recall_score(val_y, distribution, average='micro')\n print(\"micro: p: %.3f - r: %.3f - f1: %.3f\" % (p, r, f1))",
"title": ""
},
{
"docid": "d4b5a2e8ccc39473c9e6240035c8ee52",
"score": "0.63597065",
"text": "def test_train_models_with_best_params_CD5(self, real_genomic_data, real_labels_cat, real_idx):\n\t\tdisease_id = disease_IDs[int(1-1)]\n\t\tchrom = 5\n\n\n\n\t\t# Load data, hp & labels\n\t\tdata = real_genomic_data(disease_id, chrom)\n\t\tfm = char_matrix_to_featmat(data, '3d', real_pnorm_feature_scaling)\n\n\t\tlabels_cat = real_labels_cat(disease_id)\n\n\t\thp = pickle.load(open(os.path.join(FINAL_RESULTS_DIR, 'hyperparams', disease_id, 'chrom{}.p'.format(chrom)), 'rb'))\n\t\tprint(hp)\n\n\t\thp['epochs'] = int(hp['epochs'])\n\t\thp['n_snps'] = int(fm.shape[1])\n\t\t#hp['epochs'] = 250\n\t\t#hp['hidden_neurons'] = 6\n\t\t#hp['lr'] = 1e-4\n\t\t#hp['l1_reg'] = 1e-5 # TODO change me back\n\t\t# Train\n\t\tos.makedirs(os.path.join(FINAL_RESULTS_DIR, 'csv_logs', disease_id), exist_ok=True)\n\n\t\tmodel = create_montaez_dense_model(hp)\n\t\tmodel.fit(x=fm,\n\t\t\t\t\ty=labels_cat,\n\t\t\t\t\tepochs=hp['epochs'],\n\t\t\t\t\tcallbacks=[\n\t\t\t\t\t\tCSVLogger(os.path.join(FINAL_RESULTS_DIR, 'csv_logs', disease_id, '{}'.format(chrom)))\n\t\t\t\t\t],\n\t\t\t\t\tverbose=0)\n\t\t\t\t\t\n\t\t# Calculate AUC from the best model\n\t\ty_pred = model.predict(x=fm)\n\n\t\t#print(classification_report(np.argmax(Y, axis=-1), np.argmax(y_pred, axis=-1), output_dict=False))\n\n\t\tauc = roc_auc_score(labels_cat, y_pred, average='weighted')\n\t\tprec = average_precision_score(labels_cat, y_pred, average='weighted')\n\t\tprint('ROC AUC score for best model: %f' % auc)\t\n\t\tprint('Prec-Rec AUC score for best model: %f' % prec)\t\n\t\t\n\t\tfilename = os.path.join(FINAL_RESULTS_DIR, 'trained_models', disease_id, 'model{}.h5'.format(chrom))\n\t\tos.makedirs(os.path.dirname(filename), exist_ok=True)\n\t\tmodel.save(filename)\n\t\tK.clear_session()\n\t\tdel data, fm, model",
"title": ""
},
{
"docid": "d4f5f13f17dc5afe9e9378e83539e768",
"score": "0.63550985",
"text": "def cvrand(model,\n data,\n traindatagen,\n testdatagen,\n itercol='subject',\n n_iterations=3,\n val_subjects=3,\n epochs=1,\n batch_size=32,\n steps_per_epoch=None,\n validation_steps=None,\n target_size=(227, 227),\n random_state=None,\n min_delta=0,\n patience=3):\n\n # Raise error if selected columns are numeric\n if pd.api.types.is_numeric_dtype(data[itercol]):\n raise TypeError('Columns must not be numeric')\n\n # set random seed\n np.random.seed(random_state)\n\n # Create empty lists\n sampledvalues = []\n validation_accuracies = []\n train_accuracies = []\n\n # Save initial default model weights\n wsave = model.get_weights()\n\n # Designate model checkpoint and callbacks_list\n checkpoint = ModelCheckpoint('weights.hdf5',\n mode='max',\n monitor='val_accuracy',\n save_best_only=True)\n\n earlystop = EarlyStopping(monitor='val_accuracy', min_delta=min_delta, patience=patience)\n\n callbacks_list = [checkpoint, earlystop]\n\n # Initialize iteration counter\n counter = 0\n\n # Pull unique values from itercol\n valuelist = data[itercol].unique()\n\n for i in range(n_iterations):\n sampledvalues.append(np.random.choice(valuelist, size=val_subjects, replace=False))\n\n # n_iteration for loop\n for i in range(n_iterations):\n # Substep counter\n counter += 1\n\n # Print iteration and substep progress\n print('CV iteration ' + str(counter) + ' of ' + str(n_iterations))\n\n # reset model states for fresh training\n model.set_weights(wsave)\n\n # Sample the validation values\n print('Validation subjects are ' + str(sampledvalues[i]))\n\n # Split train and test sets, iterating through each subject to be excluded from training\n cvtest = data[data[itercol].isin(sampledvalues[i])]\n cvtrain = data[~data[itercol].isin(sampledvalues[i])]\n\n # Split training data\n train = traindatagen.flow_from_dataframe(cvtrain,\n x_col='imgpath',\n y_col='classname',\n batch_size=batch_size,\n target_size=target_size,\n seed=random_state)\n # Split validation data\n val = testdatagen.flow_from_dataframe(cvtest,\n x_col='imgpath',\n y_col='classname',\n target_size=target_size,\n seed=random_state)\n # Fit model\n model.fit(train,\n epochs=epochs,\n steps_per_epoch=steps_per_epoch,\n validation_data=val,\n validation_steps=validation_steps,\n callbacks=callbacks_list)\n\n # Append lists\n valmax = max(model.history.history['val_accuracy'])\n valmaxindex = model.history.history['val_accuracy'].index(valmax)\n validation_accuracies.append(round(valmax, 3))\n train_accuracies.append(round(model.history.history['accuracy'][valmaxindex], 3))\n\n # Fill dataframe with stats\n dftemp = pd.DataFrame({'validation_subjects': sampledvalues,\n 'train_accuracies': train_accuracies,\n 'validation_accuracy': validation_accuracies})\n\n return dftemp",
"title": ""
},
{
"docid": "1f029f3238380f5112a162693418c945",
"score": "0.634898",
"text": "def main():\n ###!! Generate dataset at first.\n generate_train_test_files()",
"title": ""
},
{
"docid": "93341f85a925dac3cb2a7d86eebd4847",
"score": "0.6332978",
"text": "def Classification_test(config):\n\n # 1. Retrieve information from config dict\n device = config['device']\n device_name = torch.cuda.get_device_name(device)\n print('Device name: {}'.format(device_name))\n input_shape = config['input_shape']\n batch_size = config['batch_size']\n number_of_tools = config['number_of_tools']\n output_features = number_of_tools\n random_frames = config['random_frames']\n nr_videos = config['nr_videos']\n nr_frames = config['nr_frames']\n weight_decay = config['weight_decay']\n msg_bot = config['msg_bot']\n dataset_name = config['dataset']\n model_name = config['model']\n agent_name = 'TransNetAgent'\n\n\n # 2. Define data\n data = Data()\n data.add_dataset(Cholec80(random_frames, nr_videos, nr_frames))\n test_ds = (dataset_name, 'test')\n\n\n # 3. Split data (0% train, 100% test) and define path\n splits = dict()\n for ds_name, ds in data.datasets.items():\n splits[ds_name] = split_dataset(ds, test_ratio=1.0, \n val_ratio=0, nr_repetitions=config['nr_runs'], \n cross_validation=config['cross_validation'])\n pathr = os.path.join(model_result_path, 'models', dataset_name+'_'+model, 'test_results')\n if not os.path.exists(pathr):\n os.makedirs(pathr)\n else:\n # Empty directory\n shutil.rmtree(pathr)\n os.makedirs(pathr)\n\n\n # 4. Bring data to Pytorch format\n print('Bring data to PyTorch format..')\n # Repeat for each repition\n for run_ix in range(config['nr_runs']):\n # 5. Bring data to Pytorch format\n datasets = dict()\n for ds_name, ds in data.datasets.items():\n for split, data_ixs in splits[ds_name][run_ix].items():\n if len(data_ixs) > 0: # Sometimes val indexes may be an empty list\n aug = config['augmentation'] if not('test' in split) else 'none'\n datasets[(ds_name, split)] = PytorchClassification2DDataset(ds, \n ix_lst=data_ixs, size=input_shape, aug_key=aug, \n resize=config['resize'])\n \n # 6. Build test dataloader, and visualize\n dl = DataLoader(datasets[(test_ds)], \n batch_size=batch_size, shuffle=True,\n num_workers=1)\n\n # 7. Load pretrained model\n model = torch.load(os.path.join(model_result_path, 'models_', model_name, 'model.zip'))\n model.eval()\n model.to(device)\n\n # 8. Define loss and optimizer\n loss_f = LossBCE(device=device)\n \n # 9. Test model\n agent = getattr(agents, agent_name)(model=model, device=device)\n print('Testing model in batches of {}..'.format(batch_size))\n losses_test, _, accuracy_test, accuracy_det_test = agent.test(loss_f, dl, msg_bot=msg_bot)\n\n # 10. Save results\n save_only_test_results(pathr, losses_test, accuracy_test, accuracy_det_test)",
"title": ""
},
{
"docid": "19b4953eda7a0d519efa29d1a869444b",
"score": "0.63083833",
"text": "def train_validation_split(dataset_path, output_path, split_ratio, seed=120):\n img_categories = os.listdir(dataset_path) # all the image categories\n if os.path.exists(output_path):\n print('Dataset already exists at the given path')\n else:\n os.makedirs(output_path)\n os.mkdir(output_path + '/train')\n os.mkdir(output_path + '/validation')\n\n # for every image category in the dataset build train and val folders with images in them a/c to split_ratio\n print('Splitting dataset into train and validation sets: ')\n for img_category in img_categories:\n print('.', end='')\n # list all the images for this category\n imgs = os.listdir(dataset_path + '/' + img_category)\n # sort and shuffle images randomly\n imgs.sort()\n random.seed(seed)\n random.shuffle(imgs)\n # split the imgs into two halves train and test\n train_split = imgs[:int(split_ratio * len(imgs))]\n test_split = imgs[int(split_ratio * len(imgs)):]\n\n # built the train set and copy images\n if not os.path.exists(os.path.join(output_path, 'train', img_category)):\n os.mkdir(os.path.join(output_path, 'train', img_category))\n for img in train_split:\n source = os.path.join(dataset_path, img_category, img)\n dest = os.path.join(output_path, 'train', img_category, img)\n shutil.copy(source, dest)\n\n # built the test set and copy images\n if not os.path.exists(os.path.join(output_path, 'validation', img_category)):\n os.mkdir(os.path.join(output_path, 'validation', img_category))\n for img in test_split:\n source = os.path.join(dataset_path, img_category, img)\n dest = os.path.join(output_path, 'validation', img_category, img)\n shutil.copy(source, dest)\n print('\\nSuccess!!')",
"title": ""
},
{
"docid": "cf6efe5146cb309dfdc58ab445a2fca0",
"score": "0.6281842",
"text": "def create_sets():\n\n X_training_1, Y_training_1, y_training_1 = LoadBatch('../../cifar-10-batches-py/data_batch_1')\n X_training_2, Y_training_2, y_training_2 = LoadBatch('../../cifar-10-batches-py/data_batch_2')\n X_training_3, Y_training_3, y_training_3 = LoadBatch('../../cifar-10-batches-py/data_batch_3')\n X_training_4, Y_training_4, y_training_4 = LoadBatch('../../cifar-10-batches-py/data_batch_4')\n X_training_5, Y_training_5, y_training_5 = LoadBatch('../../cifar-10-batches-py/data_batch_5')\n\n X_training = np.concatenate((X_training_1, X_training_3), axis=1)\n X_training = np.copy(np.concatenate((X_training, X_training_4), axis=1))\n X_training = np.copy(np.concatenate((X_training, X_training_5), axis=1))\n\n X_training = np.concatenate((X_training, X_training_2[:, :9000]), axis=1)\n\n Y_training = np.concatenate((Y_training_1, Y_training_3), axis=1)\n Y_training = np.copy(np.concatenate((Y_training, Y_training_4), axis=1))\n Y_training = np.copy(np.concatenate((Y_training, Y_training_5), axis=1))\n\n Y_training = np.concatenate((Y_training, Y_training_2[:, :9000]), axis=1)\n\n y_training = y_training_1 + y_training_3 + y_training_4 + y_training_5 + y_training_2[:9000]\n\n X_validation = np.copy(X_training_2[:, 9000:])\n Y_validation = np.copy(Y_training_2[:, 9000:])\n y_validation = y_training_2[9000:]\n\n X_test, _, y_test = LoadBatch('../../cifar-10-batches-py/test_batch')\n\n mean = np.mean(X_training)\n X_training -= mean\n X_validation -= mean\n X_test -= mean\n\n return [X_training, Y_training, y_training], [X_validation, Y_validation, y_validation], [X_test, y_test]",
"title": ""
},
{
"docid": "d56f2c2b2a3f8242aba1a9f7544a3eb7",
"score": "0.62642205",
"text": "def train_test_original_dataset(norm, target_cat):\n x_train, x_test, y_train, y_test = train_test_split(norm, target_cat, test_size=0.15)\n\n model = keras.Sequential()\n model.add(keras.layers.Dense(10, input_dim=13, activation='relu'))\n model.add(keras.layers.Dense(8, activation='relu'))\n model.add(keras.layers.Dense(6, activation='relu'))\n model.add(keras.layers.Dense(3, activation='softmax'))\n\n\n model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=['accuracy'])\n\n model.fit(x_train, y_train, batch_size=15, epochs=2000, verbose=0)\n\n test_loss, test_acc = model.evaluate(x_test, y_test)\n\n print('Original dataset - test accuracy:', test_acc)",
"title": ""
},
{
"docid": "236f3049bbff14ac691a0f3dff2befdb",
"score": "0.62390447",
"text": "def model_cv(make_model, data, featurizer, n_folds = 10, random_state = 1, getX = Features.getX, getY = Features.getY, stratified = False, **kwargs):\n nobs = len(data)\n cv_accuracies = []\n if stratified:\n folds = StratifiedKFold(getY(data), n_folds = n_folds, random_state = random_state, **kwargs)\n else:\n folds = KFold(n = nobs, n_folds= n_folds, random_state = random_state, **kwargs)\n get_elems_at = lambda vals, indices: [vals[i] for i in indices]\n for fold_id, (train_indices, test_indices) in enumerate(folds):\n print \"Running fold %d\" % fold_id\n train_data = get_elems_at(data, train_indices)\n test_data = get_elems_at(data, test_indices)\n # Featurize each time since our features can depend on training data\n matrices = Features.make_experiment_matrices(train_data, test_data, featurizer, getX, getY)\n # always make a new version of model...safer, not sure if model.fit would overwrite if re-trained\n # as we want\n model = make_model()\n model.fit(matrices['train_X'], matrices['train_Y'])\n accuracy = model.score(matrices['test_X'], matrices['test_Y'])\n cv_accuracies.append(accuracy)\n mean_accuracy = numpy.mean(cv_accuracies)\n std_accuracy = numpy.std(cv_accuracies)\n return (mean_accuracy, std_accuracy)",
"title": ""
},
{
"docid": "ad94c4de6b9d3187bd9e40b7ca5000ae",
"score": "0.62292963",
"text": "def compare_model_algorithms(data, Nrep=2, Nfolds=5):\n\t\n\t# set RNG\n\tnp.random.seed(0)\n\trandom.seed(0)\n\t\n\t# set KNN algorithm options\n\tuser_opt_cos = {\"name\":\"cosine\", \"user_based\":True}\n\titem_opt_cos = {\"name\":\"cosine\", \"user_based\":False}\n\t\n\t# The algorithms to cross-validate\t\n\ts_SVD = SVD()\n\ts_SVDpp = SVDpp()\n\ts_NMF = NMF()\n\ts_SlopeOne = SlopeOne()\n\tu_KNNBasic = KNNBasic(sim_options=user_opt_cos)\n\tu_KNNWithMeans = KNNWithMeans(sim_options=user_opt_cos)\n\tu_KNNBaseline = KNNBaseline(sim_options=user_opt_cos)\n\ti_KNNBasic = KNNBasic(sim_options=item_opt_cos)\n\ti_KNNWithMeans = KNNWithMeans(sim_options=item_opt_cos)\n\ti_KNNBaseline = KNNBaseline(sim_options=item_opt_cos)\n\ts_CoClustering = CoClustering()\n\ts_BaselineOnly = BaselineOnly()\n\ts_NormalPredictor = NormalPredictor()\n\t\n\tclasses = [s_SVD, s_SVDpp, s_NMF, s_SlopeOne, u_KNNBasic, u_KNNWithMeans, \n\t\t\tu_KNNBaseline, i_KNNBasic, i_KNNWithMeans, i_KNNBaseline,\n\t\t\ts_CoClustering, s_BaselineOnly, s_NormalPredictor]\n\t\n\tclass_names = [\"SVD\", \"SVDpp\", \"NMF\", \"SlopeOne\", \"user-KNNBasic\", \"user-KNNWithMeans\", \n\t\t\t\t \"user-KNNBaseline\", \"item-KNNBasic\", \"item-KNNWithMeans\", \"item-KNNBaseline\",\n\t\t\t\t \"CoClustering\", \"BaselineOnly\", \"NormalPredictor\"]\n\t\n\t# repeat cross validation for different kfold splits for higher reliability\n\tperformance_list = []\n\theaders = ['RMSE', 'MAE', 'Time (min)']\n\tfor irep in range(0,Nrep):\n\t\t\n\t\t# cross validation folds will be the same for all algorithms. \n\t\tkf = KFold(n_splits=Nfolds,random_state=0) \n\n\t\t# cross validate for each algorithm\n\t\ttable = np.zeros((len(classes),len(headers)))\n\t\tfor ik, klass in enumerate(classes):\n\t\t start = time.time()\n\t\t out = cross_validate(klass, data, ['rmse', 'mae'], kf)\n\t\t cv_time = (time.time() - start) / 60\n\t\t mean_rmse = np.mean(out['test_rmse'])\n\t\t mean_mae = np.mean(out['test_mae'])\n\t\t table[ik,:] = np.array([mean_rmse, mean_mae, cv_time])\n\t\t\t\n\t\t# Accumulate results for each cross-validation\t\n\t\tperformance_list.append(table)\n\t\n\t# Show averaged results over cross validation iterations\n\tperformance = sum(performance_list)/len(performance_list)\n\tprint(tabulate(performance.tolist(), headers=headers, showindex=class_names))\n\n\treturn performance_list, performance",
"title": ""
},
{
"docid": "93ec9a46bb7b1e1cef72b0996fd24145",
"score": "0.62239105",
"text": "def split_data(n_folds,output, path_train=False, path_test=False):\n \n #define stratified K-fold\n kf = StratifiedKFold(n_splits=n_folds,shuffle=True,random_state=57)\n \n #loads files\n if path_train:\n X_train,Y_train = read_corpus(path_train)\n if path_test:\n X_test,Y_test = read_corpus(path_test)\n\n #cross-validation process with train and test in different input files\n if path_train and path_test:\n i=0\n for train_index, test_index in kf.split(X_train,Y_train):\n X_train = X_train[test_index]\n y_train = Y_train[test_index]\n\n print(i)\n print(X_train.shape, y_train.shape)\n print(\" \")\n\n save_corpus(output+\"train\"+str(i), y_train, X_train)\n \n i=i+1\n\n i=0\n for train_index, test_index in kf.split(X_test,Y_test):\n\n x_test = X_test[test_index]\n y_test = Y_test[test_index]\n\n print(i)\n print(x_test.shape, y_test.shape)\n print(\" \")\n\n save_corpus(output+\"test\"+str(i), y_test,x_test)\n i=i+1\n \n #cross-validation process with train and test in the same file \n if path_train and not path_test:\n i=0\n for train_index, test_index in kf.split(X_train,Y_train):\n X_train_fold, X_test_fold = X_train[train_index], X_train[test_index]\n y_train_fold, y_test_fold = Y_train[train_index], Y_train[test_index]\n \n print(i)\n print(X_train_fold.shape, y_train_fold.shape)\n print(X_test_fold.shape, y_test_fold.shape)\n print(\" \")\n \n save_corpus(output+\"train\"+str(i), train_index, y_train_fold,X_train_fold)\n save_corpus(output+\"test\"+str(i), test_index, y_test_fold, X_test_fold)\n save_corpus1(output+\"train\"+str(i)+'.csv', train_index, y_train_fold,X_train_fold)\n save_corpus1(output+\"test\"+str(i)+'.csv', test_index, y_test_fold, X_test_fold)\n i=i+1",
"title": ""
},
{
"docid": "08aafd6739f852cd594573eafc608366",
"score": "0.6206666",
"text": "def trainModel(self):\n\n # load data throught the provided dataset object\n X, y = self.dataset.load()\n \n # count number of classes\n unique, _ = np.unique(y, return_counts=True)\n \n # call buildDNN method\n model = self.buildDNN(n_classes = len(unique))\n \n # preprocess data thorugh the dataset class\n X_train = self.dataset.preProcessData(X)\n y_train = self.dataset.labalEncoding(y, n_classes = len(unique))\n \n # define optimizer\n optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)\n\n # compile the model\n model.compile(optimizer = optimizer , loss = \"categorical_crossentropy\", metrics= [\"accuracy\"])\n\n # set a learning rate annealer\n learning_rate_reduction = ReduceLROnPlateau(monitor='acc', \n patience=3, \n verbose=1, \n factor=0.5, \n min_lr=0.00001)\n \n # data augmentation in order to improve model performance\n datagen = ImageDataGenerator(\n featurewise_center=False, # set input mean to 0 over the dataset\n samplewise_center=False, # set each sample mean to 0\n featurewise_std_normalization=False, # divide inputs by std of the dataset\n samplewise_std_normalization=False, # divide each input by its std\n zca_whitening=False, # apply ZCA whitening\n rotation_range=10, # randomly rotate images\n zoom_range = 0.1, # randomly zoom image \n width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)\n height_shift_range=0.1, # randomly shift images vertically (fraction of total height)\n horizontal_flip=False, # randomly flip images\n vertical_flip=False) # randomly flip images\n\n datagen.fit(X_train)\n\n # fit the model\n history = model.fit_generator(datagen.flow(X_train,y_train, batch_size=self.batch_size),\n epochs = self.epochs, steps_per_epoch=X_train.shape[0] // self.batch_size\n ,callbacks=[learning_rate_reduction])\n\n # save the model\n if self.char:\n model.save(os.path.join(self.dirBin, 'model_char.h5'))\n model.save_weights(os.path.join(self.dirBin, 'model_char_weights.h5'))\n else:\n model.save(os.path.join(self.dirBin, 'model_digit.h5'))\n model.save_weights(os.path.join(self.dirBin, 'model_digit_weights.h5'))\n logging.info('Model saved in {}'.format(self.dirBin))\n \n if self.verbose:\n self.plotHistory(history)\n \n return model",
"title": ""
},
{
"docid": "07fc7c74abfd563d4f5047f0410fa1b2",
"score": "0.62013394",
"text": "def cross_validation():\n iris = datasets.load_iris()\n X, y = iris.data, iris.target\n\n kf = KFold(n_splits=5, shuffle=True)\n models = [NeuralNetwork([8, 2], alpha=1e-5), LogisticRegression(), GradientBoostingClassifier()]\n predictions = [np.zeros(y.shape) for i in models]\n for i, m in enumerate(models):\n for train_index, test_index in kf.split(X):\n m.fit(X[train_index], y[train_index])\n predictions[i][test_index] = m.predict(X[test_index])\n\n return [f1_score(y, predict, average='micro') for predict in predictions]",
"title": ""
},
{
"docid": "afb5b0d1989fd237fa72cd69d2ae8cda",
"score": "0.62008345",
"text": "def test_model(config, test_dataloader, device, model):\n model = model.to(device)\n model.eval()\n config[\"logger\"].info(\"Testing Model...\")\n correct_top1 = 0\n correct_top5 = 0\n total = len(test_dataloader.dataset)\n for (x, y, extra) in test_dataloader:\n if config[\"test\"][\"task\"] == \"CNN\":\n x = x[\"image\"].to(device)\n else:\n x = x.to(device)\n y = y.to(device)\n if config[\"train\"][\"task\"] == \"BDNN\":\n out = model(x)[0]\n else:\n out = model(x)\n y_pred = F.softmax(out, dim=1)\n y_pred = y_pred.cpu()\n y = y.cpu()\n # Calc Top-1\n pred = y_pred.argmax(dim=1)\n correct_top1 += torch.eq(pred, y).sum().float().item()\n # Calc Top-5\n maxk = max((1, 5))\n y_resize = y.view(-1, 1)\n _, pred = y_pred.topk(maxk, 1, True, True)\n correct_top5 += torch.eq(pred, y_resize).sum().float().item()\n correct_top1 /= total\n correct_top5 /= total\n config[\"logger\"].info(\"============Testing Results============\")\n config[\"logger\"].info(\"Top-1 Acc. {} Top-5 Acc. {}\".format(correct_top1, correct_top5))",
"title": ""
},
{
"docid": "a71666f94037f615546d1a382c18a5b1",
"score": "0.6186508",
"text": "def ex_2_1(X_train, y_train, X_test, y_test):\n\n randomSeed = np.random.randint(1, 100, 1)\n\n n_hidd = [100]\n\n score_train, score_test = [], []\n\n best_score = 0\n\n bestNetwork = MLPClassifier()\n\n classes = [\"T-shirt/top\", \"trousers/pants\", \"pullover shirt\", \"dress\", \"coat\", \"sandal\", \"shirt\", \"sneaker\", \"bag\", \"ankle boot\"]\n\n for n, n_h in enumerate(n_hidd):\n for s, seed in enumerate(randomSeed):\n nn = MLPClassifier(hidden_layer_sizes=(n_h,), activation='tanh', max_iter=50, random_state=seed)\n\n nn.fit(X_train, y_train)\n\n scoretrain = nn.score(X_train, y_train)\n scoretest = nn.score(X_test, y_test)\n\n score_train.append(scoretrain)\n score_test.append(scoretest)\n\n if scoretest > best_score:\n bestNetwork = nn\n best_score = scoretest\n\n print(100 / (len(n_hidd) * len(randomSeed)) * ((n*len(randomSeed)) + (s+1)), \"%\")\n\n \n plot_boxplot(score_train, score_test)\n\n\n prediction = bestNetwork.predict(X_test)\n confusionMatrix = confusion_matrix(y_test, prediction)\n\n #confusion matrix\n print(\"Confusion matrix:\")\n print(classes)\n\n print(confusionMatrix)\n\n #Weight\n print(len(bestNetwork.coefs_))\n\n plot_hidden_layer_weights(bestNetwork.coefs_[0])\n\n print(\"Misclassified Pictures\")\n\n falseList = prediction == y_test\n\n indexPosList = []\n\n for i, index in enumerate(falseList):\n if index == False:\n indexPosList.append(i)\n\n print(indexPosList)\n\n for i in range(5):\n print(\"MLPClassifer think it is\", prediction[indexPosList[i]] + 1, \"but it is\", y_test[indexPosList[i]]+1)\n plot_image(X_test[indexPosList[i]])\n ## TODO\n pass",
"title": ""
},
{
"docid": "49fbd35b39d01fd5d6ab4711badb3868",
"score": "0.6181956",
"text": "def make_problem(graphs, labels, n_validation=50, seed=0):\n if seed is not None:\n np.random.seed(seed)\n \n # convert label to 1 hot encoding\n y = convertToOneHot(labels)\n\n # Shuffle\n indexes = np.random.permutation(len(graphs))\n graphs_shuffle = np.array(graphs)[indexes]\n y_shuffle = np.array(y)[indexes]\n\n dataset_train = Dataset(graphs_shuffle[:-n_validation], y_shuffle[:-n_validation])\n dataset_validation = Dataset(graphs_shuffle[-n_validation:], y_shuffle[-n_validation:])\n return dataset_train, dataset_validation",
"title": ""
},
{
"docid": "47cf27be7827aae960dbc445f6eccda9",
"score": "0.61763227",
"text": "def test():\n test_dataset, test_dataset_length = dataloader(args_opt.data_url, epoch=1, batch_size=1,\n mode=args_opt.mode, shuffle=False)\n predict = []\n test_dataset_iter = test_dataset.create_dict_iterator()\n print('Valid dataset length:', test_dataset_length)\n deepid = get_network(args_opt, args_opt.num_class, True)\n param_network = load_checkpoint(args_opt.ckpt_url)\n load_param_into_net(deepid, param_network)\n print('Start Testing!')\n for data in test_dataset_iter:\n img1 = \"img1\"\n img2 = \"img2\"\n img1_test = data['image1']\n img2_test = data['image2']\n label = data['label']\n output1 = deepid(img1_test)\n output2 = deepid(img2_test)\n cosdistance = mindspore.ops.tensor_dot(output1, output2, (1, 1))\n norm1 = P.norm(output1, dim=1)\n norm2 = P.norm(output2, dim=1)\n cosdistance = cosdistance / (norm1 * norm2 + 1e-5)\n cosdistance = float(cosdistance.asnumpy())\n predict.append('{}\\t{}\\t{}\\t{}\\n'.format(img1, img2, cosdistance, int(label.asnumpy())))\n accuracy = []\n thd = []\n folds = KFold(n=3120, n_folds=10, shuffle=False)\n thresholds = np.arange(-1.0, 1.0, 0.005)\n ans = lambda line: line.strip('\\n').split()\n predicts = np.array([ans(x) for x in predict])\n for idx, (train, ktest) in enumerate(folds):\n print(\"now the idx is %d\", idx)\n best_thresh = find_best_threshold(thresholds, predicts[train])\n accuracy.append(eval_acc(best_thresh, predicts[ktest]))\n thd.append(best_thresh)\n print('ACC={:.4f} std={:.4f} thd={:.4f}'.format(np.mean(accuracy), np.std(accuracy), np.mean(thd)))",
"title": ""
},
{
"docid": "7b286e936ab6cb1f5e29a430cdc5b803",
"score": "0.6162955",
"text": "def train_model(model):\n\n # Pre-processing\n print('preprocessing')\n # Scale the image down to 90x90 & Apply random transform,\n datagenerator = ImageDataGenerator(\n # featurewise_center=True, featurewise_std_normalization=True,\n rotation_range=360, shear_range=0.6,\n horizontal_flip=True, vertical_flip=True,\n zoom_range=0.6, rescale=1./255,\n # preprocessing_function=preprocess_data\n )\n \n train_path = '/content/drive/My Drive/Colab Notebooks/Data/Train_data'\n test_path = '/content/drive/My Drive/Colab Notebooks/Data/Test_data'\n \n train_batches = datagenerator.flow_from_directory(train_path, target_size=scaled_img_dimensions, classes=['cherry', 'strawberry', 'tomato'], batch_size=32, class_mode='categorical')\n \n # Training\n # model.fit(generated_imgs, train_labels[0], epochs=20, batch_size=2000)\n model.fit_generator(train_batches, shuffle=True, epochs=2, steps_per_epoch=4500) \n\n return model",
"title": ""
},
{
"docid": "76296cbc051c6cd750cbcb7ec6e89219",
"score": "0.6159247",
"text": "def Classification_initialize_and_train(config):\n\n # 1. Retrieve information from config dict\n device = config['device']\n device_name = torch.cuda.get_device_name(device)\n print('Device name: {}'.format(device_name))\n input_shape = config['input_shape']\n batch_size = config['batch_size'] \n number_of_tools = config['number_of_tools']\n output_features = number_of_tools\n random_frames = config['random_frames']\n nr_videos = config['nr_videos']\n nr_frames = config['nr_frames']\n weight_decay = config['weight_decay']\n save_interval = config['save_interval']\n msg_bot = config['msg_bot']\n bot_msg_interval = config['bot_msg_interval']\n dataset_name = config['dataset']\n model_name = config['model']\n agent_name = 'TransNetAgent'\n\n\n # 2. Define data\n data = Data()\n data.add_dataset(Cholec80(random_frames, nr_videos, nr_frames))\n train_ds = (dataset_name, 'train')\n val_ds = (dataset_name, 'val')\n test_ds = (dataset_name, 'test')\n\n\n # 3. Split data and define path\n splits = dict()\n for ds_name, ds in data.datasets.items():\n splits[ds_name] = split_dataset(ds, test_ratio=config['test_ratio'], \n val_ratio=config['val_ratio'], nr_repetitions=config['nr_runs'], \n cross_validation=config['cross_validation'])\n\n # Include the model name, Alexnet, CNN, Resnet etc. what has been used\n paths = os.path.join(storage_data_path, 'models', dataset_name+'_'+model_name, 'states')\n pathr = os.path.join(model_result_path, 'models', dataset_name+'_'+model_name, 'results')\n if not os.path.exists(paths):\n os.makedirs(paths)\n else:\n # Empty directory\n shutil.rmtree(paths)\n os.makedirs(paths)\n if not os.path.exists(pathr):\n os.makedirs(pathr)\n else:\n # Empty directory\n shutil.rmtree(pathr)\n os.makedirs(pathr)\n\n # Save split\n if splits is not None:\n lr.save_json(splits, path=paths, name='data_splits')\n\n\n # 4. Create data splits for each repetition\n print('Bring data to PyTorch format..')\n # Repeat for each repition\n for run_ix in range(config['nr_runs']):\n # 5. Bring data to Pytorch format\n datasets = dict()\n for ds_name, ds in data.datasets.items():\n for split, data_ixs in splits[ds_name][run_ix].items():\n if len(data_ixs) > 0: # Sometimes val indexes may be an empty list\n aug = config['augmentation'] if not('test' in split) else 'none'\n datasets[(ds_name, split)] = PytorchClassification2DDataset(ds, \n ix_lst=data_ixs, size=input_shape, aug_key=aug, \n resize=config['resize']) #TODO: Test with resize=True and without approach from dataset_classification.py\n\n # 6. Build train dataloader, and visualize\n dl = DataLoader(datasets[(train_ds)], \n batch_size=batch_size, shuffle=True,\n num_workers=1)\n dl_val = DataLoader(datasets[(val_ds)], \n batch_size=batch_size, shuffle=True,\n num_workers=1)\n\n # 7. Initialize model\n model = getattr(models, model_name)(output_features)\n model.to(device)\n\n # 8. Define loss and optimizer\n loss_f = LossBCE(device=device)\n optimizer = optim.Adam(model.parameters(), lr=config['lr'],\n weight_decay=weight_decay)\n\n # 9. Train model\n print('Training model in batches of {}..'.format(batch_size))\n\n agent = getattr(agents, agent_name)(model=model, device=device)\n losses_train, losses_cum_train, losses_val, losses_cum_val,\\\n accuracy_train, accuracy_det_train, accuracy_val,\\\n accuracy_det_val = agent.train(optimizer, loss_f, dl,\n dl_val, nr_epochs=config['nr_epochs'],\n save_path=paths,\n save_interval=save_interval, msg_bot=msg_bot,\n bot_msg_interval=bot_msg_interval)\n \n # 10. Build test dataloader, and visualize\n dl = DataLoader(datasets[(test_ds)], \n batch_size=batch_size, shuffle=True)\n \n # 11. Test model\n print('Testing model in batches of {}..'.format(batch_size))\n losses_test, losses_cum_test, accuracy_test, accuracy_det_test = agent.test(loss_f, dl, msg_bot=msg_bot)\n\n # 12. Save results\n save_results(model, model_name, dataset_name, paths, pathr, losses_train, losses_val, accuracy_train,\n accuracy_det_train, accuracy_val, accuracy_det_val, losses_test, accuracy_test,\n accuracy_det_test, losses_cum_train, losses_cum_val)",
"title": ""
},
{
"docid": "e57cf40d2e063373cdc94bc0fc4667a8",
"score": "0.6158664",
"text": "def auth_train(client,random_class,train_samples):\n T = 16\n model = get_model()\n model.load_weights('./weights/new_14.129-0.9992.h5')\n print (len(model.layers))\n model.pop()\n frozen_layers = len(model.layers)-2\n model.add(Dense(2, activation='sigmoid'))\n for layer in model.layers[:frozen_layers]:\n layer.trainable = False\n model.summary()\n\n random_class[random_class.index(client)], random_class[0] = random_class[0], client\n\n rand_train_class = random_class[:50]\n SGD = sgd(lr=0.01, momentum=0.9, decay=1e-3)\n model.compile(loss='binary_crossentropy', optimizer=SGD, metrics=['accuracy'])\n early_stop = EarlyStopping(patience=7, verbose=1, monitor='val_loss')\n save_best = ModelCheckpoint('./weights/'+ str(client) + '.h5', monitor='val_loss', verbose=1,save_best_only=True)\n train_gen = get_imagedata.ImageDataGenerator(preprocessing_function=preprocess, client=client,\n height_shift_range=0.1,width_shift_range=0.1,\n rotation_range=10,zoom_range=(0.9,1))\n val_gen = get_imagedata.ImageDataGenerator(preprocessing_function=preprocess, client=client)\n\n model.fit_generator(\n train_gen.flow_from_directory('../data/5sec10',sub_dir=train_samples[:3], sequence=T,\n auth_rand_train=rand_train_class,\n target_size=(100, 100),batch_size=32,authentication=True)\n , steps_per_epoch=200, epochs=15, callbacks=[early_stop, save_best],\n validation_data=val_gen.flow_from_directory('../data/5sec10', sub_dir=train_samples[3:],\n sequence=T,auth_rand_train=rand_train_class,\n target_size=(100, 100), batch_size=32,authentication=True)\n , validation_steps=200, workers=4,use_multiprocessing=True)",
"title": ""
},
{
"docid": "ddc5e9ee025b64e904f4501a4eb8bb03",
"score": "0.61550874",
"text": "def train_on_test():\n create_cbk()\n train()\n test(\"res186/\", \"models_file186.txt\", \"train186.txt\")",
"title": ""
},
{
"docid": "397e5482e4215a42a3729ca79e7a3d4e",
"score": "0.61392045",
"text": "def cross_validate_all(model, data, labels):\n\n print(\"Running Cross Validation\")\n model = model\n dum_unifrom = DummyClassifier(strategy='uniform') #uniform most_frequent stratified\n dum_stratified = DummyClassifier(strategy='stratified')\n dum_freq = DummyClassifier(strategy='most_frequent')\n\n scoring = ['accuracy', 'f1_micro', 'f1_macro']\n\n cv = KFold(n_splits=5, shuffle=True, random_state=42)\n scores = cross_validate(model, data, labels, scoring=scoring, cv=cv, return_train_score=False, n_jobs=-1)\n dum_uniform_scores = cross_validate(dum_unifrom, data, labels, scoring=scoring, cv=cv, return_train_score=False, n_jobs=-1)\n dum_stratified_scores = cross_validate(dum_stratified, data, labels, scoring=scoring, cv=cv, return_train_score=False, n_jobs=-1)\n dum_freq_scores = cross_validate(dum_freq, data, labels, scoring=scoring, cv=cv, return_train_score=False, n_jobs=-1)\n\n acc_mean = np.around(scores['test_accuracy'].mean(), decimals=3)\n acc_std = np.around(scores['test_accuracy'].std(), decimals=3)\n\n micro_mean = np.around(scores['test_f1_micro'].mean(), decimals=3)\n micro_std = np.around(scores['test_f1_micro'].std(), decimals=3)\n\n macro_mean = np.around(scores['test_f1_macro'].mean(), decimals=3)\n macro_std = np.around(scores['test_f1_macro'].std(), decimals=3)\n\n # calculate the lift over random score\n dum_mean = dum_uniform_scores['test_f1_micro'].mean()\n strat_mean = dum_stratified_scores['test_f1_micro'].mean()\n freq_mean = dum_freq_scores['test_f1_micro'].mean()\n\n dum_lift_mean = np.around(((acc_mean-dum_mean)/dum_mean)*100, decimals=2)\n strat_lift_mean = np.around(((acc_mean-strat_mean)/strat_mean)*100, decimals=2)\n freq_lift_mean = np.around(((acc_mean-freq_mean)/freq_mean)*100, decimals=2)\n\n return acc_mean, acc_std, micro_mean, micro_std, macro_mean, macro_std, dum_lift_mean, strat_lift_mean, freq_lift_mean\n\n #acc = sk_ms.cross_val_score(model, data, labels, cv=10, scoring='accuracy', n_jobs=-1)\n #f1_macro = sk_ms.cross_val_score(model, data, labels, cv=10, scoring='f1_macro', n_jobs=-1)\n #f1_micro = sk_ms.cross_val_score(model, data, labels, cv=10, scoring='f1_micro', n_jobs=-1)\n\n #return acc, f1_macro, f1_micro",
"title": ""
},
{
"docid": "bce4e31380d3fe6a6391c7c869f47861",
"score": "0.61378807",
"text": "def train():\n # Set the random seeds for reproducibility\n # np.random.seed(42)\n\n onehot_input, y, _ = cross_entropy_input_to_onehot()\n\n LEARNING_RATE_DEFAULT = 3e-3\n MAX_STEPS_DEFAULT = 4000000\n\n cnet_a = CrossNet2(onehot_input.shape[1])\n script_directory = os.path.split(os.path.abspath(__file__))[0]\n filepath = 'grubbyStarCE2.model'\n model_to_train = os.path.join(script_directory, filepath)\n\n cnet_b = SimpleMLP(onehot_input.shape[1])\n script_directory_b = os.path.split(os.path.abspath(__file__))[0]\n filepath_b = 'grubbyStarCrossEntropy.model'\n model_b = os.path.join(script_directory_b, filepath_b)\n\n print(cnet_a)\n print(onehot_input.shape)\n print(onehot_input.shape[1])\n\n optimizer = torch.optim.SGD(cnet_a.parameters(), lr=1e-3, momentum=0.9, weight_decay=1e-5)\n optimizer_b = torch.optim.SGD(cnet_b.parameters(), lr=1e-3, momentum=0.9, weight_decay=1e-5)\n\n accuracies = []\n losses = []\n vag_losses = []\n min_loss = 100\n min_loss_b = 100\n\n vag_games = get_validation_ids()\n vag_games = np.array(vag_games)\n vag_ids = vag_games[-200:]\n validation_games = 100\n vag_input = onehot_input[vag_ids, :]\n vag_targets = y[vag_ids]\n\n for epoch in range(1):\n val_ids = [i for i in range(onehot_input.shape[0]-validation_games, onehot_input.shape[0])]\n val_ids = np.append(val_ids, vag_ids)\n val_ids = np.unique(val_ids)\n val_ids = np.array(val_ids)\n print(len(val_ids), \"val ids\")\n print(val_ids)\n\n train_ids = [i for i in range(onehot_input.shape[0]) if i not in val_ids]\n\n X_train = onehot_input[train_ids, :]\n print(X_train.shape)\n y_train = y[train_ids]\n\n X_test = onehot_input[val_ids, :]\n y_test = y[val_ids]\n\n print(\"epoch \" + str(epoch))\n saturation = 1\n p = 1\n bce = True\n ace = True\n\n for iteration in range(MAX_STEPS_DEFAULT):\n BATCH_SIZE_DEFAULT = 8\n cnet_a.train()\n cnet_b.train()\n if iteration % 20000 == 0:\n # saturation *= 0.5\n # saturation = max(0.5, saturation)\n print(iteration)\n print(saturation)\n\n ids = np.random.choice(X_train.shape[0], size=BATCH_SIZE_DEFAULT, replace=False)\n\n X_train_batch = X_train[ids, :]\n y_train_batch = y_train[ids]\n\n X_train_batch = np.reshape(X_train_batch, (BATCH_SIZE_DEFAULT, -1))\n X_train_batch = Variable(torch.FloatTensor(X_train_batch))\n\n output = cnet_a.forward(X_train_batch)\n output_b = cnet_b.forward(X_train_batch)\n\n y_train_batch = np.reshape(y_train_batch, (BATCH_SIZE_DEFAULT, -1))\n y_train_batch = Variable(torch.FloatTensor(y_train_batch))\n\n if iteration % 1 == 0:\n loss = center_my_loss(output, y_train_batch, output_b, saturation)\n else:\n loss = torch.nn.functional.binary_cross_entropy(output, y_train_batch)\n\n if True:\n loss_b = center_my_loss(output_b, y_train_batch, output, saturation)\n else:\n loss_b = torch.nn.functional.binary_cross_entropy(output_b, y_train_batch)\n\n ce_loss = torch.nn.functional.binary_cross_entropy(output, y_train_batch)\n ce_loss_b = torch.nn.functional.binary_cross_entropy(output_b, y_train_batch)\n\n if iteration % EVAL_FREQ_DEFAULT == 0:\n cnet_a.eval()\n cnet_b.eval()\n\n ids = np.array(range(len(X_test)))\n\n x = X_test[ids, :]\n targets = y_test[ids]\n\n x = np.reshape(x, (len(X_test), -1))\n\n x = Variable(torch.FloatTensor(x))\n\n pred = cnet_a.forward(x)\n pred_b = cnet_b.forward(x)\n acc = accuracy(pred, targets)\n\n targets = np.reshape(targets, (len(X_test), -1))\n targets = Variable(torch.FloatTensor(targets))\n\n calc_loss = torch.nn.functional.binary_cross_entropy(pred, targets)\n calc_loss_b = torch.nn.functional.binary_cross_entropy(pred_b, targets)\n\n accuracies.append(acc)\n\n ###################\n\n if p*calc_loss.item()+(1-p)*ce_loss.item() < p*calc_loss_b.item()+(1-p)*ce_loss_b.item():\n cnet_b.train()\n cnet_b.zero_grad()\n loss_b.backward(retain_graph=True)\n optimizer_b.step()\n cnet_b.eval()\n\n if min_loss_b > calc_loss_b.item():\n min_loss_b = calc_loss_b.item()\n torch.save(cnet_b, model_b)\n\n ids = np.array(range(len(X_train)))\n x = X_train[ids, :]\n targets = y_train[ids]\n\n x = np.reshape(x, (len(X_train), -1))\n\n x = Variable(torch.FloatTensor(x))\n\n pred_b = cnet_b.forward(x)\n\n train_acc = accuracy(pred_b, targets)\n\n targets = np.reshape(targets, (len(X_train), -1))\n targets = Variable(torch.FloatTensor(targets))\n\n train_loss = torch.nn.functional.binary_cross_entropy(pred_b, targets)\n losses.append(train_loss.item())\n\n print(\"iteration: \" + str(iteration) + \" train acc \" + str(train_acc) + \" val acc \" + str(\n acc) + \" a \" + str(round(calc_loss.item()*1000)/1000) + \" b \" + str(\n round(calc_loss_b.item() * 1000)/1000))\n\n if p*calc_loss.item()+(1-p)*ce_loss.item() > p*calc_loss_b.item()+(1-p)*ce_loss_b.item():\n cnet_a.train()\n cnet_a.zero_grad()\n loss.backward(retain_graph=True)\n optimizer.step()\n cnet_a.eval()\n\n if min_loss > calc_loss.item():\n min_loss = calc_loss.item()\n torch.save(cnet_a, model_to_train)\n\n ids = np.array(range(len(X_train)))\n x = X_train[ids, :]\n targets = y_train[ids]\n\n x = np.reshape(x, (len(X_train), -1))\n\n x = Variable(torch.FloatTensor(x))\n\n pred = cnet_a.forward(x)\n\n train_acc = accuracy(pred, targets)\n\n targets = np.reshape(targets, (len(X_train), -1))\n targets = Variable(torch.FloatTensor(targets))\n\n print(\"iteration: \" + str(iteration) + \" train acc \" + str(train_acc) + \" val acc \" + str(\n acc) + \" a \" + str(round(calc_loss.item()*1000)/1000) + \" b \" + str(\n round(calc_loss_b.item() * 1000)/1000))\n\n test_nn.test_all(model_to_train)\n print(model_to_train)\n\n plt.plot(accuracies)\n plt.ylabel('accuracies')\n plt.show()\n\n plt.plot(vag_losses, 'r')\n plt.plot(losses, 'b')\n plt.ylabel('losses')\n plt.show()",
"title": ""
},
{
"docid": "1ce0d687e29d6e3788e4836fe28ef733",
"score": "0.6130701",
"text": "def train():\n net = model(load=False, shape=(100, 100, 3))\n X, y = get_X_y('./data/t1_1/driving_log.csv')\n net.fit_generator(_generator(256, X, y), samples_per_epoch=20224, nb_epoch=2)\n net.save('checkpoints/short.h5')",
"title": ""
},
{
"docid": "5b79916dd2fd528c87eaac3cf7c4d4f1",
"score": "0.61234725",
"text": "def test_performance_cross_validation(dataset, classifier, label_col, n_folds, seed='46'):\n rand_col = \"uid_rand\"\n h = 1.0 / n_folds\n df = dataset.select(\"*\", f.rand(seed).alias(rand_col))\n\n metrics_dict = {\"roc_auc\": [], # roc: y=tpr x=fpr\n \"true_pos_rate\": [], # recall = true pos rate \n \"false_pos_rate\": [],\n \"precision\": [],\n \"n_true_neg\": [],\n \"n_false_neg\": [],\n \"n_false_pos\": [],\n \"n_true_pos\": [], }\n\n model = None\n for i in range(n_folds):\n\n validate_lb = i * h # lower bound\n validate_ub = (i + 1) * h # upper bound\n condition = (df[rand_col] >= validate_lb) & (df[rand_col] < validate_ub)\n validation = df.filter(condition)\n train = df.filter(~condition)\n \n # train\n model = classifier.fit(train)\n \n # predict\n prediction = model.transform(validation)\n \n # assess performance metrics\n prediction_and_labels = prediction.rdd.map(lambda x: (x['prediction'], x[label_col]))\n print(prediction_and_labels)\n metrics = MulticlassMetrics(prediction_and_labels)\n metrics_areas = BinaryClassificationMetrics(prediction_and_labels) # gets roc and precRecall curves\n metrics_dict['roc_auc'].append(metrics_areas.areaUnderROC)\n # a bit slow, have to calc outside loop\n cm = metrics.confusionMatrix().toArray()\n n_true_neg = cm[0, 0]\n n_false_neg = cm[1, 0]\n n_true_pos = cm[1, 1]\n n_false_pos = cm[0, 1]\n #\n metrics_dict['n_true_neg'].append(n_true_neg) \n metrics_dict['n_false_neg'].append(n_false_neg)\n metrics_dict['n_true_pos'].append(n_true_pos)\n metrics_dict['n_false_pos'].append(n_false_pos) \n metrics_dict['true_pos_rate'].append(n_true_pos / (n_true_pos+n_false_neg))\n metrics_dict['false_pos_rate'].append(n_false_pos / (n_false_pos+n_true_neg))\n metrics_dict['precision'].append(n_true_pos / (n_true_pos+n_false_pos))\n\n return model, metrics_dict",
"title": ""
},
{
"docid": "7b6f4a8324ee07037fa8904b56f6e4dc",
"score": "0.610934",
"text": "def prep_datasets(test_size, validation_size):\n X, y = load_data(DATA_PATH)\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size)\n X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=validation_size)\n\n return X_train, X_test, X_validation, y_train, y_test, y_validation",
"title": ""
},
{
"docid": "8be6173f0a97bc23d9661ba1438c9277",
"score": "0.6109022",
"text": "def few_label_classification(self, list_num_shots=[1, 10, 100, -1], num_runs=10, batch_size=1000, classifier='logistic'):\n if not os.path.exists('results/few_label/'):\n os.makedirs('results/few_label/')\n results = {'Num_Shots': [], 'Mean': [], 'Std': []}\n train_loader, im_h, im_w, im_channels = setup_data_loader(data=self.data, \n data_dir=self.data_dir, \n batch_size=batch_size, \n train=True, \n normalize=False if self.model_name in ['VAE', 'VAE_GMM'] else True, \n shuffle=False, \n shot_random_seed=None)\n zs_train, ys_train = self.encode_dataset(train_loader)\n \n test_loader, im_h, im_w, im_channels = setup_data_loader(data=self.data, \n data_dir=self.data_dir, \n batch_size=batch_size, \n train=False, \n normalize=False if self.model_name in ['VAE', 'VAE_GMM'] else True, \n shuffle=False, \n shot_random_seed=None)\n zs_test, ys_test = self.encode_dataset(test_loader)\n \n num_classes = len(np.unique(ys_train))\n \n for num_shots in tqdm(list_num_shots):\n Accuracy = []\n if num_shots == -1:\n clf = LogisticRegression(random_state=0, \n multi_class='auto', \n solver='liblinear', \n max_iter=10000).fit(zs_train, ys_train)\n Accuracy.append(np.array([clf.score(zs_test, ys_test)])) \n else:\n for i in tqdm(range(num_runs)):\n # torch.cuda.empty_cache()\n zs_train_selected = []\n ys_train_selected = []\n set_seed(i)\n for k in range(num_classes):\n indk = np.argwhere(ys_train == k)[:,0]\n ind_of_indk = np.random.permutation(len(indk))[:num_shots]\n indk_selected = indk[ind_of_indk]\n zs_train_selected.append(zs_train[indk_selected])\n ys_train_selected.append(ys_train[indk_selected])\n zs_train_selected = np.concatenate(zs_train_selected, 0)\n ys_train_selected = np.concatenate(ys_train_selected, 0)\n\n# print(indk_selected)\n \n clf = LogisticRegression(random_state=0, \n multi_class='auto', \n solver='liblinear', \n max_iter=10000).fit(zs_train_selected, ys_train_selected)\n Accuracy.append(np.array([clf.score(zs_test, ys_test)])) \n# return 0\n Accuracy = np.concatenate(Accuracy)\n results['Num_Shots'].append(num_shots)\n results['Mean'].append(Accuracy.mean())\n results['Std'].append(Accuracy.std())\n pd.DataFrame.from_dict(results).to_csv('results/few_label/{}-{}-runs={}.csv'.format(self.model_name, self.data, num_runs), index=False)\n return results",
"title": ""
},
{
"docid": "e4dbe68065116d05964931885b20db3b",
"score": "0.61042625",
"text": "def main():\r\n print \"running now\"\r\n print \"reading in data\"\r\n inputs_test = pandas.read_csv(\"random_baseline_test_inputs.csv\").values\r\n labels_test = pandas.read_csv(\"random_baseline_test_labels.csv\").values\r\n inputs_train = pandas.read_csv(\"two_layer_generated2_train_inputs.csv\").values\r\n labels_train = pandas.read_csv(\"two_layer_generated2_train_labels.csv\").values\r\n\r\n #Keeps track of the number of samples in each dataset.\r\n train_size = len(labels_train)\r\n test_size = len(labels_test)\r\n\r\n \"\"\"Batch size is how many samples to train/evaluate the model on at once.\r\n In my experience, this doesn't effect performance of the model but if it is\r\n too high, your machine can run out of memory and crash and if it is too low\r\n your training can take too long. 100 has worked well so far.\"\"\"\r\n batch_size=100\r\n\r\n \"\"\"The model will train for a number of epochs equal to num_epochs. One epoch\r\n passes when the model has looked at every point in the dataset for training.\r\n The more epochs, the better trained the model will be and the better performance\r\n it will have. More complex models tend to take more epochs to train properly. \r\n There will be a point of diminishing returns with the epochs. For this model\r\n improvements start to flatten out around 10 epochs\"\"\"\r\n num_epochs = 10 \r\n\r\n \"\"\"This is a small constant used in batch regularization calculations to prevent\r\n divide by zero errors. At present batch regularization code is not implemented\r\n but is commented out (more on that further down)\"\"\"\r\n delta = 10^-8 \r\n\r\n \"\"\"These are paramaters for dropout. Dropout is a way of preventing overfitting by\r\n temporarily removing neurons (by setting them to 0) during training. The idea is \r\n that you are forcing the rest of the neurons to compensate for the lost neuron and\r\n you are preventing neurons from \"conspiring\" to overfit. drop_input and drop_hidden\r\n are the proportions (out of 1) of the dropout inputs that are kept when dropout is \r\n called with those variables. Standard practice is to keep .8 of the input units and \r\n .5 of the units in each hidden layer. Here both keep probabilities are set to 1 meaning\r\n no dropout occurs. This is because overfitting has not yet become an issue. If it does\r\n (signified by a large difference between train and test errors), dropout can be \r\n implemented as a potential fix by changing these variables.\"\"\"\r\n drop_input = 1\r\n drop_hidden = 1\r\n\r\n\r\n print \"creating model\"\r\n \"\"\" x is a placeholder for the input data that is going to be passed in to the model.\r\n Later on when we tell the model to start training, we will tell it to look at the \r\n data we took from the CSVs to use as values for x. A value of [None, 5] in the shape \r\n argument means that there can be an arbitrary amount of length 5 vectors passed in\"\"\"\r\n x = tf.placeholder(tf.float64, [None, 5], name = \"input_placeholder\")\r\n\r\n\r\n \"\"\"These are placeholders for the dropout keep probabilities. We want dropout to occur only during \r\n training and not testing so we make keep probabilities placeholders and pass in the value we want\r\n later when we know which (training/testing) we are doing.\"\"\"\r\n drop_input_placeholder = tf.placeholder(tf.float64, shape = (), name = \"drop_input_placeholder\")\r\n drop_hidden_placeholder = tf.placeholder(tf.float64, shape = (), name = \"drop_hidden_placeholder\")\r\n\r\n #Applies dropout to inputs\r\n x_drop = tf.nn.dropout(x, drop_input_placeholder)\r\n\r\n \"\"\"If reuse is set to false, weights and biases are initialized according to defaults. If true,\r\n it reads in the initial values of weights and biases from saved CSVs\"\"\"\r\n reuse = False\r\n\r\n \"\"\"This is the first layer of the neural network. This layer accepts [batch_size x 5] input vectors \r\n and multiplies them be a bias and adds a weight to create a [batch_size x 10] hidden vector. It then\r\n applies a ReLU activation function to force all elements of h_1 to be non-negative. This effectively\r\n either turns off each neuron or turns them on with an intensity linerly proportional to the value of\r\n the hidden unit. Subsequent layers are shaped similarly.\"\"\"\r\n\r\n if (reuse):\r\n W_1 = tf.Variable(pandas.read_csv(\"two_layer_weights_1.csv\", header = None).values)\r\n b_1 = tf.Variable(pandas.read_csv(\"two_layer_biases_1.csv\", header = None).values)\r\n else:\r\n W_1 = weight_variable([5,10])\r\n b_1 = bias_variable([10])\r\n h_1 = tf.nn.relu(tf.matmul(x_drop, W_1) + b_1)\r\n\r\n \"\"\"Batch normalization is a technique that has been shown in other cases to dramatically help convergence.\r\n The commented out lines here show the form that it would take in this implementation. If one wanted to \r\n apply batch normalization to this entire program, they would just have to uncomment the lines below and\r\n copy them in every step of the neural network. (e.g. change sig_1 to sig_2, mu_1 to mu_2 etc. and put the \r\n steps between the calculations of h_2 and h_2_drop.) Imperically this does not help the current model as it\r\n stands but it should be retried every once in a while if hte model is changing becuase it has been so \r\n effective in practice.\r\n mu_1 = tf.reduce_mean(h_1)\r\n sig_1 = tf.sqrt(tf.reduce_mean(tf.squared_difference(h_1, mu_1))+delta)\r\n h_1 = tf.divide((h_1-mu_1),sig_1)\"\"\"\r\n\r\n #Applies dropout to the first hidden layer.\r\n h_1_drop = tf.nn.dropout(h_1, drop_hidden_placeholder)\r\n\r\n #Second hidden layer, takes [batch_size x 10] input and gives [batch_size x 5] output\r\n if (reuse):\r\n W_2 = tf.Variable(pandas.read_csv(\"two_layer_weights_2.csv\", header = None).values)\r\n b_2 = tf.Variable(pandas.read_csv(\"two_layer_biases_2.csv\", header = None).values)\r\n else: \r\n W_2 = weight_variable([10,5])\r\n b_2 = bias_variable([5])\r\n h_2 = tf.nn.relu(tf.matmul(h_1_drop, W_2) + b_2)\r\n h_2_drop = tf.nn.dropout(h_2, drop_hidden_placeholder)\r\n\r\n\r\n #[batch_size x 5] -> [batch_size x 1]\r\n if (reuse):\r\n W_3 = tf.Variable(pandas.read_csv(\"two_layer_weights_3.csv\", header = None).values)\r\n b_3 = tf.Variable(pandas.read_csv(\"two_layer_biases_3.csv\", header = None).values)\r\n else: \r\n W_3 = weight_variable([5,1])\r\n b_3 = bias_variable([1])\r\n y = tf.matmul(h_2_drop, W_3) + b_3\r\n\r\n\r\n print \"making definitions\"\r\n\r\n\r\n # y_ is a placeholder for the actual costs. It will later be filled in with label data.\r\n y_ = tf.placeholder(tf.float64, [None,], name=\"label_placeholder\")\r\n\r\n\r\n \"\"\"This is the cost for the neural network (not the pedestrian). Higher cost reflects\r\n predictions being further from reality.\"\"\"\r\n cost = tf.squared_difference(y, y_)\r\n\r\n #Accuracy is the average cost for the batch\r\n accuracy = tf.reduce_mean(cost) \r\n\r\n \"\"\"This is where the magic happens. Your optimizer will modify the weight and bias variables\r\n to minimize this cost as best it can\"\"\"\r\n train_step = tf.train.AdamOptimizer().minimize(cost)\r\n\r\n #Creates the session in which your model will run\r\n sess = tf.InteractiveSession()\r\n\r\n #In different versions of tensorflow, initialize_all_variables is used instead of global_variables_initializer\r\n tf.global_variables_initializer().run() \r\n \r\n print \"training\"\r\n\r\n #Calculating the number of batches you need based on number of samples and batch size\r\n num_batches = int(np.floor(train_size/batch_size))\r\n\r\n\r\n \"\"\"The way I've set this up, the entire dataset doesn't get looked at, it can only look at up to a \r\n multiple of the batch size. There are ways to account for everything but it isn't super critical to\r\n the effectiveness of the model so I've let it slide for now. These printouts give you a sense of how \r\n much data you are ignoring\"\"\"\r\n print \"train size\"\r\n print train_size\r\n print \"actual size I'm sampling from\"\r\n print num_batches*batch_size\r\n\r\n for j in range (num_epochs): #This for loop iterated over epochs to train the model\r\n print \"epoch %d of %d\" % (j+1, num_epochs)\r\n\r\n \"\"\"In order to keep consecutive samples independent of each other and allow training to converge better,\r\n the dataset must be shuffled before being trained. The following lines shuffle the data once per epoch. \r\n It is important that inputs and labels are shuffled in the same order so that their rows still correspond\r\n to each other.\"\"\"\r\n permutation = np.random.permutation(len(labels_train))\r\n\r\n print \"shuffling\"\r\n shuffled_inputs_train = np.copy(inputs_train)\r\n shuffled_labels_train = np.copy(labels_train)\r\n\r\n for old_index, new_index in enumerate(permutation):\r\n shuffled_labels_train[new_index, 0] = np.copy(labels_train[old_index,0])\r\n shuffled_inputs_train[new_index, 0:5] = np.copy(inputs_train[old_index,0:5])\r\n \r\n print \"training\"\r\n\r\n #Iterates training over every batch \r\n batch_error_total = 0 #resets the error counter\r\n for i in range(num_batches): \r\n batch_xs = shuffled_inputs_train[i*batch_size:(i+1)*batch_size,0:5] #takes states and actions for the current batch\r\n batch_ys = shuffled_labels_train[i*batch_size:(i+1)*batch_size,0] #takes labels (costs) for the current batch\r\n #the line below initiates the training and keeps track of accuracy. The feed_dict shows where to look for placeholder values\r\n _, batch_error = sess.run([train_step, accuracy], feed_dict={x: batch_xs, y_: batch_ys, drop_hidden_placeholder: drop_hidden, drop_input_placeholder: drop_input})\r\n batch_error_total += float(batch_error)/num_batches #keeps track of error\r\n avg_train_error = batch_error_total\r\n print \"epoch %d has average training error of %d\" % (j+1, avg_train_error)\r\n \r\n\r\n print \"testing\"\r\n \"\"\"keeps track of the maximum and minimum predictions. This is useful to see how \"adventurous\" the model gets in\r\n its predictions. A small range means it isn't really making much different precitions every time while a larger range\r\n means it is picking up on patterns and is really able to discriminate between high likelihood and low likelihood crash\r\n scenarios\"\"\"\r\n y_max = tf.reduce_max(y)\r\n y_min = tf.reduce_min(y)\r\n\r\n\r\n #Similar to during training, our testing is batched\r\n num_batches = int(np.floor(test_size/batch_size))\r\n print \"test size\"\r\n print test_size\r\n print \"actual size I'm sampling from\"\r\n print num_batches*batch_size\r\n\r\n batch_error_total = 0\r\n ymax = 0\r\n ymin = 9999999999999999999999999999\r\n for i in range(num_batches): \r\n batch_xs = inputs_test[i*batch_size:(i+1)*batch_size,0:5] \r\n batch_ys = labels_test[i*batch_size:(i+1)*batch_size,0]\r\n batch_error, y_max_, y_min_ = sess.run([accuracy, y_max, y_min], feed_dict={x: batch_xs, y_: batch_ys, drop_input_placeholder: 1, drop_hidden_placeholder: 1})\r\n batch_error_total += float(batch_error)/num_batches\r\n #updates ymax and ymin from batch\r\n if (y_max_ > ymax):\r\n ymax = y_max_\r\n if (y_min_ < ymin):\r\n ymin = y_min_\r\n avg_test_error = batch_error_total\r\n\r\n \r\n print(\"Test Error: \", avg_test_error)\r\n print(\"Range of Predicted Outputs: \", ymin,\" - \", ymax)\r\n\r\n w1_ = W_1.eval()\r\n b1_ = b_1.eval()\r\n w2_ = W_2.eval()\r\n b2_ = b_2.eval()\r\n w3_ = W_3.eval()\r\n b3_ = b_3.eval()\r\n\r\n \r\n #save numpy arrays to CSV\r\n np.savetxt(\"two_layer_weights_1.csv\", w1_, delimiter=\",\")\r\n np.savetxt(\"two_layer_biases_1.csv\", b1_, delimiter=\",\")\r\n np.savetxt(\"two_layer_weights_2.csv\", w2_, delimiter=\",\")\r\n np.savetxt(\"two_layer_biases_2.csv\", b2_, delimiter=\",\")\r\n np.savetxt(\"two_layer_weights_3.csv\", w3_, delimiter=\",\")\r\n np.savetxt(\"two_layer_biases_3.csv\", b3_, delimiter=\",\")",
"title": ""
},
{
"docid": "fd96f3ccb40454f64b27151c187fc1df",
"score": "0.609836",
"text": "def gen_imgs_1_to_5(samples, batch_size, crop_size,\n folder_to_save=False, shuffle=True):\n\n num_samples = len(samples)\n while 1:\n if shuffle:\n # if frac = 1 will reorganized list randomly\n samples = samples.sample(frac=1)\n\n for offset in range(0, num_samples, batch_size):\n batch_samples = samples.iloc[offset:offset+batch_size]\n images = []\n labels = []\n for _, batch_sample in batch_samples.iterrows():\n img = io.imread(batch_sample.patch_path)\n img = img[:, :, :3]\n image_name = osp.splitext(\n osp.basename(batch_sample.patch_path))[0]\n\n img = patch_aug_flip_rotate_crop_1_to_5(\n img, crop_size, image_name, folder_to_save)\n # img = patch_aug_flip_rotate_crop(\n # img, crop_size, image_name, folder_to_save)\n label = batch_sample['is_tumor']\n\n # images.append(img)\n images.extend(img)\n labels.append(label)\n labels.append(label)\n labels.append(label)\n labels.append(label)\n labels.append(label)\n\n X_train = np.array(images)\n y_train = np.array(labels)\n y_train = to_categorical(y_train, num_classes=2)\n\n yield X_train, y_train",
"title": ""
},
{
"docid": "97bd1848fb338eac7a886eda8b7ed8e8",
"score": "0.60963017",
"text": "def train_CNNs(datasets, input_size, num_of_classes, num_of_epochs, batch_size):\n i = 0\n for X_train, y_train, X_test, y_test in datasets:\n X_train, y_train, X_test, y_test = prepare_data(X_train, y_train, X_test, y_test, input_size)\n model = simple_cnn(input_size, num_of_classes)\n model.fit(X_train, y_train, epochs=num_of_epochs, batch_size=batch_size, validation_split=0.1, verbose=2)\n # model.save('cifar-100_disjoint_dataset_models/cnn_model_%d.h5' % i)\n i += 1",
"title": ""
},
{
"docid": "a7ca3b812c59bde2937561c954ce682a",
"score": "0.6095634",
"text": "def build_basic_model():\n model = Sequential(name='Age_CNN')\n\n model.add(Conv2D(filters=64,kernel_size=(5,5),input_shape=(48, 48, 1),activation='elu',padding='same',kernel_initializer='he_normal',name='conv2d_1'))\n model.add(BatchNormalization(name='batchnorm_1'))\n model.add(Conv2D(filters=64,kernel_size=(5,5),activation='elu',padding='same',kernel_initializer='he_normal',name='conv2d_2'))\n model.add(BatchNormalization(name='batchnorm_2'))\n \n model.add(MaxPooling2D(pool_size=(2,2), name='maxpool2d_1'))\n model.add(Dropout(0.4, name='dropout_1'))\n\n model.add(Conv2D(filters=128,kernel_size=(3,3),activation='elu',padding='same',kernel_initializer='he_normal',name='conv2d_3'))\n model.add(BatchNormalization(name='batchnorm_3'))\n model.add(Conv2D(filters=128,kernel_size=(3,3),activation='elu',padding='same',kernel_initializer='he_normal',name='conv2d_4'))\n model.add(BatchNormalization(name='batchnorm_4'))\n \n model.add(MaxPooling2D(pool_size=(2,2), name='maxpool2d_2'))\n model.add(Dropout(0.4, name='dropout_2'))\n\n model.add(Conv2D(filters=256,kernel_size=(3,3),activation='elu',padding='same',kernel_initializer='he_normal',name='conv2d_5'))\n model.add(BatchNormalization(name='batchnorm_5'))\n \n model.add(Conv2D(filters=256,kernel_size=(3,3),activation='elu',padding='same',kernel_initializer='he_normal',name='conv2d_6'))\n model.add(BatchNormalization(name='batchnorm_6'))\n \n model.add(MaxPooling2D(pool_size=(2,2), name='maxpool2d_3'))\n model.add(Dropout(0.5, name='dropout_3'))\n\n model.add(Flatten(name='flatten')) \n model.add(Dense(128,activation='elu',kernel_initializer='he_normal',name='dense_1'))\n model.add(BatchNormalization(name='batchnorm_7'))\n model.add(Dropout(0.6, name='dropout_4'))\n \n \n return model",
"title": ""
},
{
"docid": "244d2d4b98e214ff4bfdb3fd37ebc29f",
"score": "0.608587",
"text": "def cross_validate(self, folds):\n\t\tdata_size = len(self.MH.all_dat)\n\n\t\tif folds == -1:\n\t\t\tfolds = data_size # Leave one out cross validation\n\n\t\tgroup_size = int(data_size/folds)\n\n\t\trand_indices = random.sample([x for x in range(data_size)], data_size)\n\n\t\tvalidations = {}\n\t\tfor header in self.MH.output_headers:\n\t\t\tvalidations[header] = []\n\n\t\t# Go through each fold, train on everything else, and test it\n\t\tfor i in range(folds):\n\t\t\ttrain_indices = [x for x in (rand_indices[:i*group_size] + rand_indices[(i+1)*group_size:])]\n\t\t\tself.MH.make_train_data(train_indices)\n\t\t\tself.forward_model = ForwardModel()\n\t\t\ttest_indices = rand_indices[i*group_size:(i+1)*group_size]\n\t\t\ttest_dat = [self.MH.all_dat[x] for x in test_indices]\n\t\t\tfor i,dat_point in enumerate(test_dat):\n\t\t\t\tret_vals = self.validate_model(dat_point)\n\t\t\t\tfor header in ret_vals:\n\t\t\t\t\tvalidations[header].append(ret_vals[header]) # Validations dict says how well we did for the point\n\n\t\t# Data for the cross validation is written out at all_preds_droplet_size.csv and all_preds_generation_rate.csv\n\t\t# This data only has the shown file headers. For bulk statistics (like coefficient of determination), you will\n\t\t# need to run the data through DAFD/model_data/disp_graphs.py. See that file for more information.\n\t\tfor header in validations:\n\t\t\tfile_headers = [\"actual_val\" ,\"pred_val\" ,\"deviation\", \"deviation_percent\",\"actual_regime\" ,\"pred_regime\" ,\"chip_number\"]\n\t\t\twith open(\"all_preds_\" + header + \".csv\" ,\"w\") as f:\n\t\t\t\tf.write(\",\".join(file_headers) + \"\\n\")\n\t\t\t\tfor x in validations[header]:\n\t\t\t\t\tf.write(\",\".join([str(xi) for xi in x]) + \"\\n\")",
"title": ""
},
{
"docid": "1047f260623681f410855f4afdae04d8",
"score": "0.6082897",
"text": "def cross_validation_split(df, folds=10, is_shuffle=True):\n dataset_split = []\n df_copy = shuffle(df) if is_shuffle else df\n fold_size = int(df_copy.shape[0] / folds)\n training_dataset = []\n testing_dataset = []\n for i in range(folds):\n fold = []\n while len(fold) < fold_size:\n r = randrange(df_copy.shape[0])\n index = df_copy.index[r]\n fold.append(df_copy.loc[[index]])\n df_copy = df_copy.drop(index)\n dataset_split.append(pd.concat(fold))\n for i in range(folds):\n r = list(range(folds))\n r.pop(i)\n for j in r :\n if j == r[0]:\n cv = dataset_split[j]\n else: \n cv=pd.concat([cv,dataset_split[j]])\n training_data = cv.reset_index(drop=True)\n testing_data = dataset_split[i].reset_index(drop=True)\n training_dataset.append(training_data)\n testing_dataset.append(testing_data)\n return training_dataset, testing_dataset",
"title": ""
},
{
"docid": "93e1fba646891df795a989eba6ff3c8f",
"score": "0.60722953",
"text": "def main():\n size = 200 #size of the images\n imdim = size - 20 #strip 10 pixels buffer from each size\n direct = \"../data/images\"+str(size)+\"/\" #directory containing the images\n ld = listdir(direct) #contents of that directory\n numEx = len(ld)\n \n \n DUMP_WEIGHTS = True # will we dump the weights of conv layers for visualization\n \n shuffle(ld)\n \n trainTestSplit = 0.80\n \n with open(sys.argv[1]+\"testdata.csv\",'rb') as f:\n testFs = [pn for pn in f.read().split(\"\\n\") if pn != '']\n testL = len(testFs)\n \n print \"number of examples: \", numEx\n print \"test examples : \", testL\n \n batch_size = 32\n chunkSize = 2048\n testChunkSize = testL\n numTestEx = min(testL,testChunkSize)\n \n \n ecfps = getECFPvecs()\n atomlist = getAtomList()\n \n outsize = len(ecfps[ecfps.keys()[0]])\n \n testImages = np.zeros((testChunkSize,1,imdim,imdim),dtype=np.float)\n testTargets = np.zeros((testChunkSize,outsize),dtype=np.float)\n \n \n with open(sys.argv[1]+\"wholeModel.pickle\",'rb') as f:\n model = cPickle.load(f)\n \n \n numIterations = 1\n for i in range(0,numIterations):\n shuffle(testFs)\n count = 0\n cids = []\n while count < testChunkSize: \n for x in testFs: \n if x.find(\".png\") > -1:\n CID = x[:x.find(\".png\")]\n cids.append(CID)\n image = io.imread(direct+x,as_grey=True)[10:-10,10:-10] \n #image = np.where(image > 0.1,1.0,0.0)\n testImages[count,0,:,:] = image\n testTargets[count] = ecfps[CID]\n count +=1\n \n preds = model.predict(testImages)\n RMSE = np.sqrt(mean_squared_error(testTargets, preds)) \n ranks = [] \n print RMSE\n if RMSE < 300:\n for ind1 in range(0,len(preds)):\n p = [preds[ind1][ind2] for ind2 in range(0,len(preds[0]))]\n t = [int(testTargets[ind1][ind2]) for ind2 in range(0,len(testTargets[0]))]\n #printFormula(p,t,atomlist,cids[ind1])\n ranks.append(getRank(cids[ind1],p,ecfps))\n print ranks[ind1]\n print np.mean(ranks)\n with open(\"~/CHECKME.txt\",'wb') as f:\n f.write('\\n'.join(ranks))",
"title": ""
},
{
"docid": "281c22a40a9ccb649189afa665cbfa75",
"score": "0.60718745",
"text": "def preprocessing_test():\n # create folders for test data\n data_base_path = '../../../datasets/tiny-imagenet-200/'\n val_path = data_base_path + 'val/'\n test_path = data_base_path + 'test/'\n print(val_path)\n print(test_path)\n with open(data_base_path + 'wnids.txt', 'r') as wnids:\n i = 0\n for line in wnids:\n i += 1\n label = line.split('\\n')[0]\n print('%d. label: ' % i + label)\n val_label_path = val_path + label + '/'\n test_label_path = test_path + label + '/'\n if os.path.exists(val_label_path):\n if not os.path.exists(test_label_path):\n os.mkdir(test_label_path)\n # random choose images\n path = os.listdir(val_label_path)\n sample = random.sample(path, 25)\n print(len(sample))\n print(sample)\n for name in sample:\n shutil.move(val_label_path + name, test_label_path)",
"title": ""
},
{
"docid": "56056e5202e7ca56d503117d09dfcc0d",
"score": "0.6071002",
"text": "def train(batch, epochs, num_classes, size, weights, tclasses):\n\n train_generator, validation_generator, count1, count2 = generate(batch, size)\n\n train_generator = ImageDataGenerator(\n rescale=1. / 255,\n shear_range=0.2,\n zoom_range=0.2,\n rotation_range=90,\n width_shift_range=0.2,\n height_shift_range=0.2,\n brightness_range=(1, 1.3),\n horizontal_flip=True)\n \n directory=\"/home/gnss/Desktop/garbage_train\"\n train_generator = MixupImageDataGenerator(generator=train_generator,\n directory=directory,\n batch_size=32,\n img_height=224,\n img_width=224,\n subset='training')\n\n if weights:\n model = MobileNetv2((size, size, 3), tclasses)\n model = fine_tune(num_classes, weights, model)\n print(num_classes)\n else:\n model = MobileNetv2((size, size, 3), num_classes)\n print(num_classes)\n\n opt = Adam(1e-2)\n # earlystop = EarlyStopping(monitor='val_acc', patience=30, verbose=0, mode='auto')\n tensorboard = TensorBoard('/home/gnss/Desktop/MobileNetV2/logs',write_images=True)\n # reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5,patience=3, verbose=0, mode='auto', epsilon=0.0001, cooldown=0, min_lr=0)\n\n warmup_epoch = 5\n \n warm_up_lr = WarmUpCosineDecayScheduler(learning_rate_base=1e-2,\n total_steps=count1 // batch)\n\n checkpointer = ModelCheckpoint(filepath='/home/gnss/Desktop/MobileNetV2/mobilenet.h5',verbose=1,save_best_only=True)\n\n model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])\n\n \n\n # lr=XTensorBoard('/home/gnss/Desktop/MobileNetV2/logslr')\n\n hist = model.fit_generator(\n train_generator,\n validation_data=validation_generator,\n steps_per_epoch=count1 // batch,\n validation_steps=count2 // batch,\n epochs=epochs,\n callbacks=[warm_up_lr,tensorboard,checkpointer])\n '''\n learning_rate_base=1e-2\n total_steps=count1 // batch\n plt.plot(warm_up_lr.learning_rates)\n plt.xlabel('Step', fontsize=20)\n plt.ylabel('lr', fontsize=20)\n plt.axis([0, total_steps, 0, learning_rate_base*1.1])\n plt.xticks(np.arange(0, total_steps, 50))\n plt.grid()\n plt.title('Cosine decay with warmup', fontsize=20)\n plt.show()\n '''\n if not os.path.exists('model'):\n os.makedirs('model')\n\n df = pd.DataFrame.from_dict(hist.history)\n df.to_csv('model/hist.csv', encoding='utf-8', index=False)\n model.save_weights('model/weights.h5')",
"title": ""
},
{
"docid": "cabcd943cbdaf1e3270a11e84444f668",
"score": "0.6066594",
"text": "def _train_and_test_classifier(train_data, train_labels, test_data, test_labels, classifier_model):\n if classifier_model == 'random_forest':\n model = RandomForestClassifier()\n model.fit(train_data, train_labels)\n # out = codecs.open('/Users/mayankkejriwal/git-projects/dig-random-indexing-extractor/model', 'wb', 'utf-8')\n # joblib.dump(model, '/Users/mayankkejriwal/git-projects/dig-random-indexing-extractor/test/model')\n # out.close()\n predicted_labels = model.predict(test_data)\n print predicted_labels\n predicted_probabilities = model.predict_proba(test_data)\n # print predicted_labels[0:10]\n # print predicted_probabilities[0:10]\n elif classifier_model == 'knn':\n k = 1\n model = neighbors.KNeighborsClassifier(n_neighbors=k, weights='uniform')\n model.fit(train_data, train_labels)\n predicted_labels = model.predict(test_data)\n predicted_probabilities = model.predict_proba(test_data)\n elif classifier_model == 'manual_knn':\n # this is not an scikit-learn model; does not support predicted_probabilities\n k = 5\n predicted_labels = list()\n # print len(test_data)\n for t in test_data:\n scores_dict = dict()\n for i in range(0, len(train_data)):\n score = SimFunctions.SimFunctions.abs_dot_product_sim(train_data[i], t)\n label = train_labels[i]\n if score not in scores_dict:\n scores_dict[score] = list()\n scores_dict[score].append(label)\n results = kNearestNeighbors._extract_top_k(scores_dict, k=k)\n predicted_labels.append(TokenSupervised._compute_majority_label_in_vector(results))\n predicted_labels = np.array(predicted_labels)\n elif classifier_model == 'logistic_regression':\n model = LogisticRegression()\n model.fit(train_data, train_labels)\n predicted_labels = model.predict(test_data)\n predicted_probabilities = model.predict_proba(test_data)\n elif classifier_model == 'linear_regression': # this is a regressor; be careful.\n model = LinearRegression()\n model.fit(train_data, train_labels)\n predicted_labels = model.predict(test_data)\n print 'AUC (Area Under Curve): ',\n print roc_auc_score(test_labels, predicted_labels)\n # precision, recall, thresholds = precision_recall_curve(test_labels, predicted_labels)\n # plt.clf()\n # plt.plot(recall, precision, label='precision-recall-curve')\n # plt.xlabel('Recall')\n # plt.ylabel('Precision')\n # plt.ylim([0.0, 1.05])\n # plt.xlim([0.0, 1.0])\n # plt.title('Precision-Recall curve')\n # plt.savefig('/home/mayankkejriwal/Downloads/memex-cp4-october/tmp/fig.png')\n if classifier_model not in ['linear_regression']:\n print 'Accuracy: ',\n print accuracy_score(test_labels, predicted_labels)\n # print precision_score(test_labels, predicted_labels)\n prf = ['Precision: ', 'Recall: ', 'F-score: ', 'Support: ']\n print 'Class 0\\tClass 1'\n k = precision_recall_fscore_support(test_labels, predicted_labels)\n for i in range(0, len(k)):\n print prf[i],\n print k[i]",
"title": ""
},
{
"docid": "7ac78e1d8cb9eb12f562d133ce490d5c",
"score": "0.6062178",
"text": "def main():\n\n #I/O files\n masterTrainingData = \"train_sample.csv\"\n masterTestData = \"test.csv\"\n sampleTrainingData = \"train_sample_10000.csv\"\n submissionTemplate = \"sample_submission.csv\"\n submissionOutput = \"mySubmission.csv\"\n \n # fix random seed for reproducibility\n seed = 69\n np.random.seed(seed)\n\n #load training data from csv\n dataframe = pd.read_csv(masterTrainingData, header=0)\n\n # split into input (X) and output (Y) variables\n x_train_master, y_train_master = preprocessTraining(dataframe);\n\n #print(x_train)\n #print(len(x_train))\n #print(y_train)\n \n #downsample to avoid unbalanced data\n dataframe_train_neg = dataframe[(dataframe['is_attributed'] == 0)]\n dataframe_train_pos = dataframe[(dataframe['is_attributed'] == 1)]\n print(len(dataframe_train_neg))\n print(len(dataframe_train_pos))\n\n dataframe_train_neg_sample = dataframe_train_neg.sample(n=4000)\n dataframe_train_pos_sample = dataframe_train_pos.sample(n=227)\n \n print(len(dataframe_train_neg_sample))\n print(len(dataframe_train_pos_sample))\n\n dataframe_train_comb = pd.concat([dataframe_train_neg_sample,dataframe_train_pos_sample])\n x_train, y_train = preprocessTraining(dataframe_train_comb)\n \n #submission = pd.read_csv(submissionTemplate)\n #submission['is_attributed'] = y_predss\n #submission.to_csv(submissionOutput, index=False)\n #print(submission.head())\n\n # create model\n #model = Sequential()\n #model.add(Dense(6, input_dim=9, kernel_initializer='normal', activation='relu'))\n #model.add(Dense(3, kernel_initializer='normal', activation='relu'))\n #model.add(Dense(1, kernel_initializer='normal', activation='tanh'))\n # Compile model\n #model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy', 'sparse_categorical_accuracy'])\n #model.fit(x_train, y_train, epochs=8, batch_size=64)\n \n model = KerasRegressor(build_fn = create_model)\n layers = [[5], [6,3], [6,5,4,3]]\n activations = [relu, sigmoid]\n param_grid = dict(layers = layers, activation = activations, batch_size = [32, 64], epochs = [4])\n grid = GridSearchCV(estimator = model, param_grid = param_grid, scoring = 'neg_mean_squared_error')\n\n grid_result = grid.fit(x_train, y_train)\n print(grid_result.best_score_)\n print(grid_result.best_params_)\n #[grid_result.best_score_, grid_result.best_params_]\n\n model.fit(x_train, y_train)\n results = model.predict(x_train)\n results = np.where(results > 0.5, 1, 0)\n\n negCount = 0\n posCount = 0\n for i in range(0, len(results)):\n if results[i][0] == 1:\n posCount += 1\n else:\n negCount += 1\n print(negCount)\n print(posCount)\n \n score = model.evaluate(x_train_master, y_train_master, batch_size=128)\n print(score)\n \n #test on full training data\n results = model.predict(x_train_master)\n results = np.where(results > 0.5, 1, 0)\n \n negCount = 0\n posCount = 0\n for i in range(0, len(results)):\n if results[i][0] == 1:\n posCount += 1\n else:\n negCount += 1\n print(negCount)\n print(posCount)\n false_positive_rate, recall, thresholds = roc_curve(y_train_master, results)\n roc_auc = auc(false_positive_rate, recall)\n print(roc_auc)\n \n #dataframe = pd.read_csv(masterTestData, header=0)\n #x_test = preprocessTest(dataframe)\n\n #myPredictions = model.predict(x_test)\n #myPredictions = np.where(myPredictions > 0.5, 1, 0)\n\n #output to a submission file\n #mySubmission = pd.read_csv(submissionTemplate)\n #mySubmission['is_attributed'] = myPredictions\n #mySubmission.to_csv('mySubmission.csv', index=False)\n #print(mySubmission.head())",
"title": ""
},
{
"docid": "3fcacfc998e9f4ca1dfd924c0507673c",
"score": "0.60598314",
"text": "def FIDbenchmarking(models, df_ori, dropdummies = None, num_vars = None):\n \n # Set X and y for original data\n X_ori = df_ori.iloc[:,:-1]\n y_ori = df_ori.iloc[:,-1]\n\n # Split data up into 5 folds to execute gridsearch on 5 different folds to check stability of classifiers\n\n # Split into 5 stratified folds, so 5 \"different\" original sets with corresponding holdout validation sets are created\n skf = StratifiedKFold(n_splits=5, random_state=8, shuffle=True)\n\n # Save indices per split so separate dataframes can be saved later\n val_idx = []\n for train_index, test_index in skf.split(X_ori, y_ori):\n val_idx.append(test_index)\n\n # Create the 5 folds by saving the 5 validation sets\n fold_1 = df_ori.loc[val_idx[0]]\n fold_2 = df_ori.loc[val_idx[1]]\n fold_3 = df_ori.loc[val_idx[2]]\n fold_4 = df_ori.loc[val_idx[3]]\n fold_5 = df_ori.loc[val_idx[4]]\n\n folds = [fold_1, fold_2, fold_3, fold_4, fold_5]\n\n i=1\n DTscores = []\n LRscores = []\n for fold in folds:\n print(\"fold\", i)\n DTscores.extend((\"fold\", i))\n LRscores.extend(('fold', i))\n\n for model in models:\n pipe = Pipeline([('classifier' , model)])\n\n # Drop dummy variables in case of regression\n fold_copy = fold.copy()\n if model.__class__.__name__ == 'LogisticRegression':\n if dropdummies == None:\n print(\"Warning: no dummy columns specified to drop while regression is applied!\")\n else:\n df_ori_copy = fold_copy.drop(columns=dropdummies);\n print(\"Dummy columns dropped:\", dropdummies)\n\n # Scale numerical variables in case of regression\n if model.__class__.__name__ == 'LogisticRegression':\n if num_vars == None:\n print(\"Warning: no numerical variables specified to scale while regression is applied!\")\n else:\n fold_copy[num_vars] = preprocessing.minmax_scale(fold_copy[num_vars].astype(np.float64))\n print(\"Numerical variables scaled:\", num_vars)\n\n # Set X and y for the fold for this model\n X = fold_copy.iloc[:,:-1]\n y = fold_copy.iloc[:,-1]\n\n # Create param grid\n if model.__class__.__name__ == 'LogisticRegression':\n param_grid = [\n {'classifier' : [model],\n 'classifier__penalty' : ['l2'],\n 'classifier__C' : [0.0001, .001, .01, .1, 1, 10, 100],\n 'classifier__solver' : ['lbfgs'],\n 'classifier__max_iter' : [4000]}\n ]\n elif model.__class__.__name__ == 'DecisionTreeClassifier':\n param_grid = [\n {'classifier' : [model],\n 'classifier__criterion' : ['entropy'], # both (also gini) are considered, but after experimentation to speed up the process only entropy is kept\n 'classifier__min_samples_leaf' : [0.005], # Avoid overfitting by minimizing the number of samples in leaf node\n 'classifier__max_depth' : list(range(1,6))} # Avoid overfitting by limiting the depth\n ]\n else:\n print(\"param grid not available for this model\")\n\n # Create grid search object with random state for cv folds so DT and LR are tested on same folds\n clf = GridSearchCV(pipe, param_grid = param_grid, cv = StratifiedKFold(5, shuffle=True, random_state=2))\n\n print(\"model\", model)\n\n # Fit on data\n best_clf_ori = clf.fit(X, y)\n\n best_mean = best_clf_ori.cv_results_['mean_test_score'][best_clf_ori.best_index_] \n best_std = best_clf_ori.cv_results_['std_test_score'][best_clf_ori.best_index_] \n\n print(\"best parameters:\", best_clf_ori.best_estimator_.get_params()['classifier'])\n print(\"mean\", best_mean)\n print(\"std\", best_std)\n \n if model.__class__.__name__ == 'LogisticRegression':\n LRscores.extend((\"mean\", best_mean, \"std\", best_std))\n elif model.__class__.__name__ == 'DecisionTreeClassifier': \n DTscores.extend((\"mean\", best_mean, \"std\", best_std))\n i+=1\n\n return LRscores, DTscores",
"title": ""
},
{
"docid": "294bbf1213338bdd9c9735f086c160b5",
"score": "0.60585856",
"text": "def crossvalidation(input_data):",
"title": ""
},
{
"docid": "9e551fa8e33e7f8992c3da7730c1ec9d",
"score": "0.6049004",
"text": "def training():\n # red color training images\n for f in os.listdir('../data/training_dataset/red'):\n color_histogram_of_training_image('../data/training_dataset/red/' + f)\n\n # yellow color training images\n for f in os.listdir('../data/training_dataset/yellow'):\n color_histogram_of_training_image('../data/training_dataset/yellow/' + f)\n\n # green color training images\n for f in os.listdir('../data/training_dataset/green'):\n color_histogram_of_training_image('../data/training_dataset/green/' + f)\n\n # orange color training images\n for f in os.listdir('../data/training_dataset/orange'):\n color_histogram_of_training_image('../data/training_dataset/orange/' + f)\n\n # white color training images\n for f in os.listdir('../data/training_dataset/white'):\n color_histogram_of_training_image('../data/training_dataset/white/' + f)\n\n # black color training images\n for f in os.listdir('../data/training_dataset/black'):\n color_histogram_of_training_image('../data/training_dataset/black/' + f)\n\n # blue color training images\n for f in os.listdir('../data/training_dataset/blue'):\n color_histogram_of_training_image('../data/training_dataset/blue/' + f)",
"title": ""
},
{
"docid": "8306dae9d8431df121243b23398e11f9",
"score": "0.60411876",
"text": "def training(self):",
"title": ""
},
{
"docid": "30405d30ee3c4c458cb2f520effae2aa",
"score": "0.6039188",
"text": "def random_search(self, model, fold_n, _iter, scaled_X_train, fold_y_train, scaled_X_test, fold_y_test, params):\n if model == \"NN\":\n self.clfs[model] = keras.Sequential(\n [\n keras.layers.Dense(\n units=params[\"NN\"][_iter][\"neurons1\"],\n input_shape=(scaled_X_train.shape[1],),\n activation=params[\"NN\"][_iter][\"activation\"],\n kernel_initializer=keras.initializers.RandomNormal(seed=random_state),\n bias_initializer=keras.initializers.Constant(value=0.1),\n )\n ]\n )\n self.clfs[model].add(BatchNormalization())\n self.clfs[model].add(Dropout(params[\"NN\"][_iter][\"dropout_rate\"]))\n if params[\"NN\"][_iter][\"n_layers\"] == 2:\n self.clfs[model].add(\n layers.Dense(\n units=params[\"NN\"][_iter][\"neurons2\"],\n activation=params[\"NN\"][_iter][\"activation\"],\n kernel_initializer=keras.initializers.RandomNormal(seed=random_state),\n bias_initializer=keras.initializers.Constant(value=0.1),\n )\n )\n self.clfs[model].add(Dropout(params[\"NN\"][_iter][\"dropout_rate\"]))\n self.clfs[model].add(\n layers.Dense(\n 1,\n activation=\"sigmoid\",\n kernel_initializer=keras.initializers.RandomNormal(seed=random_state),\n bias_initializer=keras.initializers.Constant(value=0.1),\n )\n )\n\n opt = adam_v2.Adam(\n learning_rate=params[\"NN\"][_iter][\"learn_rate\"],\n beta_1=params[\"NN\"][_iter][\"beta_1\"],\n beta_2=params[\"NN\"][_iter][\"beta_1\"],\n )\n self.clfs[model].compile(loss=\"binary_crossentropy\", optimizer=opt, metrics=[keras.metrics.AUC()])\n\n # fit model\n self.clfs[model].fit(\n scaled_X_train,\n fold_y_train,\n batch_size=params[\"NN\"][_iter][\"batch_size\"],\n epochs=params[\"NN\"][_iter][\"epochs\"],\n shuffle=False,\n verbose=False,\n )\n # predict\n fold_pred = self.clfs[model].predict(scaled_X_test)\n fold_pred = np.concatenate(fold_pred).ravel().tolist()\n # calculate AUC\n auc_score = roc_auc_score(fold_y_test, fold_pred)\n # save results on next line\n self.cv_randomsearch_results_df.loc[len(self.cv_randomsearch_results_df) + 1] = [\n model,\n fold_n,\n _iter,\n params[\"NN\"][_iter],\n auc_score,\n ]\n elif model == \"XGBoost\":\n self.clfs[model].set_params(**params[model][_iter])\n # fit model\n self.clfs[model].fit(scaled_X_train, fold_y_train, eval_metric=\"auc\")\n # predict\n fold_pred = self.clfs[model].predict_proba(scaled_X_test)[::, 1]\n # calculate AUC\n auc_score = roc_auc_score(fold_y_test, fold_pred)\n # save results on next line\n self.cv_randomsearch_results_df.loc[len(self.cv_randomsearch_results_df) + 1] = [\n model,\n fold_n,\n _iter,\n self.clfs[model].get_params(),\n auc_score,\n ]\n else:\n self.clfs[model].set_params(**params[model][_iter])\n # fit model\n self.clfs[model].fit(scaled_X_train, fold_y_train)\n # predict\n fold_pred = self.clfs[model].predict_proba(scaled_X_test)[:, 1]\n # calculate AUC\n auc_score = roc_auc_score(fold_y_test, fold_pred)\n # save results on next line\n self.cv_randomsearch_results_df.loc[len(self.cv_randomsearch_results_df) + 1] = [\n model,\n fold_n,\n _iter,\n self.clfs[model].get_params(),\n auc_score,\n ]",
"title": ""
},
{
"docid": "15825037f710339d5328e69419d7995b",
"score": "0.60389423",
"text": "def train(train_data, test_data, foldid: int = 0):\n # Setup invariant repositories\n # we'll take I many examples for each task with different answers for each fold\n invariants = [select_invariants(train_data, i) for i in range(1, 5)] # T x (I, 1+L+1)\n invariants = np.stack(invariants) # (T, I, 1+L+1)\n # ---------------------------\n # Setup model\n model = UMLP(invariants)\n cmodel = Classifier(model)\n optimiser = C.optimizers.Adam(alpha=ARGS.learning_rate).setup(cmodel)\n train_iter = C.iterators.SerialIterator(train_data, ARGS.batch_size)\n updater = T.StandardUpdater(train_iter, optimiser, device=-1)\n trainer = T.Trainer(updater, (2000, 'iteration'), out='results/umlp_result')\n # ---------------------------\n fname = (ARGS.name.format(foldid=foldid) if ARGS.name else '') or ('debug' if ARGS.debug else '') or str(uuid.uuid4())\n # Setup trainer extensions\n if ARGS.debug:\n trainer.extend(print_vmap, trigger=(200, 'iteration'))\n test_iter = C.iterators.SerialIterator(test_data, 128, repeat=False, shuffle=False)\n trainer.extend(T.extensions.Evaluator(test_iter, cmodel, device=-1), name='test', trigger=(10, 'iteration'))\n # trainer.extend(T.extensions.snapshot(filename=fname+'_latest.npz'), trigger=(100, 'iteration'))\n trainer.extend(T.extensions.LogReport(log_name=fname+'_log.json', trigger=(10, 'iteration')))\n trainer.extend(T.extensions.FailOnNonNumber())\n train_keys = ['uloss', 'igloss', 'oloss', 'uacc', 'igacc', 'oacc', 'vloss']\n test_keys = ['uloss', 'oloss', 'uacc', 'oacc']\n trainer.extend(T.extensions.PrintReport(['iteration'] + ['main/'+k for k in train_keys] + ['test/main/'+k for k in test_keys] + ['elapsed_time']))\n # ---------------------------\n print(f\"---- FOLD {foldid} ----\")\n try:\n trainer.run()\n except KeyboardInterrupt:\n if not ARGS.debug:\n return\n # Save run parameters\n params = ['length', 'symbols', 'invariants', 'embed', 'train_size', 'learning_rate', 'nouni', 'batch_size']\n params = {k: vars(ARGS)[k] for k in params}\n params['name'] = fname\n params['foldid'] = foldid\n with open(trainer.out + '/' + fname + '_params.json', 'w') as f:\n json.dump(params, f)\n # Save learned invariants\n with open(trainer.out + '/' + fname + '.out', 'w') as f:\n f.write(\"---- META ----\\n\")\n train_data = np.stack(train_data)\n test_data = np.stack(test_data)\n meta = {'train': train_data.shape, 'train_tasks': np.unique(train_data[:,0], return_counts=True),\n 'test': test_data.shape, 'test_tasks': np.unique(test_data[:,0], return_counts=True),\n 'foldid': foldid}\n f.write(str(meta))\n f.write(\"\\n---- INVS ----\\n\")\n f.write(str(model.inv_examples))\n f.write(\"\\n--------\\n\")\n f.write(str(model.log['vmap'][0].array))\n for t in range(1, TASKS+1):\n f.write(\"\\n---- SAMPLE ----\\n\")\n test_data = np.stack(test_data) # (S, 1+L+1)\n np.random.shuffle(test_data)\n batch = test_data[test_data[:, 0] == t][:4] # (B, 1+L+1)\n f.write(\"Input:\\n\")\n f.write(str(batch))\n out = model(batch) # (B, V)\n f.write(\"\\nOutput:\\n\")\n f.write(str(out.array))\n f.write(\"\\nAtt:\\n\")\n f.write(str(model.log['uniatt'][0].array))\n f.write(\"\\n---- END ----\\n\")\n if ARGS.debug:\n print(batch)\n import ipdb; ipdb.set_trace()\n out = model(batch)",
"title": ""
},
{
"docid": "b66734712b88ba72ea46c914b473c20b",
"score": "0.6036707",
"text": "def load_6_class_classifier():\n\n required_input_shape = (*IMAGE_DIMS, IMAGE_NUM_CHANNELS)\n\n model = Sequential()\n model.add(Conv2D(32, (3, 3), padding='valid', input_shape=required_input_shape))\n model.add(BatchNormalization())\n model.add(Activation('relu'))\n model.add(MaxPooling2D(pool_size=(2, 2)))\n \n model.add(Conv2D(64, (3, 3)))\n model.add(BatchNormalization())\n model.add(Activation('relu'))\n model.add(MaxPooling2D(pool_size=(2, 2)))\n \n model.add(Conv2D(128, (3, 3)))\n model.add(BatchNormalization())\n model.add(Activation('relu'))\n model.add(MaxPooling2D(pool_size=(2, 2)))\n \n model.add(Dropout(0.5))\n\n model.add(Flatten())\n \n model.add(Dense(2048))\n model.add(BatchNormalization())\n model.add(Activation('relu'))\n model.add(Dropout(0.5))\n\n \n model.add(Dense(256))\n model.add(BatchNormalization())\n model.add(Activation('relu'))\n model.add(Dropout(0.6))\n \n model.add(Dense(6))\n model.add(Activation('softmax'))\n model.summary()\n\n # load the model weights\n model.load_weights(configurations.six_class_color_cnn_model_location)\n \n adam = optimizers.Adam(lr=0.00001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)\n model.compile(loss='sparse_categorical_crossentropy', optimizer=adam, metrics=['accuracy'])\n \n return model",
"title": ""
},
{
"docid": "e133f36ecf91955fb752183002c422c1",
"score": "0.6036274",
"text": "def setup_dataset(self):\r\n\r\n \"\"\"\r\n train_path = \"small_dataset/images/nir/\"\r\n val_path = \"small_dataset/images/nir/\"\r\n test_path = \"small_dataset/images/nir/\"\r\n\r\n train_labels_path = \"small_dataset/labels/\"\r\n val_labels_path = \"small_dataset/labels/\"\r\n test_labels_path = \"small_dataset/labels/\"\r\n \"\"\"\r\n\r\n train_path = f\"{self.cfg.data_dir}/images\"\r\n val_path = f\"{self.cfg.data_dir}/images\"\r\n test_path = f\"{self.cfg.data_dir}/images\"\r\n\r\n train_labels_path = f\"{self.cfg.data_dir}/labels/\"\r\n val_labels_path = f\"{self.cfg.data_dir}/labels/\"\r\n test_labels_path = f\"{self.cfg.data_dir}/labels/\"\r\n\r\n train = os.listdir(train_path)\r\n val = os.listdir(val_path)\r\n test = os.listdir(test_path)\r\n\r\n random.shuffle(train)\r\n random.shuffle(val)\r\n random.shuffle(test)\r\n\r\n train_img_names_index = train[:10000]\r\n val_img_names_index = val[:2000]\r\n test_img_names_index = test[:2000]\r\n\r\n labels_one_hot = {}\r\n k = self.cfg.classes # 8\r\n i=0\r\n for label in listdir_nohidden(train_labels_path):\r\n if label!=\"storm_damage\":\r\n labels_one_hot[label] = np.zeros((k,))\r\n labels_one_hot[label][i] = 1\r\n i+=1\r\n\r\n train_dataset = SegmentationDataset(\"train\", train_img_names_index, labels_one_hot, train_path, train_labels_path, use_cache=True)\r\n val_dataset = SegmentationDataset(\"validation\", val_img_names_index, labels_one_hot, val_path, val_labels_path, use_cache=True)\r\n # test_dataset = SegmentationDataset(\"test\", test_img_names_index, labels_one_hot, test_path, test_labels_path, use_cache=True)\r\n\r\n self.train_dataloader = DataLoader(train_dataset, batch_size=self.batch_size, shuffle=self.shuffle)\r\n self.val_dataloader = DataLoader(val_dataset, batch_size=self.batch_size, shuffle=self.shuffle)\r\n # self.test_dataloader = DataLoader(test_dataset, batch_size=self.batch_size)\r",
"title": ""
},
{
"docid": "7f5702df2c52b31ca43f0ceeda852595",
"score": "0.6029016",
"text": "def testUnseenDES2017(model, neural_network, n_splits): # from last time everything worked\n\n # def testUnseenDES2017(model, neural_network, n_splits, input_shape):\n\n known_des2017_array = getUnseenDES2017()\n\n num = 47\n unknown_array = getUnseenNegative(num)\n\n image_test, labels_test = loadImage(known_des2017_array, unknown_array)\n print(\"Image test shape: \", str(image_test.shape))\n image_test = image_test.reshape(image_test.shape[0], input_shape[0], input_shape[1], input_shape[2])\n\n encoder = LabelEncoder()\n y_image_labels = encoder.fit_transform(labels_test)\n\n y_pred = model.predict(image_test)\n\n y_test_index = np.round(y_pred)\n ones = np.count_nonzero(y_test_index == 1)\n zeroes = np.count_nonzero(y_test_index == 0)\n\n print(\"Ones: %s / 47\" % ones)\n print(\"Zeroes: %s / 47\" % zeroes)\n\n # Get Accuracy Score tests DES2017 on the mlpclassifier:\n _, acc = model.evaluate(image_test, y_image_labels, verbose=0)\n accuracy_score_47 = acc * 100\n print(\"Accuracy Score_47: \" + str(accuracy_score_47))\n\n # get the k fold accuracy after k fold cross validation\n scores = cross_val_score(neural_network, image_test, y_image_labels, scoring='accuracy', cv=n_splits)\n scores_mean = scores.mean() * 100\n print(\"kFold47 Scores Mean: \" + str(scores_mean))\n k_fold_std_47 = scores.std()\n print(\"kFold47 Scores Std: \" + str(k_fold_std_47))\n k_fold_accuracy_47 = scores_mean\n\n return known_des2017_array, accuracy_score_47, k_fold_accuracy_47, k_fold_std_47",
"title": ""
},
{
"docid": "4cdaa765ddb0e0fc7523a54313636171",
"score": "0.6024136",
"text": "def test_train_model(self):\n opt = {\n 'model': 'projects.self_feeding.self_feeding_agent:SelfFeedingAgent',\n 'task': 'self_feeding:all',\n 'max_train_time': 120,\n 'dia_train': 'train_hh131k_hb60k.txt',\n 'n_layers': 2,\n 'n_heads': 2,\n 'candidates': 'batch',\n 'validation_metric': 'dia_acc',\n 'optimizer': 'adamax',\n 'learning_rate': 0.0025,\n 'ffn_size': 32,\n 'batchsize': 32,\n 'embeddings_scale': False,\n }\n _, _, _ = testing_utils.train_model(opt)",
"title": ""
},
{
"docid": "5de5956f3a979ffe84b3f90233b11164",
"score": "0.60239935",
"text": "def fit(data, n1, lr, bs, bayes_scope, model_num, args):\n tf.set_random_seed(1234) # set tf seed to keep same weight initialization for each model\n n_epochs = args.n_epochs # number of epochs\n\n # track losses and accuracies of each fold\n loss = []\n accuracy = []\n test_list = []\n # perform cross validation\n with temp_seed(0):\n for counter in range(len(data.y_train_list)):\n bayes_scope += 1\n with tf.Session() as sess:\n x_train, y_train, x_test, y_test = data.return_cross_val(counter)\n if args.noise:\n x_train, y_train = data.training_augment(y_train, args.n_aug)\n low_loss, low_accuracy = run(sess, x_train, y_train, x_test, y_test, n1, lr, bs, n_epochs, str(bayes_scope), counter, model_num)\n loss.append(low_loss)\n accuracy.append(low_accuracy)\n sess.close()\n tf.reset_default_graph()\n test_list.append(y_test)\n\n # calculate mean and std of losses and accuracies across the folds\n loss = np.array(loss)\n accuracy = np.array(accuracy)\n loss_mean = np.mean(loss)\n loss_std = np.std(loss)\n accuracy_mean = np.mean(accuracy)\n accuracy_std = np.std(accuracy)\n\n return loss, accuracy, loss_mean, loss_std, accuracy_mean, accuracy_std, test_list",
"title": ""
},
{
"docid": "aea65cd17178461f0d9488bb540e9918",
"score": "0.60236037",
"text": "def prepare_train_test_split(self, test_pct, val_pct, seed):\n\n\n if self.data is None:\n raise Exception('Error: Must preporcess data first')\n\n features = list(self.data.columns)\n features.remove(\"genre\")\n\n #split to features and labels\n X = self.data[features].values\n y = self.data[\"genre\"].values\n\n #split the dataset to train, validation and test datasets\n X_train_val, self.X_test, y_train_val, self.y_test = train_test_split(X, y, test_size=test_pct, shuffle=True,\n stratify=y, random_state=seed)\n self.X_train, self.X_val, self.y_train, self.y_val = train_test_split(X_train_val, y_train_val,\n test_size=val_pct, shuffle=True,\n stratify=y_train_val,random_state=seed)\n\n print(f\"Loaded train dataset containing: {len(self.X_train)} samples\")\n print(f\"Loaded validation dataset containing: {len(self.X_val)} samples\")\n print(f\"Loaded test dataset containing: {len(self.X_test)} samples\")\n\n images_files_df = pd.read_csv(os.path.join(self.image_source_path, self.source_images_list))\n\n\n self.prepare_images_genres('images-train', self.X_train, self.y_train,\n self.preprocessed_config[\"preprocess_col_order\"].index(\"book_id\"))\n self.prepare_images_genres('images-val', self.X_val, self.y_val,\n self.preprocessed_config[\"preprocess_col_order\"].index(\"book_id\"))\n self.prepare_images_genres('images-test', self.X_test, self.y_test,\n self.preprocessed_config[\"preprocess_col_order\"].index(\"book_id\"))\n\n print(f\"All images prepared in folders\")\n\n #increase the dimention of labels to be 2 dim array\n self.y_train = self.y_train[:, np.newaxis]\n self.y_val = self.y_val[:, np.newaxis]\n self.y_test = self.y_test[:, np.newaxis]\n\n from . import dump_datasets_to_pickle, load_datasets_from_pickle\n dump_datasets_to_pickle(self.data_path, np.hstack((self.X_train, self.y_train)), \"train.pkl\")\n dump_datasets_to_pickle(self.data_path, np.hstack((self.X_val, self.y_val)), \"val.pkl\")\n dump_datasets_to_pickle(self.data_path, np.hstack((self.X_test, self.y_test)), \"test.pkl\")\n print(f\"Dataset splits saved as pickle files\")\n\n return None",
"title": ""
},
{
"docid": "244f93f0465b452b71453a0937425bb8",
"score": "0.6017135",
"text": "def train_cnn_old(self, img_folder,check_folder=params.directories['cnn_checkpoint_weights'],pre_trained_weight_path=''):\n if os.path.exists(check_folder)==0:\n os.mkdir(check_folder) \n img_file_t, lable_t,visit_file_t, img_file_v, label_v, visit_file_v=load_class_balanced_files(img_folder,max_samples=-100)\n \n nb_epoch=500\n batch_size=round(128)\n n_batch_per_epoch=len(img_file_t)//batch_size\n\n num_val_sample=len(img_file_v)\n n_batch_per_epoch_val=num_val_sample//batch_size\n # data_train, label_train=input_load_train_data(img_file_t,visit_file_t, lable_t)\n # data_val, label_val=input_load_train_data(img_file_v,visit_file_v, label_v)\n train_generator=input_generator(img_file_t, lable_t,visit_file_t, batch_size)\n val_generator = input_generator(img_file_v,label_v,visit_file_v,batch_size)\n\n model = get_cnn_model(self.params)\n #model = make_parallel(model, 4)\n if len(pre_trained_weight_path)>1:\n model.load_weights(pre_trained_weight_path,True)\n print('use pre-trained weights: ',pre_trained_weight_path) \n model.compile(optimizer=Adam(lr=self.params.cnn_adam_learning_rate), loss='categorical_crossentropy', metrics=['accuracy'])\n\n print(\"training\")\n filePath = os.path.join(check_folder, 'weights.{epoch:02d}.hdf5')\n checkpoint = ModelCheckpoint(filepath=filePath, monitor='loss', verbose=0, save_best_only=False,\n save_weights_only=False, mode='auto', period=5)\n csv_logger = CSVLogger(os.path.join(check_folder,'train.csv'))\n callbacks_list = [csv_logger,checkpoint]\n\n model.fit_generator(train_generator, steps_per_epoch=n_batch_per_epoch,\n validation_data=val_generator, validation_steps=n_batch_per_epoch_val,\n epochs=nb_epoch, callbacks=callbacks_list)\n\n #model.fit(data_train,label_train, batch_size=batch_size,validation_data=(data_val, label_val),\n # epochs=nb_epoch, callbacks=callbacks_list)",
"title": ""
},
{
"docid": "25604d96783bc22ea17ba269d69f351c",
"score": "0.601373",
"text": "def do_kfold_validation(x, y, x_test, y_test):\n # initializes kfold with 5 folds, including shuffling,\n # using 9 as seed for the shuffling\n kfold = KFold(n_splits=5, random_state=9, shuffle=True)\n\n # uses a Gaussian Naive Bayes classifier\n model = GaussianNB()\n\n # splits datasets into folds and trains the model\n for train_index, val_index in kfold.split(x):\n x_train, x_val = x.loc[train_index], x.loc[val_index]\n y_train, y_val = y.loc[train_index], y.loc[val_index]\n # training of the model\n model.fit(x_train, y_train)\n\n evaluate_accuracy(model, x_train, x_val, x_test, y_train, y_val, y_test)\n # plot_correlation(x)\n # plot_frequency(x, 'hesitations')",
"title": ""
},
{
"docid": "e4a45402288d6f35db88557c8e6796d2",
"score": "0.60123587",
"text": "def valid_model(model,device,cls_cri):\n # test datasets\n test_set = cubdataset.CubDataset(config.DATA_ROOT,training = False,resize = config.RESIZE,crop = config.CROP)\n test_loader = DataLoader(test_set,1,shuffle = False,num_workers = 1)\n\n # valid run.\n low_cls_loss = 0\n mid_cls_loss = 0\n hig_cls_loss = 0\n coa_cls_loss = 0\n com_cls_loss = 0\n\n low_acc = 0\n mid_acc = 0\n hig_acc = 0\n coa_acc = 0\n com_acc = 0\n fine_acc = 0\n ens_acc = 0\n total_num = len(test_loader)\n\n # change to eval mode.\n model.eval()\n with torch.no_grad():\n for data in tqdm.tqdm(test_loader,desc = \"valid\"):\n # data\n imgs,targets = data\n imgs = imgs.to(device)\n targets = targets.to(device)\n\n # forward\n low_logits,mid_logits,hig_logits,coa_logits,com_logits = model(imgs)\n low_loss,mid_loss,hig_loss,coa_loss,com_loss = \\\n cls_cri(low_logits,targets),cls_cri(mid_logits,targets),\\\n cls_cri(hig_logits,targets),cls_cri(coa_logits,targets),cls_cri(com_logits,targets)\n\n low_cls_loss += low_loss.item()\n mid_cls_loss += mid_loss.item()\n hig_cls_loss += hig_loss.item()\n coa_cls_loss += coa_loss.item()\n com_cls_loss += com_loss.item()\n\n # evaluation.\n b = targets.size()[0]\n fine_logits = coa_logits + com_logits\n ens_logits = low_logits + mid_logits + hig_logits + coa_logits + com_logits\n total_logits = torch.cat((low_logits,mid_logits,hig_logits,coa_logits,com_logits,fine_logits,ens_logits),dim = 0)\n total_probs = torch.softmax(total_logits,dim = -1)\n total_preds = torch.argmax(total_probs,dim = -1)\n\n low_preds,mid_preds,hig_preds,coa_preds,com_preds,fine_preds,ens_preds = torch.split(total_preds,[b,b,b,b,b,b,b],dim = 0)\n low_acc += torch.sum(low_preds == targets).item()\n mid_acc += torch.sum(mid_preds == targets).item()\n hig_acc += torch.sum(hig_preds == targets).item()\n coa_acc += torch.sum(coa_preds == targets).item()\n com_acc += torch.sum(com_preds == targets).item()\n fine_acc += torch.sum(fine_preds == targets).item()\n ens_acc += torch.sum(ens_preds == targets).item()\n\n return low_cls_loss/total_num,mid_cls_loss/total_num,hig_cls_loss/total_num,\\\n coa_cls_loss/total_num,com_cls_loss/total_num,\\\n low_acc/total_num * 100,mid_acc/total_num * 100,hig_acc/total_num * 100,\\\n coa_acc/total_num * 100,com_acc/total_num * 100,fine_acc/total_num * 100,ens_acc/total_num * 100",
"title": ""
},
{
"docid": "c8ab4fdaf53fbfcf52709f5a3362d73b",
"score": "0.6010357",
"text": "def main():\n augment_train_ds = transforms.Compose([\n transforms.RandomCrop(64, padding=2),\n transforms.RandomHorizontalFlip(),\n transforms.ToTensor(),\n transforms.Normalize((0.5, 0.5, 0.5), (0.2, 0.2, 0.2)),\n ])\n \"\"\"Normalizing Test Dataset\"\"\"\n augment_test_ds = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.5, 0.5, 0.5), (0.2, 0.2, 0.2)),\n ])\n\n \"\"\"Set seed.\"\"\"\n np.random.seed(0)\n random.seed(0)\n torch.manual_seed(0)\n torch.cuda.manual_seed(0)\n\n train_dir = '/u/training/tra287/scratch/tiny-imagenet-200/train'\n train_ds = datasets.ImageFolder(train_dir, transform=augment_train_ds)\n # print(train_ds.class_to_idx)\n train_ds_loader = data.DataLoader(train_ds, batch_size=batch_size_train, shuffle=True, num_workers=8)\n\n val_dir = '/u/training/tra287/scratch/tiny-imagenet-200/val/'\n\n # print(\"Now working on Val Dir\")\n if 'val_' in os.listdir(val_dir+'images/')[0]:\n # print(\"Calling create_val_dir() with val_dir: \", val_dir)\n create_val_folder(val_dir)\n val_dir = val_dir + 'images/'\n # print(\"changed val_dir to : \", val_dir)\n else:\n # print(\"Didnt call create_val_dir\")\n val_dir = val_dir + 'images/'\n #train_ds = torchvision.datasets.ImageNet(train_dir, split='train', download=False, transform=augment_train_ds)\n # train_ds_loader = data.DataLoader(train_ds, batch_size=batch_size_train, shuffle=True, num_workers=8)\n # test_ds = torchvision.datasets.ImageNet(val_dir, split='val', download=False, transform=augment_test_ds)\n # test_ds_loader = data.DataLoader(test_ds, batch_size=batch_size_test, shuffle=False, num_workers=8)\n\n test_ds = datasets.ImageFolder(val_dir, transform=augment_test_ds)\n #print(test_ds.class_to_idx)\n test_ds_loader = torch.utils.data.DataLoader(test_ds, batch_size=batch_size_test, shuffle=False, num_workers=8)\n\n device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n print(\"Initializing Model\")\n basic_block = BasicBlock\n res_net = ResNet(basic_block=basic_block, num_basic_blocks_list=[2, 4, 4, 2], num_classes=200,\n linear_layer_num_input=2304, max_pool_stride=2)\n res_net = res_net.to(device)\n start_epoch = 0\n\n if load_chkpt:\n print(\"Saved Model is being loaded\")\n chkpt = torch.load('./Checkpoint/model_state.pt')\n res_net.load_state_dict(chkpt['res_net_model'])\n start_epoch = chkpt['epoch']\n\n \"\"\"If multiple GPUs are available then use asynchronous training \"\"\"\n if device == 'cuda':\n res_net = torch.nn.DataParallel(res_net)\n cudnn.benchmark = True\n\n \"\"\"___________ Training ___________\"\"\"\n\n print(\"Starting Training\")\n\n \"\"\"Criterion Function: Softmax + Log-Likelihood\"\"\"\n loss_fn = nn.CrossEntropyLoss()\n \"\"\"Adam Optimizer (as it takes advantage of both RMSDrop and Momentum\"\"\"\n optimizer = optim.Adam(res_net.parameters(), lr=learning_rate)\n\n test_acc_list = []\n epochs_list = [x for x in range(epochs)]\n\n for epoch in range(start_epoch, epochs):\n\n cur_loss = 0.0\n total_correct = 0\n total_samples = 0\n\n \"\"\" Overflow error in the optimizer if the step size is not reset.\"\"\"\n if epoch > 8:\n for group in optimizer.param_groups:\n for p in group['params']:\n state = optimizer.state[p]\n if state['step'] >= 1024:\n state['step'] = 1000\n\n for i, (inputs, labels) in enumerate(train_ds_loader):\n\n \"\"\"Transfer inputs and labels to CUDA if available\"\"\"\n inputs = Variable(inputs).to(device)\n labels = Variable(labels).to(device)\n\n \"\"\"Loss function requires the inputs to be wrapped in variables\"\"\"\n # inputs = Variable(inputs)\n\n \"\"\"Torch tends to take cumulative gradients which is not required so setting it to zero after each batch\"\"\"\n optimizer.zero_grad()\n\n outputs = res_net(inputs)\n loss = loss_fn(outputs, labels)\n loss.backward()\n optimizer.step()\n\n cur_loss += loss.item()\n avg_loss = cur_loss / (i + 1)\n\n _, predicted_label = torch.max(outputs, 1)\n # print(predicted_label.shape, labels.shape)\n total_samples += labels.shape[0]\n # arr = (predicted_label == labels).numpy()\n # print(np.sum(arr))\n \"\"\"can not use numpy as the tensors are in CUDA\"\"\"\n total_correct += predicted_label.eq(labels.long()).float().sum().item()\n accuracy = total_correct / total_samples\n\n if i % 100 == 0:\n print('Training [epoch: %d, batch: %d] loss: %.3f, accuracy: %.5f' %\n (epoch + 1, i + 1, avg_loss, accuracy))\n\n test_acc_list.append(test(device, loss_fn, res_net, test_ds_loader))\n\n \"\"\"Saving model after every 5 epochs\"\"\"\n if (epoch + 1) % 5 == 0:\n print('==> Saving model ...')\n state = {\n 'res_net_model': res_net.state_dict(),\n 'epoch': epoch,\n }\n if not os.path.isdir('./Checkpoint'):\n os.mkdir('Checkpoint')\n torch.save(state, './Checkpoint/model_state.pt')\n\n print(\"Training Completed!\")\n\n \"\"\"___________ Testing ____________\"\"\"\n print(\"Testing Started\")\n \"\"\"Puts model in testing state\"\"\"\n res_net.eval()\n\n accuracy = test(device, loss_fn, res_net, test_ds_loader)\n\n print(\"Testing Completed with accuracy:\" + str(accuracy))\n\n with open('graph_resnet_tinyimagenet.csv', 'w') as result_file:\n wr = csv.writer(result_file, dialect='excel')\n wr.writerow(test_acc_list)\n\n print(\"Saved Test Accuracy list for graph\")",
"title": ""
},
{
"docid": "232173d16e2d32eed36666657126e8f9",
"score": "0.6008869",
"text": "def test_DCAKNN(noise_dataset):\n X = noise_dataset\n model = DCAKNN(d=1, T=10)\n model.fit(X)\n model.transform(X)\n model.fit_transform(X)",
"title": ""
},
{
"docid": "b5d1416b2cbec5bfd4f15e5b20dd166b",
"score": "0.6007508",
"text": "def toy5():\r\n\tX_train = torch.stack((torch.arange(2.0).repeat_interleave(10), torch.arange(0.1, 1.1, 0.1).repeat(2))).t()\r\n\tY_train = torch.tensor([0] * 2 + [1] * 8 + [0] * 8 + [1] * 2)\r\n\r\n\tX_train_min = [0.0, 0.0]\r\n\tX_train_max = [1.0, 1.0]\r\n\treturn {\"dataset_name\": \"toy5\", \"X_train\": X_train, \"Y_train\": Y_train, \"X_train_min\": X_train_min, \"X_train_max\": X_train_max}",
"title": ""
},
{
"docid": "4b8787c50ad17cac715273ab2ce03320",
"score": "0.60015285",
"text": "def _run_training(self) -> None:",
"title": ""
},
{
"docid": "5f3dba3af1538f8dde86449fc01e48cf",
"score": "0.5996881",
"text": "def initialisedataset():\r\n import initialdefs\r\n import math\r\n\r\n task_file, alltasks = initialdefs.starup()\r\n\r\n X = np.array([[np.zeros([32, 32]), np.zeros([32, 32])]])\r\n Y = [0]\r\n\r\n # make prelim Y's - labels for which problems are patterns. Prelim because we'll make more samples from each problem\r\n # so we'll only use these to inform us what label we should use\r\n Yprelim = [0] * 400\r\n\r\n # from manually going through and seeing what tasks were filling in repeating patterns / mosaics\r\n for i in [16, 60, 73, 109, 241, 286, 304, 312, 350, 399]:\r\n Yprelim[i] = 1\r\n\r\n for taskno in range(len(alltasks)):\r\n print(taskno)\r\n task = alltasks[taskno]\r\n train = task['train']\r\n\r\n # check the input & output are the same size: if not, don't use (too different, would cause too many problems)\r\n check = train[0]\r\n checkinput = np.array(check['input'])\r\n checkoutput = np.array(check['output'])\r\n\r\n # if they are the same, we can use as sample for the model.\r\n if checkoutput.shape == checkinput.shape:\r\n for trainno in range(len(train)):\r\n # dim0: samples dim1: channels (2: input, out), dim3: x dim4: y\r\n imagepair = train[trainno]\r\n imageinput = imagepair['input']\r\n imageoutput = imagepair['output']\r\n sz0l = math.floor((32 - np.size(imageinput, 0))/2) # padding for the left of dimension 0\r\n sz0r = math.ceil((32 - np.size(imageinput, 0))/2) # padding for the right of dimension 0\r\n sz1l = math.floor((32 - np.size(imageinput, 1))/2) # padding for the left of dimension 1\r\n sz1r = math.ceil((32 - np.size(imageinput, 1))/2) # padding for the right of dimension 1\r\n ippad = np.pad(imageinput, ((sz0l, sz0r), (sz1l, sz1r)), constant_values=(0, 0))\r\n oppad = np.pad(imageoutput, ((sz0l, sz0r), (sz1l, sz1r)), constant_values=(0, 0))\r\n\r\n newsample = np.array([[ippad, oppad]])\r\n\r\n X = np.concatenate((X, newsample), axis=0)\r\n Y.append(Yprelim[taskno])\r\n\r\n # create more images from the rotated versions\r\n for i in range(3):\r\n ippad = np.rot90(ippad)\r\n oppad = np.rot90(oppad)\r\n\r\n newsample = np.array([[ippad, oppad]])\r\n\r\n X = np.concatenate((X, newsample), axis=0)\r\n Y.append(Yprelim[taskno])\r\n\r\n # create more images from the transposed & rotated versions\r\n ippad = ippad.T\r\n oppad = oppad.T\r\n\r\n newsample = np.array([[ippad, oppad]])\r\n\r\n X = np.concatenate((X, newsample), axis=0)\r\n Y.append(Yprelim[taskno])\r\n\r\n for i in range(3):\r\n ippad = np.rot90(ippad)\r\n oppad = np.rot90(oppad)\r\n\r\n newsample = np.array([[ippad, oppad]])\r\n\r\n X = np.concatenate((X, newsample), axis=0)\r\n Y.append(Yprelim[taskno])\r\n\r\n X = np.delete(X, 0, axis=0)\r\n Y.__delitem__(0)\r\n\r\n # make channel the last dim\r\n X = np.moveaxis(X, 1, -1)\r\n\r\n return X, Y",
"title": ""
},
{
"docid": "595a5ffcf5e2ea58fea3e0e6cce83d4c",
"score": "0.59949315",
"text": "def test_cv_shuffled(self):\n _, y = data_iris()\n X = np.array([[i] for i in range(100)])\n pred = solution.test_cv(DummyShuffleLearner(), X, y, k=4)\n self.assertIsNotNone(pred)",
"title": ""
},
{
"docid": "5eb25dde381ce47ffc7f7e85fbed0bc4",
"score": "0.5986747",
"text": "def train(\n model, img_width, img_height, train_data_path,\n validation_data_path, no_of_epochs):\n nb_train_samples = sum(len(files)\n for _, _, files in os.walk(train_data_path))\n nb_validation_samples = sum(len(files)\n for _, _, files in os.walk(\n validation_data_path))\n\n epochs = no_of_epochs\n batch_size = 3\n checkpoint = ModelCheckpoint(\n filepath=\"checkpoint_srkw-{epoch:02d}-{val_accuracy:.2f}.h5\",\n monitor=\"val_accuracy\", verbose=0, save_best_only=True)\n\n reduce_lr = ReduceLROnPlateau(monitor=\"val_loss\", factor=0.1,\n patience=100, min_lr=1e-8)\n\n train_datagen = ImageDataGenerator(rescale=1. / 255,\n shear_range=0.2,\n zoom_range=0.2)\n\n # only rescaling\n test_datagen = ImageDataGenerator(rescale=1. / 255)\n\n train_batchsize = 1\n val_batchsize = 1\n\n train_generator = train_datagen.flow_from_directory(\n train_data_path,\n target_size=(img_width, img_height),\n batch_size=train_batchsize,\n class_mode=\"binary\",\n shuffle=True)\n\n validation_generator = test_datagen.flow_from_directory(\n validation_data_path,\n target_size=(img_width, img_height),\n batch_size=val_batchsize,\n class_mode=\"binary\",\n shuffle=False)\n\n model.fit_generator(\n train_generator,\n steps_per_epoch=nb_train_samples // batch_size,\n epochs=epochs,\n validation_data=validation_generator,\n validation_steps=nb_validation_samples // batch_size,\n callbacks=[checkpoint, reduce_lr])\n\n model.save(\"srkw_cnn.h5\")\n\n logger.info(\"Detection Model saved\")",
"title": ""
},
{
"docid": "35f184aa90c03c358209230f835e0f86",
"score": "0.5976398",
"text": "def train_model(train: Train, ds:Dataset(\"catsdogs\"), pf: Featureset(\"cat_and_dog_features\")) -> Any:\n # train: Train, df:Dataset(\"cats-and-dogs-classification\"), pf: Featureset(\"animal_features\")\n df = ds.to_pandas().merge(pf.to_pandas(), on='id')\n training_set = df[(df['path'] == 'training_set/dogs') | (df['path'] == 'training_set/cats')]\n testing_set = df[(df['path'] == 'test_set/dogs') | (df['path'] == 'test_set/cats')]\n X_train = np.stack(training_set['content'].map(load_process_images))\n X_test = np.stack(testing_set['content'].map(load_process_images))\n train.register_input(X_train)\n train.register_output(df['category'])\n train_datagen = ImageDataGenerator(rescale=1. / 255,\n shear_range=0.2,\n zoom_range=0.2,\n horizontal_flip=True,\n width_shift_range=0.1,\n height_shift_range=0.1\n )\n train_datagen.fit(X_train)\n training_data = train_datagen.flow(X_train, training_set['category'], batch_size=32)\n validation_gen = ImageDataGenerator(rescale=1. / 255)\n testing_data = validation_gen.flow(X_test, testing_set['category'], batch_size=32)\n\n model = Sequential([\n Conv2D(filters=32, kernel_size=(3, 3), input_shape=(224, 224, 3), activation='relu'),\n MaxPooling2D(pool_size=(2, 2)),\n\n Conv2D(filters=32, kernel_size=(3, 3), activation='relu'),\n MaxPooling2D(pool_size=(2, 2)),\n Dropout(0.25),\n\n Conv2D(filters=64, kernel_size=(3, 3), activation='relu'),\n MaxPooling2D(pool_size=(2, 2)),\n Dropout(0.25),\n\n Flatten(),\n Dense(128, activation='relu'),\n Dropout(0.25),\n Dense(1, activation='sigmoid')\n ])\n model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n callbacks = [\n EarlyStopping(patience=2),\n\n ]\n epochs = 10\n model.fit(\n training_data,\n epochs=epochs,\n validation_data=testing_data,\n callbacks=callbacks\n )\n test_loss, test_accuracy = model.evaluate(testing_data)\n train_loss, train_accuracy = model.evaluate(training_data)\n\n train.log_metric(\"Testing Accuracy\", test_accuracy)\n train.log_metric(\"Testing Loss\", test_loss)\n\n train.log_metric(\"Training Accuracy\", train_accuracy)\n train.log_metric(\"Training Loss\", train_loss)\n return model",
"title": ""
},
{
"docid": "0ef953872049cc8735a5f3564ce27a78",
"score": "0.5974803",
"text": "def classify(DD, CD):\n\n # do K-fold cross validation\n all_data = np.concatenate((DD,CD))\n all_class = np.vstack((np.ones((DD.shape[0], 1)), np.zeros((CD.shape[0], 1))))\n N = all_data.shape[0]\n K = 5\n accuracies = np.zeros((K, 1))\n sensitivities = np.zeros((K, 1))\n specificities = np.zeros((K, 1))\n randIndices = np.random.permutation(range(0, N))\n\n for fold in range(0, K) :\n i_test = randIndices[fold * (N // K) : (fold + 1) * (N // K)]\n i_train = [val for val in randIndices if val not in i_test]\n c_train = all_class[i_train]\n c_test = all_class[i_test] \n\n # train the model using features all_data[i_train,:] and classes c_train \n # TODO\n \n\n # compute the % errors using all_data[i_test,:], c_test, and the model you trained\n # TODO\n \n\n # report result mean and variance, over all folds, of each of accuracy, specificity, and sensitivity\n # TODO",
"title": ""
},
{
"docid": "b4f9cdb59bf6e995c9d2b296fac83e37",
"score": "0.59640414",
"text": "def train_models(models=model_list, file_name=pr.np_save, in_dims=input_dims, lrs=learning_rates, epochs=epoch_counts,\r\n n_splits=splits, saveFile=save_file):\r\n X, Y = shape_data(file_name)\r\n X = X.reshape((8732, in_dims[0], in_dims[1], in_dims[2]))\r\n kfold = KFold(n_splits=n_splits, shuffle=True)\r\n with open(saveFile, 'w') as file:\r\n writer = csv.writer(file)\r\n writer.writerow(['Model Number', 'Learning Rate', 'Number of epochs', 'Accuracy', 'STD', 'Loss'])\r\n for lr in lrs:\r\n opt = opts.Adam(learning_rate=lr)\r\n for batch_size in batch_sizes:\r\n for epoch_count in epochs:\r\n for model_num in range(len(models)):\r\n fold_no = 0\r\n model = models[model_num]\r\n acc_per_fold = []\r\n loss_per_fold = []\r\n for train, test in kfold.split(X):\r\n fold_no += 1\r\n model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\r\n model.fit(X[train], Y[train], epochs=epoch_count, batch_size=batch_size,\r\n validation_data=(X[test], Y[test]))\r\n model.summary()\r\n scores = model.evaluate(X[test], Y[test])\r\n print(\r\n f'Score for fold {fold_no}: {model.metrics_names[0]} of {scores[0]}; '\r\n f'{model.metrics_names[1]} of {scores[1] * 100}%')\r\n acc_per_fold.append(scores[1] * 100)\r\n loss_per_fold.append(scores[0])\r\n if fold_no == 1:\r\n predictions = model.predict(X[test])\r\n preds = np.argmax(predictions, axis=1)\r\n conf_matrix = confusion_matrix(np.argmax(Y[test], axis=1), preds)\r\n print('Confusion Matrix for fold 1')\r\n print(conf_matrix)\r\n print('------------------------------------------------------------------------')\r\n print('Score per fold')\r\n for i in range(0, len(acc_per_fold)):\r\n print('------------------------------------------------------------------------')\r\n print(f'> Fold {i + 1} - Loss: {loss_per_fold[i]} - Accuracy: {acc_per_fold[i]}%')\r\n print('------------------------------------------------------------------------')\r\n print('Average scores for all folds:')\r\n print(f'> Accuracy: {np.mean(acc_per_fold)} (+- {np.std(acc_per_fold)})')\r\n print(f'> Loss: {np.mean(loss_per_fold)}')\r\n print('------------------------------------------------------------------------')\r\n with open(saveFile, 'a') as file:\r\n writer = csv.writer(file)\r\n writer.writerow(\r\n [model_num + 1, lr, epoch_count, np.mean(acc_per_fold), np.std(acc_per_fold),\r\n np.mean(loss_per_fold)])",
"title": ""
},
{
"docid": "91769a32f38873175c4167a220486de4",
"score": "0.59445524",
"text": "def cross_validate(model, X, Y, k_fold=10, verbose=False, plot=False):\r\n global rand_gen, np_rand_gen\r\n\r\n n_samples = X.shape[1]\r\n n_samples_validation = n_samples / k_fold\r\n n_samples_train = n_samples - n_samples_validation\r\n fold_stats = []\r\n X, Y = model.shuffle(X, Y)\r\n\r\n for fold in range(k_fold):\r\n rand_gen = lib.random.RandomState(seed=random_generator_seed)\r\n np_rand_gen = numpy.random.RandomState(seed=random_generator_seed)\r\n\r\n _model = MultiClassNeuralNetwork(\r\n data_dim=model._data_dim,\r\n activation_func=model._activation_func,\r\n l1_hidden_size=model._l1_hidden_size,\r\n l2_hidden_size=model._l2_hidden_size,\r\n num_of_classes=model._num_of_classes,\r\n lr=model._lr,\r\n initial_lr=model._initial_lr,\r\n epochs=model._epochs,\r\n batch_size=model._batch_size,\r\n reg=model._reg,\r\n input_noise_p=model._input_noise_p,\r\n dropout_p=model._dropout_p,\r\n early_stop_max_epochs=model._early_stop_max_epochs,\r\n input_z_score_normalization=model._input_z_score_normalization,\r\n init_weights_mu=model._init_weights_mu,\r\n init_weights_sigma=model._init_weights_sigma\r\n )\r\n\r\n sliding_window_left = fold * n_samples_validation\r\n sliding_window_right = (fold + 1) * n_samples_validation\r\n\r\n X_validation = X[:, sliding_window_left:sliding_window_right]\r\n Y_validation = Y[:, sliding_window_left:sliding_window_right]\r\n\r\n X_train_left = X[:, 0:sliding_window_left]\r\n X_train_right = X[:, sliding_window_right:n_samples]\r\n Y_train_left = Y[:, 0:sliding_window_left]\r\n Y_train_right = Y[:, sliding_window_right:n_samples]\r\n\r\n X_train = lib.concatenate((X_train_left, X_train_right), axis=1)\r\n Y_train = lib.concatenate((Y_train_left, Y_train_right), axis=1)\r\n\r\n performance = _model.fit(\r\n X_train, Y_train, X_validation, Y_validation, verbose)\r\n predictions = _model.predict(X_validation)\r\n accuracy = _model.evaluate(predictions, Y_validation)\r\n fold_stats.append(accuracy)\r\n\r\n if plot is True:\r\n print(\"\")\r\n _model.plot(performance)\r\n print(\"\")\r\n\r\n print(\"\\nFold ({fold}) test accuracy: {accuracy} %\\n\".format(\r\n fold=fold + 1, accuracy=accuracy))\r\n\r\n total_avg_accuracy = round(float(lib.average(fold_stats)), 2)\r\n\r\n return total_avg_accuracy",
"title": ""
},
{
"docid": "c8363ed89ffcb5af9c876b7a99e9e4a7",
"score": "0.5938936",
"text": "def main():\r\n \r\n cudnn.enabled = True\r\n cudnn.benchmark = True \r\n os.environ['CUDA_VISIBLE_DEVICES'] = args.gpus\r\n \r\n ############# Create mask-guided classification network.\r\n model = DSI_Net(config, K = args.K)\r\n optimizer = torch.optim.Adam(model.parameters(), lr=config.LEARNING_RATE, weight_decay =config.WEIGHT_DECAY)\r\n model.cuda()\r\n if config.FP16 is True:\r\n model, optimizer = amp.initialize(model, optimizer, opt_level=\"O1\")\r\n model = torch.nn.DataParallel(model)\r\n \r\n ############# Load pretrained weights\r\n pretrained_dict = torch.load(model_urls['deeplabv3plus_xception'])\r\n net_dict = model.state_dict()\r\n pretrained_dict = {k: v for k, v in pretrained_dict.items() if (k in net_dict) and (v.shape == net_dict[k].shape)}\r\n net_dict.update(pretrained_dict)\r\n model.load_state_dict(net_dict)\r\n print(len(net_dict))\r\n print(len(pretrained_dict))\r\n model.train()\r\n model.float()\r\n ce_loss = nn.CrossEntropyLoss()\r\n dice_loss = Dice_Loss()\r\n task_interaction_loss = Task_Interaction_Loss()\r\n \r\n ############# Load training and validation data\r\n trainloader = data.DataLoader(CADCAPDataset(config.DATA_ROOT, args.image_list, config.SIZE, data_type='train', mode = 'train'), batch_size=config.BATCH_SIZE, shuffle=True, \r\n num_workers=config.NUM_WORKERS, pin_memory=True, drop_last = config.DROP_LAST)\r\n testloader = data.DataLoader(CADCAPDataset(config.DATA_ROOT, args.image_list,config.SIZE, data_type = 'test', mode='test'), \r\n batch_size=1, shuffle=False, num_workers=config.NUM_WORKERS, pin_memory=True)\r\n train_testloader = data.DataLoader(CADCAPDataset(config.DATA_ROOT, args.image_list,config.SIZE, data_type = 'train', mode = 'test'), \r\n batch_size=1, shuffle=False, num_workers=config.NUM_WORKERS, pin_memory=True) \r\n \r\n \r\n if not os.path.isdir(config.SAVE_PATH):\r\n os.mkdir(config.SAVE_PATH)\r\n if not os.path.isdir(config.SAVE_PATH+'Seg_results/'):\r\n os.mkdir(config.SAVE_PATH+'Seg_results/')\r\n if not os.path.isdir(config.LOG_PATH):\r\n os.mkdir(config.LOG_PATH) \r\n \r\n f_path = config.LOG_PATH + 'training_output.log'\r\n logfile = open(f_path, 'a')\r\n \r\n print_f(os.getcwd(), f=logfile)\r\n print_f('Device: {}'.format(args.gpus), f=logfile)\r\n print_f('==={}==='.format(time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())), f=logfile)\r\n print_f('===Setting===', f=logfile)\r\n print_f(' Data_list: {}'.format(args.image_list), f=logfile)\r\n print_f(' K: {}'.format(args.K), f=logfile)\r\n print_f(' Lost_weight: {}'.format(args.alpha), f=logfile)\r\n print_f(' LR: {}'.format(config.LEARNING_RATE), f=logfile) \r\n\r\n OA_bulk_train = []\r\n CK_bulk_train = []\r\n DI_bulk_train = []\r\n JA_bulk_train = []\r\n SE_bulk_train = []\r\n\r\n OA_bulk_test = []\r\n CK_bulk_test = []\r\n DI_bulk_test = []\r\n JA_bulk_test = []\r\n SE_bulk_test = [] \r\n \r\n \r\n for epoch in range(config.EPOCH): \r\n #cls\r\n cls_train_loss = []\r\n seg_train_loss = []\r\n train_inter_loss = [] \r\n ############# Start the training\r\n for i_iter, batch in enumerate(trainloader):\r\n step = (config.TRAIN_NUM/config.BATCH_SIZE)*epoch+i_iter \r\n images, masks, labels, name = batch\r\n images = images.cuda()\r\n labels = labels.cuda().long()\r\n masks = masks.cuda().squeeze(1)\r\n optimizer.zero_grad()\r\n lr = adjust_learning_rate(optimizer, step)\r\n model.train()\r\n preds_seg_coarse, preds_seg_fine, preds_cls = model(images)\r\n cls_loss = ce_loss(preds_cls, labels) \r\n seg_loss_fine = dice_loss(preds_seg_fine, masks)\r\n seg_loss_coarse = dice_loss(preds_seg_coarse, masks) \r\n inter_loss = task_interaction_loss(preds_cls, preds_seg_fine, labels)\r\n loss = cls_loss + seg_loss_fine + seg_loss_coarse + args.alpha * inter_loss\r\n \r\n if config.FP16 is True:\r\n with amp.scale_loss(loss, optimizer) as scaled_loss:\r\n scaled_loss.backward()\r\n else:\r\n loss.backward()\r\n optimizer.step()\r\n #cls\r\n cls_train_loss.append(cls_loss.cpu().data.numpy()) \r\n seg_train_loss.append(seg_loss_fine.cpu().data.numpy())\r\n train_inter_loss.append(inter_loss.cpu().data.numpy()) \r\n \r\n ############ train log\r\n line = \"Train-Epoch [%d/%d] [All]: Seg_loss = %.6f, Class_loss = %.6f, Inter_loss = %.6f, LR = %0.9f\\n\" % (epoch, config.EPOCH, np.nanmean(seg_train_loss), np.nanmean(cls_train_loss), np.nanmean(train_inter_loss), lr)\r\n print_f(line, f=logfile) \r\n \r\n result = test(train_testloader, model, epoch, verbose=False)\r\n #cls\r\n [CK, OA, EREC] = result['cls']\r\n OA_bulk_train.append(OA)\r\n CK_bulk_train.append(CK)\r\n \r\n # seg \r\n [AC, DI, SE, SP, JA] = result['seg'] \r\n JA_bulk_train.append(np.nanmean(JA)) \r\n DI_bulk_train.append(np.nanmean(DI))\r\n SE_bulk_train.append(np.nanmean(SE)) \r\n \r\n ############# Start the test\r\n result = test(testloader, model, epoch, config.SAVE_PATH+'Seg_results/' , verbose = config.VERBOSE)\r\n #cls\r\n [CK, OA, EREC] = result['cls']\r\n line = \"Test -Epoch [%d/%d] [Cls]: CK-Score = %f, OA = %f, Rec-N = %f, Rec-V = %f, Rec-I=%f \\n\" % (epoch, config.EPOCH, CK, OA, EREC[0],EREC[1],EREC[2] )\r\n print_f(line, f=logfile)\r\n OA_bulk_test.append(OA)\r\n CK_bulk_test.append(CK)\r\n \r\n # seg \r\n [AC, DI, SE, SP, JA] = result['seg'] \r\n line = \"Test -Epoch [%d/%d] [Seg]: AC = %f, DI = %f, SE = %f, SP = %f, JA = %f \\n\" % (epoch, config.EPOCH, np.nanmean(AC), np.nanmean(DI), np.nanmean(SE), np.nanmean(SP), np.nanmean(JA))\r\n print_f(line, f=logfile)\r\n \r\n JA_bulk_test.append(np.nanmean(JA)) \r\n DI_bulk_test.append(np.nanmean(DI))\r\n SE_bulk_test.append(np.nanmean(SE))\r\n \r\n ############# Plot val curve\r\n filename = os.path.join(config.LOG_PATH, 'cls_curves.png')\r\n data_list = [OA_bulk_train, OA_bulk_test, CK_bulk_train, CK_bulk_test]\r\n label_list = ['OA_train','OA_test','CK_train','CK_test']\r\n draw_curves(data_list = data_list, label_list = label_list, color_list = config.COLOR[0:4], filename = filename)\r\n filename = os.path.join(config.LOG_PATH, 'seg_curves.png')\r\n data_list = [JA_bulk_train, JA_bulk_test, DI_bulk_train, DI_bulk_test, SE_bulk_train, SE_bulk_test]\r\n label_list = ['JA_train','JA_test','DI_train','DI_test', 'SE_train','SE_test'] \r\n draw_curves(data_list = data_list, label_list = label_list, color_list = config.COLOR[0:6], filename = filename)",
"title": ""
},
{
"docid": "0796727cc4b78bb5a1a5ead43d7e85aa",
"score": "0.59347063",
"text": "def ms_classification(\n self,\n data_dir = \"PATDATA\",\n patient_file_path = \"PATSTAT\",\n model_path = \"MODELPATH\",\n model_name = \"model_test\",\n num_layers = 8,\n lr = 0.0005,\n drop_rate = 0.2,\n num_folds = 1,\n num_splits = 1,\n flip = False,\n random_seed = None,\n test_fraction = 0.1,\n filter_age = None,\n batch_size = 32,\n early_stopping = True,\n epochs = 100,\n log_dir = \"./logs/fit/\",\n use_spectrogram = False,\n ms_only = False,\n use_lstm = False,\n verbose = True\n ):\n job_id = str(uuid.uuid4())\n logger.info(f\"Job ID for this run: {job_id}\")\n \n # Create the paths to the directories \n data_dir = os.environ[data_dir]\n patient_file_path = os.environ[patient_file_path]\n model_path = os.environ[model_path]\n traces = data_manipulation.readin_traces(\n data_dir, patient_file_path, start_index=100\n )\n\n if filter_age is not None:\n traces = [t for t in traces if int(t.sub_age.item()) < filter_age]\n \n if verbose:\n print(f\"Number of patient traces: {len(traces)}\")\n\n # Convert the images to Eye trace features \n if ms_only:\n X, y, subject_ids = pre_processing.build_using_edss(traces)\n\n # Augment Severe EDSS indices to get rid of data imbalance\n severe_indices = np.where(y == 1)\n X_aug, y_aug, sub_id_aug = pre_processing.flip_augment(X, y, \n subject_ids,\n severe_indices)\n # Append these Augmented samples \n print(subject_ids[severe_indices].shape)\n X = np.concatenate((X, X_aug))\n y = np.concatenate((y, y_aug))\n subject_ids = np.concatenate((subject_ids, sub_id_aug))\n \n else:\n X, y, subject_ids = pre_processing.traces_to_feature(traces, \n mean_center=False, scale_std=False)\n\n # Use only distance and test it out \n # X = pre_processing.use_dist(X)\n\n # Prepare the Data. Augment the data. \n X = pre_processing.downsample_traces(X, 128)\n \n # Use spectrograms if use_spectrogram is set to True\n if use_spectrogram:\n X = pre_processing.spec_build(X)\n freq_bins = X.shape[2]\n time_sets = X.shape[3]\n X = np.reshape(X, newshape=(-1, 2, freq_bins * time_sets))\n\n if verbose:\n print(f\"Number of samples with Label 0:{y[y == False].shape[0]}\")\n print(f\"Number of samples with Label 1:{y[y == True].shape[0]}\")\n print(f\"Number of features used from Eye trace: {X.shape[1]}\")\n \n # Create the test train split here \n (\n X_train,\n X_test,\n y_train,\n y_test,\n subject_ids_train,\n subject_ids_test,\n ) = grouped_train_test_split(\n X, y, subject_ids, test_fraction, random_seed=random_seed\n )\n\n if verbose:\n print(f\"Shape of the Input is : {X_train.shape}\")\n \n # Double the dataset by including the flipped version of the inputs\n if flip:\n X_train, y_train, subject_ids_train = data_manipulation.flip_y_traces(\n X_train, y_train, subject_ids_train\n )\n\n X_train = data_manipulation.channel_last(X_train)\n X_test = data_manipulation.channel_last(X_test)\n y_train, y_test = (\n y_train.astype(np.int32).squeeze(),\n y_test.astype(np.int32).squeeze(),\n )\n \n if verbose:\n print(f\"Shape of the Input after setting channel last is {X_train.shape}\")\n \n input_shape = tuple(list(X_train.shape)[1:])\n print(f\"Shape of the input is: {input_shape}\") \n \n if use_lstm:\n model = LSTM_Model(input_shape) \n \n if verbose:\n model.model.summary()\n \n model.run_model(X_train, y_train, \n lr = lr, epochs = epochs,\n batch_size = batch_size)\n # Save the model.\n model.save_model(model_path, model_name)\n\n if verbose:\n print(f\"Model has been saved to {os.path.join(model_path, model_name) + '.h5'}\")\n else:\n # Build the Model \n # Any new model should have this Keras Classifier wrapper.\n log_dir = log_dir + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n model = KerasClassifier(\n resnet_build_fn_closure(input_shape, log_dir=log_dir,\n drop_rate=drop_rate), \n batch_size=batch_size, \n epochs=epochs)\n \n # How many input channels \n in_channel = X_train.shape[2]\n if verbose:\n print(f\"Number of Input Channels: {in_channel}\")\n print(f\"Shape of X : {X.shape}\")\n print(f\"Shape of Y: {y.shape}\")\n # Parameters that are used for the model \n param_grid = {\n \"num_layers\": [num_layers],\n \"n_feature_maps\": [in_channel],\n \"lr\": [lr],\n \"early_stopping\": [early_stopping],\n \"kernel_multiple\": [1],\n }\n\n cv_results = manual_grid_search_with_validation(\n X_train, y_train, subject_ids_train, \n num_folds, model, param_grid,\n model_path, model_name)\n \n save_path = Path(\"~\").expanduser() / \"cv_runs\"\n # Create the directory if it doesn't exist\n save_path.mkdir(parents=True, exist_ok=True)\n save_file = save_path / f\"cv_run_{job_id}.csv\"\n cv_results.to_csv(save_file)\n \n # Use the X_test set to calculate some metrics \n y_pred = model.predict_proba(X_test)\n test_accuracy = accuracy_score(y_test, np.argmax(y_pred, axis=1))\n\n # Test Accuracy Calculation\n print(f\"Test Accuracy of the model is: {test_accuracy:.2f} when tested on {X_test.shape[0]} samples.\")",
"title": ""
},
{
"docid": "27c36c1cd92a24e9acb50b85e9ed76c5",
"score": "0.5934338",
"text": "def train_model(model_params):\n cars = glob.glob('util_images/vehicles/**/*.png')\n not_cars = glob.glob('util_images/non-vehicles/**/*.png')\n sample_size = model_params[\"sample_size\"]\n cars = cars[0:sample_size]\n not_cars = not_cars[0:sample_size]\n\n color_item = model_params[\"color_item\"]\n orient = model_params[\"orient\"]\n pix_per_cell = model_params[\"pix_per_cell\"]\n cells_per_block = 2\n hog_channels = model_params[\"hog_channel\"]\n\n\n print_parameters(color_item, orient, pix_per_cell, cells_per_block, hog_channels)\n\n car_features = object_detection_utils.extract_features(cars, color_space=color_item, orient=orient,\n pix_per_cell=pix_per_cell, cell_per_block=cells_per_block,\n hog_channel=hog_channels)\n notcar_features = object_detection_utils.extract_features(not_cars, color_space=color_item, orient=orient,\n pix_per_cell=pix_per_cell, cell_per_block=cells_per_block,\n hog_channel=hog_channels)\n # Create an array stack of feature vectors\n X = np.vstack((car_features, notcar_features)).astype(np.float64)\n # Define the labels vector\n y = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features))))\n # Split up data into randomized training and test sets\n rand_state = np.random.randint(0, 100)\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=rand_state)\n # Fit a per-column scaler\n X_scaler = StandardScaler().fit(X_train)\n # Apply the scaler to X\n X_train = X_scaler.transform(X_train)\n X_test = X_scaler.transform(X_test)\n # Use a linear SVC\n svc = LinearSVC()\n # Check the training time for the SVC\n svc.fit(X_train, y_train)\n # Check the score of the SVC\n accuracy = round(svc.score(X_test, y_test), 4)\n model = dict()\n model[\"linearSVC\"] = svc\n model[\"X_scaler\"] = X_scaler\n model[\"color_item\"] = model_params[\"color_item\"]\n model[\"orient\"] = model_params[\"orient\"]\n model[\"pix_per_cell\"] = model_params[\"pix_per_cell\"]\n model[\"cells_per_block\"] = 2\n model[\"hog_channel\"] = model_params[\"hog_channel\"]\n model[\"sample_size\"] = model_params[\"sample_size\"]\n model[\"spatial_feat\"] = True\n model[\"hist_feat\"] = True\n model[\"hog_feat\"] = True\n return model",
"title": ""
},
{
"docid": "d33ed3dd79e174c6177e1851c8ce0b24",
"score": "0.5933601",
"text": "def generate_attack_inputs(sess,model,x_test,y_test,class_num,nb_imgs, load_imgs = False,load_robust = True,file_path = 'local_info/'):\n if not load_imgs:\n # if load_robust:\n # original_predict = sess.run(model.pre_softmax,feed_dict = {model.x_input:x_test.reshape(-1,784)})\n # else:\n original_predict = model.predict_prob(x_test)\n original_class = np.argmax(original_predict, axis = 1)\n true_class = np.argmax(y_test, axis = 1)\n mask = true_class == original_class\n corr_idx = np.where(true_class == original_class)[0]\n print(np.sum(mask), \"out of\", mask.size, \"are correctly labeled,\", len(x_test[mask])) \n\n # generate targeted labels, choose least likely class, untargeted attack does not require\n orig_class_vec = range(class_num)\n target_ys_one_hot = []\n target_ys = []\n orig_images = []\n orig_labels = []\n all_true_ids = []\n trans_test_images = []\n for orig_class in orig_class_vec:\n # guarantees that same sets of images are selected...\n np.random.seed(1234)\n cls_idx = np.where(true_class == orig_class)[0]\n corr_cls_idx = np.intersect1d(cls_idx,corr_idx)\n #random sample, this is doubled because I want to set aside a test set which is to measure transferability\n corr_cls_idx = np.random.choice(corr_cls_idx, size=nb_imgs*2, replace=False)\n # print(\"selected img ids:\",corr_cls_idx)\n x_sel = x_test[corr_cls_idx]\n orig_labels_tmp = y_test[corr_cls_idx]\n y_sel = model.predict_prob(x_sel)\n cls_count = np.bincount(np.argmin(y_sel,axis=1))# count number of occurence\n tar_class = np.argmax(cls_count)\n print(\"Orig: {}, Tar: {}\".format(orig_class,tar_class))\n # store the adversarial samples related files\n target_ys.extend([tar_class]*int(len(corr_cls_idx)/2))\n target_ys_one_hot.extend([np.eye(class_num)[tar_class]]*int(len(corr_cls_idx)/2))\n orig_images.extend(instance for instance in x_sel[:int(len(x_sel)/2)])\n orig_labels.extend(lab for lab in orig_labels_tmp[:int(len(orig_labels_tmp)/2)])\n all_true_ids.extend(idx for idx in corr_cls_idx[:int(len(corr_cls_idx)/2)])\n # store the transferability test related files\n trans_test_images.extend(instance for instance in x_sel[int(len(x_sel)/2):])\n\n target_ys_one_hot = np.array(target_ys_one_hot)\n orig_images = np.array(orig_images)\n target_ys = np.array(target_ys)\n orig_labels = np.array(orig_labels)\n all_true_ids = np.array(all_true_ids)\n trans_test_images = np.array(trans_test_images)\n\n # can uncomment below if not needed\n fname = '/target_ys_one_hot_{}.npy'.format(nb_imgs)\n np.save(file_path+fname,target_ys_one_hot)\n fname = '/orig_images_{}.npy'.format(nb_imgs)\n np.save(file_path+fname,orig_images)\n fname = '/orig_labels_{}.npy'.format(nb_imgs)\n np.save(file_path+fname,orig_labels)\n fname = '/all_true_ids_{}.npy'.format(nb_imgs)\n np.save(file_path+fname,all_true_ids)\n fname = '/target_ys_{}.npy'.format(nb_imgs)\n np.save(file_path+fname,target_ys)\n fname = '/trans_test_images_{}.npy'.format(nb_imgs)\n np.save(file_path+fname,trans_test_images)\n else:\n fname = '/target_ys_one_hot_{}.npy'.format(nb_imgs)\n target_ys_one_hot = np.load(file_path+fname)\n fname = '/orig_images_{}.npy'.format(nb_imgs)\n orig_images = np.load(file_path+fname)\n fname = '/orig_labels_{}.npy'.format(nb_imgs)\n orig_labels = np.load(file_path+fname)\n fname = '/all_true_ids_{}.npy'.format(nb_imgs)\n all_true_ids = np.load(file_path+fname)\n fname = '/target_ys_{}.npy'.format(nb_imgs)\n target_ys = np.load(file_path+fname)\n fname = '/trans_test_images_{}.npy'.format(nb_imgs)\n trans_test_images = np.load(file_path+fname)\n return target_ys_one_hot,orig_images,target_ys,orig_labels,all_true_ids, trans_test_images",
"title": ""
},
{
"docid": "9e73201314edf20d5183214b6e3104c5",
"score": "0.5933446",
"text": "def learn_test(self):\r\n self.net.train_step()",
"title": ""
},
{
"docid": "32d8d6f2faf543f1badf64caac4a023c",
"score": "0.59314454",
"text": "def train(self):\n steps_for_validation = self.data.validation_data.n//self.data.validation_data.batch_size\n steps_for_train = self.data.training_data.n//self.data.training_data.batch_size\n\n # Compilation process\n self.model.compile(\n optimizer=\"rmsprop\", loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\n\n # Training process\n self.model.fit_generator(self.data.training_data, epochs=5, steps_per_epoch=steps_for_train,\n validation_data=self.data.validation_data, validation_steps=steps_for_validation)\n\n # Evaluation process\n evaluation = self.model.evaluate_generator(\n generator=self.data.validation_data, steps=steps_for_validation)\n print(\n f\"EVALUATION:\\n Loss {evaluation[0]}\\nAccuracy: {evaluation[1]}\\n\")\n\n self.model.save(\"magichands_model.h5\")",
"title": ""
},
{
"docid": "13ac53ff48da1bb7ec2ed1361735ecbc",
"score": "0.59222907",
"text": "def _generate_train_batch(self):",
"title": ""
},
{
"docid": "cad73916bbe322fa35bd7d7f8483a5cb",
"score": "0.59176",
"text": "def create_validation(args):\n \n dataset_dir = args.dataset_dir\n workspace = args.workspace\n folds_num = 3\n \n rs = np.random.RandomState(0)\n \n filenames = ['BirdVoxDCASE20k_csvpublic.csv', \n 'ff1010bird_metadata_2018.csv', \n 'warblrb10k_public_metadata_2018.csv']\n \n # Read dataframe\n dataframes = []\n \n for (n, filename) in enumerate(filenames):\n \n filepath = os.path.join(dataset_dir, filename)\n \n df = pd.read_csv(filepath)\n df = pd.DataFrame(df)\n df['fold'] = n + 1\n \n dataframes.append(df)\n \n dataframes = pd.concat(dataframes)\n \n # Write out to csv\n out_path = os.path.join(workspace, 'validation.csv')\n dataframes.to_csv(out_path)\n print(\"Write out to {}\".format(out_path))",
"title": ""
},
{
"docid": "16e58975c6c39ba9ad4c420830817f5c",
"score": "0.5915359",
"text": "def __init__(self, Examples, Labels, n_folds=10, epochs=50):\r\n \r\n # Set up an n-fold test and call train_and_evaluate_model\r\n # for each fold. Keep statistics and report summary\r\n # information of mean and variance.\r\n \r\n # Randmize examples within each fold\r\n kfold = StratifiedKFold(n_folds, shuffle=True)\r\n one_hot_labels = np_utils.to_categorical(Labels)\r\n # Generate indices that can be used to split into training\r\n # and test data, e.g. examples[train_idx]\r\n accuracy = []\r\n for (train_idx, test_idx) in kfold.split(Examples, Labels):\r\n # normally, we would gather results about each fold\r\n accuracy.append(self.train_and_evaluate__model(Examples, one_hot_labels, train_idx, test_idx, 100, epochs))\r\n accuracy = np.array(accuracy)\r\n mu, sd = np.mean(accuracy), np.std(accuracy)\r\n print()\r\n print('mean accuracy {} and standard deviation {}'.format(mu,sd))",
"title": ""
},
{
"docid": "8704d8db46bfdc6fb2723045f09b85dd",
"score": "0.5912254",
"text": "def main():\n logger = logging.getLogger(__name__)\n logger.info('making final data set from raw data')\n\n split_train_test()",
"title": ""
},
{
"docid": "d15329cda45e293524f75eab42e1b7b0",
"score": "0.591102",
"text": "def train_model(self,valid_dicom_npy_lst, boolean_mask_lst):\n train_data = []\n train_target = []\n\n # Using the saved numpy information files from the DICOM images and contour files,\n for dicom_npy_file in valid_dicom_npy_lst:\n train_data.append(np.load(dicom_npy_file))\n\n for mask_npy_file in boolean_mask_lst:\n train_target.append(np.load(mask_npy_file))\n\n # Forming the single numpy array for the entire data set\n train_data = np.array(train_data, dtype=np.float32)\n train_target = np.array(train_target, dtype=np.float32)\n\n for i in range(self.nb_epoch) :\n print('Epoch {} of {}'.format(i, self.nb_epoch))\n # get the single batch of train data consists of one numpy array for images & one numpy array for targets.\n mini_random_batches = self.get_random_mini_batches(train_data, train_target)\n print 'Random Mini Batch Length : {} out of {} entire samples'.format(mini_random_batches.__len__(),train_data.__len__())\n for random_batch in mini_random_batches:\n train_data_batch = random_batch[0]\n train_target_batch = random_batch[1]",
"title": ""
},
{
"docid": "d9d7e22c90369d59220d5e7ea08937f5",
"score": "0.59054047",
"text": "def __init_dataset(self):\r\n\r\n if self.__mode == 'test':\r\n self.__test_data = pd.read_csv(self.__label_root)\r\n self.__get_column_name(self.__test_data)\r\n self.__test_img_list = [os.path.join(self.__data_root, img_name) for img_name in\r\n self.__test_data[self.__img_column_name].values]\r\n else:\r\n self.__train_csv_data = pd.read_csv(self.__label_root)\r\n self.__get_column_name(self.__train_csv_data)\r\n # 全数据训练\r\n if config.train_proportion == 1.0:\r\n\r\n if self.__mode == 'train':\r\n self.__train_img_list = [os.path.join(self.__data_root, img_name) for img_name in\r\n self.__train_csv_data[self.__img_column_name].values]\r\n self.__train_label_list = self.__train_csv_data[self.__label_column_name].values\r\n if self.__mode == 'val':\r\n val_data = self.__train_csv_data[round(0.8*len(self.__train_csv_data)):]\r\n self.__val_img_list = [os.path.join(self.__data_root, img_name) for img_name in\r\n val_data[self.__img_column_name].values]\r\n self.__val_label_list = val_data[self.__label_column_name].values\r\n\r\n\r\n else:\r\n # random split\r\n train_img, train_label, val_img, val_label = train_test_split(\r\n self.__train_csv_data[self.__img_column_name].values,\r\n self.__train_csv_data[self.__label_column_name].values,\r\n train_size=config.train_proportion,\r\n test_size=1-config.train_proportion\r\n )\r\n # data for training\r\n if self.__mode == 'train':\r\n self.__train_img_list = train_img\r\n self.__train_label_list = train_label\r\n # data for validating\r\n if self.__mode == 'val':\r\n self.__val_img_list = val_img\r\n self.__val_label_list = val_label",
"title": ""
},
{
"docid": "8f12b9848d19a13693b83ac4081c97df",
"score": "0.5903171",
"text": "def prepare_data_cross_validation(pos_data, neg_data, ADD_FEATURE=False):\n\n _pos_cross_valid_all = get_valid_data(pos_data)\n _neg_cross_valid_all = get_valid_data(neg_data)\n\n for i in range(5):\n [_pos_4_train, _pos_4_test] = _pos_cross_valid_all[i]\n [_neg_4_train, _neg_4_test] = _neg_cross_valid_all[i]\n _neg_4_train = random.sample(_neg_4_train, len(_pos_4_train))\n test_data = _pos_4_test + _neg_4_test[:len(_pos_4_test)]\n train_data = _pos_4_train + _neg_4_train\n print '---------------------------------'\n print 'cross_validation id:' + str(i)\n print 'pos for training:' + len(_pos_4_train).__str__()\n print 'neg for training:' + len(_neg_4_train).__str__()\n print 'pos for testing:' + len(_pos_4_test).__str__()\n print 'neg for testing:' + len(_neg_4_test[:len(_pos_4_test)]).__str__()\n print '---------------------------------'\n vec_train, label_train, trigger_train, aux_train = get_data_and_lable_trigger(train_data)\n vec_test, label_test, trigger_test, aux_test = get_data_and_lable_trigger(test_data)\n datas = [(np.array(vec_train), np.array(label_train),\n trigger_train, aux_train, np.array(vec_test),\n np.array(label_test), trigger_test, aux_test)]\n _path_to_save_datas = '5-cross_data/prepared_datas_with_feature_{}'.format(i) if ADD_FEATURE else '5-cross_data/prepared_datas_{}'.format(i)\n with open(_path_to_save_datas, 'w') as f:\n pickle.dump(datas, f)",
"title": ""
},
{
"docid": "ce5c3099dfe5c5a1aaf838214a3540e8",
"score": "0.5902428",
"text": "def cv_split_classification(self):\n # Define base data size and size of validation\n data_size = len(self.transformed_data)\n validation_size = int(data_size / 10)\n\n # Check and set the random seed\n if self.random_state:\n np.random.seed(self.random_state)\n\n # Sample for validation\n validation_splitter = []\n\n # Randomize a number between 0 and 10 and multiply by the index to randomly pick observations over data set\n for index in range(validation_size):\n validation_splitter.append(np.random.choice(a=10) + (10 * index))\n self.tune_data = self.transformed_data.iloc[validation_splitter]\n\n # Determine the remaining index that weren't picked for validation\n remainder = list(set(self.transformed_data.index) - set(validation_splitter))\n remainder_df = pd.DataFrame(self.transformed_data.iloc[remainder]['Class'])\n\n # Assign a random number\n remainder_df['Random_Number'] = np.random.randint(0, len(remainder), remainder_df.shape[0])\n\n # Sort over class and the random number\n remainder_df.sort_values(by=['Class', 'Random_Number'], inplace=True)\n remainder_df.reset_index(inplace=True)\n\n # Sample for CV\n for index in range(5):\n # Mod the index by 5 and there will be 5 remainder groups for the CV split\n splitter = remainder_df.loc[remainder_df.index % 5 == index]['index']\n\n # Update our attribute with the dictionary for this index\n self.test_split.update({\n index: self.transformed_data.iloc[splitter]\n })",
"title": ""
},
{
"docid": "23ef1af4360bbaa010b3774d4a46929d",
"score": "0.58991945",
"text": "def test_if_the_name_is_correct(model_name):\n \n possible_datasets = ['IconArt_v1','RMN','RASTA','Paintings']\n possible_lr = ['_big0001_modif','_small001_modif','_big001_modif','_small01_modif','_big01_modif']\n possible_llt = ['_GAP','_GMP']\n # For the last layer transformation !!! GlobalAveragePooling2D\n possible_opt = ['_adam','_Adadelta','_RMSprop']\n possible_freeze= ['_unfreeze50','_unfreeze84','_unfreeze44','_unfreeze20','_unfreeze8']\n # _unfreeze50 for InceptionV1 to train starting at mixed4a_3x3_bottleneck_pre_relu\n # but _unfreeze84 for InceptionV1_slim to train at \n # Mixed_4b_Branch_1_a_1x1_conv : because the name of the layer are not the same !\n possible_loss= ['_cosineloss']\n possibleInit = ['_RandInit','_RandForUnfreezed']\n possible_crop = ['_randomCrop']\n possible_Sup = ['_deepSupervision']\n possible_Aug = ['_dataAug','_SmallDataAug','_MediumDataAug']\n possible_epochs = ['_ep120','_ep200','_ep1']\n possible_clipnorm = ['_cn1','_cn10']\n possible_LRSched = ['_LRschedG','_RedLROnPlat'] # For LR scheduler\n possible_dropout = ['_dropout04','_dropout070704'] # For LR scheduler\n # For the parameters based on : https://www.analyticsvidhya.com/blog/2018/10/understanding-inception-network-from-scratch/\n # Use the learning rate at 0.01 and the list dropout\n possible_lastEpochs = ['_LastEpoch']\n correct_name = False\n for dataset in possible_datasets:\n #print(dataset,model_name)\n if dataset in model_name:\n correct_name = True\n model_name_new = model_name.replace(dataset,'')\n break\n if not(correct_name):\n if not(model_name in [None,'RandForUnfreezed','imagenet']):\n print('Dataset is missing in',model_name)\n return(False)\n #raise(ValueError('Dataset is missing'))\n correct_name = False\n for lr in possible_lr:\n if lr in model_name:\n correct_name = True\n model_name_new = model_name_new.replace(lr,'')\n break\n if not(correct_name):\n print('lr is missing in ',model_name)\n return(False)\n #raise(ValueError('lr is missing'))\n\n for llt in possible_llt:\n model_name_new = model_name_new.replace(llt,'')\n \n for opt in possible_opt:\n model_name_new = model_name_new.replace(opt,'')\n for f in possible_freeze:\n model_name_new = model_name_new.replace(f,'')\n for loss in possible_loss:\n model_name_new = model_name_new.replace(loss,'')\n for init in possibleInit:\n model_name_new = model_name_new.replace(init,'')\n for crop in possible_crop:\n model_name_new = model_name_new.replace(crop,'')\n for sup in possible_Sup:\n model_name_new = model_name_new.replace(sup,'')\n for aug in possible_Aug:\n model_name_new = model_name_new.replace(aug,'')\n for ep in possible_epochs:\n model_name_new = model_name_new.replace(ep,'')\n for le in possible_lastEpochs:\n model_name_new = model_name_new.replace(le,'')\n for c in possible_clipnorm:\n model_name_new = model_name_new.replace(c,'')\n for ls in possible_LRSched:\n model_name_new = model_name_new.replace(ls,'')\n for dp in possible_dropout:\n model_name_new = model_name_new.replace(dp,'')\n \n if not(model_name_new==''):\n print(model_name_new + ' parameter is unknonw sorry.')\n return(False)\n #raise(ValueError(model_name_new + 'is unknonw sorry.'))\n \n return(True)",
"title": ""
},
{
"docid": "85239ab57ee0ec63f4f87cf0827597a0",
"score": "0.5899114",
"text": "def test(self, sample_every=30, verbose=True):\n print('start testing the cnn')\n start = time.time()\n\n # visualize the test model\n merged = tf.summary.merge_all()\n writter = tf.summary.FileWriter('graphs/{0}/test'.format(self.name), self.sess.graph) \n\n # restore saved model\n saver = tf.train.Saver()\n save_path = \"model/{0}.ckpt\".format(self.name)\n saver.restore(self.sess, save_path)\n\n # total prediction in each class {Normal, AF, Other, Noise}\n total = {0: 0, 1: 0, 2: 0, 3: 0}\n # total correct prediction in each class {Normal, AF, Other, Noise}\n corrects = {0: 0, 1: 0, 2: 0, 3: 0}\n\n # run through every single data in test set\n for i in range(self.ecg.ntests):\n # run single forward pass\n X_test, Y_test = self.ecg.get_test(i)\n\n # no drop out in testing\n loss, logit, summary = self.sess.run([self.loss, self.logits, merged], feed_dict={self.X: X_test, self.Y: Y_test, self.keep_prob: 1})\n\n # get the prediction\n writter.add_summary(summary, i)\n probs = self.sess.run(tf.nn.softmax(logit))\n pred = self.sess.run(tf.argmax(probs, 1))[0]\n\n correct = np.argmax(Y_test)\n\n total[pred] += 1\n if pred == correct:\n corrects[pred] += 1\n\n if verbose:\n # plot(X_test)\n print('True label is {0}'.format(self.id_to_class_name[correct]), '- The model predicts', self.id_to_class_name[pred])\n\n # calculate the accuracy, base Scoring part at https://physionet.org/challenge/2017/#preparing\n fn = 2.0 * corrects[0] / (total[0] + self.ecg.N)\n fa = 2.0 * corrects[1] / (total[1] + self.ecg.A)\n fo = 2.0 * corrects[2] / (total[2] + self.ecg.O)\n fp = 2.0 * corrects[3] / (total[3] + self.ecg.P)\n f = (fn + fa + fo + fp) / 4.0\n print('Accuracy in the validation set is {0}'.format(f))\n print('Testing time {0}'.format(time.time() - start))",
"title": ""
},
{
"docid": "70077f7954fa6195bfbf2ddacb50c2d8",
"score": "0.58966506",
"text": "def KNN_test_objectnet_shuffle(val_loader, model, args, train_list, path_visualization_results=None):\n # crucial for use when loading a model with bn, if we don't run eval, its very bad\n model.eval()\n\n if len(train_list)==2:\n emb_list, label_list = train_list\n else:\n emb_list, label_list, output_list = train_list\n\n try:\n emb_list = torch.from_numpy(emb_list)\n output_list = torch.from_numpy(output_list)\n except:\n pass\n\n cnt=0\n top1_cnt = 0\n top1_cnt_old = 0\n top5_cnt = 0\n\n test_hidden=True\n \n if args.visualize:\n labels_path = os.getenv(\"HOME\") + '/.torch/models/imagenet_class_index.json'\n with open(labels_path) as json_data:\n idx_to_labels = json.load(json_data)\n\n\n with torch.no_grad():\n for i, example in enumerate(val_loader):\n images = example['images']\n target = example['labels']\n path = example['path']\n if args.gpu is not None:\n images = images.cuda(args.gpu, non_blocking=True)\n # target = target.cuda(args.gpu, non_blocking=True)\n\n\n # print(\"target\", target)\n # exit(0)\n\n # print('img', images.size())\n output, fea, norm = model(images)\n # fea, _ = normalize_l2(fea)\n # fea = MLP(fea)\n\n if test_hidden:\n score_mat = torch.mm(fea.cpu(), emb_list.t()) # test_num * train_num\n else:\n score_mat = torch.mm(output.cpu(), output_list.t()) # test_num * train_num\n # print('topk', TOPK_NN)\n score, pred = torch.topk(score_mat, k=TOPK_NN, dim=1, largest=True, sorted=False)\n # print(\"done\")\n pred_np = pred.data.cpu().numpy()\n # print(\"sim\", score_mat)\n # print(\"pred\", pred)\n\n # print(\"Getting scores: \", score, pred)\n\n # print('target b4', target)\n # The way to organize is that, i-th sublist in the target, contains the i-th label for all test examples\n target_num = len(target)\n target = [each.numpy() for each in target]\n # print('target after', target)\n\n pred_label = []\n\n for each in range(pred_np.shape[0]): # for each test images\n # label_gt_all = target[each]\n label_gt_list = []\n # for mogu in label_gt_all:\n # label_gt_list.append(mogu.numpy()[0])\n # print('label gt', label_gt)\n for kk in range(target_num):\n label_gt_list.append(target[kk][each]) # Since target size is: \"the multilabel dim, number of test examples\"\n\n cnt += 1\n\n index = pred[each]\n retrieved_labels = label_list[index.cpu().numpy()]\n\n retrieved_labels = np.sort(retrieved_labels)\n # print(\"retrieved labels\", len(retrieved_labels))\n\n label_num_dict = {}\n last = None\n num = None\n\n for kk in range(retrieved_labels.shape[0]):\n if kk==0:\n last = retrieved_labels[0]\n num=1\n\n else:\n if retrieved_labels[kk] == last:\n num += 1\n else:\n label_num_dict[retrieved_labels[kk-1]] = num\n num = 1\n last = retrieved_labels[kk]\n\n if kk == retrieved_labels.shape[0] - 1:\n label_num_dict[retrieved_labels[kk]] = num\n # print(label_num_dict)\n\n # print(\"label dict num\", label_num_dict)\n # Sort dict from most frequent to least frequent\n sort_topk_neighbor = sorted(label_num_dict.items(), key=lambda kv: (-kv[1], kv[0]))\n\n # print(\"sort topk neighbor\", sort_topk_neighbor, len(label_gt_list), label_gt_list,)\n\n\n # Most frequent label for this test image is in sort_topk_neighbour[0][0], and gt is label_gt_list, so check if our top prediction is among\n if args.visualize:\n #path_viz = os.path.join(path_visualization_results,'knn' )\n #path_viz = os.path.join(path_visualization_results,<category_objnet>/knn_prediction/<basename> )\n \n\n path_image = path[each]\n list_path_split = path_image.split('/')\n objnet_category = list_path_split[-2]\n image_basename = list_path_split[-1]\n path_to_append = objnet_category + '/' + 'knn_output' + '/' + image_basename\n path_destination_image = os.path.join(path_visualization_results, path_to_append)\n # print(\"__path__\",path[each])\n # path_dest_visualization_img = os.path.join(path_viz, 'dfd')\n \n true_label = objnet_category\n predicted_labels_id = [kv[0] for kv in sort_topk_neighbor[:5]]\n predicted_labels = [idx_to_labels[str(id)][1] for id in predicted_labels_id]\n predicted_label = objnet_category \n \n temp = images[each].detach().numpy()\n temp = np.moveaxis(temp, 0, 2)\n mean = np.array([0.485, 0.456, 0.406])\n std = np.array([0.229, 0.224, 0.225])\n temp = np.clip((temp*std)+mean,0,1)\n f,ax = plt.subplots()\n ax.imshow(temp)\n str_title = 'Truth Objnet category: {} \\n Predicted labels: {} '.format(true_label,predicted_labels)\n f.suptitle(str_title, fontsize=12)\n print(\"savign at : \", path_destination_image)\n if i%500 ==0:\n print(i, 'done') \n dirname = os.path.dirname(path_destination_image)\n if not os.path.exists(dirname):\n os.makedirs(dirname)\n f.savefig(path_destination_image,bbox_inches='tight')\n plt.close()\n\n top1_cnt_old=top1_cnt\n try:\n for label_gt in label_gt_list:\n if sort_topk_neighbor[0][0] == label_gt:\n top1_cnt += 1\n break\n except:\n print(sort_topk_neighbor, label_gt_list, top1_cnt)\n\n if top1_cnt-top1_cnt_old > 1:\n print(top1_cnt-top1_cnt_old, label_gt_list)\n\n # Make sure if see one category correct, then stop loop\n flag = True\n for nnn in range(5):\n try:\n if flag == False:\n break\n for label_gt in label_gt_list:\n if sort_topk_neighbor[nnn][0] == label_gt:\n top5_cnt += 1\n flag = False\n break\n except:\n # print(\"top 5 but number less than 5\")\n break\n\n print('{} Overlapping ImgNet'.format(args.dataset), i, \"/%d top 1: %.5f, top 5: %.5f\" %\n (len(val_loader), top1_cnt*100.0/cnt, top5_cnt*100.0/cnt))",
"title": ""
},
{
"docid": "80c777305f27dcd901af5ad15a5837cf",
"score": "0.58955616",
"text": "def main():\n # Data loaders\n if check_for_augmented_data(\"./data\"):\n tr_loader, va_loader, te_loader, _ = get_train_val_test_loaders(\n task=\"target\", batch_size=config(\"cnn.batch_size\"), augment=True\n )\n else:\n tr_loader, va_loader, te_loader, _ = get_train_val_test_loaders(\n task=\"target\",\n batch_size=config(\"cnn.batch_size\"),\n )\n # Model\n model = Target()\n\n # TODO: define loss function, and optimizer\n criterion = torch.nn.CrossEntropyLoss()\n optimizer = torch.optim.Adam(model.parameters(), lr = 1e-3)\n #\n\n print(\"Number of float-valued parameters:\", count_parameters(model))\n\n # Attempts to restore the latest checkpoint if exists\n print(\"Loading cnn...\")\n model, start_epoch, stats = restore_checkpoint(model, config(\"cnn.checkpoint\"))\n\n axes = utils.make_training_plot()\n\n # Evaluate the randomly initialized model\n evaluate_epoch(\n axes, tr_loader, va_loader, te_loader, model, criterion, start_epoch, stats\n )\n\n # initial val loss for early stopping\n prev_val_loss = stats[0][1]\n\n # TODO: define patience for early stopping\n patience = 5\n curr_patience = 0\n #\n\n # Loop over the entire dataset multiple times\n # for epoch in range(start_epoch, config('cnn.num_epochs')):\n epoch = start_epoch\n while curr_patience < patience:\n # Train model\n train_epoch(tr_loader, model, criterion, optimizer)\n\n # Evaluate model\n evaluate_epoch(\n axes, tr_loader, va_loader, te_loader, model, criterion, epoch + 1, stats\n )\n\n # Save model parameters\n save_checkpoint(model, epoch + 1, config(\"cnn.checkpoint\"), stats)\n\n # update early stopping parameters\n curr_patience, prev_val_loss = early_stopping(\n stats, curr_patience, prev_val_loss\n )\n\n epoch += 1\n print(\"Finished Training\")\n # Save figure and keep plot open\n utils.save_cnn_training_plot()\n utils.hold_training_plot()",
"title": ""
},
{
"docid": "2fbe1449e57be9c34d14dfcbde67c564",
"score": "0.5892119",
"text": "def Classification_restore_and_train(config):\n\n # 1. Retrieve information from config dict\n device = config['device']\n device_name = torch.cuda.get_device_name(device)\n print('Device name: {}'.format(device_name))\n input_shape = config['input_shape']\n batch_size = config['batch_size'] \n number_of_tools = config['number_of_tools']\n output_features = number_of_tools\n random_frames = config['random_frames']\n nr_videos = config['nr_videos']\n nr_frames = config['nr_frames']\n weight_decay = config['weight_decay']\n save_interval = config['save_interval']\n msg_bot = config['msg_bot']\n bot_msg_interval = config['bot_msg_interval']\n dataset_name = config['dataset']\n model_name = config['model']\n agent_name = 'TransNetAgent'\n\n\n # 2. Define data to restore dataset\n data = Data()\n data.add_dataset(Cholec80Restored())\n train_ds = (dataset_name, 'train')\n val_ds = (dataset_name, 'val')\n test_ds = (dataset_name, 'test')\n\n\n # 3. Restore and define path\n paths = os.path.join(storage_data_path, 'models', dataset_name+'_'+model_name, 'states')\n pathr = os.path.join(model_result_path, 'models', dataset_name+'_'+model_name, 'results')\n splits = lr.load_json(path=paths, name='data_splits')\n print('Restored existing splits')\n\n # 4. Create data splits for each repetition\n print('Bring data to PyTorch format..')\n # Repeat for each repition\n for run_ix in range(config['nr_runs']):\n # 5. Bring data to Pytorch format\n datasets = dict()\n for ds_name, ds in data.datasets.items():\n for split, data_ixs in splits[ds_name][run_ix].items():\n if len(data_ixs) > 0: # Sometimes val indexes may be an empty list\n aug = config['augmentation'] if not('test' in split) else 'none'\n datasets[(ds_name, split)] = PytorchClassification2DDataset(ds, \n ix_lst=data_ixs, size=input_shape, aug_key=aug, \n resize=config['resize'])\n\n # 6. Build train dataloader, and visualize\n dl = DataLoader(datasets[(train_ds)], \n batch_size=batch_size, shuffle=True,\n num_workers=0)\n dl_val = DataLoader(datasets[(val_ds)], \n batch_size=batch_size, shuffle=True,\n num_workers=1)\n\n # 7. Initialize model\n model = getattr(models, model_name)(output_features)\n model.to(device)\n\n # 8. Define loss and optimizer\n loss_f = LossBCE(device=device)\n optimizer = optim.Adam(model.parameters(), lr=config['lr'],\n weight_decay=weight_decay)\n\n # 9. Train model\n state_names = [name for name in os.listdir(paths) if '.' not in name]\n state_name = state_names[0].split('_')[0]\n for idx, state in enumerate(state_names):\n state_names[idx] = int(state.split('_')[-1])\n state_names.sort()\n state_name += '_' + str(state_names[-1])\n\n print('Restore last saved model from epoch {}..'.format(state_name.split('_')[-1]))\n agent = getattr(agents, agent_name)(model=model, device=device)\n restored, restored_results = agent.restore_state(paths, state_name, optimizer=optimizer)\n if not restored:\n print(\"Desired state could not be recovered. --> Error!\")\n raise FileNotFoundError\n\n losses_train_r, losses_cum_train_r, losses_val_r, losses_cum_val_r, accuracy_train_r,\\\n accuracy_det_train_r, accuracy_val_r, accuracy_det_val_r = restored_results\n\n print('Training model in batches of {}..'.format(batch_size))\n losses_train, losses_cum_train, losses_val, losses_cum_val,\\\n accuracy_train, accuracy_det_train, accuracy_val,\\\n accuracy_det_val = agent.train(optimizer, loss_f, dl,\n dl_val, nr_epochs=config['nr_epochs'],\n start_epoch=int(state_name.split('_')[-1]),\n save_path=paths, losses=losses_train_r.tolist(),\n losses_cum=losses_cum_train_r.tolist(),\n losses_val=losses_val_r.tolist(),\n losses_cum_val=losses_cum_val_r.tolist(),\n accuracy=accuracy_train_r.tolist(),\n accuracy_detailed=accuracy_det_train_r.tolist(),\n accuracy_val=accuracy_val_r.tolist(),\n accuracy_val_detailed=accuracy_det_val_r.tolist(),\n save_interval=save_interval, msg_bot=msg_bot,\n bot_msg_interval=bot_msg_interval)\n\n # 10. Build test dataloader, and visualize\n dl = DataLoader(datasets[(test_ds)], \n batch_size=batch_size, shuffle=True)\n \n # 11. Test model\n print('Testing model in batches of {}..'.format(batch_size))\n losses_test, losses_cum_test, accuracy_test, accuracy_det_test = agent.test(loss_f, dl, msg_bot=msg_bot)\n\n # 12. Save results\n save_results(model, model_name, dataset_name, paths, pathr, losses_train, losses_val, accuracy_train,\n accuracy_det_train, accuracy_val, accuracy_det_val, losses_test, accuracy_test,\n accuracy_det_test, losses_cum_train, losses_cum_val)",
"title": ""
},
{
"docid": "a5a2ed71f70a2f7851513e0fa81e36e1",
"score": "0.58881956",
"text": "def main(feature_pkl='C:\\\\Users\\Cory\\\\Documents\\\\DataScienceWorkshop\\\\avito_kaggle\\\\new-feat-full\\\\train_data.pkl', model=SGDClassifier(loss='log',penalty='l2',alpha=1e-4,class_weight='auto'), KFOLD=10):\n # DEFAULT MODEL:\n # Stochastic Gradient Descent (online learning)\n # loss (cost) = log ~ Logistic Regression\n # L2 norm used for cost, alpha ~ Regularization\n # class_weight = auto\n\n # Wrapper function may pre-load these large variables and pass as tuple instead of doing this step iteratively.\n if type(feature_pkl) is tuple: \n featureIndex, trainFeatures, trainTargets, trainItemIds, testFeatures, testItemIds = feature_pkl\n else:\n print 'Loading .pkl data for fitting/cross-validation...'\n if feature_pkl.find('new-feat')>-1:\n # Benchmark code did not save column names (featureIndex)\n trainFeatures, trainTargets, trainItemIds, testFeatures, testItemIds = joblib.load(feature_pkl)\n else:\n featureIndex, trainFeatures, trainTargets, trainItemIds, testFeatures, testItemIds = joblib.load(feature_pkl)\n if type(model) is str:\n model = eval(model)\n KFOLD = int(KFOLD)\n \n font = {'family' : 'normal',\n 'weight' : 'bold',\n 'size' : 22}\n matplotlib.rc('font', **font)\n \n # convert features to CSR for row-slicing\n trainFeatures = trainFeatures.tocsr()\n\n #Cross validation split into 10 folds for cross-validation\n kf_total = cross_validation.KFold(len(trainItemIds),n_folds=KFOLD,shuffle=True,indices=True)\n \n #conversion of targets to numpy \n trainTargets = np.asarray(trainTargets)\n count = 0\n total_conf=np.zeros(shape=(2,2))\n mean_tpr = 0\n mean_fpr = np.linspace(0, 1, 100)\n \n #Iterate through the folds of the dataset\n for train_indices, test_indices in kf_total:\n #Calculation of the confusion matrix values for each fold \n model.fit(trainFeatures[train_indices], trainTargets[train_indices])\n predicted = model.predict(trainFeatures[test_indices])\n conf_arr = metrics.confusion_matrix(trainTargets[test_indices],predicted)\n norm_conf = [] \n for i in conf_arr:\n a = 0\n tmp_arr = []\n a = sum(i, 0)\n for j in i:\n tmp_arr.append(float(j)/float(a))\n norm_conf.append(tmp_arr)\n total_conf += norm_conf\n #Calculation of the ROC/AUC for each fold\n prob = model_predicted_prob(model,trainFeatures[test_indices])\n fpr, tpr, thresholds = metrics.roc_curve(trainTargets[test_indices],prob)\n mean_tpr += interp(mean_fpr, fpr, tpr)\n mean_tpr[0] = 0.0\n print \"Finished with fold number \" + str(count+1)\n count += 1\n \n #Calculate mean values and plot the results\n mean_tpr /= KFOLD\n mean_tpr[-1] = 1.0\n total_conf /= KFOLD\n \n #Plot the confusion matrix\n labels = ['not blocked','blocked']\n fig = plt.figure(figsize=(10,8))\n plt.clf()\n ax = fig.add_subplot(111)\n cax = ax.matshow(np.array(norm_conf), cmap=plt.cm.jet, \n interpolation='nearest')\n plt.title('Confusion matrix')\n fig.colorbar(cax)\n ax.set_xticklabels([''] + labels)\n ax.set_yticklabels([''] + labels)\n plt.xlabel('Predicted')\n plt.ylabel('True')\n \n #Add confusion matrix values to the graph\n width = len(norm_conf)\n height = len(norm_conf[0])\n for x in xrange(width):\n for y in xrange(height):\n ax.annotate('%.4f' % norm_conf[x][y], xy=(y, x), \n horizontalalignment='center',\n verticalalignment='center')\n print \"Confusion Matrix \\n\" + str(total_conf)\n for ext in ['.png','.pdf','.jpg']:\n fig.savefig('confusion'+ext)\n \n #Plot the ROC\n fig = plt.figure(figsize=(10,8))\n plt.plot(mean_fpr,mean_tpr)\n plt.xlim([-0.05, 1.05])\n plt.ylim([-0.05, 1.05])\n plt.xlabel('False Positive Rate')\n plt.ylabel('True Positive Rate')\n plt.title('Receiver operating characteristic')\n for ext in ['.png','.pdf','.jpg']:\n fig.savefig('roc'+ext)\n \n auc_score = metrics.auc(mean_fpr,mean_tpr)\n print \"AUC score\\n\" + str(auc_score)\n \n logging.info(\"Done with cross-validation\")\n return",
"title": ""
},
{
"docid": "e4903e00d13c6d1a5d4967e817001609",
"score": "0.58879054",
"text": "def dataset_setup(self):\n settings = self.settings\n if settings.crowd_dataset == 'World Expo':\n train_transform = torchvision.transforms.Compose([data.RandomlySelectPathWithNoPerspectiveRescale(),\n data.RandomHorizontalFlip(),\n data.NegativeOneToOneNormalizeImage(),\n data.NumpyArraysToTorchTensors()])\n validation_transform = torchvision.transforms.Compose([data.RandomlySelectPathWithNoPerspectiveRescale(),\n data.NegativeOneToOneNormalizeImage(),\n data.NumpyArraysToTorchTensors()])\n dataset_path = '../World Expo/'\n with open(os.path.join(dataset_path, 'viable_with_validation_and_random_test.json')) as json_file:\n cameras_dict = json.load(json_file)\n self.train_dataset = CrowdDataset(dataset_path, camera_names=cameras_dict['train'],\n number_of_cameras=settings.number_of_cameras,\n number_of_images_per_camera=settings.number_of_images_per_camera,\n transform=train_transform, seed=settings.labeled_dataset_seed)\n self.train_dataset_loader = DataLoader(self.train_dataset, batch_size=settings.batch_size, shuffle=True,\n pin_memory=True, num_workers=settings.number_of_data_workers)\n # self.unlabeled_dataset = CrowdDataset(dataset_path, camera_names=cameras_dict['validation'],\n # transform=train_transform, unlabeled=True,\n # seed=100)\n self.unlabeled_dataset = CrowdDataset(dataset_path, camera_names=cameras_dict['train'],\n number_of_cameras=settings.number_of_cameras,\n transform=train_transform, unlabeled=True,\n seed=settings.labeled_dataset_seed)\n self.unlabeled_dataset_loader = DataLoader(self.unlabeled_dataset, batch_size=settings.batch_size, shuffle=True,\n pin_memory=True, num_workers=settings.number_of_data_workers)\n self.validation_dataset = CrowdDataset(dataset_path, camera_names=cameras_dict['validation'],\n transform=validation_transform, seed=101)\n elif settings.crowd_dataset == 'ShanghaiTech':\n train_transform = torchvision.transforms.Compose([data.ExtractPatchForRandomPosition(),\n data.RandomHorizontalFlip(),\n data.NegativeOneToOneNormalizeImage(),\n data.NumpyArraysToTorchTensors()])\n validation_transform = torchvision.transforms.Compose([data.ExtractPatchForRandomPosition(),\n data.NegativeOneToOneNormalizeImage(),\n data.NumpyArraysToTorchTensors()])\n self.train_dataset = ShanghaiTechDataset(transform=train_transform, seed=settings.labeled_dataset_seed)\n self.train_dataset_loader = DataLoader(self.train_dataset, batch_size=settings.batch_size, shuffle=True,\n pin_memory=True, num_workers=settings.number_of_data_workers)\n self.unlabeled_dataset = ShanghaiTechDataset(transform=train_transform, seed=settings.labeled_dataset_seed,\n unlabeled=True)\n self.unlabeled_dataset_loader = DataLoader(self.unlabeled_dataset, batch_size=settings.batch_size, shuffle=True,\n pin_memory=True, num_workers=settings.number_of_data_workers)\n self.validation_dataset = ShanghaiTechDataset(dataset='test', transform=validation_transform, seed=101)\n else:\n raise ValueError('{} is not an understood crowd dataset.'.format(settings.crowd_dataset))",
"title": ""
},
{
"docid": "914e7b701c70b5ff67c1331a07c46010",
"score": "0.588063",
"text": "def initiate_training(augmentation, backbone, batch_size, dataset_path, epochs, model, random_occlusions,\n snapshot_base_name, snapshot_path, steps_per_epoch, tensorboard_dir, validation_set,\n validation_split, counting_model, balance_datasets=False):\n if augmentation:\n transform_generator = random_transform_generator(\n min_rotation=-0.1,\n max_rotation=0.1,\n min_translation=(-0.1, -0.1),\n max_translation=(0.1, 0.1),\n min_shear=-0.1,\n max_shear=0.1,\n min_scaling=(0.9, 0.9),\n max_scaling=(1.1, 1.1),\n flip_x_chance=0.5,\n flip_y_chance=0.5)\n else:\n transform_generator = random_transform_generator(flip_x_chance=0.5)\n\n dataset = CarsDataset(dataset_path, validation_split=validation_split, validation_set=validation_set,\n balance_datasets=balance_datasets)\n\n val_generator = None\n validation_steps = None\n train_generator = CarsGenerator(dataset.train,\n preprocess_image=backbone.get_preprocess_image(),\n counting_model=counting_model,\n transform_generator=transform_generator,\n batch_size=batch_size,\n image_min_side=720,\n image_max_side=1280,\n perform_random_occlusions=random_occlusions)\n\n if dataset.validation:\n val_generator = CarsGenerator(dataset.validation,\n preprocess_image=backbone.get_preprocess_image(),\n counting_model=counting_model,\n batch_size=batch_size,\n image_min_side=720,\n image_max_side=1280,\n perform_random_occlusions=False)\n validation_steps = len(val_generator)\n os.makedirs(snapshot_path, exist_ok=True)\n with open('{}/validation.txt'.format(snapshot_path), \"wt\") as f:\n for img_path in dataset.validation.keys():\n print(img_path, file=f)\n\n callbacks = create_callbacks(model,\n batch_size=batch_size,\n tensorboard_dir=tensorboard_dir,\n snapshot_path=snapshot_path,\n snapshot_name_base=snapshot_base_name)\n if steps_per_epoch is None:\n steps_per_epoch = len(train_generator)\n\n model.fit_generator(\n generator=train_generator,\n steps_per_epoch=steps_per_epoch,\n callbacks=callbacks,\n epochs=epochs,\n validation_data=val_generator,\n validation_steps=validation_steps,\n verbose=1,\n workers=1,\n use_multiprocessing=True,\n max_queue_size=10\n )",
"title": ""
},
{
"docid": "703a4c5a748da4a3e27519a47d5fd13e",
"score": "0.587915",
"text": "def evaluate_lenet5(learning_rate=0.1, n_epochs=200,\n dataset='mnist.pkl.gz',\n nkerns=[20,50],batch_size=500):\n \n rng = numpy.random.RandomState(1234)\n print('Loading Data'+'.'*20)\n datasets = load_data(dataset)\n \n trainSetX, trainSetY = datasets[0]\n validSetX, validSetY = datasets[1]\n testSetX, testSetY = datasets[2]\n \n n_train_batches = trainSetX.get_value(borrow=True).shape[0] // batch_size\n n_valid_batches = validSetX.get_value(borrow=True).shape[0] // batch_size\n n_test_batches = testSetX.get_value(borrow=True).shape[0] // batch_size\n \n print('Building Data'+'.'*20)\n \n index = T.lscalar('index')\n x = T.matrix('x')\n y = T.ivector('y')\n \n # Reshape matrix of rasterized images of shape (batch_size, 28*28)\n # to a 4D tensor, compatible with our LeNetConvPoolLayer\n layer0_input = x.reshape((batch_size,1,28,28))\n \n # construct the first convolutional pooling layer\n # filtering reduces the image size to (28-5+1,28-5+1) = (24, 24)\n # maxpooling reduces this further to (24/2, 24/2) = (12, 12)\n # 4D output tensor is thus of shape (batch_size,nkerns[0],12,12)\n layer0 = LeNetConvPoolLayer(\n rng=rng,\n input=layer0_input,\n image_shape=(batch_size,1,28,28),\n filter_shape=(nkerns[0],1,5,5),\n poolsize=(2,2)\n )\n \n # construct the second convolutional pooling layer\n # filtering reduces the image size to (12-5+1,12-5+1) = (8, 8)\n # maxpooling reduces this further to (8/2, 8/2) = (4, 4)\n # 4D output tensor is thus of shape (batch_size,nkerns[1],4,4)\n layer1 = LeNetConvPoolLayer(\n rng=rng,\n input=layer0.output,\n image_shape=(batch_size,nkerns[0],12,12),\n filter_shape=(nkerns[1],nkerns[0],5,5),\n poolsize=(2,2)\n )\n \n layer2_input = layer1.output.flatten(2)\n layer2 = HiddenLayer(\n rng=rng,\n input=layer2_input,\n n_in=nkerns[1]*4*4,\n n_out=500,\n activation=T.tanh\n )\n \n layer3 = LogisticRegression(input=layer2.output,n_in=500,n_out=10)\n \n testModel = theano.function(\n inputs=[index],\n outputs=layer3.errors(y),\n givens={\n x:testSetX[index*batch_size:(index+1)*batch_size], \n y:testSetY[index*batch_size:(index+1)*batch_size] \n } \n )\n validModel = theano.function(\n inputs=[index],\n outputs=layer3.errors(y),\n givens={\n x:validSetX[index*batch_size:(index+1)*batch_size], \n y:validSetY[index*batch_size:(index+1)*batch_size] \n }\n )\n \n params = layer3.params+layer2.params+layer1.params+layer0.params\n cost = layer3.negative_log_likelihood(y)\n grads = T.grad(cost,params)\n \n updates= [(param, param - learning_rate*grad)\n for param,grad in zip(params,grads)\n ]\n \n trainModel = theano.function(\n inputs=[index],\n outputs=cost,\n updates=updates,\n givens={\n x:trainSetX[index*batch_size:(index+1)*batch_size], \n y:trainSetY[index*batch_size:(index+1)*batch_size] \n } \n )\n \n print('Training'+'.'*20)\n \n patience = 10000\n patience_increase = 2\n improvement_threshold = 2\n validation_frequence = min(n_train_batches, patience/2)\n \n best_validation_loss = numpy.inf\n best_iter = 0\n test_score =0.\n start_time = timeit.default_timer()\n \n epoch = 0\n done_looping = False\n \n while (epoch < n_epochs) and (not done_looping):\n epoch += 1\n \n for mini_batch_index in range(n_train_batches):\n iter = (epoch - 1) * n_train_batches + mini_batch_index\n if iter % 100 == 0:\n print('training @ iter = ' , iter)\n \n cost_ij = trainModel(mini_batch_index)\n if (iter + 1) % validation_frequence ==0:\n validation_losses = [validModel(i) for i \n in range(n_valid_batches)]\n this_validation_losses = numpy.mean(validation_losses)\n print('epoch %i, minibatch %i/%i, validation error %f %%' %\n (epoch, mini_batch_index+1, n_train_batches,\n this_validation_losses*100)\n )\n if this_validation_losses < best_validation_loss:\n best_validation_loss = this_validation_losses\n best_iter = iter\n if this_validation_losses < best_validation_loss * \\\n improvement_threshold:\n patience = max(patience, patience*patience_increase)\n \n test_losses = [testModel(i) for i in range(n_test_batches)]\n test_score = numpy.mean(test_losses)\n \n print(' epoch %i, minibatch %i/%i, test error of'\n 'best model %f %%'%\n (epoch, mini_batch_index+1, n_train_batches,\n this_validation_losses*100)\n )\n if patience <= iter:\n done_looping = True\n break\n endtime = timeit.default_timer()\n print('Optimization complete.')\n print('Best validation score of %f %% obtained at iteration %i, '\n ' with test performance %f %%' %\n (best_validation_loss*100, best_iter+1, test_score*100.)\n )\n \n print('The code for file ' + os.path.split(__file__)[1]+\n ' ran for %.2fm' % (endtime - start_time)/60. \n )",
"title": ""
},
{
"docid": "cf981ff48b6e7565b993d0c026bd7c2e",
"score": "0.58766633",
"text": "def ModelTraindata():\n data = request.get_json()\n with open(app.config[\"DATA_PATH\"] + \"/config/params.json\") as json_file:\n default_configs = json.load(json_file)\n default_configs[\"experiment\"][\"model\"] = data[\"model\"]\n default_configs[\"experiment\"][\"batch_size\"] = int(data[\"batch_size\"])\n default_configs[\"experiment\"][\"epochs\"] = int(data[\"epochs\"])\n default_configs[\"experiment\"][\"learning_rate\"] = float(\n data[\"learning_rate\"])\n default_configs[\"experiment\"][\"class_ids\"] = list(\n range(0, 43)) + (np.array(data[\"class_ids\"]) + 43).tolist()\n default_configs[\"experiment\"][\"num_classes\"] = int(\n data[\"num_classes\"]) + 43\n\n max_train = -1\n for file in os.listdir(app.config[\"DATA_PATH\"] + \"checkpoints/logs/\"):\n if file.startswith(\"temp\"):\n number = int(file.split(\"_\")[-1])\n max_train = max(max_train, number)\n\n max_train+=1\n next_config = \"temp_config_\"+ str(max_train)\n with open(app.config[\"DATA_PATH\"] + \"config/\" + next_config + \".json\", \"w\") as outfile:\n json.dump(default_configs, outfile, indent=4)\n\n try:\n shutil.move(os.path.join(app.config[\"DATA_PATH\"], \"dataset/EXTRA\"),\n os.path.join(app.config[\"DATA_PATH\"], \"dataset/EXTRA_copy\"))\n remake_EXTRA_folder(\n 0, 0, new_classes=default_configs[\"experiment\"][\"class_ids\"], next_config = max_train)\n remake_EXTRA_folder(\n 0, 1, new_classes=default_configs[\"experiment\"][\"class_ids\"], next_config = max_train)\n remake_EXTRA_folder(\n 1, 0, new_classes=default_configs[\"experiment\"][\"class_ids\"], next_config = max_train)\n \n \n t1 = threading.Thread(target=train.train, args=[next_config])\n t1.start()\n while t1.is_alive():\n continue\n\n except KeyboardInterrupt:\n \"Stopped Abruptly\"\n finally:\n print(\"finally\")\n f = open(os.path.join(\"data/traffic_sign_interiit/dataset/\", \"TrainInfo.txt\"), \"w+\")\n f.close()\n shutil.rmtree(app.config[\"DATA_PATH\"] + \"dataset/EXTRA\")\n shutil.move(os.path.join(app.config[\"DATA_PATH\"], \"dataset/EXTRA_copy\"),\n os.path.join(app.config[\"DATA_PATH\"], \"dataset/EXTRA\"))\n\n check = True\n if check:\n return make_response(jsonify({\"message\": \"Model Trained Successfuly! Saved as: \"+ next_config +\".pt. Reload to see it in test\"}), 200)\n else:\n return make_response(jsonify({\"error\": \"Something is Wrong. Try Again!\"}), 400)",
"title": ""
},
{
"docid": "5e135a1d790910c67e738f3c6dfc769e",
"score": "0.5874946",
"text": "def train_coxnet_model_for_baseline(cancer_data_path, folds_data_path,\r\n test_fold_index, max_iter, top_k_variance_limit,\r\n selected_features_path, selected_features_count, present_output=True,\r\n model_output_path=\"\"):\r\n\r\n # create train and test sets\r\n X_train, y_train = create_X_and_y(cancer_data_path, top_k_variance_limit, folds_data_path,\r\n [i for i in range(5) if i != test_fold_index], selected_features_path,\r\n selected_features_count)\r\n X_test, y_test = create_X_and_y(cancer_data_path, top_k_variance_limit, folds_data_path, [test_fold_index],\r\n selected_features_path, selected_features_count)\r\n\r\n # train a simple coxnet model to get a range of possible alphas\r\n cox_net = CoxnetSurvivalAnalysis(max_iter=max_iter)\r\n cox_net.fit(X_train, y_train)\r\n initial_alphas = cox_net.alphas_\r\n\r\n params = {\"alphas\": [[v] for i, v in enumerate(initial_alphas) if i % 5 == 0] + [[v * 10] for i, v in\r\n enumerate(initial_alphas) if\r\n i % 5 == 0]\r\n , \"l1_ratio\": [0.00101, 0.1, 0.5, 0.7, 1]}\r\n\r\n gcv_results = get_grid_search_results(params, X_train, y_train, max_iter)\r\n gcv_results_table = pd.DataFrame(gcv_results.cv_results_)\r\n best_model = gcv_results.best_estimator_\r\n alphas = gcv_results_table.param_alphas.map(lambda x: x[0])\r\n\r\n best_l1_ratio = best_model.l1_ratio\r\n best_coefs = pd.DataFrame(\r\n best_model.coef_,\r\n index=X_train.columns,\r\n columns=[\"coefficient\"]\r\n )\r\n\r\n best_model_cv = get_ci_score(best_model, X_test, y_test)\r\n\r\n if present_output:\r\n # present results\r\n print(\"Best model C.I for test set on fold #{} is: {}\".format(test_fold_index, best_model_cv))\r\n print(\"Best alpha: {}\".format(best_model.alphas_))\r\n print(\"Best l1 ratio: {}\".format(best_l1_ratio))\r\n visualize_alpha_values(best_model, best_coefs, alphas)\r\n visualize_ci_for_alpha_values(gcv_results_table, gcv_results, alphas)\r\n print(\"Internal CV scores while training: \")\r\n print(cross_val_score(best_model, X_train, y_train,\r\n cv=5)) # check what to actually cross validate on - holdout or all?\r\n\r\n # save model\r\n if model_output_path:\r\n pickle.dump(best_model, open(model_output_path, \"wb\"))\r\n return best_model, best_model_cv",
"title": ""
},
{
"docid": "f8f59f72f592409cc0bd15f81fb5b465",
"score": "0.5869422",
"text": "def setup(data, \n target, \n train_size = 0.7,\n sampling = True,\n sample_estimator = None,\n categorical_features = None,\n categorical_imputation = 'constant',\n ordinal_features = None,\n high_cardinality_features = None, \n high_cardinality_method = 'frequency', \n numeric_features = None,\n numeric_imputation = 'mean',\n date_features = None,\n ignore_features = None,\n normalize = False,\n normalize_method = 'zscore',\n transformation = False,\n transformation_method = 'yeo-johnson',\n handle_unknown_categorical = True, \n unknown_categorical_method = 'least_frequent',\n pca = False,\n pca_method = 'linear',\n pca_components = None, \n ignore_low_variance = False, \n combine_rare_levels = False,\n rare_level_threshold = 0.10,\n bin_numeric_features = None, \n remove_outliers = False,\n outliers_threshold = 0.05,\n remove_multicollinearity = False,\n multicollinearity_threshold = 0.9,\n remove_perfect_collinearity = False, #added in pycaret==2.0.0\n create_clusters = False,\n cluster_iter = 20,\n polynomial_features = False, \n polynomial_degree = 2,\n trigonometry_features = False,\n polynomial_threshold = 0.1,\n group_features = None,\n group_names = None,\n feature_selection = False,\n feature_selection_threshold = 0.8,\n feature_interaction = False,\n feature_ratio = False,\n interaction_threshold = 0.01, \n transform_target = False,\n transform_target_method = 'box-cox',\n data_split_shuffle = True, #added in pycaret==2.0.0\n folds_shuffle = False, #added in pycaret==2.0.0\n n_jobs = -1, #added in pycaret==2.0.0\n use_gpu = False, #added in pycaret==2.1\n html = True, #added in pycaret==2.0.0\n session_id = None,\n log_experiment = False, #added in pycaret==2.0.0\n experiment_name = None, #added in pycaret==2.0.0\n log_plots = False, #added in pycaret==2.0.0\n log_profile = False, #added in pycaret==2.0.0\n log_data = False, #added in pycaret==2.0.0\n silent = False,\n verbose = True, #added in pycaret==2.0.0\n profile = False):\n \n #exception checking \n import sys\n \n from pycaret.utils import __version__\n ver = __version__()\n\n import logging\n\n # create logger\n global logger\n\n logger = logging.getLogger('logs')\n logger.setLevel(logging.DEBUG)\n \n # create console handler and set level to debug\n\n if logger.hasHandlers():\n logger.handlers.clear()\n \n ch = logging.FileHandler('logs.log')\n ch.setLevel(logging.DEBUG)\n\n # create formatter\n formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(message)s')\n\n # add formatter to ch\n ch.setFormatter(formatter)\n\n # add ch to logger\n logger.addHandler(ch)\n\n logger.info(\"PyCaret Regression Module\")\n logger.info('version ' + str(ver))\n logger.info(\"Initializing setup()\")\n\n #generate USI for mlflow tracking\n import secrets\n global USI\n USI = secrets.token_hex(nbytes=2)\n logger.info('USI: ' + str(USI))\n\n logger.info(\"\"\"setup(data={}, target={}, train_size={}, sampling={}, sample_estimator={}, categorical_features={}, categorical_imputation={}, ordinal_features={},\n high_cardinality_features={}, high_cardinality_method={}, numeric_features={}, numeric_imputation={}, date_features={}, ignore_features={}, normalize={},\n normalize_method={}, transformation={}, transformation_method={}, handle_unknown_categorical={}, unknown_categorical_method={}, pca={}, pca_method={},\n pca_components={}, ignore_low_variance={}, combine_rare_levels={}, rare_level_threshold={}, bin_numeric_features={}, remove_outliers={}, outliers_threshold={},\n remove_multicollinearity={}, multicollinearity_threshold={}, remove_perfect_collinearity={}, create_clusters={}, cluster_iter={},\n polynomial_features={}, polynomial_degree={}, trigonometry_features={}, polynomial_threshold={}, group_features={},\n group_names={}, feature_selection={}, feature_selection_threshold={}, feature_interaction={}, feature_ratio={}, interaction_threshold={}, transform_target={},\n transform_target_method={}, data_split_shuffle={}, folds_shuffle={}, n_jobs={}, html={}, session_id={}, log_experiment={},\n experiment_name={}, log_plots={}, log_profile={}, log_data={}, silent={}, verbose={}, profile={})\"\"\".format(\\\n str(data.shape), str(target), str(train_size), str(sampling), str(sample_estimator), str(categorical_features), str(categorical_imputation), str(ordinal_features),\\\n str(high_cardinality_features), str(high_cardinality_method), str(numeric_features), str(numeric_imputation), str(date_features), str(ignore_features),\\\n str(normalize), str(normalize_method), str(transformation), str(transformation_method), str(handle_unknown_categorical), str(unknown_categorical_method), str(pca),\\\n str(pca_method), str(pca_components), str(ignore_low_variance), str(combine_rare_levels), str(rare_level_threshold), str(bin_numeric_features), str(remove_outliers),\\\n str(outliers_threshold), str(remove_multicollinearity), str(multicollinearity_threshold), str(remove_perfect_collinearity), str(create_clusters), str(cluster_iter),\\\n str(polynomial_features), str(polynomial_degree), str(trigonometry_features), str(polynomial_threshold), str(group_features), str(group_names),\\\n str(feature_selection), str(feature_selection_threshold), str(feature_interaction), str(feature_ratio), str(interaction_threshold), str(transform_target),\\\n str(transform_target_method), str(data_split_shuffle), str(folds_shuffle), str(n_jobs), str(html), str(session_id),\\\n str(log_experiment), str(experiment_name), str(log_plots), str(log_profile), str(log_data), str(silent), str(verbose), str(profile)))\n\n #logging environment and libraries\n logger.info(\"Checking environment\")\n \n from platform import python_version, platform, python_build, machine\n\n try:\n logger.info(\"python_version: \" + str(python_version()))\n except:\n logger.warning(\"cannot find platform.python_version\")\n\n try:\n logger.info(\"python_build: \" + str(python_build()))\n except:\n logger.warning(\"cannot find platform.python_build\")\n\n try:\n logger.info(\"machine: \" + str(machine()))\n except:\n logger.warning(\"cannot find platform.machine\")\n\n try:\n logger.info(\"platform: \" + str(platform()))\n except:\n logger.warning(\"cannot find platform.platform\")\n\n try:\n import psutil\n logger.info(\"Memory: \" + str(psutil.virtual_memory()))\n logger.info(\"Physical Core: \" + str(psutil.cpu_count(logical=False)))\n logger.info(\"Logical Core: \" + str(psutil.cpu_count(logical=True)))\n except:\n logger.warning(\"cannot find psutil installation. memory not traceable. Install psutil using pip to enable memory logging. \")\n \n logger.info(\"Checking libraries\")\n\n try:\n from pandas import __version__\n logger.info(\"pd==\" + str(__version__))\n except:\n logger.warning(\"pandas not found\")\n\n try:\n from numpy import __version__\n logger.info(\"numpy==\" + str(__version__))\n except:\n logger.warning(\"numpy not found\")\n\n try:\n from sklearn import __version__\n logger.info(\"sklearn==\" + str(__version__))\n except:\n logger.warning(\"sklearn not found\")\n\n try:\n from xgboost import __version__\n logger.info(\"xgboost==\" + str(__version__))\n except:\n logger.warning(\"xgboost not found\")\n\n try:\n from lightgbm import __version__\n logger.info(\"lightgbm==\" + str(__version__))\n except:\n logger.warning(\"lightgbm not found\")\n\n try:\n from catboost import __version__\n logger.info(\"catboost==\" + str(__version__))\n except:\n logger.warning(\"catboost not found\")\n\n try:\n from mlflow.version import VERSION\n import warnings\n warnings.filterwarnings('ignore') \n logger.info(\"mlflow==\" + str(VERSION))\n except:\n logger.warning(\"mlflow not found\")\n\n #run_time\n import datetime, time\n runtime_start = time.time()\n\n logger.info(\"Checking Exceptions\")\n\n #checking data type\n if hasattr(data,'shape') is False:\n sys.exit('(Type Error): data passed must be of type pandas.DataFrame')\n\n #checking train size parameter\n if type(train_size) is not float:\n sys.exit('(Type Error): train_size parameter only accepts float value.')\n \n #checking sampling parameter\n if type(sampling) is not bool:\n sys.exit('(Type Error): sampling parameter only accepts True or False.')\n \n #checking sampling parameter\n if target not in data.columns:\n sys.exit('(Value Error): Target parameter doesnt exist in the data provided.') \n\n #checking session_id\n if session_id is not None:\n if type(session_id) is not int:\n sys.exit('(Type Error): session_id parameter must be an integer.') \n \n #checking sampling parameter\n if type(profile) is not bool:\n sys.exit('(Type Error): profile parameter only accepts True or False.')\n \n #checking normalize parameter\n if type(normalize) is not bool:\n sys.exit('(Type Error): normalize parameter only accepts True or False.')\n \n #checking transformation parameter\n if type(transformation) is not bool:\n sys.exit('(Type Error): transformation parameter only accepts True or False.')\n \n #checking categorical imputation\n allowed_categorical_imputation = ['constant', 'mode']\n if categorical_imputation not in allowed_categorical_imputation:\n sys.exit(\"(Value Error): categorical_imputation param only accepts 'constant' or 'mode' \")\n \n #ordinal_features\n if ordinal_features is not None:\n if type(ordinal_features) is not dict:\n sys.exit(\"(Type Error): ordinal_features must be of type dictionary with column name as key and ordered values as list. \")\n \n #ordinal features check\n if ordinal_features is not None:\n data_cols = data.columns\n data_cols = data_cols.drop(target)\n ord_keys = ordinal_features.keys()\n \n for i in ord_keys:\n if i not in data_cols:\n sys.exit(\"(Value Error) Column name passed as a key in ordinal_features param doesnt exist. \")\n \n for k in ord_keys:\n if data[k].nunique() != len(ordinal_features.get(k)):\n sys.exit(\"(Value Error) Levels passed in ordinal_features param doesnt match with levels in data. \")\n\n for i in ord_keys:\n value_in_keys = ordinal_features.get(i)\n value_in_data = list(data[i].unique().astype(str))\n for j in value_in_keys:\n if j not in value_in_data:\n text = \"Column name '\" + str(i) + \"' doesnt contain any level named '\" + str(j) + \"'.\"\n sys.exit(text)\n \n #high_cardinality_features\n if high_cardinality_features is not None:\n if type(high_cardinality_features) is not list:\n sys.exit(\"(Type Error): high_cardinality_features param only accepts name of columns as a list. \")\n \n if high_cardinality_features is not None:\n data_cols = data.columns\n data_cols = data_cols.drop(target)\n for i in high_cardinality_features:\n if i not in data_cols:\n sys.exit(\"(Value Error): Column type forced is either target column or doesn't exist in the dataset.\")\n \n #checking numeric imputation\n allowed_numeric_imputation = ['mean', 'median']\n if numeric_imputation not in allowed_numeric_imputation:\n sys.exit(\"(Value Error): numeric_imputation param only accepts 'mean' or 'median' \")\n \n #checking normalize method\n allowed_normalize_method = ['zscore', 'minmax', 'maxabs', 'robust']\n if normalize_method not in allowed_normalize_method:\n sys.exit(\"(Value Error): normalize_method param only accepts 'zscore', 'minxmax', 'maxabs' or 'robust'. \") \n \n #checking transformation method\n allowed_transformation_method = ['yeo-johnson', 'quantile']\n if transformation_method not in allowed_transformation_method:\n sys.exit(\"(Value Error): transformation_method param only accepts 'yeo-johnson' or 'quantile' \") \n \n #handle unknown categorical\n if type(handle_unknown_categorical) is not bool:\n sys.exit('(Type Error): handle_unknown_categorical parameter only accepts True or False.')\n \n #unknown categorical method\n unknown_categorical_method_available = ['least_frequent', 'most_frequent']\n \n if unknown_categorical_method not in unknown_categorical_method_available:\n sys.exit(\"(Type Error): unknown_categorical_method only accepts 'least_frequent' or 'most_frequent'.\")\n \n #check pca\n if type(pca) is not bool:\n sys.exit('(Type Error): PCA parameter only accepts True or False.')\n \n #pca method check\n allowed_pca_methods = ['linear', 'kernel', 'incremental',]\n if pca_method not in allowed_pca_methods:\n sys.exit(\"(Value Error): pca method param only accepts 'linear', 'kernel', or 'incremental'. \") \n \n #pca components check\n if pca is True:\n if pca_method != 'linear':\n if pca_components is not None:\n if(type(pca_components)) is not int:\n sys.exit(\"(Type Error): pca_components parameter must be integer when pca_method is not 'linear'. \")\n\n #pca components check 2\n if pca is True:\n if pca_method != 'linear':\n if pca_components is not None:\n if pca_components > len(data.columns)-1:\n sys.exit(\"(Type Error): pca_components parameter cannot be greater than original features space.\") \n \n #pca components check 3\n if pca is True:\n if pca_method == 'linear':\n if pca_components is not None:\n if type(pca_components) is not float:\n if pca_components > len(data.columns)-1: \n sys.exit(\"(Type Error): pca_components parameter cannot be greater than original features space or float between 0 - 1.\") \n \n #check ignore_low_variance\n if type(ignore_low_variance) is not bool:\n sys.exit('(Type Error): ignore_low_variance parameter only accepts True or False.')\n \n #check ignore_low_variance\n if type(combine_rare_levels) is not bool:\n sys.exit('(Type Error): combine_rare_levels parameter only accepts True or False.')\n \n #check rare_level_threshold\n if type(rare_level_threshold) is not float:\n sys.exit('(Type Error): rare_level_threshold must be a float between 0 and 1. ')\n \n #bin numeric features\n if bin_numeric_features is not None:\n all_cols = list(data.columns)\n all_cols.remove(target)\n \n for i in bin_numeric_features:\n if i not in all_cols:\n sys.exit(\"(Value Error): Column type forced is either target column or doesn't exist in the dataset.\")\n \n #check transform_target\n if type(transform_target) is not bool:\n sys.exit('(Type Error): transform_target parameter only accepts True or False.')\n \n #transform_target_method\n allowed_transform_target_method = ['box-cox', 'yeo-johnson']\n if transform_target_method not in allowed_transform_target_method:\n sys.exit(\"(Value Error): transform_target_method param only accepts 'box-cox' or 'yeo-johnson'. \") \n \n #remove_outliers\n if type(remove_outliers) is not bool:\n sys.exit('(Type Error): remove_outliers parameter only accepts True or False.') \n \n #outliers_threshold\n if type(outliers_threshold) is not float:\n sys.exit('(Type Error): outliers_threshold must be a float between 0 and 1. ') \n \n #remove_multicollinearity\n if type(remove_multicollinearity) is not bool:\n sys.exit('(Type Error): remove_multicollinearity parameter only accepts True or False.')\n \n #multicollinearity_threshold\n if type(multicollinearity_threshold) is not float:\n sys.exit('(Type Error): multicollinearity_threshold must be a float between 0 and 1. ') \n \n #create_clusters\n if type(create_clusters) is not bool:\n sys.exit('(Type Error): create_clusters parameter only accepts True or False.')\n \n #cluster_iter\n if type(cluster_iter) is not int:\n sys.exit('(Type Error): cluster_iter must be a integer greater than 1. ') \n \n #polynomial_features\n if type(polynomial_features) is not bool:\n sys.exit('(Type Error): polynomial_features only accepts True or False. ') \n \n #polynomial_degree\n if type(polynomial_degree) is not int:\n sys.exit('(Type Error): polynomial_degree must be an integer. ')\n \n #polynomial_features\n if type(trigonometry_features) is not bool:\n sys.exit('(Type Error): trigonometry_features only accepts True or False. ') \n \n #polynomial threshold\n if type(polynomial_threshold) is not float:\n sys.exit('(Type Error): polynomial_threshold must be a float between 0 and 1. ') \n \n #group features\n if group_features is not None:\n if type(group_features) is not list:\n sys.exit('(Type Error): group_features must be of type list. ') \n \n if group_names is not None:\n if type(group_names) is not list:\n sys.exit('(Type Error): group_names must be of type list. ') \n \n #cannot drop target\n if ignore_features is not None:\n if target in ignore_features:\n sys.exit(\"(Value Error): cannot drop target column. \") \n \n #feature_selection\n if type(feature_selection) is not bool:\n sys.exit('(Type Error): feature_selection only accepts True or False. ') \n \n #feature_selection_threshold\n if type(feature_selection_threshold) is not float:\n sys.exit('(Type Error): feature_selection_threshold must be a float between 0 and 1. ') \n \n #feature_interaction\n if type(feature_interaction) is not bool:\n sys.exit('(Type Error): feature_interaction only accepts True or False. ') \n \n #feature_ratio\n if type(feature_ratio) is not bool:\n sys.exit('(Type Error): feature_ratio only accepts True or False. ') \n \n #interaction_threshold\n if type(interaction_threshold) is not float:\n sys.exit('(Type Error): interaction_threshold must be a float between 0 and 1. ') \n\n #cannot drop target\n if ignore_features is not None:\n if target in ignore_features:\n sys.exit(\"(Value Error): cannot drop target column. \") \n \n #forced type check\n all_cols = list(data.columns)\n all_cols.remove(target)\n \n #categorical\n if categorical_features is not None:\n for i in categorical_features:\n if i not in all_cols:\n sys.exit(\"(Value Error): Column type forced is either target column or doesn't exist in the dataset.\")\n \n #numeric\n if numeric_features is not None:\n for i in numeric_features:\n if i not in all_cols:\n sys.exit(\"(Value Error): Column type forced is either target column or doesn't exist in the dataset.\") \n \n #date features\n if date_features is not None:\n for i in date_features:\n if i not in all_cols:\n sys.exit(\"(Value Error): Column type forced is either target column or doesn't exist in the dataset.\") \n \n #drop features\n if ignore_features is not None:\n for i in ignore_features:\n if i not in all_cols:\n sys.exit(\"(Value Error): Feature ignored is either target column or doesn't exist in the dataset.\") \n \n #silent\n if type(silent) is not bool:\n sys.exit(\"(Type Error): silent parameter only accepts True or False. \")\n\n #remove_perfect_collinearity\n if type(remove_perfect_collinearity) is not bool:\n sys.exit('(Type Error): remove_perfect_collinearity parameter only accepts True or False.')\n \n #html\n if type(html) is not bool:\n sys.exit('(Type Error): html parameter only accepts True or False.')\n\n #folds_shuffle\n if type(folds_shuffle) is not bool:\n sys.exit('(Type Error): folds_shuffle parameter only accepts True or False.')\n\n #data_split_shuffle\n if type(data_split_shuffle) is not bool:\n sys.exit('(Type Error): data_split_shuffle parameter only accepts True or False.')\n\n #log_experiment\n if type(log_experiment) is not bool:\n sys.exit('(Type Error): log_experiment parameter only accepts True or False.')\n\n #log_plots\n if type(log_plots) is not bool:\n sys.exit('(Type Error): log_plots parameter only accepts True or False.')\n\n #log_data\n if type(log_data) is not bool:\n sys.exit('(Type Error): log_data parameter only accepts True or False.')\n\n #log_profile\n if type(log_profile) is not bool:\n sys.exit('(Type Error): log_profile parameter only accepts True or False.')\n\n logger.info(\"Preloading libraries\")\n\n #pre-load libraries\n import pandas as pd\n import ipywidgets as ipw\n from IPython.display import display, HTML, clear_output, update_display\n import datetime, time\n import os\n\n #pandas option\n pd.set_option('display.max_columns', 500)\n pd.set_option('display.max_rows', 500)\n \n #global html_param\n global html_param\n \n #create html_param\n html_param = html\n\n #silent parameter to also set sampling to False\n if silent:\n sampling = False\n\n logger.info(\"Preparing display monitor\")\n\n #progress bar\n if sampling:\n max = 10 + 3\n else:\n max = 3\n \n progress = ipw.IntProgress(value=0, min=0, max=max, step=1 , description='Processing: ')\n if verbose:\n if html_param:\n display(progress)\n \n timestampStr = datetime.datetime.now().strftime(\"%H:%M:%S\")\n monitor = pd.DataFrame( [ ['Initiated' , '. . . . . . . . . . . . . . . . . .', timestampStr ], \n ['Status' , '. . . . . . . . . . . . . . . . . .' , 'Loading Dependencies' ],\n ['ETC' , '. . . . . . . . . . . . . . . . . .', 'Calculating ETC'] ],\n columns=['', ' ', ' ']).set_index('')\n \n if verbose:\n if html_param:\n display(monitor, display_id = 'monitor')\n \n logger.info(\"Importing libraries\")\n\n #general dependencies\n import numpy as np\n from sklearn.linear_model import LinearRegression\n from sklearn.model_selection import train_test_split\n from sklearn import metrics\n import random\n import seaborn as sns\n import matplotlib.pyplot as plt\n import plotly.express as px\n \n #setting sklearn config to print all parameters including default\n import sklearn\n sklearn.set_config(print_changed_only=False)\n \n #define highlight function for function grid to display\n def highlight_max(s):\n is_max = s == True\n return ['background-color: yellow' if v else '' for v in is_max]\n \n #cufflinks\n import cufflinks as cf\n cf.go_offline()\n cf.set_config_file(offline=False, world_readable=True)\n \n #ignore warnings\n import warnings\n warnings.filterwarnings('ignore') \n \n\n logger.info(\"Declaring global variables\")\n\n #declaring global variables to be accessed by other functions\n global X, y, X_train, X_test, y_train, y_test, seed, prep_pipe, target_inverse_transformer, experiment__,\\\n preprocess, folds_shuffle_param, n_jobs_param, create_model_container, master_model_container,\\\n display_container, exp_name_log, logging_param, log_plots_param, data_before_preprocess, target_param,\\\n gpu_param\n\n logger.info(\"Copying data for preprocessing\")\n #copy original data for pandas profiler\n data_before_preprocess = data.copy()\n \n #generate seed to be used globally\n if session_id is None:\n seed = random.randint(150,9000)\n else:\n seed = session_id\n \n\n \"\"\"\n preprocessing starts here\n \"\"\"\n \n monitor.iloc[1,1:] = 'Preparing Data for Modeling'\n if verbose:\n if html_param:\n update_display(monitor, display_id = 'monitor')\n \n #define parameters for preprocessor\n \n logger.info(\"Declaring preprocessing parameters\")\n\n #categorical features\n if categorical_features is None:\n cat_features_pass = []\n else:\n cat_features_pass = categorical_features\n \n #numeric features\n if numeric_features is None:\n numeric_features_pass = []\n else:\n numeric_features_pass = numeric_features\n \n #drop features\n if ignore_features is None:\n ignore_features_pass = []\n else:\n ignore_features_pass = ignore_features\n \n #date features\n if date_features is None:\n date_features_pass = []\n else:\n date_features_pass = date_features\n \n #categorical imputation strategy\n if categorical_imputation == 'constant':\n categorical_imputation_pass = 'not_available'\n elif categorical_imputation == 'mode':\n categorical_imputation_pass = 'most frequent'\n \n #transformation method strategy\n if transformation_method == 'yeo-johnson':\n trans_method_pass = 'yj'\n elif transformation_method == 'quantile':\n trans_method_pass = 'quantile'\n \n #pass method\n if pca_method == 'linear':\n pca_method_pass = 'pca_liner'\n \n elif pca_method == 'kernel':\n pca_method_pass = 'pca_kernal'\n \n elif pca_method == 'incremental':\n pca_method_pass = 'incremental'\n \n elif pca_method == 'pls':\n pca_method_pass = 'pls'\n \n #pca components\n if pca is True:\n if pca_components is None:\n if pca_method == 'linear':\n pca_components_pass = 0.99\n else:\n pca_components_pass = int((len(data.columns)-1)*0.5)\n \n else:\n pca_components_pass = pca_components\n \n else:\n pca_components_pass = 0.99\n \n if bin_numeric_features is None:\n apply_binning_pass = False\n features_to_bin_pass = []\n \n else:\n apply_binning_pass = True\n features_to_bin_pass = bin_numeric_features\n \n #trignometry\n if trigonometry_features is False:\n trigonometry_features_pass = []\n else:\n trigonometry_features_pass = ['sin', 'cos', 'tan']\n \n #group features\n #=============#\n \n #apply grouping\n if group_features is not None:\n apply_grouping_pass = True\n else:\n apply_grouping_pass = False\n \n #group features listing\n if apply_grouping_pass is True:\n \n if type(group_features[0]) is str:\n group_features_pass = []\n group_features_pass.append(group_features)\n else:\n group_features_pass = group_features\n \n else:\n \n group_features_pass = [[]]\n \n #group names\n if apply_grouping_pass is True:\n\n if (group_names is None) or (len(group_names) != len(group_features_pass)):\n group_names_pass = list(np.arange(len(group_features_pass)))\n group_names_pass = ['group_' + str(i) for i in group_names_pass]\n\n else:\n group_names_pass = group_names\n \n else:\n group_names_pass = []\n \n #feature interactions\n \n if feature_interaction or feature_ratio:\n apply_feature_interactions_pass = True\n else:\n apply_feature_interactions_pass = False\n \n interactions_to_apply_pass = []\n \n if feature_interaction:\n interactions_to_apply_pass.append('multiply')\n \n if feature_ratio:\n interactions_to_apply_pass.append('divide')\n \n #unknown categorical\n if unknown_categorical_method == 'least_frequent':\n unknown_categorical_method_pass = 'least frequent'\n elif unknown_categorical_method == 'most_frequent':\n unknown_categorical_method_pass = 'most frequent'\n\n #ordinal_features\n if ordinal_features is not None:\n apply_ordinal_encoding_pass = True\n else:\n apply_ordinal_encoding_pass = False\n \n if apply_ordinal_encoding_pass is True:\n ordinal_columns_and_categories_pass = ordinal_features\n else:\n ordinal_columns_and_categories_pass = {}\n \n if high_cardinality_features is not None:\n apply_cardinality_reduction_pass = True\n else:\n apply_cardinality_reduction_pass = False\n \n if high_cardinality_method == 'frequency':\n cardinal_method_pass = 'count'\n elif high_cardinality_method == 'clustering':\n cardinal_method_pass = 'cluster'\n \n if apply_cardinality_reduction_pass:\n cardinal_features_pass = high_cardinality_features\n else:\n cardinal_features_pass = []\n \n if silent:\n display_dtypes_pass = False\n else:\n display_dtypes_pass = True\n \n #transform target method\n if transform_target_method == 'box-cox':\n transform_target_method_pass = 'bc'\n elif transform_target_method == 'yeo-johnson':\n transform_target_method_pass = 'yj'\n\n logger.info(\"Importing preprocessing module\")\n \n #import library\n import pycaret.preprocess as preprocess\n \n logger.info(\"Creating preprocessing pipeline\")\n\n data = preprocess.Preprocess_Path_One(train_data = data, \n target_variable = target,\n categorical_features = cat_features_pass,\n apply_ordinal_encoding = apply_ordinal_encoding_pass, \n ordinal_columns_and_categories = ordinal_columns_and_categories_pass, \n apply_cardinality_reduction = apply_cardinality_reduction_pass,\n cardinal_method = cardinal_method_pass, \n cardinal_features = cardinal_features_pass,\n numerical_features = numeric_features_pass,\n time_features = date_features_pass,\n features_todrop = ignore_features_pass,\n numeric_imputation_strategy = numeric_imputation,\n categorical_imputation_strategy = categorical_imputation_pass,\n scale_data = normalize,\n scaling_method = normalize_method,\n Power_transform_data = transformation,\n Power_transform_method = trans_method_pass,\n apply_untrained_levels_treatment= handle_unknown_categorical,\n untrained_levels_treatment_method = unknown_categorical_method_pass, \n apply_pca = pca, \n pca_method = pca_method_pass, \n pca_variance_retained_or_number_of_components = pca_components_pass, \n apply_zero_nearZero_variance = ignore_low_variance,\n club_rare_levels = combine_rare_levels,\n rara_level_threshold_percentage = rare_level_threshold,\n apply_binning = apply_binning_pass,\n features_to_binn = features_to_bin_pass,\n remove_outliers = remove_outliers,\n outlier_contamination_percentage = outliers_threshold,\n outlier_methods = ['pca'], #pca hardcoded\n remove_multicollinearity = remove_multicollinearity,\n maximum_correlation_between_features = multicollinearity_threshold,\n remove_perfect_collinearity = remove_perfect_collinearity, \n cluster_entire_data = create_clusters, \n range_of_clusters_to_try = cluster_iter, \n apply_polynomial_trigonometry_features = polynomial_features, \n max_polynomial = polynomial_degree, \n trigonometry_calculations = trigonometry_features_pass, \n top_poly_trig_features_to_select_percentage = polynomial_threshold, \n apply_grouping = apply_grouping_pass, \n features_to_group_ListofList = group_features_pass, \n group_name = group_names_pass, \n apply_feature_selection = feature_selection, \n feature_selection_top_features_percentage = feature_selection_threshold, \n apply_feature_interactions = apply_feature_interactions_pass, \n feature_interactions_to_apply = interactions_to_apply_pass, \n feature_interactions_top_features_to_select_percentage=interaction_threshold, \n display_types = display_dtypes_pass, \n target_transformation = transform_target, \n target_transformation_method = transform_target_method_pass, \n random_state = seed)\n\n progress.value += 1\n logger.info(\"Preprocessing pipeline created successfully\")\n \n if hasattr(preprocess.dtypes, 'replacement'):\n label_encoded = preprocess.dtypes.replacement\n label_encoded = str(label_encoded).replace(\"'\", '')\n label_encoded = str(label_encoded).replace(\"{\", '')\n label_encoded = str(label_encoded).replace(\"}\", '')\n\n else:\n label_encoded = 'None'\n\n try:\n res_type = ['quit','Quit','exit','EXIT','q','Q','e','E','QUIT','Exit']\n res = preprocess.dtypes.response\n if res in res_type:\n sys.exit(\"(Process Exit): setup has been interupted with user command 'quit'. setup must rerun.\" )\n except:\n pass\n \n #save prep pipe\n prep_pipe = preprocess.pipe\n \n\n #save target inverse transformer\n try:\n target_inverse_transformer = preprocess.pt_target.p_transform_target\n except:\n target_inverse_transformer = None\n logger.info(\"No inverse transformer found\")\n\n \n logger.info(\"Creating grid variables\")\n\n #generate values for grid show\n missing_values = data_before_preprocess.isna().sum().sum()\n if missing_values > 0:\n missing_flag = True\n else:\n missing_flag = False\n \n if normalize is True:\n normalize_grid = normalize_method\n else:\n normalize_grid = 'None'\n \n if transformation is True:\n transformation_grid = transformation_method\n else:\n transformation_grid = 'None'\n \n if pca is True:\n pca_method_grid = pca_method\n else:\n pca_method_grid = 'None'\n \n if pca is True:\n pca_components_grid = pca_components_pass\n else:\n pca_components_grid = 'None'\n \n if combine_rare_levels:\n rare_level_threshold_grid = rare_level_threshold\n else:\n rare_level_threshold_grid = 'None'\n \n if bin_numeric_features is None:\n numeric_bin_grid = False\n else:\n numeric_bin_grid = True\n \n if remove_outliers is False:\n outliers_threshold_grid = None\n else:\n outliers_threshold_grid = outliers_threshold\n \n if remove_multicollinearity is False:\n multicollinearity_threshold_grid = None\n else:\n multicollinearity_threshold_grid = multicollinearity_threshold\n \n if create_clusters is False:\n cluster_iter_grid = None\n else:\n cluster_iter_grid = cluster_iter\n \n if polynomial_features:\n polynomial_degree_grid = polynomial_degree\n else:\n polynomial_degree_grid = None\n \n if polynomial_features or trigonometry_features:\n polynomial_threshold_grid = polynomial_threshold\n else:\n polynomial_threshold_grid = None\n \n if feature_selection:\n feature_selection_threshold_grid = feature_selection_threshold\n else:\n feature_selection_threshold_grid = None\n \n if feature_interaction or feature_ratio:\n interaction_threshold_grid = interaction_threshold\n else:\n interaction_threshold_grid = None\n \n if ordinal_features is not None:\n ordinal_features_grid = True\n else:\n ordinal_features_grid = False\n \n if handle_unknown_categorical:\n unknown_categorical_method_grid = unknown_categorical_method\n else:\n unknown_categorical_method_grid = None\n \n if group_features is not None:\n group_features_grid = True\n else:\n group_features_grid = False\n \n if high_cardinality_features is not None:\n high_cardinality_features_grid = True\n else:\n high_cardinality_features_grid = False\n \n if high_cardinality_features_grid:\n high_cardinality_method_grid = high_cardinality_method\n else:\n high_cardinality_method_grid = None\n \n learned_types = preprocess.dtypes.learent_dtypes\n learned_types.drop(target, inplace=True)\n\n float_type = 0 \n cat_type = 0\n\n for i in preprocess.dtypes.learent_dtypes:\n if 'float' in str(i):\n float_type += 1\n elif 'object' in str(i):\n cat_type += 1\n elif 'int' in str(i):\n float_type += 1\n \n #target transformation method\n if transform_target is False:\n transform_target_method_grid = None\n else:\n transform_target_method_grid = preprocess.pt_target.function_to_apply\n \n \"\"\"\n preprocessing ends here\n \"\"\"\n \n #reset pandas option\n pd.reset_option(\"display.max_rows\")\n pd.reset_option(\"display.max_columns\")\n \n logger.info(\"Creating global containers\")\n\n #create an empty list for pickling later.\n experiment__ = []\n \n #create folds_shuffle_param\n folds_shuffle_param = folds_shuffle\n\n #create n_jobs_param\n n_jobs_param = n_jobs\n\n #create create_model_container\n create_model_container = []\n\n #create master_model_container\n master_model_container = []\n\n #create display container\n display_container = []\n\n #create logging parameter\n logging_param = log_experiment\n\n #create exp_name_log param incase logging is False\n exp_name_log = 'no_logging'\n\n #create an empty log_plots_param\n if log_plots:\n log_plots_param = True\n else:\n log_plots_param = False\n\n # create target param\n target_param = target\n\n # create gpu param\n gpu_param = use_gpu\n\n #sample estimator\n if sample_estimator is None:\n model = LinearRegression(n_jobs=n_jobs_param)\n else:\n model = sample_estimator\n \n model_name = str(model).split(\"(\")[0]\n \n if 'CatBoostRegressor' in model_name:\n model_name = 'CatBoostRegressor'\n \n #creating variables to be used later in the function\n X = data.drop(target,axis=1)\n y = data[target]\n \n progress.value += 1\n \n if sampling is True and data.shape[0] > 25000: #change back to 25000\n \n split_perc = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.99]\n split_perc_text = ['10%','20%','30%','40%','50%','60%', '70%', '80%', '90%', '100%']\n split_perc_tt = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.99]\n split_perc_tt_total = []\n split_percent = []\n\n metric_results = []\n metric_name = []\n \n counter = 0\n \n for i in split_perc:\n \n progress.value += 1\n \n t0 = time.time()\n \n '''\n MONITOR UPDATE STARTS\n '''\n \n perc_text = split_perc_text[counter]\n monitor.iloc[1,1:] = 'Fitting Model on ' + perc_text + ' sample'\n if verbose:\n if html_param:\n update_display(monitor, display_id = 'monitor')\n\n '''\n MONITOR UPDATE ENDS\n '''\n \n X_, X__, y_, y__ = train_test_split(X, y, test_size=1-i, random_state=seed, shuffle=data_split_shuffle)\n X_train, X_test, y_train, y_test = train_test_split(X_, y_, test_size=1-train_size, random_state=seed, shuffle=data_split_shuffle)\n model.fit(X_train,y_train)\n pred_ = model.predict(X_test)\n \n r2 = metrics.r2_score(y_test,pred_)\n metric_results.append(r2)\n metric_name.append('R2')\n split_percent.append(i)\n \n t1 = time.time()\n \n '''\n Time calculation begins\n '''\n \n tt = t1 - t0\n total_tt = tt / i\n split_perc_tt.pop(0)\n \n for remain in split_perc_tt:\n ss = total_tt * remain\n split_perc_tt_total.append(ss)\n \n ttt = sum(split_perc_tt_total) / 60\n ttt = np.around(ttt, 2)\n \n if ttt < 1:\n ttt = str(np.around((ttt * 60), 2))\n ETC = ttt + ' Seconds Remaining'\n\n else:\n ttt = str (ttt)\n ETC = ttt + ' Minutes Remaining'\n \n monitor.iloc[2,1:] = ETC\n if verbose:\n if html_param:\n update_display(monitor, display_id = 'monitor')\n \n \n '''\n Time calculation Ends\n '''\n \n split_perc_tt_total = []\n counter += 1\n\n model_results = pd.DataFrame({'Sample' : split_percent, 'Metric' : metric_results, 'Metric Name': metric_name})\n fig = px.line(model_results, x='Sample', y='Metric', color='Metric Name', line_shape='linear', range_y = [0,1])\n fig.update_layout(plot_bgcolor='rgb(245,245,245)')\n title= str(model_name) + ' Metric and Sample %'\n fig.update_layout(title={'text': title, 'y':0.95,'x':0.45,'xanchor': 'center','yanchor': 'top'})\n fig.show()\n \n monitor.iloc[1,1:] = 'Waiting for input'\n if verbose:\n if html_param:\n update_display(monitor, display_id = 'monitor')\n \n \n print('Please Enter the sample % of data you would like to use for modeling. Example: Enter 0.3 for 30%.')\n print('Press Enter if you would like to use 100% of the data.')\n \n print(' ')\n \n sample_size = input(\"Sample Size: \")\n \n if sample_size == '' or sample_size == '1':\n \n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1-train_size, random_state=seed, shuffle=data_split_shuffle)\n\n else:\n \n sample_n = float(sample_size)\n X_selected, X_discard, y_selected, y_discard = train_test_split(X, y, test_size=1-sample_n, \n random_state=seed, shuffle=data_split_shuffle)\n \n X_train, X_test, y_train, y_test = train_test_split(X_selected, y_selected, test_size=1-train_size, \n random_state=seed, shuffle=data_split_shuffle)\n\n else:\n \n monitor.iloc[1,1:] = 'Splitting Data'\n if verbose:\n if html_param:\n update_display(monitor, display_id = 'monitor')\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1-train_size, random_state=seed, shuffle=data_split_shuffle)\n progress.value += 1\n\n '''\n Final display Starts\n '''\n clear_output()\n if verbose:\n print(' ')\n if profile:\n print('Setup Succesfully Completed. Loading Profile Now... Please Wait!')\n else:\n if verbose:\n print('Setup Succesfully Completed.')\n functions = pd.DataFrame ( [ ['session_id', seed ],\n ['Transform Target ', transform_target],\n ['Transform Target Method', transform_target_method_grid],\n ['Original Data', data_before_preprocess.shape ],\n ['Missing Values ', missing_flag],\n ['Numeric Features ', str(float_type) ],\n ['Categorical Features ', str(cat_type) ],\n ['Ordinal Features ', ordinal_features_grid],\n ['High Cardinality Features ', high_cardinality_features_grid],\n ['High Cardinality Method ', high_cardinality_method_grid],\n ['Sampled Data', '(' + str(X_train.shape[0] + X_test.shape[0]) + ', ' + str(data_before_preprocess.shape[1]) + ')' ], \n ['Transformed Train Set', X_train.shape ], \n ['Transformed Test Set',X_test.shape ],\n ['Numeric Imputer ', numeric_imputation],\n ['Categorical Imputer ', categorical_imputation],\n ['Normalize ', normalize ],\n ['Normalize Method ', normalize_grid ],\n ['Transformation ', transformation ],\n ['Transformation Method ', transformation_grid ],\n ['PCA ', pca],\n ['PCA Method ', pca_method_grid],\n ['PCA Components ', pca_components_grid],\n ['Ignore Low Variance ', ignore_low_variance],\n ['Combine Rare Levels ', combine_rare_levels],\n ['Rare Level Threshold ', rare_level_threshold_grid],\n ['Numeric Binning ', numeric_bin_grid],\n ['Remove Outliers ', remove_outliers],\n ['Outliers Threshold ', outliers_threshold_grid],\n ['Remove Multicollinearity ', remove_multicollinearity],\n ['Multicollinearity Threshold ', multicollinearity_threshold_grid],\n ['Clustering ', create_clusters],\n ['Clustering Iteration ', cluster_iter_grid],\n ['Polynomial Features ', polynomial_features],\n ['Polynomial Degree ', polynomial_degree_grid],\n ['Trignometry Features ', trigonometry_features],\n ['Polynomial Threshold ', polynomial_threshold_grid], \n ['Group Features ', group_features_grid],\n ['Feature Selection ', feature_selection],\n ['Features Selection Threshold ', feature_selection_threshold_grid],\n ['Feature Interaction ', feature_interaction],\n ['Feature Ratio ', feature_ratio],\n ['Interaction Threshold ', interaction_threshold_grid],\n ], columns = ['Description', 'Value'] )\n \n functions_ = functions.style.apply(highlight_max)\n if verbose:\n if html_param:\n display(functions_)\n else:\n print(functions_.data)\n \n if profile:\n try:\n import pandas_profiling\n pf = pandas_profiling.ProfileReport(data_before_preprocess)\n clear_output()\n display(pf)\n except:\n print('Data Profiler Failed. No output to show, please continue with Modeling.')\n \n '''\n Final display Ends\n ''' \n \n #log into experiment\n experiment__.append(('Regression Setup Config', functions))\n experiment__.append(('X_training Set', X_train))\n experiment__.append(('y_training Set', y_train))\n experiment__.append(('X_test Set', X_test))\n experiment__.append(('y_test Set', y_test))\n experiment__.append(('Transformation Pipeline', prep_pipe))\n try:\n experiment__.append(('Target Inverse Transformer', target_inverse_transformer))\n except:\n pass\n \n #end runtime\n runtime_end = time.time()\n runtime = np.array(runtime_end - runtime_start).round(2)\n\n if logging_param:\n \n logger.info(\"Logging experiment in MLFlow\")\n \n import mlflow\n from pathlib import Path\n\n if experiment_name is None:\n exp_name_ = 'reg-default-name'\n else:\n exp_name_ = experiment_name\n\n URI = secrets.token_hex(nbytes=4) \n exp_name_log = exp_name_\n \n try:\n mlflow.create_experiment(exp_name_log)\n except:\n pass\n\n #mlflow logging\n mlflow.set_experiment(exp_name_log)\n\n run_name_ = 'Session Initialized ' + str(USI)\n with mlflow.start_run(run_name=run_name_) as run:\n\n # Get active run to log as tag\n RunID = mlflow.active_run().info.run_id\n \n k = functions.copy()\n k.set_index('Description',drop=True,inplace=True)\n kdict = k.to_dict()\n params = kdict.get('Value')\n mlflow.log_params(params)\n\n #set tag of compare_models\n mlflow.set_tag(\"Source\", \"setup\")\n \n import secrets\n URI = secrets.token_hex(nbytes=4)\n mlflow.set_tag(\"URI\", URI)\n mlflow.set_tag(\"USI\", USI) \n mlflow.set_tag(\"Run Time\", runtime)\n mlflow.set_tag(\"Run ID\", RunID)\n\n # Log the transformation pipeline\n logger.info(\"SubProcess save_model() called ==================================\")\n save_model(prep_pipe, 'Transformation Pipeline', verbose=False)\n logger.info(\"SubProcess save_model() end ==================================\")\n mlflow.log_artifact('Transformation Pipeline' + '.pkl')\n os.remove('Transformation Pipeline.pkl')\n\n # Log pandas profile\n if log_profile:\n import pandas_profiling\n pf = pandas_profiling.ProfileReport(data_before_preprocess)\n pf.to_file(\"Data Profile.html\")\n mlflow.log_artifact(\"Data Profile.html\")\n os.remove(\"Data Profile.html\")\n clear_output()\n display(functions_)\n\n # Log training and testing set\n if log_data:\n X_train.join(y_train).to_csv('Train.csv')\n X_test.join(y_test).to_csv('Test.csv')\n mlflow.log_artifact(\"Train.csv\")\n mlflow.log_artifact(\"Test.csv\")\n os.remove('Train.csv')\n os.remove('Test.csv')\n\n logger.info(\"create_model_container: \" + str(len(create_model_container)))\n logger.info(\"master_model_container: \" + str(len(master_model_container)))\n logger.info(\"display_container: \" + str(len(display_container)))\n\n logger.info(\"setup() succesfully completed......................................\")\n\n return X, y, X_train, X_test, y_train, y_test, seed, prep_pipe, target_inverse_transformer,\\\n experiment__, folds_shuffle_param, n_jobs_param, html_param, create_model_container,\\\n master_model_container, display_container, exp_name_log, logging_param, log_plots_param, USI,\\\n data_before_preprocess, target_param",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="787"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">012e9d167c4fb65431c816f0d2bcbe37</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">The name of the category.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "e9c3dee8d88435fba7afa382d2833fa5",
"score": "0.0",
"text": "def name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"name\")",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "89d5292e894ed33a2743b5ac6e40c96c",
"score": "0.9356435",
"text": "def category_name(self) -> str:\n return pulumi.get(self, \"category_name\")",
"title": ""
},
{
"docid": "0708909944d1765b981bfd898b8dc4cb",
"score": "0.93421",
"text": "def category_name(self):\n return self.category.name",
"title": ""
},
{
"docid": "837b2e5849f05c1d3a5c0c8c54b14d6d",
"score": "0.8533845",
"text": "def display_category(self):\n return self.category.name",
"title": ""
},
{
"docid": "847ddfc9482fb33002fac12dfe2a54ac",
"score": "0.8499749",
"text": "def category_name(self):\r\n for short, name in Log.CATEGORIES:\r\n if short == self.category:\r\n return name",
"title": ""
},
{
"docid": "d64baca15ee050ea116217e1713805d9",
"score": "0.8397046",
"text": "def category_string(self):\n return self.category.category",
"title": ""
},
{
"docid": "6fbfa1cd29b03bac59ef2d15304c2d49",
"score": "0.8353404",
"text": "def category(self) -> str:\n return pulumi.get(self, \"category\")",
"title": ""
},
{
"docid": "6fbfa1cd29b03bac59ef2d15304c2d49",
"score": "0.8353404",
"text": "def category(self) -> str:\n return pulumi.get(self, \"category\")",
"title": ""
},
{
"docid": "cc084d9b86ab30a8c39027b34a50ae84",
"score": "0.82416683",
"text": "def category(self) -> str:\n return self.__category",
"title": ""
},
{
"docid": "784e11c23c379d212eda73f408b01c39",
"score": "0.8241514",
"text": "def category(self) -> str:\n return self._category",
"title": ""
},
{
"docid": "784e11c23c379d212eda73f408b01c39",
"score": "0.8241514",
"text": "def category(self) -> str:\n return self._category",
"title": ""
},
{
"docid": "bb9fa73c2381cb09ce34ed6335171b31",
"score": "0.8165807",
"text": "def get_category(self) -> str:\n return self.category",
"title": ""
},
{
"docid": "bb9fa73c2381cb09ce34ed6335171b31",
"score": "0.8165807",
"text": "def get_category(self) -> str:\n return self.category",
"title": ""
},
{
"docid": "1dd37b8c9e970c52b2838189a550ddd5",
"score": "0.8058707",
"text": "def __str__(self):\n return self.category_name",
"title": ""
},
{
"docid": "0b193e584f63e1639bfb75e19c6f946c",
"score": "0.79800016",
"text": "def __str__(self):\n return f'{self.category_name}'",
"title": ""
},
{
"docid": "0dc710ad2f02611012cdcbc487823c8b",
"score": "0.79722446",
"text": "def category(self):\n return self.name.rsplit('/', 1)[0] + '/'",
"title": ""
},
{
"docid": "06374a70d4477972bd07ac41a314264f",
"score": "0.7918446",
"text": "def category() -> str:",
"title": ""
},
{
"docid": "3c3f8f7b4b813ea0bddad61c6bf90c67",
"score": "0.7842589",
"text": "def category(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"category\")",
"title": ""
},
{
"docid": "3c3f8f7b4b813ea0bddad61c6bf90c67",
"score": "0.7842589",
"text": "def category(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"category\")",
"title": ""
},
{
"docid": "77ff1a6143a22e370cd3a6a6f7e521fd",
"score": "0.77653986",
"text": "def name(self):\r\n return self.cat_constructor.Name",
"title": ""
},
{
"docid": "95a247d99194548d29a1d23c029c3dcd",
"score": "0.7652714",
"text": "def __str__(self):\r\n return 'Category %s - %s' % (self.uuid, self.name)",
"title": ""
},
{
"docid": "78645ae986863b172ce083ee07a5f12a",
"score": "0.7547565",
"text": "def category(self) -> Optional[str]:\n return pulumi.get(self, \"category\")",
"title": ""
},
{
"docid": "78645ae986863b172ce083ee07a5f12a",
"score": "0.7547565",
"text": "def category(self) -> Optional[str]:\n return pulumi.get(self, \"category\")",
"title": ""
},
{
"docid": "78645ae986863b172ce083ee07a5f12a",
"score": "0.7547565",
"text": "def category(self) -> Optional[str]:\n return pulumi.get(self, \"category\")",
"title": ""
},
{
"docid": "78645ae986863b172ce083ee07a5f12a",
"score": "0.7547565",
"text": "def category(self) -> Optional[str]:\n return pulumi.get(self, \"category\")",
"title": ""
},
{
"docid": "78645ae986863b172ce083ee07a5f12a",
"score": "0.7547565",
"text": "def category(self) -> Optional[str]:\n return pulumi.get(self, \"category\")",
"title": ""
},
{
"docid": "a57544f9261000503d64e99de99db7e4",
"score": "0.7472379",
"text": "def category(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"category\")",
"title": ""
},
{
"docid": "4a855dd9f24799bbcfbed783efb17b92",
"score": "0.7465466",
"text": "def display_category(self):\n return ', '.join(category.name for category in self.category.all()[:3])",
"title": ""
},
{
"docid": "4a855dd9f24799bbcfbed783efb17b92",
"score": "0.7465466",
"text": "def display_category(self):\n return ', '.join(category.name for category in self.category.all()[:3])",
"title": ""
},
{
"docid": "7a1ae1c7692d99ade070e5b08bd50d0b",
"score": "0.74586797",
"text": "def category(self):\n return self._spec.get(\"category\", \"cat1\")",
"title": ""
},
{
"docid": "72ac3527e07c63658a0e557a89bec324",
"score": "0.74584407",
"text": "def display_category_names(self):\n print(\"Categories in '{}':\".format(self.name))\n for x in self.categories:\n print(\" {}\".format(x.get_category()))",
"title": ""
},
{
"docid": "1657d74940af32240709239acb26e1d6",
"score": "0.7458107",
"text": "def atomCatName(self):\n return \"/\".join([self.category, self.package])",
"title": ""
},
{
"docid": "21af89cdf7e410fa22fad75d418ec611",
"score": "0.74382997",
"text": "def category(self):\r\n\t\treturn self._category",
"title": ""
},
{
"docid": "f32c49533b9a54ab9eb1fbcf5a54abc8",
"score": "0.7434136",
"text": "def category(self):\n return self._category",
"title": ""
},
{
"docid": "f32c49533b9a54ab9eb1fbcf5a54abc8",
"score": "0.7434136",
"text": "def category(self):\n return self._category",
"title": ""
},
{
"docid": "f32c49533b9a54ab9eb1fbcf5a54abc8",
"score": "0.7434136",
"text": "def category(self):\n return self._category",
"title": ""
},
{
"docid": "f32c49533b9a54ab9eb1fbcf5a54abc8",
"score": "0.7434136",
"text": "def category(self):\n return self._category",
"title": ""
},
{
"docid": "1a0d0a787f626a0f90d29e2250f74fd8",
"score": "0.7415323",
"text": "def __unicode__(self):\n return '%s' % (self.category_name)",
"title": ""
},
{
"docid": "d2660afd93fca59c30089d0744754271",
"score": "0.7400903",
"text": "def display_category(self):\n return \", \".join(category.name for category in self.category.all()[:3])",
"title": ""
},
{
"docid": "dd5bee67836f1b8eca6d998ee562dee9",
"score": "0.7382513",
"text": "def category(self):\n return self.data.get('category', None)",
"title": ""
},
{
"docid": "d74a87d0efd15837adfe8fbcbeef56fa",
"score": "0.73162085",
"text": "def getcategory(self):\n return self._category",
"title": ""
},
{
"docid": "d906b9603a9910e2aa620469645f0078",
"score": "0.7305942",
"text": "def __str__(self):\n return \"{}_{}\".format(self.naics_code, self.category)",
"title": ""
},
{
"docid": "abb0f496a90b8fa3b807169ea66e4710",
"score": "0.73001766",
"text": "def __repr__(self):\n return \"<Category: {}>\".format(self.name)",
"title": ""
},
{
"docid": "dc3ea140787dcb7fa956825f78c1cdcf",
"score": "0.7292876",
"text": "def get_category(self):\n return self.category",
"title": ""
},
{
"docid": "8a469419a3fb137705f86403cfe1dec9",
"score": "0.7285824",
"text": "def category_control_name(v):\n return \"%s_category\" % (str(v.key()))",
"title": ""
},
{
"docid": "d60f3e48b3561d736891b1aa8476c110",
"score": "0.72473085",
"text": "def name_subcategory(self):\n name_cat = sc.select()\n return name_cat",
"title": ""
},
{
"docid": "307206fa4cddf4e8180af82b538bca12",
"score": "0.7234101",
"text": "def full_name(self):\n #Todo category.name to str\n names = [unicode(category.name) for category in self.get_ancestors_and_self()]\n return self._full_name_separator.join(names)",
"title": ""
},
{
"docid": "6a0dca4c22be27656fb9052f77fd4393",
"score": "0.72217125",
"text": "def getCategory(self):\n\t\treturn self.category",
"title": ""
},
{
"docid": "70ec38ab3039627e38d4f3bc3dd6b110",
"score": "0.7205014",
"text": "def getCategory(self):\n return self.category",
"title": ""
},
{
"docid": "00ad380e04978509eb45ba207d3b8189",
"score": "0.7178186",
"text": "def get_formatted_category(self):\n if (self.category == \"dinner\"):\n return \"Dinner\"\n elif (self.category == \"pregame\"):\n return \"Pregame\"\n elif (self.category == \"mainEvent\"):\n return \"Main Event\"",
"title": ""
},
{
"docid": "deb598c830491d4dd73290aa76ee3871",
"score": "0.71753734",
"text": "def getCategory(self):\r\n return self._category",
"title": ""
},
{
"docid": "fc37e7c612504a7bc7a075be43fcc727",
"score": "0.71447045",
"text": "def __repr__(self):\n return f\"Category('{self.name}', '{self.link}')\"",
"title": ""
},
{
"docid": "cabedc836d9d0b6efe6a18c61f0c911e",
"score": "0.7112214",
"text": "def category(self):\n first_slash = self._category.find('/')\n return self._category[first_slash+1:]",
"title": ""
},
{
"docid": "980cc4ca2d2bfed62c4c539e2f29acac",
"score": "0.7083927",
"text": "def __str__(self):\n return \"{}_{}\".format(self.o_net_soc_code, self.category_name)",
"title": ""
},
{
"docid": "d0077bcc85160eb5ee87403a2706536c",
"score": "0.7082638",
"text": "def category(self) -> int:\n return pulumi.get(self, \"category\")",
"title": ""
},
{
"docid": "b36ea215313f96dddf7b2ae0a7bf4281",
"score": "0.7078961",
"text": "def get_cat_name(self, catIds):\n coco_cat = self.coco.loadCats(self.coco.getCatIds())\n cat_id_list = [cat['id'] for cat in coco_cat]\n if catIds in cat_id_list:\n idx = cat_id_list.index(catIds)\n return coco_cat[idx]['name']\n else:\n print('Category ID: {} is not found'.format(catIds))",
"title": ""
},
{
"docid": "e61c3f38f95d7548bb62d8f86bbc1c5b",
"score": "0.7058682",
"text": "def get_category_name(self, val):\n for x,y in self.categories.iteritems():\n if val == y:\n return x",
"title": ""
},
{
"docid": "db58e08706bfa0189036feac713d888b",
"score": "0.70486426",
"text": "def category(self):\r\n return self.family.category",
"title": ""
},
{
"docid": "8559ebcd0f10e79f10a33f692a55a3c6",
"score": "0.70230514",
"text": "def category(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"category\")",
"title": ""
},
{
"docid": "8559ebcd0f10e79f10a33f692a55a3c6",
"score": "0.70230514",
"text": "def category(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"category\")",
"title": ""
},
{
"docid": "8559ebcd0f10e79f10a33f692a55a3c6",
"score": "0.70230514",
"text": "def category(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"category\")",
"title": ""
},
{
"docid": "e81567a46738762e670c313b26e83e92",
"score": "0.7003943",
"text": "def __unicode__(self):\n return '%s' % (self.category)",
"title": ""
},
{
"docid": "04e61fdcad64605a82bffa55e70e636d",
"score": "0.68851626",
"text": "def __repr__(self):\n return (\n f\"Category=(id={self.id},category_name={self.category_name}\"\n f\",category_slug={self.category_slug})\"\n )",
"title": ""
},
{
"docid": "132385416c41c8a88f2d74b077a54a78",
"score": "0.68466485",
"text": "def category(self, session) -> str:\n return None",
"title": ""
},
{
"docid": "132385416c41c8a88f2d74b077a54a78",
"score": "0.68466485",
"text": "def category(self, session) -> str:\n return None",
"title": ""
},
{
"docid": "09f79861b611cd9716d7a0dd4e6ff761",
"score": "0.6819605",
"text": "def get_category_name_from_file(self, file_path):\n\t\treturn os.path.basename(file_path).split(\"_\")[0].lower()",
"title": ""
},
{
"docid": "78426cf0ec4c27103dc9c5c81f641b79",
"score": "0.67999417",
"text": "def getCat(self):\n return self.__cat",
"title": ""
},
{
"docid": "0f4baac6e9e2ee62778ab614918eac22",
"score": "0.67872024",
"text": "def name_get(self):\n if self._context.get('base_category_display') == 'short':\n return super(BaseCategory, self).name_get()\n\n res = []\n for cat in self:\n names = []\n current = cat\n while current:\n names.append(current.name)\n current = current.parent_id\n res.append((cat.id, ' / '.join(reversed(names))))\n return res",
"title": ""
},
{
"docid": "ec4fa44ae9c32427f82d1f86209f8cae",
"score": "0.67868376",
"text": "def _getCategory(self):\n return self._category",
"title": ""
},
{
"docid": "58c2687b3486d37c06537a719680373a",
"score": "0.6763114",
"text": "def category_for_sidebar(self):\n if self.category == '/':\n return self.name + '/'\n else:\n return self.category",
"title": ""
},
{
"docid": "5481b7a5b087a23d40cf673ab900756a",
"score": "0.6749293",
"text": "def get_category(self, item_name):\n\t\treturn self._get_inner(item_name, \"category\")",
"title": ""
},
{
"docid": "131ebf67d44098cb9345aa71c2fa0484",
"score": "0.6714853",
"text": "def category(self):\r\n if len(self.name) > 0:\r\n return \"{0} Commands\".format(self.name)\r\n else:\r\n return \"Commands\"",
"title": ""
},
{
"docid": "3e9e273c04faa5f31a9d8ecc2837dc05",
"score": "0.6709203",
"text": "def __repr__(self):\r\n\r\n return \"<Category category_id=%s name=%s>\" % (self.category_id,\r\n self.name)",
"title": ""
},
{
"docid": "d3c8382079a50d9066a6b6b7eb36702f",
"score": "0.6706454",
"text": "def category_names(self):\n return self._category_names",
"title": ""
},
{
"docid": "f3abc7d54f964dd2a02568ca92aa6288",
"score": "0.6704538",
"text": "def category_name(value):\n return {\n 0: \"idle\",\n 1: \"unassigned\",\n 2: \"work\",\n 3: \"private\",\n 4: \"break\",\n }.get(value, \"?\")",
"title": ""
},
{
"docid": "7a7b61604dda3071777b0ac773df792d",
"score": "0.6700998",
"text": "def name(self) -> str:\n return self.mcls_data[\"name\"]",
"title": ""
},
{
"docid": "72131489f884be4ee198b3f775d36bc6",
"score": "0.66930056",
"text": "def add_category(self, name):\n pass",
"title": ""
},
{
"docid": "d4ab0a3b54bb0838df83bb4f7a2e44cf",
"score": "0.6680661",
"text": "def category(self) -> str:\n return self._match_json[\"rule\"][\"category\"][\"id\"]",
"title": ""
},
{
"docid": "e0845856b3b4613543378128ee2696b5",
"score": "0.66691476",
"text": "def description(self):\r\n return \"List of {0}\".format(self.category)",
"title": ""
},
{
"docid": "1ce8cbd5fcad52b41ad30ed17c7d600a",
"score": "0.66662973",
"text": "def category(self) -> str:\n return self._match_json[\"sentence\"]",
"title": ""
},
{
"docid": "859cedc6a3acc81eee9bfe33949e594c",
"score": "0.66522664",
"text": "def _classifier_name(self):\n pass",
"title": ""
},
{
"docid": "45b3e084e12e07d059d13bc9c39c85ae",
"score": "0.66405225",
"text": "def category(self):\r\n return None",
"title": ""
},
{
"docid": "0f98736dac789271f2092ab254ca1f78",
"score": "0.6631525",
"text": "def __unicode__(self):\n return '{cat} ({name})'.format(cat=self.cat, name=self.name)",
"title": ""
},
{
"docid": "1256cc671eed3e9c05ac7098aa629bc3",
"score": "0.6622872",
"text": "def ccle_name(self) -> str:\n return self._ccle_name",
"title": ""
},
{
"docid": "a7c7b7feeeb4368fd49ec71bd21a782c",
"score": "0.6616305",
"text": "def get_name(self):\n return self.nacp.title[0].get_name()",
"title": ""
},
{
"docid": "4e0676a63812c08b59a27a79805cd8d3",
"score": "0.6608744",
"text": "def name(self):\n return self._channel.display_name",
"title": ""
},
{
"docid": "2942a49de69b38fe0787f4947804c86b",
"score": "0.65840465",
"text": "def name(self):\n return self.get_name()",
"title": ""
},
{
"docid": "2942a49de69b38fe0787f4947804c86b",
"score": "0.65840465",
"text": "def name(self):\n return self.get_name()",
"title": ""
},
{
"docid": "2d6fffbfd28ae4bbf8b3adfe2759111f",
"score": "0.6575162",
"text": "def name(self):\n return self.title",
"title": ""
},
{
"docid": "8662746c1da3997d5994d94cb6a21e60",
"score": "0.6558912",
"text": "def name(self):\n\n return self.get_name()",
"title": ""
},
{
"docid": "55d93fe321b4c0aae22e789ba10cb083",
"score": "0.65544754",
"text": "def name(self, value):\r\n self.cat_constructor.Name = value",
"title": ""
},
{
"docid": "30537caf198aed0fefa485c28010e25c",
"score": "0.65399724",
"text": "def get_category_by_name(self, category_name):\n category = self.st.category.find(filters=\"name=%s\" % category_name)\n return category",
"title": ""
},
{
"docid": "2e5a1670aa466fd329bb723522948fea",
"score": "0.6515061",
"text": "def primary_category(self) -> str:\n assert self.primary_classification is not None\n return str(self.primary_classification.category)",
"title": ""
},
{
"docid": "cfedbc43ed83e949155c875195d82c3f",
"score": "0.649954",
"text": "def display_categories(self):\n return \", \".join([category.title for category in self.categories.all()])",
"title": ""
},
{
"docid": "18f03ba6408b62ee86f7f67aa07c8d55",
"score": "0.6494066",
"text": "def label(self) -> str:\n return self._str_representation_for(\"name\")",
"title": ""
},
{
"docid": "961ff47bd328452e856e4e249ea6ff51",
"score": "0.6491235",
"text": "def get_name(self):\n return",
"title": ""
},
{
"docid": "bf02a874f1168c6a3c8ab1b8ea4513e5",
"score": "0.64903516",
"text": "def name(self):\r\n return self.name_",
"title": ""
},
{
"docid": "3c617e4b1cdbe7f4d6973fcc7c3a40bc",
"score": "0.6483566",
"text": "def __repr__(self):\n\n return f\"\"\"< Resource Category: code = {self.code}, \n name = {self.name} >\"\"\"",
"title": ""
},
{
"docid": "d09b4e6cb66b2ab70cf087407fcbb8e5",
"score": "0.64835054",
"text": "def get_name(self):\r\n\r\n return self.get_title()",
"title": ""
},
{
"docid": "d09b4e6cb66b2ab70cf087407fcbb8e5",
"score": "0.64835054",
"text": "def get_name(self):\r\n\r\n return self.get_title()",
"title": ""
},
{
"docid": "db0061ef3cf4c041256576c24e2ad0ed",
"score": "0.6476098",
"text": "def get_name(self) -> str:\r\n\r\n return self.name",
"title": ""
},
{
"docid": "c99c902a9a1c269a9dea7384547d60da",
"score": "0.6466613",
"text": "def GetName(self):\n return _lldb.SBTypeCategory_GetName(self)",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="788"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">25b75ea7bf5cb76a12789e7989129fb6</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Returns a ApplicationCache Object to interact with the browser app cache.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "30b841e126a587fbf9af547ebf5dc15d",
"score": "0.83621204",
"text": "def get_application_cache(self) -> ApplicationCache:\n return self._selenium_web_driver().application_cache",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "ee705e4bfe2f7971b08deb8b63d3f503",
"score": "0.6836842",
"text": "def create_cache():\n class _App(object):\n config = {}\n\n cache_config = {\n 'CACHE_TYPE': 'filesystem',\n 'CACHE_DEFAULT_TIMEOUT': 60 * 60 * 24 * 7,\n 'CACHE_THRESHOLD': 100,\n 'CACHE_DIR': CACHE_DIR\n }\n return Cache(app=_App(), with_jinja2_ext=False, config=cache_config)",
"title": ""
},
{
"docid": "334a688276c7862ca0bb5905d0a49fd0",
"score": "0.6681711",
"text": "def register_cache(app):\n cache.init_app(app, config=app.config['FLASK_CACHING'])\n return cache",
"title": ""
},
{
"docid": "7642dc511277a83d32862396659c17f1",
"score": "0.6571262",
"text": "def get_app(self, app_name: str) -> 'Application':\n return self._cache.get(Application, app_name)",
"title": ""
},
{
"docid": "869dfebcc5223afdd7f3a0efe886514c",
"score": "0.6241348",
"text": "def request_bingo_cache(self):\n if self.is_requesting_archives():\n return BingoCache.load_from_datastore(archives=True)\n else:\n return BingoCache.get()",
"title": ""
},
{
"docid": "67664eccbc30c0e7d178394b6d8b3ad6",
"score": "0.6231686",
"text": "def cache(self):\n\t\tif self.is_cache_outdated():\n\t\t\tself.build_cache()\n\t\treturn self._cache",
"title": ""
},
{
"docid": "153b33b20666b9c4cedce670d4310f24",
"score": "0.61012405",
"text": "def GetCache(self, *args, **kwargs):\n pass",
"title": ""
},
{
"docid": "289d4072202e1d90878dcb9292403b8d",
"score": "0.60995215",
"text": "def get_cache() -> dict:\n return cache_data",
"title": ""
},
{
"docid": "de836b5da6ef2795c54a70be15ae3ff8",
"score": "0.59789747",
"text": "def __init__(self, app, config=None, environ_key='beaker.cache', **kwargs):\r\n self.app = app\r\n config = config or {}\r\n \r\n self.options = {}\r\n \r\n # Update the options with the parsed config\r\n self.options.update(parse_cache_config_options(config))\r\n \r\n # Add any options from kwargs, but leave out the defaults this\r\n # time\r\n self.options.update(\r\n parse_cache_config_options(kwargs, include_defaults=False))\r\n \r\n # Assume all keys are intended for cache if none are prefixed with\r\n # 'cache.'\r\n if not self.options and config:\r\n self.options = config\r\n \r\n self.options.update(kwargs)\r\n self.cache_manager = CacheManager(**self.options)\r\n self.environ_key = environ_key",
"title": ""
},
{
"docid": "c476ea367ecbb6929bd81bc289a10d6d",
"score": "0.5942076",
"text": "def _cache(self):\r\n if getattr(self, '_client', None) is None:\r\n self._client = self._lib.Client(self._servers)\r\n\r\n return self._client",
"title": ""
},
{
"docid": "bca59bca7ecf59bdae92c0021209b061",
"score": "0.5933238",
"text": "def cache(self):\n return self.__cache",
"title": ""
},
{
"docid": "3d301b63ac638716d047a343058c1758",
"score": "0.5932765",
"text": "def getApplication(self, request):\n return self.app",
"title": ""
},
{
"docid": "a5b049f6b924c0d60adde586af89db49",
"score": "0.59322166",
"text": "def _cache(self) -> Any:\n return caches[self.cache_alias]",
"title": ""
},
{
"docid": "28af4f8e54bd26c7cd73a2cf5ea2fdf1",
"score": "0.5918129",
"text": "def get_cache_item(cls, app_name, name, project=None):\n cls._check_app_name(app_name)\n query_string = {'app_name': app_name, 'name': name}\n if project:\n query_string['project'] = project\n return JSONCacheItem.objects.filter(**query_string).first()",
"title": ""
},
{
"docid": "05dd9a0157a7106df30e4a315653f530",
"score": "0.5873467",
"text": "def cache(self):\n return self._cache",
"title": ""
},
{
"docid": "19c0bbeba4aae70fe2b037660d1f5e44",
"score": "0.58454627",
"text": "def __get_application(self):\n if self.request.method in ['GET', 'DELETE']:\n access_token = self.request.get('accessToken')\n else:\n try:\n access_token = loads(self.request.body).get('accessToken')\n except ValueError:\n access_token = None\n if access_token is None:\n return None\n application_key = get_application_key(access_token)\n if not application_key:\n return None\n return Application.get_by_id(application_key)",
"title": ""
},
{
"docid": "baf7ac30be61fcda29e2c1467fe74286",
"score": "0.57707256",
"text": "def _cache(self):\n if hasattr(self, '_c_cache'):\n return self._c_cache",
"title": ""
},
{
"docid": "8871d716d8b9fc95d49422015115f9d1",
"score": "0.5753878",
"text": "def __init__(self):\n self.cache = Cache.Cache()",
"title": ""
},
{
"docid": "f0dba87b29c1fb9cdab9b96416208122",
"score": "0.5749011",
"text": "def get_system_cache_configuration(self):\n return SystemCacheConfiguration.from_json(self.service_client.GET('configurations/cache'))",
"title": ""
},
{
"docid": "5e06a73f248ad6fa75d9c193c09452ec",
"score": "0.5745871",
"text": "async def get_application(self):\n self.api_server = RestApi()\n return self.api_server.app",
"title": ""
},
{
"docid": "02b22474b0390b3b51772482ab1e97ae",
"score": "0.57296216",
"text": "async def get_application(self):\n app = web.Application()\n setup_routes(app)\n await read_json(app, NEWS_FILENAME, COMMENTS_FILENAME)\n return app",
"title": ""
},
{
"docid": "cf9facd2e37038a2167d32b13ef1af14",
"score": "0.57231057",
"text": "def get_cache_backend():\n cache_alias = getattr(settings, 'METATAGS_CACHE_ALIAS', DEFAULT_CACHE_ALIAS)\n return caches[cache_alias]",
"title": ""
},
{
"docid": "9fe2fbaf73204d994e5343659fa9a5f4",
"score": "0.5628217",
"text": "def load(self, app: OrmCacheApp) -> _T:\n\n session = app.session_manager.session()\n\n # before accessing any cached values we need to make sure that all\n # pending changes are properly flushed -> this leads to some extra cpu\n # cycles spent but eliminates the chance of accessing a stale entry\n # after a change\n if session.dirty:\n session.flush()\n\n # we use a secondary request cache for even more lookup speed and to\n # make sure that inside a request we always get the exact same instance\n # (otherwise we don't see changes reflected)\n if self.cache_key in app.request_cache:\n\n # it is possible for objects in the request cache to become\n # detached - in this case we need to merge them again\n # (the merge function only does this if necessary)\n return self.merge(session, app.request_cache[self.cache_key])\n\n else:\n obj = app.cache.get_or_create(\n key=self.cache_key,\n creator=lambda: self.create(app)\n )\n\n # named tuples\n if isinstance(obj, tuple) and hasattr(obj.__class__, '_make'):\n obj = obj._make(self.merge(session, o) for o in obj) # type:ignore\n\n # lists (we can save some memory here)\n elif isinstance(obj, list):\n for ix, o in enumerate(obj):\n obj[ix] = self.merge(session, o)\n\n # generic iterables\n elif isinstance(obj, (tuple, set)):\n obj = obj.__class__(self.merge(session, o) for o in obj)\n\n # generic objects\n else:\n obj = self.merge(session, obj)\n\n app.request_cache[self.cache_key] = obj\n\n return obj",
"title": ""
},
{
"docid": "db4887c5a66ab5ae0f5986923dfe4261",
"score": "0.5611781",
"text": "def GetApplication():\r\n global applicationinstance\r\n if applicationinstance is None:\r\n applicationinstance = Application()\r\n return applicationinstance",
"title": ""
},
{
"docid": "5a17577055d30528e507006564db0a89",
"score": "0.5601422",
"text": "def get_backend(self):\n try:\n cache = caches[machina_settings.ATTACHMENT_CACHE_NAME]\n except InvalidCacheBackendError:\n raise ImproperlyConfigured(\n 'The attachment cache backend ({}) is not configured'.format(\n machina_settings.ATTACHMENT_CACHE_NAME,\n ),\n )\n return cache",
"title": ""
},
{
"docid": "3cfb9d05acb64afffe315c5561f7c5ed",
"score": "0.5581193",
"text": "async def get_application(self):\n\n return self.api_server.app",
"title": ""
},
{
"docid": "b70c4becdaada755059aa07437d4c524",
"score": "0.5558913",
"text": "def cache_controller_in_ram():\r\n\r\n t = time.ctime()\r\n return dict(time=t, link=A('click to reload', _href=URL(r=request)))",
"title": ""
},
{
"docid": "66f96f0b609062c43880b76822d679dd",
"score": "0.5556888",
"text": "def db_cache() -> DbCache:\n return DbCache()",
"title": ""
},
{
"docid": "ace87eb211e4148baa97d2cf1c61bab1",
"score": "0.55439925",
"text": "def repopulate_app_cache(self):\r\n cache._populate()",
"title": ""
},
{
"docid": "3832c8bc07725352eed7d22b1008df38",
"score": "0.54964185",
"text": "def getApplication(self):\n return FacebookApplication(self.base.get(\"application\", []))",
"title": ""
},
{
"docid": "13cb7837c3f4c82e42eb920acc825d86",
"score": "0.5493108",
"text": "def setup_cache(self):\n if config.get('redis.host'):\n if 'redis.timeout' in config:\n default_timeout = asint(config['redis.timeout'])\n else:\n default_timeout = None\n cache = RedisCache(\n host=config['redis.host'],\n port=asint(config.get('redis.port', 6379)),\n db=asint(config.get('redis.db', 0)),\n prefix=config.get('redis.prefix', ''),\n default_timeout=default_timeout\n )\n else:\n cache = None\n\n config['pylons.app_globals'].cache = cache",
"title": ""
},
{
"docid": "ca1e444b3ac520643889a7a48a17951c",
"score": "0.5489794",
"text": "def get_cache(cache_class, namespace, max_entries=1000,\n redis_host='localhost', redis_port=6379, redis_protocol=None):\n\n def got_connection(client):\n return cache_class(namespace, max_entries=max_entries,\n client=client)\n\n d = get_redis_client(redis_host, redis_port, redis_protocol)\n d.addCallback(got_connection)\n return d",
"title": ""
},
{
"docid": "d04ba07ed214d1d987240b5b6a464de4",
"score": "0.54842114",
"text": "def get_object_cache(self, key, **kwargs):\n if key not in self._object_caches:\n return self.create_object_cache(key, **kwargs)\n return self._object_caches[key]['cache']",
"title": ""
},
{
"docid": "ee9dce7b8e65156d08727068dcb8d6b4",
"score": "0.5458705",
"text": "def registry(self):\n if webapp2.get_app().registry:\n return webapp2.get_app().registry\n else:\n return AppCachedProperty.GLOBAL_CACHE",
"title": ""
},
{
"docid": "42af6e00cbfc49d384648dc212af1b3c",
"score": "0.5456047",
"text": "def init_cache(app):\r\n if not hasattr(app.env, 'programoutput_cache'):\r\n app.env.programoutput_cache = ProgramOutputCache()",
"title": ""
},
{
"docid": "3a4b88fb5fb29ec511ce0e004d4edcca",
"score": "0.5446816",
"text": "def _get_form_cache(self):\n\t\tcache = FormCache.get_form_cache(cache_name=self.cache_name)\n\t\tif cache is None:\n\t\t\tcache = self.default_cache\n\t\t\tFormCache.set_form_cache(\n\t\t\t\tcache_name=self.cache_name,\n\t\t\t\tvalues=cache,\n\t\t\t)\n\t\treturn cache",
"title": ""
},
{
"docid": "e73b8c3756d0a2bb5d74d0a08ddd2e9a",
"score": "0.5431631",
"text": "def application(self):\n return self.__instance__.application",
"title": ""
},
{
"docid": "91441c5ce0580d43fe81d264c119a49d",
"score": "0.5430361",
"text": "def memcached(self):\n if self._memcached is None:\n self._memcached = MemcachedConnect()\n return self._memcached",
"title": ""
},
{
"docid": "91441c5ce0580d43fe81d264c119a49d",
"score": "0.5430361",
"text": "def memcached(self):\n if self._memcached is None:\n self._memcached = MemcachedConnect()\n return self._memcached",
"title": ""
},
{
"docid": "b1517e2db769d7f9eb9d90956f0bb1fd",
"score": "0.54203856",
"text": "def cache(self) -> typing.Optional[hikari.api.Cache]:",
"title": ""
},
{
"docid": "b1517e2db769d7f9eb9d90956f0bb1fd",
"score": "0.54203856",
"text": "def cache(self) -> typing.Optional[hikari.api.Cache]:",
"title": ""
},
{
"docid": "0796dc1f4c91c06c9a14153e5aaa4e93",
"score": "0.5419042",
"text": "async def get_application(self):\n parser, args = httpproxy.parse_args(self.get_args())\n return httpproxy.get_app(args)",
"title": ""
},
{
"docid": "6696ca05a9b63e58dcf94a1e9366ece7",
"score": "0.5381189",
"text": "def application(self):\r\n return Live.Application.get_application()",
"title": ""
},
{
"docid": "1f4e7f91068174b1f596a5964a4c1ea0",
"score": "0.5379811",
"text": "def cache(self):\n self.is_cached = True\n self.persist(StorageLevel.MEMORY_ONLY_SER)\n return self",
"title": ""
},
{
"docid": "e0c06854fdff64f5801b9ceb5e7f3389",
"score": "0.5374801",
"text": "def app_cache_restorer():\n state = _app_cache_deepcopy(apps.__dict__)\n try:\n yield state\n finally:\n with apps_lock():\n apps.__dict__ = state\n # Rebind the app registry models cache to\n # individual app config ones.\n for app_conf in apps.get_app_configs():\n app_conf.models = apps.all_models[app_conf.label]\n apps.clear_cache()",
"title": ""
},
{
"docid": "6010386b54023d11ab2b9e1343cbeb01",
"score": "0.53698903",
"text": "def get_application(routes):\n cookie_secret = None\n if not on_dev_server():\n cookie_secret = get_project_metadata('cookie_secret')\n else:\n cookie_secret = get_dev_secrets()\n\n config = {}\n config['webapp2_extras.sessions'] = {\n 'secret_key': str(cookie_secret),\n }\n\n if cookie_secret:\n return webapp2.WSGIApplication(routes=routes, config=config)\n else:\n return error_app(get_cookie_secret_error())",
"title": ""
},
{
"docid": "8c455cac6227c2052174516c27f9a823",
"score": "0.53665507",
"text": "def __init__(self, simplecache=None):\n if not simplecache:\n from simplecache import SimpleCache\n self.cache = SimpleCache()\n else:\n self.cache = simplecache",
"title": ""
},
{
"docid": "59c4f863d36bcaa265d22d8af732f9ba",
"score": "0.5362422",
"text": "def cache(self, *args, **kwargs):\n return self.get_query_set().cache(*args, **kwargs)",
"title": ""
},
{
"docid": "c0c2da69bbe01bfedc16759813f47b0c",
"score": "0.5360249",
"text": "def __call__(self, *args, **kwargs):\n return self._cache_wrapper(None, *args, **kwargs)",
"title": ""
},
{
"docid": "152df095861d66b1e00b698bae74a35d",
"score": "0.5358146",
"text": "def add_get_application_json_to_weak_caching(context):\n registry = getUtility(IRegistry)\n try:\n from plone.app.caching.interfaces import IPloneCacheSettings\n except ImportError:\n # plone.app.caching is optional.\n return\n\n try:\n settings = registry.forInterface(IPloneCacheSettings)\n except KeyError:\n # It is available, but not activated. Nothing to do.\n return\n mapping = settings.templateRulesetMapping\n key = \"GET_application_json_\"\n if key in mapping:\n # already set, do not change\n return\n mapping[key] = \"plone.content.folderView\"\n # Note: if we edit templateRulesetMapping, our change will not be persisted,\n # because it is a simple dict. We have to set the entire mapping.\n settings.templateRulesetMapping = mapping",
"title": ""
},
{
"docid": "9d71866de901d0a4f6fe621ae055dfa8",
"score": "0.53514236",
"text": "def cache():\n pass",
"title": ""
},
{
"docid": "19bdd5a6c663d5975005537d1ddad3cb",
"score": "0.5350763",
"text": "def cache(self, cache = None):\n try:\n currentCache = self.__request[\"cache\"]\n except Exception:\n currentCache = None\n if type(cache) is bool:\n self.__request[\"cache\"] = cache\n elif not (cache is None):\n raise Exception(\"cache must be of type bool\") \n return(currentCache)",
"title": ""
},
{
"docid": "09cc63cffc95a64464f289ff60a972a0",
"score": "0.5331978",
"text": "def get(self):\n return pickle.loads(self.db.get(self.cache_key))",
"title": ""
},
{
"docid": "143320186be55d43f35a0cbec3bb1a6c",
"score": "0.5324998",
"text": "def get_project_cache(cls, project, data_type='json'):\n cls._check_data_type(data_type)\n return JSONCacheItem.objects.filter(project=project)",
"title": ""
},
{
"docid": "4e63ad3426854737590231ef1f5a14a2",
"score": "0.5324482",
"text": "def cache(self):\n self.rdd.cache()\n return self",
"title": ""
},
{
"docid": "b9cb2ff8bf3587177d0a21c392d0a3aa",
"score": "0.5307121",
"text": "def __init__(self, cache, cache_key=None):\n self.cache = cache\n self.cache_key = cache_key",
"title": ""
},
{
"docid": "7cda2a535c305eb27ff3312122683ee2",
"score": "0.5298131",
"text": "async def get_application(self):\n return await servinit()",
"title": ""
},
{
"docid": "223ac8ecb438e90a6a126a73aa19c711",
"score": "0.52853745",
"text": "def _get_activity(_cache=[]):\n if _cache:\n return _cache[0]\n # See MainActivity.onCreate() for initialization of .singletonThis:\n # https://github.com/beeware/briefcase-android-gradle-template/blob/3.7/%7B%7B%20cookiecutter.formal_name%20%7D%7D/app/src/main/java/org/beeware/android/MainActivity.java\n # This can't be tested because if it isn't set, nothing else will work.\n if not MainActivity.singletonThis: # pragma: no cover\n raise ValueError(\n \"Unable to find MainActivity.singletonThis from Python. This is typically set by \"\n \"org.beeware.android.MainActivity.onCreate().\"\n )\n _cache.append(MainActivity.singletonThis.__global__())\n return _cache[0]",
"title": ""
},
{
"docid": "117a4865f41357039d3e9afaa14aadbf",
"score": "0.52616775",
"text": "def create_app(config_name):\n\n # instantiate an app where config will be in an instance\n app = Flask(__name__, instance_relative_config=True)\n api = Api(app)\n cor_app = CORS(app)\n\n # Set the config parameters\n app.config.from_object(config[config_name])\n app.config.from_pyfile('config.py')\n \n # Initialize the cache using the config params\n cache = Cache(\n app.config['NUMBER_OF_SLOTS'], \n app.config['TIME_TO_LIVE'], \n EvictionStrategies(app.config['EVICTION_POLICY'])\n )\n CacheApi.initialize_cache(cache)\n\n # Set the routes\n \n api.add_resource(CacheApi, '/object/<string:key>')\n\n return app",
"title": ""
},
{
"docid": "2150e2dc9809741e315158ab94b40be0",
"score": "0.524874",
"text": "def _init_cache(self):\n self.cache = Cache()",
"title": ""
},
{
"docid": "1d53deeaee79dd16889659342055f392",
"score": "0.52353615",
"text": "def cache_in_ram():\r\n\r\n t = cache.ram('time', lambda: time.ctime(), time_expire=5)\r\n return dict(time=t, link=A('click to reload', _href=URL(r=request)))",
"title": ""
},
{
"docid": "4cce37b55384c1355090c31bcabae321",
"score": "0.5233077",
"text": "def cache_controller_on_disk():\r\n\r\n t = time.ctime()\r\n return dict(time=t, link=A('click to reload', _href=URL(r=request)))",
"title": ""
},
{
"docid": "1d9e34cb1536560b88113c751ed5d066",
"score": "0.5232964",
"text": "def cache(context):\n pass",
"title": ""
},
{
"docid": "21781c1219feb51c4c32df6a3bdfc805",
"score": "0.5228117",
"text": "def copy(self) -> \"LRUCache\":\n rv = self.__class__(self.capacity)\n rv._mapping.update(self._mapping)\n rv._queue.extend(self._queue)\n return rv",
"title": ""
},
{
"docid": "25bbc181be207acf18ae5ad7ca2ac443",
"score": "0.52091885",
"text": "def application(self):\n return self._application",
"title": ""
},
{
"docid": "25bbc181be207acf18ae5ad7ca2ac443",
"score": "0.52091885",
"text": "def application(self):\n return self._application",
"title": ""
},
{
"docid": "25bbc181be207acf18ae5ad7ca2ac443",
"score": "0.52091885",
"text": "def application(self):\n return self._application",
"title": ""
},
{
"docid": "25bbc181be207acf18ae5ad7ca2ac443",
"score": "0.52091885",
"text": "def application(self):\n return self._application",
"title": ""
},
{
"docid": "c2637b40860ccde8ff311f2db227d1a9",
"score": "0.51805556",
"text": "def synchronize_apps_cache():\n sync_id = start_synchronization()\n try:\n cached_requests = get_cached_session()\n synced_apps = _sync_golab_translations(cached_requests, force_reload = False)\n _sync_regular_apps(cached_requests, synced_apps, force_reload = False)\n finally:\n end_synchronization(sync_id)",
"title": ""
},
{
"docid": "e881a491de4108eb2309b0abc0489a0e",
"score": "0.51803803",
"text": "def appinit(self):\n return Appinit(self)",
"title": ""
},
{
"docid": "0955005ce38d9fb33f9c61ea62849cf5",
"score": "0.5178926",
"text": "def load(self):\n return app",
"title": ""
},
{
"docid": "aa5be44577fef68e21bd261cbb8eb343",
"score": "0.5176882",
"text": "def cache_object(*args, **kwargs):\n global __loop__\n if __loop__ is None:\n __loop__ = DefaultEventLoop(GLOBAL_NAME)\n\n __loop__.cache_object(*args, **kwargs)",
"title": ""
},
{
"docid": "826ff4c5dfa46ce200d716f8c979c71f",
"score": "0.51712286",
"text": "def cache(self) -> Path:\n return self._impl.get_cache_path()",
"title": ""
},
{
"docid": "ba34225b33d8ee6d77a88f36f6da0eb1",
"score": "0.5167296",
"text": "def create_app(self):\n # Create APP and set configuration\n app = APP\n config = Config()\n\n app.config['TESTING'] = True\n app.config['LIVESERVER_PORT'] = config.agent_api_ip_bind_port()\n os.environ['FLASK_ENV'] = 'development'\n\n # Clear the flask cache\n cache = Cache(config={'CACHE_TYPE': 'null'})\n cache.init_app(app)\n\n # Return\n return app",
"title": ""
},
{
"docid": "ea095adf9d10118bfac112c4d08bab27",
"score": "0.5156244",
"text": "def __get__(self, instance: RedisCache, owner: Any) -> RedisCache:\n if self.bot:\n return self\n\n if self._namespace is None:\n error_message = \"RedisCache must be a class attribute.\"\n log.error(error_message)\n raise NoNamespaceError(error_message)\n\n if instance is None:\n error_message = (\n \"You must access the RedisCache instance through the cog instance \"\n \"before accessing it using the cog's class object.\"\n )\n log.error(error_message)\n raise NoParentInstanceError(error_message)\n\n for attribute in vars(instance).values():\n if isinstance(attribute, Bot):\n self.bot = attribute\n self._redis = self.bot.redis_session\n return self\n else:\n error_message = (\n \"Critical error: RedisCache has no `Bot` instance. \"\n \"This happens when the class RedisCache was created in doesn't \"\n \"have a Bot instance. Please make sure that you're instantiating \"\n \"the RedisCache inside a class that has a Bot instance attribute.\"\n )\n log.error(error_message)\n raise NoBotInstanceError(error_message)",
"title": ""
},
{
"docid": "ecdcf580e6dadac9b79940b89047d257",
"score": "0.5144671",
"text": "def get_app(self):\n return app",
"title": ""
},
{
"docid": "ecdcf580e6dadac9b79940b89047d257",
"score": "0.5144671",
"text": "def get_app(self):\n return app",
"title": ""
},
{
"docid": "6376a6e74ef6bcf425e908c4432620fd",
"score": "0.5133707",
"text": "def __install_cache(self) -> None:\n if USE_CACHE:\n CACHE_PATH.mkdir(exist_ok=True)\n requests_cache.install_cache(\n str(CACHE_PATH.joinpath(self.get_class_name()))\n )",
"title": ""
},
{
"docid": "5da1adaa6297fe43915b98a452f62088",
"score": "0.51205796",
"text": "def static_config(self):\n\n return CacheConfig(1024, None)",
"title": ""
},
{
"docid": "b16be2098fbca620e32869fc48ffd6ac",
"score": "0.51037395",
"text": "def __call__(self, environ, start_response):\n ctx = ndb.get_context()\n ctx.set_cache_policy(False)\n\n response = self.application(environ, start_response)\n\n ctx.clear_cache()\n return response",
"title": ""
},
{
"docid": "b5977509f45af093701976e2ade8bac2",
"score": "0.5083971",
"text": "def get_metadata_cache_manager():\n global _METADATA_CACHE_MANAGER\n return _METADATA_CACHE_MANAGER",
"title": ""
},
{
"docid": "7fc4e69a79bbf77e3395e0aaa6245980",
"score": "0.5079018",
"text": "def get_full_cache(self):\n self.update_time()\n return self._cache",
"title": ""
},
{
"docid": "25af86466512750d1c59fda9de5954c4",
"score": "0.50715613",
"text": "def _get_cache_obj(self, type, id):\n cache = self._resolve_cache(type)\n return cache.get(id, None)",
"title": ""
},
{
"docid": "773e0199280cb8d13f98978e26d54757",
"score": "0.5059492",
"text": "def get(cls) -> MemCacheHandlerBase:\n if not hasattr(cls, \"instance\"):\n cls_name = cls.__class__.__name__\n raise MemCacheHandlerError(f\"Before calling {cls_name}.get(), you should call {cls_name}.create() first.\")\n\n return cls.instance",
"title": ""
},
{
"docid": "03c3efd180677017fc9a9577570c04f2",
"score": "0.5054254",
"text": "def get_app(self):\n if self.app is None:\n self._register_app()\n return self.app",
"title": ""
},
{
"docid": "153fed25f31f98f5702716cb7a2be054",
"score": "0.5037384",
"text": "def get_app(self):\n return self.app",
"title": ""
},
{
"docid": "e44ac8b2c1de1c94cd56bd19fe5aa44a",
"score": "0.50314593",
"text": "def __init__(self, app):\n self._initialize_app(app)\n self._initialize_cache(SortedDict(key=lambda p: p.date, reverse=True))",
"title": ""
},
{
"docid": "54cc4030140cae06ac04f930aa3ab44a",
"score": "0.5023157",
"text": "def app(self):\n return self._app",
"title": ""
},
{
"docid": "38c7a5050d751a7deed2a178db436be1",
"score": "0.5017547",
"text": "def load_cache(cache_file):\n fd = open(cache_file)\n cache_data = load(fd)\n fd.close()\n return cache_data",
"title": ""
},
{
"docid": "d1e74660c616410ec8202c90cda4c051",
"score": "0.50134623",
"text": "def get(self, url):\n session = self.sessionmaker()\n try:\n cache_result = session.query(HTTPCacheContent) \\\n .filter(HTTPCacheContent.url == url) \\\n .one_or_none()\n\n # if expiration is enabled then don't return anything that is expired\n if cache_result is not None and \\\n not self.dont_expire and \\\n cache_result.expire_on_dt is not None and \\\n cache_result.expire_on_dt < datetime.utcnow():\n print(\n f\"URL '{url}' found in cache, but set for expiration in the past at \"\n f\"{cache_result.expire_on_dt}, so not returned.\"\n )\n cache_result = None\n finally:\n session.close()\n\n if cache_result is None:\n return None\n elif cache_result.content is not None:\n return cache_result.content\n else:\n assert cache_result.content_bzip2 is not None\n return bz2.decompress(cache_result.content_bzip2)",
"title": ""
},
{
"docid": "abb9359623ada198ed4ffdf06225b3fc",
"score": "0.500541",
"text": "def api_with_cache(epirr_id):\n if epirr_id not in cache:\n cache[epirr_id] = api_call_epirr(epirr_id)\n return cache[epirr_id]",
"title": ""
},
{
"docid": "5cd670ae42f6218ae3950ce5d1b15fac",
"score": "0.50006336",
"text": "def _get_cache(self, param, namespace = None):\n\n return self._get_cache_map([param], namespace).get(param, None)",
"title": ""
},
{
"docid": "6db3e2c53e4f23305597543bddd7e1f0",
"score": "0.49994013",
"text": "def get_route_cache(cls):\n if hasattr(cls.thread_locals, 'request'):\n request = cls.thread_locals.request\n if not hasattr(request, 'route_cache'):\n request.route_cache = {}\n return request.route_cache\n return {}",
"title": ""
},
{
"docid": "5d085c0843cd6a2fcaecbd641b4c7cb7",
"score": "0.4996278",
"text": "def get_session(cls, app: object = None) -> 'SessionStore':\n config = get_application_config(app)\n host = config.get('REDIS_HOST', 'localhost')\n port = int(config.get('REDIS_PORT', '7000'))\n db = int(config.get('REDIS_DATABASE', '0'))\n token = config.get('REDIS_TOKEN', None)\n cluster = config.get('REDIS_CLUSTER', '1') == '1'\n secret = config['JWT_SECRET']\n duration = int(config.get('SESSION_DURATION', '7200'))\n fake = config.get('REDIS_FAKE', False)\n return cls(host, port, db, secret, duration, token=token,\n cluster=cluster, fake=fake)",
"title": ""
},
{
"docid": "92662aad18d3d08d9394e5a41bd4354b",
"score": "0.4986594",
"text": "def cache_data(self):\n # Initialize key variables\n result = self.data['cache_data']\n return result",
"title": ""
},
{
"docid": "0ba0b7031e90c85452d1a720f6a03a9e",
"score": "0.49527672",
"text": "def get_app_singleton():\n return _singleton",
"title": ""
},
{
"docid": "1d671bb769197d57a80411b7f2fe1661",
"score": "0.4948296",
"text": "def get_cache(self, queue_name: str) -> dict:\n\n # Check queue name. Raise exception if not exists\n self.check_queue_name(queue_name)\n\n # Get cache from queue\n cache = self.queues[queue_name].get_cache()\n return cache",
"title": ""
},
{
"docid": "b35e91efbb19a6fb8e708214fffa593b",
"score": "0.49419135",
"text": "def get_default_cache_ctrl():\n\n if settings.get(\"DEFAULT_CACHE\") is None: # missing or None\n return CacheCtrl([])\n\n return make_cache_ctrl(settings[\"DEFAULT_CACHE\"])",
"title": ""
},
{
"docid": "679b837e1b0576c33d4c25c0bed3086d",
"score": "0.4934507",
"text": "def _create_session(self):\n kwargs = {\"connection\": self.memcached, \"ip_address\": self.request.remote_ip}\n new_session = MemcachedSession(**kwargs)\n new_session.save()\n return new_session",
"title": ""
},
{
"docid": "d7e28d69713a7047707a1f602dceba8d",
"score": "0.49208707",
"text": "def save_in_cache(self):\n memcache.set(self.code(), self)",
"title": ""
},
{
"docid": "ede37ea1f20c4e14732e6445d4722ac0",
"score": "0.49168393",
"text": "def get_application():\n description = '''Turbinia API server'''\n _app = FastAPI(\n title='Turbinia API Server', description=description, version='1.0.0',\n license_info={\n 'name': 'Apache License 2.0',\n 'url': 'https://www.apache.org/licenses/LICENSE-2.0.html'\n }, routes=router.routes)\n return _app",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="789"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">e6be7d743ec3c3a768eef75172fd2f8f</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">find users with their username that start with letter that user has entered</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "2c1b08376ea3c1ba98ed3949f10267b2",
"score": "0.83855855",
"text": "def users(letter):\n return User.objects.filter(username__istartswith=letter)",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "d8e566029b9c771acb958a39552c0017",
"score": "0.85593385",
"text": "def getUsersStartingWith(letters):",
"title": ""
},
{
"docid": "8664fa1d80739b81e26240163caa7420",
"score": "0.6641168",
"text": "def check_username(self, username):\n match = re.findall(r\"^[a-zA-Z]+\\w+$\", username)\n if match and username == match[0] and len(username) >= 4:\n if not settings.first_run:\n query = f\"SELECT user FROM {settings.users_table}\"\n users = database.get_record(query)\n if username in users[0]:\n return None, \"Your username is taken\\n\"\n return username, \"\"\n else:\n return None, \"Username is incorrect, check the tip in the input field\\n\"",
"title": ""
},
{
"docid": "b0d07a24adf7d116bc2430e9a8b3be28",
"score": "0.6440513",
"text": "def search_users_byusername(self, uid):\n raise RBFatalError(\"NOT IMLEMENTED YET\")\n self.cur.execute(\n ('SELECT username, usertype, id, name, course, year, email '\n 'FROM users WHERE username LIKE %s'), ('%%%s%%' % uid, ))\n return self.cur.fetchall()",
"title": ""
},
{
"docid": "53d6cdc39eb7021e73e9ba4c4996a088",
"score": "0.64390105",
"text": "def searchname(partialname, maxresults=32):\n people = Person.select().where( \\\n Person.name.contains(partialname) | \\\n Person.username.contains(partialname)) \\\n .order_by(Person.username).limit(maxresults)\n return [p.username for p in people]",
"title": ""
},
{
"docid": "afc18538883cccf5725b2dce2f8c60a0",
"score": "0.6423979",
"text": "def usernames_in(message):\n # Don't parse usernames in the commands\n if _command_re.match(message.split(\" \", 1)[0]):\n message = message.split(\" \", 1)[1]\n\n # Strip email addresses from the message, in order to avoid matching the\n # user's domain. Also strip URLs, in order to avoid usernames in them.\n message = strip_urls(message)\n\n results = []\n for result in _username_re.finditer(message):\n if result.group(1):\n results.append(result.group(1))\n\n return results",
"title": ""
},
{
"docid": "fc4f8574b1f5698bd6006dda181c5d0b",
"score": "0.6417107",
"text": "def get_username(self):\n is_valid_username = False\n while not is_valid_username:\n print(\"Username: \", end=\"\")\n username = input().strip()\n user_regex = re.compile(r\"^[A-z0-9]+$\")\n is_valid_username = user_regex.match(username) is not None\n if not is_valid_username:\n print(\"{} is not a valid username\".format(username))\n # check if username already exists in the system\n if self.db.is_username_exists(username):\n is_valid_username = False\n print(\"The username {} already exists\".format(username))\n return username",
"title": ""
},
{
"docid": "e43cb84cbc70ae2360760c291046ee1c",
"score": "0.63649565",
"text": "def usernames(request):\n term = request.GET.get('term', '')\n query = request.GET.get('query', '')\n pre = term or query\n\n if not pre:\n return []\n if not request.user.is_authenticated():\n return []\n with statsd.timer('users.api.usernames.search'):\n profiles = (\n Profile.objects.filter(Q(name__istartswith=pre))\n .values_list('user_id', flat=True))\n users = (\n User.objects.filter(\n Q(username__istartswith=pre) | Q(id__in=profiles))\n .extra(select={'length': 'Length(username)'})\n .order_by('length').select_related('profile'))\n\n if not waffle.switch_is_active('users-dont-limit-by-login'):\n last_login = datetime.now() - timedelta(weeks=12)\n users = users.filter(last_login__gte=last_login)\n\n return [{'username': u.username,\n 'display_name': display_name_or_none(u)}\n for u in users[:10]]",
"title": ""
},
{
"docid": "08a2c22b67b0f2a978e7780ae7c0336d",
"score": "0.6349212",
"text": "def username_condition(environ, match):\n map_user = match.get('map_username')\n if not map_user:\n return False \n if len(map_user) < 4:\n return False\n if map_user in CONFIG.BANNED_USERNAMES:\n return False\n\n return True",
"title": ""
},
{
"docid": "2a13f74a9806a22a6449e0636d9524fe",
"score": "0.6297197",
"text": "def findUsers(conn, username):\n curs = dbi.dict_cursor(conn)\n curs.execute('''select * from user_accounts where username like %s ''', \n ['%' + username + '%'])\n return curs.fetchall()",
"title": ""
},
{
"docid": "594beefe0a7b7fdf252067fc0a2c3576",
"score": "0.6248869",
"text": "def get_usernames(keyword):\n con = get_binded_connection()\n usernames = [result[1][\"uid\"][0] for result in\\\n con.search_s(\"%s,%s\" % (settings.USERS_OU, settings.DC),\n ldap.SCOPE_SUBTREE, \"(uid=*)\", ['uid'])]\n con.unbind_s()\n return filter(lambda username: re.search(keyword, username, re.IGNORECASE), usernames)",
"title": ""
},
{
"docid": "2a8f38999dd1d2fd8616deec79bde372",
"score": "0.6243985",
"text": "def valid_name(username):\n return username and re.match(r'[a-zA-Z][a-zA-Z0-9_]{2,}$', username)",
"title": ""
},
{
"docid": "edbdd034511c2b18b1c0215116f2b0f0",
"score": "0.62313473",
"text": "def matching_user(self, name):",
"title": ""
},
{
"docid": "dccf8958baf7b0b08b5e164ed7102675",
"score": "0.6210032",
"text": "def check_username(cls, username):\n return cls.db.keys('user:' + username)",
"title": ""
},
{
"docid": "bb3730a4ec076f9fc8d1347cddd7de83",
"score": "0.6201446",
"text": "def check_username(username):\n\n usernames = db.execute(\"SELECT * FROM users WHERE username = :username\", username=username)\n\n return usernames",
"title": ""
},
{
"docid": "c5acc83ec17386263af6a5d412e0fd45",
"score": "0.616268",
"text": "def search_users_byname(self, name):\n raise RBFatalError(\"NOT IMLEMENTED YET\")\n return self.search_users('name ILIKE', name)",
"title": ""
},
{
"docid": "cc1ef7ea82d61048aa09dd3ed3e44c15",
"score": "0.6128579",
"text": "def search_usernames(self, query):\n\t\tresults = self.t.users.search(q=query)\n\t\tusernames = [(user['name'], user['screen_name']) for user in results]\n\t\treturn usernames",
"title": ""
},
{
"docid": "df9359dba65ecb00cd096116cbc81acd",
"score": "0.60037506",
"text": "def test_user_is_a_johnny(self):\n one_user = User.objects.first()\n self.assertTrue(one_user.username.startswith('Johnny'))",
"title": ""
},
{
"docid": "c06d376bfd8ed8b7d44e8d44ecd462c9",
"score": "0.5999096",
"text": "def validate_username(self, username_field):\n if re.match(r'[A-Z]', username_field.data):\n raise ValidationError(\"you only can using lowercase for username\")\n \n if User.query.filter_by(username=username_field.data).first():\n raise ValidationError(\"username already registered, try another one\")",
"title": ""
},
{
"docid": "0d3ad88fbc1aaab9ad1becb48ae653c5",
"score": "0.5974542",
"text": "def search_users(self, where, var):\n raise RBFatalError(\"NOT IMLEMENTED YET\")\n self.cur.execute(\n ('SELECT username, usertype, id, name, course, year, email '\n 'FROM users WHERE ') + where + ' %s', ('%%%s%%' % var, ))\n return self.cur.fetchall()",
"title": ""
},
{
"docid": "4b384e75efcc1d67949f0ef8c80505b6",
"score": "0.5971448",
"text": "def search_for_users(self, query: str) -> list:",
"title": ""
},
{
"docid": "a02b78a0e0147084bb2f8d657650deb0",
"score": "0.59693974",
"text": "def search(request):\n global unknow_user_list\n unknow_user_list = []\n user_name = request.POST['res_user']\n first = user_name.split(' ')[0]\n second = user_name.split(' ')[1]\n for i in SCUser.objects.filter(first_name=first,last_name=second)[:]:\n unknow_user_list.append(i)",
"title": ""
},
{
"docid": "ffd486a999e8dc1924a47854a645327c",
"score": "0.59549856",
"text": "def valid_username(username):\n return username and USER_RE.match(username)",
"title": ""
},
{
"docid": "00be54a5087127aa2f0dc169f7128251",
"score": "0.5947252",
"text": "def userPrefix(self):\n return self.userName[0].lower()",
"title": ""
},
{
"docid": "368ecd47b076d6bda76aab3a613cba06",
"score": "0.5938804",
"text": "def username(Username):\n\treturn Username.strip().lower().replace(\"_\", \"\").replace(\" \", \"-\")",
"title": ""
},
{
"docid": "717f9b0b7330473e2adc7cf854da0e6e",
"score": "0.59299797",
"text": "def user_searcher(self, bot, name: str, max_users=5) -> List[any]:\n username, discriminator = self.username_parser(name)\n if discriminator:\n users = [\n x\n for x in bot.get_all_members()\n if x.name.lower() == username.lower()\n and x.discriminator == discriminator\n ]\n else:\n users = [\n x for x in bot.get_all_members() if x.name.lower() == username.lower()\n ]\n if not users:\n name = re.escape(name)\n users = [\n x\n for x in bot.get_all_members()\n if re.search(name.lower(), x.display_name.lower())\n ]\n if len(users) > max_users:\n raise ValueError(\"Too many users returned.\")\n return users",
"title": ""
},
{
"docid": "49e9d832ff89507a1c9d784f031875b5",
"score": "0.5920381",
"text": "def find_account(cls,username):\n for account in cls.users:\n if account.firstName == username:\n return account",
"title": ""
},
{
"docid": "cf9af93bf0b1284fc4bd295ec2b5eef8",
"score": "0.5917308",
"text": "def _findUserNameCLEARCHAT(self):\n\t\ttry:\n\t\t\tuserName = re.sub('[^a-zA-Z0-9_]','', self.data.split(':')[2])\n\t\t\treturn userName\n\t\texcept:\n\t\t\treturn \"error.finding.userName\"",
"title": ""
},
{
"docid": "cb6ee67c968d2081cd7cd8f23dba9b5f",
"score": "0.59135354",
"text": "def clean_username(self):\n if not ALNUM_REGEXP.search(self.cleaned_data['username']):\n raise forms.ValidationError(_(u'Uživatelské jméno může obsahovat pouze písmena, číslice a podtržítko'))\n try:\n user = User.objects.get(username__iexact=self.cleaned_data['username'])\n except User.DoesNotExist:\n return self.cleaned_data['username']\n raise forms.ValidationError(_(u'Toto uživatelské jméno už je zabráno'))",
"title": ""
},
{
"docid": "45977a361e20948d23517a0937d6ba6b",
"score": "0.5911364",
"text": "def test_contains_lowercase(self):\n assert re.search(\"[a-z]\", PasswordGenerator().create())",
"title": ""
},
{
"docid": "1747b598c0bd18602f0a68ac2c4399ca",
"score": "0.58801764",
"text": "def userfilter(text):\n return stringutil.shortuser(text)",
"title": ""
},
{
"docid": "df0d9d1c946f81589b520c6d0eb77721",
"score": "0.5878371",
"text": "def get_user_input():\r\n valid_inputs = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't',\r\n 'u', 'v', 'w', 'x', 'y', 'z', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N',\r\n 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', ' ']\r\n username = input()\r\n if len(username) < 1:\r\n print(\"Name must be at least one letter. Please enter another name:\")\r\n username = get_user_input()\r\n for letter in username:\r\n if letter not in valid_inputs:\r\n print(\"Username can only contain letters. Please enter another name:\")\r\n username = get_user_input()\r\n return username",
"title": ""
},
{
"docid": "009a78a17b2eecd6200119ecaf2d1859",
"score": "0.58749855",
"text": "def validate_username(self, username):\n regex = re.compile(r'[@_!#$%^&*()<>?/\\|}{~:]')\n check_username = User.objects.filter(username=username)\n if check_username.exists():\n raise serializers.ValidationError(\"Provided username already exist, please provide a different one\")\n elif regex.search(username):\n raise serializers.ValidationError(\"should not contain special characters @_!#$%^&*()<>?/\\|}{~:\")\n elif len(username.split()) > 1:\n raise serializers.ValidationError(\n \"Username should not contain spaces\"\n )",
"title": ""
},
{
"docid": "dfebcbddfded50c6d9ec60be675c99bd",
"score": "0.587404",
"text": "def clean_username(self):\n username = self.cleaned_data[\"username\"]\n\n if Albergatore.objects.filter(username=username).exists():\n raise forms.ValidationError('Username non disponibile!')\n else:\n return username",
"title": ""
},
{
"docid": "31b35357c4c9938c12a2a94fd81842d0",
"score": "0.5864838",
"text": "def find_user(self):\n users = []\n place = -1\n not_found = True\n # until a user is chosen\n while not_found:\n name = raw_input(\"Name: \")\n users = self.searchForUsers(name)\n # Printout 4 best matches\n for i, user in enumerate(users[:4], 1):\n print(f'#{i} {user.name}')\n place = int(input(\"Enter number, in case the user is not listed, press enter and try their name again:\"))\n if 0 <= place <= 3:\n not_found = False\n return users[place - 1]",
"title": ""
},
{
"docid": "3d070a3ee437c821903d36715620acb2",
"score": "0.58620286",
"text": "def username_validator(form, field):\n username = field.data\n if len(username) < 3:\n raise ValidationError(_('Username must be at least 3 characters long'))\n valid_chars = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789-._'\n chars = list(username)\n for char in chars:\n if char not in valid_chars:\n raise ValidationError(_(\"Username may only contain letters, numbers, '-', '.' and '_'.\"))",
"title": ""
},
{
"docid": "6480d21680c8dd6952e8aea6c8929864",
"score": "0.5858849",
"text": "def clean_username(self):\r\n existing = User.objects.filter(username__iexact=self.cleaned_data['username'])\r\n if existing.exists():\r\n raise forms.ValidationError(_(\"O email já se encontra registado.\"))\r\n else:\r\n return self.cleaned_data['username']",
"title": ""
},
{
"docid": "bb509753ebe1a402b4655be04aca08b9",
"score": "0.585632",
"text": "def check():\n userNames = db.execute(\"SELECT username FROM users\")\n userName = request.args.get(\"username\")\n search = {'username' : userName}\n if search in userNames:\n print(\"user name found\")\n return jsonify(\"false\")\n return jsonify(\"TODO\")",
"title": ""
},
{
"docid": "c50cb820f73822e2b0f5cc90572b7b68",
"score": "0.5852539",
"text": "def valid_username(self, username):\n return self.USER_RE.match(username)",
"title": ""
},
{
"docid": "4695547fd1518cdc497f17e06805a09c",
"score": "0.58498293",
"text": "def valid_username(self, username):\n return username and USER_RE.match(username)",
"title": ""
},
{
"docid": "97828fd3b7f52be6efdb1d914cf15d1b",
"score": "0.584555",
"text": "def get_username(infile):\n username = 0\n myfile = open(infile, mode=\"r\", encoding=\"latin_1\")\n what_to_look = 'Uploaded by '\n for line_username in myfile:\n if what_to_look in line_username:\n username_length = line_username.find('</font>')\n username = line_username[58:username_length]\n print(\"Username: \" + username.strip())\n return username.strip()\n myfile.close()",
"title": ""
},
{
"docid": "8b086f5cca7f18c70d9deeb692a32faf",
"score": "0.58317703",
"text": "def process_username(self, value) -> str:",
"title": ""
},
{
"docid": "0cde03d4228e8685827660495aa1a6b1",
"score": "0.58283216",
"text": "def clean_username(self):\n\t\tusername = self.cleaned_data['username']\n\t\tif not re.search(r'^\\w+$', username):\n\t\t\traise forms.ValidationError ('Username can only contain '\n\t\t\t'alphanumeric characters and the underscore.')\n\t\ttry:\n\t\t\tUser.objects.get(username=username)\n\t\texcept User.DoesNotExist:\n\t\t\treturn username\n\t\traise forms.ValidationError('Username already taken.')",
"title": ""
},
{
"docid": "886622e20a72c5ffc26f7711c383fbfc",
"score": "0.5823204",
"text": "def clean_username(self):\n existing = Users.objects.filter(username=self.cleaned_data['username'])\n if existing.exists():\n raise forms.ValidationError(_('A user with that username already exists.'))\n else:\n return self.cleaned_data['username']",
"title": ""
},
{
"docid": "e27cb3f8ef14d1b9c8665d9cd4f0f134",
"score": "0.5819259",
"text": "def handle_search(request):\n\n query = request.GET.get('query')\n if query:\n users = User.objects.filter(username__icontains=query\n ).values_list('username', flat=True\n ).order_by('profile__score')\n else:\n users = User.objects.order_by('profile__score').values_list('username', flat=True)\n\n users = list(users[:20])\n # Return list of users matching username\n return ajax_success(users=users, msg=\"Username searched\")",
"title": ""
},
{
"docid": "402f6542db3a31a58cd1343b3fc307c8",
"score": "0.5805711",
"text": "def validate_username(cls, username, skip_uniqueness_check=False):\n un = username.lower()\n\n if (un in Config['blocked_usernames']\n or any(fragment in un for fragment in Config['blocked_username_fragments'])\n or any(fragment in un for fragment in Config['autoflag_words'])):\n return \"Sorry, this username is not allowed.\"\n\n if not un:\n return \"Please enter a username.\"\n elif len(un) < 3:\n return \"Username must be 3 or more characters.\"\n elif len(un) > 16:\n return \"Username must be 16 characters or less.\"\n\n alphabet = string.lowercase + string.uppercase + string.digits + '_'\n if not all(char in alphabet for char in username):\n return \"Usernames can only contain letters, digits and underscores.\"\n\n if not skip_uniqueness_check:\n if cls.objects.filter(username__iexact=username):\n return \"Sorry! This username is taken. Please pick a different username.\"",
"title": ""
},
{
"docid": "ccf82e5c207b54640641b69971396aec",
"score": "0.5800986",
"text": "def clean_username(self):\n username = self.cleaned_data['username'].lower()\n if (User.objects.filter(username__iexact=username).exists() or\n username in settings.ACCOUNT_USERNAME_BLACKLIST):\n raise ValidationError(_('A user with that username already exists.'))\n return username",
"title": ""
},
{
"docid": "8b2719c2a52bb1843d98111dca318d8e",
"score": "0.5790296",
"text": "def get_users_like(self, name):\n name_length = len(name)\n if name_length > USER_ALL_NAME_LENGTH_MAX_WITH_DISCRIMINATOR:\n return []\n \n users = self.users\n \n # name with discriminator\n \n name_with_discriminator = _parse_name_with_discriminator(name)\n if (name_with_discriminator is not None):\n for user in users.values():\n if _is_user_matching_name_with_discriminator(user, name_with_discriminator):\n return [user]\n \n if name_length > USER_ALL_NAME_LENGTH_MAX:\n return []\n \n user_name_pattern = re_compile('.*?'.join(re_escape(char) for char in name), re_ignore_case)\n matches = []\n guild_id = self.id\n \n for user in users.values():\n # name\n \n parsed = user_name_pattern.search(user.name)\n if (parsed is not None):\n match_start = parsed.start()\n match_length = parsed.end() - match_start\n \n match_rate = (USER_MATCH_WEIGHT_NAME, match_length, match_start)\n \n matches.append((user, match_rate))\n continue\n \n # display_name\n \n user_display_name = user.display_name\n if (user_display_name is not None):\n parsed = user_name_pattern.search(user_display_name)\n if (parsed is not None):\n match_start = parsed.start()\n match_length = parsed.end() - match_start\n \n match_rate = (USER_MATCH_WEIGHT_DISPLAY_NAME, match_length, match_start)\n \n matches.append((user, match_rate))\n continue\n \n # nick\n \n try:\n guild_profile = user.guild_profiles[guild_id]\n except KeyError:\n pass\n else:\n user_nick = guild_profile.nick\n if (user_nick is not None):\n parsed = user_name_pattern.search(user_nick)\n if (parsed is not None):\n match_start = parsed.start()\n match_length = parsed.end() - match_start\n \n match_rate = (USER_MATCH_WEIGHT_NICK, match_length, match_start)\n \n matches.append((user, match_rate))\n continue\n \n \n return [item[0] for item in sorted(matches, key = _user_match_sort_key)]",
"title": ""
},
{
"docid": "b9555fdcc9e54fd5f5f4380bc80d68d4",
"score": "0.57825446",
"text": "def validate_username_field(value):\n msg = []\n if value:\n if not value.isalnum() and \"_\" not in value:\n msg.append(\n \"field mustn't contain any special character other than _\")\n if not ((re.search(\"[a-z]\", value))or(re.search(\"[A-Z]\", value))):\n msg.append(\"field must contain atleast one character\")\n if len(msg) > 0:\n raise serializers.ValidationError(msg)",
"title": ""
},
{
"docid": "a35b0a5cfdaf998533757ab81ef80b7a",
"score": "0.5759538",
"text": "def validate_username(username: str) -> bool:\n return True if re.match(\"^([a-zA-Z0-9]|\\.){6,32}$\", username) else False",
"title": ""
},
{
"docid": "bccfcb1a4faa69fafb35985b1726fcc8",
"score": "0.5750044",
"text": "def sanity_check_username(name):\n VALID_CHARACTERS = string.ascii_letters+string.digits+\"_-.\"\n rules = [ \n len(name) > 3, # User name is longer than 3 characters\n all([k in VALID_CHARACTERS for k in list(name)]) # Username is made of valid characters\n ]\n return all(rules)",
"title": ""
},
{
"docid": "73419325271abbbdc5c616ca0cd524bf",
"score": "0.574605",
"text": "def usersname_length(username):\n try:\n username = str(username)\n # if username.length() > 3 and < 21:\n if (len(username) >= 3) and (len(username) < 21):\n return True\n except ValueError:\n return False",
"title": ""
},
{
"docid": "901627988b4967b6959f5399749d0ce6",
"score": "0.57419056",
"text": "def is_valid_username(u_name):\n if not re.match(r\"^[A-Za-z\\.\\+_-]*$\", u_name) != None:\n return False\n return True",
"title": ""
},
{
"docid": "921e0723b5599cc772717ee38636de85",
"score": "0.57408136",
"text": "def findUsersByName(conn, name):\n query = \"SELECT * FROM users WHERE name LIKE %s\"\n params = [\"%\" + name + \"%\"]\n return getSqlQuery(conn, query, params)",
"title": ""
},
{
"docid": "11dd260e48c8b537315631ad539f4f7a",
"score": "0.5739077",
"text": "def clean_username(self):\n username = self.cleaned_data[\"username\"]\n if not Albergatore.objects.filter(username=username).exists():\n raise forms.ValidationError(\"Username non registrato\")\n else:\n return username",
"title": ""
},
{
"docid": "10457fa52cefb6a629aa87718148e306",
"score": "0.5726845",
"text": "def check_user_name(self, username):\n query = \"SELECT username FROM users WHERE username = '%s'\" % (username)\n result = self.fetch_single_data_row(query)\n if result is None:\n return False\n return True",
"title": ""
},
{
"docid": "e0f0e453b2520b68c9cb1c756d008987",
"score": "0.57148904",
"text": "def check_valid_username(self, username : str):\n\t\treturn True",
"title": ""
},
{
"docid": "c9c54c91fd6a80bbb5edc552561ef5b2",
"score": "0.57078695",
"text": "def security_check_username(self, bot, update):\n\n full_name = (update.message.from_user.first_name + \" \"\n + update.message.from_user.last_name)\n if self.name_ban_re and self.name_ban_re.search(full_name):\n # Logging\n log_message = \"Ban match full name: {}\".format(full_name.encode('utf-8'))\n logger.info(log_message)\n for notify_user_id in self.notify_user_ids:\n logger.info(notify_user_id,\"gets notified\")\n bot.send_message(\n chat_id=notify_user_id,\n text=log_message)\n # Ban the user\n self.ban_user(update)\n # Log in database\n\n if self.name_ban_re and self.name_ban_re.search(update.message.from_user.username or ''):\n # Logging\n log_message = \"Ban match username: {}\".format(update.message.from_user.username.encode('utf-8'))\n logger.info(log_message)\n for notify_user_id in self.notify_user_ids:\n bot.send_message(\n chat_id=notify_user_id,\n text=log_message)\n # Ban the user\n self.ban_user(update)",
"title": ""
},
{
"docid": "60497a226d11be3cafe143f5f4baf5a3",
"score": "0.56966424",
"text": "def clean_username(self):\r\n username = self.cleaned_data[\"username\"]\r\n if '@' in username:\r\n # The user entered an email, so try to log them in by e-mail\r\n emails = ContactValue.objects.filter(value=username,\r\n field__field_type='email',\r\n contact__trash=False,\r\n contact__related_user__isnull=False)\r\n users = [email.contact.related_user.user for email in emails]\r\n else:\r\n users = User.objects.filter(user__username=username)\r\n if len(users) == 0:\r\n raise forms.ValidationError(\r\n _(\"Sorry, we don't know that user or e-mail.\"))\r\n else:\r\n username = users[0]\r\n return username",
"title": ""
},
{
"docid": "c1353c764d75f75d8f8558f24aaa1ab2",
"score": "0.5692759",
"text": "def clean_username(self):\n try:\n user = User.objects.get(username__iexact=self.cleaned_data['username'])\n except User.DoesNotExist:\n return self.cleaned_data['username']\n raise forms.ValidationError(_(\"A user with that username already exists.\"))",
"title": ""
},
{
"docid": "c3de59944fb73806d9a0d76fdef129df",
"score": "0.5677581",
"text": "def auto_complete_model(request):\n if request.is_ajax():\n searchString=request.POST[\"search\"]\n search_qs = User.objects.filter(username__startswith=searchString)\n print search_qs\n results = []\n for r in search_qs:\n results.append(r.username)\n print results\n return HttpResponse(json.dumps(results), content_type=\"application/json\", status=200)",
"title": ""
},
{
"docid": "a5e0005f4d22a3f09122843f9e940b11",
"score": "0.5675287",
"text": "def is_letter_in_pattern(user_input, word):\r\n for letter in list(word):\r\n if user_input == letter:\r\n return True\r\n return False",
"title": ""
},
{
"docid": "7de1e6e4e7af2ec958fd08cc5adad7c4",
"score": "0.56643456",
"text": "def get_userName():\n userName = input(\"Tapez Votre nom d'utilisateur: \")\n\n userName = userName.capitalize() # 1st letter in cap\n\n if not userName.isalnum() or len(userName)<4:\n\n print(\"Nom invalide\")\n return get_userName()\n\n else:\n return userName",
"title": ""
},
{
"docid": "11bb76360b33e6a116242866fab5edd2",
"score": "0.5660173",
"text": "def _check_username(self, username):\n # Username cannot contain spaces\n if ' ' in username:\n return False\n else:\n return True",
"title": ""
},
{
"docid": "dc451def748fc8dda4ebad1e3f2dd02c",
"score": "0.5657971",
"text": "def clean_username(self):\n username = self.cleaned_data.get('username')\n if len(username) < 3:\n raise forms.ValidationError(\"001,Your username must be longer than 3 words.\")\n elif len(username) > 20:\n raise forms.ValidationError(\"002,Your username must be shorter than 20 words.\")\n else:\n\n #To check whether the username already exists\n \n checkresult = User_Info.objects.filter(username=username)\n if len(checkresult) > 0:\n raise forms.ValidationError(\"003,Your username already exists.\")\n return username",
"title": ""
},
{
"docid": "0411422ddb7c66213d92669fbfea4606",
"score": "0.5654673",
"text": "def find(word, letter, start):\n\n index = start\n hits = []\n\n while index < len(word):\n if word[index].lower() == letter.lower():\n hits.append(index)\n index += 1\n\n return(hits)",
"title": ""
},
{
"docid": "fe06db84edc8d4419919a5d393e0d0da",
"score": "0.5648181",
"text": "def user_name():\n while 1:\n username = input(\"enter username, please: \")\n if not len(username) == 0:\n return username",
"title": ""
},
{
"docid": "9c82479f2897275c5c78a4cb5bfe4b88",
"score": "0.56426215",
"text": "def clean_username(self, username):\n return username",
"title": ""
},
{
"docid": "58c8af63dd87814589e29192c85d4b4c",
"score": "0.5637166",
"text": "def clean_username(self):\n username = self.cleaned_data['username']\n if User.objects.filter(username=username):\n raise forms.ValidationError('Este correo electronico ya existe')\n return username",
"title": ""
},
{
"docid": "1e7833e36e5978bb94e34dd72ed08206",
"score": "0.5629253",
"text": "def _contains_lowercase_name(self, name):\n return name in (x.lower() for x in self._distinct())",
"title": ""
},
{
"docid": "4746a4438fcd4d437bbb5a83cd66e336",
"score": "0.56258893",
"text": "def userSearch(self, username, pwd=\"abc\", usneed=False):\n try:\n self.cur.execute('''\n SELECT * FROM \"Student\"\n WHERE \"UserName\" = ?''', (username,))\n\n rows = self.cur.fetchall()\n # print(rows)\n if usneed:\n return rows[0][1]\n elif len(rows) == 0:\n return \"Empty\" # if no account yet created or wrong username\n elif len(rows) == 1 and rows[0][4] == pwd:\n return True\n return False # if wrong password inserted\n except Exception as e:\n print(\"userSearch(), error -\", e)\n self.con.close()\n return -1",
"title": ""
},
{
"docid": "cc295b480fcf5c6b853d0df7ea8f1d4f",
"score": "0.561518",
"text": "def _is_first_name(self, word):\n names = self._first_name_list\n word = word.lower()\n ind = bisect.bisect_left(names, word)\n if ind < len(names) and names[ind] == word:\n return True\n return False",
"title": ""
},
{
"docid": "0d71da97eb837b9720e897203c7687db",
"score": "0.56023335",
"text": "def test_get_user_by_username_r(self):\n pass",
"title": ""
},
{
"docid": "280fe52791e9ae728929c968bfb34157",
"score": "0.5601374",
"text": "def filter_user(tweets):\n \n return tweets.str.replace('<user>', '', case=False)",
"title": ""
},
{
"docid": "801fc57aa073f025190e7da688415a5e",
"score": "0.559805",
"text": "def _findUserNamePRIVMSG(self):\n\t\ttry:\n\t\t\tuserName = re.sub('[^a-zA-Z0-9_]','', self.data.split('!')[0])\n\t\t\treturn userName\n\t\texcept:\n\t\t\treturn \"error.finding.userName\"",
"title": ""
},
{
"docid": "f6d7f959bb24b39ec9fb5bb435b9539a",
"score": "0.55943894",
"text": "def search_for_letters(phrase:str,letters:str='aeiou') ->set:\r\n return set(letters).intersection(set(phrase))",
"title": ""
},
{
"docid": "38c88ffee191a5c1841c81b8482b5ccf",
"score": "0.558165",
"text": "def get_service_usernames():\n usernames = [k[:-len('_passwd')] for k in leader_get()\n if k.endswith('_passwd') and k != 'admin_passwd']\n # now match against the service list.\n valid_service_names = valid_services.keys()\n known_service_usernames = []\n for username in usernames:\n # if a username has '_' in it, then it is a compound name.\n parts = username.split('_')\n if 'keystone' in parts:\n continue\n if not all(p in valid_service_names for p in parts):\n continue\n known_service_usernames.append(username)\n return known_service_usernames",
"title": ""
},
{
"docid": "39cb53ddf55ec2a3546f1b818d47090e",
"score": "0.5574706",
"text": "def clean_username(self):\n username = self.cleaned_data['username']\n if User.objects.filter(username=username):\n raise forms.ValidationError('Nombre de usuario ya registrado.')\n return username",
"title": ""
},
{
"docid": "1d4427c955f075306234e38ed62c4af2",
"score": "0.5553871",
"text": "def generate_username(name):\n username = ''.join(name.split(' ')).lower()\n if not User.objects.filter(username=username).exists():\n return username\n else:\n random_username = username + str(randint(0, 1000))\n return generate_username(random_username)",
"title": ""
},
{
"docid": "aa644612e55a8ebbd61835fc8da001c0",
"score": "0.5548313",
"text": "def parse_name(guild, username):\r\n if '@' in username:\r\n try:\r\n return guild.get_member(int(username[3:-1]))\r\n except:\r\n raise NameError(username)\r\n else:\r\n return get_member_from_guild(guild.members, username)",
"title": ""
},
{
"docid": "8970f68548e77c567d874853c18c5454",
"score": "0.55467904",
"text": "def check():\r\n username = request.args.get(\"username\")\r\n if len(username) >= 1:\r\n usernames = [dct[\"username\"] for dct in db.execute(\"SELECT username FROM users\")]\r\n if username not in usernames:\r\n return jsonify(True)\r\n else:\r\n return jsonify(False)",
"title": ""
},
{
"docid": "d7f90c6d8429bb1d64141f7955edbd74",
"score": "0.55457246",
"text": "def username_exists_validator(value):\n try:\n User.objects.get(username=value)\n\n except User.DoesNotExist:\n raise ValidationError(_('Requested username does not exist.'))",
"title": ""
},
{
"docid": "e76690e229888ca6a3b5515f7b6a088f",
"score": "0.55418396",
"text": "def validate_username(cls, nick):\n if nick and nick != \"\":\n nick = nick.strip()\n if len(nick) > 0:\n if re.match(\"^[A-z0-9_\\-]+$\", nick):\n return nick, \"\"\n else:\n return \"\", \"Username can only contain letters, numbers, underscores, and dashes.\"\n return \"\", \"Username must not be empty.\"",
"title": ""
},
{
"docid": "566aa32677a8d6f412d11e734703c54a",
"score": "0.55270416",
"text": "def check_username_lower_limit(username):\n if len(username) >= 4:\n return True",
"title": ""
},
{
"docid": "8286201ec8571426d3ce568b4ff7d700",
"score": "0.55268127",
"text": "def get_by_natural_key(self, username):\r\n anycase_username_field = f'{self.model.USERNAME_FIELD}__iexact'\r\n return self.get(**{anycase_username_field: username})",
"title": ""
},
{
"docid": "40e99474b264ed23dbb9d1046298888f",
"score": "0.552202",
"text": "def check_username(cls, uid):\n if not uid:\n raise RBFatalError('Username must be given')\n if re.search(r'[^a-z0-9_.-]', uid):\n raise RBFatalError(\"Invalid characters in username\")\n if len(uid) > rbconfig.maxlen_uname:\n raise RBFatalError(\"Username can not be longer than %d characters\"\n % rbconfig.maxlen_uname)\n if re.search(r'^[^a-z0-9]', uid):\n raise RBFatalError(\"Username must begin with letter or number\")",
"title": ""
},
{
"docid": "bf3b9bd84177442eda8c6433534e636c",
"score": "0.5512801",
"text": "def validate_username(self, username):\n # onyl allow alphanumeric character, . _ -\n if username is not None:\n pattern = r\"^[a-zA-Z0-9_.-]+$\"\n if len(username) < 1:\n raise ValidationError('Invalid username, minimum is 1 character')\n if len(username) > 32:\n raise ValidationError('Invalid username, max is 32 character')\n if re.match(pattern, username) is None:\n raise ValidationError('Invalid username, only alphanumeric, . _ - allowed')",
"title": ""
},
{
"docid": "28fd7d5e2650faf027dced619ce0ed7e",
"score": "0.5505782",
"text": "def get_first_name(self):\n is_valid_first_name = False\n while not is_valid_first_name:\n print(\"First Name: \", end=\"\")\n fname = input().strip()\n fn_regex = re.compile(r\"^[A-z]+$\")\n is_valid_first_name = fn_regex.match(fname) is not None\n if not is_valid_first_name:\n print(\"{} is not a valid first name\".format(fname))\n return fname",
"title": ""
},
{
"docid": "d677b66b8bcbcc5abf29fbca1af46b39",
"score": "0.54982305",
"text": "def lookup_by_letter(letter):\n letterlist = []\n if len(letter) == 1 and letter.isalpha(): #tests if input is a letter and only one letter\n letter = letter.capitalize() #allows for case insensitivity, makes uppercase to match dictionary keys\n for i in name_dict.keys():\n if i[0] == letter:\n letterlist.append(i)\n letterlist.sort()\n return (letterlist)\n else:\n return (\"Invalid input\")",
"title": ""
},
{
"docid": "451862806c0c5103f25ac4f009c789e5",
"score": "0.54910004",
"text": "def checkUsername(self, username, cursor, UserAccount):\n \n query = \"SELECT username FROM accounts WHERE username = %s\"\n cursor.execute(query, (username,))\n result = cursor.fetchall()\n\n while result:\n print(\"\\n\\n\\n\\n\\nSorry that username is taken\\n\")\n username = input(\"Please select another username: \")\n query = \"SELECT username FROM accounts WHERE username = %s\"\n cursor.execute(query, (username,))\n result = cursor.fetchall()\n UserAccount.username = username",
"title": ""
},
{
"docid": "bcaf8476203abba535b097330b03e65a",
"score": "0.54815954",
"text": "def UIDStartsWith(uid: Text, prefix: Text) -> bool:\n return uid.find(prefix + '.') == 0",
"title": ""
},
{
"docid": "e9c7c4389e4800fba66a37280252c525",
"score": "0.5481196",
"text": "def get_user_by_username_or_user_id(cls, string):\n username = str(string).replace(\"@\", \"\").strip().lower()\n if username.isdigit(): # user_id\n return cls.objects.filter(user_id=int(username)).first()\n return cls.objects.filter(username__iexact=username).first()",
"title": ""
},
{
"docid": "ec61766f47b6fa904df432ea8eee3942",
"score": "0.54762703",
"text": "def find_person(self, name=None):\n try:\n if name != '' or name != ' ':\n fname = lname = ''\n if ' ' in name:\n fname = name.split(' ')[0]\n lname = name.split(' ')[1]\n else:\n fname = lname = name\n return (x.first_name for x in\n self.user.select().where(Person.last_name.contains(lname) & Person.first_name.contains(fname)))\n except Exception as ex:\n logging.error(\"Error in searching Person\" + str(ex))",
"title": ""
},
{
"docid": "880446522c0701f47fb1caa6d64f0ad7",
"score": "0.5466264",
"text": "def searchUser(self, nick=None):\n with open('/usr/local/apache2/cookies.csv', 'r') as csvFile:\n reader = csv.reader(csvFile)\n for row in reader:\n if nick == row[0]:\n return True\n return False",
"title": ""
},
{
"docid": "6183092b076a2e135b74342a8d89997e",
"score": "0.54581034",
"text": "def clean_username(self):\n\t\tusername = self.cleaned_data['username']\n\t\tusername = username.strip()\n\t\tif not username:\n\t\t\traise ValidationError('Nickname mein harf likhna zaruri hain')\n\t\tvalidate_nickname_chars(username)\n\t\tif username[:1] == '.':\n\t\t\traise ValidationError('Nickname ke shuru mein . nah dalein')\n\t\tif username[-1:] == '.':\n\t\t\traise ValidationError('Nickname ke akhir mein . nah dalein')\n\t\texists = nick_already_exists(nickname=username)\n\t\taltered = {}\n\t\tif exists is None:\n\t\t\t#the redis DB is compromised, use PSQL DB. Check nick against DB, that's it\n\t\t\tif ' ' in username:\n\t\t\t\tusername_original = username\n\t\t\t\tusername = ''.join(username.split())\n\t\t\t\taltered = {'status':'joined'}\n\t\t\t\tif User.objects.filter(username__iexact=username).exists():\n\t\t\t\t\traise ValidationError('\"%s\" kisi aur ka nickname hai. Kuch aur likhein' % username_original)\n\t\t\telse:\n\t\t\t\tif User.objects.filter(username__iexact=username).exists():\n\t\t\t\t\traise ValidationError('\"%s\" kisi aur ka nickname hai. Kuch aur likhein' % username)\n\t\t\treturn [username], altered, username\n\t\t############################################\n\t\telse:\n\t\t\t# form variants and suggestions\n\t\t\t# check al against redis DB\n\t\t\tif ' ' in username:\n\t\t\t\talternatives = form_variants(username) #returns list of tuples containing variants and their statuses\n\t\t\t\talt_choices = process_choices(alternatives)\n\t\t\t\tif not alt_choices:\n\t\t\t\t\t# no suggestions could be unearthed\n\t\t\t\t\traise ValidationError('\"%s\" kisi aur ka nickname hai. Kuch aur likhein' % username)\n\t\t\t\telse:\n\t\t\t\t\t# some suggestions unearthed\n\t\t\t\t\taltered = {'status':'joined'}\n\t\t\t\t\treturn alt_choices, altered, username\n\t\t\telse:\n\t\t\t\tif exists:\n\t\t\t\t\t# nick is not available\n\t\t\t\t\talternatives = form_suggestions(username) #returns list of tuples containing suggestions and their statuses\n\t\t\t\t\talt_choices = process_choices(alternatives)\n\t\t\t\t\tif not alt_choices:\n\t\t\t\t\t\traise ValidationError('\"%s\" kisi aur ka nickname hai. Kuch aur likhein' % username)\n\t\t\t\t\telse:\n\t\t\t\t\t\taltered = {'status':'replaced'}\n\t\t\t\t\t\treturn alt_choices, altered, username\n\t\t\t\telse:\n\t\t\t\t\t#nick is available\n\t\t\t\t\t# altered = False\n\t\t\t\t\treturn [username], altered, username",
"title": ""
},
{
"docid": "00e4c3f38dce907d0d53bcbc58efa430",
"score": "0.54509556",
"text": "def _validate_username(self):\n username = self.cleaned_data.get(\"username\")\n\n if not username:\n return\n\n if settings.ST_CASE_INSENSITIVE_USERNAMES:\n username = username.lower()\n\n is_found = (\n User.objects\n .filter(username=username)\n .exists())\n if is_found:\n return\n\n if settings.ST_CASE_INSENSITIVE_EMAILS:\n username = username.lower()\n\n is_found_email = (\n User.objects\n .filter(email=username)\n .exists())\n if is_found_email:\n return\n\n raise forms.ValidationError(\n _(\"No account matches %(username)s.\") % {\n 'username': username})",
"title": ""
},
{
"docid": "9db4028ad184739fd82612b64ce1df4d",
"score": "0.5444739",
"text": "def fuzzy_nickname_search(request):\n logger('views', 'main: {}'.format(request.json_body))\n return fuzzy_string_matcher.get_nicknames(request.validated['user'], request.validated['value'])",
"title": ""
},
{
"docid": "ddc4a07d8aba14ec188a596bc8b9f270",
"score": "0.5443438",
"text": "def searchLetters(phrase: str, letters: str = 'aeiou') -> set:\n return set(letters).intersection(set(phrase))",
"title": ""
},
{
"docid": "373f0e4b464d81fd513c3c7bf09ae95f",
"score": "0.5439793",
"text": "def security_check_username(self, bot, update):\n\n full_name = ((update.message.from_user.first_name or \"\") + \" \"\n + (update.message.from_user.last_name or \"\"))\n if self.name_ban_re and self.name_ban_re.search(full_name):\n # Logging\n log_message = \"Ban match full name: {}\".format(\n full_name.encode('utf-8'))\n if self.debug:\n update.message.reply_text(log_message)\n print(log_message)\n for notify_user_id in self.notify_user_ids:\n print(notify_user_id, \"gets notified\")\n bot.send_message(\n chat_id=notify_user_id,\n text=log_message)\n # Ban the user\n self.ban_user(update)\n # Log in database\n s = session()\n userBan = UserBan(\n user_id=update.message.from_user.id,\n reason=log_message)\n s.add(userBan)\n s.commit()\n s.close()\n\n if self.name_ban_re and self.name_ban_re.search(update.message.from_user.username or ''):\n # Logging\n log_message = \"Ban match username: {}\".format(\n update.message.from_user.username.encode('utf-8'))\n if self.debug:\n update.message.reply_text(log_message)\n print(log_message)\n for notify_user_id in self.notify_user_ids:\n bot.send_message(\n chat_id=notify_user_id,\n text=log_message)\n # Ban the user\n self.ban_user(update)\n # Log in database\n s = session()\n userBan = UserBan(\n user_id=update.message.from_user.id,\n reason=log_message)\n s.add(userBan)\n s.commit()\n s.close()",
"title": ""
},
{
"docid": "b46757792a081b3c67557f1b7341f309",
"score": "0.5436486",
"text": "def justice_league_by_real_name(real_name):\n print(\"i'm here though\")\n canonicalized = real_name.replace(\" \", \"\").lower()\n for character in justice_league_members:\n search_term = character[\"real_name\"].replace(\" \", \"\").lower()\n\n if search_term == canonicalized:\n return jsonify(character)\n\n return jsonify({\"error\": f\"Character with real_name {real_name} not found.\"}), 404",
"title": ""
},
{
"docid": "caded9656e227eede695bae6071645e2",
"score": "0.54354316",
"text": "def validate_first_name(self, value):\n if not all(c in string.ascii_letters for c in value.lower().replace(\" \", \"\")):\n raise serializers.ValidationError(\"First name is not valid, please enter again\")\n return value",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="790"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">60a13f80bbbd52ab881d64ae67e7d878</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Test LimitTracker used in auvimport.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "376124658927b62692caa4f6e6bed547",
"score": "0.5882347",
"text": "def test_limit_tracker(self):\n\n # check direct values\n direct_track = LimitTracker()\n\n direct_track.check(1.0)\n\n self.assertEqual(direct_track.minimum, 1.0)\n self.assertEqual(direct_track.maximum, 1.0)\n\n direct_track.check(10.0)\n direct_track.check(-10.0)\n\n self.assertEqual(direct_track.minimum, -10.0)\n self.assertEqual(direct_track.maximum, 10.0)\n\n # check values in a dictionary\n dict_value_track = LimitTracker('val')\n\n dict_value_track.check({'val': 1.0})\n\n self.assertEqual(dict_value_track.minimum, 1.0)\n\n dict_value_track.check({'val': 10.0})\n dict_value_track.check({'val': -10.0})\n\n self.assertEqual(dict_value_track.maximum, 10.0)\n self.assertEqual(dict_value_track.minimum, -10.0)\n\n # the final (odd) cases\n # should throw error... or silently ignore?\n\n # using a dict, but give it a value\n self.assertRaises(TypeError, dict_value_track.check, (20.0))\n # or a dict with the wrong key\n dict_value_track.check({'other': -20.0})\n\n self.assertEqual(dict_value_track.maximum, 10.0)\n self.assertEqual(dict_value_track.minimum, -10.0)\n\n # using a value but give it a dict\n self.assertRaises(TypeError, direct_track.check, ({'val': 20.0}))\n\n self.assertEqual(direct_track.minimum, -10.0)\n self.assertEqual(direct_track.maximum, 10.0)",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "5e84c8ee36f03c4d423562b65adc6087",
"score": "0.6538664",
"text": "def test_rate_limit_interface():\n per_user_rate_def = RateDefinition(per_week=50000, per_day=13000, per_second=.001)\n min_rate_def = RateDefinition(per_second=10)\n per_user_rate_def = PerUserRateDefinition(per_user_rate_def, min_rate_def)\n my_feature_rate_limiter = RateLimiter('my_feature', per_user_rate_def.get_rate_limits)\n if my_feature_rate_limiter.allow_usage('my_domain'):\n # ...do stuff...\n my_feature_rate_limiter.report_usage('my_domain')",
"title": ""
},
{
"docid": "3d93729a630c34495d64c4b65d915e78",
"score": "0.6360713",
"text": "def test_activity_api_limits(self):\n\n async def run_test():\n with self.assertRaises(ValueError):\n await self.test_camera.query_activity_history(limit=ACTIVITY_API_LIMIT + 1)\n\n self.loop.run_until_complete(run_test())",
"title": ""
},
{
"docid": "577aaff1b88b187d68a0fc9eb0d0a150",
"score": "0.60561013",
"text": "def on_limit(self, track):\n print 'Limitation notice has arrived...'\n return",
"title": ""
},
{
"docid": "f731d809af601dfc71952e039aedadcb",
"score": "0.5922246",
"text": "def test_no_participations(self):\n user = users.models.User.objects.create_user('test-user')\n limit = self.limiter.get_limit(user)\n self.assertEqual(limit.min_level, self.c_prime)",
"title": ""
},
{
"docid": "cc47a7b5642e0be6e1843560d450fa4d",
"score": "0.58270365",
"text": "def test_limit_reached(self):\r\n chip_counter = Chips()\r\n limit_reached(chip_counter)\r\n self.assertEqual(chip_counter.total, 2000)",
"title": ""
},
{
"docid": "7f073275b6c9022ea447378594acfed2",
"score": "0.57859415",
"text": "def setUp(self):\n super(LimitsControllerTestV21, self).setUp()\n self.controller = wsgi.Resource(self.limits_controller())\n self.ctrler = self.limits_controller()",
"title": ""
},
{
"docid": "3e651565f2fa68e798f85ce96e80e1e5",
"score": "0.5757954",
"text": "def testCPULimit(self):\n result = self.RunFlow(\"CPULimitFlow\", cpu_limit=300)\n self.assertEqual(result[\"cpulimit\"], [300, 295, 255])",
"title": ""
},
{
"docid": "028e2c972cedb228cc34afa6e47ecdd1",
"score": "0.5748576",
"text": "def test_lagLimitExceeded(self):\n logger = OperationLogger(outfile=StringIO())\n for lag in [100.0, 1100.0, 1200.0]:\n logger.observe(dict(\n type='operation', phase='start', user='user01',\n label='testing', lag=lag)\n )\n self.assertEqual(\n [\"Median TESTING scheduling lag greater than 1000.0ms\"],\n logger.failures())",
"title": ""
},
{
"docid": "f0ddee65968dc0db1bba54298341b73b",
"score": "0.5728882",
"text": "def test_rate_limiting_error(self):\n self.sources[0].RATE_LIMIT_HTTP_CODES = ('429', '456')\n\n error_body = json_dumps({\"meta\": {\n \"code\": 429, \"error_message\": \"The maximum number of requests...\",\n \"error_type\": \"OAuthRateLimitException\"}})\n self.expect_get_activities().AndRaise(\n urllib.error.HTTPError('url', 429, 'Rate limited', {},\n io.StringIO(error_body)))\n\n self.post_task(expect_poll=FakeSource.RATE_LIMITED_POLL,\n expect_last_polled=util.EPOCH)\n source = self.sources[0].key.get()\n self.assertEqual('error', source.poll_status)\n self.assertTrue(source.rate_limited)",
"title": ""
},
{
"docid": "429a1b753c13a4d10d6c92ab2a74cd5b",
"score": "0.57227284",
"text": "def l_rate_limit(self):",
"title": ""
},
{
"docid": "ce44371742d077c6779a50b7ed42e1ea",
"score": "0.5719269",
"text": "def test_failureLimitExceeded(self):\n logger = OperationLogger(outfile=StringIO())\n for _ignore in range(98):\n logger.observe(dict(\n type='operation', phase='end', user='user01',\n duration=0.25, label='testing', success=True)\n )\n logger.observe(dict(\n type='operation', phase='end', user='user01',\n duration=0.25, label='testing', success=False)\n )\n self.assertEqual(\n [\"Greater than 1% TESTING failed\"],\n logger.failures())",
"title": ""
},
{
"docid": "e227dcc7289ee272f60fa0da1a07c9ce",
"score": "0.5711501",
"text": "def test_limit_complete(self):\n limit = Limit(item_limit=0)\n self.assertTrue(limit.complete)",
"title": ""
},
{
"docid": "0feeedd5f4c50591d83f4d5fcc7f880b",
"score": "0.57075286",
"text": "def patch_max_test_time(limit):\n from corehq.tests.noseplugins.timing import patch_max_test_time\n return patch_max_test_time(limit)",
"title": ""
},
{
"docid": "3cf5b7cc716afd8c4bbca17ff7c3acd0",
"score": "0.57040715",
"text": "def testNetworkLimit(self):\n result = self.RunFlow(\"NetworkLimitFlow\", network_bytes_limit=10000)\n self.assertEqual(result[\"networklimit\"], [10000, 9820, 8820, 8240])",
"title": ""
},
{
"docid": "ae6c67987520abd015cd9de754883cf2",
"score": "0.5701207",
"text": "def read_limits(self, for_who):",
"title": ""
},
{
"docid": "ff22e7cb9c2a3e700ca9155736fb84ab",
"score": "0.5698678",
"text": "def test_throttle(throttle_app: App, test_request):\n\n app = throttle_app\n\n # clear counter from previous test run\n clear_throttle(test_request, \"throttle_sample\")\n\n app.get(\"/\", status=200)\n\n # We exceeded the limit of 1 request per hour\n app.get(\"/\", status=429)\n\n # Let's clear the counter\n clear_throttle(test_request, \"throttle_sample\")\n\n app.get(\"/\", status=200)",
"title": ""
},
{
"docid": "5aaa63580662051245a41e49f13c4230",
"score": "0.56840473",
"text": "def setUp(self):\n self.max_integer = __import__('6-max_integer').max_integer",
"title": ""
},
{
"docid": "2543c7d0beb0ba1c91f9362f2f265831",
"score": "0.56803393",
"text": "def test_p_rate_limit_switch(self):\n return self._rate_limit_switch(apk_type='P')",
"title": ""
},
{
"docid": "e9ed7d357f3e9e2d9f4dc79158c4f26f",
"score": "0.5655471",
"text": "def test_custom_threshold_works(self, mock_req):\n uri = re.compile(\n r\"http://services\\.swpc\\.noaa\\.gov/text/aurora-nowcast-map\\.txt\"\n )\n mock_req.get(uri, text=load_fixture(\"aurora.txt\"))\n\n entities = []\n\n def mock_add_entities(new_entities, update_before_add=False):\n \"\"\"Mock add entities.\"\"\"\n if update_before_add:\n for entity in new_entities:\n entity.update()\n\n for entity in new_entities:\n entities.append(entity)\n\n config = {\"name\": \"Test\", \"forecast_threshold\": 1}\n self.hass.config.longitude = 18.987\n self.hass.config.latitude = 69.648\n\n aurora.setup_platform(self.hass, config, mock_add_entities)\n\n aurora_component = entities[0]\n assert aurora_component.aurora_data.visibility_level == \"16\"\n assert aurora_component.is_on",
"title": ""
},
{
"docid": "85bf876290c6c3c5d0355470cbad4ed4",
"score": "0.5648686",
"text": "def test_below_threshold(self):\n throughput_difference = self.throughput_difference\n\n self.set_limits_for_all_users(self.resource_name, self.resource_limit)\n\n # Check that a throughput below the limit succeeds\n for task in self.tasks:\n task.set_throughput(self.resource_limit * self.units - throughput_difference)\n\n for task in self.tasks:\n self.get_throughput = task.get_throughput() + self.error\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(),\n task.get_throughput(), self.error)))\n\n self.get_stats(self.get_throughput, False)\n\n self.set_throughput_to_zero()",
"title": ""
},
{
"docid": "141469ad46b520837dd553482f37f6fe",
"score": "0.5639672",
"text": "def test_change_default_throttling_settings_ws_with_overwrite_throttled_rate_above_50():",
"title": ""
},
{
"docid": "0469a79b4e91fdf1eb75b14522575b4f",
"score": "0.5631249",
"text": "def test_invocation_unchanged_limits(self):\n self.test_response[\"X-RateLimit-Usage\"] = \"0, 0\"\n self.test_response[\"X-RateLimit-Limit\"] = \"10000, 1000000\"\n rule = SleepingRateLimitRule()\n self.assertEqual(10000, rule.short_limit)\n self.assertEqual(1000000, rule.long_limit)\n rule(self.test_response)\n self.assertEqual(10000, rule.short_limit)\n self.assertEqual(1000000, rule.long_limit)",
"title": ""
},
{
"docid": "2599d758adb11097d473b0bfb0780c11",
"score": "0.56051433",
"text": "def _set_limits(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "a2319fb6aad76ef1c6ddf1cae92cd6c5",
"score": "0.5590528",
"text": "def __init__(self, reason, lim=0):\n self.successes = 0\n self.tests = 0\n self.reason = reason\n self.limit = lim",
"title": ""
},
{
"docid": "c4e9f03b8b2adbadb4b28bae3405579f",
"score": "0.5579144",
"text": "def test_invocation_changed_limits(self):\n self.test_response[\"X-RateLimit-Usage\"] = \"0, 0\"\n self.test_response[\"X-RateLimit-Limit\"] = \"600, 30000\"\n\n # without limit enforcement (default)\n rule = SleepingRateLimitRule()\n rule(self.test_response)\n self.assertEqual(600, rule.short_limit)\n self.assertEqual(30000, rule.long_limit)\n\n # with limit enforcement\n rule = SleepingRateLimitRule(force_limits=True)\n rule(self.test_response)\n self.assertEqual(10000, rule.short_limit)\n self.assertEqual(1000000, rule.long_limit)",
"title": ""
},
{
"docid": "a03eb84647800d9093f40be90d5ba017",
"score": "0.5568088",
"text": "def test_validate_number_limits(self):\n # Reset limits first.\n self.reset_limits()\n\n # Request as often as allowed.\n for i in range(0, self._app.limit_amount):\n self.http_client.fetch(self.get_url('/validate_number/?number=%2B49176123456'), self.stop)\n response = self.wait()\n self.assert_json_response(response, {'status': 'ok'})\n\n # Request again and expect error.\n self.http_client.fetch(self.get_url('/validate_number/?number=%2B49176123456'), self.stop)\n response = self.wait()\n self.assert_json_response(response, {'status': 'error', \"error\": \"limit_acceded\"})",
"title": ""
},
{
"docid": "eca65a20a04453254c85360acb20ea5e",
"score": "0.55434823",
"text": "def test_statistics_challenge(self):\n pass",
"title": ""
},
{
"docid": "df7e313bfb7a80e371ff9385d8417c86",
"score": "0.5538704",
"text": "def test_change_default_throttling_settings_ws_with_overwrite_throttled_rate_above_account_quota():",
"title": ""
},
{
"docid": "d018af22930d29f4c353b8dbfa60cafe",
"score": "0.5533047",
"text": "def test_no_quota(self, mock_google_credential):\n ae_api_client = ae.AppEngineClient(global_configs={})\n self.assertEqual(None, ae_api_client.repository._rate_limiter)",
"title": ""
},
{
"docid": "c788ea84b5ffad0c5ab893d8e475ac34",
"score": "0.5523266",
"text": "def test_below_to_above_threshold(self):\n throughput_difference = self.throughput_difference\n\n self.set_limits_for_all_users(self.resource_name, self.resource_limit)\n\n # Check that a throughput below the limit succeeds\n for task in self.tasks:\n task.set_throughput(self.resource_limit * self.units - throughput_difference)\n\n for task in self.tasks:\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(), task.get_throughput(), self.error)))\n\n self.sleep(5)\n\n # Check that a throughput above the threshold is constrained by the\n # resource limit\n for task in self.tasks:\n task.set_throughput(self.resource_limit * self.units + throughput_difference)\n\n for task in self.tasks:\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(), self.resource_limit * self.units, self.error)))\n\n self.set_throughput_to_zero()",
"title": ""
},
{
"docid": "21efddcaf5513005cc4e51e4780125a8",
"score": "0.552136",
"text": "def test_too_high_limit_value(self):\n self.mockRequest.args['limit'] = ['100']\n d = self.app.paginate_me(self.mockRequest)\n self.assertEqual(self.successResultOf(d), {'limit': 10})",
"title": ""
},
{
"docid": "fb84850547b9daa3559033fa86466ce2",
"score": "0.55199075",
"text": "def test_vehicle_error_quota_limit() -> None:\n response: models.KamereonVehicleDataResponse = fixtures.get_file_content_as_schema(\n f\"{fixtures.KAMEREON_FIXTURE_PATH}/error/quota_limit.json\",\n schemas.KamereonVehicleDataResponseSchema,\n )\n with pytest.raises(exceptions.QuotaLimitException) as excinfo:\n response.raise_for_error_code()\n assert excinfo.value.error_code == \"err.func.wired.overloaded\"\n assert excinfo.value.error_details == \"You have reached your quota limit\"",
"title": ""
},
{
"docid": "2db0e6d6b9538d21be903b3567d6778a",
"score": "0.551946",
"text": "def test_limits_boundary_values(self):\n def check_error_msg(status, output, storagelimit=False):\n import json\n if status == False:\n content = json.loads(output)[\"errors\"]\n if storagelimit:\n actual_error = content[\"dataStorageLimit\"]\n expected_error = '\"dataStorageLimit\" must be an integer between -1 and 100000'\n else:\n actual_error = content[\"dataThrottleLimit\"]\n expected_error = '\"dataThrottleLimit\" must be an integer between -1 and 2147483647'\n self.assertEqual(actual_error, expected_error)\n else:\n self.fail(\"expected to fail but passsed\")\n\n bucket = self.bucket_util.get_all_buckets(self.cluster)[0]\n server = random.choice(bucket.servers)\n bucket_helper = BucketHelper(server)\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\n throttle_limit=-2)\n check_error_msg(status, content)\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\n throttle_limit=2147483648)\n check_error_msg(status, content)\n\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\n storage_limit=-2)\n check_error_msg(status, content, True)\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\n storage_limit=2147483648)\n check_error_msg(status, content, True)\n\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\n throttle_limit=-2,\n storage_limit=-2)\n check_error_msg(status, content)\n check_error_msg(status, content, True)\n status, content = bucket_helper.set_throttle_n_storage_limit(bucket.name,\n throttle_limit=2147483648,\n storage_limit=2147483648)\n check_error_msg(status, content)\n check_error_msg(status, content, True)",
"title": ""
},
{
"docid": "2c8cfd4e6547c5da4018fce338552fc1",
"score": "0.55178887",
"text": "def test_absLimits_get(self):\n # To check if all limits are present in the response (will be checked\n # by schema)\n self.client.show_limits()",
"title": ""
},
{
"docid": "f4559bad2a7fa45c28b461e0872b9faa",
"score": "0.55167836",
"text": "def testModel10(self):\n m = self.createModel ( 10 )\n ulComp = UpperLimitComputer( cl=.95 )\n ulProf = ulComp.getUpperLimitOnMu( m )\n self.assertAlmostEqual( ulProf / 365.6091713369213, 1.0, 2 )\n ## Nick's profiling code gets for n=10 ul=357.568",
"title": ""
},
{
"docid": "605f7410262f6675d7c4426a9b4c6acf",
"score": "0.5482882",
"text": "def test_above_to_below_threshold(self):\n throughput_difference = self.throughput_difference\n\n self.set_limits_for_all_users(self.resource_name, self.resource_limit)\n\n # Check that a throughput above the threshold is constrained by the\n # resource limit\n for task in self.tasks:\n task.set_throughput(self.resource_limit * self.units + throughput_difference)\n\n for task in self.tasks:\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(), self.resource_limit * self.units, self.error)))\n\n self.sleep(5)\n\n # Check that a throughput below the limit succeeds\n for task in self.tasks:\n task.set_throughput(self.resource_limit * self.units - throughput_difference)\n\n for task in self.tasks:\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(), task.get_throughput(), self.error)))\n\n self.set_throughput_to_zero()",
"title": ""
},
{
"docid": "d52e7cbd76427c029fb67e8abebba80c",
"score": "0.5475557",
"text": "def test_investment_limit(self):\n bus1 = solph.Bus(label='Bus1')\n solph.components.GenericStorage(\n label='storage_invest_limit',\n invest_relation_input_capacity=0.2,\n invest_relation_output_capacity=0.2,\n inputs={bus1: solph.Flow()},\n outputs={bus1: solph.Flow()},\n investment=solph.Investment(ep_costs=145))\n solph.Source(label='Source', outputs={bus1: solph.Flow(\n investment=solph.Investment(ep_costs=123))})\n om = self.get_om()\n solph.constraints.investment_limit(om, limit=900)\n\n self.compare_lp_files('investment_limit.lp', my_om=om)",
"title": ""
},
{
"docid": "b7463779b823b34dc5aa1bf07a24eca5",
"score": "0.5472236",
"text": "def __init__(self, \n loading_limit: float = 1.0,\n voltage_limit: dict = {\n 'overvoltage_threshold': 1.05,\n 'undervoltage_threshold': 0.95\n }\n ):\n\n self.loading_limit = data_model.ThermalLoadingLimit(threshold=loading_limit)\n self.voltage_limit = data_model.VoltageViolationLimit(**voltage_limit)\n self.sardi_aggregated = 0\n self.counter = 0",
"title": ""
},
{
"docid": "c4e2b672d4dc7204199b80307baf975e",
"score": "0.5468406",
"text": "def setUp(self):\n self.maxDiff = None\n pass",
"title": ""
},
{
"docid": "7bc25d8909e6aa209e9f2e2d0be7b68e",
"score": "0.54441905",
"text": "def test_warn_count_all_unnecessary(marker_trackerstore: TrackerStore):\n with pytest.warns(UserWarning):\n MarkerTrackerLoader(marker_trackerstore, STRATEGY_ALL, 3)",
"title": ""
},
{
"docid": "14d2bfa5e14c5ed85e25032ec06138e7",
"score": "0.54241455",
"text": "def _monkey_update_limits(self):\n self.monkey_update_limits_counter += 1",
"title": ""
},
{
"docid": "147689e8ba9730d21bd90b07a88450ca",
"score": "0.5423483",
"text": "async def test_warn_count_exceeds_store(marker_trackerstore: TrackerStore):\n loader = MarkerTrackerLoader(marker_trackerstore, STRATEGY_SAMPLE_N, 6)\n with pytest.warns(UserWarning):\n # Need to force the generator to evaluate to produce the warning\n [tracker async for tracker in loader.load()]",
"title": ""
},
{
"docid": "f563c2e499abfb074d540962c3d9af1f",
"score": "0.54190344",
"text": "def test_change_default_throttling_settings_ws_with_overwrite_throttled_burst_above_50():",
"title": ""
},
{
"docid": "861ab1172fa87007229aece7506960ca",
"score": "0.5406477",
"text": "def setUp(self):\n self.maxDiff = None",
"title": ""
},
{
"docid": "861ab1172fa87007229aece7506960ca",
"score": "0.5406477",
"text": "def setUp(self):\n self.maxDiff = None",
"title": ""
},
{
"docid": "effe8908484025bd651d30f0924e1980",
"score": "0.5385588",
"text": "def test_backend_job_limit(self):\n job_limit = self.backend.job_limit()\n self.assertIsNotNone(job_limit)\n self.assertIsNotNone(job_limit.active_jobs)\n if job_limit.maximum_jobs:\n self.assertGreater(job_limit.maximum_jobs, 0)",
"title": ""
},
{
"docid": "e6a5ca508718aa8278ec74bef4ab52dd",
"score": "0.5382612",
"text": "def test_api_loss_livestock_read(self):\n pass",
"title": ""
},
{
"docid": "aedb392579d434e71a5f9e1599a33c1a",
"score": "0.53748673",
"text": "def test_above_threshold(self):\n throughput_difference = self.throughput_difference\n\n self.set_limits_for_all_users(self.resource_name, self.resource_limit)\n\n # Check that a throughput above the threshold is constrained by the\n # resource limit\n for task in self.tasks:\n task.set_throughput(self.resource_limit * self.units + throughput_difference)\n\n for task in self.tasks:\n self.get_throughput = self.resource_limit * self.units\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check(task.get_throughput_success(),\n self.get_throughput, self.error)))\n\n # Once above threshold, ensure the expected error message is thrown\n if self.tasks and self.tasks[0].expected_error():\n self.assertTrue(retry_with_timeout(self.retry_timeout, lambda: self.check_error(self.tasks[0].error(), self.tasks[0].expected_error())))\n\n self.run_compaction()\n\n # set a higher limit to validate stats\n self.set_limits_for_all_users(self.resource_name, self.resource_limit*2)\n\n self.get_stats(self.get_throughput)\n self.set_throughput_to_zero()",
"title": ""
},
{
"docid": "6e9d17c01cda4ebe3809accad1368a5f",
"score": "0.5370684",
"text": "def test_change_default_throttling_settings_ws_with_overwrite_throttled_burst_above_account_quota():",
"title": ""
},
{
"docid": "22680aa0d2c40892f6f36114bc2bf5e0",
"score": "0.53467035",
"text": "def test_change_default_throttling_settings_ws_with_overwrite_throttled():",
"title": ""
},
{
"docid": "1483b065c225ad7c2faed1cc34cd61e5",
"score": "0.5340418",
"text": "def test_api_pings():",
"title": ""
},
{
"docid": "01008ac4145ca5c9d4ec1f51c92bfdf4",
"score": "0.5332597",
"text": "def test_datapoints_agent(self):\n pass",
"title": ""
},
{
"docid": "8e5e5b0d87f58a1a1bfbdbbd9d919cd0",
"score": "0.53280985",
"text": "def test_api_vulnerability_read(self):\n pass",
"title": ""
},
{
"docid": "8f91b4ce1ad365a009b2d61390489c8c",
"score": "0.53207844",
"text": "def test_get_stats_works(fixture):\n\tpass",
"title": ""
},
{
"docid": "8e9dc9174213a944552c00c74e42c711",
"score": "0.53145677",
"text": "def test_post_view_hits_rate_limit_will_return_429(self, throttled_function):\n url = reverse(self.VIEW_NAME)\n\n text = \"I am eating a hamburger\"\n data = {\"text\": text, \"email\": text, \"title\": text}\n\n client = self.registered_user_client\n\n for _ in range(3):\n response = client.post(url, data=data)\n\n # by the 3rd request, it should have hit the rate limit\n self.assertEqual(response.status_code, 429)",
"title": ""
},
{
"docid": "f7ce635d7ca31075dfd703bbb75fad7e",
"score": "0.5311275",
"text": "def test_stats_get(self):\n pass",
"title": ""
},
{
"docid": "9613fa7ebf25c4158b0f0913a2a1fffe",
"score": "0.53091764",
"text": "def test_api_rejects_invalid_limit_parameter(self):\n\n res = self.client().get(\"/bucketlists/v1.0/?limit=dkj\",\n headers={\"Authorization\": self.token}\n )\n self.assertEqual(res.status_code, 400)\n self.assertIn('\"limit\": \"Limit has to be a number between 1 and 100\"', str(res.data))",
"title": ""
},
{
"docid": "c38455204f979970e200d0fbc8af07fa",
"score": "0.5301058",
"text": "def test_setup(self):\n self.assertEqual(8, len(self.raw_metrics_list), \"incorrect number json files read\")",
"title": ""
},
{
"docid": "35f8ec51e964a32376250ad4abfdc991",
"score": "0.53001773",
"text": "def test_api_pollution_read(self):\n pass",
"title": ""
},
{
"docid": "1c00c0289ce5b30ecc63191737bf7a91",
"score": "0.5295825",
"text": "def test_no_quota(self, mock_google_credential):\n iam_api_client = iam.IAMClient(global_configs={})\n self.assertEqual(None, iam_api_client.repository._rate_limiter)",
"title": ""
},
{
"docid": "140403ea97ead15f21e0234c6e3bb831",
"score": "0.5290854",
"text": "def monkey_update_limits(self):\n self._monkey_update_limits_counter += 1",
"title": ""
},
{
"docid": "5210bac5e5864645dce2f6149ab7bd66",
"score": "0.52772236",
"text": "def throttle_failure(self):\n ...",
"title": ""
},
{
"docid": "cc2cb6f3650dbdde8c333a597208dd49",
"score": "0.5274103",
"text": "def test_4(self):\n pass",
"title": ""
},
{
"docid": "311741480132ebdb87164609f0028ed4",
"score": "0.526988",
"text": "def test_get_task_counts_for_project(self):\n pass",
"title": ""
},
{
"docid": "4bfcc5fa516f4fe2340f52fde18e6100",
"score": "0.5268455",
"text": "def test_limit_batches():\n command = [\n \"run.py\",\n \"trainer=default\",\n \"trainer.max_epochs=1\",\n \"trainer.limit_train_batches=0.25\",\n \"trainer.limit_val_batches=0.25\",\n \"trainer.limit_test_batches=0.25\",\n ]\n run_command(command)",
"title": ""
},
{
"docid": "791f0692a94751a7eb9dc52bcefd5e5c",
"score": "0.5268286",
"text": "def limit(self, count):\n ...",
"title": ""
},
{
"docid": "f988e2113eb41b9ef4a78efee021d3e0",
"score": "0.5256502",
"text": "def test_calc_requests_used_most_recent_quarterly_finished_mdm_import_dataset(self):\n pass",
"title": ""
},
{
"docid": "5d7cacf4f06d0c7199faf623f92e22eb",
"score": "0.52531564",
"text": "def test_get_loyalty_statistics(self):\n pass",
"title": ""
},
{
"docid": "90b4c969440d7d9ad5e7d45c2049f85c",
"score": "0.5247421",
"text": "def test_api_vulnerability_list(self):\n pass",
"title": ""
},
{
"docid": "eeadd5017709442f6f24c683f78a6ce2",
"score": "0.5246757",
"text": "def limits(self):\n pass",
"title": ""
},
{
"docid": "c8b4b1cdf058f1a8088794045a3af17d",
"score": "0.5246362",
"text": "def test_defaultmessage_limits(self):\n # Reset limits first.\n self.reset_limits()\n\n # Request as often as allowed.\n for i in range(0, self._app.limit_amount):\n self.http_client.fetch(self.get_url('/message/?receiver=%2B49176123456'), self.stop)\n response = self.wait()\n self.assert_json_response(response, {'status': 'ok'})\n\n # Request again and expect error.\n self.http_client.fetch(self.get_url('/message/?receiver=%2B49176123456'), self.stop)\n response = self.wait()\n self.assert_json_response(response, {'status': 'error', \"error\": \"limit_acceded\"})",
"title": ""
},
{
"docid": "ba552db0941824167440874d2c526ad2",
"score": "0.5243879",
"text": "def test_api_loss_livestock_list(self):\n pass",
"title": ""
},
{
"docid": "ca5463fd9a59ec9898f825406dd45249",
"score": "0.52336663",
"text": "def test_assign_resources_on_sdp_in_low():",
"title": ""
},
{
"docid": "64401e16acebcdf491cc6144a1a120cc",
"score": "0.5232181",
"text": "def test_q_rate_limit_switch(self):\n return self._rate_limit_switch(apk_type='Q')",
"title": ""
},
{
"docid": "5648c8fcec5cc214d4d05a429bde0046",
"score": "0.5230283",
"text": "def test_mock_handle_rate_limiting_429(self, app, worker):\n mock_exception = SpotifyException(http_status=429, code=-1, msg=\"Mock exception\", headers={'Retry-After': 1})\n job = findartist.spotify.handle_rate_limiting(mock_exception)\n\n assert job in app.task_queue.jobs\n worker.work(burst=True)\n assert job not in app.task_queue.jobs\n assert job.is_finished",
"title": ""
},
{
"docid": "af1ca4a11a40dbf7e53ee6a5ca9c4704",
"score": "0.52235043",
"text": "def monkey_update_limits(self, *args):\n self._monkey_update_limits_counter += 1",
"title": ""
},
{
"docid": "65a1187552ae4ec4473c938eb901fbb1",
"score": "0.52184767",
"text": "def test_update_limits(self, monkeypatch):\n # call function to test\n monkeypatch.setattr(DoublyFedInductionMotor, '__init__', self.monkey_init)\n\n test_object = DoublyFedInductionMotor()\n test_object._motor_parameter = self._motor_parameter\n test_object._limits = self._limit_values\n test_object._nominal_values = self._nominal_values\n # verify the expected results\n test_object._update_limits()\n\n assert test_object._limits['u_rbeta'] == 0.5 * self._limit_values['u']\n assert test_object._nominal_values['i_ralpha'] == (test_object._nominal_values.get('i', None) or test_object._nominal_values['u_ralpha'] / test_object._motor_parameter['r_r'])\n\n monkeypatch.setattr(InductionMotor, '_update_limits', self.monkey_super_update_limits)\n test_object._update_limits()\n assert self._monkey_super_update_limits_counter == 1, 'super().update_limits() is not called once'",
"title": ""
},
{
"docid": "6cfebc8db03c686495c135c813f57f06",
"score": "0.52165115",
"text": "def test_set_real_limit(self):\n ## Create a card\n self.app.post('/wallet/cards',\n data={**self.card_arguments,\n 'due_day': 27,\n 'max_limit': 1000.0},\n headers=dict(token=self.token), follow_redirects=True)\n\n # set real limit\n result = self.app.put('/wallet/real_limit/{}'.format(500.0), headers=dict(token=self.token),\n follow_redirects=True)\n self.assertEqual(result.status_code, 200)\n\n # Get wallet information and test free limit\n result = self.app.get('/wallet', headers=dict(token=self.token), follow_redirects=True)\n wallet = json.loads(result.get_data(as_text=True))\n\n self.assertEqual(wallet['real_limit'], 500.0)",
"title": ""
},
{
"docid": "5c001b9151f78ba6fa1c78d66717bde8",
"score": "0.52106315",
"text": "def test_1_0(self):\n pass",
"title": ""
},
{
"docid": "4eca299efee3d1176cf39ea28978c28a",
"score": "0.5209944",
"text": "def test_add_vote(self):\n pass",
"title": ""
},
{
"docid": "fc0a30a1ff8e7d599561ab6219a85a5a",
"score": "0.5197893",
"text": "def metrics_to_use(self):",
"title": ""
},
{
"docid": "e584f5b1e1944ae9128eb9a748fbacfe",
"score": "0.51940686",
"text": "def limit( bw=10, cpu=.1 ):\n intf = custom( TCIntf, bw=bw )\n myTopo = TreeTopo( depth=1, fanout=2 )\n for sched in 'rt', 'cfs':\n info( '*** Testing with', sched, 'bandwidth limiting\\n' )\n if sched == 'rt':\n release = quietRun( 'uname -r' ).strip('\\r\\n')\n output = quietRun( 'grep CONFIG_RT_GROUP_SCHED /boot/config-%s'\n % release )\n if output == '# CONFIG_RT_GROUP_SCHED is not set\\n':\n info( '*** RT Scheduler is not enabled in your kernel. '\n 'Skipping this test\\n' )\n continue\n host = custom( CPULimitedHost, sched=sched, cpu=cpu )\n net = Mininet( topo=myTopo, intf=intf, host=host )\n net.start()\n testLinkLimit( net, bw=bw )\n net.runCpuLimitTest( cpu=cpu )\n net.stop()",
"title": ""
},
{
"docid": "2dc1e789b8060a80568434a0a92ef969",
"score": "0.51926",
"text": "def test_get_target_low(self):\n self.assertEqual(90, int(self.decider.get_target_low()))",
"title": ""
},
{
"docid": "689d8b3e3e9cdd38fcfe0e2e1c61ad6f",
"score": "0.5189289",
"text": "def test_mock_handle_rate_limiting_other(self, app, worker):\n mock_exception = SpotifyException(http_status=500, code=-1, msg=\"Mock exception\")\n job = findartist.spotify.handle_rate_limiting(mock_exception)\n assert job is None",
"title": ""
},
{
"docid": "46fcb98abd2ca1365a843b1d2872353e",
"score": "0.51855177",
"text": "def test_invalid_usage_limits_count(self):\n kwargs = {'usage_limits_count': 'invalid'}\n self.assertRaisesRegex(\n TypeError,\n \"Usage limits count must be an integer.\",\n payloads.CheckRequestPayload,\n **kwargs\n )\n\n payload = payloads.CheckRequestPayload()\n args = (payload, 'usage_limits_count', 'invalid')\n self.assertRaisesRegex(\n TypeError,\n \"Usage limits count must be an integer.\",\n setattr,\n *args\n )",
"title": ""
},
{
"docid": "552576c0a98079e2d0328df7aa439700",
"score": "0.51854384",
"text": "def test_add_loyalty_points(self):\n pass",
"title": ""
},
{
"docid": "90285dc105febdd849e5bb9c230d1dc5",
"score": "0.5181207",
"text": "def test_4_0(self):\n pass",
"title": ""
},
{
"docid": "99da783c6eb090328bfabeb8870bf314",
"score": "0.51730084",
"text": "def setUp(self):\n\n Test.maxDiff = None",
"title": ""
},
{
"docid": "5a92616e769a5d1cb3217b13b20a9983",
"score": "0.5170803",
"text": "def test_non_algorithmic(self):\n user = users.models.User.objects.create_user('test-user')\n user.school_participations.create(\n school=self.prev_school_1, parallel=self.s1_c_prime)\n user.school_participations.create(\n school=self.prev_school_2, parallel=self.s2_p)\n limit = self.limiter.get_limit(user)\n self.assertEqual(limit.min_level, self.c)",
"title": ""
},
{
"docid": "e2de9a73ba436aa8b5014b39fd686afd",
"score": "0.5166248",
"text": "def test_change_default_throttling_settings_ws_with_overwrite_not_throttled():",
"title": ""
},
{
"docid": "25f824ca954e055d2f435e9035469b80",
"score": "0.516279",
"text": "def test_replica_rebuild_per_volume_limit():\n pass",
"title": ""
},
{
"docid": "ed82c281f0922ac9a7ecd142073448e4",
"score": "0.51620966",
"text": "def test_get_account_analytics(self):\n pass",
"title": ""
},
{
"docid": "9c2bcb75ddb3b932aa7bab229253b583",
"score": "0.51556665",
"text": "def test_case_4(self):",
"title": ""
},
{
"docid": "f464cac8f81208d141ebf17c1a20cacd",
"score": "0.51537263",
"text": "def test_functionality(self):\n pass",
"title": ""
},
{
"docid": "46fd953d06a0aa2f1c6ab23ae66d95a8",
"score": "0.515275",
"text": "def test_redundancy_increased_when_not_max(self, mock_client):\n n_answers = 3\n target = 'example.com'\n task = self.ctx.create_task(n_answers, target, max_answers=4)\n for i in range(n_answers):\n TaskRunFactory.create(task=task, info={\n 'reference': i,\n 'control_number': i,\n 'comments': ''\n })\n result = self.result_repo.filter_by(project_id=task.project_id)[0]\n fake_search = MagicMock()\n fake_search.return_value = []\n mock_client.search_annotations = fake_search\n self.z3950_analyst.analyse(result.id)\n assert_equal(mock_client.create_annotation.called, False)\n\n updated_task = self.task_repo.get_task(task.id)\n assert_equal(updated_task.n_answers, n_answers + 1)",
"title": ""
},
{
"docid": "927d00ec51cb64265439588d482b3f5f",
"score": "0.5147975",
"text": "def testCaseTBA(self):",
"title": ""
},
{
"docid": "96093b7512519cf8aa209c17e16fbbd9",
"score": "0.51442605",
"text": "def test_invalid_usage_limits_count(self):\n kwargs = {'usage_limits_count': 'invalid'}\n self.assertRaisesRegex(\n TypeError,\n \"Usage limits count must be an integer.\",\n payloads.CheckResponsePayload,\n **kwargs\n )\n\n payload = payloads.CheckResponsePayload()\n args = (payload, 'usage_limits_count', 'invalid')\n self.assertRaisesRegex(\n TypeError,\n \"Usage limits count must be an integer.\",\n setattr,\n *args\n )",
"title": ""
},
{
"docid": "0605695f28a7671760cd03cd4dd896e0",
"score": "0.5142084",
"text": "def test_redundancy_not_increased_when_max(self, mock_client):\n n_answers = 3\n target = 'example.com'\n task = self.ctx.create_task(n_answers, target, max_answers=3)\n for i in range(n_answers):\n TaskRunFactory.create(task=task, info={\n 'reference': i,\n 'control_number': i,\n 'comments': ''\n })\n result = self.result_repo.filter_by(project_id=task.project_id)[0]\n fake_search = MagicMock()\n fake_search.return_value = []\n mock_client.search_annotations = fake_search\n self.z3950_analyst.analyse(result.id)\n assert_equal(mock_client.create_annotation.called, False)\n\n updated_task = self.task_repo.get_task(task.id)\n assert_equal(updated_task.n_answers, n_answers)",
"title": ""
},
{
"docid": "c61b8cf2b199172f15801994e9c81fea",
"score": "0.513838",
"text": "def test_api_loss_list(self):\n pass",
"title": ""
},
{
"docid": "001da76050180758b4e0921641d6f7fe",
"score": "0.51352376",
"text": "def test_max_local_block_devices_0_force_bfv(self):\n self.flags(max_local_block_devices=0)\n server = self._build_server()\n ex = self.assertRaises(api_client.OpenStackApiException,\n self.admin_api.post_server,\n {'server': server})\n self.assertEqual(400, ex.response.status_code)\n self.assertIn('You specified more local devices than the limit allows',\n str(ex))",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="791"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">ec66effa18f2468179bea5514cc33f27</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Changes each face when a turn is made</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "fbd8f96f5e48ea6fb17378d7c6c7e8a1",
"score": "0.0",
"text": "def turnSide(side, copy1, copy2, copy3, numMoves, isPrime,\n falseSide1, falseSide2,\n falseCopy1, falseCopy2,\n trueSide1, trueSide2, \n trueCopy1, trueCopy2,\n twoSide1, twoSide2, \n twoCopy1, twoCopy2,\n ):\n if numMoves == 1:\n if not isPrime:\n side[falseSide1][falseSide2] = copy1[falseCopy1][falseCopy2]\n elif isPrime:\n side[trueSide1][trueSide2] = copy2[trueCopy1][trueCopy2]\n elif numMoves == 2:\n side[twoSide1][twoSide2] = copy3[twoCopy1][twoCopy2]",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "ffc8669634c0df6b38b564f4c3edc344",
"score": "0.6458799",
"text": "def __init__(self):\n self.face = 0",
"title": ""
},
{
"docid": "0695201c5269bde707b3ec1907317f36",
"score": "0.6264643",
"text": "def changeTurn(self):\n self._players[self._current].deactivateAll()\n self._current += 1\n self._current %= 2\n self._players[self._current].activateAll()",
"title": ""
},
{
"docid": "5ca7ba0f0334c899a03277ab756a5f20",
"score": "0.6256967",
"text": "def faces(self, faces_list):\n self._faces = faces_list.copy()",
"title": ""
},
{
"docid": "d429617ce921bc8119fb2e901c727028",
"score": "0.62325376",
"text": "def faces(self) -> int:\n ...",
"title": ""
},
{
"docid": "1c1c70ce28713225d7cbc55049a7fbc4",
"score": "0.6092525",
"text": "def turn(self):\n pass",
"title": ""
},
{
"docid": "e08a8bc7b41d149c96a19da9d6335b8c",
"score": "0.60666806",
"text": "def face_north():\n while not_facing_north():\n turn_left()",
"title": ""
},
{
"docid": "e08a8bc7b41d149c96a19da9d6335b8c",
"score": "0.60666806",
"text": "def face_north():\n while not_facing_north():\n turn_left()",
"title": ""
},
{
"docid": "f0baaefbddfd7d10faccd30853b301b8",
"score": "0.60647076",
"text": "def setFaceValue(self, faceValue): # Private Method Please\n\t\tself.faceValue = faceValue",
"title": ""
},
{
"docid": "a177cbec4a85d538f345a12d654ba86f",
"score": "0.59847814",
"text": "def cube_perform_action(self, action):\n\n if action == 'left':\n self.turn_left()\n elif action == 'right':\n self.turn_right()\n elif action == 'front':\n self.turn_front()\n elif action == 'back':\n self.turn_back()\n elif action == 'up':\n self.turn_up()\n elif action == 'down':\n self.turn_down()\n self.__faces__ = [self.front(), self.back(), self.left(), self.right(), self.up(), self.down()]",
"title": ""
},
{
"docid": "a061d87f98199f1f9668b8d1a691a827",
"score": "0.5982775",
"text": "def __init__(self, model, size, faces_list):\n self.model = model\n self.screen = pygame.display.set_mode(size)\n self.faceImages = []\n for face in faces_list: #puts images of all available faces in a list\n self.faceImages.append(pygame.image.load('faces/'+face))\n for i in range(len(self.faceImages)): #scales the images to 80 x 80\n self.faceImages[i] = pygame.transform.scale(self.faceImages[i],(80,80))\n self.my_font = pygame.font.SysFont('Comic Sans MS', 32)\n self.text_playing = self.my_font.render('press spacebar to stack heads',\n False, (200,200,200))\n self.text_won = self.my_font.render('You Win! Press Enter to play again'\n , False, (127,255,63))",
"title": ""
},
{
"docid": "bff17381e1e3637051f3ef1d1ebe8330",
"score": "0.5980718",
"text": "def flip_card(self):\n if self.facedown:\n self.image = self.face\n self.facedown = False\n else:\n self.image = self.back\n self.facedown = True",
"title": ""
},
{
"docid": "700dc088225caa6f7c58cafc3c90f8cd",
"score": "0.5977117",
"text": "def _flip_all_card_face_up(self):\n card_update_dict = {}\n for card in self:\n card_update_dict[card.value]='U'\n card.face = 'U'\n new_event = gamestate.Event(\n type='Flip',\n cards_status=card_update_dict,\n )\n self._update_event_handle(new_event)",
"title": ""
},
{
"docid": "24eaf5602f6a342992aedd3c61992df8",
"score": "0.5971587",
"text": "def rotate_face(self, face):\n new_face = [[], [], []]\n for i in reversed(range(self.size)):\n for y in range(self.size):\n new_face[self.size - 1 - i].append(face[i][self.size - 1 - y])\n return new_face",
"title": ""
},
{
"docid": "f51b0f91c874812753159befb0d33d42",
"score": "0.5963223",
"text": "def update_ai_turn(self):\n self.is_ai_turn ^= True",
"title": ""
},
{
"docid": "e8aa1309b0c0dfe363ec073df8c793fa",
"score": "0.59623414",
"text": "def next(self):\n\n for i in range(len(self.face_names)):\n\n if self.face_names[i][0] == \"\":\n\n display_size_image = 600\n display_scale_image = display_size_image / 1000\n\n img_full = cv2.imread(self.face_rcg.get_known_face_files()[self.face_rcg.get_known_face_ids().index(self.face_ids[i][0])]) #, cv2.IMREAD_UNCHANGED)\n scale_percent = display_scale_image*1000/img_full.shape[1] # percent of original size\n width = int(img_full.shape[1] * scale_percent )\n height = int(img_full.shape[0] * scale_percent )\n dim = (width, height)\n # resize image\n img_bgr = cv2.resize(img_full, dim)\n\n (top, right, bottom, left) = self.face_rcg.get_known_face_locations()[self.face_rcg.get_known_face_ids().index(self.face_ids[i][0])]\n\n top = int(display_scale_image*top)\n right = int(display_scale_image*right)\n bottom = int(display_scale_image*bottom)\n left = int(display_scale_image*left)\n\n # Draw a box around the face\n cv2.rectangle(img_bgr, (left, top), (right, bottom), (255, 255, 255), 2)\n\n # Draw a label with a name below the face\n cv2.rectangle(img_bgr, (left, bottom), (right+120, bottom+30), (255, 255, 255), cv2.FILLED)\n font = cv2.FONT_HERSHEY_DUPLEX\n cv2.putText(img_bgr, self.face_ids[i][0], (left + 6, bottom + 25), font, 0.6, (0, 0, 0), 2)\n\n # change color channel\n b,g,r = cv2.split(img_bgr)\n img = cv2.merge((r,g,b))\n\n # Convert the Image object into a TkPhoto object\n im = Image.fromarray(img)\n imgtk = ImageTk.PhotoImage(image=im) \n self.canvas.configure(image=imgtk)\n self.canvas.image = imgtk\n\n\n self.status.set(\"Please type in the name of \" + self.face_ids[i][0] + \" and click 'Save' and 'Next'\")\n break\n\n else:\n\n if self.new_file_list:\n\n self.new_file_list = False\n\n self.face_rcg.encode([file[1] for file in self.file_list])\n\n self.face_clusters = self.face_rcg.cluster_faces_dbscan()\n #self.face_clusters = self.face_rcg.cluster_faces()\n self.face_ids,self.face_names,self.face_cluster_id = self.face_rcg.get_processed_clusters(self.face_clusters)\n\n self.status.set(\"Faces recognized. Click 'Next'\")\n\n \"\"\"else:\n\n self.face_rcg.write_images_with_names(\"/home/manuel/Pictures/test_face_recognition/known/\")\n self.status.set(\"Finished. Type path and click 'Get file list' to start new.\")\"\"\"",
"title": ""
},
{
"docid": "128d7614f3f4e709ea448f91f67b08b3",
"score": "0.59122604",
"text": "def alternateFace(self, alternateFace):\n pass",
"title": ""
},
{
"docid": "128d7614f3f4e709ea448f91f67b08b3",
"score": "0.591115",
"text": "def alternateFace(self, alternateFace):\n pass",
"title": ""
},
{
"docid": "2591ed688bb5b393fd66d89278a57ae7",
"score": "0.5884116",
"text": "def cycle_state_variable(self, step):\n if self.detection_state is DetectionState.FACELETS:\n self.curr_facelet_index = (self.curr_facelet_index + step) % len(self.facelets)\n if self.detection_state is DetectionState.COLORS:\n self.write_data()\n self.curr_face_index = (self.curr_face_index + step) % len(self.faces)",
"title": ""
},
{
"docid": "da933096f83382cb6f8e3a32d9ac169c",
"score": "0.58704185",
"text": "def before_turn(self):",
"title": ""
},
{
"docid": "86474847e2f3e487405a26113e2d7bb8",
"score": "0.58687705",
"text": "def make_turn(self, current_set):\n pass",
"title": ""
},
{
"docid": "603495cb7320afd8f075a41dd5a741b2",
"score": "0.57598776",
"text": "def flip_turn(self):\n self.current_player = self.other_player",
"title": ""
},
{
"docid": "1bba631564d46848b6f1088d1c18c4f5",
"score": "0.57119805",
"text": "def _turn(self):\n\n if self.dyingCounter:\n return\n for i in range(0, len(self.idle_anim)):\n self.idle_anim[i] = pygame.transform.flip(self.idle_anim[i], True, False)\n for i in range(0, len(self.sprint_anim)):\n self.sprint_anim[i] = pygame.transform.flip(\n self.sprint_anim[i], True, False\n )\n for i in range(0, len(self.death_anim)):\n self.death_anim[i] = pygame.transform.flip(self.death_anim[i], True, False)",
"title": ""
},
{
"docid": "6784a6fa82f3e8bc5c6a69e64aaa58aa",
"score": "0.5680338",
"text": "def face_west():\n while not_facing_west():\n turn_left()",
"title": ""
},
{
"docid": "6784a6fa82f3e8bc5c6a69e64aaa58aa",
"score": "0.5680338",
"text": "def face_west():\n while not_facing_west():\n turn_left()",
"title": ""
},
{
"docid": "549706dee04edf2bd2a164a4b62a7607",
"score": "0.5671197",
"text": "def change_turn(self):\n player = self.get_player_turn()\n if player == 1:\n self.set_player_turn(2) # change to player 2's turn\n elif player == 2:\n self.set_player_turn(1) # change to player 1's turn",
"title": ""
},
{
"docid": "aac0ed475a41f2c2daddd82cfcd1d9fe",
"score": "0.5666877",
"text": "def turn_to_face_v(self, direction=Vector3()):\n if direction.is_zero_vector():\n return\n direction = normalize(direction)\n forward = self.get_forward()\n axis = cross(forward, direction)\n angle = math.degrees(math.acos(dot(forward, direction)))\n self.rotate(angle=angle, axis=axis)",
"title": ""
},
{
"docid": "5ea4cf811fc403de72b690b5f67d56ef",
"score": "0.56437415",
"text": "def face_south():\n while not_facing_south():\n turn_left()",
"title": ""
},
{
"docid": "5ea4cf811fc403de72b690b5f67d56ef",
"score": "0.56437415",
"text": "def face_south():\n while not_facing_south():\n turn_left()",
"title": ""
},
{
"docid": "b5e928f657c1906b34842d1250d8fff6",
"score": "0.56321895",
"text": "def circularFace(self, circularFace):\n pass",
"title": ""
},
{
"docid": "189760f95d5ce7e234edde57548b548b",
"score": "0.5585415",
"text": "def face_east():\n while not_facing_east():\n turn_left()",
"title": ""
},
{
"docid": "189760f95d5ce7e234edde57548b548b",
"score": "0.5585415",
"text": "def face_east():\n while not_facing_east():\n turn_left()",
"title": ""
},
{
"docid": "ed4b503b10258e58a5c6803affaa2ed9",
"score": "0.55851406",
"text": "def update_turn(self):\n if self.get_turn() is True:\n self._turn = False\n elif self.get_turn() is False:\n self._turn = True",
"title": ""
},
{
"docid": "bb44be567a173befa4572a4772b065a6",
"score": "0.55826795",
"text": "def changePerspective(self):\n\n # We only need to loop through the 'top' of the board\n for index in range(32):\n # Set our current row and column with respect to index\n row = index//8\n column = index % 8\n # If we are currently on one of our peices\n if self.boardArray[row][column].isupper():\n flipPeice = self.boardArray[row][column].lower() # Set it as an enemy peice\n else:\n flipPeice = self.boardArray[row][column].upper() # Set enemy peice as friendly otherwise\n\n # We compute the same evaluations as above but for the peice at the opposite corner\n # If currently one of our peices\n if self.boardArray[7-row][7-column].isupper():\n self.boardArray[row][column] = self.boardArray[7-row][7-column].lower() # Set current peice to enemy peice\n else:\n self.boardArray[row][column] = self.boardArray[7-row][7-column].upper() # Set enemy peice as friendly otherwise\n\n self.boardArray[7-row][7-column] = flipPeice # Set peice to flipped peice\n\n kingFlipped = self.kingPosition_White # Set temporary kingFlipped variable for white position\n # Set new white and black king positions\n self.kingPosition_White = 63 - self.kingPosition_Black\n self.kingPosition_Black = 63 - kingFlipped",
"title": ""
},
{
"docid": "488f70727fcc85e584cfc0d19d827c55",
"score": "0.5572259",
"text": "def change_game(self,game):\r\n for i in range(0,len(game)):\r\n if(game[i] == UNEXPOSED):\r\n self.replace_character_at_index(i,UNEXPOSED)\r\n elif(game[i] == POKEMON):\r\n self.replace_character_at_index(i,POKEMON)\r\n elif(game[i] == FLAG):\r\n self.replace_character_at_index(i,FLAG)\r\n else:\r\n self.replace_character_at_index(i,game[i])",
"title": ""
},
{
"docid": "b8370e789cd50eff989a7a93fb7d4fe2",
"score": "0.5558109",
"text": "def SetFaceAttribute(*args):\r\n return _pynewton.TreeCollision_SetFaceAttribute(*args)",
"title": ""
},
{
"docid": "b69495d8208a4864bde80fc6e5d84f93",
"score": "0.5540498",
"text": "def on_face_known(self, faces):\n for person in faces:\n if self.is_greeting_appropriate(person.name):\n self.say(\"Hello, {}!\".format(person.name))",
"title": ""
},
{
"docid": "47e9fdbf3407bf11b9cddd6bc48483fa",
"score": "0.5509818",
"text": "def flip_faceverts(faces):\n flipped_faces = []\n for face in faces:\n flipped_face = []\n for vert in reversed(face):\n flipped_face.append(vert)\n flipped_faces.append(tuple(flipped_face))\n return flipped_faces",
"title": ""
},
{
"docid": "7863737e7a3789fdc123f7429e7e3abc",
"score": "0.5505532",
"text": "def turn(self, angle):\r\n self.angle += math.radians(angle)",
"title": ""
},
{
"docid": "170d87b5663fb2ec759bdc462737210a",
"score": "0.55026007",
"text": "def update(self):\n self.game_manager.play_turn()",
"title": ""
},
{
"docid": "6d48510aa9b5615f55195f6c7d5543b6",
"score": "0.54960114",
"text": "def GetFaces(self):\n ...",
"title": ""
},
{
"docid": "7cd6d059d0b5ecc8a587f69906f0edd0",
"score": "0.54950124",
"text": "def do_turn(self, player_index, game_info):",
"title": ""
},
{
"docid": "ff37c865ae0d503c86e439146c2d490a",
"score": "0.549461",
"text": "def poppy_turn(poppy, facee, frame):\r\n # Format of facee: [x, y, w, h]\r\n # print(facee)\r\n w = facee[2]\r\n x = facee[0] + 0.5 * w\r\n\r\n f_dist = find_distance(w)\r\n theta = find_angle(x - x_centre, f_dist)\r\n\r\n # print(\"angle: {0}\".format(theta)) \r\n # print(\"distance: {0}\".format(f_dist))\r\n # print()\r\n cv2.putText(frame, \"angle: \" + str(theta), \r\n (10, 100),\r\n cv2.FONT_HERSHEY_SIMPLEX,\r\n 1, (0, 255, 0), 2)\r\n\r\n if -TOLERANCE < theta < TOLERANCE:\r\n wave()\r\n else:\r\n poppy.head_z.goto_position(theta, wait=True)",
"title": ""
},
{
"docid": "ee602941d266468d60ff79ca0ad7f44d",
"score": "0.5484665",
"text": "def switch_turn(self):\n if self.whose_turn == self.player1:\n self.whose_turn = self.player2\n else:\n self.whose_turn = self.player1",
"title": ""
},
{
"docid": "9fc217b92dd7f13d55b1d8fe06e5bf9f",
"score": "0.54783547",
"text": "def switch_turn(self):\n self.black_turn = not self.black_turn",
"title": ""
},
{
"docid": "6c5dd10bcddb3d490fb9bd7562ec5609",
"score": "0.5469843",
"text": "def find_faces(self):\n if self.is_face:\n self.image = self.get_image()\n self.is_face = False\n self.rotate_right()\n else:\n if self.is_gray:\n self.set_image_colorful()\n self.is_gray = True\n gray = cv2.cvtColor(self.image, cv2.COLOR_BGR2GRAY)\n faces = FACE_CASCADE.detectMultiScale(gray, 1.3, 5)\n for (x, y, w, h) in faces:\n cv2.rectangle(self.image, (x, y), (x+w, y+h), (255, 0, 0), 2)\n self.is_face = True",
"title": ""
},
{
"docid": "10358d736ae3f6c576c6bbcb9c6a8731",
"score": "0.5469517",
"text": "def rot(cube, face, direction):\n\n cube = np.copy(cube)\n if face == Man.U.value:\n cube[0:4] = np.roll(cube[0:4], -direction, axis=0)\n cube[4] = np.rot90(cube[4], k=-direction)\n cube[5] = np.rot90(cube[5], k=direction)\n elif face == Man.L.value:\n sl = [cube[Man.F.value], cube[Man.D.value], cube[Man.B.value, ::-1], cube[Man.U.value]]\n sl = np.roll(sl, direction, axis=0)\n cube[Man.F.value] = sl[0]\n cube[Man.D.value] = sl[1]\n cube[Man.B.value, ::-1] = sl[2]\n cube[Man.U.value] = sl[3]\n cube[Man.L.value] = np.rot90(cube[Man.L.value], k=-direction)\n cube[Man.R.value] = np.rot90(cube[Man.R.value], k=direction)\n elif face == Man.F.value:\n sl = np.array([cube[Man.U.value], cube[Man.R.value], cube[Man.D.value], cube[Man.L.value]])\n sl = np.rot90(sl, k = -direction, axes=(1,2))\n sl = np.roll(sl, direction, axis=0)\n cube[Man.U.value] = sl[0]\n cube[Man.R.value] = sl[1]\n cube[Man.D.value] = sl[2]\n cube[Man.L.value] = sl[3]\n cube[Man.F.value] = np.rot90(cube[Man.F.value], k=-direction)\n cube[Man.B.value] = np.rot90(cube[Man.B.value], k=direction)\n return cube",
"title": ""
},
{
"docid": "7300ea72bb79fa732ac3314284a70b79",
"score": "0.54683673",
"text": "def _update_turn(self) -> None:\n self._player = self._next_player(self._player)\n self._turns += 1",
"title": ""
},
{
"docid": "d90968dcc01c6270f40ffe213d050cb8",
"score": "0.54581696",
"text": "def do_viewfaces(self, arg):\n if len(self.inner) == 0:\n print(\"There are no faces to view. 1. loadchoices default 2. alterproperty 3. loadfaces personal 4. savechoices\")\n if len(self.inner) > con.MAX_PORTRAITS:\n print(\"Over 50 images have been selected. If you would like to view all fifty then adjust def do_viewfaces(self, arg)\")\n print(\"Otherwise, narrow your selection.\")\n return False\n filenames = []\n for face in self.inner:\n filename = face[0][0:-3]\n filenames.append(\"{}png\\n\".format(filename))\n with open(con.SAVE_IMAGE_NAMES, 'w') as f:\n for filename in filenames:\n # print(filename)\n f.write(filename)\n # for face in self.inner:\n # print(\"elem[0]: \", face[0])\n # # ----\n # filename = face[0][0:-3]\n # filename = \"{}png\".format(filename)\n # filepath = os.path.join(IMAGES_DIRECTORY, filename)\n # # print(filepath)\n # print(filepath)\n # img = mpimg.imread(filepath)\n # imgplot = plt.imshow(img)\n # plt.show()",
"title": ""
},
{
"docid": "81af7233c98af91768a4284a206a2ea5",
"score": "0.54511875",
"text": "def turn(self) -> None:\n #current position reset\n self.current = 0\n #increase sequence and play it\n self.addseq()\n self.playseq()",
"title": ""
},
{
"docid": "cdb50a9792a666e9020ee95cb88b7d81",
"score": "0.544412",
"text": "def turn_to_face_p(self, target=Point3()):\n my_pos = self.position()\n if my_pos == target:\n return\n direction = normalize(target - my_pos)\n self.turn_to_face_v(direction)",
"title": ""
},
{
"docid": "7cc4f5944b094acbfaeefcca5129c1f7",
"score": "0.5429818",
"text": "def draw(self):\n # rect_head = (self.head[0] * UNIT_SIZE, self.head[1] * UNIT_SIZE, UNIT_SIZE - 0.5, UNIT_SIZE - 0.5)\n # pygame.draw.rect(screen, GREY + ALPHA, rect_head, 0)\n ''' I want to add two eyes! OR, use FACE and NECK! '''\n ball_face = (int((self.head[0] + 0.5) * UNIT_SIZE), int((self.head[1] + 0.5) * UNIT_SIZE))\n pygame.draw.circle(screen, GREY + ALPHA, ball_face, int(UNIT_SIZE / 2) - 1)\n if self.__direction == 'U':\n rect_neck = (self.head[0] * UNIT_SIZE, int((self.head[1] + 0.5) * UNIT_SIZE),\n UNIT_SIZE - 0.5, UNIT_SIZE / 2 - 0.5)\n # ball_eye1 = (self.head[0] * UNIT_SIZE, self.head[1] * UNIT_SIZE)\n # ball_eye2 = ((self.head[0] + 1) * UNIT_SIZE, self.head[1] * UNIT_SIZE)\n elif self.__direction == 'D':\n rect_neck = (self.head[0] * UNIT_SIZE, self.head[1] * UNIT_SIZE,\n UNIT_SIZE - 0.5, UNIT_SIZE / 2 - 0.5)\n elif self.__direction == 'L':\n rect_neck = (int((self.head[0] + 0.5) * UNIT_SIZE), self.head[1] * UNIT_SIZE,\n UNIT_SIZE / 2 - 0.5, UNIT_SIZE - 0.5)\n else:\n rect_neck = (self.head[0] * UNIT_SIZE, self.head[1] * UNIT_SIZE,\n UNIT_SIZE / 2 - 0.5, UNIT_SIZE - 0.5)\n\n pygame.draw.rect(screen, GREY + ALPHA, rect_neck, 0)\n # pygame.draw.circle(screen, WHITE + ALPHA, ball_eye1, 5)\n # pygame.draw.circle(screen, WHITE + ALPHA, ball_eye2, 5)\n for body_node in self.body:\n rect_node = (body_node[0] * UNIT_SIZE, body_node[1] * UNIT_SIZE, UNIT_SIZE - 0.5, UNIT_SIZE - 0.5)\n pygame.draw.rect(screen, GREY + ALPHA, rect_node, 0)",
"title": ""
},
{
"docid": "e005cb9924b5e7e58763ca9d7dd07829",
"score": "0.54284596",
"text": "def find_faces():\n\n labels = {}\n with open(\"./labels.pickle\", 'rb') as f:\n org_labels = pickle.load(f)\n labels = {v: k for k, v in org_labels.items()}\n\n face_cascade = CascadeClassifier(\n './haarcascade/haarcascade_frontalface_default.xml')\n recognizer = cv2.face.LBPHFaceRecognizer_create()\n recognizer.read(\"./trainer.yml\")\n\n lefteye_cascade = CascadeClassifier('./haarcascade/haarcascade_lefteye.xml')\n video_capture = cv2.VideoCapture(0)\n picture_counter = 0\n\n picture_flag = False\n while True:\n\n # Capture Frame by Frame\n ret, frame = video_capture.read()\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n faces = face_cascade.detectMultiScale(\n gray, scaleFactor=1.05, minNeighbors=8)\n face_dic = {\n \"known\": 0,\n \"total\": 0}\n \n # Draws a rectagle around the face\n for(x, y, w, h) in faces:\n face_dic[\"total\"] += 1\n roi_gray = gray[y:y+h, x:x+w]\n roi_color = frame[y:y+h, x:x+w]\n id_, conf = recognizer.predict(roi_gray)\n eyes = lefteye_cascade.detectMultiScale(roi_gray)\n test = [i for i in eyes]\n if test:\n frame = cv2.rectangle(\n frame, (x, y,), (x+w, y+h), (0, 255, 0), 3)\n\n # conf is the confidence level that detected face has been trained\n if conf >= 85:\n print(id_)\n face_dic[\"known\"] += 1\n name = labels[id_]\n font = cv2. FONT_HERSHEY_SIMPLEX\n color = (255, 255, 255)\n stroke = 2\n cv2.putText(frame, name, (x, y), font, 1,\n color, stroke, cv2.LINE_AA)\n\n for (ex, ey, ew, eh) in eyes:\n cv2.rectangle(roi_color, (ex, ey),\n (ex+ew, ey+eh), (255, 0, 0), 3)\n else:\n for (ex, ey, ew, eh) in eyes:\n cv2.rectangle(roi_color, (ex, ey),\n (ex+ew, ey+eh), (255, 0, 255), 3)\n \n \n # Shows Number faces dectected/known in frame\n cv2.putText(frame, \"Number of faces detected: \" + str(\n face_dic[\"total\"]), (0, 100), cv2.FONT_HERSHEY_TRIPLEX, 0.5, (255, 0, 0), 1)\n cv2.putText(frame, \"Number of faces known: \" + str(\n face_dic[\"known\"]), (0, 120), cv2.FONT_HERSHEY_TRIPLEX, 0.5, (255, 0, 0), 1)\n\n # Dipsplay the resulting frame\n cv2.imshow('Video', frame)\n \n \n #Saves Picture \n if face_dic[\"known\"] == 0 and face_dic[\"total\"] >= 1:\n if not picture_flag:\n picture_counter += 1\n cv2.imwrite(\"./test_subjects/\" +\n str(picture_counter)+\".png\", frame)\n picture_flag = True\n picture_counter += 1\n if picture_counter == 120:\n picture_flag = False\n picture_counter = 0\n\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\n video_capture.release()\n cv2.destroyAllWindows()",
"title": ""
},
{
"docid": "aca6c77e4382d682458f04ff0ba933f6",
"score": "0.5426279",
"text": "def roll(cube, face, direction):\n #print([\"L\", \"F\", \"R\", \"B\", \"U\", \"D\"][face] + str(direction))\n cube = np.copy(cube)\n if face == Man.U.value:\n cube[0:4, 0] = np.roll(cube[0:4, 0], -direction, axis=0)\n cube[Man.U.value] = np.rot90(cube[Man.U.value], k=-direction)\n elif face == Man.D.value:\n cube[0:4, -1] = np.roll(cube[0:4, -1], direction, axis=0)\n cube[Man.D.value] = np.rot90(cube[Man.D.value], k=-direction)\n elif face == Man.F.value:\n sl = np.array([cube[Man.U.value, -1, :], cube[Man.R.value, :, 0],\n cube[Man.D.value, 0, ::-1], cube[Man.L.value, ::-1, -1]])\n sl = np.roll(sl, direction, axis=0)\n cube[Man.U.value, -1, :] = sl[0]\n cube[Man.R.value, :, 0] = sl[1]\n cube[Man.D.value, 0, ::-1] = sl[2]\n cube[Man.L.value, ::-1, -1] = sl[3]\n cube[Man.F.value] = np.rot90(cube[Man.F.value], k=-direction)#TODO: Use map function\n elif face == Man.B.value:\n sl = np.array([cube[Man.U.value, 0, :], cube[Man.R.value, :, -1], cube[Man.D.value, -1, ::-1], cube[Man.L.value, ::-1, 0]])\n sl = np.roll(sl, -direction, axis=0)\n cube[Man.U.value, 0, :] = sl[0]\n cube[Man.R.value, :, -1] = sl[1]\n cube[Man.D.value, -1, ::-1] = sl[2]\n cube[Man.L.value, ::-1, 0] = sl[3]\n cube[Man.B.value] = np.rot90(cube[Man.B.value], k=-direction)#TODO: Use map function\n elif face == Man.L.value:\n sl = np.array([cube[Man.F.value, :, 0], cube[Man.D.value, :, 0], cube[Man.B.value, ::-1, -1], cube[Man.U.value, :, 0]])\n sl = np.roll(sl, direction, axis=0)\n cube[Man.F.value, :, 0] = sl[0]\n cube[Man.D.value, :, 0] = sl[1]\n cube[Man.B.value, ::-1, -1] = sl[2]\n cube[Man.U.value, :, 0] = sl[3]\n cube[Man.L.value] = np.rot90(cube[Man.L.value], k=-direction)\n elif face == Man.R.value:\n sl = np.array([cube[Man.F.value, :, -1], cube[Man.D.value, :, -1], cube[Man.B.value, ::-1, 0], cube[Man.U.value, :, -1]])\n sl = np.roll(sl, -direction, axis=0)\n cube[Man.F.value, :, -1] = sl[0]\n cube[Man.D.value, :, -1] = sl[1]\n cube[Man.B.value, ::-1, 0] = sl[2]\n cube[Man.U.value, :, -1] = sl[3]\n cube[Man.R.value] = np.rot90(cube[Man.R.value], k=-direction)\n return cube\n #print(Man.__str__())",
"title": ""
},
{
"docid": "004a42186846ed8f1490595eec53dab7",
"score": "0.54207855",
"text": "def _run_initial_turn(self) -> None:",
"title": ""
},
{
"docid": "ba9e97f7f2908033657be66d1f35b5b9",
"score": "0.54142267",
"text": "def ChangeTurn(self):\r\n self.lsPlayer[self.turn] = self.ActivePlayer.copy()\r\n self.turn = (self.turn + 1) % self.numPlayers\r\n self.ActivePlayer = self.lsPlayer[self.turn].copy()\r\n if not self.CheckForValidMoves(self.ActivePlayer):\r\n self.ChangeTurn()",
"title": ""
},
{
"docid": "e3e708f7fd07391ba176ad2189cd3335",
"score": "0.5413524",
"text": "def faces(self):\n raise NotImplementedError(f\"faces not implemented for {type(self)}\")",
"title": ""
},
{
"docid": "36d5ea98cfd7541db5061101436cfeb6",
"score": "0.5406411",
"text": "def advance(self):\n self.x_shape_rot += self.x_rot_speed\n self.x_shape_rot %= 360\n self.y_shape_rot += self.y_rot_speed\n self.y_shape_rot %= 360\n self.z_shape_rot += self.z_rot_speed\n self.z_shape_rot %= 360\n self.updateGL()",
"title": ""
},
{
"docid": "fcb9346c79e67186b2b99f01f562d783",
"score": "0.53899795",
"text": "def turnLeft(self):",
"title": ""
},
{
"docid": "ce23e46ad1a8999548893082b51db47f",
"score": "0.5377675",
"text": "def setNext(self,game):\r\n self.nextFixture = game",
"title": ""
},
{
"docid": "85bf3aa342f06d6abe03dfabe04ff9e8",
"score": "0.53709286",
"text": "def faces(self):\r\n\t\tif self._faces is None:\r\n\t\t\tself._faces = [Face(self, i) for i in xrange(len(self.faceVertArray))]\r\n\t\treturn self._faces",
"title": ""
},
{
"docid": "4666aaedfe037607c73a5c0fa530b353",
"score": "0.53502315",
"text": "def targetFaces(self, targetFaces):\n pass",
"title": ""
},
{
"docid": "4666aaedfe037607c73a5c0fa530b353",
"score": "0.5349725",
"text": "def targetFaces(self, targetFaces):\n pass",
"title": ""
},
{
"docid": "9e7321b77232091343736848210ba289",
"score": "0.5335583",
"text": "def save_loop(camera, name: str) -> None:\n faces = []\n process_this = True\n face_to_be_saved = None\n while True:\n ret, frame = camera.read()\n if process_this:\n faces = prc.process_faces(frame)\n process_this = not process_this\n # changing name of each face to empty string\n message = ''\n for face in faces:\n face.assign_name('')\n if len(faces) == 1:\n face_to_be_saved = faces[0]\n elif len(faces) > 1:\n message = 'More than 1 face detected'\n else:\n message = 'No face detected'\n prc.mark_faces(frame, faces)\n cv2.putText(frame, message, (20, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 255), 2)\n cv2.imshow('Video', frame)\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n if cv2.getWindowProperty('Video',cv2.WND_PROP_VISIBLE) < 1:\n break;\n\n if face_to_be_saved is not None:\n save(face_to_be_saved, name)\n else:\n print(\"Couldn't save the face\")",
"title": ""
},
{
"docid": "f83a0ae88bdaf135a50b3fd54acc6388",
"score": "0.53346217",
"text": "def on_face_new(self, faces):\n\n if self.is_greeting_appropriate(\"new\"):\n self.say(\"I see a new person!, Hello stranger!\")",
"title": ""
},
{
"docid": "0a042e9a13a6051ebb7a09d57af4c404",
"score": "0.53295404",
"text": "def face_detect(self):\n self.face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')\n \n self.gray = cv2.cvtColor(self.cv2img, cv2.COLOR_BGR2GRAY)\n self.cv2img = cv2.cvtColor(self.cv2img, cv2.COLOR_BGR2RGB)\n \n # create an array of the positions of each face \n # and then iterates through it to draw a rectangle around all faces\n faces = self.face_cascade.detectMultiScale(self.gray, 1.3, 5)\n for (x, y, w, h) in faces:\n self.cv2img = cv2.rectangle(self.cv2img, (x, y), (x+w, y+h), (255, 0, 0), 2)\n \n self.cv2img = cv2.cvtColor(self.cv2img, cv2.COLOR_BGR2RGB)\n faceimg = Image.fromarray(self.cv2img)\n faceimg = ImageTk.PhotoImage(faceimg)\n self.panel.configure(image = faceimg)\n self.panel.image = faceimg\n \n #makes the button for crop faces visible\n self.btn.pack(side = 'right')\n\n self.states.append(self.cv2img)",
"title": ""
},
{
"docid": "05dddf2a206011046beb22ae40ed5ec9",
"score": "0.5327832",
"text": "def switch_turns(self):\n # TODO: This can be a one-liner, too lazy to do it now. Somehow it's\n # easier to write a comment about something that would take 10 seconds\n # to do than actually doing it\n if self.turn == self.tictactoe.player1:\n self.turn = self.tictactoe.player2\n else:\n self.turn = self.tictactoe.player1",
"title": ""
},
{
"docid": "8388d88bb7a30ab65434d4545028ee86",
"score": "0.53252715",
"text": "def flip(self):\r\n self.buffer,self.backbuffer = self.backbuffer,self.buffer\r\n self.Refresh()",
"title": ""
},
{
"docid": "264d4cbd2ab4610bba0e52cfc6791f00",
"score": "0.53248394",
"text": "def move_forward_or_back(self, face):\n\n while not ((self.get_face_distance(face) < 1.1) or (self.get_face_distance(face) > .9)):\n # checks if face is too small, then moves back\n if self.get_face_distance(face) < .9:\n self.move_bot(300)\n # checks if face is too big, then moves forward\n elif self.get_face_distance(face) > 1.1:\n self.move_bot(-300)\n self.motors = 6000\n self.tango.setTarget(self.MOTORS, self.motors)",
"title": ""
},
{
"docid": "8f1a405824a24625be6ae6172b8f61fd",
"score": "0.5323996",
"text": "def assBody(self):\n \n \n self.surfaceBody = self.spriteList[self.spriteIndex]\n self.surfaceBody.set_colorkey(constants.TRANS)",
"title": ""
},
{
"docid": "4717439d1124655b3c1bcf0d0ccb4854",
"score": "0.532059",
"text": "def update(self):\n self.surface.fill((0, 0, 0))\n # head\n self.surface.blit(pygame.transform.rotate(self.images[\"head\"],\n -90 * self.directions[0]),\n (CASE_SIZE[0] * self.coords[0][0],\n CASE_SIZE[1] * self.coords[0][1]))\n # body\n size_x, size_y = CASE_SIZE\n\n for i in range(len(self.coords[1:-1])):\n x, y = self.coords[i + 1]\n direction = self.directions[i + 1]\n direction_prec = self.directions[i]\n if direction == direction_prec: # straight\n self.surface.blit(self.rotate(\"body_straight\", -90*direction),\n (size_x * x, size_y * y))\n # turn right>top or botom>left\n if (direction == 0 and direction_prec == 1) or \\\n (direction == 3 and direction_prec == 2):\n self.surface.blit(self.rotate(\"body_turn\",0),\n (size_x * x, size_y * y))\n # turn left>top or botom>right\n if (direction == 0 and direction_prec == 3) or \\\n (direction == 1 and direction_prec == 2):\n self.surface.blit(self.rotate(\"body_turn\",-90),\n (size_x * x, size_y * y))\n # turn left>botom or top>right\n if (direction == 2 and direction_prec == 3) or \\\n (direction == 1 and direction_prec == 0):\n self.surface.blit(self.rotate(\"body_turn\",180),\n (size_x * x, size_y * y))\n # turn top>left or right>botom\n if (direction == 3 and direction_prec == 0) or \\\n (direction == 2 and direction_prec == 1):\n self.surface.blit(self.rotate(\"body_turn\",90),\n (size_x * x, size_y * y))\n # Tail\n self.surface.blit(pygame.transform.rotate(self.images[\"tail\"],\n -90 * self.directions[-2]),\n (size_x * self.coords[-1][0],\n size_y * self.coords[-1][1]))",
"title": ""
},
{
"docid": "9e9993859b70cb4a82352b878a93e3a4",
"score": "0.53183514",
"text": "def EditSkin(\n self,\n name: str = \"\",\n faces: tuple[Face] = (),\n edges: tuple[Edge] = (),\n elementFaces: tuple[MeshFace] = (),\n elementEdges: tuple[MeshEdge] = (),\n ):\n pass",
"title": ""
},
{
"docid": "4697beee66114fbfa28fcab3d916a7b4",
"score": "0.5313693",
"text": "def update(self, image):\n\n self._faces = []\n\n if (utils.is_gray(image)):\n image = cv2.equalizeHist(image)\n else:\n image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n image = cv2.equalizeHist(image)\n\n min_size = utils.widthHeightDividedBy(image, 8)\n\n face_rects = self._face_classifier.detectMultiScale(\n image, self.scale_factor, self.min_neighbors, self.flags, min_size)\n\n if (face_rects is not None):\n for face_rect in face_rects:\n face = Face()\n face.face_rect = face_rect\n x, y, w, h = face_rect\n\n # look for an eye in the upper-left part of the face\n search_rect = (x+w//7, y, w*2//7, h//2)\n face.left_eye_rect = self._detect_one_object(\n self._eye_classifier, image, search_rect, 64\n )\n\n # look for an eye in the upper-right part of the face\n search_rect = (x + (w*4)//7, y, w*2//7, h//2)\n face.right_eye_rect = self._detect_one_object(\n self._eye_classifier, image, search_rect, 64\n )\n\n # look for an node in the middle part of the face\n search_rect = (x+w//4, y+h//4, w*2, h//2)\n face.nose_rect = self._detect_one_object(\n self._nose_classifier, image, search_rect, 32\n )\n\n # look for an mouth in the lower-middle part of the face\n search_rect = (x+w//6, y + (h*2)//3, w*2//3, h//3)\n face.mouth_rect = self._detect_one_object(\n self._mouth_classifier, image, search_rect, 16\n )\n\n self._faces.append(face)",
"title": ""
},
{
"docid": "2f9a35c4e36f54cadb68c18c698cde3f",
"score": "0.5311607",
"text": "def faceSet(self):\r\n\t\tret = FaceSet(self, [])\r\n\t\tret.update(range(len(self.faceVertArray)))\r\n\t\treturn ret",
"title": ""
},
{
"docid": "7ee3af718f04065414eca6b36fe668b4",
"score": "0.53067994",
"text": "def face_amount(self, face_amount):\n\n self._face_amount = face_amount",
"title": ""
},
{
"docid": "b21fe36df6c0fae07d86a2b23bd7f9bb",
"score": "0.5304956",
"text": "def add_face_to_list(self, points):\n ids = [point.id for point in points]\n self.faces.append(ids)",
"title": ""
},
{
"docid": "a4bb2a8e7b4881fca6ca45efc0183e63",
"score": "0.53038234",
"text": "def online_face_recognition(profile_names, n_pictures=15):\n images = []\n labels = []\n label_names = []\n for i, name in enumerate(profile_names):\n p = load_profile(name)\n p = p[0:n_pictures, ]\n images += [p]\n labels += [np.ones(p.shape[0]) * (i + 1)]\n label_names += [name]\n faces = np.vstack(images)\n labels = np.hstack(labels).astype(np.int)\n # Generate model\n model = IncrementalKCenters(faces, labels)\n # Start camera\n cam = cv.VideoCapture(0)\n while True:\n ret_val, img = cam.read()\n grey_image = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\n working_image = cv.bilateralFilter(grey_image, 9, 75, 75)\n working_image = cv.equalizeHist(working_image)\n working_image = cv.GaussianBlur(working_image, (5, 5), 0)\n box = face_haar_cascade.detectMultiScale(working_image)\n for b0 in box:\n x, y = b0[0], b0[1]\n x_range, y_range = b0[2], b0[3]\n # look for eye classifier\n local_image = img[y:(y + y_range), x:(x + x_range)]\n eye_box = eye_haar_cascade.detectMultiScale(local_image)\n if len(eye_box) == 0:\n cv.rectangle(img, tuple([b0[0] - 4, b0[1] - 4]), tuple([b0[0] + b0[2] + 4, b0[1] + b0[3] + 4]),\n (0, 0, 255), 2)\n continue\n # select face\n local_image = grey_image[y:(y + y_range), x:(x + x_range)]\n x_t = preprocess_face(local_image)\n\n \"\"\"\n Centroids are updated here\n \"\"\"\n model.online_ssl_update_centroids(x_t)\n p1, p2 = tuple([b0[0] - 4, b0[1] - 4]), tuple([b0[0] + b0[2] + 4, b0[1] + b0[3] + 4])\n\n \n \"\"\"\n HardHFS solution is computed here\n \"\"\"\n f = model.online_ssl_compute_solution()\n lab = np.argsort(f)\n \n \n \"\"\"\n Change False by something else to be able to disregard faces it cannot recognize (question 3.4)\n \"\"\" \n \n mean_dist_new_to_labeled = np.mean(distance.cdist(np.array([model.centroids[model.last_face]]), faces)[0])\n \n mean_dist_existing=0\n for person in set(labels):\n #Average distance between the faces of a same person\n mean_dist_existing += np.mean(distance.cdist(faces[labels==person], faces[labels==person])[0])\n mean_dist_existing=mean_dist_existing/len(list(set(labels))) \n \n if any(f==0.) and mean_dist_new_to_labeled>mean_dist_existing:\n color = (100, 100, 100)\n txt = \"unknown\"\n cv.putText(img, txt, (p1[0], p1[1] - 5), cv.FONT_HERSHEY_COMPLEX_SMALL, 1, color)\n else:\n for i, l in enumerate(lab):\n color = [(0, 255, 0), (255, 0, 0), (0, 0, 255)][l]\n txt = label_names[l] + \" \" + ('%.4f' % np.abs(f[l]))\n cv.putText(img, txt, (p1[0], p1[1] - 5 - 10 * i), cv.FONT_HERSHEY_COMPLEX_SMALL,\n 0.5 + 0.5 * (i == f.shape[0] - 1), color)\n cv.rectangle(img, p1, p2, color, 2)\n cv.imshow(\"cam\", img)\n key = cv.waitKey(1)\n if key in [27, 101]:\n break\n if key == ord('s'):\n # Save face\n print('saved')\n cv.imwrite(\"frame.png\", img)\n ## cv.waitKey(1)\n cv.destroyAllWindows()",
"title": ""
},
{
"docid": "5a8f35e3b27d9bfb457cba006ae270ae",
"score": "0.5301556",
"text": "def turn():\n global o_both_steering\n\n o_both_steering.on_for_rotations(100, SpeedPercent(40), 1) # OPTIMAL VALUE 1",
"title": ""
},
{
"docid": "391dc969f17266ea0bca1dc38375864f",
"score": "0.5300656",
"text": "def generator(surface, angles):\n original_surf = surface.copy()\n for angle in angles:\n x, y = self.rect.center\n self.surface = pygame.transform.rotate(original_surf, angle)\n self.instant_move(x, y)\n yield True",
"title": ""
},
{
"docid": "f6eceb20ea200d1fa8569da8eae1e7bc",
"score": "0.5300277",
"text": "def updateCameraVectors(self):\n # Calculate the new Front vector\n x = np.cos(np.radians(self.Yaw)) * np.cos(np.radians(self.Pitch))\n y = np.sin(np.radians(self.Pitch))\n z = np.sin(np.radians(self.Yaw)) * np.cos(np.radians(self.Pitch))\n \n front = np.array([x, y, z], dtype=GLfloat)\n print(front)\n self.Front = front / np.sqrt(x**2 + y**2 + z**2)\n print(self.Front)\n \n temp = np.cross(self.Front, self.WorldUp)\n self.Right = temp / np.sqrt(temp[0]**2 + temp[1]**2 + temp[2]**2)\n print(self.Right)\n \n temp = np.cross(self.Right, self.Front)\n self.Up = temp / np.sqrt(temp[0]**2 + temp[1]**2 + temp[2]**2)\n print(self.Up)\n \n \n \n \n # front = glm.vec3(x, y, z)\n # print('*=========')\n # self.Front = np.array(glm.normalize(front), dtype=GLfloat)\n # print(self.Front)\n # print('#=========')\n #\n # # Also re-calculate the Right and Up vector\n # # Normalize the vectors, because their length gets closer to 0 the more you\n # # look up or down which results in slower movement.\n # print('**=========')\n # temp = np.cross(self.Front, self.WorldUp)\n # print(temp)\n # print('##=========')\n # self.Right = np.array(glm.normalize(glm.vec3(temp)), dtype=GLfloat)\n #\n # print('***=========')\n # temp = np.cross(self.Right, self.Front)\n # print(temp)\n # self.Up = np.array(glm.normalize(glm.vec3(temp)), dtype=GLfloat)\n # print('###=========')",
"title": ""
},
{
"docid": "8904960cd5a8b6234de2d8d57a573dcd",
"score": "0.5300243",
"text": "def inputFaces(self, inputFaces):\n pass",
"title": ""
},
{
"docid": "8904960cd5a8b6234de2d8d57a573dcd",
"score": "0.5300041",
"text": "def inputFaces(self, inputFaces):\n pass",
"title": ""
},
{
"docid": "8904960cd5a8b6234de2d8d57a573dcd",
"score": "0.5300041",
"text": "def inputFaces(self, inputFaces):\n pass",
"title": ""
},
{
"docid": "8904960cd5a8b6234de2d8d57a573dcd",
"score": "0.5300041",
"text": "def inputFaces(self, inputFaces):\n pass",
"title": ""
},
{
"docid": "ad3aa8cbac3e97b9e7256bd79a6f9a6c",
"score": "0.5299885",
"text": "def next_turn(self):\n temp = self.current_player\n self.current_player = self.opponent\n self.opponent = temp",
"title": ""
},
{
"docid": "57f4d58e0f16c22078ce9dd38381e616",
"score": "0.52914584",
"text": "def test_batch_redetect_with_one_face(self):\n for detector in self.detectors:\n with self.subTest(detectorType=detector.detectorType):\n detection = detector.detectOne(image=VLIMAGE_ONE_FACE)\n redetect = detector.redetect(\n images=[ImageForRedetection(image=VLIMAGE_ONE_FACE, bBoxes=[detection.boundingBox.rect])]\n )[0]\n self.assertFaceDetection(redetect, VLIMAGE_ONE_FACE)",
"title": ""
},
{
"docid": "37798494830af70cc4a3e549576796e7",
"score": "0.52862424",
"text": "def build_face():\n masterScale = 0.2\n create_hooks(masterScale)\n inputHooks = create_joint_drivers(masterScale)\n\n for poseInfo in faceposes:\n zone = poseInfo['zone']\n pose = poseInfo['pose']\n poseDriver = poseInfo['driver']\n poseRange = poseInfo['mapping']\n poseOverrides = poseInfo['overrides']\n poseOverrideMaps = poseInfo['overridemaps']\n\n create_pose_node(pose, zone, inputHooks, masterScale, poseDriver, poseRange, poseOverrides, poseOverrideMaps)\n print 'done'",
"title": ""
},
{
"docid": "7596177b3c91fc479cf4def251516cac",
"score": "0.5286083",
"text": "def UpdateRectColors(self, selectedRectId):\n\n for (rectId, rect) in enumerate(self.rects):\n\n (x, y, w, h) = rect\n color = (0,255,0) if rectId == selectedRectId-1 else (0,0,255) #selection is 1 based\n\n cv2.rectangle(self.img500, (x, y), ((x + w), (y + h)), color, 2)\n\n # show the face number\n cv2.putText(self.img500,\n \"Face #{}\".format(rectId + 1),\n (x - 10, y - 10),\n cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)\n\n cv2.imshow(self.faceSelectionWin, self.img500)",
"title": ""
},
{
"docid": "07ac0645a023800fc4dcf11143ec82b7",
"score": "0.5282528",
"text": "def setup_face_indices(self, reset=True):\n if reset or not self.fis:\n fa = self.domain.get_facets(force_faces=True)[1]\n\n if self.faces:\n faces = self.faces\n else:\n faces = self.edges\n\n self.fis = {}\n for ig in self.igs:\n rfaces = faces[ig]\n fi = fa.indices[rfaces]\n assert_(nm.all(fi[:,0] == ig))\n self.fis[ig] = fi[:,1:].copy()",
"title": ""
},
{
"docid": "8e58cb333ee43454817f059a30915849",
"score": "0.5273543",
"text": "def another_turn(self, pit: PitData) -> None:\n self.update_color(pit, 0)",
"title": ""
},
{
"docid": "4fc7f93fc6c3dd1ed8fcd5dd822f522a",
"score": "0.52639437",
"text": "def on_draw(self):\n arcade.start_render()",
"title": ""
},
{
"docid": "4b0b0f3d2a4cf4ba6735ee43f6bad2a4",
"score": "0.5263633",
"text": "def update_turn(self):\n self.p1.turn = not self.p1.turn\n self.p2.turn = not self.p2.turn\n self.current_player = self.p1 or self.p2",
"title": ""
},
{
"docid": "65698da0371ffe24258d721bc412f014",
"score": "0.52610844",
"text": "def updateCams(self, interface=\"match\"):\n activecams = getActiveCams(len(self.cameras[interface]))\n for activeind in activecams:\n camera = self.cameras[interface][activeind]\n if camera in self.gridded:\n camera.updateImgOnLabel()",
"title": ""
},
{
"docid": "09cf5d165983357f2dcc6a505dae41e3",
"score": "0.5259303",
"text": "def RubiksCube():\n solved = False\n while (input(\"Start game?: \") == \"yes\") and (not solved):\n c = Cube()\n c.shuffle()\n plotCube(c)\n plt.ion()\n plt.show()\n plt.pause(1)\n # pause needed to update matplotlib plots with keyboard inputs\n print(\"The possible faces to rotate are: U,D,L,R,F,B.\\n The possible directions to rotate are: clockwise, counterclockwise.\\n The possible numbers of rotations are: 1,2,3.\\n\")\n while not solved:\n interact(c)\n plt.close()\n plotCube(c)\n plt.show()\n plt.pause(1)\n if c._p == Cube()._p:\n solved = True\n if solved:\n print(\"You win!\")",
"title": ""
},
{
"docid": "0a4f80d41e6e547f5490b5de12845018",
"score": "0.52588135",
"text": "def get_any_face(self):\n return choice(list(self.faces()))",
"title": ""
},
{
"docid": "f2d84dce15832a9e4b877937b180ce8e",
"score": "0.5258472",
"text": "def Turn(self, amount):\n \n self.Angle -= amount\n self.Angle %= 360\n\n self.Image = pygame.transform.rotate(self.Orig, self.Angle)\n self.UpdateMask()\n \n newSize = self.Image.get_size()\n self.ModX = -(newSize[0]-self.Size[0])/2\n self.ModY = -(newSize[1]-self.Size[1])/2",
"title": ""
},
{
"docid": "ee707779d583c98c6499bd7652922ed3",
"score": "0.524997",
"text": "def toggle_turn(self):\n if self._turn == \"player1\":\n self._turn = \"player2\"\n else:\n self._turn = \"player1\"",
"title": ""
},
{
"docid": "c18c82af0b0743c846de867ee1593851",
"score": "0.52392316",
"text": "def update_surface(self):\n self.surface = pygame.surfarray.make_surface(self.arr)",
"title": ""
},
{
"docid": "2a23ba794220991e6386c2a7006d82da",
"score": "0.5238691",
"text": "def getSurface(self):\n self.frame += 1\n if self.frame == 3:\n self.isFirstPic = not self.isFirstPic\n self.frame = 0\n\n if self.direction == 0:\n self.surface = Pacman.images[self.isFirstPic]\n elif self.direction == 1:\n self.surface = pygame.transform.rotate(Pacman.images[self.isFirstPic], 90)\n elif self.direction == 2:\n self.surface = pygame.transform.rotate(Pacman.images[self.isFirstPic], 180)\n elif self.direction == 3:\n self.surface = pygame.transform.rotate(Pacman.images[self.isFirstPic], 270)",
"title": ""
},
{
"docid": "9a00db76457ab7532cc3af1fdac12f64",
"score": "0.5236641",
"text": "def turn_inc(self) -> None:\n self.__current_turn += 1",
"title": ""
},
{
"docid": "b7e1b59538be1a46c3c180e932f6b9ca",
"score": "0.52359444",
"text": "def draw_changing(self):",
"title": ""
},
{
"docid": "20f025928f844ba5664d57864501e178",
"score": "0.5231487",
"text": "def turn(self):\n\n #print(self.coords[0] ,self.dx,self.dy, self.field.width,self.field.height)\n mis=list(self.field.mice.keys())\n if len(mis)>0:\n x=self.coords[0][0]\n y=self.coords[0][1]\n mx=1000\n my=1000\n for (tx,ty) in mis:\n if abs(x-tx)+abs(y-ty)<abs(x-mx)+abs(y-my):\n mx=tx\n my=ty\n\n #print(mx,my)\n if self.coords[0][0] < mx:\n if self.dx!=1:\n if random.randint(0,10) < 5:\n if random.randint(0,1) == 1:\n self.turn_left()\n else:\n self.turn_right()\n \n\n elif self.coords[0][0] > mx:\n if self.dx!=-1:\n if random.randint(0,10) < 5:\n if random.randint(0,1) == 1:\n self.turn_left()\n else:\n self.turn_right()\n else:\n if self.coords[0][1] < my:\n if self.dy!=1:\n if random.randint(0,10) < 5:\n if random.randint(0,1) == 1:\n self.turn_left()\n else:\n self.turn_right()\n elif self.coords[0][1] > my:\n if self.dy!=-1:\n if random.randint(0,10) < 5:\n if random.randint(0,1) == 1:\n self.turn_left()\n else:\n self.turn_right()",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="792"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">9003693612e2496d1b8ec20c13e967fc</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Takes a project UUID and either creates a new document in the desprojects index or updates the document if one already exists for that project.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "809657dc207ab465236a7ea3711a9617",
"score": "0.74024814",
"text": "def index_or_update_project(self, uuid):\n from designsafe.apps.api.projects.models import Project\n\n client = get_service_account_client()\n project_model = Project(client)\n project = project_model.search({'uuid': uuid}, client)[0]\n project_meta = project.to_dict()\n\n to_index = {key: value for key, value in project_meta.iteritems() if key != '_links'}\n to_index['value'] = {key: value for key, value in project_meta['value'].iteritems() if key != 'teamMember'}\n\n project_search = IndexedProject.search().filter(\n Q({'term': \n {'uuid._exact': uuid}\n })\n )\n res = project_search.execute()\n\n if res.hits.total == 0:\n # Create an ES record for the new metadata.\n # project_info_args = {key:value for key,value in project_info.iteritems() if key != '_links'}\n project_ES = IndexedProject(**to_index)\n project_ES.save()\n elif res.hits.total == 1:\n # Update the record.\n doc = res[0]\n doc.update(**to_index)\n else:\n # If we're here we've somehow indexed the same project multiple times. \n # Delete all records and replace with the metadata passed to the task.\n for doc in res:\n doc.delete()\n project_ES = IndexedProject(**to_index) \n project_ES.save()",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "3eb70541f86495a1a812503637a60949",
"score": "0.58575755",
"text": "def reindex_projects(self):\n client = get_service_account_client()\n query = {'name': 'designsafe.project'}\n\n in_loop = True\n offset = 0\n while in_loop:\n listing = client.meta.listMetadata(q=json.dumps(query), offset=offset, limit=100)\n offset += 100\n\n if len(listing) == 0:\n in_loop = False\n else:\n for project in listing:\n index_or_update_project.apply_async(args=[project.uuid], queue='api')",
"title": ""
},
{
"docid": "25f60d64bc8a92d3f3948d857b5134ec",
"score": "0.57789236",
"text": "def create(self, document):\n self.index(document)",
"title": ""
},
{
"docid": "1e467fdbb386338ac8ce8d4cd52f433a",
"score": "0.57180494",
"text": "def create_or_update_doc(self, index, doc):\n body = self.bulk_body[index]\n body.write(json.dumps({'index': {'_id': doc['id']}}))\n body.write('\\n')\n body.write(json.dumps(doc))\n body.write('\\n')",
"title": ""
},
{
"docid": "56f028452184cb6eea1ae6844841d69e",
"score": "0.5655644",
"text": "def update_document(self, **fields):\r\n \r\n # Check which of the supplied fields are unique\r\n unique_fields = [name for name, field\r\n in self.index.schema.fields()\r\n if name in fields and field.unique]\r\n if not unique_fields:\r\n raise IndexingError(\"None of the fields in %r are unique\" % fields.keys())\r\n \r\n # Delete documents in which the supplied unique fields match\r\n s = self.searcher()\r\n for name in unique_fields:\r\n self.delete_by_term(name, fields[name])\r\n \r\n # Add the given fields\r\n self.add_document(**fields)",
"title": ""
},
{
"docid": "8b403dda7150c949fc6ce049474e9990",
"score": "0.56132174",
"text": "def create_document(doc_id, data):\n conn = get_conn()\n for alias in get_active_aliases(conn, object_types=[data[\"object_type\"]]):\n conn.create(index=alias, doc_type=GLOBAL_DOC_TYPE, body=data, id=doc_id)",
"title": ""
},
{
"docid": "a5241a27b6867e7f2755bbd925f12a74",
"score": "0.5546349",
"text": "def update_doc(c, i, d, u=False):\n try:\n c.update_one({'_id': i}, {'$set': d}, upsert = u)\n return True\n except:\n return False",
"title": ""
},
{
"docid": "4b4b83ea1b879b88eda2c72b53e3c52c",
"score": "0.5532153",
"text": "def post_project_document(project_id,\n document_type,\n document_name,\n document_content_type,\n document_content,\n document_preamble,\n document_legal_entity_name,\n new_major_version=None,\n username=None):\n project = get_project_instance()\n try:\n project.load(str(project_id))\n except DoesNotExist as err:\n return {'errors': {'project_id': str(err)}}\n project_acl_verify(username, project)\n document = get_document_instance()\n document.set_document_name(document_name)\n document.set_document_content_type(document_content_type)\n document.set_document_content(document_content)\n document.set_document_preamble(document_preamble)\n document.set_document_legal_entity_name(document_legal_entity_name)\n if document_type == 'individual':\n major, minor = cla.utils.get_last_version(project.get_project_individual_documents())\n if new_major_version:\n document.set_document_major_version(major + 1)\n document.set_document_minor_version(0)\n else:\n if major == 0:\n major = 1\n document.set_document_major_version(major)\n document.set_document_minor_version(minor + 1)\n project.add_project_individual_document(document)\n else:\n major, minor = cla.utils.get_last_version(project.get_project_corporate_documents())\n if new_major_version:\n document.set_document_major_version(major + 1)\n document.set_document_minor_version(0)\n else:\n if major == 0:\n major = 1\n document.set_document_major_version(major)\n document.set_document_minor_version(minor + 1)\n project.add_project_corporate_document(document)\n project.save()\n return project.to_dict()",
"title": ""
},
{
"docid": "f019a8aa87ac1017e067247c87599fa5",
"score": "0.5523262",
"text": "def process_project(project_data: dict):\n project_id = project_data.get('projectid')\n\n if project_id is not None:\n Project.objects.update_or_create(\n ona_pk=project_id,\n defaults={\n 'name': project_data.get('name'),\n 'deleted_at': project_data.get('deleted_at'),\n 'last_updated': project_data.get('date_modified'),\n 'json': project_data,\n })",
"title": ""
},
{
"docid": "a74cc25a8d7944eb373349e99b2e6179",
"score": "0.5489646",
"text": "def update_project(uuid, **kwargs):\n project = Project.query.get(uuid)\n\n if project is None:\n raise NotFound(\"The specified project does not exist\")\n\n if \"name\" in kwargs:\n name = kwargs[\"name\"]\n if name != project.name:\n check_project_name = db_session.query(Project).filter_by(name=name).first()\n if check_project_name:\n raise BadRequest(\"a project with that name already exists\")\n\n data = {\"updated_at\": datetime.utcnow()}\n data.update(kwargs)\n\n try:\n db_session.query(Project).filter_by(uuid=uuid).update(data)\n db_session.commit()\n except (InvalidRequestError, ProgrammingError) as e:\n raise BadRequest(str(e))\n\n return project.as_dict()",
"title": ""
},
{
"docid": "968af9fd450edbe0146f7b5d25cc0cd7",
"score": "0.5369272",
"text": "def post_project_document_template(project_id,\n document_type,\n document_name,\n document_preamble,\n document_legal_entity_name,\n template_name,\n new_major_version=None,\n username=None):\n project = get_project_instance()\n try:\n project.load(str(project_id))\n except DoesNotExist as err:\n return {'errors': {'project_id': str(err)}}\n project_acl_verify(username, project)\n document = get_document_instance()\n document.set_document_name(document_name)\n document.set_document_preamble(document_preamble)\n document.set_document_legal_entity_name(document_legal_entity_name)\n if document_type == 'individual':\n major, minor = cla.utils.get_last_version(project.get_project_individual_documents())\n if new_major_version:\n document.set_document_major_version(major + 1)\n document.set_document_minor_version(0)\n else:\n document.set_document_minor_version(minor + 1)\n project.add_project_individual_document(document)\n else:\n major, minor = cla.utils.get_last_version(project.get_project_corporate_documents())\n if new_major_version:\n document.set_document_major_version(major + 1)\n document.set_document_minor_version(0)\n else:\n document.set_document_minor_version(minor + 1)\n project.add_project_corporate_document(document)\n # Need to take the template, inject the preamble and legal entity name, and add the tabs.\n tmplt = getattr(cla.resources.contract_templates, template_name)\n template = tmplt(document_type=document_type.capitalize(),\n major_version=document.get_document_major_version(),\n minor_version=document.get_document_minor_version())\n content = template.get_html_contract(document_legal_entity_name, document_preamble)\n pdf_generator = get_pdf_service()\n pdf_content = pdf_generator.generate(content)\n document.set_document_content_type('storage+pdf')\n document.set_document_content(pdf_content, b64_encoded=False)\n document.set_raw_document_tabs(template.get_tabs())\n project.save()\n return project.to_dict()",
"title": ""
},
{
"docid": "18db39f126f585a872029737f5e29364",
"score": "0.52629536",
"text": "def create_document(self, entity, wait=False):\n docs = self.build_document(entity, 'single')\n for doc in docs:\n logger.info(f\"Making Doc={doc}\")\n res = self.es.index(index=self.index_name(entity.project.pk),\n id=doc['_id'],\n refresh=wait,\n routing=1,\n body={**doc['_source']})",
"title": ""
},
{
"docid": "397a433b2bb4643fa322625c06217544",
"score": "0.5236747",
"text": "def setup():\n index_template = Doc._index.as_template(\"base\")\n index_template.save()",
"title": ""
},
{
"docid": "cd1f125fa2856f065487eb0b6eafe3e6",
"score": "0.5214013",
"text": "def update_project(self, project_name, from_path):\n session = self.session\n\n # check if project exists.\n query_result = session.query(Project).filter_by(\n name=project_name).first()\n if not query_result:\n logging.info(f\"{project_name} not found, skipping.\")\n return\n\n if query_result.fully_indexed and query_result.external:\n logging.debug(\n f\"{project_name} already fully indexed, skipping.\")\n return\n\n if query_result.external and from_path:\n module_name = Path(from_path).name\n if query_result.is_module_in_project(module_name):\n logging.info(f\"{project_name} is already enough indexed, skipping.\")\n return\n\n logging.info(f\"Indexing {project_name} from {from_path}.\")\n query_result.index(from_path)\n logging.info(f\"Building {project_name}.\")\n query_result.build()\n\n session.commit()\n # session.close()",
"title": ""
},
{
"docid": "f69d0545c80cf079a73906a79de1b5e3",
"score": "0.52051336",
"text": "def create_index(index=None):\n index = index or INDEX\n document_types = ['project', 'component', 'registration', 'user', 'file', 'institution', 'preprint', 'collectionSubmission']\n guid_metadata_types = ['project', 'component', 'registration', 'preprint', 'file']\n\n client().indices.create(index, ignore=[400]) # HTTP 400 if index already exists\n for type_ in document_types:\n if type_ == 'collectionSubmission':\n mapping = {\n 'properties': {\n 'collectedType': NOT_ANALYZED_PROPERTY,\n 'subjects': NOT_ANALYZED_PROPERTY,\n 'status': NOT_ANALYZED_PROPERTY,\n 'issue': NOT_ANALYZED_PROPERTY,\n 'volume': NOT_ANALYZED_PROPERTY,\n 'programArea': NOT_ANALYZED_PROPERTY,\n 'provider': NOT_ANALYZED_PROPERTY,\n 'title': ENGLISH_ANALYZER_PROPERTY,\n 'abstract': ENGLISH_ANALYZER_PROPERTY,\n 'schoolType': NOT_ANALYZED_PROPERTY,\n 'studyDesign': NOT_ANALYZED_PROPERTY,\n }\n }\n else:\n mapping = {\n 'properties': {\n 'tags': NOT_ANALYZED_PROPERTY,\n 'license': {\n 'properties': {\n 'id': NOT_ANALYZED_PROPERTY,\n 'name': NOT_ANALYZED_PROPERTY,\n # Elasticsearch automatically infers mappings from content-type. `year` needs to\n # be explicitly mapped as a string to allow date ranges, which break on the inferred type\n 'year': {'type': 'string'},\n }\n },\n\n }\n }\n if type_ in guid_metadata_types:\n mapping['properties'].update({\n 'title': ENGLISH_ANALYZER_PROPERTY,\n 'description': ENGLISH_ANALYZER_PROPERTY,\n 'language': NOT_ANALYZED_PROPERTY,\n 'resource_type_general': NOT_ANALYZED_PROPERTY,\n 'funder_name': {'type': 'string'},\n 'funder_identifier': NOT_ANALYZED_PROPERTY,\n 'award_number': NOT_ANALYZED_PROPERTY,\n 'award_uri': NOT_ANALYZED_PROPERTY,\n 'award_title': {'type': 'string'},\n })\n\n if type_ == 'user':\n fields = {\n 'job': {\n 'type': 'string',\n 'boost': '1',\n },\n 'all_jobs': {\n 'type': 'string',\n 'boost': '0.01',\n },\n 'school': {\n 'type': 'string',\n 'boost': '1',\n },\n 'all_schools': {\n 'type': 'string',\n 'boost': '0.01'\n },\n }\n mapping['properties'].update(fields)\n client().indices.put_mapping(index=index, doc_type=type_, body=mapping, ignore=[400, 404])",
"title": ""
},
{
"docid": "8f0e31de144dba037550aa04b481e8d6",
"score": "0.5165092",
"text": "def update_index():\n # TODO: check that it's been more than a day\n deb.update_index()",
"title": ""
},
{
"docid": "105792b5a3b9e9dbd05f13689425dbf9",
"score": "0.5146721",
"text": "def update_projects():",
"title": ""
},
{
"docid": "105792b5a3b9e9dbd05f13689425dbf9",
"score": "0.5146721",
"text": "def update_projects():",
"title": ""
},
{
"docid": "68dbdd7332da5f3c6e2202cf083bbe01",
"score": "0.51065445",
"text": "def update(self, document):\n pass",
"title": ""
},
{
"docid": "dfb05f5d631251af0f718137c6fa6bec",
"score": "0.5101003",
"text": "async def replace_project(project_id: ProjectID, _replace: ProjectReplace):",
"title": ""
},
{
"docid": "05a62e64c02e498d0d8bdea8b6527342",
"score": "0.5084844",
"text": "def reindex_document(document):\n\n try:\n record = document._record or DocumentRecord.objects.get(pk=document.id)\n except DocumentRecord.DoesNotExist:\n return\n\n qs = TokenFieldIndex.objects.filter(\n record_id=document.id,\n revision=document.revision\n )\n defer_iteration_with_finalize(\n qs, _destroy_record, _finalize, _shards=1, _queue=_SEARCH_QUEUE\n )\n\n # Generate a brand new revision ID for this document\n record.revision = uuid.uuid4()\n record.save()\n\n index_document(document.index_name, document)",
"title": ""
},
{
"docid": "2bf09988a232c0f4a78f1201328c3b11",
"score": "0.50620395",
"text": "def test_get_project_by_id(app):\n\n # insert a projects into the database",
"title": ""
},
{
"docid": "33d3852abae5ff5e392637b5bcc96cc4",
"score": "0.5043024",
"text": "def addDocumentToIndex(self, name, vrr, nodeIdNum, title, content, tags):\n raise NotImplementedError",
"title": ""
},
{
"docid": "e705ba684fa2a02064f83326f33c8ec5",
"score": "0.5042402",
"text": "def projects_delete(self, perun_id):\n self.projects_update(perun_id, scratched=True)",
"title": ""
},
{
"docid": "3ba7858e4f524c1eb763fba5c6cd64be",
"score": "0.50327057",
"text": "def add(self, document):\n return self.db.update({document['id']: document})",
"title": ""
},
{
"docid": "4bcd77babd05f969641766c089631885",
"score": "0.5025321",
"text": "def delete_project_document(project_id, document_type, major_version, minor_version, username=None):\n project = get_project_instance()\n try:\n project.load(str(project_id))\n except DoesNotExist as err:\n return {'errors': {'project_id': str(err)}}\n project_acl_verify(username, project)\n document = cla.utils.get_project_document(project, document_type, major_version, minor_version)\n if document is None:\n return {'errors': {'document': 'Document version not found'}}\n if document_type == 'individual':\n project.remove_project_individual_document(document)\n else:\n project.remove_project_corporate_document(document)\n project.save()\n return {'success': True}",
"title": ""
},
{
"docid": "437d1058dc5f0f770d721c6f2b83c40c",
"score": "0.50245684",
"text": "def update_project(self, database):\n opended_after_140630 = comp_dates('2014-06-30', self.ordered_opened)\n try:\n LOG.info('Handeling {proj}'.format(proj = self.name))\n project = database.ProjectDB(lims, self.id, self.samp_db)\n key = find_proj_from_view(self.proj_db, self.name)\n project.obj['_id'] = find_or_make_key(key)\n if not opended_after_140630:\n project.obj = load_status_from_google_docs.get(self.name, project.obj)\n if self.upload_data:\n info = save_couchdb_obj(self.proj_db, project.obj)\n else:\n info = self.print_couchdb_obj_to_file(project.obj)\n return \"project {name} is handled and {info}: _id = {id}\".format(\n name=self.name, info=info, id=project.obj['_id'])\n except:\n return ('Issues geting info for {name}. The \"Application\" udf might'\n ' be missing'.format(name = self.name))",
"title": ""
},
{
"docid": "311ac2a59dda5e4adc79b73d50426ccc",
"score": "0.5006261",
"text": "def create_day_doc(doc_datetime):\n timestampStr = doc_datetime.strftime(\"%Y-%m-%d\")\n\n if not COL_TELEMETRY.document(timestampStr).get().exists:\n try:\n COL_TELEMETRY.document(timestampStr).set({\"Date\": timestampStr})\n return \"Documents Created\", 201\n except Exception as e:\n reporting_client.report_exception()\n return f\"An Error Occured: {e}\", 400\n return \"Document already exists\", 200",
"title": ""
},
{
"docid": "5b85dff9afb87d73bc6a1318ae161f8e",
"score": "0.5004508",
"text": "def create_legacy_projects():\n for slug, title, pubdate in dbs:\n r = requests.get('https://physionet.org/physiobank/database/{}/HEADER.shtml'.format(slug))\n if r.status_code != 200:\n r = requests.get('https://physionet.org/physiobank/database/{}/index.shtml'.format(slug))\n if r.status_code != 200:\n print('{} does not exist'.format(slug))\n continue\n content = load_legacy_html(slug=slug)\n p = LegacyProject.objects.create(title=title, slug=slug,\n full_description=content, publish_date=datetime.datetime.strptime(pubdate.strip(), '%d %B %Y'))",
"title": ""
},
{
"docid": "f2401688b53bc9cf60745ef665dd2c14",
"score": "0.5003635",
"text": "def update_project(self):\n pass",
"title": ""
},
{
"docid": "192c820a883dfe716f2d4126f0080317",
"score": "0.49894994",
"text": "def test_create_project(self):\n payload = {\n \"name\": {\"en\": \"Test project\", \"fr\": \"Projet test\"},\n \"description\": {\"en\": [\"Test desc\"], \"fr\": [\"Test desc\"]}\n }\n res = self.admin_client.post(\n PROJECT_URL, payload, format=\"json\")\n self.assertEqual(res.status_code, status.HTTP_201_CREATED)\n exists = Project.objects.all().filter(\n name=payload['name']).exists()\n self.assertTrue(exists)\n Project.objects.all().filter(name=payload['name']).delete()",
"title": ""
},
{
"docid": "98e81e7036e3f2d0f04ad8b471ec4984",
"score": "0.49818078",
"text": "def update_document(id, body):\n\n es.update(\n index=INDEX_NAME,\n doc_type=DOC_TYPE,\n id=id,\n body=body\n )",
"title": ""
},
{
"docid": "0130808ec40d1e1a19bb584b322e719c",
"score": "0.4969886",
"text": "def test_update(dvc):\n index = Index.from_repo(dvc)\n new_stage = Stage(dvc, path=\"path1\")\n new_index = index.update({new_stage})\n\n assert not index.stages\n assert new_index.stages == [new_stage]\n\n dup_stage1 = Stage(dvc, path=\"path1\")\n dup_stage2 = Stage(dvc, path=\"path2\")\n dup_index = index.update([dup_stage1, dup_stage2])\n assert not index.stages\n assert len(new_index.stages) == 1\n assert new_index.stages[0] is new_stage\n assert set(map(id, dup_index.stages)) == {id(dup_stage1), id(dup_stage2)}",
"title": ""
},
{
"docid": "61353c81ed9deebb3ce5644b5255533a",
"score": "0.4959378",
"text": "def projects_create(self, perun_id, name=None, description=None, members=None, enabled=True):\n\n perun_id = str(perun_id)\n if name is None:\n name = perun_id\n\n if not self.ro:\n os_project = self.keystone.projects.create(name=str(name),\n perun_id=perun_id,\n domain=self.target_domain_id,\n description=description,\n enabled=bool(enabled),\n scratched=False,\n flag=self.flag,\n parent=self.parent_project_id if self.nested else None)\n denbi_project = {'id': str(os_project.id),\n 'name': str(os_project.name),\n 'perun_id': str(os_project.perun_id),\n 'description': os_project.description,\n 'enabled': bool(os_project.enabled),\n 'scratched': bool(os_project.scratched),\n 'members': []}\n else:\n denbi_project = {'id': 'read-only-fake',\n 'name': name,\n 'perun_id': perun_id,\n 'description': description,\n 'enabled': enabled,\n 'scratched': False,\n 'members': []}\n # Log keystone update\n self.log2.debug(f\"project [{denbi_project['perun_id']},{denbi_project['id']}]: created.\")\n\n self.denbi_project_map[denbi_project['perun_id']] = denbi_project\n self.__project_id2perun_id__[denbi_project['id']] = denbi_project['perun_id']\n\n # if a list of members is given append them to current project\n if members:\n for member in members:\n self.projects_append_user(perun_id, member)\n\n return denbi_project",
"title": ""
},
{
"docid": "f37cbd0c4d6a7707fd3977bd2ed8fbb1",
"score": "0.49592552",
"text": "def projects_create(context, values):\n session = get_session()\n project = models.Project()\n if not values.get('id'):\n values['id'] = uuid.uuid4()\n with session.begin():\n project.update(values)\n project.save(session)\n return project",
"title": ""
},
{
"docid": "a68db4d9ea28e8d68eef7684f1c15da2",
"score": "0.4956459",
"text": "def put_design(self, designname, doc, rebuild=True):\n response = self.server._GET(self.name, \"_design\", designname,\n errors={404: None})\n if response.status_code == 200:\n current_doc = response.json(object_pairs_hook=odict)\n doc[\"_id\"] = current_doc[\"_id\"]\n doc[\"_rev\"] = current_doc[\"_rev\"]\n if doc == current_doc:\n return False\n response = self.server._PUT(self.name, \"_design\", designname, json=doc)\n if rebuild:\n for view in doc.get(\"views\", {}):\n self.view(designname, view, limit=1)\n return True",
"title": ""
},
{
"docid": "8b15b5501a521c21a539203e0e8fe5b9",
"score": "0.49440858",
"text": "def check_and_update_run(self, run_number):\n run_doc = self.get_run(run_number)\n if not run_doc:\n run_doc = self.run_doc_class.build_document(run_number)\n if run_doc:\n print \"Run %i is not in database, inserting...\" % int(run_number)\n self.insert_rundoc(run_doc)",
"title": ""
},
{
"docid": "1e868dbc23aaa9f84999b81f877cb4a9",
"score": "0.49372107",
"text": "def _add_document(doc_id, data, database=MAIN_DB):\n return requests.put(\n os.path.join(COUCH_URL, database, doc_id),\n json=data,\n headers={\"Content-Type\": \"application/json\"}\n )",
"title": ""
},
{
"docid": "4667675eb9f0fee441a3449ca58fd4e8",
"score": "0.4923755",
"text": "def test_project_index(project_client, session, project, project2):\n assert project.releases\n assert project.tags\n assert project.comments\n assert project.user\n assert project.user.projects\n assert project.user.projectcomments\n assert project.tag_counts == [('arcade', 2, 16), ('game', 1, 14)]\n\n resp = project_client.get('/project/1/')\n assert resp.status_code == 200\n assert b'<h1>Some project title 1' in resp.data\n assert b'<h1>Some project title 2' not in resp.data\n assert b'game' in resp.data\n assert b'arcade' in resp.data\n\n resp = project_client.get('/project-blabla+bla-1-.html')\n assert resp.status_code == 200, 'because this url works too.'\n assert b'<h1>Some project title 1' in resp.data\n\n resp = project_client.get('/project/1/1')\n assert resp.status_code == 200\n assert b'A release title.' in resp.data\n\n resp = project_client.get('/project-blabla+blasbla+-1-1.html')\n assert resp.status_code == 200, 'because this url works too.'\n assert b'A release title.' in resp.data\n\n resp = project_client.get('/project/66/')\n assert resp.status_code == 404, 'when the project is not there'\n resp = project_client.get('/project/1/66')\n assert resp.status_code == 404, 'when the release is not there either'",
"title": ""
},
{
"docid": "3899bcc0fbdc4a74c83b775d8510dce1",
"score": "0.49015632",
"text": "def set_document(request, doc_id, body, errors):\n if doc_id in DB:\n errors['doc_id'].add('Document already exists')\n\n if errors:\n raise errors\n\n body['doc_id'] = doc_id\n DB[doc_id] = body\n\n # dict response for jsonify middleware\n return body",
"title": ""
},
{
"docid": "68855beaa907077bdd1096e7fe3a3d2d",
"score": "0.48790357",
"text": "def get_or_create(self, index_name): \r\n raise NotImplementedError",
"title": ""
},
{
"docid": "68855beaa907077bdd1096e7fe3a3d2d",
"score": "0.48790357",
"text": "def get_or_create(self, index_name): \r\n raise NotImplementedError",
"title": ""
},
{
"docid": "6423f40815445f520211d18edecdff79",
"score": "0.487891",
"text": "def create_doc_instances(self, **kwargs):\n kwargs[u\"cdb_project_id\"] = self.cdb_project_id\n return self.Super(ProjectTemplateDocRef).create_doc_instances(**kwargs)",
"title": ""
},
{
"docid": "8ef3b2bce4ef4353b9113c49562ff49c",
"score": "0.4876358",
"text": "def add_project():\n\n # Get an access class\n da=DomainAccess()\n ea=EmployeeAccess()\n pa=ProjectAccess()\n\n # Acquire form data\n title = request.form[\"Title\"]\n maxStudents = request.form[\"Maxstudents\"]\n descriptionTextNl = request.form[\"nlDescription\"]\n descriptionTextEng = request.form[\"engDescription\"]\n\n researchGroupNrs = request.form.getlist(\"Researchgroup\")\n typeNrs = request.form.getlist(\"Type\")\n disciplineNrs = request.form.getlist(\"Discipline\")\n tags = request.form.getlist(\"Tags\")\n related = request.form.getlist(\"Related\")\n promotorNames = request.form.getlist(\"Promotors\")\n supervisorNames = request.form.getlist(\"Supervisors\")\n externNames = request.form.getlist(\"Extern\")\n\n # Acquire request files\n files_nl = request.files.getlist(\"nlUploads\")\n files_en = request.files.getlist(\"engUploads\")\n\n # Create basic project\n project = Project(None, title, maxStudents, True)\n\n # Assign research group ID's\n for researchGroupNr in researchGroupNrs:\n if int(researchGroupNr) == 0:\n continue\n project.researchGroup.append(int(researchGroupNr))\n\n # Create dutch document\n docNL = Document(None, \"dutch\", descriptionTextNl)\n # Link attachments to document\n this_dir = os.path.dirname(__file__)\n upload_folder = os.path.join(this_dir, app.config['UPLOAD_FOLDER'])\n for file in files_nl:\n nameFile = secure_filename(title + '_' + file.filename)\n file.save(os.path.join(upload_folder, nameFile))\n docNL.attachment.append(nameFile)\n # Assign document as description\n project.desc.append(docNL)\n\n # Create english document\n docEn = Document(None, \"english\", descriptionTextEng)\n # Link attachments to document\n for file in files_en:\n nameFile = secure_filename(title + '_' + file.filename)\n file.save(os.path.join(os.path.join(upload_folder, nameFile)))\n docEn.attachment.append(nameFile)\n # Assign document as description\n project.desc.append(docEn)\n\n # Append types to the project\n typeOptions = da.get_projectType()\n for typeNr in typeNrs:\n if int(typeNr) == 0:\n continue\n project.type.append(typeOptions[int(typeNr) - 1])\n\n # Append disciplines to the project\n disciplineOptions = da.get_disciplines()\n for disciplineNr in disciplineNrs:\n if int(disciplineNr) == 0:\n continue\n project.discipline.append(disciplineOptions[int(disciplineNr) - 1])\n\n # Fetch all employees from the database\n employeeOptions = ea.get_employees()\n\n # Add promotors to the project\n # Create dictionary of promotor name and his id, used to easily append ID to the project\n promotor_id_dict = {promotorOption.name: promotorOption.id for promotorOption in employeeOptions}\n # Loop over names and connect the associated ID's to the project\n for promotorName in promotorNames:\n if promotorName in promotor_id_dict:\n project.promotors.append(promotor_id_dict[promotorName])\n\n # Add supervisors to the project\n # Create dictionary of supervisor name and his id, used to easily append ID to the project\n supervisor_id_dict = {staffOption.name: staffOption.id for staffOption in\n employeeOptions}\n # Loop over names and connect the associated ID's to the project\n for staffName in supervisorNames:\n if staffName in supervisor_id_dict:\n project.supervisors.append(supervisor_id_dict[staffName])\n\n # Add extern employees to the project\n for name in externNames:\n project.extern_employees.append(name)\n\n # Add tags to the project\n project.tag = list(tags)\n\n # Add related projects to the project\n # Fetch all projects from the database\n relatedProjectOptions = pa.get_projects()\n # Create dictionary of project title and its id, used to easily find the required ID\n related_project_id_dict = {relatedProjectOption.title: relatedProjectOption.ID for relatedProjectOption in\n relatedProjectOptions}\n # Loop over related projects and append the associated ID to the project relations\n for relatedProjectTitle in related:\n if relatedProjectTitle in related_project_id_dict:\n project.relatedProject.append(related_project_id_dict[relatedProjectTitle])\n\n # Assign active year\n now = datetime.now()\n project.activeYear.append(now.year)\n\n # Finalize project and add it to the database\n pa.add_project(project)\n findTag(project)\n\n # Return result to javascript\n return jsonify(result=True)",
"title": ""
},
{
"docid": "dcbe91f25d0a54837a18c3ca816e5125",
"score": "0.4869392",
"text": "def add_doc():\n c_id = request.args[\"c_id\"]\n doc_number = request.args[\"doc_number\"]\n\n if s.query(Documents). \\\n filter(and_(Documents.c_id == c_id,\n Documents.qpulse_no == doc_number)). \\\n count() == 0:\n doc = Documents(c_id=c_id, qpulse_no=doc_number)\n s.add(doc)\n s.commit()\n return jsonify({\"success\": True})",
"title": ""
},
{
"docid": "198535543a3034d8ac97e5d93610f18c",
"score": "0.48673",
"text": "def test_existing_index(self, orion_db):\n assert (\n \"new_field_1\" not in get_db(orion_db)[\"new_collection\"].index_information()\n )\n\n orion_db.ensure_index(\"new_collection\", \"new_field\", unique=True)\n assert \"new_field_1\" in get_db(orion_db)[\"new_collection\"].index_information()\n\n # reattempt\n orion_db.ensure_index(\"new_collection\", \"new_field\", unique=True)\n assert \"new_field_1\" in get_db(orion_db)[\"new_collection\"].index_information()",
"title": ""
},
{
"docid": "c466a7bd7ef34e6dfc1db2eba47deba4",
"score": "0.4845863",
"text": "def update_inv_index(self, processed_tokens, document_id):\n for token_index in range(0, len(processed_tokens)):\n if processed_tokens[token_index] not in self.terms:\n self.terms.append(processed_tokens[token_index])\n new_postings_list = list()\n if self.purpose == \"vsm\":\n new_doc = Document(document_id, store_term_weights=True)\n else:\n new_doc = Document(document_id, token_index + 1)\n new_postings_list.append(new_doc)\n self.posting_lists.append(new_postings_list)\n else:\n existing_posting_list = self.get_postings_list(\n processed_tokens[token_index])\n doc_exists = False\n for i in range(len(existing_posting_list)):\n if existing_posting_list[i].id == document_id:\n if self.purpose == \"vsm\":\n existing_posting_list[i].increment_frequency()\n else:\n existing_posting_list[i].add_position(\n token_index + 1)\n doc_exists = True\n if doc_exists is False:\n if self.purpose == \"vsm\":\n new_doc = Document(\n document_id, store_term_weights=True)\n else:\n new_doc = Document(document_id, token_index + 1)\n existing_posting_list.append(new_doc)",
"title": ""
},
{
"docid": "c0486cd11ffc6867d49d30635859999a",
"score": "0.48408973",
"text": "def create_index():\n try:\n print(\"[*] creating an index in elasticsearch\")\n r = requests.put(INDEX_URL)\n if \"index_already_exists_exception\" in r.text:\n print(\"[*] index already exists\")\n return\n mapping = {\n \"capture\": {\n \"properties\": { \n \"timestamp\": {\n \"type\": \"date\",\n },\n \"capture\": {\n \"type\": \"string\",\n },\n }\n }\n }\n r = requests.put(TYPE_URL + \"/_mapping\", data=json.dumps(mapping))\n print(r.text)\n except:\n raise Exception(\"Elasticsearch is not running\")",
"title": ""
},
{
"docid": "79550ceba64c00d7cd1ed2e8ab6d2e3b",
"score": "0.48389933",
"text": "def insert(self, doc):\n params = self._params_for_insert\n params.update({'body': doc, 'refresh': True})\n if not self._index_exists():\n self._create_index()\n doc = ELASTICSEARCH.index(**params)\n return doc['_id']",
"title": ""
},
{
"docid": "91c9aafa4e4dd3f4de10a590538ed54f",
"score": "0.48380858",
"text": "def create(self, id: str, data: Optional[JsonDict] = None) -> Document:\n if any(id == d.id for d in self._docs):\n raise ValueError(\n f\"There is already another Document instance for {id} part of the BulkOperation\"\n )\n\n doc = Document(self._database, id, data=data)\n self._docs.append(doc)\n\n return doc",
"title": ""
},
{
"docid": "c338b5a256e496f6123de3c7a5c4fc5b",
"score": "0.48324662",
"text": "def _import_project_data(self):\n for data in self._get_fyle_data(\n self.fyle_api.Projects, {'active_only': False}):\n defaults = {\n 'name': data['name'],\n 'description': data['description'],\n 'active': data['active'],\n }\n Project.objects.update_or_create(\n id=data['id'], user=self.batch.user, defaults=defaults)",
"title": ""
},
{
"docid": "8266958ea8686836d449b0440c676007",
"score": "0.48165312",
"text": "def index(self, doctype, docid, document):\n dbname = self.dbname\n self.broker.DB.index(index=dbname, doc_type=doctype, id=docid, body=document)",
"title": ""
},
{
"docid": "b95af5d41d231f0ee4aff7660a7f0d47",
"score": "0.48141915",
"text": "def add_new_doc(self, document):\n document_dictionary = document.term_doc_dictionary\n document_capitals = document.capital_letter_indexer\n document_date = document.tweet_date\n for key_term in document_capitals:\n if key_term not in self.global_capitals:\n self.global_capitals[key_term] = document_capitals[key_term]\n else:\n if not document_capitals[key_term]:\n self.global_capitals[key_term] = False\n\n document_entities = document.named_entities\n\n for entity in document_entities:\n self.entities_dict[entity] += 1\n document_vec = np.zeros(shape=25)\n is_covid = False\n for term in document_dictionary:\n if term == 'covid':\n is_covid = True\n if term in self.glove_dict:\n document_vec += self.glove_dict[term]\n document_vec /= len(document_dictionary)\n # document_vec, # numpy array of size 25 which\n # represents the document in 25 dimensional space(GloVe)\n if is_covid:\n self.document_posting_covid[document.tweet_id] = (document_vec, document_date)\n self.document_dict[document.tweet_id] = \"doc_posting_covid\" + str(self.doc_posting_covid_counter)\n else:\n self.doc_posting_dict[document.tweet_id] = (document_vec, document_date)\n self.document_dict[document.tweet_id] = \"doc_posting\"+str(self.doc_posting_counter)\n\n if len(self.doc_posting_dict) == 100000:\n self.save_doc_posting()\n if len(self.document_posting_covid) == 100000:\n self.save_doc_covid()\n\n\n # Go over each term in the doc\n for term in document_dictionary.keys():\n try:\n if term in self.inverted_idx:\n if term not in self.posting_dict:\n self.posting_dict[term] = None\n self.inverted_idx[term][0] += 1\n\n else:\n self.inverted_idx[term] = [1, str(self.counter_of_postings)]\n self.posting_dict[term] = None\n\n insert_tuple = (document.tweet_id, # tweet id\n document.doc_length, # total number of words in tweet\n document.max_tf, # number of occurrences of most common term in tweet\n document.unique_terms, # number of unique words in tweet\n document_dictionary[term], # number of times term is in tweet - tf!\n )\n # if there're no documents for the current term, insert the first document\n if self.posting_dict[term] is None:\n self.posting_list.append((term, [insert_tuple]))\n self.posting_dict[term] = len(self.posting_list) - 1\n else:\n tuple_idx = self.posting_dict[term]\n self.posting_list[tuple_idx][1].append(insert_tuple)\n\n # check if posting_dict is full\n if len(self.posting_list) == self.postingDict_size:\n self.save_postings()\n\n except:\n print('problem with the following key {}'.format(term))",
"title": ""
},
{
"docid": "3ad5d272bb1d56172468a84a457c5e10",
"score": "0.4809561",
"text": "def test_indexed_doc(es):\n investor_company = CompanyFactory()\n\n large_investor_profile = LargeCapitalInvestorProfileFactory(\n investor_company=investor_company,\n )\n\n doc = ESLargeInvestorProfile.es_document(large_investor_profile)\n elasticsearch.bulk(actions=(doc, ), chunk_size=1)\n\n es.indices.refresh()\n\n indexed_large_investor_profile = es.get(\n index=ESLargeInvestorProfile.get_write_index(),\n id=large_investor_profile.pk,\n )\n\n assert indexed_large_investor_profile['_id'] == str(large_investor_profile.pk)\n assert indexed_large_investor_profile['_source'] == {\n '_document_type': LargeInvestorProfileSearchApp.name,\n 'id': str(large_investor_profile.pk),\n 'asset_classes_of_interest': [],\n 'country_of_origin': {\n 'id': str(large_investor_profile.country_of_origin.pk),\n 'name': str(large_investor_profile.country_of_origin.name),\n },\n 'investor_company': {\n 'id': str(investor_company.pk),\n 'name': str(investor_company.name),\n 'trading_names': investor_company.trading_names,\n },\n 'created_by': None,\n 'investor_type': None,\n 'required_checks_conducted': None,\n 'deal_ticket_sizes': [],\n 'investment_types': [],\n 'minimum_return_rate': None,\n 'time_horizons': [],\n 'restrictions': [],\n 'construction_risks': [],\n 'minimum_equity_percentage': None,\n 'desired_deal_roles': [],\n 'uk_region_locations': [],\n 'other_countries_being_considered': [],\n 'investable_capital': None,\n 'investor_description': '',\n 'created_on': '2019-01-01T00:00:00+00:00',\n 'notes_on_locations': '',\n 'global_assets_under_management': None,\n 'modified_on': '2019-01-01T00:00:00+00:00',\n }",
"title": ""
},
{
"docid": "51f2637b70c16067a94a6bd4e0258048",
"score": "0.48014212",
"text": "def read_document(self, d):\n if not self.in_collection(d):\n self.documents[d] = self.index.add_document(d)\n\n if len(self.index) >= READ_LIMIT_TO_WRITE_TO_MONGO:\n self.flush_to_mongo()",
"title": ""
},
{
"docid": "ce5a9df77a8dea388f8abce2e0a3ba20",
"score": "0.47926098",
"text": "def project(name, description, skip_if_exists):\n if skip_if_exists:\n result = Client().graphql(\n query={\n \"query\": {\n with_args(\"project\", {\"where\": {\"name\": {\"_eq\": name}}}): {\n \"id\": True\n }\n }\n }\n )\n if result.data.project:\n click.secho(\"{} already exists\".format(name), fg=\"green\")\n return\n\n try:\n Client().create_project(project_name=name, project_description=description)\n except ClientError as exc:\n click.echo(f\"{type(exc).__name__}: {exc}\")\n click.secho(\"Error creating project\", fg=\"red\")\n return\n\n click.secho(\"{} created\".format(name), fg=\"green\")",
"title": ""
},
{
"docid": "d308c3fb5e0f7925f8a90beabd7769b1",
"score": "0.47813076",
"text": "def test_create(self):\n doc = Document(DocumentID=\"123\")",
"title": ""
},
{
"docid": "3653cfc90294d606c691f6002160e051",
"score": "0.47683114",
"text": "async def update_project(project_id: ProjectID, update: ProjectUpdate):",
"title": ""
},
{
"docid": "3cb2ea4fbdea7b30cfb8fc035ec33b63",
"score": "0.4765857",
"text": "def delete(project, version=None):",
"title": ""
},
{
"docid": "304ed14f24d509d80cc30d9e20a000bb",
"score": "0.47605696",
"text": "def update_document(self, document_id, mappings):\n\n if len(mappings) == 0:\n return None\n\n requests = []\n for (src, dest) in mappings.items():\n requests.append(\n {\n \"replaceAllText\": {\n \"containsText\": {\n \"text\": src,\n \"matchCase\": \"true\",\n },\n \"replaceText\": dest,\n }\n }\n )\n\n body = {\"requests\": requests}\n results = self.docs.batchUpdate(documentId=document_id, body=body).execute()\n\n return results",
"title": ""
},
{
"docid": "0b0ff57f645529136b9fca1f29ca8a3d",
"score": "0.47586724",
"text": "def assign_weather_to_project(delphin_id: str, weather_ids: list) -> str:\n\n # Save climate class to delphin document\n delphin_document = delphin_db.Delphin.objects(id=delphin_id).first()\n\n for id_ in weather_ids:\n weather_document = weather_db.Weather.objects(id=id_).first()\n delphin_document.update(push__weather=weather_document)\n\n return delphin_document.id",
"title": ""
},
{
"docid": "da1b60c6b85b7a75cb23675cb817f78d",
"score": "0.4754898",
"text": "def insert_document(self, index_name, type, document_id, document):\n\n try:\n if self.es_client is None or not self.es_client.ping():\n self.connect()\n\n if document_id is not None and document_id != \"\":\n return self.es_client.index(index=index_name, doc_type=type, id=document_id, body=document, refresh='wait_for', request_timeout=30)\n else:\n return self.es_client.index(index=index_name, doc_type=type, body=document, refresh='wait_for', request_timeout=30)\n except:\n logger.exception(\"could not insert document is %s in index %s \"%(document_id, index_name))",
"title": ""
},
{
"docid": "1f767eaef4e45cf976afc1f230f7464f",
"score": "0.47510174",
"text": "def upsert(self):\n\n # Raise error if index is not writable\n if not self.config.get(\"writable\"):\n raise ReadOnlyError(\"Attempting to upsert a read-only index (writable != True)\")\n\n if self.embeddings and self.documents:\n with self.lock:\n # Run upsert\n self.embeddings.upsert(self.documents)\n\n # Save index if path available, otherwise this is an memory-only index\n if self.config.get(\"path\"):\n self.embeddings.save(self.config[\"path\"], self.config.get(\"cloud\"))\n\n # Reset document stream\n self.documents.close()\n self.documents = None",
"title": ""
},
{
"docid": "da970d3a07959bb70343da54b9e0f1dc",
"score": "0.4724849",
"text": "def updateDocs():\n docs = Dataset.search()\n for doc in docs.scan():\n created = getattr(getattr(doc, \"_ts\", None), \"date_created\", None)\n updated = getattr(getattr(doc, \"_ts\", None), \"last_updated\", None)\n if not created and not updated:\n existing_dc = getattr(getattr(doc, \"_meta\", None), \"date_created\", None)\n existing_lu = getattr(getattr(doc, \"_meta\", None), \"last_updated\", None)\n\n if existing_dc and existing_lu:\n doc.update(**{\"_ts\": {\"date_created\": existing_dc, \"last_updated\": existing_lu}})\n logging.info(\"Updating fields for doc with existing\")\n else:\n doc.update(\n **{\n \"_ts\": {\n \"date_created\": default_create,\n \"last_updated\": default_last_updated,\n }\n }\n )\n logging.info(\"Adding fields for doc with defaults\")\n else:\n created_bug = getattr(getattr(doc, \"_ts\", None), \"guide\", None)\n if created_bug:\n doc.update(\n **{\n \"_ts\": {\n \"date_created\": default_create,\n \"last_updated\": default_last_updated,\n }\n }\n )\n else:\n logging.info(\"Doc already has TS last updated field\")",
"title": ""
},
{
"docid": "4c0614bb9a344bbcb7774f40d315a663",
"score": "0.4718103",
"text": "def _write_projects(projects):\n path = _get_projects_index_file()\n contents = util.read_yaml(path)\n contents.update({'projects': projects})\n util.write_yaml(path, contents)",
"title": ""
},
{
"docid": "f3b73dd77df050a21496f43cdef09149",
"score": "0.47118863",
"text": "def delete(self, project, query):\n self.es.delete_by_query(\n index=self.index_name(project),\n body=query,\n conflicts='proceed',\n )",
"title": ""
},
{
"docid": "3024c1ab542c8ad983221027239d933a",
"score": "0.47102568",
"text": "def add_doc(self, doc_id, contents):\n self.__es.index(index=self.__index_name, doc_type=self.DOC_TYPE, id=doc_id, body=contents)",
"title": ""
},
{
"docid": "af9147664a4952bc73e1c324e25ce1b2",
"score": "0.47069666",
"text": "def test_designs_id_put(self):\n pass",
"title": ""
},
{
"docid": "6d6dd2c023bd9b98b8654db1f500b71a",
"score": "0.4705125",
"text": "def insert(dictObject, db):\n\n \"\"\" res = db[\"menus\"].update_one(\n {\"id\": dictObject[\"id\"],\n \"date\": dictObject[\"date\"],\n \"mensaName\": dictObject[\"mensaName\"],\n \"lang\": dictObject[\"lang\"]\n }, {\"$set\": dictObject}, upsert=True)\n\n print(\"modifed: id: \" + str(dictObject[\"id\"].encode('utf-8')) + \" Date: \" + dictObject[\"date\"] + \" lang: \" +\n dictObject[\"lang\"])\n if res.upserted_id is None:\n print(\"res: modified: \" + str(res.modified_count) + \" matched: \" + str(res.matched_count))\n else:\n print(\"res: inserted\") \"\"\"",
"title": ""
},
{
"docid": "876fbf36475b5fdab80415b990d77feb",
"score": "0.46986815",
"text": "def scrape_new_data(index=\"verdicts\"):\n if es.indices.exists(index=index):\n tic = time.time()\n scraper.update_database()\n toc = time.time()\n print(\"Scraping done! Time needed: {}\".format(str(toc - tic)))\n else:\n print(\"Index to be updated does not exists!\")",
"title": ""
},
{
"docid": "7842c0d6747dbdef941acf436cb28de4",
"score": "0.4698259",
"text": "def create(self, index_name):\r\n raise NotImplementedError",
"title": ""
},
{
"docid": "7842c0d6747dbdef941acf436cb28de4",
"score": "0.4698259",
"text": "def create(self, index_name):\r\n raise NotImplementedError",
"title": ""
},
{
"docid": "7842c0d6747dbdef941acf436cb28de4",
"score": "0.4698259",
"text": "def create(self, index_name):\r\n raise NotImplementedError",
"title": ""
},
{
"docid": "620ced752c4b779874c9474695caa26d",
"score": "0.4695401",
"text": "def create_doc_instances(self, **kwargs):\n kwargs[u\"cdb_project_id\"] = self.cdb_project_id\n return self.Super(TaskTemplateDocRef).create_doc_instances(**kwargs)",
"title": ""
},
{
"docid": "de775a146f47d74e09cd6d9324f8dbab",
"score": "0.4694352",
"text": "def create_update_project(request, slug=None):\n request_user = request.user\n if slug is not None:\n project = Project.objects.get(slug=slug)\n if project.owner != request.user:\n raise PermissionDenied\n else:\n project_form = CreateProjectForm(instance=project)\n position_formset = PositionFormset(\n queryset=Position.objects.filter(\n project=project\n ))\n else:\n project = None\n project_form = CreateProjectForm()\n position_formset = PositionFormset(\n queryset=Position.objects.none()\n )\n\n if request.method == 'POST':\n project_form = CreateProjectForm(\n request.POST, \n instance=project\n )\n position_formset = PositionFormset(\n request.POST,\n queryset=Position.objects.filter(project=project)\n )\n if project_form.is_valid() and position_formset.is_valid():\n project = project_form.save(commit=False)\n project.owner = request_user\n new_project = False\n if project.status is None:\n new_project = True\n project.status = 'A'\n project.save()\n positions = position_formset.save(commit=False)\n for position in positions:\n position.project = project\n if position.status not in ('E', 'F'):\n position.status = 'E'\n position.save()\n position_formset.save_m2m()\n position_formset.save()\n if new_project:\n messages.success(\n request, 'Project {} created!'.format(project.title))\n else:\n messages.success(\n request, 'Project {} updated!'.format(project.title))\n return HttpResponseRedirect(reverse('projects:project', kwargs={'slug': project.slug}))\n\n context = {\n 'project_form': project_form,\n 'position_formset': position_formset,\n }\n return render(request, 'projects/create_update_project.html', context)",
"title": ""
},
{
"docid": "1df57eb7f6c6f2712debcc484e3347d8",
"score": "0.46942607",
"text": "def test_update_project(self):\n pass",
"title": ""
},
{
"docid": "c17dc383ee2e366b1a5fc4e1604f9b99",
"score": "0.4692427",
"text": "def refresh(self, project):\n self.es.indices.refresh(index=self.index_name(project))",
"title": ""
},
{
"docid": "6d957caf110468efdc93c17755a223f5",
"score": "0.46918374",
"text": "def test_rud_document(self):\n\n # upload a documnet\n self.upload_document()\n\n # retrieve document PK to test\n test_document = Document.objects.all().first()\n\n # test retrive document with invvalid pk\n rud_document_url = reverse(\n 'documents:document',\n kwargs={'pk': 99}\n )\n response = self.client.get(\n rud_document_url,\n )\n self.assertEqual(response.status_code, 404)\n\n # test update document with invvalid pk\n response = self.client.patch(\n rud_document_url,\n {\n 'language': 'af',\n 'document_type': 'pdf'\n }\n )\n self.assertEqual(response.status_code, 404)\n\n # test delete document with invvalid pk\n response = self.client.delete(\n rud_document_url\n )\n self.assertEqual(response.status_code, 404)\n\n # test retrieve document with pk\n rud_document_url = reverse(\n 'documents:document',\n kwargs={'pk': test_document.pk}\n )\n response = self.client.get(\n rud_document_url,\n )\n self.assertEqual(response.status_code, 200)\n\n # test update document with pk\n rud_document_url = reverse(\n 'documents:document',\n kwargs={'pk': test_document.pk}\n )\n response = self.client.patch(\n rud_document_url,\n {\n 'language': 'af',\n 'document_type': 'pdf'\n }\n )\n test_document.refresh_from_db()\n self.assertEqual(response.status_code, 200)\n self.assertEqual(test_document.language.short_name, 'af')\n self.assertEqual(test_document.document_type.short_name, 'pdf')\n self.assertEqual(Document.objects.all().count(), 1)\n\n # test delete document with pk\n rud_document_url = reverse(\n 'documents:document',\n kwargs={'pk': test_document.pk}\n )\n response = self.client.delete(\n rud_document_url\n )\n self.assertEqual(response.status_code, 204)\n self.assertEqual(Document.objects.all().count(), 0)",
"title": ""
},
{
"docid": "a42d660681fa0dea1386fc66c5c6f7ae",
"score": "0.46850002",
"text": "def indexDocElement(es_Url, awsauth, docData):\n try:\n headers = {\"Content-Type\": \"application/json\"}\n resp = requests.put(es_Url, auth=awsauth,\n headers=headers, json=docData)\n print(resp.content)\n if resp.status_code == 201:\n logger.info('INFO: Successfully created element into ES')\n elif resp.status_code == 200:\n logger.info('INFO: Successfully updated element into ES')\n else:\n logger.error(f'FAILURE: Unable to index element {resp.content}')\n raise\n except Exception as e:\n logger.error(f'ERROR: {str(e)}')\n logger.error(f\"ERROR: Unable to index line:{docData['content']}\")\n raise",
"title": ""
},
{
"docid": "11e4d54117e769ca06d0c90afeab7608",
"score": "0.46833104",
"text": "def unindex_object(documentId):",
"title": ""
},
{
"docid": "7a6374eccd3ecd3ac8f9c0c4077d249a",
"score": "0.46808988",
"text": "async def update_data(doc_id: int, response: IntakeData,\n db_file: str = tinydb_file):\n db0 = await get_db(db_file)\n db0.update(response.dict(), doc_ids=[doc_id])\n return db0",
"title": ""
},
{
"docid": "f6ca95cb848f9130b79bfd3e7d693228",
"score": "0.4669554",
"text": "def uploadDesignDocuments(self,directory):\n\n import glob\n pattern = os.path.join(directory,'*.py')\n viewFiles = glob.glob(pattern)\n for viewFile in viewFiles:\n execfile(viewFile)\n for view in views:\n viewID = os.path.join('_design',view)\n print (\"uploading \", view, \" of \", viewFile, \" to \", self.databaseName)\n document = self.db.get(viewID)\n if document:\n self.db.delete(document)\n self.db[viewID] = views[view]",
"title": ""
},
{
"docid": "4c6b0bfb6c8ba313b8b13141fe7abd40",
"score": "0.46691608",
"text": "def update(self, index, document, **options):\n\n index = base._resolve_index(index)\n\n index_name = index.get_name()\n index_doc_type = options.pop('doc_type', index.get_doc_type())\n\n id, adapted_document = index.adapt_document(document)\n\n if id:\n options.setdefault(\"id\", id)\n\n self._es.index(index_name, index_doc_type, adapted_document, **options)",
"title": ""
},
{
"docid": "e43a3a310e72a471695196fe25c4e8b9",
"score": "0.46609166",
"text": "def test_delete_project(self):\n name = {\"en\": \"Test project\", \"fr\": \"Projet test\"}\n project = Project.objects.create(\n name=name,\n description={\"en\": [\"Test desc\"], \"fr\": [\"Test desc\"]}\n )\n detail_url = reverse('rest:project-detail', kwargs={'pk': project.id})\n res = self.admin_client.delete(detail_url)\n self.assertEqual(res.status_code, status.HTTP_204_NO_CONTENT)\n exists = Project.objects.all().filter(name=name).exists()\n self.assertFalse(exists)",
"title": ""
},
{
"docid": "0e611165d35dc8ad0d765ab9c84890b1",
"score": "0.4655304",
"text": "def update_project(project_id, update_dict):\n # update_dict = convert_project_object_to_dict(update_dict)\n # try:\n Project.objects.get_or_404(id=project_id).update(**update_dict)\n return (True, \"success\")\n # except Exception, e:\n # return (False, repr(e))",
"title": ""
},
{
"docid": "4ec582f4f431c4caf8adbe8f976d8607",
"score": "0.46348995",
"text": "def project(id):\n\t#Call load() in the data layer to load the database and store it's contents in the variable db\n\tdb = data.load('data.json')\n\t#Call get_project() in the data layer and store the project in the variable project\n\tproject = data.get_project(db, id)\n\t#If None was returned by get_project():\n\t\t#No project with the specified ID exists, ergo a nonexistent page has been visited, ergo abort with a 404\n\tif project is None:\n\t\tabort(404)\n\telse:\n\t\t#adding a counter to the project-page.\n\t\tcounter = open(\"doc/counter.log\", \"r+\")\t\t\t\t\t#opens counter.log\n\t\tcount = 1\n\t\tpid = str(\"project \")+str(project['project_id'])+\"\\n\"\n\t\tfor line in counter:\n\t\t\tif pid in line:\n\t\t\t\tcount += 1\n\t\tproject['counter'] = count\n\t\tcounter.write(pid)\n\t\tcounter.close()\n\t\t#Render project.html with:\n\t\t\t#project --> used to display information about the project\n\t\treturn render_template('project.html', project=project)",
"title": ""
},
{
"docid": "cb39d6df0b7b3498a570ea1bba71feb3",
"score": "0.46262908",
"text": "def _update_document_by_id(doc_id, body, object_type, *, retry_on_conflict=0, **kwargs):\n conn = get_conn()\n for alias in get_active_aliases(conn, object_types=[object_type]):\n try:\n conn.update(\n index=alias,\n doc_type=GLOBAL_DOC_TYPE,\n body=body,\n id=doc_id,\n params={\"retry_on_conflict\": retry_on_conflict, **kwargs},\n )\n # Our policy for document update-related version conflicts right now is to log them\n # and allow the app to continue as normal.\n except ConflictError:\n log.error(\n \"Update API request resulted in a version conflict (alias: %s, doc id: %s)\",\n alias,\n doc_id,\n )",
"title": ""
},
{
"docid": "331d110c41a2530b147f583ddccbeb0a",
"score": "0.46188155",
"text": "def create_opensearch_index(index_name: str):\n with open(Path(__file__).parent / \"pipeline_logs_mapping.json5\") as fd:\n index_mappings = pyjson5.load(fd)\n res = requests.put(\n f\"{OPENSEARCH_ENDPOINT}/{index_name}\",\n data=json.dumps(index_mappings),\n headers={\"Content-Type\": \"application/json\"},\n auth=(OPENSEARCH_USERNAME, OPENSEARCH_PASSWORD),\n )\n if res.status_code >= 400:\n logging.error(\n f'Failed to create opensearch index \"{index_name}\", '\n f\"server responded with status {res.status_code}\"\n )\n try:\n logging.error(res.json())\n except json.JSONDecodeError:\n logging.error(res.text)",
"title": ""
},
{
"docid": "74d8d50b59c45411d8f14763398d3d38",
"score": "0.46130043",
"text": "def createDoc(self, docInfos):\n if docInfos['id'] != '':\n newDocId = self.plominoDatabase.invokeFactory(docInfos['type'], \n id=docInfos['id'])\n newDoc = self.plominoDatabase.getDocument(docInfos['id'])\n else:\n newDoc = self.plominoDatabase.createDocument()\n \n if newDoc is not None:\n if self.plominoDatabase.getForm(docInfos['form']) is not None:\n newDoc.setItem('Form', docInfos['form'])\n\n # TODO: create a default form if form does not exist ? in a later version\n # REM: doc is not saved if not associated with a existing form\n \n # Set the items of this document\n for itemInfos in docInfos['items']:\n newDoc.setItem(itemInfos['name'], itemInfos['value'])\n # TODO: check the value before set ?\n #self.createItem(itemInfos, newDoc)\n\n # Add the files inserted in the document\n if docInfos['files'] != []:\n if not hasattr(newDoc.getForm(), 'imported_files'):\n fieldId = newDoc.getForm().invokeFactory('PlominoField', \n id='imported_files',\n title='Imported Files',\n FieldType='ATTACHMENT')\n newDoc.getForm().setFormLayout(newDoc.getForm().getFormLayout() + \\\n '<p>Imported files: <span class=\"plominoFieldClass\">imported_files</span></p>')\n for fileInfos in docInfos['files']:\n newDoc.setfile(a2b_base64(str(fileInfos['content'])), fileInfos['name'])\n\n newDoc.save()\n\n else:\n raise Exception\n\n self.plominoDatabase.getIndex().refresh()",
"title": ""
},
{
"docid": "a5eea1dedb85eb1c7282df2ad603ce7f",
"score": "0.46129036",
"text": "def upsert_document(doc_id, doc, object_type, *, retry_on_conflict=0, **kwargs):\n _update_document_by_id(\n doc_id,\n {\"doc\": doc, \"doc_as_upsert\": True},\n object_type,\n retry_on_conflict=retry_on_conflict,\n **kwargs,\n )",
"title": ""
},
{
"docid": "10ab54ba9546b01682521f015050756d",
"score": "0.46108896",
"text": "def delete_project(uuid):\n project = Project.query.get(uuid)\n\n if project is None:\n raise NotFound(\"The specified project does not exist\")\n\n experiments = Experiment.query.filter(Experiment.project_id == uuid).all()\n for experiment in experiments:\n # remove dependencies\n operators = db_session.query(Operator).filter(Operator.experiment_id == experiment.uuid).all()\n for operator in operators:\n Dependency.query.filter(Dependency.operator_id == operator.uuid).delete()\n\n # remove operators\n Operator.query.filter(Operator.experiment_id == experiment.uuid).delete()\n\n Experiment.query.filter(Experiment.project_id == uuid).delete()\n\n db_session.delete(project)\n db_session.commit()\n\n prefix = join(\"experiments\", uuid)\n remove_objects(prefix=prefix)\n\n return {\"message\": \"Project deleted\"}",
"title": ""
},
{
"docid": "1acfc33f6ce86672083340d2f05e83f7",
"score": "0.46102795",
"text": "def index_document(body):\n es.index(\n index='documents',\n doc_type='document',\n body=body\n )",
"title": ""
},
{
"docid": "ce2ba6ec3062bc8b00d66042e7c58f95",
"score": "0.46075854",
"text": "def post(self, request, *args, **kwargs):\n self.project_slug = self.kwargs.get('project_slug', None)\n self.project = Project.objects.get(slug=self.project_slug)\n return super(VersionDeleteView, self).post(request, *args, **kwargs)",
"title": ""
},
{
"docid": "7cca9d7d23d55aea633a1d309353db55",
"score": "0.46073022",
"text": "def add(self, document_or_documents):\n\n added_document_ids = []\n\n if isinstance(document_or_documents, Document):\n was_list = False\n documents = [document_or_documents]\n else:\n was_list = True\n documents = document_or_documents[:]\n\n # First-pass validation\n self._validate_documents(documents)\n\n with transaction.atomic(independent=True):\n for document in documents:\n record = document._record\n\n # We go through the document fields, pull out the values that have been set\n # then we index them.\n field_data = {\n f: getattr(document, document.get_field(f).attname)\n for f in document.get_fields() if f != \"id\"\n }\n\n # Generate a database representation of this Document use\n # the passed ID if there is one\n record, created = DocumentRecord.objects.update_or_create(\n pk=document.id,\n defaults={\n \"index_stats\": self.index,\n \"data\": field_data\n }\n )\n document.id = record.id\n document._record = record\n\n if created:\n index_document(self.name, document)\n added_document_ids.append(record.id)\n else:\n # This wipes out any existing document, bumps the revision\n # and then indexes this one\n reindex_document(document)\n\n return added_document_ids if was_list else (added_document_ids[0] if added_document_ids else 0)",
"title": ""
},
{
"docid": "7df4233a622d8556ec5399e88e8dcaf1",
"score": "0.4595747",
"text": "def create_document(record):\n # TODO: Write an iterator over the INDEX_MAP to fetch fields from the record and\n # return a dictionary representing the document.\n return None",
"title": ""
},
{
"docid": "b994723d25f0a197f745292df18fa72c",
"score": "0.45953628",
"text": "def test_update_project(self):\n project = Project.objects.create(\n name={\"en\": \"Test project\", \"fr\": \"Projet test\"},\n description={\"en\": [\"Test desc\"], \"fr\": [\"Test desc\"]}\n )\n payload = {\"name\": {\"en\": \"New name\", \"fr\": \"Nouveau nom\"}}\n detail_url = reverse('rest:project-detail', kwargs={'pk': project.id})\n res = self.admin_client.patch(detail_url, payload, format=\"json\")\n self.assertEqual(res.status_code, status.HTTP_200_OK)\n project.refresh_from_db()\n self.assertEqual(project.name, payload['name'])",
"title": ""
},
{
"docid": "4b3a5c81140b78c9a4413be72272e6f4",
"score": "0.45898458",
"text": "def CreateProjects(projRef):\r\n\r\n print(\"Creating projects\")\r\n # make root directory with permit date\r\n if (len(projRef) == 0):\r\n print(\"No project available\")\r\n return\r\n\r\n dt = str(projRef.values()[0].PERMIT_DATE).split(\" \")[0]\r\n project_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), os.path.join(NFOutputPath.OUTPUT, dt))\r\n if not os.path.exists(project_root):\r\n os.makedirs(project_root)\r\n\r\n summary = []\r\n\r\n try:\r\n edit = arcpy.da.Editor(NFDataSource.COMMONGIS_CONN)\r\n edit.startEditing(False, False)\r\n edit.startOperation()\r\n editCounter = 0\r\n\r\n # create one folder for each project ref\r\n for key in projRef:\r\n arcpy.AddMessage(key)\r\n\r\n project_folder = os.path.join(project_root, key)\r\n\r\n # if the project folder already contains plans - skip; this project has been imported before; will not be included in new MaxDia report too\r\n if not os.path.exists(project_folder):\r\n os.makedirs(project_folder)\r\n file_list = []\r\n else:\r\n file_list = os.listdir(project_folder)\r\n\r\n pdf_exists = False\r\n\r\n for f in file_list:\r\n\r\n if \".pdf\" in f:\r\n arcpy.AddMessage(\"PDF in project folder: \" + project_folder)\r\n pdf_exists = True\r\n break\r\n if pdf_exists:\r\n continue\r\n\r\n cwdPdf = os.path.join(os.path.dirname(os.path.abspath(__file__)),\r\n os.path.join(project_folder, \"{0}-{1}.pdf\".format(key, NFAnnex.CWD)))\r\n\r\n UpdateCWD(projRef[key])\r\n GenerateCWD(arcpy.mapping.MapDocument(NFTemplate.CWDTEMPLATE), projRef[key], cwdPdf, [], \"\")\r\n\r\n wsnPdf = os.path.join(os.path.dirname(os.path.abspath(__file__)),\r\n os.path.join(project_folder, \"{0}-{1}.pdf\".format(key, NFAnnex.WSN)))\r\n wsnasset = UpdateWSN(projRef[key])\r\n\r\n if (len(wsnasset) > 0):\r\n # GenerateCWD(arcpy.mapping.MapDocument(NFTemplate.WSNTEMPLATE), projRef[key], wsnPdf, wsnasset, \"WSN\")\r\n print \"wsnasset\"\r\n\r\n wrnPdf = os.path.join(os.path.dirname(os.path.abspath(__file__)),\r\n os.path.join(project_folder, \"{0}-{1}.pdf\".format(key, NFAnnex.WRN)))\r\n wrnasset = UpdateWRN(projRef[key])\r\n\r\n if (len(wrnasset) > 0):\r\n GenerateCWD(arcpy.mapping.MapDocument(NFTemplate.WRNTEMPLATE), projRef[key], wrnPdf, wrnasset, \"WRN\")\r\n summary.extend(GenerateReport(projRef[key], wrnasset))\r\n\r\n editCounter = editCounter + 1\r\n\r\n if (editCounter == 10):\r\n print (\"intermediate save @ 10\")\r\n edit.stopOperation()\r\n edit.stopEditing(True)\r\n\r\n edit.startEditing(False, False)\r\n edit.startOperation()\r\n editCounter = 0\r\n\r\n edit.stopOperation()\r\n edit.stopEditing(True)\r\n del edit\r\n\r\n max_dia_report_name = GenerateMaxDiaReport(summary, dt, projRef)\r\n arcpy.AddMessage(\"END Creating projects\")\r\n arcpy.AddMessage(max_dia_report_name)\r\n print(\"Generated maxdia report\")\r\n return max_dia_report_name\r\n\r\n except Exception as err:\r\n print err\r\n Error_Handler(\"Create Projects Failed\")\r\n Error_Handler(repr(traceback.format_exc()))",
"title": ""
},
{
"docid": "86d93df5e9bcd0bbd1dbc95aa839899d",
"score": "0.45813572",
"text": "def insert_into_index(es_object, index_name, record): \n try:\n outcome = es_object.index(index=index_name, body=record)\n print(f\"Inserted object into Index '{index_name}'.\")\n print(str(record))\n except Exception as ex:\n print('Error in indexing data')\n print(str(ex))",
"title": ""
},
{
"docid": "f4a20f89cdf668abaec4a099362659d0",
"score": "0.4577445",
"text": "def test_get_or_create_project__call():\n project_name = str(uuid.uuid1())\n project = synapseclient.Project(name=project_name)\n returned = synapseclient.Project(name=project_name,\n id=str(uuid.uuid1()))\n with patch.object(CREATE_CLS,\n \"_find_by_obj_or_create\",\n return_value=returned) as patch_find_or_create:\n new_project = CREATE_CLS.get_or_create_project(name=project_name)\n assert new_project == returned\n patch_find_or_create.assert_called_once_with(project)",
"title": ""
},
{
"docid": "01878f3733f7e6fa22c36b45e3f46ae8",
"score": "0.45740703",
"text": "def test_create_project_adds_a_project(db: Session) -> None:\n\n # there is no project to start with\n assert db.query(models.Project).count() == 0\n\n # create a project\n project = schemas.ProjectCreate(\n name=\"My Project\", directory=\"/wherever\", command=\"whatever\"\n )\n create_project(db, project)\n\n # there is a project now...\n assert db.query(models.Project).count() == 1\n\n # ... and it has the correct content\n db_project = db.query(models.Project).first()\n assert db_project.id is not None\n assert db_project.command == project.command\n assert db_project.directory == project.directory\n assert db_project.name == project.name",
"title": ""
},
{
"docid": "8dd10092dad3a1e670dc70a758f1a80d",
"score": "0.45728135",
"text": "def add_guid_index():\n catalog = api.portal.get_tool('portal_catalog')\n if GUID_INDEX_NAME not in catalog.indexes():\n log.info(\"Adding GUID index %r\" % GUID_INDEX_NAME)\n catalog.addIndex(GUID_INDEX_NAME, 'FieldIndex')\n else:\n log.info(\"GUID index %r already exists\" % GUID_INDEX_NAME)",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="793"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">68e37ec088ae4267c593779e2ace4a03</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">r"""The number of graphs in the dataset.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "179a0ee94a6a7e7d1a87da777afe7be5",
"score": "0.0",
"text": "def __len__(self): # -> Literal[1]:\n ...",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "0355f1833468a8bd7a3a10e653f39a43",
"score": "0.8215049",
"text": "def number_of_graphs(self):\n return self.get_table_row_count(self.GRAPH_TABLE._name_)",
"title": ""
},
{
"docid": "d5cf06f4008d0a5795c841c5b8657580",
"score": "0.75362337",
"text": "def n_datasets(self) -> int:\n n_others = len(self._dcs().keys())\n\n return n_others + 1",
"title": ""
},
{
"docid": "bfee6f97e0581fbcdbcde40548c486ef",
"score": "0.73872626",
"text": "def number_of_edges(self):\n ...",
"title": ""
},
{
"docid": "fb0738fdb10f050aab0ef67e1a4adca5",
"score": "0.73707473",
"text": "def n_plots(self):\n return 0",
"title": ""
},
{
"docid": "08bad50437dea7ff65c93298e103c51c",
"score": "0.7311434",
"text": "def number_of_nodes(self):\n ...",
"title": ""
},
{
"docid": "6d31cacdd1126a7080e038de5ebd6812",
"score": "0.71918505",
"text": "def size(self):\n\t\treturn len(self._datasets)",
"title": ""
},
{
"docid": "1d4a94f52e9f13a3c54d0cf45633160c",
"score": "0.71886915",
"text": "def number_of_nodes(self):\r\n return len(self.nodes)",
"title": ""
},
{
"docid": "d2591fef6647a6fc549f24fb36a1d180",
"score": "0.71552324",
"text": "def number_of_nodes(self):\n return len(self.nodes)",
"title": ""
},
{
"docid": "3558f2eb5a7ab0338cede409267fcf02",
"score": "0.71441734",
"text": "def number_of_edges(self):\r\n return len(self.edges)",
"title": ""
},
{
"docid": "0d8b2036bbf102da85429eefe047c8bb",
"score": "0.7133862",
"text": "def number_of_nodes(self):\n return self.number_of_vertices()",
"title": ""
},
{
"docid": "b1aa83c24a0ee2ef2a53638baa576fba",
"score": "0.70328444",
"text": "def num_of_node(graph):\r\n return len(list(graph.nodes()))",
"title": ""
},
{
"docid": "5dfad0d59845d099bb0f3b0393f7ef0a",
"score": "0.7019014",
"text": "def get_number_of_nodes(self):\r\n return len(self.node_Ids)",
"title": ""
},
{
"docid": "76154412818646208517a4ae81add28b",
"score": "0.6967071",
"text": "def n_nodes(self):\n return self._G.number_of_nodes()",
"title": ""
},
{
"docid": "df2614d63ee491efd9a8e9666e3a4e4b",
"score": "0.6942869",
"text": "def get_number_of_nodes(self):\n return len(self.__adjacency_list)",
"title": ""
},
{
"docid": "6afa6f8cf49e64e4b7e5c061e51ef1ae",
"score": "0.6938715",
"text": "def count(self) -> int:\n return self.vis[\"count\"]",
"title": ""
},
{
"docid": "e93592d0020887f7c40a21c6d1cb6144",
"score": "0.6929193",
"text": "def getGraphSize(self):\n return self.graphSize",
"title": ""
},
{
"docid": "2edb31256fae3bd25a640fed242c1827",
"score": "0.692044",
"text": "def number_of_nodes(self):\r\n return len(self.node)",
"title": ""
},
{
"docid": "04898e55628d19987e50d0a139129b10",
"score": "0.68733644",
"text": "def n_data_points(self)->int:\n return self._n_data_points",
"title": ""
},
{
"docid": "04898e55628d19987e50d0a139129b10",
"score": "0.68733644",
"text": "def n_data_points(self)->int:\n return self._n_data_points",
"title": ""
},
{
"docid": "4a5be17166b29fc298d961638225e1c0",
"score": "0.68576306",
"text": "def number_of_edges(self):\n return sum(1 for _ in self.edges())",
"title": ""
},
{
"docid": "1f728335c8c6b9ff1422511a87bbe86c",
"score": "0.6832707",
"text": "def num_edges(self):\n return",
"title": ""
},
{
"docid": "bcc6f50b575f6617d5901ee7b86f07fe",
"score": "0.6823159",
"text": "def number_of_nodes(self):\n return sum(1 for _ in self.nodes_iter())",
"title": ""
},
{
"docid": "4e249348a7eb7edd50e8b905bb41dfed",
"score": "0.68057483",
"text": "def dataset_image_count(self):\n return self._dataset_image_count",
"title": ""
},
{
"docid": "474979992ba794a05d9cb3e18ca10e96",
"score": "0.6793165",
"text": "def n_edges(self) -> int:\n return len(self.edges())",
"title": ""
},
{
"docid": "62ae97345eb9260c4e91c922f20583df",
"score": "0.67880934",
"text": "def num_nodes(self):\n return len(self.nodes())",
"title": ""
},
{
"docid": "89ac151061059e2521eaf9256758b63b",
"score": "0.67822707",
"text": "def num_nodes(self):\n return len(self.adj_list)",
"title": ""
},
{
"docid": "5c116b37f3519214798b58931e4d1faa",
"score": "0.6774768",
"text": "def node_count(self):\n return len(self.nodes)",
"title": ""
},
{
"docid": "08727f4e597c0ab82643a0042d218190",
"score": "0.6736805",
"text": "def numNodes(self):\n return len(self.dicN)",
"title": ""
},
{
"docid": "ab03c6856efb9d62a0fac78658e743a5",
"score": "0.67211145",
"text": "def number_of_hidden_nodes(self):\r\n return len(self.hidden_nodes)",
"title": ""
},
{
"docid": "c920b53677faaa969bbfeff023116cf0",
"score": "0.6689106",
"text": "def number_of_hidden_edges(self):\r\n return len(self.hidden_edges)",
"title": ""
},
{
"docid": "87c4b1315c12da4ea656b9cbb315a03c",
"score": "0.6685002",
"text": "def connection_count(self):\n\n connections = 0\n if \"num-connections\" in self.profile:\n connections = self.profile[\"num-connections\"]\n\n return connections",
"title": ""
},
{
"docid": "690e135c7c131dc14231b535172a0282",
"score": "0.66796535",
"text": "def nconsiderednodes(self):\n assert self._node_count is not None\n return self._node_count",
"title": ""
},
{
"docid": "3e1aa29ec90d6b77fd0e7c4c31b8563f",
"score": "0.66766924",
"text": "def size(self):\n\n return self.n_edges",
"title": ""
},
{
"docid": "e6faad0a0d83c063cfbc933606dce2f7",
"score": "0.6675746",
"text": "def number_of_edges(self):\n num_edges = graph_wrapper.number_of_edges(self.graph_ptr)\n\n return num_edges",
"title": ""
},
{
"docid": "cf57ddcd35b62ee350b622ab89f97184",
"score": "0.66686505",
"text": "def n_nodes(self) -> int:\n raise NotImplementedError",
"title": ""
},
{
"docid": "cf57ddcd35b62ee350b622ab89f97184",
"score": "0.66686505",
"text": "def n_nodes(self) -> int:\n raise NotImplementedError",
"title": ""
},
{
"docid": "936e2734149056a985cf042958e7a1e8",
"score": "0.66666746",
"text": "def numNodes(self) -> int:\n return self._call_java(\"numNodes\")",
"title": ""
},
{
"docid": "a4f7605f501cc4a7659341266aecc6d0",
"score": "0.6652915",
"text": "def number_of_docs(self) -> int:\n return self._G.number_of_nodes()",
"title": ""
},
{
"docid": "d1efe6a5c532bd8a32dcb9d7931a89fe",
"score": "0.66516787",
"text": "def num_edges(self):\n return lib.graph_num_edges(self._obj)",
"title": ""
},
{
"docid": "540eef509583d426ab07af850429427c",
"score": "0.6622153",
"text": "def num_nodes(self) -> int:\n return self._c_csc_graph.num_nodes()",
"title": ""
},
{
"docid": "8f56c5b5b9c9b332a52dd9b25e86da94",
"score": "0.6622133",
"text": "def _get_number_of_instances(self):\n return self.__number_of_instances",
"title": ""
},
{
"docid": "8f56c5b5b9c9b332a52dd9b25e86da94",
"score": "0.6622133",
"text": "def _get_number_of_instances(self):\n return self.__number_of_instances",
"title": ""
},
{
"docid": "d3bbbace7d5e517b7c1f0c9cd2c64b76",
"score": "0.66186965",
"text": "def example_count(self):\n return len(self.torch_loader.dataset)",
"title": ""
},
{
"docid": "78f630810ff63c673af6a4e80f02b9fd",
"score": "0.6618202",
"text": "def number_of_vertices(self):\n num_vertices = graph_wrapper.number_of_vertices(self.graph_ptr)\n\n return num_vertices",
"title": ""
},
{
"docid": "a3a4abff35b148272092124f43be0f8b",
"score": "0.6610146",
"text": "def dataset_count():\n count = 0\n result = toolkit.get_action('package_search')({}, {'rows': 1})\n if result.get('count'):\n count = result.get('count')\n return count",
"title": ""
},
{
"docid": "6574458beda02c6c4c70bbfeed0306cd",
"score": "0.66042715",
"text": "def get_point_count(self):\n return len(self.edges)",
"title": ""
},
{
"docid": "49918347258b797a343003e19f1f7cfb",
"score": "0.6601892",
"text": "def GetNodeCount(self):\r\n pass",
"title": ""
},
{
"docid": "ed8ffb037c28d99430f82940f7ff1ebe",
"score": "0.65973693",
"text": "def num_instances(self):\n return _NvRules.IMetric_num_instances(self)",
"title": ""
},
{
"docid": "d071495a425e30a7937c01a0e1cd00d3",
"score": "0.6597358",
"text": "def nnodes(self):\n return len(self.matrix.keys())",
"title": ""
},
{
"docid": "5bb32be2f53661002c27c87273f37b8d",
"score": "0.65965056",
"text": "def num_datapoints(self):\n\t\treturn self.predictions.shape[0]",
"title": ""
},
{
"docid": "3464e5965295ae9a8d80778d86c6411c",
"score": "0.6596318",
"text": "def count(self):\n return self._label_fdist.N()",
"title": ""
},
{
"docid": "d0b4f79eedd7716a980fec7fd1fcc86c",
"score": "0.65911674",
"text": "def size(self):\n return self.nodes.size()",
"title": ""
},
{
"docid": "a0788dee9a35d6dfc6c7c25db7fe1e68",
"score": "0.65862036",
"text": "def size(self):\n return self.digr.num_vertices()",
"title": ""
},
{
"docid": "1e40b28ac771634a9df3b7c41ccd5c0c",
"score": "0.658214",
"text": "def number_of_nodes(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"number_of_nodes\")",
"title": ""
},
{
"docid": "6b5270f9cba24d292600124909b27116",
"score": "0.6581056",
"text": "def plot_rank(self):\n return len(self.plot_shape)",
"title": ""
},
{
"docid": "4748f20ccc2d3768007027e69e5cabb6",
"score": "0.65771747",
"text": "def node_count(graph):\n return len(graph.keys())",
"title": ""
},
{
"docid": "958ed92b89a2de823e72cf2d18df1875",
"score": "0.6570017",
"text": "def num_nodes(self):\n return len(self._nodes)",
"title": ""
},
{
"docid": "460f42002a43c9de8ba845e635ed3be5",
"score": "0.6568672",
"text": "def dataset_size(self):\n return self.dataset.size",
"title": ""
},
{
"docid": "a5956dcf005f5a551ed08f05bb482489",
"score": "0.6568589",
"text": "def number_of_paths(self):\n return self.num_paths",
"title": ""
},
{
"docid": "562189beb582fc5db5475ef106ed37f2",
"score": "0.65611106",
"text": "def __len__(self) -> int:\n return len(list(self.graph.items(self.uri)))",
"title": ""
},
{
"docid": "d90f41a954dbd4af2a4f891ba47a0b2f",
"score": "0.65585494",
"text": "def totalConnections(analyzer):\n return gr.numEdges(analyzer['connections'])",
"title": ""
},
{
"docid": "5b99a28bdd0c7483f89649d3657a11fa",
"score": "0.6558188",
"text": "def num_edges(self) -> int:\n return self._c_csc_graph.num_edges()",
"title": ""
},
{
"docid": "0af950419a6e9ba755be627bf4a55852",
"score": "0.6527257",
"text": "def __len__(self):\n return self.num_edges",
"title": ""
},
{
"docid": "9edc1dad30b39aefcfd0285b42b8a328",
"score": "0.65193063",
"text": "def number_of_monitors(): ###\n return len(monitors())",
"title": ""
},
{
"docid": "d15ec172779f9d6b32207a8a088d435d",
"score": "0.65185875",
"text": "def numNodes(self):\n return len(self.G)",
"title": ""
},
{
"docid": "2fc7ddd99e64240ecf316bf6d1594e93",
"score": "0.65136",
"text": "def get_frame_count(self):\n\n return len(self.plot_list)",
"title": ""
},
{
"docid": "34cb4e9110db43ea35180523b3ca3d70",
"score": "0.6512665",
"text": "def dimensions(self):\n return self._N",
"title": ""
},
{
"docid": "4aee5267e5f73468783933e887a0efdd",
"score": "0.65110767",
"text": "def n_records(self) -> int:\n\n n_records = sum([len(data.initial_molecules) for data in self.dataset.values()])\n return n_records",
"title": ""
},
{
"docid": "719d02468234d1aa3c2dec1d8689c58b",
"score": "0.65079",
"text": "def getNdim(self):\n try:\n ndim = self.segments.ndim\n except AttributeError:\n ndim = self._ndim\n return ndim",
"title": ""
},
{
"docid": "dddcc96fb145ad82d5cb2cacd1bd7931",
"score": "0.6505912",
"text": "def get_num_nodes(self):\n return len([sec for sec in self.config.sections if self.is_node(sec)])",
"title": ""
},
{
"docid": "a6b1e31b262dd6bc1276e39245392cb2",
"score": "0.6504585",
"text": "def num_datapoints(self):\n\t\treturn self.pred_probs.shape[0]",
"title": ""
},
{
"docid": "9cd1df339740f4fb4043640e049d656c",
"score": "0.6499093",
"text": "def n_scales(self):\n return len(self.scales)",
"title": ""
},
{
"docid": "9cd1df339740f4fb4043640e049d656c",
"score": "0.6499093",
"text": "def n_scales(self):\n return len(self.scales)",
"title": ""
},
{
"docid": "df460ebad4eecf19324ad19e3c7f59de",
"score": "0.64894354",
"text": "def num_subgraphs(self, subgraph):\n return self.num_subgraphs_by_parameters(\n subgraph.num_verts(),\n subgraph.num_edges(),\n subgraph.automorphism_group().order()\n )",
"title": ""
},
{
"docid": "df52ea3bf5d0498742b099a84939ac22",
"score": "0.64827776",
"text": "def __len__(self):\n\t\treturn self._nnodes",
"title": ""
},
{
"docid": "94ce00d1dbe4096809876f0f4d3ae624",
"score": "0.6477803",
"text": "def n_nodes(self):\n return len(self.xs)",
"title": ""
},
{
"docid": "df97dcfd539cfe6c227b63c8b4f920c5",
"score": "0.64721024",
"text": "def num_per_epoch(self):\n\n return len(self.images)",
"title": ""
},
{
"docid": "52821838253f58673b5a1a49ed8e44ff",
"score": "0.64649993",
"text": "def getDimension(self):\n return len(self.components)",
"title": ""
},
{
"docid": "b4a960f137d0cfa646f638b56d43f468",
"score": "0.64609027",
"text": "def __len__(self):\n return self._n_nodes",
"title": ""
},
{
"docid": "4a26f0ff4891642c6b0af7c69c3bfb35",
"score": "0.6458422",
"text": "def _get_dataset_size(self):",
"title": ""
},
{
"docid": "717dba2db4cbce87a82ddae8a8cd9c7f",
"score": "0.6457629",
"text": "def dimension(self):\r\n return self.n",
"title": ""
},
{
"docid": "98d322b0f1269415c560ad757aef43fe",
"score": "0.64518964",
"text": "def get_count(self):\n return len(self._bars)",
"title": ""
},
{
"docid": "d413aebbb9a88ec559169b4fa899caac",
"score": "0.6450254",
"text": "def dimension(self):\n return self.n+1",
"title": ""
},
{
"docid": "c49bcb084caca4964216fc2fefd6e5af",
"score": "0.6448196",
"text": "def nums(self):\n return len(self.data)",
"title": ""
},
{
"docid": "0f345f1295ad50bfc21811d69dea3f0d",
"score": "0.6447515",
"text": "def get_count(self) -> int:\n return len(self._bars)",
"title": ""
},
{
"docid": "8bc33751eeb84b817b313f95c0b2585c",
"score": "0.64436454",
"text": "def number_of_layers(self):\n return self.len",
"title": ""
},
{
"docid": "4ec5df087a97bd61e2a2f090a708bec3",
"score": "0.6435416",
"text": "def get_dataset_size(self, dataset):\n \n i = 0\n for batch, (image, audio, label) in enumerate(dataset):\n assert tf.shape(image)[0] == tf.shape(audio)[0] == tf.shape(label)[0]\n i += tf.shape(image)[0]\n \n return i",
"title": ""
},
{
"docid": "6e5b492619ae4b1681fc7d6804c9e76e",
"score": "0.642131",
"text": "def __len__(self):\r\n return len(Dataset(self._id).shape)",
"title": ""
},
{
"docid": "383ab9ad77c69e54df5b8d330e1e757c",
"score": "0.6417246",
"text": "def qg8_graph_get_number_chunks(graph: qg8_graph):\n if not isinstance(graph, qg8_graph):\n raise TypeError(\"Argument is not a qg8_graph\")\n return len(graph.chunks)",
"title": ""
},
{
"docid": "640890e72855245f1a2545c881ce98fb",
"score": "0.640243",
"text": "def num_node_groups(self) -> int:\n return pulumi.get(self, \"num_node_groups\")",
"title": ""
},
{
"docid": "725a9f9c5e60e3ed272d60d7960d7fb6",
"score": "0.6393146",
"text": "def dim(self):\n return len(self)",
"title": ""
},
{
"docid": "c5fbe652b1d9985ecc168146e0cfc634",
"score": "0.63930035",
"text": "def num_nodes(self):\n return len(self.myDict.keys())",
"title": ""
},
{
"docid": "4a45541a366dd2442b53dbbeb223cb07",
"score": "0.6390137",
"text": "def getNodes(self):\n return self.getParameter(\"NODE_COUNT\")",
"title": ""
},
{
"docid": "ba0f614b73a3c53ccb3754672c4c9142",
"score": "0.63867104",
"text": "def N(self):\n return self.inputdata().GetNumberOfPoints()",
"title": ""
},
{
"docid": "54c448bb78dddb0d6d8e65382178e863",
"score": "0.6385197",
"text": "def number_of_selfloops(self):\r\n return len(self.selfloop_edges())",
"title": ""
},
{
"docid": "ba62341117af99dc48f054cda06be3a1",
"score": "0.6381643",
"text": "def totalNumNodes(self) -> int:\n return self._call_java(\"totalNumNodes\")",
"title": ""
},
{
"docid": "79be64a597a7eff7ef9ee35a4799f694",
"score": "0.63810533",
"text": "def num_of_edge(graph):\r\n return len(list(graph.edges()))",
"title": ""
},
{
"docid": "9b618bf04b85a724d0516cc64b89edc5",
"score": "0.6379052",
"text": "def dim(self) -> int:\n return self._dim",
"title": ""
},
{
"docid": "9b618bf04b85a724d0516cc64b89edc5",
"score": "0.6379052",
"text": "def dim(self) -> int:\n return self._dim",
"title": ""
},
{
"docid": "e1c525285756cd9712e8ee8baecfed8d",
"score": "0.63684386",
"text": "def number_of_values(self):",
"title": ""
},
{
"docid": "d31cf30662de8229efe8544a66397df2",
"score": "0.63648313",
"text": "def num_frames( self ):\r\n return self.data.num_frames",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="794"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">e436a651273cc71240d278e3bae026c5</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Show the given variable on the plot. Undoes `ignore_variable`. If a dataframe name is specified, all of its columns will be shown.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "13c4e6f3f61e09eaa750fd207f32f767",
"score": "0.4953621",
"text": "def show_variable(self, toast: Toast, var_name: str) -> None:\n try:\n return self.active_view.show_variable(toast, var_name)\n except NotImplementedError:\n toast.show(f\"{self._active} does not implement showing variables\", ToastType.warning)",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "f3416a3c42880a57556602bd26db1797",
"score": "0.57033026",
"text": "def ignore_variable(self, toast: Toast, var_name: str) -> None:\n try:\n return self.active_view.ignore_variable(toast, var_name)\n except NotImplementedError:\n toast.show(f\"{self._active} does not implement ignoring variables\", ToastType.warning)",
"title": ""
},
{
"docid": "a30be2073bb4ee8b98885fcb1f16c1f5",
"score": "0.5620072",
"text": "def plot_proper(df: pd.DataFrame, xax: str, yax: str, var: str, title: str) -> None:\n pd.options.plotting.backend = \"plotly\"\n fig = df.plot(title=title, template=\"simple_white\",\n labels=dict(index=xax, value=yax, variable=var))\n fig.update_layout(\n xaxis_title=xax,\n yaxis_title=yax,\n )\n fig.show()",
"title": ""
},
{
"docid": "0372b371887346e02e57dac3f55b655a",
"score": "0.545876",
"text": "def ignore_variable(self, toast: Toast, var_name: str) -> None:\n raise NotImplementedError",
"title": ""
},
{
"docid": "b21493415e595924fcbbdc5510e9902e",
"score": "0.51881444",
"text": "def show(*args, block=True, **kwargs):\n # Remove reserved rewords\n try:\n kwargs.pop('block')\n except:\n pass\n # Get the variable names in the scope show() was called from\n callers_local_vars = inspect.currentframe().f_back.f_locals.items()\n\n # Make a dictionary of the DataFrames from the position args and get their variable names using inspect\n dataframes = {}\n for i, df_object in enumerate(args):\n df_name = 'untitled' + str(i + 1)\n\n for var_name, var_val in callers_local_vars:\n if var_val is df_object:\n df_name = var_name\n\n dataframes[df_name] = df_object\n\n # Add the dictionary of positional args to the kwargs\n if (any([key in kwargs.keys() for key in dataframes.keys()])):\n print(\"Warning! Duplicate DataFrame names were given, duplicates were ignored.\")\n kwargs = {**kwargs, **dataframes}\n\n pandas_gui = PandasGUI(**kwargs)\n\n if block:\n pandas_gui.app.exec_()",
"title": ""
},
{
"docid": "a51ef5db5e8ccb230c8b182b1588e161",
"score": "0.51808095",
"text": "def no_detour_boxplot(df,country,save=False,fig=None, ax=None,**kwargs):\n df = df.loc[df['country'] == country]\n\n if (fig == None and ax == None): # if No axes and no figure is provided\n fig, ax = plt.subplots(figsize=(12, 6))\n\n df.boxplot(by='AoI combinations', column='no detour', grid=False, ax=ax,**kwargs)\n ax.set_xlabel(\"Number of combinations of flood events (AoI)\")\n ax.set_ylabel(\"% No detour\")\n ax.set_title(\"% routes between NUTS-3 regions in {} without detour\".format(country))\n\n #Todo (possible): give fig ax as args; enable saving possiblity\n\n if save: # TODO REPLACE ALL INSTANCES OF THIS PART OF CODE WITH A SPECIAL FUNCTION\n save_figs = load_config(config_file)['paths']['output_images'] / 'no_detour_boxplot'\n if not save_figs.exists(): save_figs.mkdir()\n filename = \"noDT_boxplot_{}.png\".format(country)\n fig.savefig(save_figs / filename)\n\n return fig,ax",
"title": ""
},
{
"docid": "53a5704bd479ea2ddbc6c7a3c6679479",
"score": "0.51140994",
"text": "def display_data_frame(data_frame):\n\n with pd.option_context('display.max_rows', None, 'display.max_columns', None): # more options can be specified also\n print(data_frame)",
"title": ""
},
{
"docid": "cf1e92a529a23ea32701ac6ec9e46e36",
"score": "0.5068718",
"text": "def plot_log(df, var) -> None:\n\n df = df.copy()\n df[var] = np.log(df[var] + 1)\n sns.displot(df, x=var)",
"title": ""
},
{
"docid": "0af0d61f97e9c1df8737454662caad4a",
"score": "0.5064501",
"text": "def plot_continuous(df, var) -> None:\n\n df = df.copy()\n df['Income'] = df['Income'].astype('category')\n var_name = var\n if var in config.SKEWED_NUMERIC_VARS:\n df[var] = df[var]\n var_name = 'Log of ' + var_name\n ax = sns.boxplot(x='Income', y=var, data=df)\n ax.set(ylabel=var_name, title=f\"Effect of {var} on Income\")\n plt.show()",
"title": ""
},
{
"docid": "005efd6f5fe0c2f341addce0816a3c8e",
"score": "0.50505435",
"text": "def visualize_continuous_variables(df, label, method={'type': 'histogram', 'bins': 20}, outlier='on'):\n # create vector of the variable of interest\n v = df[label]\n # define mean and standard deviation\n m = v.mean()\n s = v.std()\n # prep the figure\n fig, ax = plt.subplots(1, 2, figsize=(14, 4))\n ax[0].set_title('Distribution of ' + label)\n ax[1].set_title('Tip % by ' + label)\n if outlier == 'off': # remove outliers accordingly and update titles\n v = v[(v - m) <= 3 * s]\n ax[0].set_title('Distribution of ' + label + '(no outliers)')\n ax[1].set_title('Tip % by ' + label + '(no outliers)')\n if method['type'] == 'histogram': # plot the histogram\n v.hist(bins=method['bins'], ax=ax[0])\n if method['type'] == 'boxplot': # plot the box plot\n df.loc[v.index].boxplot(label, ax=ax[0])\n ax[1].plot(v, df.loc[v.index].tip_percentage, '.', alpha=0.4)\n ax[0].set_xlabel(label)\n ax[1].set_xlabel(label)\n ax[0].set_ylabel('Count')\n ax[1].set_ylabel('Tip (%)')",
"title": ""
},
{
"docid": "a3c79f8c6106edcd31610020abb8ff8b",
"score": "0.5001463",
"text": "def plot_continuous_column(df, target_variable, column_name, fig_size=(9, 12)):\n f, (ax1, ax2) = plt.subplots(2, figsize=fig_size)\n tmp_df = df.dropna()\n sns.distplot(tmp_df[column_name], ax=ax1)\n sns.boxplot(x=target_variable, y=column_name, data=tmp_df, ax=ax2)",
"title": ""
},
{
"docid": "db0fbaeaed5291893aca4beb006c0d75",
"score": "0.49557826",
"text": "def set_df_violin_plot(data, header):\n df = records_to_df(data)\n try:\n df[['user', 'condition', 'block', 'task']] = df[['user', 'condition', 'block', 'task']].astype('category')\n except KeyError:\n col_names = [c['id'] for c in header]\n df = pd.DataFrame(columns=col_names)\n df.rename(columns={'df1 mean': 'df1', 'df2 mean': 'df2'}, inplace=True)\n fig = plotting.generate_violin_figure(df, columns=['df1', 'df2'], ytitle='Mean', legend_title=\"DOF\")\n return fig",
"title": ""
},
{
"docid": "e2c885c87d0ff416a36c8e524bf40d4a",
"score": "0.49128348",
"text": "def plot_var(var='positiveIncrease',\n state='NY'):\n assert type(var) == str, \"Expected string as the variable name\"\n assert type(state) == str, \"Expected string as the state name\"\n\n y = df[df['state'] == state][var]\n x = df[df['state'] == state]['date']\n plt.figure(figsize=(12, 4))\n plt.title(\"Plot of \\\"{}\\\" for {}\".format(var, state), fontsize=18)\n plt.bar(x=x, height=y, edgecolor='k', color='orange')\n plt.grid(True)\n plt.xticks(fontsize=14, rotation=45)\n plt.yticks(fontsize=14)\n plt.show()",
"title": ""
},
{
"docid": "5c80fdeda37d80a6aae7f35d9019dc81",
"score": "0.48900345",
"text": "def show_labels(self, var, datasets, text_key):\n\t\tif datasets == ['all']: datasets = self.ds_names\n\t\tds = [self[datasets]] if len(datasets)==1 else self[datasets]\n\t\talias = self._get_alias(datasets)\n\t\ttk = text_key.split('~')\n\t\tetk = tk[1].split()[0] if len(tk) > 1 else None\n\t\ttk = tk[0]\n\t\tall_df = []\n\t\tfor v in var:\n\t\t\tif not all(v in d for d in ds): continue\n\t\t\ttexts = [d.text(v, False, tk, etk) if v in d else None\n\t\t\t\t\t for d in ds]\n\t\t\tindex = pd.MultiIndex.from_tuples([(v, a) for a in alias])\n\t\t\tdf = pd.DataFrame({text_key: texts}, index=index)\n\t\t\tall_df.append(df)\n\t\tif not all_df:\n\t\t\tprint('No variables to show.')\n\t\telse:\n\t\t\treturn pd.concat(all_df, axis=0)",
"title": ""
},
{
"docid": "7f303d01580491b054b8ee02bf003a5d",
"score": "0.48365968",
"text": "def plot(ds: DatasetLike.TYPE,\n var: VarName.TYPE,\n indexers: DictLike.TYPE = None,\n title: str = None,\n properties: DictLike.TYPE = None,\n file: str = None) -> Figure:\n ds = DatasetLike.convert(ds)\n\n var_name = VarName.convert(var)\n if not var_name:\n raise ValidationError(\"Missing name for 'var'\")\n var = ds[var_name]\n\n indexers = DictLike.convert(indexers)\n properties = DictLike.convert(properties) or {}\n\n figure = plt.figure()\n ax = figure.add_subplot(111)\n\n var_data = get_var_data(var, indexers)\n var_data.plot(ax=ax, **properties)\n\n if title:\n ax.set_title(title)\n\n figure.tight_layout()\n\n if file:\n figure.savefig(file)\n\n return figure if not in_notebook() else None",
"title": ""
},
{
"docid": "147a7cca10cae4658a76af694603d9ef",
"score": "0.47928968",
"text": "def removeVariable(self, *args):\n return _libsedml.SedDataGenerator_removeVariable(self, *args)",
"title": ""
},
{
"docid": "739695fe3ea743b4e0a8fb833a68fa2c",
"score": "0.4776736",
"text": "def plot_discrete(df, var) -> None:\n\n temp = df[['Income', var]].groupby(var).mean().reset_index()\n axis = sns.barplot(x=var, y='Income', data=temp)\n axis.set(ylabel=\"Probability of earning over 50K\")\n plt.title(f\"Effect of {var} on Income\")\n if var == 'weeks worked in year':\n plt.title(f\"Effect of Months worked in year on Income\")\n axis.set(xlabel=\"Months worked in year\")\n plt.show()",
"title": ""
},
{
"docid": "4ce3d4781b9c8774a39083c0bd03c66a",
"score": "0.4768098",
"text": "def plot_missing(self, axis = 1, show=True, **figure_kwargs):\n if axis == 1:\n if self.record_missing_cols is None:\n raise NotImplementedError(\"Columns missing values have not been calculated. Run `find_missing with axis = 1`\")\n elif axis == 0:\n if self.record_missing_rows is None:\n raise NotImplementedError(\"Rows missing values have not been calculated. Run `find_missing with axis = 0`\")\n self.reset_plot()\n # Histogram of missing values\n plt.style.use('seaborn-white')\n plt.figure(figsize = (7, 5))\n if axis == 1:\n plt.hist(self.missing_stats_cols['missing_fraction'], bins = np.linspace(0, 1, 11), edgecolor = 'k', linewidth = 0.5)\n elif axis == 0:\n plt.hist(self.missing_stats_rows['missing_fraction'], bins = np.linspace(0, 1, 11), edgecolor = 'k', linewidth = 0.5)\n plt.xticks(np.linspace(0, 1, 11))\n plt.xlabel('Missing Fraction', size = 14)\n plt.ylabel('Count of Features', size = 14)\n plt.title(\"Fraction of Missing Values Histogram\", size = 16)\n missing_plot = self.prepro_dir + '/missing.png'\n plt.savefig(missing_plot, dpi=300)\n if self.isDisplay == False:\n plt.close()",
"title": ""
},
{
"docid": "f81b4edbff3dfdaaacd61d1269c7a70d",
"score": "0.47633633",
"text": "def displayDataFrame(dataFrame):\n pd.set_option('display.max_rows', dataFrame.shape[0] + 1)\n pd.set_option('display.max_columns', dataFrame.shape[1] + 1)\n print(dataFrame)\n pd.reset_option('display.max_rows')\n pd.reset_option('display.max_columns')",
"title": ""
},
{
"docid": "d6b28d65ea32c34efacc762f1e62c65c",
"score": "0.4750205",
"text": "def removeVariable(self, *args):\n return _libsedml.SedComputeChange_removeVariable(self, *args)",
"title": ""
},
{
"docid": "c57f8fcc11be295aa3a4e5d2127121d3",
"score": "0.47035733",
"text": "def plot_graph(dfs,\n variables_to_plot,\n titles,\n ylim=None,\n xlim=None,\n save=None,\n eventname=None,\n show=True):\n answer = {} \n custom_lim = np.ndarray((2,1))\n def printspanselector(xmin, xmax):\n if isinstance(xmin, np.datetime64):\n xmin = date2num(xmin)\n xmax = date2num(xmax)\n\n ind = np.logical_and(date2num(df.index) >= xmin,\n date2num(df.index) <= xmax)\n axs[-1].plot(df.index[ind],\n df[v][ind],\n 'r+',\n gid='selected')\n if 'delete' not in answer:\n answer['delete'] = []\n answer['delete'].append([xmin, xmax])\n\n def onclick(event, ax):\n # Only clicks inside this axis are valid.\n try: # use try/except in case we are not using Qt backend\n zooming_panning = ( fig.canvas.cursor().shape() != 0 ) # 0 is the arrow, which means we are not zooming or panning.\n except:\n zooming_panning = False\n if zooming_panning: \n return\n\n if eventname == 'choose':\n plt.close('all')\n answer['axes'] = event.inaxes.get_title()\n \n \n plt.close('all')\n fig = plt.figure(figsize = (11.69*(2/3), 8.27/2))\n gs1 = gridspec.GridSpec(len(dfs), 1)\n axs=[]\n for n, df in enumerate(dfs):\n if n == 0:\n axs.append(fig.add_subplot(gs1[n],) )\n else:\n axs.append(fig.add_subplot(gs1[n], sharex=axs[0]) )\n\n plt.title(titles[n])\n\n for i,v in enumerate(variables_to_plot):\n if v in df:\n if not check_timeseries(df[v].values):\n typ = '-'\n else:\n typ = '+'\n\n axs[-1].plot(df.index,\n df[v],\n typ,\n label=v)\n\n if xlim:\n plt.xlim(xlim[0], xlim[1])\n\n if ylim:\n plt.ylim(ylim[0], ylim[1])\n\n plt.legend(bbox_to_anchor=(0.0, -.5, 1, 0),\n loc=\"lower left\",\n mode=\"expand\", ncol=2)\n \n fig.autofmt_xdate()\n\n if save:\n plt.savefig(save,\n format='png',\n bbox_inches=\"tight\")\n\n if eventname=='choose':\n cid = fig.canvas.mpl_connect('button_press_event', lambda event: onclick(event, axs[-1]))\n plt.show()\n elif eventname=='clean':\n selector = SpanSelector(axs[-1],\n onselect=printspanselector,\n direction='horizontal',\n useblit=True,\n span_stays=False,\n button=1,\n rectprops={'facecolor': 'grey', 'alpha': 0.3})\n fig.canvas.mpl_connect('key_press_event',\n selector)\n plt.show()\n else:\n if show:\n plt.show()\n\n return answer",
"title": ""
},
{
"docid": "84b3cc6dad55e823688a60a9dd9f1d6a",
"score": "0.47027907",
"text": "def drop_superfluous_trace_vars(data):\n return data.drop([\"P0\", \"co2vmr\", \"gw\", \"hyai\", \"date\", \"date_written\",\n \"datesec\", \"hyai\", \"hyam\", \"hybi\", \"hybm\", \"mdt\",\n \"nbdate\", \"nbsec\", \"ndbase\", \"ndcur\", \"nlon\", \"nsbase\",\n \"nscur\", \"nsteph\", \"ntrk\", \"ntrm\", \"ntrn\",\n \"time_written\", \"wnummax\"])",
"title": ""
},
{
"docid": "62e424f4c821b66a9f0683dc1362dd3c",
"score": "0.468913",
"text": "def plot_variable(lon, lat, variable, varname):\n\n # Create a figure\n plt.figure(figsize=(12,12))\n ax = plt.axes(projection=ccrs.PlateCarree())\n ax.coastlines()\n \n # Plot data\n plt.pcolor(lon, lat, variable, transform=ccrs.PlateCarree(), cmap='RdYlBu_r')\n plt.title(varname)\n plt.colorbar(orientation=\"horizontal\", pad=0.06)#fraction=0.05)\n \n # Extent from metadata: ULC: 060.00 -120.00 LRC: 000.00 -030.00 RES:1.00 1.0\n ax.set_xticks([-120,-60,-30])\n ax.set_yticks([60,30,0])\n \n plt.savefig('snapshot_' + varname + '.png', dpi=300, bbox_inches='tight')\n plt.show()\n plt.close()",
"title": ""
},
{
"docid": "7e890b9f93a94f0a34c0d76af1446424",
"score": "0.4683768",
"text": "def show_variable(self, toast: Toast, var_name: str) -> None:\n raise NotImplementedError",
"title": ""
},
{
"docid": "b971ee4f2989e077c9d161fa3847216b",
"score": "0.46831873",
"text": "def plot_3d(df, target_variable):\n unique_labels = target_variable.unique()\n ordinal_encoding = [np.where(unique_labels == label)[0][0]\n for label in target_variable]\n color_dict = {0: 'red', 1: 'green', 2: 'blue'}\n colors = [color_dict[each] for each in ordinal_encoding]\n threedee = plt.figure().gca(projection='3d')\n threedee.scatter(df[[0]], df[[1]],\n df[[2]], color=colors)\n threedee.set_xlabel(df.columns.values[0])\n threedee.set_ylabel(df.columns.values[1])\n threedee.set_zlabel(df.columns.values[2])\n plt.show()\n return None",
"title": ""
},
{
"docid": "fbd0f849e14a511d0e2aa2a2fe32b21e",
"score": "0.46819878",
"text": "def visualize(self, df:DataFrame):# -> dict:\n pass",
"title": ""
},
{
"docid": "bc766e7a0cd6d7c24b37fe6c46efb114",
"score": "0.46767002",
"text": "def get_var(data, variable):\n\n var = data.variables[variable]\n var_plot = ma.masked_equal(var, var.missing_value)\n\n lons = data.variables['lon'][...]\n lats = data.variables['lat'][...]\n\n return var_plot, lons, lats",
"title": ""
},
{
"docid": "0ad4c798ac7dc8256ba71957ce08c2f2",
"score": "0.46681195",
"text": "def plot_solution(self, variable):\n all_points = [*(self.nodes), *self.internal_points]\n x = sorted(set([point.coords[0] for point in all_points]))\n y = sorted(set([point.coords[1] for point in all_points]))\n z = [[None] * len(x)] * len(y)\n variable = variable.lower()\n for point in all_points:\n point_x = x.index(point.coords[0])\n point_y = y.index(point.coords[1])\n if variable == \"ux\":\n z[point_y][point_x] = point.u[0]\n elif variable == \"uy\":\n z[point_y][point_x] = point.u[1]\n elif variable == \"sx\":\n z[point_y][point_x] = point.p[0]\n elif variable == \"sy\":\n z[point_y][point_x] = point.p[1]\n elif variable == \"sxy\":\n z[point_y][point_x] = point.p[2]\n else:\n print(f\"Invalid option: {variable}\")\n self.plot_model(auto_open=False)\n self.fig.add_contour(\n name=variable,\n z=z,\n x=x,\n y=y,\n connectgaps=True,\n contours=dict(coloring=\"heatmap\", showlabels=True),\n colorbar=dict(title=variable, titleside=\"right\", x=1.2),\n )\n self.fig[\"layout\"].update(title=f\"<b>{self.name} ({variable})</b>\")\n return plotly.offline.plot(self.fig, filename=f\"{self.name}-{variable}.html\")",
"title": ""
},
{
"docid": "aa5d60f3a2f6d30cef38d85daa28dec1",
"score": "0.46618274",
"text": "def set_proj_var_plot(data):\n df = records_to_df(data)\n long_df = analysis.wide_to_long(df, ['parallel', 'orthogonal'], suffixes='variance', j='projection')\n fig = plotting.generate_lines_plot(long_df, \"variance\", by='user', color_col='projection')\n return fig",
"title": ""
},
{
"docid": "ee269e097d579cb2a24ab667fa58037b",
"score": "0.46423653",
"text": "def plot_against_target(self, feature):\n assert feature != self._target_variable\n\n if feature in self.numeric_features:\n self._dataset[[feature, self._target_variable]].plot.scatter(x=feature,\n y=self._target_variable,\n alpha=0.1,\n title='{0} vs. target (`{1}`)'.\n format(feature,\n self._target_variable))\n else:\n title = '{0} vs. target (`{1}`)'.format(feature, self.target_variable)\n self._dataset[[feature, self._target_variable]].boxplot(by=feature)\n plt.ylabel(self.target_variable)\n plt.title(title)\n plt.suptitle(\"\")\n plt.tight_layout()",
"title": ""
},
{
"docid": "9e9637d4e9abc6369930a68c1502b95e",
"score": "0.46198583",
"text": "def set_variance_graph(table_data):\n df = records_to_df(table_data)\n df.dropna(inplace=True)\n return plotting.generate_means_figure(df)",
"title": ""
},
{
"docid": "82efd8c8b683ed6091a8576b3df1086d",
"score": "0.46060088",
"text": "def plotData(dataFrame, titleString, saveToFile=False, nsamples = 100, vars=None):\n\n\t#plot nsamples random elements from the sample\n\trandom_elements = random.sample(range(dataFrame.shape[0]), nsamples)\n\tdataFrame_plot = dataFrame.iloc[random_elements]\n\n\tif vars != None:\n\t\tp = sns.pairplot(dataFrame_plot, hue='AHUNumber', diag_kind='kde', vars=vars, dropna=True)\n\telse:\n\t\tp = sns.pairplot(dataFrame_plot, hue='AHUNumber', diag_kind='kde', dropna=True)\n\n\tplt.suptitle(titleString, y = 1.025)\n\n\tif saveToFile == True:\n\t\tplt.savefig('testfig.png', format='png', pad_inches=0.5, bbox_inches='tight')\n\t\tplt.close()\n\telse:\n\t\tpass\n\t\t#plt.show()",
"title": ""
},
{
"docid": "174b0d1fc32bac49ceac43e70e10a012",
"score": "0.46051374",
"text": "def plot(self, **kwargs):\n plotdata = self.plottable_data\n if plotdata:\n plotdata.plot(**kwargs)\n else:\n raise NeXusError(\"There is no plottable data\")",
"title": ""
},
{
"docid": "d347fcfa2ffeceefe3ec788c6b9ec835",
"score": "0.45513475",
"text": "def removeVariable(self, *args):\n return _libsedml.SedSetValue_removeVariable(self, *args)",
"title": ""
},
{
"docid": "52d761854d1c29b36faeda1e5af7e583",
"score": "0.4534381",
"text": "def show_cats(self, var, text_key):\n\t\ttext_key = text_key.split('~')\n\t\tetk = text_key[1].split()[0] if len(text_key) > 1 else None\n\t\ttext_key = text_key[0]\n\t\tdf_all_v = []\n\t\tfor v in var:\n\t\t\tall_df = []\n\t\t\tfor name in list(self.ds_alias.values()):\n\t\t\t\tif v in self[name]:\n\t\t\t\t\tval = self[name].value_texts(v, text_key, etk)\n\t\t\t\t\tcodes = self[name].codes(v)\n\t\t\t\t\tindex = pd.MultiIndex.from_tuples([(v, c) for c in codes])\n\t\t\t\t\tdf = pd.DataFrame(val, index=index, columns=[name])\n\t\t\t\t\tall_df.append(df)\n\t\t\tall_df = pd.concat(all_df, axis=1)\n\t\t\tdf_all_v.append(all_df)\n\t\tif not df_all_v:\n\t\t\tprint('No variables to show.')\n\t\telse:\n\t\t\treturn pd.concat(df_all_v, axis=0)",
"title": ""
},
{
"docid": "c460d6f20fc4d69bed840f326ee71a3a",
"score": "0.44996905",
"text": "def plot_categorical_column(df, target_variable, column_name, fig_size=(9, 12), y_lim=None):\n f, (ax1, ax2) = plt.subplots(2, figsize=fig_size)\n tmp_df = df.dropna()\n sns.countplot(x=column_name, data=tmp_df, ax=ax1)\n sns.pointplot(x=column_name, y=target_variable, data=tmp_df, ax=ax2)\n if y_lim is not None:\n ax2.set_ylim(0, y_lim)",
"title": ""
},
{
"docid": "31dcef4c8847e4789643aaf5cb79239e",
"score": "0.44970563",
"text": "def require_variable(df, variable, unit=None, year=None, exclude_on_fail=False,\n **kwargs):\n fdf = df.filter(**kwargs)\n if len(fdf.data) > 0:\n vdf = fdf.require_variable(variable=variable, unit=unit, year=year,\n exclude_on_fail=exclude_on_fail)\n df.meta['exclude'] |= fdf.meta['exclude'] # update if any excluded\n return vdf",
"title": ""
},
{
"docid": "5a9b710f33eaca9bece16fa9e4362da0",
"score": "0.44876218",
"text": "def unsuppress(self):\n self._pipe.send((None, 'show', None))",
"title": ""
},
{
"docid": "9a073a4e1e8b5823d0c2be47feae53f7",
"score": "0.4475288",
"text": "def plot(self, df, plotdir):\n df.plot()",
"title": ""
},
{
"docid": "4aed811f4a0431f31970dec2f4ca576a",
"score": "0.44718388",
"text": "def plot_single_variable(self, results, xlab=None, ylab=None, save=None):\n\n n, m = self.x.shape\n if m != 2:\n raise ValueError('Error: can only plot single explanatory variable. Current X shape: ({},{})'.format(n, m))\n X = self.x.drop('const', axis=1)\n X = np.squeeze(X.values)\n Y = np.squeeze(self.y.values)\n plt.plot(X, Y, 'o', c='steelblue')\n Xmin = np.argmin(X)\n Xmax = np.argmax(X)\n plt.plot([X[Xmin], X[Xmax]], [self.preds[Xmin], self.preds[Xmax]], c='darkorange')\n if xlab:\n plt.xlabel(xlab)\n if ylab:\n plt.ylabel(ylab)\n if save:\n plt.savefig('{}.png'.format(save), dpi=200)\n plt.show()",
"title": ""
},
{
"docid": "acb0f674d7c2303a1019fee9596426f2",
"score": "0.44626507",
"text": "def display(df):",
"title": ""
},
{
"docid": "7f92ec7c3a117c72a81c791bdbfc54a3",
"score": "0.4445461",
"text": "def print_dataframe(data):\n with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n print(data)",
"title": ""
},
{
"docid": "215c3df97025653743c1890ed0d2c221",
"score": "0.44444445",
"text": "def remove_variable(self, varname):\n self.graph.remove_node(self.variables[varname])\n \n #removes the class of varname (should there be no other variables that share that class) \n #from classes_imported set \n for key,value in self.classes_available.items():\n if isinstance(self.variables[varname], value):\n self.classes_imported.remove(key) \n\n #remove the varname from the variables dictionary \n del self.variables[varname]\n self.convert_to_script()\n self.graph_changed_event = True",
"title": ""
},
{
"docid": "edc07226f64f3c33738730d8089e6428",
"score": "0.44427666",
"text": "def _plot_var(self, ax, **kwargs):\n self.__xmin, self.__xmax = ax.get_xlim()\n self.__ymin, self.__ymax = ax.get_ylim()\n plot_digit = kwargs.get('plot_digit', None)\n if plot_digit is not None:\n self.__prec = '%.' + '%d' % plot_digit + 'f'",
"title": ""
},
{
"docid": "05543b631cc28eab1499d4a0303f5622",
"score": "0.44299516",
"text": "def set_dropextradata(self, dropextradata):\n self.options['dropextradata'] = dropextradata",
"title": ""
},
{
"docid": "498c7472384572bf9b63e89977b3b9a2",
"score": "0.4422164",
"text": "def box_plot() -> str:\n\n session_manager.cache_analysis_option()\n return StatsModel().get_box_plot()",
"title": ""
},
{
"docid": "2b2024b879b7e0c085a117ee9ab652b2",
"score": "0.4414972",
"text": "def logplot(self, **kwargs):\n plotdata = self.plottable_data\n if plotdata:\n plotdata.logplot(**kwargs)\n else:\n raise NeXusError(\"There is no plottable data\")",
"title": ""
},
{
"docid": "bc7637e0f986c8d599c82f0c7bfa9bbd",
"score": "0.44135877",
"text": "def plot(filename, column):\n df=pd.read_csv(filename)\n if column is None:\n df.hist();\n else:\n df[column].hist()\n plt.show() # if slow, this blocks all subsequent code from running # it waits for the window to close before continuing",
"title": ""
},
{
"docid": "c17307ab1248182037bdaadea9cc6336",
"score": "0.44075608",
"text": "def x_vs_y_plots(X,y,save_toggle=False,file_prefix=''):\n if len(file_prefix)>0:\n file_prefix += '_'\n \n df = pd.concat([X, pd.DataFrame(y, index=X.index)], axis=1)\n for x in X.columns:\n df[[x,y.name]].groupby(x).mean().plot()\n if save_toggle:\n plt.savefig(file_prefix+x+'_vs_'+y.name+'.png')\n plt.show()\n return",
"title": ""
},
{
"docid": "0889a47d34fccef58e97603b04c39123",
"score": "0.43964615",
"text": "def show_figure(self, *args, **kwargs):\n if 'divider' in dir(self):\n del self.divider\n plt.show(*args, **kwargs)",
"title": ""
},
{
"docid": "e42c056ce7bb2a7c997fe17f18e2da84",
"score": "0.43961328",
"text": "def implot(self, **kwargs):\n plotdata = self.plottable_data\n if plotdata:\n plotdata.implot(**kwargs)\n else:\n raise NeXusError(\"There is no plottable data\")",
"title": ""
},
{
"docid": "94c1e8bd58d88d907afa606c72266977",
"score": "0.4391739",
"text": "def plot_xy(varx='totalTestResultsIncrease',\n vary='positiveIncrease',\n state='NY'):\n assert type(varx) == str, \"Expected string as the variable x name\"\n assert type(vary) == str, \"Expected string as the variable y name\"\n\n y = df[df['state'] == state][vary]\n x = df[df['state'] == state][varx]\n if (x.nunique() != 1) and (y.nunique() != 1):\n plt.figure(figsize=(12, 4))\n plt.title(\"Plot of \\\"{}\\\" vs. \\\"{}\\\" for {}\".format(varx, vary, state), fontsize=18)\n plt.scatter(x=x, y=y, edgecolor='k', color='lightgreen', s=100)\n plt.grid(True)\n plt.xticks(fontsize=14, rotation=45)\n plt.yticks(fontsize=14)\n plt.show()\n else:\n print(\"Some of the data unavailable for a scatter plot. Sorry!\")",
"title": ""
},
{
"docid": "bb8afd28e29829a317cacd43faa15d70",
"score": "0.4388029",
"text": "def plot(\n self,\n components=None,\n *,\n ax=None,\n logy=False,\n show=False,\n dates=False,\n legend=True,\n grid=True,\n ):\n ax = ax or plt.gca()\n kwargs = {\"logy\": logy, \"ax\": ax, \"grid\": grid, \"legend\": legend}\n\n def get_column(col):\n if dates:\n col += \":dates\"\n data = self[col]\n return data\n\n components = self.meta.variables if components is None else components\n for col in components:\n data = get_column(col)\n data.plot(**kwargs)\n if show:\n plt.show()",
"title": ""
},
{
"docid": "3cf0aa71c26b31f4c997c8c2afaf8893",
"score": "0.4385104",
"text": "def print_df(df: pd.DataFrame) -> None:\n\twith pd.option_context('display.max_rows', None, 'display.max_columns', None):\n\t\tprint(df)",
"title": ""
},
{
"docid": "72e57320b475c20dc2f96fd48da6fca7",
"score": "0.43605706",
"text": "def display_na(df):\r\n \r\n display(HTML(\"<h4>Percentage of missing variables for each feature:\"))\r\n print(df.isnull().sum(axis=0) * 100 / len(df))",
"title": ""
},
{
"docid": "7d004de844130a45592187867f7c68fd",
"score": "0.4355372",
"text": "def show(self, *args, **kwargs):\n self.get_plot(*args, **kwargs).show()",
"title": ""
},
{
"docid": "c08b4f4854337048144d53f798e073eb",
"score": "0.43472216",
"text": "def plot_data(df, title=\"Stock prices\", xlabel=\"Date\", ylabel=\"Price\"):\n ax = df.plot(title=title, fontsize=12)\n ax.set_xlabel(xlabel)\n ax.set_ylabel(ylabel)\n plt.show() # must be called to show plots in some environments",
"title": ""
},
{
"docid": "2e2c6fd0d9dca232109a4340a5c5a1a9",
"score": "0.43427277",
"text": "def extra_plot_variance_explained(var_explained, component,var_explained_2, component_2, *args):\n opt_n_comp = np.argmin(var_explained) - 1\n stdout.write(\"\\n\")\n # plot var_explained for each component\n with plt.style.context((\"ggplot\")):\n ax = plt.subplot(111)\n plt.plot(component, np.array(var_explained), \"-v\", color=\"red\", mfc=\"red\", label=\"train\")\n plt.plot(\n component[opt_n_comp], np.array(var_explained)[opt_n_comp], \"P\", ms=10, mfc=\"red\"\n )\n plt.plot(component_2, np.array(var_explained_2), \"-v\", color=\"blue\", mfc=\"blue\", label=\"test\")\n plt.plot(\n component_2[opt_n_comp], np.array(var_explained_2)[opt_n_comp], \"P\", ms=10, mfc=\"red\"\n )\n plt.xlabel(\"Number of PLS components\")\n plt.ylabel(\"Variance explained %\")\n plt.title(\"PLS\")\n plt.xlim(0-1)\n ax.legend()\n plt.show()",
"title": ""
},
{
"docid": "0a4f7b6cbfef0a5defdf6be992e65669",
"score": "0.4340944",
"text": "def plot_runner(ds, variable, s3upload=False) -> str:\n\n model_type = get_model_type(ds)\n if model_type == 'roms':\n imagename = plot_roms(ds_ofs, var, s3upload=True)\n elif model_type == 'fvcom':\n imagename = plot_fvcom(ds_ofs, var, s3upload=True)\n else:\n print(f\"ERROR: Unsupported model type - {modeltype}\")",
"title": ""
},
{
"docid": "473bf765662306bc925b6e3848dabee6",
"score": "0.43398735",
"text": "def hide_column(self, column_name):\n if not isinstance(column_name, str):\n raise TypeError(\"column_name must be of type str\")\n\n column_names = self.get_column_names()\n if column_name not in column_names:\n raise ValueError(f\"{column_name} not in available columns ({column_names})\")\n new_column_names = [cn for cn in self.get_column_names(True)\n if cn not in column_name]\n return self.set_visible_columns(new_column_names)",
"title": ""
},
{
"docid": "715c6a713767779ca425cadeff548c21",
"score": "0.43344194",
"text": "def display(self, *fields):\n try:\n if self.df is None:\n self.warning(\"Dataframe is empty: nothing to display\")\n return\n df2 = self.df[list(fields)]\n return df2.head()\n except Exception as e:\n self.err(e, self.display, \"Can not display dataframe\")",
"title": ""
},
{
"docid": "ab189fb4de3b5e34800eba35dea524ba",
"score": "0.43340218",
"text": "def plot_hovmoeller(ds: xr.Dataset,\n var: VarName.TYPE = None,\n x_axis: DimName.TYPE = None,\n y_axis: DimName.TYPE = None,\n method: str = 'mean',\n contour: bool = True,\n title: str = None,\n file: str = None,\n monitor: Monitor = Monitor.NONE,\n **kwargs) -> Figure:\n var_name = None\n if not var:\n for key in ds.data_vars.keys():\n var_name = key\n break\n else:\n var_name = VarName.convert(var)\n var = ds[var_name]\n\n if not x_axis:\n x_axis = var.dims[0]\n else:\n x_axis = DimName.convert(x_axis)\n\n if not y_axis:\n try:\n y_axis = var.dims[1]\n except IndexError:\n raise ValidationError('Given dataset variable should have at least two dimensions.')\n else:\n y_axis = DimName.convert(y_axis)\n\n if x_axis == y_axis:\n raise ValidationError('Dimensions should differ between plot axis.')\n\n dims = list(var.dims)\n try:\n dims.remove(x_axis)\n dims.remove(y_axis)\n except ValueError:\n raise ValidationError('Given dataset variable: {} does not feature requested dimensions:\\\n {}, {}.'.format(var_name, x_axis, y_axis))\n\n ufuncs = {'min': np.nanmin, 'max': np.nanmax, 'mean': np.nanmean,\n 'median': np.nanmedian, 'sum': np.nansum}\n\n with monitor.starting(\"Plot Hovmoeller\", total_work=100):\n monitor.progress(5)\n with monitor.child(90).observing(\"Aggregate\"):\n var = var.reduce(ufuncs[method], dim=dims)\n monitor.progress(5)\n\n figure = plt.figure()\n ax = figure.add_subplot(111)\n if x_axis == 'time':\n figure.autofmt_xdate()\n\n if contour:\n var.plot.contourf(ax=ax, x=x_axis, y=y_axis, **kwargs)\n else:\n var.plot.pcolormesh(ax=ax, x=x_axis, y=y_axis, **kwargs)\n\n if title:\n ax.set_title(title)\n\n figure.tight_layout()\n\n if file:\n figure.savefig(file)\n\n return figure if not in_notebook() else None",
"title": ""
},
{
"docid": "e45e26120e690f3dad49ac9e218a7480",
"score": "0.43270665",
"text": "def remove_variable(self, var):\n self.vars.remove(var)",
"title": ""
},
{
"docid": "6f45be729aa0e30132fc58c88ddb34ca",
"score": "0.4326928",
"text": "def subset_variables(ds, variableList): # {{{\n\n allvars = ds.data_vars.keys()\n\n # get set of variables to drop (all ds variables not in vlist)\n dropvars = set(allvars) - set(variableList)\n\n # drop spurious variables\n ds = ds.drop(dropvars)\n\n # must also drop all coordinates that are not associated with the variables\n coords = set()\n for avar in ds.data_vars.keys():\n coords |= set(ds[avar].coords.keys())\n dropcoords = set(ds.coords.keys()) - coords\n\n # drop spurious coordinates\n ds = ds.drop(dropcoords)\n\n if len(ds.data_vars.keys()) == 0:\n raise ValueError(\n 'Empty dataset is returned.\\n'\n 'Variables {}\\n'\n 'are not found within the dataset '\n 'variables: {}.'.format(variableList, allvars))\n\n return ds # }}}",
"title": ""
},
{
"docid": "64cfecc9efd06e5098ce91f2f6a118a5",
"score": "0.43260407",
"text": "def distribution_plot(dataframe: pd.DataFrame, var: str, target: str = None, **kwargs):\n\n row = kwargs.get('row', None)\n col = kwargs.get('col', None)\n\n size = kwargs.get('size', None)\n aspect = kwargs.get('aspect', None)\n\n facet = sns.FacetGrid(\n dataframe,\n hue=target,\n size=size if size else 3,\n aspect=aspect if aspect else 4,\n row=row,\n col=col\n )\n\n facet.map(sns.kdeplot, var, shade=True)\n facet.set(title=\"Distribution of '{}' over '{}'\".format(var, target))\n\n # Defines limits\n if kwargs.get('clean', False):\n xmin = dataframe[var].quantile(0.02)\n xmax = dataframe[var].quantile(0.98)\n facet.set(xlim=(xmin, xmax))\n\n facet.add_legend()\n plt.show()",
"title": ""
},
{
"docid": "54230d6985c38be8af9232a0bf01ee18",
"score": "0.43237677",
"text": "def rename_variable(self, toast: Toast, var_name: str, display_name: str) -> None:\n try:\n return self.active_view.rename_variable(toast, var_name, display_name)\n except NotImplementedError:\n toast.show(f\"{self._active} does not implement variable renaming\", ToastType.warning)",
"title": ""
},
{
"docid": "5290b0dbee071b9ff806e7ed58e3ceb1",
"score": "0.43195853",
"text": "def setShowInX(self, x):\r\n\t\tself.__showInX=x",
"title": ""
},
{
"docid": "1ca95d946d23156598f83a8c528ad2ac",
"score": "0.43146688",
"text": "def plot_dis(df_master, df_dis, units):\n modul = df_master.modul\n stor = '{}_stor'.format(modul)\n loss = '{}_loss'.format(modul)\n relax = '{}_relax'.format(modul)\n\n if df_master.domain == 'freq':\n fig, (ax1, ax2) = plt.subplots(1,2, figsize=(8,0.75*4))\n df_master.plot(x='f', y=[stor], label=[\"{}'(filter)\".format(modul)],\n ax=ax1, logx=True, logy=True, color=['C0'], alpha=0.5)\n df_master.plot(x='f', y=[loss], label=[\"{}''(filter)\".format(modul)],\n ax=ax2, logx=True, logy=True, color=['C1'], alpha=0.5)\n df_dis.plot(x='f', y=[stor], label=['tau_i'], ax=ax1, \n logx=True, logy=True, ls='', marker='o', color=['C0'])\n df_dis.plot(x='f', y=[loss], label=['tau_i'], ax=ax2, \n logx=True, logy=True, ls='', marker='o', color=['C1'])\n ax1.set_xlabel('Frequency (Hz)')\n ax1.set_ylabel('Storage modulus ({})'.format(units[stor]))\n ax2.set_xlabel('Frequency (Hz)')\n ax2.set_ylabel('Loss modulus ({})'.format(units[stor])) \n ax1.legend()\n ax2.legend()\n fig.show()\n return fig\n elif df_master.domain == 'time':\n fig, ax1 = plt.subplots(figsize=(4,0.75*4))\n df_master.plot(x='t', y=[relax], \n ax=ax1, logx=True, logy=True, color=['k'])\n df_dis.plot(x='t', y=[relax], label = ['tau_i'], \n ax=ax1, logx=True, logy=True, ls='', marker='o', color=['red'])\n ax1.set_xlabel('Time ({})'.format(units['t']))\n ax1.set_ylabel('Relaxation modulus ({})'.format(units[relax]))\n ax1.legend()\n fig.show()\n return fig",
"title": ""
},
{
"docid": "35832c1e396d3f8c41dcf63d2a46273b",
"score": "0.43139654",
"text": "def hide_xticks(ax):\n for tick in ax.axes.get_xticklabels():\n tick.set_visible(False)",
"title": ""
},
{
"docid": "5e63b785758284832390cce3c328b022",
"score": "0.43101996",
"text": "def plot_data(df, title=\"Stock prices\", xlabel=\"Date\", ylabel=\"Price\"):\n ax = df.plot(title=title, fontsize=12)\n ax.set_xlabel(xlabel)\n ax.set_ylabel(ylabel)\n plt.show()",
"title": ""
},
{
"docid": "5e63b785758284832390cce3c328b022",
"score": "0.43101996",
"text": "def plot_data(df, title=\"Stock prices\", xlabel=\"Date\", ylabel=\"Price\"):\n ax = df.plot(title=title, fontsize=12)\n ax.set_xlabel(xlabel)\n ax.set_ylabel(ylabel)\n plt.show()",
"title": ""
},
{
"docid": "eb45a6f55ba0db29f2a75fa25b824f96",
"score": "0.43083325",
"text": "def show(self, *args, **kwds):\n self.plot(*args, **kwds).show()\n return",
"title": ""
},
{
"docid": "0b798880aba7d93b422ba79a8dafd758",
"score": "0.43063605",
"text": "def plot_discharge(self, data, **kw):\n x = 0\n print 'data', data\n for key in data:\n print 'key', key\n if key == '11462125 peak':\n print 'skipping peak discharge df'\n elif key == 'CLV - Cloverdale hourly':\n for col in self._col_list[:2]:\n ser = data[key]['Dataframe'][col]\n if not ser.empty:\n self._plot(col, key, ser, **kw)\n\n elif key == 'COY - Coyote hourly':\n for col in self._col_list:\n ser = data[key]['Dataframe'][col]\n if not ser.empty:\n self._plot(col, key, ser, **kw)\n else:\n for col in self._col_list:\n ser = data[key]['Dataframe'][col]\n print 'nan values: {}'.format(count_nonzero(isnan(ser)))\n print 'len ser: {}'.format(len(ser))\n if len(ser) != count_nonzero(isnan(ser)):\n self._plot(col, key, ser, **kw)",
"title": ""
},
{
"docid": "8ae8dc9da6845384c103464f8a1f1451",
"score": "0.43056202",
"text": "def set_display():\r\n # Plots display settings\r\n plt.style.use('fivethirtyeight')\r\n plt.rcParams['figure.figsize'] = 12, 8\r\n plt.rcParams.update({'font.size': 14})\r\n # DataFrame display settings\r\n pd.set_option('display.max_columns', None)\r\n pd.set_option('display.max_rows', None)\r\n pd.options.display.float_format = '{:.4f}'.format",
"title": ""
},
{
"docid": "30b66c30a50d93c31d6e8fbe9796c40a",
"score": "0.42991424",
"text": "def boxplot_one_country(df,country,AoIs= 'All',save=False,fig=None,ax=None,**kwargs):\n df = df.loc[df['country'] == country]\n #if AoIs != 'All': df2 = df2.loc[df2['AoI combinations'].isin(AoIs)]\n\n if (fig == None and ax == None): # if No axes and no figure is provided\n fig, ax = plt.subplots(figsize=(12, 6))\n\n df.boxplot(by='AoI combinations', column='disrupted', ax=ax,grid=False,**kwargs)\n ax.set_xlabel(\"Number of combinations of flood events (AoI)\")\n ax.set_ylabel(\"% preferred routes disrupted\")\n ax.set_title(\"% routes between NUTS-3 regions in {} disrupted\".format(country))\n\n if save:\n save_figs = load_config(config_file)['paths']['output_images'] / 'disrupted'\n if not save_figs.exists(): save_figs.mkdir()\n filename = \"disrupted_boxplot_{}.png\".format(country)\n fig.savefig(save_figs / filename)\n\n return fig,ax",
"title": ""
},
{
"docid": "7cf843c3246768fbe1cd025b86929885",
"score": "0.42943284",
"text": "def plot_categoricial(df, var) -> None:\n temp = df[['Income', var]].groupby(var).mean().reset_index()\n axis = sns.barplot(x=var, y='Income', data=temp)\n axis.set(ylabel=\"Probability of earning over 50K\")\n axis.set_xticklabels(config.vis_dict[var], rotation=75)\n plt.title(f\"Effect of {var} on Income\")\n plt.show()",
"title": ""
},
{
"docid": "e6b30d96205aae6597f9e8a95779b74e",
"score": "0.42883715",
"text": "def plot_line_chart(df: pd.DataFrame, title: str, y: str = 'y', x: str = 'x',):\n px.line(df, x=x, y=y, title=title).show()",
"title": ""
},
{
"docid": "99f63f08277116886413b5dfbbc45efa",
"score": "0.42838115",
"text": "def datatoplot(self, track_choice, track_no=None, track_index=None):\r\n if track_choice == 'evo':\r\n tracks = self.data\r\n elif track_choice == 'ranged':\r\n if self.ranged_tracks is None:\r\n raise NameError('No ranged tracks!')\r\n else: tracks = self.ranged_tracks\r\n else: raise NameError('Wrong track name!')\r\n \r\n max_tracks = tracks['track_no'].nunique()\r\n uniques = tracks['track_no'].unique()\r\n if track_no != None:\r\n if max_tracks > track_no:\r\n if type(track_index)==type(None):\r\n track_index=np.random.choice(uniques, track_no)\r\n else: track_index = uniques\r\n else: track_index = uniques\r\n if len(track_index)>200:\r\n raise ValueError('Too many tracks, are you sure you want to plot '+str(len(track_index))+' tracks??')\r\n \r\n selected = tracks.loc[tracks['track_no'].isin(track_index)]\r\n plot_tracks = [selected['Teff'], selected['L']]\r\n plot_m = selected['mass']\r\n return plot_tracks, plot_m",
"title": ""
},
{
"docid": "5513626174155422f31e167cfb56c003",
"score": "0.42826742",
"text": "def set_df_mean_plot(data):\n df = records_to_df(data)\n try:\n # For error bars we need standard deviation.\n df[['df1 std', 'df2 std']] = df[['df1 variance', 'df2 variance']].transform(np.sqrt)\n except KeyError:\n pass\n # Convert to long format for easier plotting.\n long_df = analysis.wide_to_long(df, stubs=['df1', 'df2'], suffixes=['mean', 'std'], j='dof')\n fig = plotting.generate_lines_plot(long_df, \"mean\", by='user', color_col='dof', errors='std', jitter=True)\n return fig",
"title": ""
},
{
"docid": "a2d4e630a8059c1b12652584c43c00fd",
"score": "0.4279645",
"text": "def easy_print_data_frame(data_frame):\n\n print(data_frame.to_string())",
"title": ""
},
{
"docid": "eef7324d0a5850a8c8ac3a856b4b9dfb",
"score": "0.42760804",
"text": "def _plot_variance_explained(var_explained, component, *args):\n opt_n_comp = np.argmin(var_explained) - 1\n stdout.write(\"\\n\")\n # plot var_explained for each component\n with plt.style.context((\"ggplot\")):\n plt.plot(component, np.array(var_explained), \"-v\", color=\"blue\", mfc=\"blue\")\n plt.plot(\n component[opt_n_comp], np.array(var_explained)[opt_n_comp], \"P\", ms=10, mfc=\"red\"\n )\n plt.xlabel(\"Number of PLS components\")\n plt.ylabel(\"Variance explained %\")\n plt.title(\"PLS\")\n plt.xlim(0-1)\n\n plt.show()",
"title": ""
},
{
"docid": "d83e8777568cac18f63c1b5039fa7e43",
"score": "0.4275368",
"text": "def show():\n plt.show(block=False)",
"title": ""
},
{
"docid": "4441f564cdf782290c8b56b76d3b9153",
"score": "0.4270931",
"text": "def show_plot(self, table):\n assert (self._current_panel is not None)\n graph = wxtbx.plots.iotbx_data_plot_base(\n parent=self._current_panel,\n tables=[table],\n size=(TEXT_WIDTH - 40, min(500, TEXT_WIDTH * 0.75)))\n graph.set_plot(table.only_plot())\n self._graphs.append((graph, table.title))\n self._current_sizer.Add(graph, 0, wx.ALL|wx.EXPAND, 5)",
"title": ""
},
{
"docid": "a774101cb8988ac9fa189c9a6f651b21",
"score": "0.42703143",
"text": "def plot_two_vars(self,\n xy_data, # ['my_datadict_out', e.g., 'merge_summstat_out']\n x_name, # [name of x variable to use for x-axis]\n y_name, # [name of y variable to use for y-axis]\n ent_axis = 'col', # [<'row', 'col'>] => how variables are situated]\n err_data = None, # [<None, 'my_datadict_out'> => e.g., 'merge_summstat_out']\n x_err = None, # [<None, size, name of x variable to use for error statistic> => create bubbles]\n y_err = None, # [<None, size, name of y variable to use for error statistic> => create bubbles]\n color_by = None, # [<None, 'g', '0.75', (0.5,0.2,0.7), 'rand', ['gender', {'male':'b', 'female':'r'}], ['age', 'g']> => color-code bubbles]\n cosine_correct = 'coord|corr', # [<None, 'coord', 'corr', 'coord|corr'> => correct for cosine between variables]\n max_cos = 0.95, # <unsigned corr> => trigger exception when cos(x, y) > max_cos]\n plot = None, # [<None, {plotting parameters}> => see docs to customize]\n savefig = 'xy_plot.png' # [<None, 'show', 'filename.png'> => how to output chart]\n ):\n\n if self.verbose is True:\n print 'plot_two_vars() is working...\\n'\n\n # Run the damon utility\n plot_two_vars_out = dmn.utils._plot_two_vars(locals())\n self.plot_two_vars_out = plot_two_vars_out\n\n if self.verbose is True:\n print 'plot_two_vars() is done -- see my_obj.plot_two_vars_out'\n\n return None",
"title": ""
},
{
"docid": "0d4531cd130c5272a5d9ffa4d886b3ee",
"score": "0.42631972",
"text": "def _plot_skipped_well_file(config, raw_data, well_name):\n if config.plot_skipped:\n plot_params = dict()\n plot_params['savepath'] = config.work_dir\n plot_params['well_name'] = well_name\n plot_params['fg_conc_edges'] = config.fg_conc_edges\n plot_params['fg_fret_edges'] = config.fg_fret_edges\n plot_params['data_df'] = raw_data\n plot_params['fine_grid_xlim'] = None\n plot_params['fine_grid_ylim'] = None\n plot_params['plot_type'] = config.plot_type\n plot_fine_grid_profiles(plot_params)",
"title": ""
},
{
"docid": "11adb8baf5e82cdd7884c2a1f79cd1e1",
"score": "0.42607546",
"text": "def do_variable(self, arg):\n self.display_cmake_help('variable', arg)",
"title": ""
},
{
"docid": "1862e5d6a2dd0e4bdab216750261911f",
"score": "0.42592025",
"text": "def strip_data(df: pd.DataFrame, data_to_keep: dict):\n gene_name = 'symbol'\n if df.index.name == data_to_keep.get(gene_name):\n data_to_keep.pop(gene_name)\n new_df = df[data_to_keep.values()]\n return new_df",
"title": ""
},
{
"docid": "a745c78232fa241c5ee33a4d7e4af63e",
"score": "0.4254119",
"text": "def bokeh_show(bokeh_object, filename=None, **save_kwargs):\n if filename is None:\n filename = \"bokehplot_00000000.html\"\n i = 1\n while i < 99999999 and os.path.isfile(filename):\n filename = \"bokehplot_{0:08d}.html\".format(i)\n i += 1\n\n if i == 99999999:\n raise RuntimeError(\n \"Cannot generate generic file name, as all filenames are used.\"\n )\n\n if \"resources\" in save_kwargs:\n resources = save_kwargs.pop(\"resources\")\n else:\n resources = bokeh.resources.CDN\n\n if \"title\" in save_kwargs:\n title = save_kwargs.pop(\"title\")\n else:\n title = \"Bokeh Plot\"\n\n bokeh.io.save(\n bokeh_object, filename=filename, resources=resources, title=title, **save_kwargs\n )\n\n return HTML(filename)",
"title": ""
},
{
"docid": "338a9905885d36026a7596b50df165de",
"score": "0.42535764",
"text": "def oplot(self, **kwargs):\n plotdata = self.plottable_data\n if plotdata:\n plotdata.oplot(**kwargs)\n else:\n raise NeXusError(\"There is no plottable data\")",
"title": ""
},
{
"docid": "8d7c065190303a0310e9f02a8760beaa",
"score": "0.4251952",
"text": "def print_1d_visual(self, df, tuning_param):\n axis = {\n \"accuracy\": \"Accuracy (%)\",\n \"false_positive\": \"False Positive Rate (%)\",\n \"false_negative\": \"False Negative Rate (%)\"\n }\n plt.clf()\n sns.set(style='darkgrid')\n sns.lineplot(x=tuning_param, y=axis[self.metric], data=df)\n fig = plt.gcf()\n fig.savefig('%s/OptimizationLineGraph.png' % self.path, bbox_inches='tight')\n plt.close()\n df.to_csv(\"%s/optimize_results.csv\" % self.path)",
"title": ""
},
{
"docid": "11a762413a0129120eb397564ca2dc97",
"score": "0.42496905",
"text": "def plot(self, variables_values, axes=None):\n assert len(self.variables) == len(variables_values), \\\n f\"You need to pass as many variable values as this visualiser has variables. Required:\" \\\n f\"{len(self.variables)}, Given: {len(variables_values)}\"\n\n fig = None\n if axes is None:\n fig, axes = self.create_default_axes()\n # TODO: Probably should not do that, but it prevents an empty plot from popping up in IPython\n plt.close(fig)\n\n self.plot_init(variables_values, axes)\n self.plot_update(variables_values, axes)\n if fig is not None and in_ipython_context:\n ip_display(fig)",
"title": ""
},
{
"docid": "81280fdc16829c5aafa79a33822634eb",
"score": "0.4248225",
"text": "def plot_my_data(x,y,verbose=True):\n\n # Note the indentation for the if block\n if (verbose):\n print \"\"\n print \"Now plotting the data.\"\n\n # In the plot command below, the 'bo' tells the program to plot blue ('b')\n # circles ('o')\n print \"\"\n #print \"Close the plot window to continue...\"\n plt.plot(x,y,'bo')",
"title": ""
},
{
"docid": "45ad991ab71a43a553c913e63ea1f2af",
"score": "0.42471483",
"text": "def variable_importance_plot(model, log, result_path=\"result\"):\n algo = model.params['model_id']['actual']['name']\n\n if model.varimp() is not None:\n plt.tight_layout()\n\n plt.figure(figsize=(12, 8))\n var_imp = model.varimp_plot()\n plt.title(\"Variable Importance : \" + algo)\n plt.xlabel(\"Relative Importance\")\n\n plt.savefig(result_path + \"/\" + \"variable_importance.png\", dpi=600)\n plt.close()\n else:\n log.warning(\"[\" + algo + \"]\" + \"This model doesn't have variable importances\")",
"title": ""
},
{
"docid": "2e224ac6f166a66c30181dc30cb08c5b",
"score": "0.42448515",
"text": "def turn_visualization_off(self):\n self.visualize = False",
"title": ""
},
{
"docid": "aaa4f7f0ce31f350d847feb2209d7261",
"score": "0.4238392",
"text": "def drop(self, var_index):\n return self.masked([i != var_index for i in range(self.num_vars)])",
"title": ""
},
{
"docid": "da9570ff3d6ed2dd5db6f03efbc01781",
"score": "0.4237229",
"text": "def display_only(self, frame):\n cv2.imshow(self.display_name, frame)",
"title": ""
},
{
"docid": "f997c2c75ad0ef1126c60105cf176510",
"score": "0.42370698",
"text": "def remove_NoVal_col(df: pd.core.frame.DataFrame, label: str) -> pd.core.series.Series:\n mask = df[label] != 99.0\n col = df[label][mask]\n return col",
"title": ""
},
{
"docid": "fa0ae72e1dcc3d68e8cfcd5580d6d362",
"score": "0.42370147",
"text": "def display_data(df):\n response= ['yes', 'no']\n rawdata = ''\n #counter variable is initialized as a tag to ensure only details from a particular point is displayed\n counter = 0\n while rawdata not in response:\n print(\"\\nDo you wish to view the raw data?\")\n print(\"\\nAccepted responses:\\nYes or no\")\n rawdata = input().lower()\n #the raw data from the df is displayed if user opts for it\n if rawdata == \"yes\":\n print(df.head())\n elif rawdata not in response:\n print(\"\\nPlease check your input.\")\n print(\"Input does not seem to match any of the accepted responses.\")\n print(\"\\nRestarting...\\n\")\n\n #Extra while loop here to ask user if they want to continue viewing data\n while rawdata == 'yes':\n print(\"Do you wish to view raw data?\")\n counter += 5\n rawdata = input().lower()\n #If user opts for it, this displays next 5 rows of data\n if rawdata == \"yes\":\n print(df[counter:counter+5])\n elif rawdata != \"yes\":\n break\n\n print('-'*80)",
"title": ""
},
{
"docid": "d09bbfaaacfe251e374b812438d2d174",
"score": "0.42354107",
"text": "def viewVariables(dataset):\r\n for entry in dataset['DATA'][0]:\r\n pprint.pprint(str(entry) + \" : \" + str(dataset['VARIABLES'][str(entry)]))",
"title": ""
},
{
"docid": "227b29295221cfa88a8937ea82c250ad",
"score": "0.42240646",
"text": "def remove_plot(self):\n \n if self.has_plant: \n #Updates the variables to show that the plot is empty\n self.plant_type = None\n self.has_plant = False\n self.age = 0\n self.time_planted = 0\n self.can_harvest = False\n \n else:\n #Once the plot itself is removed, the switch turns off and the plot is disabled\n self.plotted = False",
"title": ""
},
{
"docid": "68478c1344086aa8df9dc257fd0cc6cd",
"score": "0.42235586",
"text": "def suppress(self):\n self._pipe.send((None, 'hide', None))",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="795"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">2be6d369e19ba2cf155c71d55a982262</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Start a player off with the default starting deck, an empty hand, and an empty discard pile. Set the game stage to 'early_game'. Initialize a turn counter. Draw a random starting hand from the deck.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "9ab052ff693422df14ac76b389410470",
"score": "0.7242505",
"text": "def __init__(self):\n\n self.hand = [] # empty hand\n self.discard = [] # empty discard pile\n self.deck = 3 * [estate] + 7 * [copper] # starting deck\n self.game_stage = GameStage.early_game # start the game in early stage\n self.turn_count = 0\n\n random.shuffle(\n self.deck\n ) # shuffle the deck before drawing (for the first time)\n self.draw_hand() # start by drawing a hand",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "5100836fd6ace5c376bcbe31fb141463",
"score": "0.7105495",
"text": "def start_new_game(self, player_hand):\n self.deck_of_cards += self.hand\n self.deck_of_cards += player_hand\n self.deck_of_cards.shuffle()",
"title": ""
},
{
"docid": "73b8d8128052bfd7bd5a587d480672ce",
"score": "0.6954077",
"text": "def start_game(self):\n initial_draw = int((len(self.stock.cards) * 0.5) / len(self.participants))\n\n if initial_draw == 0:\n initial_draw = 1\n\n for player in self.participants:\n cards = [self.stock.deal() for x in range(initial_draw)]\n player.hand = cards\n\n self.act_player = self.participants[0]\n self.turn = self.next_turn()",
"title": ""
},
{
"docid": "6662d1a0a763307463d759a7f7232e3a",
"score": "0.66947067",
"text": "def init_game(self):\n if self.is_basic: #create quick simple game\n p1 = 6 #priest\n p2 = 7 #rogue\n deck1 = random_draft(CardClass(p1))\n deck2 = random_draft(CardClass(p2))\n self.players[0] = Player(\"Player1\", deck1, CardClass(p1).default_hero)\n self.players[1] = Player(\"Player2\", deck2, CardClass(p2).default_hero)\n self.game = Game(players=self.players)\n self.game.start()\n\n #Skip mulligan\n for player in self.game.players:\n cards_to_mulligan = random.sample(player.choice.cards, 0)\n player.choice.choose(*cards_to_mulligan)\n\n return self.game\n\n else:\n p1 = random.randint(1, 9)\n p2 = random.randint(1, 9)\n #initialize players and randomly draft decks\n #pdb.set_trace()\n deck1 = random_draft(CardClass(p1))\n deck2 = random_draft(CardClass(p2))\n self.players[0] = Player(\"Player1\", deck1, CardClass(p1).default_hero)\n self.players[1] = Player(\"Player2\", deck2, CardClass(p2).default_hero)\n #begin the game\n self.game = Game(players=self.players)\n self.game.start()\n\n #Skip mulligan for now\n for player in self.game.players:\n cards_to_mulligan = random.sample(player.choice.cards, 0)\n player.choice.choose(*cards_to_mulligan)\n\n return self.game",
"title": ""
},
{
"docid": "db336ace779d9c035eacad3d1e43a2c3",
"score": "0.6674635",
"text": "def play_hand(self, player):\n if not player.has_blackjack():\n while player.get_hand_value() <= 21:\n self.print_current_state(player, sd=False)\n action = player.get_action()\n if action == 'Hit':\n self.deal_top(player)\n if action == 'Stand':\n break\n if action == 'Split':\n pass\n if action == 'Double':\n player.stack -= player.amount_bet\n player.amount_bet *= 2\n self.deal_top(player)\n break\n # If player did not bust dealer plays\n if (player.get_hand_value() <= 21):\n self.dealer_plays()\n self.print_current_state(player, sd=True)\n self.declare_winner(player)\n self.reset_table()\n print(self.players[0])",
"title": ""
},
{
"docid": "cefe9cdd368f1bc5c556b592bd28b0ba",
"score": "0.6657983",
"text": "def computer_turn():\n global winner, winning_hand, time\n computer.draw_card(deck.pile[0])\n winning_hand = computer.declare_game()\n if winning_hand[0]:\n #winning condition\n winner = computer\n round_over()\n stage[0] += 1\n else:\n score = [0] * 14\n min_pile = (0, 100)\n for i in range(len(computer.hand)):\n score[i] = calculate_score(computer.hand[i], computer.hand)\n for i in range(len(score)):\n if score[i] < min_pile[1]:\n min_pile = (i,score[i])\n elif score[i] == min_pile[1]:\n if computer.hand[i].rank >= computer.hand[min_pile[0]].rank:\n min_pile = (i, score[i])\n\n computer.discard_card(deck.pile[0])\n\n joker_card = Card('3','spades',True)\n computer.draw_card(joker_card)\n score = [0] * 14\n min_deck = (0, 100)\n for i in range(len(computer.hand)):\n score[i] = calculate_score(computer.hand[i], computer.hand)\n for i in range(len(score)):\n if score[i] < min_deck[1]:\n min_deck = (i,score[i])\n elif score[i] == min_deck[1]:\n if computer.hand[i].rank >= computer.hand[min_deck[0]].rank:\n min_deck = (i, score[i])\n computer.discard_card(joker_card)\n\n\n if min_pile[1] <= min_deck[1]:\n #draw from pile\n if time == 0 :\n computer.draw_card(deck.pile.pop(0))\n deal_card_sound.play()\n time += 1\n # print(\"pile initialisation\")\n elif time >= computer_delay:\n #DELAY\n deck.update_pile(computer.hand[min_pile[0]])\n computer.discard_card(computer.hand[min_pile[0]])\n deal_card_sound.play()\n # print(\"pile time condition\")\n computer.hand = sort_hand(computer.hand)\n computer.turn = False\n user.turn = True\n time = 0\n else:\n # print(\"pile increment\")\n time += 1\n else:\n #draw from deck\n #DELAY\n if time == 0:\n computer.draw_card(deck.draw_card())\n deal_card_sound.play()\n # print(\"deck initialisation\")\n time += 1\n elif time >= computer_delay:\n # print(\"Deck time condition\")\n winning_hand = computer.declare_hand()\n if winning_hand[0] :\n #winning condition\n winner = computer\n round_over()\n stage[0] += 1\n time = 0\n else:\n score = [0] * 14\n min_deck = (0, 100)\n for i in range(len(computer.hand)):\n score[i] = calculate_score(computer.hand[i], computer.hand)\n for i in range(len(score)):\n if score[i] < min_deck[1]:\n min_deck = (i,score[i])\n elif score[i] == min_deck[1]:\n if computer.hand[i].rank >= computer.hand[min_deck[0]].rank:\n min_deck = (i, score[i])\n deck.update_pile(computer.hand[min_deck[0]])\n computer.discard_card(computer.hand[min_deck[0]])\n time = 0\n deal_card_sound.play()\n computer.hand = sort_hand(computer.hand)\n computer.turn = False\n user.turn = True\n else:\n # print(\"deck increment condition\")\n time += 1",
"title": ""
},
{
"docid": "ae5ce86f91fa809eba12df1bb5fd7837",
"score": "0.66456765",
"text": "def start_hand(player, dealer, bet_amount):\n\n player.place_bet(bet_amount)\n player.get_hand(dealer.give_card())\n player.get_hand(dealer.give_card())\n\n print(player.show_hand())\n\n dealer.get_hand(dealer.give_card())\n dealer.get_hand(dealer.give_card())\n\n print(dealer.show_first_hand())\n\n if player.hand_total_value() == 21:\n player.has_blackjack = True\n\n if dealer.hand_total_value() == 21:\n dealer.has_blackjack = True\n\n if player.has_blackjack:\n print(\"Blackjack!!!\")",
"title": ""
},
{
"docid": "f23e94ed0fa96fae14e8750676a9722f",
"score": "0.65526325",
"text": "def play(self):\n\n #############\n # Game loop #\n #############\n number_bets = 0\n print \"###############\"\n print \"# Game starts #\"\n print \"###############\"\n while self.player.chips > 0:\n self.play_hands()\n number_bets += 1\n #wants_exit = raw_input(\"> Press e for exit \").lower()\n #if wants_exit == 'e':\n # break\n print \"###############\"\n print \"#########################\"\n print \"# Game end with %s bets #\" % (number_bets)\n print \"#########################\"",
"title": ""
},
{
"docid": "cb175cb8d195e3b844ef7dd03c55afb8",
"score": "0.6526096",
"text": "def initialSetUp():\n deck = cardGame.getDeck()\n cardGame.shuffleDeck(deck)\n numberOfPlayers = getNumberOfPlayers()\n hands = cardGame.dealCards(deck, 0, numberOfPlayers)\n sortHands(hands)\n startingPlayer = getStartingPlayer(hands)\n return deck, hands, numberOfPlayers, startingPlayer",
"title": ""
},
{
"docid": "b1ec6eee21dc39ea3e1cc703759281ee",
"score": "0.6525598",
"text": "def start(self):\n\n self.view.display_game_start()\n self.cardgame.add_players()\n\n game_over = False\n\n while not game_over:\n for player in self.cardgame.get_players():\n move = self.parse_move(player.name)\n self.cardgame.step(player, move)\n self.view.display_hand(player.name, player.get_hand())\n game_over = self.cardgame.is_game_over()\n\n winner, score = self.cardgame.pick_winner()\n\n if not winner:\n self.view.display_draw_game()\n else:\n self.view.display_winner(winner.name, score, winner.get_hand())",
"title": ""
},
{
"docid": "a3e313dae13a0e559e08ea35e111a9f2",
"score": "0.65084654",
"text": "def new_game():\n global cards, exposed, hand, state\n global card_1, card_2, turns\n\n hand = cards[:]\n random.shuffle(hand) # hand for the game\n\n exposed = 16 * [False] # initialize uncovered cards\n card_1 = None\n card_2 = None\n turns = 0\n\n state = 0 # number of cards initially uncovered",
"title": ""
},
{
"docid": "fee53164e852b2bd36967c77375c4a68",
"score": "0.64621794",
"text": "def setup_game(self):\n if self.is_ready():\n for player in self.players:\n player.draw(5)\n self.wild.draw(5)\n self.starting_positions = [p.player_id for p in self.players]\n self.current_player = self.next_player()\n else:\n print 'Not enough players to setup game'",
"title": ""
},
{
"docid": "caf9e7f2d74b953dbf52004e56ba4100",
"score": "0.6429044",
"text": "def start_hand(self):\n [self.draw() for _ in range(5)] #initially draw 5",
"title": ""
},
{
"docid": "d389423337a21da474a9da118a77413d",
"score": "0.64132434",
"text": "def start_game(self):\n self.display_info()\n temporary_board = Board_game(self.board_size, self.number_of_ships,\n \"Computer\", player=False)\n self.computer_board = temporary_board\n player_name = input(\"Please enter your name: \\n\")\n print(\"-\" * 60)\n temporary_board = Board_game(self.board_size, self.number_of_ships,\n player_name, player=True)\n self.player_board = temporary_board\n\n self.play_game()",
"title": ""
},
{
"docid": "eb5a90f61dcbcc1ddbbe1831103505af",
"score": "0.6401434",
"text": "def reset_new_game(self):\n\t\tself._piles['deck'] = Pile('copper',7)\n\t\tself._piles['deck'].add_new_card_to_pile('estate',3)\n\t\tself._piles['deck'].shuffle()\n\t\tself._piles['hand'] = Pile()\n\t\tself._piles['discard'] = Pile()\n\t\tself._piles['in_play'] = Pile()\n\t\tself.draw_n(5)",
"title": ""
},
{
"docid": "6d74b8502b3dc12a43ce7f6e15d3c209",
"score": "0.6401383",
"text": "def play_hands(self):\n\n #############\n # First hand\n #############\n # Deal two cards to each opponent\n self.player.cards = self.dealer.deal(2)\n self.dealer.cards = self.dealer.deal(2)\n\n # Show hand\n self.player.show_hand()\n self.dealer.show_hand(1) # hide second card\n\n # End if player has blackjack\n if self.player.has_blackjack():\n self.end_bet()\n return\n\n #################################\n # Hand loop for player and dealer\n #################################\n opponents = [self.player, self.dealer]\n dealer_turn = 0 # 0: is player turn, 1: is dealer turn\n continue_bet = True\n round = 1\n # Bet\n self.player.chips -= self.player.chips_per_bet\n # Repeat until bet end condition\n while continue_bet:\n # Set who plays: player or dealer\n opponent = opponents[dealer_turn]\n continue_hand = True\n while (continue_hand):\n # Ask for action: hit or stand?\n action = opponent.choose_action()\n # If hit, deal cards and show hand\n if action == settings.ACTION_HIT:\n opponent.cards.extend(self.dealer.deal())\n opponent.show_hand()\n # check end conditions\n if opponent.has_blackjack() or opponent.has_busted():\n continue_hand = False\n continue_bet = False\n # If stand, it's dealer's turn\n elif action == settings.ACTION_STAND:\n dealer_turn = 1 - dealer_turn # pass turn to other opponent\n round+=1\n continue_hand = False\n # Player stands and dealer stands so end bet\n if round == 4:\n continue_bet = False\n else:\n raise Exception(\"Unknown action: '%s'\" % action)\n self.end_bet()",
"title": ""
},
{
"docid": "bd044cf068a3a9a276486c7f13ec9422",
"score": "0.6370955",
"text": "def start_game(self):\n print(\"\"\"\n Welcome to the Hi/Lo game!\n \n Guess if the next card will be higher\n or lower than the last drawn card.\n If correct, you get 100pts! Guess wrong \n and you lose 75pts!\n \n You lose if you hit, or go below, 0pts.\n You win if you score 1,000 or more pts.\n \n Good luck!\n \"\"\")\n while self.keep_playing == True:\n print(f\"The card is : {self.old_card}\")\n self.player.guess()\n self.draw_card()\n self.display_card()\n self.get_points()\n print(f\"Score: {self.points}\")\n self.keep_playing = self.to_play()\n self.card_update()",
"title": ""
},
{
"docid": "a74aa24f1c77ebf72c7629e86f7d25ca",
"score": "0.6350413",
"text": "def main():\n newdeck = deck.Deck()\n newdeck.shuffle()\n firstplayer_cards = []\n secondplayer_cards = []\n for x in range(5):\n firstplayer_cards.append(newdeck.dealacard())\n secondplayer_cards.append(newdeck.dealacard())\n playing_game(firstplayer_cards, secondplayer_cards)",
"title": ""
},
{
"docid": "c6a583f2c39d810c3d4c6a06f580b22f",
"score": "0.6337075",
"text": "def start(self):\n while self._pyramid[0][0].rank is not None: # if top card is not None\n self._menu()\n command = input('\\nCommand: ').strip()\n if self._enter_command(command) is None:\n system('cls')\n continue\n else:\n system('cls')\n input('\\nGame Over')\n print(game.instruction())\n input('Press any key to continue...') # wait user\n system('cls')\n self.start()",
"title": ""
},
{
"docid": "293c5613cb568b29524346857329a8d3",
"score": "0.6327848",
"text": "def reset(self) -> None:\n\n self.deck = make_cards()\n self.player = Player([self.deck.pop(), self.deck.pop()], 0, self.player_name)\n self.dealer = Dealer([self.deck.pop(), self.deck.pop()])\n self.player_split = Player([], 0, self.player_name + \" hand_two\")\n self.split = False",
"title": ""
},
{
"docid": "b7122669f6b8f76c9b6655a98923e290",
"score": "0.63025635",
"text": "def start_round(self):\r\n self.round_number += 1\r\n self.dealer = (self.dealer + 1) % 4\r\n self.round = Round(self.round_number, self.dealer)\r\n trump_suit = None\r\n self.deal()\r\n self.round.starting_hands = [player.cards[:] for player in self.players] + [\r\n self.kitty.cards[:]\r\n ]\r\n\r\n print(\"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\")\r\n print(\"Round %s\" % (self.round_number))\r\n print(\"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\")",
"title": ""
},
{
"docid": "e90754c4fcc810d1e8439fabac48bf0d",
"score": "0.6301797",
"text": "def start_game(self):\n\t\tpass\n\t\t# Welcome the player\n\t\t# Get the player name\n\t\t# Tell the player how to navigate the game\n\t\t\t# How to access the rules\n\t\t\t# How to quit at any input \n\t\t# Reveal the blank game board \n\t\t# \"Ready to start? (Y)es or (N)o\"",
"title": ""
},
{
"docid": "5f40568fed3de59176404cedb08d0e3a",
"score": "0.62817025",
"text": "def user_start_play(tower, discard):\n\n print('\\nNOW IT\\'S YOUR TURN!')\n print('Your Tower', end = ': ')\n print(tower)\n\n top_discard_brick = see_top_brick_discard(discard)\n print('The top brick on the discard pile is', top_discard_brick)",
"title": ""
},
{
"docid": "713dc46b4c49cbc94a8c0af15906bb56",
"score": "0.6278261",
"text": "def game_reset():\n global deck, user, computer, draw, swap, insert, sort, declare, discard, showCP, user_name, user_name_rect, computer_name, computer_name_rect\n global name_text, name_rect, score_text, score_rect, discard_mode, swap_mode, insert_mode, index\n deck = Deck(2)\n deck.shuffle_cards()\n deck.set_joker()\n user.deal_cards(deck)\n computer.deal_cards(deck)\n cardss = deck.draw_card()\n deck.update_pile(cardss)\n user.turn = True\n discard_mode = False\n swap_mode = False\n insert_mode = False\n index = []",
"title": ""
},
{
"docid": "4235cb633939d598736e432d17bdfc7f",
"score": "0.62765485",
"text": "def play_turn(game):\n print('=================================')\n if game.is_current_player_a_computer():\n print(game.get_current_player_name() + \"'s turn to play\")\n time.sleep(0.5)\n cards_drawn = game.play_computer_turn()\n for card in cards_drawn:\n if card is not None:\n print(\"Hit! Drew card %s\" % card)\n time.sleep(0.5)\n else:\n print(\"Deck is empty! Finished turn.\")\n if (game.current_player.is_over_21()):\n print(game.get_current_player_name() + \" lost! \" + game.get_current_player_name() + \" is out.\")\n time.sleep(0.5)\n else:\n if game.get_current_player_name != 'Dealer':\n print(game.get_current_player_name() + \" finished with count of \" + str(game.get_current_player_hand_count()))\n time.sleep(0.5)\n else:\n print(\"Your turn to play! Your count is: %s\" % game.get_current_player_hand_count())\n print(\"Your hand is: %s\" % game.get_current_player_hand())\n while game.human_can_draw() and input(\"Do you want to hit? [y/N] \") is 'y':\n card = game.human_player_draw()\n if card is not None:\n print(\"Hit! Delt card %s. Your count is now: %s\" % (card, game.get_current_player_hand_count()))\n print(\"Your hand is: %s\" % game.get_current_player_hand())\n else:\n print(\"Deck is empty! Finished turn.\")\n break\n if game.is_current_player_over_21():\n print(\"You lost! You score is over %s.\" % game.max_count())",
"title": ""
},
{
"docid": "2222c78926dcf1f22dafb55d4be49df5",
"score": "0.626647",
"text": "def computer_turn(self):\n print(\"You have \" + str(len(self.player1.deck)) + \" cards\")\n print(self.player2.name + \" has \" + str(len(self.player2.deck)) + \" cards\")\n print(\"Here is your top card\\n\")\n print_card(self.player1.deck[0])\n print(\"\\nYour opponent is choosing a trump stat\")\n rand_num = randint(1, 6)\n chosen_stat = None\n\n if rand_num == 1:\n chosen_stat = \"hp\"\n elif rand_num == 2:\n chosen_stat = \"attack\"\n elif rand_num == 3:\n chosen_stat = \"defense\"\n elif rand_num == 4:\n chosen_stat = \"speed\"\n elif rand_num == 5:\n chosen_stat = \"special attack\"\n else:\n chosen_stat = \"special defense\"\n time.sleep(2)\n print(\"\\n Your opponent picked \" + chosen_stat + \"!\\n\")\n\n self.check_stats(chosen_stat)",
"title": ""
},
{
"docid": "334b7600b2adc90874380ae9d38902c7",
"score": "0.625842",
"text": "def new_game():\n global exposed, deck_cards, state, turns\n turns = 0\n # Game state\n state = 0\n exposed = [False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False] \n # STEP 1: Model the deck of cards \n deck_cards = [1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8]\n correct_cards = [False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False] \n # STEP 3: Shuffle the deck using random.shuffle\n random.shuffle(deck_cards)",
"title": ""
},
{
"docid": "98b10c5fbb5553c5b37d0874b9667ae6",
"score": "0.624704",
"text": "def __init__(self):\n self.keep_playing = True\n self.old_card = r.randint(1,13)\n self.new_card = 0\n self.player = Player()\n self.guess = \"\"\n self.points = 300",
"title": ""
},
{
"docid": "2efe4a4c4937f3e8e134bfdda416f06d",
"score": "0.62439346",
"text": "def reset_game(self):\n # The player always goes first.\n self.current_turn = 'player'\n\n # Set the current cards to an empty array.\n self.cards = {\n 'dealer': [],\n 'player': []\n }\n\n # Set the totals for each player.\n self.totals = {\n 'dealer': 0,\n 'player': 0\n }\n\n # Create a new new deck of cards.\n self.deck = self.deck_generator.get_new_deck()",
"title": ""
},
{
"docid": "4315b4a7c52331d868ebcd95500313ae",
"score": "0.6240726",
"text": "def take_initial_turn(self, player, dealer, deck_list):\n\n dist_counter = 0\n\n # Distribute first 2 cards to player and dealer\n while dist_counter < 2:\n \n # Take 1 card for player\n player, deck_list = self.take_1_card(player, deck_list)\n print(f'{player.name} hand: {[pair[0] for pair in player.hand]}\\nCurrent deck len: {len(deck_list)}')\n \n # Take 1 card for dealer, showing only the first drawn card\n dealer, deck_list = self.take_1_card(dealer, deck_list)\n if dist_counter == 0:\n print(f'{dealer.name} hand: {[pair[0] for pair in dealer.hand]}\\nCurrent deck len: {len(deck_list)}')\n else:\n print(f'Current deck len: {len(deck_list)}')\n\n dist_counter += 1\n \n print('\\nEnd of initial distribution.')\n\n return player, dealer, deck_list",
"title": ""
},
{
"docid": "aeccbe58a2576153b7edf0796e66999c",
"score": "0.62265307",
"text": "def black_jack_hand(player1, dealer): \n #this should be the black_jack hand, while the begging should be setting up a new game\n dealer.get_all_bets(player1)\n ##deal two cards each, one to the dealer should be set to face down\n for x in xrange(1,3):\n dealer.give_card_to_player(player1)\n dealer.give_card_to_player(dealer)\n\n ###Now the game is ready to start showing the hands of the dealer and the player should be done\n print \"Dealer's Hand\"\n dealer.get_hand().showHand()\n print \"Player's Hand\"\n player1.get_hand().showHand()\n while(player1.want_card()):\n dealer.give_card_to_player(player1)\n print \"Players Hand\"\n player1.get_hand().showHand()\n\n \n dealer.flip_card_over()\n while(dealer.want_card()):\n dealer.give_card_to_player(dealer)\n \n print \"Dealers Hand\"\n dealer.get_hand().showHand()\n\n dealer.set_winner(player1)",
"title": ""
},
{
"docid": "06a062fdec911de20686003a63b70bb4",
"score": "0.6199962",
"text": "def play_game(self):\n\n # Print the beginning game message.\n print(\"\\nBeginning \" + str(self))\n\n time.sleep(1.5)\n\n os.system('cls' if os.name == 'nt' else 'clear')\n\n # It is the initial turn.\n turn = 0\n\n # This is the game loop. When the game ends we will exit this loop.\n has_ended = False\n while not has_ended:\n\n # Increment the turn number.\n turn += 1\n\n # Iterate through each player that is playing.\n for player_num, player in enumerate(self.config.players):\n \n # Set the current player.\n self.config.set_current_player(player_num)\n \n # Before each player's turn, check to see if the game ended.\n if self.config.supply.check_end_game():\n has_ended = True\n break\n \n # The player's turn starts here.\n try:\n print(f\"\\n********** Turn #{turn} **********\")\n player.turn(self.config)\n time.sleep(1.5)\n os.system('cls' if os.name == 'nt' else 'clear')\n \n # Here we catch unexpected errors while playing the game.\n except DominionError as e:\n print(\"Encountered an error while playing. \" + e)\n return\n\n # If anyone quits, exit the game here.\n except DominionQuitError:\n print(f\"Game quit by {player.name}\")\n return\n\n # Once the game has ended, run the end_game method.\n self.end_game(turn)",
"title": ""
},
{
"docid": "e85dcd6a656f2d43ae2291615c21760e",
"score": "0.6190354",
"text": "def start_game(self):\n\n # Change starting screen to Chess board\n self.board = Board(square_width=self.view.square_width, \n square_height=self.view.square_height,\n is_flipped=self.is_flipped)\n\n # Create players\n self.board.player_list.append('Human')\n \n if self.settings['vs_computer']: self.board.player_list.append('AI')\n else: self.board.player_list.append('Human')",
"title": ""
},
{
"docid": "f4717ddff3d01a3cc83e1621cdcdf452",
"score": "0.6188858",
"text": "def playAgain(self):\n self.game_setup()",
"title": ""
},
{
"docid": "08f12d4c17062b04529efa4006ecc7bb",
"score": "0.6175251",
"text": "def _start_new_game(self):\n D = DeckGenerator()\n D.shuffle()\n T = TableCard(D)\n T.generate_pyramid()\n gL = GameLogic(T)\n gL.level = self._level\n for item in T.pyramid_deck[-1]:\n item.status = True\n return Game(D, T, gL)",
"title": ""
},
{
"docid": "6dcbee6750907da85989d529de59453d",
"score": "0.6170448",
"text": "def initTurn(self,players,current_player,draw):\n if self.__oneSide:\n for player in players:\n i=0\n for card in player.getHand():\n if callable(getattr(player, \"getSpecial\", None)):\n self.__canvas.itemconfigure(self.__canvas_items[\"player{}-card-{}\".format(player.getId(),i)],image=self.__imgs[\"back\"])\n else:\n self.__canvas.itemconfigure(self.__canvas_items[\"player{}-card-{}\".format(player.getId(),i)],image=self.__imgs[card.getName()])\n i+=1\n i=0\n for card in player.getDiscard():\n self.__canvas.itemconfigure(self.__canvas_items[\"player{}-discard-{}\".format(player.getId(),i)],image=self.__imgs[card.getName()])\n i+=1\n else:\n for player in players:\n i=0\n for card in player.getHand():\n if current_player.getId() == player.getId():\n self.__canvas.itemconfigure(self.__canvas_items[\"player{}-card-{}\".format(player.getId(),i)],image=self.__imgs[\"back\"])\n self.__canvas.move(self.__canvas_items[\"player{}-card-{}\".format(player.getId(),i)],0,-716)\n else:\n self.__canvas.itemconfigure(self.__canvas_items[\"player{}-card-{}\".format(player.getId(),i)],image=self.__imgs[card.getName()])\n self.__canvas.move(self.__canvas_items[\"player{}-card-{}\".format(player.getId(),i)],0,716)\n i+=1\n i=0\n for card in player.getDiscard():\n if current_player.getId() == player.getId():\n self.__canvas.itemconfigure(self.__canvas_items[\"player{}-discard-{}\".format(player.getId(),i)],image=self.__imgs[card.getName()])\n self.__canvas.move(self.__canvas_items[\"player{}-discard-{}\".format(player.getId(),i)],0,-358)\n else :\n self.__canvas.itemconfigure(self.__canvas_items[\"player{}-discard-{}\".format(player.getId(),i)],image=self.__imgs[card.getName()])\n self.__canvas.move(self.__canvas_items[\"player{}-discard-{}\".format(player.getId(),i)],0,358)\n i+=1\n if len(draw) == 0:\n self.__canvas.itemconfigure(self.__canvas_items[\"draw\"],image=\"\")\n self.__updateDisplay()",
"title": ""
},
{
"docid": "020fa0581c40ec60e6532db6e2adb650",
"score": "0.6161986",
"text": "def main():\n game = poker_game()\n game.add_player(player_1) #Temp solution to add players to game\n game.add_player(player_2)\n game.form_player_hands() #Deal phase one, creating card objects and card_pack \n game.update_all_hands() #Used after each round, to sort hand + calculate each persons strongest hand\n game.deal_flop()\n game.update_all_hands()\n game.deal_turn()\n game.update_all_hands()\n game.deal_river()\n game.update_all_hands()\n print(game.player_list[1].hand_value) #Prints hand_value of player at index 1\n #hand_compare.hand_compare(player_hands, board) --Incomplete\n return",
"title": ""
},
{
"docid": "8e986e6106c6e9afad81891509fe2a59",
"score": "0.6153859",
"text": "def start_game(self):\n if self.gameover:\n self.init_game()\n self.gameover = False",
"title": ""
},
{
"docid": "ff428e6a7a8d16851ada513c3fdd2f4b",
"score": "0.61526495",
"text": "def run_play_loop(self): \n player = Player('Player', STARTING_CHIP_NUMBER)\n dealer = Player('Dealer', STARTING_CHIP_NUMBER)\n self.gui.create_play_screen()\n self.pause(500)\n \n while True:\n deck = Deck()\n\n # Prompt the user to place a wager\n wager = self.get_wager(player, dealer)\n\n # Handle the card selection process in triple pocket holdem\n # Present the user with his/her options for cards and have him/her select the cards\n self.select_cards(deck, player, dealer)\n community_cards = deck.draw_five_community_cards()\n \n # Now, reveal the cards held by the user and the dealer, as well as the \n # five community cards\n self.gui.explain_card_reveal()\n self.pause(2000)\n self.gui.reveal_player_cards(player, dealer)\n self.pause(2000)\n self.gui.reveal_common_cards(community_cards)\n self.pause(5000)\n\n # Determine the best hand held by each player and who won the round\n # Add or subtract chips from the player and dealer's totals depending on the outcome\n # Output to the user which poker hand the dealer had and what the consequent result was\n (player_hand, dealer_hand, player_wager_multiple, dealer_wager_multiple) = determine_outcome(\n player.hands[0], \n dealer.hands[0], \n dealer.hands[1], \n community_cards\n )\n player.alter_chip_balance(player_wager_multiple * wager)\n dealer.alter_chip_balance(dealer_wager_multiple * wager)\n self.gui.explain_outcome(\n player_hand, \n dealer_hand, \n player_wager_multiple, \n player.num_chips, \n dealer.num_chips\n )\n self.pause(4000)\n \n # Get the next action from the user\n # If the game has ended (i.e. the user or dealer has run out of chips),\n # the user can elect to play again. Otherwise, the user can elect to continue with\n # another round in the current game or to exit\n next_action = self.get_action_at_round_end(player, dealer)\n if next_action == MENU_NOW:\n return next_action\n elif next_action == PLAY_AGAIN:\n player = Player('Player', STARTING_CHIP_NUMBER)\n dealer = Player('Dealer', STARTING_CHIP_NUMBER)\n else:\n player.clear_hands()\n dealer.clear_hands()",
"title": ""
},
{
"docid": "94b5e7b4a39d3ad3dd48208c7ef542b8",
"score": "0.614256",
"text": "def play(self):\n # Clear/Reset attributes/values\n self.winners = []\n self.count = 0\n for player in self.players:\n # Reset hands\n del(player.cards[:])\n # Increment number of unique games played\n player.gamesPlayed += 1.0\n # Increment total number of games played\n self.totalGamesPlayed += 1.0\n # Prepare the deck, deal the cards\n self.deck = Deck()\n self.deck.shuffle()\n self.deck.deal(3, self)\n # Top card is placed onto discard pile\n self.discard.add(self.deck.pop())\n # While no one has satisfied winning conditions, take turns\n while not self.check_won():\n self.turn_seq()",
"title": ""
},
{
"docid": "ad995044d769ec0161ae9924becdf18b",
"score": "0.61161804",
"text": "def initiate_deck(self):\n\n deck = Deck()\n deck.build_deck()\n deck.shuffle()\n self.deck1, self.deck2 = deck.split_deck()",
"title": ""
},
{
"docid": "0706d71c979b7b26af67ecd63b9ff690",
"score": "0.61084616",
"text": "def reset_hands(self):\n self.my_player.reset_hand()\n self.my_computer.reset_hand()",
"title": ""
},
{
"docid": "b7dc1ffc7670027b24ea1494978535af",
"score": "0.61073416",
"text": "def reset(self, test=None):\n self.health = self.start_health\n self.armor = 0\n self.this_turn_mana = 0\n self._init_heropower()\n self._init_deck(self.fix_deck)\n self.inhands = [] # cards in self hands\n self.intable = [] # cards on self table\n self.used_cards = defaultdict(int) # used card counts. key: cidx, value: used counts\n if self.first_player:\n self.draw_as_first_player()\n else:\n self.draw_as_second_player()\n if test is not None:\n self.test = test",
"title": ""
},
{
"docid": "f1a212e0c2daeb3463048aa45a876849",
"score": "0.60954237",
"text": "def setup_game():\n\t\n\tglobal bullet_chamber\n\tglobal current_chamber\n\tglobal player_turn\n\t\n\tbullet_chamber = math.ceil(random.random()*CHAMBER_COUNT)\n\tcurrent_chamber = 1\n\t\n\tprint \"Gun prepared...\"\n\t# determine who's first\n\tif random.random() >= 0.5:\n\t\tplayer_turn = True\n\t\tprint \"Player goes first\"\n\telse:\n\t\tplayer_turn = False\n\t\tprint \"Computer goes first\"",
"title": ""
},
{
"docid": "dd4b311a54ba395986edf37e3444a0a1",
"score": "0.60823447",
"text": "def start_new_game(players):\n game = SchnapsenGame()\n game.create_cards()\n game.shuffle_cards()\n for player in players:\n for dummy in range(5):\n card = game.draw_from_stock()\n player.add_card(card)\n return game",
"title": ""
},
{
"docid": "b8c0d6666ca0db5b99f471105ab90a1f",
"score": "0.6074004",
"text": "def show_next_hand(*args):\n global twentyfive_cards\n # disable show_next and enable show_best\n # show_best25_hand_button[\"state\"] = \"normal\"\n # show_next_hand_button[\"state\"] = \"disabled\"\n\n window.title(\"Play Pyramid Poker\")\n w.delete(\"all\") # clear out last hand\n show_twentyfive_cards()",
"title": ""
},
{
"docid": "0d3d9925f75608d29e6f3f04e3b60fe8",
"score": "0.60738045",
"text": "def start_hand(deck, onCoin):\n size = 4 if onCoin else 3\n return [deck.pop() for i in range(size)]",
"title": ""
},
{
"docid": "abc03548cf453e649529ab559af2bfe6",
"score": "0.607288",
"text": "def initialise_deck(self):\n deck = Deck()\n shuffle(deck.card)\n self.deck = deck\n return",
"title": ""
},
{
"docid": "40e5d29991114394fd48b8ffdeb3fb70",
"score": "0.60725707",
"text": "def main():\n numHands = eval(input('Enter number of player: '))\n while (numHands < 2 or numHands > 10):\n numHands = eval(input('Enter number of hands to play[2~6]: '))\n game = Poker(numHands)\n print(\"## Game start\")\n game.play()\n print(\"## Caculating hands\")\n print(game.evaluate(game.hands))",
"title": ""
},
{
"docid": "d4317741c257ecaff2039978f0c82dd3",
"score": "0.60713416",
"text": "def choose_hand(self):\n\n w = random.randint(0, 4)\n if len(Player.history) > 0:\n if Player.history[len(Player.history)-1] == \"rock\":\n w = 4\n self.weapon = Weapons.weapon[w]\n self.turtle.shape(Weapons.computer_images[w])",
"title": ""
},
{
"docid": "de83074b480ba3c367ce9ddc04565f9a",
"score": "0.60338426",
"text": "def start(self):\n self.start_game()\n self.init_playing_order()\n while self.winning_camel is None:\n self.run_leg()\n self.report()\n print(\"------------------- End leg ---------------------\")\n self.game_stage = \"result\"\n self.losing_camel = self.orders[-1]\n self.game_scoring_round()\n self.determine_game_result()",
"title": ""
},
{
"docid": "5f2270c443ff6f39200566891a90e09b",
"score": "0.60226154",
"text": "def playUpgrade(self, card, target = None):\n logging.info(card)\n active = self.activePlayer.board[\"Creature\"]\n inactive = self.inactivePlayer.board[\"Creature\"]\n hand = self.activePlayer.hand\n broken = False\n \n if target:\n self.activePlayer.board[\"Upgrade\"].append(card)\n if card in hand:\n hand.remove(card)\n side, choice = target\n if side == \"fr\":\n active[choice].upgrade.append(card)\n else:\n inactive[choice].upgrade.append(card)\n eval(f\"upgrade.{card.title}(self, card, side, choice)\")\n self.cardChanged(True) # this would actually make the play abilities I put on blood of titans, a bit redundant, but redundancy is ok\n return\n \n drawMe = []\n # surfs\n selectedSurf = Surface((self.target_cardw, self.target_cardh))\n selectedSurf.convert_alpha()\n selectedSurf.set_alpha(80)\n selectedSurf.fill(COLORS[\"LIGHT_GREEN\"])\n\n selectedSurfTapped = Surface((self.target_cardh, self.target_cardw))\n selectedSurfTapped.convert_alpha()\n selectedSurfTapped.set_alpha(80)\n selectedSurfTapped.fill(COLORS[\"LIGHT_GREEN\"])\n # draw all targets\n for temp_card in active + inactive:\n if temp_card.ready:\n drawMe.append((selectedSurf, temp_card.rect))\n else:\n drawMe.append((selectedSurfTapped, temp_card.tapped_rect))\n \n if self.canDiscard(card, reset = False):\n ## discard stuff here - if you can play it, you can discard it\n discSurf = Surface((self.target_cardw, self.target_cardh))\n discSurf.convert_alpha()\n discSurf.set_alpha(80)\n discSurf.fill(COLORS[\"LIGHT_GREEN\"])\n discRect = discSurf.get_rect()\n discRect.topleft = self.discard1_rect.topleft\n drawMe.append((discSurf, discRect))\n \n while True:\n self.extraDraws = drawMe.copy()\n \n for e in pygame.event.get():\n if e.type == MOUSEMOTION:\n #update mouse position\n self.mousex, self.mousey = e.pos\n \n if e.type == QUIT:\n pygame.quit()\n\n if e.type == MOUSEBUTTONUP and e.button == 1:\n activeHit = [(Rect.collidepoint(x.rect, (self.mousex, self.mousey)) or Rect.collidepoint(x.tapped_rect, (self.mousex, self.mousey))) for x in active]\n inactiveHit = [(Rect.collidepoint(x.rect, (self.mousex, self.mousey)) or Rect.collidepoint(x.tapped_rect, (self.mousex, self.mousey))) for x in inactive]\n if Rect.collidepoint(self.hand1_rect, (self.mousex, self.mousey)):\n l = len(hand)\n for x in range(len(hand)):\n temp_card = hand[x]\n if x == 0 and self.mousex < temp_card.rect.centerx:\n hand.insert(0, self.dragging.pop())\n self.extraDraws = []\n self.cardChanged()\n return\n elif temp_card.rect.centerx < self.mousex and x < l-1 and self.mousex < hand[x+1].rect.centerx:\n hand.insert(x + 1, self.dragging.pop())\n self.extraDraws = []\n self.cardChanged()\n return\n hand.append(self.dragging.pop())\n self.extraDraws = []\n return\n elif True in activeHit:\n self.activePlayer.board[\"Upgrade\"].append(self.dragging.pop())\n choice = activeHit.index(True)\n target = active[choice]\n target.upgrade.append(card)\n eval(f\"upgrade.{card.title}(self, card, target)\")\n broken = True\n break\n elif True in inactiveHit:\n self.activePlayer.board[\"Upgrade\"].append(self.dragging.pop())\n choice = inactiveHit.index(True)\n target = active[choice]\n target.upgrade.append(card)\n eval(f\"upgrade.{card.title}(self, card, target)\")\n broken = True\n break\n elif self.canDiscard(card, reset=False) and Rect.collidepoint(discRect, (self.mousex, self.mousey)):\n hand.append(self.dragging.pop())\n self.discardCard(-1)\n self.extraDraws = []\n self.cardChanged()\n return\n else:\n hand.append(self.dragging.pop())\n self.extraDraws = []\n self.cardChanged()\n return\n\n if e.type == MOUSEBUTTONDOWN and e.button == 1:\n logging.error(\"You shouldn't be able to trigger MOUSEBUTTONDOWN in dragCard.\")\n\n if Rect.collidepoint(self.hand1_rect, (self.mousex, self.mousey)):\n l = len(hand)\n for x in range(len(hand)):\n temp_card = hand[x]\n if x == 0 and self.mousex < temp_card.rect.centerx:\n hand.insert(0, self.invisicard)\n self.cardChanged()\n break\n elif temp_card.rect.centerx < self.mousex and x < l-1 and self.mousex < hand[x+1].rect.centerx:\n hand.insert(x + 1, self.invisicard)\n self.cardChanged()\n break\n\n self.CLOCK.tick(self.FPS)\n self.hovercard = []\n self.check_hover()\n self.draw(False)\n try:\n hand.remove(self.invisicard)\n except:\n pass\n pygame.display.flip()\n self.extraDraws = []\n if broken:\n self.cardChanged()\n break\n \n if not target and card.amber > 0:\n self.activePlayer.gainAmber(card.amber, self)\n logging.info(f\"{card.title} gave you {str(card.amber)} amber. You now have {str(self.activePlayer.amber)} amber.\\n\\nChange to a log when you fix the amber display issue.\"\"\")\n if card not in self.playedThisTurn:\n self.playedThisTurn[card] = 1\n else:\n self.playedThisTurn[card] += 1\n self.cardChanged(True)\n return",
"title": ""
},
{
"docid": "c09bb9eea918d6798de1ca216dd3a63a",
"score": "0.60097355",
"text": "def player_start(self):\n for card in self._target_hand:\n if card.pip is False:\n self._dealer.flip_deal(card)\n return self._target_hand_stats",
"title": ""
},
{
"docid": "fe63242c7be7c36cf950c836bb9a76e8",
"score": "0.60043347",
"text": "def initialize_game():\n\n MIN_SCORE = 0\n\n p1_card = create_score_card()\n p2_card = create_score_card()\n p1_bonus = MIN_SCORE\n p2_bonus = MIN_SCORE\n\n round_ongoing = True\n\n while round_ongoing:\n if -1 in p1_card.values():\n print(\"-- IT IS PLAYER ONE'S TURN --\")\n take_turn(p1_card, p1_bonus)\n\n print(\"\\n\")\n\n if -1 in p2_card.values():\n print(\"-- IT IS PLAYER TWO'S TURN --\")\n take_turn(p2_card, p2_bonus)\n\n if -1 not in p1_card.values() and -1 not in p2_card.values():\n round_ongoing = False\n\n game_verdict(calculate_score(p1_card), p1_bonus, calculate_score(p2_card), p2_bonus)",
"title": ""
},
{
"docid": "45c738d403cfef32cc315c30b913debe",
"score": "0.6004059",
"text": "def __init__(self):\n self.shoe = deck.Deck(False)\n self.shuffle_shoe()\n self.hand = []",
"title": ""
},
{
"docid": "d0f4de22f0f5ef385e52aa0594aba3bc",
"score": "0.6000726",
"text": "def begin_game(self):\n pass",
"title": ""
},
{
"docid": "307a4bb97dc9ff3d6387bcf1682a04b0",
"score": "0.59878075",
"text": "def play(self):\n # randomly remove a Cards in cards\n card_picked = choice(self.cards)\n self.cards.remove(card_picked)\n\n # add the Card to the Player's history\n self.history.append(card_picked)\n\n # increment the Player's turn count by 1\n self.turn_count += 1\n\n # decrement the Player's number of cards by 1\n self.number_of_cards -= 1\n \n # if self.number_of_cards == 0:\n # print(f\"{self.name} {self.turn_count} has no card\")\n\n # display the play\n print(f\"{self.name} {self.turn_count} played: {card_picked}\")\n\n return card_picked",
"title": ""
},
{
"docid": "06c3a907ddc1c52dcecd2b011db5a5c4",
"score": "0.5981489",
"text": "def black_jack():\n dealer = Dealer()\n player1 = Player(input(\"How much money does the player have?\"))\n want_to_play = 1\n while(want_to_play == 1):\n dealer.shuffle_deck()\n dealer.ready_for_next_hand()\n player1.ready_for_next_hand()\n\n black_jack_hand(player1, dealer)\n want_to_play = input(\"Would you like to play another hand? Select 1 for Yes and 0 for no\")",
"title": ""
},
{
"docid": "3cdc313404535e52d43ab7e2d590aff0",
"score": "0.59767616",
"text": "def startTurn():\n\tglobal current_piece\n\tcurrent_piece = 0\n\tlimbL.move_to_joint_positions(coords.ResetPos)\n\tlimbL.move_to_joint_positions(coords.PickU)\n\tlimbL.move_to_joint_positions(coords.PickD)\n\ti = 0\n\twhile current_piece == 0:\n\t\tcurrent_piece = IDBlock(left_image)\n\t\trospy.sleep(5)\n\t\ti+=1\t\t\n\t\tif i >= 6:#After 30 seconds of inaction by the human player, Lucas assumes victory\n\t\t\tprint \"Huzzah! Another glorious victory for your robotic overlord!\"\n\t\t\tsys.exit()\n\tlimbL.move_to_joint_positions(coords.PickU)\n\tlimbL.move_to_joint_positions(coords.ResetPos)\n\tscanBoard()\n\tlimbL.move_to_joint_positions(coords.ResetPos)",
"title": ""
},
{
"docid": "9c181062fcadfcfb7154f204b991d32b",
"score": "0.5975167",
"text": "def play_one_turn(self):\n #XXX: My laziness knows no bounds. Only allows two players.\n #XXX: I pass the whole game object through to players. Ugh.\n \n if self.current_player == 1:\n self.p1.new_hand()\n self.p1.play_one_turn(self)\n self.current_player = 2\n else:\n self.p2.new_hand()\n self.p2.play_one_turn(self)\n self.current_player = 1",
"title": ""
},
{
"docid": "cd5f840201383788c385fab45b96b7da",
"score": "0.59595335",
"text": "def reset(self, bank=50):\n self.gm = Game(\"P1\", \"Qtable\", \"P2\", \"Qtable\", bank)\n\n # Random starting money distribution p=0.4\n if self.rc is not None:\n if random() < self.rc:\n left = round(random() * ((bank * 2) - 4))\n self.gm.sb_player().bank = left + 2\n self.gm.bb_player().bank = ((bank * 2) - 4) - left + 2\n\n self.gm.place_blinds()\n if self.dc:\n self.gm.players_draw_cards()\n # Return observation\n # [ Small Blind Player, Big Blind Player ]\n self.ob = [self.gm.sb_player(), self.gm.bb_player()]\n return self.ob",
"title": ""
},
{
"docid": "21166b5cdbe4125442fd133b0444ff07",
"score": "0.5957577",
"text": "def test_playcard_cost0(self):\n self.plr.play_card(self.card)\n self.plr.gain_card(\"Copper\")\n self.plr.test_input = [\"end phase\", \"end phase\"]\n self.plr.turn()\n self.assertNotIn(\"Horse\", self.plr.piles[Piles.DISCARD])",
"title": ""
},
{
"docid": "6436110836fe4d65b16594b43045efd8",
"score": "0.59550923",
"text": "def play_round(self):\n playing_players = self.enter_bets()\n self.give_cards()\n for player in self.players:\n if player.name in playing_players:\n self.play_hand(player)",
"title": ""
},
{
"docid": "60e9ce3200b27a029258f903fb33497d",
"score": "0.5942199",
"text": "def play_1(player):\r\n playing = True\r\n deck = Deck()\r\n deck.shuffle()\r\n score = 0\r\n print(input(\"Welcome to the casino, \" + player + \". Press Enter to play the game.\"))\r\n while playing:\r\n if deck.out():\r\n print(\"Deck is out of cards. Game over. Your score was \", str(score))\r\n playing = False\r\n else:\r\n hand_a = PokerHand()\r\n hand_b = PokerHand()\r\n for i in range(0, MAX_HAND_SIZE):\r\n card = deck.deal()\r\n hand_a.add_card(card)\r\n for i in range(0, MAX_HAND_SIZE):\r\n card = deck.deal()\r\n hand_b.add_card(card)\r\n print(\"Hand #1: \", hand_a)\r\n print(\"Hand #2: \", hand_b, \"\\n\")\r\n user_answer = int(input(\"Which hand is worth more? \"\r\n \"Type 1 if Hand #1, -1 if Hand #2, and 0 if they are worth the same\\n \"))\r\n correct_answer = hand_a.compare_to(hand_b)\r\n if user_answer == correct_answer:\r\n print(\"Correct! Let's try again\\n\")\r\n score += 1\r\n else:\r\n print(\"Game over. Your score was \", str(score))\r\n playing = False",
"title": ""
},
{
"docid": "e2e8b14207df9b875bc1f700b9f25012",
"score": "0.5939224",
"text": "def start_self_play(self, player, display=False):\n player.reset_player()\n self.init_game_state()\n\n states, mcts_probs, current_players = [], [], []\n while True:\n move, move_probs = player.get_action(self.game_state)\n\n if move == PASS_MOVE:\n move = PASS_MOVE\n else:\n move = (move // self.game_state.size, move % self.game_state.size)\n\n # store the data\n states.append(self.game_state.copy())\n mcts_probs.append(move_probs)\n current_players.append(self.game_state.current_player)\n\n # perform a move\n is_game_end = self.game_state.do_move(move)\n if display:\n self.print_board(self.game_state, str(player), str(player), move_probs)\n if is_game_end:\n winner_id = self.game_state.get_winner()\n # winner from the perspective of the current player of each state\n winners_z = np.zeros(len(current_players))\n if winner_id is not None:\n winners_z[np.array(current_players) == winner_id] = 1.0\n winners_z[np.array(current_players) != winner_id] = -1.0\n if display:\n if winner_id is not None:\n print(\n \"Game end. Winner is player:\",\n \"X\" if winner_id == go.BLACK else \"O\",\n )\n else:\n print(\"Game end. Tie\")\n return winner_id, zip(states, mcts_probs, winners_z)",
"title": ""
},
{
"docid": "a2d452612ccda9a3382eabefd3e5e2c2",
"score": "0.5927983",
"text": "def UCTPlaySampleGame():\n # state = OthelloState(4) # uncomment to play Othello on a square board of the given size\n # state = OXOState() # uncomment to play OXO\n # state = NimState(15) # uncomment to play Nim with the given number of starting chips\n # state = SimpState((5,5)) # uncomment to play Simp with the given number of starting chips\n state = CapGoState(sz) # uncomment to play Capture Go with the given board size\n UCTPlayGame(state)",
"title": ""
},
{
"docid": "3c125026b6cbd995192ce2d8c656d968",
"score": "0.59240186",
"text": "def resetGame():\n del CompHand[:] \n del UserHand[:]\n del CompWinningList[:]\n del UserWinningList[:]\n del deck[:]\n deck.append(\"Ah\")\n deck.append(\"Ad\")\n deck.append(\"Ac\")\n deck.append(\"As\")\n deck.append(\"2h\")\n deck.append(\"2d\")\n deck.append(\"2c\")\n deck.append(\"2s\")\n deck.append(\"3h\")\n deck.append(\"3d\")\n deck.append(\"3k\")\n deck.append(\"3s\")\n deck.append(\"4h\")\n deck.append(\"4d\")\n deck.append(\"4c\")\n deck.append(\"4s\")\n deck.append(\"5h\")\n deck.append(\"5d\")\n deck.append(\"5c\")\n deck.append(\"5s\")\n deck.append(\"6h\")\n deck.append(\"6d\")\n deck.append(\"6c\")\n deck.append(\"6s\")\n deck.append(\"7h\")\n deck.append(\"7d\")\n deck.append(\"7c\")\n deck.append(\"7s\")\n deck.append(\"8h\")\n deck.append(\"8d\")\n deck.append(\"8c\")\n deck.append(\"8s\")\n deck.append(\"9h\")\n deck.append(\"9c\")\n deck.append(\"9d\")\n deck.append(\"9s\")\n deck.append(\"10h\")\n deck.append(\"10d\")\n deck.append(\"10c\")\n deck.append(\"10s\")\n deck.append(\"Jh\")\n deck.append(\"Jd\")\n deck.append(\"Jc\")\n deck.append(\"Js\")\n deck.append(\"Qh\")\n deck.append(\"Qd\")\n deck.append(\"Qc\")\n deck.append(\"Qs\")\n deck.append(\"Kh\")\n deck.append(\"Kd\")\n deck.append(\"Kc\")\n deck.append(\"Ks\") \n return deck",
"title": ""
},
{
"docid": "55054711f5324eaa84db3999ab4d245c",
"score": "0.59172213",
"text": "def test_play_no_pickup(self):\n self.plr.piles[Piles.HAND].set(\"Copper\", \"Copper\", \"Estate\", \"Duchy\")\n self.plr.piles[Piles.DECK].set(\"Gold\", \"Silver\", \"Copper\", \"Copper\")\n self.plr.add_card(self.card, Piles.HAND)\n self.plr.test_input = [\n \"Discard\",\n \"Discard Estate\",\n \"Discard Duchy\",\n \"Finish\",\n \"nothing\",\n ]\n self.plr.play_card(self.card)\n self.assertEqual(self.plr.coins.get(), 2)\n self.assertEqual(self.plr.piles[Piles.HAND].size(), 4 + 1 - 2)\n self.assertNotIn(\"Duchy\", self.plr.piles[Piles.HAND])\n self.assertNotIn(\"Silver\", self.plr.piles[Piles.HAND])",
"title": ""
},
{
"docid": "f18317543fe0e81636342952f64a9f20",
"score": "0.5912951",
"text": "def start_game():\n\tprint intro_msg\n\tplayer_name = raw_input(\"What' do they call you son ? \")\n\tend = False\t\t\n\twhile not end :\n\t\tchoice,door = show_options()\n\t\tif choice == 'yes':\n\t\t\t#Enter the door chosen\n\t\t\tproceed = proceed_in_door(door)\n\t\t\tif proceed:\n\t\t\t\tprint \"Proceeding....\"\n\t\t\t\ttime.sleep(3)\n\t\t\t\tos.system('clear')\n\t\t\t\tcontinue\t\n\t\t\telse :\n\t\t\t\tend = True\n\n\t\telif choice == 'no' :\n\t\t\t#Show the random waiting message\n\t\t\tprint random.randint(0,len(waiting_messages))\n\t\t\ttime.sleep(5) #wait\n\n\t\telif choice == 'info':\n\t\t\t#shows the player info\n\t\t\tshow_player_info()\n\t\telse :\n\t\t\tprint \"Whaaaaaaaat the heck is that ?, Just YES or no\"",
"title": ""
},
{
"docid": "754749c503bb5ff4f5d8ff50d7501951",
"score": "0.5910271",
"text": "def start_game(self, name):\n\t\tself.guesses = ''\n\t\tself.incorrect = 0\n\t\tself.correct = 0\n\t\tself.gameover = False\n\t\tself.won = False\n\t\tself.username = name\n\n\t\tword_list = ['3dhubs', 'marvin', 'print', 'filament', 'order', 'layer']\n\t\tself.target = word_list[random.randint(0, 5)]",
"title": ""
},
{
"docid": "f5c861602d872a650894343f6703dbbb",
"score": "0.5908629",
"text": "def start(self):\n # <<-- Creer-Merge: start -->> - Code you add between this comment and the end comment will be preserved between Creer re-runs.\n # replace with your start logic\n self.game_data = GameData()\n\n self.random = random.random()\n # <<-- /Creer-Merge: start -->>\n\n self.game_data.humans = [human for human in self.player.units if human.job.title == 'fresh human']\n\n self.first_run = True\n self.no_income = True\n self.harvester_returning = False",
"title": ""
},
{
"docid": "c9805fd2876acedb227ef8bbf31c53e4",
"score": "0.59040797",
"text": "def reset_game():\n global tmp_score, current_player\n for data in players:\n data[1] = 0\n tmp_score = 0\n current_player = 0 # 'the user always starts'\n cls()\n start_game()",
"title": ""
},
{
"docid": "2a582deca9da8cb98d30740d848ec22b",
"score": "0.5902361",
"text": "def startGame(self):\n while True:\n whiteType = input('Welcome to King-Queen Endgame Simulatior \\nWould you like a (1) Computer or (2) Human to play as White (the attacker with a queen and a king)?\\n')\n if whiteType == (1 or 'computer' or 'Computer'):\n whiteType = 'comp'\n break\n elif whiteType == (2 or 'human' or 'Human'):\n whiteType = 'human'\n break\n else:\n print 'Please input 1 or 2.'\n while True:\n blackType = input('Would you like a (1) Computer or (2) Human to play as Black (the defender with a king)?\\n')\n if blackType == 1 or blackType=='computer' or blackType=='Computer':\n blackType = 'comp'\n break\n elif blackType == 2 or blackType=='human' or blackType=='Human':\n blackType = 'human'\n break\n else:\n print 'Please input 1 or 2.'\n self.setBoard('Q')\n print b\n self.playGame(whiteType, blackType)",
"title": ""
},
{
"docid": "c413ae6f91a0d0648f1e7f342387a287",
"score": "0.58975714",
"text": "def _start_game(self):\n\t\tself.settings.initialize_dynamic_settings()\n\n\t\t# Reset the game statistics\n\t\tself.stats.reset_stats()\n\t\tself.stats.game_active = True\n\t\tself.sb.prep_score()\n\t\tself.sb.prep_level()\n\t\tself.sb.prep_strikers()\n\n\t\t# Get rid of any remaining kids and candy.\n# \t\tself.kids.empty()\n# \t\tself.candies.empty()\n\n\t\t# Hide the mouse cursor.\n\t\tpygame.mouse.set_visible(False)\n\n\t\tpygame.mixer.music.set_endevent(self.SONG_END)\n\t\tpygame.mixer.music.load(self.song_fn)\n\t\tpygame.mixer.music.play()",
"title": ""
},
{
"docid": "26f3a2bfdeb59a4fb079e12aaccf6ffa",
"score": "0.5896248",
"text": "def create_random(cls, players, deck='52-card'):\n\n current_deck = Card.get_shuffled_deck(size=int(deck[:2]))\n\n bottom_card = current_deck[0]\n\n player_hands = {}\n\n for uid, name in players:\n player_hands[uid] = set(current_deck.pop() for _ in xrange(6))\n\n if deck[-4:] == 'fast':\n current_deck = current_deck[:len(players)]\n\n ordered_players = list(players)\n urandom_shuffle_inplace(ordered_players)\n\n cls._choose_first_player(bottom_card.suit, ordered_players, player_hands)\n\n return GameState(ordered_players, player_hands, [], current_deck, [], bottom_card)",
"title": ""
},
{
"docid": "ca6db0fb483345ac87b4cf31144f780c",
"score": "0.58839583",
"text": "def initialize_trump(self) -> None:\r\n input(\"Time to decide trump. Pass the computer to \" + self.first_player.name)\r\n euchre_utils.clear_screen()\r\n starting = self.players.index(self.first_player)\r\n i=starting\r\n\r\n self.add_card(self.deck.get_card())\r\n \r\n while i <= starting+7:\r\n player = self.players[i%4]\r\n self.print_status_update(player)\r\n face_up_card: Card = self.cards_on_table[0]\r\n\r\n if i <= starting+3: #first time around\r\n answer = euchre_utils.get_yes_no(\"Do you want the dealer\"+\r\n \" to pick up the card on the table and that card to\" +\r\n \" become trump?\".format(player.name))\r\n if answer:\r\n #alone_answer = euchre_utils.get_yes_no(\"Do you want to go alone?\")\r\n euchre_utils.clear_screen()\r\n input(\"\\nOK, Announce that {} is trump. Pass the computer to the dealer({})\".format(\r\n face_up_card.suit, self.dealer.name))\r\n euchre_utils.clear_screen()\r\n self.dealer.new_card(face_up_card)\r\n self.clear_cards_on_table()\r\n self.trump = face_up_card.suit\r\n self.player_that_called_suit = player\r\n \r\n #dealer chooses which cards to remove\r\n self.dealer.sort_hand(self.trump)\r\n self.print_status_update(self.dealer)\r\n print(\"Dealer, {} told you to pick it up. \".format(player.name)+\r\n \"Which card in your hand do you want to remove?\")\r\n self.dealer.remove_card(self.dealer.select_card(self.dealer.hand))\r\n euchre_utils.clear_screen()\r\n input(\"Pass the computer to {} to start the round\".format(self.first_player.name))\r\n euchre_utils.clear_screen()\r\n break\r\n else: \r\n euchre_utils.clear_screen()\r\n input(\"\\nPass the computer to {}\".format(self.players[(i+1)%4].name))\r\n euchre_utils.clear_screen()\r\n \r\n else: #second time around, choose which suit to be trump\r\n response = euchre_utils.get_answer_no(\"Do you want to select the trump suit?\"+\r\n \" If so, say which. You're playing 'stick the dealer'\\n\", \"diamonds|hearts|spades|clubs\")\r\n if response and response != face_up_card.suit: #if they say an availible suit\r\n print(\"OK, {} is Trump. Pass the computer to {} to start the round\".format(\r\n response, self.first_player.name))\r\n self.trump = response\r\n self.player_that_called_suit = player\r\n self.clear_cards_on_table()\r\n break\r\n elif response == face_up_card.suit:\r\n input(\"Sorry, you can't choose the suit that was initially face up. Try again\")\r\n i-=1 # loop around to the same player again to give another chance\r\n elif i == starting+7: #STICK THE DEALER\r\n #this if clause needs to come after the first two to take lower precedence\r\n input(\"You're playing 'stick the dealer', so the dealer has to choose a suit.\")\r\n i-=1\r\n else: #empty string, they said no\r\n euchre_utils.clear_screen()\r\n input(\"\\nOK, pass the computer to {}\".format(self.players[(i+1)%4].name))\r\n euchre_utils.clear_screen()\r\n \r\n euchre_utils.clear_screen()\r\n i+=1\r\n euchre_utils.clear_screen()\r\n #sort everybodys hands according to trump\r\n [player.sort_hand(self.trump) for player in self.players]",
"title": ""
},
{
"docid": "25edc669fdded7b1f82cd0c502985d64",
"score": "0.5880057",
"text": "def make_step(self, action=\"reset\"):\n reward = 0\n if not self.active and action != \"reset\":\n print(\"You specified an action, but no active game is being played. Please reset the game using action='reset'.\")\n elif self.active and action == \"reset\":\n print(\"WARNING! You specified action='reset' although the last game has not ended, yet.\")\n elif action == \"reset\":\n self.reset_game()\n if self.player_sum == 21:\n self.active = False\n if self.verbose:\n print(\"Player has Blackjack!\")\n print(\"The dealer's cards are:\", [int(x) for x in self.dealer_cards])\n if np.sum(self.dealer_cards == 1) == 1 and np.sum(self.dealer_cards == 10) == 1:\n if self.verbose:\n print(\"Dealer has Blackjack, too!\")\n print(\"DRAW!\")\n else:\n if self.verbose:\n print(\"PLAYER WINS!\")\n reward = 1\n elif action == \"hit\":\n new_card = self.draw_cards(size=1)\n if self.verbose:\n print(\"Player draws card:\", new_card)\n self.player_sum += new_card\n if self.player_sum > 21:\n if self.player_has_usable_ace:\n if self.verbose:\n print(\"Player converts a usable ace (11) into 1.\")\n self.player_cards[self.player_cards == 11] = 1\n self.player_has_usable_ace = False\n self.player_sum -= 10\n if self.player_sum > 21:\n # Player goes bust\n if self.verbose:\n print(\"Player goes BUST!\")\n self.active = False\n reward = -1\n else:\n self.state = np.array([int(self.player_sum), self.dealers_showing_card, self.player_has_usable_ace])\n if self.verbose:\n print(\"New sum of player's cards:\", self.player_sum)\n reward = 0\n elif action == \"stick\":\n self.active = False\n if self.verbose:\n print(\"The dealer's cards are:\", [int(x) for x in self.dealer_cards])\n print(\"The dealer has\", np.sum(self.dealer_cards), \"points.\")\n # Let dealer play\n if np.any(self.dealer_cards == 1) and 17 <= self.dealer_sum + 10 <= 21:\n if self.verbose:\n print(\"Dealer converts 1 into 11\")\n self.dealer_sum += 10\n self.dealer_cards[self.dealer_cards == 1] = 11\n while self.dealer_sum < 17:\n new_card = self.draw_cards(size=1)\n if self.verbose:\n print(\"Dealer draws card:\", new_card)\n self.dealer_cards = np.append(self.dealer_cards, new_card)\n self.dealer_sum += new_card\n if np.any(self.dealer_cards == 1) and 17 <= self.dealer_sum + 10 <= 21:\n if self.verbose:\n print(\"Dealer converts 1 into 11\")\n self.dealer_sum += 10\n self.dealer_cards[np.where(self.dealer_cards == 1)[0]] = 11\n if self.verbose:\n print(\"New dealer sum\", self.dealer_sum)\n\n # Check whether dealer goes bust.\n dealer_difference = 21 - self.dealer_sum\n if dealer_difference < 0:\n if self.verbose:\n print(\"Dealer goes BUST!\")\n reward = 1\n else:\n # Compare player to dealer\n player_difference = 21 - self.player_sum\n if player_difference == dealer_difference:\n if self.verbose:\n print(\"DRAW!\")\n reward = 0\n elif player_difference < dealer_difference:\n if self.verbose:\n print(\"PLAYER WINS!\")\n reward = 1\n else:\n if self.verbose:\n print(\"DEALER WINS!\")\n reward = -1\n else:\n raise ValueError(\"'action' has to be either 'stick' or 'hit' when gameover is False.\")\n\n if not self.active:\n self.state = np.array([-1, -1, -1])\n\n return copy.deepcopy(self.state), reward",
"title": ""
},
{
"docid": "2b5ed06f45caf7e937b1b9ad3a3a65f4",
"score": "0.5879681",
"text": "def test_others_playing(self):\n while True:\n card = self.g[\"Clashes\"].remove()\n if card.name == \"Warlord\":\n break\n self.plr.add_card(card, Piles.HAND)\n self.plr.play_card(card)\n mil = self.g[\"Militia\"].remove()\n self.oth.add_card(mil, Piles.HAND)\n self.oth.piles[Piles.PLAYED].set(\"Militia\", \"Militia\", \"Copper\")\n self.oth.play_card(mil)\n self.g.print_state()",
"title": ""
},
{
"docid": "2063217bde657141e0f5d1728c6fe9fb",
"score": "0.5867167",
"text": "def playCard(self, chosen: int, played_from: str = \"Hand\", cheat: bool = False, flank = \"Right\", ask = True):\n logging.info(f\"numPlays: {sum(v for k,v in self.playedThisTurn.items())}\")\n if played_from == \"Deck\":\n source = self.activePlayer.deck\n elif played_from == \"Discard\":\n source = self.activePlayer.discard\n elif played_from == \"Mimicry\":\n source = self.inactivePlayer.discard\n else:\n source = self.activePlayer.hand\n card = source[chosen]\n if not self.canPlay(card, cheat = cheat):\n return\n # canPlay() makes sure that you can't try to play a card you're not allowed to play\n # Increases amber, adds the card to the action section of the board, then calls the card's play function\n if card.amber > 0:\n self.activePlayer.gainAmber(card.amber, self)\n logging.info(f\"{source[chosen].title} gave {self.activePlayer.name} {card.amber} amber. {self.activePlayer.name} now has {self.activePlayer.amber} amber.\")\n if ask:\n if card.type == \"Creature\" and len(self.activePlayer.board[\"Creature\"]) > 0:\n flank = self.chooseFlank(card)\n # left flank\n if card.type != \"Upgrade\" and flank == \"Left\":\n self.activePlayer.board[card.type].insert(0, source.pop(chosen))\n logging.info(f\"numPlays: {sum(v for k,v in self.playedThisTurn.items())}\")\n # default case: right flank\n elif card.type != \"Upgrade\" and played_from != \"Mimicry\":\n self.activePlayer.board[card.type].append(source.pop(chosen))\n logging.info(f\"numPlays: {sum(v for k,v in self.playedThisTurn.items())}\")\n elif played_from == \"Mimicry\":\n self.activePlayer.board[card.type].append(source[chosen])\n logging.info(f\"Mimicry branch.\")\n else:\n targeted = self.chooseCards(\"Creature\", \"Choose a creature to attach the upgrade to:\")\n if targeted:\n targeted = targeted[0]\n else:\n return\n self.playUpgrade(card, targeted)\n self.cardChanged()\n return\n #once the card has been added, then we trigger any play effects (eg smaaash will target himself if played on an empty board), use stored new position\n if card not in self.playedThisTurn:\n self.playedThisTurn[card] = 1\n else:\n self.playedThisTurn[card] += 1\n self.cardChanged(True) # definitely need to recalc power here, in case we play something next to a staunch knight so now it is dead\n self.draw()\n pygame.display.update()\n card.play(self, card)\n logging.info(f\"{card.title} play ability resolved.\")\n logging.info(f\"numPlays: {sum(v for k,v in self.playedThisTurn.items())}\")\n # if the card is an action, now add it to the discard pile - remote access or poltergeist or nexus on masterplan can potentially play cards that belong to the other player\n if card.type == \"Action\" and played_from != \"Mimicry\":\n if card.title == \"library_access\":\n if card not in self.activePlayer.board[\"Action\"]:\n return\n if card.deck == self.activePlayer.name:\n self.activePlayer.purged.append(self.activePlayer.board[\"Action\"].pop())\n else:\n self.inactivePlayer.purged.append(self.activePlayer.board[\"Action\"].pop())\n else:\n if card not in self.activePlayer.board[\"Action\"]:\n return\n if card.deck == self.activePlayer.name:\n self.activePlayer.discard.append(self.activePlayer.board[\"Action\"].pop())\n else:\n self.inactivePlayer.discard.append(self.activePlayer.board[\"Action\"].pop())\n elif played_from == \"Mimicry\":\n if card.title == \"library_access\":\n purge_mimic = True\n self.activePlayer.board[\"Action\"].pop()\n logging.info(f\"Removing Mimicried card.\")\n if purge_mimic:\n if card.deck == self.activePlayer.name:\n self.activePlayer.purged.append(self.activePlayer.board[\"Action\"].pop())\n else:\n self.inactivePlayer.purged.append(self.activePlayer.board[\"Action\"].pop())\n self.cardChanged(True)",
"title": ""
},
{
"docid": "430302c7890fa57ebc3b3eed949f5b6c",
"score": "0.5861373",
"text": "def new_game():\n \n # Initialize global variable that will hold the \"deck\n # of cards\"; we model the \"deck of cards\" as a list \n # consisting of 16 numbers with each number lying in \n # the range [0,8) and appearing twice. The list is\n # created by concatenating two lists with range [0,8) \n # together. Although Player can play the game with\n # \"textual numbers\" or \"images\", the above mentioned \n # technique is being used modeling the game in both\n # game \"modes\". \n global deck_of_cards\n deck_of_cards = range(CARDS_NUMBER // 2) + range(CARDS_NUMBER // 2)\n # Shuffle the \"deck\".\n random.shuffle(deck_of_cards)\n # Remove comment if in DEBUG mode.\n #print deck_of_cards\n \n # Initialize global variable that will hold the a list,\n # with size equal to the size of the \"deck of cards\"\n # consisting of boolean values. The boolean value\n # at a certain list index indicates whether the \"card\"\n # is \"exposed\" (True) or not (False). Particularly,\n # the ith entry should be \"True\" if the ith card is \n # face up and its value is visible or \"False\" if the \n # ith card is face down and it's value is hidden. \n global deck_of_cards_exposed\n deck_of_cards_exposed = [False] * CARDS_NUMBER\n \n # Initialize global variable that will hold the game\n # state (0,1 and 2), i.e. beginning of the game, single \n # \"exposed\" unpaired \"card\" and end of a \"turn\" \n # respectively (have a look at the comments of \n # \"mouseclick()\" for a detailed description \n # concerning this variable).\n global state\n state = 0 \n \n # Initialize global variable that will hold the number\n # of \"turns\" playing the game.\n global turn \n turn = 0\n label.set_text(\"Turns = \" + str(turn))\n \n # Initialize global variable that will hold a \"helper\"\n # list, keeping the index of the cards \"exposed\" in \n # a single \"turn\". \n global index_of_cards_exposed_in_a_turn \n index_of_cards_exposed_in_a_turn = [-1, -1]\n \n return None",
"title": ""
},
{
"docid": "92bac313e3373abca83de4f803c9c470",
"score": "0.5860306",
"text": "def play(deck, verbose):\r\n\r\n\t#generate first 2 'visible' cards from deck\r\n\tvisible = [deck.pop(0), deck.pop(0)]\r\n\t\r\n\twhile len(visible) <= 9: #game over if there are more than 9 'piles'\r\n\t\t\t\r\n\t\twhile add_to_11(visible) or jqk(visible):\t\t\r\n\t\t\r\n\t\t\tif add_to_11(visible):\r\n\t\t\t\r\n\t\t\t\t#avoid popping from empty list:\r\n\t\t\t\tif len(deck) >= 2:\r\n\t\t\t\t\t\r\n\t\t\t\t\tfor a in add_to_11(visible):\r\n\t\t\t\t\t\tvisible[a] = deck.pop(0) #'cover' cards\r\n\t\t\t\t\t\t\r\n\t\t\t\t\tif verbose:\r\n\t\t\t\t\t\tprint(visible)\r\n\t\t\t\r\n\t\t\t\telse: \r\n\t\t\t\t\treturn 0 \r\n\t\t\t\t\t#if len(deck) < 2 and 2 cards need to be covered then player wins\r\n\t\r\n\t\t\tif jqk(visible):\r\n\t\r\n\t\t\t\t#avoid popping from empty list:\r\n\t\t\t\tif len(deck) >= 3 : \r\n\t\t\t\t\t\r\n\t\t\t\t\tfor j in jqk(visible):\r\n\t\t\t\t\t\tvisible[j] = deck.pop(0)\r\n\t\t\t\t\t\t\r\n\t\t\t\t\tif verbose:\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t\tprint(visible)\r\n\t\t\r\n\t\t\t\telse:\r\n\t\t\t\t\treturn 0\r\n\t\r\n\t\tif len(visible) <= 9 and len(deck) > 0: \r\n\t\t# no cards can be covered but game not over:\r\n\t\r\n\t\t\tvisible.append(deck.pop(0)) #add new pile\r\n\t\t\t\r\n\t\t\tif verbose:\r\n\t\t\t\tprint(visible)\r\n\t\t\t\r\n\t\tif len(visible) > 9 or len(deck) == 0: \r\n\t\t\tbreak #once there are 10 piles/deck empty game is over\r\n\t\r\n\treturn(len(deck))",
"title": ""
},
{
"docid": "d95982ee89a1979bb0988195bad9f0f4",
"score": "0.58343667",
"text": "def reset_game(self):\n if self.verbose:\n print(\"The game is reset.\")\n self.active = True\n self.player_cards = []\n self.player_sum = 0\n self.player_has_usable_ace = False\n while self.player_sum < 12:\n new_card = self.draw_cards(size=1)\n if new_card == 1 and self.player_sum < 11:\n new_card = 11\n self.player_has_usable_ace = True\n self.player_cards = np.append(self.player_cards, new_card)\n self.player_sum += new_card\n\n self.dealer_cards = self.draw_cards(size=2)\n self.dealer_sum = np.sum(self.dealer_cards)\n self.dealers_showing_card = self.dealer_cards[1]\n\n self.state = np.array([int(self.player_sum), self.dealers_showing_card, self.player_has_usable_ace])\n if self.verbose:\n print(\"Player's cards:\", [int(x) for x in self.player_cards]) # , \"; player sum:\", self.player_sum[0]\n print(\"Dealer's showing card:\", [self.dealer_cards[1]])",
"title": ""
},
{
"docid": "6159fbc53dc2432a972ab889872f9fc7",
"score": "0.5832458",
"text": "def start_turn(self, character):\n character.db.combat_actionsleft = ACTIONS_PER_TURN # Replenish actions\n # Prompt the character for their turn and give some information.\n character.msg(\"|wIt's your turn! You have %i HP remaining.|n\" % character.db.hp)",
"title": ""
},
{
"docid": "53efab2740a1c1c957c7cc649f014514",
"score": "0.5830657",
"text": "def initial(self, state):\n\n self.player_number = min(state.turn_order, 0) % self.size\n self.current_player = self.players[self.player_number]",
"title": ""
},
{
"docid": "d948450c72319e3b6261eb24a0525aa5",
"score": "0.581744",
"text": "def play(self):\n print('Welcome to Blackjack v1.0')\n # give dealer 2 cards\n # give player 2 cards\n # player goes first in the gameplay, dealer hits until the player stays\n # dealer then goes\n # if player wins, they double their bet\n\n # if both the player and dealer are the same value, it's called a PUSH and\n # no money changes hands.\n\n # it's a 1:1 payout in blackjack, if you put down 10, you win another 10.\n\n # if you hit BLACKJACK! - 21, then you will get a 3:1 payout, so for putting down\n # 10, you get 15.\n\n while True:\n\n break",
"title": ""
},
{
"docid": "f9ef2eb3926070baf2d966906da516c8",
"score": "0.5817214",
"text": "def shuffle_deck(self):\n self.__deck = Deck()",
"title": ""
},
{
"docid": "dece00dfe6866bb147e30f9a31ea4d1c",
"score": "0.58131605",
"text": "def select_cards(self, deck, player, dealer):\n self.gui.show_hand_select_instructions()\n self.pause(2000)\n \n first_hand = deck.draw_two_card_hand()\n second_hand = deck.draw_two_card_hand()\n third_hand = deck.draw_two_card_hand()\n \n self.gui.show_first_hand(first_hand[0].img_path, first_hand[1].img_path)\n if self.picked_up_cards():\n player.add_hand(first_hand)\n dealer.add_hand(second_hand)\n dealer.add_hand(third_hand)\n else:\n dealer.add_hand(first_hand)\n self.gui.show_second_hand(second_hand[0].img_path, second_hand[1].img_path)\n\n if self.picked_up_cards():\n player.add_hand(second_hand)\n dealer.add_hand(third_hand)\n else:\n dealer.add_hand(second_hand)\n player.add_hand(third_hand)\n self.gui.alert_to_third_hand()\n self.pause(1500)",
"title": ""
},
{
"docid": "2589a760be7715f758b3933ef4b784ee",
"score": "0.58054376",
"text": "def game(self) -> None:\n print(RULES) \n print(f\"You have been credited with ${self.start_amount}\")\n\n # main game loop, loop infinitely until\n # player decides to quit or player looses\n # all his/her money\n while True: \n if self.start_amount < 1:\n print(\"You've run out of money\")\n print(\"Good thing you weren't using real money.\")\n print(\"Try to seek professional help for your gambling problems.\")\n sys.exit() #player is broke, exit game \n\n print(f\"Money available: ${self.start_amount}\") \n\n # Get the amount player wants to stake.\n amt_to_bet = self.entity_bet(self.player, self.start_amount)\n print(f\"Player bet ${amt_to_bet}\")\n\n # Display player scores\n self.display_scores()\n\n # play for both player and player's second\n # hand if he does split\n self.entity_play(self.player)\n self.entity_play(self.player_split)\n\n # after player busts or (St)ands\n # check if dealer needs to play\n self.check_if_dealer_plays()\n\n # check which hand - player main or second hand\n # if existent- won.\n self.check_who_wins(self.player)\n self.check_who_wins(self.player_split)\n\n # start all over again\n input(\"Press Enter to continue...\")\n print('\\n')\n\n # reset stats.\n self.reset()\n # clear console\n os.system('cls') if sys.platform == \"win32\" else os.system('clear')",
"title": ""
},
{
"docid": "35cf530c7c6f7b8e383a4fe612d5570d",
"score": "0.58013165",
"text": "def __init__(self, deck=None, p1_class=Player, p2_class=Player):\n #XXX: Currently defaults to playing with VP and Money only.\n self.card_set = 'trivial'\n self.current_player = 1\n self.banked_cards = copy.deepcopy(cards.CARD_SETS[self.card_set])\n\n # Variables for game stats\n self.turn_num = 1\n self.winning_player = []\n self.player_vp = []\n\n initial_deck = deck or cards.STARTING_DECK\n self.p1 = p1_class(initial_deck)\n self.p2 = p2_class(initial_deck)",
"title": ""
},
{
"docid": "fa0e423f621be5257e0f20a164254566",
"score": "0.5795721",
"text": "def main():\n\n print(\"Welcome to BlackJack!!!\")\n\n # Ask for number of players\n n_players = get_num_players()\n\n # Initialize the game\n human_name = 'You'\n game = Game(n_players, human_name=human_name)\n\n while (True):\n # Deal\n print_deal(game.deal())\n\n # Play one turn per player\n while game.can_play():\n play_turn(game)\n game.next_player()\n\n # Output results\n print_score(game, *game.get_winners())\n if (input(\"Play again? [Y/n] \") is 'n'):\n break\n\n # End the game\n print(\"Thanks for playing! Have a great day!\")",
"title": ""
},
{
"docid": "74b353cb03f1abea549580a71817cbcb",
"score": "0.57908016",
"text": "def play(self):\n self._shuffle()\n self._deal()\n self._display_all()\n results = {}\n for player in self.player_list:\n score = player.play_hand()\n results[f'{player.type} {player.name}'] = score\n print('Dealer flips over second card...')\n print(self._dealer.hand)\n dealer_score = self._dealer.play_hand()\n print('\\n---------------------------------\\n')\n for name, score in results.items():\n if score == 0:\n print(f'{name} lost')\n elif score > dealer_score:\n print(f'{name} wins')\n elif score == dealer_score:\n print(f'{name} pushes')\n else:\n print(f'{name} lost')",
"title": ""
},
{
"docid": "64492e3d35024b21d7f7db18a48eadff",
"score": "0.57886285",
"text": "def draw_initial_cards(self):\n\n # Draw cards for player and dealer.\n for current in ('dealer', 'player'):\n for i in range(0, 2):\n card = self.draw_card()\n self.cards[current].append(card)",
"title": ""
},
{
"docid": "a3cba110962d7ea7079e68e96fc17b49",
"score": "0.5776046",
"text": "def main():\n game = WarGame()\n game.deal()\n while not game.winner():\n game.step()\n print(game)\n print(game.winner())",
"title": ""
},
{
"docid": "2ad22698074c27bd70352a6b42bde555",
"score": "0.57631284",
"text": "def test_Engine__insure_zero(engine, hand):\n player = players.BetterPlayer(name='Eric', chips=100)\n player.bet = 20\n player.hands = (cards.Hand(),)\n engine.dealer.hands = (hand,)\n engine._insure(player)\n assert player.insured == 0\n assert player.chips == 100\n assert engine.ui.mock_calls == []",
"title": ""
},
{
"docid": "b156f8f72b972997f3e5d63cee14036e",
"score": "0.5762807",
"text": "def pollPlay(self, state):\n hand = self.player.getHand()\n if not len(hand) == 0:\n cardOfChoice = random.choice(hand)\n # returns the choosen card here\n return cardOfChoice\n print(\"Oops, you are trying to play a card from an empty hand\")\n return None",
"title": ""
},
{
"docid": "7976e1729bf6638650ad8b2cd77acfb2",
"score": "0.5760314",
"text": "def start(self):\n Screen.clear()\n print('STARTING NEW GAME')\n print('ENTERING MAIN GAME STATE IN A MOMENT...')\n self.pause(pause_time=1)",
"title": ""
},
{
"docid": "39032af7fa0996e1f761e776ab5a59b6",
"score": "0.5759432",
"text": "def drawPlayerCard(self):\n player = self.level.players[0]\n card = self.level.playerDeck.draw()\n if card is not None:\n controller = PlayerDrawController(player, card)\n controller.run()",
"title": ""
},
{
"docid": "c4bb3a70ea09d37ce93a834cc0900ec8",
"score": "0.57525396",
"text": "def resetHand(self):\n self.hand = Hand()",
"title": ""
},
{
"docid": "b29b99ebb0ebc7579986cd9ef088010b",
"score": "0.5751541",
"text": "def prep(self, hand):\r\n self.hand = hand\r\n active_count = 0\r\n\r\n for player in self.players:\r\n if player.stack >= self.BLINDS[1]:\r\n player.activate()\r\n active_count += 1\r\n else:\r\n player.deactivate()\r\n \r\n if active_count < 2:\r\n c = \"Table needs at least 2 active players for a game.\"\r\n raise StructureError(c)\r\n\r\n if self.button == None:\r\n self.button = 0\r\n else:\r\n self.button += 1",
"title": ""
},
{
"docid": "2114aa08ab856de7ecb735cbc2255963",
"score": "0.574097",
"text": "def start_game(self):\n self.round = 0\n self.drawing_player = None\n self.score = {}\n\n for client_id in self.clients:\n self.score[client_id] = 0\n\n self.notify_all('game_starting')\n self.next_turn()",
"title": ""
},
{
"docid": "1fdb99a447976b8d96b16eef1d320019",
"score": "0.5738883",
"text": "def PlayRound(self):\n with self._lock:\n if self._active_round:\n logging.error('HypeJack game already active.')\n return\n bets = self._core.bets.LookupBets(\n self.name, resolver=self._core.name.lower())\n if not bets:\n logging.error('Attempted to start HypeJack with no players.')\n return\n\n self._pending_start = False\n\n # Shuffle the deck when it gets low. We assume a reasonable number of\n # cards needed per player, but with lots of splits / low cards we may\n # still run out of cards to play the hand.\n if len(self._shoe) < (len(self._peeps) + 1) * 7:\n self._ShuffleCards()\n\n # Deal cards to plebs.\n for user_id, user_bets in bets.items():\n hand = Hand(user_bets[0], self._shoe.pop(), self._shoe.pop())\n self._peeps[user_id] = [hand]\n self._DisplayUser(user_bets[0].user)\n\n # Deal cards to hypebot.\n self._dealer_hand = Hand(None, self._shoe.pop(), self._shoe.pop())\n # self._dealer_hand = Hand(playing_cards_lib.Card('Hearts', 8),\n # playing_cards_lib.Card('Spades', 8))\n self._msg_fn(None, 'Dealer: [%s, %s]' % (self._dealer_hand.cards[0], '🂠'))\n\n self._active_round = True\n\n # Short-circuit game play if the dealer has a hypejack or if all peeps\n # have hypejacks.\n if not self._dealer_hand.IsHypeJack() and any(\n [self._IsActive(user_id) for user_id in self._peeps.keys()]):\n # Force the round to end after some time if some peep ran away. Waiting\n # on a condition releases the lock while waiting, then reacquires it\n # automatically. Will shortcircuit if notified when all peeps have\n # finished their hands.\n self._game_ender.wait(timeout=self.MAX_ROUND_LENGTH)\n\n # Complete dealer hand.\n self._msg_fn(None, 'Dealer: %s' % self._dealer_hand)\n while self._dealer_hand.Score() < 17:\n self._dealer_hand.cards.append(self._shoe.pop())\n self._msg_fn(None, 'Dealer: %s' % self._dealer_hand)\n\n self._core.bets.SettleBets(self, self._core.name.lower(), self._msg_fn)\n\n # Reset game state.\n self._peeps = {}\n self._active_round = False",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="796"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">878430aedb1cf2faa0d7a646f80b93dd</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Returns True if `n` is a prime number. Uses a sieve based approach.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "f6562e93978739d735e33a2f03e547ef",
"score": "0.82913023",
"text": "def is_prime(n):\n if n < 2:\n return False\n ps = primes()\n p = next(ps)\n while p * p <= n:\n if n % p == 0:\n return False\n p = next(ps)\n return True",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "78a25bc4f1590805de217f19646d793b",
"score": "0.8519759",
"text": "def isPrime(n):\n\tif n > 1:\n\t\tsqrtN = int(math.sqrt(n))\n\t\tfor i in xrange(2, sqrtN + 1):\n\t\t\tif n % i == 0:\n\t\t\t\treturn False\n\t\treturn True\n\telse:\n\t\treturn False",
"title": ""
},
{
"docid": "dc7a4b3b514e80626aae9ddf8b01d248",
"score": "0.85149896",
"text": "def is_prime(n):\n return divisors_count(n) == 2",
"title": ""
},
{
"docid": "8552f164b83d92b64ee1a20078ac78f5",
"score": "0.85020345",
"text": "def is_prime(n):\n n=int(n)\n if n <= 1:\n return False\n else:\n val = round(m.sqrt(n))\n for i in range(2, val + 1):\n if n % i == 0:\n return False\n return True",
"title": ""
},
{
"docid": "270a8e44a4956d1698003b4d5c40e13c",
"score": "0.8487371",
"text": "def isPrime(n):\n primes=getPrimesLT(n)\n return n in primes",
"title": ""
},
{
"docid": "ba5d05a02c5d60a51d738a646aff60ba",
"score": "0.8479271",
"text": "def is_prime(self, n: int) -> bool:\n assert isinstance(n, int) and n >= 2, \"n is an integer greater than 1\"\n\n s = math.sqrt(n)\n for p in self.first_1e6_prime:\n if p <= s and n % p == 0:\n return False\n\n while p <= s:\n p += 2\n if n % p == 0:\n return False\n\n return True",
"title": ""
},
{
"docid": "b19d63e9d42c2b218f79ec6c0d98fd6f",
"score": "0.84770465",
"text": "def isprime(n):\n # range starts with 2 and only needs to go up the squareroot of n\n if n < 2:\n return False\n for x in xrange(2, int(n ** 0.5) + 1):\n if n % x == 0:\n return False\n return True",
"title": ""
},
{
"docid": "477eda70153ff8b8c7ccaf2fc536d8b1",
"score": "0.8416152",
"text": "def IsPrime(n):\n if n%2 == 0:\n return False\n else:\n prime = True\n for j in range(2,int(np.sqrt(n))+1):\n if n%j == 0:\n prime = False\n break\n return prime",
"title": ""
},
{
"docid": "901a0f3a40b40d64ffc53c9ea7bf1c30",
"score": "0.84116584",
"text": "def isprime(n):\n if n < 2:\n return False\n if n == 2:\n return True\n for i in range(2, int(math.ceil(math.sqrt(n)))+1):\n if n % i == 0:\n return False\n return True",
"title": ""
},
{
"docid": "dfeec13bc07ab476d32355abbc4c85d4",
"score": "0.8396946",
"text": "def prime(n):\n\tif n <= 1:\n\t\treturn False\n\tfor i in range(2, int(sqrt(n) + 1)):\n\t\tif n % i == 0:\n\t\t\treturn False\n\treturn True",
"title": ""
},
{
"docid": "36a9b3e3ec56f716139c2690f52c09f0",
"score": "0.8389916",
"text": "def isPrime(n):\n result = False;\n if isinstance(n, int) == False:\n raise TypeError(\"Input is not an Integer.\");\n elif n <= 0:\n raise ValueError(\"Input is zero or less.\");\n elif n < 2:\n result = False;\n elif n == 2:\n result = True;\n else:\n for i in range(2, n):\n if i == n:\n return;\n elif n % i == 0:\n result = False;\n break;\n else:\n result = True;\n\n return result;",
"title": ""
},
{
"docid": "50906bb1d2d6ef7da5820bf7438979a9",
"score": "0.8389429",
"text": "def isprime(n):\r\n prime_ind = False\r\n init_primes = [\r\n 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61,\r\n 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137,\r\n 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, 193, 197, 199\r\n ]\r\n\r\n if n in init_primes:\r\n prime_ind = True\r\n\r\n elif (n<=1 or n%2==0 or n%3==0 or n%5==0):\r\n prime_ind = False\r\n\r\n else:\r\n # Determine upper limit of test range.\r\n ulimit = (int(math.ceil(math.sqrt(n)))+1)\r\n prime_ind = not any(n%k==0 for k in range(3, ulimit, 2))\r\n return(prime_ind)",
"title": ""
},
{
"docid": "60a114091880ba14ed9eca16e709d998",
"score": "0.83860743",
"text": "def is_prime(n):\n\tfor num in range(2, floor(sqrt(n)) + 1):\n\t\tif n % num == 0:\n\t\t\treturn False\n\treturn True",
"title": ""
},
{
"docid": "8d62ce4c046658b6480d22f00130fb9f",
"score": "0.8377889",
"text": "def is_prime(n):\n\n\t# Find the square root of n.\n\n\tp = int(math.sqrt(n))\n\n\twhile p > 1:\n\t\tif n % p == 0:\n\t\t\treturn 0\n\n\t\tp = p - 1\n\n\treturn 1",
"title": ""
},
{
"docid": "56dadae9af134db9fa497ca8a6ff4e8b",
"score": "0.8371619",
"text": "def is_prime(n):\n if n in (0, 1):\n return False\n for i in range(2, n):\n\tif n % i == 0:\n\t return False\n return True",
"title": ""
},
{
"docid": "7635e87c98a226c663071d70fe5328cb",
"score": "0.83457136",
"text": "def is_prime(n):\n for i in range(2,n):\n if n % i == 0:\n return False\n return True",
"title": ""
},
{
"docid": "3c2d0c0ed09c36e073b03e7a086dc47b",
"score": "0.83377486",
"text": "def is_prime(n):\n\tfor i in range(2,int(abs(n)**0.5)+1):\n\t\tif n%i == 0:\n\t\t\treturn False\n\treturn True",
"title": ""
},
{
"docid": "856b5803e82eb6c5d40330c721906474",
"score": "0.8323594",
"text": "def prime_test(n):\n if n < 2:\n return False\n for number in islice(count(2), int(sqrt(n)-1)):\n if not n % number:\n return False\n return True",
"title": ""
},
{
"docid": "589b2f8b21d0d8b67d7c6e53a4057c7e",
"score": "0.83179694",
"text": "def is_prime(n):\n \n if n == 2: return True\n if n % 2 == 0: \n return False\n root_n = int(math.sqrt(n))+1\n up_to_root_n = root_n\n quotients = map(lambda divisor: n % divisor, \n range(3, up_to_root_n))\n n_is_evenly_divisible = 0 in quotients\n return False if n_is_evenly_divisible else True",
"title": ""
},
{
"docid": "272d0471d12b21e6b69f97f48baac25a",
"score": "0.83177346",
"text": "def is_prime(n: int) -> bool:\n result: bool = True\n\n if n <= 1 or (n % 2 == 0 and n > 2) or (n % 3 == 0 and n > 3):\n result = False\n\n if n == 2:\n result = True\n\n for x in range(3, int(math.sqrt(n)) + 1, 2):\n if n % x == 0:\n result = False\n\n return result",
"title": ""
},
{
"docid": "dd2265a5869ef4466755a84a4f2bdbe7",
"score": "0.8312044",
"text": "def is_prime(n):\n if n == 1:\n return False\n\n if n == 2:\n return True\n\n if n % 2 == 0:\n return False\n\n for i in range(3, math.ceil(math.sqrt(n)) + 1, 2):\n if n % i == 0:\n return False\n\n return True",
"title": ""
},
{
"docid": "74aa430d379576b0fc6af3d55d5f6748",
"score": "0.8284265",
"text": "def is_prime(n):",
"title": ""
},
{
"docid": "0fed4b5a70448034b4da8242726ede89",
"score": "0.82800364",
"text": "def is_prime(n):\n from math import sqrt\n\n # Primes starts on 2\n if n <= 1: return False\n # Caso especial da porra\n if n == 2: return True\n # Numeros pares > 2 nao sao primos\n if n % 2 == 0: return False\n\n square_root = int(sqrt(n))\n for m in xrange(3, square_root + 1, 2): # lista de nums impares ate sqrt\n if n % m == 0:\n return False\n return True",
"title": ""
},
{
"docid": "5f6716ef495b945079b2a2119c7894cf",
"score": "0.82623875",
"text": "def is_prime(n):\n if n < 2:\n return False\n else:\n for x in range(2,n):\n if(n % x == 0):\n return False\n return True",
"title": ""
},
{
"docid": "511006c76add766235762394700bec07",
"score": "0.8257107",
"text": "def is_prime(n):\n if n == 2:\n return True\n if n == 0 or n == 1 or n % 2 == 0:\n return False\n for i in range(3, int(math.sqrt(n))+1, 2):\n if n % i == 0:\n return False\n return True",
"title": ""
},
{
"docid": "79fb7fa454db5d1e238880467ff4ddee",
"score": "0.82552886",
"text": "def is_prime(n):\n\n if n <= 1:\n return False # 0 and 1 are prime numbers\n if n == 2:\n return True # 2 is a prime number\n if n % 2 == 0:\n return False # even numbers greater than 2 are not prime\n # For every integer 1 < i < n, if n is divisible by i,\n # then n is not prime. We only need to check from 3 to\n # sqrt(n), because sqrt(n) is the second largest factor of n,\n # and we can increase i by 2 at every iteration, since we know\n # that n is not even\n limit = math.floor( math.sqrt(n) ) + 1\n for i in range(3, limit, 2):\n if n % i == 0:\n return False\n return True",
"title": ""
},
{
"docid": "2afa11d6ef7e50b2a833a1cf51f971e6",
"score": "0.82412285",
"text": "def is_prime(n):\n if n <= 0:\n return False\n if n == 1:\n return False\n if n == 2:\n return True\n for m in range (2, n): # We only have to check until n/2 \n if n % m == 0:\n return False\n return True",
"title": ""
},
{
"docid": "422b20b7c441b1037d7b6a7cc0e0ae1b",
"score": "0.8240911",
"text": "def is_prime(n):\n if n<2:\n return False\n\n i=int(n**0.5)\n\n if has_divisor_smaller_than(n, i) == False:\n return True\n return False",
"title": ""
},
{
"docid": "f6e0478c7ea72acda213a781f826bddd",
"score": "0.82399696",
"text": "def is_prime(n: int) -> bool: # not solved\n if n == 2 or n == 3:\n return True\n if n % 2 == 0 or n % 3 == 0 or n == 1:\n return False\n else:\n for i in range(2, int(n**.5) + 1):\n if n % i == 0:\n return False\n return True",
"title": ""
},
{
"docid": "725ba95a76ce6d82f495fe8d1132f873",
"score": "0.82367325",
"text": "def isprime(n):\n # make sure n is a positive integer\n n = abs(int(n))\n # 0 and 1 are not primes\n if n < 2:\n return False\n # 2 is the only even prime number\n if n == 2:\n return True\n # all other even numbers are not primes\n if not n & 1:\n return False\n # range starts with 3 and only needs to go up the square root of n\n # for all odd numbers\n for x in range(3, int(n**0.5)+1, 2):\n if n % x == 0:\n return False\n return True",
"title": ""
},
{
"docid": "fc31081f55ec0723a789771bb6d30774",
"score": "0.82352555",
"text": "def is_prime(n):\n if n==1:\n return False\n elif n==2:\n return True\n else:\n for i in range(2,n):\n if(n % i==0):\n return False\n return True",
"title": ""
},
{
"docid": "07348f41c7be602f12b436214ff72d1a",
"score": "0.82299316",
"text": "def isprime(n):\n n = abs(n)\n if n < 2:\n return False\n if n == 2:\n return True\n if not n % 1 == 0:\n return False\n if n % 2 == 0:\n return False\n for x in range(3, round(math.sqrt(n)) + 1, 2):\n if n % x == 0:\n return False\n return True",
"title": ""
},
{
"docid": "08cee8ee7adddb537a8b7d371a94a6ce",
"score": "0.8216398",
"text": "def prime(n):\n if n == 2:\n return True\n elif n < 2 or n % 2 == 0:\n return False\n else:\n return not any(n % x == 0\n for x in range(3, math.ceil(math.sqrt(n)) + 1, 2))",
"title": ""
},
{
"docid": "a2ce1f52c9105aa1973c3712ae2bb925",
"score": "0.82160985",
"text": "def is_prime(n: int) -> bool:\n # simple test for small n: 2 and 3 are prime, but 1 is not\n if n <= 3:\n return n > 1\n\n # check if multiple of 2 or 3\n if n % 2 == 0 or n % 3 == 0:\n return False\n\n # search for subsequent prime factors around multiples of 6\n max_factor = int(math.sqrt(n))\n for i in range(5, max_factor + 1, 6):\n if n % i == 0 or n % (i + 2) == 0:\n return False\n return True",
"title": ""
},
{
"docid": "a2ce1f52c9105aa1973c3712ae2bb925",
"score": "0.82160985",
"text": "def is_prime(n: int) -> bool:\n # simple test for small n: 2 and 3 are prime, but 1 is not\n if n <= 3:\n return n > 1\n\n # check if multiple of 2 or 3\n if n % 2 == 0 or n % 3 == 0:\n return False\n\n # search for subsequent prime factors around multiples of 6\n max_factor = int(math.sqrt(n))\n for i in range(5, max_factor + 1, 6):\n if n % i == 0 or n % (i + 2) == 0:\n return False\n return True",
"title": ""
},
{
"docid": "f0820534545c1052093724d0be6a8cb0",
"score": "0.82139057",
"text": "def isprime(n):\n if n == 2: return True\n if n == 3: return True\n if n % 2 == 0: return False\n if n % 3 == 0: return False\n\n i = 5\n w = 2\n while i * i <= n:\n if n % i == 0:\n return False\n\n i += w\n w = 6 - w\n\n return True",
"title": ""
},
{
"docid": "09f7f61a6383ab12105f728487bdd534",
"score": "0.82113105",
"text": "def isprime(n):\n if n % 2 == 0 and n > 2:\n return False\n for i in range(3, n):\n if n % i == 0:\n return False\n return True",
"title": ""
},
{
"docid": "d0cccf3620b1352e5c60bf0346de7b01",
"score": "0.82056844",
"text": "def is_prime(n):\n if n <= 1:\n return False\n m = 2\n while m <= sqrt(n):\n if n % m == 0:\n return False\n m += 1\n return True",
"title": ""
},
{
"docid": "635525dbae85557bdc340c8b9b10edc4",
"score": "0.8201573",
"text": "def is_prime(n):\n\n for i in range(2, int(n**(.5))+1):\n if n % i == 0:\n return False\n return True",
"title": ""
},
{
"docid": "41ac96a2995b84c5582172f7c60743c7",
"score": "0.81952757",
"text": "def is_prime(n):\n if n < 2: return False\n if n % 2 == 0: return n == 2\n d = int(math.sqrt(n))\n for k in range(3, d+1, 2):\n if n % k == 0: return False\n return True",
"title": ""
},
{
"docid": "d7bafae931b71a704e193fea70413010",
"score": "0.8193707",
"text": "def is_prime(n):\n if n < 2:\n return False\n for i in range(2, int(n**0.5) + 1):\n if n % i == 0:\n return False\n return True",
"title": ""
},
{
"docid": "af516e3c8b4214cefaaeb5e242c9e5de",
"score": "0.8186336",
"text": "def is_prime(n):\r\n end = int(sqrt(n))\r\n for i in range(2, end + 1):\r\n if n % i == 0:\r\n return False\r\n return True",
"title": ""
},
{
"docid": "c278d5413913dd21598d0be7b8b5beeb",
"score": "0.81617874",
"text": "def is_prime(n):\n if n < 2:\n return False\n\n if n == 2:\n return True\n \n if n % 2 == 0:\n return False\n \n for i in range(3, n):\n if n % i == 0:\n return False\n \n return True",
"title": ""
},
{
"docid": "bc24c2eda47040b95d6de2a18c0f451a",
"score": "0.81556326",
"text": "def is_prime(n):\n for i in range(2,int(abs(n)**0.5)+1):\n if(n%i == 0):\n return False\n return True",
"title": ""
},
{
"docid": "1b4342cdcfc8e1b7e350391ff6fdd457",
"score": "0.8154981",
"text": "def is_prime(n):\n if n < 2:\n return False\n i = 2\n while i*i <= n:\n if n % i == 0:\n return False\n i = i + 1\n return True",
"title": ""
},
{
"docid": "0e4c66c8caa96007d8a7ed30223725ce",
"score": "0.81473047",
"text": "def isPrime(n):\n n = abs(n)\n if n < 2:\n return False\n if n == 2:\n return True\n if not n % 1 == 0:\n return False\n if n % 2 == 0:\n return False\n for x in range(3, round(math.sqrt(n)) + 1, 2):\n if n % x == 0:\n return False\n return True",
"title": ""
},
{
"docid": "0e4c66c8caa96007d8a7ed30223725ce",
"score": "0.81473047",
"text": "def isPrime(n):\n n = abs(n)\n if n < 2:\n return False\n if n == 2:\n return True\n if not n % 1 == 0:\n return False\n if n % 2 == 0:\n return False\n for x in range(3, round(math.sqrt(n)) + 1, 2):\n if n % x == 0:\n return False\n return True",
"title": ""
},
{
"docid": "78c57cc85d45fb203d479ef719ae28e9",
"score": "0.81402576",
"text": "def isprime(n):\n # range starts with 2 and only needs to go up the squareroot of n\n if n % 2 == 0:\n return False\n for x in range(3, int(n**0.5)+1,2):\n if n % x == 0:\n return False\n return True",
"title": ""
},
{
"docid": "0718b32ecb5751a279a2c58ec2ec1cf7",
"score": "0.8138055",
"text": "def is_prime(n):\n if n == 2:\n return True\n if n == 3:\n return True\n if n % 2 == 0:\n return False\n if n % 3 == 0:\n return False\n\n i = 5\n w = 2\n while i*i <= n:\n if n % i == 0:\n return False\n i += w\n w = 6 - w\n\n return True",
"title": ""
},
{
"docid": "54ae235b788cc942b94ba04ca81c3c9d",
"score": "0.8134483",
"text": "def is_prime(n):\n for i in range(2, int(n ** 0.5) + 1):\n if not n % i:\n return False\n return True",
"title": ""
},
{
"docid": "6809b1cd33dd1d0ebe49e0e30145952f",
"score": "0.8124355",
"text": "def is_prime(n):\n prime = True # assume prime until proven otherwise\n\n for i in range(2, int(sqrt(n) + 1)):\n if n % i == 0: # factor found -> not prime\n prime = False\n break\n\n return prime",
"title": ""
},
{
"docid": "a1e711a66dbcaeb4c4f5cd3fef7cac5c",
"score": "0.81242764",
"text": "def is_prime_v2(n):\n if n == 1:\n return False\n\n max_divisor = math.floor(math.sqrt(n))\n for d in range(2, 1 + max_divisor):\n if n % d == 0:\n return False\n return True",
"title": ""
},
{
"docid": "5d81ef705192336ab08758c7cb15039d",
"score": "0.81232667",
"text": "def isPrime(n):\n\n if n == 0 or n == 1:\n return False\n\n for div in range(2, int(n**0.5)+1):\n if not n % div:\n return False\n return True",
"title": ""
},
{
"docid": "148222020cd78bf2d22c5e4326000805",
"score": "0.81167865",
"text": "def is_prime(n):\n if n == 2:\n return True\n if n == 3:\n return True\n if n % 2 == 0:\n return False\n if n % 3 == 0:\n return False\n\n i = 5\n w = 2\n\n while i * i <= n:\n if n % i == 0:\n return False\n\n i += w\n w = 6 - w\n\n return True",
"title": ""
},
{
"docid": "4c5a33abd4d265953025486942673076",
"score": "0.81161934",
"text": "def isPrime(n):\n\tif n <= 1: return False\n\telif n < 4: return True #2, 3\n\telif n % 2 == 0: return False\n\telif n < 9: return True #5, 7\n\telif n % 3 == 0: return False\n\telse:\n\t\t#make use of the fact that all primes > 5 have have the form 6k +- 1\n\t\tr = math.floor(math.sqrt(n))\n\t\tf = 5\n\t\twhile f <= r:\n\t\t\tif n % f == 0: return False\n\t\t\tif n % (f + 2) == 0: return False\n\t\t\tf += 6\n\t\treturn True",
"title": ""
},
{
"docid": "18968f383dac5e1e0b0a60d0bb0988ba",
"score": "0.80963194",
"text": "def is_prime(n):\n if n < 2:\n return False\n if n == 2:\n return True\n d = 2\n while d <= int(math.sqrt(n)) + 1:\n if n % d == 0:\n return False\n d += 1\n return True",
"title": ""
},
{
"docid": "40d0e26c0162295a4d5cec18cca267db",
"score": "0.8092706",
"text": "def isprime(n):\n return len(factors(n)) == 2",
"title": ""
},
{
"docid": "bda83ee0a8ba13b9b662c98c048c8736",
"score": "0.80838317",
"text": "def isprime(n):\n if n == 2:\n return True\n if n == 3:\n return True\n if n % 2 == 0:\n return False\n if n % 3 == 0:\n return False\n\n i = 5\n w = 2\n\n while i * i <= n:\n if n % i == 0:\n return False\n\n i += w\n w = 6 - w\n\n return True",
"title": ""
},
{
"docid": "6f088fa3e414e6a8725d83eb028394b2",
"score": "0.8081018",
"text": "def is_prime_v1(n):\n if n == 1:\n return False\n\n for d in range(2, n):\n if n % d == 0:\n return False\n return True",
"title": ""
},
{
"docid": "ae0faea9835cf4d2c6ce4970efa17b8f",
"score": "0.8067031",
"text": "def is_prime(n):\n def divisible_by_known_prime(n):\n for x in known_primes:\n if divides(x, n):\n return x\n return False\n\n # Quick filters\n if n < 2:\n return False\n elif n in known_primes:\n return True\n elif divisible_by_known_prime(n):\n return False\n else:\n # Start searching again at the max known prime\n start = known_primes[-1] + 2\n for y in xrange(start, int(sqrt(n)), 2):\n if divisible_by_known_prime(y):\n continue\n else:\n if n % y == 0:\n return False\n known_primes.append(y)\n return True",
"title": ""
},
{
"docid": "464021a33de3106c49185f714bf98982",
"score": "0.8051832",
"text": "def test_prime(n):\n if n==2:\n return True\n #we only need to check divisors up to square root of a number\n for i in range(2,int(n**(.5))+1):\n if n % i ==0:\n return False\n else:\n return True",
"title": ""
},
{
"docid": "4c6829a46657473e9890c8e4a7c737a3",
"score": "0.8046635",
"text": "def isprime(n):\n\n if n == 2:\n return True\n if n == 3:\n return True\n if n % 2 == 0:\n return False\n if n % 3 == 0:\n return False\n i = 5\n w = 2\n while i * i <= n:\n if n % i == 0:\n return False\n i += w\n w = 6 - w\n return True",
"title": ""
},
{
"docid": "4c6829a46657473e9890c8e4a7c737a3",
"score": "0.8046635",
"text": "def isprime(n):\n\n if n == 2:\n return True\n if n == 3:\n return True\n if n % 2 == 0:\n return False\n if n % 3 == 0:\n return False\n i = 5\n w = 2\n while i * i <= n:\n if n % i == 0:\n return False\n i += w\n w = 6 - w\n return True",
"title": ""
},
{
"docid": "e993486b8ff4626cd7a77a6f4541959e",
"score": "0.80411047",
"text": "def is_prime_number(n:int) -> bool:\r\n\r\n i = 0\r\n is_prime = True\r\n\r\n if (n == 2 or n == 3):\r\n return True\r\n while(i < len(_prime_list)):\r\n if (not n % _prime_list[i] and is_prime):\r\n is_prime = False\r\n return is_prime\r\n i += 1\r\n if (is_prime):\r\n _prime_list.append(n)\r\n return is_prime",
"title": ""
},
{
"docid": "bc04899d0ad229677ff3b7c4f040ac5d",
"score": "0.8024999",
"text": "def isPrime(n):\n Dmax = scipy.sqrt(n)\n if n == 2:\n return True\n if isEven(n):\n return False\n d = 3\n while n%d != 0 and d <= Dmax:\n d += 2\n return d > Dmax",
"title": ""
},
{
"docid": "b5a485022c94922bb7f2ae2c0922016c",
"score": "0.7937437",
"text": "def is_prime(n):\n test_vals = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37]\n if n in test_vals:\n return True\n d = n - 1\n s = 0\n while not d & 1:\n d = d >> 1\n s += 1\n for a in test_vals:\n for r in range(0, s):\n if (a ** (d * (1 << r))) % n != (n - 1) \\\n and (a ** d) % n != 1:\n return False\n return True",
"title": ""
},
{
"docid": "7a6588f6a60f59af54b5b30212aef90f",
"score": "0.78840125",
"text": "def is_prime(n):\n if n < 2 :\n return False\n\n # OFF BY ONE Error, solution is to add +1 \n #for i in range (2, int(math.sqrt(n)) + 1 ):\n for i in range (2, int(math.sqrt(n)) ):\n if n % i == 0 :\n return False\n return True",
"title": ""
},
{
"docid": "8c240bfae6683bf9bd96ef3272a3f9b7",
"score": "0.78501946",
"text": "def prime_number(n):\n\n if n > 1:\n\n for divisor in range(2,n):\n\n if n%divisor == 0:\n #if n and divisor cannot complete full division, n is not a \n #prime number\n\n return False\n\n return True\n\n else:\n #negative number is not prime number\n\n return False",
"title": ""
},
{
"docid": "9168442a99017fb7004b5deea6142afb",
"score": "0.7763757",
"text": "def is_prime(n):\n \n if n == 2:\n return True\n if n < 2 or n % 2 == 0:\n return False\n \n trail_factor = 3\n \n root = sqrt(n)\n \n while trail_factor <= root:\n if n % trail_factor == 0:\n return False\n trail_factor += 2 \n \n return True",
"title": ""
},
{
"docid": "e931ff2a59cbd8806ee6f819d4efc108",
"score": "0.77405703",
"text": "def is_prime(n):\n def prime_helper(index):\n if index == n:\n return True\n elif n % index == 0 or n == 1:\n return False\n else:\n return prime_helper(index + 1)\n return prime_helper(2)",
"title": ""
},
{
"docid": "1ad782a50c1962258b23b6d5a2dfd97c",
"score": "0.7739098",
"text": "def primality(n):\n if n == 1:\n return NOT_PRIME\n return PRIME if all(n % i for i in range(2, int(sqrt(n)) + 1)) else NOT_PRIME",
"title": ""
},
{
"docid": "2440dc2a1445e687d40f229c33daa7cd",
"score": "0.77390164",
"text": "def isprime(n):\r\n if n % 3 == 0:\r\n return (False, 3)\r\n\r\n i = 5\r\n w = 2\r\n\r\n while i * i <= n:\r\n if n % i == 0:\r\n return (False, i)\r\n\r\n i += w\r\n w = 6 - w\r\n\r\n return (True, None)",
"title": ""
},
{
"docid": "1373ba4fdce84a62e9becedccf9c9fad",
"score": "0.7735379",
"text": "def is_prime(n):\n prime = True\n d = 2\n while prime and d <= round(n**(1/2)):\n if n%d != 0:\n d += 1\n else:\n prime = False\n return prime",
"title": ""
},
{
"docid": "5fb53f51042964c6e86ff53aeb84cd0f",
"score": "0.76981086",
"text": "def is_prime_sieve(x):\n\n if (x <= 1):\n return False\n elif (x <= 3):\n return True\n x_mod_6 = x % 6\n if ((x_mod_6 != 1) and (x_mod_6 != 5)):\n return False\n\n sqrt_x = int_sqrt(x)\n for i in generate_primes(sqrt_x):\n if ((x % i) == 0):\n return False\n return True",
"title": ""
},
{
"docid": "ebc0590b9895849fc3e3f49891941a7f",
"score": "0.7671711",
"text": "def is_prime(n):\n\n # (This is used to study the risk of false positives:)\n global miller_rabin_test_count\n\n miller_rabin_test_count = 0\n\n if n <= smallprimes[-1]:\n if n in smallprimes:\n return True\n else:\n return False\n\n if gcd(n, 2 * 3 * 5 * 7 * 11) != 1:\n return False\n\n # Choose a number of iterations sufficient to reduce the\n # probability of accepting a composite below 2**-80\n # (from Menezes et al. Table 4.4):\n\n t = 40\n n_bits = 1 + int(math.log(n, 2))\n for k, tt in (\n (100, 27),\n (150, 18),\n (200, 15),\n (250, 12),\n (300, 9),\n (350, 8),\n (400, 7),\n (450, 6),\n (550, 5),\n (650, 4),\n (850, 3),\n (1300, 2),\n ):\n if n_bits < k:\n break\n t = tt\n\n # Run the test t times:\n\n s = 0\n r = n - 1\n while (r % 2) == 0:\n s = s + 1\n r = r // 2\n for i in xrange(t):\n a = smallprimes[i]\n y = pow(a, r, n)\n if y != 1 and y != n - 1:\n j = 1\n while j <= s - 1 and y != n - 1:\n y = pow(y, 2, n)\n if y == 1:\n miller_rabin_test_count = i + 1\n return False\n j = j + 1\n if y != n - 1:\n miller_rabin_test_count = i + 1\n return False\n return True",
"title": ""
},
{
"docid": "07e51bd8d669673e6777f4dbb96d36b8",
"score": "0.76641953",
"text": "def is_prime(n):\n\n # Checks if given a list...\n if type(n) == list:\n if_prime_list = [False, ] * len(n)\n c = 0\n for i in n:\n if is_prime(i):\n if_prime_list[c] = True\n c += 1\n # and returns one with weather or not it's prime in place\n return if_prime_list\n\n # When a single number ..\n\n if n < 2:\n return False\n if n == 2 or n == 3:\n return True\n\n for i in range(2, int(sqrt(n))+1):\n if n % i == 0:\n return False\n return True",
"title": ""
},
{
"docid": "b167f7b47ee5341083441f6a31a32272",
"score": "0.7655478",
"text": "def isPrime(n):\n\n if n % 2 == 0:\n return n == 2\n d = 3\n while d * d <= n and n % d != 0:\n d += 2\n return d * d > n",
"title": ""
},
{
"docid": "0be316dbe44f91fe2b2e34bac2ceba31",
"score": "0.7647462",
"text": "def is_prime_cacheless(n):\n if n < 2:\n return False\n for i in xrange(2, int(math.sqrt(n)) + 1):\n if n % i == 0:\n return False\n return True",
"title": ""
},
{
"docid": "5809882f1b0d4c24bf26aadb26e9c4cb",
"score": "0.7642497",
"text": "def is_superprime(n):\n while n > 0:\n if not is_prime(n):\n return False\n n = n // 10\n return True",
"title": ""
},
{
"docid": "6f9804bea1d4da046f3722898d4b15ae",
"score": "0.7615486",
"text": "def is_prime_exact(n):\n if n <= 3:\n return n >= 2\n if n % 2 == 0 or n % 3 == 0:\n return False\n for i in range(5, int(n ** 0.5) + 1, 6):\n if n % i == 0 or n % (i + 2) == 0:\n return False\n return True",
"title": ""
},
{
"docid": "5259c60cb0e66026bd62a7cf1ba6f28e",
"score": "0.76105756",
"text": "def is_prime(number: int) -> bool:\n prop_div = proper_divisors(number)\n return len(prop_div) == 1",
"title": ""
},
{
"docid": "2e333a6a531610d72ac921f35578efc7",
"score": "0.76068914",
"text": "def is_prime_v3(n):\n if n == 1:\n return False\n\n # If it's even and not 2, then it's not prime\n if n == 2:\n return True\n if n > 2 and n % 2 == 0:\n return False\n\n max_divisor = math.floor(math.sqrt(n))\n for d in range(3, 1 + max_divisor, 2):\n if n % d == 0:\n return False\n return True",
"title": ""
},
{
"docid": "2e333a6a531610d72ac921f35578efc7",
"score": "0.76068914",
"text": "def is_prime_v3(n):\n if n == 1:\n return False\n\n # If it's even and not 2, then it's not prime\n if n == 2:\n return True\n if n > 2 and n % 2 == 0:\n return False\n\n max_divisor = math.floor(math.sqrt(n))\n for d in range(3, 1 + max_divisor, 2):\n if n % d == 0:\n return False\n return True",
"title": ""
},
{
"docid": "bd441534aae1f3e8f4a3213bf868fdb0",
"score": "0.75916487",
"text": "def is_prime(n):\n\n # (This is used to study the risk of false positives:)\n global miller_rabin_test_count\n\n miller_rabin_test_count = 0\n\n if n <= smallprimes[-1]:\n if n in smallprimes:\n return True\n else:\n return False\n\n if gcd(n, 2 * 3 * 5 * 7 * 11) != 1:\n return False\n\n # Choose a number of iterations sufficient to reduce the\n # probability of accepting a composite below 2**-80\n # (from Menezes et al. Table 4.4):\n\n t = 40\n n_bits = 1 + int(math.log(n, 2))\n for k, tt in ((100, 27),\n (150, 18),\n (200, 15),\n (250, 12),\n (300, 9),\n (350, 8),\n (400, 7),\n (450, 6),\n (550, 5),\n (650, 4),\n (850, 3),\n (1300, 2),\n ):\n if n_bits < k:\n break\n t = tt\n\n # Run the test t times:\n\n s = 0\n r = n - 1\n while (r % 2) == 0:\n s = s + 1\n r = r // 2\n for i in range(t):\n a = smallprimes[i]\n y = modular_exp(a, r, n)\n if y != 1 and y != n - 1:\n j = 1\n while j <= s - 1 and y != n - 1:\n y = modular_exp(y, 2, n)\n if y == 1:\n miller_rabin_test_count = i + 1\n return False\n j = j + 1\n if y != n - 1:\n miller_rabin_test_count = i + 1\n return False\n return True",
"title": ""
},
{
"docid": "31c7eb72160691d2a58b025131a986ac",
"score": "0.758809",
"text": "def is_prime(n):\n r = 0\n d = n - 1\n while d % 2 == 0:\n r += 1\n d //= 2\n\n for _ in range(40):\n a = random.randint(2, n - 2)\n x = pow(a, d, n)\n if x == 1 or x == n - 1:\n continue\n \n for _ in range(r - 1):\n x = pow(x, 2, n)\n if x == n - 1:\n break\n\n else:\n return False\n\n return True",
"title": ""
},
{
"docid": "0b537c6a55e7ef96df0d686053d637c4",
"score": "0.75501156",
"text": "def is_prime(i, primes):\n pass",
"title": ""
},
{
"docid": "1fbbb15cd7dd7e9b4d3490d51da7e317",
"score": "0.74986815",
"text": "def is_prime(number):\n for i in range(2, number):\n if number % i == 0:\n return False\n\n return True",
"title": ""
},
{
"docid": "d3e14a0482f5920322f17e25db1e4d4f",
"score": "0.7495828",
"text": "def is_prime(n, prime_numbers = []):\n if n < 2:\n return False\n\n if len(prime_numbers) == 0 or prime_numbers[-1] < n:\n prime_numbers = _generate_primes(n + 1, prime_numbers)\n\n for p in prime_numbers:\n if n < p:\n return False\n if n == p:\n return True\n\n return False",
"title": ""
},
{
"docid": "83d11c11d34ac275d0984b8b705ea571",
"score": "0.7481891",
"text": "def is_prime(number):\r\n if number <= 0:\r\n return False\r\n elif number > 2:\r\n for i in range(2, number):\r\n if number % i == 0:\r\n return False\r\n return True",
"title": ""
},
{
"docid": "2bbe1ee06cee20708acd22de4008554f",
"score": "0.7473216",
"text": "def prime(n):\n # https://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n-in-python/3035188#3035188\n sieve = np.ones(int(n/3 + (n%6==2)), dtype=np.bool)\n sieve[0] = False\n for i in range(int(n**0.5/3+1)):\n if sieve[i]:\n k=3*i+1|1\n sieve[ int((k*k)/3) ::2*k] = False\n sieve[int((k*k+4*k-2*k*(i&1))/3)::2*k] = False\n return np.r_[2,3,((3*np.nonzero(sieve)[0]+1)|1)]",
"title": ""
},
{
"docid": "589dda1ed65ef55d34a2407a75760f86",
"score": "0.74541706",
"text": "def is_prime(num):\r\n for n in range(2, num):\r\n if num % n == 0:\r\n return False\r\n return True",
"title": ""
},
{
"docid": "557caac191fadc8d79ddadae1a0b768c",
"score": "0.74398",
"text": "def _is_prime(number):\n if number <= 1:\n return False\n\n divisor = 2\n while divisor < number:\n if ((number % divisor) == 0) or (divisor > (number / 2)):\n return False\n divisor += 1\n return True",
"title": ""
},
{
"docid": "07f0a11ff3c7fc1e8870986532afedf0",
"score": "0.7438905",
"text": "def is_prime(number: int) -> bool:\n min_divisor = 2\n max_divisor = math.ceil(math.sqrt(number))\n for divisor in range(min_divisor, max_divisor + 1):\n if number % divisor == 0:\n return False\n return True",
"title": ""
},
{
"docid": "0b7752fe5a1cf97814e82aa201face01",
"score": "0.7425838",
"text": "def probable_prime(n):\n return pow(2, n-1, n) == 1",
"title": ""
},
{
"docid": "89e87a69f7b9cd088605a16cdbb01c27",
"score": "0.74213374",
"text": "def is_prime_v1(n):\n if n == 1:\n return False # 1 is not prime (1 is not composite, either)\n # d stands for divisor. Since any number greater than 1 (prime or composite) can be exactly divided by 1 and itself,\n # we skip 1 and n and only iterate over the items in the sequence from 2 through n - 1 (Remember in the range()\n # function the stop value is not included.)\n for d in range(2, n):\n # % is the modulus operator. The right-hand side is the remainder of the operation of division of n by d.\n # If n is divisible by d, the remainder is equal to 0; if not, the remainder is not equal to 0.\n # So the test_expression of n % d == 0 means n is divisible by d. If the test_expression is True, control of the\n # program flows to the body of if and execute the return statement. The function exits and meanwhile the boolean\n # value of False is returned.\n if n % d == 0:\n return False\n # If each items in the sequence generated from the range doesn't make the test_expression True, meaning n is not\n # divisible by any smaller number, then we can consider n is prime and return True after the items in the sequence\n # exhaust without the for loop being stopped halfway.\n return True",
"title": ""
},
{
"docid": "460b8ab60da7496aa79fef6e6731ca9f",
"score": "0.74172294",
"text": "def isPrime(p):\n\n return True",
"title": ""
},
{
"docid": "6248ff0357b4b512961650451529bc9c",
"score": "0.7411656",
"text": "def is_prime(number: int) -> bool:\n\n if 1 < number < 4:\n # 2 and 3 are primes\n return True\n elif number < 2 or number % 2 == 0 or number % 3 == 0:\n # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes\n return False\n\n # All primes number are in format of 6k +/- 1\n for i in range(5, int(math.sqrt(number) + 1), 6):\n if number % i == 0 or number % (i + 2) == 0:\n return False\n return True",
"title": ""
},
{
"docid": "6d50325cb8c5ccefa49472a84ce23913",
"score": "0.7410716",
"text": "def is_prime(number):\n if number <= 1:\n return False\n if number == 2:\n return True\n else:\n maximum = int(math.sqrt(number)) + 1\n for i in range(2, maximum):\n if number % i == 0:\n return False\n return True",
"title": ""
},
{
"docid": "b2b3903b7ed71035196f37c9778c5e81",
"score": "0.74050426",
"text": "def check_prime(number):\n if number in primes:\n return True\n return False",
"title": ""
},
{
"docid": "1a3617d3ebdd114f5eeefb2a8c559fad",
"score": "0.73954546",
"text": "def primes(n):\n sieve = [True] * n\n for i in range(3,int(n**0.5)+1,2): #in py3, range and xrange are same\n if sieve[i]:\n sieve[i*i::2*i]=[False]*((n-i*i-1)//(2*i)+1)\n return [2] + [i for i in range(3,n,2) if sieve[i]]",
"title": ""
},
{
"docid": "9d7593a29f9a46b123950c8a508ec279",
"score": "0.7380684",
"text": "def is_prime(number: int) -> bool:\r\n\r\n if 1 < number < 4:\r\n # 2 and 3 are primes\r\n return True\r\n elif number < 2 or number % 2 == 0 or number % 3 == 0:\r\n # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes\r\n return False\r\n\r\n # All primes number are in format of 6k +/- 1\r\n for i in range(5, int(math.sqrt(number) + 1), 6):\r\n if number % i == 0 or number % (i + 2) == 0:\r\n return False\r\n return True",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="797"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">c4d430e66bb3d21f6d5a3c0b14362f75</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Gets the EMD language between the two languages</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "62965b2c20affbafeeb5c0cc7a3c475e",
"score": "0.0",
"text": "def apply(lang1, lang2, variant=Variants.PYEMD, parameters=None):\n return exec_utils.get_variant(variant).apply(lang1, lang2, parameters=parameters)",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "7d19c984c03b3e59c0e29927e52e446b",
"score": "0.6914142",
"text": "def get_language(self):",
"title": ""
},
{
"docid": "38cf8daef8d0e9165e88916da0f08868",
"score": "0.67045707",
"text": "def getLanguage(self) -> ghidra.program.model.lang.Language:\n ...",
"title": ""
},
{
"docid": "88efbc8344a0bc887062d526ad6868f2",
"score": "0.6524406",
"text": "def Language(self) -> int:",
"title": ""
},
{
"docid": "25f46e6f2f129aea0b62e208273c88ba",
"score": "0.64493454",
"text": "def get_translation(language):",
"title": ""
},
{
"docid": "92e349e11ef96d37ee720c9e44ff81c6",
"score": "0.6397181",
"text": "def language(self):\n return self._mapped_lang",
"title": ""
},
{
"docid": "83cfc614d26d41e5c5b4cd9dcfed6d54",
"score": "0.6361355",
"text": "def get_translated_languages():",
"title": ""
},
{
"docid": "72af2abe9ac887512114fc6b329f6a58",
"score": "0.6298945",
"text": "def language(self):\n return getattr(\n self,\n constants.CONST_LANGUAGE,\n constants.DEFAULT_LANGUAGE,\n )",
"title": ""
},
{
"docid": "7374b79e2ca26f3f6ef3c6f14ae42a97",
"score": "0.62752056",
"text": "def getLanguage (self,c,setting):\n \n language = self.getString(c,setting)\n # g.trace(setting,language)\n \n return language",
"title": ""
},
{
"docid": "d018b5936457ce548d59d069e02048a5",
"score": "0.626775",
"text": "def get_language(event, context):\n logger.info('Calling get_language with event')\n logger.info(event)\n\n # Use UTF-8 encoding for comprehend\n if 'Text' in event:\n text = str(event['Text'])\n else:\n logger.error(\"There is no Text!\")\n raise Exception('Please provide Text!')\n\n # comprehend accepts up to 5000 UTF-8 encoded characters\n if len(text) >= 4900:\n text = text[:4899]\n elif len(text) < 20:\n logger.warning(\"Text is shorter than 20 characters!\")\n raise Exception('Please provide a text of at least 20 characters!')\n # logger.info(text)\n\n try:\n response = comprehend.detect_dominant_language(Text=text)\n except ClientError as e:\n logger.error(\"Received error: %s\", e, exc_info=True)\n raise\n except ParamValidationError as e:\n logger.error(\"The provided parameters are incorrect: %s\", e, exc_info=True)\n raise\n\n # logger.info(response)\n return response['Languages'][0]['LanguageCode']",
"title": ""
},
{
"docid": "91f4df655801a0f81560021370fb0325",
"score": "0.6215751",
"text": "def language(self):\n try:\n return self._get_response(\n url=self.LANGID_URL, sentence=self.sentence)\n except:\n return None",
"title": ""
},
{
"docid": "a6852d214fd73ca3c8b3c76ed0880cbe",
"score": "0.62144786",
"text": "def lang(self):\n # pick a sample to find language (don't pick the truth file)\n sample = [choose for choose in os.listdir(self.folder)\n if 'truth' not in choose][0]\n with open(os.path.join(self.folder, sample), 'r') as sample_file:\n first_line = sample_file.readlines()[0]\n lang = [match.group('lang')\n for match in re.finditer(Pan.LANG_REGEX, first_line)][0]\n return lang.lower()",
"title": ""
},
{
"docid": "64b70a35a3529c04521ca03c1783af78",
"score": "0.62131405",
"text": "def language(self):\n return self.language_version()[0]",
"title": ""
},
{
"docid": "762ed9baf7428d3c2c27c92906c3f34d",
"score": "0.61848724",
"text": "def language(self):\n lang=None\n if self.__dict__['TAG:language']:\n lang=self.__dict__['TAG:language']\n return lang",
"title": ""
},
{
"docid": "d9442ebc089fe4982af945a7c4eee87f",
"score": "0.6183641",
"text": "def lang(self):\n return self.lex_meta.lang",
"title": ""
},
{
"docid": "ff6c3d8673b836167af732a1190347c6",
"score": "0.617456",
"text": "def test_lang() -> str:\n lang = locale.getdefaultlocale()[0][0:2]\n if lang == 'fr':\n return 'dtc'\n return 'qdb'",
"title": ""
},
{
"docid": "86b093671c104aa8770479841f816a10",
"score": "0.615984",
"text": "def gncEmployeeGetLanguage(*args):\n return _gnucash_core_c.gncEmployeeGetLanguage(*args)",
"title": ""
},
{
"docid": "036561cb4fe47e070a4c9ddc370ac2e2",
"score": "0.60845643",
"text": "def get_language(self):\n\n return self.__msg_lang",
"title": ""
},
{
"docid": "99a60ae13aaed279959529adedcdb43d",
"score": "0.6080517",
"text": "def Language (self):\n return (self.headers.get ('index_language', 'any'),\n self.headers.get ('contents_language', 'any'))",
"title": ""
},
{
"docid": "1790cd720d52235c595dab8ffb7999cd",
"score": "0.6073051",
"text": "def get_lang():\n if os() == 'Windows':\n # Windows\n lang = locale.windows_locale[ctypes.windll.kernel32.GetUserDefaultUILanguage()]\n else:\n # Linux || MacOS\n lang = locale.getlocale(locale.LC_MESSAGES)[0]\n return lang",
"title": ""
},
{
"docid": "8f04f28b59d21d0f9e23d1b811974cc3",
"score": "0.6020242",
"text": "def moment_lang(self):\n return load_moment_locales().get(self.lang, 'en')",
"title": ""
},
{
"docid": "e019d75c7257253db6436ddc0730b7da",
"score": "0.5989116",
"text": "def lang(self):\n return self._lang",
"title": ""
},
{
"docid": "3065207a9592be2eab56e31bb0e0f886",
"score": "0.59747165",
"text": "def get_language(self):\n locale = self.get_locale()\n language = locale\n if '_' in locale:\n language = locale[0:locale.index('_')]\n return language",
"title": ""
},
{
"docid": "5123c9ed66643d7c0daa4ede7f2b7ffc",
"score": "0.5971464",
"text": "def check_language(text):\n if detect(text) == 'de':\n language = 'de'\n elif detect(text) == 'en':\n language = 'en'\n else:\n language = None\n return language",
"title": ""
},
{
"docid": "65d1e9eae9ab232b88af6f359a4eb6c0",
"score": "0.596331",
"text": "def language_tag(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"language_tag\")",
"title": ""
},
{
"docid": "6c4045103df8f4bf07a4b594e60c7fc2",
"score": "0.5960778",
"text": "def get_locale():\r\n # this is a demo case, we use url to get locale\r\n code = request.args.get('lang', 'en')\r\n return code",
"title": ""
},
{
"docid": "3250b795723d483231050f75ffc0f981",
"score": "0.59486884",
"text": "def language(self):\n return self.__language",
"title": ""
},
{
"docid": "3250b795723d483231050f75ffc0f981",
"score": "0.59486884",
"text": "def language(self):\n return self.__language",
"title": ""
},
{
"docid": "3250b795723d483231050f75ffc0f981",
"score": "0.59486884",
"text": "def language(self):\n return self.__language",
"title": ""
},
{
"docid": "12f6c63ad26a4e47296bddee9b9653f2",
"score": "0.5934641",
"text": "def lang_(self):\n return self.doc.vocab.store[self.lex_meta.lang]",
"title": ""
},
{
"docid": "216919c5a4cf9279f362cc048048645b",
"score": "0.5924688",
"text": "def get_main_language(project_report):\n required1 = {\"MetaDataCollector\": {\"main_language\": \"\"}}\n required2 = {\"LanguageDetector\": {\"main_language\": \"\"}}\n\n if containedStructure(required1, project_report):\n lang = project_report[\"MetaDataCollector\"][\"main_language\"]\n return shorten_language(lang)\n elif containedStructure(required2, project_report):\n lang = project_report[\"LanguageDetector\"][\"main_language\"]\n return shorten_language(lang)\n return '-'",
"title": ""
},
{
"docid": "3125c2dce33044e62c80ca613fa15952",
"score": "0.59202147",
"text": "def convert_language(language):\n\n if language == 'StoreKing_NW':\n language = 'Kannada'\n if language not in LANGUAGES:\n language = 'Hindi'\n\n return language",
"title": ""
},
{
"docid": "8ce21ad58cb3eab50ede578a17775fc6",
"score": "0.59194607",
"text": "def get_locale():\n if \"lang\" in session:\n return session[\"lang\"]\n #Decides the most suitable language option\n #fi=Finnish has higher priority\n lang = request.accept_languages.best_match([\"fi\", \"en\"])\n return lang",
"title": ""
},
{
"docid": "fb14f5c2099e436683e91382ce62e904",
"score": "0.59086585",
"text": "def language_code(self):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "71d7bb6cef50f1f302e8a531796e5ef0",
"score": "0.590221",
"text": "def __get_language__(self, lang_code: str) -> str:\n\n if lang_code not in self.ALLOWED_LANGUAGES.keys():\n raise ValueError('{} is not a supported language code'.format(lang_code))\n\n return self.ALLOWED_LANGUAGES[lang_code]",
"title": ""
},
{
"docid": "c660a06708cf14ec63c36cbcdc98b5f0",
"score": "0.5900211",
"text": "def get_lang(country_iso: str) -> str:\n res = country_lang.get(country_iso)\n if res:\n return res\n else:\n return 'en'",
"title": ""
},
{
"docid": "f71b058ed50499dc5964b614bdebe35c",
"score": "0.5899539",
"text": "def get_lang(self, language):\n if language not in self.compilers:\n self.console.print(\"Language is not supported!\", style=\"bold red\")\n Utils.close()\n return language",
"title": ""
},
{
"docid": "31f035ca7232dd83ae68d0fd8680c087",
"score": "0.5898755",
"text": "def getAvailableLanguages():\n\t#Make a list of all the locales found in NVDA's locale dir\n\tl=[x for x in os.listdir(\"locales\") if not x.startswith('.')]\n\tl=[x for x in l if os.path.isfile('locales/%s/LC_MESSAGES/twblue-documentation.mo' % x)]\n\t#Make sure that en (english) is in the list as it may not have any locale files, but is default\n\tif 'en' not in l:\n\t\tl.append('en')\n\t\tl.sort()\n\t#For each locale, ask Windows for its human readable display name\n\td=[]\n\tfor i in l:\n\t\tdesc=getLanguageDescription(i)\n\t\tlabel=\"%s, %s\"%(desc,i) if desc else i\n\t\td.append(label)\n\t#include a 'user default, windows' language, which just represents the default language for this user account\n\tl.append(\"system\")\n\t# Translators: the label for the Windows default NVDA interface language.\n\td.append(_(\"User default\"))\n\t#return a zipped up version of both the lists (a list with tuples of locale,label)\n\treturn zip(l,d)",
"title": ""
},
{
"docid": "f8a9d1153b9d1d09c331da8bf1f45873",
"score": "0.5883816",
"text": "def get_language(self):\n return self._language",
"title": ""
},
{
"docid": "29738bd87cc78dd4e2ff5aa73bc07f2f",
"score": "0.58760846",
"text": "def lang(self):\n return self.config_core.get('lang')",
"title": ""
},
{
"docid": "d7cf60544184706f9f81cdc9296ec99b",
"score": "0.5870653",
"text": "def language(self) -> Optional[Text]:\n\n return self.get(\"language\")",
"title": ""
},
{
"docid": "49d004470495efbf7e4526a851502aa6",
"score": "0.58666897",
"text": "def l_get(\n language_dict: Dict[str, Type[T_Lang]], update_msg_user_or_language_code: T_update_msg_user_or_language_code = None\n) -> Type[T_Lang]:\n lang = get_language_code(update_msg_user_or_language_code)\n\n # if it is None (default, or getting from message failed), use the default\n if lang is None:\n return language_dict['default']\n # end if\n\n if lang in language_dict:\n return language_dict[lang]\n # end if\n\n # try replacing \"de_DE\" => \"de-DE\"\n part = lang.replace(\"_\", \"-\")\n if part != lang and part in language_dict:\n return language_dict[part]\n # end of\n\n # try splitting it \"de-DE\" => \"de\"\n part = lang.split('-')[0]\n if part != lang and part in language_dict:\n return language_dict[part]\n # end of\n\n # try splitting it \"de_DE\" => \"de\"\n part = lang.split('_')[0]\n if part != lang and part in language_dict:\n return language_dict[part]\n # end if\n\n # nothing did match, use the default\n return language_dict['default']",
"title": ""
},
{
"docid": "8235dc2fa6170ed344df28e1700dbbc2",
"score": "0.5864678",
"text": "def getLanguage(self):\n spd = self.get(sessionPkgDataId, {})\n return spd.get('language', None)",
"title": ""
},
{
"docid": "16a9b23a1950dc54cc078c8a113ee638",
"score": "0.58440614",
"text": "def language(self) -> LangEnum:\n return self._lang_enum",
"title": ""
},
{
"docid": "e305eccb0d97870a645edc54619fa11d",
"score": "0.58301246",
"text": "def get_language(self):\n # Look through ancestors of this page for its language homepage\n # The language homepage is located at depth 3\n language_homepage = self.get_ancestors(inclusive=True).get(depth=3)\n\n # The slug of language homepages should always be set to the language code\n return language_homepage.slug",
"title": ""
},
{
"docid": "01a7c30c4fb5b54b7e1163687d05331e",
"score": "0.58280015",
"text": "def get_keyboard_language():\n # only language not variant layout\n user32 = ctypes.WinDLL('user32', use_last_error=True)\n curr_window = user32.GetForegroundWindow()\n thread_id = user32.GetWindowThreadProcessId(curr_window, 0)\n klid = user32.GetKeyboardLayout(thread_id)\n lid = klid & (2 ** 16 - 1)\n lid_hex = f\"{lid:#0{6}x}\"\n root = os.path.dirname(__file__)\n with open(root + '/misc/win-language-id.json', 'r') as f:\n win_layout = json.load(f)\n return win_layout[lid_hex]",
"title": ""
},
{
"docid": "35060b60835a1adab92ba7649db7b1db",
"score": "0.5827729",
"text": "def language_code(self):\n return self._language_code",
"title": ""
},
{
"docid": "c83856b033f38001233ddd1ccea8962e",
"score": "0.58120084",
"text": "def language(self):\n return self._language",
"title": ""
},
{
"docid": "c83856b033f38001233ddd1ccea8962e",
"score": "0.58120084",
"text": "def language(self):\n return self._language",
"title": ""
},
{
"docid": "c83856b033f38001233ddd1ccea8962e",
"score": "0.58120084",
"text": "def language(self):\n return self._language",
"title": ""
},
{
"docid": "60cd8d4c2b50df7167c55b050d8b4fed",
"score": "0.57999927",
"text": "def getLanguageDescription(language):\n\tdesc=None\n\tif platform.system() == \"Windows\":\n\t\tLCID=localeNameToWindowsLCID(language)\n\t\tif LCID!=0:\n\t\t\tbuf=ctypes.create_unicode_buffer(1024)\n\t\t\tif '_' not in language:\n\t\t\t\tres=ctypes.windll.kernel32.GetLocaleInfoW(LCID,LOCALE_SLANGDISPLAYNAME,buf,1024)\n\t\t\telse:\n\t\t\t\tres=0\n\t\t\tif res==0:\n\t\t\t\tres=ctypes.windll.kernel32.GetLocaleInfoW(LCID,LOCALE_SLANGUAGE,buf,1024)\n\t\t\tdesc=buf.value\n\telif platform.system() == \"Linux\" or not desc:\n\t\tdesc={\n\t\t\t\"am\":pgettext(\"languageName\",\"Amharic\"),\n\t\t\t\"an\":pgettext(\"languageName\",\"Aragonese\"),\n\t\t\t\"es\":pgettext(\"languageName\",\"Spanish\"),\n\t\t\t\"pt\":pgettext(\"languageName\",\"Portuguese\"),\n\t\t\t\"ru\":pgettext(\"languageName\",\"Russian\"),\n\t\t\t\"it\":pgettext(\"languageName\",\"italian\"),\n\t\t\t\"tr\":pgettext(\"languageName\",\"Turkey\"),\n\t\t\t\"gl\":pgettext(\"languageName\",\"Galician\"),\n\t\t\t\"ca\":pgettext(\"languageName\",\"Catala\"),\n\t\t\t\"eu\":pgettext(\"languageName\",\"Vasque\"),\n\t\t\t\"pl\":pgettext(\"languageName\",\"polish\"),\n\t\t\t\"ar\":pgettext(\"languageName\",\"Arabic\"),\n\t\t\t\"ne\":pgettext(\"languageName\",\"Nepali\"),\n\t\t\t\"sr\":pgettext(\"languageName\",\"Serbian (Latin)\"),\n\t\t}.get(language,None)\n\treturn desc",
"title": ""
},
{
"docid": "2e521127c2c1644e5b57354454f398c9",
"score": "0.5784645",
"text": "def SelectSupportedLanguage(language_codes):\n for lang in map(NormalizeLang, language_codes.split(',')):\n if lang in ALL_LANGUAGES:\n return lang\n first = lang.split('-')[0]\n if first in ALL_LANGUAGES:\n return first\n return None",
"title": ""
},
{
"docid": "45fcd201506a586053c88430aa858de5",
"score": "0.57824755",
"text": "def language(self):\n return self.json[\"response\"].get(\"language\", \"\")",
"title": ""
},
{
"docid": "8a89e360fd1b1356d970be4fb6b21d74",
"score": "0.57764006",
"text": "def test_23_language_detection_greek(self):\n res = detect_language(self.greek_text)\n self.assertEqual(res, 'el')\n return res",
"title": ""
},
{
"docid": "ef21f9e15c70ba159a29472690cf3950",
"score": "0.5774823",
"text": "def get_language(source, code, language=None):\n\n if language is not None:\n for l in extensions_mapping.values():\n if l.name == language:\n return l\n else:\n raise ValueError(\"Unknown forced language: \" + language)\n\n m = re.match(r'.*(\\..+)', os.path.basename(source))\n\n if m and m.group(1) in extensions_mapping:\n return extensions_mapping[m.group(1)]\n else:\n lang = lexers.guess_lexer(code).name.lower()\n for l in extensions_mapping.values():\n if l.name == lang:\n return l\n else:\n return None",
"title": ""
},
{
"docid": "8da823ae121d2b255c5a33172318a03e",
"score": "0.57725",
"text": "def Language(self):\n return \"\"",
"title": ""
},
{
"docid": "8bc68f83d53055dd7588e2b309bc48f3",
"score": "0.5762527",
"text": "def format_language(language):\n\n if language == ACCEPTED_LANGUAGES[0]:\n return 'eng'\n elif language == ACCEPTED_LANGUAGES[1]:\n return 'spa'\n elif language == ACCEPTED_LANGUAGES[2]:\n return 'fra'\n elif language == ACCEPTED_LANGUAGES[3]:\n return 'hin'",
"title": ""
},
{
"docid": "37f64e0382dccc912af07a8ecffe82e2",
"score": "0.5761884",
"text": "def get(ietf_tag: str) -> int:\n ietf_tag = ietf_tag.lower()\n languages = {\n \"en\": SupportedLanguage.EN,\n \"en-us\": SupportedLanguage.EN,\n \"en-gb\": SupportedLanguage.EN\n }\n\n return languages.get(ietf_tag, SupportedLanguage.EN)",
"title": ""
},
{
"docid": "49dcf7e294119248c293aa1a1ecfa77a",
"score": "0.5746692",
"text": "def get_language_from_message(message):\n if hasattr(message,'language'):\n return message.language\n return get_language_from_connection(message.persistant_connection)",
"title": ""
},
{
"docid": "fd487efa338808a4a785366461405f65",
"score": "0.5726152",
"text": "def language(self):\n\n return self._repo_data[\"language\"]",
"title": ""
},
{
"docid": "3dd19c7078cc54387873d7772600e4f7",
"score": "0.57207394",
"text": "def get_language(khoros_object, community_details=None):\n return get_community_field(khoros_object, 'language', community_details)",
"title": ""
},
{
"docid": "daadb6a97a97c036e36905d7fcc266de",
"score": "0.57153803",
"text": "def max_delang (self):\n return self._max_delang",
"title": ""
},
{
"docid": "a0c7697bba718c2fda31db17eabbad33",
"score": "0.5704921",
"text": "def get_default_language():\n return properties.LANGUAGE_CODE",
"title": ""
},
{
"docid": "4347a1ff6618c88e66f09364dd7fa2ae",
"score": "0.5693334",
"text": "def test_english(self):\n self.assertEqual(get_language('this is english'), 'en')",
"title": ""
},
{
"docid": "4459ad0d764cdc5ea1d93398d52beea2",
"score": "0.56914985",
"text": "def nb_language(self, engine_name, nb, language=None):\n return self.get_engine(engine_name).nb_language(nb, language)",
"title": ""
},
{
"docid": "8aa2db27f0e2c9c6eec736f9e249e08f",
"score": "0.5668158",
"text": "def get_language_code(self, handler):\n l = self.get_locale(handler)\n return str(l)",
"title": ""
},
{
"docid": "5ea780555d4bf7864d77a55a9e794d2e",
"score": "0.5646527",
"text": "def langsvlakheid(self):\n return self._langsvlakheid.get_waarde()",
"title": ""
},
{
"docid": "39076118f799675f003835a70c26b96e",
"score": "0.5592201",
"text": "def default_language(self):\n return self._lang",
"title": ""
},
{
"docid": "36cc39483704c665adf2d8a5f9ef33c2",
"score": "0.55814254",
"text": "def learning_language(self):\n if getattr(self, '_learning_language', None) is None:\n self._learning_language = self.app.get_setting(\n u'learning_language', u'en')\n return self._learning_language",
"title": ""
},
{
"docid": "597c596cd22ca4a0fb9414329da19b56",
"score": "0.5578478",
"text": "def get_language_choices():\n return properties.LANGUAGES",
"title": ""
},
{
"docid": "1314574931b7caa48350cdb3e69b9ef0",
"score": "0.5577607",
"text": "def language_GetCapLang(self):\n status = False\n ch_langs = ['de', 'deu', 'de-CH', 'ger', # Deutsch\n 'en', 'eng', 'en-US', 'en-GB', 'en-CA', # Englisch\n 'fr', 'fra', 'fr-CH', 'fre', # Französisch\n 'it', 'ita', 'it-CH', # Italienisch\n 'roh', 'rm'] # Romanisch\n \n url_status = False\n url_results = []\n langs_found = []\n results = []\n \n \"\"\"\n By GetCapabilities\n \"\"\"\n f = None\n if self.restful:\n url = urllib.basejoin(self.base_url, self.service_settings.REST.GetCapabilities)\n else:\n kvp = {\n 'request': 'GetCapabilities',\n 'service': self.service,\n 'version': self.version\n #'language':'de-CH'\n }\n url = self.build_kvp_url(self.base_url, kvp, swapcases=self.swapcases)\n try:\n f = URL2File(url, auth=self.auth)\n for line in f.readlines():\n # <Title xml:lang=\"fr\">\n l = re.compile('xml:lang=\"(\\w+)\"')\n found = l.findall(line)\n if found:\n langs_found.append(found[0])\n except Exception, e:\n url_results.append(\"%s for URL %s\" %(e, url))\n\n finally:\n if f:\n f.close()\n \n # Check Capabilities.Languages\n try:\n for lang in dict2list(self.gc_xmlroot.Languages.Language): \n langs_found.append(lang.value)\n results.append('Found following languages: ' + ', '.join(langs_found))\n except KeyError:\n results.append('XML Element \"Languages\" not found.')\n \n if DEBUG: print langs_found\n \n \"\"\"\n LANG-02\n Suche in Operation.Parameter(name=Language).Value\n \"\"\"\n # Get the Service Operations (same as in meta_ServiceOperations)\n \"\"\"\n if self.ows_common:\n #OWS_Common Workaround \n service_ops = dict2list(self.gc_xmlroot.OperationsMetadata.Operation)\n else:\n service_ops = dict2list(self.gc_xmlroot.Capability.Request)\n \n #if DEBUG: print service_ops\n langs_in_param_found = []\n for operation in service_ops:\n if not operation.has_key('Parameter'):\n continue\n else:\n params = dict2list(operation.Parameter)\n for param in params:\n if \"language\" == param.name.lower():\n for lang in dict2list(param.Value):\n langs_in_param_found.append(lang.value)\n langs_found.append(lang.value)\n \n if len(langs_in_param_found):\n self.setResultsOverview(\"LANG-02\", True, \"Found following languages: \" + ', '.join(langs_in_param_found))\n else:\n self.setResultsOverview(\"LANG-02\", False, \"Found no languages in Parameters\")\n\n \"\"\"\n\n \"\"\"\n By URL-naming\n LANG-03\n LANG-04\n \"\"\"\n #else:\n urls = self.base_url.split(\"/\")\n url_results = \"Service does not support languages via URL-Path\"\n for url_lang in urls:\n if url_lang in ch_langs:\n url_status = True\n langs_found.append(url_lang)\n results.append(\"Supported language %s found\" %url_lang)\n url_results = \"Supports languages via URL-Path '%s'\"%url_lang\n break\n \n \"\"\"\n Checking for HTTP status 300\n \"\"\"\n \n if url_status:\n http_status_code_redirect = 300\n http_status_code = None\n http_300 = False \n http_300_results = 'Server responded with HTTP status code 300 (multiple choices)'\n \n base_url = self.base_url.replace(\"/%s/\" %(url_lang), \"/\")\n url = self.build_kvp_url(base_url, kvp, self.swapcases)\n results.append('Checking for URL %s' %(url))\n try: \n f = URL2File(url, auth=self.auth)\n http_status_code = f.code \n except Exception, e:\n http_status_code = e.code\n finally:\n f.close()\n \n http_300 = http_status_code_redirect == http_status_code\n if not http_300:\n http_300_results = 'Server responded with HTTP status code %i (should be 300)' %(http_status_code)\n \n else:\n http_300 = False\n http_300_results = 'Service does not support redirection (HTTP Status Code 300)'\n self.setResultsOverview(\"LANG-04\", http_300, http_300_results)\n \n \"\"\"\n Checking equalness\n \"\"\"\n equal_langs = []\n \n for l in ch_langs:\n if l in langs_found:\n equal_langs.append(l)\n else:\n pass\n results.append(\"Found %s eCH-languages (%s) in GetCapabilities.\" %(len(equal_langs), ', '.join(equal_langs)))\n status = bool(len(equal_langs))\n \n if not status:\n results.append(\"Service does not support languages (via GetCapabilities)\")\n if not url_status:\n url_results = \"Service does not support languages (via URL)\"\n \n hints = \"LANG-xx\"\n # Anzahl > 0 der gefundenen übereinstimmenden Sprachangaben erfüllt LANG-01\n equal_langs = [\"'%s'\" %l for l in equal_langs]\n self.setResultsOverview(\"LANG-01\", status, results, data={'code':', '.join(equal_langs)})\n self.setResultsOverview(\"LANG-03\", url_status, url_results, data={'code':url_lang})\n\n #status = any([status, url_status, http_300, langs_in_param_found])\n status = any([status, url_status, http_300])\n return ResponseDict('language_GetCapLang', results, status, hints)",
"title": ""
},
{
"docid": "70af76ab2e7ccc1894522298d7193001",
"score": "0.5564876",
"text": "def language_tag(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"language_tag\")",
"title": ""
},
{
"docid": "b825cbe6d7edd3f663e27a2df5e2a694",
"score": "0.55642813",
"text": "def get_language_code(language):\n\n # if SelfcheckLanguage[language] in SelfcheckLanguage:\n\n try:\n return SelfcheckLanguage[language].value\n except KeyError:\n return SelfcheckLanguage.UNKNOWN.value",
"title": ""
},
{
"docid": "b2b80e66f39eb3694cc93f0dfc6eb6c8",
"score": "0.55572534",
"text": "def _example_word(lang):\n return {\n \"de\": \"mann\",\n \"es\": \"hombre\"\n }.get(lang)",
"title": ""
},
{
"docid": "23e8ef306206adbf0712ef6783425a84",
"score": "0.5551857",
"text": "def test_22_language_detection_georgian(self):\n res = detect_language(self.georgian_text)\n self.assertEqual(res, 'ka')\n return res",
"title": ""
},
{
"docid": "bbe8eabf96228e3318a7dbef78bed21e",
"score": "0.55382514",
"text": "def get_restricted_translation(language):",
"title": ""
},
{
"docid": "6e1ae8d5c5b2b0da951bde342b697e7f",
"score": "0.55368966",
"text": "def detect_language(html) :\n h = html2text.HTML2Text()\n return langdetect.detect(h.handle(html))",
"title": ""
},
{
"docid": "f1b043acb4293797c4d7d8782085b0fb",
"score": "0.5533975",
"text": "def SelectLanguage(*langs):\n for lang in langs:\n if lang:\n supported_lang = SelectSupportedLanguage(lang)\n if supported_lang:\n return supported_lang\n # All arguments were None or invalid.\n return DEFAULT_LANGUAGE",
"title": ""
},
{
"docid": "b2519838fae7143708da9ef1a0071e33",
"score": "0.55263555",
"text": "def get_langinfo(isocode):\n page = \"http://www.ethnologue.com/language/\"+isocode",
"title": ""
},
{
"docid": "116ebdb2170d7d99284a0fc999bedc0c",
"score": "0.5515722",
"text": "def detect_language(text):\n\n languages = langdetect.detect_langs(text)\n # This is a list, I should return the topmost language 2 letters code in uppercase\n\n language = str(languages[0]).upper()\n language = language[0:2]\n return language",
"title": ""
},
{
"docid": "de840d1a4f23b9a8c04ad4ee0c78d02c",
"score": "0.5501173",
"text": "def get_language(self, request):\n if request.META.get('HTTP_ACCEPT_LANGUAGE'):\n best = self.get_best_language(\n request.META['HTTP_ACCEPT_LANGUAGE'])\n if best:\n return best\n return settings.LANGUAGE_CODE",
"title": ""
},
{
"docid": "812a0731fd886aff6bfc486fda7c291e",
"score": "0.5500768",
"text": "def detect_B(text):\n\n if text is None:\n return \"None\"\n try:\n lang = detect(text)\n\n if lang == 'es':\n return lang\n else:\n return 'en'\n except UnicodeDecodeError:\n return \"None\"",
"title": ""
},
{
"docid": "12550f06daa0c939cd1f11a17e4b4901",
"score": "0.54871047",
"text": "def find_language(regnskab_dict):\n for key, regnskab_tuples in regnskab_dict.items():\n for t in regnskab_tuples:\n if t.unitIdXbrl is not None and t.unitIdXbrl[0:5] == 'lang:':\n return t.unitIdXbrl[5:]\n return 'da'",
"title": ""
},
{
"docid": "c0c7a68ed0fd1e6022fd42027c80e24e",
"score": "0.54816854",
"text": "def nb_language(cls, nb, language=None):\n return nb_language(nb, language)",
"title": ""
},
{
"docid": "53339468ec2621b4d9b68bb8c646d467",
"score": "0.54690015",
"text": "def language_tag(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"language_tag\")",
"title": ""
},
{
"docid": "5aceab5793405918a4af8ac38437e72d",
"score": "0.5465471",
"text": "def gengo_language_to_locale(lang):\n lang = REVERSE_LANGMAP.get(lang, lang)\n parts = lang.partition('-')\n return parts[0] + parts[1].replace('-', '_') + parts[2].upper()",
"title": ""
},
{
"docid": "fc684fc552261aa1a0a111055fcf3db9",
"score": "0.5461347",
"text": "def test_get_default_language():\n assert messages.get('UNKNOWN', 'foo') == messages.get('UNKNOWN', 'en')",
"title": ""
},
{
"docid": "b1490a48a304c69ef904edc4f23508b1",
"score": "0.5448692",
"text": "def get_language(self):\n return translation.get_language() # Assumes that middleware has set this properly.",
"title": ""
},
{
"docid": "4ba269d2ce9b75575efa709c03ac1679",
"score": "0.54374075",
"text": "def getLocations(self, language: str):\n if language not in self.locations.keys():\n return self.locations['de']\n else:\n return self.locations[language]",
"title": ""
},
{
"docid": "17e2768c96b0c43adf784c2cc7a648b3",
"score": "0.54363334",
"text": "def _language_name(self):\n languages = dict(settings.ALL_LANGUAGES)\n try:\n return languages[self.course_team.language]\n except KeyError:\n return self.course_team.language",
"title": ""
},
{
"docid": "c050815f7d7c5a0e33dbd062754bf878",
"score": "0.5428089",
"text": "def babel_language_in_locale(locale_id = 'en'):\n locale_obj = Locale.parse(flask_babel.get_locale())\n return locale_obj.get_language_name(locale_id)",
"title": ""
},
{
"docid": "6c2feee86d66db6f05c9ff0d67fc3f37",
"score": "0.5426774",
"text": "def get_default_programming_language(\n language: str\n) -> str:\n language = language.lower()\n for lang in config.DEFAULT_PROGRAMMING_LANGUAGES:\n if lang.replace('\\\\', '').lower() == language:\n return lang\n return \"\"",
"title": ""
},
{
"docid": "025879eba30135849ee96cfb440d0bcc",
"score": "0.54237074",
"text": "def language_version(self):\n return (None, None)",
"title": ""
},
{
"docid": "2e76f4cb6729101e7eac8a3c2918919f",
"score": "0.5418951",
"text": "def target_language(self):\n return self.target.config.name",
"title": ""
},
{
"docid": "ada6b3bf7115085ac89d052826564c84",
"score": "0.54109055",
"text": "def english(self) -> str:\n return self._english",
"title": ""
},
{
"docid": "277d65c1ed69f90c859004a3071a4fc7",
"score": "0.53834516",
"text": "def ui_language(self):\n return self._ui_language",
"title": ""
},
{
"docid": "90711d26c4fbf62f59ed7f992d8a1552",
"score": "0.5372895",
"text": "def canonizeLanguage(language):\n for code, name in LANGUAGES:\n if language == code or language == name:\n return code\n return ''",
"title": ""
},
{
"docid": "00223d98f7771684b2488a26b6afcac4",
"score": "0.53686786",
"text": "def LanguageIndex(self) -> int:",
"title": ""
},
{
"docid": "d041a335c9018f52897117de0d6b43ec",
"score": "0.5344354",
"text": "def current_language(request):\n languages = [x.split(';', 1)[0].lower() for x in request.META.get('HTTP_ACCEPT_LANGUAGE', '').split(',')]\n for language in languages:\n if language.startswith('zh'):\n return 'zh-tw'\n return 'en'",
"title": ""
},
{
"docid": "31c0766968b51ecda9a7dd834d2a9841",
"score": "0.53424793",
"text": "def get_lang(uid):\n return webdb.select('USERS', where='uid = \"' + str(uid) + '\"', limit=1)[0]['lang']",
"title": ""
},
{
"docid": "87e595a25126aeef4b825d780e5a1ebf",
"score": "0.5316689",
"text": "def get_lang_ext(self, lang):\n if lang not in self.language_extensions:\n return None\n return self.language_extensions[lang]",
"title": ""
},
{
"docid": "dc3a93bf0886f81449c29a183972b5bc",
"score": "0.53152317",
"text": "def get_language_with_ip(request):\n country = get_country_with_request(request)\n if country:\n return country.language_code\n else: # defaults to English\n return 'en'",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="798"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">dcb6c0a8a52a7bb0ac92e1bdf7df1718</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">get all the bookmars that a certai project has</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "4705871157183a2403386664a34c0255",
"score": "0.0",
"text": "def get_projectBookmarks(self, projectId):\n from Bookmark import Bookmark\n cursor = self.dbconnect.get_cursor()\n cursor.execute('select * from bookmark where project=%s', (projectId,))\n bookmarks = list()\n for row in cursor:\n bookmark = Bookmark(row[0], row[1])\n bookmarks.append(bookmark)\n return bookmarks",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "edff7cd81254a3f14281af7177212839",
"score": "0.6072002",
"text": "def getBooks():\n ret = {}\n books = []\n iter = model.getBooks()\n while iter.next():\n books.append((iter.id, iter.name))\n\n ret['books'] = books\n return ret",
"title": ""
},
{
"docid": "c20ccd70c0b4dc0f21126c5b35a2add6",
"score": "0.60219944",
"text": "def get_all_books():\n\n # return Book.query.all()\n return Book.query.order_by(Book.title).all()",
"title": ""
},
{
"docid": "7bf9f7f1f1c9dad337225ac773f549ca",
"score": "0.5911421",
"text": "def books(self):\n return self._results_dict['books']",
"title": ""
},
{
"docid": "ffaac922289e589946ee40ee82d085cd",
"score": "0.59017754",
"text": "def makeBooksArray():\n books=[]\n metadata = metainfo.readmetadata()\n for key in metadata:\n if( (len(metadata[key]['subjects']) > 0) and( metadata[key]['subjects'] is not None) and (metadata[key]['downloads']>75) and ( metadata[key]['author'] is not None) and (\"en\" in metadata[key]['language']) ):\n #authorNames = extractNames(metadata[key]['author'])\n genreString = ', '.join(metadata[key]['subjects'])\n books.append(Book(key,metadata[key]['title'],metadata[key]['author'],genreString,0,0))\n return books",
"title": ""
},
{
"docid": "dd0291c5e68e86d1df97ac53b11c36a3",
"score": "0.5664254",
"text": "def bookkeeping_all():\n result = handlers.BookKeeping().retrieve_bookkeeping_all()\n return result",
"title": ""
},
{
"docid": "d1614d4de7151421f0c9791faa015e5c",
"score": "0.5599402",
"text": "def get_all_books() -> List[Dict]:\n with open(BOOKS, 'r') as in_file:\n return [_parse_from_line(line) for line in in_file]",
"title": ""
},
{
"docid": "237be531bc784d39ef4a4f3921d9d799",
"score": "0.5585883",
"text": "def get_books():\n books = Book.query.all()\n json_books = []\n for book in books:\n json_books.append(book.to_json())\n return jsonify(json_books), 200",
"title": ""
},
{
"docid": "ef68a6b4baab0918e4133a86782e5c38",
"score": "0.5565488",
"text": "def getClips(book_id):\n ret = {}\n clips = []\n it = model.getClipsByBookId(book_id)\n while it.next():\n clips.append((it.id, it.content))\n pass\n\n ret['clips'] = clips\n return ret",
"title": ""
},
{
"docid": "9822c9b8267f4bbe3be1a2a68d825825",
"score": "0.5564752",
"text": "def books():\n import raw\n return jsonify(raw.get_books())",
"title": ""
},
{
"docid": "dbc1d921dc9c507e31bd65a8ccb389bf",
"score": "0.55092233",
"text": "def get_book(self, top_n=None):\n return {\n 'asks': self.get_asks(top_n=top_n),\n 'bids': self.get_bids(top_n=top_n)\n }",
"title": ""
},
{
"docid": "97c3fb06b8fb3ae945e9ec38ceb52e37",
"score": "0.5496954",
"text": "def get_all_books_alphabetical(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "4e546d6dc1b8c1cf1b01fb12e3d27bfc",
"score": "0.54952437",
"text": "def get_all_books_release_year(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "3530625f47dfbaac23e3bb2548db518b",
"score": "0.5468389",
"text": "def getBorrowedBooks(self):\n return self.__borrowedBooks",
"title": ""
},
{
"docid": "9732865d0e328131cc0a5c7aed478c34",
"score": "0.546294",
"text": "def get_all_books(self, book_type):\n validate_type(book_type)\n return [book.serialize() for book in self.list_books if book.type == Type(book_type)]",
"title": ""
},
{
"docid": "1a73881df25c2175e71e8c0915b410a8",
"score": "0.5461085",
"text": "def get_bible(out):\n for book in BOOKS:\n book_chapters = IBook(book).chapter_to_text(version=\"basicenglish\")\n with open(Path(out) / f\"{book}.json\", \"w\") as out_json:\n json.dump(book_chapters, out_json)",
"title": ""
},
{
"docid": "df876f5293768f809cb674acdd1282ae",
"score": "0.5460983",
"text": "def get_books(self):\n files = [f for f in listdir(self.text_dir)]\n for author, title, period, url in self.json():\n filename = format_filename(author, title)\n if not filename in files:\n logger.debug(\"Getting %s\" % filename)\n book = self.extractor.download_book(url, False, author, title, period)\n else:\n logger.debug(\"%s already downloaded\" % filename)",
"title": ""
},
{
"docid": "1c25455989ae0ff94927d623ecc64382",
"score": "0.54381627",
"text": "def getNyTimesBooks():\n lists = [lst['list_name_encoded'] for lst in requests.get('https://api.nytimes.com/svc/books/v3/lists/names.json?api-key=1bfa24a95061415dbc8d4a4f136329a5').json()['results']]\n global printout\n for l in lists[:1]:\n results = requests.get('https://api.nytimes.com/svc/books/v3/lists.json?list='+l+'&api-key=1bfa24a95061415dbc8d4a4f136329a5').json()\n for r in results['results']:\n isbn = r['book_details'][0]['primary_isbn13']\n list = r['list_name']\n try:\n GRBook_req = requests.get('https://www.goodreads.com/book/isbn_to_id/'+isbn+'?key='+API_KEY['GOODREADS'])\n if(GRBook_req.status_code == 200):\n getGRBookByID(int(GRBook_req.text), list)\n except ExpatError as e:\n print(e)",
"title": ""
},
{
"docid": "48efe85434fc0b68dd59a1906bbfe648",
"score": "0.54263264",
"text": "def citations(self):\n return []",
"title": ""
},
{
"docid": "48ee710991bee60d2ba6ceafc328780f",
"score": "0.5416807",
"text": "def getLooks(self):\n pass",
"title": ""
},
{
"docid": "b3cb373aca17c84403726111cf0ef93f",
"score": "0.5414597",
"text": "def report_books(self):\n for book in self._books.itervalues():\n print(book.report())",
"title": ""
},
{
"docid": "6304b84bc10f74f192ff44ede1114e7a",
"score": "0.5382276",
"text": "def get_all_project_details() -> list:\n\n list_project = db_connector.collection(Collections.PROJECTS).find(\n {}, {\"_id\": 0}).sort(\"project_name\", pymongo.ASCENDING)\n list_projects_to_be_send = []\n for project in list_project:\n list_projects_to_be_send.append(project)\n return list_projects_to_be_send",
"title": ""
},
{
"docid": "672c738042ef32362a7eaa5ce36e731a",
"score": "0.5363474",
"text": "def get_chapters():\n q = 'project/'\n chapters = exec_jira_query(q)\n tcs = []\n for c in chapters:\n k = c['key']\n if k in relevant_chapter_keys:\n tcs.append(_build_chapter(c))\n return tcs",
"title": ""
},
{
"docid": "8ad5bfe68833e5763701cec6d8345a9c",
"score": "0.5345248",
"text": "def get_books(self, shelf):\n titles = self.parse_titles(shelf)\n isbns = self.parse_isbns(shelf)\n isbn13s = self.parse_isbn13s(shelf)\n authors = self.parse_author(shelf)\n\n books = []\n for i in range(len(titles)):\n b = Book(titles[i], isbns[i], isbn13s[i], authors[i])\n books.append(b)\n return books",
"title": ""
},
{
"docid": "74cf32b3febfbaa7e8c684f007192f04",
"score": "0.53447914",
"text": "def books(self):\n if not hasattr(self, '_books'):\n self._books = list(Bestseller.select().where(Bestseller.author == self))\n\n return self._books",
"title": ""
},
{
"docid": "e1a081d8885482958cb5ffbf91794e50",
"score": "0.53246725",
"text": "def bookshelves_subjects(self, book_id):\n select = \"SELECT DISTINCT ?subject ?bookshelf\"\n pattern = f\"\"\"\n WHERE {{\n <{book_id}> dcterms:subject [dcterms:title ?subject];\n pgterms:bookshelf [dcterms:title ?bookshelf] ;\n dcterms:type dcmitype:Text. \n }}\n ORDER BY ?subject\n \"\"\"\n query = self._namespace + select + pattern\n query = self._get_query_results(query)\n\n subjects = [result[\"subject\"] for result in query]\n bookshelves = [result[\"bookshelf\"] for result in query]\n\n return {\"bookshelves\": set(bookshelves), \"subjects\": set(subjects)}",
"title": ""
},
{
"docid": "035f5538e005f363b6d8bec20bd36a8e",
"score": "0.5317984",
"text": "def book_list(username):\n reviews=Review.objects.filter(creator__username__contains=username)\n return {'books_read':[r.book.title for r in reviews]}",
"title": ""
},
{
"docid": "bd584a6e594699b465eb8376480b73b8",
"score": "0.5293692",
"text": "def get_all_book_ids(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "be9122f82f2ad38b2c9797839d77dae1",
"score": "0.5280353",
"text": "def test_get_all_company_fundamentals(self):\n pass",
"title": ""
},
{
"docid": "a1fb51aa792d13daf074d9ca509842b5",
"score": "0.5274824",
"text": "def get_bibliography(self, id):\n select = \"SELECT DISTINCT ?title\"\n pattern = f\"\"\"\n WHERE {{\n ?book_id purl:creator <{id}> .\n {self._books}\n }}\n ORDER BY ?title\n \"\"\"\n\n query = self._namespace + select + pattern\n return [result[\"title\"] for result in self._get_query_results(query)]",
"title": ""
},
{
"docid": "35ed5a246c763eda36d66954f9ac9472",
"score": "0.5273621",
"text": "def getCaptials():\n query_strings = request.args.get('query')\n search_strings = request.args.get('search')\n book = capital.Capital()\n if not query_strings and not search_strings:\n return jsonify(book.fetch_20Capitals()), 200\n else:\n result = book.fetch_capitals(query_strings, search_strings)\n return jsonify(result), 200",
"title": ""
},
{
"docid": "f94f6c712c4fb595164c705a98aca96a",
"score": "0.5262072",
"text": "def report_all_books(self):\n print(\"= All Books at {library} =\".format(library=self))\n print(\"+ {:^30} | {:^30} | {:^10} +\"\n .format(\"Title\", \"Author\", \"Copies\"))\n for shelf in self._shelves.itervalues():\n print(\"Shelf - {shelf}\".format(shelf=shelf))\n shelf.report_books()",
"title": ""
},
{
"docid": "852fd0932e249ca1b9b0513846f2beb5",
"score": "0.52414143",
"text": "def get_all(self):\n\n self._init_dict_chal_keys()\n\n # REC FUTURE BIG : woups. Seems that underscore is a special char in the LIKE clauses. Will fix that later.\n # Gets award informations\n filter_award_name = 'plugin_intermflag_' + str(self.chal_id) + '_%'\n awards_query_result = db.session.query(\n Awards.id.label('award_id'), Awards.date, Awards.name.label('award_name'),\n Teams.id.label('team_id'), Teams.name.label('team_name')\n ).join(Teams).filter(Awards.name.like(filter_award_name)).order_by(Awards.date).all()\n\n # Joins award datas and key datas\n awards = [\n (award.award_id, award.date, award.team_id, award.team_name, self._award_infos(award.award_name))\n for award in awards_query_result\n ]\n awards = [\n award\n for award in awards\n if award[-1] is not None\n ]\n\n # Hides descrip, icon and scores that the team should not know.\n key_ids_of_team = [\n award_infos['key_id']\n for award_id, _, award_team_id, __, award_infos in awards\n if award_team_id == self.team_id ]\n for award in awards:\n award_id, _, award_team_id, __, award_infos = award\n if award_infos['key_id'] not in key_ids_of_team and not award_infos['public']:\n award_infos['congrat_msg'] = None\n award_infos['congrat_img_url'] = None\n award_infos['score'] = None\n\n # Removes unneeded fields\n awards = [\n (award_id, award_date.strftime('%Y-%m-%d %H:%M:%S'), award_team_name, award_infos['congrat_msg'], award_infos['congrat_img_url'], award_infos['score'])\n for award_id, award_date, award_team_id, award_team_name, award_infos in awards ]\n return awards",
"title": ""
},
{
"docid": "a10e9faa2f17a446fabe0eb42492f79a",
"score": "0.52363074",
"text": "def book(items):\n list1 = [ ]\n for i in items:\n if i[1] == 'book':\n list1.append(i)\n \n\n #print(list1)\n for i in list1:\n print(i[0],i[1],i[2],i[3])\n \n #print('\\n')\n #print(\"Book Category List: \")",
"title": ""
},
{
"docid": "9011e7ed69e422937eb5327e0fd5a057",
"score": "0.52341586",
"text": "def get_popularAuthors():",
"title": ""
},
{
"docid": "5bae812189214d0eca9a33a1b8283017",
"score": "0.5205308",
"text": "def all_books(self, date_format=DATE_FORMAT):\n return self.execute(queries.ALL_BOOKS, (date_format,)).fetchall()",
"title": ""
},
{
"docid": "8dab7fefb4df6626bf48f6bf501e8abe",
"score": "0.52029043",
"text": "def fetch_titles_from_page(page_number, get_books=True) -> ([Book], bool):\n assert type(page_number) is int\n assert type(get_books) is bool\n\n logger.debug(f\"Fetching catalog page #{page_number}... \")\n url = TITLES_URL + f\"&search_page={page_number}\"\n result = requests.get(url, headers=_get_scrape_headers())\n\n if result.status_code != 200:\n return [], False\n\n json_result = json.loads(result.text)\n if json_result[\"status\"] != \"SUCCESS\":\n return [], False\n\n if json_result[\"results\"] == \"No results\":\n return [], False\n\n books = []\n if get_books:\n html_results = json_result[\"results\"]\n scraper = BeautifulSoup(html_results, 'html.parser')\n catalog_results = scraper.find_all(\"li\", class_=\"catalog-result\")\n\n if catalog_results:\n for catalog_result in catalog_results:\n book = Book()\n result_data = catalog_result.find(\"div\", class_=\"result-data\")\n book.title = result_data.a.text.strip()\n # Remove enclosing \" or ' if any\n if book.title[0] is \"\\\"\" or book.title[0] is \"'\":\n book.title = book.title[1:]\n\n if book.title[-1] is \"\\\"\" or book.title[-1] is \"'\":\n book.title = book.title[:-1]\n\n book.url = result_data.a.attrs[\"href\"]\n book_author = result_data.find(class_=\"book-author\")\n if book_author and book_author.text:\n book.author = book_author.text.strip()\n else:\n logger.error(f\"Failed to scrape the author for book \\\"{book.url}\\\" \"\n f\"in catalog page #{page_number}\")\n if book_author and book_author.a and book_author.a.attrs[\"href\"]:\n book.author_url = book_author.a.attrs[\"href\"]\n else:\n # Books with various authors don't have an author url so this might be fine\n logger.warn(f\"Failed to scrape the author url for book \\\"{book.url}\\\" \"\n f\"in catalog page #{page_number}\")\n book.download_url = catalog_result.find(class_=\"download-btn\").a.attrs[\"href\"]\n book.size = catalog_result.find(class_=\"download-btn\").span.text.strip()\n books.append(book)\n logger.debug(f\"Fetched metadata for {len(books)} books from page #{page_number}\")\n\n else:\n logger.warn(f\"Failed to fetch books from the catalog page #{page_number}\")\n\n else:\n logger.debug(f\"Skipping scraping page #{page_number} for book metadata...\")\n\n return books, True",
"title": ""
},
{
"docid": "24177aea31d1974e83f92029e9fdd9e4",
"score": "0.51874995",
"text": "def get_project_cla_groups() -> List[dict]:\n return [project.to_dict() for project in get_project_cla_group_instance().all()]",
"title": ""
},
{
"docid": "20a060a2a5bce25663f6e65a1093082b",
"score": "0.51766706",
"text": "def get_book_all(self) -> (tuple, list):\n sql_expr = \\\n '''\n SELECT * FROM Books;\n '''\n sql_result = self._my_connect.execute_sql_read(sql_expr)\n return sql_result",
"title": ""
},
{
"docid": "4e5e47b31b5d480b438d8b922ac26c47",
"score": "0.5172181",
"text": "def allReviews(self,book):\n return self.query.filter_by(book_id=book.id).all()",
"title": ""
},
{
"docid": "c34a455b5b6e5dacc98dd48267a7c8db",
"score": "0.5162416",
"text": "def get_all_obgyn():\n \n ##first find where this condition is true for facility for obgyn\n obgyn_facility_list = Facility.query.filter_by(facility_category_name_english='Obstetrics and Gynecology').all()\n \n return obgyn_facility_list",
"title": ""
},
{
"docid": "36d5129ff7c35b11a63cd60cba04f239",
"score": "0.51620466",
"text": "def get_bible_books() -> {int: dict}:\n genres = get_bible_book_genres()\n\n with open(KEY_ENGLISH_PATH, 'r') as csvfile:\n reader = csv.reader(csvfile)\n headers = next(reader)\n\n book_index = headers.index(BOOK_KEY)\n name_index = headers.index(NAME_KEY)\n testament_index = headers.index(TESTAMENT_KEY)\n genre_index = headers.index(GENRE_KEY)\n dataset_index = headers.index(DATASET_KEY)\n\n return {\n int(book[book_index]): {\n 'name': book[name_index],\n 'testament': book[testament_index],\n 'genre_id': int(book[genre_index]),\n 'genre': genres[int(book[genre_index])],\n 'dataset': book[dataset_index]\n } for book in reader\n }",
"title": ""
},
{
"docid": "d1039cac6e05df4019fe627b9cd9b488",
"score": "0.5157313",
"text": "def harvest_all():\n db = db_connect()\n dois = None\n get_commits()\n get_citations(db, dois)\n get_mentions(since_version=None)\n list_records(dois)",
"title": ""
},
{
"docid": "7ea38c63d3d310e833fa6010f016c207",
"score": "0.5141538",
"text": "def cli(ctx, biomaterial_name=\"\", provider_id=\"\", biomaterial_id=\"\", organism_id=\"\", dbxref_id=\"\"):\n return ctx.gi.expression.get_biomaterials(biomaterial_name=biomaterial_name, provider_id=provider_id, biomaterial_id=biomaterial_id, organism_id=organism_id, dbxref_id=dbxref_id)",
"title": ""
},
{
"docid": "0552a8791165f00bd82c35a9076ab387",
"score": "0.5130115",
"text": "def get_test_bible_book_ids() -> {int}:\n return {book_id for (book_id, book) in get_bible_books().items() if book['dataset'] == 'test' }",
"title": ""
},
{
"docid": "b90c8edc53393d9501476ac214728a50",
"score": "0.5111732",
"text": "def get_all_books():\n\n book_info = db.books.find({}, {\"_id\": 0})\n book_info_cur = list(book_info)\n # print(book_info_cur[0][\"rating\"])\n sorted_cur = sorted(book_info_cur, key = lambda i: int(i.get('rating', 0)), reverse=True)\n if len(book_info_cur) != 0:\n return json.dumps(sorted_cur, indent=4), 200\n else:\n return \"Empty book collection in database\", 404",
"title": ""
},
{
"docid": "10b924ef88152a9fac406b6d482606bf",
"score": "0.51091206",
"text": "def get_all_cb_comps():\n u = urllib2.urlopen('http://api.crunchbase.com/v/1/companies.js')\n \n comps = u.read()\n\n return comps",
"title": ""
},
{
"docid": "37744c1787adacba8b8ce6ee9b38eb8a",
"score": "0.5083007",
"text": "def fetch_all_books(start_page=1, end_page=MAX_KNOWN_PAGE, need_update_page=False) -> [Book]:\n assert type(start_page) is int\n assert type(end_page) is int\n assert start_page < end_page\n assert type(need_update_page) is bool\n\n while need_update_page:\n logger.debug(f\"Checking if the # of LibriVox catalog pages is larger than {end_page}...\")\n _, need_update_page = fetch_titles_from_page(end_page, False)\n if need_update_page:\n end_page += 1\n logger.debug(f\"Found new catalog page #{end_page}\")\n\n logger.info(f\"Fetching LibriVox's book catalog from pages #{start_page} till #{end_page}...\")\n\n all_books = []\n \"\"\"\n # Commenting out multiprocess code for now as it crashes in Python 3.6.2 with the error:\n # \"__NSPlaceholderDate initialize] may have been in progress in another thread when fork() was called.\"\n fetched_titles = []\n with Pool(NUM_PROCESSES) as pool:\n fetched_titles = pool.map(fetch_titles_from_page, [n for n in range(start_page, end_page)])\n for result in fetched_titles:\n if result and result[1]:\n all_books.extend(result[0])\n \"\"\"\n for page_num in range(start_page, end_page):\n catalog_page, could_fetch = fetch_titles_from_page(page_num)\n if could_fetch and catalog_page:\n all_books.extend(catalog_page)\n\n return all_books",
"title": ""
},
{
"docid": "8f76e593d6b23f2a302b628d8dc58ba0",
"score": "0.5065584",
"text": "def listConsents(self):\n Response = self.make_request()\n return Response",
"title": ""
},
{
"docid": "8e6648cba3aa87f3160051c3d7766ec1",
"score": "0.5063357",
"text": "def get_all(self):\n return [DocumentCorpus(self.collection, document) for document in self.collection.find()]",
"title": ""
},
{
"docid": "df84247f23d395f335bc7958cdf6d32f",
"score": "0.5062308",
"text": "def fetchCompanies():\n return desk.companies.list_companies()",
"title": ""
},
{
"docid": "afb6780e1b14530393c586bdff951aa2",
"score": "0.50400996",
"text": "def get_citations_needed_report(URL):\n # print('top report')\n response = requests.get(URL)\n content = response.content\n soup = BeautifulSoup(content, 'html.parser')\n result = soup.find_all('a', href=\"/wiki/Wikipedia:Citation_needed\")\n\n dictionary = []\n\n for link in result:\n P = link.parent.parent.parent\n P = P.text.strip()\n dictionary.append(P)\n\n # print('above dictionary')\n for P in dictionary:\n print(P, sep='\\n')\n print('\\n')\n\n return dictionary",
"title": ""
},
{
"docid": "2c0aea7c5e824b0c9aecad3c418b9157",
"score": "0.5029921",
"text": "def books_title(self):\n return [i.title for i in shelf.books_list]",
"title": ""
},
{
"docid": "a1ad1e8add187059eb6f82e5671699c0",
"score": "0.5021048",
"text": "def getBucketlists():",
"title": ""
},
{
"docid": "900b206cfe54379257569656e674a907",
"score": "0.5011742",
"text": "def test_books_get(self):\n pass",
"title": ""
},
{
"docid": "c0920b7316709655aa523d2dd5b7398d",
"score": "0.49976185",
"text": "def fetch_all_chapters(book) -> [Chapter]:\n logger.debug(f\"Downloading info for chapters in book: \\\"{book.title[:70]}\\\"...\")\n session = download_session.make_session()\n try:\n book_page = session.get(book.url, headers=_get_scrape_headers(), timeout=70)\n except download_session.get_download_exceptions() as e:\n logger.error(f\"Scraping timed out to download chapters from book \\\"{book.url}\\\"\")\n logger.error(e)\n return []\n\n if book_page.status_code != 200:\n logger.warn(f\"Failed to download chapters information for book \\\"{book.url}\\\"\")\n return []\n\n scraper = BeautifulSoup(book_page.text, \"html.parser\")\n _fetch_missing_book_metadata(book, scraper)\n\n # Get the chapters information\n chapters = []\n chapters_table = scraper.find(\"table\", class_=\"chapter-download\")\n if not chapters_table:\n logger.error(f\"Scraping failed to find the chapters table for book \\\"{book.url}\\\"\")\n return chapters\n\n chapter_rows = chapters_table.find_all(\"tr\")[1:] # The first row is the table headers\n if not chapter_rows:\n logger.error(f\"Scraping failed to find the chapters rows in the chapters table for book \\\"{book.url}\\\"\")\n return chapters\n\n num_row_elements = len(chapter_rows[0].find_all(\"td\"))\n if num_row_elements == 7:\n for row in chapter_rows:\n chapter = Chapter(book)\n chapter.title = row.find(\"a\", class_=\"chapter-name\").text\n chapter.download_url = row.find(\"a\", class_=\"chapter-name\").attrs[\"href\"]\n row_elements = row.find_all(\"td\")\n if not row_elements or not row_elements[0] or not row_elements[0].a:\n logger.error(f\"Scraping failed for chapter metadata of book \\\"{book.url}\\\"\")\n break\n\n chapter.number = int(row_elements[0].text.replace(row_elements[0].a.text, \"\").strip())\n if len(row_elements) > 2:\n chapter.author = row_elements[2].text.strip()\n if row_elements[2].a:\n chapter.author_url = row_elements[2].a.attrs[\"href\"]\n\n if len(row_elements) > 3:\n chapter.source_text = row_elements[3].text.strip()\n if row_elements[3].a:\n chapter.source_text_url = row_elements[3].a.attrs[\"href\"]\n\n if len(row_elements) > 4:\n chapter.reader_name = row_elements[4].text.strip()\n if row_elements[4].a:\n chapter.reader_url = row_elements[4].a.attrs[\"href\"]\n\n if len(row_elements) > 5:\n chapter.duration = row_elements[5].text.strip()\n\n if len(row_elements) > 6:\n chapter.language_code = row_elements[6].text.strip()\n # If no language_code was found, we fallback to use the book's language\n # This is not perfect as the book's language can be \"multilingual\" and chapters\n # are supposed to be recorded in only one language\n if len(row_elements) < 6:\n logger.warn(f\"Found an unexpected number of columns in the chapter row \\\"{chapter.title}\\\"\")\n\n chapters.append(chapter)\n\n else:\n logger.error(f\"Scraping failed for chapter metadata of book \\\"{book.url}\\\"\")\n\n elif num_row_elements == 4:\n for row in chapter_rows:\n chapter = Chapter(book)\n chapter.title = row.find(\"a\", class_=\"chapter-name\").text\n chapter.download_url = row.find(\"a\", class_=\"chapter-name\").attrs[\"href\"]\n row_elements = row.find_all(\"td\")\n if not row_elements or not row_elements[0] or not row_elements[0].a:\n logger.error(f\"Scraping failed for chapter metadata of book \\\"{book.url}\\\"\")\n break\n\n chapter.number = int(row_elements[0].text.replace(row_elements[0].a.text, \"\").strip())\n if len(row_elements) > 2:\n chapter.reader_name = row_elements[2].text.strip()\n # Chapters read by a group of people don't have a link to the reader's profile\n if row_elements[2].a:\n chapter.reader_url = row_elements[2].a.attrs[\"href\"]\n\n if len(row_elements) > 3:\n chapter.duration = row_elements[3].text.strip()\n # The chapter's language will be set to the book's language via the chapter's init\n chapter.author = book.author\n chapters.append(chapter)\n\n else:\n logger.error(f\"Scraping failed for metadata of chapter \\\"{chapter.title}\\\"\")\n else:\n logger.error(f\"Found an unknown number ({num_row_elements}) of chapter_rows in \"\n f\"the book page of \\\"{book.url}\\\"\")\n\n logger.debug(f\"Finished downloading info for {len(chapters)} chapters in book \\\"{book.title[:50]}\\\"\")\n return chapters",
"title": ""
},
{
"docid": "82f50f6b850e4250535ba4a318356de3",
"score": "0.49940458",
"text": "def get_campos(self):\n campos = DBSession.query(Campos).all()\n return campos",
"title": ""
},
{
"docid": "04b04e5dc6586c5a26cd4999c0e5428a",
"score": "0.49917156",
"text": "def group_by_author(self, author: Author):\n return list(filter(lambda book: book.author == author, self.books))\n # return [book for book in self.books if book.author == author]",
"title": ""
},
{
"docid": "575ee14284891d05a646ecd5e8b93d04",
"score": "0.49862415",
"text": "def get_books(date, list_name):\n \n response = requests.get('http://api.nytimes.com/svc/books/v3/lists/%s/%s.json?api-key=%s'%(str(date), list_name, key))\n response = response.json()\n\n num_results = response['num_results']\n fetched = 20\n names = [book['title'] for book in response['results']['books']]\n\n while num_results > fetched:\n time.sleep(0.125)\n response = requests.get('http://api.nytimes.com/svc/books/v3/lists/%s/%s.json?offset=%d&api-key=%s'%(str(date), list_name, fetched, key))\n response = response.json()\n names += [book['title'] for book in response['results']['books']]\n fetched += 20\n\n return set(names), datetime.datetime.strptime(response['results']['published_date'], \"%Y-%m-%d\")\n\n import datetime",
"title": ""
},
{
"docid": "6754e5ddfdd274ebad8c7dc16e92ecf5",
"score": "0.49814323",
"text": "def get_books_by_author_by(author_id):\n\n return Book.query.filter(Book.author_id == author_id).all()",
"title": ""
},
{
"docid": "059da709ae06cf5dc3abb55bfd751068",
"score": "0.49789444",
"text": "def address_book():\n return [(beings[k], locales[v]) for k,v in residencies.items()]",
"title": ""
},
{
"docid": "13ed1135b97bc4d8ad273ec5643edb03",
"score": "0.49710968",
"text": "def get_books(self, top_k=None, cache=True):\n # Reset the books saved in the cache if its length is under the threshold\n if top_k and len(self._books) < top_k:\n self._books = []\n # Get the top-k books, ordered from the author's book page\n books = []\n for i, book in enumerate(self.books(cache=cache)):\n book.register_author(self)\n books.append(book)\n if top_k and i + 1 >= top_k:\n break\n return books",
"title": ""
},
{
"docid": "fa7897ea3b6bb81356248b187a8dd014",
"score": "0.4967275",
"text": "def get_books_contained_by_version(bible_version: dict) -> [int]:\n table_path = TABLE_DIRECTORY / TABLE_NAME_FORMAT.format(table = bible_version['table'])\n\n with open(table_path, 'r', encoding='utf-8') as csvfile:\n reader = csv.reader(csvfile)\n\n headers = next(reader)\n book_index = headers.index(BOOK_KEY)\n\n return sorted({int(verse[book_index]) for verse in reader})",
"title": ""
},
{
"docid": "ad55ea24f2e82e232ae5f2893b4d496c",
"score": "0.49647325",
"text": "def get_bills(self):\n return Bill.objects.filter(club=self)",
"title": ""
},
{
"docid": "75f7e52d5e64c5f32df2d433bc20aaa9",
"score": "0.4963884",
"text": "def get_finished_books_by_criteria(search_criteria, patron_id):\n criteria = '%' + search_criteria + '%'\n search_result = (db_session.query(Finished_Book,\n Book,\n Book_Author,\n Author)\n .filter(Finished_Book.book_id == Book.id,\n Finished_Book.patron_id == patron_id,\n Book.id == Book_Author.book_id,\n Book_Author.author_id == Author.id,\n Book.title.like(criteria) |\n Author.name.like(criteria))\n .order_by(Book.title).all()) \n\n books_grouped_by_id = itertools.groupby(search_result, lambda x: x[1].id)\n\n list_of_books = []\n for book_id, result_set in books_grouped_by_id:\n\n\n for d in result_set:\n list_of_books += [(d[1].title, d[1].cover_png, d[0].date, d[3].name,\n \"\".join(r[3].name for r in result_set))]\n #end for\n #end for\n\n return list_of_books",
"title": ""
},
{
"docid": "8eefcef1d4d158585d11392c8d9cf137",
"score": "0.49480593",
"text": "def html_sample(self,limit=20):\n books = self.query.order_by(self.isbn).all()\n books = books[:limit]\n data = []\n for book in books:\n isbn = book.isbn\n desc = GoodReads.getDescription(isbn)\n data.append(desc)\n return data",
"title": ""
},
{
"docid": "f9fb78dccddb588333440f822406b273",
"score": "0.49452752",
"text": "def testBook():\n\n # book build\n books = []\n books.append(Book(\"The Hobbit\", \"J.R.R. Tolkien\", \"Doug\", [\"Wendy\"]))\n books.append(Book(\"Greek Myths\", \"Troy Miller\", \"Bob\", []))\n books.append(Book(\"Python Programming\", \"Ken Lambert\", \"Wendy\", [\"Bob\", \"Doug\"]))\n books.append(Book(\"Introduction to Computers\", \"Ted Murphy\", \"Wendy\", []))\n books.append(Book(\"P.H.P. Basics\", \"Michael Smith\", \"Wendy\", [\"Bob\"]))\n\n print(\"Current Books:\")\n print()\n for i in range(len(books)):\n print(\"\\tTitle: \", books[i].getTitle())\n print(\"\\tAuthor: \", books[i].getAuthor())\n print(\"\\tChecked Out To: \", books[i].getCurrentPatron())\n print(\"\\tCurrent Waiting List: \", books[i].getWaitingList())\n print()",
"title": ""
},
{
"docid": "4f274280e00eaca4f1c5bad1369dd8c2",
"score": "0.49442163",
"text": "def books(self, cache=True):\n if len(self._books) > 0 and cache:\n yield from self._books\n else:\n yield from self._search_books()",
"title": ""
},
{
"docid": "6af53be12cad589993d2fd84f01ca72c",
"score": "0.49382204",
"text": "def get(self):\n courses = list(Course.objects.all())\n return courses",
"title": ""
},
{
"docid": "6c9c566457fe64b0dab5057945228a3c",
"score": "0.4929902",
"text": "def get_book():\n # Check if an ID was provided as part of the URL.\n if 'id' in request.args:\n # If ID is provided, assign it to a variable.\n id = int(request.args['id'])\n else:\n # If no ID is provided, display an error in the browser.\n return \"Request error: no id field provided\"\n\n # prepare results variable (empty list)\n results = []\n\n import raw\n for book in raw.get_books():\n if book['id'] == id:\n results.append(book)\n\n # return data in json format\n return jsonify(results)",
"title": ""
},
{
"docid": "b48cade1199a9ddcd0bc3ec12b8a4ef6",
"score": "0.4925487",
"text": "def get_projects_by_accession(accession):\n project = Project()\n print(project.get_by_accession(accession))",
"title": ""
},
{
"docid": "09c8ca693e70b4c298d05c01aa7a575d",
"score": "0.492406",
"text": "def load_all_chapters(self):\n folder = join(\"/Volumes/SSD01/dialogues\",self.this_Book.txt_file_path)\n\n # self.files = listdir(join(\"data/Kindle-combined-txt\",str(isbn)))\n # no reason to get too complicated here\n self.txtfiles = [x for x in listdir(folder) if \".txt\" in x]\n\n # print(\"a sample of the text files:\")\n # print(self.txtfiles[:10])\n\n rawtext_by_chapter = []\n for fname in self.txtfiles:\n f = open(join(folder,fname),\"r\")\n rawtext_by_chapter.append(f.read())\n f.close()\n # word_lists_by_chapter = [listify(t) for t in rawtext_by_chapter]\n # apply spacy to each of them...\n word_lists_by_chapter = [[str(token) for token in nlp(t) if ((not token.is_punct) and (len(str(token)) != 0))] for t in rawtext_by_chapter]\n self.chapter_ends = cumsum(list(map(len,word_lists_by_chapter)))\n # add a 0 to the start, clip (to get the starts)\n # could just move the above array around too...\n self.chapter_beginnings = cumsum([0]+list(map(len,word_lists_by_chapter[:-1])))\n self.chapter_centers = (self.chapter_ends+self.chapter_beginnings)/2\n# print(list(map(len,self.word_lists_by_chapter)))\n# print(self.chapter_ends)\n# print(self.chapter_beginnings)\n# print(self.chapter_centers)\n# print(len(self.chapter_ends))\n# print(len(self.word_lists_by_chapter))\n self.all_word_list = list(itertools.chain(*word_lists_by_chapter))",
"title": ""
},
{
"docid": "f2cca76f77e96af07bcbb2595db675b9",
"score": "0.49230716",
"text": "def get_book():\n soup = Soup(CONTENT)\n title = str(soup.find_all(\"h2\")[0]).replace('<h2>', '').replace('</h2>', '').strip()\n desc = str([entry for entry in soup.find_all('div', class_=\"dotd-main-book-summary float-left\")][0]).split('<div')[4][2:-7].strip()\n img = soup.findAll('img')[5]['src']\n link = soup.find_all('div', class_=\"dotd-main-book-image float-left\")\n\n for div in link:\n link_p = div.find('a')['href']\n return Book(title, desc, img, link)",
"title": ""
},
{
"docid": "5b56f9049b0a5af56044011ccf3ea9a3",
"score": "0.49147058",
"text": "def get_all():\n return Doctor.query.all()",
"title": ""
},
{
"docid": "c50746a02d51d94ca427f68de0b57a4c",
"score": "0.49130303",
"text": "def get_mine(self):\n\n self._init_dict_chal_keys()\n\n # Gets award informations\n filter_award_name = 'plugin_intermflag_' + str(self.chal_id) + '_%'\n awards_query_result = db.session.query(\n Awards.id.label('award_id'), Awards.date, Awards.name.label('award_name'),\n ).filter(Awards.teamid==self.team_id).filter(Awards.name.like(filter_award_name)).order_by(Awards.date).all()\n\n # Joins award datas and key datas\n awards = [\n (award.award_id, award.date, self._award_infos(award.award_name))\n for award in awards_query_result\n ]\n awards = [\n award\n for award in awards\n if award[-1] is not None\n ]\n\n # Removes unneeded fields\n awards = [\n (award_id, award_date, award_infos['congrat_msg'], award_infos['congrat_img_url'], award_infos['score'])\n for award_id, award_date, award_infos in awards ]\n return awards",
"title": ""
},
{
"docid": "57ba6f0cc57d7662c698b5a07c807065",
"score": "0.4912252",
"text": "def get(self):\n all_blogss = get_all_blogss()\n print(\"All blogss=\",all_blogss)\n return all_blogss",
"title": ""
},
{
"docid": "147720b020aedaa97fc349619755f841",
"score": "0.49097124",
"text": "def get_authors(venue):\r\n papers=0\r\n results = dblp.search_pub(venue)\r\n\r\n hits = json.loads(results)[\"result\"][\"hits\"]\r\n for i in hits[\"hit\"]:\r\n info = i[\"info\"]\r\n if filter_papers(info, venue):\r\n papers+=1\r\n # print(info)\r\n if 'authors' in info:\r\n if isinstance(info[\"authors\"][\"author\"], list):\r\n for author in info[\"authors\"][\"author\"]:\r\n update_authors(author[\"@pid\"], author[\"text\"], info[\"key\"])\r\n else:\r\n author = info[\"authors\"][\"author\"]\r\n update_authors(author[\"@pid\"], author[\"text\"], info[\"key\"])\r\n\r\n return papers",
"title": ""
},
{
"docid": "87519999fe1b78f5a4f1835d9dfd27a6",
"score": "0.49049982",
"text": "def list_csrs():\n cmd = subprocess.Popen([PUPPETCA, '--list'], stdout = subprocess.PIPE, stderr = subprocess.STDOUT)\n out, err = cmd.communicate()\n csrs = out.split('\\n')\n return [r for r in csrs if r]",
"title": ""
},
{
"docid": "d9009a2f36c1438806386d3cfd6803ea",
"score": "0.4901347",
"text": "def resolve_labbook_list(self, info):\n return LabbookList(id=\"\")",
"title": ""
},
{
"docid": "16b462d0dbc43003f675551cd3ba7b18",
"score": "0.48985198",
"text": "def testimonials(self):\n testimonials = self.context.getRefs(relationship=\"course_testimonial\") \n return testimonials",
"title": ""
},
{
"docid": "ad48868230e24c2b9483e22cdb8e3c83",
"score": "0.48958287",
"text": "def springer_bib(year, conf, link):\n html_file = download_to_hash(link)\n soup = BeautifulSoup(open(html_file), 'lxml')\n res = ''\n for paper in soup.select('.chapter-item'):\n meta = paper.select('.content-type-list__meta')[0]\n title = meta.select('div')[0].get_text()\n authors_str = meta.select('div')[1].get_text()\n authors = authors_str2lst(authors_str)\n pdflink_a = paper.select('a.test-book-toc-download-link')\n pdflink = ''\n # some conference may not have a pdflink, e.g.\n # https://link.springer.com//book/10.1007/BFb0015518\n if pdflink_a:\n pdflink = pdflink_a[0]['href']\n if not pdflink.startswith('http'):\n pdflink = 'https://link.springer.com/' + pdflink\n id = gen_id(year, conf, authors, title)\n bib = gen_single_bib(id, title, ' and '.join(authors), pdflink, year, conf)\n res += bib\n return res",
"title": ""
},
{
"docid": "72543b0b93f999ed139508daec9bd423",
"score": "0.4895037",
"text": "def test_get_all_quotes_by_book(self):\n # hit the API endpoint\n response = self.client.post(\n reverse(\"quotes-book\", kwargs={\"version\": \"v1\"}), {\"book\": \"Don Quijote de la Mancha\"}\n )\n # fetch the data from db\n book = Book.objects.filter(title=\"Don Quijote de la Mancha\")[:1].get()\n expected = book.quotes\n serialized = QuoteSerializer(expected, many=True)\n self.assertEqual(response.data, serialized.data)\n self.assertEqual(response.status_code, status.HTTP_200_OK)",
"title": ""
},
{
"docid": "67cc7015b51ab776bba4148fff31bcc3",
"score": "0.4884307",
"text": "def list():\n return Company.query.all(), HTTPStatus.OK",
"title": ""
},
{
"docid": "b51ba71ff07fffae14bce8c9be190339",
"score": "0.48839167",
"text": "def get_CA(self):\n\n\t\treturn [r.Ca for r in self.res]",
"title": ""
},
{
"docid": "c8e49af7ea1ca8720574179626671b90",
"score": "0.4882444",
"text": "def getProjectMaintainers(self, project):\n tree = ElementTree.fromstring(''.join(core.show_project_meta(self.apiurl,\n project)))\n maintainers = []\n for person in tree.findall('person'):\n if person.get('role') == \"maintainer\":\n maintainers.append(person.get('userid'))\n return maintainers",
"title": ""
},
{
"docid": "46ce485964905983bcd771a5595c703a",
"score": "0.4879896",
"text": "def readBook2(self):\n # summary from https://www.amazon.ca/Essential-Calvin-Hobbes-Bill-\n # Watterson/dp/0836218051/ref=sr_1_1?crid=1YAN87VEA3JWD&keywords=the+\n # essential+calvin+and+hobbes&qid=1579321108&sprefix=the+essential+cal\n # %2Caps%2C181&sr=8-1\n print(form.border(\"\"\"\nTHE ESSENTIAL CALVIN AND HOBBES by Bill Watterson\n Beginning with the day Hobbes sprang into Calvin's tuna fish trap, the\n first two Calvin and Hobbes collections, Calvin and Hobbes and Something\n Under The Bed Is Drooling, are brought together in this treasury.\n Including black-and-white dailies and color Sundays, The Essential Calvin\n and Hobbes also features an original full-color 16-page story.\n \"\"\"))",
"title": ""
},
{
"docid": "0806836cfdeb94bad73bc76a354702ab",
"score": "0.4878542",
"text": "def most_client_report_order_books(self):\n\t\tclient_list = self.__borrow_controller.getReverseListClients()\n\t\tclient_list = self.__client_controller.getListFromIdsOrderBooks(client_list)\n\t\tprint(\"\\n\" + '-------=======-------')\n\t\tfor client in client_list:\n\t\t\tprint(client, end='')",
"title": ""
},
{
"docid": "bf2980bb0e099750f2f635458aa2c677",
"score": "0.4877945",
"text": "def get_book():\n id = get_input(\"id? \")\n r = requests.get(url = API_ENDPOINT + \"/\" + id)\n\n result_text = r.text\n print(\"\")\n print(\"result: \" + result_text)",
"title": ""
},
{
"docid": "230e1d7187be7c6342949083411ad154",
"score": "0.4870439",
"text": "def bid_list():\n # Grab all projects\n projects = request_api(project_path)\n\n # Parse and find \"Schnabel\" project id\n sfc_id = None\n for project in projects:\n if project[\"name\"] == \"Schnabel\":\n sfc_id = project[\"id\"]\n break\n\n # Grab all sections in \"Schnabel\" project\n params = {\"project_id\": sfc_id}\n sections = request_api(section_path, params=params)\n\n # Determine \"Bids\" section id\n bidding_id = None\n for section in sections:\n if section[\"name\"] == \"Bids\":\n bidding_id = section[\"id\"]\n break\n \n # Grab all tasks in \"Schnabel\" project\n params = {\"project_id\": sfc_id}\n tasks = request_api(task_path, params=params)\n\n # Filter tasks based on section id\n bid_tasks = []\n for task in tasks:\n if task[\"section_id\"] == bidding_id:\n bid_tasks.append(task)\n\n # Sort and return bid_tasks\n bid_tasks_sorted = quicksort(bid_tasks)\n return bid_tasks_sorted",
"title": ""
},
{
"docid": "01480a553da0b344569b406ff2d515bc",
"score": "0.4866259",
"text": "def getBook(self, _id):\n db = self.getBookDB()\n book=None\n try:\n value = (_id,)\n db.execute('select * from books where _id=?',value)\n book = self.getBookFromList(db.fetchone())\n except Exception as e:\n print(e)\n return book",
"title": ""
},
{
"docid": "7e2fbd99ee5e9dca5b830621f90e17f6",
"score": "0.48623312",
"text": "def get_available_cops():\n allIncidents = Incident.get_all()\n cops = []\n \n for i in allIncidents:\n if(inicioAmostragem <= i.reporting_date and i.reporting_date <=terminoAmostragem):\n# cops.append(i['operations_center']['id'])\n#conf\n cops.append(i['operations_center'])\n \n allReports = RelatoDeSituacao.get_all()\n \n for r in allReports:\n if (\n inicioAmostragem <= r.data_hora and \n r.data_hora <=terminoAmostragem and\n 'cop' in r.relator \n# and # todos tem que ter o COP\n#conf 'id' in r.relator['cop'] # todos tem que ter o id \n ):\n# cops.append(r.relator['cop']['id'])\n#conf \n cops.append(r.relator['cop'])\n\n allSincronizations = Sincronizacao.get_all()\n for sinc in allSincronizations:\n for action in sinc.acoes:\n if ( \n ((action.tipo == 'PONTUAL') and (action.inicio >= inicioAmostragem) and (action.inicio <= terminoAmostragem)) or\n ((action.tipo == 'INTERVALO') and (action.inicio >= inicioAmostragem and action.fim <= terminoAmostragem))\n ):\n cops.append(sinc.cop_responsavel['id'])\n \n return set(cops)",
"title": ""
},
{
"docid": "42bd5fff3bf83a8c1d6b707a4b206d95",
"score": "0.48550457",
"text": "def get_book(self, request, pk):\n query_set = Library.objects.get(id=pk)\n data = self.serializer_class(query_set, context={'request': request}).data\n return Response({'result': data}, status=status.HTTP_200_OK)",
"title": ""
},
{
"docid": "8243269654818ba0873eeae14ec49e4f",
"score": "0.48539725",
"text": "def print_catalog(self):\n print(\"THIS IS THE CATALOG OF ALL OUR BOOKS:\")\n for book, amount in self.books.items():\n print(book.title + ' - rating: ' + str(amount))",
"title": ""
},
{
"docid": "a58870f9f4fa8d5d3502ac32f7e3d318",
"score": "0.48537493",
"text": "def get_accounts():",
"title": ""
},
{
"docid": "a58870f9f4fa8d5d3502ac32f7e3d318",
"score": "0.48537493",
"text": "def get_accounts():",
"title": ""
},
{
"docid": "229b0e45cd060f50cefc112660732f2e",
"score": "0.4853242",
"text": "def extract_books(soup):\n books = {}\n for d in soup.find_all('div', 'bookMain'):\n asin = sub(r'_.*$', '', d['id'])\n title = d.find('span', 'title').text.strip()\n author = sub(r'by ', '', d.find('span', 'author').text.strip())\n author = sub('\\n', '', author)\n\n books[asin] = dict(asin=asin, title=title, author=author)\n\n return books",
"title": ""
},
{
"docid": "31d79332d6e68cd849b57c61ecb12b0d",
"score": "0.4850605",
"text": "def getBestBooks(catalog, number):\n books = catalog['books']\n bestbooks = lt.newList()\n for cont in range(1, number+1):\n book = lt.getElement(books, cont)\n lt.addLast(bestbooks, book)\n return bestbooks",
"title": ""
},
{
"docid": "c515019aac9c9b246e05855a0943f385",
"score": "0.4849605",
"text": "def get_all_biblio(page=1):\n query = BiblioEntry.query.paginate(page, ITEMS_PER_PAGE, False).items\n bibdat = convert_rows_to_dict(query)\n years = [str(value.year)\n for value in db.session.query(BiblioEntry.year).distinct()]\n tags = [str(value.tag)\n for value in db.session.query(BiblioEntry.tag).distinct()]\n templateVars = format_bibdatabase(bibdat)\n years.sort()\n templateVars[\"years\"] = years[::-1]\n templateVars[\"tags\"] = tags\n templateVars[\"nentries\"] = BiblioEntry.query.count()\n return render_template(\"references.html\", **templateVars)",
"title": ""
},
{
"docid": "0bbdb4c670cd4d67bb669d273d3d2c90",
"score": "0.48495772",
"text": "def papers(self):\n if self.all_papers == None:\n name = self.name\n orcid = self.orcid\n if not isinstance(name, str) and name != None:\n papers_name = list(ads.SearchQuery(\n author=name[0], fl=self.fl, **self.kw))\n papers_name_id = [x.id for x in papers_name]\n for kname in range(1, len(name)):\n papers_kname = list(ads.SearchQuery(\n author=name[kname], fl=self.fl, **self.kw))\n for pkn in papers_kname:\n if pkn.id not in papers_name_id:\n papers_name.append(pkn)\n papers_name_id.append(pkn.id)\n elif name != None:\n papers_name = list(ads.SearchQuery(\n author=name, fl=self.fl, **self.kw))\n papers_name_id = [x.id for x in papers_name]\n else:\n papers_name = []\n papers_name_id = []\n\n if orcid != None:\n papers_orcid = list(ads.SearchQuery(\n orcid=orcid, fl=self.fl, **self.kw))\n for porc in papers_orcid:\n if porc.id not in papers_name_id:\n papers_name.append(porc)\n papers_name_id.append(porc.id)\n\n self.ncite_all = [x.citation_count for x in papers_name]\n self.all_papers = PapersManager(papers_name)\n\n return self.all_papers",
"title": ""
},
{
"docid": "43387e866359b532e0f7ae9d8daf3237",
"score": "0.48454508",
"text": "def list_bitbucketserver_projects(self, almSettings):",
"title": ""
},
{
"docid": "321584db0441ace5dec0e13bdd715a5f",
"score": "0.48432878",
"text": "def test_get_all_audiobook_ok(client):\n\n with app.test_request_context():\n response = client.get(url_for(\"get_all_audio\", audioFileType=\"audiobook\"))\n\n assert response.status_code == 200\n res_data = response.get_json()\n record = list(filter(lambda x: x[\"id\"] == AUDIOBOOK_ID, res_data))\n assert len(record) == 1\n assert record[0][\"title\"] == \"Tale of two cities\"",
"title": ""
},
{
"docid": "e6c1dc6814ec66c490722a9594aee29a",
"score": "0.4835043",
"text": "def books_surname(self):\n return [i.author_sur for i in shelf.books_list]",
"title": ""
}
]</span></div></div></div>
</td>
</tr><tr class="group cursor-pointer space-x-4 divide-x border-b outline-offset-[-2px] odd:bg-gray-50 hover:bg-gray-100 dark:odd:bg-gray-925 dark:hover:bg-gray-850 " tabindex="0" data-row-idx="799"><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">e048fbeb7c6ddb0b67d7a05a70285c70</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="" dir="auto"><span class="">Gets the side of the line p is on.</span></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "61392af388f6f7d1f80e07e4e0dd26b4",
"score": "0.711668",
"text": "def line_side(pl, p, pr):\n\tv1 = (pr[0]-pl[0], pr[1]-pl[1]) # Vector 1\n\tv2 = (pr[0]-p[0], pr[1]-p[1]) # Vector 1\n\txp = v1[0]*v2[1] - v1[1]*v2[0] # Cross product\n\tif xp > 0:\n\t return 1\n\telif xp < 0:\n\t return -1\n\telse:\n\t return 0",
"title": ""
}
]</span></div></div></div>
</td><td class="min-w-fit max-w-sm break-words p-2 "><div class="line-clamp-2 "><div class="text-left" dir="auto"><div class="group relative"><span class="block ">[
{
"docid": "cb2c6c21f616e1864d15179f7c84d096",
"score": "0.7492487",
"text": "def get_side(self):\n return self.get_perimeter() / 4",
"title": ""
},
{
"docid": "dc079eec7d6fd53e500597fcb4a05fcb",
"score": "0.7384879",
"text": "def point_side_line(p1, p2, p):\n\n # Get vector from p1 to p2\n a = p2 - p1\n # Get vector from p1 to q\n b = p - p1\n # The sign of the cross product determines point side\n s = np.sign(np.cross(a,b))\n\n # s>0 -> LEFT side of the line\n # s=0 -> ON side of the line\n # s<0 -> RIGHT side of the line\n return s",
"title": ""
},
{
"docid": "d9f66d4996016c63cce2787af84ca213",
"score": "0.731372",
"text": "def getSide(self):\n return self.side",
"title": ""
},
{
"docid": "c6b22f78f373225ad3d6b028b74f755a",
"score": "0.7087271",
"text": "def side(self) -> int:\n return self._side",
"title": ""
},
{
"docid": "546c66e2ae21ff0ed1122999b1f1a98a",
"score": "0.6932154",
"text": "def side(self):\n return self._side",
"title": ""
},
{
"docid": "546c66e2ae21ff0ed1122999b1f1a98a",
"score": "0.6932154",
"text": "def side(self):\n return self._side",
"title": ""
},
{
"docid": "319b8e8eaec902c8d4a61daea65f714e",
"score": "0.67851067",
"text": "def side(self):\n\t\treturn self.get(54)",
"title": ""
},
{
"docid": "99ce10f146becbd285f82f30503daa8d",
"score": "0.64582276",
"text": "def get_side_left(self):\n return self.filter_var(self.side_left)",
"title": ""
},
{
"docid": "9bac596a77cc5e3988f13cf4d3cf5c97",
"score": "0.6424401",
"text": "def point_line_side(pointx, pointy, x1, y1, x2, y2):\n return (x2-x1) * (pointy-y1) - (y2-y1) * (pointx-x1)",
"title": ""
},
{
"docid": "ca1c895aa5e8ba15ce9142beafd4aa8b",
"score": "0.6313351",
"text": "def get_side(self, player):\n if self.__team == player.get_team():\n return \"ALLIES\"\n return \"ENEMIES\"",
"title": ""
},
{
"docid": "388ef22d1cbe686519f8df605e5cceda",
"score": "0.61742723",
"text": "def get_side_right(self):\n return self.filter_var(self.side_right)",
"title": ""
},
{
"docid": "5bf47b198470cfb534d7252195d40d25",
"score": "0.61605984",
"text": "def side1(self) -> float:\n return self._side1",
"title": ""
},
{
"docid": "c96cab5dc570f59ec86ef0529726dbd8",
"score": "0.61603254",
"text": "def get_side(a, b):\n x = cosine_sign(a, b) #computes z component of out of plane component\n if x < 0:\n return LEFT\n elif x > 0: \n return RIGHT\n elif x == 0.:\n return \"zero\"\n else:\n return None",
"title": ""
},
{
"docid": "d44143d085f4d3f1bf40a5ecb61ddcff",
"score": "0.61546004",
"text": "def side2(self) -> float:\n return self._side2",
"title": ""
},
{
"docid": "5cb2c8f9f85a895ec83d33b86194efab",
"score": "0.6146379",
"text": "def distance_from_line(self, p):\n rp = pt.diff(p, self.start) # make relative to start of line\n rp_along_d = self.direction.copy().scale(rp.dot_product(self.direction)) # Get component in direction of line\n rpr = pt.diff(rp, rp_along_d) # perpendicular\n return rpr.length()",
"title": ""
},
{
"docid": "30bc1e85f3f9f64b5b8e912b0e9b661e",
"score": "0.6144422",
"text": "def get_flop(self):\n return self.Flop",
"title": ""
},
{
"docid": "4812ef8c1c078aebffa2a372682cc043",
"score": "0.60142285",
"text": "def complementary_side(self):\n if self == PuzzlePieceSide.top:\n return PuzzlePieceSide.bottom\n\n if self == PuzzlePieceSide.right:\n return PuzzlePieceSide.left\n\n if self == PuzzlePieceSide.bottom:\n return PuzzlePieceSide.top\n\n if self == PuzzlePieceSide.left:\n return PuzzlePieceSide.right",
"title": ""
},
{
"docid": "fe908da91ed5367c91c1ecdf043f099e",
"score": "0.5988404",
"text": "def _determineSide(self, testPlayer):\n playerList = self._playerOrder(self._playerTurn)\n lastPlayer = playerList[-1]\n if testPlayer < lastPlayer:\n testPlayer += 4\n return testPlayer - lastPlayer - 1",
"title": ""
},
{
"docid": "ac3dc604b14c12375571c029bbfb43ba",
"score": "0.5897389",
"text": "def get_side(given_name):\n return get_group(given_name, group='side') or ''",
"title": ""
},
{
"docid": "452b59b17bdf485a91f4f306fc899d97",
"score": "0.58860266",
"text": "def point_is_on_leftside_of_line(self, point):\n\n if (point.y < self.point2.y and point.y >= self.point1.y) or (point.y >= self.point2.y and point.y < self.point1.y):\n\n # The equation to determine this comes from here: https://math.stackexchange.com/questions/274712/calculate-on-which-side-of-a-straight-line-is-a-given-point-located\n\n d = ((point.x - self.point1.x)*(self.point2.y - self.point1.y)) - ((point.y - self.point1.y)*(self.point2.x - self.point1.x))\n\n left = (self.point1.y - self.point2.y)\n # If d and left are both positive or both negative, then the point is on the left side of the line\n\n if (d < 0 and left < 0) or (d > 0 and left > 0):\n return True\n\n else:\n return False",
"title": ""
},
{
"docid": "e4811fb638efc5f5d24f044525e886ee",
"score": "0.5875098",
"text": "def linha_pos(p):\n\treturn p[0]",
"title": ""
},
{
"docid": "9f2e4c3181ec788edc55e9f45175d7f0",
"score": "0.5852361",
"text": "def side(pitch):\n return pitch / SQRT3",
"title": ""
},
{
"docid": "c7ecd08a42dbabcd7f85d608af4e0d73",
"score": "0.58335465",
"text": "def getPerimeter(self):\n return self.side * 4",
"title": ""
},
{
"docid": "505dc43b635a26a2b30b27a6ba5dc9e6",
"score": "0.5772587",
"text": "def side(self, value):\n if value in (self.LEFT, self.RIGHT):\n self._side = value\n else:\n raise ValueError('Side value must be LEFT (-1) or RIGHT (1)')",
"title": ""
},
{
"docid": "65b7f0c5539dfef34becf6dbd667105f",
"score": "0.57487196",
"text": "def left(self):\n return self.corner.x()",
"title": ""
},
{
"docid": "c813cffbd7d40171bb2fb28d280983df",
"score": "0.5639955",
"text": "def side_name(self):\n return str(self).split(\".\")[1]",
"title": ""
},
{
"docid": "3adb71702fb9cd2cc0ece1f8045760ba",
"score": "0.56043947",
"text": "def get_semi_perimeter(self):\n return (self.side1 + self.side2 + self.side3) / 2",
"title": ""
},
{
"docid": "9a066b8bd9a856bd12319defc923aef4",
"score": "0.559953",
"text": "def getSide( name ):\n \n # make sure is not a full dag path\n edit = name.split('|')[-1]\n \n # make sure is lowerCase and return the side\n edit = edit.lower()\n \n if edit.startswith('l_'): return 'l'\n if edit.startswith('r_'): return 'r'\n \n return ''",
"title": ""
},
{
"docid": "5e2991d7d4d56b1bce69d751f18d4576",
"score": "0.5562073",
"text": "def side_lengths(self):\n d12 = self.p1.euclidean_distance(self.p2)\n d23 = self.p2.euclidean_distance(self.p3)\n d31 = self.p3.euclidean_distance(self.p1)\n return d12, d23, d31",
"title": ""
},
{
"docid": "e3bd52e2d680737939d8ba267bcddba0",
"score": "0.5545313",
"text": "def grating_direction(self, p):\n normal = self.normal(p)\n xaxis = Vec(1, 0, 0)\n a = normal.cross(xaxis)\n rotation = Mat().rotateAxis(90, a)\n return normal.transformDir(rotation).normalize()",
"title": ""
},
{
"docid": "60b97ef5e685d6009ee908fbd9a55829",
"score": "0.55079645",
"text": "def getQuarter(self, point):\n # Checks on which side of the bottom-left to top-right diagonal the point\n # is.\n posDiag = point[0] + point[1] > 0\n\n # Checks on which side of the top-left to bottom-right diagonal the point\n # is.\n negDiag = point[1] > point[0]\n\n if posDiag:\n if negDiag:\n return 0\n else:\n return 1\n if negDiag:\n return 3\n return 2",
"title": ""
},
{
"docid": "7cace02a999fdba75f2c696856f3d90b",
"score": "0.5465378",
"text": "def get_location_to_move_to(self, side):\n temp = PoseStamped()\n if self.get_domino_direction() == \"L\":\n if side == \"top\":\n temp.pose.position.y += VERT_VERT_OFFSET\n if side == \"bottom\":\n temp.pose.position.y -= VERT_VERT_OFFSET\n if self.get_domino_direction() == \"R\":\n if side == \"top\":\n temp.pose.position.y -= VERT_VERT_OFFSET\n if side == \"bottom\":\n temp.pose.position.y += VERT_VERT_OFFSET\n return temp\n #if \"side\" == \"left\":\n # temp.pose.position.y += HORIZ_VERT_OFFSET\n #if \"side\" == \"right\":\n # temp.pose.position.y -= HORIZ_VERT_OFFSET",
"title": ""
},
{
"docid": "2ba517229a96a0ae281a986f32b12561",
"score": "0.5462002",
"text": "def get_perimeter(self):\n return self.side1 + self.side2 + self.side3",
"title": ""
},
{
"docid": "f0c094c6beb4f14393aec743aeed28b9",
"score": "0.54574496",
"text": "def get_distance_from_starting_side(img, mode, x_edge_left, x_edge_right):\n if mode == \"left\":\n return img.shape[1] - x_edge_right\n else:\n return x_edge_left",
"title": ""
},
{
"docid": "4f67f669222f5bb0958ed5913d501004",
"score": "0.5418983",
"text": "def get_pos(self, x, y):\n return x * self.SIDE_SQUARES + y",
"title": ""
},
{
"docid": "f46217b5d4a3bf9930b48694b85137e4",
"score": "0.54149246",
"text": "def get_relative(self, p):\n return (p - self.absolute.bottom_left) / self.absolute.size.to_float()",
"title": ""
},
{
"docid": "feafb19adaf2e87952196d4b826f8166",
"score": "0.5411935",
"text": "def direction_ratio(self, point):\n return [(point.x - self.x),(point.y - self.y),(point.z - self.z)]",
"title": ""
},
{
"docid": "39aa45a24134572f06f9aed8b4fcc24a",
"score": "0.5406185",
"text": "def area(self):\n return self.side**2",
"title": ""
},
{
"docid": "39aa45a24134572f06f9aed8b4fcc24a",
"score": "0.5406185",
"text": "def area(self):\n return self.side**2",
"title": ""
},
{
"docid": "39aa45a24134572f06f9aed8b4fcc24a",
"score": "0.5406185",
"text": "def area(self):\n return self.side**2",
"title": ""
},
{
"docid": "eaad10f40fe76b681998e34ee83a1a8c",
"score": "0.5405452",
"text": "def get_right(self):\n\t\tif self.computed:\n\t\t\treturn self.dimensions[1]\n\t\treturn 1",
"title": ""
},
{
"docid": "d6e47540c05fecce63ccfae23df225ce",
"score": "0.53937054",
"text": "def getPerpendicularLineHomogenous(p1, p2):\n b1 = (p2[1] - p1[1]) / (p2[0] - p1[0]) if p1[0] != p2[0] else float('inf')\n chord_cent = [(p2[0] + p1[0]) / 2, (p2[1] + p1[1]) / 2, 1]\n print(\"Chord_cent1 \", chord_cent)\n if b1 == 0:\n return float('inf'), chord_cent\n if b1 == float('inf'):\n return 0, chord_cent\n return -1 / b1, chord_cent",
"title": ""
},
{
"docid": "ff766003e683a29f7d2c6416c280a635",
"score": "0.53896916",
"text": "def getArea(self):\n #return self.side * self.side\n return 2*self.side",
"title": ""
},
{
"docid": "7df14a41fa16a48c86b48839d61fdc82",
"score": "0.53799415",
"text": "def midpoint(self, p):\n\n x = (p[0][0] + p[1][0] + p[2][0] + p[3][0]) / 4\n y = (p[0][1] + p[1][1] + p[2][1] + p[3][1]) / 4\n return x, y",
"title": ""
},
{
"docid": "f73dab94634347f82d0dc28547542c25",
"score": "0.53726375",
"text": "def getLineWidth(self):\n return self._lineWidth",
"title": ""
},
{
"docid": "06f05f2ccca3c0281d049deaa4f03d8c",
"score": "0.53612983",
"text": "def linestyle(self):\n return self._linestyle",
"title": ""
},
{
"docid": "4076cda62c00ab929cf05a1aae6cf65c",
"score": "0.53612137",
"text": "def get_offset_right(self):\n\t\treturn self.offset[1]",
"title": ""
},
{
"docid": "f90514e3e77fcadcb9a2c86e09e4e1b1",
"score": "0.5358349",
"text": "def getdLine(self):\n return self._dline",
"title": ""
},
{
"docid": "b6fa7a36eb53e525f484638168cc8b5b",
"score": "0.53573775",
"text": "def height(self):\r\n return self._side",
"title": ""
},
{
"docid": "1b5f5bd755b344a8a75bc6c57f86475c",
"score": "0.5345058",
"text": "def calculate_perimeter(self):\n perimeter = self.__side * 4\n return perimeter",
"title": ""
},
{
"docid": "f87b30c945a5567bfc2240bcd77dd08c",
"score": "0.5343993",
"text": "def quadrant(self, pt):\r\n if pt[X] >= self.origin[X]:\r\n if pt[Y] >= self.origin[Y]:\r\n return NE\r\n else:\r\n return SE\r\n else:\r\n if pt[Y] >= self.origin[Y]:\r\n return NW\r\n else:\r\n return SW",
"title": ""
},
{
"docid": "bd23c5c0554d9d9997c69b4457f3c274",
"score": "0.5329019",
"text": "def line_from_pts(p1, p2):\n\n ##### STUDENT CODE START #####\n l = np.cross(p1, p2)\n l = l / np.linalg.norm(l[:2], 2)\n\n ##### STUDENT CODE END #####\n \n return l",
"title": ""
},
{
"docid": "21a35e959d4908f30b97bf4848e75ec6",
"score": "0.5325119",
"text": "def side_classification(self):\n sides = self.side_lengths()\n\n if isequal(sides[0], sides[1]) and isequal(sides[1], sides[2]) and isequal(sides[2], sides[0]):\n return \"equilateral\" # três lados iguais: equilátero\n\n if isequal(sides[0], sides[1]) or isequal(sides[1], sides[2]) or isequal(sides[2], sides[0]):\n return \"isosceles\" # dois lados iguais, mas não os três: isósceles\n\n return \"scalene\" # lados diferentes: escaleno",
"title": ""
},
{
"docid": "2641821b1ed811817a778bb890aaa36c",
"score": "0.532211",
"text": "def get_direction(self):\n if self.is_left_pressed():\n self._current = Point(-1, 0)\n elif self.is_right_pressed():\n self._current = Point(1, 0)\n elif self.is_up_pressed():\n self._current = Point(0, -1)\n elif self.is_down_pressed():\n self._current = Point(0, 1)\n\n return self._current",
"title": ""
},
{
"docid": "be4b4aedcdfc2898a74a38f5ad3bef25",
"score": "0.5311293",
"text": "def get_prow_direction(self):\r\n return self.__prow_direction",
"title": ""
},
{
"docid": "4824af83cbfcd0c9b4827977a55c8929",
"score": "0.5307189",
"text": "def right(self):\n return self.corner.x() + self.size.width()",
"title": ""
},
{
"docid": "1e76bbc1bed440819b0e30feab13317b",
"score": "0.52988553",
"text": "def left_position(self):\n try:\n return self.__left_position\n except AttributeError:\n self.__left_position = self.start_position if self.feature.clockwise \\\n else 2 * self.start_position - self.stop_position\n return self.__left_position",
"title": ""
},
{
"docid": "31b0b084c0645ffe85794cb9c11aaf33",
"score": "0.52933043",
"text": "def Direction(self, *args):\n return _Geom2d.Geom2d_Line_Direction(self, *args)",
"title": ""
},
{
"docid": "3c17ae41ecdb1315c0c732d7dacbe2d1",
"score": "0.52901244",
"text": "def getLeftQuadraturePosition(self):\r\n return -self.leftTalon.getSensorCollection().getQuadraturePosition()",
"title": ""
},
{
"docid": "508bb380003269349fc110de0c40a413",
"score": "0.528571",
"text": "def getEdgeThickness(self):\n front = self[0].point.z + self[0].edgePlane()\n back = self[1].point.z + self[1].edgePlane()\n return back - front",
"title": ""
},
{
"docid": "ab32697a754caf7edf5eef8b455737f6",
"score": "0.5277734",
"text": "def signed_dist_to_line(self, p, q) -> float:\n if p.y == q.y:\n return self.y - p.y\n elif p.x == q.x:\n return self.x - p.x\n else:\n a = 1 / (q.x - p.x)\n b = -1 / (q.y - p.y)\n c = p.y / (q.y - p.y) - p.x / (q.x - p.x)\n return (a * self.x + b * self.y + c) / math.sqrt(a ** 2 + b ** 2)",
"title": ""
},
{
"docid": "8e3305c5054dc703ff0d2b23d82d1e5e",
"score": "0.52753776",
"text": "def line_orientation(p: Position, q: Position, r: Position) -> OrientationResult:\r\n val = ((q[1] - p[1]) * (r[0] - q[0])) - ((q[0] - p[0]) * (r[1] - q[1]))\r\n\r\n if np.isclose(val, 0.0):\r\n return OrientationResult.COLLINEAR\r\n\r\n if val > 0:\r\n return OrientationResult.CLOCKWISE\r\n return OrientationResult.ANTICLOCKWISE",
"title": ""
},
{
"docid": "dfb13ef9256803cb070b6e95a2d28ecf",
"score": "0.52742714",
"text": "def side_func(x):\n y=(window_height-HEIGHT)/2.0 - sqrt(3) * (x-window_width/2.0)\n if (x>=250):\n y=(window_height-HEIGHT)/2.0 + sqrt(3) * (x-window_width/2.0)\n return y",
"title": ""
},
{
"docid": "1086cb68103222f6a4abfcea670b4adb",
"score": "0.52640045",
"text": "def perimeter(self) -> float:\n return 2*(self.side1+self.side2)",
"title": ""
},
{
"docid": "87f215dbff347887aad936ea533ed38a",
"score": "0.5243852",
"text": "def on_surface(self, point):\n return point[0] - self.rect.x, point[1] - self.rect.y",
"title": ""
},
{
"docid": "064023801890f585ef985bafa019b550",
"score": "0.5243692",
"text": "def perpendicular(self, through: Point | Line) -> Line | Plane:\n if self.dim != 3 and isinstance(through, Line):\n raise NotImplementedError(f\"Expected dimension 3 but found dimension {self.dim}.\")\n\n p = self.array[..., :-1]\n p = Point(np.append(p, np.zeros(p.shape[:-1] + (1,), dtype=p.dtype), axis=-1), copy=False)\n return through.join(p)",
"title": ""
},
{
"docid": "b20216c62a07a2c561a93c157149e1ef",
"score": "0.5235033",
"text": "def frame_left(self) -> float:\r\n return self.position.x - self.half_width",
"title": ""
},
{
"docid": "028d0164af79b05c3ac256b9c71d08c3",
"score": "0.522857",
"text": "def direction(self) -> Point:\n first_vec = self.first.subtract(self.second)\n second_vec = self.first.subtract(self.third)\n return first_vec.vector_product(second_vec)",
"title": ""
},
{
"docid": "8268a4baf77c92f8ebf10101ad82ba1c",
"score": "0.52246726",
"text": "def SPL(self):\n return self.S().P().L()",
"title": ""
},
{
"docid": "d8d39586dbd0d67cf187c96d7c2580c5",
"score": "0.52229756",
"text": "def lineLength (line):\r\n return math.sqrt((line[1].x - line[0].x) ** 2 + (line[1].y - line[0].y) ** 2)",
"title": ""
},
{
"docid": "69259cea9e83b1ffc23ae212e788078f",
"score": "0.5217991",
"text": "def side_length(X1,y1,x2,y2):\n return math.sqrt((X1-X2)**2 + (y1 - y2)**2))",
"title": ""
},
{
"docid": "7b047d858bc17cad65646b9791437d70",
"score": "0.52150047",
"text": "def getThickness(self):\n return self[1].point.z - self[0].point.z",
"title": ""
},
{
"docid": "7bf01ad6f6db18d2c958c702f21d424a",
"score": "0.5213559",
"text": "def linewidth():",
"title": ""
},
{
"docid": "732448dd60c29e209140f9bca58d8ae5",
"score": "0.52098674",
"text": "def frame_right(self) -> float:\r\n return self.position.x + self.half_width",
"title": ""
},
{
"docid": "acc3c505cc6af72b33e15aa92f07b3c7",
"score": "0.51981115",
"text": "def get_connected(self, side=None):\n end_side=[]\n if self._orientation==1:\n end_side.append('W')\n return end_side\n elif self._orientation==2:\n end_side.append('N')\n return end_side\n elif self._orientation==3:\n end_side.append('E')\n return end_side\n else:\n end_side.append('S')\n return end_side",
"title": ""
},
{
"docid": "c439f1be62b3408051c86203f5f0318b",
"score": "0.51950645",
"text": "def _width_of_current_line(self) -> int:\n return self._tw.width_of_current_line",
"title": ""
},
{
"docid": "c92873d5d9b9e8feafb4a5fe82708e6a",
"score": "0.5189287",
"text": "def dof_point(self) -> PointType:\n return self.ref.sub_entity(*self.entity).midpoint()",
"title": ""
},
{
"docid": "e6575bde7e407ed2671b78943f03ad74",
"score": "0.51873684",
"text": "def pentagon_perimeter(side: Number) -> Number:\n return 5*side",
"title": ""
},
{
"docid": "de905baaaa716709c4113fc4938a03bb",
"score": "0.5185853",
"text": "def get_side_of_head(s:bs4.element.Tag) -> SideOfHead:\n title = s.title.contents[0].lower()\n description = s.description.contents[0].lower()\n if 'rt' in title or 'right' in description:\n return SideOfHead.RIGHT\n elif 'lt' in title or 'left' in description:\n return SideOfHead.LEFT\n else:\n error(f'side of head cannot be determined from:\\n\\ttitle:{title}\\n\\t description:{description}')\n return None",
"title": ""
},
{
"docid": "3d092e967e0ec30a79077167779e0415",
"score": "0.51793754",
"text": "def get_offset_left(self):\n\t\treturn self.offset[0]",
"title": ""
},
{
"docid": "a4be2789fba83d16e1bf5707f434fb23",
"score": "0.517089",
"text": "def column(self) -> int:\n\n return int(self[-2:])",
"title": ""
},
{
"docid": "5743ed02767f576faa0f5a98e9b8430c",
"score": "0.51668185",
"text": "def side_lobe_level():\n return -13.26",
"title": ""
},
{
"docid": "3a8cacc4e6a6e14b4df8722e1f377b38",
"score": "0.5161773",
"text": "def get_shape(sides):\n pass",
"title": ""
},
{
"docid": "31be2fda9a74fdd41f7ebe74fe131f23",
"score": "0.5157701",
"text": "def get_pin_direction(self, line):\n is_input = self._get_bit_in_register('DIRECTION', line)\n return Directions.IN if is_input else Directions.OUT",
"title": ""
},
{
"docid": "c9ac0edf3447d8db00b4d251ccdc5281",
"score": "0.51407963",
"text": "def _get_level(self):\n if not hasattr(self.__p, 'pPr'):\n return 0\n return int(self.__p.pPr.get('lvl', 0))",
"title": ""
},
{
"docid": "18fc0904c9be3ee6edecbd6ba0bd0794",
"score": "0.51407915",
"text": "def pointDistance( self, p ):\n disp = self.p2 - self.p1\n segLen = disp.magnitude()\n norm = disp / segLen\n dispP = p - self.p1\n dp = norm.dot( dispP )\n if ( dp < 0 ): \n return (p - self.p1).magnitude()\n elif ( dp > segLen ):\n return ( p - self.p2).magnitude()\n else:\n A = -norm.y\n B = norm.x\n C = -( A * self.p1.x + B * self.p1.y )\n return abs( A * p.x + B * p.y + C )",
"title": ""
},
{
"docid": "79fa7b3b4b53af8196ccbd5dc769c301",
"score": "0.5140242",
"text": "def sideinfo(self):\n return self._get_element(*self._side_info_locator)",
"title": ""
},
{
"docid": "a688a18fc0e8075e19decfe1f28a08e0",
"score": "0.5128",
"text": "def line(self):\n return self.__line",
"title": ""
},
{
"docid": "5d85367c9d9286952cef6560643372c3",
"score": "0.5127137",
"text": "def right(self):\n return self._x + self._width",
"title": ""
},
{
"docid": "6b5e166a12cb1538013848c4b2f848a2",
"score": "0.51181984",
"text": "def line(p1, p2):\n A = (p1[1] - p2[1])\n B = (p2[0] - p1[0])\n C = (p1[0]*p2[1] - p2[0]*p1[1])\n return A, B, -C",
"title": ""
},
{
"docid": "611a49d6ca4dc570aaf11508efcccfa2",
"score": "0.5113819",
"text": "def perpendicular(self):\n Vperp = Vector2(-self.mData[1], self.mData[0])\n return Vperp",
"title": ""
},
{
"docid": "ae882b581d115c24f67cdfe1172c7eac",
"score": "0.5109769",
"text": "def distance_to(self, p):\n\t\treturn (self - p).length()",
"title": ""
},
{
"docid": "ac64bdf0462135e4d1d630ee1a66a143",
"score": "0.5109033",
"text": "def float2SideLeft(self):\n self.mLeft = self.getFloatSideLeft()\n return True",
"title": ""
},
{
"docid": "02812c3794e0b2e346c612557edd9d1d",
"score": "0.5107515",
"text": "def midPoint( self ):\n try:\n return ( self.p1 + self.p2 ) * 0.5\n except TypeError:\n print type( self.p1 ), type( self.p2 )",
"title": ""
},
{
"docid": "984e3ad3e8d3118d918353047d3d1428",
"score": "0.510563",
"text": "def coluna_pos(p):\n\treturn p[1]",
"title": ""
},
{
"docid": "bfae792a2165e1b3e43234a27b076f87",
"score": "0.51040936",
"text": "def right(self) -> float:\n return self.x + self.w",
"title": ""
},
{
"docid": "55dd12c453218080573b3268eebd75a4",
"score": "0.5101551",
"text": "def right(self):\n return self.left + self.width",
"title": ""
},
{
"docid": "59f689ee7b14e64d8b2fd2cde977e06b",
"score": "0.50990087",
"text": "def point_to_line(tower):\n return True, tower",
"title": ""
},
{
"docid": "5388fc94cbc0fdd59754edf92694cbee",
"score": "0.5096413",
"text": "def line_width(self):\n return self._line_width",
"title": ""
},
{
"docid": "8175b4c1543f79275f8b383fec11f42c",
"score": "0.5087364",
"text": "def perpendicular(self, through):\n if self.contains(through):\n n = self.dim + 1\n\n l = self\n\n if n > 3:\n # additional point is required to determine the exact line\n arr = np.zeros(n)\n for i in range(n):\n arr[-i - 1] = 1\n o = Point(arr)\n if not self.contains(o):\n break\n e = join(self, o)\n basis = e.basis_matrix\n line_pts = basis.dot(self.basis_matrix.T)\n l = Line(np.cross(*line_pts.T))\n\n from .operators import harmonic_set\n p = l.meet(infty)\n q = harmonic_set(I, J, p)\n\n if n > 3:\n q = Point(basis.T.dot(q.array))\n\n return Line(through, q)\n\n return self.mirror(through).join(through)",
"title": ""
}
]</span></div></div></div>
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